diff --git a/doc/LectureNotes/DataFiles/cancer.dot b/doc/LectureNotes/DataFiles/cancer.dot index cfa886d17..590609fe5 100644 --- a/doc/LectureNotes/DataFiles/cancer.dot +++ b/doc/LectureNotes/DataFiles/cancer.dot @@ -6,23 +6,23 @@ edge [fontname="helvetica"] ; 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; 2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e78946"] ; 1 -> 2 ; -3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; +3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; 2 -> 3 ; 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ; 3 -> 4 ; -5 [label="mean perimeter <= 78.51\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; +5 [label="worst compactness <= 0.085\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; 3 -> 5 ; 6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ; 5 -> 6 ; 7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139"] ; 5 -> 7 ; -8 [label="mean texture <= 20.84\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ; +8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ; 2 -> 8 ; 9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139"] ; 8 -> 9 ; 10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139"] ; 8 -> 10 ; -11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; +11 [label="area error <= 13.475\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; 1 -> 11 ; 12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 11 -> 12 ; @@ -30,11 +30,11 @@ edge [fontname="helvetica"] ; 11 -> 13 ; 14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ; 0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -15 [label="worst concavity <= 0.318\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; +15 [label="worst radius <= 17.74\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; 14 -> 15 ; 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ; 15 -> 16 ; -17 [label="worst perimeter <= 115.95\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; +17 [label="worst smoothness <= 0.106\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; 15 -> 17 ; 18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 17 -> 18 ; @@ -48,10 +48,10 @@ edge [fontname="helvetica"] ; 21 -> 22 ; 23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ; 21 -> 23 ; -24 [label="fractal dimension error <= 0.013\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; +24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; 20 -> 24 ; -25 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ; +25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 24 -> 25 ; -26 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; +26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ; 24 -> 26 ; } \ No newline at end of file diff --git a/doc/LectureNotes/DataFiles/cancer.png b/doc/LectureNotes/DataFiles/cancer.png new file mode 100644 index 000000000..7afaf0ca0 Binary files /dev/null and b/doc/LectureNotes/DataFiles/cancer.png differ diff --git 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9ebf1ec3e..ae594637e 100644 Binary files a/doc/LectureNotes/_build/html/_images/statistics_181_0.png and b/doc/LectureNotes/_build/html/_images/statistics_181_0.png differ diff --git a/doc/LectureNotes/_build/html/_images/statistics_188_1.png b/doc/LectureNotes/_build/html/_images/statistics_188_1.png index 8fe8c9418..ba13a577f 100644 Binary files a/doc/LectureNotes/_build/html/_images/statistics_188_1.png and b/doc/LectureNotes/_build/html/_images/statistics_188_1.png differ diff --git a/doc/LectureNotes/_build/html/_sources/week35.ipynb b/doc/LectureNotes/_build/html/_sources/week35.ipynb index baa97aaf4..6c23bf707 100644 --- a/doc/LectureNotes/_build/html/_sources/week35.ipynb +++ b/doc/LectureNotes/_build/html/_sources/week35.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7577bda1", + "id": "be53b5a0", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "2f58994e", + "id": "ea9aa8ba", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "984a751a", + "id": "1682c283", "metadata": { "editable": true }, @@ -49,7 +49,7 @@ }, { "cell_type": "markdown", - "id": "b85fab3f", + "id": "06e8536b", "metadata": { "editable": true }, @@ -65,7 +65,7 @@ }, { "cell_type": "markdown", - "id": "6156ea7b", + "id": "78f54d26", "metadata": { "editable": true }, @@ -99,7 +99,7 @@ }, { "cell_type": "markdown", - "id": "b3a257b8", + "id": "94c25631", "metadata": { "editable": true }, @@ -115,7 +115,7 @@ }, { "cell_type": "markdown", - "id": "d409d413", + "id": "a42b88dc", "metadata": { "editable": true }, @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "92019658", + "id": "3171d71c", "metadata": { "editable": true }, @@ -137,7 +137,7 @@ }, { "cell_type": "markdown", - "id": "98b024de", + "id": "e70f9acc", "metadata": { "editable": true }, @@ -149,7 +149,7 @@ }, { "cell_type": "markdown", - "id": "f08b4dcd", + "id": "2379d06d", "metadata": { "editable": true }, @@ -170,7 +170,7 @@ }, { "cell_type": "markdown", - "id": "12b17f2d", + "id": "643717b0", "metadata": { "editable": true }, @@ -182,7 +182,7 @@ }, { "cell_type": "markdown", - "id": "d7bb3d6a", + "id": "378c280f", "metadata": { "editable": true }, @@ -195,7 +195,7 @@ }, { "cell_type": "markdown", - "id": "6e628309", + "id": "e52a2672", "metadata": { "editable": true }, @@ -207,7 +207,7 @@ }, { "cell_type": "markdown", - "id": "b8e38e10", + "id": "cce68c3d", "metadata": { "editable": true }, @@ -219,7 +219,7 @@ }, { "cell_type": "markdown", - "id": "9e391414", + "id": "f8c11724", "metadata": { "editable": true }, @@ -229,7 +229,7 @@ }, { "cell_type": "markdown", - "id": "20b2a848", + "id": "a139ab45", "metadata": { "editable": true }, @@ -241,7 +241,7 @@ }, { "cell_type": "markdown", - "id": "8ba76773", + "id": "089dec59", "metadata": { "editable": true }, @@ -254,7 +254,7 @@ }, { "cell_type": "markdown", - "id": "78a49ebf", + "id": "570e9155", "metadata": { "editable": true }, @@ -266,7 +266,7 @@ }, { "cell_type": "markdown", - "id": "b6562367", + "id": "2c6cbd01", "metadata": { "editable": true }, @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "3fdcc186", + "id": "daaf53c3", "metadata": { "editable": true }, @@ -288,7 +288,7 @@ }, { "cell_type": "markdown", - "id": "a0a15628", + "id": "186a0c23", "metadata": { "editable": true }, @@ -300,7 +300,7 @@ }, { "cell_type": "markdown", - "id": "ad1e37fe", + "id": "e83c76fe", "metadata": { "editable": true }, @@ -311,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "bfc33b62", + "id": "48869faa", "metadata": { "editable": true }, @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "05d1562e", + "id": "09ef84bc", "metadata": { "editable": true }, @@ -342,7 +342,7 @@ }, { "cell_type": "markdown", - "id": "2615bb43", + "id": "a3573593", "metadata": { "editable": true }, @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "4c278d09", + "id": "0cf80555", "metadata": { "editable": true }, @@ -365,7 +365,7 @@ }, { "cell_type": "markdown", - "id": "4362df64", + "id": "9a5943d2", "metadata": { "editable": true }, @@ -377,7 +377,7 @@ }, { "cell_type": "markdown", - "id": "c9d3f7d2", + "id": "614492f6", "metadata": { "editable": true }, @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "5bdc470d", + "id": "e151cfa1", "metadata": { "editable": true }, @@ -399,7 +399,7 @@ }, { "cell_type": "markdown", - "id": "ae831fa3", + "id": "1e2877d6", "metadata": { "editable": true }, @@ -409,7 +409,7 @@ }, { "cell_type": "markdown", - "id": "28e87953", + "id": "62ea2b3f", "metadata": { "editable": true }, @@ -421,7 +421,7 @@ }, { "cell_type": "markdown", - "id": "2e1444b7", + "id": "8b2e0f0e", "metadata": { "editable": true }, @@ -432,7 +432,7 @@ }, { "cell_type": "markdown", - "id": "a2b49226", + "id": "08983f5f", "metadata": { "editable": true }, @@ -444,7 +444,7 @@ }, { "cell_type": "markdown", - "id": "d7e1556d", + "id": "0220251b", "metadata": { "editable": true }, @@ -454,7 +454,7 @@ }, { "cell_type": "markdown", - "id": "4015b0da", + "id": "d80df328", "metadata": { "editable": true }, @@ -466,7 +466,7 @@ }, { "cell_type": "markdown", - "id": "289d01ec", + "id": "838e6fcc", "metadata": { "editable": true }, @@ -476,7 +476,7 @@ }, { "cell_type": "markdown", - "id": "4c084023", + "id": "49bab4db", "metadata": { "editable": true }, @@ -488,7 +488,7 @@ }, { "cell_type": "markdown", - "id": "9e0db3c9", + "id": "a9f8042e", "metadata": { "editable": true }, @@ -508,7 +508,7 @@ }, { "cell_type": "markdown", - "id": "32ac6fde", + "id": "41a2f792", "metadata": { "editable": true }, @@ -535,7 +535,7 @@ }, { "cell_type": "markdown", - "id": "adcd3708", + "id": "2cd68700", "metadata": { "editable": true }, @@ -547,7 +547,7 @@ }, { "cell_type": "markdown", - "id": "1187bf5d", + "id": "45e00b5c", "metadata": { "editable": true }, @@ -559,7 +559,7 @@ }, { "cell_type": "markdown", - "id": "51e56bc5", + "id": "0d8f4073", "metadata": { "editable": true }, @@ -575,7 +575,7 @@ }, { "cell_type": "markdown", - "id": "1198e0f1", + "id": "32b84460", "metadata": { "editable": true }, @@ -593,7 +593,7 @@ }, { "cell_type": "markdown", - "id": "88ee3246", + "id": "b1603479", "metadata": { "editable": true }, @@ -605,7 +605,7 @@ }, { "cell_type": "markdown", - "id": "5c8002e1", + "id": "6b964217", "metadata": { "editable": true }, @@ -617,7 +617,7 @@ }, { "cell_type": "markdown", - "id": "64f56d8b", + "id": "ef7da021", "metadata": { "editable": true }, @@ -628,7 +628,7 @@ }, { "cell_type": "markdown", - "id": "436eb900", + "id": "f36afc00", "metadata": { "editable": true }, @@ -640,7 +640,7 @@ }, { "cell_type": "markdown", - "id": "1f93191d", + "id": "c86b67b1", "metadata": { "editable": true }, @@ -650,7 +650,7 @@ }, { "cell_type": "markdown", - "id": "88a779b0", + "id": "9f824fc3", "metadata": { "editable": true }, @@ -662,7 +662,7 @@ }, { "cell_type": "markdown", - "id": "dadd9b73", + "id": "13294ac7", "metadata": { "editable": true }, @@ -676,7 +676,7 @@ }, { "cell_type": "markdown", - "id": "81a3d747", + "id": "d7e5700e", "metadata": { "editable": true }, @@ -688,7 +688,7 @@ }, { "cell_type": "markdown", - "id": "97d69ab9", + "id": "1f1b9622", "metadata": { "editable": true }, @@ -700,7 +700,7 @@ }, { "cell_type": "markdown", - "id": "eb053306", + "id": "d22ff920", "metadata": { "editable": true }, @@ -712,7 +712,7 @@ }, { "cell_type": "markdown", - "id": "2cafbbd7", + "id": "308eae89", "metadata": { "editable": true }, @@ -722,7 +722,7 @@ }, { "cell_type": "markdown", - "id": "359b9cab", + "id": "69df69e0", "metadata": { "editable": true }, @@ -734,7 +734,7 @@ }, { "cell_type": "markdown", - "id": "bba294f8", + "id": "989c0888", "metadata": { "editable": true }, @@ -746,7 +746,7 @@ }, { "cell_type": "markdown", - "id": "26084341", + "id": "796e59d4", "metadata": { "editable": true }, @@ -758,7 +758,7 @@ }, { "cell_type": "markdown", - "id": "4bfeb04f", + "id": "0ecc2fbb", "metadata": { "editable": true }, @@ -772,7 +772,7 @@ }, { "cell_type": "markdown", - "id": "d6251d99", + "id": "7f541061", "metadata": { "editable": true }, @@ -784,7 +784,7 @@ }, { "cell_type": "markdown", - "id": "260ddc35", + "id": "150fcbf6", "metadata": { "editable": true }, @@ -796,7 +796,7 @@ }, { "cell_type": "markdown", - "id": "1f4eda24", + "id": "27b04c69", "metadata": { "editable": true }, @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "6e23b4e8", + "id": "9d121c2b", "metadata": { "editable": true }, @@ -818,7 +818,7 @@ }, { "cell_type": "markdown", - "id": "6978c685", + "id": "de64d430", "metadata": { "editable": true }, @@ -830,7 +830,7 @@ }, { "cell_type": "markdown", - "id": "0e872773", + "id": "60392be8", "metadata": { "editable": true }, @@ -840,7 +840,7 @@ }, { "cell_type": "markdown", - "id": "25f9231f", + "id": "3eeb0762", "metadata": { "editable": true }, @@ -852,7 +852,7 @@ }, { "cell_type": "markdown", - "id": "67715022", + "id": "5acbe964", "metadata": { "editable": true }, @@ -862,7 +862,7 @@ }, { "cell_type": "markdown", - "id": "8a045d81", + "id": "a5e67241", "metadata": { "editable": true }, @@ -874,7 +874,7 @@ }, { "cell_type": "markdown", - "id": "3a570fb6", + "id": "d80219d2", "metadata": { "editable": true }, @@ -886,7 +886,7 @@ }, { "cell_type": "markdown", - "id": "3649bf07", + "id": "7ec6b553", "metadata": { "editable": true }, @@ -898,7 +898,7 @@ }, { "cell_type": "markdown", - "id": "36191007", + "id": "bcd2cdff", "metadata": { "editable": true }, @@ -913,7 +913,7 @@ }, { "cell_type": "markdown", - "id": "2f0ccff1", + "id": "7160434b", "metadata": { "editable": true }, @@ -925,7 +925,7 @@ }, { "cell_type": "markdown", - "id": "5ad5a1f9", + "id": "ae1d7aa6", "metadata": { "editable": true }, @@ -935,7 +935,7 @@ }, { "cell_type": "markdown", - "id": "f2894537", + "id": "5ced56fe", "metadata": { "editable": true }, @@ -947,7 +947,7 @@ }, { "cell_type": "markdown", - "id": "db94de78", + "id": "78444018", "metadata": { "editable": true }, @@ -957,7 +957,7 @@ }, { "cell_type": "markdown", - "id": "aea39aca", + "id": "4207abea", "metadata": { "editable": true }, @@ -969,7 +969,7 @@ }, { "cell_type": "markdown", - "id": "bc220993", + "id": "5ca388cb", "metadata": { "editable": true }, @@ -979,7 +979,7 @@ }, { "cell_type": "markdown", - "id": "3d04bd63", + "id": "76bcca31", "metadata": { "editable": true }, @@ -991,7 +991,7 @@ }, { "cell_type": "markdown", - "id": "d82cae70", + "id": "72a7c5d6", "metadata": { "editable": true }, @@ -1003,7 +1003,7 @@ }, { "cell_type": "markdown", - "id": "4abb369d", + "id": "8ddd5a6c", "metadata": { "editable": true }, @@ -1015,7 +1015,7 @@ }, { "cell_type": "markdown", - "id": "9e36ffb6", + "id": "4ce22956", "metadata": { "editable": true }, @@ -1025,7 +1025,7 @@ }, { "cell_type": "markdown", - "id": "6639866c", + "id": "7a7a8220", "metadata": { "editable": true }, @@ -1037,7 +1037,7 @@ }, { "cell_type": "markdown", - "id": "f509d862", + "id": "660f5feb", "metadata": { "editable": true }, @@ -1050,7 +1050,7 @@ }, { "cell_type": "markdown", - "id": "a151fb3a", + "id": "35130b19", "metadata": { "editable": true }, @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "5c393665", + "id": "43929a90", "metadata": { "editable": true }, @@ -1072,7 +1072,7 @@ }, { "cell_type": "markdown", - "id": "fff34d82", + "id": "b9484098", "metadata": { "editable": true }, @@ -1084,7 +1084,7 @@ }, { "cell_type": "markdown", - "id": "cd0739ee", + "id": "a8366c58", "metadata": { "editable": true }, @@ -1094,7 +1094,7 @@ }, { "cell_type": "markdown", - "id": "3e3c1eb2", + "id": "22ff6985", "metadata": { "editable": true }, @@ -1106,7 +1106,7 @@ }, { "cell_type": "markdown", - "id": "a2462961", + "id": "8129f022", "metadata": { "editable": true }, @@ -1116,7 +1116,7 @@ }, { "cell_type": "markdown", - "id": "bb3ecaaa", + "id": "d8ff6bb2", "metadata": { "editable": true }, @@ -1128,7 +1128,7 @@ }, { "cell_type": "markdown", - "id": "48423fd4", + "id": "c8f81b3f", "metadata": { "editable": true }, @@ -1138,7 +1138,7 @@ }, { "cell_type": "markdown", - "id": "4605744e", + "id": "69af2c51", "metadata": { "editable": true }, @@ -1150,7 +1150,7 @@ }, { "cell_type": "markdown", - "id": "1d2566d1", + "id": "c9e254ef", "metadata": { "editable": true }, @@ -1160,7 +1160,7 @@ }, { "cell_type": "markdown", - "id": "041a2cd3", + "id": "b1377365", "metadata": { "editable": true }, @@ -1172,7 +1172,7 @@ }, { "cell_type": "markdown", - "id": "4376380c", + "id": "52258121", "metadata": { "editable": true }, @@ -1184,7 +1184,7 @@ }, { "cell_type": "markdown", - "id": "22ff508a", + "id": "7785c30e", "metadata": { "editable": true }, @@ -1196,7 +1196,7 @@ }, { "cell_type": "markdown", - "id": "55d8ced5", + "id": "ba371063", "metadata": { "editable": true }, @@ -1208,7 +1208,7 @@ }, { "cell_type": "markdown", - "id": "bab2bcb0", + "id": "905ad22c", "metadata": { "editable": true }, @@ -1220,7 +1220,7 @@ }, { "cell_type": "markdown", - "id": "9d2d479d", + "id": "0f770989", "metadata": { "editable": true }, @@ -1236,7 +1236,7 @@ }, { "cell_type": "markdown", - "id": "071a9f00", + "id": "c4c4d825", "metadata": { "editable": true }, @@ -1248,7 +1248,7 @@ }, { "cell_type": "markdown", - "id": "f080cd68", + "id": "bca9faf0", "metadata": { "editable": true }, @@ -1258,7 +1258,7 @@ }, { "cell_type": "markdown", - "id": "113143ab", + "id": "678d6d8d", "metadata": { "editable": true }, @@ -1270,7 +1270,7 @@ }, { "cell_type": "markdown", - "id": "3ec7da1f", + "id": "9f18b73a", "metadata": { "editable": true }, @@ -1288,7 +1288,7 @@ }, { "cell_type": "markdown", - "id": "45ef802b", + "id": "a738b78b", "metadata": { "editable": true }, @@ -1300,7 +1300,7 @@ }, { "cell_type": "markdown", - "id": "b0ae87b4", + "id": "c4325fac", "metadata": { "editable": true }, @@ -1312,7 +1312,7 @@ }, { "cell_type": "markdown", - "id": "2e05e4cd", + "id": "d32fe699", "metadata": { "editable": true }, @@ -1322,7 +1322,7 @@ }, { "cell_type": "markdown", - "id": "0f916a45", + "id": "bdabaf26", "metadata": { "editable": true }, @@ -1334,7 +1334,7 @@ }, { "cell_type": "markdown", - "id": "fb292357", + "id": "1a13e693", "metadata": { "editable": true }, @@ -1344,7 +1344,7 @@ }, { "cell_type": "markdown", - "id": "d560a000", + "id": "b26dc6ec", "metadata": { "editable": true }, @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "f112cba6", + "id": "f42cbab8", "metadata": { "editable": true }, @@ -1366,7 +1366,7 @@ }, { "cell_type": "markdown", - "id": "b74c63a9", + "id": "a73bb0ea", "metadata": { "editable": true }, @@ -1380,7 +1380,7 @@ }, { "cell_type": "markdown", - "id": "65bbad29", + "id": "c1c8da9d", "metadata": { "editable": true }, @@ -1392,7 +1392,7 @@ }, { "cell_type": "markdown", - "id": "881bc42f", + "id": "2ca0aef5", "metadata": { "editable": true }, @@ -1403,7 +1403,7 @@ }, { "cell_type": "markdown", - "id": "580a86e0", + "id": "d18683ee", "metadata": { "editable": true }, @@ -1416,7 +1416,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "5ab57679", + "id": "a4ea6710", "metadata": { "collapsed": false, "editable": true @@ -1443,7 +1443,7 @@ }, { "cell_type": "markdown", - "id": "ea4bd54b", + "id": "6fb0024c", "metadata": { "editable": true }, @@ -1454,7 +1454,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "9a384f3b", + "id": "72a80735", "metadata": { "collapsed": false, "editable": true @@ -1467,7 +1467,7 @@ }, { "cell_type": "markdown", - "id": "76aab9da", + "id": "3768a40c", "metadata": { "editable": true }, @@ -1481,7 +1481,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "74a5da7d", + "id": "d5af18c7", "metadata": { "collapsed": false, "editable": true @@ -1494,7 +1494,7 @@ }, { "cell_type": "markdown", - "id": "a9b3c131", + "id": "92c5153f", "metadata": { "editable": true }, @@ -1505,7 +1505,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "f6cd6c32", + "id": "09511b52", "metadata": { "collapsed": false, "editable": true @@ -1517,7 +1517,7 @@ }, { "cell_type": "markdown", - "id": "44a68a85", + "id": "d92d6b51", "metadata": { "editable": true }, @@ -1528,7 +1528,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "00287e4f", + "id": "4e16843d", "metadata": { "collapsed": false, "editable": true @@ -1544,7 +1544,7 @@ }, { "cell_type": "markdown", - "id": "50c01b79", + "id": "acef1cf8", "metadata": { "editable": true }, @@ -1555,7 +1555,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "311db3ab", + "id": "11d5e613", "metadata": { "collapsed": false, "editable": true @@ -1569,7 +1569,7 @@ }, { "cell_type": "markdown", - "id": "e0d346fd", + "id": "216b1096", "metadata": { "editable": true }, @@ -1590,7 +1590,7 @@ }, { "cell_type": "markdown", - "id": "147ef91e", + "id": "8fa67728", "metadata": { "editable": true }, @@ -1601,7 +1601,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "6707a4db", + "id": "28c9c5f2", "metadata": { "collapsed": false, "editable": true @@ -1654,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "455d14ce", + "id": "97af1cca", "metadata": { "editable": true }, @@ -1665,7 +1665,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "526cd78a", + "id": "e4ca68c4", "metadata": { "collapsed": false, "editable": true @@ -1690,7 +1690,7 @@ }, { "cell_type": "markdown", - "id": "a60215de", + "id": "16a219af", "metadata": { "editable": true }, @@ -1702,7 +1702,7 @@ }, { "cell_type": "markdown", - "id": "a46a7630", + "id": "d99baccc", "metadata": { "editable": true }, @@ -1731,7 +1731,7 @@ }, { "cell_type": "markdown", - "id": "674d374b", + "id": "382627de", "metadata": { "editable": true }, @@ -1756,7 +1756,7 @@ }, { "cell_type": "markdown", - "id": "baeb079c", + "id": "dbe9c5e7", "metadata": { "editable": true }, @@ -1776,7 +1776,7 @@ }, { "cell_type": "markdown", - "id": "cb3a3371", + "id": "76afa0fa", "metadata": { "editable": true }, @@ -1803,7 +1803,7 @@ }, { "cell_type": "markdown", - "id": "4de3b3d6", + "id": "7a3ee226", "metadata": { "editable": true }, @@ -1816,7 +1816,7 @@ }, { "cell_type": "markdown", - "id": "b14ecb9f", + "id": "a3b3ad3e", "metadata": { "editable": true }, @@ -1828,7 +1828,7 @@ }, { "cell_type": "markdown", - "id": "9ac3aa36", + "id": "5c0f36af", "metadata": { "editable": true }, @@ -1839,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "56f36ee9", + "id": "3de5eca9", "metadata": { "editable": true }, @@ -1855,7 +1855,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "167ea26b", + "id": "e836c1eb", "metadata": { "collapsed": false, "editable": true @@ -1889,7 +1889,7 @@ }, { "cell_type": "markdown", - "id": "fdf2d4d8", + "id": "03c9b6b3", "metadata": { "editable": true }, @@ -1899,7 +1899,7 @@ }, { "cell_type": "markdown", - "id": "aae3e78a", + "id": "ecabe53c", "metadata": { "editable": true }, @@ -1914,7 +1914,7 @@ }, { "cell_type": "markdown", - "id": "e1b73f82", + "id": "daa74ff5", "metadata": { "editable": true }, @@ -1926,7 +1926,7 @@ }, { "cell_type": "markdown", - "id": "796edd11", + "id": "4bf5d4cc", "metadata": { "editable": true }, @@ -1936,7 +1936,7 @@ }, { "cell_type": "markdown", - "id": "7316c9ad", + "id": "bff437d3", "metadata": { "editable": true }, @@ -1952,7 +1952,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "95995d2e", + "id": "e4815d9d", "metadata": { "collapsed": false, "editable": true @@ -1969,7 +1969,7 @@ }, { "cell_type": "markdown", - "id": "f22540c4", + "id": "ac106c70", "metadata": { "editable": true }, @@ -1982,7 +1982,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "beabe048", + "id": "b3b4d1c5", "metadata": { "collapsed": false, "editable": true @@ -2029,7 +2029,7 @@ }, { "cell_type": "markdown", - "id": "5c668e12", + "id": "588605a3", "metadata": { "editable": true }, @@ -2040,7 +2040,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "3b36f302", + "id": "eac5c19b", "metadata": { "collapsed": false, "editable": true @@ -2143,7 +2143,7 @@ }, { "cell_type": "markdown", - "id": "5c92044f", + "id": "e09cfcf0", "metadata": { "editable": true }, @@ -2171,7 +2171,7 @@ }, { "cell_type": "markdown", - "id": "85314a61", + "id": "2bbf93a9", "metadata": { "editable": true }, @@ -2206,7 +2206,7 @@ }, { "cell_type": "markdown", - "id": "b314c1bb", + "id": "51f3121a", "metadata": { "editable": true }, @@ -2221,7 +2221,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "d6fc1b8a", + "id": "e5bfd742", "metadata": { "collapsed": false, "editable": true @@ -2246,7 +2246,7 @@ }, { "cell_type": "markdown", - "id": "09f8edc9", + "id": "61e91fe1", "metadata": { "editable": true }, @@ -2262,7 +2262,7 @@ }, { "cell_type": "markdown", - "id": "4c1fa1f8", + "id": "02fa2126", "metadata": { "editable": true }, @@ -2274,7 +2274,7 @@ }, { "cell_type": "markdown", - "id": "7e4999d3", + "id": "c5ea5b96", "metadata": { "editable": true }, @@ -2289,7 +2289,7 @@ }, { "cell_type": "markdown", - "id": "c3316efa", + "id": "6dbfbe9a", "metadata": { "editable": true }, @@ -2301,7 +2301,7 @@ }, { "cell_type": "markdown", - "id": "d61e55f2", + "id": "e3318d8a", "metadata": { "editable": true }, @@ -2311,7 +2311,7 @@ }, { "cell_type": "markdown", - "id": "c4cdc607", + "id": "7a7f2bb0", "metadata": { "editable": true }, @@ -2323,7 +2323,7 @@ }, { "cell_type": "markdown", - "id": "977f0ee7", + "id": "7b9072eb", "metadata": { "editable": true }, @@ -2333,7 +2333,7 @@ }, { "cell_type": "markdown", - "id": "cba7a885", + "id": "fb9fc6af", "metadata": { "editable": true }, @@ -2345,7 +2345,7 @@ }, { "cell_type": "markdown", - "id": "d0b8d2c4", + "id": "5b1d9bae", "metadata": { "editable": true }, @@ -2358,7 +2358,7 @@ }, { "cell_type": "markdown", - "id": "5bb6c52f", + "id": "aa9d7398", "metadata": { "editable": true }, @@ -2370,7 +2370,7 @@ }, { "cell_type": "markdown", - "id": "15d60b36", + "id": "1ba94b15", "metadata": { "editable": true }, @@ -2380,7 +2380,7 @@ }, { "cell_type": "markdown", - "id": "c09def6d", + "id": "2c91f4b9", "metadata": { "editable": true }, @@ -2392,7 +2392,7 @@ }, { "cell_type": "markdown", - "id": "75385062", + "id": "f227a642", "metadata": { "editable": true }, @@ -2402,7 +2402,7 @@ }, { "cell_type": "markdown", - "id": "85490229", + "id": "744d6fcc", "metadata": { "editable": true }, @@ -2414,7 +2414,7 @@ }, { "cell_type": "markdown", - "id": "0ea2efcc", + "id": "c07272e6", "metadata": { "editable": true }, @@ -2424,7 +2424,7 @@ }, { "cell_type": "markdown", - "id": "1cd99c24", + "id": "28b1cf18", "metadata": { "editable": true }, @@ -2436,7 +2436,7 @@ }, { "cell_type": "markdown", - "id": "e40f46ea", + "id": "d9e5287b", "metadata": { "editable": true }, @@ -2446,7 +2446,7 @@ }, { "cell_type": "markdown", - "id": "7898f91b", + "id": "d2b5ee59", "metadata": { "editable": true }, @@ -2458,7 +2458,7 @@ }, { "cell_type": "markdown", - "id": "684197d4", + "id": "ae591a22", "metadata": { "editable": true }, @@ -2468,7 +2468,7 @@ }, { "cell_type": "markdown", - "id": "3e1e2b1c", + "id": "bbb4efc5", "metadata": { "editable": true }, @@ -2480,7 +2480,7 @@ }, { "cell_type": "markdown", - "id": "538de526", + "id": "76c37a89", "metadata": { "editable": true }, @@ -2490,7 +2490,7 @@ }, { "cell_type": "markdown", - "id": "e996dff2", + "id": "ac78f014", "metadata": { "editable": true }, @@ -2502,7 +2502,7 @@ }, { "cell_type": "markdown", - "id": "b42651a5", + "id": "01c57df2", "metadata": { "editable": true }, @@ -2514,7 +2514,7 @@ }, { "cell_type": "markdown", - "id": "3ba0968a", + "id": "5c77be60", "metadata": { "editable": true }, @@ -2526,7 +2526,7 @@ }, { "cell_type": "markdown", - "id": "8eaff46a", + "id": "7448d02c", "metadata": { "editable": true }, @@ -2539,7 +2539,7 @@ }, { "cell_type": "markdown", - "id": "c417449e", + "id": "6b16f287", "metadata": { "editable": true }, @@ -2551,7 +2551,7 @@ }, { "cell_type": "markdown", - "id": "72e2f493", + "id": "d6f810af", "metadata": { "editable": true }, @@ -2561,7 +2561,7 @@ }, { "cell_type": "markdown", - "id": "c93a45ca", + "id": "6b506524", "metadata": { "editable": true }, @@ -2575,7 +2575,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "b3681d9a", + "id": "d39358da", "metadata": { "collapsed": false, "editable": true @@ -2672,7 +2672,7 @@ }, { "cell_type": "markdown", - "id": "b5f66b2e", + "id": "1c2292c1", "metadata": { "editable": true }, @@ -2693,7 +2693,7 @@ }, { "cell_type": "markdown", - "id": "e15e9033", + "id": "1fa67201", "metadata": { "editable": true }, @@ -2705,7 +2705,7 @@ }, { "cell_type": "markdown", - "id": "0a46c80f", + "id": "137f3aa8", "metadata": { "editable": true }, @@ -2715,7 +2715,7 @@ }, { "cell_type": "markdown", - "id": "9665a7bb", + "id": "b5fd0444", "metadata": { "editable": true }, @@ -2727,7 +2727,7 @@ }, { "cell_type": "markdown", - "id": "39245662", + "id": "04e6de19", "metadata": { "editable": true }, @@ -2737,7 +2737,7 @@ }, { "cell_type": "markdown", - "id": "949b0425", + "id": "2cd0f6df", "metadata": { "editable": true }, @@ -2749,7 +2749,7 @@ }, { "cell_type": "markdown", - "id": "25265f0a", + "id": "f833976e", "metadata": { "editable": true }, @@ -2765,7 +2765,7 @@ }, { "cell_type": "markdown", - "id": "37309898", + "id": "0526f8a3", "metadata": { "editable": true }, @@ -2809,7 +2809,7 @@ }, { "cell_type": "markdown", - "id": "313c63b9", + "id": "132ef74f", "metadata": { "editable": true }, @@ -2821,7 +2821,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "c85aaed0", + "id": "25ecbae5", "metadata": { "collapsed": false, "editable": true @@ -2837,7 +2837,7 @@ }, { "cell_type": "markdown", - "id": "cb66e4d4", + "id": "dd07b613", "metadata": { "editable": true }, @@ -2848,7 +2848,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "018dcf23", + "id": "217c66c6", "metadata": { "collapsed": false, "editable": true @@ -2866,7 +2866,7 @@ }, { "cell_type": "markdown", - "id": "733a4641", + "id": "a74cd412", "metadata": { "editable": true }, @@ -2877,7 +2877,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "3d092054", + "id": "7252d534", "metadata": { "collapsed": false, "editable": true @@ -2891,7 +2891,7 @@ }, { "cell_type": "markdown", - "id": "721cd730", + "id": "9bc51de3", "metadata": { "editable": true }, @@ -2902,7 +2902,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "8d6060bb", + "id": "5209e335", "metadata": { "collapsed": false, "editable": true @@ -2915,7 +2915,7 @@ }, { "cell_type": "markdown", - "id": "756d6e73", + "id": "2f7e2bce", "metadata": { "editable": true }, @@ -2926,7 +2926,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "29bde3bc", + "id": "f40dddbb", "metadata": { "collapsed": false, "editable": true @@ -2943,7 +2943,7 @@ }, { "cell_type": "markdown", - "id": "71b1deab", + "id": "4c784974", "metadata": { "editable": true }, @@ -2954,7 +2954,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "b2f69544", + "id": "f41e4472", "metadata": { "collapsed": false, "editable": true @@ -2970,7 +2970,7 @@ }, { "cell_type": "markdown", - "id": "1785df9d", + "id": "ddbe7e53", "metadata": { "editable": true }, @@ -2981,7 +2981,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "69c203fc", + "id": "fb697c78", "metadata": { "collapsed": false, "editable": true @@ -3005,7 +3005,7 @@ }, { "cell_type": "markdown", - "id": "5662334d", + "id": "e9883aae", "metadata": { "editable": true }, @@ -3016,7 +3016,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "e595038c", + "id": "3b54b6a2", "metadata": { "collapsed": false, "editable": true @@ -3029,7 +3029,7 @@ }, { "cell_type": "markdown", - "id": "9445dd2f", + "id": "787c8475", "metadata": { "editable": true }, @@ -3040,7 +3040,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "92dbf123", + "id": "c88e54a9", "metadata": { "collapsed": false, "editable": true @@ -3060,7 +3060,7 @@ }, { "cell_type": "markdown", - "id": "e12a5f00", + "id": "6facc9e0", "metadata": { "editable": true }, @@ -3071,7 +3071,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "febeaf82", + "id": "0fe2de24", "metadata": { "collapsed": false, "editable": true @@ -3114,7 +3114,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "c07aa9ac", + "id": "33e2f09a", "metadata": { "collapsed": false, "editable": true @@ -3129,7 +3129,7 @@ }, { "cell_type": "markdown", - "id": "0cd41c03", + "id": "7af3d057", "metadata": { "editable": true }, @@ -3139,7 +3139,7 @@ }, { "cell_type": "markdown", - "id": "bd868c50", + "id": "c5cc4ff1", "metadata": { "editable": true }, @@ -3153,7 +3153,7 @@ }, { "cell_type": "markdown", - "id": "65fb290e", + "id": "e187c407", "metadata": { "editable": true }, @@ -3165,7 +3165,7 @@ }, { "cell_type": "markdown", - "id": "463f75e0", + "id": "80c1e1d2", "metadata": { "editable": true }, @@ -3177,7 +3177,7 @@ }, { "cell_type": "markdown", - "id": "b0b45b31", + "id": "d08c63a8", "metadata": { "editable": true }, @@ -3189,7 +3189,7 @@ }, { "cell_type": "markdown", - "id": "9f88e79e", + "id": "e4199d04", "metadata": { "editable": true }, @@ -3199,7 +3199,7 @@ }, { "cell_type": "markdown", - "id": "4f7242ca", + "id": "1cd0a12a", "metadata": { "editable": true }, @@ -3211,7 +3211,7 @@ }, { "cell_type": "markdown", - "id": "a6e63461", + "id": "c4de93ba", "metadata": { "editable": true }, @@ -3221,7 +3221,7 @@ }, { "cell_type": "markdown", - "id": "29b9afea", + "id": "27136288", "metadata": { "editable": true }, @@ -3233,7 +3233,7 @@ }, { "cell_type": "markdown", - "id": "082571e4", + "id": "a088b960", "metadata": { "editable": true }, @@ -3244,7 +3244,7 @@ }, { "cell_type": "markdown", - "id": "6ec2b7ca", + "id": "1a02ddbb", "metadata": { "editable": true }, @@ -3256,7 +3256,7 @@ }, { "cell_type": "markdown", - "id": "e6a5f733", + "id": "ed18d091", "metadata": { "editable": true }, @@ -3268,7 +3268,7 @@ }, { "cell_type": "markdown", - "id": "d56a3f53", + "id": "3d5b79af", "metadata": { "editable": true }, @@ -3278,7 +3278,7 @@ }, { "cell_type": "markdown", - "id": "3947d992", + "id": "dda4570b", "metadata": { "editable": true }, @@ -3290,7 +3290,7 @@ }, { "cell_type": "markdown", - "id": "84faa9d9", + "id": "11d8d3e3", "metadata": { "editable": true }, @@ -3302,7 +3302,7 @@ }, { "cell_type": "markdown", - "id": "db86df7f", + "id": "46901f0f", "metadata": { "editable": true }, @@ -3312,7 +3312,7 @@ }, { "cell_type": "markdown", - "id": "d78105c6", + "id": "9189fcdb", "metadata": { "editable": true }, @@ -3324,7 +3324,7 @@ }, { "cell_type": "markdown", - "id": "ec1aa593", + "id": "90000eee", "metadata": { "editable": true }, @@ -3334,7 +3334,7 @@ }, { "cell_type": "markdown", - "id": "f34bef40", + "id": "e82d1f9c", "metadata": { "editable": true }, @@ -3346,7 +3346,7 @@ }, { "cell_type": "markdown", - "id": "ceaf9a9e", + "id": "072720c0", "metadata": { "editable": true }, @@ -3356,7 +3356,7 @@ }, { "cell_type": "markdown", - "id": "ab1d059d", + "id": "dd15f44d", "metadata": { "editable": true }, @@ -3396,7 +3396,7 @@ }, { "cell_type": "markdown", - "id": "ad796da6", + "id": "6fb5a31f", "metadata": { "editable": true }, @@ -3413,7 +3413,7 @@ }, { "cell_type": "markdown", - "id": "df8c83cc", + "id": "179c61b1", "metadata": { "editable": true }, @@ -3436,7 +3436,7 @@ }, { "cell_type": "markdown", - "id": "410aac9c", + "id": "5c43a81e", "metadata": { "editable": true }, @@ -3453,7 +3453,7 @@ }, { "cell_type": "markdown", - "id": "c8b42e28", + "id": "29134082", "metadata": { "editable": true }, @@ -3472,7 +3472,7 @@ }, { "cell_type": "markdown", - "id": "9696acb0", + "id": "8ef42f32", "metadata": { "editable": true }, @@ -3483,7 +3483,7 @@ }, { "cell_type": "markdown", - "id": "6402cd93", + "id": "076c58c7", "metadata": { "editable": true }, @@ -3495,7 +3495,7 @@ }, { "cell_type": "markdown", - "id": "7aa90576", + "id": "04db202f", "metadata": { "editable": true }, @@ -3513,7 +3513,7 @@ }, { "cell_type": "markdown", - "id": "8d9194f3", + "id": "b60cb6b5", "metadata": { "editable": true }, @@ -3529,7 +3529,7 @@ }, { "cell_type": "markdown", - "id": "fa1477f5", + "id": "d29615f2", "metadata": { "editable": true }, @@ -3541,7 +3541,7 @@ }, { "cell_type": "markdown", - "id": "5aad52f5", + "id": "1f2c3f1e", "metadata": { "editable": true }, @@ -3551,7 +3551,7 @@ }, { "cell_type": "markdown", - "id": "67a7edc8", + "id": "759ab85a", "metadata": { "editable": true }, @@ -3566,7 +3566,7 @@ }, { "cell_type": "markdown", - "id": "50428ec4", + "id": "4538c162", "metadata": { "editable": true }, @@ -3578,7 +3578,7 @@ }, { "cell_type": "markdown", - "id": "8f8769e8", + "id": "a5617e01", "metadata": { "editable": true }, @@ -3588,7 +3588,7 @@ }, { "cell_type": "markdown", - "id": "51a88b15", + "id": "fe0b46d4", "metadata": { "editable": true }, @@ -3600,7 +3600,7 @@ }, { "cell_type": "markdown", - "id": "2b7cc475", + "id": "7695cd6a", "metadata": { "editable": true }, @@ -3610,7 +3610,7 @@ }, { "cell_type": "markdown", - "id": "a655c3e6", + "id": "26601b01", "metadata": { "editable": true }, @@ -3622,7 +3622,7 @@ }, { "cell_type": "markdown", - "id": "72d1c378", + "id": "6a2d467f", "metadata": { "editable": true }, @@ -3634,7 +3634,7 @@ }, { "cell_type": "markdown", - "id": "15a44ecc", + "id": "a830ae89", "metadata": { "editable": true }, @@ -3649,7 +3649,7 @@ }, { "cell_type": "markdown", - "id": "0bb7dca3", + "id": "488dd05c", "metadata": { "editable": true }, @@ -3660,7 +3660,7 @@ }, { "cell_type": "markdown", - "id": "c45e48e8", + "id": "0383a3b6", "metadata": { "editable": true }, @@ -3680,7 +3680,7 @@ }, { "cell_type": "markdown", - "id": "856e267f", + "id": "10e82320", "metadata": { "editable": true }, @@ -3692,7 +3692,7 @@ }, { "cell_type": "markdown", - "id": "c304b541", + "id": "59a6e34a", "metadata": { "editable": true }, @@ -3702,7 +3702,7 @@ }, { "cell_type": "markdown", - "id": "13a7fb5e", + "id": "ab9dc4a5", "metadata": { "editable": true }, @@ -3714,7 +3714,7 @@ }, { "cell_type": "markdown", - "id": "91d79dd2", + "id": "51c1c028", "metadata": { "editable": true }, @@ -3743,7 +3743,7 @@ }, { "cell_type": "markdown", - "id": "afe923ac", + "id": "ec252687", "metadata": { "editable": true }, @@ -3770,7 +3770,7 @@ }, { "cell_type": "markdown", - "id": "e3b0107b", + "id": "156bfe10", "metadata": { "editable": true }, @@ -3781,7 +3781,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "df6b7c05", + "id": "3c182484", "metadata": { "collapsed": false, "editable": true @@ -3821,7 +3821,7 @@ }, { "cell_type": "markdown", - "id": "9e91f9a0", + "id": "98a0e5ec", "metadata": { "editable": true }, @@ -3838,7 +3838,7 @@ }, { "cell_type": "markdown", - "id": "415fc97a", + "id": "b69361d0", "metadata": { "editable": true }, @@ -3861,7 +3861,7 @@ }, { "cell_type": "markdown", - "id": "c537ea64", + "id": "8d7c0cc8", "metadata": { "editable": true }, @@ -3875,7 +3875,7 @@ }, { "cell_type": "markdown", - "id": "ba856156", + "id": "0a45fa90", "metadata": { "editable": true }, @@ -3894,7 +3894,7 @@ }, { "cell_type": "markdown", - "id": "376b56d3", + "id": "add34bfc", "metadata": { "editable": true }, @@ -3904,7 +3904,7 @@ }, { "cell_type": "markdown", - "id": "94383f7f", + "id": "f00ff6da", "metadata": { "editable": true }, @@ -3916,7 +3916,7 @@ }, { "cell_type": "markdown", - "id": "836c6ad5", + "id": "348cd8df", "metadata": { "editable": true }, @@ -3930,7 +3930,7 @@ }, { "cell_type": "markdown", - "id": "e56306d7", + "id": "f7697c17", "metadata": { "editable": true }, @@ -3942,7 +3942,7 @@ }, { "cell_type": "markdown", - "id": "cd33b717", + "id": "64417096", "metadata": { "editable": true }, @@ -3952,7 +3952,7 @@ }, { "cell_type": "markdown", - "id": "f81bbc5e", + "id": "ded28c47", "metadata": { "editable": true }, @@ -3964,7 +3964,7 @@ }, { "cell_type": "markdown", - "id": "bdc865ea", + "id": "f70d6ea7", "metadata": { "editable": true }, @@ -3981,7 +3981,7 @@ }, { "cell_type": "markdown", - "id": "6560516f", + "id": "68ecbbfa", "metadata": { "editable": true }, @@ -3991,7 +3991,7 @@ }, { "cell_type": "markdown", - "id": "0913014f", + "id": "73db495d", "metadata": { "editable": true }, @@ -4007,7 +4007,7 @@ }, { "cell_type": "markdown", - "id": "7960d3eb", + "id": "44412861", "metadata": { "editable": true }, @@ -4017,7 +4017,7 @@ }, { "cell_type": "markdown", - "id": "a2c6cab1", + "id": "985de8ad", "metadata": { "editable": true }, @@ -4033,7 +4033,7 @@ }, { "cell_type": "markdown", - "id": "d026c4fa", + "id": "bae93d68", "metadata": { "editable": true }, @@ -4043,7 +4043,7 @@ }, { "cell_type": "markdown", - "id": "bc5999f4", + "id": "e29bd644", "metadata": { "editable": true }, @@ -4059,7 +4059,7 @@ }, { "cell_type": "markdown", - "id": "1753c718", + "id": "3abf4a2d", "metadata": { "editable": true }, @@ -4069,7 +4069,7 @@ }, { "cell_type": "markdown", - "id": "fb1ffbac", + "id": "fe1e2ce7", "metadata": { "editable": true }, @@ -4086,7 +4086,7 @@ }, { "cell_type": "markdown", - "id": "9d5ba9ce", + "id": "d23d044b", "metadata": { "editable": true }, @@ -4098,7 +4098,7 @@ }, { "cell_type": "markdown", - "id": "fc09fc11", + "id": "36e38f22", "metadata": { "editable": true }, @@ -4110,7 +4110,7 @@ }, { "cell_type": "markdown", - "id": "36822227", + "id": "ad22843b", "metadata": { "editable": true }, @@ -4122,7 +4122,7 @@ }, { "cell_type": "markdown", - "id": "de6cc474", + "id": "fabcf0f8", "metadata": { "editable": true }, @@ -4132,7 +4132,7 @@ }, { "cell_type": "markdown", - "id": "453ff6f8", + "id": "2c2f44c5", "metadata": { "editable": true }, @@ -4144,7 +4144,7 @@ }, { "cell_type": "markdown", - "id": "3fb1165b", + "id": "105762d0", "metadata": { "editable": true }, @@ -4156,7 +4156,7 @@ }, { "cell_type": "markdown", - "id": "fb0b59b1", + "id": "35a42e41", "metadata": { "editable": true }, @@ -4168,7 +4168,7 @@ }, { "cell_type": "markdown", - "id": "1c998fc7", + "id": "8e21698f", "metadata": { "editable": true }, @@ -4178,7 +4178,7 @@ }, { "cell_type": "markdown", - "id": "8b91e913", + "id": "a240efc0", "metadata": { "editable": true }, @@ -4190,7 +4190,7 @@ }, { "cell_type": "markdown", - "id": "2d8c0246", + "id": "3919a2cb", "metadata": { "editable": true }, @@ -4200,7 +4200,7 @@ }, { "cell_type": "markdown", - "id": "f73a9f8b", + "id": "ddf99a3e", "metadata": { "editable": true }, @@ -4212,7 +4212,7 @@ }, { "cell_type": "markdown", - "id": "0ce5f423", + "id": "5a790d25", "metadata": { "editable": true }, @@ -4226,7 +4226,7 @@ }, { "cell_type": "markdown", - "id": "3ec391cc", + "id": "113a5d16", "metadata": { "editable": true }, @@ -4238,7 +4238,7 @@ }, { "cell_type": "markdown", - "id": "11a3f35c", + "id": "ec65ec89", "metadata": { "editable": true }, @@ -4250,7 +4250,7 @@ }, { "cell_type": "markdown", - "id": "c6b8757c", + "id": "ed891134", "metadata": { "editable": true }, @@ -4260,7 +4260,7 @@ }, { "cell_type": "markdown", - "id": "4e2f4fc0", + "id": "5266f146", "metadata": { "editable": true }, @@ -4272,7 +4272,7 @@ }, { "cell_type": "markdown", - "id": "db3e6c79", + "id": "666ab5b3", "metadata": { "editable": true }, @@ -4283,7 +4283,7 @@ }, { "cell_type": "markdown", - "id": "4d95e8aa", + "id": "ab9a3eec", "metadata": { "editable": true }, @@ -4295,7 +4295,7 @@ }, { "cell_type": "markdown", - "id": "a7dfc330", + "id": "252e7533", "metadata": { "editable": true }, @@ -4305,7 +4305,7 @@ }, { "cell_type": "markdown", - "id": "888796de", + "id": "201b9300", "metadata": { "editable": true }, @@ -4317,7 +4317,7 @@ }, { "cell_type": "markdown", - "id": "99561aeb", + "id": "4f6287cf", "metadata": { "editable": true }, @@ -4327,7 +4327,7 @@ }, { "cell_type": "markdown", - "id": "0a4556b6", + "id": "1ace6d06", "metadata": { "editable": true }, @@ -4339,7 +4339,7 @@ }, { "cell_type": "markdown", - "id": "9844b480", + "id": "274f98fb", "metadata": { "editable": true }, @@ -4350,7 +4350,7 @@ }, { "cell_type": "markdown", - "id": "3731b9ff", + "id": "70dc1b76", "metadata": { "editable": true }, @@ -4362,7 +4362,7 @@ }, { "cell_type": "markdown", - "id": "2debf83f", + "id": "096c55e9", "metadata": { "editable": true }, @@ -4380,7 +4380,7 @@ }, { "cell_type": "markdown", - "id": "7fd0c7b2", + "id": "0ac9bdae", "metadata": { "editable": true }, @@ -4396,19 +4396,19 @@ }, { "cell_type": "markdown", - "id": "db45a990", + "id": "a456e251", "metadata": { "editable": true }, "source": [ "$$\n", - "\\frac{\\partial^2 C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}^T\\partial \\boldsymbol{\\beta}} =\\frac{2}{n}\\boldsymbol{X}^T\\boldsymbol{X}.\n", + "\\frac{\\partial^2 C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T} =\\frac{2}{n}\\boldsymbol{X}^T\\boldsymbol{X}.\n", "$$" ] }, { "cell_type": "markdown", - "id": "476e4063", + "id": "7c817401", "metadata": { "editable": true }, @@ -4420,7 +4420,7 @@ }, { "cell_type": "markdown", - "id": "f7caedf0", + "id": "20d7bb5d", "metadata": { "editable": true }, @@ -4432,7 +4432,7 @@ }, { "cell_type": "markdown", - "id": "a9469af7", + "id": "52a9f64e", "metadata": { "editable": true }, @@ -4445,7 +4445,7 @@ }, { "cell_type": "markdown", - "id": "dae869bb", + "id": "77820e66", "metadata": { "editable": true }, @@ -4461,7 +4461,7 @@ }, { "cell_type": "markdown", - "id": "d718bf56", + "id": "dc6ab9cd", "metadata": { "editable": true }, @@ -4475,7 +4475,7 @@ }, { "cell_type": "markdown", - "id": "f5ad97e4", + "id": "8a8ee502", "metadata": { "editable": true }, @@ -4485,7 +4485,7 @@ }, { "cell_type": "markdown", - "id": "b76f1367", + "id": "f377b529", "metadata": { "editable": true }, @@ -4497,7 +4497,7 @@ }, { "cell_type": "markdown", - "id": "dc7b9319", + "id": "e572a54d", "metadata": { "editable": true }, @@ -4507,7 +4507,7 @@ }, { "cell_type": "markdown", - "id": "8159fef4", + "id": "1b9c72f5", "metadata": { "editable": true }, @@ -4519,7 +4519,7 @@ }, { "cell_type": "markdown", - "id": "d34ac113", + "id": "15c56784", "metadata": { "editable": true }, @@ -4529,7 +4529,7 @@ }, { "cell_type": "markdown", - "id": "1e0b77c9", + "id": "a03c1a32", "metadata": { "editable": true }, @@ -4543,7 +4543,7 @@ }, { "cell_type": "markdown", - "id": "01cb77e3", + "id": "af372da2", "metadata": { "editable": true }, @@ -4554,13 +4554,13 @@ "method corrects the bias in the estimation of the population variance\n", "and covariance. It also partially corrects the bias in the estimation\n", "of the population standard deviation. If you use a library like\n", - "**Scikit-Learn** or **nunmpy's** function calculate the covariance, this\n", + "**Scikit-Learn** or **nunmpy's** function to calculate the covariance, this\n", "quantity will be computed with a factor $1/(n-1)$." ] }, { "cell_type": "markdown", - "id": "e69ed735", + "id": "9200f285", "metadata": { "editable": true }, @@ -4576,7 +4576,7 @@ }, { "cell_type": "markdown", - "id": "f0da7f4b", + "id": "d5becae6", "metadata": { "editable": true }, @@ -4588,7 +4588,7 @@ }, { "cell_type": "markdown", - "id": "935ca456", + "id": "27e6e5b8", "metadata": { "editable": true }, @@ -4601,7 +4601,7 @@ }, { "cell_type": "markdown", - "id": "06f10a4e", + "id": "06454f55", "metadata": { "editable": true }, @@ -4615,7 +4615,7 @@ }, { "cell_type": "markdown", - "id": "be61cd6a", + "id": "833b2511", "metadata": { "editable": true }, @@ -4625,7 +4625,7 @@ }, { "cell_type": "markdown", - "id": "0e25f2ad", + "id": "056aceed", "metadata": { "editable": true }, @@ -4638,7 +4638,7 @@ }, { "cell_type": "markdown", - "id": "fe5ccfec", + "id": "950f361e", "metadata": { "editable": true }, @@ -4657,7 +4657,7 @@ }, { "cell_type": "markdown", - "id": "eefbdf95", + "id": "2d8ac5c2", "metadata": { "editable": true }, @@ -4669,7 +4669,7 @@ }, { "cell_type": "markdown", - "id": "05b5b021", + "id": "62d04779", "metadata": { "editable": true }, @@ -4681,7 +4681,7 @@ }, { "cell_type": "markdown", - "id": "17df2dd0", + "id": "797b13b8", "metadata": { "editable": true }, @@ -4691,7 +4691,7 @@ }, { "cell_type": "markdown", - "id": "286fc60b", + "id": "bd705147", "metadata": { "editable": true }, @@ -4703,7 +4703,7 @@ }, { "cell_type": "markdown", - "id": "9918e3f5", + "id": "542850cc", "metadata": { "editable": true }, @@ -4716,7 +4716,7 @@ }, { "cell_type": "markdown", - "id": "11803309", + "id": "db1aaf48", "metadata": { "editable": true }, @@ -4735,7 +4735,7 @@ }, { "cell_type": "markdown", - "id": "81195690", + "id": "6dc65433", "metadata": { "editable": true }, @@ -4745,7 +4745,7 @@ }, { "cell_type": "markdown", - "id": "7a402445", + "id": "ccd95a67", "metadata": { "editable": true }, @@ -4764,7 +4764,7 @@ }, { "cell_type": "markdown", - "id": "4c0f4547", + "id": "c15cc257", "metadata": { "editable": true }, @@ -4782,7 +4782,7 @@ }, { "cell_type": "markdown", - "id": "238844a1", + "id": "cc4a5463", "metadata": { "editable": true }, @@ -4796,7 +4796,7 @@ }, { "cell_type": "markdown", - "id": "33ddecf3", + "id": "b8cfd6fa", "metadata": { "editable": true }, @@ -4811,7 +4811,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "5b7e3d47", + "id": "31e1da88", "metadata": { "collapsed": false, "editable": true @@ -4832,7 +4832,7 @@ }, { "cell_type": "markdown", - "id": "b5203c81", + "id": "24a933c2", "metadata": { "editable": true }, @@ -4849,7 +4849,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "6ca9fe2f", + "id": "8d9779ce", "metadata": { "collapsed": false, "editable": true @@ -4881,7 +4881,7 @@ }, { "cell_type": "markdown", - "id": "0a33aee4", + "id": "c7e322b5", "metadata": { "editable": true }, @@ -4895,7 +4895,7 @@ }, { "cell_type": "markdown", - "id": "be321ad7", + "id": "ea5682d7", "metadata": { "editable": true }, @@ -4908,7 +4908,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "df41b33a", + "id": "0e83fcbc", "metadata": { "collapsed": false, "editable": true @@ -4933,7 +4933,7 @@ }, { "cell_type": "markdown", - "id": "37e53b7c", + "id": "1226fab9", "metadata": { "editable": true }, @@ -4943,7 +4943,7 @@ }, { "cell_type": "markdown", - "id": "470d603b", + "id": "923ffc58", "metadata": { "editable": true }, @@ -4954,7 +4954,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "d15a64cf", + "id": "145c2622", "metadata": { "collapsed": false, "editable": true @@ -5008,7 +5008,7 @@ }, { "cell_type": "markdown", - "id": "450109da", + "id": "8a657c92", "metadata": { "editable": true }, @@ -5025,7 +5025,7 @@ }, { "cell_type": "markdown", - "id": "15d78e68", + "id": "522f204b", "metadata": { "editable": true }, @@ -5037,7 +5037,7 @@ }, { "cell_type": "markdown", - "id": "05d71468", + "id": "6ab032e5", "metadata": { "editable": true }, @@ -5049,7 +5049,7 @@ }, { "cell_type": "markdown", - "id": "579b77d0", + "id": "423a9a47", "metadata": { "editable": true }, @@ -5059,7 +5059,7 @@ }, { "cell_type": "markdown", - "id": "0b77d639", + "id": "817b323b", "metadata": { "editable": true }, @@ -5076,7 +5076,7 @@ }, { "cell_type": "markdown", - "id": "b6f64deb", + "id": "93f4ff8f", "metadata": { "editable": true }, @@ -5086,7 +5086,7 @@ }, { "cell_type": "markdown", - "id": "6416bd42", + "id": "437c6704", "metadata": { "editable": true }, @@ -5101,7 +5101,7 @@ }, { "cell_type": "markdown", - "id": "cd76a3d6", + "id": "fe76cb03", "metadata": { "editable": true }, @@ -5111,7 +5111,7 @@ }, { "cell_type": "markdown", - "id": "7667e919", + "id": "71d190a9", "metadata": { "editable": true }, @@ -5125,7 +5125,7 @@ }, { "cell_type": "markdown", - "id": "a364fdec", + "id": "21d676ff", "metadata": { "editable": true }, @@ -5137,7 +5137,7 @@ }, { "cell_type": "markdown", - "id": "9e30f36d", + "id": "95bf7f81", "metadata": { "editable": true }, @@ -5149,7 +5149,7 @@ }, { "cell_type": "markdown", - "id": "a8706a80", + "id": "3e6fccbc", "metadata": { "editable": true }, @@ -5161,7 +5161,7 @@ }, { "cell_type": "markdown", - "id": "b9e7b5b3", + "id": "8c261002", "metadata": { "editable": true }, @@ -5171,7 +5171,7 @@ }, { "cell_type": "markdown", - "id": "58f9f374", + "id": "f9aedabd", "metadata": { "editable": true }, @@ -5183,7 +5183,7 @@ }, { "cell_type": "markdown", - "id": "ccd3b380", + "id": "b9c3ddc5", "metadata": { "editable": true }, @@ -5193,7 +5193,7 @@ }, { "cell_type": "markdown", - "id": "4febae0d", + "id": "ed30a2b1", "metadata": { "editable": true }, @@ -5210,7 +5210,7 @@ }, { "cell_type": "markdown", - "id": "5264a8ef", + "id": "dfb0eb4a", "metadata": { "editable": true }, @@ -5220,7 +5220,7 @@ }, { "cell_type": "markdown", - "id": "6306d337", + "id": "39664ac4", "metadata": { "editable": true }, @@ -5232,7 +5232,7 @@ }, { "cell_type": "markdown", - "id": "0b85ad53", + "id": "36858bcd", "metadata": { "editable": true }, @@ -5242,7 +5242,7 @@ }, { "cell_type": "markdown", - "id": "3f462bd9", + "id": "1938dd21", "metadata": { "editable": true }, @@ -5254,7 +5254,7 @@ }, { "cell_type": "markdown", - "id": "3e998d31", + "id": "d89b7fce", "metadata": { "editable": true }, @@ -5268,7 +5268,7 @@ }, { "cell_type": "markdown", - "id": "93ad8f0c", + "id": "a30a69b4", "metadata": { "editable": true }, @@ -5280,7 +5280,7 @@ }, { "cell_type": "markdown", - "id": "ec4e112e", + "id": "34639fb6", "metadata": { "editable": true }, @@ -5302,7 +5302,7 @@ }, { "cell_type": "markdown", - "id": "36422c93", + "id": "20ad8e3f", "metadata": { "editable": true }, @@ -5314,7 +5314,7 @@ }, { "cell_type": "markdown", - "id": "c726caf1", + "id": "d9478bfc", "metadata": { "editable": true }, @@ -5329,7 +5329,7 @@ }, { "cell_type": "markdown", - "id": "13624ec5", + "id": "04d7397f", "metadata": { "editable": true }, @@ -5341,7 +5341,7 @@ }, { "cell_type": "markdown", - "id": "a5f5dbbb", + "id": "59f21f2c", "metadata": { "editable": true }, @@ -5353,7 +5353,7 @@ }, { "cell_type": "markdown", - "id": "f5a06622", + "id": "a309e6d5", "metadata": { "editable": true }, @@ -5363,7 +5363,7 @@ }, { "cell_type": "markdown", - "id": "472afc38", + "id": "2c4186ec", "metadata": { "editable": true }, @@ -5375,7 +5375,7 @@ }, { "cell_type": "markdown", - "id": "719c02f4", + "id": "ccd854d0", "metadata": { "editable": true }, @@ -5385,7 +5385,7 @@ }, { "cell_type": "markdown", - "id": "b2a62207", + "id": "74eaeda4", "metadata": { "editable": true }, @@ -5397,7 +5397,7 @@ }, { "cell_type": "markdown", - "id": "6194792e", + "id": "053b8482", "metadata": { "editable": true }, @@ -5407,7 +5407,7 @@ }, { "cell_type": "markdown", - "id": "c957d867", + "id": "6f8a528a", "metadata": { "editable": true }, @@ -5419,7 +5419,7 @@ }, { "cell_type": "markdown", - "id": "7d0a4281", + "id": "112070df", "metadata": { "editable": true }, @@ -5436,7 +5436,7 @@ }, { "cell_type": "markdown", - "id": "3a982382", + "id": "33e268b0", "metadata": { "editable": true }, @@ -5449,7 +5449,7 @@ }, { "cell_type": "markdown", - "id": "9fa67077", + "id": "0de7adcd", "metadata": { "editable": true }, @@ -5461,7 +5461,7 @@ }, { "cell_type": "markdown", - "id": "d70a4c10", + "id": "fa30d846", "metadata": { "editable": true }, @@ -5471,7 +5471,7 @@ }, { "cell_type": "markdown", - "id": "61ce3a16", + "id": "d577ef22", "metadata": { "editable": true }, @@ -5484,7 +5484,7 @@ }, { "cell_type": "markdown", - "id": "a7110d4e", + "id": "ce39ce20", "metadata": { "editable": true }, @@ -5494,7 +5494,7 @@ }, { "cell_type": "markdown", - "id": "102fd8e6", + "id": "960bd21b", "metadata": { "editable": true }, @@ -5506,7 +5506,7 @@ }, { "cell_type": "markdown", - "id": "ea3f1c3b", + "id": "7736561e", "metadata": { "editable": true }, @@ -5519,7 +5519,7 @@ }, { "cell_type": "markdown", - "id": "2e7d3edc", + "id": "f1043146", "metadata": { "editable": true }, @@ -5532,7 +5532,7 @@ }, { "cell_type": "markdown", - "id": "6138e4d3", + "id": "56e791a2", "metadata": { "editable": true }, @@ -5544,7 +5544,7 @@ }, { "cell_type": "markdown", - "id": "8406ec32", + "id": "dbcb265a", "metadata": { "editable": true }, @@ -5556,7 +5556,7 @@ }, { "cell_type": "markdown", - "id": "8725c04e", + "id": "9e1a00c1", "metadata": { "editable": true }, @@ -5566,7 +5566,7 @@ }, { "cell_type": "markdown", - "id": "dcb02b56", + "id": "6ca6c71d", "metadata": { "editable": true }, @@ -5579,7 +5579,7 @@ }, { "cell_type": "markdown", - "id": "fdc3d5dc", + "id": "797658d0", "metadata": { "editable": true }, @@ -5591,7 +5591,7 @@ }, { "cell_type": "markdown", - "id": "1a06957b", + "id": "ed64a13e", "metadata": { "editable": true }, @@ -5603,7 +5603,7 @@ }, { "cell_type": "markdown", - "id": "5d3c2482", + "id": "fc6a2aeb", "metadata": { "editable": true }, @@ -5615,7 +5615,7 @@ }, { "cell_type": "markdown", - "id": "e94ba1d9", + "id": "1c956ba4", "metadata": { "editable": true }, @@ -5627,7 +5627,7 @@ }, { "cell_type": "markdown", - "id": "ebee0c20", + "id": "ea5516df", "metadata": { "editable": true }, @@ -5641,7 +5641,7 @@ }, { "cell_type": "markdown", - "id": "e30e7753", + "id": "b6d61e24", "metadata": { "editable": true }, @@ -5653,7 +5653,7 @@ }, { "cell_type": "markdown", - "id": "bf0db09e", + "id": "84765a82", "metadata": { "editable": true }, @@ -5663,7 +5663,7 @@ }, { "cell_type": "markdown", - "id": "09c45e7a", + "id": "973019d8", "metadata": { "editable": true }, @@ -5675,7 +5675,7 @@ }, { "cell_type": "markdown", - "id": "3691ef57", + "id": "7f074350", "metadata": { "editable": true }, @@ -5687,7 +5687,7 @@ }, { "cell_type": "markdown", - "id": "86e182d9", + "id": "0570ceae", "metadata": { "editable": true }, @@ -5699,7 +5699,7 @@ }, { "cell_type": "markdown", - "id": "eb5f1b1c", + "id": "5d16ea9d", "metadata": { "editable": true }, @@ -5711,7 +5711,7 @@ }, { "cell_type": "markdown", - "id": "64a361ad", + "id": "b739c33e", "metadata": { "editable": true }, @@ -5723,7 +5723,7 @@ }, { "cell_type": "markdown", - "id": "736ab040", + "id": "dc8bc23b", "metadata": { "editable": true }, @@ -5742,7 +5742,7 @@ }, { "cell_type": "markdown", - "id": "1e5e13b7", + "id": "d89da6dc", "metadata": { "editable": true }, @@ -5754,7 +5754,7 @@ }, { "cell_type": "markdown", - "id": "f5b34bc6", + "id": "3b871cb3", "metadata": { "editable": true }, @@ -5764,7 +5764,7 @@ }, { "cell_type": "markdown", - "id": "05fed0df", + "id": "018e1163", "metadata": { "editable": true }, @@ -5776,7 +5776,7 @@ }, { "cell_type": "markdown", - "id": "0853969f", + "id": "24f364d8", "metadata": { "editable": true }, @@ -5786,7 +5786,7 @@ }, { "cell_type": "markdown", - "id": "e1e0e802", + "id": "4f695ec4", "metadata": { "editable": true }, @@ -5798,7 +5798,7 @@ }, { "cell_type": "markdown", - "id": "5349338e", + "id": "a4599188", "metadata": { "editable": true }, @@ -5810,7 +5810,7 @@ }, { "cell_type": "markdown", - "id": "3856bb34", + "id": "0edf0a0c", "metadata": { "editable": true }, @@ -5826,7 +5826,7 @@ }, { "cell_type": "markdown", - "id": "96bb9c00", + "id": "9afad20c", "metadata": { "editable": true }, @@ -5838,7 +5838,7 @@ }, { "cell_type": "markdown", - "id": "343e7f2a", + "id": "33df2d17", "metadata": { "editable": true }, @@ -5850,7 +5850,7 @@ }, { "cell_type": "markdown", - "id": "d9a0dd13", + "id": "972e10e7", "metadata": { "editable": true }, @@ -5860,7 +5860,7 @@ }, { "cell_type": "markdown", - "id": "47f89929", + "id": "86f89bca", "metadata": { "editable": true }, @@ -5872,7 +5872,7 @@ }, { "cell_type": "markdown", - "id": "6a8d40da", + "id": "c286d017", "metadata": { "editable": true }, @@ -5882,7 +5882,7 @@ }, { "cell_type": "markdown", - "id": "87a36c85", + "id": "4dcbe7e5", "metadata": { "editable": true }, @@ -5894,7 +5894,7 @@ }, { "cell_type": "markdown", - "id": "6237d1b0", + "id": "43db48d4", "metadata": { "editable": true }, @@ -5911,7 +5911,7 @@ }, { "cell_type": "markdown", - "id": "39430adb", + "id": "17d8c95a", "metadata": { "editable": true }, @@ -5923,7 +5923,7 @@ }, { "cell_type": "markdown", - "id": "b97b5db8", + "id": "3c9d6593", "metadata": { "editable": true }, @@ -5935,7 +5935,7 @@ }, { "cell_type": "markdown", - "id": "06fb16da", + "id": "a1606a04", "metadata": { "editable": true }, @@ -5945,7 +5945,7 @@ }, { "cell_type": "markdown", - "id": "552d08cd", + "id": "d192512a", "metadata": { "editable": true }, @@ -5957,7 +5957,7 @@ }, { "cell_type": "markdown", - "id": "4e2d1b42", + "id": "a60675a1", "metadata": { "editable": true }, @@ -5967,7 +5967,7 @@ }, { "cell_type": "markdown", - "id": "fbc4cda5", + "id": "942fc37f", "metadata": { "editable": true }, @@ -5979,7 +5979,7 @@ }, { "cell_type": "markdown", - "id": "e36f9245", + "id": "4f5e067f", "metadata": { "editable": true }, @@ -5989,7 +5989,7 @@ }, { "cell_type": "markdown", - "id": "06b74c95", + "id": "d330f743", "metadata": { "editable": true }, @@ -6001,7 +6001,7 @@ }, { "cell_type": "markdown", - "id": "1cbd57d2", + "id": "55f9b635", "metadata": { "editable": true }, @@ -6011,7 +6011,7 @@ }, { "cell_type": "markdown", - "id": "17a0dae2", + "id": "5a6d1b0b", "metadata": { "editable": true }, @@ -6023,7 +6023,7 @@ }, { "cell_type": "markdown", - "id": "abc993af", + "id": "12fd13cb", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/html/chapter1.html b/doc/LectureNotes/_build/html/chapter1.html index b982f1929..aa83d5f68 100644 --- a/doc/LectureNotes/_build/html/chapter1.html +++ b/doc/LectureNotes/_build/html/chapter1.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+ @@ -957,13 +984,13 @@ example of the functionality of Scikit-Learn.

The intercept alpha: 
- [1.72261919]
+ [2.11366595]
 Coefficient beta : 
- [[5.39493843]]
-Mean squared error: 0.23
-Variance score: 0.91
+ [[4.95136998]]
+Mean squared error: 0.28
+Variance score: 0.89
 Mean squared log error: 0.01
-Mean absolute error: 0.39
+Mean absolute error: 0.41
_images/chapter1_19_1.png @@ -1063,7 +1090,7 @@ a linear \(x\)-dependence we s
_images/chapter1_33_0.png -
0.0050000000000000044
+
0.004999999999999996
diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index 2f4af6bb2..dd6974658 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+
@@ -1274,7 +1301,7 @@ the Hadamard product, meaning element-wise multiplication.

Old accuracy on training data: 0.1440501043841336
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1608,7 +1635,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1617,7 +1644,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1626,7 +1653,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1635,7 +1662,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1644,7 +1671,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1653,7 +1680,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1662,7 +1689,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1671,11 +1698,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1684,11 +1711,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1697,11 +1724,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1710,11 +1737,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1723,11 +1750,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1736,7 +1763,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1745,11 +1772,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1758,11 +1785,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1771,11 +1798,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1784,11 +1811,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1797,11 +1824,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1810,11 +1837,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1823,11 +1850,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1836,11 +1863,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1893,15 +1920,15 @@ Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -2160,26 +2187,27 @@ Accuracy score on test set:  0.9861111111111112
 
Learning rate  =  0.01
 Lambda =  0.1
 Accuracy score on test set:  0.9888888888888889
-
-Learning rate  =  0.01
-Lambda =  1.0
-Accuracy score on test set:  0.9722222222222222
 

 

 
Learning rate  =  0.01
+Lambda =  1.0
+Accuracy score on test set:  0.9722222222222222
+
+Learning rate  =  0.01
 Lambda =  10.0
 Accuracy score on test set:  0.9527777777777777
-
-Learning rate  =  0.1
-Lambda =  1e-05
-Accuracy score on test set:  0.9027777777777778
 

 

 
Learning rate  =  0.1
-Lambda =  0.0001
-Accuracy score on test set:  0.8583333333333333
+Lambda =  1e-05
+Accuracy score on test set:  0.9027777777777778
 
 Learning rate  =  0.1
+Lambda =  0.0001
+Accuracy score on test set:  0.8583333333333333
+

+

+
Learning rate  =  0.1
 Lambda =  0.001
 Accuracy score on test set:  0.8722222222222222
 

@@ -2200,43 +2228,44 @@ Accuracy score on test set:  0.8722222222222222
 Learning rate  =  0.1
 Lambda =  10.0
 Accuracy score on test set:  0.8666666666666667
-
-Learning rate  =  1.0
-Lambda =  1e-05
-Accuracy score on test set:  0.08611111111111111
 
Learning rate  =  1.0
+Lambda =  1e-05
+Accuracy score on test set:  0.08611111111111111
+
+Learning rate  =  1.0
 Lambda =  0.0001
 Accuracy score on test set:  0.10555555555555556
 
 Learning rate  =  1.0
 Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  1.0
-Lambda =  0.01
-Accuracy score on test set:  0.17777777777777778
 
Learning rate  =  1.0
+Lambda =  0.01
+Accuracy score on test set:  0.17777777777777778
+
+Learning rate  =  1.0
 Lambda =  0.1
 Accuracy score on test set:  0.08333333333333333
 
 Learning rate  =  1.0
 Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
-
-Learning rate  =  1.0
+
+
+
Learning rate  =  1.0
 Lambda =  10.0
 Accuracy score on test set:  0.09444444444444444
+
+Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on test set:  0.17222222222222222
 
Learning rate  =  10.0
-Lambda =  1e-05
-Accuracy score on test set:  0.17222222222222222
-
-Learning rate  =  10.0
 Lambda =  0.0001
 Accuracy score on test set:  0.11666666666666667
 
@@ -2247,18 +2276,17 @@ Accuracy score on test set:  0.10555555555555556
 Learning rate  =  10.0
 Lambda =  0.01
 Accuracy score on test set:  0.1388888888888889
-
-
-
Learning rate  =  10.0
-Lambda =  0.1
-Accuracy score on test set:  0.11388888888888889
 
 Learning rate  =  10.0
-Lambda =  1.0
-Accuracy score on test set:  0.10555555555555556
+Lambda =  0.1
+Accuracy score on test set:  0.11388888888888889
 
Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  10.0
 Lambda =  10.0
 Accuracy score on test set:  0.09444444444444444
diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html index 9361d8bea..767cc874d 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+
@@ -2583,6 +2610,43 @@ Using TensorFlow results in a much better execution time. Try it!

19 x = tuple(args[i] for i in argnum) ---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:57, in jacobian(fun, x) + 47 @unary_to_nary + 48 def jacobian(fun, x): + 49 """ + 50 Returns a function which computes the Jacobian of `fun` with respect to + 51 positional argument number `argnum`, which must be a scalar or array. Unlike + (...) + 55 (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...). + 56 """ +---> 57 vjp, ans = _make_vjp(fun, x) + 58 ans_vspace = vspace(ans) + 59 jacobian_shape = ans_vspace.shape + vspace(x).shape + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x) + 8 def make_vjp(fun, x): + 9 start_node = VJPNode.new_root() +---> 10 end_value, end_node = trace(start_node, fun, x) + 11 if end_node is None: + 12 def vjp(g): return vspace(x).zeros() + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x) + 8 with trace_stack.new_trace() as t: + 9 start_box = new_box(x, t, start_node) +---> 10 end_box = fun(start_box) + 11 if isbox(end_box) and end_box._trace == start_box._trace: + 12 return end_box._value, end_box._node + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x) + 13 else: + 14 subargs = subvals(args, zip(argnum, x)) +---> 15 return fun(*subargs, **kwargs) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs) + 18 else: + 19 x = tuple(args[i] for i in argnum) +---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs) + File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:61, in jacobian(fun, x) 59 jacobian_shape = ans_vspace.shape + vspace(x).shape 60 grads = map(vjp, ans_vspace.standard_basis()) @@ -2631,19 +2695,45 @@ Using TensorFlow results in a much better execution time. Try it!

83 defvjp(anp.cos, lambda ans, x : lambda g: - g * anp.sin(x)) 84 defvjp(anp.tan, lambda ans, x : lambda g: g / anp.cos(x) **2) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:37, in primitive.<locals>.f_wrapped(*args, **kwargs) - 35 @wraps(f_raw) - 36 def f_wrapped(*args, **kwargs): ----> 37 boxed_args, trace, node_constructor = find_top_boxed_args(args) - 38 if boxed_args: - 39 argvals = subvals(args, [(argnum, box._value) for argnum, box in boxed_args]) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:27, in ArrayBox.__mul__(self, other) +---> 27 def __mul__(self, other): return anp.multiply(self, other) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:70, in find_top_boxed_args(args) - 68 top_node_type = None - 69 for argnum, arg in enumerate(args): ----> 70 if isbox(arg): - 71 trace = arg._trace - 72 if trace > top_trace: +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45, in primitive.<locals>.f_wrapped(*args, **kwargs) + 43 argnums = tuple(argnum for argnum, _ in boxed_args) + 44 ans = f_wrapped(*argvals, **kwargs) +---> 45 node = node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents) + 46 return new_box(ans, trace, node) + 47 else: + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36, in VJPNode.__init__(self, value, fun, args, kwargs, parent_argnums, parents) + 33 fun_name = getattr(fun, '__name__', fun) + 34 raise NotImplementedError("VJP of {} wrt argnums {} not defined" + 35 .format(fun_name, parent_argnums)) +---> 36 self.vjp = vjpmaker(parent_argnums, value, args, kwargs) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:76, in defvjp.<locals>.vjp_argnums(argnums, ans, args, kwargs) + 73 except KeyError: + 74 raise NotImplementedError( + 75 "VJP of {} wrt argnums 0, 1 not defined".format(fun.__name__)) +---> 76 vjp_0 = vjp_0_fun(ans, *args, **kwargs) + 77 vjp_1 = vjp_1_fun(ans, *args, **kwargs) + 78 return lambda g: (vjp_0(g), vjp_1(g)) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:34, in <lambda>(ans, x, y) + 30 # ----- Binary ufuncs ----- + 32 defvjp(anp.add, lambda ans, x, y : unbroadcast_f(x, lambda g: g), + 33 lambda ans, x, y : unbroadcast_f(y, lambda g: g)) +---> 34 defvjp(anp.multiply, lambda ans, x, y : unbroadcast_f(x, lambda g: y * g), + 35 lambda ans, x, y : unbroadcast_f(y, lambda g: x * g)) + 36 defvjp(anp.subtract, lambda ans, x, y : unbroadcast_f(x, lambda g: g), + 37 lambda ans, x, y : unbroadcast_f(y, lambda g: -g)) + 38 defvjp(anp.divide, lambda ans, x, y : unbroadcast_f(x, lambda g: g / y), + 39 lambda ans, x, y : unbroadcast_f(y, lambda g: - g * x / y**2)) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:659, in unbroadcast_f(target, f) + 658 def unbroadcast_f(target, f): +--> 659 target_meta = anp.metadata(target) + 660 return lambda g: unbroadcast(f(g), target_meta) KeyboardInterrupt:
diff --git a/doc/LectureNotes/_build/html/chapter12.html b/doc/LectureNotes/_build/html/chapter12.html index 0bd3d3e60..c85ed04ce 100644 --- a/doc/LectureNotes/_build/html/chapter12.html +++ b/doc/LectureNotes/_build/html/chapter12.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+
@@ -1129,8 +1156,12 @@ labels = (n_inputs) = (1797,)
+
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lib/__init__.py:32: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.
+  warnings.warn("JAX on Mac ARM machines is experimental and minimally tested. "
+
+
---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
+AttributeError                            Traceback (most recent call last)
 Input In [4], in <cell line: 1>()
 ----> 1 from tensorflow.keras import datasets, layers, models
       2 from tensorflow.keras.layers import Input
@@ -1216,14 +1247,122 @@ labels = (n_inputs) = (1797,)
      30 from tensorflow.lite.python import wrap_toco
      31 from tensorflow.lite.python.convert_phase import Component
 
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:26, in <module>
-     23 import six
-     24 from six.moves import range
----> 26 import flatbuffers
-     27 from tensorflow.core.protobuf import config_pb2 as _config_pb2
-     28 from tensorflow.core.protobuf import graph_debug_info_pb2
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:51, in <module>
+     47 # Jax functions used by TFLite
+     48 # pylint: disable=g-import-not-at-top
+     49 # pylint: disable=unused-import
+     50 try:
+---> 51   from jax import xla_computation as _xla_computation
+     52 except ImportError:
+     53   _xla_computation = None
 
-ModuleNotFoundError: No module named 'flatbuffers'
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/__init__.py:116, in <module>
+     40 from ._src.config import (
+     41   config as config,
+     42   enable_checks as enable_checks,
+   (...)
+     51   numpy_rank_promotion as numpy_rank_promotion,
+     52 )
+     53 from ._src.api import (
+     54   ad,  # TODO(phawkins): update users to avoid this.
+     55   checkpoint as checkpoint,
+   (...)
+    114   xla_computation as xla_computation,
+    115 )
+--> 116 from .experimental.maps import soft_pmap as soft_pmap
+    117 from .version import __version__ as __version__
+    119 # These submodules are separate because they are in an import cycle with
+    120 # jax and rely on the names imported above.
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/experimental/maps.py:26, in <module>
+     23 from functools import wraps, partial, partialmethod
+     24 from enum import Enum
+---> 26 from .. import numpy as jnp
+     27 from .. import core
+     28 from .. import linear_util as lu
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/__init__.py:19, in <module>
+      1 # Copyright 2018 Google LLC
+      2 #
+      3 # Licensed under the Apache License, Version 2.0 (the "License");
+   (...)
+     17 
+     18 # flake8: noqa: F401
+---> 19 from . import fft as fft
+     20 from . import linalg as linalg
+     22 from jax.interpreters.xla import DeviceArray as DeviceArray
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/fft.py:17, in <module>
+      1 # Copyright 2020 Google LLC
+      2 #
+      3 # Licensed under the Apache License, Version 2.0 (the "License");
+   (...)
+     14 
+     15 # flake8: noqa: F401
+---> 17 from jax._src.numpy.fft import (
+     18   ifft as ifft,
+     19   ifft2 as ifft2,
+     20   ifftn as ifftn,
+     21   ifftshift as ifftshift,
+     22   ihfft as ihfft,
+     23   irfft as irfft,
+     24   irfft2 as irfft2,
+     25   irfftn as irfftn,
+     26   fft as fft,
+     27   fft2 as fft2,
+     28   fftfreq as fftfreq,
+     29   fftn as fftn,
+     30   fftshift as fftshift,
+     31   hfft as hfft,
+     32   rfft as rfft,
+     33   rfft2 as rfft2,
+     34   rfftfreq as rfftfreq,
+     35   rfftn as rfftn,
+     36 )
+     38 # Module initialization is encapsulated in a function to avoid accidental
+     39 # namespace pollution.
+     40 _NOT_IMPLEMENTED = []
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/numpy/fft.py:19, in <module>
+     16 import operator
+     17 import numpy as np
+---> 19 from jax import lax
+     20 from jax._src.lib import xla_client
+     21 from jax._src.util import safe_zip
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/lax/__init__.py:332, in <module>
+    299 from jax._src.lax.lax import (_reduce_sum, _reduce_max, _reduce_min, _reduce_or,
+    300                   _reduce_and, _reduce_window_sum, _reduce_window_max,
+    301                   _reduce_window_min, _reduce_window_prod,
+   (...)
+    306                   _upcast_fp16_for_computation, _broadcasting_shape_rule,
+    307                   _eye, _tri, _delta, _ones, _zeros, _dilate_shape)
+    308 from jax._src.lax.control_flow import (
+    309   associative_scan as associative_scan,
+    310   cond as cond,
+   (...)
+    330   while_p as while_p,
+    331 )
+--> 332 from jax._src.lax.fft import (
+    333   fft as fft,
+    334   fft_p as fft_p,
+    335 )
+    336 from jax._src.lax.parallel import (
+    337   all_gather as all_gather,
+    338   all_to_all as all_to_all,
+   (...)
+    355   xeinsum as xeinsum,
+    356 )
+    357 from jax._src.lax.other import (
+    358   conv_general_dilated_patches as conv_general_dilated_patches
+    359 )
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lax/fft.py:145, in <module>
+    143 batching.primitive_batchers[fft_p] = fft_batching_rule
+    144 if pocketfft:
+--> 145   xla.backend_specific_translations['cpu'][fft_p] = pocketfft.pocketfft
+
+AttributeError: module 'jaxlib.pocketfft' has no attribute 'pocketfft'
diff --git a/doc/LectureNotes/_build/html/chapter13.html b/doc/LectureNotes/_build/html/chapter13.html index 42d4fab6a..430d7f1de 100644 --- a/doc/LectureNotes/_build/html/chapter13.html +++ b/doc/LectureNotes/_build/html/chapter13.html @@ -55,6 +55,7 @@ const thebe_selector_output = ".output, .cell_output" + @@ -241,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+ @@ -538,8 +566,12 @@ systems such as automatic translation and speech-to-text.

+
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lib/__init__.py:32: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.
+  warnings.warn("JAX on Mac ARM machines is experimental and minimally tested. "
+
+
---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
+AttributeError                            Traceback (most recent call last)
 Input In [1], in <cell line: 7>()
       5 import numpy as np
       6 import matplotlib.pyplot as plt
@@ -627,14 +659,122 @@ systems such as automatic translation and speech-to-text.

      30 from tensorflow.lite.python import wrap_toco
      31 from tensorflow.lite.python.convert_phase import Component
 
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:26, in <module>
-     23 import six
-     24 from six.moves import range
----> 26 import flatbuffers
-     27 from tensorflow.core.protobuf import config_pb2 as _config_pb2
-     28 from tensorflow.core.protobuf import graph_debug_info_pb2
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:51, in <module>
+     47 # Jax functions used by TFLite
+     48 # pylint: disable=g-import-not-at-top
+     49 # pylint: disable=unused-import
+     50 try:
+---> 51   from jax import xla_computation as _xla_computation
+     52 except ImportError:
+     53   _xla_computation = None
 
-ModuleNotFoundError: No module named 'flatbuffers'
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/__init__.py:116, in <module>
+     40 from ._src.config import (
+     41   config as config,
+     42   enable_checks as enable_checks,
+   (...)
+     51   numpy_rank_promotion as numpy_rank_promotion,
+     52 )
+     53 from ._src.api import (
+     54   ad,  # TODO(phawkins): update users to avoid this.
+     55   checkpoint as checkpoint,
+   (...)
+    114   xla_computation as xla_computation,
+    115 )
+--> 116 from .experimental.maps import soft_pmap as soft_pmap
+    117 from .version import __version__ as __version__
+    119 # These submodules are separate because they are in an import cycle with
+    120 # jax and rely on the names imported above.
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/experimental/maps.py:26, in <module>
+     23 from functools import wraps, partial, partialmethod
+     24 from enum import Enum
+---> 26 from .. import numpy as jnp
+     27 from .. import core
+     28 from .. import linear_util as lu
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/__init__.py:19, in <module>
+      1 # Copyright 2018 Google LLC
+      2 #
+      3 # Licensed under the Apache License, Version 2.0 (the "License");
+   (...)
+     17 
+     18 # flake8: noqa: F401
+---> 19 from . import fft as fft
+     20 from . import linalg as linalg
+     22 from jax.interpreters.xla import DeviceArray as DeviceArray
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/fft.py:17, in <module>
+      1 # Copyright 2020 Google LLC
+      2 #
+      3 # Licensed under the Apache License, Version 2.0 (the "License");
+   (...)
+     14 
+     15 # flake8: noqa: F401
+---> 17 from jax._src.numpy.fft import (
+     18   ifft as ifft,
+     19   ifft2 as ifft2,
+     20   ifftn as ifftn,
+     21   ifftshift as ifftshift,
+     22   ihfft as ihfft,
+     23   irfft as irfft,
+     24   irfft2 as irfft2,
+     25   irfftn as irfftn,
+     26   fft as fft,
+     27   fft2 as fft2,
+     28   fftfreq as fftfreq,
+     29   fftn as fftn,
+     30   fftshift as fftshift,
+     31   hfft as hfft,
+     32   rfft as rfft,
+     33   rfft2 as rfft2,
+     34   rfftfreq as rfftfreq,
+     35   rfftn as rfftn,
+     36 )
+     38 # Module initialization is encapsulated in a function to avoid accidental
+     39 # namespace pollution.
+     40 _NOT_IMPLEMENTED = []
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/numpy/fft.py:19, in <module>
+     16 import operator
+     17 import numpy as np
+---> 19 from jax import lax
+     20 from jax._src.lib import xla_client
+     21 from jax._src.util import safe_zip
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/lax/__init__.py:332, in <module>
+    299 from jax._src.lax.lax import (_reduce_sum, _reduce_max, _reduce_min, _reduce_or,
+    300                   _reduce_and, _reduce_window_sum, _reduce_window_max,
+    301                   _reduce_window_min, _reduce_window_prod,
+   (...)
+    306                   _upcast_fp16_for_computation, _broadcasting_shape_rule,
+    307                   _eye, _tri, _delta, _ones, _zeros, _dilate_shape)
+    308 from jax._src.lax.control_flow import (
+    309   associative_scan as associative_scan,
+    310   cond as cond,
+   (...)
+    330   while_p as while_p,
+    331 )
+--> 332 from jax._src.lax.fft import (
+    333   fft as fft,
+    334   fft_p as fft_p,
+    335 )
+    336 from jax._src.lax.parallel import (
+    337   all_gather as all_gather,
+    338   all_to_all as all_to_all,
+   (...)
+    355   xeinsum as xeinsum,
+    356 )
+    357 from jax._src.lax.other import (
+    358   conv_general_dilated_patches as conv_general_dilated_patches
+    359 )
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lax/fft.py:145, in <module>
+    143 batching.primitive_batchers[fft_p] = fft_batching_rule
+    144 if pocketfft:
+--> 145   xla.backend_specific_translations['cpu'][fft_p] = pocketfft.pocketfft
+
+AttributeError: module 'jaxlib.pocketfft' has no attribute 'pocketfft'
@@ -1863,6 +2003,13 @@ latent space.

16. Convolutional Neural Networks

+ +
+

next

+

Exercises week 34

+
+ + diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html index dd825b377..20579950c 100644 --- a/doc/LectureNotes/_build/html/chapter2.html +++ b/doc/LectureNotes/_build/html/chapter2.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+ @@ -1206,10 +1233,10 @@ covariance matrix through the np.linalg.eig() function.

-
-0.08873443359350565
-3.7851533175757255
-[[ 0.98248312  3.05483267]
- [ 3.05483267 10.24784064]]
+
0.008947823579448178
+4.14822021080294
+[[0.73947737 2.16006961]
+ [2.16006961 7.24782786]]
@@ -1246,10 +1273,10 @@ a more brute force way. Here we scale the mean values for each column of the des
-
0.07858099596662704
-2.071920625289855
-[[1.         0.71822416]
- [0.71822416 1.        ]]
+
0.08657894597048958
+2.219222942590059
+[[1.        0.6343356]
+ [0.6343356 1.       ]]
@@ -1279,30 +1306,30 @@ this matrix we easily see that it is a positive definite matrix.

-
[[ -2.84861838 -10.07337358]
- [  0.53938383   2.59445979]
- [ -0.40980089  -0.48871288]
- [  0.05834332  -0.39384255]
- [  2.25385387   7.58112299]
- [  0.68246434   2.46650488]
- [ -0.25366775  -1.97047717]
- [  0.79081838   2.03807267]
- [ -0.06150169  -0.57109235]
- [ -0.75127504  -1.18266178]]
-          0          1
-0 -2.848618 -10.073374
-1  0.539384   2.594460
-2 -0.409801  -0.488713
-3  0.058343  -0.393843
-4  2.253854   7.581123
-5  0.682464   2.466505
-6 -0.253668  -1.970477
-7  0.790818   2.038073
-8 -0.061502  -0.571092
-9 -0.751275  -1.182662
+
[[ 0.29724806  0.26804287]
+ [-0.15626984 -1.36853738]
+ [-0.77070756 -2.13536532]
+ [-0.45372697 -3.1582408 ]
+ [ 0.52580392  2.72567956]
+ [-0.86515815 -1.35704388]
+ [-0.73738602 -2.12933164]
+ [-0.10486183  1.06292011]
+ [ 1.75670484  5.27381733]
+ [ 0.50835355  0.81805914]]
           0         1
-0  1.000000  0.984525
-1  0.984525  1.000000
+0  0.297248  0.268043
+1 -0.156270 -1.368537
+2 -0.770708 -2.135365
+3 -0.453727 -3.158241
+4  0.525804  2.725680
+5 -0.865158 -1.357044
+6 -0.737386 -2.129332
+7 -0.104862  1.062920
+8  1.756705  5.273817
+9  0.508354  0.818059
+          0         1
+0  1.000000  0.915549
+1  0.915549  1.000000
@@ -1359,37 +1386,37 @@ this matrix we easily see that it is a positive definite matrix.

     0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.090241  0.082140  0.090564  0.084086  0.078082  0.082282  0.076619   
-2   0.0  0.082140  0.075227  0.083102  0.077428  0.072150  0.075982  0.070945   
-3   0.0  0.090564  0.083102  0.096893  0.090268  0.084107  0.091647  0.085571   
-4   0.0  0.084086  0.077428  0.090268  0.084286  0.078707  0.085655  0.080120   
-5   0.0  0.078082  0.072150  0.084107  0.078707  0.073657  0.080061  0.075020   
-6   0.0  0.082282  0.075982  0.091647  0.085655  0.080061  0.089082  0.083380   
-7   0.0  0.076619  0.070945  0.085571  0.080120  0.075020  0.083380  0.078158   
-8   0.0  0.071394  0.066284  0.079944  0.074984  0.070333  0.078082  0.073299   
-9   0.0  0.066569  0.061966  0.074729  0.070216  0.065973  0.073159  0.068776   
-10  0.0  0.073831  0.068541  0.084523  0.079224  0.074258  0.083779  0.078587   
-11  0.0  0.068867  0.064081  0.079021  0.074183  0.069640  0.078484  0.073716   
-12  0.0  0.064284  0.059952  0.073925  0.069506  0.065349  0.073567  0.069187   
-13  0.0  0.060048  0.056127  0.069202  0.065165  0.061359  0.068999  0.064974   
-14  0.0  0.056131  0.052581  0.064823  0.061133  0.057648  0.064753  0.061054   
+1   0.0  0.086925  0.087377  0.087520  0.087878  0.088164  0.079680  0.079580   
+2   0.0  0.087377  0.089240  0.088074  0.089063  0.089924  0.079827  0.080060   
+3   0.0  0.087520  0.088074  0.094227  0.094252  0.094139  0.089441  0.088974   
+4   0.0  0.087878  0.089063  0.094252  0.094610  0.094812  0.089040  0.088778   
+5   0.0  0.088164  0.089924  0.094139  0.094812  0.095315  0.088488  0.088425   
+6   0.0  0.079680  0.079827  0.089441  0.089040  0.088488  0.087315  0.086524   
+7   0.0  0.079580  0.080060  0.088974  0.088778  0.088425  0.086524  0.085876   
+8   0.0  0.079499  0.080295  0.088502  0.088506  0.088348  0.085716  0.085210   
+9   0.0  0.079448  0.080548  0.088037  0.088238  0.088275  0.084906  0.084541   
+10  0.0  0.071751  0.071438  0.082839  0.082089  0.081194  0.082509  0.081481   
+11  0.0  0.071392  0.071284  0.082139  0.081533  0.080780  0.081559  0.080641   
+12  0.0  0.071071  0.071164  0.081471  0.081007  0.080395  0.080635  0.079827   
+13  0.0  0.070794  0.071084  0.080839  0.080518  0.080048  0.079741  0.079043   
+14  0.0  0.070562  0.071051  0.080249  0.080070  0.079745  0.078880  0.078293   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.071394  0.066569  0.073831  0.068867  0.064284  0.060048  0.056131  
-2   0.066284  0.061966  0.068541  0.064081  0.059952  0.056127  0.052581  
-3   0.079944  0.074729  0.084523  0.079021  0.073925  0.069202  0.064823  
-4   0.074984  0.070216  0.079224  0.074183  0.069506  0.065165  0.061133  
-5   0.070333  0.065973  0.074258  0.069640  0.065349  0.061359  0.057648  
-6   0.078082  0.073159  0.083779  0.078484  0.073567  0.068999  0.064753  
-7   0.073299  0.068776  0.078587  0.073716  0.069187  0.064974  0.061054  
-8   0.068841  0.064684  0.073750  0.069268  0.065095  0.061209  0.057588  
-9   0.064684  0.060863  0.069242  0.065118  0.061272  0.057686  0.054340  
-10  0.073750  0.069242  0.079948  0.075028  0.070450  0.066189  0.062220  
-11  0.069268  0.065118  0.075028  0.070494  0.066270  0.062333  0.058663  
-12  0.065095  0.061272  0.070450  0.066270  0.062370  0.058732  0.055337  
-13  0.061209  0.057686  0.066189  0.062333  0.058732  0.055369  0.052227  
-14  0.057588  0.054340  0.062220  0.058663  0.055337  0.052227  0.049318  
+1   0.079499  0.079448  0.071751  0.071392  0.071071  0.070794  0.070562  
+2   0.080295  0.080548  0.071438  0.071284  0.071164  0.071084  0.071051  
+3   0.088502  0.088037  0.082839  0.082139  0.081471  0.080839  0.080249  
+4   0.088506  0.088238  0.082089  0.081533  0.081007  0.080518  0.080070  
+5   0.088348  0.088275  0.081194  0.080780  0.080395  0.080048  0.079745  
+6   0.085716  0.084906  0.082509  0.081559  0.080635  0.079741  0.078880  
+7   0.085210  0.084541  0.081481  0.080641  0.079827  0.079043  0.078293  
+8   0.084685  0.084157  0.080434  0.079704  0.078999  0.078326  0.077688  
+9   0.084157  0.083772  0.079379  0.078759  0.078165  0.077604  0.077079  
+10  0.080434  0.079379  0.079152  0.078033  0.076935  0.075863  0.074818  
+11  0.079704  0.078759  0.078033  0.077004  0.075996  0.075014  0.074061  
+12  0.078999  0.078165  0.076935  0.075996  0.075079  0.074187  0.073325  
+13  0.078326  0.077604  0.075863  0.075014  0.074187  0.073388  0.072618  
+14  0.077688  0.077079  0.074818  0.074061  0.073325  0.072618  0.071942
diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index aeb83f7e6..07507e971 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+
@@ -760,10 +787,10 @@ number \(i\) is left out. Usin
-
Runtime: 0.0893679 sec
+
Runtime: 0.0907831 sec
 Jackknife Statistics :
 original           bias      std. error
- 99.9524        99.9424        0.148854
+ 100.142        100.132        0.149864
@@ -982,7 +1009,7 @@ theorem.

Bootstrap Statistics :
 original           bias      std. error
- 100.188  15.1133         100.19        0.149655
+ 100.033  14.9292        100.032        0.149452
@@ -1216,9 +1243,7 @@ Error: 0.03781367141738902 Bias^2: 0.03365768507152769 Var: 0.0041559863458613296 0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902 -
-
-
Polynomial degree: 7
+Polynomial degree: 7
 Error: 0.027609773491022394
 Bias^2: 0.022999498260366198
 Var: 0.004610275230656182
@@ -1238,7 +1263,9 @@ Error: 0.021592704588021178
 Bias^2: 0.010516485576646504
 Var: 0.01107621901137467
 0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174
-Polynomial degree: 11
+
+
+
Polynomial degree: 11
 Error: 0.07160048164232538
 Bias^2: 0.014436800088896381
 Var: 0.05716368155342902
@@ -1508,12 +1535,12 @@ Mean squared error on test data: 0.17446471
 Degree of polynomial:  13
 Mean squared error on training data: 0.00759119
 Mean squared error on test data: 1.08131003
-
-
-
Degree of polynomial:  14
+Degree of polynomial:  14
 Mean squared error on training data: 0.00472199
 Mean squared error on test data: 0.81333804
-Degree of polynomial:  15
+
+
+
Degree of polynomial:  15
 Mean squared error on training data: 0.00410478
 Mean squared error on test data: 92.09172409
 Degree of polynomial:  16
@@ -1528,12 +1555,12 @@ Mean squared error on test data: 108.27092910
 Degree of polynomial:  19
 Mean squared error on training data: 0.00156376
 Mean squared error on test data: 1371.99051150
-
-
-
Degree of polynomial:  20
+Degree of polynomial:  20
 Mean squared error on training data: 0.00137818
 Mean squared error on test data: 1887.86252988
-Degree of polynomial:  21
+
+
+
Degree of polynomial:  21
 Mean squared error on training data: 0.00118508
 Mean squared error on test data: 14859.69908626
 Degree of polynomial:  22
@@ -1548,12 +1575,12 @@ Mean squared error on test data: 1277.61702282
 Degree of polynomial:  25
 Mean squared error on training data: 0.00079129
 Mean squared error on test data: 128664.31650694
-
-
-
Degree of polynomial:  26
+Degree of polynomial:  26
 Mean squared error on training data: 0.00076905
 Mean squared error on test data: 19003.94822514
-Degree of polynomial:  27
+
+
+
Degree of polynomial:  27
 Mean squared error on training data: 0.00068946
 Mean squared error on test data: 2379.66219404
 Degree of polynomial:  28
@@ -1564,9 +1591,9 @@ Mean squared error on training data: 0.00060705
 Mean squared error on test data: 3250.17647619
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 

 

@@ -1800,7 +1827,7 @@ cross-validation (LOOCV).
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 

 

@@ -2689,9 +2716,9 @@ linear system as an equation would reduce this down to
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

@@ -2835,9 +2862,9 @@ with the form utilized in linear regression, viz.
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

@@ -2877,9 +2904,9 @@ cost function is given by
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

@@ -2914,9 +2941,9 @@ cost function is given by
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

@@ -2969,43 +2996,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
-
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+
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/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00
   model = cd_fast.enet_coordinate_descent(
 
- 10%|███████████▉                                                                                                           | 1/10 [00:00<00:06,  1.50it/s]
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+
 90%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎              | 9/10 [00:02<00:00,  6.85it/s]
 

 

-
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:02<00:00,  5.96it/s]
+
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:02<00:00,  7.30it/s]
 

 

-
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:02<00:00,  3.97it/s]
+
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:02<00:00,  4.68it/s]
 

 

 

@@ -3152,9 +3179,9 @@ which polynomial fits the data best.

 

 

 

-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection='3d')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
   fig.colorbar(surf, shrink=0.5, aspect=5)
 

 

diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html
index d06f34eed..bb3c8e5f9 100644
--- a/doc/LectureNotes/_build/html/chapter4.html
+++ b/doc/LectureNotes/_build/html/chapter4.html
@@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
+

+ 
+  Weekly material, notes and exercises
+ 
+

+
 
     

  
diff --git a/doc/LectureNotes/_build/html/chapter5.html b/doc/LectureNotes/_build/html/chapter5.html
index 76d73571c..eecfa0f14 100644
--- a/doc/LectureNotes/_build/html/chapter5.html
+++ b/doc/LectureNotes/_build/html/chapter5.html
@@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
+

+ 
+  Weekly material, notes and exercises
+ 
+

+
 
     

  
@@ -1345,17 +1372,6 @@ converge. So, welcome to the promised land of quadratic programming.

 

 

 

-

-
---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Input In [4], in <cell line: 2>()
-      1 import numpy
-----> 2 import cvxopt
-
-ModuleNotFoundError: No module named 'cvxopt'
-

-

-

 

 
This will make our life much easier. You don’t need t write your own optimizer.

 
We remind ourselves about the general problem we want to solve

@@ -1419,6 +1435,14 @@ sol[’primal objective’]
 

 

 

+

+
  Input In [5]
+    P = matrix(numpy.diag([1,0]), tc=’d’)
+                                     ^
+SyntaxError: invalid character '’' (U+2019)
+

+

+

 

 
We are now ready to return to our setup of the optmization problem for a more realistic case. Introducing the slack parameter \(C\) we have

 

diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html
index 3f80fa6dd..79fd9ac75 100644
--- a/doc/LectureNotes/_build/html/chapter6.html
+++ b/doc/LectureNotes/_build/html/chapter6.html
@@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
+

+ 
+  Weekly material, notes and exercises
+ 
+

+
 
     

  
@@ -688,9 +715,9 @@ predicting the target features of query instances is as follows:

 

 

 
2nd degree coefficients:
-zero power:  0.18790439176058887
-first power:  -0.014599964106338128
-second power:  0.00010403373827253124
+zero power:  -4.653578701904388
+first power:  0.17297886491529482
+second power:  -0.0007790285013223805
 

 

 _images/chapter6_1_1.png
@@ -923,16 +950,102 @@ s = -\sum_{k=1}^K p_{mk}\log{p_{mk}}.
 

 

 

-
---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Input In [2], in <cell line: 9>()
-      6 from sklearn.tree import export_graphviz
-      8 from IPython.display import Image 
-----> 9 from pydot import graph_from_dot_data
-     10 import pandas as pd
-     11 import numpy as np
+
     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \
+0          17.99         10.38          122.80     1001.0          0.11840   
+1          20.57         17.77          132.90     1326.0          0.08474   
+2          19.69         21.25          130.00     1203.0          0.10960   
+3          11.42         20.38           77.58      386.1          0.14250   
+4          20.29         14.34          135.10     1297.0          0.10030   
+..           ...           ...             ...        ...              ...   
+564        21.56         22.39          142.00     1479.0          0.11100   
+565        20.13         28.25          131.20     1261.0          0.09780   
+566        16.60         28.08          108.30      858.1          0.08455   
+567        20.60         29.33          140.10     1265.0          0.11780   
+568         7.76         24.54           47.92      181.0          0.05263   
 
-ModuleNotFoundError: No module named 'pydot'
+     mean compactness  mean concavity  mean concave points  mean symmetry  \
+0             0.27760         0.30010              0.14710         0.2419   
+1             0.07864         0.08690              0.07017         0.1812   
+2             0.15990         0.19740              0.12790         0.2069   
+3             0.28390         0.24140              0.10520         0.2597   
+4             0.13280         0.19800              0.10430         0.1809   
+..                ...             ...                  ...            ...   
+564           0.11590         0.24390              0.13890         0.1726   
+565           0.10340         0.14400              0.09791         0.1752   
+566           0.10230         0.09251              0.05302         0.1590   
+567           0.27700         0.35140              0.15200         0.2397   
+568           0.04362         0.00000              0.00000         0.1587   
+
+     mean fractal dimension  ...  worst radius  worst texture  \
+0                   0.07871  ...        25.380          17.33   
+1                   0.05667  ...        24.990          23.41   
+2                   0.05999  ...        23.570          25.53   
+3                   0.09744  ...        14.910          26.50   
+4                   0.05883  ...        22.540          16.67   
+..                      ...  ...           ...            ...   
+564                 0.05623  ...        25.450          26.40   
+565                 0.05533  ...        23.690          38.25   
+566                 0.05648  ...        18.980          34.12   
+567                 0.07016  ...        25.740          39.42   
+568                 0.05884  ...         9.456          30.37   
+
+     worst perimeter  worst area  worst smoothness  worst compactness  \
+0             184.60      2019.0           0.16220            0.66560   
+1             158.80      1956.0           0.12380            0.18660   
+2             152.50      1709.0           0.14440            0.42450   
+3              98.87       567.7           0.20980            0.86630   
+4             152.20      1575.0           0.13740            0.20500   
+..               ...         ...               ...                ...   
+564           166.10      2027.0           0.14100            0.21130   
+565           155.00      1731.0           0.11660            0.19220   
+566           126.70      1124.0           0.11390            0.30940   
+567           184.60      1821.0           0.16500            0.86810   
+568            59.16       268.6           0.08996            0.06444   
+
+     worst concavity  worst concave points  worst symmetry  \
+0             0.7119                0.2654          0.4601   
+1             0.2416                0.1860          0.2750   
+2             0.4504                0.2430          0.3613   
+3             0.6869                0.2575          0.6638   
+4             0.4000                0.1625          0.2364   
+..               ...                   ...             ...   
+564           0.4107                0.2216          0.2060   
+565           0.3215                0.1628          0.2572   
+566           0.3403                0.1418          0.2218   
+567           0.9387                0.2650          0.4087   
+568           0.0000                0.0000          0.2871   
+
+     worst fractal dimension  
+0                    0.11890  
+1                    0.08902  
+2                    0.08758  
+3                    0.17300  
+4                    0.07678  
+..                       ...  
+564                  0.07115  
+565                  0.06637  
+566                  0.07820  
+567                  0.12400  
+568                  0.07039  
+
+[569 rows x 30 columns]
+     malignant  benign
+0            1       0
+1            1       0
+2            1       0
+3            1       0
+4            1       0
+..         ...     ...
+564          1       0
+565          1       0
+566          1       0
+567          1       0
+568          0       1
+
+[569 rows x 2 columns]
+

+

+
0
 

 

 

@@ -966,6 +1079,11 @@ s = -\sum_{k=1}^K p_{mk}\log{p_{mk}}.
 

 

 

+

+
0
+

+

+

 

 

 

@@ -983,6 +1101,28 @@ s = -\sum_{k=1}^K p_{mk}\log{p_{mk}}.
 

 

 

+

+
[Text(0.5, 0.9166666666666666, 'X[2] <= 2.45\ngini = 0.667\nsamples = 150\nvalue = [50, 50, 50]'),
+ Text(0.4230769230769231, 0.75, 'gini = 0.0\nsamples = 50\nvalue = [50, 0, 0]'),
+ Text(0.5769230769230769, 0.75, 'X[3] <= 1.75\ngini = 0.5\nsamples = 100\nvalue = [0, 50, 50]'),
+ Text(0.3076923076923077, 0.5833333333333334, 'X[2] <= 4.95\ngini = 0.168\nsamples = 54\nvalue = [0, 49, 5]'),
+ Text(0.15384615384615385, 0.4166666666666667, 'X[3] <= 1.65\ngini = 0.041\nsamples = 48\nvalue = [0, 47, 1]'),
+ Text(0.07692307692307693, 0.25, 'gini = 0.0\nsamples = 47\nvalue = [0, 47, 0]'),
+ Text(0.23076923076923078, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
+ Text(0.46153846153846156, 0.4166666666666667, 'X[3] <= 1.55\ngini = 0.444\nsamples = 6\nvalue = [0, 2, 4]'),
+ Text(0.38461538461538464, 0.25, 'gini = 0.0\nsamples = 3\nvalue = [0, 0, 3]'),
+ Text(0.5384615384615384, 0.25, 'X[2] <= 5.45\ngini = 0.444\nsamples = 3\nvalue = [0, 2, 1]'),
+ Text(0.46153846153846156, 0.08333333333333333, 'gini = 0.0\nsamples = 2\nvalue = [0, 2, 0]'),
+ Text(0.6153846153846154, 0.08333333333333333, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
+ Text(0.8461538461538461, 0.5833333333333334, 'X[2] <= 4.85\ngini = 0.043\nsamples = 46\nvalue = [0, 1, 45]'),
+ Text(0.7692307692307693, 0.4166666666666667, 'X[1] <= 3.1\ngini = 0.444\nsamples = 3\nvalue = [0, 1, 2]'),
+ Text(0.6923076923076923, 0.25, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]'),
+ Text(0.8461538461538461, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]'),
+ Text(0.9230769230769231, 0.4166666666666667, 'gini = 0.0\nsamples = 43\nvalue = [0, 0, 43]')]
+

+

+_images/chapter6_24_1.png
+

 

 
Alternatively, the tree can also be exported in textual format with the function exporttext.
 This method doesn’t require the installation of external libraries and is more compact:

@@ -999,6 +1139,17 @@ This method doesn’t require the installation of external libraries and is more
+
+
|--- petal width (cm) <= 0.80
+|   |--- class: 0
+|--- petal width (cm) >  0.80
+|   |--- petal width (cm) <= 1.75
+|   |   |--- class: 1
+|   |--- petal width (cm) >  1.75
+|   |   |--- class: 2
+
+
+
@@ -1163,6 +1314,20 @@ humidity and weak and strong for wind.

+
+
---------------------------------------------------------------------------
+FileNotFoundError                         Traceback (most recent call last)
+Input In [6], in <cell line: 37>()
+     34 def save_fig(fig_id):
+     35     plt.savefig(image_path(fig_id) + ".png", format='png')
+---> 37 infile = open(data_path("rideclass.csv"),'r')
+     39 # Read the experimental data with Pandas
+     40 from IPython.display import display
+
+FileNotFoundError: [Errno 2] No such file or directory: 'DataFiles/rideclass.csv'
+
+
+

The above functions (gini, entropy and misclassification error) are important components of the so-called CART algorithm. We will discuss diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html index a4134fde8..735ae8dbe 100644 --- a/doc/LectureNotes/_build/html/chapter7.html +++ b/doc/LectureNotes/_build/html/chapter7.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+ diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html index 3356d10f5..c25b2f5eb 100644 --- a/doc/LectureNotes/_build/html/chapter8.html +++ b/doc/LectureNotes/_build/html/chapter8.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+ @@ -642,10 +669,10 @@ covariance matrix through the np.linalg.eig() function.

-
0.046785461905835435
-4.240670854503034
-[[0.94986593 2.88137798]
- [2.88137798 9.93586895]]
+
0.264540221699101
+4.673457751724773
+[[0.83632853 2.54078623]
+ [2.54078623 8.44021223]]
@@ -685,10 +712,10 @@ a more brute force way. Here we scale the mean values for each column of the des
-
0.08271198519070039
-1.7306310662842432
-[[1.         0.58084359]
- [0.58084359 1.        ]]
+
0.08374032367704139
+1.7696835316227453
+[[1.         0.66443521]
+ [0.66443521 1.        ]]
@@ -717,30 +744,30 @@ this matrix we easily see that it is a positive definite matrix.

-
[[-0.50488131 -2.2493023 ]
- [-0.26115367 -1.92631966]
- [-1.43556723 -2.99992698]
- [ 0.64528459  2.57643113]
- [-0.55273102 -2.09964817]
- [ 0.31681097  1.26466619]
- [-0.04673082 -0.56607416]
- [ 1.53394148  5.38629412]
- [ 0.15092012 -0.22014758]
- [ 0.15410688  0.83402742]]
+
[[ 1.20708879  4.33201844]
+ [-0.3838783  -1.24125217]
+ [ 0.74722409  1.60194224]
+ [-0.04086326  0.37419664]
+ [ 0.62422045  2.05587489]
+ [ 1.75357263  5.63710438]
+ [-2.53968309 -7.28219089]
+ [-1.42054337 -4.90629812]
+ [-0.13019959 -0.83320794]
+ [ 0.18306165  0.26181254]]
           0         1
-0 -0.504881 -2.249302
-1 -0.261154 -1.926320
-2 -1.435567 -2.999927
-3  0.645285  2.576431
-4 -0.552731 -2.099648
-5  0.316811  1.264666
-6 -0.046731 -0.566074
-7  1.533941  5.386294
-8  0.150920 -0.220148
-9  0.154107  0.834027
-         0        1
-0  1.00000  0.95302
-1  0.95302  1.00000
+0  1.207089  4.332018
+1 -0.383878 -1.241252
+2  0.747224  1.601942
+3 -0.040863  0.374197
+4  0.624220  2.055875
+5  1.753573  5.637104
+6 -2.539683 -7.282191
+7 -1.420543 -4.906298
+8 -0.130200 -0.833208
+9  0.183062  0.261813
+          0         1
+0  1.000000  0.992504
+1  0.992504  1.000000
@@ -797,37 +824,37 @@ this matrix we easily see that it is a positive definite matrix.

     0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.088104  0.081216  0.088760  0.086997  0.085010  0.080343  0.079306   
-2   0.0  0.081216  0.075612  0.080764  0.079618  0.078300  0.072530  0.071897   
-3   0.0  0.088760  0.080764  0.094925  0.092249  0.089310  0.089233  0.087615   
-4   0.0  0.086997  0.079618  0.092249  0.089982  0.087470  0.086257  0.084927   
-5   0.0  0.085010  0.078300  0.089310  0.087470  0.085410  0.083021  0.081989   
-6   0.0  0.080343  0.072530  0.089233  0.086257  0.083021  0.086109  0.084269   
-7   0.0  0.079306  0.071897  0.087615  0.084927  0.081989  0.084269  0.082642   
-8   0.0  0.078323  0.071329  0.086021  0.083629  0.080998  0.082431  0.081022   
-9   0.0  0.077372  0.070810  0.084426  0.082339  0.080026  0.080571  0.079383   
-10  0.0  0.071946  0.064637  0.081990  0.079001  0.075770  0.080642  0.078768   
-11  0.0  0.071044  0.064041  0.080699  0.077930  0.074922  0.079218  0.077511   
-12  0.0  0.070222  0.063524  0.079476  0.076927  0.074144  0.077846  0.076306   
-13  0.0  0.069475  0.063084  0.078314  0.075987  0.073433  0.076518  0.075145   
-14  0.0  0.068799  0.062719  0.077203  0.075102  0.072783  0.075222  0.074019   
+1   0.0  0.092290  0.091376  0.091772  0.091659  0.091579  0.083691  0.083321   
+2   0.0  0.091376  0.091209  0.090620  0.090923  0.091266  0.082166  0.082105   
+3   0.0  0.091772  0.090620  0.098069  0.097435  0.096845  0.093556  0.092724   
+4   0.0  0.091659  0.090923  0.097435  0.097111  0.096833  0.092478  0.091896   
+5   0.0  0.091579  0.091266  0.096845  0.096833  0.096872  0.091445  0.091113   
+6   0.0  0.083691  0.082166  0.093556  0.092478  0.091445  0.092007  0.090828   
+7   0.0  0.083321  0.082105  0.092724  0.091896  0.091113  0.090828  0.089857   
+8   0.0  0.083051  0.082145  0.091996  0.091418  0.090888  0.089744  0.088982   
+9   0.0  0.082877  0.082285  0.091369  0.091044  0.090768  0.088754  0.088201   
+10  0.0  0.075829  0.073985  0.087419  0.086021  0.084663  0.087860  0.086440   
+11  0.0  0.075277  0.073684  0.086460  0.085272  0.084124  0.086624  0.085384   
+12  0.0  0.074819  0.073477  0.085603  0.084624  0.083685  0.085484  0.084422   
+13  0.0  0.074453  0.073363  0.084846  0.084076  0.083348  0.084438  0.083555   
+14  0.0  0.074180  0.073344  0.084187  0.083627  0.083111  0.083485  0.082779   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.078323  0.077372  0.071946  0.071044  0.070222  0.069475  0.068799  
-2   0.071329  0.070810  0.064637  0.064041  0.063524  0.063084  0.062719  
-3   0.086021  0.084426  0.081990  0.080699  0.079476  0.078314  0.077203  
-4   0.083629  0.082339  0.079001  0.077930  0.076927  0.075987  0.075102  
-5   0.080998  0.080026  0.075770  0.074922  0.074144  0.073433  0.072783  
-6   0.082431  0.080571  0.080642  0.079218  0.077846  0.076518  0.075222  
-7   0.081022  0.079383  0.078768  0.077511  0.076306  0.075145  0.074019  
-8   0.079622  0.078211  0.076886  0.075797  0.074760  0.073768  0.072813  
-9   0.078211  0.077033  0.074969  0.074050  0.073183  0.072364  0.071585  
-10  0.076886  0.074969  0.076632  0.075201  0.073811  0.072452  0.071115  
-11  0.075797  0.074050  0.075201  0.073904  0.072647  0.071421  0.070216  
-12  0.074760  0.073183  0.073811  0.072647  0.071523  0.070428  0.069355  
-13  0.073768  0.072364  0.072452  0.071421  0.070428  0.069465  0.068525  
-14  0.072813  0.071585  0.071115  0.070216  0.069355  0.068525  0.067719  
+1   0.083051  0.082877  0.075829  0.075277  0.074819  0.074453  0.074180  
+2   0.082145  0.082285  0.073985  0.073684  0.073477  0.073363  0.073344  
+3   0.091996  0.091369  0.087419  0.086460  0.085603  0.084846  0.084187  
+4   0.091418  0.091044  0.086021  0.085272  0.084624  0.084076  0.083627  
+5   0.090888  0.090768  0.084663  0.084124  0.083685  0.083348  0.083111  
+6   0.089744  0.088754  0.087860  0.086624  0.085484  0.084438  0.083485  
+7   0.088982  0.088201  0.086440  0.085384  0.084422  0.083555  0.082779  
+8   0.088317  0.087746  0.085108  0.084230  0.083446  0.082756  0.082158  
+9   0.087746  0.087386  0.083859  0.083159  0.082553  0.082040  0.081620  
+10  0.085108  0.083859  0.085252  0.083832  0.082501  0.081258  0.080098  
+11  0.084230  0.083159  0.083832  0.082569  0.081395  0.080305  0.079298  
+12  0.083446  0.082553  0.082501  0.081395  0.080374  0.079437  0.078582  
+13  0.082756  0.082040  0.081258  0.080305  0.079437  0.078652  0.077949  
+14  0.082158  0.081620  0.080098  0.079298  0.078582  0.077949  0.077397
@@ -1016,10 +1043,10 @@ We can write our own code or simply use either the functionaly of numpy< 
          0         1
-0  3.967536  1.983164
-1  1.983164  2.000755
-[[3.9675364  1.98316352]
- [1.98316352 2.00075534]]
+0  3.956454  1.972286
+1  1.972286  1.977089
+[[3.95645365 1.97228638]
+ [1.97228638 1.97708897]]
@@ -1046,8 +1073,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t 
Centered covariance using own code
-[[3.9675364  1.98316352]
- [1.98316352 2.00075534]]
+[[3.95645365 1.97228638]
+ [1.97228638 1.97708897]]
_images/chapter8_65_1.png @@ -1107,16 +1134,16 @@ questions.

Eigenvalues of Covariance matrix
-5.197738983259782
-0.7705527590466072
+5.173439546289586
+0.7601030735620569
 First eigenvector
-[0.84977962 0.52713812]
+[0.85102768 0.52512084]
 Second eigenvector
-[-0.52713812  0.84977962]
+[-0.52512084  0.85102768]
 
Eigenvector of largest eigenvalue
-[-0.84977962 -0.52713812]
+[-0.85102768 -0.52512084]
diff --git a/doc/LectureNotes/_build/html/chapter9.html b/doc/LectureNotes/_build/html/chapter9.html index 5b5e72cac..66dc4a971 100644 --- a/doc/LectureNotes/_build/html/chapter9.html +++ b/doc/LectureNotes/_build/html/chapter9.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+ diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html index 6610c1f92..09ade64da 100644 --- a/doc/LectureNotes/_build/html/chapteroptimization.html +++ b/doc/LectureNotes/_build/html/chapteroptimization.html @@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output" +

+ + Weekly material, notes and exercises + +

+ @@ -938,11 +965,11 @@ which equals

-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_9414/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20669/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection="3d")
 

 

-
<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x13b240850>
+
<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1336a6040>
 

 

 _images/chapteroptimization_61_2.png
@@ -1000,7 +1027,7 @@ which equals

 

 

 

-
[<matplotlib.lines.Line2D at 0x13b8c72e0>]
+
[<matplotlib.lines.Line2D at 0x133c2e1c0>]
 

 

 _images/chapteroptimization_69_1.png
@@ -1257,11 +1284,11 @@ when \(||\nabla_\beta C(\beta_k) || \
 

 

 

-
[0.25881631 4.66111673]
-[[4.01840062]
- [2.89545727]]
-[[4.01840062]
- [2.89545727]]
+
[0.29972182 4.52744746]
+[[4.01247056]
+ [2.97656972]]
+[[4.01247056]
+ [2.97656972]]
 

 

 _images/chapteroptimization_123_1.png
@@ -1290,9 +1317,9 @@ when \(||\nabla_\beta C(\beta_k) || \
 

 

 

-
[[3.79441434]
- [3.07608141]]
-[3.80994952] [3.12302855]
+
[[4.02158709]
+ [2.93023603]]
+[4.00882596] [2.93522293]
 

 

 

@@ -1363,10 +1390,10 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|
 

 

 

-
[[3.78596961]
- [3.12387751]]
-[[3.71441535]
- [3.17942122]]
+
[[4.09781386]
+ [2.97980332]]
+[[4.0527437 ]
+ [3.01510929]]
 

 

 _images/chapteroptimization_132_1.png
@@ -1616,15 +1643,15 @@ function.

 

 

 
Own inversion
-[[4.42130182]
- [2.83757843]]
-Eigenvalues of Hessian Matrix:[0.27660123 4.17938393]
+[[3.87618586]
+ [3.13847924]]
+Eigenvalues of Hessian Matrix:[0.3313155  4.62759057]
 theta from own gd
-[[4.42130182]
- [2.83757843]]
+[[3.87618586]
+ [3.13847924]]
 theta from own sdg
-[[4.44198566]
- [2.79512696]]
+[[3.87379129]
+ [3.15508406]]
 

 

 _images/chapteroptimization_148_1.png
diff --git a/doc/LectureNotes/_build/html/clustering.html b/doc/LectureNotes/_build/html/clustering.html
index 68212adf7..5a39179c8 100644
--- a/doc/LectureNotes/_build/html/clustering.html
+++ b/doc/LectureNotes/_build/html/clustering.html
@@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
+

+ 
+  Weekly material, notes and exercises
+ 
+

+
 
     

  
@@ -492,8 +519,12 @@ Gaussian distribution.

 

 

 

+
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lib/__init__.py:32: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.
+  warnings.warn("JAX on Mac ARM machines is experimental and minimally tested. "
+

+

 
---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
+AttributeError                            Traceback (most recent call last)
 Input In [1], in <cell line: 5>()
       3 import time
       4 import numpy as np
@@ -581,14 +612,122 @@ Gaussian distribution.

      30 from tensorflow.lite.python import wrap_toco
      31 from tensorflow.lite.python.convert_phase import Component
 
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:26, in <module>
-     23 import six
-     24 from six.moves import range
----> 26 import flatbuffers
-     27 from tensorflow.core.protobuf import config_pb2 as _config_pb2
-     28 from tensorflow.core.protobuf import graph_debug_info_pb2
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:51, in <module>
+     47 # Jax functions used by TFLite
+     48 # pylint: disable=g-import-not-at-top
+     49 # pylint: disable=unused-import
+     50 try:
+---> 51   from jax import xla_computation as _xla_computation
+     52 except ImportError:
+     53   _xla_computation = None
 
-ModuleNotFoundError: No module named 'flatbuffers'
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/__init__.py:116, in <module>
+     40 from ._src.config import (
+     41   config as config,
+     42   enable_checks as enable_checks,
+   (...)
+     51   numpy_rank_promotion as numpy_rank_promotion,
+     52 )
+     53 from ._src.api import (
+     54   ad,  # TODO(phawkins): update users to avoid this.
+     55   checkpoint as checkpoint,
+   (...)
+    114   xla_computation as xla_computation,
+    115 )
+--> 116 from .experimental.maps import soft_pmap as soft_pmap
+    117 from .version import __version__ as __version__
+    119 # These submodules are separate because they are in an import cycle with
+    120 # jax and rely on the names imported above.
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/experimental/maps.py:26, in <module>
+     23 from functools import wraps, partial, partialmethod
+     24 from enum import Enum
+---> 26 from .. import numpy as jnp
+     27 from .. import core
+     28 from .. import linear_util as lu
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/__init__.py:19, in <module>
+      1 # Copyright 2018 Google LLC
+      2 #
+      3 # Licensed under the Apache License, Version 2.0 (the "License");
+   (...)
+     17 
+     18 # flake8: noqa: F401
+---> 19 from . import fft as fft
+     20 from . import linalg as linalg
+     22 from jax.interpreters.xla import DeviceArray as DeviceArray
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/fft.py:17, in <module>
+      1 # Copyright 2020 Google LLC
+      2 #
+      3 # Licensed under the Apache License, Version 2.0 (the "License");
+   (...)
+     14 
+     15 # flake8: noqa: F401
+---> 17 from jax._src.numpy.fft import (
+     18   ifft as ifft,
+     19   ifft2 as ifft2,
+     20   ifftn as ifftn,
+     21   ifftshift as ifftshift,
+     22   ihfft as ihfft,
+     23   irfft as irfft,
+     24   irfft2 as irfft2,
+     25   irfftn as irfftn,
+     26   fft as fft,
+     27   fft2 as fft2,
+     28   fftfreq as fftfreq,
+     29   fftn as fftn,
+     30   fftshift as fftshift,
+     31   hfft as hfft,
+     32   rfft as rfft,
+     33   rfft2 as rfft2,
+     34   rfftfreq as rfftfreq,
+     35   rfftn as rfftn,
+     36 )
+     38 # Module initialization is encapsulated in a function to avoid accidental
+     39 # namespace pollution.
+     40 _NOT_IMPLEMENTED = []
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/numpy/fft.py:19, in <module>
+     16 import operator
+     17 import numpy as np
+---> 19 from jax import lax
+     20 from jax._src.lib import xla_client
+     21 from jax._src.util import safe_zip
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/lax/__init__.py:332, in <module>
+    299 from jax._src.lax.lax import (_reduce_sum, _reduce_max, _reduce_min, _reduce_or,
+    300                   _reduce_and, _reduce_window_sum, _reduce_window_max,
+    301                   _reduce_window_min, _reduce_window_prod,
+   (...)
+    306                   _upcast_fp16_for_computation, _broadcasting_shape_rule,
+    307                   _eye, _tri, _delta, _ones, _zeros, _dilate_shape)
+    308 from jax._src.lax.control_flow import (
+    309   associative_scan as associative_scan,
+    310   cond as cond,
+   (...)
+    330   while_p as while_p,
+    331 )
+--> 332 from jax._src.lax.fft import (
+    333   fft as fft,
+    334   fft_p as fft_p,
+    335 )
+    336 from jax._src.lax.parallel import (
+    337   all_gather as all_gather,
+    338   all_to_all as all_to_all,
+   (...)
+    355   xeinsum as xeinsum,
+    356 )
+    357 from jax._src.lax.other import (
+    358   conv_general_dilated_patches as conv_general_dilated_patches
+    359 )
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lax/fft.py:145, in <module>
+    143 batching.primitive_batchers[fft_p] = fft_batching_rule
+    144 if pocketfft:
+--> 145   xla.backend_specific_translations['cpu'][fft_p] = pocketfft.pocketfft
+
+AttributeError: module 'jaxlib.pocketfft' has no attribute 'pocketfft'
 

 

 

diff --git a/doc/LectureNotes/_build/html/exercisesweek34.html b/doc/LectureNotes/_build/html/exercisesweek34.html
index 7f55ce9e2..febe54b5d 100644
--- a/doc/LectureNotes/_build/html/exercisesweek34.html
+++ b/doc/LectureNotes/_build/html/exercisesweek34.html
@@ -5,7 +5,7 @@
   
     
     
-    19. Exercises week 34 — Applied Data Analysis and Machine Learning
+    Exercises week 34 — Applied Data Analysis and Machine Learning
     
   
   
@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
     
     
     
@@ -250,12 +250,22 @@ const thebe_selector_output = ".output, .cell_output"
 
@@ -331,22 +341,22 @@ const thebe_selector_output = ".output, .cell_output"
                 
@@ -371,22 +381,22 @@ const thebe_selector_output = ".output, .cell_output"
                             
@@ -401,15 +411,15 @@ const thebe_selector_output = ".output, .cell_output"
   
 

-
19. Exercises week 34

+
Exercises week 34

 
FYS-STK3155/4155

 
Date: August 21-25, 2023

 

-
19.1. Exercises

+
Exercises

 
Here are three possible exercises for week 34

 

 

-
19.2. Exercise 1: Setting up various Python environments

+
Exercise 1: Setting up various Python environments

 
The first exercise here is of a mere technical art. We want you to have

 
    
 
  • git as a version control software and to establish a user account on a provider like GitHub. Other providers like GitLab etc are equally fine. You can also use the University of Oslo GitHub facilities.
    • 
@@ -465,7 +475,7 @@ license.
    
 
    We recommend using Anaconda if you are not too familiar with setting paths in a terminal environment.
    
 

 

-
19.3. Exercise 2: making your own data and exploring scikit-learn

+
Exercise 2: making your own data and exploring scikit-learn

 
We will generate our own dataset for a function \(y(x)\) where \(x \in [0,1]\) and defined by random numbers computed with the uniform distribution. The function \(y\) is a quadratic polynomial in \(x\) with added stochastic noise according to the normal distribution \(\cal {N}(0,1)\).
 The following simple Python instructions define our \(x\) and \(y\) values (with 100 data points).

 

@@ -512,7 +522,7 @@ R^2(\boldsymbol{y}, \tilde{\boldsymbol{y}}) = 1 - \frac{\sum_{i=0}^{n - 1} (y_i
 Discuss the meaning of these results. Try also to vary the coefficient in front of the added stochastic noise term and discuss the quality of the fits.

 

 

-
19.4. Exercise 3: Split data in test and training data

+
Exercise 3: Split data in test and training data

 
In this exercise we want you to to compute the MSE for the training
 data and the test data as function of the complexity of a polynomial,
 that is the degree of a given polynomial.

@@ -575,7 +585,7 @@ Add now a model which allows you to make polynomials up to degree 
     

         
next

-        
20. Week 34: Introduction to the course, Logistics and Practicalities

+        
Week 34: Introduction to the course, Logistics and Practicalities

     

     
     
diff --git a/doc/LectureNotes/_build/html/exercisesweek35.html b/doc/LectureNotes/_build/html/exercisesweek35.html
index 5e4a1c58b..31b481daa 100644
--- a/doc/LectureNotes/_build/html/exercisesweek35.html
+++ b/doc/LectureNotes/_build/html/exercisesweek35.html
@@ -56,7 +56,7 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
     
     
     
@@ -250,12 +250,12 @@ const thebe_selector_output = ".output, .cell_output"
 

  
@@ -544,8 +571,8 @@ matrices and vectors.

 

 

 

-
[-1.25902112 -0.51174395 -0.29276615  1.6489862  -1.69115646  1.62620724
-  0.65444431 -1.35346808 -0.20316225  1.12630042]
+
[-0.38763091 -2.70501534 -0.3581571  -0.96251494 -1.26223899  0.35309734
+  2.28186376 -1.85104809 -0.37114298 -1.20893188]
 

 

 

@@ -766,26 +793,26 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
 

 

 

-
[[0.26185107 0.82454365 0.97434186 0.56556315 0.58187347 0.72509099
-  0.19158446 0.3263505  0.0536097  0.90066122]
- [0.34678929 0.66981186 0.44152248 0.20299677 0.1614891  0.68485505
-  0.43333886 0.45741697 0.31311243 0.88001352]
- [0.29661191 0.05268304 0.98153145 0.9007164  0.69481746 0.35319678
-  0.88063413 0.06319374 0.06695337 0.75350216]
- [0.15089627 0.58671946 0.13734823 0.72394787 0.38019139 0.422275
-  0.35821426 0.49282737 0.19144544 0.84653115]
- [0.38637915 0.8049181  0.49672291 0.98699753 0.8192798  0.05850532
-  0.00152188 0.13131825 0.31229747 0.40183706]
- [0.8705211  0.1472032  0.22567203 0.55202922 0.62683307 0.41566661
-  0.23659936 0.85086629 0.85120833 0.79135075]
- [0.31377492 0.38629844 0.33956555 0.64064128 0.42028578 0.58474054
-  0.71760245 0.51732028 0.31211671 0.35894575]
- [0.78286771 0.72594302 0.22821344 0.23962594 0.48739546 0.59190877
-  0.8557822  0.44830642 0.90595152 0.9626883 ]
- [0.34498451 0.90694878 0.15442554 0.43560678 0.89045167 0.21654926
-  0.00355118 0.18695705 0.61123608 0.7386068 ]
- [0.30813073 0.26549135 0.96812218 0.94321297 0.81592628 0.60980325
-  0.4284066  0.8792323  0.92968793 0.82073684]]
+
[[0.78459267 0.75453081 0.05363779 0.57350724 0.69764852 0.65795279
+  0.51507839 0.20461136 0.38788697 0.96496641]
+ [0.25028968 0.96081861 0.18931988 0.51108791 0.30337713 0.43036842
+  0.52839842 0.15321987 0.78561443 0.09030825]
+ [0.10097956 0.50584526 0.34989509 0.55626454 0.69154964 0.2895238
+  0.13393141 0.15503141 0.26015755 0.42902155]
+ [0.25789255 0.9492866  0.90252116 0.904221   0.51933924 0.14432948
+  0.54445121 0.02699523 0.18657863 0.971688  ]
+ [0.13392097 0.27801122 0.50931378 0.04234339 0.22442417 0.44065609
+  0.74943449 0.42451192 0.33736485 0.97952271]
+ [0.95824108 0.59950055 0.91346044 0.58042237 0.13228567 0.31519573
+  0.12427889 0.64736858 0.60236782 0.18036103]
+ [0.95911004 0.82027884 0.27547877 0.84317815 0.89842298 0.68322599
+  0.02377668 0.39943328 0.00162091 0.0525221 ]
+ [0.94076632 0.88335933 0.75492292 0.7860324  0.41923956 0.86181269
+  0.45979894 0.44190304 0.07829878 0.00458014]
+ [0.42915006 0.68127341 0.23875722 0.31988705 0.54956992 0.24801014
+  0.65335632 0.97713364 0.05635863 0.12160173]
+ [0.93386901 0.74935095 0.96534137 0.98400474 0.98581925 0.30313128
+  0.41386599 0.88450476 0.87099757 0.22566121]]
 

 

 

@@ -845,13 +872,13 @@ covariance matrix through the np.linalg.eig() function.

 

 

 

-
-0.2246674023625205
-3.345687771875474
--0.6084611325305795
-[[ 1.17709473  3.57810065  3.67258699]
- [ 3.57810065 11.8548082  11.12152272]
- [ 3.67258699 11.12152272 15.62249103]]
-[26.06969872  0.07319349  2.51150176]
+
0.0626202708457115
+4.327883798133277
+0.3178411477108273
+[[ 1.01422853  3.21909039  2.82750687]
+ [ 3.21909039 11.42694395  9.22887861]
+ [ 2.82750687  9.22887861 17.2086376 ]]
+[24.72855434  0.09533574  4.82592   ]
 

 

 

diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 360eb70ee..1d7fb3865 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -240,6 +240,33 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
+

+ 
+  Weekly material, notes and exercises
+ 
+

+
 
     

  
diff --git a/doc/LectureNotes/_build/html/search.html b/doc/LectureNotes/_build/html/search.html
index 5152a17f6..05130ca4d 100644
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-   19. Exercises week 34
+   Exercises week 34
   
  
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  • 
   
-   20. Week 34: Introduction to the course, Logistics and Practicalities
+   Week 34: Introduction to the course, Logistics and Practicalities
   
  
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index 72d2d5d61..b2905d4d1 100644
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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso 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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/statistics.html b/doc/LectureNotes/_build/html/statistics.html
index a88abd420..ba447d4b5 100644
--- a/doc/LectureNotes/_build/html/statistics.html
+++ b/doc/LectureNotes/_build/html/statistics.html
@@ -242,6 +242,33 @@ const thebe_selector_output = ".output, .cell_output"
   
  
    • 
 

+

+ 
+  Weekly material, notes and exercises
+ 
+

+
 
     

  
@@ -916,27 +943,37 @@ uncorrelated.

 

 

 

-
1.1297300314822336
-[[ 9.30839676 15.51664729 15.66620847  8.85065653 10.38302314  7.56183518
-   8.50754416 10.88560514  9.76063234  3.03168642]
- [15.51664729 25.86550074 26.11481199 14.75361646 17.3079975  12.6052136
-  14.18166474 18.1457774  16.27050214  5.05367466]
- [15.66620847 26.11481199 26.3665263  14.89582298 17.47482507 12.72671218
-  14.31835835 18.32068012 16.42732954  5.1023858 ]
- [ 8.85065653 14.75361646 14.89582298  8.41542567  9.87243817  7.18998208
-   8.08918584 10.35030572  9.28065343  2.8826033 ]
- [10.38302314 17.3079975  17.47482507  9.87243817 11.58171189  8.43482628
-   9.48971452 12.14231548 10.88746795  3.38168549]
- [ 7.56183518 12.6052136  12.72671218  7.18998208  8.43482628  6.14298603
-   6.91124889  8.84310736  7.92921648  2.46284227]
- [ 8.50754416 14.18166474 14.31835835  8.08918584  9.48971452  6.91124889
-   7.77559332  9.94905663  8.92087142  2.77085375]
- [10.88560514 18.1457774  18.32068012 10.35030572 12.14231548  8.84310736
-   9.94905663 12.73005463 11.41446721  3.54537329]
- [ 9.76063234 16.27050214 16.42732954  9.28065343 10.88746795  7.92921648
-   8.92087142 11.41446721 10.23483916  3.17897671]
- [ 3.03168642  5.05367466  5.1023858   2.8826033   3.38168549  2.46284227
-   2.77085375  3.54537329  3.17897671  0.98740124]]
+
3.0818000034712947
+[[1.82949718e-02 3.31658671e-01 3.68886089e-01 6.49666527e-01
+  1.49052317e-01 5.97305528e-01 4.57810898e-01 2.51578522e-01
+  2.89396164e-01 5.90342093e-01]
+ [3.31658671e-01 6.01244295e+00 6.68731669e+00 1.17774184e+01
+  2.70208087e+00 1.08281969e+01 8.29938171e+00 4.56071752e+00
+  5.24629107e+00 1.07019610e+01]
+ [3.68886089e-01 6.68731669e+00 7.43794243e+00 1.30993886e+01
+  3.00537912e+00 1.20436207e+01 9.23095558e+00 5.07264063e+00
+  5.83516719e+00 1.19032152e+01]
+ [6.49666527e-01 1.17774184e+01 1.30993886e+01 2.30700873e+01
+  5.29294618e+00 2.12107137e+01 1.62571673e+01 8.93371943e+00
+  1.02766489e+01 2.09634375e+01]
+ [1.49052317e-01 2.70208087e+00 3.00537912e+00 5.29294618e+00
+  1.21435514e+00 4.86635201e+00 3.72986500e+00 2.04965397e+00
+  2.35776087e+00 4.80961969e+00]
+ [5.97305528e-01 1.08281969e+01 1.20436207e+01 2.12107137e+01
+  4.86635201e+00 1.95011994e+01 1.49468927e+01 8.21369083e+00
+  9.44838453e+00 1.92738529e+01]
+ [4.57810898e-01 8.29938171e+00 9.23095558e+00 1.62571673e+01
+  3.72986500e+00 1.49468927e+01 1.14561980e+01 6.29546690e+00
+  7.24181044e+00 1.47726407e+01]
+ [2.51578522e-01 4.56071752e+00 5.07264063e+00 8.93371943e+00
+  2.04965397e+00 8.21369083e+00 6.29546690e+00 3.45951629e+00
+  3.97955570e+00 8.11793498e+00]
+ [2.89396164e-01 5.24629107e+00 5.83516719e+00 1.02766489e+01
+  2.35776087e+00 9.44838453e+00 7.24181044e+00 3.97955570e+00
+  4.57776818e+00 9.33823452e+00]
+ [5.90342093e-01 1.07019610e+01 1.19032152e+01 2.09634375e+01
+  4.80961969e+00 1.92738529e+01 1.47726407e+01 8.11793498e+00
+  9.33823452e+00 1.90491568e+01]]
 

 

 

@@ -1204,15 +1241,15 @@ more practically oriented methods like the blocking technique.

 

 

 

-
-0.09083636328656121
-3.6943316601792833
--0.35793003441520066
-0.8866190885623907 9.823585220707946 13.828190347382744
-2.7923086060375724 2.5472246386316972 7.87667593906086
-[[ 0.88661909  2.79230861  2.54722464]
- [ 2.79230861  9.82358522  7.87667594]
- [ 2.54722464  7.87667594 13.82819035]]
-[20.65578316  0.07532297  3.80728853]
+
-0.054244842835462305
+4.000854409696581
+0.13083543199018746
+0.8437169762110144 8.948675162607389 10.317825933186352
+2.601583341274718 2.1245596497124075 6.443538568902246
+[[ 0.84371698  2.60158334  2.12455965]
+ [ 2.60158334  8.94867516  6.44353857]
+ [ 2.12455965  6.44353857 10.31782593]]
+[16.80422999  0.07128434  3.23470374]
 

 

 

@@ -1542,7 +1579,7 @@ assumption for approximating \(\sigma
 

 

 

-
0.008818897251043893 0.9940672992288855
+
-0.048547423739546604 0.9959293935368551
 

 

 _images/statistics_188_1.png
diff --git a/doc/LectureNotes/_build/html/teachers.html b/doc/LectureNotes/_build/html/teachers.html
index 29fdbcac9..361da019d 100644
--- a/doc/LectureNotes/_build/html/teachers.html
+++ b/doc/LectureNotes/_build/html/teachers.html
@@ -240,6 +240,33 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
+

+ 
+  Weekly material, notes and exercises
+ 
+

+
 
     

  
diff --git a/doc/LectureNotes/_build/html/textbooks.html b/doc/LectureNotes/_build/html/textbooks.html
index 0f8094a0f..8176c6e2c 100644
--- a/doc/LectureNotes/_build/html/textbooks.html
+++ b/doc/LectureNotes/_build/html/textbooks.html
@@ -240,6 +240,33 @@ const thebe_selector_output = ".output, .cell_output"
   
  
 
+

+ 
+  Weekly material, notes and exercises
+ 
+

+
 
     

  
diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html
index a81f80a5e..b0278d538 100644
--- a/doc/LectureNotes/_build/html/week34.html
+++ b/doc/LectureNotes/_build/html/week34.html
@@ -5,7 +5,7 @@
   
     
     
-    20. Week 34: Introduction to the course, Logistics and Practicalities — Applied Data Analysis and Machine Learning
+    Week 34: Introduction to the course, Logistics and Practicalities — Applied Data Analysis and Machine Learning
     
   
   
@@ -55,7 +55,8 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
+    
     
     
     
@@ -249,12 +250,22 @@ const thebe_selector_output = ".output, .cell_output"
 
@@ -330,192 +341,192 @@ const thebe_selector_output = ".output, .cell_output"
                 
@@ -744,192 +755,192 @@ const thebe_selector_output = ".output, .cell_output"
                             
@@ -1148,11 +1159,11 @@ const thebe_selector_output = ".output, .cell_output"
   
 

-
20. Week 34: Introduction to the course, Logistics and Practicalities

+
Week 34: Introduction to the course, Logistics and Practicalities

 
Morten Hjorth-Jensen, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University, USA

 
Date: Week 34, August 21-25, 2023

 

-
20.1. Overview of first week

+
Overview of first week

 
    
 
  1. The sessions on Tuesdays and Wednesdays last four hours for each group (four groups in total) and will include lectures in a flipped mode (promoting active learning) and work on exercices and projects.
    2. 
 
  2. The sessions will begin with lectures, discussions, questions and answers about the material to be covered every week. Videos and teaching material will be announced in due time.
    3. 
@@ -1168,7 +1179,7 @@ doconce format html week34.do.txt --no_mako -->
 
    The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learning material with reading suggestions will be made available before each week starts.
    
 

 

-
20.2. Schedule first week

+
Schedule first week

 
    
 
  • August 22: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.
    • 
 
  • August 23: Presentation of the course, aims and content. Introduction to software and repetition of Python Programming, linear algebra and basic elements of statistics. Please select group.
    • 
@@ -1176,7 +1187,7 @@ doconce format html week34.do.txt --no_mako -->
 

 

 

-
20.3. Lectures and ComputerLab

+
Lectures and ComputerLab

 
    
 
  • The sessions on Tuesdays and Wednesdays last four hours and will include partly lectures in a flipped mode (promoting active learning) and work on exercices and projects.
    • 
 
  • Thursdays: regular lectures (12.15pm-2pm)
    • 
@@ -1188,14 +1199,14 @@ doconce format html week34.do.txt --no_mako -->
 

 

 

-
20.4. Communication channels

+
Communication channels

 
    
 
  • Chat and communications via <canvas.uio.no>
    • 
 
  • Discord channel will be added asap
    • 
 

 

 

-
20.5. Course Format

+
Course Format

 
    
 
  • Three compulsory projects. Electronic reports only using Canvas to hand in projects and git as version control software and GitHub for repository (or GitLab) of all your material.
    • 
 
  • Evaluation and grading: The three projects are graded and each counts 1/3 of the final mark. No final written or oral exam.
    • 
@@ -1208,7 +1219,7 @@ doconce format html week34.do.txt --no_mako -->
 

 

 

-
20.6. Teachers

+
Teachers

 

 

-
20.7. Deadlines for projects (tentative)

+
Deadlines for projects (tentative)

 
    
 
  1. Project 1: October 9 (available September 4) graded with feedback)
    2. 
 
  2. Project 2: November 6 (available October 6, graded with feedback)
    3. 
@@ -1235,7 +1246,7 @@ doconce format html week34.do.txt --no_mako -->
 
    Extra Credit (not mandatory),  weekly exercise assignments, 10 in total (due Friday same week), 10% additional score. The extra credit assignments are due each Friday and can be uploaed to Canvas in your preferred format (although we prefer jupyter-notebooks). First assignment is for week 35. Each weekly exercise set counts 1%.
    
 

 

-
20.8. Grading

+
Grading

 
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. There are three projects which are graded and each project counts 1/3 of the final grade. The total score is thus the average from all three projects.

 
The final number of points is based on the average of all projects and the grade follows the following table:

 
    
@@ -1249,12 +1260,12 @@ doconce format html week34.do.txt --no_mako -->
 
    In addition you can get an extra 10% score for weekly assignments (10 in total and due each Friday). Each weekly assignment counts 1%.
    
 

 

-
20.9. Reading material

+
Reading material

 
The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.

 
In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see next slide for links.

 

 

-
20.10. Textbooks

+
Textbooks

 
 

 

-
20.11. Reading suggestions week 34

+
Reading suggestions week 34

 
This week: Refresh linear algebra, GBC chapters 1 and 2. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html (these notes).

 

 

-
20.12. Prerequisites

+
Prerequisites

 
Basic knowledge in programming and mathematics, with an emphasis on
 linear algebra. Knowledge of Python or/and C++ as programming
 languages is strongly recommended and experience with Jupiter notebook
@@ -1280,7 +1291,7 @@ offer nowadays a basic programming course (often compulsory) where
 Python is the recurring programming language.

 

 

-
20.13. Topics covered in this course: Statistical analysis and optimization of data

+
Topics covered in this course: Statistical analysis and optimization of data

 
The course has two central parts

 
    
 
  1. Statistical analysis and optimization of data
    2. 
@@ -1289,7 +1300,7 @@ Python is the recurring programming language.
    
 
    These topics will be scattered thorughout the course and may not  necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms.
    
 

 

-
20.14. Statistical analysis and optimization of data

+
Statistical analysis and optimization of data

 
We plan to cover the following topics:

 
    
 
  • Basic concepts, expectation values, variance, covariance, correlation functions and errors;
    • 
@@ -1302,7 +1313,7 @@ Python is the recurring programming language.
    
 

 

 

-
20.15. Machine Learning

+
Machine Learning

 
    
 
  • Pre deep-learning revolution (2008 approx)
    
 
      
@@ -1326,7 +1337,7 @@ Python is the recurring programming language.
      
 
      Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
      
 

 
 

-
20.17. Other courses on Data science and Machine Learning  at UiO

+
Other courses on Data science and Machine Learning  at UiO

 
 

 

-
20.18. Other courses on Data science and Machine Learning  at UiO, contn

+
Other courses on Data science and Machine Learning  at UiO, contn

 
 

 

-
20.19. Learning outcomes

+
Learning outcomes

 
This course aims at giving you insights and knowledge about many of
 the central algorithms used in Data Analysis and Machine Learning.
 The course is project based and through various numerical projects,
@@ -1382,7 +1393,7 @@ specifically, after this course you will

 
 

 

-
20.20. Introduction

+
Introduction

 
Our emphasis throughout this series of lectures

 is on understanding the mathematical aspects of
 different algorithms used in the fields of data analysis and machine learning.

@@ -1414,7 +1425,7 @@ well as allowing you to set up models and produce your own data and
 get started with programming.

 

 

-
20.21. AI/ML and some statements you may have heard (and what do they mean?)

+
AI/ML and some statements you may have heard (and what do they mean?)

 
    
 
  1. Fei-Fei Li on ImageNet: map out the entire world of objects (The data that transformed AI research)
    2. 
 
  2. Russell and Norvig in their popular textbook: relevant to any intellectual task; it is truly a universal field (Artificial Intelligence, A modern approach)
    3. 
@@ -1424,7 +1435,7 @@ get started with programming.
    
 
    Here: with AI/ML we intend a collection of machine learning methods with an emphasis on statistical learning and data analysis
    
 

 

-
20.22. What is Machine Learning?

+
What is Machine Learning?

 
Statistics, data science and machine learning form important fields of
 research in modern science.  They describe how to learn and make
 predictions from data, as well as allowing us to extract important
@@ -1485,7 +1496,7 @@ Carlo methods are central elements in a proper understanding of many
 of algorithms and methods we will discuss.

 

 

-
20.23. Types of Machine Learning

+
Types of Machine Learning

 
The approaches to machine learning are many, but are often split into
 two main categories.  In supervised learning we know the answer to a
 problem, and let the computer deduce the logic behind it. On the other
@@ -1504,7 +1515,7 @@ desired output of a system.  Some of the most common tasks are:

 
 

 

-
20.24. Essential elements of ML

+
Essential elements of ML

 
The methods we cover have three main topics in common, irrespective of
 whether we deal with supervised or unsupervised learning.

 
    
@@ -1514,11 +1525,11 @@ whether we deal with supervised or unsupervised learning.
    
 

 

 

-
20.25. An optimization/minimization problem

+
An optimization/minimization problem

 
At the heart of basically all Machine Learning algorithms we will encounter so-called minimization or optimization algorithms. A large family of such methods are so-called gradient methods.

 

 

-
20.26. A Frequentist approach to data analysis

+
A Frequentist approach to data analysis

 
When you hear phrases like predictions and estimations and
 correlations and causations, what do you think of?  May be you think
 of the difference between classifying new data points and generating
@@ -1543,7 +1554,7 @@ used in turn to make estimations and find causations such as given \(B\).

 

 

-
20.27. What is a good model?

+
What is a good model?

 
In science and engineering we often end up in situations where we want to infer (or learn) a
 quantitative model \(M\) for a given set of sample points \(\boldsymbol{X} \in [x_1, x_2,\dots x_N]\).

 
As we will see repeatedely in these lectures, we could try to fit these data points to a model given by a
@@ -1561,7 +1572,7 @@ is that if we are not specific about what we mean by a correct model, t
 could easily be many different models that fit the given data set equally well.

 

 

-
20.28. What is a good model? Can we define it?

+
What is a good model? Can we define it?

 
The central question is this: what leads us to say that a model is correct or
 optimal for a given data set? To make the model inference problem well posed, i.e.,
 to guarantee that there is a unique optimal model for the given data, we need to
@@ -1582,7 +1593,7 @@ may first try the simplest class of models, namely linear models, followed obvio
 
How to evaluate which model fits best the data is something we will come back to over and over again in these sets of lectures.

 

 

-
20.29. Software and needed installations

+
Software and needed installations

 
We will make extensive use of Python as programming language and its
 myriad of available libraries.  You will find
 Jupyter notebooks invaluable in your work.  You can run R
@@ -1611,7 +1622,7 @@ you can use pip as well and simply install Python as

 
etc etc.

 

 

-
20.30. Python installers

+
Python installers

 
If you don’t want to perform these operations separately and venture
 into the hassle of exploring how to set up dependencies and paths, we
 recommend two widely used distrubutions which set up all relevant
@@ -1635,7 +1646,7 @@ license.

 no setup and runs entirely in the cloud. Try it out!

 

 

-
20.31. Useful Python libraries

+
Useful Python libraries

 
Here we list several useful Python libraries we strongly recommend (if you use anaconda many of these are already there)

 
    
 
  • NumPy is a highly popular library for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays
    • 
@@ -1653,7 +1664,7 @@ no setup and runs entirely in the cloud. Try it out!
    
 

 

 

-
20.32. Installing R, C++, cython or Julia

+
Installing R, C++, cython or Julia

 
You will also find it convenient to utilize R. We will mainly
 use Python during our lectures and in various projects and exercises.
 Those of you
@@ -1668,7 +1679,7 @@ lectures.

 follow the link here

 

 

-
20.33. Installing R, C++, cython, Numba etc

+
Installing R, C++, cython, Numba etc

 
For the C++ aficionados, Jupyter/IPython notebook allows you also to
 install C++ and run codes written in this language interactively in
 the browser. Since we will emphasize writing many of the algorithms
@@ -1691,7 +1702,7 @@ further processing. For example, convert to latex as

 
Finally, we recommend strongly using Autograd or JAX for automatic differentiation.

 

 

-
20.34. Numpy examples and Important Matrix and vector handling packages

+
Numpy examples and Important Matrix and vector handling packages

 
There are several central software libraries for linear algebra and eigenvalue problems. Several of the more
 popular ones have been wrapped into ofter software packages like those from the widely used text Numerical Recipes. The original source codes in many of the available packages are often taken from the widely used
 software package LAPACK, which follows two other popular packages
@@ -1703,7 +1714,7 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 
 

 

-
20.35. Numpy and arrays

+
Numpy and arrays

 
Numpy provides an easy way to handle arrays in Python. The standard way to import this library is as

 

 

@@ -1722,8 +1733,8 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 

 

 

-
[ 0.39864527 -1.50219982 -0.24611674 -0.52819229 -0.24533566 -0.85694362
- -0.81955926  0.14252712 -1.00625843 -0.23732849]
+
[-0.03483677 -1.06136773  0.56481272 -0.35947075 -1.64972151 -1.58590795
+ -1.17428293  0.7225597  -1.14061742 -1.22893751]
 

 

 

@@ -1832,7 +1843,7 @@ The attentive reader will also notice that the output is 
-
20.36. Matrices in Python

+
Matrices in Python

 
Having defined vectors, we are now ready to try out matrices. We can
 define a \(3 \times 3 \) real matrix \(\boldsymbol{A}\) as (recall that we user
 lowercase letters for vectors and uppercase letters for matrices)

@@ -1948,26 +1959,26 @@ lowercase letters for vectors and uppercase letters for matrices)

 

 

 

-
[[0.99374388 0.68029036 0.03324117 0.4017308  0.92888885 0.12434807
-  0.9538239  0.15899253 0.48442336 0.72057967]
- [0.16840081 0.01852793 0.10950766 0.25522012 0.78674758 0.65280103
-  0.220968   0.61634314 0.25115233 0.05622775]
- [0.87927601 0.35147532 0.62432074 0.16167387 0.49242688 0.12934015
-  0.50812169 0.35123041 0.95054046 0.66638078]
- [0.08887708 0.95994091 0.21927988 0.05833369 0.49974854 0.20999563
-  0.28102554 0.01479194 0.84942942 0.30702075]
- [0.29672381 0.99820454 0.56848886 0.32190363 0.13320535 0.31768693
-  0.80280228 0.59654302 0.25116851 0.81681019]
- [0.50799086 0.13261136 0.33078983 0.53623614 0.14174062 0.06662062
-  0.32183101 0.8338786  0.32766274 0.88595926]
- [0.12539459 0.60176806 0.12885048 0.00708794 0.66614025 0.70727837
-  0.87849647 0.05491655 0.37157712 0.93325497]
- [0.56343926 0.23056012 0.97840468 0.82484316 0.9956582  0.53822787
-  0.53219937 0.58312967 0.14617985 0.59468796]
- [0.67054028 0.4304167  0.38299798 0.16397496 0.81912272 0.54894095
-  0.53254035 0.17826349 0.01551946 0.54969168]
- [0.92373266 0.86361743 0.27525917 0.59367954 0.85843684 0.40389922
-  0.8074242  0.59323905 0.51075117 0.79552752]]
+
[[0.24279008 0.63112036 0.80947943 0.97509292 0.19425617 0.3957482
+  0.1655226  0.83760781 0.07995375 0.76400155]
+ [0.75621895 0.20893514 0.93082503 0.79419162 0.02783644 0.21296315
+  0.64298419 0.34578026 0.60975366 0.46369869]
+ [0.77859437 0.23477043 0.35438626 0.63115792 0.2460037  0.35568525
+  0.0825971  0.94117118 0.14900336 0.30035718]
+ [0.36692356 0.78972773 0.67655635 0.67160204 0.80108096 0.31507591
+  0.21328866 0.41340248 0.3005849  0.40672425]
+ [0.21922061 0.88274486 0.86572911 0.06486061 0.07565581 0.26678445
+  0.03265139 0.22090974 0.33135331 0.66973261]
+ [0.7221662  0.96941962 0.39707147 0.24929083 0.31531613 0.33079801
+  0.06538944 0.42352791 0.94227931 0.27809912]
+ [0.07195822 0.31719317 0.47248297 0.18264218 0.64033527 0.51146442
+  0.49545491 0.91936525 0.81656508 0.78329097]
+ [0.58666142 0.01646892 0.11029323 0.67442363 0.6914791  0.87902877
+  0.98950411 0.27090196 0.08732305 0.89543736]
+ [0.25253892 0.57505712 0.24848907 0.9631064  0.46312791 0.96431281
+  0.28744729 0.09772449 0.17676228 0.51656406]
+ [0.68664664 0.63281351 0.62806444 0.36809474 0.98129668 0.62914124
+  0.37885034 0.6577093  0.57947706 0.24688732]]
 

 

 

@@ -2022,13 +2033,13 @@ covariance matrix through the np.linalg.eig() function.

 

 

 

-
-0.007334080205654388
-3.9196398947631166
-0.49680475019226
-[[ 0.83982725  2.39595799  2.20255857]
- [ 2.39595799  8.04496448  5.72101205]
- [ 2.20255857  5.72101205 10.77494715]]
-[15.97477238  0.08363087  3.60133562]
+
-0.19542686939553625
+3.3929303193461995
+-0.3886211842840971
+[[ 0.96552318  2.67354673  2.55640013]
+ [ 2.67354673  8.3318361   7.41646135]
+ [ 2.55640013  7.41646135 11.19890453]]
+[18.10686259  0.09289204  2.29650918]
 

 

 

@@ -2067,7 +2078,7 @@ covariance matrix through the np.linalg.eig() function.

 

 

 

-
20.37. Meet the Pandas

+
Meet the Pandas

 
 
 
Figure 1: 

@@ -2251,7 +2262,7 @@ Name: Aragorn, dtype: object
 

 

 

-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_65914/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20702/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
   data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))
 

 

@@ -2766,7 +2777,7 @@ most operations are vectorized, achieving thereby a high performance when dealin
 As we will see below it leads also to a very concice code close to the mathematical operations we may be interested in.
 For multidimensional arrays, we recommend strongly xarray. xarray has much of the same flexibility as pandas, but allows for the extension to higher dimensions than two. We will see examples later of the usage of both pandas and xarray.

 

-
20.37.1. Simple linear regression model using scikit-learn

+
Simple linear regression model using scikit-learn

 
We start with perhaps our simplest possible example, using Scikit-Learn to perform linear regression analysis on a data set produced by us.

 
What follows is a simple Python code where we have defined a function
 \(y\) in terms of the variable \(x\). Both are defined as vectors with  \(100\) entries.
@@ -3031,7 +3042,7 @@ H_{\delta}(\boldsymbol{a})=\left\{\begin{array}{cc}\frac{1}{2} \boldsymbol{a}^{2
 various lectures and lab sessions.

 

 

-
20.37.2. To our real data: nuclear binding energies. Brief reminder on masses and binding energies

+
To our real data: nuclear binding energies. Brief reminder on masses and binding energies

 
Let us now dive into  nuclear physics and remind ourselves briefly about some basic features about binding
 energies.  A basic quantity which can be measured for the ground
 states of nuclei is the atomic mass \(M(N, Z)\) of the neutral atom with
@@ -3094,7 +3105,7 @@ arises from the tendency of proton pairs and neutron pairs to
 occur. An even number of particles is more stable than an odd number.

 

 

-
20.37.3. Organizing our data

+
Organizing our data

 
Let us start with reading and organizing our data.
 We start with the compilation of masses and binding energies from 2016.
 After having downloaded this file to our own computer, we are now ready to read the file and start structuring our data.

@@ -3320,7 +3331,7 @@ Now we can print measures of how our fit is doing, the coefficients from the fit
 

 

 

-
20.37.4. And what about using neural networks?

+
And what about using neural networks?

 
The seaborn package allows us to visualize data in an efficient way. Note that we use scikit-learn’s multi-layer perceptron (or feed forward neural network)
 functionality.

 

@@ -3361,7 +3372,7 @@ functionality.

 

 

 

-
20.38. A first summary

+
A first summary

 
The aim behind these introductory words was to present to you various
 Python libraries and their functionalities, in particular libraries like
 numpy, pandas, xarray and matplotlib and other that make our life much easier
@@ -3372,7 +3383,7 @@ Machine Learning algorithms for supervised learning. Later we will meet 
 Now it is time to dive more into the details of various methods. We will start with linear regression and try to take a deeper look at what it entails.

 

 

-
20.39. Why Linear Regression (aka Ordinary Least Squares and family)

+
Why Linear Regression (aka Ordinary Least Squares and family)

 
Fitting a continuous function with linear parameterization in terms of the parameters  \(\boldsymbol{\beta}\).

 
    
 
  • Method of choice for fitting a continuous function!
    • 
@@ -3389,7 +3400,7 @@ Now it is time to dive more into the details of various methods. We will start w
 Similarly, Mehta et al’s article is also recommended.
    
 

 

-
20.40. Regression analysis, overarching aims

+
Regression analysis, overarching aims

 
Regression modeling deals with the description of  the sampling distribution of a given random variable \(y\) and how it varies as function of another variable or a set of such variables \(\boldsymbol{x} =[x_0, x_1,\dots, x_{n-1}]^T\).
 The first variable is called the dependent, the outcome or the response variable while the set of variables \(\boldsymbol{x}\) is called the independent variable, or the predictor variable or the explanatory variable, or simply just the inputs.

 
A regression model aims at finding a likelihood function \(p(\boldsymbol{y}\vert \boldsymbol{x})\) or in the more traditional sense a function \(\boldsymbol{y}(\boldsymbol{x})\), that is the conditional distribution for \(\boldsymbol{y}\) with a given \(\boldsymbol{x}\). The estimation of  \(p(\boldsymbol{y}\vert \boldsymbol{x})\) is made using a data set with

@@ -3401,7 +3412,7 @@ The first variable is called the dependent, the outcome
 
The goal of the regression analysis is to extract/exploit relationship between \(\boldsymbol{y}\) and \(\boldsymbol{x}\) in order to infer specific dependencies, approximations to the likelihood functions, functional relationships and to make predictions, making fits and many other things.

 

 

-
20.41. Regression analysis, overarching aims II

+
Regression analysis, overarching aims II

 
Consider an experiment in which \(p\) characteristics/features of \(n\) samples are
 measured. The data from this experiment, for various explanatory variables \(p\) are normally represented by a matrix

 \(\mathbf{X}\).

@@ -3419,7 +3430,7 @@ the linear regression model where \(\beta_j\).

 

 

-
20.42. Examples

+
Examples

 
In order to understand the relation among the predictors (or features or properties) \(p\), the set of data \(n\) and the target (outcome, output etc) \(\boldsymbol{y}\),
 consider the model we discussed for describing nuclear binding energies.

 
There we assumed that we could parametrize the data using a polynomial approximation based on the liquid drop model.
@@ -3435,7 +3446,7 @@ This gives \(p=0,1,2,3,4\). Fu
 so-called credit card default data from Taiwan. The data set contains data on \(n=30000\) credit card holders with predictors like gender, marital status, age, profession, education, etc. In total there are \(24\) such predictors or attributes leading to a design matrix of dimensionality \(24 \times 30000\). This is however a classification problem and we will come back to it when we discuss Logistic Regression.

 

 

-
20.43. General linear models and linear algebra

+
General linear models and linear algebra

 
Before we proceed let us study a case where we aim at fitting a set of data \(\boldsymbol{y}=[y_0,y_1,\dots,y_{n-1}]\). We could think of these data as a result of an experiment or a complicated numerical experiment. These data are functions of a series of variables \(\boldsymbol{x}=[x_0,x_1,\dots,x_{n-1}]\), that is \(y_i = y(x_i)\) with \(i=0,1,2,\dots,n-1\). The variables \(x_i\) could represent physical quantities like time, temperature, position etc. We assume that \(y(x)\) is a smooth function.

 
Since obtaining these data points may not be trivial, we want to use these data to fit a function which can allow us to make predictions for values of \(y\) which are not in the present set. The perhaps simplest approach is to assume we can parametrize our function in terms of a polynomial of degree \(n-1\) with \(n\) points, that is

 

@@ -3445,7 +3456,7 @@ y=y(x) \rightarrow y(x_i)=\tilde{y}_i+\epsilon_i=\sum_{j=0}^{n-1} \beta_j x_i^j+
 
where \(\epsilon_i\) is the error in our approximation.

 

 

-
20.44. Rewriting the fitting procedure as a linear algebra problem

+
Rewriting the fitting procedure as a linear algebra problem

 
For every set of values \(y_i,x_i\) we have thus the corresponding set of equations

 

 \[\begin{split}
@@ -3459,7 +3470,7 @@ y_{n-1}&=\beta_0+\beta_1x_{n-1}^1+\beta_2x_{n-1}^2+\dots+\beta_{n-1}x_{n-1}^
 \end{split}\]

 

 

-
20.45. Rewriting the fitting procedure as a linear algebra problem, more details

+
Rewriting the fitting procedure as a linear algebra problem, more details

 
Defining the vectors

 

 \[
@@ -3495,7 +3506,7 @@ y_{n-1}&=\beta_0+\beta_1x_{n-1}^1+\beta_2x_{n-1}^2+\dots+\beta_{n-1}x_{n-1}^
 
The above design matrix is called a Vandermonde matrix.

 

 

-
20.46. Generalizing the fitting procedure as a linear algebra problem

+
Generalizing the fitting procedure as a linear algebra problem

 
We are obviously not limited to the above polynomial expansions.  We
 could replace the various powers of \(x\) with elements of Fourier
 series or instead of \(x_i^j\) we could have \(\cos{(j x_i)}\) or \(\sin{(j
@@ -3516,7 +3527,7 @@ y_{n-1}&=\beta_0x_{n-1,0}+\beta_1x_{n-1,2}+\beta_2x_{n-1,2}+\dots+\beta_{n-1
 
Note that we have \(p=n\) here. The matrix is symmetric. This is generally not the case!

 

 

-
20.47. Generalizing the fitting procedure as a linear algebra problem

+
Generalizing the fitting procedure as a linear algebra problem

 
We redefine in turn the matrix \(\boldsymbol{X}\) as

 

 \[\begin{split}
@@ -3537,7 +3548,7 @@ x_{n-1,0}& x_{n-1,1} &x_{n-1,2}& \dots & \dots &x_{n-1,n-1}\
 
The left-hand side of this equation is kwown. Our error vector \(\boldsymbol{\epsilon}\) and the parameter vector \(\boldsymbol{\beta}\) are our unknow quantities. How can we obtain the optimal set of \(\beta_i\) values?

 

 

-
20.48. Optimizing our parameters

+
Optimizing our parameters

 
We have defined the matrix \(\boldsymbol{X}\) via the equations

 

 \[\begin{split}
@@ -3556,7 +3567,7 @@ y_{n-1}&=\beta_0x_{n-1,0}+\beta_1x_{n-1,2}+\beta_2x_{n-1,2}+\dots+\beta_{n-1
 our matrix as \(\boldsymbol{X}\in {\mathbb{R}}^{n\times p}\), with the predictors refering to the column numbers and the entries \(n\) being the row elements.

 

 

-
20.49. Our model for the nuclear binding energies

+
Our model for the nuclear binding energies

 
In our introductory notes we looked at the so-called liquid drop model. Let us remind ourselves about what we did by looking at the code.

 
We restate the parts of the code we are most interested in.

 

@@ -3642,7 +3653,7 @@ our matrix as \(\boldsymbol{X}\in {\m
 
throughout these lectures.

 

 

-
20.50. Optimizing our parameters, more details

+
Optimizing our parameters, more details

 
With the above we use the design matrix to define the approximation \(\boldsymbol{\tilde{y}}\) via the unknown quantity \(\boldsymbol{\beta}\) as

 

 \[
@@ -3668,7 +3679,7 @@ C(\boldsymbol{\beta})=\frac{1}{2n}\sum_{i=0}^{n-1}\left(y_i-\tilde{y}_i\right)^2
 
since when taking the first derivative with respect to the unknown parameters \(\beta\), the factor of \(2\) cancels out.

 

 

-
20.51. Interpretations and optimizing our parameters

+
Interpretations and optimizing our parameters

 
The function

 

 \[
@@ -3711,7 +3722,7 @@ will treat \(y_i\) as our exac
 \]

 

 

-
20.52. Interpretations and optimizing our parameters

+
Interpretations and optimizing our parameters

 
We can rewrite

 

 \[
@@ -3740,12 +3751,12 @@ allow for the usage of direct linear algebra methods such as LU
 
Small question: Do you think the example we have at hand here (the nuclear binding energies) can lead to problems in inverting the matrix  \(\boldsymbol{X}^T\boldsymbol{X}\)? What kind of problems can we expect?

 

 

-
20.53. Some useful matrix and vector expressions

+
Some useful matrix and vector expressions

 
See the handwritten notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2022/NotesExercise5Week452022.pdf

 
These notes will be discussed during one of the lectures.

 

 

-
20.54. Interpretations and optimizing our parameters

+
Interpretations and optimizing our parameters

 
The residuals \(\boldsymbol{\epsilon}\) are in turn given by

 

 \[
@@ -3765,7 +3776,7 @@ allow for the usage of direct linear algebra methods such as LU
 
Let us now return to our nuclear binding energies and simply code the above equations.

 

 

-
20.55. Own code for Ordinary Least Squares

+
Own code for Ordinary Least Squares

 
It is rather straightforward to implement the matrix inversion and obtain the parameters \(\boldsymbol{\beta}\). After having defined the matrix \(\boldsymbol{X}\) we simply need to
 write

 

@@ -3808,7 +3819,7 @@ write

 

 

 

-
20.56. Adding error analysis and training set up

+
Adding error analysis and training set up

 
We can easily test our fit by computing the \(R2\) score that we discussed in connection with the functionality of Scikit-Learn in the introductory slides.
 Since we are not using Scikit-Learn here we can define our own \(R2\) function as

 

@@ -3851,7 +3862,7 @@ Since we are not using Scikit-Learn here we can define our own
 

 

 

-
20.57. The \(\chi^2\) function

+
The \(\chi^2\) function

 
Normally, the response (dependent or outcome) variable \(y_i\) is the
 outcome of a numerical experiment or another type of experiment and is
 thus only an approximation to the true value. It is then always
@@ -3869,7 +3880,7 @@ as

 
where the matrix \(\boldsymbol{\Sigma}\) is a diagonal matrix with \(\sigma_i\) as matrix elements.

 

 

-
20.58. The \(\chi^2\) function

+
The \(\chi^2\) function

 
In order to find the parameters \(\beta_i\) we will then minimize the spread of \(\chi^2(\boldsymbol{\beta})\) by requiring

 

 \[
@@ -3888,7 +3899,7 @@ as

 
where we have defined the matrix \(\boldsymbol{A} =\boldsymbol{X}/\boldsymbol{\Sigma}\) with matrix elements \(a_{ij} = x_{ij}/\sigma_i\) and the vector \(\boldsymbol{b}\) with elements \(b_i = y_i/\sigma_i\).

 

 

-
20.59. The \(\chi^2\) function

+
The \(\chi^2\) function

 
We can rewrite

 

 \[
@@ -3906,7 +3917,7 @@ as

 \]

 

 

-
20.60. The \(\chi^2\) function

+
The \(\chi^2\) function

 
If we then introduce the matrix

 

 \[
@@ -3929,7 +3940,7 @@ as

 \]

 

 

-
20.61. The \(\chi^2\) function

+
The \(\chi^2\) function

 
The first step here is to approximate the function \(y\) with a first-order polynomial, that is we write

 

 \[
@@ -3947,7 +3958,7 @@ y=y(x) \rightarrow y(x_i) \approx \beta_0+\beta_1 x_i.
 \]

 

 

-
20.62. The \(\chi^2\) function

+
The \(\chi^2\) function

 
For a linear fit (a first-order polynomial) we don’t need to invert a matrix!!

 Defining

 

@@ -3985,7 +3996,7 @@ unknown coefficients \(\beta_i\)
 

 

-
20.63. Fitting an Equation of State for Dense Nuclear Matter

+
Fitting an Equation of State for Dense Nuclear Matter

 
Before we continue, let us introduce yet another example. We are going to fit the
 nuclear equation of state using results from many-body calculations.
 The equation of state we have made available here, as function of
@@ -4002,7 +4013,7 @@ sneak in Ridge regression (to be discussed below) which include
 hyperparameter \(\lambda\), also to be explained below.

 

 

-
20.64. The code

+
The code

 

 

 
# Common imports
@@ -4101,7 +4112,7 @@ standard OLS and the Ridge regression at higher densities. We discuss this in mo
 below.

 

 

-
20.65. Splitting our Data in Training and Test data

+
Splitting our Data in Training and Test data

 
It is normal in essentially all Machine Learning studies to split the
 data in a training set and a test set (sometimes also an additional
 validation set).  Scikit-Learn has an own function for this. There
@@ -4184,11 +4195,11 @@ but now splitting the data into a training set and a test set.

 

 

 

-
20.66. Exercises

+
Exercises

 
Here are three possible exercises for week 34

 

 

-
20.67. Exercise 1: Setting up various Python environments

+
Exercise 1: Setting up various Python environments

 
The first exercise here is of a mere technical art. We want you to have

 
    
 
  • git as a version control software and to establish a user account on a provider like GitHub. Other providers like GitLab etc are equally fine. You can also use the University of Oslo GitHub facilities.
    • 
@@ -4244,7 +4255,7 @@ license.
    
 
    We recommend using Anaconda if you are not too familiar with setting paths in a terminal environment.
    
 

 

-
20.68. Exercise 2: making your own data and exploring scikit-learn

+
Exercise 2: making your own data and exploring scikit-learn

 
We will generate our own dataset for a function \(y(x)\) where \(x \in [0,1]\) and defined by random numbers computed with the uniform distribution. The function \(y\) is a quadratic polynomial in \(x\) with added stochastic noise according to the normal distribution \(\cal {N}(0,1)\).
 The following simple Python instructions define our \(x\) and \(y\) values (with 100 data points).

 

@@ -4280,7 +4291,7 @@ R^2(\boldsymbol{y}, \tilde{\boldsymbol{y}}) = 1 - \frac{\sum_{i=0}^{n - 1} (y_i
 Discuss the meaning of these results. Try also to vary the coefficient in front of the added stochastic noise term and discuss the quality of the fits.

 

 

-
20.69. Exercise 3: Split data in test and training data

+
Exercise 3: Split data in test and training data

 
In this exercise we want you to to compute the MSE for the training
 data and the test data as function of the complexity of a polynomial,
 that is the degree of a given polynomial.

@@ -4337,9 +4348,16 @@ Add now a model which allows you to make polynomials up to degree 
         

             
previous

-            
19. Exercises week 34

+            
Exercises week 34

         

     
+    
+    

+        
next

+        
Exercises week 35

+    

+    
+    
 

             
         

diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
index f72a80f83..7f2425580 100644
--- a/doc/LectureNotes/_build/html/week35.html
+++ b/doc/LectureNotes/_build/html/week35.html
@@ -249,12 +249,12 @@ const thebe_selector_output = ".output, .cell_output"
 

 

 

-
0.9963864072152893
+
0.9959898232423614
 

 

 

@@ -1628,7 +1628,7 @@ Since we are not using Scikit-Learn here we can define our own
 

 

 

-
0.008435888964550838
+
0.008278885543361304
 

 

 

@@ -1643,23 +1643,23 @@ Since we are not using Scikit-Learn here we can define our own
 

 

 

-
[0.00168141 0.0025074  0.01497781 0.06168748 0.04101587 0.03079528
- 0.0211496  0.04046799 0.00054272 0.01029589 0.02442398 0.04419696
- 0.02566258 0.00868337 0.02649453 0.00147197 0.00546438 0.00233441
- 0.01990155 0.00808144 0.00681644 0.07093644 0.01693    0.00965032
- 0.00287154 0.00758025 0.02521597 0.00057106 0.01550656 0.04495095
- 0.03995478 0.01056549 0.00820764 0.00089459 0.02082761 0.03583342
- 0.01907538 0.00018425 0.02539993 0.02166269 0.0035417  0.0025125
- 0.01742183 0.01586048 0.03791967 0.02344111 0.02195273 0.03500198
- 0.01539    0.01176895 0.02868501 0.00201915 0.01588619 0.00754532
- 0.01135107 0.01242803 0.06121384 0.01678286 0.05855607 0.02206138
- 0.06031681 0.00523269 0.00911938 0.06038053 0.01957153 0.00449439
- 0.00191249 0.01152107 0.02335522 0.04573105 0.02612167 0.010154
- 0.00867698 0.0814721  0.01693278 0.01844381 0.00781035 0.01725808
- 0.00645183 0.00054144 0.00452742 0.00406231 0.01619802 0.01073921
- 0.00074389 0.07943621 0.01788423 0.04637311 0.0348171  0.00689391
- 0.04087592 0.09631112 0.03634298 0.04516608 0.0183718  0.01817919
- 0.07297557 0.00578738 0.00465403 0.00174508]
+
[0.02970592 0.01381723 0.01572164 0.02401344 0.06008452 0.0084603
+ 0.00506501 0.08734015 0.00145838 0.00779538 0.00046714 0.02971158
+ 0.02263868 0.02541796 0.02724253 0.04120281 0.00578776 0.01574375
+ 0.08923158 0.03510756 0.00373019 0.03206303 0.02008059 0.03533092
+ 0.01558461 0.00733497 0.01770163 0.03026672 0.05698425 0.02766981
+ 0.02421521 0.00943261 0.00207627 0.03352725 0.00139198 0.00770648
+ 0.03078597 0.00355175 0.00432302 0.04554454 0.05892262 0.0097018
+ 0.04100738 0.03232013 0.02747522 0.04208001 0.00252344 0.03080664
+ 0.00437363 0.01614046 0.00158723 0.01471878 0.03235599 0.01285424
+ 0.01494533 0.01006893 0.01248731 0.0317159  0.02226654 0.0189082
+ 0.01923991 0.01816821 0.02900131 0.0123679  0.08684675 0.00934728
+ 0.04331431 0.01574091 0.00017471 0.0164286  0.00118368 0.00839839
+ 0.00548686 0.02596838 0.02058701 0.00661104 0.02708158 0.03187054
+ 0.0171347  0.04573057 0.04735806 0.00131939 0.00704468 0.02422503
+ 0.02818883 0.003738   0.02212893 0.00732273 0.02212776 0.0202731
+ 0.0214539  0.02712723 0.02200044 0.01588614 0.03610331 0.02808201
+ 0.00801078 0.01885351 0.00458671 0.01133171]
 

 

 

@@ -1728,15 +1728,15 @@ but now splitting the data into a training set and a test set.

 

 

 

-
[ 2.03739357 -0.54302039  7.21493572 -3.15550086  1.44651551]
+
[ 2.00025531  0.8850944  -0.44894073 10.27580794 -5.9383327 ]
 Training R2
-0.9973102337301051
+0.9960039497353536
 Training MSE
-0.006158537606875468
+0.007839875253055148
 Test R2
-0.9954911579937146
+0.9962074631757628
 Test MSE
-0.010751622516868333
+0.006275173922218319
 

 

 

@@ -3510,7 +3510,7 @@ least squares is proportional to the second derivative of the cost
 function, that is we have

 

 \[
-\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}^T\partial \boldsymbol{\beta}} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.
+\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.
 \]

 
This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).

 
The Hessian matrix plays an important role and is defined in this course as

@@ -3558,7 +3558,7 @@ with a factor \(1/(n-1)\). Thi
 method corrects the bias in the estimation of the population variance
 and covariance. It also partially corrects the bias in the estimation
 of the population standard deviation. If you use a library like
-Scikit-Learn or nunmpy’s function calculate the covariance, this
+Scikit-Learn or nunmpy’s function to calculate the covariance, this
 quantity will be computed with a factor \(1/(n-1)\).

 

 

diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
index b64f202bc..9b2dd9a85 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
@@ -343,7 +343,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -515,7 +515,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -583,18 +583,18 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [1.72261919]\n",
+      " [2.11366595]\n",
       "Coefficient beta : \n",
-      " [[5.39493843]]\n",
-      "Mean squared error: 0.23\n",
-      "Variance score: 0.91\n",
+      " [[4.95136998]]\n",
+      "Mean squared error: 0.28\n",
+      "Variance score: 0.89\n",
       "Mean squared log error: 0.01\n",
-      "Mean absolute error: 0.39\n"
+      "Mean absolute error: 0.41\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -822,7 +822,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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YN28e/fr1Y/To0VxwwQWUlJT4unkiIuJrax8DDDBKzcdGqfl4rfMFWwOVywHKN998w4UXXkiLFi2wWCzMmTOnwvPlewEct169elXYp7CwkNtvv52EhAQaNWrEkCFD2LlzZ70+SLCw2WwkJSXRsmVLunXrxoMPPsgnn3zCvHnzmD59OgDPPPMMnTt3plGjRqSmpnLrrbeSl5cHwOLFi7nuuuvIzs4uO/8TJkwAYMaMGaSnpxMdHU1SUhJXXXUVe/bs8dEnFRGROjm05lhw4mCUmtuDiMsByuHDh+natSsvvvhitfs4egEct88//7zC83feeSezZ8/mvffe49tvvyUvL48LLriA0tLSao7oI34yxnf22WfTtWtXPv74YwBCQkJ4/vnnWbt2LW+++SZff/019957LwB9+vTh2WefJSYmpuz8jxkzBoCioiIeffRRfvnlF+bMmcPWrVsZOXKkTz6TiIjUUVxnsFgrbrNYze1BJNTVFwwePJjBgwfXuI+jF8CZ7Oxs3njjDd5++2369+8PmN/sU1NTWbhwIYMGDXK1SZ7hGONzdKMVZEHWQui/GBL7er05HTt25NdffwXMAM+hTZs2PProo9xyyy1MmTKF8PBwYmNjsVgsVX4H119/fdnPbdu25fnnn6dHjx7k5eXRuHFjr3wOERGpp7Rx5vXIYjWvTxYrYIG0h3zdMrfySA7K4sWLadasGR06dGDUqFEVhhFWrFhBcXExAwcOLNvWokUL0tLS+O6775wer7CwkJycnAo3j/OzMT7DMLBYLAAsWrSIAQMG0LJlS6Kjo7n22mvZv38/hw8frvEYq1at4qKLLqJVq1ZER0eTkZEBwJ9//unp5ouIiLsk9jW/LCcNgMiW5n3/JZDYx9ctcyu3ByiDBw9m5syZfP3110yePJmff/6Zs88+m8LCQgCysrIIDw+nSZMmFV7XvHlzsrKynB5z4sSJxMbGlt1SU1Pd3eyq/GyMb8OGDbRp04bt27dz3nnnkZaWxkcffcSKFSt46aWXACguLq729YcPH2bgwIE0btyYGTNm8PPPPzN79mzAHPoREZEAktgX+s2DS3aa90EWnEAdhniOZ9iwYWU/p6WlkZ6eTqtWrfjss88YOnRota8r30NQ2QMPPMBdd91V9jgnJ8fzQUpcZ3NYp3yQ4qMxvq+//po1a9bwr3/9i+XLl1NSUsLkyZMJCTHjy/fff7/C/uHh4VXyeX777Tf27dvHk08+WXbuli9f7p0PICIi4iKPTzNOTk6mVatWbN68GYCkpCSKioo4ePBghf327NlD8+bNnR7DZrMRExNT4eZxaeMAy7FEJC+N8RUWFpKVlcWuXbtYuXIlTzzxBBdddBEXXHAB1157Le3ataOkpIQXXniBLVu28Pbbb/PKK69UOEbr1q3Jy8vjq6++Yt++feTn53PCCScQHh5e9rq5c+fy6KOPevSziIiI1JXHA5T9+/ezY8cOkpOTAejevTthYWEsWLCgbJ/MzEzWrl1Lnz5+1EXlozG++fPnk5ycTOvWrTn33HNZtGgRzz//PJ988glWq5VTTz2VZ555hqeeeoq0tDRmzpzJxIkTKxyjT58+3HzzzQwbNozExEQmTZpEYmIi06dP54MPPuCUU07hySef5N///rdHP4uIiEhdWQzDMFx5QV5eHr///jsAp512Gs888wz9+vUjPj6e+Ph4JkyYwKWXXkpycjLbtm3jwQcf5M8//2TDhg1ER0cDcMstt/Dpp58yffp04uPjGTNmDPv372fFihVYrdaa3h4wh3hiY2PJzs6u0ptSUFDA1q1badOmDREREa58NAkw+l2LiASWmq7flbmcg7J8+XL69etX9tiRGzJixAhefvll1qxZw1tvvcWhQ4dITk6mX79+zJo1qyw4AfjPf/5DaGgoV1xxBUeOHOGcc85h+vTptQpOREREJPi53IPiD9SDIqDftYhIoHGlB0Vr8YiIiIjfUYAiIiIifkcBioiIiPidoA1QAjC1Rlyk37GISPAKugAlLCwMgPz8fB+3RDzN8Tt2/M5FRCR4uL3Uva9ZrVbi4uLKFiiMioqqtoS+BCbDMMjPz2fPnj3ExcVperqISBAKugAFzHL6QIVVlCX4xMXFlf2uRUQkuARlgGKxWEhOTqZZs2Y1rvArgSssLEw9JyIiQSwoAxQHq9Wqi5iIiEgACrokWREREQl8ClBERETE7yhAEREREb+jAEVERET8jgIUERER8TsKUERERALF3mWwaDDMTjHv9y7zdYs8JqinGYuIiASNvctgYQZggFEKBVmQtRD6L4bEvj5unPupB0VERCQQrH2MsuAEjt4bR7cHHwUoIiIigeDQmmPBiYNRam4PQgpQREREAkFcZ7BUqo5usZrb3clP8lwUoIiIiASCtHGA5ViQYrGaj9Mecv1Y1QUhjjyXrAVwZJd5vzDDJ0GKAhQREZFAkNjXTIhNGgCRLc37/ksgsY9rx6kpCPGjPBfN4hEREQkUiX2h37z6HcNZEGKxmtv9KM9FPSgiIiINSU1BiLfyXGpBAYqIiEhDUlMQ4s48l3pSgCIiItKQ1BSEuCvPxQ2UgyIiItKQOIIQR85JXOejwUmfY8/XN8/FDRSgiIiINDR+EoTUREM8IiIi/s5Piqd5k3pQRERE/FkDWyTQQT0oIiIi/syPiqd5kwIUERERf+ZHxdO8SQGKiIiIP/Oj4mnepABFRETEn/lR8TRvUoAiIiLiz/yoeJo3aRaPiIiIvwuAuiXuph4UERER8TsKUERERMTvuBygfPPNN1x44YW0aNECi8XCnDlzyp4rLi7mvvvuo3PnzjRq1IgWLVpw7bXXsnv37grHyMjIwGKxVLgNHz683h9GREREgoPLAcrhw4fp2rUrL774YpXn8vPzWblyJQ899BArV67k448/ZtOmTQwZMqTKvqNGjSIzM7Ps9uqrr9btE4iIiEjQcTlJdvDgwQwePNjpc7GxsSxYsKDCthdeeIEePXrw559/csIJJ5Rtj4qKIikpydW3FxERkQbA4zko2dnZWCwW4uLiKmyfOXMmCQkJdOrUiTFjxpCbm1vtMQoLC8nJyalwExERkeDl0WnGBQUF3H///Vx11VXExMSUbb/66qtp06YNSUlJrF27lgceeIBffvmlSu+Lw8SJE3n44Yc92VQRERHxIxbDMIw6v9hiYfbs2Vx88cVVnisuLubyyy/nzz//ZPHixRUClMpWrFhBeno6K1asoFu3blWeLywspLCwsOxxTk4OqampZGdn13hcERER8R85OTnExsbW6vrtkSGe4uJirrjiCrZu3cqCBQuO24hu3boRFhbG5s2bnT5vs9mIiYmpcBMRERE32rsMFg2G2Snm/d5lPm2O24d4HMHJ5s2bWbRoEU2bNj3ua9atW0dxcTHJycnubo6IiIgcz95lsDADMMyVkguyIGuhWWI/sa9PmuRygJKXl8fvv/9e9njr1q2sXr2a+Ph4WrRowWWXXcbKlSv59NNPKS0tJSsrC4D4+HjCw8P5448/mDlzJueddx4JCQmsX7+eu+++m9NOO42+fX1zEkRERBq0tY9RFpyAeW+xmtt9VGLf5RyUxYsX069fvyrbR4wYwYQJE2jTpo3T1y1atIiMjAx27NjB3//+d9auXUteXh6pqamcf/75jB8/nvj4+Fq1wZUxLBERETmO2SlwZFfV7ZEt4ZKdbnsbV67fLvegZGRkUFNMc7x4JzU1lSVLlrj6tiIiIuIpcZ3NYR1HDwqYPShxnX3WJK3FIyIicjx+lkDqdmnjAIsZlMDRewukPeSzJilAERERqYkjgTRrgTkMkrXAfBxMQUpiXzMhNmmAOayTNAD6L4HEPj5rkkcLtYmIiAQ8P0wg9YjEvn71edSDIiIiUpNDayrmZoD5+NAa37SngVCAIiIiUpO4zsdyMxx8nEDaEChAERERqYkfJpA2BApQREREauKHCaQNgZJkRUREjsfPEkgbAvWgiIiIiN9RgCIiIiJ+RwGKiIiI+B0FKCIiIuJ3FKCIiIiI31GAIiIiIn5HAYqIiIj4HQUoIiIitbV3GSwaDLNTzPtgWtHYz6hQm4iISG3sXQYLMyhb2bggC7IWmlVmE/v6uHHBRz0oIiIitbH2McqCEzh6bxzdLu6mAEVERKQ2Dq05Fpw4GKXmdnE7BSgiIhL83JE7Etf52IrGDharuV3cTgGKiIgEN0fuSNYCOLLLvF+Y4XqQkjYOsBwLUixW83HaQ+5trwAKUEREJNi5K3cksa+ZEJs0ACJbmvf9l0BiH/e0UzOEKtAsHhERCW7uzB1J7Av95rmnXeVphlAV6kEREZHgFgi5I5ohVIUCFBERCW6BkDviZzOE3l++gwOHi3zy3g4KUEREJLh5OnfEHfyol+e73/dx74e/MvA/35CdX+z193dQDoqIiAQ/T+WOuEvaODPnxGI1e0581MtzuLCEez/6FYBz05oTGxXm1fcvTz0oIiIivuYnvTxPzf+NnQeP0DIukvsHn+zV965MPSgiIiL+wMe9PD9s2c9b328H4KlLu9DY5tsQQT0oIiIiDVx+UQn3fmgO7VzZI5W/tU/wcYsUoIiIiDR4T3+xkT8P5NMiNoIHz/Pt0I6DAhQREZEG7OdtB5j+3TYAJl7ahegI3yXGlqcARUREpIE6UlTKvR/+imHAFekpnNUh0ddNKqMARUREpIGa/OVGtu47TFJMBGPPP8XXzalAAYqIiEgDtGL7Ad5YthWAiUM7ExvpH0M7DgpQREREGpiC4lLuOTq0c2m3FPp1bObrJlWhAEVERKSBeWr+b2zZe5hm0Tb+7wL/GtpxUIAiIiISDPYug0WDYXaKeb93mdPdvvtjH9OWbQPgqcu6+LScfU1cDlC++eYbLrzwQlq0aIHFYmHOnDkVnjcMgwkTJtCiRQsiIyPJyMhg3bp1FfYpLCzk9ttvJyEhgUaNGjFkyBB27txZrw8iIiLSYO1dBgszIGsBHNll3i/MqBKk5BQUc88HZkG2q3qeQL+T/G9ox8HlAOXw4cN07dqVF1980enzkyZN4plnnuHFF1/k559/JikpiQEDBpCbm1u2z5133sns2bN57733+Pbbb8nLy+OCCy6gtLTU6TFFRESkBmsfAwxzoUE4em8c3X7Mo/9bz65DRzghPoqxflKQrTouF9ofPHgwgwcPdvqcYRg8++yzjB07lqFDhwLw5ptv0rx5c9555x1uuukmsrOzeeONN3j77bfp378/ADNmzCA1NZWFCxcyaNCgenwcERGRBujQmmPBiYNRam4/asH6v/hgxU4sFph8RVca+XitneNxaw7K1q1bycrKYuDAgWXbbDYbZ511Ft999x0AK1asoLi4uMI+LVq0IC0trWyfygoLC8nJyalwExEROa5a5mXU+zW+FtcZLNaK2yxWczuwP6+QBz42h3ZuPKMtp7eO93YLXebWACUrKwuA5s2bV9jevHnzsueysrIIDw+nSZMm1e5T2cSJE4mNjS27paamurPZIiISjGqZl1Hv1/iDtHGA5ViQYrGaj9MewjAMxs5ey768Ijo0b8y/BnTwZUtrzSOzeCwWS4XHhmFU2VZZTfs88MADZGdnl9127NjhtraKiEiQqmVeRp1e42+9LIl9of9iSBoAkS3N+/5LILEPc1bvYv66LEJDLDxzxalEhFmPezh/4NYBqKSkJMDsJUlOTi7bvmfPnrJelaSkJIqKijh48GCFXpQ9e/bQp08fp8e12WzYbDZ3NlVERALR3mVmsHBojTl8kTbOvDg7U4u8jDq9xtHL4ghkCrIga6EZIFTXFm9I7Av95lXYtPvQEf7vE3Mm7ehz2pPWMtYXLasTt/agtGnThqSkJBYsWFC2raioiCVLlpQFH927dycsLKzCPpmZmaxdu7baAEVERMTl4Zfj5GXU+TV16ZnxAbvd4N4PfyW3oISuqXHcktHO101yics9KHl5efz+++9lj7du3crq1auJj4/nhBNO4M477+SJJ56gffv2tG/fnieeeIKoqCiuuuoqAGJjY7nhhhu4++67adq0KfHx8YwZM4bOnTuXzeoRERGpwllgYLGa2yv1HABm70rWQnMfx75H8zKqVZvX1KVnxgemLtvKt7/vIyIshGeu6EqoNbBqs7ocoCxfvpx+/fqVPb7rrrsAGDFiBNOnT+fee+/lyJEj3HrrrRw8eJCePXvy5ZdfEh0dXfaa//znP4SGhnLFFVdw5MgRzjnnHKZPn47VGhjjYiIi4gOuBgaOvIwKQ0IPQWINvfW1eU1cZ3NYp3xbjtcz42Xrd+cwaf5GAB664BTaJTb2cYtcZzEMw/B1I1yVk5NDbGws2dnZxMTE+Lo5IiLiDYsGm8M6lQODpAHOe1A8pXIOiqOX5WhSqq8VFJdy4QvfsnlPHv1Pbs5r13Y/7kQVb3Hl+h1Y/T0iItJw1TCV1qtqmDHjDyZ+voHNe/JIaGzjqUs7+01w4ir/LiMnIiLiUJchG0+2xZu9NrW06Lc9vPn9dgD+fXkXmjYO3BmwClBERCRw+GNg4MrUZw/al1fIPR/+AsDIPq3JqM9CgMXFEObbVY41xCMiIsHFm0XU/KTyrGEY3Pfhr+zLK+Kk5tHcP7hj3Q+Wlwe9e8Pzz4MP01QVoIiISPDwdsDgJzVRZvz4J1/9tofw0BCeu7Ie1WJLS+Hqq2HFCnj8cThwwL0NdYECFBERCR7eDhj8oCbK73tyeezT9QDcd25HOibVY3brvffC3Llgs8Enn0DTpm5qpesUoIiISPDwdsBQl2q1blRQXMo/31lFYYmdM9oncF2f1nU/2KuvwjPPmD+/+Sb06uWWNtaVAhQREQke3g4YKk99BjMgKjrolTyUJz7fwG9ZuTRtFM7ky7sSElLHKcULFsBtt5k/P/ooDBvmvkbWkQIUEREJHt6uleKY+hyfXnH7geUeT5advzaTt45OKZ58RVeaxUTU7UAbNsDll5v5J9dcA2PHurGVdacARUREgocviqgl9oXwJlV7UTyY+7LzYD73fvgrADed1bbuU4r/+gvOOw+ys+Fvf4PXXgM/KeymOigiIhJcfFErxYu5L8Wldu54dxU5BSWcmhrHmIEn1e1Ahw/DBRfAtm1w4okwe7aZHOsn1IMiIiJSX85yXwgBe6Hb67H8Z8EmVv55iOiIUF648jTC6rJKcUkJDB8Oy5dDQgLMm2fe+xEFKCIiIvVVJVk2BLBD4QG31mNZunkvLy/5A4Anh3YhNT7K9YMYBtxxB3z6KUREmNOKTzyxXu3yBAUoIiIi9VU598UWT1mQAm7JSdmbW8i/Zv2CYcBVPU/g/C7JdTvQv/8NL79s5prMnGlWjfVDykERERFxh/K5L7NTKAtOHOqRk2K3G9z1/mr25RVyUvNo/u+CU+rWxlmzzGJsYNY8GTq0bsfxAvWgiIiIuJub67G8vOQPlm7eR0RYCC9edVrdStkvXQrXXmv+PHo03HlnndriLQpQRERE3M2N9Vi+/2M/k7/cCMAjQ9Jo3zza9fZs3AgXXQRFRXDJJTB5suvH8DIFKCIiIu52vHostVxxeU9OAbe/uwq7AZd1T+Hy9BTX2/LXXzB4MBw8aJavnzEDrHVcTNCLlIMiIiLiCdXVY3GsuOxY1LAgC7IWmgFNYt+y3UpK7dz+7ir25RXSMSmaRy9Kw+JqEbXDh+HCC2HrVmjXzpyxE1WHmT8+oB4UERERb6rlisvPLNjEj1sP0CjcyktXdyMy3MVej5ISuPJK+Plnc1Xizz+HxES3fARvUIAiIiLiTbWoOvv1b38xZbFZ7+Spy7rQLrGxa+9hGHDjjfC//x2rddKhQ31b7lUKUERERLzpODN8dh7M51+zfgFgRO9WXNClhevv8cADMG2amWsyaxb08eBaRB6iAEVERMSbapjhU1hSym0zV5J9pJiuKbE8eP7Jrh//P/+Bp54yf/7vf2HIEHe13KsUoIiIiFSnlrNtXFLDDJ8nPtvALzuziY0M46Wru2ELdTHvZMYMuOsu8+eJE+H66+vfXh/RLB4RERFnajnbpk6czPD5ZPUu3vx+OwD/GdaVlCYuzraZNw+uu878+c474b776tdGH1MPioiIiDO1nG3jDut353DfR78CcFu/dpzdsblrB/jhB7jsMnPmztVXm4XYXJ2S7GcUoIiIiDhTi9k2bnmb/CJumrGcgmI7Z3ZI5K4BJ7l2gA0b4PzzIT8fzj3XTI4NCfzLe+B/AhEREU9w83o6zpTaDUa/t5odB46QGh/J88NPxRriQs/Hjh0wcCAcOAA9e8KHH0JYmNva50sKUEREpGE6XgKsG9fTqc5/Fmxiyaa9RISF8Orf04mLCq/9i/fvN4OTnTuhY0f47DNo1MhtbfM1BSgiItLwOBJgsxbAkV3m/cKMikHK8dbTqacv1mXx4qLfAXhyaBdOaRFT+xfn5prDOr/9Bikp8MUXZrXY8jwxA8mLNItHREQaHmcJsBarub387Jrq1tOpp9/35HH3+2Yxtuv6tubi01rW/sVHjpi1TX78EeLjzeDkhBMq7uPJGUheoh4UERFpeLyUAOtMXmEJN729nLzCEnq0iefB81woxlZUZM7WWbwYoqPN4OSUU6ru58UZSJ6iAEVERNyvLsML3hyS8EICrDN2u8GY93/hj72HaR5j46WruhFmreWluKQE/v53c9G/yEgz5yQ93fm+PgzA3EVDPCIi4l51GV7w9pBE2jjz+BbrseEdNyfAOvP815uZvy6LMKuFl//encRoW+1eaLfDqFHwwQfmLJ3Zs+GMM6rfP66zeQ7LByleCMDcST0oIiLiXnUZXvD2kISHE2Cdmbcmk2cXbgbg8Ys70+2EJuYTx+s5MgwYPRqmTzcX/3vvPRg0qOY388IMJE9TD4qIiLhXXYYXfDEk4aEEWGfW787hrnJJsVecnmo+UZueo7Fj4cUXzZ+nTYOhQ4//ho4AbO1j5jmM62wGJx4MwNxNAYqIiLhXXYYXgmBIojr78goZ9dZyjhSXckb7BMaWT4o93myiiRPNG8CUKXDNNbV/Yy8GYJ7g9iGe1q1bY7FYqtxuu+02AEaOHFnluV69erm7GSIi4it1GV4IgiEJZ4pK7NwyYwW7Dh2hddMoXryyG6Hlk2Jr6jl64QV48EFz26RJcMst3mu4H3B7gPLzzz+TmZlZdluwYAEAl19+edk+5557boV9Pv/8c3c3Q0REfKUu+R0+yAnxNMMw+L9P1vLztoNE20J5fUQ6sVGVytBXN5vo+yZwxx3m44cegnvu8U6j/Yjbh3gSExMrPH7yySdp164dZ511Vtk2m81GUlKSu99aRET8RV2GFwJ8SKKyt77fzns/78BigeevPI0Tm0VX3cnZbKJvDXh5nfn8nXfCww97td3+wqOzeIqKipgxYwbXX389lnLLPi9evJhmzZrRoUMHRo0axZ49e2o8TmFhITk5ORVuIiIi/mrZ7/t45NP1ADwwuCP9OjZzvmPlnqN1neAVzJk7N90EzzwDFhcWDwwiHg1Q5syZw6FDhxg5cmTZtsGDBzNz5ky+/vprJk+ezM8//8zZZ59NYWFhtceZOHEisbGxZbfU1FRPNltERBoSNxeI+31PHrfMWEGp3WDoaS0ZdUbbml/g6Dkq/DdMXGvWPPnHP8yk2AYanABYDMMwPHXwQYMGER4ezv/+979q98nMzKRVq1a89957DK1m6lRhYWGFACYnJ4fU1FSys7OJiXFhcSUREZHyKk/zdSTn1rFA3IHDRVz80jL+PJBPtxPieGdULyLCrMd/4fvvw1VXQWkpXH89vPYahARfqbKcnBxiY2Nrdf322DTj7du3s3DhQj7++OMa90tOTqZVq1Zs3ry52n1sNhs2Wy2r7YmIiNRWbRcNrIXCklJufGs5fx7IJzU+kv9em1674OTDD48FJyNHBm1w4iqPnYFp06bRrFkzzj///Br3279/Pzt27CA5OdlTTREREV/x5vo6deGmAnGGYXDfh7+yfPtBoiNCmTridBIa1+KL9ccfw5VXmsHJtdfC6697Nzjx49+PR3pQ7HY706ZNY8SIEYSGHnuLvLw8JkyYwKWXXkpycjLbtm3jwQcfJCEhgUsuucQTTREREV/x9vo6deGmAnHPfbWZOat3Expi4eWru9O+uZMZO5XNmQPDhh1bBHDqVLOUvbf4+e/HI2HawoUL+fPPP7n++usrbLdaraxZs4aLLrqIDh06MGLECDp06MD3339PdHQtfpkiIhI4vL2+Tl24oUDcnFW7ytbYefTiNP7WPuH4L5o7F664wgxOrrrq2Do73uTnvx+P9KAMHDgQZ7m3kZGRfPHFF554SxER8Te+WF/HVfVcs2b5tgPc++GvANx0Zluu7HHC8V/06adw2WVQXAzDh8Obb3o/OAG///1oLR4REfEMT6+vs3dZpcBiXN2GJupYIG77/sPc+PYKikrtDOrUnPvO7Xj8F82ZY/acFBfD5ZfD229DqI8uxX6+/pHShEVExDM8ub6OI38iawEc2WXeL8zwWpLn/rxCRk77mQOHi+iSEsuzw04jJOQ4NUtmzTrWc3L55TBzpu+CE/D79Y8UoIiIiGd4cn0dH+ZPHCkq5YY3l7N132FaxkXy+rXpRIYfZ4jmrbeOTSX++9/hnXcgLKzm13ian69/pCEeERHxHE+tr+Oj/ImSUju3v7uK1TsOERsZxpvXn06zmIiaX/Tf/8LNN5vl6//xD3jlFd/knDjjx+sfqQdFREQCT3WrAHswf8IwDMbPXcfCDX9hCw3hjRHpzhcALO+FF8w1dQwDbrsNXn3Vf4ITP6cARUREAo8P8iemLP6DmT/+icUCzw0/jfTW8TW/4Omn4Y47zJ/HjDGDFVWIrTWdKRERCTxezp/4aMVOnv5iIwATLuzEuWlJ1e9sGPDoo3DvvebjceNg0qQGvfBfXSgHRUREApOX8ie+2bSX+z46WuvkrLaM6NO6+p0NwwxInnjCfPzYYzB2rMfbGIzUgyIiIlKNtbuyuWXGCkrsBhef2oL7BtVQ68Qw4O67jwUnkyd7Pjjx47V06ks9KCIiIk5s2ZvHiKk/cbiolD7tmjLpsq7V1zopKTGTYadONR+/9BLceqtnG+jna+nUl3pQRETENUH8rd0hM/sI17zxE/sPF5HWMoZXr+lOeGg1l8yCArM67NSpZhLs1KmeD07A79fSqS/1oIiISO0F+bd2gIOHi7j2jZ/YdegIbRMaMf26HkRHVFNULTcXLr4Yvv4awsPNarEXX+ydhvr5Wjr1pR4UERGpvSD/1n64sISR039m8548kmIieOuGHiQ0tjnfed8+OPtsMzhp3Bjmz/decAI+qQXjTQpQRESk9oL4W3thSSk3z1jBLzsOERcVxts39CClSZTznXfsgDPOgOXLISEBFi2Cfv2822A/X0unvhSgiIhI7QXpt/ZSu8Fds35h6eZ9RIVbmX5dD9o3r6ZK7MaN0Lcv/PYbpKbC0qWQnu7dBoPfr6VTX8pBERGR2ksbZ+acWKxmz0kQfGs3DINxc9by2ZpMwqwW/ntNOqemxjnfeeVKGDTIHN456ST48ks44QSvtrcCP15Lp77UgyIiIrUXZN/aDcPgic838O5Px0rY/619gvOdFy+GjAwzOOne3ew58WVwEuTUgyIiIq4Jom/t/1m4mdeWbgVg4iWdOa9zsvMdP/wQ/v53KCw0c03mzIGYGO81tAFSD4qIiDRIUxb/zvNfbQZg/IWnMLxHNb0hzz5r1jkpLDRn6Xz+uYITL1CAIiIiDc60ZVuZNN9c/O++cztyXd82VXey283S9f/6l1nG/tZbzZ6UiAgvt7Zh0hCPiIg0KO/+9CcP/289AHec055bMtpV3amgAEaMgPffNx8/+aS5OrFWJPYaBSgiItJgzF61kwdnmzVbbjyzLf/q377qTgcPmkM533wDYWEwbRpcfbV3GyoKUEREpGGYtyaTu9//BcOAa3q14oHBHbFU7hH5808491zYsMHMM/n4YzjnHN80uIFTgCIiIkFv/tosbn93FXYDLu+ewsNDOlUNTlavhvPOg8xMaNnSTIbt0sUn7RUlyYqISJCbvzaLf76zkhK7wUWntuDJS7sQElIpOFmwAM480wxOOnWC779XcOJjClBERMR/7V0GiwbD7BTzfu8yl15eOTh55opTsVYOTt54w+w5yc01C7F9+61Zwl58SkM8IiLin/Yug4UZlK2eXJBlltnvv9gsFncc89dm8s93VlFiN7j41BZMrhyclJbCAw/A00+bj4cPh+nTwVbN6sXiVepBEREJBvXsafBLax+jLDiBo/fG0e01O25wkpcHl156LDgZPx7eeUfBiR9RD4qISKCrZ0+D3zq05lhw4mCUmttrUD44ueS0lvz78q4Vg5OdO+HCC82kWJsNpk6Fq65yf/ulXtSDIiIS6OrR0+DX4jofXS25HIvV3F6N4wYny5dDjx5mcNKsGSxapODETylAEREJdHXsafB7aeMAy7EgxWI1H6c95HT3Oat2cVtNwcmHHx6bqZOWBj/+CL17e/xjSN0oQBERCXTOehoA7IXOc1ECJV8lsa85TJU0ACJbmvf9l0Binyq7vvvTn/zr/dWU2g0u655SMTgxDJg4ES6/HI4cgcGDYdkyaN3aqx9HXGMxDMPwdSNclZOTQ2xsLNnZ2cRoRUkRaegcOSiGHbCXeyIELCEVc1Eq56s4eiWqy1fZu8wcKjq0xgyE0sb5XV7L1G+38sin5to61/ZuxYQLOx2rc1JYCDfdBG++aT6+4w6YPBlClYLpC65cv9WDIiIS6Bw9Dbb4Sk/YqZKL4kq+iiOYyVoAR3aZ9wsz/KrH5aVFv5cFJzed2ZaHh5QLTnbvhrPOMoMTqxVeegmee07BSYBQgCIiEgwS+0KIkymylXNRXMlX8ePkW8MwePqL33j6i40A3Nm/PfeXX1vn+++he3czz6RJE5g3D2691YctFlcpQBERCRa1mfXiyswYP02+NQyDRz5dz0uL/gDgwfM6cmf/DseCk9dfN3tOsrLMZNiff4YBA3zYYqkLBSgiIsGiNrNeXJkZU4dpvp5WUmrn/o/WMG3ZNgAevagTN57ZznyyqAhuuw1GjYLiYhg61OxJadfOZ+2VulOAIiISLGoz68WFmTGuTvP1tILiUm6duZJZy3cQYoFJl3Xhmt6tzSf/+gv694cpU8BigcceM6cVN27sk7ZK/bk9QJkwYQIWi6XCLSkpqex5wzCYMGECLVq0IDIykoyMDNatW+fuZoiINEyJfaHfPLhkp3nvLPCozT6O/WobzHhYTkExI6b+xJfr/yI8NIQpV3fnivSjC/otXw7p6bB0KcTEwNy5MHasGahIwPJIKnOnTp1YuHBh2WOr9VgX4aRJk3jmmWeYPn06HTp04LHHHmPAgAFs3LiR6OhoTzRHRETqyhHM+NDe3EJGTP2J9Zk5NLaF8tq16fRu19R88u23zSGdwkI46ST45BPzXgKeR4Z4QkNDSUpKKrslJiYCZu/Js88+y9ixYxk6dChpaWm8+eab5Ofn884773iiKSIiEsB2HMjn8le+Y31mDk0bhfPejb3M4KSwEP75T7j2WvPnCy4wZ+woOAkaHglQNm/eTIsWLWjTpg3Dhw9ny5YtAGzdupWsrCwGDhxYtq/NZuOss87iu+++q/Z4hYWF5OTkVLiJiEhw+y0rh0tf/o5t+/NJaRLJh7f0Ia1lLGzfbpasf+klc8eHHjJ7TmJjfdtgh0Cp1Ovn3B6g9OzZk7feeosvvviC1157jaysLPr06cP+/fvJysoCoHnz5hVe07x587LnnJk4cSKxsbFlt9TUVHc3W0RE/MhPWw9wxSvfsye3kJOaR/PRLX1ok9DIrGfSrRv89JNZ3+Szz+CRRyDET+Z8BEBxu0Dh9t/o4MGDufTSS+ncuTP9+/fns88+A+BNR5lhODZX/SjDMKpsK++BBx4gOzu77LZjxw53N1tERPzE/37Zzd9f/5GcghK6t2rC+zf1pnmjMLOn5Lzz4MABMyl21SrzsT/x4+J2gcbjIWejRo3o3LkzmzdvLpvNU7m3ZM+ePVV6Vcqz2WzExMRUuImISHAxDIP/fvMHt7+7iqJSO4M6NWfGDT2JzTsIgwaZU4fBrAj77bfQqpX52J+GVPy0uF0g8niAUlhYyIYNG0hOTqZNmzYkJSWxYMGCsueLiopYsmQJffp4f9qaiIj4h1K7wfi563ji898AGNmnNVOu7k7kzz+YQzpffQVRUTBzppl7Yjta1t/fhlT8sLhdoHJ7gDJmzBiWLFnC1q1b+fHHH7nsssvIyclhxIgRWCwW7rzzTp544glmz57N2rVrGTlyJFFRUVx11VXuboqIiASAI0Wl3DxjBW99vx2AceefzPgLTsb63LOQkQG7dkHHjmbJ+srXCn8bUvGz4naBzO11UHbu3MmVV17Jvn37SExMpFevXvzwww+0OtoVd++993LkyBFuvfVWDh48SM+ePfnyyy9VA0VEpAHal1fIDW8u55cdhwgPDeE/V5zK+S3D4ZJLzIJrAMOHw2uvOa8K629DKo7idmsfM9sQ19kMTnxQ3C7QWQzDMHzdCFfl5OQQGxtLdna28lFERALU73vyuOHNn9m+P5+4qDBeuzad07evgauvNntNwsPhmWfMnJPqJlIsGmwO65QPUixWs+qtjwvMSVWuXL/9ZF6WiEgD5E/JnV62dPNeLpmyjO3780mNj+SjUT04ffrzcPbZZnBy0klm4bXbbqu5ZL2GVIKWR0rdi4jIcTiSOx35EwVZkLXQHB5I7OvjxnnW299vY8L/1lNqN0hv1YT/9mtG/LAh5lo6ANddB88/X7uF/jSkErQUoIiI+IKz5E6L1dwepEMTJaV2Hv10PW8eTYYd2q0lT1m3ENbnAjh4EKKj4dVX4corXTuwH6wXJO6nAEVExBf8LbnTw3IKivnnO6v4ZtNeAB44uzU3znkJy8svmzucfjq8+y60a+fDVoo/UYAiIuILcZ3NYZ3KyZ1BWC9j+/7D3PDmcn7fk0dkmJXXukfyt3uugl9/NXe45x6zCFt4uG8bKn5FAYqIiC+kjTNzTizWo0FKiHl/YLmZMJsyBHbOLZdXMS4gc1O+3byPf767kkP5xSQ3Dufjkp9JHv6IuQJxs2bw1ltmlViRSjTNWETEV/YuM3NO9i+Hov2ABbBjTrAsd++YmRJACbSGYfD60q1MnLcBuwHnRBXw8sIXCF+6xNzhvPPgjTfg6BIo0jBomrGISCBwJHc2TQeLIyih6r2vq6O66EhRKXfOWs3jn2/Abjd44vAvvD75ejM4iYoyE2E//VTBidRIQzwiIr7mLGG2sgBJoN1xIJ+b3l7B+swcEgpy+fDXt2i96OgMm1694O234cQTfdtICQjqQRER8TVnC8xVFgAJtN/9vo8hL37L+swcLtj9C9+9e6cZnISGmkmwS5cqOHFFAy7kB+pBERHxPWcJs85yUPy0OqphGLzx7VYmzvuN8IIjTPl5Bud9O8d88uSTzV6T7t192saA04AL+TmoB0VExNcc1VCTBkB4AtjiISzWvLclmNv7L/HL6qg5BcXcMmMlj322gR5bf+HbmaOPBSejR8OKFQpO6sLfVmn2AfWgiIj4g8S+Zk9K+W/NZT0n4yoGJ47ZPz6egrxudza3zVzJ3t37eGLJdK5a9bn5RGoqTJ0K/ft7vU1Bo4EV8nNGPSgiIt5WXW5Bbb41O7r+sxbAkV3m/cIMr+cnvP/zDoZO+Y4Tfl7KV9P/eSw4ueUWWLtWwUl9OctLCoA8JHdSD4qIiDfVlFtQm2/NPl7D50hRKQ99spYvl/3GI1+/wbA1C8wn2rQx65r06+fxNjQIlfOS/DwPyRMUoIiIeFNNAUZtyt/7sOt/y948bp25khbffsWXX7xIUt4BDIsFy+23wxNPQKNGHm9Dg6FVmhWgiIh4VU0Bxt9mHf9bc33W8Klj7ophGHy4YifPvPc998x/haHrFplPtG+PZepU+Nvfjv/e4roGvkqzAhQREW+qKcCozbfmunb913Haam5BMeNmr8E6cwaffv0GTY/kYISEYLnrLnjkEYiMrPu5EKmB1uIREfGmyoFC2To7LkwjrtITUouu/0WDzYTayoFR0oBqv6X/suMQTz0/l3++P5k+f5orDxtpaVhefx169qxdW0XKceX6rR4UERFvckduQV26/l3IXbHbDd74agP5jzzBtO9nYSstwR4RSciE8WbPSViYa+8tUgcKUEREvM0XuQW1zF3Zk1PAa49OZfjUibQ7sBOA4gGDCHtlCrRt680WSwOnOigiIs4E2zooaeMAy7HaGk5yVxZ+s5bvzxrC2H/fRrsDOznSNBHjvfcI+2KeghNvCrZ/e3WkHBQRkcqqzRNZHNjroFSTu5KTX8i8u55gwFvPEn8kB7vFQs611xP37L8hLs7XrW5YgvXf3lHKQRERqQ8fF0PzGCdDS2tmL8B6x+0M27kRgL2tOxD31lTizgj8i2FACtZ/e3WgIR4REajYrf7XopoTSoOgC75gVyarB15K56EDOWXnRg7botjx4CMkblpLWKAGJ0Hwe9EaPMeoB0VEpHK3ujOOhNI61hPxGyUl7H5iMjETH+PUgjwAVmZcyEnTp5DaKsXHjauHQP+9ONSnEF+QUQ+KiEjlbvXKyieU1mZBPz9VtPAr9rY/hRbj76dxQR4bWpzIzzP+R7dFc2kUyMEJBPTvpYJaJDM3FApQREScdasDhNggsqVZzMxRSC3QuuD3LoNZGRzslUj4gP4kbtvMgcgY3r3+QZqt/4XTr77A1y10j0D7vVTHUScnaUDVf3sNjIZ4RESq61Zv3q9qYmIgdcFvXUDJnedhfG7QpKSUUksIH3XrR9PHbufKcy/ydevcK5B+L8fTwNfgcVAPioiIK93qgdAFX1ICr75K8WlDCJ1bQlhJKT+mpvGff17FwNHLOcf2iq9b6H6B8HsRlyhAERFxpVvdn7vgDQPmzaO0Sxe4+WbCsgvY0qQF91x2B3kPRDCm1wzirNmBN+xRG/78e/EnATTTSYXaRETcqUoxtHHemUXy668YY8ZgWbAAgIMR0TzX90qKzg7nvtRpxFoPm/sdZ4FACWJ+UAROhdpERHzBF1Ndd++G//s/jKlTsRgGhdZQ3ux2IZ9deB0PXhBDz9/6H20PgTXs4atAL5gFWBE4BSgiIu7izQvAgQPw1FMYL7yA5cgRLMCnHc/gP/1GctGlZ/D+WW2xhVohZXH9Vk72hWCpaeJvAmymkwIUERF38cYFIC8PnnsOnn4asrOxACtadOTxfjdgO/NvvHZJGm0TGx/bPxBnhATYN/2AEWAznRSgiIi4iycvAIWF8N//wmOPwZ49AGxIbM3TZ17Lqs59GHtBJy7t1hKLxVL/9/K1APumHzDSxpk9URZrxRwUPx3yU4AiIuIunrgAlJbC22/DhAmwfTsAfzZJ5t9/u5rPTjmTq3q14ZmBHYiLCnfLR3CJp/JEAuybfsBwzHQKkCE/t08znjhxIqeffjrR0dE0a9aMiy++mI0bN1bYZ+TIkVgslgq3Xr16ubspIiLe5c6prnY7fPghdO4M110H27ezL6YpYwfeytk3vMyuwZfwye1n8ujFab4LThZmQNYCOLLLvF+Y4Z5pq6pp4jmOIb9Ldpr3fhqcgAd6UJYsWcJtt93G6aefTklJCWPHjmXgwIGsX7+eRo0ale137rnnMm3atLLH4eE++A8mIuJu9c35cAQmjz4Ka9cCkNcohudPv5S3up1P4yaxTDqvI5ec5uPhHE/miQTYN33xDLcHKPPnz6/weNq0aTRr1owVK1Zw5plnlm232WwkJSW5++1FRAJTaSl88IEZmKxfD0BBVGNeO+0C/nv6JeRHNmZkn9aM7t+emIgwHzcWz+eJBGJyr7iVx3NQsrOzAYiPj6+wffHixTRr1oy4uDjOOussHn/8cZo1a+b0GIWFhRQWFpY9zsnJ8VyDRUS8qbQU3nvPTH797TcAChtHM7X7EF4+9UJyIhrT/+Rm3D/4ZE5s1vg4B/Mi5YmIh3m0kqxhGFx00UUcPHiQpUuXlm2fNWsWjRs3plWrVmzdupWHHnqIkpISVqxYgc1mq3KcCRMm8PDDD1fZrkqyIuI3XE0YLSmBd981A5NNmwAoionj7V6X8Owp55Jra0THpGgeuuAU+p6Y4KUPUYPKny9lCCy/g6pVSVVeXqrnSiVZjwYot912G5999hnffvstKSkp1e6XmZlJq1ateO+99xg6dGiV5531oKSmpipAERH/4EoJ8YICmD4d/v1v+OMPAIrjmvD+GZcxsd0A8mxRJDS2cc+gDlzWPRVriB9MG67u86U/DzvnKk9Eas0vSt3ffvvtzJ07l2+++abG4AQgOTmZVq1asXnzZqfP22w2pz0rIiJ+oTYJo4cOwcsvm0XW/voLgJIm8cw5exjjU/tx2BZFRFgI//xbW27OaEdj29E/z/5Q8r26z7dzrnvyRPzhM4rfcXuAYhgGt99+O7Nnz2bx4sW0adPmuK/Zv38/O3bsIDk52d3NERHxvJoSRnfvhmefhVdegdxcAEpapvBJ/yt5qGlP8sMisIZYuPr0VO44pz3NYyKOHcNfSr57MiHWXz6j+B23Byi33XYb77zzDp988gnR0dFkZWUBEBsbS2RkJHl5eUyYMIFLL72U5ORktm3bxoMPPkhCQgKXXHKJu5sjIuJ5zhJGM0PgawsMbwNFRQAUn9KJuQOuZqytEwVHa3xc2LUFdw3oQJuERlWP6y8l3z2ZEOsvn1H8jtsDlJdffhmAjIyMCtunTZvGyJEjsVqtrFmzhrfeeotDhw6RnJxMv379mDVrFtHR0e5ujoiI5zkqyBICG+zwBbDcDsZOAIp69+GD/lfzcGlrikrNtL8zOyRy76CTSGsZW/1x/aXkuydLpPvLZxS/45EhnppERkbyxRdfuPttRUSq50qOQ13yIWLSYf998NwL8PuxMgiF513AjDOH8VRuU4qK7IDB6a2b8K8BHejTrhYzc/xlKq8nC6f5y2cUv+PRWTye4koWsIg0ENUFFq7MsHFlXzAX7XvlFZgypSzxlchIjgy/imndh/BcZhiFJXYA0ls5ApOmta8AW2173DyV15dJqt76jOIX/GaasacoQBGRCmoKLNY+Zq4TU/kbetKAqjkOiwbXbt9ffjFn48ycWZZfQsuWHLr+Rl46sR9vbsqj6Ghg0r1VE/7VvwN9T3QhMKn82TxZ8t3VoMwTPP0ZxW/4xTRjERGvqSnR0pUch5r2LSqCOXPMqcKLFx97vkcPMq+/mX83TmPOur2UrjeHeLqdEMed/TtwRvuE+q2ZU9eS77XtFfGHJFVvlrXXlOaAoQBFRAJfTYGFKzkOzvbdFwI/2eCWE44N41itcNllbBx+Pf/OiWfB+r+APQCc0T6B2/qdSM828b5bzM+VqbsNKUlVU5oDigIUEQl8NQUhrsxAcexrhMBqO3yFeW9sMZ9PTsa44Qa+zbiEl/4o5IcfDgB/YbHAuZ2SuDXjRDqnOJmV4+1v7a70ijSkJFV/6C2SWlOAIiKBr6YgJLFP7Weg2E+EdTfAG29B1pFj2885h8JRN/FBy9OY+sNOtizIBCA0xMLFp7Xk5rPaVb+QX+Vv7Ud2QeZ8aNoTuk32TKDiSq+IJ6cQ+5uG1FsUBBSgiEjgqK4n4njTYGvKcSgpgXnzYNo0+N//zMcATZrAyJHs//t1TN0bxswf/+TQqo0AREeEclXPExjRuzUt4iJrbnPlb+0O+380AxdPDC+40iviySnE/qah9BYFSZ6NZvGISGBw92yT334zg5K33oKjFa8B6NkT4+abWdlrAG+t3sPnazIpPlpc7YT4KK7v25rL01NpZDvO9zvHRSLzS8DufJ/qZhPVl6buOtcQzos/zMqqgWbxiEjwcUf+QG4uvP8+TJ0K3313bHtiIlxzDflXX8Oc4nje/mE7G6avLnv69NZNuOFvbRlwSvParS5c/iJRXXDi+AyeGF5oSL0irmgI5yWI8mwUoIiIb9W2O7qu+QOlpfD112bNkg8+gPx8c3sI0C0CLjyZ3y9+mhnbkvjok53kFu4CwBYawkWntuDvvVrRJSXOtc9U3bBOZZ4cXvDm1N3y/H14wVfnxVuCKM9GAYqI+I4r0z5dyR8wDFi50gxK3nsPMjOPPdcuFU7fRfHfrCwM6cbb+8/nu3cKgG0AtG4axd97teKy7inERYXX7XM5u0hUFozJqJ6cxuvvgY+/CKI8GwUoIlI/9blwuNIdXZvZJlu2wDvvwIwZsHHjse2NLdAvBW58gE32pXywOYrZmRnsK2kCQAilnNNsO9dcMJy/nZhASG2GcWpS3UUiPh3Cm2h4wVWqX1J7QTQrSwGKiNRdfS8crnRHV5c/UNoWXnrJ7C35/vtj+0eEw6nF0NdCdqcI/pfXhQ9WFvBL/tVluySEHmRY/JdcGT+flNhw6HB7HU6CE9VdJLo9E1wBSWWeGl4IorwKjwuiPBsFKCJSd/W9cLjaHe3IH8jMhI8+ggkPwNKl5pAOQEgInHMOXH019vgZfJd5gA8OnM38Tb0pNGwAhFpK6Be9nCuafElGzArCLEfbHDeg7ufBWTuD5CLhEk8NLwRRXoVXBEmejQIUEam7+l44XOmO3rXLDEo++ACWLTsWlAD07AnDhsHw4Ww8tJm5Sxcx56cr2VXcvGyXDrbtXBG/gIubfEtC6L5jr/VUF3iQXCRc4qnhhSDKq5DaU4AiInVX3wvH8Xoatm6FTz4xg5Ly04IBeveGyy+HSy/lz0YJ/O/X3cyduZqN++xAOgDRIXlc1GQJlzdZSJfIzThdGic+PfiHXrzFUz1HQZRXIbWnAEVE6q4+F47KybV/mwVNe8OKFfDcOJg7F9ZU6onp29cMSoYOZU9sIp/+msnc/+1g9Y5j+4VbijkrejkXxi1lYMwPRIQUVd8Gi9VMWlVw4j6e6DlqqENmDZwqyYpI/VSZxVOLC0f55NrCUlgfAisNWBsPf+0/tp/VCmecAZdcApdeyp7opny5/i8+X5PJD1v2Y3eknligT7sEhpROZlDk58SGHq70hiEQEgb2wqptiWwJl+ysxwkQkdpSJVkRqV7lgCJlCOycW/f6EjV9Y65uCvKicbCoFFYb8CtQ6Ki2uh8aN4bBg2HIEDjvPLbZbXzx0w988fY8Vh1qjkFI2eG7nRDHkK4tOK9LMs2iI2BRLmQVmAVcHRzl5AGyFiiPQSRAqAdFpCGpPC2YEMxS7Efv3bluR/n3Ki6FTSHwiwF/tIENWyru2wToDvRuivHgTtbtL+TLdVl8se4vNv6VW2HXU6M2Mij2By44/5+knnhmxSAoKgUOLDd3rLzWCkbwr8PiTiqMJh6gHhQRca5KCXZ7xXt31pdYOBYWlppByTqgwPFeW8ACtAO6AN2g4IQwvs8/lcWll7Dw2e/YdehI2WFCLaX0arSGQTHfMSD2R5LC9ptt3JELsdaqdVgMoGk65O+sOuSkPIba8URhNAU84iIFKCINSW1KsNe1vsTevbBoEXz1lXn744+Kz8cAXYHTm8CNM9j2/U0szunGotxu/LC+c1mdEjhCRFgIZ3VIZFCnJM7542xiizc7b2N1dVjCm8CgH6q2sSFO/a0LdxdGqy7gSX++fsOLEtQUoIgEs8rfWqNSqk4Lrqy2eRm5ufDNN8cCkl9/rfh8iAVONMygpCsUpIbxw5GuLC69hCX/i2Lrvlcq7J7c2ELGKan0OymRM9onEhluNZ/IbgdZW5znjjT0Al6e6JXYuwz+WuTe8+os4CEEfr4NLCEqXy9OKUARCVbOvrUamMMrjmnB1eWgOJsmnJ1t1iKZ/w589Sn8dggqxzmdO8PZZ8M551B8Uhi/fjOG7/I6syy3Cys3nEyREXZ0x8OEhlhIb92Efic1I+OkZnRo3hiLs0IlNU1lXvuo7wt4+WrowlPDMAszwCip+lx9zqvTnrtyw4qO+2AuX68hLpcpQBEJVtV101desC7lItj5SdW8jL/+MsvIL11q9pT8+ivY7RXfIxFIs8Dw8ZReeBMb7JF898c+vvtjPz/9fID8oqcq7J4UVUJGk41kRC2l7wkWok+9DxLb1fw5aqqB4esCXr5cxM4T69M4julUPc6rs4J+zgRr75cWO6wTzeIRCVazU+DIrqrbndX9sNvht9/ghx/MXpKlS2HTpqqvbREFJx6BkwwKO1pZG92eFfmd+Ln0LH7KPYnsI8UVdm8SFUbvdk3p3S6BPk2203bFOVgslWfRLK7fH+m61GGp93s4pksPdj51OWmA53sBXPn91veYITY45+u6n9cqs8eq4a1z522+/HfiZzSLR8RfeCpHoDbHrKkM/f798OOPZkDyww/mzzk5FV9vsUBaGpx5JpxxBjnpvVi+5CZ+PpjEivyTWb23A0V7wsu9oJjG1iJ6Rv9G7+bZ9OlxHh1PPoOQkKPDNotuBoubv/GD5xNfa/r268scGE+sT1PdMZv3q1/QV74XLPNLjs0eq8zH5es9NQzT0HOl6kgBioinuLtbd+8yWHk37P/x2LYjuyBzPjTtCd0mHzvu3mVQdPDo+wLbgW0W2GKH3etgS0LV40dFwemnQ8+elPTpy6YTu7I6z8KvOw+xcssuNq1aC9xe4SXx1my6N1rP6Y3Wkx61ni5RfxBqKTEvar9OhmblPmug/pGuaSjFl4vYeWJ4y5NDZo5A0llvAoAtAc78xHfTvj05DKPFDutEQzwidXW8b1t16dat7pg1JS+WHTsUenwKazfDB6NhmwFbDcjEeVrBSSdBr17Ye/RkZ8eurGjcgl+yDvPLzkOs332IQidv1TZ8J+mN1pPeaAPdo9bT1rbL+QJ8zj7r8c6HvyYR1jTs0f0/sPwOfFb8zRPDW54eMqscCPhLwTxPDsP462f2AVeu3wpQRI7H8Qd7/3JzBgxA43bVVCxdfOyi6mqOQLV/xBab71/+j6cBHAJ2AH8CW4FtQFY1n6EJ0DGRknNuZkeHrvyS3IHV+SFsyMxh/e4ccp1EIzHWPLpEbqZL5Ga6Rm2ie6MNJIRm13yuavqsNf2RrrbK62LfBynVfeMHMyisUsvDAzkwwcYbeUOu8kROT3n++Jl9QDkoIvVVPigp2n90Y7lYvnBfxf2d5VO40q27dxl8c3HFHhLHMX8YD2t+gT9KzYBkJ+Z9XjVtT7RitCrl8AmRbGnRgpWJHVkZfjIbCjuwpbAlpesMWPdnhZdEhIWQ1iKWLilxdM15lS7FH9MqbBchlnp8f6n8WWuajbNoMFXrZGCekzPnOA9SvNXj4hj2cMowg5MGluhYb/5YMM/TwzD++Jn9nAIUadicXeSgdjMOKiufT1E+B8ShuvF8R8/C4RJzOGY3ZhCyE9hRCvu/cv5+FjCSIL+ljayUBH5LbsNPqefwK6n8ntuYXHtjc7/8ozezkTSJCuPk5JiyW6cWMbRv1pjQA9/D2rsh90sIry6J0SEEMI4V2aptPZXq/khXV+G2cJ95bir3pHhz2qYjsPrqnKqrIQdCDo3Ujq+nrEsVClCk4aruIhffHZeDEzj2bav8ccuLPglCGsP7QyG7JRT3hl1F8OOHsL0Eahg9KU1OJK/pEbKSmrIlsSW/Nj2R72K7sLG0NQVGxLEdyx0jhFLa2HZzcuQ2To7Yxim9b+Hkjj1pHmOrWhCtQpuPE5w4/nCnv1Cxfkp19VRqo8Y6GUbVmT6eqAFSk8S+5kwWrYYcvGrq4ROfUA6KBB53de1Xm1vg6A1wQfl8ipUTYM1XsMcOezh2ywT+omr11XKKYkI52CyaXYnN+C2xNSvjO/Jt095khTau9jXhlmLaRmRyYmQmJ8YU0L7RXk5kFa0j/sIWUmIWZnP2h7b8ebQXQuEB55/bYqXGBficcfV3dLwk4Mp5AJ7OF3BGiY4i9aYcFAlOTqfZZprTbG0JRy/ELgQr1S6cd5zgxA4cBPZxLPjIaQJ5LeGeK2CXkwtnOSVhIexPiGVX02ZsaXoC6+Jasyr2JP6ITyXX1sjpa0Is0CIuktZNG3FC0yhaxUfRNrEx7UM3krp8AFZLadXPUuLooh7nPDip1TBWiDmLwZVvknUZfnF8e/3m4qr5Pc56KXwxbTMYv2H768wpEdSDIoGiVtNsnX3TL/cHt/If46KD5kyc8hc5O5AL7C93O3D0/qAV9pWawclxYpiC8DD+atKUHXHN+T02lS0xLdkSn8KWpi3JjE7AsIRUeU2YpZjUsL9oZcukVXgmrWxZtA7fTauIv0gJ20v4wIVVLx41zTBxnBNn0ySP9zrHayuXxa/NBaw+0zVr20vhzt6MhnqRrmnWmKufv6GeQ3GZphmLf3E2TdfR2wEV/7ClDKm6/Do4/2Z9POX/4BYVw+z+cMiAQ3bItpjTdLM5ug2MQ8BBsNQQAzmUhFjJjG7Kn3FJ/BmbxI64JHbENjcfxyVxMDKGygVCEkIP0SJsD8lh+2gRvpeWYXtJDttb9nNC6CFCrOEQFl11uKW6C3x1Qx3lRbaEv82qeJ4PLK/5fDqCPcdHcOUCVt/hl9pOx3THtE13XqQDjbvqfjTkc1gXDTyYU4AivlP5P1/KELOQlWGnYrdDCGCpeAGsPBOkplyQIsxptnmYPR55YORASa6V4txQSnOskA0h2XbCsksJP1zs/DhO2LGwp3ETMqMT2R2TQGZ0Apkxiewud7+vURz2ECtg1gtJjCiiWfP2JEbbaBZtIzEkk2YH59KseB0tEhJIbtePiNX/dHIeKolsad7X9gJfmx6U+HQ4sIIKF5AK59shBGzxZgGy6nqYanMBC6R1RwKpreDei5u78ngC7Rz6koK5wMlBmTJlCk8//TSZmZl06tSJZ599ljPOOMOXTQoctflDVV2BMTg2BOLosXC2T+4f5jZ7MYSEHbuHqj0g+5eDUQzF5aaRHMnE2D2fEqwUG2EU2cMoMkIpMsIotodSUmClNM+K/YgFe34I5IM93/GzBY4YhOQbhB4uIfxwCRGHC4nKLyQq/wgRRVUDDgsQRilh1WShFodY2duoydFbHHsbNWFP4/iybfsaxZEVnUBuTCTR4fnEh+bQxJpDvK2EJh2HcUpUOH3sO0j8fTyJYQdoFrqfxLAcIkKKj85oedg8D46ZqC3SIe3hY7+XhLSae4LK51A4m9FiLzR/p+V/z5WnRlY+3rEIsFKNEcc04UpTKsuXGp+dUrfS9O6crunpb5uBVH7f3VOr3ZXHE0jn0Ne8PfsswPmsB2XWrFlcc801TJkyhb59+/Lqq6/y+uuvs379ek444YQaX+uxHpTKF/TyF+TaXLSr26e+r6+8T3Eu5Kwva7bdsJgX/tCmFBNJUWQ7iosLKMrZRpERZgYERmjFAKHCvflccfl9jTCK7FZKi60YJRbsRRYoBkuRgbWwFGuBgbWolNCjt7CikrJbeFExtqJibEVF2IqLiCouJLK4gMZFR4guzCe68DCNC/MJNVycKVNJiSWEg5ExHIqM5mBkNAcjY44+bkx+o0hyoyMpiLFRHBNKcZyV0EZ2okPzibEeLrtFWw8TYy2gSZdbiN/6FHGl282Aw8HZN8EqvUQXwfLbnfcSWUJqV10WzKqk5auq1uZ4ldsTlWJuKz/b5tsrnL9neIKZr1PdEEl9c0kqn6MKU5BrEWh449tmIH37d3db3ZXHE0jn0Nd8MfvMzwTEEE/Pnj3p1q0bL7/8ctm2k08+mYsvvpiJEyfW+FqPBCh7l8H8s+AvO+Z/WI6VsajmZ7sdCo0wCu3hFJaGU2g3fy4ywiixW81eAsNKsT2MYru1wrYSI5RieyilduvRfcztpYaVktIQ7I6b3YJRat6wA6WYj+0WLKUG2CHEbmC1lxJqtxNi2Am1lx59XIrVKCWstARbaTHhJcWElxZjO3pfdispxlZaRHhJifl8aTG2kiJspbUfFqmrEksIhyMiOWyLID8ikiM2G4WRYRRFhFEcGUpppJXiRlZKG1uxN7ZgaQxEG1jjo7D1eJBIWwiN1t5NZMlfRIUUEG09TKOQAtcqoDr+mKaNq9sf7BpLoVuPvx4NVF0ozVFZ1tmMFlf/8Nf1AuKuC1hdAw1vXPgCaeqwJy5uHs3j8cNz6GsK5vx/iKeoqIgVK1Zw//33V9g+cOBAvvvuuyr7FxYWUlh4rIJjTuVl4d1h7WPs3x1N0/sP1folIUAkxURSDBx2f5v8TKnVQmm4lRKbFXt4CKW2EIxwC3abBcNmARtgA0uEgcVmEBJhEGKzY40oxRphJzSylJAoA0sUEAVEQqjNTqzlMLGunD9HT4Pjj1/qc3Wr/Org6I6u6zTSaqcrlzu2Q3XDH5VXcU3sa+aCHO94tVHXIRd3Tauta7e2N4YOAmnqsCemVruj/HognUNfU7Val/gkQNm3bx+lpaU0b968wvbmzZuTlVV1tbOJEyfy8MMPe7ZRh9aQZ7cRamuEYbFgYDl6T9ljLFTcXvazBSzGsUkbFo7mfxplP5dtO/rN3mKh7DWO/SwWw3w+BLBytEffACtYrAYWq/nYYjWwhBiEWA1CrHYsVgOr1U5IiJ2Qo/thPXYMrEAY5m+7/H1YNdured4aYmClhHBqMc2lOpawo39gazu8UymRs2lP6PZM1Yt5+T+QUSkVF/I7bpvK/ZGvyx/smqqgurIeTW2OW5cLUn0uIO64gNU10PBWrZNAWSPFny9ugXIOfU3BnEt8miRbudy2YRhVS3ADDzzwAHfddVfZ45ycHFJTU93bmLjOxKV8x5THLycypJCokAIiLYVEhhQQGVJY4RZVts2xT2H9FlULapXWZum/GNY+WscaHDX8R678B7LKYn8WnM4Qcscf+bLF5JzMinFlPZrqjuuOC5IvLyB1DTT8+YLsC7q4BQcFc7XmkwAlISEBq9Vapbdkz549VXpVAGw2Gzabk+5ud0obR2xWBg8kv4XLZc4DQVismWjrJMnW6T5QfbKuvRiKczh20a/0ekdpdccaKpX/mFaZeVJN0FC5p8QV5f8IHDdhs55/5MtfOKrUeqnHsYPlguTrIaZgooubNCA+TZLt3r07U6ZMKdt2yimncNFFF/kmSRYCZxYPlJsmfPRiW9NU4uOtw1KXP/zufr27gwbxL+5IxhSRgBcQs3gc04xfeeUVevfuzX//+19ee+011q1bR6tWrWp8rQq1iYiIBB6/n8UDMGzYMPbv388jjzxCZmYmaWlpfP7558cNTkRERCT4qdS9iIiIeIUr1++qS6qKiIiI+JgCFBEREfE7ClBERETE7yhAEREREb+jAEVERET8jgIUERER8TsKUERERMTvKEARERERv6MARURERPyOz0rd14ej+G1OTo6PWyIiIiK15bhu16aIfUAGKLm5uQCkpqb6uCUiIiLiqtzcXGJjY2vcJyDX4rHb7ezevZvo6GgsFkutXpOTk0Nqaio7duzQ+j1epPPuGzrvvqHz7hs6775Rl/NuGAa5ubm0aNGCkJCas0wCsgclJCSElJSUOr02JiZG/4B9QOfdN3TefUPn3Td03n3D1fN+vJ4TByXJioiIiN9RgCIiIiJ+p8EEKDabjfHjx2Oz2XzdlAZF5903dN59Q+fdN3TefcPT5z0gk2RFREQkuDWYHhQREREJHApQRERExO8oQBERERG/owBFRERE/E5QBShTpkyhTZs2RERE0L17d5YuXVrj/kuWLKF79+5ERETQtm1bXnnlFS+1NLi4ct4//vhjBgwYQGJiIjExMfTu3ZsvvvjCi60NHq7+e3dYtmwZoaGhnHrqqZ5tYJBy9bwXFhYyduxYWrVqhc1mo127dkydOtVLrQ0erp73mTNn0rVrV6KiokhOTua6665j//79XmptcPjmm2+48MILadGiBRaLhTlz5hz3NW69rhpB4r333jPCwsKM1157zVi/fr0xevRoo1GjRsb27dud7r9lyxYjKirKGD16tLF+/XrjtddeM8LCwowPP/zQyy0PbK6e99GjRxtPPfWU8dNPPxmbNm0yHnjgASMsLMxYuXKll1se2Fw97w6HDh0y2rZtawwcONDo2rWrdxobROpy3ocMGWL07NnTWLBggbF161bjxx9/NJYtW+bFVgc+V8/70qVLjZCQEOO5554ztmzZYixdutTo1KmTcfHFF3u55YHt888/N8aOHWt89NFHBmDMnj27xv3dfV0NmgClR48exs0331xhW8eOHY3777/f6f733nuv0bFjxwrbbrrpJqNXr14ea2MwcvW8O3PKKacYDz/8sLubFtTqet6HDRtmjBs3zhg/frwClDpw9bzPmzfPiI2NNfbv3++N5gUtV8/7008/bbRt27bCtueff95ISUnxWBuDXW0CFHdfV4NiiKeoqIgVK1YwcODACtsHDhzId9995/Q133//fZX9Bw0axPLlyykuLvZYW4NJXc57ZXa7ndzcXOLj4z3RxKBU1/M+bdo0/vjjD8aPH+/pJgalupz3uXPnkp6ezqRJk2jZsiUdOnRgzJgxHDlyxBtNDgp1Oe99+vRh586dfP755xiGwV9//cWHH37I+eef740mN1juvq4G5GKBle3bt4/S0lKaN29eYXvz5s3Jyspy+pqsrCyn+5eUlLBv3z6Sk5M91t5gUZfzXtnkyZM5fPgwV1xxhSeaGJTqct43b97M/fffz9KlSwkNDYr/9l5Xl/O+ZcsWvv32WyIiIpg9ezb79u3j1ltv5cCBA8pDqaW6nPc+ffowc+ZMhg0bRkFBASUlJQwZMoQXXnjBG01usNx9XQ2KHhQHi8VS4bFhGFW2HW9/Z9ulZq6ed4d3332XCRMmMGvWLJo1a+ap5gWt2p730tJSrrrqKh5++GE6dOjgreYFLVf+vdvtdiwWCzNnzqRHjx6cd955PPPMM0yfPl29KC5y5byvX7+eO+64g//7v/9jxYoVzJ8/n61bt3LzzTd7o6kNmjuvq0HxVSohIQGr1Volmt6zZ0+VaM4hKSnJ6f6hoaE0bdrUY20NJnU57w6zZs3ihhtu4IMPPqB///6ebGbQcfW85+bmsnz5clatWsU///lPwLxwGoZBaGgoX375JWeffbZX2h7I6vLvPTk5mZYtW1ZYXv7kk0/GMAx27txJ+/btPdrmYFCX8z5x4kT69u3LPffcA0CXLl1o1KgRZ5xxBo899ph6yD3E3dfVoOhBCQ8Pp3v37ixYsKDC9gULFtCnTx+nr+ndu3eV/b/88kvS09MJCwvzWFuDSV3OO5g9JyNHjuSdd97RmHAduHreY2JiWLNmDatXry673XzzzZx00kmsXr2anj17eqvpAa0u/9779u3L7t27ycvLK9u2adMmQkJCSElJ8Wh7g0Vdznt+fj4hIRUvb1arFTj2jV7cz+3X1Tql1vohxzS0N954w1i/fr1x5513Go0aNTK2bdtmGIZh3H///cY111xTtr9jOtS//vUvY/369cYbb7yhacZ14Op5f+edd4zQ0FDjpZdeMjIzM8tuhw4d8tVHCEiunvfKNIunblw977m5uUZKSopx2WWXGevWrTOWLFlitG/f3vjHP/7hq48QkFw979OmTTNCQ0ONKVOmGH/88Yfx7bffGunp6UaPHj189RECUm5urrFq1Spj1apVBmA888wzxqpVq8qmd3v6uho0AYphGMZLL71ktGrVyggPDze6detmLFmypOy5ESNGGGeddVaF/RcvXmycdtppRnh4uNG6dWvj5Zdf9nKLg4Mr5/2ss84ygCq3ESNGeL/hAc7Vf+/lKUCpO1fP+4YNG4z+/fsbkZGRRkpKinHXXXcZ+fn5Xm514HP1vD///PPGKaecYkRGRhrJycnG1VdfbezcudPLrQ5sixYtqvHvtaevqxbDUH+XiIiI+JegyEERERGR4KIARURERPyOAhQRERHxOwpQRERExO8oQBERERG/owBFRERE/I4CFBEREfE7ClBERETE7yhAEREREb+jAEVERET8jgIUERER8TsKUERERMTv/D+dIr2y+yqPJAAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -838,7 +838,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0050000000000000044\n"
+      "0.004999999999999996\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
index 966db3dc9..ff15fcc41 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
@@ -1077,7 +1077,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1655,7 +1655,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1673,7 +1673,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1691,7 +1691,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1709,7 +1709,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1727,7 +1727,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1745,7 +1745,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1763,7 +1763,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1781,11 +1781,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1803,11 +1803,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1825,11 +1825,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1847,11 +1847,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1869,11 +1869,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1891,7 +1891,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -1909,11 +1909,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1931,11 +1931,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1953,11 +1953,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1975,11 +1975,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -1997,11 +1997,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2019,11 +2019,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2041,11 +2041,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2063,11 +2063,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
       "  exp_term = np.exp(self.z_o)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
      ]
     },
@@ -2128,15 +2128,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_94478/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20528/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
       "  return 1/(1 + np.exp(-x))\n"
      ]
     },
@@ -2669,24 +2669,20 @@
       "Learning rate  =  0.01\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.9888888888888889\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  0.01\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.9722222222222222\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  0.01\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.9527777777777777\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.9027777777777778\n",
       "\n"
      ]
     },
@@ -2694,10 +2690,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9027777777777778\n",
+      "\n",
       "Learning rate  =  0.1\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.8583333333333333\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  0.1\n",
       "Lambda =  0.001\n",
       "Accuracy score on test set:  0.8722222222222222\n",
@@ -2729,10 +2735,6 @@
       "Learning rate  =  0.1\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.8666666666666667\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  1e-05\n",
-      "Accuracy score on test set:  0.08611111111111111\n",
       "\n"
      ]
     },
@@ -2740,6 +2742,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.08611111111111111\n",
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.10555555555555556\n",
@@ -2747,10 +2753,6 @@
       "Learning rate  =  1.0\n",
       "Lambda =  0.001\n",
       "Accuracy score on test set:  0.10555555555555556\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.01\n",
-      "Accuracy score on test set:  0.17777777777777778\n",
       "\n"
      ]
     },
@@ -2758,6 +2760,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.17777777777777778\n",
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.08333333333333333\n",
@@ -2765,10 +2771,6 @@
       "Learning rate  =  1.0\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.08888888888888889\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  10.0\n",
-      "Accuracy score on test set:  0.09444444444444444\n",
       "\n"
      ]
     },
@@ -2776,10 +2778,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  10.0\n",
+      "Accuracy score on test set:  0.09444444444444444\n",
+      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.17222222222222222\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.11666666666666667\n",
@@ -2791,20 +2803,10 @@
       "Learning rate  =  10.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.1388888888888889\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.11388888888888889\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.10555555555555556\n",
       "\n"
      ]
     },
@@ -2812,6 +2814,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  10.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.09444444444444444\n",
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
index 44f6fad75..d921b1909 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb
@@ -3029,15 +3029,24 @@
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:78\u001b[0m, in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhessian\u001b[39m(fun, x):\n\u001b[1;32m     77\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturns a function that computes the exact Hessian.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 78\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:57\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjacobian\u001b[39m(fun, x):\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;124;03m    Returns a function which computes the Jacobian of `fun` with respect to\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;124;03m    positional argument number `argnum`, which must be a scalar or array. Unlike\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;124;03m    (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m     vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     58\u001b[0m     ans_vspace \u001b[38;5;241m=\u001b[39m vspace(ans)\n\u001b[1;32m     59\u001b[0m     jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m      9\u001b[0m     start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m     end_value, end_node \u001b[38;5;241m=\u001b[39m  \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      9\u001b[0m     start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m     end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:61\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     59\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m     60\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(vjp, ans_vspace\u001b[38;5;241m.\u001b[39mstandard_basis())\n\u001b[0;32m---> 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m)\u001b[49m, jacobian_shape)\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m     arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m     90\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m     84\u001b[0m     \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m     arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m     90\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp..vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m toposort(end_node):\n\u001b[1;32m     20\u001b[0m     outgrad \u001b[38;5;241m=\u001b[39m outgrads\u001b[38;5;241m.\u001b[39mpop(node)\n\u001b[0;32m---> 21\u001b[0m     ingrads \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrad\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[1;32m     23\u001b[0m         outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(outgrads\u001b[38;5;241m.\u001b[39mget(parent), ingrad)\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m     65\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[1;32m     66\u001b[0m     vjp \u001b[38;5;241m=\u001b[39m vjpfun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 67\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m,)\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m     69\u001b[0m     argnum_0, argnum_1 \u001b[38;5;241m=\u001b[39m argnums\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:82\u001b[0m, in \u001b[0;36m\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m     80\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlog10,  \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m x \u001b[38;5;241m/\u001b[39m anp\u001b[38;5;241m.\u001b[39mlog(\u001b[38;5;241m10\u001b[39m))\n\u001b[1;32m     81\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlog1p,  \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m (x \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m---> 82\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39msin,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcos\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     83\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mcos,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39m g \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39msin(x))\n\u001b[1;32m     84\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mtan,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m anp\u001b[38;5;241m.\u001b[39mcos(x) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:37\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f_raw)\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf_wrapped\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 37\u001b[0m     boxed_args, trace, node_constructor \u001b[38;5;241m=\u001b[39m \u001b[43mfind_top_boxed_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     38\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m boxed_args:\n\u001b[1;32m     39\u001b[0m         argvals \u001b[38;5;241m=\u001b[39m subvals(args, [(argnum, box\u001b[38;5;241m.\u001b[39m_value) \u001b[38;5;28;01mfor\u001b[39;00m argnum, box \u001b[38;5;129;01min\u001b[39;00m boxed_args])\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:70\u001b[0m, in \u001b[0;36mfind_top_boxed_args\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m     68\u001b[0m top_node_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m argnum, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(args):\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43misbox\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m     71\u001b[0m         trace \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_trace\n\u001b[1;32m     72\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m trace \u001b[38;5;241m>\u001b[39m top_trace:\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:82\u001b[0m, in \u001b[0;36m\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m     80\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlog10,  \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m x \u001b[38;5;241m/\u001b[39m anp\u001b[38;5;241m.\u001b[39mlog(\u001b[38;5;241m10\u001b[39m))\n\u001b[1;32m     81\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlog1p,  \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m (x \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m---> 82\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39msin,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[43mg\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcos\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     83\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mcos,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39m g \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39msin(x))\n\u001b[1;32m     84\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mtan,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x : \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m anp\u001b[38;5;241m.\u001b[39mcos(x) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:27\u001b[0m, in \u001b[0;36mArrayBox.__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__mul__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultiply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     43\u001b[0m     argnums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(argnum    \u001b[38;5;28;01mfor\u001b[39;00m argnum, _   \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[1;32m     44\u001b[0m     ans \u001b[38;5;241m=\u001b[39m f_wrapped(\u001b[38;5;241m*\u001b[39margvals, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 45\u001b[0m     node \u001b[38;5;241m=\u001b[39m \u001b[43mnode_constructor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_wrapped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36\u001b[0m, in \u001b[0;36mVJPNode.__init__\u001b[0;34m(self, value, fun, args, kwargs, parent_argnums, parents)\u001b[0m\n\u001b[1;32m     33\u001b[0m     fun_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fun, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__name__\u001b[39m\u001b[38;5;124m'\u001b[39m, fun)\n\u001b[1;32m     34\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnums \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     35\u001b[0m                               \u001b[38;5;241m.\u001b[39mformat(fun_name, parent_argnums))\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpmaker\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent_argnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:76\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums\u001b[0;34m(argnums, ans, args, kwargs)\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m     75\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnums 0, 1 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[0;32m---> 76\u001b[0m vjp_0 \u001b[38;5;241m=\u001b[39m \u001b[43mvjp_0_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     77\u001b[0m vjp_1 \u001b[38;5;241m=\u001b[39m vjp_1_fun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (vjp_0(g), vjp_1(g))\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:34\u001b[0m, in \u001b[0;36m\u001b[0;34m(ans, x, y)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;66;03m# ----- Binary ufuncs -----\u001b[39;00m\n\u001b[1;32m     32\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39madd,         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m     33\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: g))\n\u001b[0;32m---> 34\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mmultiply,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : \u001b[43munbroadcast_f\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m     35\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: x \u001b[38;5;241m*\u001b[39m g))\n\u001b[1;32m     36\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39msubtract,    \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m     37\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39mg))\n\u001b[1;32m     38\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mdivide,      \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g:   g \u001b[38;5;241m/\u001b[39m y),\n\u001b[1;32m     39\u001b[0m                         \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39m g \u001b[38;5;241m*\u001b[39m x \u001b[38;5;241m/\u001b[39m y\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m))\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:659\u001b[0m, in \u001b[0;36munbroadcast_f\u001b[0;34m(target, f)\u001b[0m\n\u001b[1;32m    658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munbroadcast_f\u001b[39m(target, f):\n\u001b[0;32m--> 659\u001b[0m     target_meta \u001b[38;5;241m=\u001b[39m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    660\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: unbroadcast(f(g), target_meta)\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb
index a0e2d2c34..67a50bdf0 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb
@@ -1298,12 +1298,20 @@
    },
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'flatbuffers'",
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lib/__init__.py:32: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.\n",
+      "  warnings.warn(\"JAX on Mac ARM machines is experimental and minimally tested. \"\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'jaxlib.pocketfft' has no attribute 'pocketfft'",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
       "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets, layers, models\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential      \u001b[38;5;66;03m#This allows appending layers to existing models\u001b[39;00m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m autograph\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m bitwise\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/__init__.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03m\"\"\"Compatibility functions.\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \n\u001b[1;32m      5\u001b[0m \u001b[38;5;124;03mThe `tf.compat` module contains two sets of compatibility functions.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     32\u001b[0m \n\u001b[1;32m     33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v1\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v2\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m forward_compatibility_horizon\n",
@@ -1315,8 +1323,15 @@
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/experimental/authoring/__init__.py:8\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03m\"\"\"Public API for tf.lite.experimental.authoring namespace.\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compatible\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/authoring/authoring.py:43\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m convert\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m converter_error_data_pb2\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/convert.py:29\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite_constants\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m util\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wrap_toco\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert_phase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Component\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:26\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmoves\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;28mrange\u001b[39m\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mflatbuffers\u001b[39;00m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprotobuf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config_pb2 \u001b[38;5;28;01mas\u001b[39;00m _config_pb2\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprotobuf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m graph_debug_info_pb2\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'flatbuffers'"
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;66;03m# Jax functions used by TFLite\u001b[39;00m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;66;03m# pylint: disable=unused-import\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m   \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_computation \u001b[38;5;28;01mas\u001b[39;00m _xla_computation\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m   _xla_computation \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/__init__.py:116\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     41\u001b[0m   config \u001b[38;5;28;01mas\u001b[39;00m config,\n\u001b[1;32m     42\u001b[0m   enable_checks \u001b[38;5;28;01mas\u001b[39;00m enable_checks,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     51\u001b[0m   numpy_rank_promotion \u001b[38;5;28;01mas\u001b[39;00m numpy_rank_promotion,\n\u001b[1;32m     52\u001b[0m )\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     54\u001b[0m   ad,  \u001b[38;5;66;03m# TODO(phawkins): update users to avoid this.\u001b[39;00m\n\u001b[1;32m     55\u001b[0m   checkpoint \u001b[38;5;28;01mas\u001b[39;00m checkpoint,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    114\u001b[0m   xla_computation \u001b[38;5;28;01mas\u001b[39;00m xla_computation,\n\u001b[1;32m    115\u001b[0m )\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmaps\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m soft_pmap \u001b[38;5;28;01mas\u001b[39;00m soft_pmap\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__ \u001b[38;5;28;01mas\u001b[39;00m __version__\n\u001b[1;32m    119\u001b[0m \u001b[38;5;66;03m# These submodules are separate because they are in an import cycle with\u001b[39;00m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;66;03m# jax and rely on the names imported above.\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/experimental/maps.py:26\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wraps, partial, partialmethod\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01menum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Enum\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m jnp\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linear_util \u001b[38;5;28;01mas\u001b[39;00m lu\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/__init__.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2018 Google LLC\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m \n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fft \u001b[38;5;28;01mas\u001b[39;00m fft\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linalg \u001b[38;5;28;01mas\u001b[39;00m linalg\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterpreters\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mxla\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DeviceArray \u001b[38;5;28;01mas\u001b[39;00m DeviceArray\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/fft.py:17\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 Google LLC\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     18\u001b[0m   ifft \u001b[38;5;28;01mas\u001b[39;00m ifft,\n\u001b[1;32m     19\u001b[0m   ifft2 \u001b[38;5;28;01mas\u001b[39;00m ifft2,\n\u001b[1;32m     20\u001b[0m   ifftn \u001b[38;5;28;01mas\u001b[39;00m ifftn,\n\u001b[1;32m     21\u001b[0m   ifftshift \u001b[38;5;28;01mas\u001b[39;00m ifftshift,\n\u001b[1;32m     22\u001b[0m   ihfft \u001b[38;5;28;01mas\u001b[39;00m ihfft,\n\u001b[1;32m     23\u001b[0m   irfft \u001b[38;5;28;01mas\u001b[39;00m irfft,\n\u001b[1;32m     24\u001b[0m   irfft2 \u001b[38;5;28;01mas\u001b[39;00m irfft2,\n\u001b[1;32m     25\u001b[0m   irfftn \u001b[38;5;28;01mas\u001b[39;00m irfftn,\n\u001b[1;32m     26\u001b[0m   fft \u001b[38;5;28;01mas\u001b[39;00m fft,\n\u001b[1;32m     27\u001b[0m   fft2 \u001b[38;5;28;01mas\u001b[39;00m fft2,\n\u001b[1;32m     28\u001b[0m   fftfreq \u001b[38;5;28;01mas\u001b[39;00m fftfreq,\n\u001b[1;32m     29\u001b[0m   fftn \u001b[38;5;28;01mas\u001b[39;00m fftn,\n\u001b[1;32m     30\u001b[0m   fftshift \u001b[38;5;28;01mas\u001b[39;00m fftshift,\n\u001b[1;32m     31\u001b[0m   hfft \u001b[38;5;28;01mas\u001b[39;00m hfft,\n\u001b[1;32m     32\u001b[0m   rfft \u001b[38;5;28;01mas\u001b[39;00m rfft,\n\u001b[1;32m     33\u001b[0m   rfft2 \u001b[38;5;28;01mas\u001b[39;00m rfft2,\n\u001b[1;32m     34\u001b[0m   rfftfreq \u001b[38;5;28;01mas\u001b[39;00m rfftfreq,\n\u001b[1;32m     35\u001b[0m   rfftn \u001b[38;5;28;01mas\u001b[39;00m rfftn,\n\u001b[1;32m     36\u001b[0m )\n\u001b[1;32m     38\u001b[0m \u001b[38;5;66;03m# Module initialization is encapsulated in a function to avoid accidental\u001b[39;00m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# namespace pollution.\u001b[39;00m\n\u001b[1;32m     40\u001b[0m _NOT_IMPLEMENTED \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/numpy/fft.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01moperator\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lax\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_client\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m safe_zip\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/lax/__init__.py:332\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m    299\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (_reduce_sum, _reduce_max, _reduce_min, _reduce_or,\n\u001b[1;32m    300\u001b[0m                   _reduce_and, _reduce_window_sum, _reduce_window_max,\n\u001b[1;32m    301\u001b[0m                   _reduce_window_min, _reduce_window_prod,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    306\u001b[0m                   _upcast_fp16_for_computation, _broadcasting_shape_rule,\n\u001b[1;32m    307\u001b[0m                   _eye, _tri, _delta, _ones, _zeros, _dilate_shape)\n\u001b[1;32m    308\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontrol_flow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    309\u001b[0m   associative_scan \u001b[38;5;28;01mas\u001b[39;00m associative_scan,\n\u001b[1;32m    310\u001b[0m   cond \u001b[38;5;28;01mas\u001b[39;00m cond,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    330\u001b[0m   while_p \u001b[38;5;28;01mas\u001b[39;00m while_p,\n\u001b[1;32m    331\u001b[0m )\n\u001b[0;32m--> 332\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    333\u001b[0m   fft \u001b[38;5;28;01mas\u001b[39;00m fft,\n\u001b[1;32m    334\u001b[0m   fft_p \u001b[38;5;28;01mas\u001b[39;00m fft_p,\n\u001b[1;32m    335\u001b[0m )\n\u001b[1;32m    336\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mparallel\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    337\u001b[0m   all_gather \u001b[38;5;28;01mas\u001b[39;00m all_gather,\n\u001b[1;32m    338\u001b[0m   all_to_all \u001b[38;5;28;01mas\u001b[39;00m all_to_all,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    355\u001b[0m   xeinsum \u001b[38;5;28;01mas\u001b[39;00m xeinsum,\n\u001b[1;32m    356\u001b[0m )\n\u001b[1;32m    357\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mother\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    358\u001b[0m   conv_general_dilated_patches \u001b[38;5;28;01mas\u001b[39;00m conv_general_dilated_patches\n\u001b[1;32m    359\u001b[0m )\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lax/fft.py:145\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m    143\u001b[0m batching\u001b[38;5;241m.\u001b[39mprimitive_batchers[fft_p] \u001b[38;5;241m=\u001b[39m fft_batching_rule\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pocketfft:\n\u001b[0;32m--> 145\u001b[0m   xla\u001b[38;5;241m.\u001b[39mbackend_specific_translations[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m][fft_p] \u001b[38;5;241m=\u001b[39m \u001b[43mpocketfft\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpocketfft\u001b[49m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'jaxlib.pocketfft' has no attribute 'pocketfft'"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb
index 21ef5b16b..23ad81342 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb
@@ -60,12 +60,20 @@
    },
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'flatbuffers'",
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lib/__init__.py:32: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.\n",
+      "  warnings.warn(\"JAX on Mac ARM machines is experimental and minimally tested. \"\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'jaxlib.pocketfft' has no attribute 'pocketfft'",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
       "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets, layers, models\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m autograph\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m bitwise\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/__init__.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03m\"\"\"Compatibility functions.\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \n\u001b[1;32m      5\u001b[0m \u001b[38;5;124;03mThe `tf.compat` module contains two sets of compatibility functions.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     32\u001b[0m \n\u001b[1;32m     33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v1\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v2\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m forward_compatibility_horizon\n",
@@ -77,8 +85,15 @@
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/experimental/authoring/__init__.py:8\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03m\"\"\"Public API for tf.lite.experimental.authoring namespace.\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compatible\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/authoring/authoring.py:43\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m convert\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m converter_error_data_pb2\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/convert.py:29\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite_constants\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m util\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wrap_toco\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert_phase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Component\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:26\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmoves\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;28mrange\u001b[39m\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mflatbuffers\u001b[39;00m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprotobuf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config_pb2 \u001b[38;5;28;01mas\u001b[39;00m _config_pb2\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprotobuf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m graph_debug_info_pb2\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'flatbuffers'"
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;66;03m# Jax functions used by TFLite\u001b[39;00m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;66;03m# pylint: disable=unused-import\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m   \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_computation \u001b[38;5;28;01mas\u001b[39;00m _xla_computation\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m   _xla_computation \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/__init__.py:116\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     41\u001b[0m   config \u001b[38;5;28;01mas\u001b[39;00m config,\n\u001b[1;32m     42\u001b[0m   enable_checks \u001b[38;5;28;01mas\u001b[39;00m enable_checks,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     51\u001b[0m   numpy_rank_promotion \u001b[38;5;28;01mas\u001b[39;00m numpy_rank_promotion,\n\u001b[1;32m     52\u001b[0m )\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     54\u001b[0m   ad,  \u001b[38;5;66;03m# TODO(phawkins): update users to avoid this.\u001b[39;00m\n\u001b[1;32m     55\u001b[0m   checkpoint \u001b[38;5;28;01mas\u001b[39;00m checkpoint,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    114\u001b[0m   xla_computation \u001b[38;5;28;01mas\u001b[39;00m xla_computation,\n\u001b[1;32m    115\u001b[0m )\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmaps\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m soft_pmap \u001b[38;5;28;01mas\u001b[39;00m soft_pmap\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__ \u001b[38;5;28;01mas\u001b[39;00m __version__\n\u001b[1;32m    119\u001b[0m \u001b[38;5;66;03m# These submodules are separate because they are in an import cycle with\u001b[39;00m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;66;03m# jax and rely on the names imported above.\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/experimental/maps.py:26\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wraps, partial, partialmethod\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01menum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Enum\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m jnp\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linear_util \u001b[38;5;28;01mas\u001b[39;00m lu\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/__init__.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2018 Google LLC\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m \n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fft \u001b[38;5;28;01mas\u001b[39;00m fft\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linalg \u001b[38;5;28;01mas\u001b[39;00m linalg\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterpreters\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mxla\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DeviceArray \u001b[38;5;28;01mas\u001b[39;00m DeviceArray\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/fft.py:17\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 Google LLC\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     18\u001b[0m   ifft \u001b[38;5;28;01mas\u001b[39;00m ifft,\n\u001b[1;32m     19\u001b[0m   ifft2 \u001b[38;5;28;01mas\u001b[39;00m ifft2,\n\u001b[1;32m     20\u001b[0m   ifftn \u001b[38;5;28;01mas\u001b[39;00m ifftn,\n\u001b[1;32m     21\u001b[0m   ifftshift \u001b[38;5;28;01mas\u001b[39;00m ifftshift,\n\u001b[1;32m     22\u001b[0m   ihfft \u001b[38;5;28;01mas\u001b[39;00m ihfft,\n\u001b[1;32m     23\u001b[0m   irfft \u001b[38;5;28;01mas\u001b[39;00m irfft,\n\u001b[1;32m     24\u001b[0m   irfft2 \u001b[38;5;28;01mas\u001b[39;00m irfft2,\n\u001b[1;32m     25\u001b[0m   irfftn \u001b[38;5;28;01mas\u001b[39;00m irfftn,\n\u001b[1;32m     26\u001b[0m   fft \u001b[38;5;28;01mas\u001b[39;00m fft,\n\u001b[1;32m     27\u001b[0m   fft2 \u001b[38;5;28;01mas\u001b[39;00m fft2,\n\u001b[1;32m     28\u001b[0m   fftfreq \u001b[38;5;28;01mas\u001b[39;00m fftfreq,\n\u001b[1;32m     29\u001b[0m   fftn \u001b[38;5;28;01mas\u001b[39;00m fftn,\n\u001b[1;32m     30\u001b[0m   fftshift \u001b[38;5;28;01mas\u001b[39;00m fftshift,\n\u001b[1;32m     31\u001b[0m   hfft \u001b[38;5;28;01mas\u001b[39;00m hfft,\n\u001b[1;32m     32\u001b[0m   rfft \u001b[38;5;28;01mas\u001b[39;00m rfft,\n\u001b[1;32m     33\u001b[0m   rfft2 \u001b[38;5;28;01mas\u001b[39;00m rfft2,\n\u001b[1;32m     34\u001b[0m   rfftfreq \u001b[38;5;28;01mas\u001b[39;00m rfftfreq,\n\u001b[1;32m     35\u001b[0m   rfftn \u001b[38;5;28;01mas\u001b[39;00m rfftn,\n\u001b[1;32m     36\u001b[0m )\n\u001b[1;32m     38\u001b[0m \u001b[38;5;66;03m# Module initialization is encapsulated in a function to avoid accidental\u001b[39;00m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# namespace pollution.\u001b[39;00m\n\u001b[1;32m     40\u001b[0m _NOT_IMPLEMENTED \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/numpy/fft.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01moperator\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lax\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_client\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m safe_zip\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/lax/__init__.py:332\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m    299\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (_reduce_sum, _reduce_max, _reduce_min, _reduce_or,\n\u001b[1;32m    300\u001b[0m                   _reduce_and, _reduce_window_sum, _reduce_window_max,\n\u001b[1;32m    301\u001b[0m                   _reduce_window_min, _reduce_window_prod,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    306\u001b[0m                   _upcast_fp16_for_computation, _broadcasting_shape_rule,\n\u001b[1;32m    307\u001b[0m                   _eye, _tri, _delta, _ones, _zeros, _dilate_shape)\n\u001b[1;32m    308\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontrol_flow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    309\u001b[0m   associative_scan \u001b[38;5;28;01mas\u001b[39;00m associative_scan,\n\u001b[1;32m    310\u001b[0m   cond \u001b[38;5;28;01mas\u001b[39;00m cond,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    330\u001b[0m   while_p \u001b[38;5;28;01mas\u001b[39;00m while_p,\n\u001b[1;32m    331\u001b[0m )\n\u001b[0;32m--> 332\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    333\u001b[0m   fft \u001b[38;5;28;01mas\u001b[39;00m fft,\n\u001b[1;32m    334\u001b[0m   fft_p \u001b[38;5;28;01mas\u001b[39;00m fft_p,\n\u001b[1;32m    335\u001b[0m )\n\u001b[1;32m    336\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mparallel\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    337\u001b[0m   all_gather \u001b[38;5;28;01mas\u001b[39;00m all_gather,\n\u001b[1;32m    338\u001b[0m   all_to_all \u001b[38;5;28;01mas\u001b[39;00m all_to_all,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    355\u001b[0m   xeinsum \u001b[38;5;28;01mas\u001b[39;00m xeinsum,\n\u001b[1;32m    356\u001b[0m )\n\u001b[1;32m    357\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mother\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    358\u001b[0m   conv_general_dilated_patches \u001b[38;5;28;01mas\u001b[39;00m conv_general_dilated_patches\n\u001b[1;32m    359\u001b[0m )\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lax/fft.py:145\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m    143\u001b[0m batching\u001b[38;5;241m.\u001b[39mprimitive_batchers[fft_p] \u001b[38;5;241m=\u001b[39m fft_batching_rule\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pocketfft:\n\u001b[0;32m--> 145\u001b[0m   xla\u001b[38;5;241m.\u001b[39mbackend_specific_translations[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m][fft_p] \u001b[38;5;241m=\u001b[39m \u001b[43mpocketfft\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpocketfft\u001b[49m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'jaxlib.pocketfft' has no attribute 'pocketfft'"
      ]
     }
    ],
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
index ae45ecc7a..acc85f976 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
@@ -1798,10 +1798,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.08873443359350565\n",
-      "3.7851533175757255\n",
-      "[[ 0.98248312  3.05483267]\n",
-      " [ 3.05483267 10.24784064]]\n"
+      "0.008947823579448178\n",
+      "4.14822021080294\n",
+      "[[0.73947737 2.16006961]\n",
+      " [2.16006961 7.24782786]]\n"
      ]
     }
    ],
@@ -1845,10 +1845,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.07858099596662704\n",
-      "2.071920625289855\n",
-      "[[1.         0.71822416]\n",
-      " [0.71822416 1.        ]]\n"
+      "0.08657894597048958\n",
+      "2.219222942590059\n",
+      "[[1.        0.6343356]\n",
+      " [0.6343356 1.       ]]\n"
      ]
     }
    ],
@@ -1905,30 +1905,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ -2.84861838 -10.07337358]\n",
-      " [  0.53938383   2.59445979]\n",
-      " [ -0.40980089  -0.48871288]\n",
-      " [  0.05834332  -0.39384255]\n",
-      " [  2.25385387   7.58112299]\n",
-      " [  0.68246434   2.46650488]\n",
-      " [ -0.25366775  -1.97047717]\n",
-      " [  0.79081838   2.03807267]\n",
-      " [ -0.06150169  -0.57109235]\n",
-      " [ -0.75127504  -1.18266178]]\n",
-      "          0          1\n",
-      "0 -2.848618 -10.073374\n",
-      "1  0.539384   2.594460\n",
-      "2 -0.409801  -0.488713\n",
-      "3  0.058343  -0.393843\n",
-      "4  2.253854   7.581123\n",
-      "5  0.682464   2.466505\n",
-      "6 -0.253668  -1.970477\n",
-      "7  0.790818   2.038073\n",
-      "8 -0.061502  -0.571092\n",
-      "9 -0.751275  -1.182662\n",
+      "[[ 0.29724806  0.26804287]\n",
+      " [-0.15626984 -1.36853738]\n",
+      " [-0.77070756 -2.13536532]\n",
+      " [-0.45372697 -3.1582408 ]\n",
+      " [ 0.52580392  2.72567956]\n",
+      " [-0.86515815 -1.35704388]\n",
+      " [-0.73738602 -2.12933164]\n",
+      " [-0.10486183  1.06292011]\n",
+      " [ 1.75670484  5.27381733]\n",
+      " [ 0.50835355  0.81805914]]\n",
       "          0         1\n",
-      "0  1.000000  0.984525\n",
-      "1  0.984525  1.000000\n"
+      "0  0.297248  0.268043\n",
+      "1 -0.156270 -1.368537\n",
+      "2 -0.770708 -2.135365\n",
+      "3 -0.453727 -3.158241\n",
+      "4  0.525804  2.725680\n",
+      "5 -0.865158 -1.357044\n",
+      "6 -0.737386 -2.129332\n",
+      "7 -0.104862  1.062920\n",
+      "8  1.756705  5.273817\n",
+      "9  0.508354  0.818059\n",
+      "          0         1\n",
+      "0  1.000000  0.915549\n",
+      "1  0.915549  1.000000\n"
      ]
     }
    ],
@@ -1974,37 +1974,37 @@
      "text": [
       "     0         1         2         3         4         5         6         7   \\\n",
       "0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-      "1   0.0  0.090241  0.082140  0.090564  0.084086  0.078082  0.082282  0.076619   \n",
-      "2   0.0  0.082140  0.075227  0.083102  0.077428  0.072150  0.075982  0.070945   \n",
-      "3   0.0  0.090564  0.083102  0.096893  0.090268  0.084107  0.091647  0.085571   \n",
-      "4   0.0  0.084086  0.077428  0.090268  0.084286  0.078707  0.085655  0.080120   \n",
-      "5   0.0  0.078082  0.072150  0.084107  0.078707  0.073657  0.080061  0.075020   \n",
-      "6   0.0  0.082282  0.075982  0.091647  0.085655  0.080061  0.089082  0.083380   \n",
-      "7   0.0  0.076619  0.070945  0.085571  0.080120  0.075020  0.083380  0.078158   \n",
-      "8   0.0  0.071394  0.066284  0.079944  0.074984  0.070333  0.078082  0.073299   \n",
-      "9   0.0  0.066569  0.061966  0.074729  0.070216  0.065973  0.073159  0.068776   \n",
-      "10  0.0  0.073831  0.068541  0.084523  0.079224  0.074258  0.083779  0.078587   \n",
-      "11  0.0  0.068867  0.064081  0.079021  0.074183  0.069640  0.078484  0.073716   \n",
-      "12  0.0  0.064284  0.059952  0.073925  0.069506  0.065349  0.073567  0.069187   \n",
-      "13  0.0  0.060048  0.056127  0.069202  0.065165  0.061359  0.068999  0.064974   \n",
-      "14  0.0  0.056131  0.052581  0.064823  0.061133  0.057648  0.064753  0.061054   \n",
+      "1   0.0  0.086925  0.087377  0.087520  0.087878  0.088164  0.079680  0.079580   \n",
+      "2   0.0  0.087377  0.089240  0.088074  0.089063  0.089924  0.079827  0.080060   \n",
+      "3   0.0  0.087520  0.088074  0.094227  0.094252  0.094139  0.089441  0.088974   \n",
+      "4   0.0  0.087878  0.089063  0.094252  0.094610  0.094812  0.089040  0.088778   \n",
+      "5   0.0  0.088164  0.089924  0.094139  0.094812  0.095315  0.088488  0.088425   \n",
+      "6   0.0  0.079680  0.079827  0.089441  0.089040  0.088488  0.087315  0.086524   \n",
+      "7   0.0  0.079580  0.080060  0.088974  0.088778  0.088425  0.086524  0.085876   \n",
+      "8   0.0  0.079499  0.080295  0.088502  0.088506  0.088348  0.085716  0.085210   \n",
+      "9   0.0  0.079448  0.080548  0.088037  0.088238  0.088275  0.084906  0.084541   \n",
+      "10  0.0  0.071751  0.071438  0.082839  0.082089  0.081194  0.082509  0.081481   \n",
+      "11  0.0  0.071392  0.071284  0.082139  0.081533  0.080780  0.081559  0.080641   \n",
+      "12  0.0  0.071071  0.071164  0.081471  0.081007  0.080395  0.080635  0.079827   \n",
+      "13  0.0  0.070794  0.071084  0.080839  0.080518  0.080048  0.079741  0.079043   \n",
+      "14  0.0  0.070562  0.071051  0.080249  0.080070  0.079745  0.078880  0.078293   \n",
       "\n",
       "          8         9         10        11        12        13        14  \n",
       "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-      "1   0.071394  0.066569  0.073831  0.068867  0.064284  0.060048  0.056131  \n",
-      "2   0.066284  0.061966  0.068541  0.064081  0.059952  0.056127  0.052581  \n",
-      "3   0.079944  0.074729  0.084523  0.079021  0.073925  0.069202  0.064823  \n",
-      "4   0.074984  0.070216  0.079224  0.074183  0.069506  0.065165  0.061133  \n",
-      "5   0.070333  0.065973  0.074258  0.069640  0.065349  0.061359  0.057648  \n",
-      "6   0.078082  0.073159  0.083779  0.078484  0.073567  0.068999  0.064753  \n",
-      "7   0.073299  0.068776  0.078587  0.073716  0.069187  0.064974  0.061054  \n",
-      "8   0.068841  0.064684  0.073750  0.069268  0.065095  0.061209  0.057588  \n",
-      "9   0.064684  0.060863  0.069242  0.065118  0.061272  0.057686  0.054340  \n",
-      "10  0.073750  0.069242  0.079948  0.075028  0.070450  0.066189  0.062220  \n",
-      "11  0.069268  0.065118  0.075028  0.070494  0.066270  0.062333  0.058663  \n",
-      "12  0.065095  0.061272  0.070450  0.066270  0.062370  0.058732  0.055337  \n",
-      "13  0.061209  0.057686  0.066189  0.062333  0.058732  0.055369  0.052227  \n",
-      "14  0.057588  0.054340  0.062220  0.058663  0.055337  0.052227  0.049318  \n"
+      "1   0.079499  0.079448  0.071751  0.071392  0.071071  0.070794  0.070562  \n",
+      "2   0.080295  0.080548  0.071438  0.071284  0.071164  0.071084  0.071051  \n",
+      "3   0.088502  0.088037  0.082839  0.082139  0.081471  0.080839  0.080249  \n",
+      "4   0.088506  0.088238  0.082089  0.081533  0.081007  0.080518  0.080070  \n",
+      "5   0.088348  0.088275  0.081194  0.080780  0.080395  0.080048  0.079745  \n",
+      "6   0.085716  0.084906  0.082509  0.081559  0.080635  0.079741  0.078880  \n",
+      "7   0.085210  0.084541  0.081481  0.080641  0.079827  0.079043  0.078293  \n",
+      "8   0.084685  0.084157  0.080434  0.079704  0.078999  0.078326  0.077688  \n",
+      "9   0.084157  0.083772  0.079379  0.078759  0.078165  0.077604  0.077079  \n",
+      "10  0.080434  0.079379  0.079152  0.078033  0.076935  0.075863  0.074818  \n",
+      "11  0.079704  0.078759  0.078033  0.077004  0.075996  0.075014  0.074061  \n",
+      "12  0.078999  0.078165  0.076935  0.075996  0.075079  0.074187  0.073325  \n",
+      "13  0.078326  0.077604  0.075863  0.075014  0.074187  0.073388  0.072618  \n",
+      "14  0.077688  0.077079  0.074818  0.074061  0.073325  0.072618  0.071942  \n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
index e347cd688..b7e0134a0 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
@@ -489,10 +489,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime: 0.0893679 sec\n",
+      "Runtime: 0.0907831 sec\n",
       "Jackknife Statistics :\n",
       "original           bias      std. error\n",
-      " 99.9524        99.9424        0.148854\n"
+      " 100.142        100.132        0.149864\n"
      ]
     }
    ],
@@ -917,7 +917,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 100.188  15.1133         100.19        0.149655\n"
+      " 100.033  14.9292        100.032        0.149452\n"
      ]
     }
    ],
@@ -975,7 +975,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -1308,13 +1308,7 @@
       "Error: 0.03781367141738902\n",
       "Bias^2: 0.03365768507152769\n",
       "Var: 0.0041559863458613296\n",
-      "0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902\n",
       "Polynomial degree: 7\n",
       "Error: 0.027609773491022394\n",
       "Bias^2: 0.022999498260366198\n",
@@ -1334,7 +1328,13 @@
       "Error: 0.021592704588021178\n",
       "Bias^2: 0.010516485576646504\n",
       "Var: 0.01107621901137467\n",
-      "0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174\n",
+      "0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Polynomial degree: 11\n",
       "Error: 0.07160048164232538\n",
       "Bias^2: 0.014436800088896381\n",
@@ -1634,16 +1634,16 @@
       "Mean squared error on test data: 0.17446471\n",
       "Degree of polynomial:  13\n",
       "Mean squared error on training data: 0.00759119\n",
-      "Mean squared error on test data: 1.08131003\n"
+      "Mean squared error on test data: 1.08131003\n",
+      "Degree of polynomial:  14\n",
+      "Mean squared error on training data: 0.00472199\n",
+      "Mean squared error on test data: 0.81333804\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Degree of polynomial:  14\n",
-      "Mean squared error on training data: 0.00472199\n",
-      "Mean squared error on test data: 0.81333804\n",
       "Degree of polynomial:  15\n",
       "Mean squared error on training data: 0.00410478\n",
       "Mean squared error on test data: 92.09172409\n",
@@ -1658,16 +1658,16 @@
       "Mean squared error on test data: 108.27092910\n",
       "Degree of polynomial:  19\n",
       "Mean squared error on training data: 0.00156376\n",
-      "Mean squared error on test data: 1371.99051150\n"
+      "Mean squared error on test data: 1371.99051150\n",
+      "Degree of polynomial:  20\n",
+      "Mean squared error on training data: 0.00137818\n",
+      "Mean squared error on test data: 1887.86252988\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Degree of polynomial:  20\n",
-      "Mean squared error on training data: 0.00137818\n",
-      "Mean squared error on test data: 1887.86252988\n",
       "Degree of polynomial:  21\n",
       "Mean squared error on training data: 0.00118508\n",
       "Mean squared error on test data: 14859.69908626\n",
@@ -1682,16 +1682,16 @@
       "Mean squared error on test data: 1277.61702282\n",
       "Degree of polynomial:  25\n",
       "Mean squared error on training data: 0.00079129\n",
-      "Mean squared error on test data: 128664.31650694\n"
+      "Mean squared error on test data: 128664.31650694\n",
+      "Degree of polynomial:  26\n",
+      "Mean squared error on training data: 0.00076905\n",
+      "Mean squared error on test data: 19003.94822514\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Degree of polynomial:  26\n",
-      "Mean squared error on training data: 0.00076905\n",
-      "Mean squared error on test data: 19003.94822514\n",
       "Degree of polynomial:  27\n",
       "Mean squared error on training data: 0.00068946\n",
       "Mean squared error on test data: 2379.66219404\n",
@@ -1707,9 +1707,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -2051,7 +2051,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
      ]
     },
@@ -3725,9 +3725,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4037,9 +4037,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4116,9 +4116,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4203,9 +4203,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
       "  cb = fig.colorbar(im)\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_41811/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20614/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -4266,7 +4266,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  0%|                                                                                                                               | 0/10 [00:00\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcvxopt\u001b[39;00m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cvxopt'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy\n",
     "import cvxopt"
@@ -1944,12 +1932,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid character '’' (U+2019) (3974140161.py, line 5)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Input \u001b[0;32mIn [5]\u001b[0;36m\u001b[0m\n\u001b[0;31m    P = matrix(numpy.diag([1,0]), tc=’d’)\u001b[0m\n\u001b[0m                                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid character '’' (U+2019)\n"
+     ]
+    }
+   ],
    "source": [
     "# Import the necessary packages\n",
     "import numpy\n",
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter5.py b/doc/LectureNotes/_build/jupyter_execute/chapter5.py
index 771895dc7..0b081eb47 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter5.py
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter5.py
@@ -1066,7 +1066,7 @@ import cvxopt
 # Since we don't have any equalities the matrix $\boldsymbol{A}$ is set to zero
 # The following code solves the equations for us
 
-# In[ ]:
+# In[5]:
 
 
 # Import the necessary packages
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb
index 1e59edd55..9a93ed3d7 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb
@@ -86,14 +86,14 @@
      "output_type": "stream",
      "text": [
       "2nd degree coefficients:\n",
-      "zero power:  0.18790439176058887\n",
-      "first power:  -0.014599964106338128\n",
-      "second power:  0.00010403373827253124\n"
+      "zero power:  -4.653578701904388\n",
+      "first power:  0.17297886491529482\n",
+      "second power:  -0.0007790285013223805\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "
"
       ]
@@ -107,7 +107,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "
"
       ]
@@ -537,15 +537,113 @@
    },
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'pydot'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtree\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m export_graphviz\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image \n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpydot\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m graph_from_dot_data\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pydot'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+      "0          17.99         10.38          122.80     1001.0          0.11840   \n",
+      "1          20.57         17.77          132.90     1326.0          0.08474   \n",
+      "2          19.69         21.25          130.00     1203.0          0.10960   \n",
+      "3          11.42         20.38           77.58      386.1          0.14250   \n",
+      "4          20.29         14.34          135.10     1297.0          0.10030   \n",
+      "..           ...           ...             ...        ...              ...   \n",
+      "564        21.56         22.39          142.00     1479.0          0.11100   \n",
+      "565        20.13         28.25          131.20     1261.0          0.09780   \n",
+      "566        16.60         28.08          108.30      858.1          0.08455   \n",
+      "567        20.60         29.33          140.10     1265.0          0.11780   \n",
+      "568         7.76         24.54           47.92      181.0          0.05263   \n",
+      "\n",
+      "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+      "0             0.27760         0.30010              0.14710         0.2419   \n",
+      "1             0.07864         0.08690              0.07017         0.1812   \n",
+      "2             0.15990         0.19740              0.12790         0.2069   \n",
+      "3             0.28390         0.24140              0.10520         0.2597   \n",
+      "4             0.13280         0.19800              0.10430         0.1809   \n",
+      "..                ...             ...                  ...            ...   \n",
+      "564           0.11590         0.24390              0.13890         0.1726   \n",
+      "565           0.10340         0.14400              0.09791         0.1752   \n",
+      "566           0.10230         0.09251              0.05302         0.1590   \n",
+      "567           0.27700         0.35140              0.15200         0.2397   \n",
+      "568           0.04362         0.00000              0.00000         0.1587   \n",
+      "\n",
+      "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
+      "0                   0.07871  ...        25.380          17.33   \n",
+      "1                   0.05667  ...        24.990          23.41   \n",
+      "2                   0.05999  ...        23.570          25.53   \n",
+      "3                   0.09744  ...        14.910          26.50   \n",
+      "4                   0.05883  ...        22.540          16.67   \n",
+      "..                      ...  ...           ...            ...   \n",
+      "564                 0.05623  ...        25.450          26.40   \n",
+      "565                 0.05533  ...        23.690          38.25   \n",
+      "566                 0.05648  ...        18.980          34.12   \n",
+      "567                 0.07016  ...        25.740          39.42   \n",
+      "568                 0.05884  ...         9.456          30.37   \n",
+      "\n",
+      "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
+      "0             184.60      2019.0           0.16220            0.66560   \n",
+      "1             158.80      1956.0           0.12380            0.18660   \n",
+      "2             152.50      1709.0           0.14440            0.42450   \n",
+      "3              98.87       567.7           0.20980            0.86630   \n",
+      "4             152.20      1575.0           0.13740            0.20500   \n",
+      "..               ...         ...               ...                ...   \n",
+      "564           166.10      2027.0           0.14100            0.21130   \n",
+      "565           155.00      1731.0           0.11660            0.19220   \n",
+      "566           126.70      1124.0           0.11390            0.30940   \n",
+      "567           184.60      1821.0           0.16500            0.86810   \n",
+      "568            59.16       268.6           0.08996            0.06444   \n",
+      "\n",
+      "     worst concavity  worst concave points  worst symmetry  \\\n",
+      "0             0.7119                0.2654          0.4601   \n",
+      "1             0.2416                0.1860          0.2750   \n",
+      "2             0.4504                0.2430          0.3613   \n",
+      "3             0.6869                0.2575          0.6638   \n",
+      "4             0.4000                0.1625          0.2364   \n",
+      "..               ...                   ...             ...   \n",
+      "564           0.4107                0.2216          0.2060   \n",
+      "565           0.3215                0.1628          0.2572   \n",
+      "566           0.3403                0.1418          0.2218   \n",
+      "567           0.9387                0.2650          0.4087   \n",
+      "568           0.0000                0.0000          0.2871   \n",
+      "\n",
+      "     worst fractal dimension  \n",
+      "0                    0.11890  \n",
+      "1                    0.08902  \n",
+      "2                    0.08758  \n",
+      "3                    0.17300  \n",
+      "4                    0.07678  \n",
+      "..                       ...  \n",
+      "564                  0.07115  \n",
+      "565                  0.06637  \n",
+      "566                  0.07820  \n",
+      "567                  0.12400  \n",
+      "568                  0.07039  \n",
+      "\n",
+      "[569 rows x 30 columns]\n",
+      "     malignant  benign\n",
+      "0            1       0\n",
+      "1            1       0\n",
+      "2            1       0\n",
+      "3            1       0\n",
+      "4            1       0\n",
+      "..         ...     ...\n",
+      "564          1       0\n",
+      "565          1       0\n",
+      "566          1       0\n",
+      "567          1       0\n",
+      "568          0       1\n",
+      "\n",
+      "[569 rows x 2 columns]\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -586,12 +684,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -630,12 +739,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 0.9166666666666666, 'X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n",
+       " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
+       " Text(0.5769230769230769, 0.75, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
+       " Text(0.3076923076923077, 0.5833333333333334, 'X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
+       " Text(0.15384615384615385, 0.4166666666666667, 'X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
+       " Text(0.07692307692307693, 0.25, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
+       " Text(0.23076923076923078, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
+       " Text(0.46153846153846156, 0.4166666666666667, 'X[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
+       " Text(0.38461538461538464, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
+       " Text(0.5384615384615384, 0.25, 'X[2] <= 5.45\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
+       " Text(0.46153846153846156, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
+       " Text(0.6153846153846154, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
+       " Text(0.8461538461538461, 0.5833333333333334, 'X[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
+       " Text(0.7692307692307693, 0.4166666666666667, 'X[1] <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
+       " Text(0.6923076923076923, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
+       " Text(0.8461538461538461, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
+       " Text(0.9230769230769231, 0.4166666666666667, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {
+      "filenames": {
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_24_1.png"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn import tree\n",
@@ -656,12 +806,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|--- petal width (cm) <= 0.80\n",
+      "|   |--- class: 0\n",
+      "|--- petal width (cm) >  0.80\n",
+      "|   |--- petal width (cm) <= 1.75\n",
+      "|   |   |--- class: 1\n",
+      "|   |--- petal width (cm) >  1.75\n",
+      "|   |   |--- class: 2\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
@@ -821,12 +986,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'DataFiles/rideclass.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msave_fig\u001b[39m(fig_id):\n\u001b[1;32m     35\u001b[0m     plt\u001b[38;5;241m.\u001b[39msavefig(image_path(fig_id) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 37\u001b[0m infile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata_path\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrideclass.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# Read the experimental data with Pandas\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m display\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'DataFiles/rideclass.csv'"
+     ]
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6.py b/doc/LectureNotes/_build/jupyter_execute/chapter6.py
index e9803c016..1b9ee13f5 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter6.py
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter6.py
@@ -422,7 +422,7 @@ cmd = 'dot -Tpng DataFiles/cancer.dot -o DataFiles/cancer.png'
 os.system(cmd)
 
 
-# In[ ]:
+# In[3]:
 
 
 # Common imports
@@ -455,7 +455,7 @@ os.system(cmd)
 # 
 # **Scikit-Learn** has also another way to visualize the trees which is very useful, here with the Iris data.
 
-# In[ ]:
+# In[4]:
 
 
 from sklearn.datasets import load_iris
@@ -470,7 +470,7 @@ tree.plot_tree(tree_clf)
 # Alternatively, the tree can also be exported in textual format with the function exporttext.
 # This method doesn’t require the installation of external libraries and is more compact:
 
-# In[ ]:
+# In[5]:
 
 
 from sklearn.datasets import load_iris
@@ -584,7 +584,7 @@ print(r)
 # 
 # ### Simple Python Code to read in Data and perform Classification
 
-# In[ ]:
+# In[6]:
 
 
 # Common imports
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png
index e92d8c52d..0a10641b4 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png
index ca1164933..2221d7900 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_24_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_24_1.png
index 572004dc7..385267394 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_24_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_24_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
index 3f8c2bbea..ca0c6655a 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
@@ -295,10 +295,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.046785461905835435\n",
-      "4.240670854503034\n",
-      "[[0.94986593 2.88137798]\n",
-      " [2.88137798 9.93586895]]\n"
+      "0.264540221699101\n",
+      "4.673457751724773\n",
+      "[[0.83632853 2.54078623]\n",
+      " [2.54078623 8.44021223]]\n"
      ]
     }
    ],
@@ -340,10 +340,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.08271198519070039\n",
-      "1.7306310662842432\n",
-      "[[1.         0.58084359]\n",
-      " [0.58084359 1.        ]]\n"
+      "0.08374032367704139\n",
+      "1.7696835316227453\n",
+      "[[1.         0.66443521]\n",
+      " [0.66443521 1.        ]]\n"
      ]
     }
    ],
@@ -397,30 +397,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.50488131 -2.2493023 ]\n",
-      " [-0.26115367 -1.92631966]\n",
-      " [-1.43556723 -2.99992698]\n",
-      " [ 0.64528459  2.57643113]\n",
-      " [-0.55273102 -2.09964817]\n",
-      " [ 0.31681097  1.26466619]\n",
-      " [-0.04673082 -0.56607416]\n",
-      " [ 1.53394148  5.38629412]\n",
-      " [ 0.15092012 -0.22014758]\n",
-      " [ 0.15410688  0.83402742]]\n",
+      "[[ 1.20708879  4.33201844]\n",
+      " [-0.3838783  -1.24125217]\n",
+      " [ 0.74722409  1.60194224]\n",
+      " [-0.04086326  0.37419664]\n",
+      " [ 0.62422045  2.05587489]\n",
+      " [ 1.75357263  5.63710438]\n",
+      " [-2.53968309 -7.28219089]\n",
+      " [-1.42054337 -4.90629812]\n",
+      " [-0.13019959 -0.83320794]\n",
+      " [ 0.18306165  0.26181254]]\n",
       "          0         1\n",
-      "0 -0.504881 -2.249302\n",
-      "1 -0.261154 -1.926320\n",
-      "2 -1.435567 -2.999927\n",
-      "3  0.645285  2.576431\n",
-      "4 -0.552731 -2.099648\n",
-      "5  0.316811  1.264666\n",
-      "6 -0.046731 -0.566074\n",
-      "7  1.533941  5.386294\n",
-      "8  0.150920 -0.220148\n",
-      "9  0.154107  0.834027\n",
-      "         0        1\n",
-      "0  1.00000  0.95302\n",
-      "1  0.95302  1.00000\n"
+      "0  1.207089  4.332018\n",
+      "1 -0.383878 -1.241252\n",
+      "2  0.747224  1.601942\n",
+      "3 -0.040863  0.374197\n",
+      "4  0.624220  2.055875\n",
+      "5  1.753573  5.637104\n",
+      "6 -2.539683 -7.282191\n",
+      "7 -1.420543 -4.906298\n",
+      "8 -0.130200 -0.833208\n",
+      "9  0.183062  0.261813\n",
+      "          0         1\n",
+      "0  1.000000  0.992504\n",
+      "1  0.992504  1.000000\n"
      ]
     }
    ],
@@ -461,37 +461,37 @@
      "text": [
       "     0         1         2         3         4         5         6         7   \\\n",
       "0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-      "1   0.0  0.088104  0.081216  0.088760  0.086997  0.085010  0.080343  0.079306   \n",
-      "2   0.0  0.081216  0.075612  0.080764  0.079618  0.078300  0.072530  0.071897   \n",
-      "3   0.0  0.088760  0.080764  0.094925  0.092249  0.089310  0.089233  0.087615   \n",
-      "4   0.0  0.086997  0.079618  0.092249  0.089982  0.087470  0.086257  0.084927   \n",
-      "5   0.0  0.085010  0.078300  0.089310  0.087470  0.085410  0.083021  0.081989   \n",
-      "6   0.0  0.080343  0.072530  0.089233  0.086257  0.083021  0.086109  0.084269   \n",
-      "7   0.0  0.079306  0.071897  0.087615  0.084927  0.081989  0.084269  0.082642   \n",
-      "8   0.0  0.078323  0.071329  0.086021  0.083629  0.080998  0.082431  0.081022   \n",
-      "9   0.0  0.077372  0.070810  0.084426  0.082339  0.080026  0.080571  0.079383   \n",
-      "10  0.0  0.071946  0.064637  0.081990  0.079001  0.075770  0.080642  0.078768   \n",
-      "11  0.0  0.071044  0.064041  0.080699  0.077930  0.074922  0.079218  0.077511   \n",
-      "12  0.0  0.070222  0.063524  0.079476  0.076927  0.074144  0.077846  0.076306   \n",
-      "13  0.0  0.069475  0.063084  0.078314  0.075987  0.073433  0.076518  0.075145   \n",
-      "14  0.0  0.068799  0.062719  0.077203  0.075102  0.072783  0.075222  0.074019   \n",
+      "1   0.0  0.092290  0.091376  0.091772  0.091659  0.091579  0.083691  0.083321   \n",
+      "2   0.0  0.091376  0.091209  0.090620  0.090923  0.091266  0.082166  0.082105   \n",
+      "3   0.0  0.091772  0.090620  0.098069  0.097435  0.096845  0.093556  0.092724   \n",
+      "4   0.0  0.091659  0.090923  0.097435  0.097111  0.096833  0.092478  0.091896   \n",
+      "5   0.0  0.091579  0.091266  0.096845  0.096833  0.096872  0.091445  0.091113   \n",
+      "6   0.0  0.083691  0.082166  0.093556  0.092478  0.091445  0.092007  0.090828   \n",
+      "7   0.0  0.083321  0.082105  0.092724  0.091896  0.091113  0.090828  0.089857   \n",
+      "8   0.0  0.083051  0.082145  0.091996  0.091418  0.090888  0.089744  0.088982   \n",
+      "9   0.0  0.082877  0.082285  0.091369  0.091044  0.090768  0.088754  0.088201   \n",
+      "10  0.0  0.075829  0.073985  0.087419  0.086021  0.084663  0.087860  0.086440   \n",
+      "11  0.0  0.075277  0.073684  0.086460  0.085272  0.084124  0.086624  0.085384   \n",
+      "12  0.0  0.074819  0.073477  0.085603  0.084624  0.083685  0.085484  0.084422   \n",
+      "13  0.0  0.074453  0.073363  0.084846  0.084076  0.083348  0.084438  0.083555   \n",
+      "14  0.0  0.074180  0.073344  0.084187  0.083627  0.083111  0.083485  0.082779   \n",
       "\n",
       "          8         9         10        11        12        13        14  \n",
       "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-      "1   0.078323  0.077372  0.071946  0.071044  0.070222  0.069475  0.068799  \n",
-      "2   0.071329  0.070810  0.064637  0.064041  0.063524  0.063084  0.062719  \n",
-      "3   0.086021  0.084426  0.081990  0.080699  0.079476  0.078314  0.077203  \n",
-      "4   0.083629  0.082339  0.079001  0.077930  0.076927  0.075987  0.075102  \n",
-      "5   0.080998  0.080026  0.075770  0.074922  0.074144  0.073433  0.072783  \n",
-      "6   0.082431  0.080571  0.080642  0.079218  0.077846  0.076518  0.075222  \n",
-      "7   0.081022  0.079383  0.078768  0.077511  0.076306  0.075145  0.074019  \n",
-      "8   0.079622  0.078211  0.076886  0.075797  0.074760  0.073768  0.072813  \n",
-      "9   0.078211  0.077033  0.074969  0.074050  0.073183  0.072364  0.071585  \n",
-      "10  0.076886  0.074969  0.076632  0.075201  0.073811  0.072452  0.071115  \n",
-      "11  0.075797  0.074050  0.075201  0.073904  0.072647  0.071421  0.070216  \n",
-      "12  0.074760  0.073183  0.073811  0.072647  0.071523  0.070428  0.069355  \n",
-      "13  0.073768  0.072364  0.072452  0.071421  0.070428  0.069465  0.068525  \n",
-      "14  0.072813  0.071585  0.071115  0.070216  0.069355  0.068525  0.067719  \n"
+      "1   0.083051  0.082877  0.075829  0.075277  0.074819  0.074453  0.074180  \n",
+      "2   0.082145  0.082285  0.073985  0.073684  0.073477  0.073363  0.073344  \n",
+      "3   0.091996  0.091369  0.087419  0.086460  0.085603  0.084846  0.084187  \n",
+      "4   0.091418  0.091044  0.086021  0.085272  0.084624  0.084076  0.083627  \n",
+      "5   0.090888  0.090768  0.084663  0.084124  0.083685  0.083348  0.083111  \n",
+      "6   0.089744  0.088754  0.087860  0.086624  0.085484  0.084438  0.083485  \n",
+      "7   0.088982  0.088201  0.086440  0.085384  0.084422  0.083555  0.082779  \n",
+      "8   0.088317  0.087746  0.085108  0.084230  0.083446  0.082756  0.082158  \n",
+      "9   0.087746  0.087386  0.083859  0.083159  0.082553  0.082040  0.081620  \n",
+      "10  0.085108  0.083859  0.085252  0.083832  0.082501  0.081258  0.080098  \n",
+      "11  0.084230  0.083159  0.083832  0.082569  0.081395  0.080305  0.079298  \n",
+      "12  0.083446  0.082553  0.082501  0.081395  0.080374  0.079437  0.078582  \n",
+      "13  0.082756  0.082040  0.081258  0.080305  0.079437  0.078652  0.077949  \n",
+      "14  0.082158  0.081620  0.080098  0.079298  0.078582  0.077949  0.077397  \n"
      ]
     }
    ],
@@ -916,10 +916,10 @@
      "output_type": "stream",
      "text": [
       "          0         1\n",
-      "0  3.967536  1.983164\n",
-      "1  1.983164  2.000755\n",
-      "[[3.9675364  1.98316352]\n",
-      " [1.98316352 2.00075534]]\n"
+      "0  3.956454  1.972286\n",
+      "1  1.972286  1.977089\n",
+      "[[3.95645365 1.97228638]\n",
+      " [1.97228638 1.97708897]]\n"
      ]
     }
    ],
@@ -949,13 +949,13 @@
      "output_type": "stream",
      "text": [
       "Centered covariance using own code\n",
-      "[[3.9675364  1.98316352]\n",
-      " [1.98316352 2.00075534]]\n"
+      "[[3.95645365 1.97228638]\n",
+      " [1.97228638 1.97708897]]\n"
      ]
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -1044,12 +1044,12 @@
      "output_type": "stream",
      "text": [
       "Eigenvalues of Covariance matrix\n",
-      "5.197738983259782\n",
-      "0.7705527590466072\n",
+      "5.173439546289586\n",
+      "0.7601030735620569\n",
       "First eigenvector\n",
-      "[0.84977962 0.52713812]\n",
+      "[0.85102768 0.52512084]\n",
       "Second eigenvector\n",
-      "[-0.52713812  0.84977962]\n"
+      "[-0.52512084  0.85102768]\n"
      ]
     },
     {
@@ -1057,7 +1057,7 @@
      "output_type": "stream",
      "text": [
       "Eigenvector of largest eigenvalue\n",
-      "[-0.84977962 -0.52713812]\n"
+      "[-0.85102768 -0.52512084]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png
index b57eff150..e3a1b1fe7 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
index 076976c77..a104689f4 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
@@ -924,14 +924,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_9414/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20669/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
       "  ax = fig.gca(projection=\"3d\")\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 1,
@@ -1101,7 +1101,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 5,
@@ -1802,16 +1802,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.25881631 4.66111673]\n",
-      "[[4.01840062]\n",
-      " [2.89545727]]\n",
-      "[[4.01840062]\n",
-      " [2.89545727]]\n"
+      "[0.29972182 4.52744746]\n",
+      "[[4.01247056]\n",
+      " [2.97656972]]\n",
+      "[[4.01247056]\n",
+      " [2.97656972]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "
"
       ]
@@ -1896,9 +1896,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.79441434]\n",
-      " [3.07608141]]\n",
-      "[3.80994952] [3.12302855]\n"
+      "[[4.02158709]\n",
+      " [2.93023603]]\n",
+      "[4.00882596] [2.93522293]\n"
      ]
     }
    ],
@@ -2002,15 +2002,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.78596961]\n",
-      " [3.12387751]]\n",
-      "[[3.71441535]\n",
-      " [3.17942122]]\n"
+      "[[4.09781386]\n",
+      " [2.97980332]]\n",
+      "[[4.0527437 ]\n",
+      " [3.01510929]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -2389,20 +2389,20 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.42130182]\n",
-      " [2.83757843]]\n",
-      "Eigenvalues of Hessian Matrix:[0.27660123 4.17938393]\n",
+      "[[3.87618586]\n",
+      " [3.13847924]]\n",
+      "Eigenvalues of Hessian Matrix:[0.3313155  4.62759057]\n",
       "theta from own gd\n",
-      "[[4.42130182]\n",
-      " [2.83757843]]\n",
+      "[[3.87618586]\n",
+      " [3.13847924]]\n",
       "theta from own sdg\n",
-      "[[4.44198566]\n",
-      " [2.79512696]]\n"
+      "[[3.87379129]\n",
+      " [3.15508406]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png
index 33d351dcf..bb2938061 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png
index 29122dd00..ff10a99de 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_148_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_148_1.png
index c5ff69126..24d7408ae 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb b/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb
index ea4fe7c2f..c72bd0283 100644
--- a/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb
@@ -283,12 +283,20 @@
    },
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'flatbuffers'",
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lib/__init__.py:32: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.\n",
+      "  warnings.warn(\"JAX on Mac ARM machines is experimental and minimally tested. \"\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'jaxlib.pocketfft' has no attribute 'pocketfft'",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
       "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m image\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m autograph\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m bitwise\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/__init__.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03m\"\"\"Compatibility functions.\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \n\u001b[1;32m      5\u001b[0m \u001b[38;5;124;03mThe `tf.compat` module contains two sets of compatibility functions.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     32\u001b[0m \n\u001b[1;32m     33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v1\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v2\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m forward_compatibility_horizon\n",
@@ -300,8 +308,15 @@
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/experimental/authoring/__init__.py:8\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;124;03m\"\"\"Public API for tf.lite.experimental.authoring namespace.\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compatible\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/authoring/authoring.py:43\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m convert\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m converter_error_data_pb2\n",
       "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/convert.py:29\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite_constants\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m util\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wrap_toco\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert_phase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Component\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:26\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmoves\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;28mrange\u001b[39m\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mflatbuffers\u001b[39;00m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprotobuf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config_pb2 \u001b[38;5;28;01mas\u001b[39;00m _config_pb2\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprotobuf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m graph_debug_info_pb2\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'flatbuffers'"
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;66;03m# Jax functions used by TFLite\u001b[39;00m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;66;03m# pylint: disable=unused-import\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m   \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_computation \u001b[38;5;28;01mas\u001b[39;00m _xla_computation\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m   _xla_computation \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/__init__.py:116\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     41\u001b[0m   config \u001b[38;5;28;01mas\u001b[39;00m config,\n\u001b[1;32m     42\u001b[0m   enable_checks \u001b[38;5;28;01mas\u001b[39;00m enable_checks,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     51\u001b[0m   numpy_rank_promotion \u001b[38;5;28;01mas\u001b[39;00m numpy_rank_promotion,\n\u001b[1;32m     52\u001b[0m )\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     54\u001b[0m   ad,  \u001b[38;5;66;03m# TODO(phawkins): update users to avoid this.\u001b[39;00m\n\u001b[1;32m     55\u001b[0m   checkpoint \u001b[38;5;28;01mas\u001b[39;00m checkpoint,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    114\u001b[0m   xla_computation \u001b[38;5;28;01mas\u001b[39;00m xla_computation,\n\u001b[1;32m    115\u001b[0m )\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmaps\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m soft_pmap \u001b[38;5;28;01mas\u001b[39;00m soft_pmap\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__ \u001b[38;5;28;01mas\u001b[39;00m __version__\n\u001b[1;32m    119\u001b[0m \u001b[38;5;66;03m# These submodules are separate because they are in an import cycle with\u001b[39;00m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;66;03m# jax and rely on the names imported above.\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/experimental/maps.py:26\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wraps, partial, partialmethod\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01menum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Enum\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m jnp\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linear_util \u001b[38;5;28;01mas\u001b[39;00m lu\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/__init__.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2018 Google LLC\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m \n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fft \u001b[38;5;28;01mas\u001b[39;00m fft\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linalg \u001b[38;5;28;01mas\u001b[39;00m linalg\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterpreters\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mxla\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DeviceArray \u001b[38;5;28;01mas\u001b[39;00m DeviceArray\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/fft.py:17\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 Google LLC\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     14\u001b[0m \n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     18\u001b[0m   ifft \u001b[38;5;28;01mas\u001b[39;00m ifft,\n\u001b[1;32m     19\u001b[0m   ifft2 \u001b[38;5;28;01mas\u001b[39;00m ifft2,\n\u001b[1;32m     20\u001b[0m   ifftn \u001b[38;5;28;01mas\u001b[39;00m ifftn,\n\u001b[1;32m     21\u001b[0m   ifftshift \u001b[38;5;28;01mas\u001b[39;00m ifftshift,\n\u001b[1;32m     22\u001b[0m   ihfft \u001b[38;5;28;01mas\u001b[39;00m ihfft,\n\u001b[1;32m     23\u001b[0m   irfft \u001b[38;5;28;01mas\u001b[39;00m irfft,\n\u001b[1;32m     24\u001b[0m   irfft2 \u001b[38;5;28;01mas\u001b[39;00m irfft2,\n\u001b[1;32m     25\u001b[0m   irfftn \u001b[38;5;28;01mas\u001b[39;00m irfftn,\n\u001b[1;32m     26\u001b[0m   fft \u001b[38;5;28;01mas\u001b[39;00m fft,\n\u001b[1;32m     27\u001b[0m   fft2 \u001b[38;5;28;01mas\u001b[39;00m fft2,\n\u001b[1;32m     28\u001b[0m   fftfreq \u001b[38;5;28;01mas\u001b[39;00m fftfreq,\n\u001b[1;32m     29\u001b[0m   fftn \u001b[38;5;28;01mas\u001b[39;00m fftn,\n\u001b[1;32m     30\u001b[0m   fftshift \u001b[38;5;28;01mas\u001b[39;00m fftshift,\n\u001b[1;32m     31\u001b[0m   hfft \u001b[38;5;28;01mas\u001b[39;00m hfft,\n\u001b[1;32m     32\u001b[0m   rfft \u001b[38;5;28;01mas\u001b[39;00m rfft,\n\u001b[1;32m     33\u001b[0m   rfft2 \u001b[38;5;28;01mas\u001b[39;00m rfft2,\n\u001b[1;32m     34\u001b[0m   rfftfreq \u001b[38;5;28;01mas\u001b[39;00m rfftfreq,\n\u001b[1;32m     35\u001b[0m   rfftn \u001b[38;5;28;01mas\u001b[39;00m rfftn,\n\u001b[1;32m     36\u001b[0m )\n\u001b[1;32m     38\u001b[0m \u001b[38;5;66;03m# Module initialization is encapsulated in a function to avoid accidental\u001b[39;00m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# namespace pollution.\u001b[39;00m\n\u001b[1;32m     40\u001b[0m _NOT_IMPLEMENTED \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/numpy/fft.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01moperator\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lax\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_client\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m safe_zip\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/lax/__init__.py:332\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m    299\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (_reduce_sum, _reduce_max, _reduce_min, _reduce_or,\n\u001b[1;32m    300\u001b[0m                   _reduce_and, _reduce_window_sum, _reduce_window_max,\n\u001b[1;32m    301\u001b[0m                   _reduce_window_min, _reduce_window_prod,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    306\u001b[0m                   _upcast_fp16_for_computation, _broadcasting_shape_rule,\n\u001b[1;32m    307\u001b[0m                   _eye, _tri, _delta, _ones, _zeros, _dilate_shape)\n\u001b[1;32m    308\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontrol_flow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    309\u001b[0m   associative_scan \u001b[38;5;28;01mas\u001b[39;00m associative_scan,\n\u001b[1;32m    310\u001b[0m   cond \u001b[38;5;28;01mas\u001b[39;00m cond,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    330\u001b[0m   while_p \u001b[38;5;28;01mas\u001b[39;00m while_p,\n\u001b[1;32m    331\u001b[0m )\n\u001b[0;32m--> 332\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    333\u001b[0m   fft \u001b[38;5;28;01mas\u001b[39;00m fft,\n\u001b[1;32m    334\u001b[0m   fft_p \u001b[38;5;28;01mas\u001b[39;00m fft_p,\n\u001b[1;32m    335\u001b[0m )\n\u001b[1;32m    336\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mparallel\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    337\u001b[0m   all_gather \u001b[38;5;28;01mas\u001b[39;00m all_gather,\n\u001b[1;32m    338\u001b[0m   all_to_all \u001b[38;5;28;01mas\u001b[39;00m all_to_all,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    355\u001b[0m   xeinsum \u001b[38;5;28;01mas\u001b[39;00m xeinsum,\n\u001b[1;32m    356\u001b[0m )\n\u001b[1;32m    357\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mother\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m    358\u001b[0m   conv_general_dilated_patches \u001b[38;5;28;01mas\u001b[39;00m conv_general_dilated_patches\n\u001b[1;32m    359\u001b[0m )\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lax/fft.py:145\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m    143\u001b[0m batching\u001b[38;5;241m.\u001b[39mprimitive_batchers[fft_p] \u001b[38;5;241m=\u001b[39m fft_batching_rule\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pocketfft:\n\u001b[0;32m--> 145\u001b[0m   xla\u001b[38;5;241m.\u001b[39mbackend_specific_translations[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m][fft_p] \u001b[38;5;241m=\u001b[39m \u001b[43mpocketfft\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpocketfft\u001b[49m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'jaxlib.pocketfft' has no attribute 'pocketfft'"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
index 29bbcb1f1..9bb1be44f 100644
--- a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
@@ -225,8 +225,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-1.25902112 -0.51174395 -0.29276615  1.6489862  -1.69115646  1.62620724\n",
-      "  0.65444431 -1.35346808 -0.20316225  1.12630042]\n"
+      "[-0.38763091 -2.70501534 -0.3581571  -0.96251494 -1.26223899  0.35309734\n",
+      "  2.28186376 -1.85104809 -0.37114298 -1.20893188]\n"
      ]
     }
    ],
@@ -662,26 +662,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.26185107 0.82454365 0.97434186 0.56556315 0.58187347 0.72509099\n",
-      "  0.19158446 0.3263505  0.0536097  0.90066122]\n",
-      " [0.34678929 0.66981186 0.44152248 0.20299677 0.1614891  0.68485505\n",
-      "  0.43333886 0.45741697 0.31311243 0.88001352]\n",
-      " [0.29661191 0.05268304 0.98153145 0.9007164  0.69481746 0.35319678\n",
-      "  0.88063413 0.06319374 0.06695337 0.75350216]\n",
-      " [0.15089627 0.58671946 0.13734823 0.72394787 0.38019139 0.422275\n",
-      "  0.35821426 0.49282737 0.19144544 0.84653115]\n",
-      " [0.38637915 0.8049181  0.49672291 0.98699753 0.8192798  0.05850532\n",
-      "  0.00152188 0.13131825 0.31229747 0.40183706]\n",
-      " [0.8705211  0.1472032  0.22567203 0.55202922 0.62683307 0.41566661\n",
-      "  0.23659936 0.85086629 0.85120833 0.79135075]\n",
-      " [0.31377492 0.38629844 0.33956555 0.64064128 0.42028578 0.58474054\n",
-      "  0.71760245 0.51732028 0.31211671 0.35894575]\n",
-      " [0.78286771 0.72594302 0.22821344 0.23962594 0.48739546 0.59190877\n",
-      "  0.8557822  0.44830642 0.90595152 0.9626883 ]\n",
-      " [0.34498451 0.90694878 0.15442554 0.43560678 0.89045167 0.21654926\n",
-      "  0.00355118 0.18695705 0.61123608 0.7386068 ]\n",
-      " [0.30813073 0.26549135 0.96812218 0.94321297 0.81592628 0.60980325\n",
-      "  0.4284066  0.8792323  0.92968793 0.82073684]]\n"
+      "[[0.78459267 0.75453081 0.05363779 0.57350724 0.69764852 0.65795279\n",
+      "  0.51507839 0.20461136 0.38788697 0.96496641]\n",
+      " [0.25028968 0.96081861 0.18931988 0.51108791 0.30337713 0.43036842\n",
+      "  0.52839842 0.15321987 0.78561443 0.09030825]\n",
+      " [0.10097956 0.50584526 0.34989509 0.55626454 0.69154964 0.2895238\n",
+      "  0.13393141 0.15503141 0.26015755 0.42902155]\n",
+      " [0.25789255 0.9492866  0.90252116 0.904221   0.51933924 0.14432948\n",
+      "  0.54445121 0.02699523 0.18657863 0.971688  ]\n",
+      " [0.13392097 0.27801122 0.50931378 0.04234339 0.22442417 0.44065609\n",
+      "  0.74943449 0.42451192 0.33736485 0.97952271]\n",
+      " [0.95824108 0.59950055 0.91346044 0.58042237 0.13228567 0.31519573\n",
+      "  0.12427889 0.64736858 0.60236782 0.18036103]\n",
+      " [0.95911004 0.82027884 0.27547877 0.84317815 0.89842298 0.68322599\n",
+      "  0.02377668 0.39943328 0.00162091 0.0525221 ]\n",
+      " [0.94076632 0.88335933 0.75492292 0.7860324  0.41923956 0.86181269\n",
+      "  0.45979894 0.44190304 0.07829878 0.00458014]\n",
+      " [0.42915006 0.68127341 0.23875722 0.31988705 0.54956992 0.24801014\n",
+      "  0.65335632 0.97713364 0.05635863 0.12160173]\n",
+      " [0.93386901 0.74935095 0.96534137 0.98400474 0.98581925 0.30313128\n",
+      "  0.41386599 0.88450476 0.87099757 0.22566121]]\n"
      ]
     }
    ],
@@ -800,13 +800,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.2246674023625205\n",
-      "3.345687771875474\n",
-      "-0.6084611325305795\n",
-      "[[ 1.17709473  3.57810065  3.67258699]\n",
-      " [ 3.57810065 11.8548082  11.12152272]\n",
-      " [ 3.67258699 11.12152272 15.62249103]]\n",
-      "[26.06969872  0.07319349  2.51150176]\n"
+      "0.0626202708457115\n",
+      "4.327883798133277\n",
+      "0.3178411477108273\n",
+      "[[ 1.01422853  3.21909039  2.82750687]\n",
+      " [ 3.21909039 11.42694395  9.22887861]\n",
+      " [ 2.82750687  9.22887861 17.2086376 ]]\n",
+      "[24.72855434  0.09533574  4.82592   ]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
index 63a8157e2..81e6aed7a 100644
--- a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
@@ -1344,27 +1344,37 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.1297300314822336\n",
-      "[[ 9.30839676 15.51664729 15.66620847  8.85065653 10.38302314  7.56183518\n",
-      "   8.50754416 10.88560514  9.76063234  3.03168642]\n",
-      " [15.51664729 25.86550074 26.11481199 14.75361646 17.3079975  12.6052136\n",
-      "  14.18166474 18.1457774  16.27050214  5.05367466]\n",
-      " [15.66620847 26.11481199 26.3665263  14.89582298 17.47482507 12.72671218\n",
-      "  14.31835835 18.32068012 16.42732954  5.1023858 ]\n",
-      " [ 8.85065653 14.75361646 14.89582298  8.41542567  9.87243817  7.18998208\n",
-      "   8.08918584 10.35030572  9.28065343  2.8826033 ]\n",
-      " [10.38302314 17.3079975  17.47482507  9.87243817 11.58171189  8.43482628\n",
-      "   9.48971452 12.14231548 10.88746795  3.38168549]\n",
-      " [ 7.56183518 12.6052136  12.72671218  7.18998208  8.43482628  6.14298603\n",
-      "   6.91124889  8.84310736  7.92921648  2.46284227]\n",
-      " [ 8.50754416 14.18166474 14.31835835  8.08918584  9.48971452  6.91124889\n",
-      "   7.77559332  9.94905663  8.92087142  2.77085375]\n",
-      " [10.88560514 18.1457774  18.32068012 10.35030572 12.14231548  8.84310736\n",
-      "   9.94905663 12.73005463 11.41446721  3.54537329]\n",
-      " [ 9.76063234 16.27050214 16.42732954  9.28065343 10.88746795  7.92921648\n",
-      "   8.92087142 11.41446721 10.23483916  3.17897671]\n",
-      " [ 3.03168642  5.05367466  5.1023858   2.8826033   3.38168549  2.46284227\n",
-      "   2.77085375  3.54537329  3.17897671  0.98740124]]\n"
+      "3.0818000034712947\n",
+      "[[1.82949718e-02 3.31658671e-01 3.68886089e-01 6.49666527e-01\n",
+      "  1.49052317e-01 5.97305528e-01 4.57810898e-01 2.51578522e-01\n",
+      "  2.89396164e-01 5.90342093e-01]\n",
+      " [3.31658671e-01 6.01244295e+00 6.68731669e+00 1.17774184e+01\n",
+      "  2.70208087e+00 1.08281969e+01 8.29938171e+00 4.56071752e+00\n",
+      "  5.24629107e+00 1.07019610e+01]\n",
+      " [3.68886089e-01 6.68731669e+00 7.43794243e+00 1.30993886e+01\n",
+      "  3.00537912e+00 1.20436207e+01 9.23095558e+00 5.07264063e+00\n",
+      "  5.83516719e+00 1.19032152e+01]\n",
+      " [6.49666527e-01 1.17774184e+01 1.30993886e+01 2.30700873e+01\n",
+      "  5.29294618e+00 2.12107137e+01 1.62571673e+01 8.93371943e+00\n",
+      "  1.02766489e+01 2.09634375e+01]\n",
+      " [1.49052317e-01 2.70208087e+00 3.00537912e+00 5.29294618e+00\n",
+      "  1.21435514e+00 4.86635201e+00 3.72986500e+00 2.04965397e+00\n",
+      "  2.35776087e+00 4.80961969e+00]\n",
+      " [5.97305528e-01 1.08281969e+01 1.20436207e+01 2.12107137e+01\n",
+      "  4.86635201e+00 1.95011994e+01 1.49468927e+01 8.21369083e+00\n",
+      "  9.44838453e+00 1.92738529e+01]\n",
+      " [4.57810898e-01 8.29938171e+00 9.23095558e+00 1.62571673e+01\n",
+      "  3.72986500e+00 1.49468927e+01 1.14561980e+01 6.29546690e+00\n",
+      "  7.24181044e+00 1.47726407e+01]\n",
+      " [2.51578522e-01 4.56071752e+00 5.07264063e+00 8.93371943e+00\n",
+      "  2.04965397e+00 8.21369083e+00 6.29546690e+00 3.45951629e+00\n",
+      "  3.97955570e+00 8.11793498e+00]\n",
+      " [2.89396164e-01 5.24629107e+00 5.83516719e+00 1.02766489e+01\n",
+      "  2.35776087e+00 9.44838453e+00 7.24181044e+00 3.97955570e+00\n",
+      "  4.57776818e+00 9.33823452e+00]\n",
+      " [5.90342093e-01 1.07019610e+01 1.19032152e+01 2.09634375e+01\n",
+      "  4.80961969e+00 1.92738529e+01 1.47726407e+01 8.11793498e+00\n",
+      "  9.33823452e+00 1.90491568e+01]]\n"
      ]
     }
    ],
@@ -2001,15 +2011,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.09083636328656121\n",
-      "3.6943316601792833\n",
-      "-0.35793003441520066\n",
-      "0.8866190885623907 9.823585220707946 13.828190347382744\n",
-      "2.7923086060375724 2.5472246386316972 7.87667593906086\n",
-      "[[ 0.88661909  2.79230861  2.54722464]\n",
-      " [ 2.79230861  9.82358522  7.87667594]\n",
-      " [ 2.54722464  7.87667594 13.82819035]]\n",
-      "[20.65578316  0.07532297  3.80728853]\n"
+      "-0.054244842835462305\n",
+      "4.000854409696581\n",
+      "0.13083543199018746\n",
+      "0.8437169762110144 8.948675162607389 10.317825933186352\n",
+      "2.601583341274718 2.1245596497124075 6.443538568902246\n",
+      "[[ 0.84371698  2.60158334  2.12455965]\n",
+      " [ 2.60158334  8.94867516  6.44353857]\n",
+      " [ 2.12455965  6.44353857 10.31782593]]\n",
+      "[16.80422999  0.07128434  3.23470374]\n"
      ]
     }
    ],
@@ -2638,7 +2648,7 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -2764,12 +2774,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.008818897251043893 0.9940672992288855\n"
+      "-0.048547423739546604 0.9959293935368551\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
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diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png b/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png
index 9ebf1ec3e..ae594637e 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png and b/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png
index 8fe8c9418..ba13a577f 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png and b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
index 56aab343c..07c22d5a8 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
@@ -985,8 +985,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.39864527 -1.50219982 -0.24611674 -0.52819229 -0.24533566 -0.85694362\n",
-      " -0.81955926  0.14252712 -1.00625843 -0.23732849]\n"
+      "[-0.03483677 -1.06136773  0.56481272 -0.35947075 -1.64972151 -1.58590795\n",
+      " -1.17428293  0.7225597  -1.14061742 -1.22893751]\n"
      ]
     }
    ],
@@ -1424,26 +1424,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "  0.53254035 0.17826349 0.01551946 0.54969168]\n",
-      " [0.92373266 0.86361743 0.27525917 0.59367954 0.85843684 0.40389922\n",
-      "  0.8074242  0.59323905 0.51075117 0.79552752]]\n"
+      "[[0.24279008 0.63112036 0.80947943 0.97509292 0.19425617 0.3957482\n",
+      "  0.1655226  0.83760781 0.07995375 0.76400155]\n",
+      " [0.75621895 0.20893514 0.93082503 0.79419162 0.02783644 0.21296315\n",
+      "  0.64298419 0.34578026 0.60975366 0.46369869]\n",
+      " [0.77859437 0.23477043 0.35438626 0.63115792 0.2460037  0.35568525\n",
+      "  0.0825971  0.94117118 0.14900336 0.30035718]\n",
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+      "  0.21328866 0.41340248 0.3005849  0.40672425]\n",
+      " [0.21922061 0.88274486 0.86572911 0.06486061 0.07565581 0.26678445\n",
+      "  0.03265139 0.22090974 0.33135331 0.66973261]\n",
+      " [0.7221662  0.96941962 0.39707147 0.24929083 0.31531613 0.33079801\n",
+      "  0.06538944 0.42352791 0.94227931 0.27809912]\n",
+      " [0.07195822 0.31719317 0.47248297 0.18264218 0.64033527 0.51146442\n",
+      "  0.49545491 0.91936525 0.81656508 0.78329097]\n",
+      " [0.58666142 0.01646892 0.11029323 0.67442363 0.6914791  0.87902877\n",
+      "  0.98950411 0.27090196 0.08732305 0.89543736]\n",
+      " [0.25253892 0.57505712 0.24848907 0.9631064  0.46312791 0.96431281\n",
+      "  0.28744729 0.09772449 0.17676228 0.51656406]\n",
+      " [0.68664664 0.63281351 0.62806444 0.36809474 0.98129668 0.62914124\n",
+      "  0.37885034 0.6577093  0.57947706 0.24688732]]\n"
      ]
     }
    ],
@@ -1557,13 +1557,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.007334080205654388\n",
-      "3.9196398947631166\n",
-      "0.49680475019226\n",
-      "[[ 0.83982725  2.39595799  2.20255857]\n",
-      " [ 2.39595799  8.04496448  5.72101205]\n",
-      " [ 2.20255857  5.72101205 10.77494715]]\n",
-      "[15.97477238  0.08363087  3.60133562]\n"
+      "-0.19542686939553625\n",
+      "3.3929303193461995\n",
+      "-0.3886211842840971\n",
+      "[[ 0.96552318  2.67354673  2.55640013]\n",
+      " [ 2.67354673  8.3318361   7.41646135]\n",
+      " [ 2.55640013  7.41646135 11.19890453]]\n",
+      "[18.10686259  0.09289204  2.29650918]\n"
      ]
     }
    ],
@@ -1916,7 +1916,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_65914/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_20702/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
       "  data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))\n"
      ]
     },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
index ea3699b40..a5d97f870 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7577bda1",
+   "id": "be53b5a0",
    "metadata": {
     "editable": true
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@@ -14,7 +14,7 @@
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   {
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-   "id": "2f58994e",
+   "id": "ea9aa8ba",
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@@ -27,7 +27,7 @@
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-   "id": "984a751a",
+   "id": "1682c283",
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@@ -49,7 +49,7 @@
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-   "id": "b85fab3f",
+   "id": "06e8536b",
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@@ -65,7 +65,7 @@
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+   "id": "78f54d26",
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@@ -99,7 +99,7 @@
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+   "id": "94c25631",
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@@ -115,7 +115,7 @@
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-   "id": "d409d413",
+   "id": "a42b88dc",
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@@ -127,7 +127,7 @@
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   {
    "cell_type": "markdown",
-   "id": "92019658",
+   "id": "3171d71c",
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@@ -137,7 +137,7 @@
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-   "id": "98b024de",
+   "id": "e70f9acc",
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@@ -149,7 +149,7 @@
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    "cell_type": "markdown",
-   "id": "f08b4dcd",
+   "id": "2379d06d",
    "metadata": {
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@@ -170,7 +170,7 @@
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   {
    "cell_type": "markdown",
-   "id": "12b17f2d",
+   "id": "643717b0",
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@@ -182,7 +182,7 @@
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-   "id": "d7bb3d6a",
+   "id": "378c280f",
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@@ -195,7 +195,7 @@
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@@ -207,7 +207,7 @@
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   {
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-   "id": "b8e38e10",
+   "id": "cce68c3d",
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@@ -219,7 +219,7 @@
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-   "id": "9e391414",
+   "id": "f8c11724",
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@@ -229,7 +229,7 @@
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   {
    "cell_type": "markdown",
-   "id": "20b2a848",
+   "id": "a139ab45",
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@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ba76773",
+   "id": "089dec59",
    "metadata": {
     "editable": true
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@@ -254,7 +254,7 @@
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   {
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-   "id": "78a49ebf",
+   "id": "570e9155",
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@@ -266,7 +266,7 @@
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-   "id": "b6562367",
+   "id": "2c6cbd01",
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@@ -276,7 +276,7 @@
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-   "id": "3fdcc186",
+   "id": "daaf53c3",
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@@ -288,7 +288,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0a15628",
+   "id": "186a0c23",
    "metadata": {
     "editable": true
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@@ -300,7 +300,7 @@
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   {
    "cell_type": "markdown",
-   "id": "ad1e37fe",
+   "id": "e83c76fe",
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@@ -311,7 +311,7 @@
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-   "id": "bfc33b62",
+   "id": "48869faa",
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@@ -323,7 +323,7 @@
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   {
    "cell_type": "markdown",
-   "id": "05d1562e",
+   "id": "09ef84bc",
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@@ -342,7 +342,7 @@
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-   "id": "2615bb43",
+   "id": "a3573593",
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@@ -355,7 +355,7 @@
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-   "id": "4c278d09",
+   "id": "0cf80555",
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@@ -365,7 +365,7 @@
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-   "id": "4362df64",
+   "id": "9a5943d2",
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@@ -377,7 +377,7 @@
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+   "id": "614492f6",
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@@ -387,7 +387,7 @@
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-   "id": "5bdc470d",
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@@ -399,7 +399,7 @@
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+   "id": "1e2877d6",
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@@ -409,7 +409,7 @@
   },
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-   "id": "28e87953",
+   "id": "62ea2b3f",
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     "editable": true
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@@ -421,7 +421,7 @@
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   {
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-   "id": "2e1444b7",
+   "id": "8b2e0f0e",
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@@ -432,7 +432,7 @@
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+   "id": "08983f5f",
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@@ -444,7 +444,7 @@
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-   "id": "d7e1556d",
+   "id": "0220251b",
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@@ -454,7 +454,7 @@
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   {
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-   "id": "4015b0da",
+   "id": "d80df328",
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@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "289d01ec",
+   "id": "838e6fcc",
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@@ -476,7 +476,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c084023",
+   "id": "49bab4db",
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     "editable": true
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@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e0db3c9",
+   "id": "a9f8042e",
    "metadata": {
     "editable": true
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@@ -508,7 +508,7 @@
   },
   {
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-   "id": "32ac6fde",
+   "id": "41a2f792",
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@@ -535,7 +535,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adcd3708",
+   "id": "2cd68700",
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     "editable": true
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@@ -547,7 +547,7 @@
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-   "id": "1187bf5d",
+   "id": "45e00b5c",
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@@ -559,7 +559,7 @@
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-   "id": "51e56bc5",
+   "id": "0d8f4073",
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@@ -575,7 +575,7 @@
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@@ -593,7 +593,7 @@
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+   "id": "b1603479",
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@@ -605,7 +605,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c8002e1",
+   "id": "6b964217",
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@@ -617,7 +617,7 @@
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+   "id": "ef7da021",
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@@ -628,7 +628,7 @@
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+   "id": "f36afc00",
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@@ -640,7 +640,7 @@
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-   "id": "1f93191d",
+   "id": "c86b67b1",
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@@ -650,7 +650,7 @@
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@@ -662,7 +662,7 @@
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+   "id": "13294ac7",
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@@ -676,7 +676,7 @@
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-   "id": "81a3d747",
+   "id": "d7e5700e",
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@@ -688,7 +688,7 @@
   },
   {
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-   "id": "97d69ab9",
+   "id": "1f1b9622",
    "metadata": {
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@@ -700,7 +700,7 @@
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+   "id": "d22ff920",
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@@ -712,7 +712,7 @@
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-   "id": "2cafbbd7",
+   "id": "308eae89",
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@@ -722,7 +722,7 @@
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+   "id": "69df69e0",
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@@ -734,7 +734,7 @@
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-   "id": "bba294f8",
+   "id": "989c0888",
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@@ -746,7 +746,7 @@
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-   "id": "26084341",
+   "id": "796e59d4",
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@@ -758,7 +758,7 @@
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-   "id": "4bfeb04f",
+   "id": "0ecc2fbb",
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@@ -772,7 +772,7 @@
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   {
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+   "id": "7f541061",
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@@ -784,7 +784,7 @@
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+   "id": "150fcbf6",
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@@ -796,7 +796,7 @@
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   {
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-   "id": "1f4eda24",
+   "id": "27b04c69",
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@@ -808,7 +808,7 @@
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+   "id": "9d121c2b",
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@@ -818,7 +818,7 @@
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@@ -830,7 +830,7 @@
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+   "id": "60392be8",
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@@ -840,7 +840,7 @@
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+   "id": "3eeb0762",
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@@ -852,7 +852,7 @@
   },
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-   "id": "67715022",
+   "id": "5acbe964",
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@@ -862,7 +862,7 @@
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+   "id": "a5e67241",
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@@ -874,7 +874,7 @@
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@@ -886,7 +886,7 @@
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@@ -898,7 +898,7 @@
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@@ -913,7 +913,7 @@
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@@ -925,7 +925,7 @@
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+   "id": "ae1d7aa6",
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@@ -935,7 +935,7 @@
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@@ -947,7 +947,7 @@
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+   "id": "78444018",
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@@ -957,7 +957,7 @@
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+   "id": "4207abea",
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@@ -969,7 +969,7 @@
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@@ -979,7 +979,7 @@
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@@ -991,7 +991,7 @@
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@@ -1003,7 +1003,7 @@
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@@ -1015,7 +1015,7 @@
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@@ -1025,7 +1025,7 @@
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@@ -1037,7 +1037,7 @@
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@@ -1050,7 +1050,7 @@
   },
   {
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@@ -1062,7 +1062,7 @@
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@@ -1072,7 +1072,7 @@
   },
   {
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@@ -1084,7 +1084,7 @@
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@@ -1094,7 +1094,7 @@
   },
   {
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@@ -1106,7 +1106,7 @@
   },
   {
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@@ -1116,7 +1116,7 @@
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@@ -1128,7 +1128,7 @@
   },
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@@ -1138,7 +1138,7 @@
   },
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@@ -1150,7 +1150,7 @@
   },
   {
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@@ -1160,7 +1160,7 @@
   },
   {
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     "editable": true
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@@ -1172,7 +1172,7 @@
   },
   {
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@@ -1184,7 +1184,7 @@
   },
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@@ -1196,7 +1196,7 @@
   },
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@@ -1208,7 +1208,7 @@
   },
   {
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@@ -1220,7 +1220,7 @@
   },
   {
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@@ -1236,7 +1236,7 @@
   },
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@@ -1248,7 +1248,7 @@
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@@ -1258,7 +1258,7 @@
   },
   {
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@@ -1270,7 +1270,7 @@
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@@ -1288,7 +1288,7 @@
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@@ -1300,7 +1300,7 @@
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   {
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@@ -1312,7 +1312,7 @@
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@@ -1322,7 +1322,7 @@
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@@ -1334,7 +1334,7 @@
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@@ -1344,7 +1344,7 @@
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@@ -1356,7 +1356,7 @@
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@@ -1366,7 +1366,7 @@
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@@ -1380,7 +1380,7 @@
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@@ -1392,7 +1392,7 @@
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@@ -1403,7 +1403,7 @@
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@@ -1416,7 +1416,7 @@
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@@ -1443,7 +1443,7 @@
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@@ -1454,7 +1454,7 @@
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@@ -1467,7 +1467,7 @@
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@@ -1481,7 +1481,7 @@
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@@ -1494,7 +1494,7 @@
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@@ -1505,7 +1505,7 @@
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@@ -1515,7 +1515,7 @@
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@@ -1525,7 +1525,7 @@
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@@ -1536,7 +1536,7 @@
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@@ -1546,7 +1546,7 @@
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@@ -1560,7 +1560,7 @@
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@@ -1571,7 +1571,7 @@
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@@ -1581,23 +1581,23 @@
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@@ -1609,7 +1609,7 @@
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@@ -1630,7 +1630,7 @@
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@@ -1641,7 +1641,7 @@
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@@ -1651,15 +1651,15 @@
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      "text": [
-      "[ 2.03739357 -0.54302039  7.21493572 -3.15550086  1.44651551]\n",
+      "[ 2.00025531  0.8850944  -0.44894073 10.27580794 -5.9383327 ]\n",
       "Training R2\n",
-      "0.9973102337301051\n",
+      "0.9960039497353536\n",
       "Training MSE\n",
-      "0.006158537606875468\n",
+      "0.007839875253055148\n",
       "Test R2\n",
-      "0.9954911579937146\n",
+      "0.9962074631757628\n",
       "Test MSE\n",
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@@ -1710,7 +1710,7 @@
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@@ -1721,7 +1721,7 @@
   {
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@@ -1746,7 +1746,7 @@
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@@ -1758,7 +1758,7 @@
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@@ -1787,7 +1787,7 @@
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@@ -1812,7 +1812,7 @@
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@@ -1832,7 +1832,7 @@
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@@ -1859,7 +1859,7 @@
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@@ -1872,7 +1872,7 @@
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@@ -1884,7 +1884,7 @@
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@@ -1895,7 +1895,7 @@
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@@ -1911,7 +1911,7 @@
   {
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-   "id": "167ea26b",
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@@ -2354,7 +2354,7 @@
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@@ -2364,7 +2364,7 @@
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@@ -2379,7 +2379,7 @@
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@@ -2391,7 +2391,7 @@
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@@ -2401,7 +2401,7 @@
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@@ -2417,7 +2417,7 @@
   {
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-   "id": "95995d2e",
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@@ -2434,7 +2434,7 @@
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@@ -2447,7 +2447,7 @@
   {
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-   "id": "beabe048",
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@@ -2509,7 +2509,7 @@
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@@ -2520,7 +2520,7 @@
   {
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@@ -2656,7 +2656,7 @@
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@@ -2684,7 +2684,7 @@
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@@ -2719,7 +2719,7 @@
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@@ -2734,7 +2734,7 @@
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@@ -2759,7 +2759,7 @@
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@@ -2775,7 +2775,7 @@
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@@ -2787,7 +2787,7 @@
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@@ -2802,7 +2802,7 @@
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@@ -2814,7 +2814,7 @@
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@@ -2824,7 +2824,7 @@
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@@ -2836,7 +2836,7 @@
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@@ -2846,7 +2846,7 @@
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@@ -2858,7 +2858,7 @@
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@@ -2871,7 +2871,7 @@
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@@ -2883,7 +2883,7 @@
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@@ -2893,7 +2893,7 @@
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@@ -2905,7 +2905,7 @@
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@@ -2915,7 +2915,7 @@
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@@ -2927,7 +2927,7 @@
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@@ -2937,7 +2937,7 @@
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@@ -2949,7 +2949,7 @@
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@@ -2959,7 +2959,7 @@
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@@ -2971,7 +2971,7 @@
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@@ -2981,7 +2981,7 @@
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@@ -2993,7 +2993,7 @@
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@@ -3003,7 +3003,7 @@
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@@ -3015,7 +3015,7 @@
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@@ -3027,7 +3027,7 @@
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@@ -3039,7 +3039,7 @@
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@@ -3052,7 +3052,7 @@
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@@ -3064,7 +3064,7 @@
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@@ -3074,7 +3074,7 @@
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@@ -3088,7 +3088,7 @@
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@@ -3221,7 +3221,7 @@
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@@ -3242,7 +3242,7 @@
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@@ -3254,7 +3254,7 @@
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@@ -3264,7 +3264,7 @@
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@@ -3276,7 +3276,7 @@
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@@ -3286,7 +3286,7 @@
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@@ -3298,7 +3298,7 @@
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@@ -3314,7 +3314,7 @@
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@@ -3358,7 +3358,7 @@
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@@ -3370,7 +3370,7 @@
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@@ -3386,7 +3386,7 @@
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@@ -3397,7 +3397,7 @@
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@@ -3468,7 +3468,7 @@
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@@ -3479,7 +3479,7 @@
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@@ -3493,7 +3493,7 @@
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@@ -3504,7 +3504,7 @@
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@@ -3542,7 +3542,7 @@
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@@ -3553,7 +3553,7 @@
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@@ -3593,7 +3593,7 @@
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@@ -3604,7 +3604,7 @@
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@@ -3645,7 +3645,7 @@
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@@ -3656,7 +3656,7 @@
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@@ -3695,7 +3695,7 @@
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@@ -3706,7 +3706,7 @@
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@@ -3719,7 +3719,7 @@
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@@ -3730,7 +3730,7 @@
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@@ -3761,7 +3761,7 @@
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@@ -3772,7 +3772,7 @@
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@@ -3832,7 +3832,7 @@
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@@ -3862,7 +3862,7 @@
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@@ -3872,7 +3872,7 @@
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@@ -3886,7 +3886,7 @@
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@@ -3898,7 +3898,7 @@
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@@ -3910,7 +3910,7 @@
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@@ -3922,7 +3922,7 @@
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@@ -3932,7 +3932,7 @@
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@@ -3944,7 +3944,7 @@
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@@ -3954,7 +3954,7 @@
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@@ -3966,7 +3966,7 @@
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@@ -3977,7 +3977,7 @@
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@@ -3989,7 +3989,7 @@
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@@ -4001,7 +4001,7 @@
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@@ -4011,7 +4011,7 @@
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@@ -4023,7 +4023,7 @@
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@@ -4035,7 +4035,7 @@
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@@ -4045,7 +4045,7 @@
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@@ -4057,7 +4057,7 @@
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@@ -4067,7 +4067,7 @@
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@@ -4079,7 +4079,7 @@
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@@ -4089,7 +4089,7 @@
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@@ -4129,7 +4129,7 @@
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@@ -4146,7 +4146,7 @@
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@@ -4169,7 +4169,7 @@
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@@ -4186,7 +4186,7 @@
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@@ -4205,7 +4205,7 @@
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@@ -4216,7 +4216,7 @@
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@@ -4228,7 +4228,7 @@
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@@ -4246,7 +4246,7 @@
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@@ -4262,7 +4262,7 @@
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@@ -4274,7 +4274,7 @@
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@@ -4284,7 +4284,7 @@
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@@ -4299,7 +4299,7 @@
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@@ -4311,7 +4311,7 @@
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@@ -4321,7 +4321,7 @@
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@@ -4333,7 +4333,7 @@
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@@ -4343,7 +4343,7 @@
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@@ -4355,7 +4355,7 @@
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@@ -4367,7 +4367,7 @@
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@@ -4382,7 +4382,7 @@
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@@ -4393,7 +4393,7 @@
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@@ -4413,7 +4413,7 @@
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@@ -4425,7 +4425,7 @@
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+   "id": "59a6e34a",
    "metadata": {
     "editable": true
    },
@@ -4435,7 +4435,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13a7fb5e",
+   "id": "ab9dc4a5",
    "metadata": {
     "editable": true
    },
@@ -4447,7 +4447,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91d79dd2",
+   "id": "51c1c028",
    "metadata": {
     "editable": true
    },
@@ -4476,7 +4476,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afe923ac",
+   "id": "ec252687",
    "metadata": {
     "editable": true
    },
@@ -4503,7 +4503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3b0107b",
+   "id": "156bfe10",
    "metadata": {
     "editable": true
    },
@@ -4514,7 +4514,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "df6b7c05",
+   "id": "3c182484",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4576,7 +4576,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e91f9a0",
+   "id": "98a0e5ec",
    "metadata": {
     "editable": true
    },
@@ -4593,7 +4593,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "415fc97a",
+   "id": "b69361d0",
    "metadata": {
     "editable": true
    },
@@ -4616,7 +4616,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c537ea64",
+   "id": "8d7c0cc8",
    "metadata": {
     "editable": true
    },
@@ -4630,7 +4630,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba856156",
+   "id": "0a45fa90",
    "metadata": {
     "editable": true
    },
@@ -4649,7 +4649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "376b56d3",
+   "id": "add34bfc",
    "metadata": {
     "editable": true
    },
@@ -4659,7 +4659,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94383f7f",
+   "id": "f00ff6da",
    "metadata": {
     "editable": true
    },
@@ -4671,7 +4671,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "836c6ad5",
+   "id": "348cd8df",
    "metadata": {
     "editable": true
    },
@@ -4685,7 +4685,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e56306d7",
+   "id": "f7697c17",
    "metadata": {
     "editable": true
    },
@@ -4697,7 +4697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd33b717",
+   "id": "64417096",
    "metadata": {
     "editable": true
    },
@@ -4707,7 +4707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f81bbc5e",
+   "id": "ded28c47",
    "metadata": {
     "editable": true
    },
@@ -4719,7 +4719,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdc865ea",
+   "id": "f70d6ea7",
    "metadata": {
     "editable": true
    },
@@ -4736,7 +4736,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6560516f",
+   "id": "68ecbbfa",
    "metadata": {
     "editable": true
    },
@@ -4746,7 +4746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0913014f",
+   "id": "73db495d",
    "metadata": {
     "editable": true
    },
@@ -4762,7 +4762,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7960d3eb",
+   "id": "44412861",
    "metadata": {
     "editable": true
    },
@@ -4772,7 +4772,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2c6cab1",
+   "id": "985de8ad",
    "metadata": {
     "editable": true
    },
@@ -4788,7 +4788,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d026c4fa",
+   "id": "bae93d68",
    "metadata": {
     "editable": true
    },
@@ -4798,7 +4798,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc5999f4",
+   "id": "e29bd644",
    "metadata": {
     "editable": true
    },
@@ -4814,7 +4814,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1753c718",
+   "id": "3abf4a2d",
    "metadata": {
     "editable": true
    },
@@ -4824,7 +4824,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb1ffbac",
+   "id": "fe1e2ce7",
    "metadata": {
     "editable": true
    },
@@ -4841,7 +4841,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d5ba9ce",
+   "id": "d23d044b",
    "metadata": {
     "editable": true
    },
@@ -4853,7 +4853,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc09fc11",
+   "id": "36e38f22",
    "metadata": {
     "editable": true
    },
@@ -4865,7 +4865,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36822227",
+   "id": "ad22843b",
    "metadata": {
     "editable": true
    },
@@ -4877,7 +4877,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de6cc474",
+   "id": "fabcf0f8",
    "metadata": {
     "editable": true
    },
@@ -4887,7 +4887,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "453ff6f8",
+   "id": "2c2f44c5",
    "metadata": {
     "editable": true
    },
@@ -4899,7 +4899,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fb1165b",
+   "id": "105762d0",
    "metadata": {
     "editable": true
    },
@@ -4911,7 +4911,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb0b59b1",
+   "id": "35a42e41",
    "metadata": {
     "editable": true
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@@ -4923,7 +4923,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c998fc7",
+   "id": "8e21698f",
    "metadata": {
     "editable": true
    },
@@ -4933,7 +4933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b91e913",
+   "id": "a240efc0",
    "metadata": {
     "editable": true
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@@ -4945,7 +4945,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d8c0246",
+   "id": "3919a2cb",
    "metadata": {
     "editable": true
    },
@@ -4955,7 +4955,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f73a9f8b",
+   "id": "ddf99a3e",
    "metadata": {
     "editable": true
    },
@@ -4967,7 +4967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ce5f423",
+   "id": "5a790d25",
    "metadata": {
     "editable": true
    },
@@ -4981,7 +4981,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ec391cc",
+   "id": "113a5d16",
    "metadata": {
     "editable": true
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@@ -4993,7 +4993,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11a3f35c",
+   "id": "ec65ec89",
    "metadata": {
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@@ -5005,7 +5005,7 @@
   },
   {
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-   "id": "c6b8757c",
+   "id": "ed891134",
    "metadata": {
     "editable": true
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@@ -5015,7 +5015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e2f4fc0",
+   "id": "5266f146",
    "metadata": {
     "editable": true
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@@ -5027,7 +5027,7 @@
   },
   {
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-   "id": "db3e6c79",
+   "id": "666ab5b3",
    "metadata": {
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@@ -5038,7 +5038,7 @@
   },
   {
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+   "id": "ab9a3eec",
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@@ -5050,7 +5050,7 @@
   },
   {
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-   "id": "a7dfc330",
+   "id": "252e7533",
    "metadata": {
     "editable": true
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@@ -5060,7 +5060,7 @@
   },
   {
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-   "id": "888796de",
+   "id": "201b9300",
    "metadata": {
     "editable": true
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@@ -5072,7 +5072,7 @@
   },
   {
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-   "id": "99561aeb",
+   "id": "4f6287cf",
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     "editable": true
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@@ -5082,7 +5082,7 @@
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   {
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-   "id": "0a4556b6",
+   "id": "1ace6d06",
    "metadata": {
     "editable": true
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@@ -5094,7 +5094,7 @@
   },
   {
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-   "id": "9844b480",
+   "id": "274f98fb",
    "metadata": {
     "editable": true
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@@ -5105,7 +5105,7 @@
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   {
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+   "id": "70dc1b76",
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@@ -5117,7 +5117,7 @@
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   {
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-   "id": "2debf83f",
+   "id": "096c55e9",
    "metadata": {
     "editable": true
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@@ -5135,7 +5135,7 @@
   },
   {
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-   "id": "7fd0c7b2",
+   "id": "0ac9bdae",
    "metadata": {
     "editable": true
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@@ -5151,19 +5151,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db45a990",
+   "id": "a456e251",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\frac{\\partial^2 C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}^T\\partial \\boldsymbol{\\beta}} =\\frac{2}{n}\\boldsymbol{X}^T\\boldsymbol{X}.\n",
+    "\\frac{\\partial^2 C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T} =\\frac{2}{n}\\boldsymbol{X}^T\\boldsymbol{X}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "476e4063",
+   "id": "7c817401",
    "metadata": {
     "editable": true
    },
@@ -5175,7 +5175,7 @@
   },
   {
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-   "id": "f7caedf0",
+   "id": "20d7bb5d",
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@@ -5187,7 +5187,7 @@
   },
   {
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-   "id": "a9469af7",
+   "id": "52a9f64e",
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@@ -5200,7 +5200,7 @@
   },
   {
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-   "id": "dae869bb",
+   "id": "77820e66",
    "metadata": {
     "editable": true
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@@ -5216,7 +5216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d718bf56",
+   "id": "dc6ab9cd",
    "metadata": {
     "editable": true
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@@ -5230,7 +5230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5ad97e4",
+   "id": "8a8ee502",
    "metadata": {
     "editable": true
    },
@@ -5240,7 +5240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b76f1367",
+   "id": "f377b529",
    "metadata": {
     "editable": true
    },
@@ -5252,7 +5252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc7b9319",
+   "id": "e572a54d",
    "metadata": {
     "editable": true
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@@ -5262,7 +5262,7 @@
   },
   {
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-   "id": "8159fef4",
+   "id": "1b9c72f5",
    "metadata": {
     "editable": true
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@@ -5274,7 +5274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d34ac113",
+   "id": "15c56784",
    "metadata": {
     "editable": true
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@@ -5284,7 +5284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e0b77c9",
+   "id": "a03c1a32",
    "metadata": {
     "editable": true
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@@ -5298,7 +5298,7 @@
   },
   {
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-   "id": "01cb77e3",
+   "id": "af372da2",
    "metadata": {
     "editable": true
    },
@@ -5309,13 +5309,13 @@
     "method corrects the bias in the estimation of the population variance\n",
     "and covariance. It also partially corrects the bias in the estimation\n",
     "of the population standard deviation. If you use a library like\n",
-    "**Scikit-Learn** or **nunmpy's** function calculate the covariance, this\n",
+    "**Scikit-Learn** or **nunmpy's** function to calculate the covariance, this\n",
     "quantity will be computed with a factor $1/(n-1)$."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e69ed735",
+   "id": "9200f285",
    "metadata": {
     "editable": true
    },
@@ -5331,7 +5331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0da7f4b",
+   "id": "d5becae6",
    "metadata": {
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@@ -5343,7 +5343,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "935ca456",
+   "id": "27e6e5b8",
    "metadata": {
     "editable": true
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@@ -5356,7 +5356,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06f10a4e",
+   "id": "06454f55",
    "metadata": {
     "editable": true
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@@ -5370,7 +5370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be61cd6a",
+   "id": "833b2511",
    "metadata": {
     "editable": true
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@@ -5380,7 +5380,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e25f2ad",
+   "id": "056aceed",
    "metadata": {
     "editable": true
    },
@@ -5393,7 +5393,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe5ccfec",
+   "id": "950f361e",
    "metadata": {
     "editable": true
    },
@@ -5412,7 +5412,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eefbdf95",
+   "id": "2d8ac5c2",
    "metadata": {
     "editable": true
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@@ -5424,7 +5424,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05b5b021",
+   "id": "62d04779",
    "metadata": {
     "editable": true
    },
@@ -5436,7 +5436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17df2dd0",
+   "id": "797b13b8",
    "metadata": {
     "editable": true
    },
@@ -5446,7 +5446,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "286fc60b",
+   "id": "bd705147",
    "metadata": {
     "editable": true
    },
@@ -5458,7 +5458,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9918e3f5",
+   "id": "542850cc",
    "metadata": {
     "editable": true
    },
@@ -5471,7 +5471,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11803309",
+   "id": "db1aaf48",
    "metadata": {
     "editable": true
    },
@@ -5490,7 +5490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81195690",
+   "id": "6dc65433",
    "metadata": {
     "editable": true
    },
@@ -5500,7 +5500,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a402445",
+   "id": "ccd95a67",
    "metadata": {
     "editable": true
    },
@@ -5519,7 +5519,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c0f4547",
+   "id": "c15cc257",
    "metadata": {
     "editable": true
    },
@@ -5537,7 +5537,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "238844a1",
+   "id": "cc4a5463",
    "metadata": {
     "editable": true
    },
@@ -5551,7 +5551,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33ddecf3",
+   "id": "b8cfd6fa",
    "metadata": {
     "editable": true
    },
@@ -5566,7 +5566,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "5b7e3d47",
+   "id": "31e1da88",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5598,7 +5598,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5203c81",
+   "id": "24a933c2",
    "metadata": {
     "editable": true
    },
@@ -5615,7 +5615,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "6ca9fe2f",
+   "id": "8d9779ce",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5658,7 +5658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a33aee4",
+   "id": "c7e322b5",
    "metadata": {
     "editable": true
    },
@@ -5672,7 +5672,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be321ad7",
+   "id": "ea5682d7",
    "metadata": {
     "editable": true
    },
@@ -5685,7 +5685,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "df41b33a",
+   "id": "0e83fcbc",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5741,7 +5741,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "37e53b7c",
+   "id": "1226fab9",
    "metadata": {
     "editable": true
    },
@@ -5751,7 +5751,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "470d603b",
+   "id": "923ffc58",
    "metadata": {
     "editable": true
    },
@@ -5762,7 +5762,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "d15a64cf",
+   "id": "145c2622",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -5856,7 +5856,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "450109da",
+   "id": "8a657c92",
    "metadata": {
     "editable": true
    },
@@ -5873,7 +5873,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15d78e68",
+   "id": "522f204b",
    "metadata": {
     "editable": true
    },
@@ -5885,7 +5885,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05d71468",
+   "id": "6ab032e5",
    "metadata": {
     "editable": true
    },
@@ -5897,7 +5897,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "579b77d0",
+   "id": "423a9a47",
    "metadata": {
     "editable": true
    },
@@ -5907,7 +5907,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b77d639",
+   "id": "817b323b",
    "metadata": {
     "editable": true
    },
@@ -5924,7 +5924,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6f64deb",
+   "id": "93f4ff8f",
    "metadata": {
     "editable": true
    },
@@ -5934,7 +5934,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6416bd42",
+   "id": "437c6704",
    "metadata": {
     "editable": true
    },
@@ -5949,7 +5949,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd76a3d6",
+   "id": "fe76cb03",
    "metadata": {
     "editable": true
    },
@@ -5959,7 +5959,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7667e919",
+   "id": "71d190a9",
    "metadata": {
     "editable": true
    },
@@ -5973,7 +5973,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a364fdec",
+   "id": "21d676ff",
    "metadata": {
     "editable": true
    },
@@ -5985,7 +5985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e30f36d",
+   "id": "95bf7f81",
    "metadata": {
     "editable": true
    },
@@ -5997,7 +5997,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8706a80",
+   "id": "3e6fccbc",
    "metadata": {
     "editable": true
    },
@@ -6009,7 +6009,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9e7b5b3",
+   "id": "8c261002",
    "metadata": {
     "editable": true
    },
@@ -6019,7 +6019,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58f9f374",
+   "id": "f9aedabd",
    "metadata": {
     "editable": true
    },
@@ -6031,7 +6031,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ccd3b380",
+   "id": "b9c3ddc5",
    "metadata": {
     "editable": true
    },
@@ -6041,7 +6041,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4febae0d",
+   "id": "ed30a2b1",
    "metadata": {
     "editable": true
    },
@@ -6058,7 +6058,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5264a8ef",
+   "id": "dfb0eb4a",
    "metadata": {
     "editable": true
    },
@@ -6068,7 +6068,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6306d337",
+   "id": "39664ac4",
    "metadata": {
     "editable": true
    },
@@ -6080,7 +6080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b85ad53",
+   "id": "36858bcd",
    "metadata": {
     "editable": true
    },
@@ -6090,7 +6090,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f462bd9",
+   "id": "1938dd21",
    "metadata": {
     "editable": true
    },
@@ -6102,7 +6102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e998d31",
+   "id": "d89b7fce",
    "metadata": {
     "editable": true
    },
@@ -6116,7 +6116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93ad8f0c",
+   "id": "a30a69b4",
    "metadata": {
     "editable": true
    },
@@ -6128,7 +6128,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec4e112e",
+   "id": "34639fb6",
    "metadata": {
     "editable": true
    },
@@ -6150,7 +6150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36422c93",
+   "id": "20ad8e3f",
    "metadata": {
     "editable": true
    },
@@ -6162,7 +6162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c726caf1",
+   "id": "d9478bfc",
    "metadata": {
     "editable": true
    },
@@ -6177,7 +6177,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13624ec5",
+   "id": "04d7397f",
    "metadata": {
     "editable": true
    },
@@ -6189,7 +6189,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5f5dbbb",
+   "id": "59f21f2c",
    "metadata": {
     "editable": true
    },
@@ -6201,7 +6201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5a06622",
+   "id": "a309e6d5",
    "metadata": {
     "editable": true
    },
@@ -6211,7 +6211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "472afc38",
+   "id": "2c4186ec",
    "metadata": {
     "editable": true
    },
@@ -6223,7 +6223,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "719c02f4",
+   "id": "ccd854d0",
    "metadata": {
     "editable": true
    },
@@ -6233,7 +6233,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2a62207",
+   "id": "74eaeda4",
    "metadata": {
     "editable": true
    },
@@ -6245,7 +6245,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6194792e",
+   "id": "053b8482",
    "metadata": {
     "editable": true
    },
@@ -6255,7 +6255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c957d867",
+   "id": "6f8a528a",
    "metadata": {
     "editable": true
    },
@@ -6267,7 +6267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d0a4281",
+   "id": "112070df",
    "metadata": {
     "editable": true
    },
@@ -6284,7 +6284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a982382",
+   "id": "33e268b0",
    "metadata": {
     "editable": true
    },
@@ -6297,7 +6297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fa67077",
+   "id": "0de7adcd",
    "metadata": {
     "editable": true
    },
@@ -6309,7 +6309,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d70a4c10",
+   "id": "fa30d846",
    "metadata": {
     "editable": true
    },
@@ -6319,7 +6319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61ce3a16",
+   "id": "d577ef22",
    "metadata": {
     "editable": true
    },
@@ -6332,7 +6332,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7110d4e",
+   "id": "ce39ce20",
    "metadata": {
     "editable": true
    },
@@ -6342,7 +6342,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "102fd8e6",
+   "id": "960bd21b",
    "metadata": {
     "editable": true
    },
@@ -6354,7 +6354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea3f1c3b",
+   "id": "7736561e",
    "metadata": {
     "editable": true
    },
@@ -6367,7 +6367,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e7d3edc",
+   "id": "f1043146",
    "metadata": {
     "editable": true
    },
@@ -6380,7 +6380,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6138e4d3",
+   "id": "56e791a2",
    "metadata": {
     "editable": true
    },
@@ -6392,7 +6392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8406ec32",
+   "id": "dbcb265a",
    "metadata": {
     "editable": true
    },
@@ -6404,7 +6404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8725c04e",
+   "id": "9e1a00c1",
    "metadata": {
     "editable": true
    },
@@ -6414,7 +6414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dcb02b56",
+   "id": "6ca6c71d",
    "metadata": {
     "editable": true
    },
@@ -6427,7 +6427,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fdc3d5dc",
+   "id": "797658d0",
    "metadata": {
     "editable": true
    },
@@ -6439,7 +6439,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a06957b",
+   "id": "ed64a13e",
    "metadata": {
     "editable": true
    },
@@ -6451,7 +6451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d3c2482",
+   "id": "fc6a2aeb",
    "metadata": {
     "editable": true
    },
@@ -6463,7 +6463,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e94ba1d9",
+   "id": "1c956ba4",
    "metadata": {
     "editable": true
    },
@@ -6475,7 +6475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebee0c20",
+   "id": "ea5516df",
    "metadata": {
     "editable": true
    },
@@ -6489,7 +6489,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e30e7753",
+   "id": "b6d61e24",
    "metadata": {
     "editable": true
    },
@@ -6501,7 +6501,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf0db09e",
+   "id": "84765a82",
    "metadata": {
     "editable": true
    },
@@ -6511,7 +6511,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09c45e7a",
+   "id": "973019d8",
    "metadata": {
     "editable": true
    },
@@ -6523,7 +6523,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3691ef57",
+   "id": "7f074350",
    "metadata": {
     "editable": true
    },
@@ -6535,7 +6535,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86e182d9",
+   "id": "0570ceae",
    "metadata": {
     "editable": true
    },
@@ -6547,7 +6547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb5f1b1c",
+   "id": "5d16ea9d",
    "metadata": {
     "editable": true
    },
@@ -6559,7 +6559,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64a361ad",
+   "id": "b739c33e",
    "metadata": {
     "editable": true
    },
@@ -6571,7 +6571,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "736ab040",
+   "id": "dc8bc23b",
    "metadata": {
     "editable": true
    },
@@ -6590,7 +6590,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e5e13b7",
+   "id": "d89da6dc",
    "metadata": {
     "editable": true
    },
@@ -6602,7 +6602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5b34bc6",
+   "id": "3b871cb3",
    "metadata": {
     "editable": true
    },
@@ -6612,7 +6612,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05fed0df",
+   "id": "018e1163",
    "metadata": {
     "editable": true
    },
@@ -6624,7 +6624,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0853969f",
+   "id": "24f364d8",
    "metadata": {
     "editable": true
    },
@@ -6634,7 +6634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1e0e802",
+   "id": "4f695ec4",
    "metadata": {
     "editable": true
    },
@@ -6646,7 +6646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5349338e",
+   "id": "a4599188",
    "metadata": {
     "editable": true
    },
@@ -6658,7 +6658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3856bb34",
+   "id": "0edf0a0c",
    "metadata": {
     "editable": true
    },
@@ -6674,7 +6674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96bb9c00",
+   "id": "9afad20c",
    "metadata": {
     "editable": true
    },
@@ -6686,7 +6686,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "343e7f2a",
+   "id": "33df2d17",
    "metadata": {
     "editable": true
    },
@@ -6698,7 +6698,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9a0dd13",
+   "id": "972e10e7",
    "metadata": {
     "editable": true
    },
@@ -6708,7 +6708,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47f89929",
+   "id": "86f89bca",
    "metadata": {
     "editable": true
    },
@@ -6720,7 +6720,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a8d40da",
+   "id": "c286d017",
    "metadata": {
     "editable": true
    },
@@ -6730,7 +6730,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87a36c85",
+   "id": "4dcbe7e5",
    "metadata": {
     "editable": true
    },
@@ -6742,7 +6742,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6237d1b0",
+   "id": "43db48d4",
    "metadata": {
     "editable": true
    },
@@ -6759,7 +6759,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39430adb",
+   "id": "17d8c95a",
    "metadata": {
     "editable": true
    },
@@ -6771,7 +6771,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b97b5db8",
+   "id": "3c9d6593",
    "metadata": {
     "editable": true
    },
@@ -6783,7 +6783,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06fb16da",
+   "id": "a1606a04",
    "metadata": {
     "editable": true
    },
@@ -6793,7 +6793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "552d08cd",
+   "id": "d192512a",
    "metadata": {
     "editable": true
    },
@@ -6805,7 +6805,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e2d1b42",
+   "id": "a60675a1",
    "metadata": {
     "editable": true
    },
@@ -6815,7 +6815,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbc4cda5",
+   "id": "942fc37f",
    "metadata": {
     "editable": true
    },
@@ -6827,7 +6827,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e36f9245",
+   "id": "4f5e067f",
    "metadata": {
     "editable": true
    },
@@ -6837,7 +6837,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06b74c95",
+   "id": "d330f743",
    "metadata": {
     "editable": true
    },
@@ -6849,7 +6849,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1cbd57d2",
+   "id": "55f9b635",
    "metadata": {
     "editable": true
    },
@@ -6859,7 +6859,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17a0dae2",
+   "id": "5a6d1b0b",
    "metadata": {
     "editable": true
    },
@@ -6871,7 +6871,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abc993af",
+   "id": "12fd13cb",
    "metadata": {
     "editable": true
    },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.py b/doc/LectureNotes/_build/jupyter_execute/week35.py
index bfc513af4..4174beeb0 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week35.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week35.py
@@ -1990,7 +1990,7 @@ print(C-X)
 # function, that is we have
 
 # $$
-# \frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}^T\partial \boldsymbol{\beta}} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.
+# \frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.
 # $$
 
 # This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).
@@ -2046,7 +2046,7 @@ print(C-X)
 # method corrects the bias in the estimation of the population variance
 # and covariance. It also partially corrects the bias in the estimation
 # of the population standard deviation. If you use a library like
-# **Scikit-Learn** or **nunmpy's** function calculate the covariance, this
+# **Scikit-Learn** or **nunmpy's** function to calculate the covariance, this
 # quantity will be computed with a factor $1/(n-1)$.
 
 # ## Covariance and Correlation Matrix
diff --git a/doc/LectureNotes/gaussian.pdf b/doc/LectureNotes/gaussian.pdf
index bced77758..7e852ec73 100644
Binary files a/doc/LectureNotes/gaussian.pdf and b/doc/LectureNotes/gaussian.pdf differ
diff --git a/doc/LectureNotes/week35.ipynb b/doc/LectureNotes/week35.ipynb
index baa97aaf4..6c23bf707 100644
--- a/doc/LectureNotes/week35.ipynb
+++ b/doc/LectureNotes/week35.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7577bda1",
+   "id": "be53b5a0",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f58994e",
+   "id": "ea9aa8ba",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "984a751a",
+   "id": "1682c283",
    "metadata": {
     "editable": true
    },
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b85fab3f",
+   "id": "06e8536b",
    "metadata": {
     "editable": true
    },
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6156ea7b",
+   "id": "78f54d26",
    "metadata": {
     "editable": true
    },
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3a257b8",
+   "id": "94c25631",
    "metadata": {
     "editable": true
    },
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d409d413",
+   "id": "a42b88dc",
    "metadata": {
     "editable": true
    },
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92019658",
+   "id": "3171d71c",
    "metadata": {
     "editable": true
    },
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98b024de",
+   "id": "e70f9acc",
    "metadata": {
     "editable": true
    },
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f08b4dcd",
+   "id": "2379d06d",
    "metadata": {
     "editable": true
    },
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12b17f2d",
+   "id": "643717b0",
    "metadata": {
     "editable": true
    },
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7bb3d6a",
+   "id": "378c280f",
    "metadata": {
     "editable": true
    },
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e628309",
+   "id": "e52a2672",
    "metadata": {
     "editable": true
    },
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8e38e10",
+   "id": "cce68c3d",
    "metadata": {
     "editable": true
    },
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e391414",
+   "id": "f8c11724",
    "metadata": {
     "editable": true
    },
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20b2a848",
+   "id": "a139ab45",
    "metadata": {
     "editable": true
    },
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ba76773",
+   "id": "089dec59",
    "metadata": {
     "editable": true
    },
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78a49ebf",
+   "id": "570e9155",
    "metadata": {
     "editable": true
    },
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6562367",
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@@ -276,7 +276,7 @@
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@@ -288,7 +288,7 @@
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@@ -300,7 +300,7 @@
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@@ -311,7 +311,7 @@
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@@ -323,7 +323,7 @@
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@@ -342,7 +342,7 @@
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@@ -355,7 +355,7 @@
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@@ -365,7 +365,7 @@
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@@ -377,7 +377,7 @@
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@@ -387,7 +387,7 @@
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@@ -399,7 +399,7 @@
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@@ -409,7 +409,7 @@
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@@ -421,7 +421,7 @@
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@@ -432,7 +432,7 @@
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@@ -444,7 +444,7 @@
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@@ -454,7 +454,7 @@
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@@ -466,7 +466,7 @@
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@@ -476,7 +476,7 @@
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@@ -488,7 +488,7 @@
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@@ -508,7 +508,7 @@
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@@ -535,7 +535,7 @@
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@@ -547,7 +547,7 @@
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@@ -559,7 +559,7 @@
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@@ -575,7 +575,7 @@
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@@ -593,7 +593,7 @@
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@@ -605,7 +605,7 @@
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@@ -617,7 +617,7 @@
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@@ -628,7 +628,7 @@
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@@ -640,7 +640,7 @@
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@@ -650,7 +650,7 @@
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@@ -662,7 +662,7 @@
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@@ -676,7 +676,7 @@
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@@ -688,7 +688,7 @@
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@@ -700,7 +700,7 @@
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@@ -712,7 +712,7 @@
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@@ -722,7 +722,7 @@
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@@ -734,7 +734,7 @@
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@@ -746,7 +746,7 @@
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@@ -758,7 +758,7 @@
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@@ -772,7 +772,7 @@
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@@ -784,7 +784,7 @@
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@@ -796,7 +796,7 @@
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@@ -808,7 +808,7 @@
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@@ -818,7 +818,7 @@
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@@ -830,7 +830,7 @@
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@@ -840,7 +840,7 @@
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@@ -852,7 +852,7 @@
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@@ -862,7 +862,7 @@
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@@ -874,7 +874,7 @@
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@@ -886,7 +886,7 @@
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@@ -898,7 +898,7 @@
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@@ -913,7 +913,7 @@
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@@ -925,7 +925,7 @@
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@@ -935,7 +935,7 @@
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@@ -947,7 +947,7 @@
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@@ -957,7 +957,7 @@
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@@ -969,7 +969,7 @@
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@@ -979,7 +979,7 @@
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@@ -991,7 +991,7 @@
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@@ -1003,7 +1003,7 @@
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@@ -1015,7 +1015,7 @@
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@@ -1025,7 +1025,7 @@
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@@ -1037,7 +1037,7 @@
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@@ -1050,7 +1050,7 @@
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@@ -1062,7 +1062,7 @@
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@@ -1072,7 +1072,7 @@
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@@ -1084,7 +1084,7 @@
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@@ -1094,7 +1094,7 @@
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@@ -1106,7 +1106,7 @@
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@@ -1116,7 +1116,7 @@
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@@ -1128,7 +1128,7 @@
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@@ -1138,7 +1138,7 @@
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@@ -1150,7 +1150,7 @@
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@@ -1160,7 +1160,7 @@
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@@ -1172,7 +1172,7 @@
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@@ -1184,7 +1184,7 @@
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@@ -1196,7 +1196,7 @@
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@@ -1208,7 +1208,7 @@
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@@ -1220,7 +1220,7 @@
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@@ -1236,7 +1236,7 @@
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@@ -1248,7 +1248,7 @@
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@@ -1258,7 +1258,7 @@
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@@ -1270,7 +1270,7 @@
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@@ -1288,7 +1288,7 @@
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@@ -1300,7 +1300,7 @@
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@@ -1312,7 +1312,7 @@
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@@ -1322,7 +1322,7 @@
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@@ -1334,7 +1334,7 @@
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@@ -1344,7 +1344,7 @@
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@@ -1356,7 +1356,7 @@
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@@ -1366,7 +1366,7 @@
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@@ -1380,7 +1380,7 @@
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@@ -1392,7 +1392,7 @@
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@@ -1403,7 +1403,7 @@
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@@ -1416,7 +1416,7 @@
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@@ -1443,7 +1443,7 @@
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@@ -1454,7 +1454,7 @@
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@@ -1467,7 +1467,7 @@
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@@ -1481,7 +1481,7 @@
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@@ -1494,7 +1494,7 @@
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@@ -1505,7 +1505,7 @@
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@@ -1517,7 +1517,7 @@
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@@ -1528,7 +1528,7 @@
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@@ -1544,7 +1544,7 @@
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@@ -1555,7 +1555,7 @@
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@@ -1569,7 +1569,7 @@
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@@ -1590,7 +1590,7 @@
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@@ -1601,7 +1601,7 @@
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@@ -1654,7 +1654,7 @@
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@@ -1665,7 +1665,7 @@
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@@ -1690,7 +1690,7 @@
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@@ -1702,7 +1702,7 @@
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@@ -1731,7 +1731,7 @@
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@@ -1756,7 +1756,7 @@
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@@ -1776,7 +1776,7 @@
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@@ -1803,7 +1803,7 @@
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@@ -1816,7 +1816,7 @@
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@@ -1828,7 +1828,7 @@
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@@ -1839,7 +1839,7 @@
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@@ -1855,7 +1855,7 @@
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@@ -1889,7 +1889,7 @@
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@@ -1899,7 +1899,7 @@
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@@ -1914,7 +1914,7 @@
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@@ -1926,7 +1926,7 @@
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@@ -1936,7 +1936,7 @@
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@@ -1952,7 +1952,7 @@
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@@ -1969,7 +1969,7 @@
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@@ -1982,7 +1982,7 @@
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@@ -2029,7 +2029,7 @@
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@@ -2040,7 +2040,7 @@
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@@ -2143,7 +2143,7 @@
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@@ -2171,7 +2171,7 @@
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@@ -2206,7 +2206,7 @@
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@@ -2221,7 +2221,7 @@
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@@ -2246,7 +2246,7 @@
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@@ -2262,7 +2262,7 @@
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@@ -2274,7 +2274,7 @@
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@@ -2289,7 +2289,7 @@
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@@ -2301,7 +2301,7 @@
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@@ -2311,7 +2311,7 @@
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@@ -2323,7 +2323,7 @@
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@@ -2333,7 +2333,7 @@
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@@ -2345,7 +2345,7 @@
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@@ -2358,7 +2358,7 @@
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@@ -2370,7 +2370,7 @@
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@@ -2380,7 +2380,7 @@
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@@ -2392,7 +2392,7 @@
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@@ -2402,7 +2402,7 @@
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@@ -2414,7 +2414,7 @@
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@@ -2424,7 +2424,7 @@
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@@ -2436,7 +2436,7 @@
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@@ -2446,7 +2446,7 @@
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@@ -2458,7 +2458,7 @@
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@@ -2468,7 +2468,7 @@
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@@ -2480,7 +2480,7 @@
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@@ -2490,7 +2490,7 @@
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@@ -2502,7 +2502,7 @@
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@@ -2514,7 +2514,7 @@
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@@ -2526,7 +2526,7 @@
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@@ -2539,7 +2539,7 @@
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@@ -2551,7 +2551,7 @@
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@@ -2561,7 +2561,7 @@
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@@ -2575,7 +2575,7 @@
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@@ -2672,7 +2672,7 @@
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@@ -2693,7 +2693,7 @@
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@@ -2705,7 +2705,7 @@
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@@ -2715,7 +2715,7 @@
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@@ -2727,7 +2727,7 @@
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@@ -2737,7 +2737,7 @@
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@@ -2749,7 +2749,7 @@
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@@ -2765,7 +2765,7 @@
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@@ -2809,7 +2809,7 @@
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@@ -2821,7 +2821,7 @@
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@@ -2837,7 +2837,7 @@
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@@ -2848,7 +2848,7 @@
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@@ -2866,7 +2866,7 @@
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@@ -2877,7 +2877,7 @@
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@@ -2891,7 +2891,7 @@
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@@ -2902,7 +2902,7 @@
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@@ -2915,7 +2915,7 @@
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@@ -2926,7 +2926,7 @@
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@@ -2943,7 +2943,7 @@
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@@ -2954,7 +2954,7 @@
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@@ -2970,7 +2970,7 @@
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@@ -2981,7 +2981,7 @@
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@@ -3005,7 +3005,7 @@
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@@ -3016,7 +3016,7 @@
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@@ -3029,7 +3029,7 @@
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@@ -3040,7 +3040,7 @@
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@@ -3060,7 +3060,7 @@
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@@ -3071,7 +3071,7 @@
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@@ -3114,7 +3114,7 @@
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@@ -3129,7 +3129,7 @@
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@@ -3139,7 +3139,7 @@
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@@ -3153,7 +3153,7 @@
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@@ -3165,7 +3165,7 @@
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@@ -3177,7 +3177,7 @@
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@@ -3189,7 +3189,7 @@
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@@ -3199,7 +3199,7 @@
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@@ -3211,7 +3211,7 @@
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@@ -3221,7 +3221,7 @@
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@@ -3233,7 +3233,7 @@
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+   "id": "a088b960",
    "metadata": {
     "editable": true
    },
@@ -3244,7 +3244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ec2b7ca",
+   "id": "1a02ddbb",
    "metadata": {
     "editable": true
    },
@@ -3256,7 +3256,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6a5f733",
+   "id": "ed18d091",
    "metadata": {
     "editable": true
    },
@@ -3268,7 +3268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d56a3f53",
+   "id": "3d5b79af",
    "metadata": {
     "editable": true
    },
@@ -3278,7 +3278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3947d992",
+   "id": "dda4570b",
    "metadata": {
     "editable": true
    },
@@ -3290,7 +3290,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84faa9d9",
+   "id": "11d8d3e3",
    "metadata": {
     "editable": true
    },
@@ -3302,7 +3302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db86df7f",
+   "id": "46901f0f",
    "metadata": {
     "editable": true
    },
@@ -3312,7 +3312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d78105c6",
+   "id": "9189fcdb",
    "metadata": {
     "editable": true
    },
@@ -3324,7 +3324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec1aa593",
+   "id": "90000eee",
    "metadata": {
     "editable": true
    },
@@ -3334,7 +3334,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f34bef40",
+   "id": "e82d1f9c",
    "metadata": {
     "editable": true
    },
@@ -3346,7 +3346,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ceaf9a9e",
+   "id": "072720c0",
    "metadata": {
     "editable": true
    },
@@ -3356,7 +3356,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab1d059d",
+   "id": "dd15f44d",
    "metadata": {
     "editable": true
    },
@@ -3396,7 +3396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad796da6",
+   "id": "6fb5a31f",
    "metadata": {
     "editable": true
    },
@@ -3413,7 +3413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df8c83cc",
+   "id": "179c61b1",
    "metadata": {
     "editable": true
    },
@@ -3436,7 +3436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "410aac9c",
+   "id": "5c43a81e",
    "metadata": {
     "editable": true
    },
@@ -3453,7 +3453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8b42e28",
+   "id": "29134082",
    "metadata": {
     "editable": true
    },
@@ -3472,7 +3472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9696acb0",
+   "id": "8ef42f32",
    "metadata": {
     "editable": true
    },
@@ -3483,7 +3483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6402cd93",
+   "id": "076c58c7",
    "metadata": {
     "editable": true
    },
@@ -3495,7 +3495,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7aa90576",
+   "id": "04db202f",
    "metadata": {
     "editable": true
    },
@@ -3513,7 +3513,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d9194f3",
+   "id": "b60cb6b5",
    "metadata": {
     "editable": true
    },
@@ -3529,7 +3529,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa1477f5",
+   "id": "d29615f2",
    "metadata": {
     "editable": true
    },
@@ -3541,7 +3541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5aad52f5",
+   "id": "1f2c3f1e",
    "metadata": {
     "editable": true
    },
@@ -3551,7 +3551,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67a7edc8",
+   "id": "759ab85a",
    "metadata": {
     "editable": true
    },
@@ -3566,7 +3566,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50428ec4",
+   "id": "4538c162",
    "metadata": {
     "editable": true
    },
@@ -3578,7 +3578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f8769e8",
+   "id": "a5617e01",
    "metadata": {
     "editable": true
    },
@@ -3588,7 +3588,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51a88b15",
+   "id": "fe0b46d4",
    "metadata": {
     "editable": true
    },
@@ -3600,7 +3600,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b7cc475",
+   "id": "7695cd6a",
    "metadata": {
     "editable": true
    },
@@ -3610,7 +3610,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a655c3e6",
+   "id": "26601b01",
    "metadata": {
     "editable": true
    },
@@ -3622,7 +3622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72d1c378",
+   "id": "6a2d467f",
    "metadata": {
     "editable": true
    },
@@ -3634,7 +3634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15a44ecc",
+   "id": "a830ae89",
    "metadata": {
     "editable": true
    },
@@ -3649,7 +3649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0bb7dca3",
+   "id": "488dd05c",
    "metadata": {
     "editable": true
    },
@@ -3660,7 +3660,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c45e48e8",
+   "id": "0383a3b6",
    "metadata": {
     "editable": true
    },
@@ -3680,7 +3680,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "856e267f",
+   "id": "10e82320",
    "metadata": {
     "editable": true
    },
@@ -3692,7 +3692,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c304b541",
+   "id": "59a6e34a",
    "metadata": {
     "editable": true
    },
@@ -3702,7 +3702,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13a7fb5e",
+   "id": "ab9dc4a5",
    "metadata": {
     "editable": true
    },
@@ -3714,7 +3714,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91d79dd2",
+   "id": "51c1c028",
    "metadata": {
     "editable": true
    },
@@ -3743,7 +3743,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afe923ac",
+   "id": "ec252687",
    "metadata": {
     "editable": true
    },
@@ -3770,7 +3770,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3b0107b",
+   "id": "156bfe10",
    "metadata": {
     "editable": true
    },
@@ -3781,7 +3781,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "df6b7c05",
+   "id": "3c182484",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3821,7 +3821,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e91f9a0",
+   "id": "98a0e5ec",
    "metadata": {
     "editable": true
    },
@@ -3838,7 +3838,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "415fc97a",
+   "id": "b69361d0",
    "metadata": {
     "editable": true
    },
@@ -3861,7 +3861,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c537ea64",
+   "id": "8d7c0cc8",
    "metadata": {
     "editable": true
    },
@@ -3875,7 +3875,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba856156",
+   "id": "0a45fa90",
    "metadata": {
     "editable": true
    },
@@ -3894,7 +3894,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "376b56d3",
+   "id": "add34bfc",
    "metadata": {
     "editable": true
    },
@@ -3904,7 +3904,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94383f7f",
+   "id": "f00ff6da",
    "metadata": {
     "editable": true
    },
@@ -3916,7 +3916,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "836c6ad5",
+   "id": "348cd8df",
    "metadata": {
     "editable": true
    },
@@ -3930,7 +3930,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e56306d7",
+   "id": "f7697c17",
    "metadata": {
     "editable": true
    },
@@ -3942,7 +3942,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd33b717",
+   "id": "64417096",
    "metadata": {
     "editable": true
    },
@@ -3952,7 +3952,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f81bbc5e",
+   "id": "ded28c47",
    "metadata": {
     "editable": true
    },
@@ -3964,7 +3964,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdc865ea",
+   "id": "f70d6ea7",
    "metadata": {
     "editable": true
    },
@@ -3981,7 +3981,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6560516f",
+   "id": "68ecbbfa",
    "metadata": {
     "editable": true
    },
@@ -3991,7 +3991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0913014f",
+   "id": "73db495d",
    "metadata": {
     "editable": true
    },
@@ -4007,7 +4007,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7960d3eb",
+   "id": "44412861",
    "metadata": {
     "editable": true
    },
@@ -4017,7 +4017,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2c6cab1",
+   "id": "985de8ad",
    "metadata": {
     "editable": true
    },
@@ -4033,7 +4033,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d026c4fa",
+   "id": "bae93d68",
    "metadata": {
     "editable": true
    },
@@ -4043,7 +4043,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc5999f4",
+   "id": "e29bd644",
    "metadata": {
     "editable": true
    },
@@ -4059,7 +4059,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1753c718",
+   "id": "3abf4a2d",
    "metadata": {
     "editable": true
    },
@@ -4069,7 +4069,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb1ffbac",
+   "id": "fe1e2ce7",
    "metadata": {
     "editable": true
    },
@@ -4086,7 +4086,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d5ba9ce",
+   "id": "d23d044b",
    "metadata": {
     "editable": true
    },
@@ -4098,7 +4098,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc09fc11",
+   "id": "36e38f22",
    "metadata": {
     "editable": true
    },
@@ -4110,7 +4110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36822227",
+   "id": "ad22843b",
    "metadata": {
     "editable": true
    },
@@ -4122,7 +4122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de6cc474",
+   "id": "fabcf0f8",
    "metadata": {
     "editable": true
    },
@@ -4132,7 +4132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "453ff6f8",
+   "id": "2c2f44c5",
    "metadata": {
     "editable": true
    },
@@ -4144,7 +4144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fb1165b",
+   "id": "105762d0",
    "metadata": {
     "editable": true
    },
@@ -4156,7 +4156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb0b59b1",
+   "id": "35a42e41",
    "metadata": {
     "editable": true
    },
@@ -4168,7 +4168,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c998fc7",
+   "id": "8e21698f",
    "metadata": {
     "editable": true
    },
@@ -4178,7 +4178,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b91e913",
+   "id": "a240efc0",
    "metadata": {
     "editable": true
    },
@@ -4190,7 +4190,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d8c0246",
+   "id": "3919a2cb",
    "metadata": {
     "editable": true
    },
@@ -4200,7 +4200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f73a9f8b",
+   "id": "ddf99a3e",
    "metadata": {
     "editable": true
    },
@@ -4212,7 +4212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ce5f423",
+   "id": "5a790d25",
    "metadata": {
     "editable": true
    },
@@ -4226,7 +4226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ec391cc",
+   "id": "113a5d16",
    "metadata": {
     "editable": true
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@@ -4238,7 +4238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11a3f35c",
+   "id": "ec65ec89",
    "metadata": {
     "editable": true
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@@ -4250,7 +4250,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6b8757c",
+   "id": "ed891134",
    "metadata": {
     "editable": true
    },
@@ -4260,7 +4260,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e2f4fc0",
+   "id": "5266f146",
    "metadata": {
     "editable": true
    },
@@ -4272,7 +4272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db3e6c79",
+   "id": "666ab5b3",
    "metadata": {
     "editable": true
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@@ -4283,7 +4283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d95e8aa",
+   "id": "ab9a3eec",
    "metadata": {
     "editable": true
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@@ -4295,7 +4295,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7dfc330",
+   "id": "252e7533",
    "metadata": {
     "editable": true
    },
@@ -4305,7 +4305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "888796de",
+   "id": "201b9300",
    "metadata": {
     "editable": true
    },
@@ -4317,7 +4317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99561aeb",
+   "id": "4f6287cf",
    "metadata": {
     "editable": true
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@@ -4327,7 +4327,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a4556b6",
+   "id": "1ace6d06",
    "metadata": {
     "editable": true
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@@ -4339,7 +4339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9844b480",
+   "id": "274f98fb",
    "metadata": {
     "editable": true
    },
@@ -4350,7 +4350,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3731b9ff",
+   "id": "70dc1b76",
    "metadata": {
     "editable": true
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@@ -4362,7 +4362,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2debf83f",
+   "id": "096c55e9",
    "metadata": {
     "editable": true
    },
@@ -4380,7 +4380,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fd0c7b2",
+   "id": "0ac9bdae",
    "metadata": {
     "editable": true
    },
@@ -4396,19 +4396,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db45a990",
+   "id": "a456e251",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\frac{\\partial^2 C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}^T\\partial \\boldsymbol{\\beta}} =\\frac{2}{n}\\boldsymbol{X}^T\\boldsymbol{X}.\n",
+    "\\frac{\\partial^2 C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T} =\\frac{2}{n}\\boldsymbol{X}^T\\boldsymbol{X}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "476e4063",
+   "id": "7c817401",
    "metadata": {
     "editable": true
    },
@@ -4420,7 +4420,7 @@
   },
   {
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-   "id": "f7caedf0",
+   "id": "20d7bb5d",
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@@ -4432,7 +4432,7 @@
   },
   {
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-   "id": "a9469af7",
+   "id": "52a9f64e",
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@@ -4445,7 +4445,7 @@
   },
   {
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-   "id": "dae869bb",
+   "id": "77820e66",
    "metadata": {
     "editable": true
    },
@@ -4461,7 +4461,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d718bf56",
+   "id": "dc6ab9cd",
    "metadata": {
     "editable": true
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@@ -4475,7 +4475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5ad97e4",
+   "id": "8a8ee502",
    "metadata": {
     "editable": true
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@@ -4485,7 +4485,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b76f1367",
+   "id": "f377b529",
    "metadata": {
     "editable": true
    },
@@ -4497,7 +4497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc7b9319",
+   "id": "e572a54d",
    "metadata": {
     "editable": true
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@@ -4507,7 +4507,7 @@
   },
   {
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-   "id": "8159fef4",
+   "id": "1b9c72f5",
    "metadata": {
     "editable": true
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@@ -4519,7 +4519,7 @@
   },
   {
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-   "id": "d34ac113",
+   "id": "15c56784",
    "metadata": {
     "editable": true
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@@ -4529,7 +4529,7 @@
   },
   {
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-   "id": "1e0b77c9",
+   "id": "a03c1a32",
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     "editable": true
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@@ -4543,7 +4543,7 @@
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   {
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-   "id": "01cb77e3",
+   "id": "af372da2",
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     "editable": true
    },
@@ -4554,13 +4554,13 @@
     "method corrects the bias in the estimation of the population variance\n",
     "and covariance. It also partially corrects the bias in the estimation\n",
     "of the population standard deviation. If you use a library like\n",
-    "**Scikit-Learn** or **nunmpy's** function calculate the covariance, this\n",
+    "**Scikit-Learn** or **nunmpy's** function to calculate the covariance, this\n",
     "quantity will be computed with a factor $1/(n-1)$."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e69ed735",
+   "id": "9200f285",
    "metadata": {
     "editable": true
    },
@@ -4576,7 +4576,7 @@
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   {
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-   "id": "f0da7f4b",
+   "id": "d5becae6",
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@@ -4588,7 +4588,7 @@
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@@ -4601,7 +4601,7 @@
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@@ -4615,7 +4615,7 @@
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@@ -4625,7 +4625,7 @@
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@@ -4638,7 +4638,7 @@
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@@ -4657,7 +4657,7 @@
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@@ -4669,7 +4669,7 @@
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@@ -4681,7 +4681,7 @@
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@@ -4691,7 +4691,7 @@
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@@ -4703,7 +4703,7 @@
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@@ -4716,7 +4716,7 @@
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@@ -4735,7 +4735,7 @@
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@@ -4745,7 +4745,7 @@
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@@ -4764,7 +4764,7 @@
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@@ -4782,7 +4782,7 @@
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@@ -4796,7 +4796,7 @@
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@@ -4811,7 +4811,7 @@
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@@ -4832,7 +4832,7 @@
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@@ -4849,7 +4849,7 @@
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@@ -4881,7 +4881,7 @@
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@@ -4895,7 +4895,7 @@
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@@ -4908,7 +4908,7 @@
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@@ -4933,7 +4933,7 @@
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@@ -4943,7 +4943,7 @@
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@@ -4954,7 +4954,7 @@
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@@ -5008,7 +5008,7 @@
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@@ -5025,7 +5025,7 @@
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@@ -5037,7 +5037,7 @@
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@@ -5049,7 +5049,7 @@
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@@ -5059,7 +5059,7 @@
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@@ -5076,7 +5076,7 @@
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@@ -5086,7 +5086,7 @@
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@@ -5101,7 +5101,7 @@
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@@ -5111,7 +5111,7 @@
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@@ -5125,7 +5125,7 @@
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@@ -5137,7 +5137,7 @@
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@@ -5149,7 +5149,7 @@
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@@ -5161,7 +5161,7 @@
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@@ -5171,7 +5171,7 @@
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@@ -5183,7 +5183,7 @@
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@@ -5193,7 +5193,7 @@
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@@ -5210,7 +5210,7 @@
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@@ -5220,7 +5220,7 @@
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@@ -5232,7 +5232,7 @@
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@@ -5242,7 +5242,7 @@
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@@ -5254,7 +5254,7 @@
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@@ -5268,7 +5268,7 @@
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@@ -5280,7 +5280,7 @@
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@@ -5302,7 +5302,7 @@
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@@ -5314,7 +5314,7 @@
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@@ -5329,7 +5329,7 @@
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@@ -5341,7 +5341,7 @@
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@@ -5353,7 +5353,7 @@
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@@ -5363,7 +5363,7 @@
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@@ -5375,7 +5375,7 @@
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@@ -5385,7 +5385,7 @@
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@@ -5397,7 +5397,7 @@
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@@ -5407,7 +5407,7 @@
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@@ -5419,7 +5419,7 @@
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@@ -5436,7 +5436,7 @@
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@@ -5449,7 +5449,7 @@
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@@ -5461,7 +5461,7 @@
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@@ -5471,7 +5471,7 @@
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@@ -5484,7 +5484,7 @@
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@@ -5494,7 +5494,7 @@
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@@ -5506,7 +5506,7 @@
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@@ -5519,7 +5519,7 @@
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@@ -5532,7 +5532,7 @@
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@@ -5544,7 +5544,7 @@
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@@ -5556,7 +5556,7 @@
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@@ -5566,7 +5566,7 @@
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@@ -5579,7 +5579,7 @@
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@@ -5591,7 +5591,7 @@
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@@ -5603,7 +5603,7 @@
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@@ -5615,7 +5615,7 @@
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@@ -5627,7 +5627,7 @@
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@@ -5641,7 +5641,7 @@
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@@ -5653,7 +5653,7 @@
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@@ -5663,7 +5663,7 @@
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@@ -5675,7 +5675,7 @@
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@@ -5687,7 +5687,7 @@
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@@ -5699,7 +5699,7 @@
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@@ -5711,7 +5711,7 @@
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@@ -5723,7 +5723,7 @@
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@@ -5742,7 +5742,7 @@
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@@ -5754,7 +5754,7 @@
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@@ -5764,7 +5764,7 @@
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@@ -5776,7 +5776,7 @@
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@@ -5786,7 +5786,7 @@
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@@ -5798,7 +5798,7 @@
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@@ -5810,7 +5810,7 @@
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@@ -5826,7 +5826,7 @@
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@@ -5838,7 +5838,7 @@
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@@ -5850,7 +5850,7 @@
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@@ -5860,7 +5860,7 @@
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@@ -5872,7 +5872,7 @@
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@@ -5882,7 +5882,7 @@
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@@ -5894,7 +5894,7 @@
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@@ -5911,7 +5911,7 @@
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@@ -5923,7 +5923,7 @@
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@@ -5935,7 +5935,7 @@
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@@ -5945,7 +5945,7 @@
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-   "id": "552d08cd",
+   "id": "d192512a",
    "metadata": {
     "editable": true
    },
@@ -5957,7 +5957,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e2d1b42",
+   "id": "a60675a1",
    "metadata": {
     "editable": true
    },
@@ -5967,7 +5967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbc4cda5",
+   "id": "942fc37f",
    "metadata": {
     "editable": true
    },
@@ -5979,7 +5979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e36f9245",
+   "id": "4f5e067f",
    "metadata": {
     "editable": true
    },
@@ -5989,7 +5989,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06b74c95",
+   "id": "d330f743",
    "metadata": {
     "editable": true
    },
@@ -6001,7 +6001,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1cbd57d2",
+   "id": "55f9b635",
    "metadata": {
     "editable": true
    },
@@ -6011,7 +6011,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17a0dae2",
+   "id": "5a6d1b0b",
    "metadata": {
     "editable": true
    },
@@ -6023,7 +6023,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abc993af",
+   "id": "12fd13cb",
    "metadata": {
     "editable": true
    },