From 6e0d02923264b95d2d5652aa3bfdc4e43f95ed3b Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Mon, 18 Aug 2025 12:47:35 +0200 Subject: [PATCH] typos --- .../_build/.doctrees/environment.pickle | Bin 198882 -> 199001 bytes .../_build/.doctrees/week34.doctree | Bin 387235 -> 376324 bytes .../_build/html/_sources/week34.ipynb | 939 +++++++----------- doc/LectureNotes/_build/html/genindex.html | 2 +- doc/LectureNotes/_build/html/intro.html | 2 +- doc/LectureNotes/_build/html/search.html | 2 +- doc/LectureNotes/_build/html/searchindex.js | 2 +- doc/LectureNotes/_build/html/week34.html | 858 ++++++++-------- .../_build/jupyter_execute/week34.ipynb | 939 +++++++----------- doc/LectureNotes/week34.ipynb | 939 +++++++----------- 10 files changed, 1401 insertions(+), 2282 deletions(-) diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle index 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+++ b/doc/LectureNotes/_build/html/_sources/week34.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3b47d5e6", + "id": "0b9e53e2", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "5f010738", + "id": "d69d6c55", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "9821466c", + "id": "ed7a2caa", "metadata": { "editable": true }, @@ -52,7 +52,7 @@ }, { "cell_type": "markdown", - "id": "d635526f", + "id": "1a7fa793", "metadata": { "editable": true }, @@ -68,7 +68,7 @@ }, { "cell_type": "markdown", - "id": "46d1ebd2", + "id": "42eed58e", "metadata": { "editable": true }, @@ -92,7 +92,7 @@ }, { "cell_type": "markdown", - "id": "722a1812", + "id": "19b78895", "metadata": { "editable": true }, @@ -105,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "8724a41b", + "id": "920c8b78", "metadata": { "editable": true }, @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "a74065a9", + "id": "66791ac8", "metadata": { "editable": true }, @@ -157,7 +157,7 @@ }, { "cell_type": "markdown", - "id": "cb3519d1", + "id": "d006c6f2", "metadata": { "editable": true }, @@ -175,7 +175,7 @@ }, { "cell_type": "markdown", - "id": "f46e4ab5", + "id": "d2b59d04", "metadata": { "editable": true }, @@ -203,7 +203,7 @@ }, { "cell_type": "markdown", - "id": "18134f7b", + "id": "2b1dfff8", "metadata": { "editable": true }, @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "6e4b2cb4", + "id": "39ce46e9", "metadata": { "editable": true }, @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "2dc04ba4", + "id": "cc1139fe", "metadata": { "editable": true }, @@ -261,7 +261,7 @@ }, { "cell_type": "markdown", - "id": "b9d7446a", + "id": "f56c0bb5", "metadata": { "editable": true }, @@ -273,7 +273,7 @@ }, { "cell_type": "markdown", - "id": "0ac913df", + "id": "4b601e8d", "metadata": { "editable": true }, @@ -293,7 +293,7 @@ }, { "cell_type": "markdown", - "id": "fe4f142d", + "id": "2518a99e", "metadata": { "editable": true }, @@ -311,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "82a26310", + "id": "a0e1e9ae", "metadata": { "editable": true }, @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "5d97a91f", + "id": "8af343cb", "metadata": { "editable": true }, @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "0af85fa1", + "id": "c0cfedb4", "metadata": { "editable": true }, @@ -382,7 +382,7 @@ }, { "cell_type": "markdown", - "id": "ca1c0d4b", + "id": "4561f563", "metadata": { "editable": true }, @@ -398,7 +398,7 @@ }, { "cell_type": "markdown", - "id": "5e3a4194", + "id": "b94446df", "metadata": { "editable": true }, @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "44fe97f3", + "id": "2da0b611", "metadata": { "editable": true }, @@ -438,7 +438,7 @@ }, { "cell_type": "markdown", - "id": "3fd61872", + "id": "cb6368d0", "metadata": { "editable": true }, @@ -468,7 +468,7 @@ }, { "cell_type": "markdown", - "id": "50148149", + "id": "cd5643ee", "metadata": { "editable": true }, @@ -497,7 +497,7 @@ }, { "cell_type": "markdown", - "id": "12bc5144", + "id": "dad2568b", "metadata": { "editable": true }, @@ -515,7 +515,7 @@ }, { "cell_type": "markdown", - "id": "25428d4e", + "id": "ccf34c1f", "metadata": { "editable": true }, @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "2efac50e", + "id": "d9a80c4f", "metadata": { "editable": true }, @@ -549,7 +549,7 @@ }, { "cell_type": "markdown", - "id": "dfd0c6a0", + "id": "bf1454b7", "metadata": { "editable": true }, @@ -572,7 +572,7 @@ }, { "cell_type": "markdown", - "id": "f92f8d35", + "id": "4a01a6f0", "metadata": { "editable": true }, @@ -588,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "174d6e96", + "id": "2bf27180", "metadata": { "editable": true }, @@ -604,12 +604,12 @@ }, { "cell_type": "markdown", - "id": "0fcac42b", + "id": "15ddfa02", "metadata": { "editable": true }, "source": [ - "## Example of discriminative modeling, [taken from Generative Deeep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n", + "## Example of discriminative modeling, [taken from Generative Deep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n", "\n", "\n", "\n", @@ -620,7 +620,7 @@ }, { "cell_type": "markdown", - "id": "a8942d9a", + "id": "371a54c3", "metadata": { "editable": true }, @@ -640,7 +640,7 @@ }, { "cell_type": "markdown", - "id": "a28ea0e4", + "id": "f9ec1f0a", "metadata": { "editable": true }, @@ -675,7 +675,7 @@ }, { "cell_type": "markdown", - "id": "d3260f00", + "id": "19f2aef4", "metadata": { "editable": true }, @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "4ac16500", + "id": "e08bd4e6", "metadata": { "editable": true }, @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "825ca09e", + "id": "729c3f46", "metadata": { "editable": true }, @@ -775,7 +775,7 @@ }, { "cell_type": "markdown", - "id": "40bd0bf2", + "id": "6825a222", "metadata": { "editable": true }, @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "a356fdf0", + "id": "bad98b2e", "metadata": { "editable": true }, @@ -845,7 +845,7 @@ }, { "cell_type": "markdown", - "id": "a9730b8a", + "id": "1daa92e1", "metadata": { "editable": true }, @@ -869,7 +869,7 @@ }, { "cell_type": "markdown", - "id": "8f967a6a", + "id": "1c840067", "metadata": { "editable": true }, @@ -896,7 +896,7 @@ }, { "cell_type": "markdown", - "id": "76e167a3", + "id": "f33d6379", "metadata": { "editable": true }, @@ -906,7 +906,7 @@ }, { "cell_type": "markdown", - "id": "94a3c420", + "id": "e3a03646", "metadata": { "editable": true }, @@ -918,7 +918,7 @@ }, { "cell_type": "markdown", - "id": "c6ff6b6b", + "id": "fb6d2495", "metadata": { "editable": true }, @@ -939,7 +939,7 @@ }, { "cell_type": "markdown", - "id": "55dbb002", + "id": "3101a680", "metadata": { "editable": true }, @@ -951,13 +951,10 @@ { "cell_type": "code", "execution_count": 1, - "id": "655878bd", + "id": "66625f81", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -966,7 +963,7 @@ }, { "cell_type": "markdown", - "id": "914fa12c", + "id": "d8edb94a", "metadata": { "editable": true }, @@ -977,13 +974,10 @@ { "cell_type": "code", "execution_count": 2, - "id": "a0cfca17", + "id": "7d44772f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -994,7 +988,7 @@ }, { "cell_type": "markdown", - "id": "342c0109", + "id": "7c3a5d5e", "metadata": { "editable": true }, @@ -1006,13 +1000,10 @@ { "cell_type": "code", "execution_count": 3, - "id": "d09ad51a", + "id": "bd150398", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1023,7 +1014,7 @@ }, { "cell_type": "markdown", - "id": "be9009bc", + "id": "62bb511e", "metadata": { "editable": true }, @@ -1035,13 +1026,10 @@ { "cell_type": "code", "execution_count": 4, - "id": "19dfe7ab", + "id": "de9d1b88", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1052,7 +1040,7 @@ }, { "cell_type": "markdown", - "id": "5c78e90e", + "id": "575f4f67", "metadata": { "editable": true }, @@ -1069,13 +1057,10 @@ { "cell_type": "code", "execution_count": 5, - "id": "48603a4c", + "id": "eadd4613", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1089,7 +1074,7 @@ }, { "cell_type": "markdown", - "id": "db5cfa34", + "id": "43c2b238", "metadata": { "editable": true }, @@ -1101,13 +1086,10 @@ { "cell_type": "code", "execution_count": 6, - "id": "1c252e6c", + "id": "f1153763", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1118,7 +1100,7 @@ }, { "cell_type": "markdown", - "id": "375afb81", + "id": "b897cb85", "metadata": { "editable": true }, @@ -1129,13 +1111,10 @@ { "cell_type": "code", "execution_count": 7, - "id": "4c87de99", + "id": "3417f7b5", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1146,7 +1125,7 @@ }, { "cell_type": "markdown", - "id": "afff17c8", + "id": "421de263", "metadata": { "editable": true }, @@ -1157,13 +1136,10 @@ { "cell_type": "code", "execution_count": 8, - "id": "d5827065", + "id": "e9055b4f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1174,7 +1150,7 @@ }, { "cell_type": "markdown", - "id": "83e054e8", + "id": "52301fe3", "metadata": { "editable": true }, @@ -1189,13 +1165,10 @@ { "cell_type": "code", "execution_count": 9, - "id": "0cd4400a", + "id": "7f400b7f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1206,7 +1179,7 @@ }, { "cell_type": "markdown", - "id": "c2868228", + "id": "35677f44", "metadata": { "editable": true }, @@ -1217,13 +1190,10 @@ { "cell_type": "code", "execution_count": 10, - "id": "6e7ef557", + "id": "16b1c27a", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1235,7 +1205,7 @@ }, { "cell_type": "markdown", - "id": "47391e12", + "id": "7566ab17", "metadata": { "editable": true }, @@ -1246,13 +1216,10 @@ { "cell_type": "code", "execution_count": 11, - "id": "a9457054", + "id": "b10affa1", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1264,7 +1231,7 @@ }, { "cell_type": "markdown", - "id": "a04c51eb", + "id": "c50f969e", "metadata": { "editable": true }, @@ -1275,13 +1242,10 @@ { "cell_type": "code", "execution_count": 12, - "id": "43e1f145", + "id": "35f1236f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1294,7 +1258,7 @@ }, { "cell_type": "markdown", - "id": "e11ea608", + "id": "02cec13a", "metadata": { "editable": true }, @@ -1305,13 +1269,10 @@ { "cell_type": "code", "execution_count": 13, - "id": "ed021bcf", + "id": "821bc62d", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1324,7 +1285,7 @@ }, { "cell_type": "markdown", - "id": "7cf97a5d", + "id": "5df31e24", "metadata": { "editable": true }, @@ -1335,13 +1296,10 @@ { "cell_type": "code", "execution_count": 14, - "id": "0dd77598", + "id": "d821d3ff", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1354,7 +1312,7 @@ }, { "cell_type": "markdown", - "id": "a27628a3", + "id": "a99c04f6", "metadata": { "editable": true }, @@ -1366,7 +1324,7 @@ }, { "cell_type": "markdown", - "id": "80585c1d", + "id": "fdb302f5", "metadata": { "editable": true }, @@ -1381,7 +1339,7 @@ }, { "cell_type": "markdown", - "id": "74725f56", + "id": "4c91d693", "metadata": { "editable": true }, @@ -1391,7 +1349,7 @@ }, { "cell_type": "markdown", - "id": "fd223a73", + "id": "316ccd01", "metadata": { "editable": true }, @@ -1403,7 +1361,7 @@ }, { "cell_type": "markdown", - "id": "40dfa247", + "id": "73cf195b", "metadata": { "editable": true }, @@ -1414,7 +1372,7 @@ }, { "cell_type": "markdown", - "id": "56e47db3", + "id": "c9d6f1ba", "metadata": { "editable": true }, @@ -1429,7 +1387,7 @@ }, { "cell_type": "markdown", - "id": "26251aec", + "id": "a5bbc329", "metadata": { "editable": true }, @@ -1444,13 +1402,10 @@ { "cell_type": "code", "execution_count": 15, - "id": "dab44ca5", + "id": "74dd353c", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1474,13 +1429,10 @@ { "cell_type": "code", "execution_count": 16, - "id": "58c875cb", + "id": "f07645f8", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1501,7 +1453,7 @@ }, { "cell_type": "markdown", - "id": "8d735077", + "id": "245ee493", "metadata": { "editable": true }, @@ -1527,13 +1479,10 @@ { "cell_type": "code", "execution_count": 17, - "id": "d60ac131", + "id": "e428bcdc", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1550,7 +1499,7 @@ }, { "cell_type": "markdown", - "id": "06e4b620", + "id": "861269ad", "metadata": { "editable": true }, @@ -1564,13 +1513,10 @@ { "cell_type": "code", "execution_count": 18, - "id": "2174392e", + "id": "c34c9894", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1580,7 +1526,7 @@ }, { "cell_type": "markdown", - "id": "72c17dd0", + "id": "0ec5724d", "metadata": { "editable": true }, @@ -1591,13 +1537,10 @@ { "cell_type": "code", "execution_count": 19, - "id": "7612f9de", + "id": "83302e08", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1606,7 +1549,7 @@ }, { "cell_type": "markdown", - "id": "242d4c4b", + "id": "886277ee", "metadata": { "editable": true }, @@ -1617,13 +1560,10 @@ { "cell_type": "code", "execution_count": 20, - "id": "0171d1ad", + "id": "c1010af7", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1638,7 +1578,7 @@ }, { "cell_type": "markdown", - "id": "e5232bfb", + "id": "f3c82384", "metadata": { "editable": true }, @@ -1650,13 +1590,10 @@ { "cell_type": "code", "execution_count": 21, - "id": "cb2ab622", + "id": "70542372", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1677,7 +1614,7 @@ }, { "cell_type": "markdown", - "id": "1998bc9c", + "id": "a83eb591", "metadata": { "editable": true }, @@ -1688,13 +1625,10 @@ { "cell_type": "code", "execution_count": 22, - "id": "e0ec9915", + "id": "2f42295f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1720,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "ebe44ad1", + "id": "fb1b0336", "metadata": { "editable": true }, @@ -1731,13 +1665,10 @@ { "cell_type": "code", "execution_count": 23, - "id": "6f6d21e6", + "id": "0438c751", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1749,7 +1680,7 @@ }, { "cell_type": "markdown", - "id": "2f97c531", + "id": "f6d3d4c8", "metadata": { "editable": true }, @@ -1766,7 +1697,7 @@ }, { "cell_type": "markdown", - "id": "36a57ec4", + "id": "d552a0d0", "metadata": { "editable": true }, @@ -1778,7 +1709,7 @@ }, { "cell_type": "markdown", - "id": "ca69e9e9", + "id": "b96bd9e5", "metadata": { "editable": true }, @@ -1809,7 +1740,7 @@ }, { "cell_type": "markdown", - "id": "b4bc6d8a", + "id": "41318c5c", "metadata": { "editable": true }, @@ -1821,7 +1752,7 @@ }, { "cell_type": "markdown", - "id": "dd9f61d0", + "id": "789c6fe4", "metadata": { "editable": true }, @@ -1849,13 +1780,10 @@ { "cell_type": "code", "execution_count": 24, - "id": "81d8ecc3", + "id": "8c7472f2", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1882,7 +1810,7 @@ }, { "cell_type": "markdown", - "id": "b9f8b0e5", + "id": "25cef2d3", "metadata": { "editable": true }, @@ -1899,7 +1827,7 @@ }, { "cell_type": "markdown", - "id": "37a6973d", + "id": "58a04438", "metadata": { "editable": true }, @@ -1911,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "3b1616dd", + "id": "55191297", "metadata": { "editable": true }, @@ -1932,7 +1860,7 @@ }, { "cell_type": "markdown", - "id": "02f59dce", + "id": "2951bf64", "metadata": { "editable": true }, @@ -1945,7 +1873,7 @@ }, { "cell_type": "markdown", - "id": "37a3928f", + "id": "1109ceeb", "metadata": { "editable": true }, @@ -1976,7 +1904,7 @@ }, { "cell_type": "markdown", - "id": "8ced1c75", + "id": "7eeca264", "metadata": { "editable": true }, @@ -1988,7 +1916,7 @@ }, { "cell_type": "markdown", - "id": "2b2c7527", + "id": "fd8b7402", "metadata": { "editable": true }, @@ -2006,13 +1934,10 @@ { "cell_type": "code", "execution_count": 25, - "id": "dcc4e935", + "id": "0de5719f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2036,7 +1961,7 @@ }, { "cell_type": "markdown", - "id": "ef0a62d7", + "id": "ce194339", "metadata": { "editable": true }, @@ -2058,13 +1983,10 @@ { "cell_type": "code", "execution_count": 26, - "id": "e2a9bb21", + "id": "cde68f17", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2099,7 +2021,7 @@ }, { "cell_type": "markdown", - "id": "a6eca083", + "id": "f48a129c", "metadata": { "editable": true }, @@ -2110,7 +2032,7 @@ }, { "cell_type": "markdown", - "id": "c940fd70", + "id": "c337129d", "metadata": { "editable": true }, @@ -2123,7 +2045,7 @@ }, { "cell_type": "markdown", - "id": "7b1317ef", + "id": "d6462e3c", "metadata": { "editable": true }, @@ -2144,7 +2066,7 @@ }, { "cell_type": "markdown", - "id": "3895d32d", + "id": "c2a5a5fd", "metadata": { "editable": true }, @@ -2156,7 +2078,7 @@ }, { "cell_type": "markdown", - "id": "b88937a4", + "id": "d19d16f8", "metadata": { "editable": true }, @@ -2166,7 +2088,7 @@ }, { "cell_type": "markdown", - "id": "7d62abca", + "id": "7739ec09", "metadata": { "editable": true }, @@ -2178,7 +2100,7 @@ }, { "cell_type": "markdown", - "id": "903ee760", + "id": "7f433018", "metadata": { "editable": true }, @@ -2190,7 +2112,7 @@ }, { "cell_type": "markdown", - "id": "ef89ae2c", + "id": "20361838", "metadata": { "editable": true }, @@ -2202,7 +2124,7 @@ }, { "cell_type": "markdown", - "id": "924ac782", + "id": "02d95914", "metadata": { "editable": true }, @@ -2213,7 +2135,7 @@ }, { "cell_type": "markdown", - "id": "50053e83", + "id": "524bb980", "metadata": { "editable": true }, @@ -2225,7 +2147,7 @@ }, { "cell_type": "markdown", - "id": "dc7df77b", + "id": "6cb0b956", "metadata": { "editable": true }, @@ -2247,7 +2169,7 @@ }, { "cell_type": "markdown", - "id": "7fe669ae", + "id": "142be577", "metadata": { "editable": true }, @@ -2259,7 +2181,7 @@ }, { "cell_type": "markdown", - "id": "28ceb188", + "id": "4e765569", "metadata": { "editable": true }, @@ -2272,7 +2194,7 @@ }, { "cell_type": "markdown", - "id": "d89d075b", + "id": "f79ebfdf", "metadata": { "editable": true }, @@ -2289,7 +2211,7 @@ }, { "cell_type": "markdown", - "id": "b3816234", + "id": "eb09c88a", "metadata": { "editable": true }, @@ -2301,7 +2223,7 @@ }, { "cell_type": "markdown", - "id": "ddf946bd", + "id": "bf69319c", "metadata": { "editable": true }, @@ -2311,7 +2233,7 @@ }, { "cell_type": "markdown", - "id": "ec48e2fd", + "id": "eec8df19", "metadata": { "editable": true }, @@ -2323,7 +2245,7 @@ }, { "cell_type": "markdown", - "id": "462f1772", + "id": "313f9634", "metadata": { "editable": true }, @@ -2333,7 +2255,7 @@ }, { "cell_type": "markdown", - "id": "dabcb01d", + "id": "83b87f63", "metadata": { "editable": true }, @@ -2345,7 +2267,7 @@ }, { "cell_type": "markdown", - "id": "be2a6bd1", + "id": "be859efd", "metadata": { "editable": true }, @@ -2355,7 +2277,7 @@ }, { "cell_type": "markdown", - "id": "eb37d8f8", + "id": "a976e227", "metadata": { "editable": true }, @@ -2367,7 +2289,7 @@ }, { "cell_type": "markdown", - "id": "de325e18", + "id": "9f1dc803", "metadata": { "editable": true }, @@ -2383,7 +2305,7 @@ }, { "cell_type": "markdown", - "id": "2c68a0c1", + "id": "4fe2c4e9", "metadata": { "editable": true }, @@ -2395,7 +2317,7 @@ }, { "cell_type": "markdown", - "id": "f3492a4b", + "id": "bd8e264d", "metadata": { "editable": true }, @@ -2406,7 +2328,7 @@ }, { "cell_type": "markdown", - "id": "207b9ead", + "id": "9a0e9ac7", "metadata": { "editable": true }, @@ -2418,7 +2340,7 @@ }, { "cell_type": "markdown", - "id": "d74c7ddc", + "id": "9b20bc14", "metadata": { "editable": true }, @@ -2432,7 +2354,7 @@ }, { "cell_type": "markdown", - "id": "a02ea08c", + "id": "5fae8c2f", "metadata": { "editable": true }, @@ -2444,7 +2366,7 @@ }, { "cell_type": "markdown", - "id": "b9863119", + "id": "c955d871", "metadata": { "editable": true }, @@ -2469,7 +2391,7 @@ }, { "cell_type": "markdown", - "id": "92ca8aa6", + "id": "10feca33", "metadata": { "editable": true }, @@ -2485,14 +2407,11 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "32b99126", + "execution_count": 27, + "id": "cd57c0cd", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2533,7 +2452,7 @@ }, { "cell_type": "markdown", - "id": "185af862", + "id": "ed4e2094", "metadata": { "editable": true }, @@ -2550,13 +2469,10 @@ { "cell_type": "code", "execution_count": 28, - "id": "2ae9db3d", + "id": "309536df", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2574,7 +2490,7 @@ }, { "cell_type": "markdown", - "id": "1ade787b", + "id": "27597f0a", "metadata": { "editable": true }, @@ -2587,14 +2503,11 @@ }, { "cell_type": "code", - "execution_count": 38, - "id": "e0d18716", + "execution_count": 29, + "id": "1c60fe5f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2620,7 +2533,7 @@ }, { "cell_type": "markdown", - "id": "bd819c63", + "id": "36972bcb", "metadata": { "editable": true }, @@ -2639,38 +2552,13 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "fc0346bc", + "execution_count": 30, + "id": "85ffeaf7", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " N Z A Element Ebinding\n", - "A \n", - "4 0 1 3 4 Li 1.153760\n", - "5 2 3 2 5 He 5.512132\n", - "6 7 3 3 6 Li 5.332331\n", - "7 12 4 3 7 Li 5.606439\n", - "8 17 4 4 8 Be 7.062435\n", - "... ... ... ... ... ...\n", - "264 3297 156 108 264 Hs 7.298375\n", - "265 3303 157 108 265 Hs 7.296247\n", - "266 3310 158 108 266 Hs 7.298273\n", - "269 3331 159 110 269 Ds 7.250154\n", - "270 3337 160 110 270 Ds 7.253775\n", - "\n", - "[264 rows x 5 columns]\n" - ] - } - ], + "outputs": [], "source": [ "A = Masses['A']\n", "Z = Masses['Z']\n", @@ -2682,7 +2570,7 @@ }, { "cell_type": "markdown", - "id": "4f70ca63", + "id": "799627e9", "metadata": { "editable": true }, @@ -2693,14 +2581,11 @@ }, { "cell_type": "code", - "execution_count": 40, - "id": "d56956d6", + "execution_count": 31, + "id": "365fbba9", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2715,7 +2600,7 @@ }, { "cell_type": "markdown", - "id": "0b3df36c", + "id": "0d3c5663", "metadata": { "editable": true }, @@ -2725,14 +2610,11 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "d10ecf29", + "execution_count": 32, + "id": "60ba302b", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2742,7 +2624,7 @@ }, { "cell_type": "markdown", - "id": "2289c78e", + "id": "af13881a", "metadata": { "editable": true }, @@ -2753,38 +2635,13 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "286afac2", + "execution_count": 33, + "id": "a5cba204", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 0.02\n", - "Variance score: 0.95\n", - "Mean absolute error: 0.05\n", - "[ 0.00000000e+00 -2.96611194e-02 2.01719003e-01 1.08078025e+01\n", - " -4.03097597e+01] 5.294399745619595\n" - ] - }, - { - "data": { - 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", - "text/plain": [ - "

" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n", @@ -2810,7 +2667,7 @@ }, { "cell_type": "markdown", - "id": "8fa56ca8", + "id": "db2d6c19", "metadata": { "editable": true }, @@ -2823,95 +2680,13 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "76f0ddd2", + "execution_count": 34, + "id": "66c27da6", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 1.73\n", - "Mean squared error: 19.48\n", - "Mean squared error: 12.11\n", - "Mean squared error: 23.42\n", - "Mean squared error: 0.23\n", - "Mean squared error: 0.18\n", - "Mean squared error: 0.29\n", - "Mean squared error: 0.20\n", - "Mean squared error: 0.26\n", - "Mean squared error: 4.07\n", - "Mean squared error: 2.12\n", - "Mean squared error: 7.54\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 15.78\n", - "Mean squared error: 122.02\n", - "Mean squared error: 51.22\n", - "Mean squared error: 152.55\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1.7304881 19.47958494 12.11340253 23.41589548]\n", - " [ 0.22948497 0.18392847 0.29364655 0.20072279]\n", - " [ 0.26303845 4.06730814 2.11590451 7.5411205 ]\n", - " [ 15.77893972 122.02334824 51.2167072 152.54894451]]\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.neural_network import MLPRegressor\n", "from sklearn.metrics import accuracy_score\n", @@ -2951,7 +2726,7 @@ }, { "cell_type": "markdown", - "id": "19fec57c", + "id": "50a1e770", "metadata": { "editable": true }, @@ -2971,7 +2746,7 @@ }, { "cell_type": "markdown", - "id": "1bcb8b34", + "id": "5af218e3", "metadata": { "editable": true }, @@ -3003,7 +2778,7 @@ }, { "cell_type": "markdown", - "id": "2ebf620c", + "id": "55450c7b", "metadata": { "editable": true }, @@ -3027,7 +2802,7 @@ }, { "cell_type": "markdown", - "id": "db27019b", + "id": "be42ca6d", "metadata": { "editable": true }, @@ -3055,7 +2830,7 @@ }, { "cell_type": "markdown", - "id": "3ab4acd0", + "id": "4b7a4ceb", "metadata": { "editable": true }, @@ -3070,7 +2845,7 @@ }, { "cell_type": "markdown", - "id": "b60035b5", + "id": "e00d9920", "metadata": { "editable": true }, @@ -3082,7 +2857,7 @@ }, { "cell_type": "markdown", - "id": "29af7e67", + "id": "6243c073", "metadata": { "editable": true }, @@ -3097,7 +2872,7 @@ }, { "cell_type": "markdown", - "id": "740b4af2", + "id": "009ea7de", "metadata": { "editable": true }, @@ -3110,7 +2885,7 @@ }, { "cell_type": "markdown", - "id": "b4ad3976", + "id": "712c684d", "metadata": { "editable": true }, @@ -3122,7 +2897,7 @@ }, { "cell_type": "markdown", - "id": "0051e649", + "id": "b4bcd2a5", "metadata": { "editable": true }, @@ -3132,7 +2907,7 @@ }, { "cell_type": "markdown", - "id": "1a7712be", + "id": "143b6359", "metadata": { "editable": true }, @@ -3143,7 +2918,7 @@ }, { "cell_type": "markdown", - "id": "27e08926", + "id": "d2e56068", "metadata": { "editable": true }, @@ -3161,7 +2936,7 @@ }, { "cell_type": "markdown", - "id": "2ec61954", + "id": "2541505d", "metadata": { "editable": true }, @@ -3172,7 +2947,7 @@ }, { "cell_type": "markdown", - "id": "b7aab6da", + "id": "725efee7", "metadata": { "editable": true }, @@ -3184,7 +2959,7 @@ }, { "cell_type": "markdown", - "id": "523fa0e8", + "id": "cb15e9f1", "metadata": { "editable": true }, @@ -3194,7 +2969,7 @@ }, { "cell_type": "markdown", - "id": "a4172710", + "id": "87026e15", "metadata": { "editable": true }, @@ -3206,7 +2981,7 @@ }, { "cell_type": "markdown", - "id": "3a21de62", + "id": "cd09c85a", "metadata": { "editable": true }, @@ -3216,7 +2991,7 @@ }, { "cell_type": "markdown", - "id": "a68d2930", + "id": "c7845cfd", "metadata": { "editable": true }, @@ -3228,7 +3003,7 @@ }, { "cell_type": "markdown", - "id": "519a6c63", + "id": "20e94324", "metadata": { "editable": true }, @@ -3238,7 +3013,7 @@ }, { "cell_type": "markdown", - "id": "2f28767d", + "id": "46cc127e", "metadata": { "editable": true }, @@ -3257,7 +3032,7 @@ }, { "cell_type": "markdown", - "id": "60e193f4", + "id": "b83e3f3f", "metadata": { "editable": true }, @@ -3267,7 +3042,7 @@ }, { "cell_type": "markdown", - "id": "b76cff50", + "id": "d3c3d5b8", "metadata": { "editable": true }, @@ -3279,7 +3054,7 @@ }, { "cell_type": "markdown", - "id": "6c65fa96", + "id": "c6e741c3", "metadata": { "editable": true }, @@ -3289,7 +3064,7 @@ }, { "cell_type": "markdown", - "id": "554a1508", + "id": "510abc9b", "metadata": { "editable": true }, @@ -3305,7 +3080,7 @@ }, { "cell_type": "markdown", - "id": "ec603f5c", + "id": "1a6464a4", "metadata": { "editable": true }, @@ -3325,7 +3100,7 @@ }, { "cell_type": "markdown", - "id": "98c74b9b", + "id": "0f403e2d", "metadata": { "editable": true }, @@ -3335,7 +3110,7 @@ }, { "cell_type": "markdown", - "id": "7f4d2c5c", + "id": "4a8138dc", "metadata": { "editable": true }, @@ -3346,7 +3121,7 @@ }, { "cell_type": "markdown", - "id": "654bf9a6", + "id": "726cbaa4", "metadata": { "editable": true }, @@ -3365,7 +3140,7 @@ }, { "cell_type": "markdown", - "id": "f80909ca", + "id": "6b7adfb6", "metadata": { "editable": true }, @@ -3375,7 +3150,7 @@ }, { "cell_type": "markdown", - "id": "ee25c0f7", + "id": "84165f3e", "metadata": { "editable": true }, @@ -3387,7 +3162,7 @@ }, { "cell_type": "markdown", - "id": "c1947e0c", + "id": "ebf366ef", "metadata": { "editable": true }, @@ -3397,7 +3172,7 @@ }, { "cell_type": "markdown", - "id": "05229723", + "id": "02ffad05", "metadata": { "editable": true }, @@ -3408,7 +3183,7 @@ }, { "cell_type": "markdown", - "id": "bb0abff0", + "id": "d54237d5", "metadata": { "editable": true }, @@ -3428,7 +3203,7 @@ }, { "cell_type": "markdown", - "id": "bc16c9f8", + "id": "8a7e9dbb", "metadata": { "editable": true }, @@ -3440,7 +3215,7 @@ }, { "cell_type": "markdown", - "id": "d7f65472", + "id": "d4d5ff52", "metadata": { "editable": true }, @@ -3455,13 +3230,10 @@ { "cell_type": "code", "execution_count": 35, - "id": "acf08e73", + "id": "297415bf", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -3538,7 +3310,7 @@ }, { "cell_type": "markdown", - "id": "937591a1", + "id": "2ce99829", "metadata": { "editable": true }, @@ -3548,7 +3320,7 @@ }, { "cell_type": "markdown", - "id": "132c386c", + "id": "f6a44999", "metadata": { "editable": true }, @@ -3560,7 +3332,7 @@ }, { "cell_type": "markdown", - "id": "869bdef2", + "id": "990c34c3", "metadata": { "editable": true }, @@ -3570,7 +3342,7 @@ }, { "cell_type": "markdown", - "id": "7781a1c4", + "id": "a06f9457", "metadata": { "editable": true }, @@ -3581,7 +3353,7 @@ }, { "cell_type": "markdown", - "id": "fd8e693c", + "id": "19cd9843", "metadata": { "editable": true }, @@ -3593,7 +3365,7 @@ }, { "cell_type": "markdown", - "id": "f5c29ebf", + "id": "cc8c11ab", "metadata": { "editable": true }, @@ -3603,7 +3375,7 @@ }, { "cell_type": "markdown", - "id": "a798f40c", + "id": "d3d4e384", "metadata": { "editable": true }, @@ -3615,7 +3387,7 @@ }, { "cell_type": "markdown", - "id": "bae820af", + "id": "6187929e", "metadata": { "editable": true }, @@ -3625,7 +3397,7 @@ }, { "cell_type": "markdown", - "id": "06c75a81", + "id": "e0ba2cb1", "metadata": { "editable": true }, @@ -3637,7 +3409,7 @@ }, { "cell_type": "markdown", - "id": "59236913", + "id": "468fec32", "metadata": { "editable": true }, @@ -3650,7 +3422,7 @@ }, { "cell_type": "markdown", - "id": "81b5da1b", + "id": "ba618e10", "metadata": { "editable": true }, @@ -3662,7 +3434,7 @@ }, { "cell_type": "markdown", - "id": "e74c517f", + "id": "a74d3f1c", "metadata": { "editable": true }, @@ -3672,7 +3444,7 @@ }, { "cell_type": "markdown", - "id": "648beb7f", + "id": "fb434398", "metadata": { "editable": true }, @@ -3684,7 +3456,7 @@ }, { "cell_type": "markdown", - "id": "1d9326d3", + "id": "42558cc8", "metadata": { "editable": true }, @@ -3696,7 +3468,7 @@ }, { "cell_type": "markdown", - "id": "f51ed614", + "id": "d851da5b", "metadata": { "editable": true }, @@ -3707,7 +3479,7 @@ }, { "cell_type": "markdown", - "id": "ced37c54", + "id": "49533cb3", "metadata": { "editable": true }, @@ -3719,7 +3491,7 @@ }, { "cell_type": "markdown", - "id": "ebbeea54", + "id": "a62f6a4c", "metadata": { "editable": true }, @@ -3738,7 +3510,7 @@ }, { "cell_type": "markdown", - "id": "1b5d06a0", + "id": "381182cd", "metadata": { "editable": true }, @@ -3751,7 +3523,7 @@ }, { "cell_type": "markdown", - "id": "74890321", + "id": "04b172c8", "metadata": { "editable": true }, @@ -3761,7 +3533,7 @@ }, { "cell_type": "markdown", - "id": "ad33aa43", + "id": "265d8614", "metadata": { "editable": true }, @@ -3773,7 +3545,7 @@ }, { "cell_type": "markdown", - "id": "626df4e6", + "id": "68c1005e", "metadata": { "editable": true }, @@ -3783,7 +3555,7 @@ }, { "cell_type": "markdown", - "id": "6594383b", + "id": "61fb0fbc", "metadata": { "editable": true }, @@ -3795,7 +3567,7 @@ }, { "cell_type": "markdown", - "id": "ec04a0b3", + "id": "acb3fa59", "metadata": { "editable": true }, @@ -3805,7 +3577,7 @@ }, { "cell_type": "markdown", - "id": "a82796e0", + "id": "82f920b5", "metadata": { "editable": true }, @@ -3817,7 +3589,7 @@ }, { "cell_type": "markdown", - "id": "9607cc25", + "id": "26de4671", "metadata": { "editable": true }, @@ -3828,7 +3600,7 @@ }, { "cell_type": "markdown", - "id": "f84698d3", + "id": "071a86ff", "metadata": { "editable": true }, @@ -3840,7 +3612,7 @@ }, { "cell_type": "markdown", - "id": "1245df8a", + "id": "fdb51c1d", "metadata": { "editable": true }, @@ -3850,7 +3622,7 @@ }, { "cell_type": "markdown", - "id": "b6df8204", + "id": "5532b00a", "metadata": { "editable": true }, @@ -3862,7 +3634,7 @@ }, { "cell_type": "markdown", - "id": "6250497e", + "id": "a7c57982", "metadata": { "editable": true }, @@ -3872,7 +3644,7 @@ }, { "cell_type": "markdown", - "id": "f788dc8f", + "id": "022ce912", "metadata": { "editable": true }, @@ -3884,7 +3656,7 @@ }, { "cell_type": "markdown", - "id": "bdb72b3a", + "id": "12518c37", "metadata": { "editable": true }, @@ -3905,7 +3677,7 @@ }, { "cell_type": "markdown", - "id": "d498ca67", + "id": "89f9bbda", "metadata": { "editable": true }, @@ -3916,7 +3688,7 @@ }, { "cell_type": "markdown", - "id": "786a3fef", + "id": "51458daa", "metadata": { "editable": true }, @@ -3928,7 +3700,7 @@ }, { "cell_type": "markdown", - "id": "a0080ea0", + "id": "eaf4a93e", "metadata": { "editable": true }, @@ -3938,7 +3710,7 @@ }, { "cell_type": "markdown", - "id": "27b8b857", + "id": "2fea3537", "metadata": { "editable": true }, @@ -3950,7 +3722,7 @@ }, { "cell_type": "markdown", - "id": "3bf5241a", + "id": "ca4f1123", "metadata": { "editable": true }, @@ -3960,7 +3732,7 @@ }, { "cell_type": "markdown", - "id": "6b578bc7", + "id": "09de659a", "metadata": { "editable": true }, @@ -3972,7 +3744,7 @@ }, { "cell_type": "markdown", - "id": "11220b8a", + "id": "77f9fd08", "metadata": { "editable": true }, @@ -3984,7 +3756,7 @@ }, { "cell_type": "markdown", - "id": "a920365b", + "id": "beab9d54", "metadata": { "editable": true }, @@ -3998,13 +3770,10 @@ { "cell_type": "code", "execution_count": 36, - "id": "0b7b8c69", + "id": "ce0d14e1", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4016,7 +3785,7 @@ }, { "cell_type": "markdown", - "id": "c209e80c", + "id": "20bbbdf2", "metadata": { "editable": true }, @@ -4027,13 +3796,10 @@ { "cell_type": "code", "execution_count": 37, - "id": "947eedad", + "id": "d1b21aae", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4043,7 +3809,7 @@ }, { "cell_type": "markdown", - "id": "55e5aa0a", + "id": "02bb27d6", "metadata": { "editable": true }, @@ -4054,13 +3820,10 @@ { "cell_type": "code", "execution_count": 38, - "id": "3fb277ad", + "id": "98630c99", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4080,7 +3843,7 @@ }, { "cell_type": "markdown", - "id": "36c1d2bc", + "id": "9af04036", "metadata": { "editable": true }, @@ -4094,13 +3857,10 @@ { "cell_type": "code", "execution_count": 39, - "id": "6db56b3d", + "id": "2d8cd3da", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4110,7 +3870,7 @@ }, { "cell_type": "markdown", - "id": "e0ee98a4", + "id": "71899eed", "metadata": { "editable": true }, @@ -4121,13 +3881,10 @@ { "cell_type": "code", "execution_count": 40, - "id": "8774bce4", + "id": "f8cc2dd0", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4136,7 +3893,7 @@ }, { "cell_type": "markdown", - "id": "1d29c10a", + "id": "44f3dd69", "metadata": { "editable": true }, @@ -4147,13 +3904,10 @@ { "cell_type": "code", "execution_count": 41, - "id": "93c8b0c6", + "id": "acdf8d3d", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4166,7 +3920,7 @@ }, { "cell_type": "markdown", - "id": "ff36a22e", + "id": "26d00774", "metadata": { "editable": true }, @@ -4177,13 +3931,10 @@ { "cell_type": "code", "execution_count": 42, - "id": "35feafa3", + "id": "fcf52c3c", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4194,7 +3945,7 @@ }, { "cell_type": "markdown", - "id": "1c4ac2dc", + "id": "82083461", "metadata": { "editable": true }, @@ -4216,7 +3967,7 @@ }, { "cell_type": "markdown", - "id": "dc2eacf3", + "id": "c247a29f", "metadata": { "editable": true }, @@ -4228,7 +3979,7 @@ }, { "cell_type": "markdown", - "id": "48c5a8b6", + "id": "5c6266aa", "metadata": { "editable": true }, @@ -4238,7 +3989,7 @@ }, { "cell_type": "markdown", - "id": "abc4699e", + "id": "c36f8d5c", "metadata": { "editable": true }, @@ -4250,7 +4001,7 @@ }, { "cell_type": "markdown", - "id": "4db48e9d", + "id": "5b1a5174", "metadata": { "editable": true }, @@ -4262,7 +4013,7 @@ }, { "cell_type": "markdown", - "id": "350fec9b", + "id": "81c82695", "metadata": { "editable": true }, @@ -4272,7 +4023,7 @@ }, { "cell_type": "markdown", - "id": "b474ddf5", + "id": "7dc8a3bc", "metadata": { "editable": true }, @@ -4284,7 +4035,7 @@ }, { "cell_type": "markdown", - "id": "67fc1d79", + "id": "41789a38", "metadata": { "editable": true }, @@ -4294,7 +4045,7 @@ }, { "cell_type": "markdown", - "id": "6aa9f684", + "id": "4a2e42bf", "metadata": { "editable": true }, @@ -4306,7 +4057,7 @@ }, { "cell_type": "markdown", - "id": "ce023be0", + "id": "acd67ee6", "metadata": { "editable": true }, @@ -4316,7 +4067,7 @@ }, { "cell_type": "markdown", - "id": "3754c848", + "id": "ee90b0eb", "metadata": { "editable": true }, @@ -4328,7 +4079,7 @@ }, { "cell_type": "markdown", - "id": "4d884286", + "id": "54eaab7f", "metadata": { "editable": true }, @@ -4340,7 +4091,7 @@ }, { "cell_type": "markdown", - "id": "f8446340", + "id": "f941f1ac", "metadata": { "editable": true }, @@ -4350,7 +4101,7 @@ }, { "cell_type": "markdown", - "id": "a6830538", + "id": "89e02a88", "metadata": { "editable": true }, @@ -4362,7 +4113,7 @@ }, { "cell_type": "markdown", - "id": "84f9e6b4", + "id": "3d1f3b65", "metadata": { "editable": true }, @@ -4372,7 +4123,7 @@ }, { "cell_type": "markdown", - "id": "586509f5", + "id": "1dda0ac2", "metadata": { "editable": true }, @@ -4384,7 +4135,7 @@ }, { "cell_type": "markdown", - "id": "ff04787a", + "id": "aafe7aae", "metadata": { "editable": true }, @@ -4396,7 +4147,7 @@ }, { "cell_type": "markdown", - "id": "fab40e86", + "id": "37f9c1fa", "metadata": { "editable": true }, @@ -4408,7 +4159,7 @@ }, { "cell_type": "markdown", - "id": "175c9026", + "id": "373adac2", "metadata": { "editable": true }, @@ -4418,7 +4169,7 @@ }, { "cell_type": "markdown", - "id": "8b8d1d83", + "id": "bdd6d800", "metadata": { "editable": true }, @@ -4430,7 +4181,7 @@ }, { "cell_type": "markdown", - "id": "0c64f049", + "id": "913df005", "metadata": { "editable": true }, @@ -4440,7 +4191,7 @@ }, { "cell_type": "markdown", - "id": "2a591f8f", + "id": "76f6737e", "metadata": { "editable": true }, @@ -4452,7 +4203,7 @@ }, { "cell_type": "markdown", - "id": "956b5691", + "id": "48f6aa66", "metadata": { "editable": true }, @@ -4462,7 +4213,7 @@ }, { "cell_type": "markdown", - "id": "85b1bd72", + "id": "513a1986", "metadata": { "editable": true }, @@ -4474,7 +4225,7 @@ }, { "cell_type": "markdown", - "id": "6950fb95", + "id": "20fbbe07", "metadata": { "editable": true }, @@ -4485,7 +4236,7 @@ }, { "cell_type": "markdown", - "id": "e2d998e7", + "id": "4dd97daa", "metadata": { "editable": true }, @@ -4497,7 +4248,7 @@ }, { "cell_type": "markdown", - "id": "79e10d20", + "id": "032afd0e", "metadata": { "editable": true }, @@ -4507,7 +4258,7 @@ }, { "cell_type": "markdown", - "id": "6585dbe2", + "id": "25eac667", "metadata": { "editable": true }, @@ -4519,7 +4270,7 @@ }, { "cell_type": "markdown", - "id": "4291e4ad", + "id": "18c343cd", "metadata": { "editable": true }, @@ -4529,7 +4280,7 @@ }, { "cell_type": "markdown", - "id": "6a50444a", + "id": "9864986f", "metadata": { "editable": true }, @@ -4541,7 +4292,7 @@ }, { "cell_type": "markdown", - "id": "58d08cf2", + "id": "1ecdde02", "metadata": { "editable": true }, @@ -4554,7 +4305,7 @@ }, { "cell_type": "markdown", - "id": "8215c3c6", + "id": "9d62dddc", "metadata": { "editable": true }, @@ -4566,7 +4317,7 @@ }, { "cell_type": "markdown", - "id": "09efd53b", + "id": "db398f5b", "metadata": { "editable": true }, @@ -4578,7 +4329,7 @@ }, { "cell_type": "markdown", - "id": "6c694cf4", + "id": "2bbf293c", "metadata": { "editable": true }, @@ -4590,7 +4341,7 @@ }, { "cell_type": "markdown", - "id": "909419d5", + "id": "d82e1506", "metadata": { "editable": true }, @@ -4602,7 +4353,7 @@ }, { "cell_type": "markdown", - "id": "2d35d3a4", + "id": "1ddd417c", "metadata": { "editable": true }, @@ -4614,7 +4365,7 @@ }, { "cell_type": "markdown", - "id": "845b476b", + "id": "e7904873", "metadata": { "editable": true }, @@ -4624,7 +4375,7 @@ }, { "cell_type": "markdown", - "id": "644470d4", + "id": "19604164", "metadata": { "editable": true }, @@ -4636,7 +4387,7 @@ }, { "cell_type": "markdown", - "id": "6c4ad6c8", + "id": "0f262e03", "metadata": { "editable": true }, @@ -4648,7 +4399,7 @@ }, { "cell_type": "markdown", - "id": "839e60fb", + "id": "cdb18e0d", "metadata": { "editable": true }, @@ -4660,25 +4411,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/LectureNotes/_build/html/genindex.html b/doc/LectureNotes/_build/html/genindex.html index 905369861..907e860ee 100644 --- a/doc/LectureNotes/_build/html/genindex.html +++ b/doc/LectureNotes/_build/html/genindex.html @@ -31,7 +31,7 @@ - + diff --git a/doc/LectureNotes/_build/html/intro.html b/doc/LectureNotes/_build/html/intro.html index 179fca5f2..19dc216b0 100644 --- a/doc/LectureNotes/_build/html/intro.html +++ b/doc/LectureNotes/_build/html/intro.html @@ -32,7 +32,7 @@ - + diff --git a/doc/LectureNotes/_build/html/search.html b/doc/LectureNotes/_build/html/search.html index 506889057..31483789c 100644 --- a/doc/LectureNotes/_build/html/search.html +++ b/doc/LectureNotes/_build/html/search.html @@ -30,7 +30,7 @@ - + diff --git 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"Bagging Examples": [[12, "bagging-examples"]], "Basic Matrix Features": [[18, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[13, null]], "Basic math of the SVD": [[7, "basic-math-of-the-svd"]], "Basics": [[9, "basics"]], "Basics of a tree": [[11, "basics-of-a-tree"]], "Batch Normalization": [[3, "batch-normalization"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[7, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[12, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[8, "bootstrap"]], "Bringing it together, first back propagation equation": [[14, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[3, null]], "Building a tree, regression": [[11, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[3, "building-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, building convolutional neural networks in 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"Define model and architecture": [[3, "define-model-and-architecture"]], "Defining the cost function": [[3, "defining-the-cost-function"]], "Deliverables": [[0, "deliverables"], [1, "deliverables"]], "Derivatives and the chain rule": [[14, "derivatives-and-the-chain-rule"]], "Deriving OLS from a probability distribution": [[7, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[1, "deriving-and-implementing-ordinary-least-squares"]], "Deriving the back propagation code for a multilayer perceptron model": [[14, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[3, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[13, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[10, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[11, "disadvantages"]], "Discriminative Modeling": [[23, "discriminative-modeling"]], "Domains and probabilities": [[20, "domains-and-probabilities"]], "Dropout": [[3, "dropout"]], "Elements of Probability Theory and Statistical Data Analysis": [[20, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[12, null]], "Entropy and the ID3 algorithm": [[11, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[23, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[3, "evaluate-model-performance-on-test-data"]], "Example of discriminative modeling, taken from Generative Deeep Learning by David Foster": [[23, "example-of-discriminative-modeling-taken-from-generative-deeep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[23, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example: Exponential decay": [[4, "example-exponential-decay"]], "Example: Population growth": [[4, "example-population-growth"]], "Example: The diffusion equation": [[4, "example-the-diffusion-equation"]], "Example: binary classification problem": [[3, "example-binary-classification-problem"]], "Examples": [[23, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[9, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[1, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[0, "exercise-1-github-setup"]], "Exercise 1: Setting up various Python environments": [[2, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[1, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Setting up a Github repository": [[0, "exercise-2-setting-up-a-github-repository"]], "Exercise 2: making your own data and exploring scikit-learn": [[2, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[1, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[0, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Setting up a Python virtual environment": [[0, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3: Normalizing our data": [[2, "exercise-3-normalizing-our-data"]], "Exercise 4 - Fitting a polynomial": [[1, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - The train-test split": [[0, "exercise-4-the-train-test-split"]], "Exercise 4: Adding Ridge Regression": [[2, "exercise-4-adding-ridge-regression"]], "Exercise 5 - Comparing your code with sklearn": [[1, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5: Analytical exercises": [[2, "exercise-5-analytical-exercises"]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[8, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[8, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[8, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[8, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[8, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[8, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[2, "exercises"]], "Exercises and Projects": [[8, "exercises-and-projects"]], "Exercises week 34": [[0, null]], "Exercises week 35": [[1, null]], "Expectation values": [[20, "expectation-values"]], "Extremely useful tools, strongly recommended": [[23, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[14, "feed-forward-neural-networks"]], "Feed-forward pass": [[3, "feed-forward-pass"]], "Final back propagating equation": [[14, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[3, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[2, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "From one to many layers, the universal approximation theorem": [[14, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Further Dimensionality Remarks": [[5, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[7, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[18, "gaussian-elimination"]], "General Features": [[11, "general-features"]], "General linear models and linear algebra": [[23, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[23, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [23, "id1"]], "Generative Adversarial Networks": [[6, "generative-adversarial-networks"]], "Generative Models": [[6, "generative-models"]], "Generative Versus Discriminative Modeling": [[23, "generative-versus-discriminative-modeling"]], "Geometric Interpretation and link with Singular Value Decomposition": [[13, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Gradient Boosting, Classification Example": [[12, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[12, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[3, "gradient-clipping"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[12, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[4, "gradient-descent"]], "Grading": [[21, "grading"], [21, "id2"], [23, "grading"]], "Housing data, the code": [[2, "housing-data-the-code"]], "How to take derivatives of Matrix-Vector expressions": [[1, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[10, "hyperplanes-and-all-that"]], "Important Matrix and vector handling packages": [[18, "important-matrix-and-vector-handling-packages"]], "Improving performance": [[3, "improving-performance"]], "In summary": [[21, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[15, "including-stochastic-gradient-descent-with-autograd"]], "Incremental PCA": [[13, "incremental-pca"]], "Installing R, C++, cython or Julia": [[23, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[23, "installing-r-c-cython-numba-etc"]], "Instructor information": [[21, "instructor-information"]], "Interpretations and optimizing our parameters": [[23, "interpretations-and-optimizing-our-parameters"], [23, "id2"], [23, "id3"]], "Introducing JAX": [[15, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[13, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[2, "introduction"], [8, "introduction"], [17, "introduction"], [18, "introduction"]], "Iterative Fitting, Classification and AdaBoost": [[12, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[12, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[13, "kernel-pca"]], "Kernels and non-linearity": [[10, "kernels-and-non-linearity"]], "LU 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"Matrix multiplication": [[3, "matrix-multiplication"]], "Matrix-vector notation and activation": [[14, "matrix-vector-notation-and-activation"]], "Meet the covariance!": [[20, "meet-the-covariance"]], "Meet the Covariance Matrix": [[7, "meet-the-covariance-matrix"]], "Meet the Pandas": [[23, "meet-the-pandas"]], "Momentum based GD": [[15, "momentum-based-gd"]], "More complicated Example: The Ising model": [[8, "more-complicated-example-the-ising-model"]], "More on Dimensionalities": [[5, "more-on-dimensionalities"]], "More on Rescaling data": [[8, "more-on-rescaling-data"]], "Multilayer perceptrons": [[14, "multilayer-perceptrons"]], "Network requirements": [[4, "network-requirements"]], "Neural Networks vs CNNs": [[5, "neural-networks-vs-cnns"]], "Neural networks": [[14, null]], "Numerical experiments and the covariance, central limit theorem": [[20, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[18, "numpy-and-arrays"], [23, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[23, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization, the central part of any Machine Learning algortithm": [[15, null]], "Optimizing our parameters": [[23, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[23, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[3, "optimizing-the-cost-function"]], "Organizing our data": [[2, "organizing-our-data"], [23, "organizing-our-data"]], "Other Matrix and Vector Operations": [[18, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[6, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning at UiO": [[23, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[23, 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"wave": 4, "we": 23, "week": [0, 1, 23], "what": [2, 23], "which": 3, "why": 23, "wisconsin": 9, "write": [6, 13], "xgboost": 12, "your": [1, 2, 12]}}) \ No newline at end of file +Search.setIndex({"alltitles": {"A Classification Tree": [[11, "a-classification-tree"]], "A Frequentist approach to data analysis": [[2, "a-frequentist-approach-to-data-analysis"], [23, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[10, "a-better-approach"]], "A first summary": [[23, "a-first-summary"]], "A quick Reminder on Lagrangian Multipliers": [[10, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[6, "a-simple-example"]], "A soft classifier": [[10, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[3, "a-top-down-perspective-on-neural-networks"]], "ADAM optimizer": [[15, "adam-optimizer"]], "Activation functions": [[14, "activation-functions"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[12, "adaptive-boosting-adaboost-basic-algorithm"]], "Adding error analysis and training set up": [[23, "adding-error-analysis-and-training-set-up"]], "Adjust hyperparameters": [[3, "adjust-hyperparameters"]], "Algorithms for Setting up Decision Trees": [[11, "algorithms-for-setting-up-decision-trees"]], "An Overview of Ensemble Methods": [[12, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[6, "an-extrapolation-example"]], "An optimization/minimization problem": [[23, "an-optimization-minimization-problem"]], "And what about using neural networks?": [[23, "and-what-about-using-neural-networks"]], "Another example, the moons again": [[11, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[17, null]], "Autocorrelation function": [[20, "autocorrelation-function"]], "Automatic differentiation": [[15, "automatic-differentiation"]], "Back to the Cancer Data": [[13, "back-to-the-cancer-data"]], "Bagging": [[12, "bagging"]], "Bagging Examples": [[12, "bagging-examples"]], "Basic Matrix Features": [[18, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[13, null]], "Basic math of the SVD": [[7, "basic-math-of-the-svd"]], "Basics": [[9, "basics"]], "Basics of a tree": [[11, "basics-of-a-tree"]], "Batch Normalization": [[3, "batch-normalization"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[7, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[12, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[8, "bootstrap"]], "Bringing it together, first back propagation equation": [[14, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[3, null]], "Building a tree, regression": [[11, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[3, "building-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[5, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[11, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Choose cost function and optimizer": [[3, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[13, "classical-pca-theorem"]], "Clustering and Unsupervised Learning": [[16, null]], "Code for SVD and Inversion of Matrices": [[7, "code-for-svd-and-inversion-of-matrices"]], "Codes and Approaches": [[16, "codes-and-approaches"]], "Codes for the SVD": [[7, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[0, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[3, "collect-and-pre-process-data"]], "Communication channels": [[23, "communication-channels"]], "Compare Bagging on Trees with Random Forests": [[12, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[4, "comparing-with-a-numerical-scheme"]], "Computing the Gini index": [[11, "computing-the-gini-index"]], "Conjugate gradient method": [[15, "conjugate-gradient-method"]], "Convex functions": [[15, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[5, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[5, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[14, "convolutional-neural-network"]], "Convolutional Neural Networks": [[5, null]], "Correlation Matrix": [[13, "correlation-matrix"]], "Course Format": [[23, "course-format"]], "Course setting": [[19, null]], "Cross-validation": [[8, "cross-validation"]], "Deadlines for projects (tentative)": [[23, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[11, null]], "Deep learning methods": [[23, "deep-learning-methods"]], "Define model and architecture": [[3, "define-model-and-architecture"]], "Defining the cost function": [[3, "defining-the-cost-function"]], "Deliverables": [[0, "deliverables"], [1, "deliverables"]], "Derivatives and the chain rule": [[14, "derivatives-and-the-chain-rule"]], "Deriving OLS from a probability distribution": [[7, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[1, "deriving-and-implementing-ordinary-least-squares"]], "Deriving the back propagation code for a multilayer perceptron model": [[14, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[3, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[13, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[10, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[11, "disadvantages"]], "Discriminative Modeling": [[23, "discriminative-modeling"]], "Domains and probabilities": [[20, "domains-and-probabilities"]], "Dropout": [[3, "dropout"]], "Elements of Probability Theory and Statistical Data Analysis": [[20, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[12, null]], "Entropy and the ID3 algorithm": [[11, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[23, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[3, "evaluate-model-performance-on-test-data"]], "Example of discriminative modeling, taken from Generative Deep Learning by David Foster": [[23, "example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[23, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example: Exponential decay": [[4, "example-exponential-decay"]], "Example: Population growth": [[4, "example-population-growth"]], "Example: The diffusion equation": [[4, "example-the-diffusion-equation"]], "Example: binary classification problem": [[3, "example-binary-classification-problem"]], "Examples": [[23, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[9, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[1, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[0, "exercise-1-github-setup"]], "Exercise 1: Setting up various Python environments": [[2, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[1, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Setting up a Github repository": [[0, "exercise-2-setting-up-a-github-repository"]], "Exercise 2: making your own data and exploring scikit-learn": [[2, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[1, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[0, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Setting up a Python virtual environment": [[0, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3: Normalizing our data": [[2, "exercise-3-normalizing-our-data"]], "Exercise 4 - Fitting a polynomial": [[1, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - The train-test split": [[0, "exercise-4-the-train-test-split"]], "Exercise 4: Adding Ridge Regression": [[2, "exercise-4-adding-ridge-regression"]], "Exercise 5 - Comparing your code with sklearn": [[1, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5: Analytical exercises": [[2, "exercise-5-analytical-exercises"]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[8, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[8, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[8, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[8, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[8, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[8, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[2, "exercises"]], "Exercises and Projects": [[8, "exercises-and-projects"]], "Exercises week 34": [[0, null]], "Exercises week 35": [[1, null]], "Expectation values": [[20, "expectation-values"]], "Extremely useful tools, strongly recommended": [[23, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[14, "feed-forward-neural-networks"]], "Feed-forward pass": [[3, "feed-forward-pass"]], "Final back propagating equation": [[14, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[3, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[2, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "From one to many layers, the universal approximation theorem": [[14, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Further Dimensionality Remarks": [[5, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[7, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[18, "gaussian-elimination"]], "General Features": [[11, "general-features"]], "General linear models and linear algebra": [[23, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[23, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [23, "id1"]], "Generative Adversarial Networks": [[6, "generative-adversarial-networks"]], "Generative Models": [[6, "generative-models"]], "Generative Versus Discriminative Modeling": [[23, "generative-versus-discriminative-modeling"]], "Geometric Interpretation and link with Singular Value Decomposition": [[13, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Gradient Boosting, Classification Example": [[12, "gradient-boosting-classification-example"]], "Gradient Boosting, 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"incremental-pca"]], "Installing R, C++, cython or Julia": [[23, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[23, "installing-r-c-cython-numba-etc"]], "Instructor information": [[21, "instructor-information"]], "Interpretations and optimizing our parameters": [[23, "interpretations-and-optimizing-our-parameters"], [23, "id2"], [23, "id3"]], "Introducing JAX": [[15, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[13, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[2, "introduction"], [8, "introduction"], [17, "introduction"], [18, "introduction"]], "Iterative Fitting, Classification and AdaBoost": [[12, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[12, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[13, "kernel-pca"]], "Kernels and non-linearity": [[10, "kernels-and-non-linearity"]], "LU 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"wave": 4, "we": 23, "week": [0, 1, 23], "what": [2, 23], "which": 3, "why": 23, "wisconsin": 9, "write": [6, 13], "xgboost": 12, "your": [1, 2, 12]}}) \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html index 1364bb6bb..eb399b7e1 100644 --- a/doc/LectureNotes/_build/html/week34.html +++ b/doc/LectureNotes/_build/html/week34.html @@ -32,7 +32,7 @@ - + @@ -402,7 +402,7 @@ document.write(`
  • What Is Generative Modeling?
  • Example of generative modeling, taken from Generative Deep Learning by David Foster
  • Generative Versus Discriminative Modeling
    • -
  • Example of discriminative modeling, taken from Generative Deeep Learning by David Foster
    • +
  • Example of discriminative modeling, taken from Generative Deep Learning by David Foster
  • Discriminative Modeling
  • A Frequentist approach to data analysis
  • What is a good model?
    • @@ -771,8 +771,8 @@ counterpart, discriminative modeling. If you have studied machine learning, most problems you will have faced will have most likely been discriminative in nature.

    -
    -

    Example of discriminative modeling, taken from Generative Deeep Learning by David Foster#

    +
    +

    Example of discriminative modeling, taken from Generative Deep Learning by David Foster#

    Figure 1:

    @@ -979,7 +979,7 @@ developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly he

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

    -
    import numpy as np
+
    import numpy as np
    @@ -987,9 +987,9 @@ developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly he

    Here follows a simple example where we set up an array of ten elements, all determined by random numbers drawn according to the normal distribution,

    -
    n = 10
-x = np.random.normal(size=n)
-print(x)
+
    n = 10
+x = np.random.normal(size=n)
+print(x)
    @@ -998,9 +998,9 @@ developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly he Another alternative is to declare a vector as follows

    -
    import numpy as np
-x = np.array([1, 2, 3])
-print(x)
+
    import numpy as np
+x = np.array([1, 2, 3])
+print(x)
    @@ -1009,9 +1009,9 @@ Another alternative is to declare a vector as follows

    start numbering array elements from \(0\) and on. This means that a vector with \(n\) elements has a sequence of entities \(x_0, x_1, x_2, \dots, x_{n-1}\). We could also let (recommended) Numpy to compute the logarithms of a specific array as

    -
    import numpy as np
-x = np.log(np.array([4, 7, 8]))
-print(x)
+
    import numpy as np
+x = np.log(np.array([4, 7, 8]))
+print(x)
    @@ -1025,12 +1025,12 @@ from Python’s math module. The looping is done explicitely by logarithms of a vector would be to write

    -
    import numpy as np
-from math import log
-x = np.array([4, 7, 8])
-for i in range(0, len(x)):
-    x[i] = log(x[i])
-print(x)
+
    import numpy as np
+from math import log
+x = np.array([4, 7, 8])
+for i in range(0, len(x)):
+    x[i] = log(x[i])
+print(x)
    @@ -1039,9 +1039,9 @@ logarithms of a vector would be to write

    The attentive reader will also notice that the output is \([1, 1, 2]\). Python interprets automagically our numbers as integers (like the automatic keyword in C++). To change this we could define our array elements to be double precision numbers as

    -
    import numpy as np
-x = np.log(np.array([4, 7, 8], dtype = np.float64))
-print(x)
+
    import numpy as np
+x = np.log(np.array([4, 7, 8], dtype = np.float64))
+print(x)
    @@ -1049,9 +1049,9 @@ The attentive reader will also notice that the output is
    -
    import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0]))
-print(x)
+
    import numpy as np
+x = np.log(np.array([4.0, 7.0, 8.0]))
+print(x)
    @@ -1059,9 +1059,9 @@ The attentive reader will also notice that the output is \(x\) is actually an object which inherits the functionalities defined in Numpy) as

    -
    import numpy as np
-x = np.log(np.array([4.0, 7.0, 8.0]))
-print(x.itemsize)
+
    import numpy as np
+x = np.log(np.array([4.0, 7.0, 8.0]))
+print(x.itemsize)
    @@ -1074,9 +1074,9 @@ define a \(3 \times 3 \) real lowercase letters for vectors and uppercase letters for matrices)

    -
    import numpy as np
-A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
-print(A)
+
    import numpy as np
+A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
+print(A)
    @@ -1084,10 +1084,10 @@ lowercase letters for vectors and uppercase letters for matrices)

    If we use the shape function we would get \((3, 3)\) as output, that is verifying that our matrix is a \(3\times 3\) matrix. We can slice the matrix and print for example the first column (Python organized matrix elements in a row-major order, see below) as

    -
    import numpy as np
-A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
-# print the first column, row-major order and elements start with 0
-print(A[:,0])
+
    import numpy as np
+A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
+# print the first column, row-major order and elements start with 0
+print(A[:,0])
    @@ -1095,10 +1095,10 @@ lowercase letters for vectors and uppercase letters for matrices)

    We can continue this was by printing out other columns or rows. The example here prints out the second column

    -
    import numpy as np
-A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
-# print the first column, row-major order and elements start with 0
-print(A[1,:])
+
    import numpy as np
+A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
+# print the first column, row-major order and elements start with 0
+print(A[1,:])
    @@ -1106,11 +1106,11 @@ lowercase letters for vectors and uppercase letters for matrices)

    Numpy contains many other functionalities that allow us to slice, subdivide etc etc arrays. We strongly recommend that you look up the Numpy website for more details. Useful functions when defining a matrix are the np.zeros function which declares a matrix of a given dimension and sets all elements to zero

    -
    import numpy as np
-n = 10
-# define a matrix of dimension 10 x 10 and set all elements to zero
-A = np.zeros( (n, n) )
-print(A)
+
    import numpy as np
+n = 10
+# define a matrix of dimension 10 x 10 and set all elements to zero
+A = np.zeros( (n, n) )
+print(A)
    @@ -1118,11 +1118,11 @@ lowercase letters for vectors and uppercase letters for matrices)

    or initializing all elements to

    -
    import numpy as np
-n = 10
-# define a matrix of dimension 10 x 10 and set all elements to one
-A = np.ones( (n, n) )
-print(A)
+
    import numpy as np
+n = 10
+# define a matrix of dimension 10 x 10 and set all elements to one
+A = np.ones( (n, n) )
+print(A)
    @@ -1130,11 +1130,11 @@ lowercase letters for vectors and uppercase letters for matrices)

    or as unitarily distributed random numbers (see the material on random number generators in the statistics part)

    -
    import numpy as np
-n = 10
-# define a matrix of dimension 10 x 10 and set all elements to random numbers with x \in [0, 1]
-A = np.random.rand(n, n)
-print(A)
+
    import numpy as np
+n = 10
+# define a matrix of dimension 10 x 10 and set all elements to random numbers with x \in [0, 1]
+A = np.random.rand(n, n)
+print(A)
    @@ -1170,40 +1170,40 @@ function np.mean(x). We can also extract the eigenvalues of the covariance matrix through the np.linalg.eig() function.

    -
    # Importing various packages
-import numpy as np
+
    # Importing various packages
+import numpy as np
 
-n = 100
-x = np.random.normal(size=n)
-print(np.mean(x))
-y = 4+3*x+np.random.normal(size=n)
-print(np.mean(y))
-z = x**3+np.random.normal(size=n)
-print(np.mean(z))
-W = np.vstack((x, y, z))
-Sigma = np.cov(W)
-print(Sigma)
-Eigvals, Eigvecs = np.linalg.eig(Sigma)
-print(Eigvals)
+n = 100
+x = np.random.normal(size=n)
+print(np.mean(x))
+y = 4+3*x+np.random.normal(size=n)
+print(np.mean(y))
+z = x**3+np.random.normal(size=n)
+print(np.mean(z))
+W = np.vstack((x, y, z))
+Sigma = np.cov(W)
+print(Sigma)
+Eigvals, Eigvecs = np.linalg.eig(Sigma)
+print(Eigvals)
    -
    %matplotlib inline
+
    %matplotlib inline
 
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy import sparse
-eye = np.eye(4)
-print(eye)
-sparse_mtx = sparse.csr_matrix(eye)
-print(sparse_mtx)
-x = np.linspace(-10,10,100)
-y = np.sin(x)
-plt.plot(x,y,marker='x')
-plt.show()
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import sparse
+eye = np.eye(4)
+print(eye)
+sparse_mtx = sparse.csr_matrix(eye)
+print(sparse_mtx)
+x = np.linspace(-10,10,100)
+y = np.sin(x)
+plt.plot(x,y,marker='x')
+plt.show()
    @@ -1224,15 +1224,15 @@ analysis tools for Python. pandas stands for panel data, a term

    The following simple example shows how we can, in an easy way make tables of our data. Here we define a data set which includes names, place of birth and date of birth, and displays the data in an easy to read way. We will see repeated use of pandas, in particular in connection with classification of data.

    -
    import pandas as pd
-from IPython.display import display
-data = {'First Name': ["Frodo", "Bilbo", "Aragorn II", "Samwise"],
-        'Last Name': ["Baggins", "Baggins","Elessar","Gamgee"],
-        'Place of birth': ["Shire", "Shire", "Eriador", "Shire"],
-        'Date of Birth T.A.': [2968, 2890, 2931, 2980]
-        }
-data_pandas = pd.DataFrame(data)
-display(data_pandas)
+
    import pandas as pd
+from IPython.display import display
+data = {'First Name': ["Frodo", "Bilbo", "Aragorn II", "Samwise"],
+        'Last Name': ["Baggins", "Baggins","Elessar","Gamgee"],
+        'Place of birth': ["Shire", "Shire", "Eriador", "Shire"],
+        'Date of Birth T.A.': [2968, 2890, 2931, 2980]
+        }
+data_pandas = pd.DataFrame(data)
+display(data_pandas)
    @@ -1243,8 +1243,8 @@ Displaying these results, we see that the indices are given by the default numbe pandas is extremely flexible and we can easily change the above indices by defining a new type of indexing as

    -
    data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])
-display(data_pandas)
+
    data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])
+display(data_pandas)
    @@ -1252,7 +1252,7 @@ Displaying these results, we see that the indices are given by the default numbe

    Thereafter we display the content of the row which begins with the index Aragorn

    -
    display(data_pandas.loc['Aragorn'])
+
    display(data_pandas.loc['Aragorn'])
    @@ -1260,13 +1260,13 @@ Displaying these results, we see that the indices are given by the default numbe

    We can easily append data to this, for example

    -
    new_hobbit = {'First Name': ["Peregrin"],
-              'Last Name': ["Took"],
-              'Place of birth': ["Shire"],
-              'Date of Birth T.A.': [2990]
-              }
-data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))
-display(data_pandas)
+
    new_hobbit = {'First Name': ["Peregrin"],
+              'Last Name': ["Took"],
+              'Place of birth': ["Shire"],
+              'Date of Birth T.A.': [2990]
+              }
+data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))
+display(data_pandas)
    @@ -1275,19 +1275,19 @@ Displaying these results, we see that the indices are given by the default numbe of dimensionality \(10\times 5\) and compute the mean value and standard deviation of each column. Similarly, we can perform mathematial operations like squaring the matrix elements and many other operations.

    -
    import numpy as np
-import pandas as pd
-from IPython.display import display
-np.random.seed(100)
-# setting up a 10 x 5 matrix
-rows = 10
-cols = 5
-a = np.random.randn(rows,cols)
-df = pd.DataFrame(a)
-display(df)
-print(df.mean())
-print(df.std())
-display(df**2)
+
    import numpy as np
+import pandas as pd
+from IPython.display import display
+np.random.seed(100)
+# setting up a 10 x 5 matrix
+rows = 10
+cols = 5
+a = np.random.randn(rows,cols)
+df = pd.DataFrame(a)
+display(df)
+print(df.mean())
+print(df.std())
+display(df**2)
    @@ -1295,24 +1295,24 @@ of dimensionality \(10\times 5\)Thereafter we can select specific columns only and plot final results

    -
    df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']
-df.index = np.arange(10)
+
    df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']
+df.index = np.arange(10)
 
-display(df)
-print(df['Second'].mean() )
-print(df.info())
-print(df.describe())
+display(df)
+print(df['Second'].mean() )
+print(df.info())
+print(df.describe())
 
-from pylab import plt, mpl
-plt.style.use('seaborn')
-mpl.rcParams['font.family'] = 'serif'
+from pylab import plt, mpl
+plt.style.use('seaborn')
+mpl.rcParams['font.family'] = 'serif'
 
-df.cumsum().plot(lw=2.0, figsize=(10,6))
-plt.show()
+df.cumsum().plot(lw=2.0, figsize=(10,6))
+plt.show()
 
 
-df.plot.bar(figsize=(10,6), rot=15)
-plt.show()
+df.plot.bar(figsize=(10,6), rot=15)
+plt.show()
    @@ -1320,10 +1320,10 @@ of dimensionality \(10\times 5\)We can produce a \(4\times 4\) matrix

    -
    b = np.arange(16).reshape((4,4))
-print(b)
-df1 = pd.DataFrame(b)
-print(df1)
+
    b = np.arange(16).reshape((4,4))
+print(b)
+df1 = pd.DataFrame(b)
+print(df1)
    @@ -1381,25 +1381,25 @@ data with a straight line.

    The Python code follows here.

    -
    # Importing various packages
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.linear_model import LinearRegression
+
    # Importing various packages
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
 
-x = np.random.rand(100,1)
-y = 2*x+np.random.randn(100,1)
-linreg = LinearRegression()
-linreg.fit(x,y)
-xnew = np.array([[0],[1]])
-ypredict = linreg.predict(xnew)
+x = np.random.rand(100,1)
+y = 2*x+np.random.randn(100,1)
+linreg = LinearRegression()
+linreg.fit(x,y)
+xnew = np.array([[0],[1]])
+ypredict = linreg.predict(xnew)
 
-plt.plot(xnew, ypredict, "r-")
-plt.plot(x, y ,'ro')
-plt.axis([0,1.0,0, 5.0])
-plt.xlabel(r'$x$')
-plt.ylabel(r'$y$')
-plt.title(r'Simple Linear Regression')
-plt.show()
+plt.plot(xnew, ypredict, "r-")
+plt.plot(x, y ,'ro')
+plt.axis([0,1.0,0, 5.0])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$y$')
+plt.title(r'Simple Linear Regression')
+plt.show()
    @@ -1465,22 +1465,22 @@ to be dominated by outliers.

    We can modify easily the above Python code and plot the relative error instead

    -
    import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.linear_model import LinearRegression
+
    import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
 
-x = np.random.rand(100,1)
-y = 5*x+0.01*np.random.randn(100,1)
-linreg = LinearRegression()
-linreg.fit(x,y)
-ypredict = linreg.predict(x)
+x = np.random.rand(100,1)
+y = 5*x+0.01*np.random.randn(100,1)
+linreg = LinearRegression()
+linreg.fit(x,y)
+ypredict = linreg.predict(x)
 
-plt.plot(x, np.abs(ypredict-y)/abs(y), "ro")
-plt.axis([0,1.0,0.0, 0.5])
-plt.xlabel(r'$x$')
-plt.ylabel(r'$\epsilon_{\mathrm{relative}}$')
-plt.title(r'Relative error')
-plt.show()
+plt.plot(x, np.abs(ypredict-y)/abs(y), "ro")
+plt.axis([0,1.0,0.0, 0.5])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$\epsilon_{\mathrm{relative}}$')
+plt.title(r'Relative error')
+plt.show()
    @@ -1497,33 +1497,33 @@ other properties from the statistical data analysis.

    example of the functionality of Scikit-Learn.

    -
    import numpy as np 
-import matplotlib.pyplot as plt 
-from sklearn.linear_model import LinearRegression 
-from sklearn.metrics import mean_squared_error, r2_score, mean_squared_log_error, mean_absolute_error
+
    import numpy as np 
+import matplotlib.pyplot as plt 
+from sklearn.linear_model import LinearRegression 
+from sklearn.metrics import mean_squared_error, r2_score, mean_squared_log_error, mean_absolute_error
 
-x = np.random.rand(100,1)
-y = 2.0+ 5*x+0.5*np.random.randn(100,1)
-linreg = LinearRegression()
-linreg.fit(x,y)
-ypredict = linreg.predict(x)
-print('The intercept alpha: \n', linreg.intercept_)
-print('Coefficient beta : \n', linreg.coef_)
-# The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(y, ypredict))
-# Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(y, ypredict))
-# Mean squared log error                                                        
-print('Mean squared log error: %.2f' % mean_squared_log_error(y, ypredict) )
-# Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(y, ypredict))
-plt.plot(x, ypredict, "r-")
-plt.plot(x, y ,'ro')
-plt.axis([0.0,1.0,1.5, 7.0])
-plt.xlabel(r'$x$')
-plt.ylabel(r'$y$')
-plt.title(r'Linear Regression fit ')
-plt.show()
+x = np.random.rand(100,1)
+y = 2.0+ 5*x+0.5*np.random.randn(100,1)
+linreg = LinearRegression()
+linreg.fit(x,y)
+ypredict = linreg.predict(x)
+print('The intercept alpha: \n', linreg.intercept_)
+print('Coefficient beta : \n', linreg.coef_)
+# The mean squared error                               
+print("Mean squared error: %.2f" % mean_squared_error(y, ypredict))
+# Explained variance score: 1 is perfect prediction                                 
+print('Variance score: %.2f' % r2_score(y, ypredict))
+# Mean squared log error                                                        
+print('Mean squared log error: %.2f' % mean_squared_log_error(y, ypredict) )
+# Mean absolute error                                                           
+print('Mean absolute error: %.2f' % mean_absolute_error(y, ypredict))
+plt.plot(x, ypredict, "r-")
+plt.plot(x, y ,'ro')
+plt.axis([0.0,1.0,1.5, 7.0])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$y$')
+plt.title(r'Linear Regression fit ')
+plt.show()
    @@ -1656,39 +1656,39 @@ After having downloaded this file to our own computer, we are now ready to read

    We start with preparing folders for storing our calculations and the data file over masses and binding energies. We import also various modules that we will find useful in order to present various Machine Learning methods. Here we focus mainly on the functionality of scikit-learn.

    -
    # Common imports
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import sklearn.linear_model as skl
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
-import os
+
    # Common imports
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import sklearn.linear_model as skl
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
+import os
 
-# Where to save the figures and data files
-PROJECT_ROOT_DIR = "Results"
-FIGURE_ID = "Results/FigureFiles"
-DATA_ID = "DataFiles/"
+# Where to save the figures and data files
+PROJECT_ROOT_DIR = "Results"
+FIGURE_ID = "Results/FigureFiles"
+DATA_ID = "DataFiles/"
 
-if not os.path.exists(PROJECT_ROOT_DIR):
-    os.mkdir(PROJECT_ROOT_DIR)
+if not os.path.exists(PROJECT_ROOT_DIR):
+    os.mkdir(PROJECT_ROOT_DIR)
 
-if not os.path.exists(FIGURE_ID):
-    os.makedirs(FIGURE_ID)
+if not os.path.exists(FIGURE_ID):
+    os.makedirs(FIGURE_ID)
 
-if not os.path.exists(DATA_ID):
-    os.makedirs(DATA_ID)
+if not os.path.exists(DATA_ID):
+    os.makedirs(DATA_ID)
 
-def image_path(fig_id):
-    return os.path.join(FIGURE_ID, fig_id)
+def image_path(fig_id):
+    return os.path.join(FIGURE_ID, fig_id)
 
-def data_path(dat_id):
-    return os.path.join(DATA_ID, dat_id)
+def data_path(dat_id):
+    return os.path.join(DATA_ID, dat_id)
 
-def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+def save_fig(fig_id):
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
-infile = open(data_path("MassEval2016.dat"),'r')
+infile = open(data_path("MassEval2016.dat"),'r')
    @@ -1701,16 +1701,16 @@ data) to actually open the file and simply take a look at it!

    In particular, the program that outputs the final nuclear masses is written in Fortran with a specific format. It means that we need to figure out the format and which columns contain the data we are interested in. Pandas comes with a function that reads formatted output. After having admired the file, we are now ready to start massaging it with pandas. The file begins with some basic format information.

    -
    """                                                                                                                         
-This is taken from the data file of the mass 2016 evaluation.                                                               
-All files are 3436 lines long with 124 character per line.                                                                  
-       Headers are 39 lines long.                                                                                           
-   col 1     :  Fortran character control: 1 = page feed  0 = line feed                                                     
-   format    :  a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5                     
-   These formats are reflected in the pandas widths variable below, see the statement                                       
-   widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),                                                            
-   Pandas has also a variable header, with length 39 in this case.                                                          
-"""
+
    """                                                                                                                         
+This is taken from the data file of the mass 2016 evaluation.                                                               
+All files are 3436 lines long with 124 character per line.                                                                  
+       Headers are 39 lines long.                                                                                           
+   col 1     :  Fortran character control: 1 = page feed  0 = line feed                                                     
+   format    :  a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5                     
+   These formats are reflected in the pandas widths variable below, see the statement                                       
+   widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),                                                            
+   Pandas has also a variable header, with length 39 in this case.                                                          
+"""
    @@ -1721,24 +1721,24 @@ respectively. We add also for the sake of completeness the element name. The dat covert them into the pandas DataFrame structure.

    -
    # Read the experimental data with Pandas
-Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),
-              names=('N', 'Z', 'A', 'Element', 'Ebinding'),
-              widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),
-              header=39,
-              index_col=False)
+
    # Read the experimental data with Pandas
+Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),
+              names=('N', 'Z', 'A', 'Element', 'Ebinding'),
+              widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),
+              header=39,
+              index_col=False)
 
-# Extrapolated values are indicated by '#' in place of the decimal place, so
-# the Ebinding column won't be numeric. Coerce to float and drop these entries.
-Masses['Ebinding'] = pd.to_numeric(Masses['Ebinding'], errors='coerce')
-Masses = Masses.dropna()
-# Convert from keV to MeV.
-Masses['Ebinding'] /= 1000
+# Extrapolated values are indicated by '#' in place of the decimal place, so
+# the Ebinding column won't be numeric. Coerce to float and drop these entries.
+Masses['Ebinding'] = pd.to_numeric(Masses['Ebinding'], errors='coerce')
+Masses = Masses.dropna()
+# Convert from keV to MeV.
+Masses['Ebinding'] /= 1000
 
-# Group the DataFrame by nucleon number, A.
-Masses = Masses.groupby('A')
-# Find the rows of the grouped DataFrame with the maximum binding energy.
-Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])
+# Group the DataFrame by nucleon number, A.
+Masses = Masses.groupby('A')
+# Find the rows of the grouped DataFrame with the maximum binding energy.
+Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])
    @@ -1754,31 +1754,12 @@ to make some simple fits using both the functionalities in numpy\(A\), the number of protons \(Z\) and the number of neutrons \(N\), the element name and finally the energies themselves.

    -
    A = Masses['A']
-Z = Masses['Z']
-N = Masses['N']
-Element = Masses['Element']
-Energies = Masses['Ebinding']
-print(Masses)
-
    -
    -
    -
    -
                N    Z    A Element  Ebinding
-A                                        
-4   0       1    3    4      Li  1.153760
-5   2       3    2    5      He  5.512132
-6   7       3    3    6      Li  5.332331
-7   12      4    3    7      Li  5.606439
-8   17      4    4    8      Be  7.062435
-...       ...  ...  ...     ...       ...
-264 3297  156  108  264      Hs  7.298375
-265 3303  157  108  265      Hs  7.296247
-266 3310  158  108  266      Hs  7.298273
-269 3331  159  110  269      Ds  7.250154
-270 3337  160  110  270      Ds  7.253775
-
-[264 rows x 5 columns]
+
    A = Masses['A']
+Z = Masses['Z']
+N = Masses['N']
+Element = Masses['Element']
+Energies = Masses['Ebinding']
+print(Masses)
    @@ -1787,13 +1768,13 @@ A It has dimensionality \(p\times n\), where \(n\) is the number of data points and \(p\) are the so-called predictors. In our case here they are given by the number of polynomials in \(A\) we wish to include in the fit.

    -
    # Now we set up the design matrix X
-X = np.zeros((len(A),5))
-X[:,0] = 1
-X[:,1] = A
-X[:,2] = A**(2.0/3.0)
-X[:,3] = A**(-1.0/3.0)
-X[:,4] = A**(-1.0)
+
    # Now we set up the design matrix X
+X = np.zeros((len(A),5))
+X[:,0] = 1
+X[:,1] = A
+X[:,2] = A**(2.0/3.0)
+X[:,3] = A**(-1.0/3.0)
+X[:,4] = A**(-1.0)
    @@ -1801,8 +1782,8 @@ It has dimensionality \(p\times n\)With scikitlearn we are now ready to use linear regression and fit our data.

    -
    clf = skl.LinearRegression().fit(X, Energies)
-fity = clf.predict(X)
+
    clf = skl.LinearRegression().fit(X, Energies)
+fity = clf.predict(X)
    @@ -1811,39 +1792,29 @@ It has dimensionality \(p\times n\)
    -
    # The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(Energies, fity))
-# Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(Energies, fity))
-# Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(Energies, fity))
-print(clf.coef_, clf.intercept_)
+
    # The mean squared error                               
+print("Mean squared error: %.2f" % mean_squared_error(Energies, fity))
+# Explained variance score: 1 is perfect prediction                                 
+print('Variance score: %.2f' % r2_score(Energies, fity))
+# Mean absolute error                                                           
+print('Mean absolute error: %.2f' % mean_absolute_error(Energies, fity))
+print(clf.coef_, clf.intercept_)
 
-Masses['Eapprox']  = fity
-# Generate a plot comparing the experimental with the fitted values values.
-fig, ax = plt.subplots()
-ax.set_xlabel(r'$A = N + Z$')
-ax.set_ylabel(r'$E_\mathrm{bind}\,/\mathrm{MeV}$')
-ax.plot(Masses['A'], Masses['Ebinding'], alpha=0.7, lw=2,
-            label='Ame2016')
-ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.7, lw=2, c='m',
-            label='Fit')
-ax.legend()
-save_fig("Masses2016")
-plt.show()
+Masses['Eapprox']  = fity
+# Generate a plot comparing the experimental with the fitted values values.
+fig, ax = plt.subplots()
+ax.set_xlabel(r'$A = N + Z$')
+ax.set_ylabel(r'$E_\mathrm{bind}\,/\mathrm{MeV}$')
+ax.plot(Masses['A'], Masses['Ebinding'], alpha=0.7, lw=2,
+            label='Ame2016')
+ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.7, lw=2, c='m',
+            label='Fit')
+ax.legend()
+save_fig("Masses2016")
+plt.show()
    -
    -
    Mean squared error: 0.02
-Variance score: 0.95
-Mean absolute error: 0.05
-[ 0.00000000e+00 -2.96611194e-02  2.01719003e-01  1.08078025e+01
- -4.03097597e+01] 5.294399745619595
-
    -
    -_images/df7b764b543c6e41eeaa5ee2d1d6e85e9f6c059c7cab9b3fc7a2f38d7dfb90ec.png -
    @@ -1852,91 +1823,40 @@ Mean absolute error: 0.05 functionality.

    -
    from sklearn.neural_network import MLPRegressor
-from sklearn.metrics import accuracy_score
-import seaborn as sns
+
    from sklearn.neural_network import MLPRegressor
+from sklearn.metrics import accuracy_score
+import seaborn as sns
 
 
-X_train = X
-Y_train = Energies
-n_hidden_neurons = 50
-epochs = 100
-# store models for later use
-eta_vals = np.logspace(-3, 0, 4)
-lmbd_vals = np.logspace(-3, 0, 4)
-# store the models for later use
-DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
-train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
-sns.set()
-for i, eta in enumerate(eta_vals):
-    for j, lmbd in enumerate(lmbd_vals):
-        dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='relu', solver='adam',
-                            alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
-        dnn.fit(X_train, Y_train)
-        DNN_scikit[i][j] = dnn
-        train_accuracy[i][j] = dnn.score(X_train, Y_train)
-        fity = dnn.predict(X_train)
-        MSE = mean_squared_error(Y_train, fity)
-        print("Mean squared error: %.2f" % mean_squared_error(Y_train, fity))
-        train_accuracy[i][j] = MSE
-fig, ax = plt.subplots(figsize = (10, 10))
-sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
-ax.set_title("Training Accuracy")
-ax.set_ylabel("$\eta$")
-ax.set_xlabel("$\lambda$")
-plt.show()
-print(train_accuracy)
-
    
-
    
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Mean squared error: 1.73
-Mean squared error: 19.48
-Mean squared error: 12.11
-Mean squared error: 23.42
-Mean squared error: 0.23
-Mean squared error: 0.18
-Mean squared error: 0.29
-Mean squared error: 0.20
-Mean squared error: 0.26
-Mean squared error: 4.07
-Mean squared error: 2.12
-Mean squared error: 7.54
-
    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
    -
    -
    Mean squared error: 15.78
-Mean squared error: 122.02
-Mean squared error: 51.22
-Mean squared error: 152.55
-
    -
    -_images/1b1c59fe24a1c61677966d3734d014c13848c51a6e194c77783efd4db527d26b.png -
    [[  1.7304881   19.47958494  12.11340253  23.41589548]
- [  0.22948497   0.18392847   0.29364655   0.20072279]
- [  0.26303845   4.06730814   2.11590451   7.5411205 ]
- [ 15.77893972 122.02334824  51.2167072  152.54894451]]
+X_train = X
+Y_train = Energies
+n_hidden_neurons = 50
+epochs = 100
+# store models for later use
+eta_vals = np.logspace(-3, 0, 4)
+lmbd_vals = np.logspace(-3, 0, 4)
+# store the models for later use
+DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
+train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+sns.set()
+for i, eta in enumerate(eta_vals):
+    for j, lmbd in enumerate(lmbd_vals):
+        dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='relu', solver='adam',
+                            alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
+        dnn.fit(X_train, Y_train)
+        DNN_scikit[i][j] = dnn
+        train_accuracy[i][j] = dnn.score(X_train, Y_train)
+        fity = dnn.predict(X_train)
+        MSE = mean_squared_error(Y_train, fity)
+        print("Mean squared error: %.2f" % mean_squared_error(Y_train, fity))
+        train_accuracy[i][j] = MSE
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+print(train_accuracy)
    @@ -2145,75 +2065,75 @@ our matrix as \(\boldsymbol{X}\in {\m

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

    -
    # Common imports
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from IPython.display import display
-import os
+
    # Common imports
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from IPython.display import display
+import os
 
-# Where to save the figures and data files
-PROJECT_ROOT_DIR = "Results"
-FIGURE_ID = "Results/FigureFiles"
-DATA_ID = "DataFiles/"
+# Where to save the figures and data files
+PROJECT_ROOT_DIR = "Results"
+FIGURE_ID = "Results/FigureFiles"
+DATA_ID = "DataFiles/"
 
-if not os.path.exists(PROJECT_ROOT_DIR):
-    os.mkdir(PROJECT_ROOT_DIR)
+if not os.path.exists(PROJECT_ROOT_DIR):
+    os.mkdir(PROJECT_ROOT_DIR)
 
-if not os.path.exists(FIGURE_ID):
-    os.makedirs(FIGURE_ID)
+if not os.path.exists(FIGURE_ID):
+    os.makedirs(FIGURE_ID)
 
-if not os.path.exists(DATA_ID):
-    os.makedirs(DATA_ID)
+if not os.path.exists(DATA_ID):
+    os.makedirs(DATA_ID)
 
-def image_path(fig_id):
-    return os.path.join(FIGURE_ID, fig_id)
+def image_path(fig_id):
+    return os.path.join(FIGURE_ID, fig_id)
 
-def data_path(dat_id):
-    return os.path.join(DATA_ID, dat_id)
+def data_path(dat_id):
+    return os.path.join(DATA_ID, dat_id)
 
-def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+def save_fig(fig_id):
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
-infile = open(data_path("MassEval2016.dat"),'r')
+infile = open(data_path("MassEval2016.dat"),'r')
 
 
-# Read the experimental data with Pandas
-Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),
-              names=('N', 'Z', 'A', 'Element', 'Ebinding'),
-              widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),
-              header=39,
-              index_col=False)
+# Read the experimental data with Pandas
+Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),
+              names=('N', 'Z', 'A', 'Element', 'Ebinding'),
+              widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),
+              header=39,
+              index_col=False)
 
-# Extrapolated values are indicated by '#' in place of the decimal place, so
-# the Ebinding column won't be numeric. Coerce to float and drop these entries.
-Masses['Ebinding'] = pd.to_numeric(Masses['Ebinding'], errors='coerce')
-Masses = Masses.dropna()
-# Convert from keV to MeV.
-Masses['Ebinding'] /= 1000
+# Extrapolated values are indicated by '#' in place of the decimal place, so
+# the Ebinding column won't be numeric. Coerce to float and drop these entries.
+Masses['Ebinding'] = pd.to_numeric(Masses['Ebinding'], errors='coerce')
+Masses = Masses.dropna()
+# Convert from keV to MeV.
+Masses['Ebinding'] /= 1000
 
-# Group the DataFrame by nucleon number, A.
-Masses = Masses.groupby('A')
-# Find the rows of the grouped DataFrame with the maximum binding energy.
-Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])
-A = Masses['A']
-Z = Masses['Z']
-N = Masses['N']
-Element = Masses['Element']
-Energies = Masses['Ebinding']
+# Group the DataFrame by nucleon number, A.
+Masses = Masses.groupby('A')
+# Find the rows of the grouped DataFrame with the maximum binding energy.
+Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])
+A = Masses['A']
+Z = Masses['Z']
+N = Masses['N']
+Element = Masses['Element']
+Energies = Masses['Ebinding']
 
-# Now we set up the design matrix X
-X = np.zeros((len(A),5))
-X[:,0] = 1
-X[:,1] = A
-X[:,2] = A**(2.0/3.0)
-X[:,3] = A**(-1.0/3.0)
-X[:,4] = A**(-1.0)
-# Then nice printout using pandas
-DesignMatrix = pd.DataFrame(X)
-DesignMatrix.index = A
-DesignMatrix.columns = ['1', 'A', 'A^(2/3)', 'A^(-1/3)', '1/A']
-display(DesignMatrix)
+# Now we set up the design matrix X
+X = np.zeros((len(A),5))
+X[:,0] = 1
+X[:,1] = A
+X[:,2] = A**(2.0/3.0)
+X[:,3] = A**(-1.0/3.0)
+X[:,4] = A**(-1.0)
+# Then nice printout using pandas
+DesignMatrix = pd.DataFrame(X)
+DesignMatrix.index = A
+DesignMatrix.columns = ['1', 'A', 'A^(2/3)', 'A^(-1/3)', '1/A']
+display(DesignMatrix)
    @@ -2349,10 +2269,10 @@ allow for the usage of direct linear algebra methods such as LU write

    -
    # matrix inversion to find beta
-beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)
-# and then make the prediction
-ytilde = X @ beta
+
    # matrix inversion to find beta
+beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)
+# and then make the prediction
+ytilde = X @ beta
    @@ -2360,8 +2280,8 @@ write

    Alternatively, you can use the least squares functionality in Numpy as

    -
    fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
-ytildenp = np.dot(fit,X.T)
+
    fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
+ytildenp = np.dot(fit,X.T)
    @@ -2369,18 +2289,18 @@ write

    And finally we plot our fit with and compare with data

    -
    Masses['Eapprox']  = ytilde
-# Generate a plot comparing the experimental with the fitted values values.
-fig, ax = plt.subplots()
-ax.set_xlabel(r'$A = N + Z$')
-ax.set_ylabel(r'$E_\mathrm{bind}\,/\mathrm{MeV}$')
-ax.plot(Masses['A'], Masses['Ebinding'], alpha=0.7, lw=2,
-            label='Ame2016')
-ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.7, lw=2, c='m',
-            label='Fit')
-ax.legend()
-save_fig("Masses2016OLS")
-plt.show()
+
    Masses['Eapprox']  = ytilde
+# Generate a plot comparing the experimental with the fitted values values.
+fig, ax = plt.subplots()
+ax.set_xlabel(r'$A = N + Z$')
+ax.set_ylabel(r'$E_\mathrm{bind}\,/\mathrm{MeV}$')
+ax.plot(Masses['A'], Masses['Ebinding'], alpha=0.7, lw=2,
+            label='Ame2016')
+ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.7, lw=2, c='m',
+            label='Fit')
+ax.legend()
+save_fig("Masses2016OLS")
+plt.show()
    @@ -2392,8 +2312,8 @@ write

    Since we are not using Scikit-Learn here we can define our own \(R2\) function as

    -
    def R2(y_data, y_model):
-    return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)
+
    def R2(y_data, y_model):
+    return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)
    @@ -2401,7 +2321,7 @@ Since we are not using Scikit-Learn here we can define our own

    and we would be using it as

    -
    print(R2(Energies,ytilde))
+
    print(R2(Energies,ytilde))
    @@ -2409,11 +2329,11 @@ Since we are not using Scikit-Learn here we can define our own

    We can easily add our MSE score as

    -
    def MSE(y_data,y_model):
-    n = np.size(y_model)
-    return np.sum((y_data-y_model)**2)/n
+
    def MSE(y_data,y_model):
+    n = np.size(y_model)
+    return np.sum((y_data-y_model)**2)/n
 
-print(MSE(Energies,ytilde))
+print(MSE(Energies,ytilde))
    @@ -2421,9 +2341,9 @@ Since we are not using Scikit-Learn here we can define our own

    and finally the relative error as

    -
    def RelativeError(y_data,y_model):
-    return abs((y_data-y_model)/y_data)
-print(RelativeError(Energies, ytilde))
+
    def RelativeError(y_data,y_model):
+    return abs((y_data-y_model)/y_data)
+print(RelativeError(Energies, ytilde))
    @@ -2657,7 +2577,7 @@ Singular Value Decomposition (SVD) method discussed next week.

  • What Is Generative Modeling?
  • Example of generative modeling, taken from Generative Deep Learning by David Foster
  • Generative Versus Discriminative Modeling
    • -
  • Example of discriminative modeling, taken from Generative Deeep Learning by David Foster
    • +
  • Example of discriminative modeling, taken from Generative Deep Learning by David Foster
  • Discriminative Modeling
  • A Frequentist approach to data analysis
  • What is a good model?
    • diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb index a594ee228..f7aa1b4cc 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3b47d5e6", + "id": "0b9e53e2", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "5f010738", + "id": "d69d6c55", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "9821466c", + "id": "ed7a2caa", "metadata": { "editable": true }, @@ -52,7 +52,7 @@ }, { "cell_type": "markdown", - "id": "d635526f", + "id": "1a7fa793", "metadata": { "editable": true }, @@ -68,7 +68,7 @@ }, { "cell_type": "markdown", - "id": "46d1ebd2", + "id": "42eed58e", "metadata": { "editable": true }, @@ -92,7 +92,7 @@ }, { "cell_type": "markdown", - "id": "722a1812", + "id": "19b78895", "metadata": { "editable": true }, @@ -105,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "8724a41b", + "id": "920c8b78", "metadata": { "editable": true }, @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "a74065a9", + "id": "66791ac8", "metadata": { "editable": true }, @@ -157,7 +157,7 @@ }, { "cell_type": "markdown", - "id": "cb3519d1", + "id": "d006c6f2", "metadata": { "editable": true }, @@ -175,7 +175,7 @@ }, { "cell_type": "markdown", - "id": "f46e4ab5", + "id": "d2b59d04", "metadata": { "editable": true }, @@ -203,7 +203,7 @@ }, { "cell_type": "markdown", - "id": "18134f7b", + "id": "2b1dfff8", "metadata": { "editable": true }, @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "6e4b2cb4", + "id": "39ce46e9", "metadata": { "editable": true }, @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "2dc04ba4", + "id": "cc1139fe", "metadata": { "editable": true }, @@ -261,7 +261,7 @@ }, { "cell_type": "markdown", - "id": "b9d7446a", + "id": "f56c0bb5", "metadata": { "editable": true }, @@ -273,7 +273,7 @@ }, { "cell_type": "markdown", - "id": "0ac913df", + "id": "4b601e8d", "metadata": { "editable": true }, @@ -293,7 +293,7 @@ }, { "cell_type": "markdown", - "id": "fe4f142d", + "id": "2518a99e", "metadata": { "editable": true }, @@ -311,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "82a26310", + "id": "a0e1e9ae", "metadata": { "editable": true }, @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "5d97a91f", + "id": "8af343cb", "metadata": { "editable": true }, @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "0af85fa1", + "id": "c0cfedb4", "metadata": { "editable": true }, @@ -382,7 +382,7 @@ }, { "cell_type": "markdown", - "id": "ca1c0d4b", + "id": "4561f563", "metadata": { "editable": true }, @@ -398,7 +398,7 @@ }, { "cell_type": "markdown", - "id": "5e3a4194", + "id": "b94446df", "metadata": { "editable": true }, @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "44fe97f3", + "id": "2da0b611", "metadata": { "editable": true }, @@ -438,7 +438,7 @@ }, { "cell_type": "markdown", - "id": "3fd61872", + "id": "cb6368d0", "metadata": { "editable": true }, @@ -468,7 +468,7 @@ }, { "cell_type": "markdown", - "id": "50148149", + "id": "cd5643ee", "metadata": { "editable": true }, @@ -497,7 +497,7 @@ }, { "cell_type": "markdown", - "id": "12bc5144", + "id": "dad2568b", "metadata": { "editable": true }, @@ -515,7 +515,7 @@ }, { "cell_type": "markdown", - "id": "25428d4e", + "id": "ccf34c1f", "metadata": { "editable": true }, @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "2efac50e", + "id": "d9a80c4f", "metadata": { "editable": true }, @@ -549,7 +549,7 @@ }, { "cell_type": "markdown", - "id": "dfd0c6a0", + "id": "bf1454b7", "metadata": { "editable": true }, @@ -572,7 +572,7 @@ }, { "cell_type": "markdown", - "id": "f92f8d35", + "id": "4a01a6f0", "metadata": { "editable": true }, @@ -588,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "174d6e96", + "id": "2bf27180", "metadata": { "editable": true }, @@ -604,12 +604,12 @@ }, { "cell_type": "markdown", - "id": "0fcac42b", + "id": "15ddfa02", "metadata": { "editable": true }, "source": [ - "## Example of discriminative modeling, [taken from Generative Deeep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n", + "## Example of discriminative modeling, [taken from Generative Deep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n", "\n", "\n", "\n", @@ -620,7 +620,7 @@ }, { "cell_type": "markdown", - "id": "a8942d9a", + "id": "371a54c3", "metadata": { "editable": true }, @@ -640,7 +640,7 @@ }, { "cell_type": "markdown", - "id": "a28ea0e4", + "id": "f9ec1f0a", "metadata": { "editable": true }, @@ -675,7 +675,7 @@ }, { "cell_type": "markdown", - "id": "d3260f00", + "id": "19f2aef4", "metadata": { "editable": true }, @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "4ac16500", + "id": "e08bd4e6", "metadata": { "editable": true }, @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "825ca09e", + "id": "729c3f46", "metadata": { "editable": true }, @@ -775,7 +775,7 @@ }, { "cell_type": "markdown", - "id": "40bd0bf2", + "id": "6825a222", "metadata": { "editable": true }, @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "a356fdf0", + "id": "bad98b2e", "metadata": { "editable": true }, @@ -845,7 +845,7 @@ }, { "cell_type": "markdown", - "id": "a9730b8a", + "id": "1daa92e1", "metadata": { "editable": true }, @@ -869,7 +869,7 @@ }, { "cell_type": "markdown", - "id": "8f967a6a", + "id": "1c840067", "metadata": { "editable": true }, @@ -896,7 +896,7 @@ }, { "cell_type": "markdown", - "id": "76e167a3", + "id": "f33d6379", "metadata": { "editable": true }, @@ -906,7 +906,7 @@ }, { "cell_type": "markdown", - "id": "94a3c420", + "id": "e3a03646", "metadata": { "editable": true }, @@ -918,7 +918,7 @@ }, { "cell_type": "markdown", - "id": "c6ff6b6b", + "id": "fb6d2495", "metadata": { "editable": true }, @@ -939,7 +939,7 @@ }, { "cell_type": "markdown", - "id": "55dbb002", + "id": "3101a680", "metadata": { "editable": true }, @@ -951,13 +951,10 @@ { "cell_type": "code", "execution_count": 1, - "id": "655878bd", + "id": "66625f81", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -966,7 +963,7 @@ }, { "cell_type": "markdown", - "id": "914fa12c", + "id": "d8edb94a", "metadata": { "editable": true }, @@ -977,13 +974,10 @@ { "cell_type": "code", "execution_count": 2, - "id": "a0cfca17", + "id": "7d44772f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -994,7 +988,7 @@ }, { "cell_type": "markdown", - "id": "342c0109", + "id": "7c3a5d5e", "metadata": { "editable": true }, @@ -1006,13 +1000,10 @@ { "cell_type": "code", "execution_count": 3, - "id": "d09ad51a", + "id": "bd150398", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1023,7 +1014,7 @@ }, { "cell_type": "markdown", - "id": "be9009bc", + "id": "62bb511e", "metadata": { "editable": true }, @@ -1035,13 +1026,10 @@ { "cell_type": "code", "execution_count": 4, - "id": "19dfe7ab", + "id": "de9d1b88", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1052,7 +1040,7 @@ }, { "cell_type": "markdown", - "id": "5c78e90e", + "id": "575f4f67", "metadata": { "editable": true }, @@ -1069,13 +1057,10 @@ { "cell_type": "code", "execution_count": 5, - "id": "48603a4c", + "id": "eadd4613", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1089,7 +1074,7 @@ }, { "cell_type": "markdown", - "id": "db5cfa34", + "id": "43c2b238", "metadata": { "editable": true }, @@ -1101,13 +1086,10 @@ { "cell_type": "code", "execution_count": 6, - "id": "1c252e6c", + "id": "f1153763", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1118,7 +1100,7 @@ }, { "cell_type": "markdown", - "id": "375afb81", + "id": "b897cb85", "metadata": { "editable": true }, @@ -1129,13 +1111,10 @@ { "cell_type": "code", "execution_count": 7, - "id": "4c87de99", + "id": "3417f7b5", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1146,7 +1125,7 @@ }, { "cell_type": "markdown", - "id": "afff17c8", + "id": "421de263", "metadata": { "editable": true }, @@ -1157,13 +1136,10 @@ { "cell_type": "code", "execution_count": 8, - "id": "d5827065", + "id": "e9055b4f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1174,7 +1150,7 @@ }, { "cell_type": "markdown", - "id": "83e054e8", + "id": "52301fe3", "metadata": { "editable": true }, @@ -1189,13 +1165,10 @@ { "cell_type": "code", "execution_count": 9, - "id": "0cd4400a", + "id": "7f400b7f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1206,7 +1179,7 @@ }, { "cell_type": "markdown", - "id": "c2868228", + "id": "35677f44", "metadata": { "editable": true }, @@ -1217,13 +1190,10 @@ { "cell_type": "code", "execution_count": 10, - "id": "6e7ef557", + "id": "16b1c27a", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1235,7 +1205,7 @@ }, { "cell_type": "markdown", - "id": "47391e12", + "id": "7566ab17", "metadata": { "editable": true }, @@ -1246,13 +1216,10 @@ { "cell_type": "code", "execution_count": 11, - "id": "a9457054", + "id": "b10affa1", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1264,7 +1231,7 @@ }, { "cell_type": "markdown", - "id": "a04c51eb", + "id": "c50f969e", "metadata": { "editable": true }, @@ -1275,13 +1242,10 @@ { "cell_type": "code", "execution_count": 12, - "id": "43e1f145", + "id": "35f1236f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1294,7 +1258,7 @@ }, { "cell_type": "markdown", - "id": "e11ea608", + "id": "02cec13a", "metadata": { "editable": true }, @@ -1305,13 +1269,10 @@ { "cell_type": "code", "execution_count": 13, - "id": "ed021bcf", + "id": "821bc62d", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1324,7 +1285,7 @@ }, { "cell_type": "markdown", - "id": "7cf97a5d", + "id": "5df31e24", "metadata": { "editable": true }, @@ -1335,13 +1296,10 @@ { "cell_type": "code", "execution_count": 14, - "id": "0dd77598", + "id": "d821d3ff", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1354,7 +1312,7 @@ }, { "cell_type": "markdown", - "id": "a27628a3", + "id": "a99c04f6", "metadata": { "editable": true }, @@ -1366,7 +1324,7 @@ }, { "cell_type": "markdown", - "id": "80585c1d", + "id": "fdb302f5", "metadata": { "editable": true }, @@ -1381,7 +1339,7 @@ }, { "cell_type": "markdown", - "id": "74725f56", + "id": "4c91d693", "metadata": { "editable": true }, @@ -1391,7 +1349,7 @@ }, { "cell_type": "markdown", - "id": "fd223a73", + "id": "316ccd01", "metadata": { "editable": true }, @@ -1403,7 +1361,7 @@ }, { "cell_type": "markdown", - "id": "40dfa247", + "id": "73cf195b", "metadata": { "editable": true }, @@ -1414,7 +1372,7 @@ }, { "cell_type": "markdown", - "id": "56e47db3", + "id": "c9d6f1ba", "metadata": { "editable": true }, @@ -1429,7 +1387,7 @@ }, { "cell_type": "markdown", - "id": "26251aec", + "id": "a5bbc329", "metadata": { "editable": true }, @@ -1444,13 +1402,10 @@ { "cell_type": "code", "execution_count": 15, - "id": "dab44ca5", + "id": "74dd353c", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1474,13 +1429,10 @@ { "cell_type": "code", "execution_count": 16, - "id": "58c875cb", + "id": "f07645f8", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1501,7 +1453,7 @@ }, { "cell_type": "markdown", - "id": "8d735077", + "id": "245ee493", "metadata": { "editable": true }, @@ -1527,13 +1479,10 @@ { "cell_type": "code", "execution_count": 17, - "id": "d60ac131", + "id": "e428bcdc", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1550,7 +1499,7 @@ }, { "cell_type": "markdown", - "id": "06e4b620", + "id": "861269ad", "metadata": { "editable": true }, @@ -1564,13 +1513,10 @@ { "cell_type": "code", "execution_count": 18, - "id": "2174392e", + "id": "c34c9894", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1580,7 +1526,7 @@ }, { "cell_type": "markdown", - "id": "72c17dd0", + "id": "0ec5724d", "metadata": { "editable": true }, @@ -1591,13 +1537,10 @@ { "cell_type": "code", "execution_count": 19, - "id": "7612f9de", + "id": "83302e08", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1606,7 +1549,7 @@ }, { "cell_type": "markdown", - "id": "242d4c4b", + "id": "886277ee", "metadata": { "editable": true }, @@ -1617,13 +1560,10 @@ { "cell_type": "code", "execution_count": 20, - "id": "0171d1ad", + "id": "c1010af7", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1638,7 +1578,7 @@ }, { "cell_type": "markdown", - "id": "e5232bfb", + "id": "f3c82384", "metadata": { "editable": true }, @@ -1650,13 +1590,10 @@ { "cell_type": "code", "execution_count": 21, - "id": "cb2ab622", + "id": "70542372", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1677,7 +1614,7 @@ }, { "cell_type": "markdown", - "id": "1998bc9c", + "id": "a83eb591", "metadata": { "editable": true }, @@ -1688,13 +1625,10 @@ { "cell_type": "code", "execution_count": 22, - "id": "e0ec9915", + "id": "2f42295f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1720,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "ebe44ad1", + "id": "fb1b0336", "metadata": { "editable": true }, @@ -1731,13 +1665,10 @@ { "cell_type": "code", "execution_count": 23, - "id": "6f6d21e6", + "id": "0438c751", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1749,7 +1680,7 @@ }, { "cell_type": "markdown", - "id": "2f97c531", + "id": "f6d3d4c8", "metadata": { "editable": true }, @@ -1766,7 +1697,7 @@ }, { "cell_type": "markdown", - "id": "36a57ec4", + "id": "d552a0d0", "metadata": { "editable": true }, @@ -1778,7 +1709,7 @@ }, { "cell_type": "markdown", - "id": "ca69e9e9", + "id": "b96bd9e5", "metadata": { "editable": true }, @@ -1809,7 +1740,7 @@ }, { "cell_type": "markdown", - "id": "b4bc6d8a", + "id": "41318c5c", "metadata": { "editable": true }, @@ -1821,7 +1752,7 @@ }, { "cell_type": "markdown", - "id": "dd9f61d0", + "id": "789c6fe4", "metadata": { "editable": true }, @@ -1849,13 +1780,10 @@ { "cell_type": "code", "execution_count": 24, - "id": "81d8ecc3", + "id": "8c7472f2", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1882,7 +1810,7 @@ }, { "cell_type": "markdown", - "id": "b9f8b0e5", + "id": "25cef2d3", "metadata": { "editable": true }, @@ -1899,7 +1827,7 @@ }, { "cell_type": "markdown", - "id": "37a6973d", + "id": "58a04438", "metadata": { "editable": true }, @@ -1911,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "3b1616dd", + "id": "55191297", "metadata": { "editable": true }, @@ -1932,7 +1860,7 @@ }, { "cell_type": "markdown", - "id": "02f59dce", + "id": "2951bf64", "metadata": { "editable": true }, @@ -1945,7 +1873,7 @@ }, { "cell_type": "markdown", - "id": "37a3928f", + "id": "1109ceeb", "metadata": { "editable": true }, @@ -1976,7 +1904,7 @@ }, { "cell_type": "markdown", - "id": "8ced1c75", + "id": "7eeca264", "metadata": { "editable": true }, @@ -1988,7 +1916,7 @@ }, { "cell_type": "markdown", - "id": "2b2c7527", + "id": "fd8b7402", "metadata": { "editable": true }, @@ -2006,13 +1934,10 @@ { "cell_type": "code", "execution_count": 25, - "id": "dcc4e935", + "id": "0de5719f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2036,7 +1961,7 @@ }, { "cell_type": "markdown", - "id": "ef0a62d7", + "id": "ce194339", "metadata": { "editable": true }, @@ -2058,13 +1983,10 @@ { "cell_type": "code", "execution_count": 26, - "id": "e2a9bb21", + "id": "cde68f17", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2099,7 +2021,7 @@ }, { "cell_type": "markdown", - "id": "a6eca083", + "id": "f48a129c", "metadata": { "editable": true }, @@ -2110,7 +2032,7 @@ }, { "cell_type": "markdown", - "id": "c940fd70", + "id": "c337129d", "metadata": { "editable": true }, @@ -2123,7 +2045,7 @@ }, { "cell_type": "markdown", - "id": "7b1317ef", + "id": "d6462e3c", "metadata": { "editable": true }, @@ -2144,7 +2066,7 @@ }, { "cell_type": "markdown", - "id": "3895d32d", + "id": "c2a5a5fd", "metadata": { "editable": true }, @@ -2156,7 +2078,7 @@ }, { "cell_type": "markdown", - "id": "b88937a4", + "id": "d19d16f8", "metadata": { "editable": true }, @@ -2166,7 +2088,7 @@ }, { "cell_type": "markdown", - "id": "7d62abca", + "id": "7739ec09", "metadata": { "editable": true }, @@ -2178,7 +2100,7 @@ }, { "cell_type": "markdown", - "id": "903ee760", + "id": "7f433018", "metadata": { "editable": true }, @@ -2190,7 +2112,7 @@ }, { "cell_type": "markdown", - "id": "ef89ae2c", + "id": "20361838", "metadata": { "editable": true }, @@ -2202,7 +2124,7 @@ }, { "cell_type": "markdown", - "id": "924ac782", + "id": "02d95914", "metadata": { "editable": true }, @@ -2213,7 +2135,7 @@ }, { "cell_type": "markdown", - "id": "50053e83", + "id": "524bb980", "metadata": { "editable": true }, @@ -2225,7 +2147,7 @@ }, { "cell_type": "markdown", - "id": "dc7df77b", + "id": "6cb0b956", "metadata": { "editable": true }, @@ -2247,7 +2169,7 @@ }, { "cell_type": "markdown", - "id": "7fe669ae", + "id": "142be577", "metadata": { "editable": true }, @@ -2259,7 +2181,7 @@ }, { "cell_type": "markdown", - "id": "28ceb188", + "id": "4e765569", "metadata": { "editable": true }, @@ -2272,7 +2194,7 @@ }, { "cell_type": "markdown", - "id": "d89d075b", + "id": "f79ebfdf", "metadata": { "editable": true }, @@ -2289,7 +2211,7 @@ }, { "cell_type": "markdown", - "id": "b3816234", + "id": "eb09c88a", "metadata": { "editable": true }, @@ -2301,7 +2223,7 @@ }, { "cell_type": "markdown", - "id": "ddf946bd", + "id": "bf69319c", "metadata": { "editable": true }, @@ -2311,7 +2233,7 @@ }, { "cell_type": "markdown", - "id": "ec48e2fd", + "id": "eec8df19", "metadata": { "editable": true }, @@ -2323,7 +2245,7 @@ }, { "cell_type": "markdown", - "id": "462f1772", + "id": "313f9634", "metadata": { "editable": true }, @@ -2333,7 +2255,7 @@ }, { "cell_type": "markdown", - "id": "dabcb01d", + "id": "83b87f63", "metadata": { "editable": true }, @@ -2345,7 +2267,7 @@ }, { "cell_type": "markdown", - "id": "be2a6bd1", + "id": "be859efd", "metadata": { "editable": true }, @@ -2355,7 +2277,7 @@ }, { "cell_type": "markdown", - "id": "eb37d8f8", + "id": "a976e227", "metadata": { "editable": true }, @@ -2367,7 +2289,7 @@ }, { "cell_type": "markdown", - "id": "de325e18", + "id": "9f1dc803", "metadata": { "editable": true }, @@ -2383,7 +2305,7 @@ }, { "cell_type": "markdown", - "id": "2c68a0c1", + "id": "4fe2c4e9", "metadata": { "editable": true }, @@ -2395,7 +2317,7 @@ }, { "cell_type": "markdown", - "id": "f3492a4b", + "id": "bd8e264d", "metadata": { "editable": true }, @@ -2406,7 +2328,7 @@ }, { "cell_type": "markdown", - "id": "207b9ead", + "id": "9a0e9ac7", "metadata": { "editable": true }, @@ -2418,7 +2340,7 @@ }, { "cell_type": "markdown", - "id": "d74c7ddc", + "id": "9b20bc14", "metadata": { "editable": true }, @@ -2432,7 +2354,7 @@ }, { "cell_type": "markdown", - "id": "a02ea08c", + "id": "5fae8c2f", "metadata": { "editable": true }, @@ -2444,7 +2366,7 @@ }, { "cell_type": "markdown", - "id": "b9863119", + "id": "c955d871", "metadata": { "editable": true }, @@ -2469,7 +2391,7 @@ }, { "cell_type": "markdown", - "id": "92ca8aa6", + "id": "10feca33", "metadata": { "editable": true }, @@ -2485,14 +2407,11 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "32b99126", + "execution_count": 27, + "id": "cd57c0cd", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2533,7 +2452,7 @@ }, { "cell_type": "markdown", - "id": "185af862", + "id": "ed4e2094", "metadata": { "editable": true }, @@ -2550,13 +2469,10 @@ { "cell_type": "code", "execution_count": 28, - "id": "2ae9db3d", + "id": "309536df", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2574,7 +2490,7 @@ }, { "cell_type": "markdown", - "id": "1ade787b", + "id": "27597f0a", "metadata": { "editable": true }, @@ -2587,14 +2503,11 @@ }, { "cell_type": "code", - "execution_count": 38, - "id": "e0d18716", + "execution_count": 29, + "id": "1c60fe5f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2620,7 +2533,7 @@ }, { "cell_type": "markdown", - "id": "bd819c63", + "id": "36972bcb", "metadata": { "editable": true }, @@ -2639,38 +2552,13 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "fc0346bc", + "execution_count": 30, + "id": "85ffeaf7", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " N Z A Element Ebinding\n", - "A \n", - "4 0 1 3 4 Li 1.153760\n", - "5 2 3 2 5 He 5.512132\n", - "6 7 3 3 6 Li 5.332331\n", - "7 12 4 3 7 Li 5.606439\n", - "8 17 4 4 8 Be 7.062435\n", - "... ... ... ... ... ...\n", - "264 3297 156 108 264 Hs 7.298375\n", - "265 3303 157 108 265 Hs 7.296247\n", - "266 3310 158 108 266 Hs 7.298273\n", - "269 3331 159 110 269 Ds 7.250154\n", - "270 3337 160 110 270 Ds 7.253775\n", - "\n", - "[264 rows x 5 columns]\n" - ] - } - ], + "outputs": [], "source": [ "A = Masses['A']\n", "Z = Masses['Z']\n", @@ -2682,7 +2570,7 @@ }, { "cell_type": "markdown", - "id": "4f70ca63", + "id": "799627e9", "metadata": { "editable": true }, @@ -2693,14 +2581,11 @@ }, { "cell_type": "code", - "execution_count": 40, - "id": "d56956d6", + "execution_count": 31, + "id": "365fbba9", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2715,7 +2600,7 @@ }, { "cell_type": "markdown", - "id": "0b3df36c", + "id": "0d3c5663", "metadata": { "editable": true }, @@ -2725,14 +2610,11 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "d10ecf29", + "execution_count": 32, + "id": "60ba302b", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2742,7 +2624,7 @@ }, { "cell_type": "markdown", - "id": "2289c78e", + "id": "af13881a", "metadata": { "editable": true }, @@ -2753,38 +2635,13 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "286afac2", + "execution_count": 33, + "id": "a5cba204", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 0.02\n", - "Variance score: 0.95\n", - "Mean absolute error: 0.05\n", - "[ 0.00000000e+00 -2.96611194e-02 2.01719003e-01 1.08078025e+01\n", - " -4.03097597e+01] 5.294399745619595\n" - ] - }, - { - "data": { - "image/png": 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IRQ00toUBGR+ZSSIEFLuCkSO8sDlUjB9djCKvAw6nhgKPAyOqitARCMM08+tDV0oJ05TQYyYMPf5D103osfiPWMxELGoiFjUQixmIRU3o0fj/5WEChl2Lf5dlAzC60IGobnbum6MnTYIGAKkKGIqCmvogntvSBGgKNIeGoiInqsoLEDZMjK0sxLQjSqBwGIooJ6xcuQKmaeIb31iCceOOSHrskksuwyuvvIznn3+u3/edN+9EHHlkNX760xvxgx9cj8rKEXjhhb9gxYoXEyuDfv3re1BbW4O77/41iouL0dzcPU+xuNgHu92Or3zla/jNb36N4mIfRowYiYce+hUqKiqxYMHCPrXD4XDiwgsvxpNPPoby8nJMmzYDr7++EqtX/wv33fdQv39f/cEQM8ztquvAjjo/5kwqgyolNm5sQltTEDt3tSEai09k3e6Pwq7FJ7baNAWGGR+GkZoCaVMgNQUjKj3o0E0omoKzTxiHCp/7gNfSNAU2u9o5xyO/QowQApomoGkK4nGjb6SUMHQrHm6iRrxXJ2IgEtYRixiIRAxEIzqiYQPRiAEFgMuuwWUHvG47YoYFAcCUEu2BWHyDQNOCpcuuhgEihtbGEFq3NANCYLsi8C+XBtWuweHSMG6UF3anDVAVTBxXjBJv38a5iSg9/va3lzB37nEHBBggPt/llFO+gNdfX4kxY8b2676qquLeex/CQw/9CsuW3YhwOIxx48bj9tt/iblzj4NlWXjjjdeg6zquuebKA77+2WdfRFXVSHzrW1fCNE3ceedtiEajOOqoObjnngeShqQO59JLvwWn04lHHvkNmpoaEu3oOXQ1GIQ83LeJec40LbS0HDjM0ReapsDnK0Bra7DXSZr5ri0QxVMrNkCGdDgMCQcOvk+L1BRIuxoPLXYVUAWEokBKiSKPAxd88chEj8LBJqIO9XoOlJQSsagZDzWdQScaMRI/j4R1REI6IhEDHaEYghEDqiJgWhKRmBkfSzscIeBy22AAKPDYMXGcD4VeBwoK7BhR6YGjc5n6cMb3afqlUlNdj6G5eR9KS6sOWDlDcT03/cw3h/vzLSkpgNqH+X/siRmGLEuiodaPN/6xA0pjPOAZnT+6SFWB6rZBuDVEAUCJz0npmpA7fqQXXzxmDBraQhhR4obDpu7/MtRPQgg4nPEJwIdimlYi0MSiJhQhsGNXCwIdMcQiBmrrOxKbCR5ASoSD8ZUNbcEYPqoPJB6yaQpUTYEBoNDrxOiqQtgcGrxFDowbXQR3gQOKMrwDDhHlFoaYYWDr3na8v74ek0Z6UaYp2Lm1BW3tEbQ1J/dQSZsKuDVMmFgKYVMw58hyGIaFlat3w1tgx6K5YxCKGugI6Zg4ygtVUXDECG+WflfDl6oqKPA4UOBxJL7DHXVEcdJ3ZOFwDLGYiT21HdhT0w4YEtGIjn0NAZhREwpwwFwd3bDie+kAaI0E0NoQSHpNIQBTCBQVOVFY5IAOwFfiwswjy+FwabBpKnciJqKM4r84Q1w4auC193fDaI9g9eYmeJ02WFIiGI5/py5VBWMm+FAf1mEqAmccPxbjq5KDyWVnTU0MMRR5HKgqzfhvg/rJ5bLD5QKKilyYMbUicV1KiahuAqbE+m3N2F3jB0wLHR1RtLeFAVNC6VzJ1VPPfXTamoJo61yJVrcN2LC6Jr6JoCpQUuJGRZkbmkPD6FFejK7ywuHkEBURDQ6GmCHMNC28+dZ2mPs6oHR+KHXtJms5NUi3hsoqL875wiRYMr4c2aYdOAbJD6ChQwgBpz3+1/7oGSNw9IwRicfCUQNCAKoisG5bM+obAlAtoKEpiJbmEFQJwLRg9TYGLyWEIdHa0N2Ds+6TWtg0FYoq4CywYeSIQrg9DthdNkwYWwRvkYvDU0Q0IAwxQ9S7H+zB1vX1CHbE4nuIdAYRy6nB8tjgcNtw7JRKzJ5UGt+LBfwwGe56DgUdNbkCmNzdg2NZEkLE98XZXeOH3x+FYkns3NOG5pYwFNNCKBA7YOdj3TABI37gZ3tL967E7ysiftaVpsBT5MTIEYUo8Njh7fy5ncNSRNQH/JdiiIlFDfzr3V34fG1d0vVRY4tRPb0STcEoRpd7MLbSw51fqc+6ekxUITB+THHi+syZ3T05Md3Eph0tCAZiCAVi2LW3HcGOCIQhgf3CjWXJxEq4QEcUdXvbE4/ZNBV2hwrYFJSWFmDcmCK4PXZUlBfAW8jl4UTUjSFmCKmv9WPtRzWobeiesCvtKopGe3Hmoslw2LmCiAaP3aZiZnXyuTKyc5hyR207tu5qgzBMdLRHsa8+ABhmPOAcMMHYjPfgAAi2RbB7W3PnI/Hl4XaXBpvLhqoRHpSXe+Bw2TCirADuw6zqIqKhh3/rhwDLktj42T7s3BL/xz4Y0SEVAcvrwJKvzIC3wM55LZQVXcdBTBxdjIk9zruJ6iYiMRMCEtt2t6OuvgNm1EBLSxj+9ghgWBAHnK8lEQ7FEO6c19XUo/cGmoLiYhfcXgdKfC5Mry6Dz+eG2sscLyIaOhhi8lwsamDN+3vQ1LnfR0Q3oWsKrCI7xo0qQpHHcZg7EGWew6Ym9haaM7UC6LGCKty5x00kYuDzTQ1obg7DjBhobQ0hGtJ7CTcADCuxaqoWwIZP9qGwwIaiIicqKzzwFrvgLXKisNiJAg9DPdFQwRCTxwL+CD58dzdCgShMS6ItGEOrCkifAxACR/aYu0CUL7omGLscGk45rnsbdiklAmEdesxEU1MQu/a0IxSIIhrS0dIcgtlj1ZRpWWjriKKtI4q9tR3wuG3QVAGHXYPLoaGwyAFvkQuFRQ4UdgYcG4dbaZBcffUVWLPm414fO//8C7BlyyZUVY3EzTffAgB4991/YOTIURg/fkIGW5mfGGLyVFtLCKv/sRN6LD53oD2so9XReSQA4hMxJ1RxIzoaOoQQKHTbATdQUuxC9aSyxGOGaaKxKYSWlhDWb21GXV0HhCEhDAumZaE9EE081+nQ4GoPw6Z1wO3UEuvyXG47CoscKC5xY9QYH4QKOFzc44bSY+HC0/Gf//mjA667XC4YhgFFif/bXVe3D0uX/gD33/8wQ0wfMMTkodamIFav2gWj84RjT5ETu5TkDcqmHVHC3VNp2NBUFVWVhaiqLMT0qZXwB2PY1xzElr1t2L67HdBNCN0CDAthw0Kkw+j8OgWFbjtsmgLTis+5aawPYOfWZui6GQ9O7LWhNHA4HCgtLTvs84b4cYZpx0+5PNPeGk4EGMOUUFwafBN8iH0SAgBUlRbg9GPHoNjDA9No+PIW2OEtsGPyWB8Cc3Q0tYfR6o/is+3NaOuIApYEDAumYSGmmxBRHUq7hNuhwqYpcDlscNoVSEuivSWctMcNALg9dniLXSjyueAtdsJb7DrsmVdEB3P11VegqmokLrvsCpx//pcBANdccyW++c1v4/LLv5Pl1uU2/q3LI4GOKFb/Y2eiB6Y+HENIsbDtk5rEc46bVgFfISfzEnXxuGzwuGw4YgQw+8gyNLVH0NYRxdodLdhT34Gu73tNKdHeeeyCZppQOnQ4AEjdgtupocCpQekcWgp17oXTc38bh8uGomInvD5XZ8BxwumycTiK+qyiohKPPvo7fPvbS3D77b/EscfOy3aTch5DTJ6IRQ189O4uxDpXbjgKHQhJM7ETLxAf6x9T4clWE4lyniIEKopdqCh2oXpMMZrawmhqj6CuNYSNu1oRjZmQigBsanwZuCUBS8JvWBDhGBRDwikABwQUAaiKArdDg6YKRMM6GsI6GvZ1JF7P5tDiwaa4s8fG5+LqqAHat6cdm9fXw9R7Of5ikKk2BdXTK1E1uqjfX/vqq6/grbfeSLo2Y8Ys3Hvvg933V1UUF/sAAIWFXrjd7oE1eBhgiMkDliXxyb/2INgRn5xYWOSEc7QXWJfcxV09ugiqwn0xiPqqrNiFsmIXpozz4aQZVWjxRxAzLeysD+DTzY0wIQFFAHYV0q7CBBAEEJQSMCWEbkIEI9AsQDEsODUFbqcNdk2BpinQowaa6gOJLRAAQNWURE9NPNy44PE6eI5UH23f3IigP3r4Jw6GMLBjU1NKIebkk0/Bd797TdI1h4O95gPFEJMHtqxvQHPnoXp2p4ZjThqH13sMIRW4bHA7NMydUnGwWxDRYdg0BZUlbmiagqOnVeGUWSMQi5lo8UexfmcLWjqiiERNNPsj8SEoTUBqCqQLiAGAlIiYEu2GBRE2oBgWXIoCRUooIn5WlNOuQhoWWpuCaG3q3llbURV4i50oLonPsynyuVBQ6GCPTS8mTC7H5nXZ64kZP/nwk3N743YXYPToMWluETHE5LjGug5s29gIIL7E9OgTxsLh0rC3MR5q3E4Nl589lf/YEaWZqiiwaUBliRuVJd3d+tGYicb2MHTDQmNbGDvrOhCKGAhFdOjCigcbJ2AB6AAA04LQLbQbBhCOQdEt2BUBt1ODXYtPJLYDaGsOoa051P36mpIINEWd4cbN3bdRNboopZ6QfDHc/3z7iyEmh+kxE599WJM4W2byzEqUlBVgX3MQeufGXqMrPHzTE2WQw65idHl87tn4Ki+Om1oJADBMC7vrA6hrCaI9GENdcwj+YAxQFUhVSUwgtgAYlkRYtwDdgAhbsFkSGgRUBXDaNbidGiSAlsYgWhq7e2xsdrU72PhcKCpxw8m9bIYUl8sFANi+fSuqq6fA4+E8x0NhiMlh69fUItp50m/5iEKMr453Y26v9Seew4m8RLlBUxVMGOnFhJHdm0yGIgZ0M77Z3o59frQFYugIxdDcHoF0qIBDhQQQBRC14nNsOnQLwh+BYliwCwGnXYPdpkBTFVhSHjDHxu7UEqGmazjK4bRlvgCUFkVFxTj77C/joYfux969e3Dttddnu0k5Tcgc21lH13U88MADeOGFF9De3o6pU6fiuuuuw9FHH53S/UzTQktL8PBP7IWmKfD5CtDaGoRhZHb8tWFfBz5ctRNA/Luv+YuOhMOp4b11dVi9oSHxvEvPnJI35yNls55DFWuafpmoaUcohn3NoXiPTUsINY0BRDt3307SORQldAvQrXiwUQRURcBpV1HgtMHWyyGXTrcNxSXuzh/xYJPNwzBTqamux9DcvA+lpVWw2bjvVW80Tcnbv/eH+/MtKSmAqh7+PZtzPTG/+c1vsHz5ctx5550YM2YMHn30UXz729/G3/72N1RWVma7eRlhmhbWr9mX+PXU2VVwumzYWtOeFGBmH1mWNwGGiLoVuu3xIxR6MEwL/mAMW2vaUd8Sip+F5o/Gh6Kc8edYUsKwJIRuIahbaPGHoZoSdlWBq0ePjQzpiITaE/vYxHcejk8cLi6NBxtOHKahIOdCzBtvvIEvfelLOPnkkwEAN9xwA5599lmsWbMGixcvznLrMmPnlmaEOs96KS51I2ITCEcNrN/RknjOSTOrcMzk8mw1kYjSTFMVlHidOM7rTFwLRw3UtYTQEYqhsS2CvY0BtAdiScHGlBIxUyKoWxBRHUK3oJoSTpsKTRWwaSrcTg3+tjD8bWHs3h7/d0SzqSgqcSV6a4pL3Nx1mPJOzr1ji4uL8X//93+46KKLUFVVhT/96U+w2+2YOnVqtpuWEZGwjq1dvS1CoMOhYPU/dyV1G3pcNhwzuZzfRRENcS6HhvH7HeQqpYQ/pGPr3nbsbQygqT2MQEjvXO4d/yfdlBIxo3MoKmZABKNQJWBTFTjtKhw2FWrMRCxmoLnH/BpXgT0RaIpLXPD6XH3q0ifKlpwLMTfffDN+8IMf4LTTToOqqlAUBb/61a8wduzYlO+ppTgW3PWXN5N/iXduaYZlSQhF4IhJpXhtayOEImB2XgOAKUf4YLPl3wF02ajnUMeapl8+1LS0SEVpkRPHIz7E3tYRxZ6GAPyhGFr8EeyqDyCmm4ADkIj/sCwJXTcRjlkQkRhEzIKwJFwOFbbOpd6GJREJ6airiS8eEELAW+yErzQ+v8ZX5k5px+FUampZ/CbtULr+CIRILGDNS6oqUv6MBnIwxGzbtg1erxcPPvggKisr8eyzz2Lp0qX4wx/+gClTpvT7fooi4PMVDKhNXq9rQF9/OM3tYTz3xhYUuWwQdQHYbCo0TcGc48fhrV0tBzx/3qxRA/49ZdNg13M4Yk3TL59q6vMVYPzYksSvTUuiIxiDPxjF9lo/du3zo8UfQUco1v1FnbsOR2ImIlETiBpARxQC8W/83A4NTruGcEMQrS1hOGxtgAAcDg1llR6UVcR/lJQXQNP69k1Vf2oaiahoalIG/CE31OVy2D4UyxJQFAVFRW44nc7Df8FB5NTqpJqaGixevBhPPvkk5s6dm7h+4YUXwufz4cEHHzzEV/fONC34/eHDP7EXqqrA63XB7w/DNAdvBvj/fbwXn2xpgtIWwUiXHS6HhklTK1A2tghPrdyU9NwSrxNLzpicl0NJmarncMKapt9QrmkwrMfn1QRjaA/EsL3Wj2BE736CjJ/uLWIWRMwEdAuicxhbUwRUVYGmdi/7VhUBu01Fsc8FX6kbvrIC+MrccLmTD75MpaaxWAwNDTUoKRkBu50LGPYnRLyupmnlZU9MLBZFS0sdKipGwW4/cHWS19u3ocyc6on57LPPoOs6Zs6cmXR99uzZeOedd1K+70CXoJmmNajL2JrbI5C6CREyEFNVeNx2jJtYgl2NAUir+93pcmo4acYImGZXB3F+Gux6DkesafoNxZo6bComjuze7fbUo0aiI6yjI6RjX1MQexsD8Id0tAeiMDvn16BzNZSlm0DMgogaCIa7g48QAvaGACDiS79dDg1utw3FJS5UjSxEWUUhSsriOx73r6bxEBSLRRlietEVXPIxwADxP9e4gS0Tz6kQU1VVBQDYtGkTZs2albi+efNmjBs3LlvNGnRtgRiUkAF0TsYbO7EEdoeG1o7uQ87OnDcO1WOKs9dIIhpyhBDwuu3wuu0YVVaQOH9NN0zsbQzCH4xBNyzsbQygtjkEXTcTw1AiZnZOHDYR1bv3uAlFdDS3h7Fnnx+fr2uAqgooioCn2AlvsRNurwPFJW6MHemFr9Bx0ENrFUWFy+VBINAKALDbuSR8f5YlOr+pzR9SSsRiUQQCrXC5PFAGeGhxToWYWbNmYe7cuVi6dCmWLVuGESNG4Pnnn8d7772HZ555JtvNGxSWJdERiEKE4t/ZxEwLR0wqBYCkEOMr5HciRJQZNk1NWhXVFW4sKdHSHsGexgCCYQPBiI59zUG0+6PxE71jFkRXj42M9xibpoRpCbQ3h9DaFEp0HfxTVSBtChSnBm+JC2VlBfC4bShw2lDg1OB22lDo9sDlQiLIUDJFUWBZ+dlb6HJ54PWWHP6Jh5FTIUZRFDz00EO47777cOONN6K9vR3V1dV48skncdRRR2W7eYOiIxSDDOpQOoeNdJuS2KuhZ4gp5qZ2RJRlihAoK3ahrDh5gq6UElICjW1h1DQFEYkaaGsLo7kxiHBHFFbYhDSSdyQWpgVhWkDEgL8tgvYdbYBdgbSpkHYV0qYAisDoCg/mVpdhRKkTCntiElRVoKjIjfb2UN71xqiqNuAemC45FWIAoKioCMuWLcOyZcuy3ZSMaO2IQgl1jy/HnCoCYR0elw1tnRveFbrtvW4tTkSUC4QQEOLAE7+7qKqAYQFr19WhrTmElsb4iifDsOJLv43OnpuoCRHtDjvSpqLWH8ULu9vgKLRj8hElmD2xjD3TiK8gczqdCIfNITd3qz9yLsQMN/X1HYnZ/9KmAjYVzf4IVEUkzlIp5l9YIspjQghUlBVgzqyqxAeuZUl0tEfQ2hREU2MQjfUBhEI6TNOCaUmYlkQwosMI6kBQh9EawdraAD5fXQNPsQOjRhfjqCnlqPAdGJpo+GCIybLa3e2Jn1vu+B9Hc3sUWo+lZSUMMUQ0xCiKSJy+fcSRZZBSIhzS0doURGtTCC1NIXT4IwhFDATCOsJRI77827AQCuvYsi+AzR/VwO11oqyiAFOOLMPEI3x5u28KpYYhJosMw0J7Q+cJ20JAds6FWfVZLdw9zjBhTwwRDXVCCLgL7HAX2DFqnA8AEIsaaO2cENxYH0BNrR+BsB5fDSUlhCURbgtjT1sYezY34W27hpJSN0aN9mLi+BL4St3Q8nB3c+o7hpgsaqj1I9Y5ZCTdGoSqoGvvwVDESDyvqpTdpUQ0/NgdGipHelE50ospiO8z094SRkN9AFu3N6NuXweiURNd+2bFYgbq9vlRt8+PTz+uhdOuwVPkwBHjfBg12ouSsgLYHfzYG0r4p5lF+/a2w+jcvdJT6obmtqG5PQIgfqLt2EoPpo0vQSXHfImIoKoKSsoLUFJegCkzKiGlRHNzCBs2N2Lr9lYE2yPxFU8ADNNCIBxDIBxDXV0HHB+rsNlU+HwujBtXjPJKD0rKCuB02bL8u6KBYIjJEsOwsK/GH1+aqAj4St2YO7USa7Y0YkRJAaaP98Fp5x8PEdHBCCFQVlaA+WUFmH/iEQiEdWzf3YaNW5vQUBcAokZi4URUj2/KFwjFsLfWD7tNhd2mwOdzYeRIL0ZUFcJXVgB3wYFb4FPu4qdkhhimhZf/uRMxw8I5Jx6B1voAorH4kJF0aigudGBUWQFGleXvwY5ERNnkcdkwa3I5Zk0uh2FaaA/EsHVPGzZsbkRHazixGZ+UEtGYgWgM6AjGsHtvO5TOIxOKvE6Uj/Bg9KgilFd64E7h1G7KHIaYDNmwqxW76joAAG9+vBdVUBJLqKVTQ3lx/pyYS0SU6zRVQWmRE6VFI3D8jBHQDRN1LWGs39GMPXvbEW6PJo5OgJSwLIlgWEcwrKO2vgOffVYHl12Fq8COkjI3xo31YeQoLzxeHn+QSxhiMqTn7rtb97RB1WyI6iakIiDtCkb0skEUERGlh01TMabCgzEVHuD4+PDS3sYAdtX60dgQREtTEGZIT4QaKSVCUQOhqIHmlhC2bG6Kn+JtU1Fc4sKo0UUYNboII6s80FSugMoWhpgMcfeYES9iJiyhIhozIR0aHA6NO1ASEWVQ14neXad6m5aFmsYg6ptDqKvvwL7aDsQC0R7nQMVXR5mmhfpaHfW1fnz8wR4IRUGhzwlfmRul5R6MH1uMilI3j0jIEIaYDDF6HNIloiZ0Nb4rpXSoqPC52T1JRJRFqqJgbGUhxlYWAtPiK5/aAjG0+iPYtacNe2vaEWqPwgjpgNV9VpG0LPibQ/A3h7BrUxM+EgLCqcFZaEdpRQHGj/WhwudCidcJB/esSTuGmAzR9eQQ04H4eUnSoaKKQ0lERDlFCAFfoQO+QgcmjCpKXI/EDGzZ0YK9e9rR1hKGvzkEQ+8+70lICYR1RMI6ahqC2Lu+EdKuAnYVvjI3Kio9KCpwoLLEharSAgabAWKIyZBY1wFdndtmd1ix+BtbERjBzeyIiPKC065h5uQKzJxcAQCwLAv1jUHU7o1vstfcEEQkYsAwLcjOXYVFxAAiBtr9UbTtbIuf0t35w+Wxo6zYhbEVHlSWuFHqdcDt5N41fcUQkyGxzmPou05otTqHkgBwMzsiojylKAqqKgtRVVkIYBSklAj4o2hqCGD37jbU7+tAJGIgqpuI6VZSqAGAWEsYNXUB7N3eEv9MUAW8HgcqfC4Uuu0oL3JiVLkHhW4bpx30giEmQ7qGk0Ssu9vRsqso8jiSzkkiIqL8JYRAYZEThUVOjO882LKjPYqWxgDq6wJoqO9AJGwgHDUQMyzohgmrR6iRikCgLYqOxmC8t14VgBDQVAW+QgfGVHowsqwA5cUuFBfzG2B+emZIzIgv2+sKMVIRgE3BMZPLs9wyIiIaLEIIeIud8BY7E6d1d7RH0NwQRHNjEC2NQYQjOiJREzHDQkw3EY2akD1CjXSoMO0qGqMGGltD+FgICCXeY1NW6IC3wI6yIifGVhbCM8yOUWCIyZCYYQJmfHwUAKRNxYTRxZgxviTLLSMiokyJhxoXvMUujK+Ohxp/W1eoCaC1KQRdN2GYFgzTQiRmIhYzoQd16O0xSFXEe2icKsKKwPZAFLLHaimXU0NJoRNjK+N74pQUOuGwD93JwwwxGaIbVqIXRgiBkjI3Fh87hmOcRETDmBACRT4XinwuTJhcBsuS8LeFEz01rU1BmJ0LQ0xLxkONYSIWNhHrCEIRgLQriYnC4YiBmkgANY0BvNf5GsWFDowu92DCSC/GVHigqUr2fsNpxhCTITHdhIiZUFUFYyo8OOGUCbBzaR0REfWgKALFJW4Ul7gxcUo5LEuivTWM5oZAZ6gJwTItCEXApqkIR3TEDBORmIlIKApDEYh19tZIuwIIgbaOKNo6oli7vRk2TUFVaQGOOrIM46u82f7tDhhDTIbEDAsiZkERAqqqoKiEZyUREdGhKYqAr9QNX6kbk6bGdw1uaw6htSmEjvYo6mvboSoCLnv3x7lhSoSiOvSAAVMVCEgJ06YANgW6YWF3fQd213fgKwsmotBtQyhioKo0PzddZYjJAEtKGFEDmmlBscfP3VCHUHceERFlhqoqKK3woHKkFz5fARrq/Wio60BzQwBNDUEE2iPQVAGv2574mjIZH4YKBQxEIBFR4j01K97biZge389m0uginD53TN6NEDDEZICux3thgM5dIMsKstwiIiIaCmx2FZUjvagcGR8aikZ0NDcE0dQQQHNDEOFgDIoQcDu0xBl++5pDiPijMFoEhF0FHCq27mxFY1sE82dXYUKVN296ZRhiMiBmdB73DkARAsUcSiIiokHgcNowcmwxRo4thpQS4aDeGWjic2piEQNlxU7UNAYBS0JETIioGd+kryWCv9X4UVrhwYlzR6GiJP4Nt2FZ8LhsOXmoJUNMBuiGBdG5Y6+ixJfXERERDSYhBNweO8Z6SjB2Qkli473mhgCKdrVi7952uO0aVFWgsS2CaMyACFlo3dmKFTtbIW2dq54cKtxFTsyaVIYZ40ty6lgEhpgMiOom0NkTo9kUOF0sOxERZVbPjffGV8eXc7e3hNDUEERzQwA1NX40tYdhdC7pFroVH0UI6oi2RvFBTQc+eH8Pxo3z4egZlfAW2CGEyOoGe/w0zYBAIJrY5M7psefNWCMREQ1dihKfo+krK8CR0ypgGhaaGgP4fH0D6vd1IBbSAUhYEohE49uEIGZi9+d12LWuPtFLc8JxY3Ds1Mqs/B4YYjLA3xpJ/Lyg0JnFlhAREfVO1RRUVnlR2bl/TCxqJCYJ19a0o7EphI5QDJaVfDr3jg2NDDFDmb+tR4jxOrLYEiIior6xOzRUjSlC1ZgizDxmFIKBKBrqOrBpczPq9vkhTQmbqmDiqKKstZEhJgOC/u4Q4y1mTwwREeWfAo8D4yc5MH5S/Myn9tYwYlEDJeWerLWJISYDQh0xAPHTSN0e+2GeTURElNuEiB+PkG3cNnaQRSMG9Gj8SHVoChx5thsiERFRrmKIGWSBjigsGV+ZJDUFNo0lJyIiSoecGk56//33cckll/T62OjRo/HGG29kuEUDF+yIonN1NSR7YoiIiNImp0LMnDlzsGrVqqRrmzdvxhVXXIErr7wyS63qn7Xbm7GnMYATpo9AsceBgD8KqyvFsCeGiIgobXIqxNjtdpSXlyd+res6fv7zn2PRokU4//zzs9iyvgmEdbzx0V4AQHN7BBctmoyAPwKZGE4SsGvsiSEiIkqHnAox+3v66aexb98+/L//9/+y3ZQ+CUb0xM+b2+PLqgOdw0lSEYAiYLOxJ4aIiCgdcjbERKNRPPzww1iyZAkqKioGdC8txSEcVVWS/n84dpsKofQ8UkAiGjHiPTGaArtdg30Yz4npbz3p8FjT9GNN0481TT/WNC5nQ8wLL7yAaDSKiy++eED3URQBn69gQPfwevt26nTEBGw9Qooq1PivhYDqUFFQYB9wW4aCvtaT+o41TT/WNP1Y0/Qb7jXN2RDz/PPPY9GiRfD5fAO6j2VJ+P2hlL5WVRV4vS74/WGYpnXY57e2BaHrZuLXNXtaoesmDMOE4VIBS6K1NZhSW4aC/taTDo81TT/WNP1Y0/Qb6jX1el196mXKyRDT0tKCTz75BN/5znfScr+uY8VTZZpWn+4RjZmQXSuRALS3hiEtmZgToyliwG0ZCvpaT+o71jT9WNP0Y03Tb7jXNCcH0z7++GMIIXDcccdluyn9Ypoy6dd+fwQSAKSE1JRhPR+GiIgo3XIyxGzcuBFjxoyBy5VfY32mlRxiOvzR7mCjCrgcDDFERETpkpMhpqmpCcXFxdluRr9ZPUOMlAgGYjAtC1JVACHgdtqy1zgiIqIhJifnxNxyyy3ZbkJKTKvHuKQEdN2Mn5ukxpddux05WW4iIqK8lJM9MfnK6NkTY1qwpIRpyXhPDAC3kyGGiIgoXfipmkY9J/YKM74qyTQtQGNPDBERUbqxJyaNkoaTDAuWxZ4YIiKiwcIQk0Y9J/YKU8JMDCexJ4aIiCjd+KmaRklLrE0JS3QOMXWFGPbEEBERpQ17YtIoeU5M58ReKeOnV2sKbBr3iSEiIkoXhpg0MrrmxEgZn9hrSZgCnXvEsBeGiIgonRhi0igxJ8aSQOd8GDM+kgS3gxvdERERpRNDTBol5sR0DivphplYmeRiTwwREVFaMcSkUdecGNH5f4srk4iIiAYNQ0wadffE9NgvhnvEEBERDQqGmDTq2uxO9Fxq3dkTU8AQQ0RElFYMMWm0/5wYAJBKPMS4OJxERESUVgwxaZSYE9NLTwznxBAREaUXQ0wa7d8TIxUBiK7dernEmoiIKJ0YYtLItKz4RnddYaZzKElRBDwu9sQQERGlE0NMGpmWBCQAmRxi5k6u4JEDREREacbugTQyLRnfrbeTVAVOmDECx06pyGKriIiIhiaGmDQyzfiZSYoiMKLEjdETSnDs1MpsN4uIiGhI4nBSGpmWBVgSQgg4bCrKStzZbhIREdGQxRCTRpYlAdNC55mPcHCDOyIiokHDEJNGphVfmdS5qhoOF5dVExERDRaGmDQyTQmYEqKzL8bJnhgiIqJBwxCTRuyJISIiyhyGmDSxLAkpO5dYCwHNpkLTWF4iIqLBwk/ZNOk6ckCY8Z4YTuolIiIaXAwxadK1vBpSQgBw8JgBIiKiQcUQkyY9d+sVAnDw1GoiIqJBxRCTJqbZM8QI2BliiIiIBhVDTJp0rUwCAAHAZueBj0RERIOJISZNTMvqPr1aCNgZYoiIiAYVQ0yaxOfExH8uAA4nERERDbKcDDHPP/88zjrrLMycORNnn302XnnllWw36bBMs8dwkhAcTiIiIhpkORdiXnjhBdx00034+te/jpdffhlnnXUWfvjDH+KTTz7JdtMOydpvdRJ7YoiIiAZXToUYKSV+9atfYcmSJViyZAnGjRuH733vezjxxBPxwQcfZLt5h7T/Emv2xBAREQ2unOou2L59O2pqanDOOeckXX/88cez1KK+MywraXUSJ/YSERENrpwKMTt37gQAhEIhXH755Vi/fj1Gjx6N7373u1i4cGHK9031DCNVVZL+f2gCkACEgKIqcDg1iK6TIAlAf+tJfcGaph9rmn6safqxpnE5FWICgQAAYOnSpbj66qtx3XXXYeXKlbjqqqvwxBNP4IQTTuj3PRVFwOcrGFC7vF7XYZ/jbg5BBQABuNx2lJR4BvSaQ1lf6kn9w5qmH2uafqxp+g33muZUiLHZbACAyy+/HOeeey4AYOrUqVi/fn3KIcayJPz+UErtUVUFXq8Lfn8Ypmkd8rnt7WFYRtcaa4nW1mBKrzmU9aee1DesafqxpunHmqbfUK+p1+vqUy9TToWYESNGAACqq6uTrk+aNAlvvfVWyvc1jIH9AZumddh7RCJ6YrM7VVMH/JpDWV/qSf3DmqYfa5p+rGn6Dfea5tRg2rRp01BQUIBPP/006frmzZsxduzYLLWqb2JRM/FzrkwiIiIafDnVE+N0OvGtb30LDz74ICorKzFr1iysWLEC7777Lp588slsN++QYrHuEGN3MMQQERENtpwKMQBw1VVXweVy4d5770V9fT0mTpyIX//61zj++OOz3bRD0mPsiSEiIsqkfoWYlpYWlJSUDFZbEr75zW/im9/85qC/TjrpMSPxc7s957IhERHRkNOvOTGnnHIKrrnmGrz99tuQXSc2EwDAiHVPrOJwEhER0eDrV5fB4sWL8cYbb+C1115DeXk5zj33XHz1q1/N+Um3maAbPefEsCeGiIhosPWrJ+buu+/GqlWr8NOf/hSVlZX47W9/i8WLF+Piiy/GCy+8gGg0OljtzHk9e2IcDDFERESDrt9LrD0eDy644AI8++yzWLFiBS699FLs2LEDS5cuxUknnYRly5bhs88+G4y25jRT7+6JYYghIiIafAPaJ2bixIlYunQp3n77bTz44IM47rjjsHz5cnz9618/4BDHoc7oMZzkdDLEEBERDba0bHanqipOO+00/Pd//zcuuugiAMDWrVvTceu8Yek9hpMYYoiIiAbdgD9tTdPEm2++ieXLl2PVqlUwDAOTJ0/Geeedl4725Q3TjK/WkoqANsxPFSUiIsqElEPMli1bsHz5crz44otobW2Fx+PBeeedh/POOw8zZsxIZxvzgtV1AJcQUBWR3cYQERENA/0KMYFAAC+99BKWL1+OdevWAQDmzp2L8847D2eccQYcDsegNDIfWJ09MRCAwhBDREQ06PoVYk466STEYjGUl5fjiiuu4B4xnSxLQlqdPTEKe2KIiIgyoV8hZv78+TjvvPNwyimnQFE476OLaViQXaNJioAQDDFERESDrV8h5oEHHjjgWkdHB9asWYPW1lYsWLAARUVFaWtcvtBjBozOOTE8coCIiCgzBrQ66cEHH8Sjjz6KSCQCIQSee+45FBUVYcmSJTjppJNwxRVXpKudOS0Q1GF1niXlctqy3BoiIqLhIeUxoaeffhoPPvggzjvvPPz2t79NOhDyC1/4At566610tC+nbattx4cbG9DYFkpcK3AxxBAREWVCyj0xTz/9NC699FL8+Mc/hmmaSY+NGzcOu3btGnDjclkgrOPld3cCAGxmd4DzFNiz1CIiIqLhJeWemD179mD+/Pm9PlZQUAC/359yo/JBc3sk8XMj1h3iChliiIiIMiLlEFNYWIimpqZeH6upqUFpaWnKjcoHdluP0vUYSvMWDt+9coiIiDIp5RBzwgkn4LHHHkMo1D0fRAgBwzDwxz/+ESeffHJaGpireuQWwOr6hYDXw54YIiKiTEh5Tsw111yD8847D2effTa++MUvQgiBP/zhD9iwYQNqa2tx3333pbGZuadnhhGdv9BUAYeDhz8SERFlQso9MePGjcMf//hHTJgwAX/84x8hpcQLL7wAn8+HZ555BiNHjkxnO3NOz9VYXd0ymqZAs3GfGCIiokwYULfBpEmT8PjjjyMWi6G1tRVFRUVwOp3paltOSx5Oiv/PpirQNO5kTERElAlpGfuw2+2orKxMx63yRm89MTb2xBAREWUMuw3SQHQNJ7EnhoiIKGP61RNzySWX9Pm5Qgj87ne/63eD8sX+w0k2TYXLoUGzMcQQERFlQr9CzAcffACPx4OqqqrBak/esHqkmIpiFzyaAlVTeII1ERFRhvQrxIwZMwZ79uyBx+PBV7/6VZx11llwu92D1bb80blPDOfDEBERZU6/xj5ee+01/P73v8eYMWNw22234eSTT8bNN9+Mjz/+eLDal7N69sRYVnx5EoeSiIiIMqffn7rHHXccfvnLX2LVqlW4/vrrsXnzZlx44YU488wz8dhjjx30KIIhpyvDSAlpdq1OYk8MERFRpqTcdeDxeHDBBRfg2WefxYsvvogTTjgB9957L372s5+ls305K9ER02OCL3tiiIiIMmfAn7rbtm3DX//6V6xcuRJSSowfPz4d7cp5ieGkHsNKnBNDRESUOSltdhcMBrFixQosX74cn376KcaOHYuLL74Y55577rDb9K5nT4zKPWKIiIgypl8hZvXq1Xjuuefw6quvQkqJxYsX40c/+hGOO+64wWpfzkrs2GtJdC2qtnE4iYiIKGP6FWIuvvhieDwenHPOOTj77LPh8XgAAOvWrev1+dOnT+93g2pqarBw4cIDrt922204//zz+32/wdK5qjq+W29nitE4sZeIiChj+j2cFAgE8Oyzz+LZZ5896HOklBBCYMOGDf1u0KZNm+BwOPD6668nbRxXWFjY73sNqsScmO5LnNhLRESUOf0KMT//+c8Hqx0Jmzdvxvjx41FRUTHorzUQiexiSXTNj+bEXiIioszpV4jx+/1YtGjRoB47sGnTJkyaNGnQ7p8uPZdYi8RwEntiiIiIMqVfIea3v/0t7rzzTsyYMQOLFy/G4sWLMWbMmLQ2aPPmzSgvL8eFF16InTt3Yty4cbjqqqswf/78lO+ZarhQVSXp/z0JRcR/IB5ihCLgcGoMModwqHpSaljT9GNN0481TT/WNE5ImXQe8yFZloUPPvgAr776Kl577TU0NTWhuroaixcvxqJFiwbcgxKLxTBnzhxMnToVP/7xj+F2u/Hiiy/i97//PZ544gmccMIJ/b5n1/ycdPtoYz1WvLsDaIugUtPg9dhx2llTUFHlTftrERER0YH6FWL299FHH2HlypV4/fXXsW/fPhxxxBFYtGgRFi9ejGnTpqV0z1AoBE3TYLfbE9cuv/xyCCHw2GOP9ft+pmnB7w+n1BZVVeD1uuD3h2GaVtJjn25twhsf7YVoj2KETYPHbcPJX5wEXykPxDyYQ9WTUsOaph9rmn6safoN9Zp6va4+9TKltNldl2OOOQbHHHMMbrrpJnz22WdYuXIlXnnlFTzyyCMYOXIkFi9ejB//+Mf9umdvp2JXV1dj1apVKbfTMAb2B2ya1gH30A0L0pIQpoTUJGTnmuuBvtZw0Fs9aWBY0/RjTdOPNU2/4V7TtA2mzZo1C9dffz1effVV/OUvf8E555yDt99+u1/32LhxI+bMmYMPP/ww6fratWtzb7KvPOAnw35skoiIKJMG1BNzMFOnTsXUqVNx7bXX9uvrqqurceSRR+JnP/sZli1bBp/Phz//+c9Ys2YNnnvuucFoaspkj31iuubcKGr6594QERFR7wYlxKRKURQ8/PDDuOuuu3DttdfC7/dj2rRpeOKJJzB58uRsNy9JV/+LkOyJISIiyoacCjEAUFJSgjvuuCPbzTgs2cuOvTwAkoiIKHP4qZuinpvdAfF9YhSFw0lERESZMuCemJdeeumwzznnnHMG+jI5p3teb/wASA4lERERZdaAQ8yf//xnAPHhlU8++QTl5eUYMWIE6urq0NTUhDlz5gzNENNzYi8AlZN6iYiIMmrAIeapp54CACxbtgxnnHEGLrroosRjTz/9NDZv3jzQl8hJ3cNJ8Z8o7IkhIiLKqLR98q5YsQIXXHBB0rWvf/3rWLFiRbpeIqd09cSIRE8MQwwREVEmpe2Td+TIkQfs5fLXv/51UE+8ziYJxHthuubEaBxOIiIiyqS0LbG+7bbbcM011+CRRx5BVVUV9u3bB9M0cf/996frJXLK/kdOsSeGiIgos9IWYmbNmoXXXnsNa9asQWNjI8rLy3HUUUfBZrOl6yVyipTosURJcE4MERFRhqV1s7va2lq0trYiFouhpqYGNTU1AIB///d/T+fL5IR4iOmcFwOuTiIiIsq0tIWYhx56CI888gimTJkCl8uVuC6EGKIhRibv1sueGCIiooxKW4j5/e9/j+XLl2PixInpumVOS0zs7cQjB4iIiDIrbZ+8Ho8Ho0aNStftcl5ST4zgcBIREVGmpS3EfPvb38ZNN92EjRs3or6+PunHUCQR3yMGiM+J4cReIiKizErbcNKyZcsAAH/729+SrgshsGHDhnS9TM7oObEX4JwYIiKiTEtbiNm4cWO6bpUXkoeTBEMMERFRhvGTN0UHLLHmjr1EREQZNeCemE8//RSzZ8/Gxx9/fNDnHH300QN9mZzDJdZERETZNeAQ85Of/AQvvfQSrrvuul4fF0LgjTfeGOjL5Jyehw7wAEgiIqLMG3CIeemllwAAb7755oAbk0+kBESPib0Kl1gTERFlVFqPHXj//fexYsWKxNlJZ511FubNm5fOl8gZB+4Tw54YIiKiTErbJ+/jjz+OH/3oR/D5fDj11FNRUlKC66+/Ho899li6XiKnHLDEmjv2EhERZVTaemKeeOIJ/P73v8eECRMS17785S/jkksuwbe+9a10vUzOkJBJp1hzx14iIqLMSlv3gc1mw4gRI5KuVVRUwGazpeslcsr+S6y5Yy8REVFmDbgnputYgcsvvxzXXHMNrrzySlRWVqKurg6PPvrokOyFAbjEmoiIKNsGHGIWLFgAIUT8Qx3AqlWrkh7/xz/+gW984xsDfZncxAMgiYiIsqZfIWb58uU444wzUFBQkLg23I4b6GJZMrHEOr5jL3tiiIiIMqlfn7w/+clPsG3btqRr+/96uJCJ/8QpCntiiIiIMqlfIUZKmfRr0zTxpS99CevWrUu6XldXh+XLlw+8dbmsx8ReVVUgBEMMERFRJg14DGT/YAPEJ/v+5Cc/Geitc5rVY2Iv58MQERFlHidypKjnEmvOhyEiIso8fvqmqGf/E0MMERFR5vX707elpWUw2pF3pJQQieEkhhgiIqJM6/en73e/+10ce+yxuPjii3HHHXdACIG6urpe58YM1I4dOzBnzhz85S9/Sfu9Byq+2V33xF4iIiLKrH7tE/Pb3/4W69evx/r167Fu3TqsXr0aAHD11VfD4XDgyCOPxJQpU+ByuQbcMF3Xcd111yEUCg34XoPBsnoe/siJvURERJnWrxCzYMECLFiwIPHrtrY2rF+/HmvXrk2Em+eeey4+1DLAJce//vWvkzbVyzWW2SPEsCeGiIgo4wZ07EBxcTFOPPFEnHjiiYlrgUAAa9euxYYNG1K+7+rVq/GnP/0Jzz//PE499dSBNHHwJPXEMMQQERFl2oDPTtqfx+PBvHnzMG/evJS+3u/348c//jF+8pOfoKqqKi1t0lIMGV09LL33tEigs7fJbldTfo3h5ND1pFSwpunHmqYfa5p+rGlc2kPMQN1yyy046qijcM4556Tlfooi4PMNbFjK6z1wjo9N0xA/aUCgsNA54NcYTnqrJw0Ma5p+rGn6sabpN9xrmlMh5vnnn8eHH36Il156KW33tCwJvz+1ycGqqsDrdcHvD8M0raTHIpEYLAkISEQiOlpbg+lo7pB2qHpSaljT9GNN0481Tb+hXlOv19WnXqacCjHLly9Hc3PzAfNgli1bhscffxwrVqxI6b6GMbA/YNO0DriHZXYusRYCMg2vMZz0Vk8aGNY0/VjT9GNN02+41zSnQsxdd92FSCSSdG3RokW45pprcNZZZ2WpVb2TPd4zPMGaiIgo83IqxFRWVvZ6vbS0FKNGjcpwaw7N6pFiGGKIiIgyb3hPax6IzgwjwBBDRESUDTnVE9ObTZs2ZbsJvUocsyAAwRBDRESUceyJSVHPYwfYE0NERJR5DDGpkgwxRERE2cQQkyKrezSJIYaIiCgLGGJS1bU4SQgoCstIRESUafz0TVXP4SSVPTFERESZxhCToq7VSRxOIiIiyg6GmBTJHj0xQjDEEBERZRpDTIoSGUawJ4aIiCgbGGJS1d0RwzkxREREWcAQkyJpcU4MERFRNjHEpKrHeBJDDBERUeYxxKQoaWIvQwwREVHGMcSkSCbt2MsyEhERZRo/fVPF1UlERERZxRCTKh4ASURElFUMMSnqOZzEOTFERESZxxCTKvbEEBERZRVDTAqklD3mxAjw1AEiIqLMY4hJgezxXyF4dhIREVE2MMSkYP+eGCIiIso8hpgUSIlEiBGsIBERUVbwIzgFPeb0ciiJiIgoSxhiUiJ7rLFmiCEiIsoGhpgUWFb3z5lhiIiIsoMhJgUSEiIxJ4YphoiIKBsYYlIQn9jbtcSaIYaIiCgbGGJSYFndM3uZYYiIiLKDISYFZo9JMeyJISIiyg6GmBRYZo+eGM6JISIiygqGmBSYZs+emCw2hIiIaBhjiEkBe2KIiIiyjyEmBaZkiCEiIsq2nAsxzc3NuP766zFv3jzMmTMHV1xxBbZu3ZrtZiVJ6onheBIREVFW5FyI+e53v4s9e/bg0UcfxXPPPQen04lLL70U4XA4201LSJoTw54YIiKirMipENPa2orRo0fj1ltvxcyZMzFx4kRcddVVaGxsxJYtW7LdvAT2xBAREWWflu0G9OTz+XDPPfckft3U1ITHH38cI0aMwKRJk7LYsmSy52Z3ORUDiYiIho+cCjE9/dd//Rf+/Oc/w2634ze/+Q3cbnfK99K01JKGqipJ/0/S2QOjqiLl+w83h6wnpYQ1TT/WNP1Y0/RjTeOElD2W2uSQrVu3IhKJ4I9//CNefvllPPPMM5g+fXq/7yOlTPuQz9p19fjLn9cAAI6YUoFLLpiT1vsTERHR4eVsiOliWRbOOecczJo1Cz//+c/7/fWmacHvT21SsKoq8Hpd8PvDSZN5N2xswKuvbAYAjJlUgq+cMy2l+w83B6snpY41TT/WNP1Y0/Qb6jX1el196mXKqeGk5uZmvPfeezjzzDOhqioAQFEUTJw4EQ0NDSnf1zAG9gdsmlbSPXTdTJxiDSEGfP/hZv960sCxpunHmqYfa5p+w72mOTWY1tDQgB/96Ef44IMPEtd0Xcf69esxceLELLYsmezxfuESayIiouzIqRAzZcoUnHzyyfjZz36GDz/8EJs3b8bSpUvh9/tx6aWXZrt5CTw7iYiIKPtyKsQIIXDfffdh3rx5uPbaa3H++eejvb0dTz/9NEaOHJnt5iWYPfaJUdgTQ0RElBU5NScGAAoLC3HLLbfglltuyXZTDsqS3OyOiIgo23KqJyZfWEk9MSwhERFRNvATOAU9d+xlhiEiIsoOfgSnwLR4ACQREVG2McSkILknhiGGiIgoGxhiUmAlHQDJEENERJQNDDEpsCxO7CUiIso2fgKnwOLEXiIioqzjR3AK2BNDRESUffwEToFMmhOTxYYQERENY/wIToHFYweIiIiyjiEmBRxOIiIiyj5+AqdASvbEEBERZRtDTAosbnZHRESUdQwxKeg5J0aoDDFERETZwBCTAim7z05iRwwREVF2MMSkIJFhhODEXiIioizhJ3AKuubESADsiCEiIsoOhpgUJDa7E4AQjDFERETZwBCTAqvHEmtmGCIiouxgiEmB02MHAEi7yuEkIiKiLGGIScHI6lKYJU5YxQ52xRAREWWJlu0G5CMhFEhHvHRcYk1ERJQd7IlJgUyaE8MUQ0RElA0MMSmQh38KERERDTKGmBQkHQDJnhgiIqKsYIhJgezZFcMMQ0RElBUMMSlIyjAMMURERFnBEJMCDicRERFlH0NMCiRn9hIREWUdQ0wK2BNDRESUfQwxKeCcGCIiouxjiEmB5HgSERFR1uVciGlra8NPf/pTnHLKKTj66KNxwQUX4MMPP8x2s5L0zDDcsZeIiCg7ci7E/PCHP8Snn36Ke+65B8899xymT5+Oyy+/HNu2bct20xKSQ0z22kFERDSc5VSI2bVrF959910sW7YMc+fOxYQJE3DzzTejsrISL7/8crabl8Czk4iIiLIvp0KMz+fDI488ghkzZiSuCSEgpUR7e3sWW5aMG/YSERFln5btBvTk9XqxYMGCpGuvvPIKdu/ejZNPPjnl+2paallNVZWk/3cRQkAo8fhisykp33+4OVg9KXWsafqxpunHmqYfaxqXUyFmfx999BFuuukmnHbaaVi4cGFK91AUAZ+vYEDt8HpdSb92u+2w2VQAQHGRe8D3H272rycNHGuafqxp+rGm6Tfca5qzIeb111/Hddddh9mzZ+Oee+5J+T6WJeH3h1L6WlVV4PW64PeHYZpW4nowGIWumwAAvz8Ml8ZBpb44WD0pdaxp+rGm6ceapt9Qr6nX6+pTL1NOhpg//OEPuP3223H66afjrrvugt1uH9D9DGNgf8CmaSXdwzAtSCs+M8a05IDvP9zsX08aONY0/VjT9GNN02+41zTnBtOeeeYZ3HrrrfjGN76B++67b8ABZjAkLbHOXjOIiIiGtZzqidmxYwfuuOMOnH766fjOd76D5ubmxGNOpxOFhYVZbF235CXWWWwIERHRMJZTIWblypXQdR2vvfYaXnvttaTHzj33XNx5551ZalmynkuseQAkERFRduRUiLnyyitx5ZVXZrsZh8Wjk4iIiLIv5+bE5APu2EtERJR9DDEp4NlJRERE2ccQkwIJ9sQQERFlG0NMCrjEmoiIKPsYYlLAJdZERETZxxAzQBxOIiIiyg6GmBRYnNhLRESUdQwxKUgaTuKsGCIioqxgiEkFe2KIiIiyjiEmBRYn9hIREWUdQ8wAcTiJiIgoOxhiUsCJvURERNnHEJMCnp1ERESUfQwxA8QMQ0RElB0MMSngxF4iIqLsY4hJQfLZSUwxRERE2cAQkwpO7CUiIso6hpgUSHBiLxERUbYxxKSgaziJAYaIiCh7GGJS0DWxlxmGiIgoexhiUsGeGCIioqxjiElBYjgpu80gIiIa1hhiUmCBw0lERETZxhCTCg4nERERZR1DTAokJ/YSERFlHUNMCrp2iWFPDBERUfYwxKQg0ROT5XYQERENZwwxKbASc2Ky2w4iIqLhjCEmFZzYS0RElHUMMSngcBIREVH2McSkwG5TAQAOu5rllhAREQ1fDDEpOGlmFUZXeHDSzKpsN4WIiGjY0rLdgEN56KGH8N577+Gpp57KdlOSTBjpxYSR3mw3g4iIaFjL2Z6YJ598Evfff3+2m0FEREQ5Kud6Yurr63HzzTfjo48+wvjx47PdHCIiIspROdcTs27dOhQVFeHFF1/E7Nmzs90cIiIiylE51xOzcOFCLFy4MK331LTUspqqKkn/p4FhPdOPNU0/1jT9WNP0Y03jci7EpJuiCPh8BQO6h9frSlNrCGA9BwNrmn6safqxpuk33Gs65EOMZUn4/aGUvlZVFXi9Lvj9YZimleaWDT+sZ/qxpunHmqYfa5p+Q72mXq+rT71MQz7EAIBhDOwP2DStAd+DurGe6ceaph9rmn6safoN95oO78E0IiIiylsMMURERJSXGGKIiIgoL+X0nJg777wz200gIiKiHMWeGCIiIspLDDFERESUl4SUUma7EYNJSgnLSv23qKrKkFyDny2sZ/qxpunHmqYfa5p+Q7mmiiIghDjs84Z8iCEiIqKhicNJRERElJcYYoiIiCgvMcQQERFRXmKIISIiorzEEENERER5iSGGiIiI8hJDDBEREeUlhhgiIiLKSwwxRERElJcYYoiIiCgvMcQQERFRXmKIISIiorzEEENERER5iSGmF5Zl4f7778f8+fMxe/ZsXHbZZdi1a1e2m5VXampqMHny5AN+PPvsswCADRs24KKLLsJRRx2FU089FY8//niWW5y7HnroIVx88cVJ1w5XP76HD623mt54440HvF9POeWUxOOsabK2tjb89Kc/xSmnnIKjjz4aF1xwAT788MPE43yP9t/hasr3aC8kHeDXv/61POGEE+Rbb70lN2zYIC+77DJ5+umny2g0mu2m5Y033nhDzpw5U9bX18uGhobEj3A4LFtaWuTxxx8vb775Zrl161b53HPPyZkzZ8rnnnsu283OOU888YScPHmyvOiiixLX+lI/vocPrreaSinlueeeK++5556k92tzc3PicdY02Te/+U355S9/Wa5evVpu27ZN3nrrrXLWrFly69atfI+m6FA1lZLv0d4wxOwnGo3KOXPmyGeeeSZxrb29Xc6aNUu+/PLLWWxZfvnNb34jv/zlL/f62MMPPyznz58vdV1PXLv77rvl4sWLM9W8nFdXVycvv/xyedRRR8kzzjgj6QP3cPXje7h3h6qpYRhy5syZ8rXXXuv1a1nTZDt37pTV1dXyo48+SlyzLEuefvrp8r777uN7NAWHqynfo73jcNJ+Nm7ciGAwiHnz5iWueb1eTJs2DatXr85iy/LLpk2bMGnSpF4f+/DDD3HsscdC07TEtXnz5mHHjh1obm7OVBNz2rp161BUVIQXX3wRs2fPTnrscPXje7h3h6rpzp07EY1GMXHixF6/ljVN5vP58Mgjj2DGjBmJa0IISCnR3t7O92gKDldTvkd7px3+KcNLXV0dAKCqqirpekVFBfbt25eNJuWlzZs3o7y8HBdeeCF27tyJcePG4aqrrsL8+fNRV1eH6urqpOdXVFQAAGpra1FaWpqNJueUhQsXYuHChb0+drj68T3cu0PVdPPmzRBC4He/+x3eeecdKIqCBQsW4Nprr0VhYSFruh+v14sFCxYkXXvllVewe/dunHzyybj33nv5Hu2nw9WU79HesSdmP+FwGABgt9uTrjscDkSj0Ww0Ke/EYjHs3LkTgUAA1157LR555BHMnDkT3/72t/Hee+8hEon0Wl8ArHEfHK5+fA/335YtW6AoCkaNGoWHH34YS5cuxdtvv42rrroKlmWxpofx0Ucf4aabbsJpp52GhQsX8j2aBvvXlO/R3rEnZj9OpxNA/IO46+dA/C+ey+XKVrPyit1ux+rVq6FpWuIv1IwZM7Bt2zY8/vjjcDqdiMViSV/T9ZfM7XZnvL355nD143u4/77//e/j0ksvhdfrBQBUV1ejvLwcX//61/H555+zpofw+uuv47rrrsPs2bNxzz33AOB7dKB6qynfo71jT8x+urriGhoakq43NDRgxIgR2WhSXnK73Qd8R1BdXY36+nqMGDGi1/oCQGVlZcbamK8OVz++h/tPCJH4cOjSNRxSV1fHmh7EH/7wB3z/+9/HKaecgkcffTTx4cn3aOoOVlO+R3vHELOfKVOmwOPx4P33309c8/v9WL9+PebOnZvFluWPjRs3Ys6cOUn7GwDA2rVrMWnSJBx77LH46KOPYJpm4rH33nsP48eP53yYPjhc/fge7r8f/ehHuPzyy5Ouff755wCASZMmsaa9eOaZZ3DrrbfiG9/4Bu67776kb1r4Hk3NoWrK9+hBZHt5VC6655575HHHHSdff/31xFr7RYsWDem19ulkmqY8//zz5Ze+9CW5evVquXXrVnnHHXfIGTNmyI0bN8qmpiZ57LHHyqVLl8otW7bI5cuXy5kzZ8q//OUv2W56Tlq6dGnScuC+1I/v4UPbv6ZvvvmmnDx5snzooYfkrl275FtvvSUXLlwof/jDHyaew5p22759u5w+fbr83ve+l7RnSUNDg/T7/XyPpuBwNeV7tHdCSimzHaRyjWmauOeee/CXv/wFkUgExx57LH76059i9OjR2W5a3mhpacFdd92Fd955B36/H9OmTcN1112X+I7gs88+w+23347169ejvLwcl112GS666KIstzo33XDDDaipqcFTTz2VuHa4+vE9fGi91XTlypV4+OGHsX37dhQWFuKcc87Btddem5iQypp2e/jhh3Hvvff2+ti5556LO++8k+/RfupLTfkePRBDDBEREeUlzokhIiKivMQQQ0RERHmJIYaIiIjyEkMMERER5SWGGCIiIspLDDFERESUlxhiiIiIKC8xxBAREVFeYoghon7bsmULpk+fjsmTJ6OpqSkjr/n2229j8uTJmDx5MtavX3/A41dffTVOOOGEQW3DvHnzEm042I8bb7xxUNtARN20bDeAiPLPbbfdBsMwAAAbNmzA/PnzB/01161bBwBwOBxYuXIlpk2blvT4+vXrD7iWTqZp4uGHH+71sdraWtx0002wLAtf+cpXBq0NRJSMPTFE1C9///vf8a9//QunnnoqgHiIyYT169ejuLgYZ555Jl599dWkx1pbW1FTU4MZM2b0+X4XX3wxbrjhhj4/X1VVHHXUUQf8qKysxN133w3TNPHggw/i2GOP7fM9iWhgGGKIqM8ikQh++ctfoqqqCnfeeSdUVcXGjRsz8trr1q3DjBkzcPrpp2P79u3YsmVL0mMABrUnpjf19fW45JJL0NDQgAceeCAjPVJE1I0hhoj67NFHH0VNTQ1+/OMfw+fzYdy4cYftiZFSwjCMPv04mNbWVtTW1mLatGmYP38+3G43Vq5cmXg8GyGmK8Ds27cP999/PxYsWJCx1yaiOM6JIaI+qa2txWOPPYbjjjsOZ511FgBg8uTJWLlyJcLhMFwuV69f98EHH+CSSy7p02u88cYbGD169AHXuybyTp8+HQ6HAwsWLMCrr76Kq6++GkA8xBQVFWHMmDG93ldKCdM0D7jWFbB60rTD/7PY2NiIJUuWoKamBr/61a/whS98oU+/PyJKL4YYIuqTO++8E7qu4+abb05cmzx5Ml555RVs3rwZs2fP7vXrpk+fjueee65Pr1FRUdHr9a6elunTpwMAFi1ahB/84AfYtWsXxo0bh7Vr1x6yF+ZgQWr16tV4/vnnk64dLEh1aWpqwpIlS7B3717cd999OO200w732yKiQcIQQ0SH9a9//QsrV67Eueeei5EjR8Lv9wNAoudjw4YNBw0xBQUFmDp1ap9e52C9IPv3tCxYsCCxSulrX/saampqcMYZZxz0vr0FqWXLlqGiogLf+973kq4fLEgBQEtLCy699FLs2rUL9957L774xS/26fdFRIODIYaIDsk0Tdx+++0AgL/+9a/461//esBzDjUvJl3DST17WgoKCnDSSSdh5cqVid6ZQ61M8ng8mDlzZtK1goICFBcXH3D9YFpaWrBkyRLs2LEDd911FxYtWtSnryOiwcMQQ0SH9PTTT2Pz5s34/ve/3+vy4WuuueaQK5QGOpzU0dGBPXv24PTTT0+6vmjRItxwww2J5daDOam3tbUV3/zmN7Ft2zbcddddOPPMMwfttYio7xhiiOigWlpa8MADD2DOnDn43ve+ByHEAc+ZPHkyPv/8c1iWBUU5cMFjb70g/bFu3TpIKQ/oaVm4cCFsNhuWL18Oj8eDcePGpfwahxIKhXDZZZdh48aNuPjiizFy5EisWbPmgOe53W5UV1cPShuIqHcMMUR0UPfddx+CwSD++7//u9cAAwBTpkzB+++/j507d2LChAlpb0PXyqT9e1qKiopw/PHHY9WqVTjqqKMO2r6BWrNmTaINTz31FJ566qlen3f22WfjnnvuGZQ2EFHvhJRSZrsRRERERP3Fze6IiIgoLzHEEBERUV5iiCEiIqK8xBBDREREeYkhhoiIiPISQwwRERHlJYYYIiIiyksMMURERJSXGGKIiIgoLzHEEBERUV5iiCEiIqK8xBBDREREeen/AzAgfTpVRFO9AAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n", @@ -2810,7 +2667,7 @@ }, { "cell_type": "markdown", - "id": "8fa56ca8", + "id": "db2d6c19", "metadata": { "editable": true }, @@ -2823,95 +2680,13 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "76f0ddd2", + "execution_count": 34, + "id": "66c27da6", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 1.73\n", - "Mean squared error: 19.48\n", - "Mean squared error: 12.11\n", - "Mean squared error: 23.42\n", - "Mean squared error: 0.23\n", - "Mean squared error: 0.18\n", - "Mean squared error: 0.29\n", - "Mean squared error: 0.20\n", - "Mean squared error: 0.26\n", - "Mean squared error: 4.07\n", - "Mean squared error: 2.12\n", - "Mean squared error: 7.54\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 15.78\n", - "Mean squared error: 122.02\n", - "Mean squared error: 51.22\n", - "Mean squared error: 152.55\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1.7304881 19.47958494 12.11340253 23.41589548]\n", - " [ 0.22948497 0.18392847 0.29364655 0.20072279]\n", - " [ 0.26303845 4.06730814 2.11590451 7.5411205 ]\n", - " [ 15.77893972 122.02334824 51.2167072 152.54894451]]\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.neural_network import MLPRegressor\n", "from sklearn.metrics import accuracy_score\n", @@ -2951,7 +2726,7 @@ }, { "cell_type": "markdown", - "id": "19fec57c", + "id": "50a1e770", "metadata": { "editable": true }, @@ -2971,7 +2746,7 @@ }, { "cell_type": "markdown", - "id": "1bcb8b34", + "id": "5af218e3", "metadata": { "editable": true }, @@ -3003,7 +2778,7 @@ }, { "cell_type": "markdown", - "id": "2ebf620c", + "id": "55450c7b", "metadata": { "editable": true }, @@ -3027,7 +2802,7 @@ }, { "cell_type": "markdown", - "id": "db27019b", + "id": "be42ca6d", "metadata": { "editable": true }, @@ -3055,7 +2830,7 @@ }, { "cell_type": "markdown", - "id": "3ab4acd0", + "id": "4b7a4ceb", "metadata": { "editable": true }, @@ -3070,7 +2845,7 @@ }, { "cell_type": "markdown", - "id": "b60035b5", + "id": "e00d9920", "metadata": { "editable": true }, @@ -3082,7 +2857,7 @@ }, { "cell_type": "markdown", - "id": "29af7e67", + "id": "6243c073", "metadata": { "editable": true }, @@ -3097,7 +2872,7 @@ }, { "cell_type": "markdown", - "id": "740b4af2", + "id": "009ea7de", "metadata": { "editable": true }, @@ -3110,7 +2885,7 @@ }, { "cell_type": "markdown", - "id": "b4ad3976", + "id": "712c684d", "metadata": { "editable": true }, @@ -3122,7 +2897,7 @@ }, { "cell_type": "markdown", - "id": "0051e649", + "id": "b4bcd2a5", "metadata": { "editable": true }, @@ -3132,7 +2907,7 @@ }, { "cell_type": "markdown", - "id": "1a7712be", + "id": "143b6359", "metadata": { "editable": true }, @@ -3143,7 +2918,7 @@ }, { "cell_type": "markdown", - "id": "27e08926", + "id": "d2e56068", "metadata": { "editable": true }, @@ -3161,7 +2936,7 @@ }, { "cell_type": "markdown", - "id": "2ec61954", + "id": "2541505d", "metadata": { "editable": true }, @@ -3172,7 +2947,7 @@ }, { "cell_type": "markdown", - "id": "b7aab6da", + "id": "725efee7", "metadata": { "editable": true }, @@ -3184,7 +2959,7 @@ }, { "cell_type": "markdown", - "id": "523fa0e8", + "id": "cb15e9f1", "metadata": { "editable": true }, @@ -3194,7 +2969,7 @@ }, { "cell_type": "markdown", - "id": "a4172710", + "id": "87026e15", "metadata": { "editable": true }, @@ -3206,7 +2981,7 @@ }, { "cell_type": "markdown", - "id": "3a21de62", + "id": "cd09c85a", "metadata": { "editable": true }, @@ -3216,7 +2991,7 @@ }, { "cell_type": "markdown", - "id": "a68d2930", + "id": "c7845cfd", "metadata": { "editable": true }, @@ -3228,7 +3003,7 @@ }, { "cell_type": "markdown", - "id": "519a6c63", + "id": "20e94324", "metadata": { "editable": true }, @@ -3238,7 +3013,7 @@ }, { "cell_type": "markdown", - "id": "2f28767d", + "id": "46cc127e", "metadata": { "editable": true }, @@ -3257,7 +3032,7 @@ }, { "cell_type": "markdown", - "id": "60e193f4", + "id": "b83e3f3f", "metadata": { "editable": true }, @@ -3267,7 +3042,7 @@ }, { "cell_type": "markdown", - "id": "b76cff50", + "id": "d3c3d5b8", "metadata": { "editable": true }, @@ -3279,7 +3054,7 @@ }, { "cell_type": "markdown", - "id": "6c65fa96", + "id": "c6e741c3", "metadata": { "editable": true }, @@ -3289,7 +3064,7 @@ }, { "cell_type": "markdown", - "id": "554a1508", + "id": "510abc9b", "metadata": { "editable": true }, @@ -3305,7 +3080,7 @@ }, { "cell_type": "markdown", - "id": "ec603f5c", + "id": "1a6464a4", "metadata": { "editable": true }, @@ -3325,7 +3100,7 @@ }, { "cell_type": "markdown", - "id": "98c74b9b", + "id": "0f403e2d", "metadata": { "editable": true }, @@ -3335,7 +3110,7 @@ }, { "cell_type": "markdown", - "id": "7f4d2c5c", + "id": "4a8138dc", "metadata": { "editable": true }, @@ -3346,7 +3121,7 @@ }, { "cell_type": "markdown", - "id": "654bf9a6", + "id": "726cbaa4", "metadata": { "editable": true }, @@ -3365,7 +3140,7 @@ }, { "cell_type": "markdown", - "id": "f80909ca", + "id": "6b7adfb6", "metadata": { "editable": true }, @@ -3375,7 +3150,7 @@ }, { "cell_type": "markdown", - "id": "ee25c0f7", + "id": "84165f3e", "metadata": { "editable": true }, @@ -3387,7 +3162,7 @@ }, { "cell_type": "markdown", - "id": "c1947e0c", + "id": "ebf366ef", "metadata": { "editable": true }, @@ -3397,7 +3172,7 @@ }, { "cell_type": "markdown", - "id": "05229723", + "id": "02ffad05", "metadata": { "editable": true }, @@ -3408,7 +3183,7 @@ }, { "cell_type": "markdown", - "id": "bb0abff0", + "id": "d54237d5", "metadata": { "editable": true }, @@ -3428,7 +3203,7 @@ }, { "cell_type": "markdown", - "id": "bc16c9f8", + "id": "8a7e9dbb", "metadata": { "editable": true }, @@ -3440,7 +3215,7 @@ }, { "cell_type": "markdown", - "id": "d7f65472", + "id": "d4d5ff52", "metadata": { "editable": true }, @@ -3455,13 +3230,10 @@ { "cell_type": "code", "execution_count": 35, - "id": "acf08e73", + "id": "297415bf", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -3538,7 +3310,7 @@ }, { "cell_type": "markdown", - "id": "937591a1", + "id": "2ce99829", "metadata": { "editable": true }, @@ -3548,7 +3320,7 @@ }, { "cell_type": "markdown", - "id": "132c386c", + "id": "f6a44999", "metadata": { "editable": true }, @@ -3560,7 +3332,7 @@ }, { "cell_type": "markdown", - "id": "869bdef2", + "id": "990c34c3", "metadata": { "editable": true }, @@ -3570,7 +3342,7 @@ }, { "cell_type": "markdown", - "id": "7781a1c4", + "id": "a06f9457", "metadata": { "editable": true }, @@ -3581,7 +3353,7 @@ }, { "cell_type": "markdown", - "id": "fd8e693c", + "id": "19cd9843", "metadata": { "editable": true }, @@ -3593,7 +3365,7 @@ }, { "cell_type": "markdown", - "id": "f5c29ebf", + "id": "cc8c11ab", "metadata": { "editable": true }, @@ -3603,7 +3375,7 @@ }, { "cell_type": "markdown", - "id": "a798f40c", + "id": "d3d4e384", "metadata": { "editable": true }, @@ -3615,7 +3387,7 @@ }, { "cell_type": "markdown", - "id": "bae820af", + "id": "6187929e", "metadata": { "editable": true }, @@ -3625,7 +3397,7 @@ }, { "cell_type": "markdown", - "id": "06c75a81", + "id": "e0ba2cb1", "metadata": { "editable": true }, @@ -3637,7 +3409,7 @@ }, { "cell_type": "markdown", - "id": "59236913", + "id": "468fec32", "metadata": { "editable": true }, @@ -3650,7 +3422,7 @@ }, { "cell_type": "markdown", - "id": "81b5da1b", + "id": "ba618e10", "metadata": { "editable": true }, @@ -3662,7 +3434,7 @@ }, { "cell_type": "markdown", - "id": "e74c517f", + "id": "a74d3f1c", "metadata": { "editable": true }, @@ -3672,7 +3444,7 @@ }, { "cell_type": "markdown", - "id": "648beb7f", + "id": "fb434398", "metadata": { "editable": true }, @@ -3684,7 +3456,7 @@ }, { "cell_type": "markdown", - "id": "1d9326d3", + "id": "42558cc8", "metadata": { "editable": true }, @@ -3696,7 +3468,7 @@ }, { "cell_type": "markdown", - "id": "f51ed614", + "id": "d851da5b", "metadata": { "editable": true }, @@ -3707,7 +3479,7 @@ }, { "cell_type": "markdown", - "id": "ced37c54", + "id": "49533cb3", "metadata": { "editable": true }, @@ -3719,7 +3491,7 @@ }, { "cell_type": "markdown", - "id": "ebbeea54", + "id": "a62f6a4c", "metadata": { "editable": true }, @@ -3738,7 +3510,7 @@ }, { "cell_type": "markdown", - "id": "1b5d06a0", + "id": "381182cd", "metadata": { "editable": true }, @@ -3751,7 +3523,7 @@ }, { "cell_type": "markdown", - "id": "74890321", + "id": "04b172c8", "metadata": { "editable": true }, @@ -3761,7 +3533,7 @@ }, { "cell_type": "markdown", - "id": "ad33aa43", + "id": "265d8614", "metadata": { "editable": true }, @@ -3773,7 +3545,7 @@ }, { "cell_type": "markdown", - "id": "626df4e6", + "id": "68c1005e", "metadata": { "editable": true }, @@ -3783,7 +3555,7 @@ }, { "cell_type": "markdown", - "id": "6594383b", + "id": "61fb0fbc", "metadata": { "editable": true }, @@ -3795,7 +3567,7 @@ }, { "cell_type": "markdown", - "id": "ec04a0b3", + "id": "acb3fa59", "metadata": { "editable": true }, @@ -3805,7 +3577,7 @@ }, { "cell_type": "markdown", - "id": "a82796e0", + "id": "82f920b5", "metadata": { "editable": true }, @@ -3817,7 +3589,7 @@ }, { "cell_type": "markdown", - "id": "9607cc25", + "id": "26de4671", "metadata": { "editable": true }, @@ -3828,7 +3600,7 @@ }, { "cell_type": "markdown", - "id": "f84698d3", + "id": "071a86ff", "metadata": { "editable": true }, @@ -3840,7 +3612,7 @@ }, { "cell_type": "markdown", - "id": "1245df8a", + "id": "fdb51c1d", "metadata": { "editable": true }, @@ -3850,7 +3622,7 @@ }, { "cell_type": "markdown", - "id": "b6df8204", + "id": "5532b00a", "metadata": { "editable": true }, @@ -3862,7 +3634,7 @@ }, { "cell_type": "markdown", - "id": "6250497e", + "id": "a7c57982", "metadata": { "editable": true }, @@ -3872,7 +3644,7 @@ }, { "cell_type": "markdown", - "id": "f788dc8f", + "id": "022ce912", "metadata": { "editable": true }, @@ -3884,7 +3656,7 @@ }, { "cell_type": "markdown", - "id": "bdb72b3a", + "id": "12518c37", "metadata": { "editable": true }, @@ -3905,7 +3677,7 @@ }, { "cell_type": "markdown", - "id": "d498ca67", + "id": "89f9bbda", "metadata": { "editable": true }, @@ -3916,7 +3688,7 @@ }, { "cell_type": "markdown", - "id": "786a3fef", + "id": "51458daa", "metadata": { "editable": true }, @@ -3928,7 +3700,7 @@ }, { "cell_type": "markdown", - "id": "a0080ea0", + "id": "eaf4a93e", "metadata": { "editable": true }, @@ -3938,7 +3710,7 @@ }, { "cell_type": "markdown", - "id": "27b8b857", + "id": "2fea3537", "metadata": { "editable": true }, @@ -3950,7 +3722,7 @@ }, { "cell_type": "markdown", - "id": "3bf5241a", + "id": "ca4f1123", "metadata": { "editable": true }, @@ -3960,7 +3732,7 @@ }, { "cell_type": "markdown", - "id": "6b578bc7", + "id": "09de659a", "metadata": { "editable": true }, @@ -3972,7 +3744,7 @@ }, { "cell_type": "markdown", - "id": "11220b8a", + "id": "77f9fd08", "metadata": { "editable": true }, @@ -3984,7 +3756,7 @@ }, { "cell_type": "markdown", - "id": "a920365b", + "id": "beab9d54", "metadata": { "editable": true }, @@ -3998,13 +3770,10 @@ { "cell_type": "code", "execution_count": 36, - "id": "0b7b8c69", + "id": "ce0d14e1", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4016,7 +3785,7 @@ }, { "cell_type": "markdown", - "id": "c209e80c", + "id": "20bbbdf2", "metadata": { "editable": true }, @@ -4027,13 +3796,10 @@ { "cell_type": "code", "execution_count": 37, - "id": "947eedad", + "id": "d1b21aae", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4043,7 +3809,7 @@ }, { "cell_type": "markdown", - "id": "55e5aa0a", + "id": "02bb27d6", "metadata": { "editable": true }, @@ -4054,13 +3820,10 @@ { "cell_type": "code", "execution_count": 38, - "id": "3fb277ad", + "id": "98630c99", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4080,7 +3843,7 @@ }, { "cell_type": "markdown", - "id": "36c1d2bc", + "id": "9af04036", "metadata": { "editable": true }, @@ -4094,13 +3857,10 @@ { "cell_type": "code", "execution_count": 39, - "id": "6db56b3d", + "id": "2d8cd3da", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4110,7 +3870,7 @@ }, { "cell_type": "markdown", - "id": "e0ee98a4", + "id": "71899eed", "metadata": { "editable": true }, @@ -4121,13 +3881,10 @@ { "cell_type": "code", "execution_count": 40, - "id": "8774bce4", + "id": "f8cc2dd0", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4136,7 +3893,7 @@ }, { "cell_type": "markdown", - "id": "1d29c10a", + "id": "44f3dd69", "metadata": { "editable": true }, @@ -4147,13 +3904,10 @@ { "cell_type": "code", "execution_count": 41, - "id": "93c8b0c6", + "id": "acdf8d3d", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4166,7 +3920,7 @@ }, { "cell_type": "markdown", - "id": "ff36a22e", + "id": "26d00774", "metadata": { "editable": true }, @@ -4177,13 +3931,10 @@ { "cell_type": "code", "execution_count": 42, - "id": "35feafa3", + "id": "fcf52c3c", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4194,7 +3945,7 @@ }, { "cell_type": "markdown", - "id": "1c4ac2dc", + "id": "82083461", "metadata": { "editable": true }, @@ -4216,7 +3967,7 @@ }, { "cell_type": "markdown", - "id": "dc2eacf3", + "id": "c247a29f", "metadata": { "editable": true }, @@ -4228,7 +3979,7 @@ }, { "cell_type": "markdown", - "id": "48c5a8b6", + "id": "5c6266aa", "metadata": { "editable": true }, @@ -4238,7 +3989,7 @@ }, { "cell_type": "markdown", - "id": "abc4699e", + "id": "c36f8d5c", "metadata": { "editable": true }, @@ -4250,7 +4001,7 @@ }, { "cell_type": "markdown", - "id": "4db48e9d", + "id": "5b1a5174", "metadata": { "editable": true }, @@ -4262,7 +4013,7 @@ }, { "cell_type": "markdown", - "id": "350fec9b", + "id": "81c82695", "metadata": { "editable": true }, @@ -4272,7 +4023,7 @@ }, { "cell_type": "markdown", - "id": "b474ddf5", + "id": "7dc8a3bc", "metadata": { "editable": true }, @@ -4284,7 +4035,7 @@ }, { "cell_type": "markdown", - "id": "67fc1d79", + "id": "41789a38", "metadata": { "editable": true }, @@ -4294,7 +4045,7 @@ }, { "cell_type": "markdown", - "id": "6aa9f684", + "id": "4a2e42bf", "metadata": { "editable": true }, @@ -4306,7 +4057,7 @@ }, { "cell_type": "markdown", - "id": "ce023be0", + "id": "acd67ee6", "metadata": { "editable": true }, @@ -4316,7 +4067,7 @@ }, { "cell_type": "markdown", - "id": "3754c848", + "id": "ee90b0eb", "metadata": { "editable": true }, @@ -4328,7 +4079,7 @@ }, { "cell_type": "markdown", - "id": "4d884286", + "id": "54eaab7f", "metadata": { "editable": true }, @@ -4340,7 +4091,7 @@ }, { "cell_type": "markdown", - "id": "f8446340", + "id": "f941f1ac", "metadata": { "editable": true }, @@ -4350,7 +4101,7 @@ }, { "cell_type": "markdown", - "id": "a6830538", + "id": "89e02a88", "metadata": { "editable": true }, @@ -4362,7 +4113,7 @@ }, { "cell_type": "markdown", - "id": "84f9e6b4", + "id": "3d1f3b65", "metadata": { "editable": true }, @@ -4372,7 +4123,7 @@ }, { "cell_type": "markdown", - "id": "586509f5", + "id": "1dda0ac2", "metadata": { "editable": true }, @@ -4384,7 +4135,7 @@ }, { "cell_type": "markdown", - "id": "ff04787a", + "id": "aafe7aae", "metadata": { "editable": true }, @@ -4396,7 +4147,7 @@ }, { "cell_type": "markdown", - "id": "fab40e86", + "id": "37f9c1fa", "metadata": { "editable": true }, @@ -4408,7 +4159,7 @@ }, { "cell_type": "markdown", - "id": "175c9026", + "id": "373adac2", "metadata": { "editable": true }, @@ -4418,7 +4169,7 @@ }, { "cell_type": "markdown", - "id": "8b8d1d83", + "id": "bdd6d800", "metadata": { "editable": true }, @@ -4430,7 +4181,7 @@ }, { "cell_type": "markdown", - "id": "0c64f049", + "id": "913df005", "metadata": { "editable": true }, @@ -4440,7 +4191,7 @@ }, { "cell_type": "markdown", - "id": "2a591f8f", + "id": "76f6737e", "metadata": { "editable": true }, @@ -4452,7 +4203,7 @@ }, { "cell_type": "markdown", - "id": "956b5691", + "id": "48f6aa66", "metadata": { "editable": true }, @@ -4462,7 +4213,7 @@ }, { "cell_type": "markdown", - "id": "85b1bd72", + "id": "513a1986", "metadata": { "editable": true }, @@ -4474,7 +4225,7 @@ }, { "cell_type": "markdown", - "id": "6950fb95", + "id": "20fbbe07", "metadata": { "editable": true }, @@ -4485,7 +4236,7 @@ }, { "cell_type": "markdown", - "id": "e2d998e7", + "id": "4dd97daa", "metadata": { "editable": true }, @@ -4497,7 +4248,7 @@ }, { "cell_type": "markdown", - "id": "79e10d20", + "id": "032afd0e", "metadata": { "editable": true }, @@ -4507,7 +4258,7 @@ }, { "cell_type": "markdown", - "id": "6585dbe2", + "id": "25eac667", "metadata": { "editable": true }, @@ -4519,7 +4270,7 @@ }, { "cell_type": "markdown", - "id": "4291e4ad", + "id": "18c343cd", "metadata": { "editable": true }, @@ -4529,7 +4280,7 @@ }, { "cell_type": "markdown", - "id": "6a50444a", + "id": "9864986f", "metadata": { "editable": true }, @@ -4541,7 +4292,7 @@ }, { "cell_type": "markdown", - "id": "58d08cf2", + "id": "1ecdde02", "metadata": { "editable": true }, @@ -4554,7 +4305,7 @@ }, { "cell_type": "markdown", - "id": "8215c3c6", + "id": "9d62dddc", "metadata": { "editable": true }, @@ -4566,7 +4317,7 @@ }, { "cell_type": "markdown", - "id": "09efd53b", + "id": "db398f5b", "metadata": { "editable": true }, @@ -4578,7 +4329,7 @@ }, { "cell_type": "markdown", - "id": "6c694cf4", + "id": "2bbf293c", "metadata": { "editable": true }, @@ -4590,7 +4341,7 @@ }, { "cell_type": "markdown", - "id": "909419d5", + "id": "d82e1506", "metadata": { "editable": true }, @@ -4602,7 +4353,7 @@ }, { "cell_type": "markdown", - "id": "2d35d3a4", + "id": "1ddd417c", "metadata": { "editable": true }, @@ -4614,7 +4365,7 @@ }, { "cell_type": "markdown", - "id": "845b476b", + "id": "e7904873", "metadata": { "editable": true }, @@ -4624,7 +4375,7 @@ }, { "cell_type": "markdown", - "id": "644470d4", + "id": "19604164", "metadata": { "editable": true }, @@ -4636,7 +4387,7 @@ }, { "cell_type": "markdown", - "id": "6c4ad6c8", + "id": "0f262e03", "metadata": { "editable": true }, @@ -4648,7 +4399,7 @@ }, { "cell_type": "markdown", - "id": "839e60fb", + "id": "cdb18e0d", "metadata": { "editable": true }, @@ -4660,25 +4411,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } \ No newline at end of file diff --git a/doc/LectureNotes/week34.ipynb b/doc/LectureNotes/week34.ipynb index 95856e1b9..899bd8e9f 100644 --- a/doc/LectureNotes/week34.ipynb +++ b/doc/LectureNotes/week34.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3b47d5e6", + "id": "0b9e53e2", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "5f010738", + "id": "d69d6c55", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "9821466c", + "id": "ed7a2caa", "metadata": { "editable": true }, @@ -52,7 +52,7 @@ }, { "cell_type": "markdown", - "id": "d635526f", + "id": "1a7fa793", "metadata": { "editable": true }, @@ -68,7 +68,7 @@ }, { "cell_type": "markdown", - "id": "46d1ebd2", + "id": "42eed58e", "metadata": { "editable": true }, @@ -92,7 +92,7 @@ }, { "cell_type": "markdown", - "id": "722a1812", + "id": "19b78895", "metadata": { "editable": true }, @@ -105,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "8724a41b", + "id": "920c8b78", "metadata": { "editable": true }, @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "a74065a9", + "id": "66791ac8", "metadata": { "editable": true }, @@ -157,7 +157,7 @@ }, { "cell_type": "markdown", - "id": "cb3519d1", + "id": "d006c6f2", "metadata": { "editable": true }, @@ -175,7 +175,7 @@ }, { "cell_type": "markdown", - "id": "f46e4ab5", + "id": "d2b59d04", "metadata": { "editable": true }, @@ -203,7 +203,7 @@ }, { "cell_type": "markdown", - "id": "18134f7b", + "id": "2b1dfff8", "metadata": { "editable": true }, @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "6e4b2cb4", + "id": "39ce46e9", "metadata": { "editable": true }, @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "2dc04ba4", + "id": "cc1139fe", "metadata": { "editable": true }, @@ -261,7 +261,7 @@ }, { "cell_type": "markdown", - "id": "b9d7446a", + "id": "f56c0bb5", "metadata": { "editable": true }, @@ -273,7 +273,7 @@ }, { "cell_type": "markdown", - "id": "0ac913df", + "id": "4b601e8d", "metadata": { "editable": true }, @@ -293,7 +293,7 @@ }, { "cell_type": "markdown", - "id": "fe4f142d", + "id": "2518a99e", "metadata": { "editable": true }, @@ -311,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "82a26310", + "id": "a0e1e9ae", "metadata": { "editable": true }, @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "5d97a91f", + "id": "8af343cb", "metadata": { "editable": true }, @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "0af85fa1", + "id": "c0cfedb4", "metadata": { "editable": true }, @@ -382,7 +382,7 @@ }, { "cell_type": "markdown", - "id": "ca1c0d4b", + "id": "4561f563", "metadata": { "editable": true }, @@ -398,7 +398,7 @@ }, { "cell_type": "markdown", - "id": "5e3a4194", + "id": "b94446df", "metadata": { "editable": true }, @@ -420,7 +420,7 @@ }, { "cell_type": "markdown", - "id": "44fe97f3", + "id": "2da0b611", "metadata": { "editable": true }, @@ -438,7 +438,7 @@ }, { "cell_type": "markdown", - "id": "3fd61872", + "id": "cb6368d0", "metadata": { "editable": true }, @@ -468,7 +468,7 @@ }, { "cell_type": "markdown", - "id": "50148149", + "id": "cd5643ee", "metadata": { "editable": true }, @@ -497,7 +497,7 @@ }, { "cell_type": "markdown", - "id": "12bc5144", + "id": "dad2568b", "metadata": { "editable": true }, @@ -515,7 +515,7 @@ }, { "cell_type": "markdown", - "id": "25428d4e", + "id": "ccf34c1f", "metadata": { "editable": true }, @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "2efac50e", + "id": "d9a80c4f", "metadata": { "editable": true }, @@ -549,7 +549,7 @@ }, { "cell_type": "markdown", - "id": "dfd0c6a0", + "id": "bf1454b7", "metadata": { "editable": true }, @@ -572,7 +572,7 @@ }, { "cell_type": "markdown", - "id": "f92f8d35", + "id": "4a01a6f0", "metadata": { "editable": true }, @@ -588,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "174d6e96", + "id": "2bf27180", "metadata": { "editable": true }, @@ -604,12 +604,12 @@ }, { "cell_type": "markdown", - "id": "0fcac42b", + "id": "15ddfa02", "metadata": { "editable": true }, "source": [ - "## Example of discriminative modeling, [taken from Generative Deeep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n", + "## Example of discriminative modeling, [taken from Generative Deep Learning by David Foster](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html)\n", "\n", "\n", "\n", @@ -620,7 +620,7 @@ }, { "cell_type": "markdown", - "id": "a8942d9a", + "id": "371a54c3", "metadata": { "editable": true }, @@ -640,7 +640,7 @@ }, { "cell_type": "markdown", - "id": "a28ea0e4", + "id": "f9ec1f0a", "metadata": { "editable": true }, @@ -675,7 +675,7 @@ }, { "cell_type": "markdown", - "id": "d3260f00", + "id": "19f2aef4", "metadata": { "editable": true }, @@ -705,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "4ac16500", + "id": "e08bd4e6", "metadata": { "editable": true }, @@ -736,7 +736,7 @@ }, { "cell_type": "markdown", - "id": "825ca09e", + "id": "729c3f46", "metadata": { "editable": true }, @@ -775,7 +775,7 @@ }, { "cell_type": "markdown", - "id": "40bd0bf2", + "id": "6825a222", "metadata": { "editable": true }, @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "a356fdf0", + "id": "bad98b2e", "metadata": { "editable": true }, @@ -845,7 +845,7 @@ }, { "cell_type": "markdown", - "id": "a9730b8a", + "id": "1daa92e1", "metadata": { "editable": true }, @@ -869,7 +869,7 @@ }, { "cell_type": "markdown", - "id": "8f967a6a", + "id": "1c840067", "metadata": { "editable": true }, @@ -896,7 +896,7 @@ }, { "cell_type": "markdown", - "id": "76e167a3", + "id": "f33d6379", "metadata": { "editable": true }, @@ -906,7 +906,7 @@ }, { "cell_type": "markdown", - "id": "94a3c420", + "id": "e3a03646", "metadata": { "editable": true }, @@ -918,7 +918,7 @@ }, { "cell_type": "markdown", - "id": "c6ff6b6b", + "id": "fb6d2495", "metadata": { "editable": true }, @@ -939,7 +939,7 @@ }, { "cell_type": "markdown", - "id": "55dbb002", + "id": "3101a680", "metadata": { "editable": true }, @@ -951,13 +951,10 @@ { "cell_type": "code", "execution_count": 1, - "id": "655878bd", + "id": "66625f81", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -966,7 +963,7 @@ }, { "cell_type": "markdown", - "id": "914fa12c", + "id": "d8edb94a", "metadata": { "editable": true }, @@ -977,13 +974,10 @@ { "cell_type": "code", "execution_count": 2, - "id": "a0cfca17", + "id": "7d44772f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -994,7 +988,7 @@ }, { "cell_type": "markdown", - "id": "342c0109", + "id": "7c3a5d5e", "metadata": { "editable": true }, @@ -1006,13 +1000,10 @@ { "cell_type": "code", "execution_count": 3, - "id": "d09ad51a", + "id": "bd150398", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1023,7 +1014,7 @@ }, { "cell_type": "markdown", - "id": "be9009bc", + "id": "62bb511e", "metadata": { "editable": true }, @@ -1035,13 +1026,10 @@ { "cell_type": "code", "execution_count": 4, - "id": "19dfe7ab", + "id": "de9d1b88", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1052,7 +1040,7 @@ }, { "cell_type": "markdown", - "id": "5c78e90e", + "id": "575f4f67", "metadata": { "editable": true }, @@ -1069,13 +1057,10 @@ { "cell_type": "code", "execution_count": 5, - "id": "48603a4c", + "id": "eadd4613", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1089,7 +1074,7 @@ }, { "cell_type": "markdown", - "id": "db5cfa34", + "id": "43c2b238", "metadata": { "editable": true }, @@ -1101,13 +1086,10 @@ { "cell_type": "code", "execution_count": 6, - "id": "1c252e6c", + "id": "f1153763", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1118,7 +1100,7 @@ }, { "cell_type": "markdown", - "id": "375afb81", + "id": "b897cb85", "metadata": { "editable": true }, @@ -1129,13 +1111,10 @@ { "cell_type": "code", "execution_count": 7, - "id": "4c87de99", + "id": "3417f7b5", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1146,7 +1125,7 @@ }, { "cell_type": "markdown", - "id": "afff17c8", + "id": "421de263", "metadata": { "editable": true }, @@ -1157,13 +1136,10 @@ { "cell_type": "code", "execution_count": 8, - "id": "d5827065", + "id": "e9055b4f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1174,7 +1150,7 @@ }, { "cell_type": "markdown", - "id": "83e054e8", + "id": "52301fe3", "metadata": { "editable": true }, @@ -1189,13 +1165,10 @@ { "cell_type": "code", "execution_count": 9, - "id": "0cd4400a", + "id": "7f400b7f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1206,7 +1179,7 @@ }, { "cell_type": "markdown", - "id": "c2868228", + "id": "35677f44", "metadata": { "editable": true }, @@ -1217,13 +1190,10 @@ { "cell_type": "code", "execution_count": 10, - "id": "6e7ef557", + "id": "16b1c27a", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1235,7 +1205,7 @@ }, { "cell_type": "markdown", - "id": "47391e12", + "id": "7566ab17", "metadata": { "editable": true }, @@ -1246,13 +1216,10 @@ { "cell_type": "code", "execution_count": 11, - "id": "a9457054", + "id": "b10affa1", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1264,7 +1231,7 @@ }, { "cell_type": "markdown", - "id": "a04c51eb", + "id": "c50f969e", "metadata": { "editable": true }, @@ -1275,13 +1242,10 @@ { "cell_type": "code", "execution_count": 12, - "id": "43e1f145", + "id": "35f1236f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1294,7 +1258,7 @@ }, { "cell_type": "markdown", - "id": "e11ea608", + "id": "02cec13a", "metadata": { "editable": true }, @@ -1305,13 +1269,10 @@ { "cell_type": "code", "execution_count": 13, - "id": "ed021bcf", + "id": "821bc62d", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1324,7 +1285,7 @@ }, { "cell_type": "markdown", - "id": "7cf97a5d", + "id": "5df31e24", "metadata": { "editable": true }, @@ -1335,13 +1296,10 @@ { "cell_type": "code", "execution_count": 14, - "id": "0dd77598", + "id": "d821d3ff", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1354,7 +1312,7 @@ }, { "cell_type": "markdown", - "id": "a27628a3", + "id": "a99c04f6", "metadata": { "editable": true }, @@ -1366,7 +1324,7 @@ }, { "cell_type": "markdown", - "id": "80585c1d", + "id": "fdb302f5", "metadata": { "editable": true }, @@ -1381,7 +1339,7 @@ }, { "cell_type": "markdown", - "id": "74725f56", + "id": "4c91d693", "metadata": { "editable": true }, @@ -1391,7 +1349,7 @@ }, { "cell_type": "markdown", - "id": "fd223a73", + "id": "316ccd01", "metadata": { "editable": true }, @@ -1403,7 +1361,7 @@ }, { "cell_type": "markdown", - "id": "40dfa247", + "id": "73cf195b", "metadata": { "editable": true }, @@ -1414,7 +1372,7 @@ }, { "cell_type": "markdown", - "id": "56e47db3", + "id": "c9d6f1ba", "metadata": { "editable": true }, @@ -1429,7 +1387,7 @@ }, { "cell_type": "markdown", - "id": "26251aec", + "id": "a5bbc329", "metadata": { "editable": true }, @@ -1444,13 +1402,10 @@ { "cell_type": "code", "execution_count": 15, - "id": "dab44ca5", + "id": "74dd353c", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1474,13 +1429,10 @@ { "cell_type": "code", "execution_count": 16, - "id": "58c875cb", + "id": "f07645f8", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1501,7 +1453,7 @@ }, { "cell_type": "markdown", - "id": "8d735077", + "id": "245ee493", "metadata": { "editable": true }, @@ -1527,13 +1479,10 @@ { "cell_type": "code", "execution_count": 17, - "id": "d60ac131", + "id": "e428bcdc", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1550,7 +1499,7 @@ }, { "cell_type": "markdown", - "id": "06e4b620", + "id": "861269ad", "metadata": { "editable": true }, @@ -1564,13 +1513,10 @@ { "cell_type": "code", "execution_count": 18, - "id": "2174392e", + "id": "c34c9894", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1580,7 +1526,7 @@ }, { "cell_type": "markdown", - "id": "72c17dd0", + "id": "0ec5724d", "metadata": { "editable": true }, @@ -1591,13 +1537,10 @@ { "cell_type": "code", "execution_count": 19, - "id": "7612f9de", + "id": "83302e08", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1606,7 +1549,7 @@ }, { "cell_type": "markdown", - "id": "242d4c4b", + "id": "886277ee", "metadata": { "editable": true }, @@ -1617,13 +1560,10 @@ { "cell_type": "code", "execution_count": 20, - "id": "0171d1ad", + "id": "c1010af7", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1638,7 +1578,7 @@ }, { "cell_type": "markdown", - "id": "e5232bfb", + "id": "f3c82384", "metadata": { "editable": true }, @@ -1650,13 +1590,10 @@ { "cell_type": "code", "execution_count": 21, - "id": "cb2ab622", + "id": "70542372", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1677,7 +1614,7 @@ }, { "cell_type": "markdown", - "id": "1998bc9c", + "id": "a83eb591", "metadata": { "editable": true }, @@ -1688,13 +1625,10 @@ { "cell_type": "code", "execution_count": 22, - "id": "e0ec9915", + "id": "2f42295f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1720,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "ebe44ad1", + "id": "fb1b0336", "metadata": { "editable": true }, @@ -1731,13 +1665,10 @@ { "cell_type": "code", "execution_count": 23, - "id": "6f6d21e6", + "id": "0438c751", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1749,7 +1680,7 @@ }, { "cell_type": "markdown", - "id": "2f97c531", + "id": "f6d3d4c8", "metadata": { "editable": true }, @@ -1766,7 +1697,7 @@ }, { "cell_type": "markdown", - "id": "36a57ec4", + "id": "d552a0d0", "metadata": { "editable": true }, @@ -1778,7 +1709,7 @@ }, { "cell_type": "markdown", - "id": "ca69e9e9", + "id": "b96bd9e5", "metadata": { "editable": true }, @@ -1809,7 +1740,7 @@ }, { "cell_type": "markdown", - "id": "b4bc6d8a", + "id": "41318c5c", "metadata": { "editable": true }, @@ -1821,7 +1752,7 @@ }, { "cell_type": "markdown", - "id": "dd9f61d0", + "id": "789c6fe4", "metadata": { "editable": true }, @@ -1849,13 +1780,10 @@ { "cell_type": "code", "execution_count": 24, - "id": "81d8ecc3", + "id": "8c7472f2", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1882,7 +1810,7 @@ }, { "cell_type": "markdown", - "id": "b9f8b0e5", + "id": "25cef2d3", "metadata": { "editable": true }, @@ -1899,7 +1827,7 @@ }, { "cell_type": "markdown", - "id": "37a6973d", + "id": "58a04438", "metadata": { "editable": true }, @@ -1911,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "3b1616dd", + "id": "55191297", "metadata": { "editable": true }, @@ -1932,7 +1860,7 @@ }, { "cell_type": "markdown", - "id": "02f59dce", + "id": "2951bf64", "metadata": { "editable": true }, @@ -1945,7 +1873,7 @@ }, { "cell_type": "markdown", - "id": "37a3928f", + "id": "1109ceeb", "metadata": { "editable": true }, @@ -1976,7 +1904,7 @@ }, { "cell_type": "markdown", - "id": "8ced1c75", + "id": "7eeca264", "metadata": { "editable": true }, @@ -1988,7 +1916,7 @@ }, { "cell_type": "markdown", - "id": "2b2c7527", + "id": "fd8b7402", "metadata": { "editable": true }, @@ -2006,13 +1934,10 @@ { "cell_type": "code", "execution_count": 25, - "id": "dcc4e935", + "id": "0de5719f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2036,7 +1961,7 @@ }, { "cell_type": "markdown", - "id": "ef0a62d7", + "id": "ce194339", "metadata": { "editable": true }, @@ -2058,13 +1983,10 @@ { "cell_type": "code", "execution_count": 26, - "id": "e2a9bb21", + "id": "cde68f17", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2099,7 +2021,7 @@ }, { "cell_type": "markdown", - "id": "a6eca083", + "id": "f48a129c", "metadata": { "editable": true }, @@ -2110,7 +2032,7 @@ }, { "cell_type": "markdown", - "id": "c940fd70", + "id": "c337129d", "metadata": { "editable": true }, @@ -2123,7 +2045,7 @@ }, { "cell_type": "markdown", - "id": "7b1317ef", + "id": "d6462e3c", "metadata": { "editable": true }, @@ -2144,7 +2066,7 @@ }, { "cell_type": "markdown", - "id": "3895d32d", + "id": "c2a5a5fd", "metadata": { "editable": true }, @@ -2156,7 +2078,7 @@ }, { "cell_type": "markdown", - "id": "b88937a4", + "id": "d19d16f8", "metadata": { "editable": true }, @@ -2166,7 +2088,7 @@ }, { "cell_type": "markdown", - "id": "7d62abca", + "id": "7739ec09", "metadata": { "editable": true }, @@ -2178,7 +2100,7 @@ }, { "cell_type": "markdown", - "id": "903ee760", + "id": "7f433018", "metadata": { "editable": true }, @@ -2190,7 +2112,7 @@ }, { "cell_type": "markdown", - "id": "ef89ae2c", + "id": "20361838", "metadata": { "editable": true }, @@ -2202,7 +2124,7 @@ }, { "cell_type": "markdown", - "id": "924ac782", + "id": "02d95914", "metadata": { "editable": true }, @@ -2213,7 +2135,7 @@ }, { "cell_type": "markdown", - "id": "50053e83", + "id": "524bb980", "metadata": { "editable": true }, @@ -2225,7 +2147,7 @@ }, { "cell_type": "markdown", - "id": "dc7df77b", + "id": "6cb0b956", "metadata": { "editable": true }, @@ -2247,7 +2169,7 @@ }, { "cell_type": "markdown", - "id": "7fe669ae", + "id": "142be577", "metadata": { "editable": true }, @@ -2259,7 +2181,7 @@ }, { "cell_type": "markdown", - "id": "28ceb188", + "id": "4e765569", "metadata": { "editable": true }, @@ -2272,7 +2194,7 @@ }, { "cell_type": "markdown", - "id": "d89d075b", + "id": "f79ebfdf", "metadata": { "editable": true }, @@ -2289,7 +2211,7 @@ }, { "cell_type": "markdown", - "id": "b3816234", + "id": "eb09c88a", "metadata": { "editable": true }, @@ -2301,7 +2223,7 @@ }, { "cell_type": "markdown", - "id": "ddf946bd", + "id": "bf69319c", "metadata": { "editable": true }, @@ -2311,7 +2233,7 @@ }, { "cell_type": "markdown", - "id": "ec48e2fd", + "id": "eec8df19", "metadata": { "editable": true }, @@ -2323,7 +2245,7 @@ }, { "cell_type": "markdown", - "id": "462f1772", + "id": "313f9634", "metadata": { "editable": true }, @@ -2333,7 +2255,7 @@ }, { "cell_type": "markdown", - "id": "dabcb01d", + "id": "83b87f63", "metadata": { "editable": true }, @@ -2345,7 +2267,7 @@ }, { "cell_type": "markdown", - "id": "be2a6bd1", + "id": "be859efd", "metadata": { "editable": true }, @@ -2355,7 +2277,7 @@ }, { "cell_type": "markdown", - "id": "eb37d8f8", + "id": "a976e227", "metadata": { "editable": true }, @@ -2367,7 +2289,7 @@ }, { "cell_type": "markdown", - "id": "de325e18", + "id": "9f1dc803", "metadata": { "editable": true }, @@ -2383,7 +2305,7 @@ }, { "cell_type": "markdown", - "id": "2c68a0c1", + "id": "4fe2c4e9", "metadata": { "editable": true }, @@ -2395,7 +2317,7 @@ }, { "cell_type": "markdown", - "id": "f3492a4b", + "id": "bd8e264d", "metadata": { "editable": true }, @@ -2406,7 +2328,7 @@ }, { "cell_type": "markdown", - "id": "207b9ead", + "id": "9a0e9ac7", "metadata": { "editable": true }, @@ -2418,7 +2340,7 @@ }, { "cell_type": "markdown", - "id": "d74c7ddc", + "id": "9b20bc14", "metadata": { "editable": true }, @@ -2432,7 +2354,7 @@ }, { "cell_type": "markdown", - "id": "a02ea08c", + "id": "5fae8c2f", "metadata": { "editable": true }, @@ -2444,7 +2366,7 @@ }, { "cell_type": "markdown", - "id": "b9863119", + "id": "c955d871", "metadata": { "editable": true }, @@ -2469,7 +2391,7 @@ }, { "cell_type": "markdown", - "id": "92ca8aa6", + "id": "10feca33", "metadata": { "editable": true }, @@ -2485,14 +2407,11 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "32b99126", + "execution_count": 27, + "id": "cd57c0cd", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2533,7 +2452,7 @@ }, { "cell_type": "markdown", - "id": "185af862", + "id": "ed4e2094", "metadata": { "editable": true }, @@ -2550,13 +2469,10 @@ { "cell_type": "code", "execution_count": 28, - "id": "2ae9db3d", + "id": "309536df", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2574,7 +2490,7 @@ }, { "cell_type": "markdown", - "id": "1ade787b", + "id": "27597f0a", "metadata": { "editable": true }, @@ -2587,14 +2503,11 @@ }, { "cell_type": "code", - "execution_count": 38, - "id": "e0d18716", + "execution_count": 29, + "id": "1c60fe5f", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2620,7 +2533,7 @@ }, { "cell_type": "markdown", - "id": "bd819c63", + "id": "36972bcb", "metadata": { "editable": true }, @@ -2639,38 +2552,13 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "fc0346bc", + "execution_count": 30, + "id": "85ffeaf7", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " N Z A Element Ebinding\n", - "A \n", - "4 0 1 3 4 Li 1.153760\n", - "5 2 3 2 5 He 5.512132\n", - "6 7 3 3 6 Li 5.332331\n", - "7 12 4 3 7 Li 5.606439\n", - "8 17 4 4 8 Be 7.062435\n", - "... ... ... ... ... ...\n", - "264 3297 156 108 264 Hs 7.298375\n", - "265 3303 157 108 265 Hs 7.296247\n", - "266 3310 158 108 266 Hs 7.298273\n", - "269 3331 159 110 269 Ds 7.250154\n", - "270 3337 160 110 270 Ds 7.253775\n", - "\n", - "[264 rows x 5 columns]\n" - ] - } - ], + "outputs": [], "source": [ "A = Masses['A']\n", "Z = Masses['Z']\n", @@ -2682,7 +2570,7 @@ }, { "cell_type": "markdown", - "id": "4f70ca63", + "id": "799627e9", "metadata": { "editable": true }, @@ -2693,14 +2581,11 @@ }, { "cell_type": "code", - "execution_count": 40, - "id": "d56956d6", + "execution_count": 31, + "id": "365fbba9", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2715,7 +2600,7 @@ }, { "cell_type": "markdown", - "id": "0b3df36c", + "id": "0d3c5663", "metadata": { "editable": true }, @@ -2725,14 +2610,11 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "d10ecf29", + "execution_count": 32, + "id": "60ba302b", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2742,7 +2624,7 @@ }, { "cell_type": "markdown", - "id": "2289c78e", + "id": "af13881a", "metadata": { "editable": true }, @@ -2753,38 +2635,13 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "286afac2", + "execution_count": 33, + "id": "a5cba204", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 0.02\n", - "Variance score: 0.95\n", - "Mean absolute error: 0.05\n", - "[ 0.00000000e+00 -2.96611194e-02 2.01719003e-01 1.08078025e+01\n", - " -4.03097597e+01] 5.294399745619595\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# The mean squared error \n", "print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, fity))\n", @@ -2810,7 +2667,7 @@ }, { "cell_type": "markdown", - "id": "8fa56ca8", + "id": "db2d6c19", "metadata": { "editable": true }, @@ -2823,95 +2680,13 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "76f0ddd2", + "execution_count": 34, + "id": "66c27da6", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 1.73\n", - "Mean squared error: 19.48\n", - "Mean squared error: 12.11\n", - "Mean squared error: 23.42\n", - "Mean squared error: 0.23\n", - "Mean squared error: 0.18\n", - "Mean squared error: 0.29\n", - "Mean squared error: 0.20\n", - "Mean squared error: 0.26\n", - "Mean squared error: 4.07\n", - "Mean squared error: 2.12\n", - "Mean squared error: 7.54\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 15.78\n", - "Mean squared error: 122.02\n", - "Mean squared error: 51.22\n", - "Mean squared error: 152.55\n" - ] - }, - { - "data": { - "image/png": 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e1Jtvvpli34ULFyRJhQsXdtpeqFAhnT9/3nINrLIFAAAAWJBeL4zYuHHj2+5ft27dXd3vwYMH9cknn2jBggXy9PRMsf/GjRuSlGJf9uzZFRkZaflx6JAAAAAAcBIbG6u33npLPXv2VMWKFVM9xsvLS5JSnMAeGxurHDlyWH4sOiQAAABABna3HZDb2bNnj44cOaJPPvlE06ZNkyTFx8crISFBNWvW1IgRI1SqVClJSSe9lyhRIvm2YWFhtwwxqSGQAAAAABZkpQsjVqtWTatXr3baNm/ePK1evVrz5s1T/vz55enpKR8fH23dujU5kERFRSk4OFht27a1/FgEEgAAAABOvLy8VLJkSadtuXPnlru7u9P2tm3bavz48cqXL5+KFi2qcePGyd/fX02bNrX8WAQSAAAAAHelT58+SkhI0NChQxUTE6PatWtr1qxZqZ4Efys2h8PhcGGN6coTHm1Ml4Aswq1SWdMlIIuwXYk2XQKyiET/fKZLQBaxetsw0yXc0i8n0+ff93qljpou4Z6wyhYAAAAAYwgkAAAAAIzhHBIAAADAAjvf5bsEryoAAAAAYwgkAAAAAIxhZAsAAACwICtdGPF+okMCAAAAwBgCCQAAAABjGNkCAAAALEh08F2+K/CqAgAAADCGQAIAAADAGEa2AAAAAAvsrLLlEnRIAAAAABhDIAEAAABgDCNbAAAAgAWJfJfvEryqAAAAAIwhkAAAAAAwhpEtAAAAwAIujOgavKoAAAAAjKFDAhUsll+f7/5Iw1tO0N6Nwake0/TVBnp7Vs9b3se4TtO1Zt5GV5WITKiAX259FvS6Rr7xlfbuOJG8/aH6FfRK98cVUN5PUVduaNPafZr78VrduB5nsFpkZAUK59GnqwdqZNdZ+mPL0eTtVeuUVbs3n1KpSkUUH5ugAztPaNb73+t8yCWD1SIjeqb5A3qudW0VLppXVyKuafPGQ/rvFz/r+rWkz61HG1TQK53qqXipAoq8cl2rV+zR11/+ooQEu+HKgfSBQJLFFSpRQB/88I588uS87XHbftylPnWHOm2z2Wzq92k3efvm0LaVu11ZJjKZQoXzaMxn7eXjm8Np+6ONKmnoxJe1d8dJvf/2ImVzd9PLXRtq7MxOeqPdF7In8scbd6ZQ0bwaPa+HfHI7v9cqPlBK7y/oqS1r9mlcn3nKnsNTbXo/ofFBfdSz6VhFRVwzVDEymtbtHlWnno20eP5v2r39hIoUz6f23RuqVJlCGvT6fNV+tKyGjX1Rq1f8rpmfrFPxUgXUqVcj5S/go8kf/GC6fNwhO8NFLkEgyaJsNpuavlpf3ca2tXR85KWrirx01Wlb895Pq3iloupXf1iKfUBqbDabmjxXU137P5Xq/rY9G+nU8Ysa2mOuEhISJUn7doVozo9v6onmD+inoB33s1xkYDabTU1a1VaXoc+nuv+l15ro9NFQvd9zjhwOhyRp/44TmrflPTVt/ZCCvvjf/SwXGZTNJrVp/5h++HanZk9fL0navf2EoiKv690PWqtcpcJq06GuDu0/q4mjv0/enzuPt17uWFefTVqtmJh4k08BSBeIeVlUQLUS6vNJZ62Zt1FjO0y749vn9cutDiNe1IrP1+jgtqP/fgNAUkB5P/Ue+h+tXb5b4wYvSbG/eOmC2vnrkeQwIkmRl6/p9PGLerh+hftZKjK4gEpF9PqY1lq7ZLvGvzE/xf5Dv4do2awNyWFEkiLConQ9OkaFSxa4n6UiA/POmV3rfvpD61ftc9p+9tRlSVKRonk1fsR3GjfiO6f98fGJcnNzUzZ3/hkGSHRIsqyLpy6pQ8U3dOnsZVWrH3jHt28//EXZE+2aM2yRC6pDZhV2PlKdnp2kS6FRqlYrIMX+yMvX5Vc0r9O2bO5uKlg4jzw8+biCdWFnI9S5/mhduhCpqnXKpti/8OM1KbZVe6SscuXJqZOHzt+PEpEJXIuO1fTxP6XY/ljDipKkk8cv6vzZiOTt3jmz64GHAtTqlUe0/qc/dC069r7VirSR6LCZLiFTMv4XPiEhQatXr9aOHTt07tw5xcXFKUeOHPL391etWrXUtGlTubsbLzPTuRpxTVfvckY6T0FfNWlbT0smrtC1yOtpXBkys+ioG4qOunHL/Wu+26WXuzVU6471tHrZTnlm91D73k3knTM7J7XjjkRHXld0pPXjc+fLqb5jX9LFcxFau2Sb6wpDphdYtZheevUx/frzQYUcv5i8PX/BXPr6h36SpPNnIzRvxgZTJQLpjtFe4alTp9SsWTMNHjxYBw8elJeXlwoWLCgPDw8dOHBA77zzjp577jmdO3fOZJn4h6c7N5LNzU3ffrzSdCnIZOZ9ul6LZm3Uq6831qINgzX7h366cS1Wm/93QLE3CCRwjXx+vvpg4WvKkz+XRnebrRjCL+5SlRolNHry/+nc2cuaOHq5076YG3Ea0Ou/GjFgkaIir+uTuV1UIoDxQEAy3CEZMWKEihUrpiVLlihXrlwp9kdFRalfv34aOXKkPvvsMwMVIjX1Wj6snWv2ciI70pw90a4vp6zW/E/Xq3CxvAq/eFXXrsboo9mddTXy1p0V4G6VqlBYI+Z0U46cnhr66mc6vPe06ZKQQTVsWllvDXtep09d0uA+C3Q1KsZp/7XoWP2+46Qkac/OEP13WR+98HIdTX5/hYFqgfTFaIdk586dGjBgQKphRJJ8fX319ttva/v27fe5MtxKgaL5VLZGgDYu2Wy6FGRCVR8spQcfLav4uASdOn5R167GyC2bmwLK++voATqlSFvVHy2nCUv7ymaT3m71sQ7sPGm6JGRQrds+okGjXtCBfWfUv9tcRYQnjUS7ZbOpQZNAlSnv73R89NUYnT9zWQX9fE2Ui3uQKLd0+ZPRGX0Gvr6+CgsLu+0x586dk5eX132qCP+mQu0ykqT9vx02XAkyo3pPVFHf95o7rTzzZPMHlMs3h35dn/pFO4G7UaZyUQ2f3UVh5yLU7/nJCjl8wXRJyKCatXhAXfs01cZ1wXqn93xdv/bXier2RIe69G6iLq83drpNQT9fFQ8oqONHQu93uUC6ZHRkq1WrVnrnnXfUp08fPfzwwypcuLA8PT0VFxen0NBQbdu2TePHj1erVq1MlpkleefKoRKBRXX+WKjTaFZAlRKKi4nT+eN8iCLt/bB4m55qWUtvjW6pVd/uVEA5f3Xq94R+XrlX+3eFmC4PmcgbH7VRNvdsWjDpJxUskkcFi+RJ3hd5OVrnQ8LNFYcMI2/+nOre70ldOHdF332zTWUrFnbaf/5M0snrbw17Xm8MflYb1uxX/oK59Ern+roaeV1BC5g2ACTDgaR3795yc3PT2LFjdf16ytWacubMqVdeeUV9+/Y1UF3WVrZmgMavG6ZxnT/Vmv/+tRJIXr/cir7CylpwjZCjYXrv9Xnq2PcJDZ/aVhHh0Vo4Y4MWzmQ1GqQd/xL5VbZqcUnSkM86pti/ZvE2Tez/1f0uCxnQQ4+Wk5eXh/yL5NGkGSnfS+NGfKfVK/boxo04vfTqY2r0ZBXFxMRr++ajmj1tva5E8Pc0o7E7Mv54VHpkc/z9qlCGxMfH68CBAwoNDdWNGzfk5eUlf39/VaxYUZ6enmn2OE94tEmz+wJux61SyuseAK5guxJtugRkEYn++UyXgCxi9bZhpku4pUVHa5suIVUvlc3Y51uniwt8eHh4qFq1aqbLAAAAAHCfpYtAAgAAAKR3mWFFq/SIVxUAAACAMQQSAAAAAMYwsgUAAABYkOiwmS4hU6JDAgAAAMAYAgkAAAAAYxjZAgAAACyw812+S/CqAgAAADCGQAIAAADAGEa2AAAAAAsSHXyX7wq8qgAAAACMIZAAAAAAMIaRLQAAAMACu7gwoivQIQEAAABgDIEEAAAAgDGMbAEAAAAWsMqWa/CqAgAAADCGQAIAAADAGEa2AAAAAAsS+S7fJXhVAQAAABhDIAEAAABgDCNbAAAAgAV2BxdGdAU6JAAAAACMIZAAAAAAMIaRLQAAAMACVtlyDV5VAAAAAMYQSAAAAAAYw8gWAAAAYIHdwXf5rsCrCgAAAMAYAgkAAAAAYxjZAgAAACxIFBdGdAU6JAAAAACMIZAAAAAAMIaRLQAAAMACVtlyDV5VAAAAAMYQSAAAAAAYw8gWAAAAYAGrbLkGHRIAAAAAxhBIAAAAABjDyBYAAABgAatsuQavKgAAAABjCCQAAAAAjGFkCwAAALAgkZEtl+BVBQAAAGAMgQQAAACAMYxsAQAAABbYuTCiS9AhAQAAAGAMgQQAAACAMYxsAQAAABawypZr8KoCAAAAMIZAAgAAAMAYRrYAAAAAC+wOVtlyBTokAAAAAIwhkAAAAAAwhpEtAAAAwIJEvst3CV5VAAAAAMYQSAAAAABo+vTpateundO29evXq2XLlqpZs6YaNWqksWPHKiYmJnm/3W7X1KlTVa9ePVWvXl2dOnVSSEjIHT0ugQQAAACwwO6wpcuftDBnzhxNnTrVaduOHTv0+uuv68knn9SyZcs0fPhwrVy5UiNGjEg+Zvr06Vq4cKFGjx6tRYsWyWazqWvXroqLi7P82AQSAAAAIIsKDQ1Vly5dNGXKFAUEBDjtW7hwoerUqaNu3bqpZMmSql+/vvr166fly5crLi5OcXFxmj17tnr37q0GDRqoYsWKmjRpkkJDQ7VmzRrLNRBIAAAAgCxq//79yp07t5YvX67q1as77evUqZMGDBiQ4jYJCQmKjo7WwYMHde3aNdWpUyd5n6+vrwIDA7V9+3bLNbDKFgAAAGCBPRN+l9+oUSM1atQo1X2BgYFOv8fFxenLL79U5cqVlS9fPu3YsUOSVLhwYafjChUqpPPnz1uugUACAAAAZGCNGze+7f5169bd82MkJCRowIABOnr0qBYsWCBJunHjhiTJ09PT6djs2bMrMjLS8n1nvpgHAAAAIM1ER0erR48eWrdunaZOnZo82uXl5SVJKU5gj42NVY4cOSzfPx0SAAAAwILENFrRKq2lRQfkVsLCwtS1a1edOXNGM2bMcDpf5OaoVlhYmEqUKOF0m4oVK1p+DDokAAAAAFKIjIxU+/btdfnyZX311VdOYUSSKlasKB8fH23dujV5W1RUlIKDg1WrVi3Lj0OHBAAAAEAKH3zwgU6fPq2ZM2cqX758unjxYvK+fPnyydPTU23bttX48eOVL18+FS1aVOPGjZO/v7+aNm1q+XEIJAAAAIAFaXURwozAbrfrxx9/VHx8vNq3b59i/7p161SsWDH16dNHCQkJGjp0qGJiYlS7dm3NmjUrxYnut2NzOByOtCw+PXvCo43pEpBFuFUqa7oEZBG2K9GmS0AWkeifz3QJyCJWbxtmuoRb6rv7ZdMlpGpKza9Nl3BPOIcEAAAAgDGMbAEAAAAW2B18l+8KvKoAAAAAjCGQAAAAADCGkS0AAADAgkRlnVW27ic6JAAAAACMIZAAAAAAMIaRLQAAAMCCrHRhxPuJDgkAAAAAYwgkAAAAAIxhZAsAAACwgAsjugavKgAAAABjCCQAAAAAjGFkCwAAALDAzoURXYIOCQAAAABjCCQAAAAAjGFkCwAAALAgkQsjugQdEgAAAADGEEgAAAAAGMPIFgAAAGABF0Z0DV5VAAAAAMYQSAAAAAAYk6VGthyJiaZLQBaRuO+Q6RIAIG2dOWu6AsA4O6tsuQQdEgAAAADGEEgAAAAAGJOlRrYAAACAu2UXI1uuQIcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABYYHfwXb4r8KoCAAAAMIZAAgAAAMAYRrYAAAAAC1hlyzXokAAAAAAwhkACAAAAwBhGtgAAAAAL7GJkyxXokAAAAAAwhkACAAAAwBhGtgAAAAALWGXLNeiQAAAAADCGQAIAAADAGEa2AAAAAAsY2XINOiQAAAAAjCGQAAAAADCGkS0AAADAAka2XIMOCQAAAABjCCQAAAAAjGFkCwAAALCAkS3XoEMCAAAAwBgCCQAAAABjGNkCAAAALLCLkS1XoEMCAAAAwBgCCQAAAABjGNkCAAAALGCVLdegQwIAAADAGAIJAAAAAGMY2QIAAAAsYGTLNeiQAAAAADCGQAIAAADAGEa2AAAAAAsY2XINOiQAAAAAjCGQAAAAADCGkS0AAADAAka2XIMOCQAAAABjCCQAAAAAjGFkCwAAALDAwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAABbYxciWK9AhAQAAAGAMgQQAAACAMYxsAQAAABZwYUTXoEMCAAAAwBgCCQAAAABjGNkCAAAALODCiK5BhwQAAACAMQQSAAAAAMYwsgUAAABYwCpbrkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjAKluuQYcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABY4HCYriBzokMCAAAAQNOnT1e7du2cth04cEBt27ZVjRo11LBhQ82aNctpv91u19SpU1WvXj1Vr15dnTp1UkhIyB09LoEEAAAAyOLmzJmjqVOnOm2LiIhQx44dVapUKQUFBal3796aMmWKgoKCko+ZPn26Fi5cqNGjR2vRokWy2Wzq2rWr4uLiLD82I1sAAACABXZlvlW2QkNDNWTIEO3cuVMBAQFO+7755ht5enpq+PDhcnd3V5kyZRQSEqIZM2aoZcuWiouL0+zZs/X222+rQYMGkqRJkyapXr16WrNmjZo1a2apBjokAAAAQBa1f/9+5c6dW8uXL1f16tWd9u3YsUO1a9eWu/tfPYw6deroxIkTCg8P18GDB3Xt2jXVqVMneb+vr68CAwO1fft2yzXQIQEAAACyqEaNGqlRo0ap7rtw4YLKly/vtK1QoUKSpHPnzunChQuSpMKFC6c45vz585ZrIJAAAAAAFjjS6YURGzdufNv969atu6v7jYmJkaenp9O27NmzS5JiY2N148YNSUr1mMjISMuPw8gWAAAAgBS8vLxSnJweGxsrSfL29paXl5ckpXpMjhw5LD8OHRIAAAAgA7vbDsi/8ff3V1hYmNO2m7/7+fkpISEheVuJEiWcjqlYsaLlx6FDAgAAAFhgd9jS5Y+r1K5dWzt37lRiYmLyts2bNysgIED58+dXxYoV5ePjo61btybvj4qKUnBwsGrVqmX5cQgkAAAAAFJo2bKloqOjNWTIEB09elRLly7V3Llz1b17d0lJ5460bdtW48eP17p163Tw4EH169dP/v7+atq0qeXHYWQLAAAAQAr58+fXzJkzNWbMGLVo0UIFCxbUgAED1KJFi+Rj+vTpo4SEBA0dOlQxMTGqXbu2Zs2aleJE99uxORwOhyueQHrU1K216RIAAABwG2vsi02XcEvVV7xruoRU7Xl2lOkS7gkjWwAAAACMIZAAAAAAMIZzSAAAAAAL0uuFETM6OiQAAAAAjCGQAAAAADCGkS0AAADAAka2XIMOCQAAAABjCCQAAAAAjGFkCwAAALDAzsiWS9AhAQAAAGAMHZIspNaTNdRxVBuVCCymyItRWvH5ai38cNktj3f3cFer/s+q6asNVbB4fl06c1nrv/pFCz9cpoT4hOTjnu7SWC/0babCpf0UduqSvv90lb6d+uN9eEZIr+70vfZ35R4orambx6hD+T4KDbnotO/R52vrlaGtVLxCEV2+cEVr52/Qwg+c34/IWlz1ufZE+4Zq1f8/KlrWX+Hnr2jtvA1aMDpIiQmJ9+FZISNw1XsPyIoIJFlE4CPlNfK7gdqw6Dd9+e5CValbUR1Hvyw3Nzd99f7SVG/Tc3IHNX21gRaMDtLh7UdV9oHSavdeaxUqWVATu3wqSXq2xxPqO72rFo5dpl1r9qjiw+XUffyr8sqZXV9/8O39fIpIJ+7mvXZT6WolNXrFO3L3SPnR9ECTanov6C1tWPSbZr2zQAFVS6jTmJeVp2BufdJ7lqueDtIxV32utejzjHpN7qiNizdrxoB5yl3AV68Of1EBVUtqRMtx9/MpIp1y1XsP6Z/DYbqCzIlAkkW0G9Zax34/qbHtP5Yk7Vj1u9w9sumlgc21ZOIKxcXEOR2fK6+Pnu3eVDMHLdDi8cslSbvX75MkdfuonWYNWqDIS1FqM7C5fv7zH4g3jylWroief/1pAkkWdafvNSnpm8PmvZ9S+5FtFHcj5X5JerLD4wo7dUkftvtYdrtdu9buVZ5CufXCG830ab85fHOdBbnic+3q5Wi1HdZaO1fv0aiXJibf9vDO45q1f5IeaFJNu9buvU/PEOmVq/6mAlkV55BkAR6e7qrWsLI2fbvVafvGJVvknSuHqtarmOI2OXN7a8Xna7R5+Q6n7WcOn5MkFS5dSJI0+OkxmjFwntMx8fEJ8sjukZZPARnE3bzXJOmhZ2qq7bDW+vr9pZo5aH7q953dXTHXYmW325O3RV6Kkmd2D3nnypF2TwIZgqs+1/L65ZZvPh9tXuF8zKkDZ3TlYpTqPPtgGj8TZDSu/JsKZFUEkiygcGk/eWb30Nk/P/huOnf0giSpWPkiKW5z4WSYPn5tZvKH5U11Wzys+LgEnTl8XpJ06uBZhZ26JCnpG6CnOzdS03b1tXz6T654Kkjn7ua9JkmHth9Tu4DX9NX7S5WYYE/1mO+m/aSi5Qqr9VvPKWdub1V6uJxe6NtMW3/YpasR0Wn7RJDuuepzLfrKNSXEJ8i/lPM/EH3y5FSuvDlTbEfW48q/qUj/HA5buvzJ6BjZygJy5skpSboWdcNp+/WrSb97+1r7drnuCw+rSbv6+nbqj4q+cs1pX+VHK2jyptGSpMM7jum7j1fea9nIgO72vRZ+7vK/3veen/frm3HfqdtH7dTto3aSpCO7juv9V6bcS8nIoFz5ufbzot/03GtP6eT+0/r1223KUyi3ek3uqIT4RHnlzJ6GzwIZ0f34mwpkNXRIsgA3tz+T8y3OxLLb//0MrXot6+idBX21d0OwZg1akGL/hRNh6t/wPb3/yhTlzO2tadvHKk+h3PdUNzKetHiv3Urfz7rpxbef1/zRS9T/8fc0vtM05S7gqw9WDlH2HJ53fb/ImFz5uTal5wytW/CL3pzRQ99enqPpO8cqeMshHdp+VDHXYtOkfmRc9+NvKpDVGO+QtGvXTjabtVbTf//7XxdXkznd/ObF29fbafvNuftrkddve/uW/Z5V14/aae/P+zWs+UeKj0u5PGH4+QiFn4+QJB3cekRzDk/VM10a/+uqSshc7vW9div5i+TTM10a6+sPvtXcYYskSXs3BOvQ9mOa8cdEPdWpkb6bxphgVuLKz7WYazGa2OVTTe/7pfxKFlDoyYuKuR6rpzo20p7j+9P4mSCjuR9/U5F+ZYbxqPTIeCB55JFH9PHHH6t06dKqVq2a6XIypXPHQpWYkKgiZf2dtt/8/VTwmVve9rWpndT89af186Lf9FH7j50+OHP4eOmR52rp4NajOnfsQvL288dDFR1xTQWL50/jZ4L07l7ea7dTqEQBubm5af+vB522n9x/WpGXolSycvG7KxgZlqs+1yTp4WYPKDrimvb/dkghf95PnoK+Klg8v47sPp7GzwQZjSvfe0BWZTyQ9OrVS97e3po6dao+//xzFStWzHRJmU58bLz2bjygui0eTl5uUJLqt6qjqxHROrjtaKq36/T+/6n5609ryaQV+rz/3BT77Yl29Z/ZU2vmbdTk7p8nby9fq4x88+fSsT0haf9kkK7d7Xvt35w7ekGJCYmqWq+Stv/0e/L2YuWLKHcBX104EXqvpSODcdXnmiQ92/0J+ebPpb6PDUne9sIbzWRPtGvLip1p+0SQ4bjyvQdkVcYDiSR16NBBmzZt0uTJkzV+/HjT5WRKX40J0tg17+rdRW/qpy/XK/DRCmr91nOaOWiB4mLi5J0rh0oGFtO5Y6GKvBSlMtVL6aUBz+vQ9qPa+M1vqvRwOaf7Cwk+o+tXb2jRR9/plaEtFRV+VbvX7lXR8kX06ntJ67Ov+vJ/hp4tTLrT95oVkZeitHTKD2r91nOSpJ1r9sqvZEG1HdZaoSEX9eOMda58SkinXPW5tuzjH/XhqnfVc1IHbV6+QzUaVdHL77ygrz/8VhdOhBl6tkhPXPXeQ/rHdRFdw+ZwpI9rToaGhio4OFiPP/64yx6jqVtrl913RvBY84f06vAXVaxCEYWfvazl03/SkokrJEnVGgRqwv9GaFzHaVo992e1H/GS2r7b6pb31f/x97R3Q7BsNpuadW+q53o+qSJl/XX1crQ2fbtVXw5dqOtRd3e+ADK+O3mv/dMT7Rvq7S9fU9uAXgoNuei0r0XfZ/Rs9yfkH1BIl89HaOeavfpyyNdcUCwLc8XnmiQ93uYx/d+QlvIPKKSwkIta/ukqffcJ5ynhL65670FaY19suoRbqrB0pOkSUnXohWGmS7gn6SaQ3A9ZPZAAAACkdwSSO5fRA0m6GNkCAAAA0jtW2XINrkMCAAAAwBgCCQAAAABjGNkCAAAArMgyZ17fX3RIAAAAABhDIAEAAABgDCNbAAAAgAWssuUadEgAAAAAGEMgAQAAAGAMI1sAAACABQ5W2XIJOiQAAAAAjCGQAAAAADCGkS0AAADAAlbZcg06JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsIALI7oGHRIAAAAAxhBIAAAAABjDyBYAAABgBSNbLkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjg4MKILkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjBKlsuQYcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABYwSpbLkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjCKluuQIcEAAAAgDEEEgAAAADGMLIFAAAAWMEqWy5BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFY4uDCiK9AhAQAAAGAMgQQAAACAMYxsAQAAABY4WGXLJeiQAAAAADCGQAIAAADAGEa2AAAAACsY2XIJOiQAAAAAjCGQAAAAADCGkS0AAADACi6M6BJ0SAAAAAAYQyABAAAAYAwjWwAAAIAFNlbZcgk6JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsIILI7oEHRIAAAAAxhBIAAAAABhDIAEAAACscKTTn3sQHx+vSZMmqWHDhqpZs6b+7//+T7t27Uref+DAAbVt21Y1atRQw4YNNWvWrHt7wFQQSAAAAIAs6tNPP1VQUJBGjx6tZcuWqXTp0uratatCQ0MVERGhjh07qlSpUgoKClLv3r01ZcoUBQUFpWkNnNQOAAAAZFHr1q3Ts88+q7p160qSBg0apMWLF+v333/XyZMn5enpqeHDh8vd3V1lypRRSEiIZsyYoZYtW6ZZDXRIAAAAACtMj2a5YGQrT548+t///qczZ84oMTFRixYtkqenpypVqqQdO3aodu3acnf/q4dRp04dnThxQuHh4ff2wH9DhwQAAADIooYMGaJ+/fqpcePGypYtm9zc3DRlyhSVKFFCFy5cUPny5Z2OL1SokCTp3Llzyp8/f5rUQCABAAAAMrDGjRvfdv+6detuue/YsWPy9fXVtGnT5Ofnp8WLF2vgwIGaP3++YmJi5Onp6XR89uzZJUmxsbH3XvifCCQAAACAFfc4HpXenD17Vm+//bbmzJmjWrVqSZKqVq2qo0eP6uOPP5aXl5fi4uKcbnMziHh7e6dZHQQSAAAAIAO7XQfkdvbu3av4+HhVrVrVaXv16tW1ceNGFSlSRGFhYU77bv7u5+d3d8WmgpPaAQAAgCyocOHCkqRDhw45bT98+LBKliyp2rVra+fOnUpMTEzet3nzZgUEBKTZ+SMSgQQAAACwxmFLnz93qVq1aqpVq5YGDhyoLVu26OTJk5o8ebI2b96sbt26qWXLloqOjtaQIUN09OhRLV26VHPnzlX37t3T8EVlZAsAAADIktzc3DR9+nRNnjxZ77zzjiIjI1W+fHnNmTNHNWrUkCTNnDlTY8aMUYsWLVSwYEENGDBALVq0SNM6bA6HI5OdnnNrTd1amy4BAAAAt7HGvth0CbdUatoE0yWk6uRr/U2XcE/okAAAAAAW2LLM1/j3F+eQAAAAADCGQAIAAADAGEa2AAAAACsY2XIJOiQAAAAAjCGQAAAAADCGQAIAAADAGAIJAAAAAGPu6KT2ixcvatq0aTp9+rQKFCigSpUqqUqVKqpcubJy5MjhqhoBAAAAZFJ3FEgGDx6sTZs2qVy5cjpz5oy+//57ORwOubm5qXTp0qpSpYqqVq2qqlWrqmLFivLw8HBV3QAAAMB9xYURXeOOAsnu3bv19ttvq1OnTpKk69eva//+/frjjz/0xx9/aPv27fr2228lSZ6entq7d2/aVwwAAAAg07ijQJI9e3YFBgYm/+7t7a3atWurdu3ayduuXLmivXv3at++fWlXJQAAAIBM6Y4CSZMmTRQcHKw6derc8pg8efKofv36ql+//j0XB2RUbpxThfvEEZ9gugRkEW6+PqZLAMxz2ExXkCnd0SpbLVu21MqVK3X06FFX1QMAAAAgC7mjDsmLL74om82m1q1b66mnnlK9evVUuXJllSxZ0lX1AQAAAMjE7iiQjB49WgcOHND+/fu1cuVKffvtt7LZbMqZM6cCAwNVpUoVDRgwwFW1AgAAAOawypZL3FEgadWqVfJ/2+12HTt2TPv379e+ffsUHByshQsXEkgAAAAAWHZHgeTv3NzcVK5cOZUrV07NmzeXJDkcxEYAAAAA1t11IEmNzcbKAwAAAMik+O7dJe5olS0AAAAASEsEEgAAAADGpOnIFgAAAJBZ2RjZcgk6JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsIKRLZegQwIAAADAGAIJAAAAAGMY2QIAAAAs4MKIrkGHBAAAAIAxBBIAAAAAxjCyBQAAAFjhsJmuIFOiQwIAAADAGAIJAAAAAGMY2QIAAACsYJUtl6BDAgAAAMAYAgkAAAAAYxjZAgAAACzgwoiuQYcEAAAAgDEEEgAAAADGMLIFAAAAWMHIlkvQIQEAAABgDIEEAAAAgDGMbAEAAAAWsMqWa9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwsuUSdEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGABF0Z0DTokAAAAAIwhkAAAAAAwhkACAAAAwBgCCQAAAABjCCQAAAAAjGGVLQAAAMAKVtlyCTokAAAAAIwhkAAAAAAwhpEtAAAAwAIujOgadEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGAFI1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWMCFEV2DDgkAAAAAYwgkAAAAAIxhZAsAAACwgpEtl6BDAgAAAMAYAgkAAAAAYxjZAgAAACxglS3XoEMCAAAAwBgCCQAAAABjGNkCAAAArGBkyyXokAAAAAAwhkACAAAAwBhGtgAAAAArGNlyCTokAAAAAIwhkAAAAAAwhpEtAAAAwAIujOgadEgAAAAAGEMgAQAAAGAMgQQAAACwwpFOf+7RsmXL9Mwzz6hq1apq1qyZVq5cmbzvwIEDatu2rWrUqKGGDRtq1qxZ9/6A/0AgAQAAALKo7777ToMHD9ZLL72kFStW6JlnntGbb76p3bt3KyIiQh07dlSpUqUUFBSk3r17a8qUKQoKCkrTGjipHQAAAMiCHA6HpkyZovbt26t9+/aSpNdee027du3Stm3btG3bNnl6emr48OFyd3dXmTJlFBISohkzZqhly5ZpVgcdEgAAAMAK06NZaTyydfz4cZ09e1b/+c9/nLbPmjVL3bt3144dO1S7dm25u//Vw6hTp45OnDih8PDwu3/gf6BDAgAAAGRgjRs3vu3+devWpbr95MmTkqTr16+rc+fOCg4OVrFixdSzZ081atRIFy5cUPny5Z1uU6hQIUnSuXPnlD9//nsvXnRIAAAAgCwpOjpakjRw4EA9++yzmj17th577DH16tVLmzdvVkxMjDw9PZ1ukz17dklSbGxsmtVBhwQAAACwIL1eGPFWHZB/4+HhIUnq3LmzWrRoIUmqVKmSgoOD9eWXX8rLy0txcXFOt7kZRLy9ve+hYmd0SAAAAIAsyN/fX5JSjGWVLVtWZ86ckb+/v8LCwpz23fzdz88vzeogkAAAAABZUGBgoHLmzKk9e/Y4bT98+LBKlCih2rVra+fOnUpMTEzet3nzZgUEBKTZ+SMSgQQAAACwxvRqWmm8ypaXl5e6dOmiadOmacWKFTp16pQ+/fRT/frrr+rYsaNatmyp6OhoDRkyREePHtXSpUs1d+5cde/e/e4fNBWcQwIAAABkUb169VKOHDk0adIkhYaGqkyZMvr444/18MMPS5JmzpypMWPGqEWLFipYsKAGDBiQfL5JWrE5HI50enpO2mvq1tp0CUbVerKGOo5qoxKBxRR5MUorPl+thR8uu+Xx7h7uatX/WTV9taEKFs+vS2cua/1Xv2jhh8uUEJ+QfFzxCkXUdWw7VWsYqMT4RO3deECfvzVXF06E3fK+Mzu3HDlMl5BuvftVH5WtUVLtA/tbOr5sjVKa8vMwdao2QKGnLrm4uozH8bf/LUJ6pnMjPdfrCRUOKKQrYVHavGKn/jtiia5fvfGvty1XM0BTNo1Qx8A3FRrCe+2f3Hx9TJeQLlR7rLw++u6tW+6fN3a5FoxbkWJ7sbL+mrllZIrtp49cUNdHhqVpjRndT5e+MF3CLVV5e5LpElK1b1w/0yXcEzokWUTgI+U18ruB2rDoN3357kJVqVtRHUe/LDc3N331/tJUb9Nzcgc1fbWBFowO0uHtR1X2gdJq915rFSpZUBO7fCpJKlgsvyZvGq3Th87pg1emKHsOT3UY9bI+XPWuulXrr7iYuFTvG1lTozaPqu7ztXQh5KKl40tXLa5RS9+UuwcfVfh3rfs/q06jXtLiiSu0e/1+FSnjp/bDW6tU5eIa9PT7t71t6WolNOq7t3mv4V8d3XNKbzz5QYrt7Qc3V/mapfTz0m2p3q5M1eKSpAHPj1dcTHzy9ti//TfSv/S6ylZGxydvFtFuWGsd+/2kxrb/WJK0Y9XvcvfIppcGNteSiStSBIdceX30bPemmjlogRaPXy5J2r1+nySp20ftNGvQAkVeitKrI17U9as3NLDpSMXeSLqPCyfCNPK7gSpfq7T2bTp4H58l0rN8/nnUa1xbXTzz71d2dffIpud7NtWr77ZU3A3+WOPf2Ww2tRnwvH6YsU6zhy6SlPSZFXU5Wu9+3VflHgjQkV0nUtzO3SObnn/tSbUf3lpxN/gCBf/uenSMDu50fi/Vebq6ajaopNEdP9PZY6lPB5SuUkwXQi5p76+H70eZQIbCSe1ZgIenu6o1rKxN32512r5xyRZ558qhqvUqprhNztzeWvH5Gm1evsNp+5nD5yRJhUsnXaWzbouH9dPs9clhRJIO7zyuNsW6E0bgpN/0ztq5bp92/xz8r8fWfrK6XnmnhRZ+tFyz3l10H6pDRuftm0Prvtqk9Qt/c9p+9sh5SVKRMqkvT/nQ0zXVdugL+vrDZZo5+GuX14nMx9PLQ70+eFlbV+/Vpu933fK4MlWK69i+0/exMiDjIJBkAYVL+8kzu4fO/hkmbjp39IIkqVj5Iiluc+FkmD5+bWZyALmpbouHFR+XoDOHz8u/VCH55Mmp0JMX1fuTzgq6OFs/XF+gkd8NVMHiBVz3hJDhPNW+gcrVLKVp/f9r6fjDO0+ofeCb+nrc905LDQK3ci3yuqb3m6vgzc7fPj/WvLYk6eT+1P8heGjHMb1arq++/vA7JSbYXV4nMp8WPZoov39ufTbk9l+elK5SXDl9c2jSyoFafmaavto/Th3fbaFs7tnuU6VIE6ZX00rjVbbSC+OB5MSJE/r44481evRobdiwIcX+6OhovfPOOwYqyzxy5skpSboW5XxS582TPL19rZ2AXfeFh9WkXX0tn/6Toq9cU+6CvpKkLh++ovxF8un9/5usiV0/U5kapTR+/Xvy8s6ehs8CGVWh4vnV7cP/0ydvzFVUeLSl24Sfj9DViGsurgyZXWCdcnrprf/o1++2KyT4bKrHhJ/jvYa75+6RTc93baQN327X+RO3PjcuT8FcyueXW8XL+WvF7A0a8uIUrZz3i1r0aKL+n3S4fwUD6ZTRc0h27typzp07y8/PTw6HQwsWLFCTJk00YcIEeXp6SpJiYmK0bNkyffBByhPIYI2bmy3pP26xoJrd/u/Rul7LOho0v4/2bgjWrEELJEnunklvn4jQSI1oOV43F2w7d/SCpm5+X43b1tMPX6xNg2eAjOzNz7po+6o92vTdjn8/GEgjVR6roJHfvqVzx8M0sVv6XbEHGVu95x9UPr/cWvzJ6tsed/1qjN55YZJOH72gS+ciJEl//HZY8bEJ6jCkub6e8INOH7lwP0oG0iWjHZIJEyaoVatWWrVqlVavXq2JEyfq119/VY8ePRQfz4msaSX6StK3f96+3k7bvXMldUauRV6/7e1b9ntWQxb20/5NB/Xuc2MVH5e0zOiNPzss23/arb+vHn1g6xFdjYhWmRoBafYckDE9172JAioX12cDF8gtm5vcsrnJZksKyH//byAtNXzxEX248h2FnrqkgU+OoQMCl6n7nwd18sBZndh/5rbHxcXEa/fGA8lh5KZta/ZKShrnQgZhejSLka20d+jQIbVt2zb596efflozZszQ7t27NWDAAIOVZS7njoUqMSFRRcr6O22/+fup4Ft/kL42tZN6TGivX5Zs0ZBm7yvmWozz/Sba5ZHdI8Xt3D3cnU50R9ZUt3lt5Snoq4XHP9bKqDlaGTVHTV+pK/+SBbUyao5eeae56RKRybR+81kN+u9rOrD1qPo3GqmI0EjTJSGTyuaeTQ82DNRGC93fYmX99EyH+vL28XLant0raRrE6jgrkFkZHdny8fFRRESESpUqlbztwQcf1Lhx49SnTx998MEH6tq1q7kCM4n42Hjt3XhAdVs8nLyEryTVb1VHVyOidXDb0VRv1+n9/1Pz15/Wkkkr9Hn/uSn2x1yL0b5fDuixFg9r9uCvkjsnNRtVUQ4fL+375YBrnhAyjKl9vlQOH+dzlNoObq5yNUvpvdaTFX4+4ha3BO5csy6N1PXD/9PPizfrow7TlRDPgghwnYDAovLKmV3BW4/967H5C+dRn/FtlRCXqNVf/Zq8vX7zWrp29YaO7AlxZalAumc0kDRo0EAjR47U8OHDFRgYKA+PpG/amzRposGDB2v06NE6f/68yRIzja/GBGnsmnf17qI39dOX6xX4aAW1fus5zRy0QHExcfLOlUMlA4vp3LFQRV6KUpnqpfTSgOd1aPtRbfzmN1V6uJzT/YUEn9H1qzc0a/BXGv+/4Rrzw2AtnrBcef3yqMuHr+jAlsMplgxG1nMmlZnoqMvRio9L0JHdSev4e+fyUomKRXX+RJgiL1293yUik8jrl1vdx7fThZMX9d201Spb03lk9PzxUMXHJqhEpaI6fzyU9xruWalKRSVJpw6n/HeKt4+XSlQorPMnLyoyPFp//HpYezYdUrdRreXl7anTRy7ooaZV9Xy3Rpo5PEjR/zI6DWR2RgNJ//791a9fP7Vp00aff/656tevn7yvbdu2cnNz0/vv3/7qurDm9//t08hWE/Tq8Bc1/NsBCj97WTMGzNOSiSskSWUfCNCE/43QuI7TtHruz6r7wsNyc3NThdplNXVzyv8f9H/8Pe3dEKwDWw7r7UbD1XH0yxq25C3FXo/Vb99t1+dv/Vd2O0to4t+VrVFK434arPHdv9Ca+ZtMl4MM6qGnasjLO7v8SxXUpJ/fS7F/XOfPFBpyUePXvqtxnT/TmnkbDVSJzCRPoaSVJq9eSXmOUtnqJfTRd29pwutfas3CzbLbHRrRbrraDvyPXujZVPn8cuv8yYua2n++fprH515GwpmPrmFzOG6x9NJ9dOrUKeXNm1e5cuVKse/EiRNavXq1unfvfs+P09St9T3fB2CFWw5rSykD98oRn2C6BGQRbr4+pktAFvHTpfS7Ml61NyeZLiFVeyf2M13CPTHaIbmpRIkSt9wXEBCQJmEEAAAAQPqTLgIJAAAAkO4ZnyvKnIxfqR0AAABA1kUgAQAAAGAMI1sAAACABTZGtlyCDgkAAAAAYwgkAAAAAIxhZAsAAACwgpEtl6BDAgAAAMAYAgkAAAAAYxjZAgAAAKxgZMsl6JAAAAAAMIZAAgAAAMAYRrYAAAAAC7gwomvQIQEAAABgDIEEAAAAgDGMbAEAAABWMLLlEnRIAAAAABhDIAEAAABgDCNbAAAAgAWssuUadEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGABq2y5Bh0SAAAAAMYQSAAAAAAYw8gWAAAAYAUjWy5BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAABZwYUTXoEMCAAAAwBgCCQAAAABjGNkCAAAArGBkyyXokAAAAAAwhkACAAAAwBhGtgAAAAALbA5mtlyBDgkAAAAAYwgkAAAAAIxhZAsAAACwgoktl6BDAgAAAMAYAgkAAAAAYxjZAgAAACywMbLlEnRIAAAAABhDIAEAAABgDCNbAAAAgBWMbLkEHRIAAAAAxhBIAAAAABjDyBYAAABgAatsuQYdEgAAAADGEEgAAAAAGMPIFgAAAGAFI1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWMAqW65BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAABawypZr0CEBAAAAYAyBBAAAAIAxjGwBAAAAVjiY2XIFOiQAAAAAjCGQAAAAADCGkS0AAADAAlbZcg06JAAAAACMIZAAAAAAMIaRLQAAAMAKRrZcgg4JAAAAAGMIJAAAAACMYWQLAAAAsMBmN11B5kSHBAAAAMjiTpw4oZo1a2rp0qXJ2w4cOKC2bduqRo0aatiwoWbNmuWSxyaQAAAAAFlYfHy83nrrLV2/fj15W0REhDp27KhSpUopKChIvXv31pQpUxQUFJTmj8/IFgAAAGBFJl1l6+OPP1bOnDmdtn3zzTfy9PTU8OHD5e7urjJlyigkJEQzZsxQy5Yt0/Tx6ZAAAAAAWdT27du1aNEijR071mn7jh07VLt2bbm7/9W/qFOnjk6cOKHw8PA0rYEOCQAAAJCBNW7c+Lb7161bl+r2qKgoDRgwQEOHDlXhwoWd9l24cEHly5d32laoUCFJ0rlz55Q/f/57qNgZgQQAAACwwJbJRraGDx+uGjVq6D//+U+KfTExMfL09HTalj17dklSbGxsmtZBIAEAAAAysFt1QG5n2bJl2rFjh77//vtU93t5eSkuLs5p280g4u3tfedF3gaBBAAAAMhigoKCFB4eroYNGzptf++99zRr1iwVKVJEYWFhTvtu/u7n55emtRBIAAAAACscmWdma/z48YqJiXHa9sQTT6hPnz565pln9MMPP2jhwoVKTExUtmzZJEmbN29WQEBAmp4/IrHKFgAAAJDl+Pn5qWTJkk4/kpQ/f34VLVpULVu2VHR0tIYMGaKjR49q6dKlmjt3rrp3757mtRBIAAAAADjJnz+/Zs6cqRMnTqhFixb65JNPNGDAALVo0SLNH4uRLQAAAMCCzLbK1j8dOnTI6fdq1app0aJFLn9cOiQAAAAAjCGQAAAAADAmS41suZctbboEZBEPLDliugRkESs/rWe6BGQRO9771HQJgHmZfGTLFDokAAAAAIwhkAAAAAAwJkuNbAEAAAB3K7OvsmUKHRIAAAAAxhBIAAAAABjDyBYAAABghYOZLVegQwIAAADAGAIJAAAAAGMY2QIAAAAsYJUt16BDAgAAAMAYAgkAAAAAYxjZAgAAAKxgZMsl6JAAAAAAMIZAAgAAAMAYRrYAAAAAC1hlyzXokAAAAAAwhkACAAAAwBhGtgAAAAAr7MxsuQIdEgAAAADGEEgAAAAAGMPIFgAAAGAFE1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWMCFEV2DDgkAAAAAYwgkAAAAAIxhZAsAAACwwsHMlivQIQEAAABgDIEEAAAAgDGMbAEAAAAWsMqWa9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwsuUSdEgAAAAAGEMgAQAAAGAMI1sAAACABTYujOgSdEgAAAAAGEMgAQAAAGAMI1sAAACAFXbTBWROdEgAAAAAGEMgAQAAAGAMI1sAAACABayy5Rp0SAAAAAAYQyABAAAAYAwjWwAAAIAVTGy5BB0SAAAAAMYQSAAAAAAYw8gWAAAAYAWrbLkEHRIAAAAAxhBIAAAAABjDyBYAAABggY2JLZegQwIAAADAGAIJAAAAAGMY2QIAAACsYJUtl6BDAgAAAMAYAgkAAAAAYxjZAgAAACyw2U1XkDnRIQEAAABgDIEEAAAAgDGMbAEAAABWsMqWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwseUSdEgAAAAAGEMgAQAAAGAMI1sAAACABTZW2XIJOiQAAAAAjCGQAAAAADCGkS0AAADACka2XIIOCQAAAABjCCQAAAAAjGFkCwAAALDCbrqAzIkOCQAAAABjCCQAAAAAjGFkCwAAALCACyO6Bh0SAAAAAMYQSAAAAAAYw8gWAAAAYAUjWy5BhwQAAACAMQQSAAAAAMYwsgUAAABYwciWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFbYTReQOdEhAQAAAGAMgQQAAACAMQQSAAAAwAKbw5Euf+7FlStXNGzYMNWvX18PPPCAXn75Ze3YsSN5/4EDB9S2bVvVqFFDDRs21KxZs+71ZUyBQAIAAABkUW+++ab27NmjiRMnasmSJapcubI6d+6sY8eOKSIiQh07dlSpUqUUFBSk3r17a8qUKQoKCkrTGjipHU4K+OfWpyv6aWSv/+qPbceTt+f381Xnt5/Rg/XKy90jmw7tPa1ZY3/UsQPnDFaL++X6pXit6X9UjwwooUKVc97yOHu8XYdXhCvk5yu6Hh6vHPk8VKJeblVsXkBuHq75/uPC7qvavzBMUWdild3XXaWfyKsKzQvIZrMlHxO2/5qCF4Up8lSM3NzdlL9CDlVr6yefwtldUhPM8/Jw1y+TX1M2N+f3XWx8gh7p87HTNnc3N81660X9tv+kPv9hy/0sEwadD5Oe7yh9Mlp6qOatjzseIjV71ZZie0AJh36c55raftkqTZklHTsp5c0jtXlO6vqK9LePNW3bLX38pXT4mOTpKdWoLL3VQypZzDU1IXMKCQnRr7/+qq+//loPPPCAJGnIkCHauHGjVqxYIS8vL3l6emr48OFyd3dXmTJlFBISohkzZqhly5ZpVgeBBMkKFcmj0bM7y8c3h9P2HDk9NW5BD8XHJ+rjYd8qLjZeL7/WWGPmdFHPZycp4uJVQxXjfrh2MU6bRoco/vq/Ly3y+5wLCtlwRZVaFlTesjl05XiMgheH6frFeNXqVTTNa7t06Lp+HXtaxR/1VeU2hXTp4HXt+zpMDrtUqWVBSVL4oev6ZdRJFamVSw/1KabEWLsOBF3S/949qScmllF2Xz4GM6NyxQoom5ubBs36QefDo5K32+3Oow3ZPbJpdMenVTWgsH7bf/I+VwlTzl6Qur4tXY22Sbr9uMuBo0n/d+5khzw9/9ru5aLvM3bvk14bLD31uNS3s7TzD2nyTMnukHq0++uYzv2lxx+TPnpXiomRPpsnte0tLf8yKcTARTLZhRHz5s2rL774QlWqVEneZrPZ5HA4FBkZqX379ql27dpyd//rb2WdOnX0+eefKzw8XPnz50+TOvhLDNlsNjVp8aC6DHwm1f0tOtSTb96c6vrU+OTwcWTfGU39to+qPVRaG37Ycz/LxX3isDsUsuGK9v431NLxcVcTdHxNhKq+4qcKzxeQJPlV9ZEk/TE/VFVf8VP23Hf+kbP/mzCF/HxFz0wvn2LfgcVhylPKSw/1SfpK0L9mLtkTHTq07JLKP5tf2bK76eC3l+RbNLvqvFlcNrekrxfzV/TWjz0O6+TPV1ThuQJ3XBPSv/LFCikuPkHrdx1Vgj31MF2zbFENeulxFczjc5+rgyl2u7TsJ+mjT63f5uBRqai/47ZdlDv1yZfStz9J6xal3DdtjlSxrPTR0KTf6z0sJSRIMxZIHV5MCkIzFkilS0qTR0g3m4A1q0qNWifdb6c2aVcrMobGjRvfdv+6detS3e7r66sGDRo4bVu5cqVOnTqlunXratKkSSpf3vnvb6FChSRJ586dS7NAwjkkUEBFf70+ornWLtul8QNSfjo+9mQVbfrpD6dOSMSlaLWr9z5hJBOLDInRrhnnVbJBHtXu/e/djfjrdpVumldFauVy2p6rSNJXitFhccnbzm2P0rqBx7T0/4L1fZdD+n32eSXE3Nni7onxdl3cf11FH3Z+vGJ1cishxq6LB69LkvKVy6GyzfInhxFJypHXQ+45sunahTghc6pQrKCOX7h8yzAiSZN6Pqfzl6/qlQ8W3MfKYNKhY9KISVLzJ6WxQ6zd5uDRpIDwb9Ztklp1k6o3leq1kN6fKl2/cWf1xcVJ236XmtZ33v5kA+n6DZt27k36vWol6dVWf4URSSqUX/Lxlk6dvbPHBP5u586dGjx4sBo3bqxGjRopJiZGnn9vDUrKnj2pPRgbG5tmj2u8QxIbG6sjR46obNmy8vLy0oEDBzR//nyFhoaqXLlyat++vfz9/U2XmamFnbuizk3G6VJopKo+VNppXzZ3N5Uo46f13+1Wu75P6KnWteWbN6cO7A7R9JHf6eThC4aqhqt5F/DQUx+Xk3d+D4Xtv/avx+f089QDXYuk2H52a5Rs2WzK9ef5Gqd+uaJtU8+qRL3cqtymkK5djNe+r5LOAan3bsmkVnGi468hij//PWlP/KtN7pbNpmuh8bInOJSriPPchI//nwHoXKxU3Sd5dOvvwvZdU/y1RPmW4BySzKpC8YJyOBya3ucFVS9dRHEJiVq767AmBW3U9dh4SVKXCd/o6Llww5XifirsJ61aIPkXSjoHw4qDR6UyJaU2PZPGt3x9pOZPSX06Sx5//itqxRrp7dE2/aepQ307J42ETZ4hHTkpzZ6QdO5HYuJf0z43c3JCwl+P4+4unT4nxcfbVLK481hQiT/PCzl5WnqsttTz1ZR1bt0lRV61qVzpzDVSlO6k05GtW3VA7sTatWv11ltvqXr16po4caIkycvLS3Fxzl/e3Qwi3t7e9/yYNxkNJMeOHVOHDh108eJFFSlSRKNHj1avXr1UrFgxlSlTRmvXrtXSpUv11VdfqUyZMiZLzdSiI28oOjL1r3F8fHPI3SObWnSopwtnwjV5aJA8PLOpXZ8nNHZeN/V6brLCQ6NSvS0yNs9c7vL898Nu68yWKIVsjFTZZ/LJ0yebHA6H/lgQKr8aPsljVpLkU9hTv4wM0YVd0Sr8YC5tGHlSl4KvO93X0jbByf/99LRyir+WKElyz+Hc6L35e/yN1L8Zj41M0M7PzilHfneVapDnHp8h0iObTSpbpIAS7XZ9++smzfxxqwJL+qlbszoqXTi/ukz8Rg6HCCNZUB5fSb7Wj790Wbp02SabzaH+3ZMCzZad0syvpQth0rh3k/59OuFzqd5DjuQxKynp5PJOb9q0YYtDDR+ROr4pbf/d+eT4qn+bslm70KGo6KT/9vnHv/Ny/nlqZ/Qtvhu6fEUaNl7yL+hQ8yetPz/gpvnz52vMmDFq2rSpxo8fn9wV8ff3V1hYmNOxN3/38/NLs8c3Gkg++ugj1axZU7169dKsWbPUs2dPPffccxo5cqRsNpsSEhI0YMAAffDBB5o5c6bJUrMsD4+/3iJDO89WzPWklHzkj7OateZt/afto5oz4SdT5SEdO7M5UtumnlXBQG9VfSXpQ+vquTjdCE9QxRa5nDoeBQNzyj2Hm0L3JgWSB7sVUfyfI1wn1kTo/K6renRgieTjc+R1143Lf361mHLxm6TNqWy/cTlev4wOUWxUguoPKyX3HNnS5skiXbHJpj7Tlik86ppOhkZIknYdPavwqOsa0+lpPRJYihPYYYlPTmnWBIcCSkiFk8bm9VCNpFWtpsy0qUc7h2w26cJFm7q1dTh1PGpXl3xyOvTbDqnhI9KI/tK160mfe998L/28WZr+/l/HFywghV5K+u/UPr8k5xGtm8IuSV3eksIjpC8nSjnT7ktrZBFfffWVRo0apXbt2mnw4MFy+9sbrXbt2lq4cKESExOVLVvS38zNmzcrICAgzc4fkQwHkm3btikoKEilS5fWwIED9f333+vll19OXq7T3d1dPXr00EsvvWSyzCzt+rWkttzebceSw4gkXTx/RaeOhal0pZQjOsDh7y9p77xQFaycU48NKK5sfy75G3c1qauxe+Z57Z55PsXtYiKS/prnKvrXKNX5nVfl5m5TvjLOq7955Ey6z4R/rP6V8GdnxMPbOWxEhsRo0wenlBBjV90hJZWvrPP9IfOwOxzaeeRMiu2/7DshSSpftCCBBJZ4ZZcerZVye4M60pSZ0sFjfwWVkZNsGjkp5bEXLyWFkIC/vlPRz5slDw+pSkXnY3P9ub7CPzsh1/4cYvD5x6rrh49JPQYlnasyY1zSuSVwsXQ6snW3Tpw4offff19NmzZV9+7dFR7+V+fYy8tLLVu21MyZMzVkyBB16dJFe/fu1dy5czVixIg0rcNoIPHy8lJMTIwkqUCBAnrxxReTT5S5KSoqSrly5Urt5rgPrkfHKOLSVXl4pnyruLtnU1xMvIGqkF45HA79PvuCjv10WcUe8VXt3kWTw4j0V4io2s5PBVO5nolnTusdCx8/T9ncpOh/nJh+83ffYn99loT9Ea3fxp2Wh7ebGo4spdwlvO7oeSFjKZg7p+pWCdCv+08q7Ep08navPzu+V67d4ZnGyLJOnJK27paebeIcBmL+PJc3b+6/QsTbPR16qEbK+/C9g3/ClCgiZcvmSHFi+qk/83WZUn9t27JLen2IlCun9N+pUnnnU0ABS1atWqX4+HitWbNGa9ascdrXokULffjhh5o5c6bGjBmjFi1aqGDBghowYIBatGiRpnUYDSR169bVqFGjNHr0aJUpU0YjR45M3udwOLRt2zaNGDFCTZo0MVgldmw8pEebVpFvXm9FRSTN9RcNKKBiAQX00+JthqtDerLvqzAd++myyjXLr2rt/ZwuTihJvkWyK3vubLoeFqd8f1tuNyYiXts+PqvSTfMmn5T+b7J5uqlApZw6uzVK5Z/Ln/xYZ7ZEyiOnW3IHJOLEDf069pRyFvJUvSEllSO/Rxo9W6RXnh7uerdtU33xwxZ9tmJz8vYnapVXot2u3UdZhgjWhF6SRky0ycPdoZbN/tq+8n9STm+HKldIOt8jf16Hzpx3Xm73Yrg0YEzSRQ1LWLwMU/bsUq1q0pqNSfd18yN01QbJ18ehan92QIIPS73ekYoWlmaOk/xSrt0BWNKjRw/16NHjtsdUq1ZNixalskZ1GjIaSN555x316NFD06dP14QJE5z2/fjjj+rfv7/q1aunN99801CFkKSvpq3TI00qa8zsLvpq2lplc8+mDm8+pYvnI7WKQJJlxV9PVNSZWPn4eSp7bnddOXFDh767pLxlvFTsUV9dPuL8LbRvsezy8M6mKi/7aefn52Rzs6nwg7kUfz1RB5Zc1PXwBOUtnXKMqvKLhVT5xUKp1lCpZQFtHBWiLRPPqFSjPAo/dF2Hl4er6it+ypY9qRuz89NzsidIga0L6np4vK6H/9XVy+7rbjkAIeM4eylSK7YEq8MTtRSfkKg/TpxXjbJF1enJ2lq8YY9C/jyvBPin6GvS0ZNJASJfnqTzQB6q4dDY6dKNmKSxqw1bpPlB0ts9pdx/dj/6dpGGT5CyuUkNH5WuRkuf/lcKvShVrpDycV7vmPSTmh6vSp3elPq9J73wjLR7vzR7odS/+18XYxz6UdIKXa93SLri/Pm/nXOcL4/1AIS7cGcr1MMio4EkX758+uabb3TlypUU+x555BEtW7ZMFStWTHlD3FcXTl9W/5emq9PbT+utj9rIbrdr969H9MX7K3TjGtdxyKoiTsRo4/CTqtWriEo9nldnt16VHFLEsRj9b8iJFMfXH15KhSrnVEDjvHLP4abD313S8bURcvdyU/4K3nqoTzHl9LuzcFCoqo8e6V9cwd+EafNHp5Ujn7uqtfNT+f8kdV+iQ+N05UTSWOiWiSnPKSjZII9qv85f7sxo9IK1OhV2Rc/WCVSXZx5W2JVofbZii/67Zofp0pCOBR+W2r9h0/uDHGrxtJQtm/TJGOmTOdKcb6SLl6XiRaTh/aUX//PX7Vo/m9QpmbVQ+maF5J1DeqCKNG6oVKzwndVQ5wFpysikiye+PlTyK5AUfjr+eTrt6XPSgSNJrZM33kt5++ZPOfTBO3f3/AFTbA5HJjs75zaeLj/QdAnIIh5YcsR0CcgiVn5az3QJyCJ2vHcHlzcH7oGb/2HTJdzSU5UtXlHzPvtp/xjTJdwT4xdGBAAAADICW9b5Hv++SmVFawAAAAC4PwgkAAAAAIxhZAsAAACwgpEtl6BDAgAAAMAYAgkAAAAAYxjZAgAAAKywM7LlCnRIAAAAABhDIAEAAABgDCNbAAAAgBWssuUSdEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGAFI1suQYcEAAAAgDEEEgAAAADGMLIFAAAAWGFnZMsV6JAAAAAAMIZAAgAAAMAYRrYAAAAAKxx20xVkSnRIAAAAABhDIAEAAABgDCNbAAAAgBVcGNEl6JAAAAAAMIZAAgAAAMAYRrYAAAAAK7gwokvQIQEAAABgDIEEAAAAgDGMbAEAAABWsMqWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFYwsuUSdEgAAAAAGEMgAQAAAGAMI1sAAACAFYxsuQQdEgAAAADGEEgAAAAAGMPIFgAAAGCF3W66gkyJDgkAAAAAYwgkAAAAAIxhZAsAAACwglW2XIIOCQAAAABjCCQAAAAAjGFkCwAAALCCkS2XoEMCAAAAwBgCCQAAAABjGNkCAAAArLAzsuUKdEgAAAAAGEMgAQAAAGAMI1sAAACABQ6H3XQJmRIdEgAAAADGEEgAAAAAGMPIFgAAAGAFq2y5BB0SAAAAAMYQSAAAAAAYw8gWAAAAYIWDkS1XoEMCAAAAwBgCCQAAAABjGNkCAAAArLBzYURXoEMCAAAAwBgCCQAAAABjGNkCAAAArGCVLZegQwIAAADAGAIJAAAAAGMY2QIAAAAscLDKlkvQIQEAAABgDIEEAAAAgDGMbAEAAABWsMqWS9AhAQAAAGAMgQQAAACAMYxsAQAAAFbYGdlyBTokAAAAAIwhkAAAAAAwhpEtAAAAwAoHF0Z0BTokAAAAAIwhkAAAAAAwhpEtAAAAwAIHq2y5BB0SAAAAAMYQSAAAAAAYw8gWAAAAYAWrbLkEHRIAAAAAxhBIAAAAABjDyBYAAABgAatsuQYdEgAAAADGEEgAAAAAGMPIFgAAAGAFq2y5BB0SAAAAAMYQSAAAAAAYY3M4HCwXAAAAAMAIOiQAAAAAjCGQAAAAADCGQAIAAADAGAIJAAAAAGMIJAAAAACMIZAAAAAAMIZAAgAAAMAYAgkAAAAAYwgkAAAAAIwhkAAAAAAwhkACAAAAwBgCCQAAAABjCCQAAAAAjCGQIFV2u11Tp05VvXr1VL16dXXq1EkhISGmy0ImN336dLVr1850GciErly5omHDhql+/fp64IEH9PLLL2vHjh2my0ImFR4errffflt16tRRzZo11a1bNx09etR0WUC6RSBBqqZPn66FCxdq9OjRWrRokWw2m7p27aq4uDjTpSGTmjNnjqZOnWq6DGRSb775pvbs2aOJEydqyZIlqly5sjp37qxjx46ZLg2ZUM+ePXX69GnNmDFDS5YskZeXlzp06KAbN26YLg1IlwgkSCEuLk6zZ89W79691aBBA1WsWFGTJk1SaGio1qxZY7o8ZDKhoaHq0qWLpkyZooCAANPlIBMKCQnRr7/+qvfee0+1atVS6dKlNWTIEPn5+WnFihWmy0MmExERoWLFimnUqFGqWrWqypQpo169eunixYs6cuSI6fKAdIlAghQOHjyoa9euqU6dOsnbfH19FRgYqO3btxusDJnR/v37lTt3bi1fvlzVq1c3XQ4yobx58+qLL75QlSpVkrfZbDY5HA5FRkYarAyZUd68eTVx4kSVK1dOknTp0iXNmjVL/v7+Klu2rOHqgPTJ3XQBSH8uXLggSSpcuLDT9kKFCun8+fMmSkIm1qhRIzVq1Mh0GcjEfH191aBBA6dtK1eu1KlTp1S3bl1DVSErePfdd/XNN9/I09NTn376qby9vU2XBKRLdEiQws0ZV09PT6ft2bNnV2xsrImSACDN7Ny5U4MHD1bjxo0Jw3Cp9u3bKygoSM8995xee+017d+/33RJQLpEIEEKXl5ekpTiBPbY2FjlyJHDREkAkCbWrl2rzp07q1q1apo4caLpcpDJlS1bVlWqVNGoUaNUrFgxzZ8/33RJQLpEIEEKN0e1wsLCnLaHhYXJ39/fREkAcM/mz5+v3r17q379+poxY0byly9AWgoPD9eKFSuUmJiYvM3NzU1lypRJ8XcVQBICCVKoWLGifHx8tHXr1uRtUVFRCg4OVq1atQxWBgB356uvvtKoUaP0yiuvaPLkySlGUoG0EhYWpv79+2vbtm3J2+Lj4xUcHKwyZcoYrAxIvzipHSl4enqqbdu2Gj9+vPLly6eiRYtq3Lhx8vf3V9OmTU2XBwB35MSJE3r//ffVtGlTde/eXeHh4cn7vLy8lCtXLoPVIbOpWLGi6tatqxEjRmj06NHy9fXVZ599pqioKHXo0MF0eUC6RCBBqvr06aOEhAQNHTpUMTExql27tmbNmsW3igAynFWrVik+Pl5r1qxJcS2lFi1a6MMPPzRUGTIjm82myZMna8KECXrjjTd09epV1apVSwsWLFCRIkVMlwekSzaHw+EwXQQAAACArIlzSAAAAAAYQyABAAAAYAyBBAAAAIAxBBIAAAAAxhBIAAAAABhDIAEAAABgDIEEAAAAgDEEEgAAAADGEEgAIIOJjY1VYGCgatasqVGjRpkuBwCAe0IgAYAMxmazae7cuapWrZrmz5+vEydOmC4JAIC7RiABgAzG09NTtWvXVpcuXSRJ+/fvN1wRAAB3j0ACABlU6dKlJUkHDhwwXAkAAHePQAIAGdSMGTMkSQcPHjRcCQAAd49AAgAZ0KZNm/T1118rd+7cCg4ONl0OAAB3jUACABlMVFSUBg8erMaNG+vll1/W5cuXFRoaarosAADuCoEEADKYESNGKCEhQaNHj1ZgYKAkxrYAABkXgQQAMpCffvpJK1as0JgxY5QvX77kQMKJ7QCAjIpAAgAZxMWLF/Xee+/ppZde0uOPPy5JKl68uHx9fTmPBACQYRFIACCDePfdd5U7d24NGjTIaXulSpUY2QIAZFgEEgDIABYvXqyNGzfqo48+kre3t9O+wMBAnTp1StHR0YaqAwDg7tkcDofDdBEAAAAAsiY6JAAAAACMIZAAAAAAMIZAAgAAAMAYAgkAAAAAYwgkAAAAAIwhkAAAAAAwhkACAAAAwBgCCQAAAABjCCQAAAAAjCGQAAAAADCGQAIAAADAGAIJAAAAAGP+HxPLK3XyXLGWAAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1.7304881 19.47958494 12.11340253 23.41589548]\n", - " [ 0.22948497 0.18392847 0.29364655 0.20072279]\n", - " [ 0.26303845 4.06730814 2.11590451 7.5411205 ]\n", - " [ 15.77893972 122.02334824 51.2167072 152.54894451]]\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.neural_network import MLPRegressor\n", "from sklearn.metrics import accuracy_score\n", @@ -2951,7 +2726,7 @@ }, { "cell_type": "markdown", - "id": "19fec57c", + "id": "50a1e770", "metadata": { "editable": true }, @@ -2971,7 +2746,7 @@ }, { "cell_type": "markdown", - "id": "1bcb8b34", + "id": "5af218e3", "metadata": { "editable": true }, @@ -3003,7 +2778,7 @@ }, { "cell_type": "markdown", - "id": "2ebf620c", + "id": "55450c7b", "metadata": { "editable": true }, @@ -3027,7 +2802,7 @@ }, { "cell_type": "markdown", - "id": "db27019b", + "id": "be42ca6d", "metadata": { "editable": true }, @@ -3055,7 +2830,7 @@ }, { "cell_type": "markdown", - "id": "3ab4acd0", + "id": "4b7a4ceb", "metadata": { "editable": true }, @@ -3070,7 +2845,7 @@ }, { "cell_type": "markdown", - "id": "b60035b5", + "id": "e00d9920", "metadata": { "editable": true }, @@ -3082,7 +2857,7 @@ }, { "cell_type": "markdown", - "id": "29af7e67", + "id": "6243c073", "metadata": { "editable": true }, @@ -3097,7 +2872,7 @@ }, { "cell_type": "markdown", - "id": "740b4af2", + "id": "009ea7de", "metadata": { "editable": true }, @@ -3110,7 +2885,7 @@ }, { "cell_type": "markdown", - "id": "b4ad3976", + "id": "712c684d", "metadata": { "editable": true }, @@ -3122,7 +2897,7 @@ }, { "cell_type": "markdown", - "id": "0051e649", + "id": "b4bcd2a5", "metadata": { "editable": true }, @@ -3132,7 +2907,7 @@ }, { "cell_type": "markdown", - "id": "1a7712be", + "id": "143b6359", "metadata": { "editable": true }, @@ -3143,7 +2918,7 @@ }, { "cell_type": "markdown", - "id": "27e08926", + "id": "d2e56068", "metadata": { "editable": true }, @@ -3161,7 +2936,7 @@ }, { "cell_type": "markdown", - "id": "2ec61954", + "id": "2541505d", "metadata": { "editable": true }, @@ -3172,7 +2947,7 @@ }, { "cell_type": "markdown", - "id": "b7aab6da", + "id": "725efee7", "metadata": { "editable": true }, @@ -3184,7 +2959,7 @@ }, { "cell_type": "markdown", - "id": "523fa0e8", + "id": "cb15e9f1", "metadata": { "editable": true }, @@ -3194,7 +2969,7 @@ }, { "cell_type": "markdown", - "id": "a4172710", + "id": "87026e15", "metadata": { "editable": true }, @@ -3206,7 +2981,7 @@ }, { "cell_type": "markdown", - "id": "3a21de62", + "id": "cd09c85a", "metadata": { "editable": true }, @@ -3216,7 +2991,7 @@ }, { "cell_type": "markdown", - "id": "a68d2930", + "id": "c7845cfd", "metadata": { "editable": true }, @@ -3228,7 +3003,7 @@ }, { "cell_type": "markdown", - "id": "519a6c63", + "id": "20e94324", "metadata": { "editable": true }, @@ -3238,7 +3013,7 @@ }, { "cell_type": "markdown", - "id": "2f28767d", + "id": "46cc127e", "metadata": { "editable": true }, @@ -3257,7 +3032,7 @@ }, { "cell_type": "markdown", - "id": "60e193f4", + "id": "b83e3f3f", "metadata": { "editable": true }, @@ -3267,7 +3042,7 @@ }, { "cell_type": "markdown", - "id": "b76cff50", + "id": "d3c3d5b8", "metadata": { "editable": true }, @@ -3279,7 +3054,7 @@ }, { "cell_type": "markdown", - "id": "6c65fa96", + "id": "c6e741c3", "metadata": { "editable": true }, @@ -3289,7 +3064,7 @@ }, { "cell_type": "markdown", - "id": "554a1508", + "id": "510abc9b", "metadata": { "editable": true }, @@ -3305,7 +3080,7 @@ }, { "cell_type": "markdown", - "id": "ec603f5c", + "id": "1a6464a4", "metadata": { "editable": true }, @@ -3325,7 +3100,7 @@ }, { "cell_type": "markdown", - "id": "98c74b9b", + "id": "0f403e2d", "metadata": { "editable": true }, @@ -3335,7 +3110,7 @@ }, { "cell_type": "markdown", - "id": "7f4d2c5c", + "id": "4a8138dc", "metadata": { "editable": true }, @@ -3346,7 +3121,7 @@ }, { "cell_type": "markdown", - "id": "654bf9a6", + "id": "726cbaa4", "metadata": { "editable": true }, @@ -3365,7 +3140,7 @@ }, { "cell_type": "markdown", - "id": "f80909ca", + "id": "6b7adfb6", "metadata": { "editable": true }, @@ -3375,7 +3150,7 @@ }, { "cell_type": "markdown", - "id": "ee25c0f7", + "id": "84165f3e", "metadata": { "editable": true }, @@ -3387,7 +3162,7 @@ }, { "cell_type": "markdown", - "id": "c1947e0c", + "id": "ebf366ef", "metadata": { "editable": true }, @@ -3397,7 +3172,7 @@ }, { "cell_type": "markdown", - "id": "05229723", + "id": "02ffad05", "metadata": { "editable": true }, @@ -3408,7 +3183,7 @@ }, { "cell_type": "markdown", - "id": "bb0abff0", + "id": "d54237d5", "metadata": { "editable": true }, @@ -3428,7 +3203,7 @@ }, { "cell_type": "markdown", - "id": "bc16c9f8", + "id": "8a7e9dbb", "metadata": { "editable": true }, @@ -3440,7 +3215,7 @@ }, { "cell_type": "markdown", - "id": "d7f65472", + "id": "d4d5ff52", "metadata": { "editable": true }, @@ -3455,13 +3230,10 @@ { "cell_type": "code", "execution_count": 35, - "id": "acf08e73", + "id": "297415bf", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -3538,7 +3310,7 @@ }, { "cell_type": "markdown", - "id": "937591a1", + "id": "2ce99829", "metadata": { "editable": true }, @@ -3548,7 +3320,7 @@ }, { "cell_type": "markdown", - "id": "132c386c", + "id": "f6a44999", "metadata": { "editable": true }, @@ -3560,7 +3332,7 @@ }, { "cell_type": "markdown", - "id": "869bdef2", + "id": "990c34c3", "metadata": { "editable": true }, @@ -3570,7 +3342,7 @@ }, { "cell_type": "markdown", - "id": "7781a1c4", + "id": "a06f9457", "metadata": { "editable": true }, @@ -3581,7 +3353,7 @@ }, { "cell_type": "markdown", - "id": "fd8e693c", + "id": "19cd9843", "metadata": { "editable": true }, @@ -3593,7 +3365,7 @@ }, { "cell_type": "markdown", - "id": "f5c29ebf", + "id": "cc8c11ab", "metadata": { "editable": true }, @@ -3603,7 +3375,7 @@ }, { "cell_type": "markdown", - "id": "a798f40c", + "id": "d3d4e384", "metadata": { "editable": true }, @@ -3615,7 +3387,7 @@ }, { "cell_type": "markdown", - "id": "bae820af", + "id": "6187929e", "metadata": { "editable": true }, @@ -3625,7 +3397,7 @@ }, { "cell_type": "markdown", - "id": "06c75a81", + "id": "e0ba2cb1", "metadata": { "editable": true }, @@ -3637,7 +3409,7 @@ }, { "cell_type": "markdown", - "id": "59236913", + "id": "468fec32", "metadata": { "editable": true }, @@ -3650,7 +3422,7 @@ }, { "cell_type": "markdown", - "id": "81b5da1b", + "id": "ba618e10", "metadata": { "editable": true }, @@ -3662,7 +3434,7 @@ }, { "cell_type": "markdown", - "id": "e74c517f", + "id": "a74d3f1c", "metadata": { "editable": true }, @@ -3672,7 +3444,7 @@ }, { "cell_type": "markdown", - "id": "648beb7f", + "id": "fb434398", "metadata": { "editable": true }, @@ -3684,7 +3456,7 @@ }, { "cell_type": "markdown", - "id": "1d9326d3", + "id": "42558cc8", "metadata": { "editable": true }, @@ -3696,7 +3468,7 @@ }, { "cell_type": "markdown", - "id": "f51ed614", + "id": "d851da5b", "metadata": { "editable": true }, @@ -3707,7 +3479,7 @@ }, { "cell_type": "markdown", - "id": "ced37c54", + "id": "49533cb3", "metadata": { "editable": true }, @@ -3719,7 +3491,7 @@ }, { "cell_type": "markdown", - "id": "ebbeea54", + "id": "a62f6a4c", "metadata": { "editable": true }, @@ -3738,7 +3510,7 @@ }, { "cell_type": "markdown", - "id": "1b5d06a0", + "id": "381182cd", "metadata": { "editable": true }, @@ -3751,7 +3523,7 @@ }, { "cell_type": "markdown", - "id": "74890321", + "id": "04b172c8", "metadata": { "editable": true }, @@ -3761,7 +3533,7 @@ }, { "cell_type": "markdown", - "id": "ad33aa43", + "id": "265d8614", "metadata": { "editable": true }, @@ -3773,7 +3545,7 @@ }, { "cell_type": "markdown", - "id": "626df4e6", + "id": "68c1005e", "metadata": { "editable": true }, @@ -3783,7 +3555,7 @@ }, { "cell_type": "markdown", - "id": "6594383b", + "id": "61fb0fbc", "metadata": { "editable": true }, @@ -3795,7 +3567,7 @@ }, { "cell_type": "markdown", - "id": "ec04a0b3", + "id": "acb3fa59", "metadata": { "editable": true }, @@ -3805,7 +3577,7 @@ }, { "cell_type": "markdown", - "id": "a82796e0", + "id": "82f920b5", "metadata": { "editable": true }, @@ -3817,7 +3589,7 @@ }, { "cell_type": "markdown", - "id": "9607cc25", + "id": "26de4671", "metadata": { "editable": true }, @@ -3828,7 +3600,7 @@ }, { "cell_type": "markdown", - "id": "f84698d3", + "id": "071a86ff", "metadata": { "editable": true }, @@ -3840,7 +3612,7 @@ }, { "cell_type": "markdown", - "id": "1245df8a", + "id": "fdb51c1d", "metadata": { "editable": true }, @@ -3850,7 +3622,7 @@ }, { "cell_type": "markdown", - "id": "b6df8204", + "id": "5532b00a", "metadata": { "editable": true }, @@ -3862,7 +3634,7 @@ }, { "cell_type": "markdown", - "id": "6250497e", + "id": "a7c57982", "metadata": { "editable": true }, @@ -3872,7 +3644,7 @@ }, { "cell_type": "markdown", - "id": "f788dc8f", + "id": "022ce912", "metadata": { "editable": true }, @@ -3884,7 +3656,7 @@ }, { "cell_type": "markdown", - "id": "bdb72b3a", + "id": "12518c37", "metadata": { "editable": true }, @@ -3905,7 +3677,7 @@ }, { "cell_type": "markdown", - "id": "d498ca67", + "id": "89f9bbda", "metadata": { "editable": true }, @@ -3916,7 +3688,7 @@ }, { "cell_type": "markdown", - "id": "786a3fef", + "id": "51458daa", "metadata": { "editable": true }, @@ -3928,7 +3700,7 @@ }, { "cell_type": "markdown", - "id": "a0080ea0", + "id": "eaf4a93e", "metadata": { "editable": true }, @@ -3938,7 +3710,7 @@ }, { "cell_type": "markdown", - "id": "27b8b857", + "id": "2fea3537", "metadata": { "editable": true }, @@ -3950,7 +3722,7 @@ }, { "cell_type": "markdown", - "id": "3bf5241a", + "id": "ca4f1123", "metadata": { "editable": true }, @@ -3960,7 +3732,7 @@ }, { "cell_type": "markdown", - "id": "6b578bc7", + "id": "09de659a", "metadata": { "editable": true }, @@ -3972,7 +3744,7 @@ }, { "cell_type": "markdown", - "id": "11220b8a", + "id": "77f9fd08", "metadata": { "editable": true }, @@ -3984,7 +3756,7 @@ }, { "cell_type": "markdown", - "id": "a920365b", + "id": "beab9d54", "metadata": { "editable": true }, @@ -3998,13 +3770,10 @@ { "cell_type": "code", "execution_count": 36, - "id": "0b7b8c69", + "id": "ce0d14e1", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4016,7 +3785,7 @@ }, { "cell_type": "markdown", - "id": "c209e80c", + "id": "20bbbdf2", "metadata": { "editable": true }, @@ -4027,13 +3796,10 @@ { "cell_type": "code", "execution_count": 37, - "id": "947eedad", + "id": "d1b21aae", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4043,7 +3809,7 @@ }, { "cell_type": "markdown", - "id": "55e5aa0a", + "id": "02bb27d6", "metadata": { "editable": true }, @@ -4054,13 +3820,10 @@ { "cell_type": "code", "execution_count": 38, - "id": "3fb277ad", + "id": "98630c99", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4080,7 +3843,7 @@ }, { "cell_type": "markdown", - "id": "36c1d2bc", + "id": "9af04036", "metadata": { "editable": true }, @@ -4094,13 +3857,10 @@ { "cell_type": "code", "execution_count": 39, - "id": "6db56b3d", + "id": "2d8cd3da", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4110,7 +3870,7 @@ }, { "cell_type": "markdown", - "id": "e0ee98a4", + "id": "71899eed", "metadata": { "editable": true }, @@ -4121,13 +3881,10 @@ { "cell_type": "code", "execution_count": 40, - "id": "8774bce4", + "id": "f8cc2dd0", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4136,7 +3893,7 @@ }, { "cell_type": "markdown", - "id": "1d29c10a", + "id": "44f3dd69", "metadata": { "editable": true }, @@ -4147,13 +3904,10 @@ { "cell_type": "code", "execution_count": 41, - "id": "93c8b0c6", + "id": "acdf8d3d", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4166,7 +3920,7 @@ }, { "cell_type": "markdown", - "id": "ff36a22e", + "id": "26d00774", "metadata": { "editable": true }, @@ -4177,13 +3931,10 @@ { "cell_type": "code", "execution_count": 42, - "id": "35feafa3", + "id": "fcf52c3c", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -4194,7 +3945,7 @@ }, { "cell_type": "markdown", - "id": "1c4ac2dc", + "id": "82083461", "metadata": { "editable": true }, @@ -4216,7 +3967,7 @@ }, { "cell_type": "markdown", - "id": "dc2eacf3", + "id": "c247a29f", "metadata": { "editable": true }, @@ -4228,7 +3979,7 @@ }, { "cell_type": "markdown", - "id": "48c5a8b6", + "id": "5c6266aa", "metadata": { "editable": true }, @@ -4238,7 +3989,7 @@ }, { "cell_type": "markdown", - "id": "abc4699e", + "id": "c36f8d5c", "metadata": { "editable": true }, @@ -4250,7 +4001,7 @@ }, { "cell_type": "markdown", - "id": "4db48e9d", + "id": "5b1a5174", "metadata": { "editable": true }, @@ -4262,7 +4013,7 @@ }, { "cell_type": "markdown", - "id": "350fec9b", + "id": "81c82695", "metadata": { "editable": true }, @@ -4272,7 +4023,7 @@ }, { "cell_type": "markdown", - "id": "b474ddf5", + "id": "7dc8a3bc", "metadata": { "editable": true }, @@ -4284,7 +4035,7 @@ }, { "cell_type": "markdown", - "id": "67fc1d79", + "id": "41789a38", "metadata": { "editable": true }, @@ -4294,7 +4045,7 @@ }, { "cell_type": "markdown", - "id": "6aa9f684", + "id": "4a2e42bf", "metadata": { "editable": true }, @@ -4306,7 +4057,7 @@ }, { "cell_type": "markdown", - "id": "ce023be0", + "id": "acd67ee6", "metadata": { "editable": true }, @@ -4316,7 +4067,7 @@ }, { "cell_type": "markdown", - "id": "3754c848", + "id": "ee90b0eb", "metadata": { "editable": true }, @@ -4328,7 +4079,7 @@ }, { "cell_type": "markdown", - "id": "4d884286", + "id": "54eaab7f", "metadata": { "editable": true }, @@ -4340,7 +4091,7 @@ }, { "cell_type": "markdown", - "id": "f8446340", + "id": "f941f1ac", "metadata": { "editable": true }, @@ -4350,7 +4101,7 @@ }, { "cell_type": "markdown", - "id": "a6830538", + "id": "89e02a88", "metadata": { "editable": true }, @@ -4362,7 +4113,7 @@ }, { "cell_type": "markdown", - "id": "84f9e6b4", + "id": "3d1f3b65", "metadata": { "editable": true }, @@ -4372,7 +4123,7 @@ }, { "cell_type": "markdown", - "id": "586509f5", + "id": "1dda0ac2", "metadata": { "editable": true }, @@ -4384,7 +4135,7 @@ }, { "cell_type": "markdown", - "id": "ff04787a", + "id": "aafe7aae", "metadata": { "editable": true }, @@ -4396,7 +4147,7 @@ }, { "cell_type": "markdown", - "id": "fab40e86", + "id": "37f9c1fa", "metadata": { "editable": true }, @@ -4408,7 +4159,7 @@ }, { "cell_type": "markdown", - "id": "175c9026", + "id": "373adac2", "metadata": { "editable": true }, @@ -4418,7 +4169,7 @@ }, { "cell_type": "markdown", - "id": "8b8d1d83", + "id": "bdd6d800", "metadata": { "editable": true }, @@ -4430,7 +4181,7 @@ }, { "cell_type": "markdown", - "id": "0c64f049", + "id": "913df005", "metadata": { "editable": true }, @@ -4440,7 +4191,7 @@ }, { "cell_type": "markdown", - "id": "2a591f8f", + "id": "76f6737e", "metadata": { "editable": true }, @@ -4452,7 +4203,7 @@ }, { "cell_type": "markdown", - "id": "956b5691", + "id": "48f6aa66", "metadata": { "editable": true }, @@ -4462,7 +4213,7 @@ }, { "cell_type": "markdown", - "id": "85b1bd72", + "id": "513a1986", "metadata": { "editable": true }, @@ -4474,7 +4225,7 @@ }, { "cell_type": "markdown", - "id": "6950fb95", + "id": "20fbbe07", "metadata": { "editable": true }, @@ -4485,7 +4236,7 @@ }, { "cell_type": "markdown", - "id": "e2d998e7", + "id": "4dd97daa", "metadata": { "editable": true }, @@ -4497,7 +4248,7 @@ }, { "cell_type": "markdown", - "id": "79e10d20", + "id": "032afd0e", "metadata": { "editable": true }, @@ -4507,7 +4258,7 @@ }, { "cell_type": "markdown", - "id": "6585dbe2", + "id": "25eac667", "metadata": { "editable": true }, @@ -4519,7 +4270,7 @@ }, { "cell_type": "markdown", - "id": "4291e4ad", + "id": "18c343cd", "metadata": { "editable": true }, @@ -4529,7 +4280,7 @@ }, { "cell_type": "markdown", - "id": "6a50444a", + "id": "9864986f", "metadata": { "editable": true }, @@ -4541,7 +4292,7 @@ }, { "cell_type": "markdown", - "id": "58d08cf2", + "id": "1ecdde02", "metadata": { "editable": true }, @@ -4554,7 +4305,7 @@ }, { "cell_type": "markdown", - "id": "8215c3c6", + "id": "9d62dddc", "metadata": { "editable": true }, @@ -4566,7 +4317,7 @@ }, { "cell_type": "markdown", - "id": "09efd53b", + "id": "db398f5b", "metadata": { "editable": true }, @@ -4578,7 +4329,7 @@ }, { "cell_type": "markdown", - "id": "6c694cf4", + "id": "2bbf293c", "metadata": { "editable": true }, @@ -4590,7 +4341,7 @@ }, { "cell_type": "markdown", - "id": "909419d5", + "id": "d82e1506", "metadata": { "editable": true }, @@ -4602,7 +4353,7 @@ }, { "cell_type": "markdown", - "id": "2d35d3a4", + "id": "1ddd417c", "metadata": { "editable": true }, @@ -4614,7 +4365,7 @@ }, { "cell_type": "markdown", - "id": "845b476b", + "id": "e7904873", "metadata": { "editable": true }, @@ -4624,7 +4375,7 @@ }, { "cell_type": "markdown", - "id": "644470d4", + "id": "19604164", "metadata": { "editable": true }, @@ -4636,7 +4387,7 @@ }, { "cell_type": "markdown", - "id": "6c4ad6c8", + "id": "0f262e03", "metadata": { "editable": true }, @@ -4648,7 +4399,7 @@ }, { "cell_type": "markdown", - "id": "839e60fb", + "id": "cdb18e0d", "metadata": { "editable": true }, @@ -4660,25 +4411,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }