diff --git a/doc/pub/week44/html/._week44-bs001.html b/doc/pub/week44/html/._week44-bs001.html
index ddcb93a03..49e8126f7 100644
--- a/doc/pub/week44/html/._week44-bs001.html
+++ b/doc/pub/week44/html/._week44-bs001.html
@@ -305,7 +305,8 @@ MathJax.Hub.Config({
- Thursday: Basics of decision trees, classification and regression algorithms
- - Note: Thursday's lecture is digital only due to "High-school post-education day":https://www.uio.no/om/samarbeid/skole/fagped-dag/"
+ - Video of lecture
+ - Note: Thursday's lecture is digital only due to High-school post-education day
- Friday: Decision trees and ensemble models (bagging and random forests)
diff --git a/doc/pub/week44/html/week44-reveal.html b/doc/pub/week44/html/week44-reveal.html
index 7f88535f6..a3865ef41 100644
--- a/doc/pub/week44/html/week44-reveal.html
+++ b/doc/pub/week44/html/week44-reveal.html
@@ -201,7 +201,9 @@ MathJax.Hub.Config({
Thursday: Basics of decision trees, classification and regression algorithms
-- Note: Thursday's lecture is digital only due to "High-school post-education day":https://www.uio.no/om/samarbeid/skole/fagped-dag/"
+- Video of lecture
+
+- Note: Thursday's lecture is digital only due to High-school post-education day
Friday: Decision trees and ensemble models (bagging and random forests)
diff --git a/doc/pub/week44/html/week44-solarized.html b/doc/pub/week44/html/week44-solarized.html
index ff8c75e04..0dfcdcf08 100644
--- a/doc/pub/week44/html/week44-solarized.html
+++ b/doc/pub/week44/html/week44-solarized.html
@@ -272,7 +272,8 @@ MathJax.Hub.Config({
- Thursday: Basics of decision trees, classification and regression algorithms
- - Note: Thursday's lecture is digital only due to "High-school post-education day":https://www.uio.no/om/samarbeid/skole/fagped-dag/"
+ - Video of lecture
+ - Note: Thursday's lecture is digital only due to High-school post-education day
- Friday: Decision trees and ensemble models (bagging and random forests)
diff --git a/doc/pub/week44/html/week44.html b/doc/pub/week44/html/week44.html
index a14dfbb93..33f46b749 100644
--- a/doc/pub/week44/html/week44.html
+++ b/doc/pub/week44/html/week44.html
@@ -349,7 +349,8 @@ MathJax.Hub.Config({
- Thursday: Basics of decision trees, classification and regression algorithms
- - Note: Thursday's lecture is digital only due to "High-school post-education day":https://www.uio.no/om/samarbeid/skole/fagped-dag/"
+ - Video of lecture
+ - Note: Thursday's lecture is digital only due to High-school post-education day
- Friday: Decision trees and ensemble models (bagging and random forests)
diff --git a/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz b/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz
index 5d3b425a1..eb5a47c27 100644
Binary files a/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz and b/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz differ
diff --git a/doc/pub/week44/ipynb/week44.ipynb b/doc/pub/week44/ipynb/week44.ipynb
index f83bb031c..b883deb25 100644
--- a/doc/pub/week44/ipynb/week44.ipynb
+++ b/doc/pub/week44/ipynb/week44.ipynb
@@ -2,8 +2,10 @@
"cells": [
{
"cell_type": "markdown",
- "id": "59fb2b9e",
- "metadata": {},
+ "id": "a253875c",
+ "metadata": {
+ "editable": true
+ },
"source": [
"\n",
@@ -12,8 +14,10 @@
},
{
"cell_type": "markdown",
- "id": "d408fbc4",
- "metadata": {},
+ "id": "bb5c0f59",
+ "metadata": {
+ "editable": true
+ },
"source": [
"# Week 44: Decision Trees, Ensemble methods and Random Forests\n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
@@ -25,14 +29,18 @@
},
{
"cell_type": "markdown",
- "id": "d0ae087e",
- "metadata": {},
+ "id": "042245bc",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Overview of week 44\n",
"\n",
"* Thursday: Basics of decision trees, classification and regression algorithms\n",
"\n",
- " * **Note**: Thursday's lecture is digital only due to \"High-school post-education day\":https://www.uio.no/om/samarbeid/skole/fagped-dag/\"\n",
+ " * [Video of lecture](https://youtu.be/7jexGH5SOOE)\n",
+ "\n",
+ " * **Note**: Thursday's lecture is digital only due to [High-school post-education day](https://www.uio.no/om/samarbeid/skole/fagped-dag/)\n",
"\n",
"* Friday: Decision trees and ensemble models (bagging and random forests)\n",
"\n",
@@ -47,8 +55,10 @@
},
{
"cell_type": "markdown",
- "id": "8374c026",
- "metadata": {},
+ "id": "767853e6",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Digression First\n",
"\n",
@@ -63,8 +73,10 @@
},
{
"cell_type": "markdown",
- "id": "5103d90f",
- "metadata": {},
+ "id": "689815b6",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Decision trees, overarching aims\n",
"\n",
@@ -92,8 +104,10 @@
},
{
"cell_type": "markdown",
- "id": "17974e99",
- "metadata": {},
+ "id": "402ea3f6",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Basics of a tree\n",
"\n",
@@ -110,8 +124,10 @@
},
{
"cell_type": "markdown",
- "id": "e1141b9b",
- "metadata": {},
+ "id": "b9855ce5",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## A Sketch of a Tree, Regression problem\n",
"\n",
@@ -122,8 +138,10 @@
},
{
"cell_type": "markdown",
- "id": "16a3a7d7",
- "metadata": {},
+ "id": "5b7ff057",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## A Sketch of a Tree, Classification problem\n",
"\n",
@@ -133,8 +151,10 @@
},
{
"cell_type": "markdown",
- "id": "0cf0e0ad",
- "metadata": {},
+ "id": "30304e56",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## A typical Decision Tree with its pertinent Jargon, Classification Problem\n",
"\n",
@@ -149,8 +169,10 @@
},
{
"cell_type": "markdown",
- "id": "7f882015",
- "metadata": {},
+ "id": "3c0aba31",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## General Features\n",
"\n",
@@ -170,8 +192,10 @@
},
{
"cell_type": "markdown",
- "id": "de640bd9",
- "metadata": {},
+ "id": "aa8eeb22",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## How do we set it up?\n",
"\n",
@@ -191,49 +215,23 @@
},
{
"cell_type": "markdown",
- "id": "73a83c4f",
- "metadata": {},
+ "id": "baef7a61",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Decision trees and Regression"
]
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "1af5ee76",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2nd degree coefficients:\n",
- "zero power: -0.03284026428293263\n",
- "first power: 0.08750136942283238\n",
- "second power: -0.0004315471812525959\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": 1,
+ "id": "b57d320b",
+ "metadata": {
+ "collapsed": false,
+ "editable": true
+ },
+ "outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
@@ -302,7 +300,7 @@
"from sklearn.tree import DecisionTreeRegressor\n",
"regr_1=DecisionTreeRegressor(max_depth=2)\n",
"regr_2=DecisionTreeRegressor(max_depth=5)\n",
- "regr_3=DecisionTreeRegressor(max_depth=13)\n",
+ "regr_3=DecisionTreeRegressor(max_depth=7)\n",
"regr_1.fit(X, distance_list)\n",
"regr_2.fit(X, distance_list)\n",
"regr_3.fit(X, distance_list)\n",
@@ -329,8 +327,10 @@
},
{
"cell_type": "markdown",
- "id": "8e7a1f48",
- "metadata": {},
+ "id": "e00aa65a",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Building a tree, regression\n",
"\n",
@@ -349,8 +349,10 @@
},
{
"cell_type": "markdown",
- "id": "b0cdf698",
- "metadata": {},
+ "id": "30b0a4c0",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n",
@@ -359,8 +361,10 @@
},
{
"cell_type": "markdown",
- "id": "73f15329",
- "metadata": {},
+ "id": "1583a89f",
+ "metadata": {
+ "editable": true
+ },
"source": [
"where $\\overline{y}_{R_j}$ is the mean response for the training observations \n",
"within box $j$."
@@ -368,8 +372,10 @@
},
{
"cell_type": "markdown",
- "id": "f7e6d1d8",
- "metadata": {},
+ "id": "061b3932",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## A top-down approach, recursive binary splitting\n",
"\n",
@@ -388,8 +394,10 @@
},
{
"cell_type": "markdown",
- "id": "1e1df1bc",
- "metadata": {},
+ "id": "3bcd4ed5",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Making a tree\n",
"\n",
@@ -399,8 +407,10 @@
},
{
"cell_type": "markdown",
- "id": "ecc178b3",
- "metadata": {},
+ "id": "da65d6f0",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"\\left\\{X\\vert x_j < s\\right\\},\n",
@@ -409,16 +419,20 @@
},
{
"cell_type": "markdown",
- "id": "51e92bd7",
- "metadata": {},
+ "id": "b6078340",
+ "metadata": {
+ "editable": true
+ },
"source": [
"and"
]
},
{
"cell_type": "markdown",
- "id": "dcae0bf1",
- "metadata": {},
+ "id": "1f8ccc33",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"\\left\\{X\\vert x_j \\geq s\\right\\},\n",
@@ -427,16 +441,20 @@
},
{
"cell_type": "markdown",
- "id": "f5493ac0",
- "metadata": {},
+ "id": "2cbfddcc",
+ "metadata": {
+ "editable": true
+ },
"source": [
"so that we obtain the lowest MSE, that is"
]
},
{
"cell_type": "markdown",
- "id": "c285a4a5",
- "metadata": {},
+ "id": "2ce74e29",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"\\sum_{i:x_i\\in R_j}(y_i-\\overline{y}_{R_1})^2+\\sum_{i:x_i\\in R_2}(y_i-\\overline{y}_{R_2})^2,\n",
@@ -445,8 +463,10 @@
},
{
"cell_type": "markdown",
- "id": "c40f850e",
- "metadata": {},
+ "id": "1c4dee1e",
+ "metadata": {
+ "editable": true
+ },
"source": [
"which we want to minimize by considering all predictors\n",
"$x_1,x_2,\\dots,x_p$. We consider also all possible values of $s$ for\n",
@@ -476,8 +496,10 @@
},
{
"cell_type": "markdown",
- "id": "2de1df16",
- "metadata": {},
+ "id": "c4c46b6a",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Pruning the tree\n",
"\n",
@@ -498,8 +520,10 @@
},
{
"cell_type": "markdown",
- "id": "62c0ce6a",
- "metadata": {},
+ "id": "84491b1e",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Cost complexity pruning\n",
"\n",
@@ -508,8 +532,10 @@
},
{
"cell_type": "markdown",
- "id": "fae9d5d9",
- "metadata": {},
+ "id": "ea087da1",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n",
@@ -518,8 +544,10 @@
},
{
"cell_type": "markdown",
- "id": "89fb5670",
- "metadata": {},
+ "id": "64b9270c",
+ "metadata": {
+ "editable": true
+ },
"source": [
"is as small as possible. Here $\\overline{T}$ is \n",
"the number of terminal nodes of the tree $T$ , $R_m$ is the\n",
@@ -544,8 +572,10 @@
},
{
"cell_type": "markdown",
- "id": "35d5d8d1",
- "metadata": {},
+ "id": "d76b1cb7",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Schematic Regression Procedure\n",
"\n",
@@ -568,8 +598,10 @@
},
{
"cell_type": "markdown",
- "id": "fe67bc38",
- "metadata": {},
+ "id": "a2b5ab7b",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## A Classification Tree\n",
"\n",
@@ -589,8 +621,10 @@
},
{
"cell_type": "markdown",
- "id": "a295171f",
- "metadata": {},
+ "id": "ce43b24b",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Growing a classification tree\n",
"\n",
@@ -614,8 +648,10 @@
},
{
"cell_type": "markdown",
- "id": "2ca5b9ba",
- "metadata": {},
+ "id": "a7679c4b",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Classification tree, how to split nodes\n",
"\n",
@@ -631,8 +667,10 @@
},
{
"cell_type": "markdown",
- "id": "f1001e0e",
- "metadata": {},
+ "id": "ef1fa15d",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i=k).\n",
@@ -641,8 +679,10 @@
},
{
"cell_type": "markdown",
- "id": "fdeba790",
- "metadata": {},
+ "id": "a0cffc89",
+ "metadata": {
+ "editable": true
+ },
"source": [
"We let $p_{mk}$ represent the majority class of observations in region\n",
"$m$. The three most common ways of splitting a node are given by\n",
@@ -652,8 +692,10 @@
},
{
"cell_type": "markdown",
- "id": "2d487375",
- "metadata": {},
+ "id": "cc50edf6",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"p_{mk} = \\frac{1}{N_m}\\sum_{x_i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n",
@@ -662,16 +704,20 @@
},
{
"cell_type": "markdown",
- "id": "b71a7fae",
- "metadata": {},
+ "id": "bc71a6ca",
+ "metadata": {
+ "editable": true
+ },
"source": [
"* Gini index $g$"
]
},
{
"cell_type": "markdown",
- "id": "b4a99914",
- "metadata": {},
+ "id": "7f4c72e2",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"g = \\sum_{k=1}^K p_{mk}(1-p_{mk}).\n",
@@ -680,16 +726,20 @@
},
{
"cell_type": "markdown",
- "id": "9a6ce2de",
- "metadata": {},
+ "id": "4bca2433",
+ "metadata": {
+ "editable": true
+ },
"source": [
"* Information entropy or just entropy $s$"
]
},
{
"cell_type": "markdown",
- "id": "8622aaae",
- "metadata": {},
+ "id": "be263447",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n",
@@ -698,128 +748,23 @@
},
{
"cell_type": "markdown",
- "id": "61315602",
- "metadata": {},
+ "id": "7b14afde",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Visualizing the Tree, Classification"
]
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "a7aa6248",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " mean radius mean texture mean perimeter mean area mean smoothness \\\n",
- "0 17.99 10.38 122.80 1001.0 0.11840 \n",
- "1 20.57 17.77 132.90 1326.0 0.08474 \n",
- "2 19.69 21.25 130.00 1203.0 0.10960 \n",
- "3 11.42 20.38 77.58 386.1 0.14250 \n",
- "4 20.29 14.34 135.10 1297.0 0.10030 \n",
- ".. ... ... ... ... ... \n",
- "564 21.56 22.39 142.00 1479.0 0.11100 \n",
- "565 20.13 28.25 131.20 1261.0 0.09780 \n",
- "566 16.60 28.08 108.30 858.1 0.08455 \n",
- "567 20.60 29.33 140.10 1265.0 0.11780 \n",
- "568 7.76 24.54 47.92 181.0 0.05263 \n",
- "\n",
- " mean compactness mean concavity mean concave points mean symmetry \\\n",
- "0 0.27760 0.30010 0.14710 0.2419 \n",
- "1 0.07864 0.08690 0.07017 0.1812 \n",
- "2 0.15990 0.19740 0.12790 0.2069 \n",
- "3 0.28390 0.24140 0.10520 0.2597 \n",
- "4 0.13280 0.19800 0.10430 0.1809 \n",
- ".. ... ... ... ... \n",
- "564 0.11590 0.24390 0.13890 0.1726 \n",
- "565 0.10340 0.14400 0.09791 0.1752 \n",
- "566 0.10230 0.09251 0.05302 0.1590 \n",
- "567 0.27700 0.35140 0.15200 0.2397 \n",
- "568 0.04362 0.00000 0.00000 0.1587 \n",
- "\n",
- " mean fractal dimension ... worst radius worst texture \\\n",
- "0 0.07871 ... 25.380 17.33 \n",
- "1 0.05667 ... 24.990 23.41 \n",
- "2 0.05999 ... 23.570 25.53 \n",
- "3 0.09744 ... 14.910 26.50 \n",
- "4 0.05883 ... 22.540 16.67 \n",
- ".. ... ... ... ... \n",
- "564 0.05623 ... 25.450 26.40 \n",
- "565 0.05533 ... 23.690 38.25 \n",
- "566 0.05648 ... 18.980 34.12 \n",
- "567 0.07016 ... 25.740 39.42 \n",
- "568 0.05884 ... 9.456 30.37 \n",
- "\n",
- " worst perimeter worst area worst smoothness worst compactness \\\n",
- "0 184.60 2019.0 0.16220 0.66560 \n",
- "1 158.80 1956.0 0.12380 0.18660 \n",
- "2 152.50 1709.0 0.14440 0.42450 \n",
- "3 98.87 567.7 0.20980 0.86630 \n",
- "4 152.20 1575.0 0.13740 0.20500 \n",
- ".. ... ... ... ... \n",
- "564 166.10 2027.0 0.14100 0.21130 \n",
- "565 155.00 1731.0 0.11660 0.19220 \n",
- "566 126.70 1124.0 0.11390 0.30940 \n",
- "567 184.60 1821.0 0.16500 0.86810 \n",
- "568 59.16 268.6 0.08996 0.06444 \n",
- "\n",
- " worst concavity worst concave points worst symmetry \\\n",
- "0 0.7119 0.2654 0.4601 \n",
- "1 0.2416 0.1860 0.2750 \n",
- "2 0.4504 0.2430 0.3613 \n",
- "3 0.6869 0.2575 0.6638 \n",
- "4 0.4000 0.1625 0.2364 \n",
- ".. ... ... ... \n",
- "564 0.4107 0.2216 0.2060 \n",
- "565 0.3215 0.1628 0.2572 \n",
- "566 0.3403 0.1418 0.2218 \n",
- "567 0.9387 0.2650 0.4087 \n",
- "568 0.0000 0.0000 0.2871 \n",
- "\n",
- " worst fractal dimension \n",
- "0 0.11890 \n",
- "1 0.08902 \n",
- "2 0.08758 \n",
- "3 0.17300 \n",
- "4 0.07678 \n",
- ".. ... \n",
- "564 0.07115 \n",
- "565 0.06637 \n",
- "566 0.07820 \n",
- "567 0.12400 \n",
- "568 0.07039 \n",
- "\n",
- "[569 rows x 30 columns]\n",
- " malignant benign\n",
- "0 1 0\n",
- "1 1 0\n",
- "2 1 0\n",
- "3 1 0\n",
- "4 1 0\n",
- ".. ... ...\n",
- "564 1 0\n",
- "565 1 0\n",
- "566 1 0\n",
- "567 1 0\n",
- "568 0 1\n",
- "\n",
- "[569 rows x 2 columns]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "0"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": 2,
+ "id": "c36e5725",
+ "metadata": {
+ "collapsed": false,
+ "editable": true
+ },
+ "outputs": [],
"source": [
"import os\n",
"from sklearn.datasets import load_breast_cancer\n",
@@ -858,8 +803,10 @@
},
{
"cell_type": "markdown",
- "id": "cd6e1605",
- "metadata": {},
+ "id": "fe181229",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Visualizing the Tree, The Moons"
]
@@ -867,8 +814,11 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "92f3f3d5",
- "metadata": {},
+ "id": "a9879bdf",
+ "metadata": {
+ "collapsed": false,
+ "editable": true
+ },
"outputs": [],
"source": [
"# Common imports\n",
@@ -899,8 +849,10 @@
},
{
"cell_type": "markdown",
- "id": "368a97c9",
- "metadata": {},
+ "id": "68514092",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Other ways of visualizing the trees\n",
"\n",
@@ -909,47 +861,13 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "5cc14381",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
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- " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
- " Text(0.5769230769230769, 0.75, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
- " Text(0.3076923076923077, 0.5833333333333334, 'X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
- " Text(0.15384615384615385, 0.4166666666666667, 'X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
- " Text(0.07692307692307693, 0.25, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
- " Text(0.23076923076923078, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
- " Text(0.46153846153846156, 0.4166666666666667, 'X[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
- " Text(0.38461538461538464, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
- " Text(0.5384615384615384, 0.25, 'X[2] <= 5.45\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
- " Text(0.46153846153846156, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
- " Text(0.6153846153846154, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
- " Text(0.8461538461538461, 0.5833333333333334, 'X[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
- " Text(0.7692307692307693, 0.4166666666666667, 'X[0] <= 5.95\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
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- " Text(0.8461538461538461, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
- " Text(0.9230769230769231, 0.4166666666666667, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
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- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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Uq1dP7XCEEC8hyYAQRqJv374cP36c6OhorKys1A7nhR48eICjoyNt2rRhx44daocjhHgJaSYQwgjs2bOHXbt2sXjxYoNPBACsrKxYtGgRO3fuZO/evcTHx5OZmal2WEKIPMiTASEM3MOHD2natCnNmjXju+++M5oZ/hRFoUePHpw/f560tDSCg4Nxc3NTOywhRC6kq68QBm769OncuXOHr776ymgSAYArV65Qvnx5bt68iYWFBVevXlU7JCFEHiQZEMKAnTlzhoULFxIUFETDhg3VDqdAKleuzLVr13j8+DGPHj3i/PnzaockhMiDNBMIYaB0Oh3t2rXjwYMHnD59GnNzc7VDKrDHjx+zYMECAgICaNKkCdHR0WqHJITIhSQDQhiYv/76i6VLl1K9enU8PT35+eef6dChg9phFcnVq1cxMzOjfv36aocihMiFJANCGJh//etfDBgwgCpVqtC7d2/Wrl1rkBMMCSHKDhlaKISBuXnzJhqNhsePH7N9+3aDWZVQCFF2SQdCIQzM8ePHURSFjIwMJkyYgLOzc6mdOy4ujsTExFI7n7GxtrbGzs5O7TCEKHaSDAhhYGxsbKhTpw779u3jjTfeKLXzxsXF4ejoSGpqaqmd09hotVqio6MlIRBljvQZEEIAcOrUKZycnNiwYQOOjo5qh2NwoqOjcXV1JTIyklatWqkdjhDFSp4MCCFycHR0lC87IUyMJAPCJEhbeE7S9i2EeJokA6LMk7bw5xVn2/eRI0fYv38/M2bMYMyYMUyYMIEvv/ySli1b4u3tTXBwMBcvXuTOnTusW7eOW7du4eHhQXh4OA4ODrkeM3tRo/LlX3yLunr1KrNmzSIlJYUtW7bk2LZ69WpOnjxJfHw8LVu2ZPbs2TRu3JguXbrQqlUrRo8eXeRrF6KskGRAlHmJiYmkpqZKW/j/l932nZiYWCzJQLt27fj222+ZOnUq9vb22NvbAzBmzBgA/Pz8AFi4cCEJCQk0a9aMjh075nqss2fPsmHDBu7cuUNISAhVq1Z94bkbNmzI6tWrcXFxeW7biBEjGDFiBN7e3voFkipXrkxaWppMfiTEMyQZECZD2sJLzpAhQ2jTpg0JCQnPbXv06BHjx48nPj6eESNG5Pr+y5cvM3LkSHr16oW3tze1atUCnjx1iIiI0O9nZmbGV199le+4MjIy+OOPP2jSpAnwpJOkTqfD2dmZDz74oCCXKESZJpMOCVEEkyZNyvX18PBwYmNjC3Qsf39/xo8f/9wxU1JScHNzY9SoUWzcuLGwoZYYnU5HYGAg27ZtY/r06c9tNzc3Jzw8HDc3N/bu3ZvrMezs7Bg3bhyXL19m4cKFREVFFUtsO3bsoG/fvvqfNRoNZmZmWFpaIgOphPgfeTIgRD5FR0czffp0mjRpQmRkJPv27dN/4bds2ZJPPvmEkydPsmrVKm7evEl6enq+jx0XF4dOp2PJkiX4+voSHx9PvXr1gCdfaIMGDaJ79+4MGjSIwYMHl8TlFVpISAhDhw6la9euHD16lMOHD+fY7uvry8OHD0lOTiYkJCTXY5ibm9O/f3/69+/P7du32bhxI/Xq1aNdu3a0a9cuz3PfvXuXL774gt9++43g4GD8/PwYMmSI/mnCtm3b9AlUTEwMs2fPBuCdd94xquWghShpkgwIkU+rVq1i/vz51K5dm169euXYVr9+fSZMmEBoaCinT59+7r137txhypQpOV7z9fWlUaNGANy4cUP/5W9nZ8f169f1P1+/fl0/+ZAhrlHg4+Oj/3dgYCDwpKyyzZs3r0DHq1mzJhMnTszXvjVq1CA8PDzHa083K+zYsUP/bwcHB77++usCxSKEqZBmAiHy6enHys8+Yq5YsSIAFSpUICMjo8DHtrW15fr16wDEx8dja2ub6zadTlfgY6vBwcHhuS/pbDExMcTGxqLVaks5KiFEXuTJgBD5NGrUKHx8fLC3t6dSpUoFeq+NjU2eX46Avlf/xIkTMTc3x87OjpCQEDp16kS/fv3w8vJi9+7d9OzZs0jXUFry6ksxadIk5syZw7p163K8Hh4eTrdu3WjQoEG+z+Hv709KSgparZY5c+boXz906BBTp06ladOmuLi45DlyQQjxP5IMCJFPtWrVwt7enrt37zJ06FAA/dj27L89PDwACvUF9PQXGoC3t7f+389+eRoDtfpYaDQaqlSpQnp6uv41IcSLSTIgRD5Vr16doKAgtcMwGmr1sejQoQPvvvsut27dws/PT/oJCJEP0mdAiBKybt069u/fX6zHbNy4MR4eHqxYsQKAc+fO4erqyuDBgzl37lyxnquo1OpjkT1KoFq1aoU6thCmSJ4MCPGUTZs2cejQIbRaLXPnzmXr1q2cPn2alJQUQkNDmTVrFg8ePCApKYlmzZpx//59rl27xoYNG3B2dqZ9+/bExsYyduxY/TGvXbtGcHAwGo2GRo0a0a1bN6ZPn46trS3Dhw+nefPm+Y7v2Rn0QkNDCQsLQ6fT4efnx/Lly4u9TApLrT4WV65cYf/+/Tx48CDH/4MQIm+SDAjxlBs3btCiRQv69OmDhYUFGo0Gc3NzYmJi9I+zXVxccHBwwM3NjZ07dzJ69GiSkpLIysrC29ub5ORkJk+eTOvWrQFYtmwZFStWRKvVcu7cOd5++22qVavGkCFDciQCL3s0Ds/PoJecnEzlypUBSE5OLuHSKRi1+li88cYbDBgwoLBhC2GSJBkQ4im+vr6cOXMGPz8/goKC2LFjBzt27CAwMJCUlBQArKyssLCwwMrKCngyYU5GRgY6nY6srCwePXqUY0IbnU6Hm5sbzZo1079ma2vLmjVrOHPmDMOGDct3fM/OoFepUiWSk5P1/zYk0sdCCOMhyYAQT1mxYgWXLl0CnkxoY2NjQ3BwMCdPnnzpb68WFhYEBQVx5coVpkyZwsmTJwHw8vIiICCAV155hcqVK9OhQwf27t3L3bt36dKli/79L3s0ntsMel5eXnh5eembCYzRunXrqF27Nt26dSu2Y7q7u3PixAn9tMbnzp1jzpw5KIqCv78/zZs3z3NoohCmSJIBIZ7y7LK22W3w2V+0TycE2cP9li5dCoClpSWzZs3Sb3/6ScCzawp06tSpwLHlNoNey5YtVektb+h9K5YvX55jJcNn+1Z88cUXeQ5NFMIUSTIgRDHJbgc3BYbet+JZz/ateNHQRCFMkSQDQogCM/S+Fc96tm+Fra0tu3fvBp4MTezdu3ehjy1EWSDJgBC5cHFxKfbf9Nu3b4+fnx/Ozs40btyYLl260KpVK0aPHp1rm/azCrNP9rwEbdq00ffcLw6G3LcC0K9k6OHhwdKlS5/rW5Hb0EQhTJoiRBkXGRmpAEpkZKSiKIoyevRo5e7du0pmZqYyePBg5caNG8qkSZMUDw8PZffu3YqiKMqgQYNy/B0WFqYcPHhQOXXqlDJmzBjFw8NDWb9+fYHiyD6WoijK66+/rgwdOlTZv3+/Pqa//vpLuX//vjJ69Ohc31/YfQ4ePKiEhYXlWR4ve724PV0OxqS0ykcINciTAWFyBg4cyLZt23j11Vfp3Lkz5cuXJzMzk5o1axIREYGzs3Oe7w0JCaFhw4ZoNBpOnz7NkCFD9Nv8/f1JSkrS/9y7d2+6d++e63EKM19Ace2jNlPqWyGEsZBkQJicTp06sWbNGiIjI5k3bx6rVq2ib9++ODk5Pdd2XK7ckxm7s9vBMzMzmThxIlWrVi1SDIWZL6C49hFCiGdJMiBMTrly5ahXrx4JCQlYWVnRtm1bli9fTpMmTTA3N8+xb+3atZk/fz6HDx/GyckJPz8/vLy8qFWrFg0aNGDcuHH6fbPnAHiZ/M4XMGfOnBxLARd2n5JU0n0r8jMXQGH6XwQGBhITE0PVqlWZOnUqderUee5c+/fvJzAwkGPHjhXr9QlhiCQZECbp6S+Wt99+m7fffjvH9uwvuJCQEAB8fHz02zZs2FCkc+dnvoCHDx+SmZlZ5H2Kwt3dndmzZ2NlZcWwYcMIDg4mNDSU+/fv07179xzNKdlJQXh4OA4ODlhZWbFq1Sp0Oh1t27bN0ZzyMnXr1sXZ2fmFyxQ/rTDrNVSoUAFLS0vMzc2pVq1arufq1q2bUS4dLURhSDIgRCmpUqUKe/bseWGfhGyVKlVi8uTJRd4nPT2dXbt20blz5wLFCur3rcjvXACF6X8REBCARqNhz549rFu3jtdff13mHRAmTZIBYTKio6NVPX/20L5Tp06V6nmzFwnKPm9+y0HtvhX5nQugMP0vsuc3qFWrFtHR0TLvgDB5kgyIMs/a2hqtVourq6vaoRgMrVaLtbX1C/dRu29FbnMBHDhwgBo1atCqVSug8P0vZs6cSVxcHPfu3SM0NJRXXnnluXMJYUo0iqIoagchREmLi4sjMTFR7TAMhrW19XNfeKdOncLJyYnIyEj9l60aXtQp8csvv8TLy4sqVark+3gPHz5k0aJFL21SeVkshlI+QpQEeTIgTIKdnZ38tvcShpIsvahvRUBAQIGPl5++FbnZv38/Wq22wO8TwhjJkwEhTNzFixdZsGABa9eu5fHjx2zYsAFHR0e1wzI40dHRuLq60rp1a2bMmEHXrl1zrK0ghDGTZEAIE3X8+HGCg4PZuXMnNWvWxM3NjdDQUFJTU9UOzWBZWlry97//nbNnz9KyZUv8/PwYOHAgFSpUUDs0IYpEkgEhTIhOp+P//u//CA4O5pdffsHe3h4fHx+GDBmCpaWl9K14CWtra+rVq8ehQ4cIDg5m//792NnZ4e3tzYgRI2TWR2G0JBkQwgQ8evSIzZs3M2/ePH7//Xf+8Y9/8Pnnn+Ps7IyZmZna4Rmt//73v8ybN4/NmzdTpUoVxo4dy7hx46hZs6baoQlRIJIMCFGGJScns3LlShYuXMj169fp1asXfn5+tG/fXtq7i9G1a9dYtGgRK1euJCsri+HDh+Pt7U3jxo3VDk2IfJFkQIgy6ObNmyxZsoRly5aRmprK4MGD8fHxoVmzZmqHVqbdu3ePsLAwFi9ezN27d+nfvz++vr60bt1a7dCEeCFJBoQoQy5cuMCCBQv4+uuvsbCwwN3dnU8//ZS6deuqHZpJSUtLY/369cyfP5/Lly/TqVMn/Pz8+OCDD+SJjDBIkgwIUQYcO3aM4OBgdu3aRa1atfj000/x8PAo8lLLomiysrLYuXMnwcHBnDx5khYtWuDn58egQYNkBIIwKOXUDkAIUTg6nY59+/bRoUMH3n77bX7//XdWrFjBH3/8waRJkyQRMABmZmYMGDCA48ePc/DgQerWrcuQIUNo1KgRixYt4uHDh2qHKAQgyYAQRufRo0esW7eOFi1a8OGHH/L48WN27txJdHQ0I0eOxNLSUu0QxTM0Gg0dO3bku+++48yZM3Ts2BFfX1/s7OyYPHkyt2/fVjtEYeKkmUAII/HXX3/pRwbcuHGDDz/8ED8/P9q1ayft0EYoLi6ORYsWsWLFCjIzMxk+fDifffaZjEAQqpBkQAgD9+eff7JkyRLCwsL0IwN8fX1p2rSp2qGJYpCUlMSyZctYsmQJd+7coX///vj5+ckIBFGqJBkQwkBduHCB+fPns379ehkZYAKeHYHQsWNH/Pz86Natmzz5ESVOkgEhDMyvv/5KcHAwu3fvplatWkyYMAF3d3fpEGgisrKy2LVrF3PnztWPQPD19cXFxUVGIIgSIx0IhTAAOp2OvXv38s4779C2bVuio6NZuXIlsbGxfP7555IImBAzMzP69+/P8ePHOXToEPXq1WPo0KE0atSIhQsXkpycrHaIogySZEAIFT09MsDZ2Vn/W+H58+cZMWIEFhYWaocoVKLRaHj33Xf59ttv+e9//6ufuCh7BMKtW7fUDlGUIdJMIIQK/vrrL1asWMGiRYtyjAxo37692qEJA/bsCAQ3Nzc+++wz/v73v6sdmjBykgwIUYr+/PNPFi9eTFhYGGlpabi6uuLj4yMjA0SBJCUl6ddAuHPnDv369cPPz4+33npL7dCEkZJkQIhScOHCBebNm0dERAQWFhZ4eHjw6aefYmtrq3Zowoilp6frRyBcunSJd999l88//1xGIIgCkz4DQpSgX3/9lb59++Lo6Mh3333HjBkziI+PJzg4WBIBUWSWlpaMHj2a6Oho/vWvf5GWlkaPHj1o2bIlERERPH78WO0QhZGQZECIYvbsyICYmBhWrVrFH3/8gZ+fH1ZWVmqHKMoYMzMz+vXrx7Fjx/j555+pX7++jEAQBSLJgBDFJCMjg7Vr19K8eXOcnZ3R6XTs3r2b33//nU8++URGBogSp9Fo6NChA/v27ePs2bM5RiB88cUXMgJB5En6DAhRRA8ePNCPDEhISMDZ2Vm/ZoAQaouPj9ePQHj8+DHDhg3js88+w97eXu3QhAGRZECIQkpISGDx4sWEh4eTlpbGkCFD8PHxwdHRUe3QhHhOUlIS4eHhLF68mNu3b9O3b1/8/Pz4xz/+oXZowgBIM4EQ+XDq1Cn++usvAGJiYhg5ciSvvvoq4eHheHh4EBsby+rVqyUREAarWrVq+Pv7Exsby/Llyzl79ixt2rTRL62sKAqKovCf//yHrKwstcMVpUySASFe4tChQ7z11lvMmzePPn366EcGzJw5k7i4OObOnUudOnXUDlOIfLG0tGTUqFFER0ezY8cO0tPT6dmzJy1btiQsLIyOHTsSEBCgdpiilEkzgRAvEBsby2uvvYZGo+HBgwc4ODjg5+fHxx9/LB0CRZmgKAqHDx8mODiYffv2YWVlxYMHD1i7di1ubm5qhydKiSQDgri4OBITE9UOwyBYW1tjZ2en/9nOzo74+HgqVKiAjY0N06dPZ+TIkSpGKETJiImJoU+fPly/fp2UlBQAEhMTqVGjhn4fuVf8z7P3CmNXXu0AhLri4uJwdHQkNTVV7VAMglarJTo6Wv8h9/f358yZM9SsWZO0tDSaN2+ucoRClAxra2sGDBgAPBkmm5qammO1TLlX5PTsvcLYyZMBE3fq1CmcnJzYsGGDyXd+i46OxtXVlcjISFq1aqV2OEIYFLlX/E9ZvFfIkwEBgKOjY5mp1EKIkiP3irJJRhMIIYQQJk6SAZFvR44cYcqUKQCMGTOGixcv4ubmRkhICACffPIJw4cPZ/jw4eh0OmJiYujYsSMxMTF5HjMzM5PMzMx8nT8lJQUnJyf279+f4/XNmzczePBgRowYQXR0NACNGzfGw8ODFStWFOZShRBFYKj3iu+++46PPvqIgQMH8u9//xuQe0U2SQZEvrVr146srCymTp2Kvb29fjrTMWPGALBmzRrWrl1LlSpVuHnzJg4ODnTs2DHXY509e5bPP/+c0aNH8/Dhw3ydf+7cuQwcOPC513fv3s26deuYN28eCxYsAKBy5cqkpaVRv379QlypEKIoDPVecfToUebMmcPixYv58ccfAblXZJM+A6JAhgwZQps2bUhISMh1+8WLF8nIyMhzEp7Lly8zcuRIevXqhbe3N7Vq1QKe/CYRERGh38/MzIyvvvpK//MPP/xA06ZNSU9Pf+6YPj4+jB8/ntq1a5OUlAQ86eyk0+lwdnbmgw8+KPT1CiEKxxDvFX379sXV1RWdTqd/EiD3iifkyYDIN51OR2BgINu2bWP69OnPbT937hzBwcEsWbIkz2PY2dkxbtw4Ll++zMKFC4mKisrXuQ8ePMixY8fYtGkTy5cvR6fT6be9+eabhIWF8fHHH+uH+Wg0GszMzLC0tEQGzAhRugz1XhEcHMzPP//Mf/7zH+bOnQvIvSKbPBkQ+RYSEsLQoUPp2rUrR48e5fDhw/ptOp2O999/n+7duzN+/HgmT55M3bp1nzuGubk5/fv3p3///ty+fZuNGzdSr1492rVr98JV/mbNmgXAunXrqF27NuXKlWPIkCFERESwb98+du/eTWpqKgsWLCAmJobZs2cD8M4776DRaIq5JIQQL2Ko9wpnZ2dGjRqFoih069ZN7hVPU4RJi4yMVAAlMjKyUO8fNmyYkpaWluf2adOmKdHR0YUNr1QVtSyEKMvkXvE/ZfFeIc0EokgcHBwIDw/PdVtMTAyxsbFotdpSjkoIYWjkXmHYpJlAFMmkSZP0f8+ZMyfHNgcHB9q0aZOjzS4//P39SUlJQavV5jjm1atXmTVrFikpKWzZsgWAw4cPs2XLFjQaDQEBATx+/Jhx48ZRo0YNmjRpwueff17EKxRCFIfSvFekpKQwduxYKlSoQMeOHRk8eDAAq1evZtOmTfz4449ER0ezePFiEhMTef/993F3dy/iFRo3eTIgCiw6OhoXFxemTZtGr169gCer+wG0bNmSRYsWMXjwYNLS0rh582auvXrzEhcXh06nY8mSJWRlZREfH6/f1rBhQ1avXp1j/9DQUMzNzbGwsKBatWpcvHgRZ2dn1qxZw7lz54p+sUKIQlPrXrFjxw4GDRrEypUr2bNnDwB//PEHd+/excbGBngyk2J4eDhbt24lMjKymK7YeEkyIAps1apVzJ8/nylTpjw3CUj9+vWZMGECbdq04fTp08+9986dO3h4eOT4c+XKFf32GzduUK9ePeBJb+Lr16+/MJbIyEjmzp3Lu+++y6ZNm3jjjTfYsGEDnTt3plOnTkW/WCFEoal1r7h+/bp+m5mZGTqdjgULFjBhwoQc59izZw/vv/8+7733XjFdsfGSZEAUmPLU8BvlmaE4FStWBKBChQpkZGQU+Ni2trb6D3V8fDy2trYv3N/R0ZEKFSpQo0YNkpOTWbt2LTNnzuSnn37iu+++K/D5hRDFR617xdPbdDodV69e5c6dO/j5+REVFaW/Nzg7O/Pjjz+yefPmAp+/rJE+A6LARo0ahY+PD/b29lSqVKlA77WxscmzExGgnydg4sSJmJubY2dnR0hICJ06dcLOzo4vvviC3377jeDgYPz8/Bg8eDCenp48fPiQkJAQbt26xfTp04mIiKBBgwZFuUwhRBGpda/o168fXl5e7N69m549e9K4cWO++eYbAG7evEmPHj04dOgQW7du5dGjR3Tt2rXwF1lGyBLGJi57WdKCLMV57949Fi1axN27d+natSu9e/cu4ShLR2HKQghTIfeK/ymL9wp5MiAKrHr16gQFBakdhhDCwMm9wnhInwFR6tatW/fcamJFldvKY2fPnqVmzZoF6qEshDAsJXG/APDw8NAPd/zzzz8ZP348Xl5eOWZLNCXyZEDky6ZNmzh06BBarZa5c+eydetWTp8+TUpKCqGhocyaNYsHDx6QlJREs2bNuH//PteuXWPDhg04OzvTvn17YmNjGTt2rP6Y165dIzg4GI1GQ6NGjejWrRvTp0/H1taW4cOH07x583zH9+zKY48fP2bVqlV079692MtCCPFihn6/2L59O2+++SaXL18GnkyfrNVqefDgwUs7LZdVkgyIfLlx4wYtWrSgT58+WFhYoNFoMDc3JyYmRj8syMXFBQcHB9zc3Ni5cyejR48mKSmJrKwsvL29SU5OZvLkybRu3RqAZcuWUbFiRbRaLefOnePtt9+mWrVqDBkyJMcH+86dO/q10bP5+vrSqFEj/c/Prjw2f/58xo0bx8yZM0u+cIQQORjy/eLWrVtERUUxatQofTJw9uxZFi1ahI2NDX5+fs/NZ2IKJBkQ+eLr68uZM2fw8/MjKCiIHTt2sGPHDgIDA0lJSQHAysoKCwsLrKysgCcLjWRkZKDT6cjKyuLRo0c5FgLR6XS4ubnRrFkz/Wu2trasWbOGM2fOMGzYsHzH9+zKY6dPn+bWrVucOHGCsLAwJk6cWEwlIYR4GUO+X/z888/cvn2boKAgzp49y8WLF7G1taV69epUqVKlUMMcywJJBkS+rFixgkuXLgFQo0YNbGxsCA4O5uTJk3Ts2PGF77WwsCAoKIgrV64wZcoUTp48CYCXlxcBAQG88sorVK5cmQ4dOrB3717u3r1Lly5d9O9/2RCj3FYeyx5G5ObmhqenZ1EuXQhRQIZ8vxg4cCADBw4kNjaW8PBw7O3t8fb2xtfXl3Llypns/UKGFpq40hgi4+Liol9LwJCVxeFCQhSX0vp8GMP9oizeK2Q0gShxhv7BFkIYDrlfqEOSASGEEMLESTIgCszFxaXYj9m+fXv96mL+/v6MHz9ePwY4L0+PE968eTODBw9mxIgRREdH57p/YGAgLi4ueHh4kJCQQHp6Om5ubi9sXxRCFJ4h3Cvc3d154403Xnrcp+8npnivkGRA5ODu7s69e/fIysrC1dWVhIQE/P398fT01H8As2V/0MPDwzl06BBRUVGMHTsWT09PIiIiCnTeunXr4uzs/MJlSZ+WPU442+7du1m3bh3z5s1jwYIFub6nQoUKWFpaYm5uTrVq1bC0tMTNza1AcQohnjCWe8Xy5ctp0qTJC4/57P3EFO8VMppA5DBw4EC2bdvGq6++SufOnSlfvjyZmZnUrFmTiIgInJ2d83xvSEgIDRs2RKPRcPr0aYYMGaLf5u/vT1JSkv7n3r175zohUG7Lkmb/nC23ccI+Pj6MHz+e2rVr5zjP0wICAtBoNOzZs4d169aZbK9hIYqDMdwr8iO3+4kp3iskGRA5dOrUiTVr1hAZGcm8efNYtWoVffv2xcnJ6blFRsqVe/JgKXvccGZmJhMnTqRq1aqFPr+trS27d+8GnixLmtvCJrmNE37zzTd58803uXTpEvfv38/12NljlmvVqpVnU4IQIn+M4V6RH7ndT+zt7QHTuldIMiByKFeuHPXq1SMhIQErKyvatm2rf8xmbm6eY9/atWszf/58Dh8+jJOTE35+fnh5eVGrVi0aNGjAuHHj9PtmzwPwMrktS3rgwAFq1KihH8KT2zjhffv2sXv3blJTU/XNBHPmzMnRljhz5kzi4uK4d+8eoaGhRSonIUydMdwrAP2y5x4eHixdupSDBw++9H5ikvcKRZi0yMhIBVAiIyNVjWPQoEF5bps1a5by4MGDAh0vOTlZmTFjxkv3O3jwoBIWFqYoiuGUhRCGyFA+H0W9VxTmfqIoZf9eIR0IhUGoUqXKc52OsgUEBFClSpUCHa9SpUpMnjz5hfukp6eza9cu6tSpU6BjCyHUU9R7RWHuJ6Zwr5BmAgGgeruYh4cH8GRmr9I0dOhQ/XnVLgMhjIHanxO5V5QMSQZMnLW1NVqtFldXV7VDMQharRZra2u1wxDC4Mi9Iqeydq+QtQkEcXFxJCYmvnCfP//8k88++4zY2FgCAwPp2rVrKUVXeNHR0fj4+JCRkUFwcHC+5hC3trbWd0wSQuSUn3tFQf373//G39+fhQsX0qFDh2I7blpaGgMGDODVV18lNDQ0xwqIxaGs3SskGRAv9csvvzBgwAC0Wi27du3i9ddfVzukfLt9+zYfffQRR48eJTQ0VP+IUQihvvv37+Po6Mjbb7/Njh07iv34+/bt48MPP2TLli0MGjSo2I9flkgHQpEnRVH46quveO+992jWrBknT540qkQAoGbNmvzwww+4u7vj6emJu7u7ya5XLoShmTx5Mg8fPmTJkiUlcvxevXrRr18/JkyYwIMHD0rkHGWFJAMiVxkZGYwaNQovLy/Gjh3Lv//9b2xsbNQOq1AqVKjA0qVLWbVqFevWraNz587cvHlT7bCEMGknTpxg2bJlBAUFUbdu3RI7z+LFi3n48CFffPFFiZ2jLJBmAvGchIQE+vfvz6lTp1i+fHmZmpP72LFj9OvXD41Gw86dO3nrrbfUDkkIk5OZmclbb72FoiicPHmS8uVLti/7okWL8Pb25vjx47Ru3bpEz2WsJBkQOZjCl2V2shMVFcXy5csZNmyY2iEJYVKyv5yPHTtWKveYzMxMWrdujUaj4cSJEyWefBgjaSYQemvWrOHdd9+lQYMG/Pbbb2UyEQCoU6cOhw4dYvDgwbi5ufHpp5/y+PFjtcMSwiRcv36dKVOm4OnpWWr3mPLly7N8+XJOnz7N0qVLS+WcxkaeDAgeP36Mt7c3S5cuZdSoUYSGhmJhYaF2WCVOURSWLVvGhAkTeOedd9i6dWuZGjcshCHq378/R48eJSYmBisrq1I999ixY1m/fj3R0dEl2k/BGEkyYOLu3LnDRx99xJEjR1iyZAkeHh7FPh7X0P38888MGDCASpUqsWvXLl577TW1QxKiTMoe6rd582ZcXFxK/fwPHjzAwcGBdu3asX379lI/vyGTZMCERUVF0adPH9LS0ti+fXuxTvhhbK5du0bfvn2JiYlh7dq1MiZZiGKWkpJCs2bNaNKkCfv371ftl45vvvkGFxcX9u3bR8+ePVWJwRBJnwETtXnzZtq1a4e1tTWRkZEmnQgA1K9fn8OHD9OnTx9cXFzw9/cnKytL7bCEKDOCgoK4efMmy5YtU/Xp48CBA+natStjx44lJSVFtTgMjSQDJiYrK4vPP/+cjz/+mP79+3P48GHq1aundlgGQavVsnHjRubNm0dwcDAffvgh9+/fVzssIYze2bNnCQkJYfLkyTRq1EjVWDQaDcuWLePmzZvMmDFD1VgMiTQTmJCkpCT++c9/cuDAAebNm8fEiRNNrn9Afn3//fe4uLhgY2PD7t27cXR0VDskIYySTqfjnXfe4d69e5w+fdpgOifPnDmT6dOnExUVRfPmzdUOR3WSDJiI33//nT59+nD37l22bt1Kly5d1A7J4F2+fJk+ffoQFxfHhg0bcHZ2VjskIYzOqlWrGDVqFAcPHqRjx45qh6OXkZHB66+/To0aNfjll18oV860H5Sb9tWbiF27dtGmTRssLS357bffJBHIp8aNG/Prr7/SpUsXevfuzYwZM9DpdGqHJYTRuH37Nn5+fgwbNsygEgEACwsLwsLCOHLkCGvWrFE7HNVJMlCG6XQ6AgMD6du3Lx988AG//vorDRs2VDsso1K5cmW2b9/O9OnTmTp1Kh999BHJyclqhyWEUfD19UWj0TBv3jy1Q8lVx44dGTZsGH5+fty5c0ftcFQlzQRl1F9//cXQoUPZs2cPM2bMICAgQPoHFNHu3btxdXWlfv367Nq1i8aNG6sdkhAG6+DBg3Tu3JlVq1YxYsQItcPJ0507d3BwcKBXr158/fXXaoejGkkGyqBLly7Ru3dvrl+/zsaNG/nwww/VDqnMOH/+PH369OHOnTt88803dO3aVe2QhDA4GRkZvPbaa9jY2PDzzz8bfHv86tWrGTlyJD/99BOdOnVSOxxVGPb/kCiw/fv307p1a7Kysjhx4oQkAsWsadOmnDhxgjZt2tC9e3fmz5+P5NNC5BQcHMyVK1cIDw83+EQAYPjw4bRv3x5PT08yMjLUDkcVhv+/JPJFURTmzp1Ljx49aN++PSdOnMDBwUHtsMqkqlWrsm/fPvz8/PD19cXV1ZXU1FS1wxJCde7u7ri7uzNr1ix8fHxo1qyZ2iHlS7ly5QgPD+fKlSsEBwerHY4qJBkwYllZWQwbNoxjx47x8ccfM2nSJAICAti9e3epLwBiaszMzJg9ezbffPMNu3bton379sTExNC/f39u3LihdnhCqOK3337j22+/pXr16tSuXVvtcAqkWbNm+Pj4MGvWLC5fvqx2OKVO+gwYse3bt/PRRx9hb2/P9evX+frrrxkwYIDaYZmcM2fO0Lt3b1JSUsjIyGDkyJGEhISoHZYQpa569eokJSVRrlw5WrduzdGjR42imSBbamoqzZo14+9//zvff/+9SXW6Np7/JZGDoihMmTKFcuXKkZiYyMCBA+nTp4/aYZmk1157DQ8PDypVqsTDhw9ZtmwZSUlJaoclRKm7f/8+ZmZmfPnll0Y5kY9Wq+Wrr77iwIEDfPPNN2qHU6qM639K6P3000/ExMSg0+lITU3l5s2bPHr0SO2wTNbNmzd58OABiqKQkZFBQECA2iEJUepmzJjBqVOn+PzzzzE3N1c7nELp0aMH/fv3Z8KECVy7do0jR46oHVKpkGYCI5WamsqMGTPo06cPrVq1okKFCmqHZPIUReHSpUts3LiRf/7zn9KBUwgjdePGDRwdHWndujWHDx8mNTUVMzMztcMqUZIMCCGEEE+ZOHEiP/30E//9738BuHXrFjVr1lQ5qpJVXu0ASkJcXByJiYlqh2EQrK2tsbOzUzuMMsmU65nUq9IhdUydOtarVy+2bt2q//nGjRuSDBibuLg4HB0dZdz3/6fVaomOjpYbdzEz9Xom9arkSR1Tr4699957xMTE4O7uzubNm02iQ3CZSwYSExNJTU1lw4YNJr8GfXR0NK6uriQmJspNu5iZcj2TelU6pI6pW8cqV67Mpk2bWL9+PeXLl7mvyueU2St0dHSkVatWaochyjipZ6KkSR1TlykkAiBDC4UQQgiTZxopTx6OHDnC/v37mTFjBmPGjGHChAl8+eWXtGzZEm9vb4KDg7l48SJ37txh3bp13Lp1Cw8PD8LDw/McNpaZmQm8PJu8evUqs2bNIiUlhS1btuTY9ueffzJ79mx0Oh0uLi60b9+exo0b06VLF1q1asXo0aOLpwBEiTPUOhYYGEhMTAxVq1Zl6tSp1KlTR+qYEXpZ/dq0aROHDh0iPT2dsLAw4uPji61+HTp0iKlTp9K0aVNcXFzo2LGjftvmzZvZt28flpaW+Pj44OjoWKL1y5Q7WuamMJ0vTToZaNeuHd9++y1Tp07F3t4ee3t7AMaMGQOAn58fAAsXLiQhIYFmzZrlqPBPO3v2LBs2bODOnTuEhIRQtWrVF567YcOGrF69GhcXl+e2hYSEoNVqefDgAba2tsCT9qu0tDTq169fyKsVajDUOlahQgUsLS0xNzenWrVqgNQxY/Sy+rV37142b97Mt99+y44dOxgyZEix1S+NRkOVKlVIT0+nXr16Obbt3r2biIgIkpOT8fPzY9WqVSVWv0y9o2VuCtP50qSTAYAhQ4bQpk0bEhISntv26NEjxo8fT3x8PCNGjMj1/ZcvX2bkyJH06tULb29vatWqBTzJ2CMiIvT7mZmZ8dVXX+UrprNnz7Jo0SJsbGzw8/Nj9erVnDp1Cp1Oh7OzMx988EEhrlSoxRDrWEBAABqNhj179rBu3To8PT2ljhmpF9Wv7Ln17ezs9GPmn1XY+tWhQwfeffddbt26hZ+fH19//bV+m4+PD+PHj6d27dr6nvglVb9MuaNlbgrb+dKkkwGdTkdgYCDbtm1j+vTpzy1daW5uTnh4ONu2bWPv3r0MHjz4uWPY2dkxbtw4Dhw4wMKFCxk0aBBvvPFGkeKytbWlevXqVKlSRb+2tkajwczMDEtLSxRFMakFNIyZodax7PpTq1YtoqOj9a9JHTMuL6tf2XPKxcXFUbdu3VyPUdj6lV0/qlWrpr9PZXvzzTd58803uXTpEvfv39fvX5L1qyQ6Wk6aNIk5c+Y893p4eDjdunWjQYMG+T6Wv78/KSkpaLXaHMdMSUlh7NixVKhQgY4dO+Z6DygNJp0MhISEMHToULp27crRo0c5fPhwju2+vr48fPiQ5OTkPFehMzc3p3///vTv35/bt2+zceNG6tWrR7t27WjXrl2e57579y5ffPEFv/32G8HBwfj5+TFkyBAiIiLw9vbG19eXcuXK4enpSUxMDLNnzwbgnXfekZu0ETHUOjZz5kzi4uK4d+8eoaGhUseM1MvqV8+ePRkzZgwpKSl5PjUqbP3avn07+/fv58GDB4wdOxZAX7/27dvH7t27SU1NZcGCBUZRv6Kjo5k+fTpNmjQhMjKSffv2ERsbC0DLli355JNPOHnyJKtWreLmzZukp6fn+9hxcXHodDqWLFmCr68v8fHx+qaVHTt2MGjQILp3786gQYNUSwZQypjIyEgFUCIjIwv1/mHDhilpaWl5bp82bZoSHR1d2PBKVVHLQuStKGVr7HVM6lXpKGw5G3v9UpSCXXtx1Udvb28lPj5eefz4sfLBBx8oiqIogwYNUhRFUXr16qUoiqIsWbJEOXr06HNlePv2bcXd3T3Hn8uXL+u3Hz16VAkNDc1xjGxffvmlcvbsWUVRFOWf//xnka5BUQpfHjK08BkODg6Eh4fnui0mJobY2Fi0Wm0pRyXKEqljoiRJ/Soc5allepRnluypWLEi8KTj7bNNIvlha2vL9evXAYiPj9d3DH92m06nK/Cxi4skA8+YNGkSEyZMYNKkSc9tc3BwoE2bNgX+D/P392f8+PHPHTMlJQU3NzdGjRrFxo0b9a+vXr2a9957D3gyPGzEiBG59ggXxmnSpEncvHkz122HDh0iMDCwQB1/8qpf8KSOOTk5sX//fv1rT9evvPYRxqs072FQdurYqFGj8PHxISgoiEqVKhXovTY2NoSHh+f406hRI/327M/zxIkT0Wg02NnZERISQlRUFP369WPLli14enrSs2fPYr2mgpBkgCdtRS4uLkybNo1evXoB5GgrWrRoEYMHDyYtLa1IbUVZWVnEx8frt2W3Fa1cuZI9e/YA8Mcff3D37l1sbGyA/w0PE8ZLrfoFMHfuXAYOHKj/+dn6lds+wvhIHSu6WrVqYW9vz927dxk6dCiAfn6O7L89PDzo2LEjgYGBBV6ifM6cOSxcuJC5c+cC4O3tzRtvvEGlSpVYt24dYWFhDBkypBivqGBMugNhtlWrVjF//nxq166t/yBlq1+/PhMmTCA0NJTTp08/9947d+4wZcqUHK/5+vrqs8IbN27oO4rY2dlx/fp1/c/Xr1/X99o1MzNDp9OxYMECfacgUTaoVb9++OEHmjZtqr/x51a/nt1HGCepY0VXvXp1goKC1A5DNZIMUPJtRbt37waetBX17t07x7br16/TvHlzdDodV69e5c6dO/j5+REVFcV3331Hjx49CnNJwoCoVb8OHjxISkoK58+fp2LFijRs2PC5+nXkyJEc+3Tt2pVy5eSBobGROlb61q1bR+3atenWrVuxHtfDw4OqVasyZ84cdu3axf79+4mPj2fq1Kn84x//KNZzPU2SAf7XVmRvb1/otqK8PN1WZG5urm8r6tSpE/369cPLy4vdu3fTs2dPGjduzDfffAPAzZs36dGjR67Dw4RxUat+zZo1C/jfTcve3v65+pWdbGbvUxZu0qZI6lj+ZE/PrNVqmTt3Llu3buX06dOkpKQQGhrKrFmzePDgAUlJSTRr1oz79+9z7do1NmzYgLOzM+3btyc2NlY/lBLg2rVrBAcHo9FoaNSoEd26dWP69OnY2toyfPhwmjdvnu/4tm/fzptvvsnly5cB6NOnD3369CEqKoqjR49KMlDS8ttWBOQ5leeLPDtphbe3t/7f69aty/U92eetUaPGCz+owvCpWb8A3NzcnnvPs2sV5LaPMB5Sx/Lnxo0btGjRgj59+mBhYYFGo8Hc3JyYmBh9E4qLiwsODg64ubmxc+dORo8eTVJSEllZWXh7e5OcnMzkyZNp3bo1AMuWLaNixYpotVrOnTvH22+/TbVq1RgyZEiOROBlzTG3bt0iKiqKUaNG6ZMBgPnz57Nz505WrlxZomUjyQDSViRKltQvUdKkjuWPr68vZ86cwc/Pj6CgIHbs2MGOHTsIDAwkJSUFACsrKywsLLCysgKeTMqUkZGBTqcjKyuLR48e5Zg0SafT4ebmRrNmzfSv2drasmbNGs6cOcOwYcPyFdvPP//M7du3CQoK4uzZs1y8eBF7e3t8fHwYNmwYAQEBJZoQSDJQDEqj7ei7775j7dq1aDQaRo4cSdeuXYv1XMI4lERdW7NmDVFRUVSuXJkvv/yy2I4rjFNx1zGdToenpydpaWlUrFiRsLCwYjluYaxYsYJLly4BT5662tjYEBwczMmTJ1/6xMTCwoKgoCCuXLnClClTOHnyJABeXl4EBATwyiuvULlyZTp06MDevXu5e/cuXbp00b//Zc0xAwcOZODAgcTGxhIeHo69vT0rV64kKiqK5ORk3N3di14AL2CyyYCxtR0dPXqUOXPmoNVqWbRokSQDRsSQ69rt27fZtm0bDg4O1KxZs6SKQJQwQ65j5cqVY/ny5QCMHDmyRK4/v55dOjk7ruy+WE8nBNlNuEuXLgXA0tJS30cCyPEk4Ol5YgA6depU6BgbNGigb5YZNWpUoY9TUCabDBhb21Hfvn1xdXVFp9OxYsWKUighUVwMua5dvXoVKysrFi5ciI+PD1euXMkxWYowDoZcxwDOnz/PjBkzcsw9YGye7QNR1phsMmBsbUfBwcH8/PPPwJOOOJs2bSquohAlzJDrWp06dahRowbwpN354cOHxXXZohQZch0DaNq0KZs3b2bs2LHcuHEjx3S8wjCYbDJgbG1Hzs7OjBo1CkVRir1vgihZhlzX7OzsqFq1Kt7e3jx69IjXXnut6BcsSp0h17GEhARmzpyJTqfD3NycOnXqFP2Ci8jFxaXYf9Nv3749fn5+ODs757lc8dPysw+8eN6B1157DQ8PD9q0aaMfLVJoRV4iycCUxopq2StZGTpZXa7klFbZGmJdk3pVOqSOFW7VwtGjRyt3795VMjMzlcGDBys3btxQJk2apHh4eCi7d+9WFOV/15z9d1hYmHLw4EHl1KlTypgxYxQPDw9l/fr1BYo5+1jXrl1T/Pz8FEVRFB8fHyUuLu65ffOzj6IoyrZt25SVK1cqn3/+eY7XT506pSxdulRRFEU5ePCgEhYWlmd55JfMMFIIZb3tSBgOqWuipJW1OjZw4EC2bdvGjz/+SOfOnSlfvjyZmZnUrFmTiIiIF743JCQEa2tratWq9dzUzf7+/nh4eOj//N///V+ux8ht+ubC7JPdd+zppzDwZN4BLy+vInVSzI0kA0IIIcqMTp068csvv7B9+3b69+9PREQEffv2JSAggOTk5Bz7Zs+GmN2vIjMzk4kTJxIYGMiCBQsKdf4XLVdckH2e7jv2448/cvHiRQB8fHzYtWsXCxcuLFR8eTH5ZKAklgZu3769fhXCFy39+TQPDw/9PqtXr8bDw4OePXvi7++f6/7Z6xZkt9Wlp6fj5uYmsxUaMLXr2oULFxgxYgQff/wx8+fPf+Fxn66P+/fvp02bNsUbuCgRatcxAHd3d/0CbLnJrR4WZx0rV64c9erVIz09HSsrK9q2bcuKFSsICQnB3Nw8x761a9dm/vz5/Oc//wGeDDH08vLis88+IzQ0NMe+s2fPzrFEcffu3XM9f27LFR84cIBTp04VaJ+BAweycuVKpk6dynvvvaefd2DMmDF4e3sXqANnvhSoUcEIPN1eYgxtR4qSd7vQxIkTlZiYmDzP82xbUXG1HYmXM8Z2yqf1798/z2251cen25WlXpUOY65j+e2H8HQ9LGwdM5T6+KJrnjVrlvLgwYMXvj8/++RG+gzkgzG0HeXVLpSRkcEff/xBkyZNCnDFQi3GUNeybd26lffeey/XbXnVR6E+Y6pj+fGiemiMqlSpon+C8qyAgACqVKnywvfnZ59npaens2vXrmIZoVGmhxZ26tSJNWvWEBkZybx581i1ahV9+/bFyckpxzKckHfbUdWqVQt9/hct/Zktr/mod+zYQd++fQt9blG6jKGuAWzevJn4+Pg8V7/Mqz4K9RlLHcuPl9XDwoiOji62YxVG9tC+px/1l4bshamyz1vYcijTyUB221FCQoK+7Wj58uU0adIkz7ajw4cP4+TkpG87qlWrFg0aNGDcuHH6fWfPnp2v8+e29OeBAweoUaMGrVq1AnKfUwBg27ZtOaa4nDNnTo52ul9//ZWQkBDu379P7dq16dOnT6HKSBQPY6hrUVFR+Pj48OGHH+Lt7U1ISEi+66NQnzHUMUC/5LqHhwdLly7l4MGDL62HRWFtbY1Wq8XV1bVIxylLtFot1tbWBXqPRlEUpYTiUcWpU6dwcnIiMjIyRwUtTS+a0OLLL7/Ey8urQI+DHj58yKJFi5g8efIL9zt06BAxMTE5MlS1y6KsMpSyLWpdy299fPo8hnLtZZ2hlLMx1LG4uDgSExNfup+psLa21idv+VWmnwyoJbvtyNnZ+bltAQEBBT5epUqVXpoIZLcdde7cucDHF8arqHUtP/vs378frVZbqPiE8TOGOmZnZ1fgLz+RU5lNBtRsPzL2tiORf2qXcWnUtZo1a+Ll5SX1SiVql7fUMdNQ5pIBaT/KqTBtR+LlTL2eSb0qeVLHpI6VpjLXZwCKv/3or7/+ol+/fjg5OTF37txiOy48GcrVv39/evXq9dKJiQqjMG1HIn+Ks5599913TJkyhdDQUNq2bVssx8y2ceNGQkJCWL9+fY4V6IpC6lXpKK46pigKnp6eJCQksHXrViwtLYshuicyMzMZMmQIGo2G9evXU7588fyOKXWslBV4hgMT5OHhoVSuXFm5ceNGiRx/4cKFikajUY4fP14ixxeG7d69e0rNmjWVjz76qESO//jxY+X1119X3njjDeXx48clcg5h2CIiIhRA2b9/f4kc/9ixY4pGo1EWLVpUIscXJa9MPhkoTseOHaNt27YsXrw4x3Cc4pSZmclbb72FoiicPHmy2DJrYRzc3d3ZvHkzMTExJba864kTJ2jTpg0LFy7k008/LZFzCMN07949HBwc6Ny5c4kuSjRmzBgiIiKIiYnJda59YdgkGXiBzMxMnJycqFChAsePH8fMzKzEzpV9s16wYAETJ04ssfMIw3L06FHatWvHkiVLSizZzObl5cXXX39NdHQ0devWLdFzCcPh7u7Oli1biImJ4ZVXXimx89y/fx9HR0fatWvH9u3bS+w8ooSo+lzCwM2fP18pV66c8ttvv5XK+caOHatUrFjxpfPKi7Lh0aNHSosWLZQ333xTyczMLPHz3b9/X6ldu7bSr1+/Ej+XMAxHjhxRAGXp0qWlcr7NmzcrgLJv375SOZ8oPvJkIA9xcXE0bdqUTz75hCVLlpTKOR88eICDgwNt2rRh586dpXJOoZ758+fz+eefc+LECZycnErlnN988w0uLi7s3buXXr16lco5hToeP36Mk5MTFhYWHDt2rESfbGZTFIUPPviAS5cu8fvvv8v8FMZE5WTEYPXu3VupU6dOoVaRKootW7YogLJnz55SPa8oXbGxsYpWq1XGjx9fqufV6XRK165dlfr16ysPHz4s1XOL0hUcHKyUK1eu1Ffzu3TpkmJhYfHcKqzCsEkykItdu3YpgLJt27ZSP7dOp1M++OADxc7OTm7WZZROp1M+/PBDVZJNRVGUy5cvy826jMtONidMmKDK+YOCgpTy5csrZ8+eVeX8ouCkmeAZDx8+pGnTpjRv3pxvv/0WjUZT6jFcuXKF5s2bM27cOIKDg0v9/KJk7dq1i759+7Jt2zYGDBigSgwzZ85k+vTpREVF0bx5c1ViECVDURScnZ2JiooiOjqaypUrl3oMGRkZvPbaa1hbW/PLL7/oV1EUBkzlZMTgfPbZZ4qlpaVy9epVVeOYOXOmYmZmppw5c0bVOETx+uuvv5S6desqPXr0UHQ6nWpxpKenKw4ODkq7du2UrKws1eIQxW/Hjh0KoPzrX/9SNY6ffvpJAZRVq1apGofIH3ky8JQzZ87g5OTEjBkz8Pf3VzWWjIwMXn/9dapVq8bhw4clsy4jPvvsM8LCwvj999959dVXVY3l559/pmPHjqxcuZKRI0eqGosoHsnJyTRt2pTXXnuNvXv3qvJk82nDhg1j3759xMTEYGNjo2os4iXUzkYMRVZWltKmTRuladOmSkZGhtrhKIqiKIcOHVIAZfny5WqHIopBVFSUYmZmpsyePVvtUPSGDRumVKtWTbl165baoYhiMHHiROVvf/ub8scff6gdiqIoinL79m2lWrVqytChQ9UORbyEJAP/X3h4uAIov/zyi9qh5ODm5qZUrVpVbtZGLjMzU3nrrbeUZs2aKY8ePVI7HL3bt28r1atXl5t1GXDq1CmlXLlyyty5c9UOJYeVK1cqgPLTTz+pHYp4AWkm4MliQQ4ODvTr14/Vq1erHU4OiYmJNGnShB49ehAREaF2OKKQwsLCGDNmDP/5z39o37692uHksHr1akaOHMlPP/1Ep06d1A5HFEJWVhZt27YlNTWVU6dOUaFCBbVD0tPpdHTo0IHExETOnDmDhYWF2iGJ3KidjRiCjz/+WKlRo4aSmJiodii5Wr16tQIoP/74o9qhiEL4888/FSsrK2XEiBFqh5KrrKwspX379kqTJk2U9PR0tcMRhfDVV18pgHL48GG1Q8nV2bNnlfLlyyszZsxQOxSRB5NPBg4cOKAAytq1a9UOJU/ZN2t7e3u5WRuhf/7zn4q1tbXBJpuKoijnzp1TypcvrwQFBakdiiigP//8U6lSpYoycuRItUN5oc8//1yxsLBQLl26pHYoIhcm3UyQnp5Oy5YtqVOnDgcPHlS95+2L/P7777z++utMmTKFqVOnqh2OyKcDBw7QtWtX1q1bx7Bhw9QO54X8/f1ZuHAh586do3HjxmqHI/Lpn//8Jz/++CMxMTFUr15d7XDylJKSQrNmzWjSpAn79+836PutSVI7G1HTtGnTlAoVKijnz59XO5R88ff3VywsLJSLFy+qHYrIh7S0NKVx48bKu+++q+qcAvmVkpKiNGjQQHn//feNIl6hKN9//70CKOvXr1c7lHzZt2+fAiibN29WOxTxDJN9MnDhwgVatmyJr68vM2fOVDucfElNTaV58+Y0atSIf//735JZG7hp06Yxe/Zs/vvf/+Lg4KB2OPny3Xff0bNnTzZv3kylSpXo0qULlpaWaoclcpGWlkaLFi2ws7Pjxx9/NJr7Qf/+/Tly5AgxMTFUrVpV7XBENrWzETXodDqlc+fOSsOGDZXU1FS1wymQb7/9VgGUjRs3qh2KeIGYmBjF3NxcmTx5stqhFNiAAQOUmjVrKhqNRuqZAZsyZYpibm6uxMTEqB1KgcTHxyuVKlVSxowZo3Yo4ikmOa3dxo0b+emnn1i2bBl/+9vf1A6nQHr06MGAAQOYOHEiSUlJaocjcqEoCp6entSrV4+AgAC1wymQn376iTNnzvDXX39Rvnx5/vzzT7VDErmIiYlhzpw5fP755zRp0kTtcAqkbt26zJw5k7CwME6cOKF2OOL/M7lmgnv37uHg4EDnzp3ZsmWL2uEUSkJCAg4ODgwePJiwsDC1wxHPiIiIYOjQoXz//fd07dpV7XAK5P79+wwfPpxdu3YBMHjwYDZs2KBuUCIHRVHo1KkTN27c4OzZs0bZjJOZmclbb72FoiicPHmS8uXLqx2SyTO5JwP+/v5kZGSwcOFCtUMptDp16jBr1iyWL1/OsWPH1A5HPOXevXt89tlnuLi4GF0iAFC1alV27tzJzp07sbS05O7du2qHJJ4RERHBzz//zLJly4wyEQAoX748y5cv58yZM4SGhqodjsCEngyMGjWKtm3b8sknn7B06VLGjh2rdkhFkpWVxT/+8Q8yMzP56KOPaNKkiWrL4QqIjo5m5syZaLVatm7dSkxMDK+88oraYRWJTqdDo9EYTce0sm7hwoVYWloydepU3n//fTZt2qR2SEXm5eXFunXrWLx4MdevX2fatGlqh2SyTCIZSEtLQ6vVUrduXapXr8727dv5+9//rnZYRbZu3To++eQTGjZsyFtvvVUmbg7GaunSpUycOJHMzEz8/PwIDAw0uv4owrC1atWKtLQ0EhIS+Ne//kWXLl3UDqnIjh07Ru/evalatSrp6elcu3ZN7ZBMlkk0E9y6dQuA69evc+HCBaZPn65yREX34MEDPDw8qFq1KlevXpUPkcoSEhLQ6XRYWVkRHBzMr7/+qnZIooyJj48nJiYGjUbDqFGjKAu/x3l5eZGWlsbFixf5888/y8Q1GSuT6LVx4cIFAMqVK8enn35aJmbws7Ky4sSJE4wePZrjx4/z+++/qx2SSfvll1/Q6XRotVoiIiLo3LmzqvHExcWRmJioagxqsba2xs7OTu0wipWiKPr/z86dO7N48eIy0Xzz3Xff4ePjQ0REBI8fP+bBgwcy94BKTCIZcHR05I033mDZsmW0adNG7XCKTcuWLTl69CgBAQH88ccfaodj0nr27ImNjQ0bNmygYsWKqsYSFxeHo6MjqampqsahFq1WS3R0dJlKCDQaDR06dMDFxQVPT0+1wyk2NWvWZP369fTo0YOQkBAqV66sdkgmyyT6DAhhSk6dOoWTkxMbNmzA0dFR7XBKVXR0NK6urkRGRtKqVSu1wxHCaJjEkwEhTJGjo6N8IQoh8qXQyYApt0nmpiDtlKZcdoVpz5XyKjuPuw2V1DH5TOZXmf1MFmYO42vXrilarVYB5M///6PVapVr165J2RVTOUl5Fa68FEVRIiMjFUCJjIzMc5/Dhw/r103w9PRULly4oAwbNkxZsGCBoiiKMnz4cMXNzU1xc3NTsrKylOjoaOXdd99VoqOj8zzm48ePlcePH780voMHDyrvvPOO4u7urhw8ePC57f/9738VGxsbJS0tTVEURWnUqJHi7u6uLF++/KXHzs+1P0vqmHwmS/ozaQwK9WQgMTGR1NRUk2yTzE12O2ViYuJLM0ZTLruClFM2Ka+ClVd+tWvXjm+//ZapU6dib2+Pvb09AGPGjAFgzZo1AHz66afcvHkTBwcHOnbsmOuxzp49y4YNG7hz5w4hISEv7Q2u0WioUqUK6enp1KtXL8e2x48fs2rVKrp3765/rXLlyqSlpVG/fv1CXu2LSR2Tz2R+leRnUm1F6jMgbZKFJ2VXMFJexW/IkCG0adOGhISEXLdfvHiRjIwM6tSpk+v2y5cvM3LkSHr16oW3tze1atUC4MiRI0REROj3MzMz46uvvtL/3KFDB959911u3bqFn58fX3/9tX7b/PnzGTduXI5lxU+dOoVOp8PZ2ZkPPvigSNf8IlLHCkbKq2wxuEmHJk2alOvr4eHhxMbGFuhY/v7+jB8//rljpqSk4ObmxqhRo9i4cWNhQzUIUl4FJ2X2ZKrhwMBAtm3bluskXOfOnSM4OJglS5bkeQw7OzvGjRvH5cuXWbhwIVFRUfk6d/b4+GrVqpGRkZFj2+nTp1m6dCknTpzQL8Kl0WgwMzPD0tLSKCalkfpVMFJehkHVZCA6OhoXFxemTZtGr169APT/+S1btmTRokUMHjyYtLQ0bt68SXp6er6PHRcXh06nY8mSJWRlZREfH6/ftmPHDgYNGsTKlSvZs2dPsV5TSZLyKjgps9yFhIQwdOhQunbtilar5fDhw/ptOp2O999/H51Ox/jx47l+/XquxzA3N6d///6Eh4fj7e3NoUOHSExMpF27doSHh+v/PP1UAGD79u2MHDmSwYMH4+HhATx5SgHwzTffsGjRIt566y08PT2JiYlh2LBhDBs2jHfeecfgJtqR+lUwUl6GS9WhhatWrWL+/PnUrl1bXzGy1a9fnwkTJhAaGsrp06efe++dO3eYMmVKjtd8fX1p1KgRADdu3NC3R9rZ2XH9+nX9z9evX+eNN94AnjzCNBZSXgUnZZY7Hx8f/b8DAwOBJ2UFT2bq/PPPPwt0vJo1azJx4sR87TtgwIDnFtV6ulkBnqy7AeDg4JCjGcHQSP0qGCkvw6Xqk4GnH/k9+/gvexa3ChUqPPcoMT9sbW31v9HEx8dja2ub6zadTlfgY6tFyqvgpMzyz8HBgfDw8Fy3xcTEEBsbi1arLeWoDJvUr4KR8jJcqj4ZGDVqFD4+Ptjb21OpUqUCvdfGxibPGxeg7+k5ceJEzM3NsbOzIyQkhE6dOtGvXz+8vLzYvXs3PXv2LNI1lCYpr4KTMsu/SZMm5dl+e+jQIQIDAwvUg9rf35+UlBS0Wi1z5szJsS0lJYUOHTowa9YsunXrBsDq1avZtGkTP/74Y577GBqpXwUj5WXACjMesTBjeXNz9+5dZcqUKcqYMWOUXbt2FelYaipIeRSl7Iy9vApz7UWta8ZcZoW99vy+7/z588qgQYOUqVOnKj179lQURVEGDRqkKIqitGjRQlm4cKHy8ccfK6mpqcq0adNeOMfAs65du6b4+fkpiqIoPj4+SlxcXI7tU6ZMUebMmaP83//9n6IoinL16lVl7ty5+vPntk9+lHYdM+b6pShSXgVVXN99hkjVJwPVq1cnKChIzRCMipRXwUmZ5U2t9tsffviBpk2b6juH6XQ6FixYoO/UmNs+hkrqV8FIeRkuo1qbYN26ddSuXbvYHxl6eHhQtWpV5syZw+rVqzl58iTx8fG0bNmS2bNnF+u51FISZefu7s6JEyfyPaTMmBR3eel0Ojw9PUlLS6NixYr6YXNqUkq4/Xb37t3Ak/bb3r1767cdPHiQlJQUzp8/T8WKFWnYsCF37tzBz8+PqKgovvvuO44cOZJjn65du1KunMGNhC4S+UwWTHGX14ULFwgODiYtLY1WrVrl6FRriko8Gdi0aROHDh1Cq9Uyd+5ctm7dyunTp0lJSSE0NJRZs2bx4MEDkpKSaNasGffv3+fatWts2LABZ2dn2rdvT2xsLGPHjtUf89q1awQHB6PRaGjUqBHdunVj+vTp2NraMnz4cJo3b57v+LZv386bb77J5cuXARgxYgQjRozA29sbNze34i6OAjH0slu+fDkuLi4lcemFYsjlVa5cOZYvXw7AyJEjS+T6C0qt9ttZs2YB/7u529vb88033wBw8+ZNevToQY8ePXLsYyiJgCHXMZDPZEHKq0mTJqxevRrgudEtpqjEk4EbN27QokUL+vTpg4WFBRqNBnNzc2JiYvSPH11cXHBwcMDNzY2dO3cyevRokpKSyMrKwtvbm+TkZCZPnkzr1q0BWLZsGRUrVkSr1XLu3DnefvttqlWrxpAhQ3JUhJc9yrx16xZRUVGMGjVKnwwAZGRk8Mcff9CkSZMSLp0XM+SyM0SGXl7nz59nxowZ2NjYlHxh5EOtWrWwt7fn7t27+sfzW7ZsyfF39jwAeU1F/CLPdhr09vbO8XNuyXb2eV+0j5oMvY4ZGmMor61bt/Lee++VbEEYgRJPBnx9fTlz5gx+fn4EBQWxY8cOduzYQWBgICkpKQBYWVlhYWGBlZUV8GQyk4yMDHQ6HVlZWTx69CjHZCM6nQ43NzeaNWumf83W1pY1a9Zw5swZhg0blq/Yfv75Z27fvk1QUBBnz57l4sWL2Nvbs2PHDvr27VuMpVA4hlx2hsjQy6tp06Zs3ryZsWPHcuPGjRxDn9Qg7bcFZ+h1zNAYenlt3ryZ+Ph4/Pz8iumKjVeJJwMrVqzg0qVLANSoUQMbGxuCg4M5efLkS3/bsLCwICgoiCtXrjBlyhROnjwJgJeXFwEBAbzyyitUrlyZDh06sHfvXu7evUuXLl3073/Zo8yBAwcycOBAYmNjCQ8P1y/Wsm3bNoOYstKQyw7giy++4LfffsPDw4OlS5dSvry6XVAMubwSEhKYOXMmOp0Oc3PzPOf7N1am0McCDLuOgXwmC1JeUVFR+Pj48OGHH+Lt7U1ISEjRL9iYFWYIQmkNr3h6mJEhK62hhQVhiGWnxtDC/Cor5VWQ923cuFEZNWqU8umnnyrp6enK+vXrFW9vb8Xd3V159OiRMm3aNGXChAnKsGHDlODgYCUgIEAZPHiwoiiK8uGHHypz585VPD09lXPnzilr165V/u///k+JjY1VxowZo4wdO1YJCQnRD1/09vZWzp49W6hyGDFiRLFfe1HfUxhlpY5JecnQwlL3bPuhyD8pu4IxxfIy9PZcQ+tjUVSmWMeKQsqrdBl0MiCEKDmG3p5raH0shCjLSm28TkkMd2nfvr1+Baq8lq58loeHh36f1atX4+HhQc+ePfH39891/3PnzuHq6srgwYM5d+4c6enpuLm5vbTtrjgZQtnlZx93d3f9YiAA+/fvp02bNsUXdD4ZQ3npdDrc3d0ZOnQonp6eQOmX14oVK9iwYQPwfHvuy2S353766af6+OFJe+6XX36Jj48P06dP5+DBgyxcuJCrV6/SuHFj/X7Z7blP/3n6qUBCQgJjxozBw8MDMzMzg+tjYQh17NnPW26ePY58JvPe58KFC4wYMYKPP/6Y+fPnA+qVlxqKJRlwd3fn3r17ZGVl4erqSkJCAv7+/nh6ej63XGR2pQgPD+fQoUNERUUxduxYPD09n1u57GXq1q2Ls7PzC5eufFr2nALZRowYQXh4OE2aNMlzCFNoaChhYWEsW7aM0NBQLC0ti3W4kzGUXX7Ld/ny5TmGY3br1o0GDRoUKK6XKSvllT3vwPr163n8+DFQMuX1IqNHj2bevHls3ryZ6tWrs3z5cvz8/Pj222/p2LEjgYGBODg4YGlpqV9FcOnSpdSuXRtLS0tmzZrFli1baNasGW5ubnTr1o369euzceNG5s+fz7Rp0+jUqRMhISF8/fXXtGvXLt+x1alTh2XLlhEeHs6SJUtKdeliY6hj8Pzn7Vm5HUc+k3nvkz3vwKZNmzh27BhQ+p9JNRVLM8HAgQPZtm0br776Kp07d6Z8+fJkZmZSs2ZNIiIicHZ2zvO9ISEhNGzYEI1Gw+nTp/XrmsOTTC4pKUn/c+/evenevftzx3jR1KfZCjunQHJyMpUrV9b/u7gZQ9nlZ5/SUpbKy5jbxMtye64x1LH8KK3PrTGUV0HKwlTnHSiWJwOdOnXil19+Yfv27fTv35+IiAj69u1LQEDAc1+g2TOJZbdJZmZmMnHiRAIDA1mwYEGhzv+ipSuzPT2nwI8//sjFixcBXjqnQKVKlUhOTuavv/4q8Cxt+WEMZZeffUpLWSqv7DbxrKwsbty4Uah4ioPaj3Bzezybm9JqhjKGOlaax3kZYyiv/JbF5s2biY2NzdH0ZSqK5clAuXLlqFevHgkJCVhZWdG2bVv9Iyxzc/Mc+9auXZv58+dz+PBhnJyc8PPzw8vLi1q1atGgQQPGjRun3ze/6wLkNvXpgQMHqFGjBq1atQLyP6fAnDlzcty0vLy88PLyQqfTlcjEFMZQdvnZB0pnjHNZKa/SmnfA3d2d2bNnY2VlxbBhwwgODiY0NJT79+/TvXv3HL+1ubi4sGXLFsLDw3FwcMDKyopVq1ah0+lo27Ztjt/aXia3R7i+vr7Ex8c/9xtZfqeFfXaq3W7duumbL4qTMdQxeP7zdvDgwZfWw5JgDOWVn31Mft6BwoxHNJSxli8ahzpr1izlwYMHBTpecnKyMmPGjJfud/DgQSUsLEz/syHOM/AyRS27/Jbv0+cx5DHNL2Ms5fXs+3744QclPDxc+f7775XVq1crt27dUnx8fJSpU6cqAwYMyHHO7L/DwsKUgwcPKq6ursrUqVOVadOmKd7e3jnOMWnSJMXd3V3/57vvvsv1Oo4ePaqEhoYqiqIoS5YsUY4ePZpn3N98842ybNmyF17bs/8Pz/4sdUw+k4piWOVlLAxj9Y9CqlKlynMdVLIFBARQpUqVAh2vUqVKTJ48+YX7pKens2vXLoPr3VxQRS27/Oyzf/9+tFptoWM0JMZaXsbwCBdM+/FsNmOtY2qR8ipeRXqOGx0dXVxxFEr2IiqnTp0q1fNmL+qSfd7ClIMplF3NmjXx8vIqUjllk/IqHGN4hJvb41k1mqGkjhWMlFcZU5jHCdeuXVO0Wq0CyJ///0er1SrXrl2TsiumcpLyKlx5KYphPMpU4xGuohTu2qWOyWeypD+TxqBQqbWdnR3R0dEkJiYW5u1lkrW1db466Jh62eW3nLJJeRWsvAxF9iPc3IaVBQQEvPT9+dmnuB7hSh2Tz2RBGOtn8mU0iqIoagchhCg+p06dwsnJicjIyByP2U2BKV+7EEUhaxMIUUaV6fbNPJjiNQtRHCQZEKKMsba2RqvV4urqqnYoqtBqtVhbW6sdhhBGRZoJhCiD4uLiiqVNNyIigsWLFxMREYGjo2MxRPY/d+7coX///nTt2vWlQ3oLoqy26QpRkiQZEELkKi4uDkdHR0aOHMnixYtL5BxfffUVXl5eHDlyhLZt25bIOYQQLyfJgBAiV3369OHkyZNER0cXeAKv/MrKyuLtt98mLS2NU6dOUaFChRI5jxDixYx6BkIhRMnYvXs3u3fvZvHixSWWCACYmZkRHh7O+fPnWbhwYYmdRwjxYvJkQAiRw8OHD2natCktWrRg3759aDSaEj/nxIkTWb58OefPnzeZ9eOFMCSSDAghcvDx8WHZsmX8/vvvvPrqq6VyzuTkZBwdHXnjjTfYs2dPqSQgQoj/kWYCIYTemTNnWLRoEVOnTi21RACgcuXKLFmyhH379rFr165SO68Q4gl5MiCEAECn09GuXTv++usvoqKinlvIqKQpisKHH37I6dOniY6OpnLlyqV6fiFMmTwZEEIAsHLlSo4dO8by5ctLPREA0Gg0LF26lHv37jFt2rRSP78QpkyeDAghuHXrFg4ODvTv359Vq1apGktwcDD+/v789ttvvPHGG6rGIoSpkGRACIGrqyvff/89MTEx1KhRQ9VYHj9+TKtWrfjb3/7Gr7/+ipmZmarxCGEKpJlACBP3ww8/sHHjRubPn696IgBQoUIFwsPDOXnyJMuXL1c7HCFMgjwZEMKEpaen07JlS+rUqcPBgwcNakjfqFGj2Lp1KxcuXKB27dpqhyNEmSZPBoQwYXPmzCE2NpawsDCDSgQA5s6di7m5ORMnTlQ7FCHKPEkGhDBRFy5cYPbs2fj5+RX7ioTFoXr16ixYsIAtW7bw73//W+1whCjTpJlACBOkKApdunQhNjaWc+fO8be//U3tkHKlKAqdO3cmPj6es2fPGmycQhg7eTIghAnauHEjP/30E8uWLTPoL1iNRkNYWBhxcXHMnj1b7XCEKLPkyYAQJiYpKYkmTZrQuXNntmzZonY4+TJ16lTmzJnDf//7XxwcHNQOR4gyR5IBIUyMu7s7W7ZsISYmhldeeUXtcPIlPT2d5s2bU69ePX766SeD6+wohLGTZgIhTMjRo0dZsWIFX375pdEkAgCWlpaEhYVx6NAhIiIi1A5HiDJHngwIYSIeP36Mk5MTlpaWRjuz38cff8yBAwe4cOEC1atXVzscIcoMeTIghIlYtGgRv//+O+Hh4UaZCACEhITw+PFjPv/8c7VDEaJMkScDQpRxf/75J48ePaJp06aMHj2ahQsXqh1SkYSFhTFmzBgOHz5Mo0aNqFWrlvQhEKKIJBkQogw7f/48zZs357333iM6Opro6GgqV66sdlhFkpWVRdu2bUlJSeHatWtERETQp08ftcMSwqhJM4EQZdiVK1dQFIUffviB1157jePHj6sdUpFduHCBOnXqEB0dzePHj7l8+bLaIQlh9CQZEKIMi42NBaB8+fIcP34crVarbkDF4G9/+xtnzpxBo9GQkZHBxYsX1Q5JCKMnyYAQZdiJEycA6NevHzExMbRt21bliIru1Vdf5dy5c/oFjE6dOqVyREIYP+kzIEQZlpSUxNmzZ+nQoYPaoZSIqKgoqlatyquvvqp2KEIYNUkGhBBCCBMnzQRCCCGEiSuvdgBCGKu4uDgSExPVDkMV1tbW2NnZFfh9UmYFLzMhSoMkA0IUQlxcHI6OjqSmpqodiiq0Wi3R0dEF+nKTMit4mQlRWiQZEKIQEhMTSU1NZcOGDTg6OqodTqmKjo7G1dWVxMTEAn2xSZkVvMyEKC2SDAhRBI6OjrRq1apYjjVp0iTmzJnz3Ovh4eF069aNBg0a5PtY/v7+pKSkoNVqcxwzJSWFsWPHUqFCBTp27MjgwYOLI/QCKa4yM5XyEqI0SAdCIVQQHR2Ni4sL06ZNo1evXsD/Jghq2bIlixYtYvDgwaSlpXHz5k3S09Pzfey4uDh0Oh1LliwhKyuL+Ph4/bYdO3YwaNAgVq5cyZ49e4r1mkqSlJcQJUueDAihglWrVjF//nxq166t/3LLVr9+fSZMmEBoaCinT59+7r137txhypQpOV7z9fWlUaNGANy4cYN69eoBYGdnx/Xr1/U/X79+nTfeeAPAqFYulPISomTJkwEhVPD09B7PTvVRsWJFACpUqEBGRkaBj21ra8v169cBiI+Px9bWNtdtOp2uwMdWi5SXECVLngwIoYJRo0bh4+ODvb09lSpVKtB7bWxsCA8Pz3N7dge1iRMnYm5ujp2dHSEhIXTq1Il+/frh5eXF7t276dmzZ5GuoTRJeQlRsmQGQiEK4dSpUzg5OREZGVmoznD37t1j0aJF3L17l65du9K7d+8SiLJkFPbai1JmxlxeUPT6IkRJkycDQqigevXqBAUFqR2G0ZDyEqJkSZ8BIYzIunXr2L9/f7Ed78KFC4wYMYKPP/6Y+fPnF9txDUVxlxeAu7u7vlOhEGWFPBkQooRt2rSJQ4cOodVqmTt3Llu3buX06dOkpKQQGhrKrFmzePDgAUlJSTRr1oz79+9z7do1NmzYgLOzM+3btyc2NpaxY8fqj3nt2jWCg4PRaDQ0atSIbt26MX36dGxtbRk+fDjNmzfPV2xNmjRh9erVAAwYMKBErr+gDLm8AJYvX46Li0tJXLoQqpFkQIgSduPGDVq0aEGfPn2wsLBAo9Fgbm5OTEyMfiici4sLDg4OuLm5sXPnTkaPHk1SUhJZWVl4e3uTnJzM5MmTad26NQDLli2jYsWKaLVazp07x9tvv021atUYMmRIji+2lw2ry7Z161bee++9ki2IfDKG8hKirJFkQIgS5uvry5kzZ/Dz8yMoKIgdO3awY8cOAgMDSUlJAcDKygoLCwusrKwAMDc3JyMjA51OR1ZWFo8ePUKj0eiPqdPpcHNzo1mzZvrXbG1tWbNmDWfOnGHYsGH5jm/z5s3Ex8fj5+dXTFdcNIZeXkKURZIMCFHCVqxYwaVLlwCoUaMGNjY2BAcHc/LkSTp27PjC91pYWBAUFMSVK1eYMmUKJ0+eBMDLy4uAgABeeeUVKleuTIcOHdi7dy93796lS5cu+ve/bFhdVFQUPj4+fPjhh3h7exMSElL0Cy4iQy4vgC+++ILffvsNDw8Pli5dSvnychsVxk+GFgpRCKU1VMzFxYUtW7aU2PELQ42hhflliOUFMrRQGD4ZTSCEATPELzZDJuUlROFIMiCEEEKYOEkGhCglJTEcrX379vrV9Pz9/Rk/fjyTJk3Kdd/8zinw7HH2799PmzZtij32l1G7vPK7z7PzDqhVXkIUhSQDQhQDd3d37t27R1ZWFq6uriQkJODv74+np+dzS99mf8mFh4dz6NAhoqKiGDt2LJ6enkRERBTovHXr1sXZ2fmFy/Bmy55TYNOmTRw7dizX4+V2nG7dutGgQYMCxfUyxlBe+dkHnsw70KRJE/3PJVFeQpQ06QYrRDEYOHAg27Zt49VXX6Vz586UL1+ezMxMatasSUREBM7Oznm+NyQkhIYNG6LRaDh9+jRDhgzRb/P39ycpKUn/c+/evenevftzx3jRMrzPetGcAgU5TlEYQ3mVVlkIYQjkyYAQxaBTp0788ssvbN++nf79+xMREUHfvn0JCAggOTk5x77lyj352GWPmc/MzGTixIkEBgayYMGCQp3/RcvwPm3z5s3Exsbi6elZpOMUlTGUV2mVhRCGQJ4MCFEMypUrR7169UhISMDKyoq2bdvqHx+bm5vn2Ld27drMnz+fw4cP4+TkhJ+fH15eXtSqVYsGDRowbtw4/b6zZ8/O1/lzW4b3wIED1KhRQz+ULbc5BZ7dJ7fjlARjKK/87AMy74AoIxQhRIFFRkYqgBIZGalqHIMGDcpz26xZs5QHDx688P352efZ8xT22g2hzNQoL0UxjGsX4kWkmUAII1alSpXnOtxlCwgIoEqVKi98f3722b9/P1qtttAxGhIpLyFyJ8+zhCiC6OhoVc/v4eEBPJnhrqTUrFkTLy8v/TmKes1qlpka5QXq1xMhXkaSASEKwdraGq1Wi6urq9qhqEKr1WJtbV2g90iZFbzMhCgtsjaBEIUUFxdHYmKi2mGowtraulCdC6XMSqZDphBFJcmAEEIIYeKkA6EQQghh4iQZEEIIIUycJANCCCGEiZNkQAghhDBxkgwIIYQQJk6SASGEEMLESTIghBBCmDhJBoQQQggTJ8mAEEIIYeIkGRBCCCFMnCQDQgghhImTZEAIIYQwcZIMCCGEECZOkgEhhBDCxEkyIIQQQpg4SQaEEEIIEyfJgBBCCGHiJBkQQgghTJwkA0IIIYSJk2RACCGEMHGSDAghhBAmTpIBIYQQwsRJMiCEEEKYOEkGhBBCCBMnyYAQQghh4v4fD+aYbLuh5VkAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": 4,
+ "id": "fb604e9d",
+ "metadata": {
+ "collapsed": false,
+ "editable": true
+ },
+ "outputs": [],
"source": [
"from sklearn.datasets import load_iris\n",
"from sklearn import tree\n",
@@ -962,8 +880,10 @@
},
{
"cell_type": "markdown",
- "id": "749201ad",
- "metadata": {},
+ "id": "482326c5",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Printing out as text\n",
"\n",
@@ -973,25 +893,13 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "id": "af6b920f",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "|--- petal width (cm) <= 0.80\n",
- "| |--- class: 0\n",
- "|--- petal width (cm) > 0.80\n",
- "| |--- petal width (cm) <= 1.75\n",
- "| | |--- class: 1\n",
- "| |--- petal width (cm) > 1.75\n",
- "| | |--- class: 2\n",
- "\n"
- ]
- }
- ],
+ "execution_count": 5,
+ "id": "2ce787ae",
+ "metadata": {
+ "collapsed": false,
+ "editable": true
+ },
+ "outputs": [],
"source": [
"from sklearn.datasets import load_iris\n",
"from sklearn.tree import DecisionTreeClassifier\n",
@@ -1005,8 +913,10 @@
},
{
"cell_type": "markdown",
- "id": "4b221e5c",
- "metadata": {},
+ "id": "09ba04e2",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Algorithms for Setting up Decision Trees\n",
"\n",
@@ -1023,8 +933,10 @@
},
{
"cell_type": "markdown",
- "id": "ef4ff80a",
- "metadata": {},
+ "id": "ab977b0b",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## The CART algorithm for Classification\n",
"\n",
@@ -1038,8 +950,10 @@
},
{
"cell_type": "markdown",
- "id": "577342d4",
- "metadata": {},
+ "id": "f4220521",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}G_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}G_{\\mathrm{right}},\n",
@@ -1048,8 +962,10 @@
},
{
"cell_type": "markdown",
- "id": "5e8aa9db",
- "metadata": {},
+ "id": "08753c68",
+ "metadata": {
+ "editable": true
+ },
"source": [
"where $G_{\\mathrm{left/right}}$ measures the impurity of the left/right subset and $m_{\\mathrm{left/right}}$\n",
" is the number of instances in the left/right subset\n",
@@ -1063,8 +979,10 @@
},
{
"cell_type": "markdown",
- "id": "277f39a4",
- "metadata": {},
+ "id": "4f6c02ab",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## The CART algorithm for Regression\n",
"\n",
@@ -1074,8 +992,10 @@
},
{
"cell_type": "markdown",
- "id": "a7ed9f9f",
- "metadata": {},
+ "id": "e0bd2f1f",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n",
@@ -1084,16 +1004,20 @@
},
{
"cell_type": "markdown",
- "id": "c1cdfa33",
- "metadata": {},
+ "id": "125b3b90",
+ "metadata": {
+ "editable": true
+ },
"source": [
"Here the MSE for a specific node is defined as"
]
},
{
"cell_type": "markdown",
- "id": "b6647d29",
- "metadata": {},
+ "id": "a42c2224",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n",
@@ -1102,16 +1026,20 @@
},
{
"cell_type": "markdown",
- "id": "13867677",
- "metadata": {},
+ "id": "5f8d6404",
+ "metadata": {
+ "editable": true
+ },
"source": [
"with"
]
},
{
"cell_type": "markdown",
- "id": "a92987fc",
- "metadata": {},
+ "id": "1dc3f2aa",
+ "metadata": {
+ "editable": true
+ },
"source": [
"$$\n",
"\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n",
@@ -1120,8 +1048,10 @@
},
{
"cell_type": "markdown",
- "id": "baac08a1",
- "metadata": {},
+ "id": "d2d29dae",
+ "metadata": {
+ "editable": true
+ },
"source": [
"the mean value of all observations in a specific node.\n",
"\n",
@@ -1131,8 +1061,10 @@
},
{
"cell_type": "markdown",
- "id": "62011b0f",
- "metadata": {},
+ "id": "d4d10bd1",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Why binary splits?\n",
"\n",
@@ -1144,8 +1076,10 @@
},
{
"cell_type": "markdown",
- "id": "68483628",
- "metadata": {},
+ "id": "6c55e62b",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Computing a Tree using the Gini Index\n",
"\n",
@@ -1168,8 +1102,10 @@
},
{
"cell_type": "markdown",
- "id": "58d114ab",
- "metadata": {},
+ "id": "cc1dfa23",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## The Table\n",
"\n",
@@ -1194,8 +1130,10 @@
},
{
"cell_type": "markdown",
- "id": "48bfbac4",
- "metadata": {},
+ "id": "4dd70dcc",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Computing the various Gini Indices\n",
"\n",
@@ -1209,8 +1147,10 @@
},
{
"cell_type": "markdown",
- "id": "407c354e",
- "metadata": {},
+ "id": "108ac502",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Computing the various Gini Indices, Hours slept\n",
"\n",
@@ -1221,8 +1161,10 @@
},
{
"cell_type": "markdown",
- "id": "68ffb69c",
- "metadata": {},
+ "id": "79c0305c",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## Computing the various Gini Indices, Hours studied\n",
"\n",
@@ -1235,165 +1177,23 @@
},
{
"cell_type": "markdown",
- "id": "249b41f6",
- "metadata": {},
+ "id": "9fe208a4",
+ "metadata": {
+ "editable": true
+ },
"source": [
"## A possible code using Scikit-Learn"
]
},
{
"cell_type": "code",
- "execution_count": 8,
- "id": "2be74ca8",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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