diff --git a/doc/pub/week45/ipynb/DataFiles/Datafiles/bank.csv b/doc/pub/week45/ipynb/DataFiles/Datafiles/bank.csv
deleted file mode 100644
index 930337395..000000000
--- a/doc/pub/week45/ipynb/DataFiles/Datafiles/bank.csv
+++ /dev/null
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--2.1668,1.5933,0.045122,-1.678,1
--1.1667,-1.4237,2.9241,0.66119,1
--2.8391,-6.63,10.4849,-0.42113,1
--4.5046,-5.8126,10.8867,-0.52846,1
--2.41,3.7433,-0.40215,-1.2953,1
-0.40614,1.3492,-1.4501,-0.55949,1
--1.3887,-4.8773,6.4774,0.34179,1
--3.7503,-13.4586,17.5932,-2.7771,1
--3.5637,-8.3827,12.393,-1.2823,1
--2.5419,-0.65804,2.6842,1.1952,1
\ No newline at end of file
diff --git a/doc/pub/week45/ipynb/DataFiles/Datafiles/cancer.dot b/doc/pub/week45/ipynb/DataFiles/Datafiles/cancer.dot
deleted file mode 100644
index 5b4b48a9b..000000000
--- a/doc/pub/week45/ipynb/DataFiles/Datafiles/cancer.dot
+++ /dev/null
@@ -1,57 +0,0 @@
-digraph Tree {
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-edge [fontname=helvetica] ;
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-0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
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-1 -> 2 ;
-3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e58139fb"] ;
-2 -> 3 ;
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-3 -> 5 ;
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-5 -> 6 ;
-7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139ff"] ;
-5 -> 7 ;
-8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#e581392c"] ;
-2 -> 8 ;
-9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139ff"] ;
-8 -> 9 ;
-10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139ff"] ;
-8 -> 10 ;
-11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#e581396b"] ;
-1 -> 11 ;
-12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ;
-11 -> 12 ;
-13 [label="gini = 0.0\nsamples = 5\nvalue = [[0, 5]\n[5, 0]]", fillcolor="#e58139ff"] ;
-11 -> 13 ;
-14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#e5813994"] ;
-0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
-15 [label="worst radius <= 17.74\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#e5813938"] ;
-14 -> 15 ;
-16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139ff"] ;
-15 -> 16 ;
-17 [label="mean texture <= 13.745\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#e5813955"] ;
-15 -> 17 ;
-18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ;
-17 -> 18 ;
-19 [label="gini = 0.0\nsamples = 4\nvalue = [[0, 4]\n[4, 0]]", fillcolor="#e58139ff"] ;
-17 -> 19 ;
-20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#e58139d0"] ;
-14 -> 20 ;
-21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#e5813900"] ;
-20 -> 21 ;
-22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139ff"] ;
-21 -> 22 ;
-23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139ff"] ;
-21 -> 23 ;
-24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e58139f7"] ;
-20 -> 24 ;
-25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ;
-24 -> 25 ;
-26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139ff"] ;
-24 -> 26 ;
-}
\ No newline at end of file
diff --git a/doc/pub/week45/ipynb/DataFiles/Datafiles/cancer.png b/doc/pub/week45/ipynb/DataFiles/Datafiles/cancer.png
deleted file mode 100644
index 2ceb5e1f8..000000000
Binary files a/doc/pub/week45/ipynb/DataFiles/Datafiles/cancer.png and /dev/null differ
diff --git a/doc/pub/week45/ipynb/DataFiles/Datafiles/ensembleoverview.png b/doc/pub/week45/ipynb/DataFiles/Datafiles/ensembleoverview.png
deleted file mode 100644
index dce581ee6..000000000
Binary files a/doc/pub/week45/ipynb/DataFiles/Datafiles/ensembleoverview.png and /dev/null differ
diff --git a/doc/pub/week45/ipynb/DataFiles/Datafiles/ride.csv b/doc/pub/week45/ipynb/DataFiles/Datafiles/ride.csv
deleted file mode 100644
index d03d4ca16..000000000
--- a/doc/pub/week45/ipynb/DataFiles/Datafiles/ride.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-Outlook,Temperature,Humidity,Wind,Ride
-0,0,0,0,0
-0,0,0,1,1
-1,0,0,0,1
-2,1,0,0,1
-2,2,1,0,1
-2,2,1,1,0
-1,2,1,1,1
-0,1,0,0,0
-0,2,1,0,1
-2,1,1,0,1
-0,1,1,1,1
-1,1,0,1,1
-1,0,1,0,1
-2,1,0,1,0
diff --git a/doc/pub/week45/ipynb/DataFiles/Datafiles/ride.csv~ b/doc/pub/week45/ipynb/DataFiles/Datafiles/ride.csv~
deleted file mode 100644
index 7a908c5f7..000000000
--- a/doc/pub/week45/ipynb/DataFiles/Datafiles/ride.csv~
+++ /dev/null
@@ -1,15 +0,0 @@
-Outlook,Temperature,Humidity,Wind,Ride
-Sunny,Hot,High,Weak,0
-Sunny,Hot,High,Strong,1
-Overcast,Hot,High,Weak,1
-Rain,Mild,High,Weak,1
-Rain,Cool,Normal,Weak,1
-Rain,Cool,Normal,Strong,0
-Overcast,Cool,Normal,Strong,1
-Sunny,Mild,High,Weak,0
-Sunny,Cool,Normal,Weak,1
-Rain,Mild,Normal,Weak,1
-Sunny,Mild,Normal,Strong,1
-Overcast,Mild,High,Strong,1
-Overcast,Hot,Normal,Weak,1
-Rain,Mild,High,Strong,0
diff --git a/doc/pub/week45/ipynb/DataFiles/Datafiles/ride.dot b/doc/pub/week45/ipynb/DataFiles/Datafiles/ride.dot
deleted file mode 100644
index 50aaa7638..000000000
--- a/doc/pub/week45/ipynb/DataFiles/Datafiles/ride.dot
+++ /dev/null
@@ -1,13 +0,0 @@
-digraph Tree {
-node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ;
-edge [fontname=helvetica] ;
-0 [label="X[7] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#39e5818b"] ;
-1 [label="X[1] <= 0.5\ngini = 0.408\nsamples = 14\nvalue = [4, 10, 0]", fillcolor="#39e58199"] ;
-0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
-2 [label="gini = 0.48\nsamples = 10\nvalue = [4, 6, 0]", fillcolor="#39e58155"] ;
-1 -> 2 ;
-3 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4, 0]", fillcolor="#39e581ff"] ;
-1 -> 3 ;
-4 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]", fillcolor="#8139e5ff"] ;
-0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
-}
\ No newline at end of file
diff --git a/doc/pub/week45/ipynb/DataFiles/Datafiles/rideclass.csv b/doc/pub/week45/ipynb/DataFiles/Datafiles/rideclass.csv
deleted file mode 100644
index a0831ac7e..000000000
--- a/doc/pub/week45/ipynb/DataFiles/Datafiles/rideclass.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-Day,Outlook,Temperature,Humidity,Wind,Ride
-1,Sunny,Hot,High,Weak,0
-2,Sunny,Hot,High,Strong,1
-3,Overcast,Hot,High,Weak,1
-4,Rain,Mild,High,Weak,1
-5,Rain,Cool,Normal,Weak,1
-6,Rain,Cool,Normal,Strong,0
-7,Overcast,Cool,Normal,Strong,1
-8,Sunny,Mild,High,Weak,0
-9,Sunny,Cool,Normal,Weak,1
-10,Rain,Mild,Normal,Weak,1
-11,Sunny,Mild,Normal,Strong,1
-12,Overcast,Mild,High,Strong,1
-13,Overcast,Hot,Normal,Weak,1
-14,Rain,Mild,High,Strong,0
diff --git a/doc/pub/week45/ipynb/DataFiles/Datafiles/zoo.csv b/doc/pub/week45/ipynb/DataFiles/Datafiles/zoo.csv
deleted file mode 100644
index ca71f7d21..000000000
--- a/doc/pub/week45/ipynb/DataFiles/Datafiles/zoo.csv
+++ /dev/null
@@ -1,101 +0,0 @@
-aardvark,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1
-antelope,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
-bass,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
-bear,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1
-boar,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-buffalo,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
-calf,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
-carp,0,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,4
-catfish,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
-cavy,1,0,0,1,0,0,0,1,1,1,0,0,4,0,1,0,1
-cheetah,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-chicken,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2
-chub,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
-clam,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,7
-crab,0,0,1,0,0,1,1,0,0,0,0,0,4,0,0,0,7
-crayfish,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7
-crow,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2
-deer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
-dogfish,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4
-dolphin,0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,1
-dove,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2
-duck,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,0,2
-elephant,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
-flamingo,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,1,2
-flea,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6
-frog,0,0,1,0,0,1,1,1,1,1,0,0,4,0,0,0,5
-frog,0,0,1,0,0,1,1,1,1,1,1,0,4,0,0,0,5
-fruitbat,1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,1
-giraffe,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
-girl,1,0,0,1,0,0,1,1,1,1,0,0,2,0,1,1,1
-gnat,0,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6
-goat,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
-gorilla,1,0,0,1,0,0,0,1,1,1,0,0,2,0,0,1,1
-gull,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2
-haddock,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4
-hamster,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,0,1
-hare,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,1
-hawk,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2
-herring,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
-honeybee,1,0,1,0,1,0,0,0,0,1,1,0,6,0,1,0,6
-housefly,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6
-kiwi,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,0,2
-ladybird,0,0,1,0,1,0,1,0,0,1,0,0,6,0,0,0,6
-lark,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
-leopard,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-lion,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-lobster,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7
-lynx,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-mink,1,0,0,1,0,1,1,1,1,1,0,0,4,1,0,1,1
-mole,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1
-mongoose,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-moth,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6
-newt,0,0,1,0,0,1,1,1,1,1,0,0,4,1,0,0,5
-octopus,0,0,1,0,0,1,1,0,0,0,0,0,8,0,0,1,7
-opossum,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1
-oryx,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1
-ostrich,0,1,1,0,0,0,0,0,1,1,0,0,2,1,0,1,2
-parakeet,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2
-penguin,0,1,1,0,0,1,1,0,1,1,0,0,2,1,0,1,2
-pheasant,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
-pike,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4
-piranha,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4
-pitviper,0,0,1,0,0,0,1,1,1,1,1,0,0,1,0,0,3
-platypus,1,0,1,1,0,1,1,0,1,1,0,0,4,1,0,1,1
-polecat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-pony,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
-porpoise,0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,1
-puma,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-pussycat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,1,1,1
-raccoon,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-reindeer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1
-rhea,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,1,2
-scorpion,0,0,0,0,0,0,1,0,0,1,1,0,8,1,0,0,7
-seahorse,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4
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-sealion,1,0,0,1,0,1,1,1,1,1,0,1,2,1,0,1,1
-seasnake,0,0,0,0,0,1,1,1,1,0,1,0,0,1,0,0,3
-seawasp,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,7
-skimmer,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2
-skua,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2
-slowworm,0,0,1,0,0,0,1,1,1,1,0,0,0,1,0,0,3
-slug,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7
-sole,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4
-sparrow,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
-squirrel,1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,0,1
-starfish,0,0,1,0,0,1,1,0,0,0,0,0,5,0,0,0,7
-stingray,0,0,1,0,0,1,1,1,1,0,1,1,0,1,0,1,4
-swan,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,1,2
-termite,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6
-toad,0,0,1,0,0,1,0,1,1,1,0,0,4,0,0,0,5
-tortoise,0,0,1,0,0,0,0,0,1,1,0,0,4,1,0,1,3
-tuatara,0,0,1,0,0,0,1,1,1,1,0,0,4,1,0,0,3
-tuna,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4
-vampire,1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,1
-vole,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,1
-vulture,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,1,2
-wallaby,1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,1,1
-wasp,1,0,1,0,1,0,0,0,0,1,1,0,6,0,0,0,6
-wolf,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1
-worm,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7
-wren,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2
diff --git a/doc/pub/week45/ipynb/week45.ipynb b/doc/pub/week45/ipynb/week45.ipynb
index e0ac773f2..07d464c23 100644
--- a/doc/pub/week45/ipynb/week45.ipynb
+++ b/doc/pub/week45/ipynb/week45.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "markdown",
    "id": "b8e048f2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
     "doconce format html week45.do.txt --no_mako -->\n",
@@ -15,9 +13,7 @@
   {
    "cell_type": "markdown",
    "id": "3c558c51",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# Week 45: Decisions Trees, Random Forests, Bagging  and Boosting\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",
@@ -30,9 +26,7 @@
   {
    "cell_type": "markdown",
    "id": "707efc8a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Overview of week 45\n",
     "\n",
@@ -54,9 +48,7 @@
   {
    "cell_type": "markdown",
    "id": "07d278bf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Decision trees, overarching aims\n",
     "\n",
@@ -85,9 +77,7 @@
   {
    "cell_type": "markdown",
    "id": "eec5673d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Basics of a tree\n",
     "\n",
@@ -105,9 +95,7 @@
   {
    "cell_type": "markdown",
    "id": "806a416a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A Sketch of a Tree, Regression problem\n",
     "\n",
@@ -117,9 +105,7 @@
   {
    "cell_type": "markdown",
    "id": "1ad01519",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A Sketch of a Tree, Classification  problem\n",
     "\n",
@@ -129,9 +115,7 @@
   {
    "cell_type": "markdown",
    "id": "f72002bc",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A typical Decision Tree with its pertinent Jargon, Classification Problem\n",
     "\n",
@@ -147,9 +131,7 @@
   {
    "cell_type": "markdown",
    "id": "efa8d295",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## General Features\n",
     "\n",
@@ -170,9 +152,7 @@
   {
    "cell_type": "markdown",
    "id": "cc015b03",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## How do we set it up?\n",
     "\n",
@@ -193,9 +173,7 @@
   {
    "cell_type": "markdown",
    "id": "49378ea8",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Decision trees and Regression"
    ]
@@ -204,10 +182,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "a6761f89",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -305,9 +280,7 @@
   {
    "cell_type": "markdown",
    "id": "a752c6b0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Building a tree, regression\n",
     "\n",
@@ -327,9 +300,7 @@
   {
    "cell_type": "markdown",
    "id": "bc72fac7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\sum_{j=1}^J\\sum_{i\\in R_j}(y_i-\\overline{y}_{R_j})^2,\n",
@@ -339,9 +310,7 @@
   {
    "cell_type": "markdown",
    "id": "00b480b3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $\\overline{y}_{R_j}$  is the mean response for the training observations \n",
     "within box $j$."
@@ -350,9 +319,7 @@
   {
    "cell_type": "markdown",
    "id": "7e9a3ebf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A top-down approach, recursive binary splitting\n",
     "\n",
@@ -372,9 +339,7 @@
   {
    "cell_type": "markdown",
    "id": "d8419201",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Making a tree\n",
     "\n",
@@ -385,9 +350,7 @@
   {
    "cell_type": "markdown",
    "id": "19b9a14f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\left\\{X\\vert x_j < s\\right\\},\n",
@@ -397,9 +360,7 @@
   {
    "cell_type": "markdown",
    "id": "2b1e0deb",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and"
    ]
@@ -407,9 +368,7 @@
   {
    "cell_type": "markdown",
    "id": "95925ab2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\left\\{X\\vert x_j \\geq s\\right\\},\n",
@@ -419,9 +378,7 @@
   {
    "cell_type": "markdown",
    "id": "6fcb38ed",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "so that we obtain the lowest MSE, that is"
    ]
@@ -429,9 +386,7 @@
   {
    "cell_type": "markdown",
    "id": "5bcfa0ee",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\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",
@@ -441,9 +396,7 @@
   {
    "cell_type": "markdown",
    "id": "3fb296e7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "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",
@@ -474,9 +427,7 @@
   {
    "cell_type": "markdown",
    "id": "ccb98b4b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Pruning the tree\n",
     "\n",
@@ -498,9 +449,7 @@
   {
    "cell_type": "markdown",
    "id": "bc3bb5b7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Cost complexity pruning\n",
     "\n",
@@ -510,9 +459,7 @@
   {
    "cell_type": "markdown",
    "id": "bd9fcc00",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\sum_{m=1}^{\\overline{T}}\\sum_{i:x_i\\in R_m}(y_i-\\overline{y}_{R_m})^2+\\alpha\\overline{T},\n",
@@ -522,9 +469,7 @@
   {
    "cell_type": "markdown",
    "id": "5e883366",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "is as small as possible. Here $\\overline{T}$ is \n",
     "the number of terminal nodes of the tree $T$ , $R_m$ is the\n",
@@ -550,9 +495,7 @@
   {
    "cell_type": "markdown",
    "id": "66cfce67",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Schematic Regression Procedure\n",
     "\n",
@@ -576,9 +519,7 @@
   {
    "cell_type": "markdown",
    "id": "a1cf3867",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## A Classification Tree\n",
     "\n",
@@ -599,9 +540,7 @@
   {
    "cell_type": "markdown",
    "id": "8a52f83d",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Growing a classification tree\n",
     "\n",
@@ -626,9 +565,7 @@
   {
    "cell_type": "markdown",
    "id": "19292eb1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Classification tree, how to split nodes\n",
     "\n",
@@ -645,9 +582,7 @@
   {
    "cell_type": "markdown",
    "id": "660b87c0",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "p_{mk} = \\frac{1}{N_m}\\sum_{i\\in R_m}I(y_i=k).\n",
@@ -657,9 +592,7 @@
   {
    "cell_type": "markdown",
    "id": "b863c773",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "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",
@@ -670,9 +603,7 @@
   {
    "cell_type": "markdown",
    "id": "3adb78ea",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{1}{N_m}\\sum_{i\\in R_m}I(y_i\\ne k) = 1-p_{mk}.\n",
@@ -682,9 +613,7 @@
   {
    "cell_type": "markdown",
    "id": "da8cf302",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "* Gini index $g$"
    ]
@@ -692,9 +621,7 @@
   {
    "cell_type": "markdown",
    "id": "8bdfa9d3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "g = \\sum_{k\\ne k'} p_{mk}p_{mk'}=\\sum_{k=1}^K p_{mk}(1-p_{mk}).\n",
@@ -704,9 +631,7 @@
   {
    "cell_type": "markdown",
    "id": "7c208942",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "* Information entropy or just entropy $s$"
    ]
@@ -714,9 +639,7 @@
   {
    "cell_type": "markdown",
    "id": "ba063690",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "s = -\\sum_{k=1}^K p_{mk}\\log{p_{mk}}.\n",
@@ -726,9 +649,7 @@
   {
    "cell_type": "markdown",
    "id": "d927c5b7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Gini Index (or Coefficient or Impurity)\n",
     "\n",
@@ -748,9 +669,7 @@
   {
    "cell_type": "markdown",
    "id": "ed21cc0a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Why binary splits?\n",
     "\n",
@@ -763,9 +682,7 @@
   {
    "cell_type": "markdown",
    "id": "8b2fc38e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing a Tree using the Gini Index\n",
     "\n",
@@ -789,9 +706,7 @@
   {
    "cell_type": "markdown",
    "id": "d2023b2a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The Table\n",
     "\n",
@@ -817,9 +732,7 @@
   {
    "cell_type": "markdown",
    "id": "05e3ff24",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing the various Gini Indices\n",
     "\n",
@@ -834,9 +747,7 @@
   {
    "cell_type": "markdown",
    "id": "94ec28db",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing the various Gini Indices, Hours slept\n",
     "\n",
@@ -848,9 +759,7 @@
   {
    "cell_type": "markdown",
    "id": "e659cbfb",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing the various Gini Indices, Hours studied\n",
     "\n",
@@ -864,22 +773,29 @@
   {
    "cell_type": "markdown",
    "id": "12d49e54",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualizing the Tree, Classification"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "7ff4578a",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pydot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-7c394b1e8b71>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpydot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgraph_from_dot_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pydot'"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "from sklearn.datasets import load_breast_cancer\n",
@@ -919,22 +835,29 @@
   {
    "cell_type": "markdown",
    "id": "487e62c8",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Visualizing the Tree, The Moons"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "f357d60c",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pydot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-597bd38b1152>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmake_moons\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mexport_graphviz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpydot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgraph_from_dot_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pydot'"
+     ]
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -965,9 +888,7 @@
   {
    "cell_type": "markdown",
    "id": "2a9c254b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Other ways of visualizing the trees\n",
     "\n",
@@ -976,13 +897,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "524b405e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(167.4, 199.32, 'X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n",
+       " Text(141.64615384615385, 163.07999999999998, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
+       " Text(193.15384615384616, 163.07999999999998, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
+       " Text(103.01538461538462, 126.83999999999999, 'X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
+       " Text(51.50769230769231, 90.6, 'X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
+       " Text(25.753846153846155, 54.359999999999985, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
+       " Text(77.26153846153846, 54.359999999999985, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
+       " Text(154.52307692307693, 90.6, 'X[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
+       " Text(128.76923076923077, 54.359999999999985, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
+       " Text(180.27692307692308, 54.359999999999985, 'X[0] <= 6.95\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
+       " Text(154.52307692307693, 18.119999999999976, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
+       " Text(206.03076923076924, 18.119999999999976, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
+       " Text(283.2923076923077, 126.83999999999999, 'X[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
+       " Text(257.53846153846155, 90.6, 'X[0] <= 5.95\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
+       " Text(231.7846153846154, 54.359999999999985, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
+       " Text(283.2923076923077, 54.359999999999985, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
+       " Text(309.04615384615386, 90.6, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn import tree\n",
@@ -996,9 +953,7 @@
   {
    "cell_type": "markdown",
    "id": "fa90608a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Printing out as text\n",
     "\n",
@@ -1008,13 +963,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "f068897d",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "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"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
@@ -1029,9 +996,7 @@
   {
    "cell_type": "markdown",
    "id": "9bc4d551",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Algorithms for Setting up Decision Trees\n",
     "\n",
@@ -1049,9 +1014,7 @@
   {
    "cell_type": "markdown",
    "id": "7d6044cb",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The CART algorithm for Classification\n",
     "\n",
@@ -1066,9 +1029,7 @@
   {
    "cell_type": "markdown",
    "id": "cbd5b927",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}G_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}G_{\\mathrm{right}},\n",
@@ -1078,9 +1039,7 @@
   {
    "cell_type": "markdown",
    "id": "c7fb3bc7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "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",
@@ -1095,9 +1054,7 @@
   {
    "cell_type": "markdown",
    "id": "a21c1456",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The CART algorithm for Regression\n",
     "\n",
@@ -1108,9 +1065,7 @@
   {
    "cell_type": "markdown",
    "id": "f1e7036a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(k,t_k) = \\frac{m_{\\mathrm{left}}}{m}\\mathrm{MSE}_{\\mathrm{left}}+ \\frac{m_{\\mathrm{right}}}{m}\\mathrm{MSE}_{\\mathrm{right}}.\n",
@@ -1120,9 +1075,7 @@
   {
    "cell_type": "markdown",
    "id": "5b6d39e7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Here the MSE for a specific node is defined as"
    ]
@@ -1130,9 +1083,7 @@
   {
    "cell_type": "markdown",
    "id": "ebe4bf16",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathrm{MSE}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}(\\overline{y}_{\\mathrm{node}}-y_i)^2,\n",
@@ -1142,9 +1093,7 @@
   {
    "cell_type": "markdown",
    "id": "565c4e66",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "with"
    ]
@@ -1152,9 +1101,7 @@
   {
    "cell_type": "markdown",
    "id": "9247bcd6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\overline{y}_{\\mathrm{node}}=\\frac{1}{m_\\mathrm{node}}\\sum_{i\\in \\mathrm{node}}y_i,\n",
@@ -1164,9 +1111,7 @@
   {
    "cell_type": "markdown",
    "id": "7dd29ac2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "the mean value of all observations in a specific node.\n",
     "\n",
@@ -1177,9 +1122,7 @@
   {
    "cell_type": "markdown",
    "id": "48fe2503",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing the Gini index\n",
     "\n",
@@ -1221,22 +1164,29 @@
   {
    "cell_type": "markdown",
    "id": "ae3d09bc",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Simple Python Code to read in Data and perform Classification"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "1c14742e",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pydot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-c812b18cba65>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompose\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mColumnTransformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpydot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgraph_from_dot_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pydot'"
+     ]
+    }
+   ],
    "source": [
     "# Common imports\n",
     "import numpy as np\n",
@@ -1310,9 +1260,7 @@
   {
    "cell_type": "markdown",
    "id": "f68b19c5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Computing the Gini Factor\n",
     "\n",
@@ -1326,13 +1274,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "38fb3318",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X1 < 0.000 Gini=0.408\n",
+      "X1 < 0.000 Gini=0.408\n",
+      "X1 < 1.000 Gini=0.394\n",
+      "X1 < 2.000 Gini=0.394\n",
+      "X1 < 2.000 Gini=0.394\n",
+      "X1 < 2.000 Gini=0.394\n",
+      "X1 < 1.000 Gini=0.394\n",
+      "X1 < 0.000 Gini=0.408\n",
+      "X1 < 0.000 Gini=0.408\n",
+      "X1 < 2.000 Gini=0.394\n",
+      "X1 < 0.000 Gini=0.408\n",
+      "X1 < 1.000 Gini=0.394\n",
+      "X1 < 1.000 Gini=0.394\n",
+      "X1 < 2.000 Gini=0.394\n",
+      "X2 < 0.000 Gini=0.408\n",
+      "X2 < 0.000 Gini=0.408\n",
+      "X2 < 0.000 Gini=0.408\n",
+      "X2 < 1.000 Gini=0.407\n",
+      "X2 < 2.000 Gini=0.407\n",
+      "X2 < 2.000 Gini=0.407\n",
+      "X2 < 2.000 Gini=0.407\n",
+      "X2 < 1.000 Gini=0.407\n",
+      "X2 < 2.000 Gini=0.407\n",
+      "X2 < 1.000 Gini=0.407\n",
+      "X2 < 1.000 Gini=0.407\n",
+      "X2 < 1.000 Gini=0.407\n",
+      "X2 < 0.000 Gini=0.408\n",
+      "X2 < 1.000 Gini=0.407\n",
+      "X3 < 0.000 Gini=0.408\n",
+      "X3 < 0.000 Gini=0.408\n",
+      "X3 < 0.000 Gini=0.408\n",
+      "X3 < 0.000 Gini=0.408\n",
+      "X3 < 1.000 Gini=0.367\n",
+      "X3 < 1.000 Gini=0.367\n",
+      "X3 < 1.000 Gini=0.367\n",
+      "X3 < 0.000 Gini=0.408\n",
+      "X3 < 1.000 Gini=0.367\n",
+      "X3 < 1.000 Gini=0.367\n",
+      "X3 < 1.000 Gini=0.367\n",
+      "X3 < 0.000 Gini=0.408\n",
+      "X3 < 1.000 Gini=0.367\n",
+      "X3 < 0.000 Gini=0.408\n",
+      "X4 < 0.000 Gini=0.408\n",
+      "X4 < 1.000 Gini=0.405\n",
+      "X4 < 0.000 Gini=0.408\n",
+      "X4 < 0.000 Gini=0.408\n",
+      "X4 < 0.000 Gini=0.408\n",
+      "X4 < 1.000 Gini=0.405\n",
+      "X4 < 1.000 Gini=0.405\n",
+      "X4 < 0.000 Gini=0.408\n",
+      "X4 < 0.000 Gini=0.408\n",
+      "X4 < 0.000 Gini=0.408\n",
+      "X4 < 1.000 Gini=0.405\n",
+      "X4 < 1.000 Gini=0.405\n",
+      "X4 < 0.000 Gini=0.408\n",
+      "X4 < 1.000 Gini=0.405\n",
+      "Split: [X3 < 1.000]\n"
+     ]
+    }
+   ],
    "source": [
     "# Split a dataset based on an attribute and an attribute value\n",
     "def test_split(index, value, dataset):\n",
@@ -1399,9 +1408,7 @@
   {
    "cell_type": "markdown",
    "id": "8e2e38be",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Entropy and the ID3 algorithm\n",
     "\n",
@@ -1438,9 +1445,7 @@
   {
    "cell_type": "markdown",
    "id": "f4316cf5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Cancer Data again now with Decision Trees and other Methods"
    ]
@@ -1449,11 +1454,37 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "64a04a67",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(426, 30)\n",
+      "(143, 30)\n",
+      "Test set accuracy with Logistic Regression: 0.94\n",
+      "Test set accuracy with SVM: 0.63\n",
+      "Test set accuracy with Decision Trees: 0.89\n",
+      "Test set accuracy Logistic Regression with scaled data: 0.96\n",
+      "Test set accuracy SVM with scaled data: 0.96\n",
+      "Test set accuracy with Decision Trees and scaled data: 0.92\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mhjensen/Software/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -1501,9 +1532,7 @@
   {
    "cell_type": "markdown",
    "id": "e8bf2b9c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Another example, the moons again"
    ]
@@ -1512,11 +1541,21 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "e52d3d63",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from __future__ import division, print_function, unicode_literals\n",
     "\n",
@@ -1587,9 +1626,7 @@
   {
    "cell_type": "markdown",
    "id": "909c35c6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Playing around with regions"
    ]
@@ -1598,11 +1635,21 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "3a5b5fca",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "np.random.seed(6)\n",
     "Xs = np.random.rand(100, 2) - 0.5\n",
@@ -1629,9 +1676,7 @@
   {
    "cell_type": "markdown",
    "id": "607883cb",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Regression trees"
    ]
@@ -1640,10 +1685,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "a917a8a7",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Quadratic training set + noise\n",
@@ -1658,11 +1700,19 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "a2329a82",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeRegressor(max_depth=2, random_state=42)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.tree import DecisionTreeRegressor\n",
     "\n",
@@ -1673,9 +1723,7 @@
   {
    "cell_type": "markdown",
    "id": "9920ac6a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Final regressor code"
    ]
@@ -1684,11 +1732,21 @@
    "cell_type": "code",
    "execution_count": 13,
    "id": "3b569d53",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.tree import DecisionTreeRegressor\n",
     "\n",
@@ -1734,11 +1792,21 @@
    "cell_type": "code",
    "execution_count": 14,
    "id": "67740201",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
     "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
@@ -1773,9 +1841,7 @@
   {
    "cell_type": "markdown",
    "id": "37b7856b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Pros and cons of trees, pros\n",
     "\n",
@@ -1797,9 +1863,7 @@
   {
    "cell_type": "markdown",
    "id": "579d15a1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Disadvantages\n",
     "\n",
@@ -1825,9 +1889,7 @@
   {
    "cell_type": "markdown",
    "id": "50e291e2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods\n",
     "\n",
@@ -1856,9 +1918,7 @@
   {
    "cell_type": "markdown",
    "id": "2a067ded",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## An Overview of Ensemble Methods\n",
     "\n",
@@ -1872,9 +1932,7 @@
   {
    "cell_type": "markdown",
    "id": "92f0694a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Bagging\n",
     "\n",
@@ -1894,9 +1952,7 @@
   {
    "cell_type": "markdown",
    "id": "35e5abc4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## More bagging\n",
     "\n",
@@ -1926,9 +1982,7 @@
   {
    "cell_type": "markdown",
    "id": "99530e36",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Making your own Bootstrap: Changing the Level of the Decision Tree\n",
     "\n",
@@ -1938,13 +1992,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "id": "77318608",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial degree: 1\n",
+      "Error: 0.050228534884996276\n",
+      "Bias^2: 0.047424636658910696\n",
+      "Var: 0.0028038982260855942\n",
+      "0.050228534884996276 >= 0.047424636658910696 + 0.0028038982260855942 = 0.05022853488499629\n",
+      "Polynomial degree: 2\n",
+      "Error: 0.035938172137818414\n",
+      "Bias^2: 0.03325162633373944\n",
+      "Var: 0.0026865458040789747\n",
+      "0.035938172137818414 >= 0.03325162633373944 + 0.0026865458040789747 = 0.035938172137818414\n",
+      "Polynomial degree: 3\n",
+      "Error: 0.019637742595026014\n",
+      "Bias^2: 0.016076691813926115\n",
+      "Var: 0.003561050781099899\n",
+      "0.019637742595026014 >= 0.016076691813926115 + 0.003561050781099899 = 0.019637742595026014\n",
+      "Polynomial degree: 4\n",
+      "Error: 0.01462046252128244\n",
+      "Bias^2: 0.011424636021768093\n",
+      "Var: 0.003195826499514345\n",
+      "0.01462046252128244 >= 0.011424636021768093 + 0.003195826499514345 = 0.014620462521282438\n",
+      "Polynomial degree: 5\n",
+      "Error: 0.012890642007324326\n",
+      "Bias^2: 0.01063737085997277\n",
+      "Var: 0.002253271147351561\n",
+      "0.012890642007324326 >= 0.01063737085997277 + 0.002253271147351561 = 0.012890642007324331\n",
+      "Polynomial degree: 6\n",
+      "Error: 0.013587860152743086\n",
+      "Bias^2: 0.011167722418296555\n",
+      "Var: 0.0024201377344465307\n",
+      "0.013587860152743086 >= 0.011167722418296555 + 0.0024201377344465307 = 0.013587860152743086\n",
+      "Polynomial degree: 7\n",
+      "Error: 0.014667745849181872\n",
+      "Bias^2: 0.011665580298884004\n",
+      "Var: 0.0030021655502978674\n",
+      "0.014667745849181872 >= 0.011665580298884004 + 0.0030021655502978674 = 0.014667745849181872\n",
+      "Polynomial degree: 8\n",
+      "Error: 0.01576461886622324\n",
+      "Bias^2: 0.012194276195040337\n",
+      "Var: 0.0035703426711829076\n",
+      "0.01576461886622324 >= 0.012194276195040337 + 0.0035703426711829076 = 0.015764618866223244\n",
+      "Polynomial degree: 9\n",
+      "Error: 0.01681890798652693\n",
+      "Bias^2: 0.0127531179009464\n",
+      "Var: 0.004065790085580534\n",
+      "0.01681890798652693 >= 0.0127531179009464 + 0.004065790085580534 = 0.016818907986526934\n",
+      "Polynomial degree: 10\n",
+      "Error: 0.017662244936164105\n",
+      "Bias^2: 0.01322098120809351\n",
+      "Var: 0.004441263728070601\n",
+      "0.017662244936164105 >= 0.01322098120809351 + 0.004441263728070601 = 0.017662244936164112\n",
+      "Polynomial degree: 11\n",
+      "Error: 0.01835866569618622\n",
+      "Bias^2: 0.013658812846630355\n",
+      "Var: 0.004699852849555868\n",
+      "0.01835866569618622 >= 0.013658812846630355 + 0.004699852849555868 = 0.018358665696186223\n",
+      "0.4792857906235924\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'save_fig' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-dce6a1daf90b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpolydegree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Variance'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0msave_fig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"baggingboot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'save_fig' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\n",
     "import matplotlib.pyplot as plt\n",
@@ -1956,7 +2093,7 @@
     "\n",
     "n = 1000\n",
     "n_boostraps = 100\n",
-    "maxdepth = 10\n",
+    "maxdepth = 12\n",
     "\n",
     "# Make data set.\n",
     "x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
@@ -2003,9 +2140,7 @@
   {
    "cell_type": "markdown",
    "id": "0db1989b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Why Voting?\n",
     "\n",
@@ -2027,9 +2162,7 @@
   {
    "cell_type": "markdown",
    "id": "db575915",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Tossing coins\n",
     "\n",
@@ -2058,9 +2191,7 @@
   {
    "cell_type": "markdown",
    "id": "e1b33601",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Standard imports first"
    ]
@@ -2069,11 +2200,20 @@
    "cell_type": "code",
    "execution_count": 16,
    "id": "2efc1afa",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pydot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-f32ca21bff0e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Common imports\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpydot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgraph_from_dot_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pydot'"
+     ]
+    }
+   ],
    "source": [
     "# Common imports\n",
     "from IPython.display import Image \n",
@@ -2117,9 +2257,7 @@
   {
    "cell_type": "markdown",
    "id": "f3567d84",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Simple Voting Example, head or tail"
    ]
@@ -2128,11 +2266,32 @@
    "cell_type": "code",
    "execution_count": 17,
    "id": "8bfc7097",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'save_fig' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-c6e552eb0b4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"lower right\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.42\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.58\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0msave_fig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"votingsimple\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'save_fig' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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TbhI0ALoO9fj6/vTWZr/nf6j5sbSqefuSzfuodbr4rKCcUruzud0tJcnz1/sIF18VBjYFHbNyB/+XXxb0GgptDo76fRudFm0kx9o6u6xCoVAo2qatx7QSIUTTo20pUOLnr6k9GMLREoJ5UwVE+Om7COiLFgUzFTgPuKNlJyGEEfgYeF9K2WQvKAWGARlo2o6Ixj6tkFLOkFIOlVIObWvhemcIUjqQ7lrcLolJpzmGntn9O20ez3zNY/aV1vHNOi2k9PnnnwfgxRdfpDxhJZWxG5jz2kbK7R7NRb27bfX+e5cOIzkyhAFp0a32dY4PI+upU1h9/7HNbbU2JzVlVhw2F4mdItHpPOXl0nvHMeWWQVz90jifeYwmPVe/7GmzNzj5uyi1Oym0Obh8c5ZPe9fFm7hpezZ9l27mis37uGTTXlIWbGje3yR0/Geb5lMzKjq8eV/HEGPz9q07cnAFSJbicHuElxezihi4zCPHDlu+lft35foVDhUKhULhn7aEjQlAude2v7/xjf8HQy0Q2aItEmgV0yml3Cul3CeldEspNwGPAGd69xFC6IAP0XxArvcaWyulXC2ldEopixr3HS+EaHnsoIkuGYSt8mVsVTNw2D1PtukR2k2nvE5ra9IoALz46y4iKzxKHIfDI1g4Tdopv744q7lteU4lne6ezQvzduJq9JcY3llTGl13TBfG90hkxb0TibJ4bpgtiQ/3aClKamx8/oQWrVxbrmlgKhxO8hqfzHV6HQZT65JzBqOn7fuX1wc81l9N36WbfW7ye8b2a9Xnh5Iq5pZWtznPrEFdKRw/kMLxA1l9VB+OivZkfk1dsIGz1nscauucLrbVNpC20CO8PLmvoNWcb+eW0mHBBhyHkV+LQqFQHM4E9NmQUi702l5wEI61EzAIIbpJKXc1tg2gtfnD73LwqvwuNAeId9AcTU+WUrZ2bvAdC/4rx7di9JiPWLXqdOw2z03J1eAJO1393RskT9S2U8KL+HlrUfO+4hpPVEmIUUe9NBIqtKUNfOB7zvKqUF8TuYufIjwWqGlLd2ACXpi3i57JEZzQJ5mV+8pJijRz54k9g1k6AG9PG8oVH6ymuNqKrV7TTIyYnMneehujftccUQuOGdDsQ+J0S4SAfQ028q0Ojo6NIOO6Xnz74x565tRQVdJAVMKB+Tf8Wd7J9VWWvdE7gzCDnuFRYays8l+S58uBXQjV6fi1vJofSqrYUWfl4/6Zrfp9PagbuVY7Q5dvBWBxRS3J89cjaLvG3vpRfUg2G7lycxbfNzqrjlyxlTWjAlkBFYclvz0Gi54FcyTYGgXVsATIHA8pg8AYAgPO1/5XKBQHjaAcRIUQLqCDlLK4RXscUCyl9FeV3QcpZZ0QYhbwiBDiCmAgMBkY5ed4JwFrpZRFQoieaBEnX3h1eR3oBRwrpWxoMXYEUAnsAmKAl4AFUsqWJhy/6HSSuNhcCgp6NLe5Hfuatwt3r2sWNgCKy9ZhEA6c0lfjYHO4WelI5xiTliSrv8H3CdkaWkBqRTF5MZqlKrTMTpPRotbmYmtjdtKi6sBhsf5Ii9WKvuXsrmxuM4ca2VDkSZveYcEGCscPZGVlLaet203/cAsba7W38enuHbmrrAiGh/P9cDA9uJwu/eM58ep+/LavlMz4MDKjDm5hOYB6l5tQvY6fS6u4b5cnk+nzPdKYkqQ50n43uFtzu93tZmF5Dd3CQvimqIKjosPRC8HgqDDu6NyhzWN1DDHx2YAuXL9tPyWNvh8tBY1dY/tRanfyWWE5d3RORt8onL3VtxP1LjeZizaSZ3OwvrqegZEH//34V7NnPkSnQ1wXyF0Dn18EsZlw2ssQ27l1/5IdsOc3mNvoZx7XFXqeAktf1F73mgRdJsLOn2BnY7p/m5dGrK4ENn2u/QH8cAuMugGOf6z9tf76iHac/ufAoAshoSdYYkA2mUUF6JRTsUIRbDRKIK2AGc2MESzXAe8CxUAZcK2UcosQIh3YCvSWUmYDE4GZjRVmi4CPgCcAhBAZwNWADShsekIHrpZSfgxkNvZNBKqBX9B8Pv4EgS8WKc7refM4uPznl3zaC6us1LhjAU3YEH6em+tNnqenyMRQyvO1p/Ywk57LZvpL2No+iRGaKaVolpacyxiiyYEftnCInF1S2ewP0SRoANy1M9en3/IeIeg2lLJw1i4ujKuHbMgdNwCDLihFUVAcv2qHzxqaKBw/MOAYk07HcfGak+vNnZIP+JjjYiPYNLovz+0r5NmsQgBSzUbO6RDLnY3CSoRBz92ZrQWXUL2OWzKS+N/+Ik5cs5ONo/qQaA5s3vpL2TMf4rtDVOqfm8dWA1W5ENcN9G1cFqQEIWDN+7Dkf1CxD7qfCFNeh1AvX/GqPIhIBl27zyEeXA541K8vN1TnwUsDte2ux8LueYHnKdvtETQAtn2v/TXRdyqU7ISLv4Mdc+Db/7SeY9nLkL8eBpwHA8/Xztntgq3fQEIvMJjhZa8yBes/1v78MexKqMqBU56DqI7a3FlL4bxPtHkVin8BbQobQohbGzclcI0QwjsTlB4YC2xvNTAAUspyYIqf9mw0B9Km17cDtweYYz9tmESklJ8AnwS7puDwvWBW7Q8nKqO2TftMjc3XudIuTT6vJVAR5nEjyU8yYUy2oC9sYNmesmaNxqI7xh/QSqNDfW96stGvYFmlbxKvlo6XgfhtQCi7Uox0KiiDOM2cMq+okuOTorE3OAkJ+3M3Wadb+hU0HkpOwGF3YfTjV3Iwua1zMrd2SqLe5SbMEPyx7srswP/2aya0Y1ZtZ/PovuiEoNbpItdmP+DQ2j/EgqdhwRPadvoouHSOdvNqqNCerttDSng4unX7Sc/AiKt922pL4MtLIWtx6/4758IzvhqHOp2FLmPnArAl00Xc7OuhWDNdcX8JGEzgaIBvrtU0AmV74Mc7218zBBY0+k4FnQE2fqa97nqsJsDsW+jpc/aH0Ps0z+tBF2p/3hRsgDeP1s41azF8205x68xjYO+CwPtXNVZ+3jnXt/3haDh9BvQ4CbJXQNpwsES3fSyF4ghFtOVV35i/ArTIjlzAOx7UjpbX4kEp5e9/1QIPFcYevWXcG//Hx3Iqu3cN9zGjRO8z4LKuaH6deXI2ujTB1eJ9AD6WU3np4xvYGtsJh96I1AtkpBFdhZ0uulLGmvaR7Ugg3VhCWHUmdZF72ZjahWVdfZ0e9dm1GLf5WnuynjrlgM9l3G0/cnadpuGY9sQoImJDuGTTXvY32Jk9pDuZi9ouTX9cXCS/lLXtePnf+Q1UFTdw0eNHERn3x26sW2obmLjKf7b7Bz7zhK5OmNaLXqPaNo38HZTanc05QFryZp8MJicGccMPhMsJhRuhwwDY8IlmCgjRNDl5tXms2PcbI769jY5OG25gQOd0AB7tdSkPbHsPgEdMnTi9/+Ww8Gm4/GcwWCB3JcT3gK+vht2/tL2GK36Fku1gCIGvLvffJ30UZC8DNAG62BRLkr2cJztdwYsZF/l0jXDWsnnZFMxeLlZuBINGfkmZMRqnzvfZ5/NUF5GJ3egZEU6ty028zgUv9IfaQrhxvaZZ6TIB+kxp//1s0sgEy94F8OkFYK8N3KfnqXCuH22G97E2fgGzrmjdxxKjCYUtOXY6jL4ZclZqpqPwhODX/C/BJSV6IZBSIgHdIdQOSSmpqdmEy1WP3VFObu5HVFevJz5+Ir17PYPTWU1JyTySkiaRn/8pu/c83eZ8RmMsIHE4KoiOGkaDNYfkpMnodCbyC74kMfEk3G4rxcU/4XC0DtcfMWIu4WHdWk98AAgh1rQXkXkwaFPY8FrMfOAMKeXBq5d+mNGWsBGzz4jTurz59cCrt7GPztwv/gtowkb8TXH8mtKblwadhX1gLO4kC8nba5i2vYDqGO2JrsZs4fOhx3Pp0m+ZMW4KACanA7tB0w7E55YRsa2YArem8ZACnrxxJOd3iDugc3n1mt+at5+JbiBjYAI7k0wMjgxlzpDuPvkoPujXmWmbPD4pFp2OfeP6A5C+YAN2P9+P8AY3t3xX2fzaOzGYN+UOJ7OKKrgkJb6V2eW3smrO37i3+fWCoT1Y8tAqCoSbEIck1O57XEuEkZGTu9DzqGR0Xom1craV47C56DwgHvE3qKQXltdwzgb/xeue7N6RS1N9zQLzy6qJNxnoF6H5eVTbqwnRh2DSe2m+bLXwpMcskmtO5MEuNzAl712uGjgTgz0Lp6kTADsTcjh6zb34zwoDYW43s/IKSHG2UazvPyvBEqvd2BY8BQueDNy32wma4NP/bM2U0IhbSp8Q5PaYWvQz0/K/Y/KgV4Ie08Tao3qTEmKiyuFkT72NvQ02yh1OLk1NwCA4uN+DmiJNWDv5WY9JqL4czBGgP0CtnssJLhuYGp3PV74Fc/wqcH055XkYcsmBmaSOcOxuN0/uLeD1nOAyK9yckeTX5FnncuGWmkn0zyClZNOmaykpbUdA/5tJSppEWtqlREUO8Lu/6X7v/Rs5rISNfwM+wsbuYRTkeyJAWmo2Bl69ja+ZypeNpV0+llPZ/vlbdNn8GteMuRTruGRo9JW46+s9VMVuwiUEbx09WZvA6mre701qRTGTNi5jpnWY1u0E7YZzeWo8j3cPnJ7cG7vVyeu3zaUifg2OisHMHB6LK9UTWVM4fiB5VjtDlm/ltd4ZnJEUw69l1QyJDKXc4SLGqCfGqD1h7qqz8m1xJf9t9Gm4vljPXGcDu1O0G+P9n5U3m5AufnI0lnBjcxbSBpebzo0alKvTEni4q3YuZXm1nLhjLznCk1fkxS4d6ba9juWzgqs4e93r49m7roStSwuaq9kC/OeNYKOwNeocdbyx4Q2u6n8VESZ/6V40Kq2VjP1sLP3j+/PhyR+iE14+PEVbqIruwu27ChECpqXEceZ6z3ns1M0ncv505L0F9Fm5h/LGRG6TKCSveiabDRMwN2zgluEP8ciefG6xLGLFple5urKaMfUN7DLHctKIWW2eR0jtfEKrZqF3VQIwosGKNTSWDdK3UGBnu4N9Ju0G+WleAX3Sj4FT/gsxnXwndFjh8RbFnE3hcG8eLdlYU8/xq3e2agf4YkAXxoZKnLXFfOeK47qtgYv8xRj0XJueyHFxkSSbjfRa8ueSyj3aNZUr01prBRxuiVNKLIdLJliXwyO0bPhU0zgFw5hbNSdWb3OZrRqqC8Dt1MwxUdo1w+22sXTZOOx23xv3wAEziYsbexBOom3yrHaiDHqMOsGbOSVcmBJHrNHgV7MZZdBT1ZZg/Cd5KW4xo2LjSUw8gbq6XZRXLCM97XKMRo9Ju6xsEVu23obD0X6NqNTUC4mJHs7mLTcCEBraBZutEJerjrCwbnTtchexsaNwOevRWetwhYRgMsUjpcTttpOb9yFxsWNxu21UVq3BbEpg1+6nEMJAUtKp7N//OslJk+nR41GstnxCzCm43Q0IYWD//nf4IHsbCRTTiy0+5nw3OmZyBb+KEwC4TT7BYNY07+/Y8WK6d3uA3NwPSE+/5PASNoQQ3dFyXaQDPg4IUsrLDv7SDi1tCxt6XNYmS5Fk4NXbeYtrWSC0JFpNwgbAs9ENzUICwBW/5LE5s4Jas4XNHbu0Om5MXTVnrF3IO2MnAXDNwm+YaR1GrzgD67wyel63+Du6dOnCBRdc0OZ5vHrNb1RHbcdmKaY8NILPh3lCZwbbdcw5of+BvTHApwVlvLi/iEXDe9Fp4Qafp+hjNtazoH8od8yqIDUxjMyB8Qw7tTMdWjzlTkqI5vqkWH59eDXPTPU4EnoLLE0MOi6do87oghCCBf+3gy2LWt/kAjHpzn40RJeTGZVJcUMxqeHaZ7Gncg8Wg4WU8BQAdlbs5LX1r/Fr9q9cN+A6rh14bcA5x3w6hiqbZt66Z/g9nN+rsX5gxX54sfH9PP5x6DwWjKG4fryLwQnXUWTWtBqfbbyNV3veyCJTRtDnEVX0FFIYqU68LegxhX1D4e1j4ab1EBrLz3t/5LbFgf0gjss4juePeT7whG63TySF0y3pvmRTm/VlvhnUlTklVUzvmtJKvV3ucLKuup7viyv51CvD666x/fw+eXqn+W/itLW7AoY/tyS61sXG4wZgCtGE53dzS7h3l//v0tEx4Syq8DWZTEmM5oWe6c0p6rMabPxaVs05ybGE/8kn5aCwVsGGz+DHVvkMkRKy3P2pTaonpkjQUa9lE3DqBfUWHTu7hFMfZsJh0K7v+aRwh3gZgJvlMwxgLUYc7Ag9C50plbCa+cx1jWQkS0khjxgqCA/rycAB72EOScTprOHtzf/H9IoRPuvY1NnJi7v2kF6XxRC+YGFkBh9yGWfxCS+JILQ27fBR+i4iYo/DnvMk75SGM5zl9GAHDgzocKPHTQXRXC/e+UPzT5Jfcy4ftdnHjQ43gghdNEMHf4spPFHTMtnrtd9bcWP2hogULYrqgs+hrhR2/awJkAXrm+eSgF0YMQ++AIZfDQk9PBorpw1yV0PpTs102WUCLkcDdXYbEZX7KF33KQlRCUw3j+ANXeD6W8FyjJzHKBZz08TvDh9hQwhxCvAVsA4tK+cqoAtaNMpiKeVpbQw/IggkbOj0Lur2FpFYn6911EkGXrmdj7iEH4UmIHgLGy9GNVB9okfYCLG5sJoDX5iuXPQdeunmjUazyjULv2FT1XD6Ra1kTt+RZMclc1J8FBlfaf4hl1xyCZ06dQIgb2cFP7yygQsfOYrQSCNut+TVqz+gPG0/DUYT74862edY5p/y2PLwCYSbDbjcEreUGA/wCW/y2l383sbF/v7Pyjn6P305vjS/zXlC7G5u/q4SY4uHmGtfG++T7RSguqwBkzWXklcu5buKR1rNtTd2A5nlmtpwY/IClnX+unlfYmgiHcI6sKHEI/w8MeYJ7l1yr88cmy7e5HedOdU5nDLrVABkozbm2ynfkmmIhC8v83U+9EICHca13mds2IDRvo/6qCl+xwUiPvsS3jluJkPWfsCnfW5ganoGc3NXce1eX/NV3ujelC1YQNzEiej12vfu611fszhvMb/s/4UuUV3YU+XRvJzW5TQeG/1Yq5t6vcuNWSeYX17Dzdv2U+oI/LR5VnIMP5dWc3dmh1Zmo7+C34uqWfT4mlamNoBN6SY2Z5iatW8APXLt7OhoatX3z5BqNvLN4G6khQSe94U1L/DOZs9NcPpR05nafWpQ80vppqZmC2FhXQGJXupBZyR7WwVz3lpIl9PuxImREhJIpgA9bpwY2Epf+rIRHdp31Y6RS8Wnf/g8e8otbA9YK/PPc6V8jXH8Sh5p6HHyOjcygV84ht8CjklNPpvY2DEkJJ2MaPTdAKhryEaHG4ulE+Bma84XZO1+hBBsODGwkYE8J+5pcz1XyNfoxwa6lZQzcFsxXyYfx4097vfpo5dOTihdxpyEo/3Ocde+t7ki7yvCXfVsD+3MTT3vxi5MbA9vnfenJbdnvUuKrYSvEo9jaczgdvtn1uewN9R/cc3+oYJnwvfSkDSa0zcFvh4XTRh0WAkba4AvpZRPCiFq0JJx5aNl8FwupWzj8ejIwFvY2LN7GPmNwoYxoo6QlZ6qrKVpiXx08tU4hOci/7y8jorPtZh8CTx2TrDlYjThAuCDkZq6a9qKnzA4wnAa6/ix70j2xyUTo9dxzm8eVfpdd93F4sVFvLw3nz1JTp5Gz47PnwYJtT21L2iT8AIwYMvvjF79G2ENdcwdcQe/3DquuWz99eO7cvsJHv+U9qhwONtUcU9cX8+vA9vPO3HztxVEWH2/e206m869B1a8xquFHkHijNi7CdFVcUyXEC5YO50wh+ZA+cZRN2FwmRi/+wI2d1hEUXgWbuFqM61bZlQme6s0H5IPT/qQgYkDAXhq+dNEvK+ZteothWyP2YTRbeZK8SWmuj5kmn8nTF+BHbgjMZ5byyvJcDrhpGf5NbI/FxR6wpsH5D1Nvkt772piLsYacSxfbriZYVXrGJ6RxghHOMXx17AieqDP2laM6EmnUG0e25497D1FE356bt5E8bP/ZdPsH7nw0RdpycrF35H2wAN+fRheWPMCb29+h5rYq7CFjyW08ksc5p44LH3pG2Zic13giPZdY/vxTm4J7+SV8nrvDMbEBDZB/Rmar00Svv/4KsJSPTegks2TKNvq/xln8s0DqYw2cOy2va32Pdo1BdsTG9HpbeyNDackykBKuYv3jo2k//46Lk25m/2GNPbQle/FGUGvtSkp3Gu9M6gtm8Ozq54CQIdkYKiLaXF23is1sdOq57G+E9BXzvYZP3bMSgoKvqS6ZjPFxXP8vx/AfI7lJ04hV6T77IuSFVSJth2Sh+2yElPr4udBYW32a4vz5AeczPdsZAA/czIbxGAfgUSPi3RXGfv0iVwk32UCP2PC0bz+YL1p4kttdCiysamPZt6IqHEyYEs1ZnsArdrRd0Kn0VpkkNOuOUL/+iiyKgd3bR46t+fY1fow7DoDK6IGcEWfg1YQ/JARVefisnnVhHtdP6tDXUyKm05onZF99iFssJ6CuUW1CZeAbWkmUsqdmBySRX0t2IyCXy8deVgJG7VAfynlXiFEOXC0lHKzEKIfMFtKmd7OFIc9wQobv549ibWxvqrE2+STpG1OpWzrqdSbBM+dHnwUQpOwMb/HIPKiE7jwd0/xtVmDjqY4UhNczlz9G/F1WoTIVddex+CtvpLq+Yu+ZX7/0fQqyKJ7cS6/9hzCriRN4r1i8XeE79+JsbaSlztfy6+3jWPic56n7gOJeMm59jpq58/HJQTHvvZ/bfa97esKrr8xjTWxSax9Zj1PnOC5KTVFm/QYYKHHuB4kZEQEDqP18iHY3+81fvilA1cmnkeRycnJaZpZpJfNzri1rwPwTZ8XOGn71ZhdHsGlOGw/lzx4DKd947lB3T70drpFd+Pqea3t5O+d8B4bSzbR8GoGHYa/S1Qnj4Pwjq9eQbo8wubI8zL47cvNhDoimNX3ORx6B+ds0BJMRV9WxZPbH+Phox5g+rIHAPhp6k/EWeIwCSPC7dTCQO31WuRHhwG4hI4fSio5Pi4KncNB6YMPUP3d90SefBLVc370+xbVhVg49X/vBvgkoHd5CbN7d8TSy1PIb+KCz9kiuwcc442lejYGezbm+t95ftwzHN/p+KDGNWFz2VhbtJYuhhq2bL2FlJSzSU05l/BwTdCVUiKlC53OgNVWiECwZKmW76+usBdhydsCzi3KHqBD5+4kZ6SjN5eTtf9NSkrmIoF5nMBMcRVHycVczwutxrqdIdQV9iai49qA829kIJ9xPhcyk15sZT8ZPMSTPg8cLRnt/IHr9O+1+Z7UEs6vHMfnQgu9TZBFPM/16HDzEyfxgbiCaFnBE9zGi9zODtG7zfkC0TPHzlnLfE1EbgEVYTriat1IwKEHk5fyyqWDnHgD3w0PY8rKKjqWgK7xVmGJ34mzIRqnNRoh3LidmiCc1icWvV7HxGm9MIUafDWUbpcWpKP3o+XNWanlHtn23R86vwOhyplErr0/+22DybSs5tfK67EaBfVmwd4kIz8O9RXEzHY318ytIrJBO3kJrOgRQkmknmG7rVSE6eiR50AnwW4Aq0nHK6dE4fY6986FDgwuyejtVk4dncaQo1OpcUvMTsmH9yxrFoKKovR8PiacGouO8ZsaGLDP1qy9axLUDkRg80aHk7PibqfY0Y1lNdOwSc+1+Po3Jx5WwkYBMFFKuVUIsQW4T0r5jRBiELBISvnXPNocQgIJG+bwekyrtGgSg2U8P00KbyVs3C8foBdb2f75WyzpFcL8/sFllPywdzqDDZKXX36Z33oMZmdyOlcv/IY3vbQS3jQJJrWmED466kSffR3Li8mN1bKRplYUE2azsjM53WdcxLbVvNy5tW/C7cd35/oJ7YdPSbeb7b09KtWS6FhqQsPo/N23mIVg9ErflCsPfFZO/42vsbG/lqdg1sgwtmSYWT28Ow1bKkn7YTghulp4oETL4lhbrDm3dWzxvV/yP5g3XdueXoXD7WBv0QbO/PnS5i5zcvIJaejI52X/C7j+Cx89CiLtPLbiMe4cdicJoQlIKen/QX+QEG6Pod5YjVvnokNVFyZvvRGdqZbuU25pNdf2z2cQ7M/+k343El8Ne1IEj4x6hH7Xv4Fjv1YoLv3ddwgb1SqJbjPlH3xA0RNtRIcACbfdSumrr2E7ZRJvTzqLGKedGQ7/prvHXv8vTr2errn7ufCRFwCIraqgPMpXQD5/7jd8PnEMk3Jnc/7MdfR66B7G57a2wc85fQ5pkZpQK6VkVeEqBicNZkvZFr7f8z1lDWXMy9byYowLd3B6TFuVBdomgnvYuSiDHuOzqHY99YfnaROpw7r9E0rLFtFxzKsUb5jKsvJo+vZYTNeU1o6wu+lGJTHspCeb6E+28OQbGSxXcT3PY8aOyxaK3lxPPRa205vnxL2t5joQ5g3tTt+IUKSUfF1cyR07cuhgNrJweE9WV9UxLCoMm1tiAnQ64dFuuRywex4//JTE/q2VzfOl94kle4v2ADDw2DTWz8shtXs0PUd1IDkzCqSWINBo1jf7wAC8c/tirLX+P1OjWc/ptw0mNMrkN1rNL8tfg/X/B1fMA2MI1loHc9/azKjTuxAbXokhMg5pCNUii51Wqksb+PDh9QCYRQ1dQ5aSbl5PZ/PvLDI/x5aczkh54LfnP3pTb8KuhxqLJsz9GSLjQ+g6JJG+E9KI8HoP7VYnOp1orm+1a3URP7+t+Y6k9ohm0vUD0HvVucLtIlA228MqGkUI8Q0wR0o5QwjxDFol1g+A09HSlR/YY85hSGBhowHTKu1DNIZNYe6pLtbG+Aobj8vb6cQ+tn02g8fODS5MNak4jw1nn0x9QwPPPPNMs9lD73Lh8if9Azd9/jK2pDQfE0l7XLnoW/SNn3H4jnW8mXYJDl1rO/NX1x7FkIy2zT+1ixeTc+VVAIirLqDmg48Jt3r2j3/dk0vNO0+GN9cmn4EOCVHpUJXt/0DTq8jLy2Pt2rWcfNJJ1D2WTo7BRe35H9A5eRBnfncmFTZPFPZ1fS/n2sE3wcPRPmaW1MwQIpNjKC+oo2ifphWKSrRw9j3DMFk8F8wqWxWzn9lGRZ539Ib2nvU8+6rmlugqB5VRHu3Loj0D0G2bQnx925FCI35/mLCGYlYOHEVt9AWMXnYvZrsnn0rYuKNJf/PNVuPs+/ez5wRfoTJ0xAjS333H/xOiF96hqGc4apllDPfb77y533LJnK8wOJ3o2rkWRJ97Dj/F5PJ81AqkEAipI9QeycVdLuXKcWfz8pqn+WTzXHoWjmFU8TjCO2zC2eM7qgt706fHkjbn9oeUgtq8gUR0XMfggbOIiR3gtU9SUbSc/JLPKCr5odXYDh3OomPq+ezd9xJVVWsYOuQr6up2EhM9EaerAikbsNmKyM55h9JSTRiq6vAQ8RGdeGHtC+wq38U1K3xNU5Hpv+Ooj8Vek4TbGYJ0ab8jY3ghLns4GROexhxZSHHV8dwS7astG7LbynHr63lxUjQNZt8Lfp/9NjoVO5k9zPep+vHX3+aVMydTkKBF1dzy8dtMWvIroQMHkv7+THTmIG/gfzFSSuwNTuZ/tJ09a9sPVT1/+ghikn3PVUrJxvm5rJm7n4bqA0lK/cdI6x1LztZyxl/Uk96jU1rtb3JOdrvcCG9hzYs9JbWkRlsw6nU4XG6GPT6PGqsTsxuObTAS69axLMTBHqMbJHR06Ti33oxXIB5jLuhBysB44sO171LTcbYXVrO7uJbbv9iA1dFaYDkqM47s8nqiQ41sya/m3GFpzFqXh93p5sFTe3PZmM5kl9VTUW/nwe+2cFRmHHef5L/G1uEmbGQC4VLKjUKIUOA5YDRacbVbGzOAHtH4ChtDyc/X1M0h4VaMqzQ7e5hxIt9NDWVttK+wMViu5DaeZsNH1/DMRccFdbw+O9bya+EtuC79iUffm8uqTj1Zk9F2wbWhW35ncGkhM8ZpIbTdsrazq1PbY5q0GgAhubuZFXkctaZI6jGRGR/G3lKPs+fyeybQISpwgq7dJ5yAY382YRecwynpX3HKSjcX/+r5Ifzy35d4IiyBT+67AQNJzRqNJq5LOj24vEpxXZlepjnfjlqylGVjRjfvmp02G6vBI+FsmLbBE4paU8j6R+5gac2lJBp2cUbcPeiHXIDrpBd544YFPofo1C8Ok8XAzpVFPu3G8GJiuswntodvlsoJR61GCB01DXtZuc6nADEA6Zn3krfnZWwNFqIejiC8roCC5KOoOtdFTNcF5P9+KdX7R2GOyqXzCQ+TuuwU5EetY/YT77iD2GkXse+MM7Dt0irSmnv2JPObr1v1PRDsLjfpLZK5xTlsnLnsx4BPcAnFxUz4bT7uMImwgnB5elZGZrJ2sBYpY4rMJ/PEh4JaR/6KK6jO1n4/Qm8jefDHRHVeTsHKS6jKGo05KgdnQzTx2Vv4cfh6dIYoUus1h+vu23eQ1bkTdrOZKbO+RgpBiM1TO8gVJtHXCbotW4o+JqbVDWJ3xW7iF26hMiWC1PTelM2Ywc/9JM9Wf6l18ErGZbZLbCbB/3X6mkWfaJe3o5Y/wPKjNBt/fOlGuu3+kuUjWzssA9SZBc9Paducet9nZQxN2cuSyhDqIvagN5oJD7eQkpJCeWYZM9e8ynv/02wb11yv54L5bo7e4rled1m4gNpffyXm7LMRhmArT/xxqmxVRJoiWV20mrSINJJCkwLmNHHYXXzxxCoqCuv97gfo2DOGMWd347sX11NfFVjAcCHRt6Fn+C3MgcwI5YLoWKiyU7hFexDJ07sI7xjGjTcPJcRkYPamAo7tlUSY+cDfqxfm7eSFebva73gEsv/pUw8PYUMIYQCOB36XUrZOYfYPQQgR8I04c0g/RnZJp0NcOs+GlLP+f58FnCfpt3UAxFW72HnHRTh3+bc1dxgzjttSwukYWsWvxl689dZbAeeMfeNjjN1702XfNtbN+pKG2f7zLhi69SLuTY8fRdGEQQHn7HniNF6+81KeXCNZP28W5T8FTq7U9B3Z1rMXZ2btY6vXBd6bM6OiuD2zK5H1dWyxWjlrf1bAOVdfGcaQFD2EJXDVp9m8tda/KrZDhw5cdZVHu/Dwww8HnPPNN9/kqi6FsPg5Zqyxc/UP1oB9X7n61+btp7+6hpxS/xeSk0+J4P1T44i/Zh9r1qxh6NDAv8nXXk+le3czxn2CV24r5Ysq/7X/unUz8fobHZkwbidZ555H5pdf+O0HMD0pmft370IfHs6MGTO4+urAeRi8f8sDB/Vmw3r/370rrriCQYMHU1KcjyX0W+66038kDsALL3alb19NoHz++RLmzK5p85yaOHZia+fMJm7oOZITMkZTmuBgbs42Pl0YOFHSQw95hJgZM2ZQUFDgt9/k+AQej49HJyWrgWk7AldR+CKjE31CND+DhwoLAn5Ovc1mvuzkMYv0bmPOB5NTGTT0csri+jJv73I+XRzYnHf3I49hdjnaPafBgwczaZImdO8r2scHb3wQcM6Lpp9ATGUOl/+i4wmblc927/bbr++AvuBlFdx8SWBn7+defo5+k/oRbY5myj1TyJ8ZOKKh78y+vHfCe1TYKrhtym1sWu//OzVp4tmc0FX7DmeX7OSZWYFDzjtd9AIyRQvxLJv7MrUbfvLbz5TUhQ6XeLRQ+58+NeCcsSdexakXnERuUTQbfvuAsjmB/Woy7vqBvqmRbM6rpmDmTdiL/OcBCh9wAnEn3sD0Sb3pF1Le5jXi5Adm0rf/IIZkxHDzDddS8Ptsv/1MSV0YdfvbXH10JqO7xpMeF9ip96RrHyT9qNM4Z3ga333yAS883DpU2vucmvA6p0MibLQb9yildAKzgCPeL+PPknTmTwRryZuwMbBEr6HdGHLro+jSpXX+DW/65Wpf8j2de7XZL7q+hl1LTgpqfV0N5SxZuID7B7lJjW6/nPbeM9r3zBdAZL2mKUm4o+0Ye+u4NymJexR5+y4tO+JBQEoJEx+EC7/SioO1QUnyIiRuItJ+x2CpbLNv/JUH9kTj6CzRTWjf8fL96V+R/F7bMf4dHnkYfbh/E0hL5r2n+RYVF/1KfV3gBGlr166ltCSPMWP/j4iIthMXmUyt69b4w+0ykbf0Gvb+2L53/76u8awaGc6+LjHURrRvChj2+8p2++Skp/HFOWfz2bnnsPjo9hNVOQwGymNiKIsLzuwZcVLb36fUhx9kyvcPc+mbpzHuvLY/+yZB40Awu9p+n4bOraKj/iRmnTmVXZGRAfvtqfAvhPjjuTXPceuCW7nsp+DSKF3606XcuuBWdlUE/r3sNxbR8x4dV78yjvKItq+l1sa70zE9Epg6OLCpMjpcMmHEBiJ63U1Er7vbnDMk+QfWuh6gOP4GzIn+hRdvNudVIIxloA98PTdGryS5/6NscbxGfm3bIf+PTO7L02f256yhHTm1f2vzTRN9U6OYf/sxnDs8nVKb/5IOTfTJqOe+yYmM75FIr5TAnz3AJ9f24r7z8kns9yjm0OI2+x5sgjWj/I7mFNpGqcUjG28zCsDiRVpdh8nOwfy2awbC4GbA5Tu4QHzV7lwGp5s7P92I07aehYM6k+52EWq3cuXRg5j12ussGn48I9YvItSqfYEvvPkqEkdOapUI67/dO1L16EYaKp7nv9f4lrs+dusqupbk4QZWdO9Nn9wsrqr/lF7s4bjBb7EpojurVpxNmk0zE8yQ55Ev/FdHfejEZDp/o/2Yw7o9gc7g+/S6cdpGtvfSPOHLIuDa6w2YdCY+O/UzLvt4CrYIMzOf9PwYe27bqqlXp0eRZ9BjTrmGkmf8mwG6zP0RY0YGjv372XPiSXx27jnN+/pu2sTmfv2oEjlc9ckyVg4fxr7MwLHqI5ctJyM7m8w5s7Hn5LDS1fZFMi+3J6kdtafVhAeMlDyq3QSS6ELvY+ag07VWt7rr6yn/6GNKnn8eaZBIPehs2kVTIgn5/hr25bzc3D+lw9n06vUkbreTurqd1NZuo6Eykn0F1wBQnTOEyr1HU1/kiTTQh1QhnSYMlipOumokHbt5znnHigLmzQwcmdHh2GKiYu8LuH/D+hMYMDDwRbYmbwARqb7fw6VLziM0tJK09E3Ex3uqAkspEEI2p/e/wGTCfMYZ6MLDefvttwE4++yz6dWrV5saqZZ07tyZMWPGkJGYiH3XLh4p+4jZ+b7aj0h7JDPHz+Tbb75FSkldXXCJvtqjV1QUDVFRZGVnc+7gwei6d+f/Pv2UhIQERo8ezYABAwKaDqxWK9u3b+ebb77xaZ8wYQJjx45lzpw5REREMGDAAKKionA6nWzbto309HSioqJazed0arGL27Zt46uv2r/uBCKmvJyJ837FYTRistv9+ub4c4i8/yI9OzsKLFbJq7WT6T/lMsxeD0alDaVU1Bh4feEPlO/Ywvbk+dQZ6v6UZ6WUOs5M+h8XjuyMwZVIp3jtib7eUc/N829meYEnKkxIQZfqLgwo9/jy5IbmYnFZsOvsdGgIXE8pNzSXrIgsbHobGbUZhDvC2Ri7kRpjTXPYR6/KXiRYE0iwHnh9mnJTObH2WCISIvjP5f8hqz6Ltxa9RcR67Zl9c8xmkuuT6dmrJ6XrSw94/kAUhxSzKXYTdp2dIaVDSLQmsiV6C9tjGrVyfqqHbr5k8+FhRgEQQpwEPAU8BKwBfH7ZjdVcj2gCCRtXWCcye+sjdLpkP4ZoV1DCRoi1nhtmPoHO2A23Yxe1XQcjjTrCt632+zvsEFLN+e8vYl11PSet2cmkDUuYcdN/EELw0uUzcdR+ybNewsbQPZsZmut5QplsWs6gK16E1zRbuARKjTEkOHxL2TzScBVui3913L28zBjnf2no+rzfi8XnT2oXvotu0xMf25G5Uz0VLM/94Vw6/biJs5eaKE8J4dg5y6nMXo7+vZMY1UmLVFia9F/ybr65zfetLDaWecd7fF7O+fQzzr5Hu+H/3v8D9l5wIV+efRYAJ/8wmzmn+g/ZPe6nn9l+RQc6dV6vNZTrITa4FMjp6VfQrauW+Mdms/HRRx8xatQoejWGjG7r6V+71GPdWnQWzd/l1988F+SJE/xrGLz7AOQsupG0zAmEZj5Jvb11XcOmpHEAxrBiupxyHxV5fdm8RzOVjep0OhsLPmJQY2rz8vIUCvK7Y7OHEh1VRGaXNa3mBMhZeBMRaauJzlzq077/17sQegcNpV1xSx1WSyFnXnEc8enhzJs3j8rKSrKysvzO2RbJyclceeWV5Obmkp6eTmlpKa+++ioAt956K5FtPJU34S+zaBMFBQVYLBbCw8Opq6sjOzubPn20CKpHHvHvX3GgDBkyhC5dulBWVkaPHj0oKCjg66/9C9P3338/hoPkTyGlpLa2Fkvj92z+/PksXer53BISEjjttNOITopm49qN/DL3wOt4DF25is77tFpJQkqfS4EEbIMG0vPBB7EmJ/Pgp0uJK1od1LwNBsHPqd/g1HmSPxhdRhKtiRRaCnHpfH+fercek9tEz8qeGNwGiixFlIaU0qWmC5WmSoaXDA/6nKKioujatStr1vj/Dfxr0QMuePjhhw8rYcPbHdZ7gACklPKIrxDUlrAxa9cT9PmPFvYWjLARX17EpZ97nm5dphDcZgvGGv917Mw6J9d/ot28bTYbDoeD8PBwrHW1vHrZuQDMOP82qiI1h7O73/0UvamOsjRNBfzQQw9pF9+vroRNn7c+wEOVAPzf9WeSazVzQvgafi7tjbVDZ1zhTRd3SWrqNp+b0pbsOD7Q1XP9V25G7JAs6m/glVNap7lembeST2d+SqQjkvzQfJ67+TlGfeIbznlSp5O4MXoKNef4rx5alJjIggnjAYhy2Dnp+9lceq2L2lDBxmkbtUyBbjd75y+g7K67iKitZflRI8nOyGiOP29+P821DB+hXfzXrD6V+voYQJKesRGXy0A01cRm+lcnDzO+S+TYcdQsXsJzv3oUeQ/efz/1y5aRc5XHZyJm2kVEnngioYN9M/3t3v00+7NnMHTIF0RF+c8CKKVk3dqrqKgKnCnRHy67BX0Qpo2G2lfYN1cg3AZKk5cAkrFH+5pssubdy7Tpl2A06XE6a1i4aCAAvXo+CfXH8/V/1xKfFs6Ei3qRkO7filpbW8uOHTv4/vvvW+0LCwvz0ThcdNFF7ZoM/0qcTic//vgjVXt3Yt+3k8l33E/+zm3sWLaY/RvXYY9JwJaspZQ3VFfgbPy9derU6YAEq/T0dAYOHEifPn0wBxExUpabTWxqWmCNicPF6qwK9pfX8dOWIhIjzDw0qTcWo545mwuxSBsT+mWg95MNuLCwkDfeeCPotfsjorqamnaEQGNoJI76titFt0WfPn2orq4mJyfngMbp9XqmTJlCnz59WL16NZs3b0YIQUJCAp07d24WNJuQUmK1Wvnoo4/Iy9NS1/fs2RObzca+fftazT916lTycnMYP2EiJpOJnTt30qlTJ8rKyli1ahUDBw4kIyMDh8OB0WikoaGBuro6lq9bzpqcNWSXZZNe50lDlXp8KvHF8ZQUlxDXIY4FOxawLWwbRZYiEHBc2nHcP+J+fsv7jdyqXM7vfj6JUYmN1WZrEEKQl5dHXFwcCQkejYvL5aKyspIVK1awatUqAPoP7M+E8RN44X8vtPkeHm7Cxri29ksp/edsPoJoS9j4bN/TDLxaU117Cxsn//olcya2jkw4esXPjFi/KOCx0kMrya6P9mkbO7ITw48ZCQMvaPaIL8/P471btJubI/pEXjh3DAAb511K4jUzmP6O5lw0ffp0z0SFm+CNMVqhpiXPgzkK7smmrKGMrzd/wMU/PIwByeulQ2moDaWm8xBCQysZMrT1zaKJDtcZEQjePl7Hz0N0fDP5G7pEd2H27NkYjUaWLVvm07868UN+CfOfa+S5nvcwOHEQ7q/nUjZjRnP7L8dPoDxW+/F81fkrRqeMZlv5No7ueDSPjvb1A8i/+x6sO3eQ9tlnrFq1iiFDhmA0Gpn95pusKSpi7NEfAlBTE8f6dZ6U7UaDjn7psUxKrUSOu5PfFtzDkiVh9B/wC1FRxaz8/QwmfL2QH0/2TfMOMGHerySUaurOzl/PIqRXYP8ZKSVOZxVGY3TAPt4sWz6Rhoas5tcbNxyHlDoiI4sJDasiKSmws6U/Ro6YR1hYZ5bN2s26n7VIism3DCKlWyi1dbuIjOjrd5zLZaW8fAkJCcce0PG8cdhtGE1mXE4n+sYn+tzcXEwmE4mJiX943j9KbXkZb1578R8eL4UOId0kpHfioqdfosFq5ZlnnmnVLyMjg6lTpxIWGsrGX+fy27vaDf6ip18isZPHDPbLW6+wcd5cLnj8efJ37WD+zNYhz5e/9yU/bynhzq82ttrXFm9eNIQxXeMJNekprbUjpSQ2zMTCnSWEmQ0UVjVQUFzGlRP7YDAYyC+r5rlPf+b3PBvF7nBidfWcaGrbP8Abo91O761bGZqSSsLNNxHSvTtut5uC/HxiampZf+mlWBoaMDidLBs9ioKUwD4KgQgJCSEkJITKykpAcxgvKCjgsssuIz394OaS1H63Dn6Z+TZb581p1xoUlZTM8Mln0XXYSJw2GwazGXNoKGW5OZTm7OfHV54DYPIdD5A5eCgV+fn8PONl6irLqSoqbJ6nY+++9BpzDF2HHUVopMecJqXEWleLKcTS/Fvyh6u6mvL3PyCkbx/CRo5E2u3UrVxJ3g03evrodAgpm01oZbGx2ELMRFZXM3zNmsNH2Pg34E/YSHBHMtk+LKCwcfZ37/L5aa39Am5/4/42v6gZYRUkHnslq7790qf9tl6L4ewPobeW5fL3rz9nyaeaB/oxl13PKSbN56Jw4Ti4dRsvv/8VkZGRXHxxi4uprQaEDp7pAic/A4OncfP8m/k1+1c62R18Wl7MimHaU9uyxVMZNbZtbU3SnUb0taLZpPHWmPnEUc3HH/r3jl+YvJBSSws7pIR+5f3ICc+h0lzJlIxjmJ09H4cbEIKp+7T3fUPsBnZHebQOdw27iwt7X9jm+rzZs2ctWfs1U0vvXkt4szF/RbP2pwU+gpofjp/7Ez+fqKWSP+dTLQqp1/bAPhPB0tDQgNHoydnx9ddf0LVrAt9+6z8fRVRUIf0HaGrxqqoESoovZtKkU9HrSzGZbKxdpxXoG3f0egyGg+fLXVdZwRtXX+TTNursCyjet5fdq5aT3KUbhXvad6C97u3/wxLR9tNxbUU5b14zje4jRnPcVTcQEh7ODy88zY7lizn2iv8w4LiTkFLisDZgsrSdOK+yqJB3bryi/RNsZPzFV9L/2JPYtWo5c156FoBr3/qY169su/DhX8ni2FFsjeiJXXdo82kIoUUBv3vRQAYkhzBnzhwGDBjAmkI7ywrh+bMHoNcJal97lbID0JoUJCeTnZ7O0NWr0bndCCDtrbcoF/DuvHl0796dQYMGkZaWRniQTtHB4rBZcbvc6PQ6DEZT84mu+/E71sz5lvS+A9k8/+e2JzlMCAkPJ84SQdyW7eyPsJBaVkW92Uh2XCTuxqRdmcUV6KQkptaKwe0mKz6KhhAT0f360+eUyRhMZvYtWUDvo46m48hRStg4lPgTNibY+5LpTmoWNqqI5DrhCZU675sZfDLlqlZz3fHG/a3avBkZl83o1zbicjp54YIpze1jE/Zh6DeZwVc/AcBz53hCuG451ooub5Vnkun+w/UCcc4P57C1bCuXxdnoH3pgJZydyyz8kh+FJIRZcZegd0RxYYhveufOnTuxb19W8+u1cWu5cswpjO11Dhajhce+fgz9Zs3ati9uI9P6bGC3VccrJSH0L+tPt2otg+kd993ByP8b2TzPZ6d+Ru+44NM0N/lC9OjxKB1Tz8dqtSKECKjO3rRpk1/nu647dzHhnrupOuvsZqfV6IoKRnbOZOTNNwW9HoDq6mrmz5/PhAkTAHjllVewBQgfbiIuLo6ysjLCw8OprfVNNX3zzTcTHR3t06aVrLai1wfOkxIsLb+XB4suQ0dSX1lBwe4dTHv2FSLi4lkz+xv2rP6dxE5d2LLwwP3Ppz3zMnEd09m+dCHLvvw/Jt18Nx/dc7NPn5iUjpxyw+3sXbeKPuMmEhkfvIZFSknuts18/nDbBbxaMuXOB1j+5acU7W0tiO23pJHRoJkLvk6eRK6lI2HOOnTSxcnFP5Fob9thMKP/IIaefyW6yHjS4zSha8aiPTwxJ3BoblvcOKEr143XQkxzKxromhj8jd5VU8POYf79JyJOOIHUZ59BmDxJBKXbTcP69ew/P7AQl3DbrcRdcQWO7Gz2X3wJKU89Sejw4Qg/2S/bQrrdZC/4jS/ffOGAxmX0H8Tos87HYXeQlNkVc2gobrcLW10ddRXl1FVVYrJYyN60oflh0B9TLrgcU1g4n8/QwnJDbQ7Sy6oRUmJ2utC73Th1OsrDLRREh+FsJ1HfX8Xtn89WwsahJBhhI49U7hQvNY85ZtkcFrSorHrGnA/pkh1YFXl6x810Cq9A97AmLHgLFE3c+un3/O/805Bu7bM5Kn4/oxJa5E07AGGjwdnA8I+1C8ILaW2H5K78/XQoreH8k8yUvPgzVbdoERrr3+xFVKdq9vbogilbs2sbjQ1ERJQSEeEmPWMR0cVuFhUeT9cuq1i37iTCwmKZNGkSa9asYccO7T3R6ZyMHvOJzzHr6yNZs3oy19x4DcmxybjcLt7b+DJ9zDaSzGZycj+ga9e76JB8BjqdEbu9jMVLhhMZOYhhQz3aIW+nywnjdwe0gbfkl19+Yd++fRwzqD//N3sunffu5RgEGR+8j9tqZfeXX/F/uz03jaOPPrpZcAiG9rQnLWmphSkpKeHVV18lIyODSy+9tI2Rf571P8/h13dea9U+9d5H2PDLHHavWtHc1mXoCPas/p3BJ52Gw2Yla8M6eo4Zx6pvv2TMudMIjY7GHBrG98+3nW69PRIyOlOyv7U9vT1CwiO49q2P0OkOzkVcut047DaQEoPZjBA6n6qjSMmSPWU8/PV6dpdrSaqMbjvHlfzGyuih6HBTbG5b0Pn4ihEM7BjJN4/eTeHu1unRvTnuquvpP9F/SK63Gaslv+8t49sN+Tw6uS96XXC/kWCoW7kSU8eOGBITg0oyVvLSy5S+1vq7FiwxF16IPjYGU1oae912Vn7/FRc88zLrPp7JyiXzg56nU0kl5WEhdC+sIL62vrkGTEtiL7+MhP/8h+qff8aYlIR0usi58so/vP5ASEAfFYW7Mf+LVrtGh0Ovx60T5MRGIC0WqjokYjQa0UVFkd5/EKERkQw44RRqSkv45IE7SOzUmfL8PGrLy+gz7liSunRlzexvqCoqJDQqGr3BiNDpuOrVd/95woYQIhZ4By1JWClwj5SyVTUvIcQljf28PeFOlVIuCGYeIcRE4FUgHfgduERKub+ttQUjbOygJ4+Ix5vHjP39ZxaP8GRqv0E+x0iWsf7NwPb823ot1jamBxY2rnrxNWbcdF3rMU30OBnO+4RguWvRXczZp1WSbBI2ltXq6R7iJt6gff67fs6kvjaS6vh+zeOE08GYCVp56h1fdaLH1CwAVq86jYaGqGbfiIiIvtTU+CYHysnuQ1aW5hwphIsxY9su2lZbE8vo0U8TFzcet7uBBQv7tdnfm/HHbKOiYgXrN2g34gH93yY+fnxQY/3dXK+87EYijjvW50nqk08+aRaYILBZpons7GxSU1OZPXs2a9e2LvIVFhaG2+3mlltuobq6mri4uKCFo4OBdLtBaGmYrXW1fP+/pxhy8mS+frp1iOqtn37vszZrXS2l2Vl07OXf96Ml9VWVvH5V+6aw9L4DOPO+R5vf9+rSEiLi4hFC4HI62bd+DbEpHYlNSaVw904+vu/WgHNd/cYHhMd40u9LKckuryejjeRI3nhHvEgpySqrJzHCTKhJT1WDg4U7SxicHkNChJmQxhoU//l4LbM3+U/Q5c19J/fijMGpzFi8l5P7diAhwoxeJ4iyGJvn8l5HQ001xpAQDAYje9et5ptnPFE1RnMIN7z/BUIIbPX1vH/7fwiJiKAkS/PzSe3ZG51Oz5S7HmTv2lUkpHciruPhVzfTXVeHq7YWZ2kpWVM9fnDm0aOwLfX4hLmEoDAqjA0ZSYTaHDh1AruxbcGmd0E5nYorMCQmkvLM080O3c6KSoqfeQZHQQENfn6jB4vwCRMIH38MkSdpOZCkw4EhRjNju+12hMHgV2vjrq+nYeMm9JERGNPS0Ef8NamuDqt05QftYEJ8gpZI7HJgIDAbGCWl3NKi3yXAFVLKMQc6jxAiHtgDXAF8DzwKjJVSjvQ3VxPBCBuPM52twnMTPPfbt/l0sscu/B/5P0axpFnYiGiwIYWgNkRTIw6KyWNC8l64Zikkaxdqf8JG9zQLO3M8clYrYePWbRAZvLNVv/f7Ealzk2qSXB5vY0GNgR+qTEyJtnNMhBaK1rTmml6+37lh/T8nJNpX5b91yziSkncTF5fX7rHzcntiMteT0FIzEwC3aQwOtw6zM7CDbUvMpiRsdi2fSJ8+L5CcNCmocdLt5vnzWpcpP/Xmu+k+crTfm/8jjzyC260FZ11++eWkpWmhvWvXruW7777DYrEwcuRI5s/3fbK69ZZbePOVl6lzOBmb0YHxF19BfVUVK7/5gj7HHMtHd9/EsNOmcvQFf63mAuDLxx9g/0Yt0+3Vb3zAm9dM89k/4PhTOPr8i9v1izhQquod5FU2cPKLiwhx2xjQNYXPrj7qT89blpdDaEwcllDf9dbbnbjckgvfWcmGnMrm9q6J4cy9aSxCCJbtKWVgWjRGvY7s8nqO/1/w37tA9O4QyXG9k7j52G4UVFmJDTO1EiL+LHk7tvHpg4EzRbZFQnonznn4aYwhIa20Pt5C6MHA3lCPMcTiM5/b5ULnx2RQXVrCzNuuw2FtIDQikvqaA4tuSaqqoyhKEyYvvPhakk4Ovpp1k3AppcRVXo6hMdmb22ql6ptvsPTvT/7992Pbug1daCjueu2hLfLkk0l57r+H9EHhYHNYCxtCCAtabZRd7WkMvMaEARVAXynlzsa2D4E8KeXdLfpeQgBho715hBBXoWkyRnn1LwUGSSkDGjX9CRvj7X3p4iVsPMKjzWWeL5z1Oh2K86iJSuWN87SUu+/J8zBhZ/2bPdEZe3Di6h/ZkRzLniRNih0Sm8sxSft8TCBluTn8cv/55DW0TuoDcGb6JjLCKn0bD8CE4nI1MPPn/nQye6KXv6owsrjWyJejLyY3WyvLvv7NXhy/aS9ru2eyb6Dne6fTORg95tOgjxcs6z/oR8YxuVTLTDIy/Me/h4SPRufOJ6XD2ej1oezY6UldXetMZkNROqNTfbNLOhKX8vHv+1m8q5Rv/jOagWnRreaVUvLeLddQUeARljKHDCcsOoZNv3oSXp1x93Q6D/L9DVqtVp56ylNx9Nxzz6UgP5+FiwLfpHr27Ene175hp1FJyT4e6U0ce8V1DDiudTRMW0gpqS4pJiw6BoOXfdwfG+fN5Ze3Aqem7zNuIide17rK7R/B6XLz6vw9fLYqm/yqwKnjm1j7wHHEhvlf/y9bi7jyg9Vsflhz1j3t5SUMzoiha2I4T/2o/azHdotn8a5S7jihByMzY5n6+nK/c/2V/H7vRJIi28/IezBwWK3Memo6udt8tYoGs5krX3mXld9+SU1pCfk7tlJbETgV0rUzPsISGcVn0+8ib/tWn31Cp+PiZ19l/vszmgVUgL7jj2PcRZdTX1VFdFIyUkryd2zl80fuJTIhkUEnTmLhh++0uX69wcCgk05j9ff+yy+0xbFXXEdpzn4MJjP9JpxATHKHA/bpUGgcVsKGEGImsFJK+ZoQwoSW2KsPYAdOl1L+GMQcg4BlUkqLV9vtwDgp5aQWfS9BM4M0AOXAh8CTUkpne/MIIV4ETFLKa732bwYeklIGDLvQhYZJQ/de9EJTslRVJhEnwzFJIyW2bMI61JNFZxrQnp7S8jUbstnhYndGV3S46YEWpVBXEIXQJxNTuRMJ1IcaseoMhDicOA1OrB3jSY9MR0ejzXffUnIbWnvqRxhsRJus0KlR5mr6rA5Aiq5vyMJm8y02VuEUJEb1JswQSl1DDrV5tRgbHFisdlw6HRURYUi9AWnQoiXCw8vRGxzY7ZaA6at1bonUdWdHuaR7rP8cFrsqMuls3o+tyoz3185oDqE6LJKEkCyf/jvKuzKicxxCQI3VSVWDg/hwE3Xl+2kor6PMEEtaTB6i0RRUbYugoC7JZ45BaTGYjZ6LUJPDnzcp3XuibzzXnK2+NR2SOndBuiU6gwEhQGcwUlVeRlVt8BkrI01G6iqDz3vXsVffoJ6UaivKcTkdVJf4Tzsck5xCeKz2hGarr0cIKNqnJRlLzuxGoZfzYlrv4MxWDpebNfsrSIm2kB4bWPNRa3OyOS+wUKwTAnc7156MuDA6RIWQU15PXmVwadMD0SkujOSoEFxuyaqswJ+FAIZ1jkUnBA6XG71OIBBt/uTW7q/A7nI3f1f/DqSUIGW7N1wpJQW7d+ByHHjK9ENBRFwCeoMBh81GZHxCs/AspaS+soLQqGglVBxkFi5ceEiEjWBT250ANHlGnoZWJyUZuAyYDrQrbADhQMurTxX+a64sAvoC+9GEms8AJ/BkEPOEAy3rHPs9TqMW5CoA4aUydti1JxOTNCKlG5prtGlXEpPTU6FQCkgqyScywZPiW++2E16X1TzCLF1YMSAkRNVJchtKKW3QPM6HJQ9DhMVAQ+sIkejkDhDipfE4wCuZ01nbStAAqHAJMhtLjoeHZqCr8GgHdFIiXE6Ey4mr8QZcW+uxfXsLG2H1LoSU1IYZiKh1ssbpQqJjf3UaGZG+yXlsVSY6NBRibWgdFeKwWXE79dgdRkwR2kWwoFYL882tqMfplhRVa0/GdcX5hLo0FWaso5y6Ygui0aOrUh8FerC4GrDqQ5AI1uVoidTCzQaSIkOQZb6mH4PJ1CxogHbTtVsbKNqrCUxNN+eW6AGXJdzzmUhJckI8hpAQCvLyob6mWTisa/TJTercBZMllKriIqpLNQEhPq0TlkZbbGl2Fg21NdSUlhAWHUPB7h2YLKHY6j2CTZNQYLc2+Ghm/FFRmE9FYetaDWFRMRhDQujQrQcVBfnEpfrWnaisd7C9sLUKO8piJNSkXTLyKxvIbxQAQox6+neMQicEhdVWskpbC2L9UqOwmPTYnW5MBh06IXC5JVvyqwkze/JCeLO/rI79Ze0LdZEhRsxGHSU1NswGPTan9ltKjgzBZNCRHBWCrvFz0usEIzPjqLe7EIDFFNi8YfSTJMsfgzParu56KBBCBHV9EEKQ0q1n83tdU1pCVYnnGtGxZx+ETofL4cDpsGOrr6OquAiD0Uh8ulaUzu1yUbJ/b6vPCyAqMZnqkmKkdNOhW4/mMFO3y4XTYUdvMKA3GLHW1WKrraWmvIz4tHTMYeFtCthCCMK8fHD+kUiw59ci7S4M0SHoo81/KvV7sMc8VAQrbMQATY9PJwJfSSmLhRCfAoELMfhSC7R8fI8EWpWRlFJ6ZzHaJIR4BLgDTdhob54DOc4MYAZoZpTY/73N83IqRYWZ7Nw5miusE7Hbyvm+4nX6TtvFfTxKlsike8EWJn+rOWjGV9dTGhlK5xNyiOqkhSjmvZ7KsE0eeSd5WAUbilOIz25AL2FrGky/UHvrF1y8ABxWnruwdXKw2z77oVWbN263E5erDqPRvwlmz57nydr/qk9bQsIJ9O/ncYh0VlSw6yhPts+0N9/gzde0PAOWUcdSXFHJ1VdfTXx8PL///jtlOdcQntTAhrd6cEv3pc3XtxnO08l2esLZksJ3cereH+lzoXbT3vRed1x27cJ+0dMvUVtZwddPtixLLhl4taYSv2/JfRTWJ+FC+70lAzfsex3IaPM9acknKWdxXr5WVfWzlKmck68pt75KnkytIZy4xHh+vn2i3xuLP3+altijE7B1yCB8+1qEbDRVpba++Yw+5yJGnuGp+1JfXUXBrh10GeIJG3S7XPzv/MmeQR3bcjMKhwxPBsGbP/6abYsXAJC7bTNbFv7qd1REXAJXveYJ356/o5hL31vFojvGM2tdLvvL6vl6XR7+q+ho1yZ/+7KB8T0SyNpR4rN/5X0TSYwI3qywNb8ak0Hw0YpsZi7Lam6/6uhM7j25Fzani49XZDPtqAwMAYQBl1tS1eAIaJJRHByk233ItAxSSqTDja4N4fBIQEqJPauakjcPLGGbCNFj6RWHMSUMJIT0isWY0Fqz2LCjnNoleUQem0H1vP2Y0iIwxIRQv64YEWLA0jMWV7WN6nkeH7q0RUf/6fMKhmCFjUKgrxCiAE3L0ZRcIhwIVh+3EzAIIbpJKZv0twOALW2MacK7TlB782wBmrNcNfpsdAnyOH4QGEI0J8pebCGLTHrt3EBCRmdKs/ZSGql94MZQT85/ndFXXKzaF0pSqUcj0DsH7alXCM774TxuGHRD875pndfwwb4hQa1s+477KCj4kgnjdyKEHru9jNVrziIqahCFhd9Q54Iwr99mz55PkJriueHZ9u1j7yRfB8nwceM4NymBbUsXMvGya3yeNsaMGUN9zWJ+//ZjpHsun2f345yMTXDHXp54ZDlG6cCh07QEGfuycdR5NAZNgsaY8y4msVMmicCNH37FlJtfYkJZUwJaweYPuzL+2vEsf+BSOt+jRdAgJTdkeZIHlZriKOl9IpOMezjlpruoLiluzrTakiZBA2gWNKoMkeRbNAfb6go7I5/4ldk3jiU2zITJ4Ll43vjBl7w07UxiUjoy5Y4HmD/zTXqNOYaivbs5ZtoV7F7zO1UhCXTtks7sx31DFfuMO5Yx503D3lBPbErripWhkVE+ggaATq9nxOnn8PvXn7Xqb0nvTkN261DIC598Aafdjt5gpNOo8dz62XrumHoFsSddTHjZPn589iEy+g/i5Btu5/UrL+CEa3xzhFz6npa75ehnfR1aM+JCObFvMucOS2fGoj18stKjqbrv5F6kxVq45qO1TB6YwrfrNe3J/B0eAfura0cx5A888fdurFo5/bQ+TD+tD/mVDURZjISZtUuV2aDnsjGd25oCvU78KwUNt9VJ1dwshFFHSM9YDHEW9BEm7NnVGBJD0YcZ25+kvWPYXUiHG+nQnr7/ClzVNmx7qqicsw+kxF3b+hYjjDoixnUkbEQHnBVWdKFGjPEHnmNGSoltTxVCB6bOUUGZL101dtAJKr7cSejgRCx94gFw19rRR5pxW53UrSxEH2kidKCWarzkzY04Sxpw17V9uwwb2QFTxwgqvvT9rUuri/p1xdDoNlM1xxMKLkw6pN3t079kVyUAtsb/m7BuLWv3/P4qgvXZeBC4DcgHLEB3KaVdCHE5cHmTM2YQ83yKJjhcgRZFMgf/0SgnAWullEVCiJ7Al8AXUsqH25tHCJEA7EYz8cwGHkbz5wg6GqWwsAu7do5q1GxUsabHg8R2r2IeJ/CeuIrr3n+SB977lOfP9biaNGUYBSic2YHBK9v+UD8bq+OrMZ4b2wMZN/H5ond45ebP+PKGm8kYOJxJt7QulyylpM5RR7gpvDmvRFOoZ07uB+zc2Tp0cY9Vx7iRP5CyIZ+wkSPRWSxUfvUVBfd5ko8l3XcfhoQEIhuzZbbFtiULmPPyfwE4/7HnSO7anZm3X0957n76TLmAazZENmohwJLQgN7oxmLow5jzLyZz0DCfuV6YtxPXW/5DGEfc/jQXfpXVPBdA1MRzKEsfxg0Tujar9ME3tXtCeidKsrMCrv/dtGnUGdoOgYwIMVBjbay6+ciJWEx6xjz9G7kVmtC48I5jmLksi/eWeo5jdDt4b7TkqFNO9THNBMv+sjqmvrqEmOJt1OtDybOk+q7JUU2tIZxoRxWxjnIS+w3niTP6EW0xsq2wmvPfal3ADeDek3uSGR9OXLiJuDAz1VYHabGhDHjYf8bEUJOe1fcf6/P+AtTZnLilJCKk9bmNfuo38iobePX8wZzSP3C1zSMZ6ZLULs3DmBKGKS0CYdIf8igEt9WJbXcldasKCR2USM3CXIRBhz2vBtztj/cm6pTO1C4vwJQeQcwZ3RA6gXS6EQYd6ATCKwdH/aYSKr/ejbveGXC+pJsHY0xu/btq2FxK2Ue+WXdjz+2Bq9refNM0pobjyKttNfaPYukTR+jgJKrnZ4NT4ijUzHHCYsAQG9LusUKHJlG/ughDUig6iwF71h+v++KPqFMzsfSMxbavipDecW0Kgq5aO9Zt5TgK62jYXAYCXJWBkwKG9I7DVWXD1DEce24tQi8IH9uR2sW52LNrMHeNJvK4DMwZmmB/WDmIAgghpqLlrfhCSpnb2HYxUCml/DbIOWKBd4HjgDLgbinl/wkh0oGtQG8pZbYQ4r/ARWiakyLgI+BRKaWjrXm8jnMs8Aqazr0pz0ZWW2sLJGzY7NWs63k/MV2ruYMXyRcduf69x7n/gy981OzewkbRR8kMWta2Q2BJJPznP/4VSyaHjksHXcH1Q25otW/Gxhm8vO5llp63lFVLBjW3jxj+I7+vPMnvfHflWpgzaCZlZ11E1JlTMcTEUvbWWz59DjQFd1smhouefokP79Ly8t/yybdBJ1Uq2b+PD+70Pedjpl3Jgg+0tR535fX0P9Z/EiN/vHvz1VQU5JGU2ZWy3BycdhuXvf4hMbGeJ+7vNuRz4yfr2phF44cbxnDqy/5TiXuj1wm2P3piu/b+K95fzbxtRXxw2XCO7p7A8z/v4KXf/DvWehNlMVLVcHCd+5bdPQGzQUeY2fCHQzSrrQ6cLnlEaBSklDhya9FHmtBH+c8s6yxroPDZ4CqaNhE6OJGokzqjCze2EkLsOTVUz9uPdYfmQxQ9KZPw0an+pvGs0y2pmLULR14tERPSEHodZR9sbXMMgD7KhKvK3m6/wx1LnzhCesUROihBC8fVCaTDTe2yfNALqn44sJpBBxtjShiO/DZ8igwCnNr91dwtGn2kmfDRKZhSDk4qdmdpA47ietxWJ6EDE32EwwPhsBM2/ukEEjas9io29L6f6Mya5rooS9LC6Nq1W0BhY/d36Rw9t5C0cWXkLIwLeMy3TtBx5U/a40hT3RFvNl28qVXbuM/GUW4tZ4DFyaXx7V9QXi02s8umZ0XPd8i++GKMqak48lo7Fh6osOGt3QjE8MlnMvb8Sw5oXimlj8aoicwhwzn9zgcPaK76qkpqK8pJ7JSJy+mkrqKcyITWGRzdbsnbS/YSF2bmti82tDnnRSMz+HCFJ9o7zKSnzt7aufe1CwaTFhPKpFc0AWXRHeNZuKuECT0T2VtSy0XvrGw1pokfbhhDrw6R/Li5gHqbizu/2khihJmld0/AoBPYnG4+XZnN9O9b33i2PHwC32/IZ2z3BGJCjfR+8Cc/R/Cw4p6JJEcdmlDNvwPpkhQ+txpL3zhql+YTdVJnvzephOsGUPJa25/9gWDuGo1td2W7/SKPz0BnMRA6KBHcEuuuSowdwiiZsdGv+cAfpk6RhI9OwdInPuANRzrdOMsaMMRbEHod1fNzqP4pi6hTMqlZkNOueh8g7qLehPSO9SQ7c7px1Tpw19qxZ9dQ+Z1/Z2qA5DuHoY80IQw6HKUNFP13NcKkI+7iPhgTQin7eBvG5FCiT+2CMP5xP5Am347Cp1fhrnMQ0isW6XQT0iOW8JEdqFmSh6vShrlzJJb+Ca2EQrfNibPMSvnH27AMSNCENrfUND9GHdItA7/HUoLEZ790atd3YTh8I2j+dmFDCDHN7w4/SCkDJ4g/Qgio2XBUs6H3vUR1rm0WNvYc3Y8wvT6gsLFnThrTHJsIiXGy7VPf5FvbOkKv3NbHD1bYGPXJKMJlFXclt5+3AODmHM2nZEn8U+Tfdnur/TEXXUTcJZoQciA4rFZeutjXqfXKV9/lrf94CtOd/9hzdOjW44DmBaguKSZ/5zZmNxbEArjmzQ8Jiz50Xv+V9Xb0OsHOohqffA1ZT2mJgj5flcP2whoenKTlXam3O9lZVMuUV5f+4WMOSIvmm+tGBa2atzldmPQ61udU0i0pArNB51ej0pSwyOFy43JLjHoda7Mr6J4UQZTlz9vx/0qcZQ0Igy6gBqLNsZU2Cp8KLNQFQ9yFvQjpFUflt7sxdgwnfLhmIpJS0rCpFJ1Zjy7ChLO0gfL/a782ScyZ3dCFGSl7v30Nhd/1TOuNpXfgB5g/inRLELT67rnqHOgshgN6anbVOTQzT/8E9DHmIzrh1b+BQyVstOUg+mqL1ybAiMcyqENzDrUBR7ywEQi9FLRMlq/3E4+0oczEgDhN0yDC7DwXH8F9IZkkDdpO0TotWqTwggk8lL6Iz58MbPf0prCukOQwj2+/zWWjxl7DaQYvZ9RacPvRykWk30G/1JMh5xT6xPXxK2ikvfM2YSNHIv5AASBjSAi3ffYDVcWFzH//LU696S4MJhMde/Uld9tm+k888Q8JGgCRCYlEJiQ2CxtT733kkAoaANGhmjlgSEYsL547kNcX7OHl8zxmq7OHpfn0DzUZGJgWzdK7J3DMs/NxuNrXGGY9dQpWh4tp76zk1uO7MzLzwG4iZoP2uQ1Kb/u9abrYG/U6mqwkwzod/mGE9ZtKKP/YcwPXhRqafQYSrx+IqWPg9M3S6Q4saAhIfWIM7loHwqij4PHfkQ43xtRwwkYko7MYsfT1TR8fc0Y33ymEILS/JxrIlBJOaP8E3HYX5Z/uwJFbg6lzFOb0CNwON+EjOqCzeC63KY+MouTNjQF9B0J6xBA9uSuGWE3rJN0S6fzrojECCRN/xKlUH2Yk8pi09jsq/lUEFDaklM2/ZCHEKWj5NG5G84EAGAE8j5YO/B/HSXbtxiIQiBYPiyF+nh7nVHiEjcxxRbyUE8F9p71HlPk+itZpGTKzTh8Kaxbh6OhG1AsM5V4/8MboFG9WFa5iUhePSSG3JpdwnWRYB4+wYVmlo268xzNsQY2B4/ecTpcsM5bL0ll89iKqbruXluXXuq9Yjr5F5dA/QlRiMlPueKD59ZQ7H6Bw9y4y+g/803OPPf8SVnz1KRn9B7Xf+S9k8sBUJg8MTvOTGm1h1+MnNyehSo22IKVkZ1EtPZIj2F5YTXG1jeGdtZt9iFHP59f8+ZTdhztSShz5dZhSfSXjJrW0LasKXYiB8s924CisI+7iPj6CBuDjnFj8ynrPDr2AQMKdDlKmj/J7k9ZHaAJl6qOj/9hJ+TucSU/8tParFOtMepJu8P1eS4cLEcBnRugE4ggP+1T8uwk29PW/wGVSSu/8v0uFEDcDM4G2E0IcYSQn7yF1Y+OTn06PrdIEaR5HIKfbyaZSXxNHsVkSHzWZ0irNV1aHZLezmi7nz4QntERMs3ZpaXlL7tUuminXeZzpzA54/Lj/8viKx6mwaU5ka4vXcmrmqQgh2LLmJwqrruOxFvc883aPsPFYQQjV7hDOn/EtxUDxM88Qf9211P+2AIDI0ybhyMkl4rjjDoqg4Q9zaNhBETRA8/kYPrl1/pEjgdRoTxieEIIeyZrs3jM5kp6Bklj8Q3FbneRP900dbki04CwOnBW0bKYWoBZ5XAZhIztQ8dUurFvLMHeLRtrd2Pd7RQcEEDQMSaEk3xJcGPnhQCBBQ6H4JxCssNEJ8Od2W48WofIPRmCt8LUXXzTnIjaXbeaSpgRTQmDWm+k36L/MX6AJG9cn2jh9zvlsungTiXffRdjoo9i/bCreKeGciRJ9CQgpCLNCXEgci85dxJy9c7hr8V18ufNL+sX346FlD2mmlxbVmGNmGDBv8sx3rfF0TrCMIJ87m9tKX/OEjaY88URQpZ8Vij+LdLqpmLWL+rX+U6m3JWg0YekfT+RE7fLSUlvQsL0cnUlPyYyNYBAkXT8IQ0Iotj2ag6Wr2o6xQ3AVXhUKxV9PsHee34GXhBAXSCnzAIQQqcD/gBV/1eIOB4TQNafDbmJzmVZbI6reSlVoCD8PzcHqcqHbMQebNGIWDjK9Cp/FXXIJv/7WhWlxet4v8wguxdMdhP+kI/JbA2FWyIzOBGB4B0+yp4eWPcQxG93IFnllEx42YiwShH73IWLnOchQ6PfgV+QTsPyLEjQUh4SCp1a2ygOgizCRct8IHCX1FD2nmRXjLu3T7HxoTA7z8dh3211t+idYemqax45PjfVpD+mu+a80mUgUCsXhQbB3n8uBb4AsIURT3GQqsAOYcvCXdXhQ3JBNXEgKQtfyVq/RLzePyAb4bKIeEPDZBUQd2wer3VNrYEvpFvrE9wFgUKiLj8p8Z3IMDYdvrbwfeQOxIdoFNMLk6/h23Ww3Zf/x2KuT7jSirxWEHzuRtO5DqZtsbDeHftff/KevVigOFtIlybvPNxdJ3CV9COkS3RzOaEwIbSUg+ONIT0utUCh8CUrYkFLuEUL0R0ui1RPt1rYVmCf/wYk6Smy5xId0ROhA0top9J0TdExYL7F6PUR5CxoAxfXF9HB6LE0hLaYRURYkDZT+7wWip5yOMSkRs95MYmgidWVFzYEwtj7aRvhcHfpa4ZMXI/Obb9k3eUrza11EBN2XLWV7v/4ApL7wAsYU3xBchaJuVSEVX2kZ/2OmdtNyAugFlp5x6COD1wy47S4Kn12Nu8aT9yXplsEYk5QZQ6FQaAStV28UKn5u/PtXIKVE12hGcTUKG5Zqjy/s8l46lvcKPL6zycVL616iC56IksdTfW3VVkMR1VN1RH6lJ+faa8icpTmRzjtzHtt7tfZqN2a3FnpCevSg1/ZtbOupLcZdU4MwGun0+WdUfvEFESccH/xJK/6ReKeMjpnarVnIaML7dSW7Sbi6P6b0CECADurXFmPuHEXhs6tIvnMY9pwacEvKP93R6lgp049CF6JMdgqFwkPQV4TGFOEnojmE+jz2SCkfOcjr+tv5Lvs1OkdoUSRCBy40ta7O1ap4bEBuSrJxc85uXK62y2TXTXRj6ylJfHwbtn37MHfujCPbU5XPFSUR9WBwhRGyPnDW0KjJp1H17XfNry39+2Pp3z/o9Sr+mUi39KlN4S1YRB6bjqvGTt3vhT5j2qpKWfj0Kr/toYMSiZiQpgQNhULRiqCuCkKIkWhFzWxAApAHdGh8nQX844QNm6ueJguR0Ems0qwZj1om3WhkZVaO33aA6atfY1o7+ZqcqRJpkOw96WQyZ//A3lM82UmLntRSCYdG9EIXspPkBx7wO0fo8BFUffsdHR77R6Y+UfwBvE0lunCjTwrslIdHoTNrQnTUyZnozHqkW5L3wNLAeSv8kHjjoINW70GhUPwzCfYR5FngY+AmoBqYgBYK+wnwzl+ztL8bicSNFE6SBpfyJecC0BBxAqHVrdOKhDQKJv22VLOpT6TPvmBd3QpecpBynclH0JBekTBVNasZuj5w/YGoM07HmJpC6IgRQR5RcSQiXW6suyoJ6R7TnPlRuiRCr227bU7yH1realyHu4cHrNHQJHQInaDj42No2FKGzmLAWWHFtruSqBM7oYsw/eFiTwqF4t9NsMJGf7RS8lII4QLMUsq9Qoi7gP9DE0T+UUgasx6GaNVb69BqjEjhP31v0yU4scxO363VbO7dJHBITDr/T4np6VeSne1bfdUVIdHXeC7o1VM8Rb4yO9/c5pqFEISNHNlmH8WRT959nvor+tgQXOVanZzQQYnEntPDv6Bx34gDKgZl6aOp4sxEETYk6U+uWKFQ/NsJVtjwdhQoQivdvg2oBf6hYQ6aZqMJd5N+QroD9PeQVGpnc+O2ATC3eBjs0f1hkpMno9eHodOZycp6xXNUM+DlFlJ3rOd4nTu3LjmvOHKp31BM+Sc7SL5jKIY4C26bk4ovd2kFvsIMJFzZH7fNhTnDoylzlPo6GDcJGgD164qpX+dJohVxTEciju6ILvTwLramUCj++QQrbKwFhgE7gQXAY0KIJOBCILAn2RGOd3aNeeJEbUMcWPy/WYdPgi+A5OQpGAyajbtzp+uprd1GaamWB8MdJnEYJW4LdBh/Bfm8AcBRIw8sT0b5FzuRNidxF7Zfp0FxaLHn11L80rrm14XPrsaQYMFZ4hEk3HVOil5Y6zMu9vyeVHy5E4CYs7tT8bm2nXBVP9wNLso+9FQSTXlwpBIyFArFYUOwwsZ9QFOmqfvRqry+jCZ8XPoXrOtvRyKR0o3b4PskKYQnEOf4jOP5eX/bkcAtQ10B9HpP/gGdzsiA/jOoqlrL6jVn4Q6D8ru0BF4DJ9zB5t80YSM0tFPQa3fVOahfUxRwv7Pcij7SdEBqdcXBQUrpI2g04S1oBMK7hHnY4CTCBvuaNwwJFtwNTs1kosp6KxSKw4hgk3qt9touAU76y1Z0GGCs0y7iEkl1h2U++1IcZTQlYn7umOd4euXTHPPbc63m6NnjcbbvuM+n7bdqAxMinX5vBAZDNACuDh4BYNnyidoxO5x9QOuv/mV/83bLSpLS4abwGS10MZhMjoqDg6OkHp3FQGljgTHQNBKmtAhqFuZSPS8bXaSJlHs9zr3S5cZZ2kDR/3w1HAnX+A9nTr5t6F+zeIVCofiTHFBAvBBiKNAF+EFKWSeECANsUkpnO0OPLIRm9pDI5u0mHtrxPPdGeV7fNfwumPOE7/hjp2M2J7aadrE1icenLPJ7SKMxGoCqqR73mIaGLADcbpufEYGpW1HQvJ33wDLiL+vbXDPCWeWZy1XnQB+mVO1/NXVriqj4YqdPW+qjo5tTeEcem0HksRmtxgm9DmNSGB2fGusJw1YaC4VCcQQSlB5dCJEkhPgdWIkWfdKkv30eaP1Yf6QjtAu7lG5kC2Ej1mX1N8IXvRmd3tKqee7UuQGHGI1RAfdZLO0X1nUU15N792KqF7TO91H67ubmbVelZ/0Fj66gau6+duf+N2HLrqbk3c1Ih39HYCklrmob5Z/vwLqzAgBXtQ3rrgq//YteXNtK0AgdlNgsaASLEEIJGgqF4oglWM3G/4BCIA7I9mr/As134x9Fk2OoRGKwxvjua+uCP/I6WPEaSDd6fajPrhHD5xBqDA0wEEQbjqedOl0fcF/tigIqv9nd/Lp6bpbffs6yBqTTTenbm33aaxbkEnViZ6TLjdD/u304apbkUfXDXgBKP9xKwmV9ASj7ZDvS7gIJ1u3lzf3r1xYTMTGdml89P4mmBFfuegeFz69pTqJlSo/AlB5J2LAkVTNEoVD86whW2JgITJRSVrR4utqDlr48KBpTnr8DHA+UAvdIKf+vnTG/AeMBY5O5RghR26KbBXhNSnmDEKITsA8t6VgTT0spg0+rKTzChi0i12dXj7osiInwMwhPdlHpQq/z1WyEh/cI+vDeTJwQOIkX4CNoeKMLM+JucIJbO5fCZ1f77QeQe/diAOKv7EdIl+g/tM6/G3e9g/xHVgDt+6KUf74De04N8Zf2RR9lAp2gdml+s6ABYNtZQf2GEso/2d7GTPgIGoBf58+Ys7oTOjhRaSYUCsW/lmCFDQu+uTaaSACCsCs082rjPEnAQGC2EGKDlHKLv85CiAv8rVFKGe7VJwwt98cXLbpF/xFfEjc6aMyvIaWbmg4rfPYnOCq4stKN0+In/3iyVkuFuK6tNBvBMHHCHmpqtpKT8x4FhbMOeLzPUu4Yii7EQPkXO/1GpqQ8dBT5D/smfyp9a9MR6zRa+L81zdvuekfAsE9nWQP1a7VcFE2Ost7owo0Ikx5XuTWgoJH66Cgw6Ch+aR2OAk2mjbukD2UzW3+NQ3rEqKRYCoXiX0+wwsYi4BLg3sbXUmh6/7uAoBJANAoFU4G+UspaYIkQ4jvgIuBuP/2jgIeAaUDrlIgezgSKgcVBnUk75JFKJ1EN+ObZ8ObGiiqoqGq9o/85kNgLOgxAby/7Q8ePiOhN797P0r37g+0KLC39CvRRJlxVmkzYVAwr9qzurYSNJoEi8cZBfp/EDwec5VYKn1mFPtJE8l3D/Jp4pJTY9lSCBHeNp+ZH/iMrAgpNtS0KjrWkw30jcNc5KHjsd5/25LuHowvR+xQZS7ppMG6bC6ETCKOOjk+NpWZRLtItqZ6bpXJdKBQKRSPBCht3AguFEMMAM5pTaB8gChgd5BzdAZeU0ttbbgMwLkD/J4DX0XxF2uJi4APZ5K7vYb8QQgK/AHdIKUtbDhRCXAVcBWDorpVnN+KAJp+NVlMGwBgG3U8AIaDDAMA3l8aE8bsCjQyIweBrqnHXOxAmvU9uDHe9w6dPk6DRktgLelG/pgjr9vLmNNQAppRwEq4bgLS6qPxujxZm+dJawo9KwTIgAVe5FWPygfkXOCusSLsLY1IYriob+ihzm/2llK3MC95Jr1zVdvLuW+pXeMi7Z4nPa0NiKM7i+lbzVs/Pxp5dQ+RxGdQu0sxiKY+MonZZfrOPS9SpmYQflYIQAn24idTHRlO3shBLv3iESYfO7P+n0lRTpImIozsCEHlMWpvnrVAoFP8mgs2zsVUI0Q+4Fq3Sawia2eJVKWVBm4M9hAMt1QFVeJKFNdMYYjsarfBbx0ATCiHS0YSVy72aS9Gyna5Hc2h9Fa12ywl+zmsGMAPA2KO3BDDg9PHZAHDTjq1dCIjo4NOk05m9dv85x0tHST1Fz2lmAu+brrtBsxIZO4bjyK3F2CEMR0Edugjfp+nQfvGE9ov3O7c5XUuFrQszQmkDjvw6Kr7a1VwpVB8bQoc7hwVcW93aIio+30nSbUOw76+m4kttXNy03pR9sBVDUihJNw/266/QpL0ASLxhEMaUMPKnL0faXK36Nmwtw9JbE5SkW1L3e+uvXfy03tQuz6d2aT5lM7cQf2lfnJVWqn/S8o5YtzU6dwrQmfREHpMWUCgQBh3ho/6hmfgVCoXiEBN0ng0pZSGaWeOPUgtEtmiLxKcSCAjtzvwacJOU0tmOU900YImUsjl+s9FE0+QNWSSEuB4oEEJESimr21ukHmdzTZSm/61ozp76QC4gjgYw+D7BH0xnwCZBA6B2eT7mbjEY4y3YsrTTiTq+E/UbS4gYm/qHIx1iz+7u14nUu/ZGSxq2lDanzPZeI0DZB1rqbGdRPZXf7SFmctdW4+37PR9H8cutzTnxV/Rtjp4p+2Ar0ZO7EH5UCnWrCqn81uM4Gz2lC6H9E9CFGgkf25HapflYd1Q0O762JO7iPgHPSaFQKBQHnzaFjUbNQbtIKbPb78VOwCCE6CalbLIrDABaetVFAkOBzxpv2E166lwhxFlSSu87yDTgqfaW1/h/kHd/0azZoNGMshktY6NL+Hm7rFUgXeCoD276ILDn1aILNWCICWm1r+km2/GpsZ5IFCmJPbP7nzqmIa51XpAm3A1OdJbW51724bag5q5bXoC0ugjpGUvogITm9vLPdgQepIOQrjF0fGpss9BQ+e0ewo9KofJrTwROywgaQ7R/s03k8Rm4auxEndDJx+9CoVAoFH897en3s9DCSAP9Ne1vFyllHTALeEQIESaEGA1MBj5s0bUKrZLswMa/kxvbhwDNXntCiFFAKi2iUIQQI4QQPYQQOiFEHPASsEBK6cejM+BqATDqtZu900cmayGzrGwsEf/7G8FP3w7FL6+j8OnWkRLeuO0ujGmaBcqU0VJh9Mfo+NRYku8ZDmjmk6hJmQDkP7ycmiV52nHrHVTO3ttswmlJSO84DPGa4BJzRrfm9vp1xZR/sh1HST2Oknqkw2MqSXnoqObtmKnamJAesc1t+liP0OWtrej41Fi/obopD470eR11cmciJ6QTM7mrEjQUCoXib6C9K6+3sV4AC4HzgVz/3dvlOuBdtOiRMuBaKeWWRg3KVqB3o5ak2SlUCNF0pylqEcp6MTBLSuljhgEy0ZxLE4FqNAfR84JdoEQgGzUbFn04wmXAqQ/wNu38GZyBzQwxMUcRGtol2ENrx3d6lbWvd/jUNfHGXWPHkaOd+sG8gRqizM1+IbYsj3zmyK3xudHXLtaED2NqONGTu1Dy2gYAIo/LwNTBY8oxd4nyMc+0NLdEntgJncVA/BV90YUaMaWEY0wJxxDvETCSbx+KdWsZZR95NClxlwY2hehCjaQ+OQZXlT2gpkOhUCgUh44271JSSp87gxDCDWySUu4NMKRNpJTlwBQ/7dloDqT+xmThxwQipbw6QP9PgE/+yPqaaU5RLgkrGUjHKAeEwvQ9r2rOoE1RKjtmg7lRq6BvfVMbPOijAz60d4RJU5IqfziKDp7ZJhDmTp4U6vXrS/z2MaVHYE6PJPXJMcgGZ6tQT0OcBWNyGI7COr/jm5w+Q7p6MrWaUn2/CkInsPSNx9w1GkdRHcm3DGk3pFQIoQQNhUKhOEz4d+en9oP0/ldqhdik1GSyvrW78JF7pIRYzdTAUf85KMd31Tr8tsdd1Iuw4cnNr5scMP9q2kvyFX2aprkRQgQUAJJuHkzHp8ZiSGjtF9KWr0hLEq7oR8p9KneFQqFQHGkoA7Y/RFPIqxupc+GU2s3N6HZqmg10IBu1H+bGyN0BQVtq2qTsY/9Ol/pIM9GndyXy2HQKnljZ3B7SI8Zv/4OJqXMU9n3+XV4OJOqmqQS6dEuq5uzTcljoVQpvhUKh+KfzR4SNIDNdHZnUEElcYzoQu9uKFC6cmAAwykatg8ECjjpAgrOxZLvB9KeP7apz4Crz7wMiTDpNexDue5z4S/v+6eO2R/zFvbHtriSkVxxCL5BuSd69Swjp7SdlexAInSD61MyDvEqFQqFQHK60F/r6XYumEOAtIYSPw4CU8rSDvbC/iyeZzpviUgBy63aQGFbIItMpAFh1ZkBoOTUcdVC4CVIGaQP9+GwcKD4ZQQ068HIW1TeGwQrdodcE6EIMWPp6koIJnThia6goFAqF4tDTnmajZYGPA/d4PMKoFRE0KW/c0o3DUsoSoYWD5oYkQ+02MFqgAchfB/3P1QYa/pywId0Sp1cCrQ53DaNuZSER49MCChgRE4MuuKtQKBQKxd9Ge9Eolx6qhRx+yOYMok2Y3A5w2cHglWxr3nTt/z8pbNQszGlOqw2gjzARGUCYiD2vB85SKxHjVf0NhUKhUBz+KAfRQAiw2HwjQ0JdDdqG0asaq7Ox7U+YUew5NT6CRtLNg9vsHzog8Q8fS6FQKBSKQ40KfQ2EgPHbtCzsJ8nvAZhQ3hgFYvQTrhko8Vc7SJek+M2NPm26MBXaqVAoFIp/DkrYCIDwCroxY0Un3RhoTLE95bWDdpy8+5b4OIKCZkJRKBQKheKfgjKjBKLRJ1NvjUbv6IwxwksgiO/mf8wBIF1uquYEVVZGoVAoFIojGqXZCIAQIHUSqXPgxIBRtk4vIqUBpzvZz+j2qVtdRO3SfJ82XbiR5LuGBRihUCgUCsWRiRI2AiEkBa84cJvqWGXpRK2u0Y8izqPVKHU8TKH9bVwyAkdhHW6bK8Bkvthzaqia3VqrYc6I9FtWXqFQKBSKIxllRgmEV2qLfGO0tnHjOrBopc/rQqdhsw4AoNJxLQ0vrMXcNZqEK/oFnNKWXU3t4jwaNpX63R86WEWZKBQKheKfhxI2AuA3jVasJ8W21dm/edshtXbb3tb1Qxq2lyMdLkL7JTSXYQ+EpU98m/sVCoVCoTgSUWaUQIi2S8C4ddHN207ZsbGx9ZiymVso/3h7u4eLPC7jgJanUCgUCsWRghI2AtFOCRJbZduOoVJKqufnNL921fkvHZ8y/SjCx6QScXTHA16iQqFQKBRHAsqMEgDvyun9GgoJiz4wzYOzuJ7qn7KaXxf9b43ffroQg6qAqlAoFIp/NEqzEQgvM8q2kAScbVtVmnGUNBbEbdHfXetfs6FQKBQKxT8dJWwEoEmxUUUkTqFntd3msz+kV6zfcUXPaRoM2SIrqDf6aK2OigjR//mFKhQKhUJxmKPMKAEYqsuhAbDhpw4K+NpZ/CDt/nNuhB3VgZjJXalZlEtI95g/uUqFQqFQKA5/DqlmQwgRK4T4WghRJ4TYL4Q4P4gxvwkhpBDC4NW2QAhhFULUNv7taDFmohDi/9u78/CqqnOP499fIBBkUARBAmKEixNoU8VbWwW9F3G2irRqnbCtN9herEN9bLW1BWer3FpqpeVqlcdWa6lTHaq2FYei1nIt1GKtgqKGMoRRSJgC7/1jrUN2DufkJHBODoT38zz7CWetPaz9JmS/WXvtvd6RVCdpuqQWP+pR0W7rx1iT1r29rMn6zRsy92xsXLAGgK7D+1G6d+eWNss555zb6bR2z8ZPgA1Ab6ASeFrSbDObk2llSeeRvY3jzOyeDNv0BB4FLgaeBG4AHgaObElDLUfPRS6pd270vuIwFv/wzYb9bmrm4A/nnHOtYuPGjVRXV7Nu3bpiN6VgysrK6NevH6WlxZlVvNWSDUmdgdHAEDNbA/xJ0m+BC4BvZ1h/d+D7wIXAay041JnAHDObFvczHlgq6UAzy/3Ci2jzlj6flicH6+atZM3L1WHrDZvpclT5lnlQ9jhlvxbvzznnXOFUV1fTtWtXKioq0Hb+obkjMjOWLVtGdXU1++1XnGtQa95G2R/YZGbvJspmA4OzrH8zMBlYlKX+FklLJc2QdGyifHDcLwBmVgvMy3QcSVWSZkqamV5nGX7eLG0yttJ9umZs2PJfNeQ07ffqxB6nDWw4ZpkPk3HOuR3JunXr6NGjR5tMNAAk0aNHj6L23LRmstEFSB8IsQrY6ootaShwFPDjLPv6FjAA6AtMAZ6UlLqiN/s4ZjbFzIaa2dCt6mJkLPl2r0SuUdK5lA7lnWnfe7etGrd5dcNjriVpyUVJR38CxTnndjRtNdFIKfb5tWaysQbollbWDVidLJBUAtwNXGZm9Zl2ZGZ/NrPVZrbezKYCM4CTW3KcXFZVhKRgcwzRhR+s3/I68k1rNrC5diNqX0K3Ef1bslvkyYZzzrkMKioqOOSQQ6isrGTo0PA38LRp0xg8eDAlJSXMnNnQCT9jxgwOPfRQjjjiCObOnQvAypUrOeGEE7bqhd8RtGaf/rtAe0mDzOy9WPYpIH1waDdgKPBwzMRSV+dqSV80s1cy7NtoeDXGHGBMqiKOFRmY4ThN2tRBccch2Tjgk83Y5tDPseSuWQCs//AT9jht4JZHWFf97gNq/5z5rk/HAbuz/v1VlHT02yjOOecymz59Oj17NkzKOWTIEB599FHGjh3baL2JEyfyyCOPMH/+fCZPnszEiRO54YYbuPbaa4vei5FJq135zKxW0qPA9ZIuJjyNcjrwubRVVwHlic/7AG8AhwM1kvYAPgO8BNQDZwPDgcvj+o8Bt0saDTwNfA/4W0sGhwJbUpdUz0Y7o6FnY2V4wdfG6vAYa+pWSfdRg7ImGz0uOJiNS+pQqb9HzTnnXPMcdNBBGctLS0tZu3YtdXV1lJaWMm/ePBYsWMAxxxzTyi1snta+8n0d6AQsAR4CvmZmcyT1j+/L6G/BotQC1MRtF5vZBqAUuDGWLwUuBc4ws38CmFkN4amXm4AVhMTknJY2NDVmI5VslNj2PbZa0qk9HfdNv7vjnHNuRyMp6zJlypQt602ZMqXJdbfluMcffzyHH354o+Nkcs0111BVVcWdd97JuHHj+M53vsMNN9zQ4mO2llbt0zez5cAZGco/IgzszLTNfBJzsMZk4ogcx/kDcOB2NBXFMZ7JZGP9vJV0GtKzia0a6/mVIdvTBOecc7uQGTNmUF5ezpIlSxg5ciQHHnggw4cPz7huZWUlr7/+OgAvv/wy5eXlmBlnn302paWlTJw4kd69e7dm85vkffrZzGs8QLQEY/mD77BmxoJmbV4+4bP+OnLnnNsJmVnWpaqqast6VVVVTa7bUuXlYQRBr169GDVqFG+88Uaz2nrjjTdy3XXXMWHCBCZMmMD555/PpEmTWnz8QvJkI4v6jSHZSD36WhJ/buqXrt2yTvcvDNpqu5LO4e1sPhDUOedcc9XW1rJ69eot/37++ecZMiR37/jUqVM55ZRT6N69O3V1dZSUlFBSUkJdXV2hm9wifkXMJm3MRruYbFi90X6vTtTXrKXswK1nfu19xWFsrvXp5J1zzjXf4sWLGTVqFAD19fWce+65nHjiiTz22GNceuml1NTUcMopp1BZWclzzz0HQF1dHVOnTuX5558H4Morr2T06NF06NCBhx56qGjnkoknG1lYjExyzEaoMEr37oxtNtp16bDVdu26dMhY7pxzzmUzYMAAZs+evVX5qFGjtiQh6XbbbTemT5++5fOwYcN46623CtbG7eHJRharzwhTxDeM2Qjq3lwChNeQO+eccy43H7ORQ6/eo4FEz0ZUX7M2w9rOOeecS+fJRg5duh4CNIzZcM4551zLeLKRw6b4Vdsw1bxzzjnnPNnIKb6h3Hs2nHPOuW3kyUYOqZ6N9DEbzjnnnGseTzZySOUYnmw455wrpExTzC9fvpyRI0cyaNAgRo4cyYoVK4Cdb4p5TzZy2GzxDaJFbodzzrm2b/r06cyaNYuZM2cCcOuttzJixAjee+89RowYwa233go0TDF/8803M3nyZIAdeop5v4bm4LdRnHPOFcsTTzzBmDFjABgzZgyPP/44sPNNMe8v9cpheX3IENMHiJaWdy5Ca5xzzhXS5ZdfzqxZs/K6z8rKSu68886c66WmmJfE2LFjqaqqYvHixfTp0weAPn36sGRJeLFkaor5Tp068cADD3DVVVf5FPM7s+s/DsnGqkN7wGs1W8p7jhlcrCY555xrgzJNMZ/NzjbFvCcbTeiw0CDM+Euno8sbJRvtdu9YpFY555wrlOb0QBRKpinme/fuzcKFC+nTpw8LFy6kV69ejbZJTTH/8MMPM27cOCZMmMD8+fOZNGkSN910UzFOIyMfs9GEDX0aBtm02/HG2zjnnGsjsk0x//nPf56pU6cCYTr5008/vdF2PsV8G9FeUG/Qp6PP5Oqcc64wsk0xf8QRR3DWWWdx77330r9/f6ZNm7ZlG59ivg25Yt+9uX3+IgZ06sjiWNb1P/cpapucc861LdmmmO/Rowd//OMfM26zM00x77dRckg9hNJO0PnIMCK46/B+xWuQc845t5Np1WRD0p6SHpNUK+lDSec2Y5sXJJmk9vFzR0n3xu1XS/qrpJMS61fE9dckluta0s5pnMOmGJpNZggokdjj9IH0vekoSsq8Q8g555xrrta+av4E2AD0BiqBpyXNNrM5mVaWdB5bt7E98DFwDPARcDLwa0mHmNn8xHp7mFn9tjTycX2RPvYvjuZlNpltGRwqyUeKOueccy3Uaj0bkjoDo4HrzGyNmf0J+C1wQZb1dwe+D1ydLDezWjMbb2bzzWyzmT0FfAAcns/21scc54O1GyjBEwznnGvLdsT5RPKp2OfXmrdR9gc2mdm7ibLZQLa3Y90MTAYWNbVTSb3jvtN7Rz6UVC3pPkk9s2xbJWmmpJkZallAX56sWcmGNv5D6Jxzu7KysjKWLVtW9AtyoZgZy5Yto6ysrGhtaM3bKF2AVWllq4Cu6StKGgocBVwGZB2NKakU+CUw1czeicVLgSOAWUAPwq2bXwInpG9vZlOAKQClBxy81U9ZDb3Si5xzzrUx/fr1o7q6mpqamtwr76TKysro1694Dze0ZrKxBuiWVtYNWJ0skFQC3A1cZmb12Wavi+s9QBgDMi5VbmZrgFRPxWJJ44CFkrqZ2SfNbWzbzG+dc86lKy0tZb/99it2M9q01ryN8i7QXtKgRNmn2Pr2RzdgKPCwpEXAX2J5taRhAAoZyL2EgaajzWxjE8dN5Q0tHnghTzmcc8657dZqPRtmVivpUeB6SRcTnkY5Hfhc2qqr2DIjCQD7AG8QBoCm+rgmAwcBx5nZ2uTGkj4DrATeA7oDk4AXzSz9Fk7T7UWUrAD2bMlWzjnnnEvX2i/1+jrQCVgCPAR8zczmSOof34fR34JFqYWGBGOxmW2QtC8wlpCsLEq8S+O8uN4A4FnC7Zm/A+uBL7W8qaLDfH/nmXPOObe91FZH37aUpNXAP4vdjl1AT8IgXlc4HuPC8xi3Do9z4R1gZls9qJFv/irMBv80s6HFbkRbJ2mmx7mwPMaF5zFuHR7nwsv86of88/sEzjnnnCsoTzacc845V1CebDSYUuwG7CI8zoXnMS48j3Hr8DgXXqvE2AeIOuecc66gvGfDOeeccwXlyYZzzjnnCmqXTzYk7SnpMUm1kj6UdG6x27Sjk9RR0r0xXqsl/VXSSYn6EZLekVQnaXp8EVuqTpJuk7QsLj9QYgIcSRVxm7q4j+Na+/x2NJIGSVon6ReJMo9xHkk6R9I/4u+BeYmpETzOeRBj8YykFZIWSbpLUvtY5zHeBpLGKcxavl7S/Wl1BYuppHPj7/5aSY9Lat57ts1sl14IbzJ9mDAr7dGE16UPLna7duQF6AyMByoICeuphDe2VhBewrMK+CJQBtwOvJ7Ydizh5Wn9gL7A28AlifrXgP8hvGl2NOHV83sV+5yLHO/ngVeAX8TPHuP8xnck8CFwZPx57hsXj3P+YvwMcH+M497AW8A3PMbbFdMzgTMI03fcnygvWEyBwYTf9cMJ18wHgV81q73FDliRv1mdCbPG7p8oewC4tdht29kW4G/xB7MKeDUtxmuBA+PnV4GqRP1XU/8RgP0Jr5fvmqh/JfkfYVdbgHOAXxOSu1Sy4THOb4xfBb6aodzjnL8Y/wM4OfH5duBnHuO8xPZGGicbBYspcDPwYKJuIOEa2jVXO3f12yj7A5vM7N1E2WxC9uaaSVJvQiznEGI3O1VnZrXAPBpi2qiexvEeDLxvZquz1O9SJHUDrge+mVblMc4TSe0Is0zvJWmupOrYxd8Jj3M+/Qg4R9JukvoCJxHmsPIY518hY5q+73nEP9hzNWpXTza6ELqbklYBBX9PfFshqRT4JTDVzN4hd0zT61cBXeI9Q/9+NHYDcK+ZfZxW7jHOn95AKfAFYBhhgsdPA9/F45xPLxEuVJ8A1cBM4HE8xoVQyJhuc8x39WRjDdAtrawb4Z6Uy0FSCeG20wZgXCzOFdP0+m7AGgt9cv79iCRVAscBP8xQ7THOn7Xx64/NbKGZLSXcrz4Zj3NexN8TzwGPErr0ewLdgdvwGBdCIWO6zTHf1ZONd4H2kgYlyj5FuB3gmhCz4HsJfxmONrONsWoOIYap9ToT7uvNyVRP43jPAQZI6pqlfldyLGHA7UeSFgFXAaMlvYnHOG/MbAXhL+1Mbzf0OOfHnsA+wF1mtt7MlgH3ERI6j3H+FTKm6fseAHQkXEubVuzBLcVegF8RnkjpDByFP43S3Lj9FHgd6JJWvleM4WjCSOjbaDwS+hLCYLG+QHn84U2OhH4duCNuO4pdaHR5Whx3I4zaTy13AL+J8fUY5zfW1wN/AXoR/uJ+hXALy+Ocvxi/D3ybMNP4HsBjhNuvHuNtj2n7eN63EHqYy2JZwWJKw62wYYRr5i/wp1Ga/Q3bk3DvsBb4CDi32G3a0RdgX8JfgusI3Wqp5bxYfxzwDqGL+kWgIrGtgB8Ay+PyA+Jr82N9RdxmLeHxrOOKfb47wkLiaRSPcd5jWwrcHX+pLgImAWUe57zGuDLGYgWwFJgG9PIYb1dMx8ffw8llfKFjCpxLuFbWAk8AezanvT43inPOOecKalcfs+Gcc865AvNkwznnnHMF5cmGc8455wrKkw3nnHPOFZQnG84555wrKE82nHPOOVdQnmw418ZIul/SU8VuR5Kk0yW9J6le0v0FOsYOd97OucCTDefyKF7wTNJ308qPjeU9i9W2IrsHeITwQrjLCnSMy4Dzt2cHki6StCZP7XHORZ5sOJd/64CrJe1V7IbkU5zhd1u224Mw+dZzZrbAzNJnjcwLM1tlZisLsW/n3PbxZMO5/JsOzAeuy7ZCpp4OSRWxbGjaOidJ+j9JayW9IqmfpGMkzZa0RtJTknpkOMZ3JS2O69wnqVOiTpKuljQv7vctSednaMuXJL0gaS0wNsu5dJc0VdKKuK8/SBqcOgfCK6oBXoj7PDbLfjpIulnSh5LWS3pf0jcS9cMl/VnSunheP5TUIVHf6DaKpBcl3R33uVTSEkl3xFlIM35PCBOEdY7tNEnjc51jrN9d0gPxGOti2y9P1I+V9G6sq5H0nKT2ifovS3o71r8r6YpkO3Nt79yOzpMN5/JvM2HSqUskDczD/iYAlwOfIUwU9jDwPaCKMDvsYMI8CUnHEGZnHEGYkOl4woRMKTcCXwX+GziYMJnTzySdkrafWwjzhhxMmEMok/tj204H/h2oA56Nyc2rsX3EdvSJZZlMBS4ErgQOiu1bCSCpL/A74K/Ap2Pdl2L7mnIeUA98DhhHiOPZWdZ9NdbXxXb2IUxIlescIcTzEOBU4EDgK8CC2PahwE8I38cDCPNWPJs6qKT/Am4mfE8PAr4JfAv4enO2d26nUOzJZHzxpS0thIvSU/Hf04kzIhKSAgN6Zvocyypi2dC0dU5IrDMulh2WKBsP/D2tDStJzMhLGMuwnjBTY2fCJEvD0tp+J/BMWlu+meN8B8X1hifKdifMOnlx/NwzrnNsM/ZzYpb6m4C5QEmi7KJ4Trulxz5+fhF4LW0/vwfuaaIdFwFrtuEcfwvcl2WfZ8Z1u2ap/wi4IK3scuDt5mzviy87w+LdcM4VztXA65LuyLlm0/6W+Pfi+PWttLJe6duYWXKg42tAB2Ag0JEwffSzkpIzMZYSbv8kzczRtoMIPTmvpQrMbJWktwi9Ic316bif6U0c5zUz25wo+xPhnP6NxjFKSi//F1vHKpfmnONk4DeSDiMkNE+a2Uux7vfAh8AHkp4DngceNbPVcVzPPoRepcmJY7YnzM7Z5PYtPA/nisZvozhXIGb2F8ITGLdlqE5dNJUoyzYAc2Nyt3Hf6WUt+b+cWvc0wtTfqWUw4XZLUm2OfamJupZMKd3UflL12fbX1HE2pn1uaaxSx84m9f34HeFJmzsIPTlPS7ov1q0GDgPOIvRiXAO8I6k80ZZLaPy9GEK8/ZRje+d2Cp5sOFdY1wLDgBPTymvi1z6Jsso8HvcQSZ0Tn48ENgDzgLcJtx/2NbO5acuHLTzO24TfI59NFUjqRhi/8HYL9vNm3M9/NHGcz6YN7jyahnPKlw1AuwzHznmOZrbUzB4ws4sIY0rGSOoY6+rN7AUzuwY4lHAr61QzW0wY2zEww/dibmLfGbfP43k7V1B+G8W5AjKzuZKmsPW7JeYCHwPjJX2bMEbiu+RPe+Dnkq4HyoFbgf81s1qAeGvnDkkCXga6EBKSzWY2pbkHMbP3JD1BuA1QRRgrchPwCfBgC/fza+AeSZcRko9+QIWZPUAYpHo5cLekHwED4jndZWZ1zT1OM8wHyiSNJAxGrWvOOcY4vwnMIcT+TOB9M1sv6VTC7auXgeWEhKor8I94zPHAjyWtBJ4h9HAdBvQ1s1uasb1zOzzv2XCu8K4nPBGxRbwNcg7hojmb8KTBtXk85kuEC9904DHgBcIYkpTrCBe5q+J6vyc8LfLBNhzry8AbhEGSbwC7EQZ6rm3hfi4kXLwnAe8QBnzuDmBmC4CTCGM7ZgE/Bx4ivzHDzF4Ffhr3XUNDzHKd43pCAjIbmEFIBk6LdSuBM4A/xPO6ijCw9JV4zHsIT69cELd/hfCk0QfN2d65nYHMWnJb1TnnnHOuZbxnwznnnHMF5cmGc8455wrKkw3nnHPOFZQnG84555wrKE82nHPOOVdQnmw455xzrqA82XDOOedcQXmy4ZxzzrmC8mTDOeeccwX1/26TpwOtq7MFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 576x252 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\n",
     "# Common imports\n",
@@ -2162,9 +2321,7 @@
   {
    "cell_type": "markdown",
    "id": "394c30b2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Using the Voting Classifier\n",
     "\n",
@@ -2175,11 +2332,23 @@
    "cell_type": "code",
    "execution_count": 18,
    "id": "04d378b8",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LogisticRegression 0.864\n",
+      "RandomForestClassifier 0.872\n",
+      "SVC 0.888\n",
+      "VotingClassifier 0.896\n",
+      "LogisticRegression 0.864\n",
+      "RandomForestClassifier 0.872\n",
+      "SVC 0.888\n",
+      "VotingClassifier 0.912\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import make_moons\n",
@@ -2228,9 +2397,7 @@
   {
    "cell_type": "markdown",
    "id": "2aa90ff1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Voting and Bagging"
    ]
@@ -2239,11 +2406,21 @@
    "cell_type": "code",
    "execution_count": 19,
    "id": "d2b80cd4",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
+       "                             ('rf', RandomForestClassifier(random_state=42)),\n",
+       "                             ('svc', SVC(random_state=42))])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.datasets import make_moons\n",
@@ -2269,10 +2446,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "86ba6caa",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
@@ -2287,10 +2461,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "id": "d009d0ef",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "log_clf = LogisticRegression(random_state=42)\n",
@@ -2307,10 +2478,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "id": "6f87139d",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.metrics import accuracy_score\n",
@@ -2324,9 +2492,7 @@
   {
    "cell_type": "markdown",
    "id": "768b6c06",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Random forests\n",
     "\n",
@@ -2347,9 +2513,7 @@
   {
    "cell_type": "markdown",
    "id": "5876341e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "m\\approx \\sqrt{p}.\n",
@@ -2359,9 +2523,7 @@
   {
    "cell_type": "markdown",
    "id": "b79d9382",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In building a random forest, at\n",
     "each split in the tree, the algorithm is not even allowed to consider\n",
@@ -2384,9 +2546,7 @@
   {
    "cell_type": "markdown",
    "id": "b02fdfb7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Random Forest Algorithm\n",
     "The algorithm described here can be applied to both classification and regression problems.\n",
@@ -2410,9 +2570,7 @@
   {
    "cell_type": "markdown",
    "id": "56589894",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Random Forests Compared with other Methods on the Cancer Data"
    ]
@@ -2421,10 +2579,7 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "6d326205",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -2498,9 +2653,7 @@
   {
    "cell_type": "markdown",
    "id": "147dd974",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Recall that the cumulative gains curve shows the percentage of the\n",
     "overall number of cases in a given category *gained* by targeting a\n",
@@ -2514,9 +2667,7 @@
   {
    "cell_type": "markdown",
    "id": "f9162cb5",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Compare  Bagging on Trees with Random Forests"
    ]
@@ -2525,10 +2676,7 @@
    "cell_type": "code",
    "execution_count": 24,
    "id": "3359897d",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bag_clf = BaggingClassifier(\n",
@@ -2540,10 +2688,7 @@
    "cell_type": "code",
    "execution_count": 25,
    "id": "fac6da67",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bag_clf.fit(X_train, y_train)\n",
@@ -2558,9 +2703,7 @@
   {
    "cell_type": "markdown",
    "id": "236ab1e3",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Boosting, a Bird's Eye View\n",
     "\n",
@@ -2578,9 +2721,7 @@
   {
    "cell_type": "markdown",
    "id": "424e0843",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## What is boosting? Additive Modelling/Iterative Fitting\n",
     "\n",
@@ -2592,9 +2733,7 @@
   {
    "cell_type": "markdown",
    "id": "5c2c6074",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n",
@@ -2604,9 +2743,7 @@
   {
    "cell_type": "markdown",
    "id": "f374a24b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $\\beta_m$ are the expansion parameters to be determined in a\n",
     "minimization process and $b(x;\\gamma_m)$ are some simple functions of\n",
@@ -2621,9 +2758,7 @@
   {
    "cell_type": "markdown",
    "id": "ec77a4de",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\sigma(t) = \\frac{1}{1+\\exp{(-t)}},\n",
@@ -2633,9 +2768,7 @@
   {
    "cell_type": "markdown",
    "id": "47aeac82",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where $t=\\gamma_0+\\gamma_1 x$ and the parameters $\\gamma_0$ and\n",
     "$\\gamma_1$ were determined by the Logistic Regression fitting\n",
@@ -2647,9 +2780,7 @@
   {
    "cell_type": "markdown",
    "id": "71d7bac6",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n",
@@ -2659,9 +2790,7 @@
   {
    "cell_type": "markdown",
    "id": "386ef484",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In this case the function $f(x)$ was replaced by the design matrix\n",
     "$\\boldsymbol{X}$ and the unknown linear regression parameters $\\boldsymbol{\\beta}$,\n",
@@ -2672,9 +2801,7 @@
   {
    "cell_type": "markdown",
    "id": "e73f130e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\boldsymbol{\\beta}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n",
@@ -2684,9 +2811,7 @@
   {
    "cell_type": "markdown",
    "id": "6bdf779b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters $\\beta_m$ and $\\gamma_m$."
    ]
@@ -2694,9 +2819,7 @@
   {
    "cell_type": "markdown",
    "id": "8d5acd31",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Iterative Fitting, Regression and Squared-error Cost Function\n",
     "\n",
@@ -2722,9 +2845,7 @@
   {
    "cell_type": "markdown",
    "id": "510fc0f7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Squared-Error Example and Iterative Fitting\n",
     "\n",
@@ -2738,9 +2859,7 @@
   {
    "cell_type": "markdown",
    "id": "40618652",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "(\\beta_m,\\gamma_m) = \\mathrm{argmin}_{\\beta,\\lambda}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2=\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta(1+\\gamma x_i))^2.\n",
@@ -2750,9 +2869,7 @@
   {
    "cell_type": "markdown",
    "id": "4a225c1e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We start our iteration by simply setting $f_0(x)=0$. \n",
     "Taking the derivatives  with respect to $\\beta$ and $\\gamma$ we obtain"
@@ -2761,9 +2878,7 @@
   {
    "cell_type": "markdown",
    "id": "c57e04fd",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial {\\cal C}}{\\partial \\beta} = -2\\sum_{i}(1+\\gamma x_i)(y_i-\\beta(1+\\gamma x_i))=0,\n",
@@ -2773,9 +2888,7 @@
   {
    "cell_type": "markdown",
    "id": "51ece285",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and"
    ]
@@ -2783,9 +2896,7 @@
   {
    "cell_type": "markdown",
    "id": "027da958",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\frac{\\partial {\\cal C}}{\\partial \\gamma} =-2\\sum_{i}\\beta x_i(y_i-\\beta(1+\\gamma x_i))=0.\n",
@@ -2795,9 +2906,7 @@
   {
    "cell_type": "markdown",
    "id": "edcf5852",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can then rewrite these equations as (defining $\\boldsymbol{w}=\\boldsymbol{e}+\\gamma \\boldsymbol{x})$ with $\\boldsymbol{e}$ being the unit vector)"
    ]
@@ -2805,9 +2914,7 @@
   {
    "cell_type": "markdown",
    "id": "ac315a21",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\gamma \\boldsymbol{w}^T(\\boldsymbol{y}-\\beta\\gamma \\boldsymbol{w})=0,\n",
@@ -2817,9 +2924,7 @@
   {
    "cell_type": "markdown",
    "id": "ecafd78c",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which gives us $\\beta = \\boldsymbol{w}^T\\boldsymbol{y}/(\\boldsymbol{w}^T\\boldsymbol{w})$. Similarly we have"
    ]
@@ -2827,9 +2932,7 @@
   {
    "cell_type": "markdown",
    "id": "3aa73aec",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\beta\\gamma \\boldsymbol{x}^T(\\boldsymbol{y}-\\beta(1+\\gamma \\boldsymbol{x}))=0,\n",
@@ -2839,9 +2942,7 @@
   {
    "cell_type": "markdown",
    "id": "d100a2cc",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which leads to $\\gamma =(\\boldsymbol{x}^T\\boldsymbol{y}-\\beta\\boldsymbol{x}^T\\boldsymbol{e})/(\\beta\\boldsymbol{x}^T\\boldsymbol{x})$.  Inserting\n",
     "for $\\beta$ gives us an equation for $\\gamma$. This is a non-linear equation in the unknown $\\gamma$ and has to be solved numerically. \n",
@@ -2853,9 +2954,7 @@
   {
    "cell_type": "markdown",
    "id": "77d884ae",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Iterative Fitting, Classification and AdaBoost\n",
     "\n",
@@ -2869,9 +2968,7 @@
   {
    "cell_type": "markdown",
    "id": "8ca1ea91",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathrm{\\overline{err}}=\\frac{1}{n} \\sum_{i=0}^{n-1} I(y_i\\ne G(x_i)).\n",
@@ -2881,9 +2978,7 @@
   {
    "cell_type": "markdown",
    "id": "e9049241",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The iterative procedure starts with defining a weak classifier whose\n",
     "error rate is barely better than random guessing.  The iterative\n",
@@ -2897,9 +2992,7 @@
   {
    "cell_type": "markdown",
    "id": "df814eab",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n",
@@ -2909,9 +3002,7 @@
   {
    "cell_type": "markdown",
    "id": "afce22c9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "will be a function of"
    ]
@@ -2919,9 +3010,7 @@
   {
    "cell_type": "markdown",
    "id": "3eb3d253",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "G_M(x) = \\mathrm{sign} \\sum_{i=1}^M \\alpha_m G_m(x).\n",
@@ -2931,9 +3020,7 @@
   {
    "cell_type": "markdown",
    "id": "db366472",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Adaptive Boosting, AdaBoost\n",
     "\n",
@@ -2943,9 +3030,7 @@
   {
    "cell_type": "markdown",
    "id": "9c289506",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "f_m(x) = f_{m-1}(x)+\\beta_mG_m(x).\n",
@@ -2955,9 +3040,7 @@
   {
    "cell_type": "markdown",
    "id": "b73caf81",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the\n",
     "exponential cost/loss function defined as"
@@ -2966,9 +3049,7 @@
   {
    "cell_type": "markdown",
    "id": "e1497919",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}\\exp{(-y_i(f_{m-1}(x_i)+\\beta G(x_i))}.\n",
@@ -2978,9 +3059,7 @@
   {
    "cell_type": "markdown",
    "id": "f61fe6a9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We optimize $\\beta$ and $G$ for each value of $m=1:M$ as we did in the regression case.\n",
     "This is normally done in two steps. Let us however first rewrite the cost function as"
@@ -2989,9 +3068,7 @@
   {
    "cell_type": "markdown",
    "id": "4aef8601",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}w_i^{m}\\exp{(-y_i\\beta G(x_i))},\n",
@@ -3001,9 +3078,7 @@
   {
    "cell_type": "markdown",
    "id": "c7165d54",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where we have defined $w_i^m= \\exp{(-y_if_{m-1}(x_i))}$."
    ]
@@ -3011,9 +3086,7 @@
   {
    "cell_type": "markdown",
    "id": "5cbade74",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Building up AdaBoost\n",
     "\n",
@@ -3023,9 +3096,7 @@
   {
    "cell_type": "markdown",
    "id": "a52cbd43",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "G_m(x) = \\mathrm{sign} \\sum_{i=0}^{n-1} w_i^m I(y_i \\ne G_(x_i)),\n",
@@ -3035,9 +3106,7 @@
   {
    "cell_type": "markdown",
    "id": "3148496e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which is the classifier that minimizes the weighted error rate in predicting $y$.\n",
     "\n",
@@ -3047,9 +3116,7 @@
   {
    "cell_type": "markdown",
    "id": "9fda11b4",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\exp{-(\\beta)}\\sum_{y_i=G(x_i)}w_i^m+\\exp{(\\beta)}\\sum_{y_i\\ne G(x_i)}w_i^m,\n",
@@ -3059,9 +3126,7 @@
   {
    "cell_type": "markdown",
    "id": "31ffcfd2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which can be rewritten as"
    ]
@@ -3069,9 +3134,7 @@
   {
    "cell_type": "markdown",
    "id": "80616747",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "(\\exp{(\\beta)}-\\exp{-(\\beta)})\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i))+\\exp{(-\\beta)}\\sum_{i=0}^{n-1}w_i^m=0,\n",
@@ -3081,9 +3144,7 @@
   {
    "cell_type": "markdown",
    "id": "8a9380dc",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which leads to"
    ]
@@ -3091,9 +3152,7 @@
   {
    "cell_type": "markdown",
    "id": "677ee319",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\beta_m = \\frac{1}{2}\\log{\\frac{1-\\mathrm{\\overline{err}}}{\\mathrm{\\overline{err}}}},\n",
@@ -3103,9 +3162,7 @@
   {
    "cell_type": "markdown",
    "id": "6283c125",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where we have redefined the error as"
    ]
@@ -3113,9 +3170,7 @@
   {
    "cell_type": "markdown",
    "id": "0d7285ca",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathrm{\\overline{err}}_m=\\frac{1}{n}\\frac{\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i)}{\\sum_{i=0}^{n-1}w_i^m},\n",
@@ -3125,9 +3180,7 @@
   {
    "cell_type": "markdown",
    "id": "bfce1bb9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "which leads to an update of"
    ]
@@ -3135,9 +3188,7 @@
   {
    "cell_type": "markdown",
    "id": "4a1d1f64",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "f_m(x) = f_{m-1}(x) +\\beta_m G_m(x).\n",
@@ -3147,9 +3198,7 @@
   {
    "cell_type": "markdown",
    "id": "9a668f75",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "This leads to the new weights"
    ]
@@ -3157,9 +3206,7 @@
   {
    "cell_type": "markdown",
    "id": "e2c4d42a",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "w_i^{m+1} = w_i^m \\exp{(-y_i\\beta_m G_m(x_i))}\n",
@@ -3169,9 +3216,7 @@
   {
    "cell_type": "markdown",
    "id": "900679d9",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Adaptive boosting: AdaBoost, Basic Algorithm\n",
     "\n",
@@ -3189,9 +3234,7 @@
   {
    "cell_type": "markdown",
    "id": "a11f8c18",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathrm{err}=\\frac{1}{n}\\sum_{i=0}^{n-1}I(y_i\\ne G(x_i)),\n",
@@ -3201,9 +3244,7 @@
   {
    "cell_type": "markdown",
    "id": "c9c47a57",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "where the function $I()$ is one if we misclassify and zero if we classify correctly."
    ]
@@ -3211,9 +3252,7 @@
   {
    "cell_type": "markdown",
    "id": "7c9edb04",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Basic Steps of AdaBoost\n",
     "\n",
@@ -3227,9 +3266,7 @@
   {
    "cell_type": "markdown",
    "id": "7fdd3cbe",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "\\mathrm{\\overline{err}}_m=\\frac{\\sum_{i=0}^{n-1}w_i^m I(y_i\\ne G(x_i))}{\\sum_{i=0}^{n-1}w_i},\n",
@@ -3239,9 +3276,7 @@
   {
    "cell_type": "markdown",
    "id": "8c5b40a7",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n",
     "\n",
@@ -3267,9 +3302,7 @@
   {
    "cell_type": "markdown",
    "id": "f5a2468f",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## AdaBoost Examples\n",
     "\n",
@@ -3280,10 +3313,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "id": "3c538b9c",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.ensemble import AdaBoostClassifier\n",
@@ -3312,9 +3342,7 @@
   {
    "cell_type": "markdown",
    "id": "944755d2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent\n",
     "\n",
@@ -3330,9 +3358,7 @@
   {
    "cell_type": "markdown",
    "id": "1db01624",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## The Squared-Error again! Steepest Descent\n",
     "\n",
@@ -3343,9 +3369,7 @@
   {
    "cell_type": "markdown",
    "id": "9f1e977e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "(\\hat{\\boldsymbol{f}}) = \\mathrm{argmin}_{\\boldsymbol{f}}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n",
@@ -3355,9 +3379,7 @@
   {
    "cell_type": "markdown",
    "id": "ad07aa37",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We define a real function $h_m(x)$ that defines our final function $f_M(x)$ as"
    ]
@@ -3365,9 +3387,7 @@
   {
    "cell_type": "markdown",
    "id": "6c5e51f2",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "f_M(x) = \\sum_{m=0}^M h_m(x).\n",
@@ -3377,9 +3397,7 @@
   {
    "cell_type": "markdown",
    "id": "ff8db114",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "In the steepest decent approach we approximate $h_m(x) = -\\rho_m g_m(x)$, where $\\rho_m$ is a scalar and $g_m(x)$ the gradient defined as"
    ]
@@ -3387,9 +3405,7 @@
   {
    "cell_type": "markdown",
    "id": "57a1832e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "g_m(x_i) = \\left[ \\frac{\\partial {\\cal L}(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x_i)=f_{m-1}(x_i)}.\n",
@@ -3399,9 +3415,7 @@
   {
    "cell_type": "markdown",
    "id": "f2d02473",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "With the new gradient we can update $f_m(x) = f_{m-1}(x) -\\rho_m g_m(x)$. Using the above squared-error function we see that\n",
     "the gradient is $g_m(x_i) = -2(y_i-f(x_i))$.\n",
@@ -3412,9 +3426,7 @@
   {
    "cell_type": "markdown",
    "id": "69d68822",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "(\\rho_1) = \\mathrm{argmin}_{\\rho}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i+2\\rho y_i)^2.\n",
@@ -3424,9 +3436,7 @@
   {
    "cell_type": "markdown",
    "id": "536cae62",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Steepest Descent Example\n",
     "\n",
@@ -3436,9 +3446,7 @@
   {
    "cell_type": "markdown",
    "id": "7afeb185",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "f_1(x) = f_{0}(x) -\\rho_1 g_1(x)=-y_i.\n",
@@ -3448,9 +3456,7 @@
   {
    "cell_type": "markdown",
    "id": "7b64caef",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We can then proceed and compute"
    ]
@@ -3458,9 +3464,7 @@
   {
    "cell_type": "markdown",
    "id": "ec411555",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "g_2(x_i) = \\left[ \\frac{\\partial {\\cal L}(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x_i)=f_{1}(x_i)=y_i}=-4y_i,\n",
@@ -3470,9 +3474,7 @@
   {
    "cell_type": "markdown",
    "id": "df5d8340",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "and find a new value for $\\rho_2=-1/2$ and continue till we have reached $m=M$. We can modify the steepest descent method, or steepest boosting, by introducing what is called **gradient boosting**."
    ]
@@ -3480,9 +3482,7 @@
   {
    "cell_type": "markdown",
    "id": "8148cb91",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Gradient Boosting, algorithm\n",
     "\n",
@@ -3496,9 +3496,7 @@
   {
    "cell_type": "markdown",
    "id": "4559496b",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "$$\n",
     "C(\\boldsymbol{y},\\boldsymbol{f})=\\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n",
@@ -3508,9 +3506,7 @@
   {
    "cell_type": "markdown",
    "id": "70dd086e",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The way we proceed in an iterative fashion is to\n",
     "1. Initialize our estimate $f_0(x)$.\n",
@@ -3529,9 +3525,7 @@
   {
    "cell_type": "markdown",
    "id": "d27d3ddf",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Gradient Boosting, Examples of Regression"
    ]
@@ -3540,10 +3534,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "id": "40bb2103",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -3597,9 +3588,7 @@
   {
    "cell_type": "markdown",
    "id": "570d4c86",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Gradient Boosting, Classification Example"
    ]
@@ -3608,10 +3597,7 @@
    "cell_type": "code",
    "execution_count": 28,
    "id": "2356e9e9",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -3659,9 +3645,7 @@
   {
    "cell_type": "markdown",
    "id": "eb735817",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## XGBoost: Extreme Gradient Boosting\n",
     "\n",
@@ -3682,9 +3666,7 @@
   {
    "cell_type": "markdown",
    "id": "5a6da2c1",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Regression Case"
    ]
@@ -3693,10 +3675,7 @@
    "cell_type": "code",
    "execution_count": 29,
    "id": "bfa7cf73",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -3750,9 +3729,7 @@
   {
    "cell_type": "markdown",
    "id": "5ad2e9bc",
-   "metadata": {
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Xgboost on the Cancer Data\n",
     "\n",
@@ -3763,10 +3740,7 @@
    "cell_type": "code",
    "execution_count": 30,
    "id": "df6c15d0",
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -3824,7 +3798,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }