1205 lines
450 KiB
Plaintext
1205 lines
450 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## Uczenie maszynowe UMZ 2017/2018\n",
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"# 3. Naiwny klasyfikator bayesowski, drzewa decyzyjne\n",
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"### Część 1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## 3.1. Naiwny klasyfikator bayesowski"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"* Naiwny klasyfikator bayesowski jest algorytmem dla problemu klasyfikacji wieloklasowej.\n",
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"* Naszym celem jest znalezienie funkcji uczącej $f \\colon x \\mapsto y$, gdzie $y$ oznacza jedną ze zdefiniowanych wcześniej klas.\n",
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"* Klasyfikacja probabilistyczna polega na wskazaniu klasy o najwyższym prawdopodobieństwie:\n",
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"$$ \\hat{y} = \\mathop{\\arg \\max}_y P( y \\,|\\, x ) $$\n",
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"* Klasyfikatory probabilistyczne → klasyfikatory bayesowskie → naiwny klasyfikator bayesowski"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"### Twierdzenie Bayesa\n",
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"\n",
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"$$ P( y_k \\,|\\, x ) = \\frac{ P( x \\,|\\, y_k ) \\cdot P( y_k ) }{ \\sum_{i} P( x \\,|\\, y_i ) \\, P( y_i ) } $$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"### Twierdzenie Bayesa\n",
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"\n",
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"$$ \\underbrace{P( y_k \\,|\\, x )}_\\textrm{ prawd. a posteriori } = \\frac{ \\overbrace{ P( x \\,|\\, y_k )}^\\textrm{ model klasy } \\cdot \\overbrace{P( y_k )}^\\textrm{ prawd. a priori } }{ \\underbrace{\\sum_{i} P( x \\,|\\, y_i ) \\, P( y_i )}_\\textrm{wyrażenie normalizacyjne} } $$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"### Rola wyrażenia normalizacyjnego w twierdzeniu Bayesa\n",
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"\n",
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"_Przykład_: obserwacja nietypowa ma małe prawdopodobieństwo względem dowolnej klasy, wyrażenie normalizacyjne sprawia, że to prawdopodobieństwo staje się porównywalne z prawdopodobieństwami typowych obserwacji, ale nie wpływa na klasyfikację!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"### Klasyfikatory dyskryminatywne a generatywne\n",
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"\n",
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"* Klasyfikatory generatywne tworzą model rozkładu prawdopodobieństwa dla każdej z klas.\n",
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"* Klasyfikatory dyskryminatywne wyznaczają granicę klas (_decision boundary_) bezpośrednio.\n",
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"* Naiwny klasyfikator baywsowski jest klasyfikatorem generatywnym (ponieważ wyznacza $P( x \\,|\\, y )$).\n",
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"* Wszystkie klasyfikatory generatywne są probabilistyczne, ale nie na odwrót.\n",
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"* Regresja logistyczna jest przykładem klasyfikatora dyskryminatywnego."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"### Założenie niezależności dla naiwnego klasyfikatora bayesowskiego\n",
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"\n",
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"* Naiwny klasyfikator bayesowski jest _naiwny_, ponieważ zakłada, że poszczególne cechy są niezależne od siebie:\n",
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"$$ P( x_1, \\ldots, x_n \\,|\\, y ) \\,=\\, \\prod_{i=1}^n P( x_i \\,|\\, x_1, \\ldots, x_{i-1}, y ) \\,=\\, \\prod_{i=1}^n P( x_i \\,|\\, y ) $$\n",
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"* To założenie jest bardzo przydatne ze względów obliczeniowych, ponieważ bardzo często mamy do czynienia z ogromną liczbą cech (bitmapy, słowniki itp.)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Naiwny klasyfikator bayesowski – przykład"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"slideshow": {
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"slide_type": "notes"
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}
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},
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"outputs": [],
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"source": [
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"# Przydtne importy\n",
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"\n",
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"import ipywidgets as widgets\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"outputs": [],
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"source": [
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"# Wczytanie danych (gatunki kosaćców)\n",
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"\n",
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"data_iris_setosa = (\n",
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" pandas.read_csv('iris.csv', usecols=['pł.dł.', 'pł.sz.', 'Gatunek'])\n",
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" .apply(lambda x: [x[0], x[1], 1 if x[2] == 'Iris-setosa' else 0], axis=1))\n",
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"data_iris_setosa.columns = ['dł. płatka', 'szer. płatka', 'Iris setosa?']\n",
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"\n",
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"m, n_plus_1 = data_iris_setosa.values.shape\n",
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"n = n_plus_1 - 1\n",
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"Xn = data_iris_setosa.values[:, 0:n].reshape(m, n)\n",
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"\n",
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"X = np.matrix(np.concatenate((np.ones((m, 1)), Xn), axis=1)).reshape(m, n_plus_1)\n",
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"Y = np.matrix(data_iris_setosa.values[:, 2]).reshape(m, 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"count: {0: 100, 1: 50}\n",
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"prior prob.: {0: 0.6666666666666666, 1: 0.3333333333333333}\n"
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]
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}
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],
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"source": [
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"classes = [0, 1]\n",
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"count = [sum(1 if y == c else 0 for y in Y.T.tolist()[0]) for c in classes]\n",
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"prior_prob = [float(count[c]) / float(Y.shape[0]) for c in classes]\n",
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"\n",
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"print 'count: ', {c: count[c] for c in classes}\n",
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"print 'prior prob.:', {c: prior_prob[c] for c in classes}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"slideshow": {
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"slide_type": "notes"
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}
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},
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"outputs": [],
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"source": [
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"# Wykres danych (wersja macierzowa)\n",
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"def plot_data_for_classification(X, Y, xlabel, ylabel): \n",
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" fig = plt.figure(figsize=(16*.6, 9*.6))\n",
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" ax = fig.add_subplot(111)\n",
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" fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
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" X = X.tolist()\n",
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" Y = Y.tolist()\n",
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" X1n = [x[1] for x, y in zip(X, Y) if y[0] == 0]\n",
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" X1p = [x[1] for x, y in zip(X, Y) if y[0] == 1]\n",
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" X2n = [x[2] for x, y in zip(X, Y) if y[0] == 0]\n",
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" X2p = [x[2] for x, y in zip(X, Y) if y[0] == 1]\n",
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" ax.scatter(X1n, X2n, c='r', marker='x', s=50, label='Dane')\n",
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" ax.scatter(X1p, X2p, c='g', marker='o', s=50, label='Dane')\n",
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" \n",
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" ax.set_xlabel(xlabel)\n",
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" ax.set_ylabel(ylabel)\n",
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" ax.margins(.05, .05)\n",
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" return fig"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 14,
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||
"metadata": {
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||
"slideshow": {
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||
"slide_type": "subslide"
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}
|
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},
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"outputs": [
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{
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"data": {
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ky8xeYmbLqkuWQQFAy/EA/fv27ZOOPDL6Od32uDjK5ebjLJeliy6Kfk63PUQcQKuKq8iX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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f805c1a8510>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(X, Y, xlabel=u'dł. płatka', ylabel=u'szer. płatka')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[matrix([[ 1. , 4.906, 1.676]]), matrix([[ 1. , 1.464, 0.244]])]\n",
|
||
"[matrix([[ 0. , 0.8214402 , 0.42263933]]), matrix([[ 0. , 0.17176728, 0.10613199]])]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"XY = np.column_stack((X, Y))\n",
|
||
"XY_split = [XY[np.where(XY[:,3] == c)[0]] for c in classes]\n",
|
||
"X_split = [XY_split[c][:,0:3] for c in classes]\n",
|
||
"Y_split = [XY_split[c][:,3] for c in classes]\n",
|
||
"\n",
|
||
"X_mean = [np.mean(X_split[c], axis=0) for c in classes]\n",
|
||
"X_std = [np.std(X_split[c], axis=0) for c in classes]\n",
|
||
"print X_mean \n",
|
||
"print X_std"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Rysowanie średnich\n",
|
||
"def draw_means(fig, means, xmin=0.0, xmax=7.0, ymin=0.0, ymax=7.0):\n",
|
||
" class_color = {0: 'r', 1: 'g'}\n",
|
||
" classes = range(len(means))\n",
|
||
" ax = fig.axes[0]\n",
|
||
" mean_x1 = [means[c].item(0, 1) for c in classes]\n",
|
||
" mean_x2 = [means[c].item(0, 2) for c in classes]\n",
|
||
" for c in classes:\n",
|
||
" ax.plot([mean_x1[c], mean_x1[c]], [xmin, xmax],\n",
|
||
" color=class_color.get(c, 'c'), linestyle='dashed')\n",
|
||
" ax.plot([ymin, ymax], [mean_x2[c], mean_x2[c]],\n",
|
||
" color=class_color.get(c, 'c'), linestyle='dashed') "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from scipy.stats import norm\n",
|
||
"\n",
|
||
"# Prawdopodobieństwo klasy dla pojedynczej cechy\n",
|
||
"def prob(x, c, feature, mean, std):\n",
|
||
" return norm(mean[c].item(0, feature), std[c].item(0, feature)).pdf(x)\n",
|
||
"\n",
|
||
"# Prawdopodobieństwo klasy\n",
|
||
"def class_prob(x, c, mean, std):\n",
|
||
" return prob(x[1], c, 1, mean, std) * prob(x[2], c, 2, mean, std)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Wykres prawdopodobieństw klas\n",
|
||
"def plot_prob(fig, X_mean, X_std, classes, xmin=0.0, xmax=7.0, ymin=0.0, ymax=7.0):\n",
|
||
" class_color = {0: 'r', 1: 'g'}\n",
|
||
" ax = fig.axes[0]\n",
|
||
" x1, x2 = np.meshgrid(np.arange(xmin, xmax, 0.02),\n",
|
||
" np.arange(xmin, xmax, 0.02))\n",
|
||
" for c in classes:\n",
|
||
" fun1 = lambda x: prob(x, c, 1, X_mean, X_std)\n",
|
||
" fun2 = lambda x: prob(x, c, 2, X_mean, X_std)\n",
|
||
" p = fun1(x1) * fun2(x2)\n",
|
||
" plt.contour(x1, x2, p, levels=np.arange(0.0, 1.0, 0.1),\n",
|
||
" colors=class_color.get(c, 'c'), lw=3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/pawel/.local/lib/python2.7/site-packages/matplotlib/contour.py:967: UserWarning: The following kwargs were not used by contour: 'lw'\n",
|
||
" s)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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YvRjWVhARERHVAAEDK2NMgjHmSe/nRWRN5JpEREREFJ8CBlYiUg6gX5TaQkRERBTXgim3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\nEyszMzOzGvkvUXZIP7e5YcEAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f805c1dad10>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(X, Y, xlabel=u'dł. płatka', ylabel=u'szer. płatka')\n",
|
||
"draw_means(fig, X_mean)\n",
|
||
"plot_prob(fig, X_mean, X_std, classes)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Prawdopodobieństwo a posteriori\n",
|
||
"def posterior_prob(x, c):\n",
|
||
" normalizer = sum(class_prob(x, c, X_mean, X_std)\n",
|
||
" * prior_prob[c]\n",
|
||
" for c in classes)\n",
|
||
" return (class_prob(x, c, X_mean, X_std) \n",
|
||
" * prior_prob[c]\n",
|
||
" / normalizer)\n",
|
||
"\n",
|
||
"# Wykres granicy klas dla naiwnego Bayesa\n",
|
||
"def plot_decision_boundary_bayes(fig, X_mean, X_std, xmin=0.0, xmax=7.0, ymin=0.0, ymax=7.0):\n",
|
||
" ax = fig.axes[0]\n",
|
||
" x1, x2 = np.meshgrid(np.arange(xmin, xmax, 0.02),\n",
|
||
" np.arange(ymin, ymax, 0.02))\n",
|
||
" p = [posterior_prob([1, x1, x2], c) for c in classes]\n",
|
||
" p_diff = p[1] - p[0]\n",
|
||
" plt.contour(x1, x2, p_diff, levels=[0.0], colors='c', lw=3);"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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g9DBLhqfly4euX758aKjK1gQz1zapuAaaZtK5545cPJ5en29s+QqsixlfsZ+vnIJcmyDH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\niUBRYMN/KAAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f8052af4090>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(X, Y, xlabel=u'dł. płatka', ylabel=u'szer. płatka')\n",
|
||
"plot_decision_boundary_bayes(fig, X_mean, X_std)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Dla porównania: regresja logistyczna na tych samych danych"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def powerme(x1,x2,n):\n",
|
||
" X = []\n",
|
||
" for m in range(n+1):\n",
|
||
" for i in range(m+1):\n",
|
||
" X.append(np.multiply(np.power(x1,i),np.power(x2,(m-i))))\n",
|
||
" return np.hstack(X)\n",
|
||
"\n",
|
||
"# Funkcja logistyczna\n",
|
||
"def safeSigmoid(x, eps=0):\n",
|
||
" y = 1.0/(1.0 + np.exp(-x))\n",
|
||
" if eps > 0:\n",
|
||
" y[y < eps] = eps\n",
|
||
" y[y > 1 - eps] = 1 - eps\n",
|
||
" return y\n",
|
||
"\n",
|
||
"# Funkcja hipotezy dla regresji logistycznej\n",
|
||
"def h(theta, X, eps=0.0):\n",
|
||
" return safeSigmoid(X*theta, eps)\n",
|
||
"\n",
|
||
"# Funkcja kosztu dla regresji logistycznej\n",
|
||
"def J(h,theta,X,y, lamb=0):\n",
|
||
" m = len(y)\n",
|
||
" f = h(theta, X, eps=10**-7)\n",
|
||
" j = -np.sum(np.multiply(y, np.log(f)) + \n",
|
||
" np.multiply(1 - y, np.log(1 - f)), axis=0)/m\n",
|
||
" if lamb > 0:\n",
|
||
" j += lamb/(2*m) * np.sum(np.power(theta[1:],2))\n",
|
||
" return j\n",
|
||
"\n",
|
||
"# Gradient funkcji kosztu\n",
|
||
"def dJ(h,theta,X,y,lamb=0):\n",
|
||
" g = 1.0/y.shape[0]*(X.T*(h(theta,X)-y))\n",
|
||
" if lamb > 0:\n",
|
||
" g[1:] += lamb/float(y.shape[0]) * theta[1:] \n",
|
||
" return g\n",
|
||
"\n",
|
||
"# Funkcja klasyfikująca\n",
|
||
"def classifyBi(theta, X):\n",
|
||
" prob = h(theta, X)\n",
|
||
" return prob"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Przygotowanie danych dla wielomianowej regresji logistycznej\n",
|
||
"\n",
|
||
"data = np.matrix(data_iris_setosa)\n",
|
||
"\n",
|
||
"Xpl = powerme(data[:, 1], data[:, 0], n)\n",
|
||
"Ypl = np.matrix(data[:, 2]).reshape(m, 1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Metoda gradientu prostego dla regresji logistycznej\n",
|
||
"def GD(h, fJ, fdJ, theta, X, y, alpha=0.01, eps=10**-3, maxSteps=10000):\n",
|
||
" errorCurr = fJ(h, theta, X, y)\n",
|
||
" errors = [[errorCurr, theta]]\n",
|
||
" while True:\n",
|
||
" # oblicz nowe theta\n",
|
||
" theta = theta - alpha * fdJ(h, theta, X, y)\n",
|
||
" # raportuj poziom błędu\n",
|
||
" errorCurr, errorPrev = fJ(h, theta, X, y), errorCurr\n",
|
||
" # kryteria stopu\n",
|
||
" if abs(errorPrev - errorCurr) <= eps:\n",
|
||
" break\n",
|
||
" if len(errors) > maxSteps:\n",
|
||
" break\n",
|
||
" errors.append([errorCurr, theta]) \n",
|
||
" return theta, errors"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"theta = [[ 4.01960795]\n",
|
||
" [ 3.89499137]\n",
|
||
" [ 0.18747599]\n",
|
||
" [-1.3524039 ]\n",
|
||
" [-2.00123783]\n",
|
||
" [-0.87625505]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Uruchomienie metody gradientu prostego dla regresji logistycznej\n",
|
||
"theta_start = np.matrix(np.zeros(Xpl.shape[1])).reshape(Xpl.shape[1], 1)\n",
|
||
"theta, errors = GD(h, J, dJ, theta_start, Xpl, Ypl, \n",
|
||
" alpha=0.1, eps=10**-7, maxSteps=100000)\n",
|
||
"print(r'theta = {}'.format(theta))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Wykres granicy klas\n",
|
||
"def plot_decision_boundary(fig, theta, Xpl, xmin=0.0, xmax=7.0):\n",
|
||
" ax = fig.axes[0]\n",
|
||
" xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.02),\n",
|
||
" np.arange(xmin, xmax, 0.02))\n",
|
||
" l = len(xx.ravel())\n",
|
||
" C = powerme(yy.reshape(l, 1), xx.reshape(l, 1), n)\n",
|
||
" z = classifyBi(theta, C).reshape(int(np.sqrt(l)), int(np.sqrt(l)))\n",
|
||
"\n",
|
||
" plt.contour(xx, yy, z, levels=[0.5], colors='m', lw=3);"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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bq6XKpzqkMP65QKfJNrGiQSgKmpmp37R+mjB/gmZWz9Tr//x6DbpikGp+WqPV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\nQED+P0U9BeCg6WQZAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f80526a7ed0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(Xpl, Ypl, xlabel=u'dł. płatka', ylabel=u'szer. płatka')\n",
|
||
"plot_decision_boundary(fig, theta, Xpl)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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Eitq8YKa1BurFpUAiIgqJb7nLt8Tme/neB1rusnqprPvyX/fY3O7AS3WBxrf7+Gh2Ht8d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\nFfeCm3z4QmoAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f80526a7ad0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(Xpl, Ypl, xlabel=u'dł. płatka', ylabel=u'szer. płatka')\n",
|
||
"plot_decision_boundary(fig, theta, Xpl)\n",
|
||
"plot_decision_boundary_bayes(fig, X_mean, X_std)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Inny przykład"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Wczytanie danych (gatunki kosaćców)\n",
|
||
"\n",
|
||
"data_iris_versicolor = (\n",
|
||
" pandas.read_csv('iris.csv', usecols=['pł.dł.', 'pł.sz.', 'Gatunek'])\n",
|
||
" .apply(lambda x: [x[0], x[1], 1 if x[2] == 'Iris-versicolor' else 0], axis=1))\n",
|
||
"data_iris_versicolor.columns = ['dł. płatka', 'szer. płatka', 'Iris versicolor?']\n",
|
||
"\n",
|
||
"m, n_plus_1 = data_iris_versicolor.values.shape\n",
|
||
"n = n_plus_1 - 1\n",
|
||
"Xn = data_iris_versicolor.values[:, 0:n].reshape(m, n)\n",
|
||
"\n",
|
||
"X = np.matrix(np.concatenate((np.ones((m, 1)), Xn), axis=1)).reshape(m, n_plus_1)\n",
|
||
"Y = np.matrix(data_iris_versicolor.values[:, 2]).reshape(m, 1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "notes"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"count: {0: 100, 1: 50}\n",
|
||
"prior prob.: {0: 0.6666666666666666, 1: 0.3333333333333333}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"classes = [0, 1]\n",
|
||
"count = [sum(1 if y == c else 0 for y in Y.T.tolist()[0]) for c in classes]\n",
|
||
"prior_prob = [float(count[c]) / float(Y.shape[0]) for c in classes]\n",
|
||
"\n",
|
||
"print 'count: ', {c: count[c] for c in classes}\n",
|
||
"print 'prior prob.:', {c: prior_prob[c] for c in classes}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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RygtW1jy+6sJVQcYI1UqJFkUAQssz+bpT0h9b5AxJL5eXsQCQo2MOP0ZrF64d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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f805a13dd50>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(X, Y, xlabel=u'dł. płatka', ylabel=u'szer. płatka')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[matrix([[ 1. , 3.508, 1.135]]), matrix([[ 1. , 4.26 , 1.326]])]\n",
|
||
"[matrix([[ 0. , 2.08373127, 0.91459007]]), matrix([[ 0. , 0.46518813, 0.19576517]])]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"XY = np.column_stack((X, Y))\n",
|
||
"XY_split = [XY[np.where(XY[:,3] == c)[0]] for c in classes]\n",
|
||
"X_split = [XY_split[c][:,0:3] for c in classes]\n",
|
||
"Y_split = [XY_split[c][:,3] for c in classes]\n",
|
||
"\n",
|
||
"X_mean = [np.mean(X_split[c], axis=0) for c in classes]\n",
|
||
"X_std = [np.std(X_split[c], axis=0) for c in classes]\n",
|
||
"print X_mean\n",
|
||
"print X_std"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/pawel/.local/lib/python2.7/site-packages/matplotlib/contour.py:1180: UserWarning: No contour levels were found within the data range.\n",
|
||
" warnings.warn(\"No contour levels were found\"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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FEitjzJTKDkREpEQLFjibD1uwdQELtvp2G0Vqi4r2WL1VqVGIiIiI+IAyEytjjJ8x5oUT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\nSik1Tv4/EcuC10H+HqAAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f8051c24090>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(X, Y, xlabel=u'dł. płatka', ylabel=u'szer. płatka')\n",
|
||
"draw_means(fig, X_mean)\n",
|
||
"plot_prob(fig, X_mean, X_std, classes)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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2pSyP707xOgBg7DBLhKfrr++7/Prr+4aqTE0ws62TCmugaSadc87gxeOp5bnGlqvAupDx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\nRK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f8051b18a10>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(X, Y, xlabel=u'dł. płatka', ylabel=u'szer. płatka')\n",
|
||
"plot_decision_boundary_bayes(fig, X_mean, X_std)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 52,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Przygotowanie danych dla wielomianowej regresji logistycznej\n",
|
||
"\n",
|
||
"data = np.matrix(data_iris_versicolor)\n",
|
||
"\n",
|
||
"Xpl = powerme(data[:, 1], data[:, 0], n)\n",
|
||
"Ypl = np.matrix(data[:, 2]).reshape(m, 1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"theta = [[-22.4168027 ]\n",
|
||
" [ 12.03703048]\n",
|
||
" [ 11.13804517]\n",
|
||
" [ -1.75684025]\n",
|
||
" [ 1.01230657]\n",
|
||
" [ -7.61403096]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Uruchomienie metody gradientu prostego dla regresji logistycznej\n",
|
||
"theta_start = np.matrix(np.zeros(Xpl.shape[1])).reshape(Xpl.shape[1], 1)\n",
|
||
"theta, errors = GD(h, J, dJ, theta_start, Xpl, Ypl, \n",
|
||
" alpha=0.05, eps=10**-7, maxSteps=100000)\n",
|
||
"print(r'theta = {}'.format(theta))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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wKJCIiLolOtwVHWKLLtH3yYa7rB4qix3+i21bKJR8qC7Z8TO9fy47je+OFIcCMx6mYhcG\nKyIi6pZkdUzBYPwarNtuM5Z49VmhUHr1W+m0LZXjp1tfls31aVY7je/OYEVERH1Hsh6rZcs691DF\n/vJMtE01+fZEoj1e0VCl2j5c3X13esdPt31m7J/LUvzuqQYrFq8TEVHu05hCbLu9bX0o1L4wWxNM\ngploWyrb4wmHjSkVFizovH/s+u4eP932mbV/Lkvhu7N4nYiI+g4RIzzdeWf79Xfe2T5UxZsEM9E2\nIL0JNEWAadO6Lh6Prk/WtmQF1um0L93vl0mpXJtUjmHmd0+lW6unFg4FEhFRtySrlUlUJ2V1jVWy\nAumlSxMff+nS9PbvzTVW6Rbes8aKiIioC+lOgmnlXYHJfnmnG5yS7d+b7wpMNxTyrkAiIqIuhMNt\nBepdrY9Oa9DV9qVLjaU7+3a1Pl77EhXOJzt+uvsna1u63y+T0im8P43vnmqwYo0VERHlvniTXUbX\n22zxt197rbF0dwJNTVLnEw4nnry0437xvt/Che3XmTWjfLJrl8kC9mTXVjW9a2PBd2ewIiIiSiTZ\nA57nz09t++TJ7Y87eXJq+0fDV7wC62x+AHW6Uvluia5NJqTSrdVTC4cCiYgo66RTGD93rjEBaDoT\nhCY7frrF9dks3Wtv4ncHa6yIiIhMks4EoulOXppKgXVvnuAz3WtjklSDFScIJSIiSoVq9yYQVU1v\n8tLo/tHPxZ4vdn2y9uWydK+NCThBKBER5Q5NoQDcyu3JOhk0SR1Pou3pTl6aSoF1svZlUrL/tule\n+2yTSrdWdxYAQwC8CWALgI8BzE22D4cCiYj6qHTnoUp3e6Iho3TrfKyukcr2CT7TGa5Ldw6wDAwF\nWhmsqgBMivxcBOATAOcm2ofBioioj8rm8JLNoS+V9mV6gs90gl+6s9b35uJ1AC8CuCLRZxisiIj6\nsHQKxM3Ynqhd3Z181IztvWGCT6uufbLJU02UarDqkeJ1ERkG4B0A41TV22HbzQBuBoChQ4dO3rdv\nn+XtISKiLKXdLACfORO4917gwQcBh6NtezBorF+wwDhuV8cHeqwAOivF+55mf/94/23NYOWxI7Km\neF1ECgEsBXB7x1AFAKr6tKpOUdUpFRUVVjeHiIiylSYoUo5OFBlvks3rrwcefhiorGy/vbLSWD9/\nfvzjL1/eeyfYTEVPTDCa6L9tNh+7e+2xdPjPCeCvAO5M5fMcCiQi6qOS1eEkm2TT71ctLzfel5cb\nn499HwjEP/5ttxlLthZ/W83q4ncrj9+DhfvIdI0VAAGwGMBPU92HwYqIqI9KtUA83iSb0e3RMBVd\nou+TFYjHFkFbWKeTtaysU7KyuL4vTRAqItMBvAtgE4DIIDbuVdVX4u3DCUKJiPooTVLnM2uWUUsV\nr8Yqun3mzM41Vi+91LY9UR0R0Hsn2EyFWlSnlOy/bTo1XFYeu4OM11ip6nuqKqp6nqpOiCxxQxUR\nEfVi4TAwbx4aWurxzNpnMO/1eXhm7TNoaKkH5s0zAtLKlZ3rYlSN9UDySTbD4a5rsKIBIdEkm0D3\n63Qi3+1UIXzH9aFQ+hNkWr2/plGnlOz4QPIJTrsrlclTe1oq3Vo9tXAokIiyWTgc1pAvpAFvQP21\nfm2tadWWfS3avLNZm7Y1aePmRm1Y36DetV71Vnu1fnW91q+q1/qVHZbV9eqt9qp3rVcb1jdo4+ZG\nbdrWpM07m7Vlb4u2HmxV3zGfBrwBDflCGu4Nw1F3363vDoUW/ZtNC/6rQHE/tOC/CrTo32z67lCo\nTp3aNrQXChn7xNZR3X134lqaF15oG8IaP97Yd/z4tnUvvBC/benW6cQOU3bV9tmz0xuuSne4y+q5\noLJ9Hi2TIMWhQEfS5EVElKPCvjACJwMIngwiWBdEsD6IUH0IQW8QIW/ktSFkLI2RpclYws1hhJpD\nCLeEjaXVWDJCAJvHZix5Ntjz7bDl22AvsMNeaDdei4zFUeyAvdh4dZQai73EDmeZE44yB5xlTthc\nPf80s4b778UM50/Q4AwDgSYAQFOgCXACM/7Bhpp7/4rCCy8F1q83epnWrDFe168HJkwApk417vyb\nOxdYuNDoiVi40Dj4okXA3r1tJ7vkEmP7JZcAGzYY61atAq67ruvGrVhhHCPesS+5pK1XqysLFgCv\nvRa/7S+8ANx1l3EswDj2HXe0nTM6DBnP7NnG56zaP/o9u/v9021fL8OHMBNRTlBVBE8G4T/ih/+I\nH4EjAfiP+RE4FjCW2gACx43X4IkgAicCCDcnD0L2wkgoiQaUwkhoybfDVmCDPc9+KtDYPDbY3DaI\nW2Bz2SAugc1pgzjFWOyRxSHG7Ts2QETaF12osWhIT71qUI3XQOTngCLsDyPsC0N9irDPCHehlrag\ndyr8NbUFw2hQ1EDiv9ftRXY4+zvhrHDCWe6Ec4ATrgEu43WgC64qF9xVbrjOcMFR6jC+Q5qeWfsM\nbn/1diNMdVDgLMCiKxfhpgk3tgWSqAkTjKAikriWZuZMY0oFnw947LG27bfdBrjdwEMPta8fimVG\nnU50GLKrtkfnz4qGjajYIJOMlfsD6X//dNuXA1KtsWKwIqKMCzYE4Tvga1sO+uCv8cNX44P/kN9Y\njvih/q7/vnKUOYyAEF1iemcc/RzGUuqAoyTSgxPp0bEX2iG23vGXfqxQa8jomas3eumCJ42gGTwZ\nROB4AMHjQSOU1kYC6dEA/Ef9UF/n62vz2OAqDcA9ugzuwW54hnjgHuKG50wPPMOMxV5g76IV7c17\nfR4e/uDhuNvvufAeLPjCAiOgdCxOjwlEDb4GLPl4CXYc34FR/Udhztg5KHIXtX3eqgLsVCRpe9pt\ny/T+mT5+hqUarDgUSESWUlUEjgfQursVrXsjyz5j8X3qg2+/D8G6YKf9HP0dp3pN8s/JN3pSKiPL\nQNepXhZHmQM2R88PbWUzu8cOu8cO10BXyvvosuUIXfcP8H9zLvz/9H34DwfgO9AK/+//Ct/aT+Hr\ndzG8+4pw7MAxaLB9AHNWOOEZ4UHeiDzkjYwsZ+ch/+x8OMucAIBR/UehwFkQt8dqZNnI+MXnkV6f\n9z59DzN+NwNhDaMp0IQCZwHu/OudeOXrr2D60OnxC7B7otckSdvTblum98/08XMIgxURpU3DCl+N\nDy2ftKBlZwtadrSgZZextO5uRagx1O7zjn6OUz0eJReXwDPUA/cgN9xD3HAPNsKU3ZO8F4TMI9fM\nhmPut+FY9CDy+zW11cmsjdbefAEQgYYU/sN+tH4aE5R3t6Jldwu8H3pxdMnRtgl2ADjLncgfk49p\nI8/H7AMzsL1yD/YO2IvaolpjuBSArbkFc8Zc174uKbZOafJkNHzwFmb8bgYa/A2njh0NaTN+NwM1\ndx5E4bz72tcKxQ5NWfkLPnYYsIu2o7q6rcaqO22LHWbLxP7JWH38HMOhQCJKWaglhOZtzcayNfL6\nSTNadrS0q2cStyBveB48Zxm9GJ4RHuP9cCNMOYr5b7qsZEKdTNgfRuueVjTvaEbL9hY0b282lo+O\nIdDS1oPm9Xixr3If9lTswOxdOzCxyoOCj1+GbcK4tl6emMDyzPwv4vbC9+LXaA38R9z07Sfbtzf2\n+yxblrgAOx3z5hmPzYmtqYoNW7NnG7VK3W1b9JE7mdo/GauPnyVYY0VE3RYOhNG8vRlNm5rQtLlt\nad3TahRfA4AN8Az3IH90PvJH5yNvlDH0kzcqD+7B7l5Zu9QnWFUnEw7DP/eHqL36X/H+/32A1o9b\nUbG/Avm78hFuMkK52MMomFCMoilFKJ5ajOLzi5E/2gP5t3sx7wuKhz/4cdzD33PhPCxoOD8zD1IO\nh43C+eiDnjuuf/BBY5LS7rYt3eJ6qyfR7MFJOjOJwYqIUhKoC6BxfWO7pXlLc9udZXYg/+x8FIwr\nQMHYAuSfm4/8c/KRPyofNjdrm3oVVTTccSuWvPsUdpQBo04Acy76DooWPpHyL8akxeUdTxlWtOxs\nQeO6RjSsbUBDtbGEvMbwsb3YjuLzi7FzxE48EngE6yrXodXV2u4Yp+4qnHTTaZ8/J/SR4JLtGKyI\nqJOgN4iGNW2/vBqqG9C6u+2XlKvKhcLxhSgYX4DC8wpRMK4A+aMZoPoEVbz3/a9ihvsFhF0ONEkQ\nBeqAzR/EK77rMf0nf0z6y7ur4nKb2NqKy1NtSljR/EkzGlY1wLvSi/r369G0uQlQIGgLYtugbVg3\nbB3WDV+HzUM2w1PgQc1dNVh/eL0p5886fWSoLdsxWBH1cRpSNH3cBO+HXnhXeeFd6UXztuZTQ3me\nYR4UTi5E0eQiFE4sRNHEotO6i4x6l4YXfo9Ba7+OBnfnbUU+oGbS71B4/Q3x9/c1YNCjg9oVl5/a\n31WEmrtqUOgq7Hb7AnUBfLjiQ/zx2T9i3O5xGHVgFOxqh8/hg+dzHgy6ehC+XPNlbCvZdqoo3szz\nZ1Si4vBeNldUNuN0C0R9TKg5ZPzr/t161L9fD+9KL0INxnCKs9yJovOLMODvB6Dos0UomlIEVzlD\nFLVZMrwJ4S1uQH2dtoU9biwZ0YybEu3/8RKEtesJWcMaxpLNS3DTpERHSMxZ6sTF37oYk26YhCWb\nl+DNA29i7O6xGL9rPBr/1ogDdx/AU3gKB/sdxAejP8D7o9/HpqGbELaHTTl/RnWcCT16cwFDVVZi\nsCLKUaGmEOrfq0fdW3Woe7sODR81GPMLCVDwmQIM/PpAFF9YjOJpxcg7K8+U2bMp86yqIdpxYiea\nughVANCkPuw8sSvh+Xcc39HlHXuAMS3CzhM7U2pHjbcG89+Yj2212zCmfAwWXL4AZxSfcaqeqHD2\nbCMgTYrsEFn/w6Z12PCHjZi2YxpmfTQLX1n5FdTl1+G9Me/hrbFvYdfk1M6ftaLhKvaOTYaqrMRg\nRZQjwsEwGlY14MTrJ1D3Rh28q7zQgEIcgqLPFmHwXYNRekkpSj5XAkcJ/9fujZJOkJmGUf1HwQYb\nwujc62SDDSPLRiY8/6j+o+BxeNAabO20v8fhMSYATeLJj57Era/ceur96prVWLxxMZ6Y8QRuOVCV\nsM5oyPwv4pHxf8WLU1+Ex+fB1J1TcfHWi3HZ5stw9dqrEXyxFTu+tQOV/1iJwomFufcPDU7AmTNY\nY0WUxVr3t+LEX07gxKsncPKNk8adUjagaFIRSi8rRb/L+qFkeklKjxSh3GZ1DdNfd/wVV/7+yrjb\n/zTnT7hh+Q1xz7/mn9fg7CfOjrv/obsOobKwMu72Gm8NBi0cFH//O2tQed9/x60zaljwIwz67wo0\nSKDdfq6AC5//5EL8d+BR1L9SD/UrCsYXoOrbVaj8h8rc+EcIa6yyAmusiHKQhhXe1V4cf+k4jr98\nHE2bjKEV91A3BswZgLIvlqH0slI4+zkz3FLqaVbXMH1j+TcSbv/75X+PeP8QD2sY//nef8Jj96A1\n1EWPld2DP3/y54Ttm//G/ITnv+eN+Xh24a+MN13UGRWJ4JUb38CMX12OsD+AJjdQ4ANsLsV9P/4R\nJpw5AYETARx97igO/fIQdn5vJ3bP242B/zAQg28bjIJzCxKeP6NWrOgcomJrri65hHcFZhEGK6IM\nC/vDOPl/J1G7rBa1L9UicCQA2IGS6SUY8eMR6D+jP/LPyc+9oYs+zIo6KKtrmOpa6xLu1+RvgqLr\nYNUUaML22u1dhioAaA21nmpfvGuzrXZbwvNvr92etM5o+pkXoWb+cSy5oBg7y4CRJ4A5Hx5HYeTa\nO8ucGHTLIAy6ZRC81V7UPFWDw88exqGfH0LZjDIMnT8UpdNLE7ajW9Kdh2r2bGNKhdjPRa/FJZcY\n6ylrMFgRZUDYH8bJv53E0T8exfEXjyNYF4S90I6yL5WhfHY5yr5Uxl6pHGVVHVRKDzFOIlENU6mn\nFLUttXH3LXAVIBgKxu2RGl0+GusOrYM/7O+03WVzJa3RGlM+BqtrVsc9/+jy0cnrjFRROO8+3LQu\nZvu8+7ocKiueUoziXxZjxEMjUPM/NTj42EGsv2g9Sj9fimEPDDM3YK1Ykd48VCJdb4+3njKKs/4R\n9RANK+rercP2f92OD6o+wKarNqF2RS36z+qPcX8ah88d+xzG/nEsBt4wkKEqRzX4Gk49KDgagJoC\nTWjwG+sb/Y3dPvacsXNgk67/yraJDXPGzUm4f423pl2oinXrK7fimS8/k3D/38/+fcIeqZsn3dxl\nqAIAf9iPKVVTEl6bf7/43xMdW+mkAAAgAElEQVSe/6HLF7SvKwqHjddFi4z14XDi7XGGMV0VLgy7\nbxim7ZuGsxaehaatTVh/0XpsmrkJTdu67iE8bbNnd25LbFvZ49SrsMeKyGIte1tw5NdHcPjXh9G6\npxW2fBvKZ5djwN8PQNkVZZzVvBexsg6qyF2EV77+StyZxZMVrierYVq+fTkuHXYp3tz7Zqdtlw67\nFEdajiS86+8Hr/8g4fG/88p3El6bd/a9gydmPNFl+HtixhOo/NvKxHVGbndadUj2fDuG3D4EZ9x8\nBg48dgCfLvgU1Z+pxuDbB+PMH54JR2Eavy45D1WfwmBFZIFwIIzaFbU49PQhnPzbSUCAfpf3w7Af\nDUP5NeXp/SVNWcusOqh4pg+djpq7arBk8xLsPLETI8tGYs64OSndDZhKDdOH3/4QK/evxMznZuJk\ny0n0y+uHl772EqYNmYZ5r8/rMlQBQGuwFXtP7k14/L11e5NemwVfWIBrz7kW9/ztHmyv3Y7R5aPx\n0BceMu4mnKKJ64xmzQKmTUu7Dsmeb8eZ95yJqpuqsHv+buz/yX4ce+EYRj8zGv0u75fSMbrEeaj6\nDP7tTmQiX40PNf9Tg0O/OAT/YT/cQ90Ydv8wVN5YCc9QT6abRxFWTbKZSh3UJ7Wf4FsrvoU9dXsw\nvHQ4np39LM4ub5umIO4EmRHeVi/e2fcOttVuQ01DDb408kvtglW8/VOqYQIwdsBYPHj5g6euzdgB\nY1P6bsP6DcOhpkNxjz+sdBi8Pm/SGrHKwko8O/vZzgdIpc7IxDokV4ULY54Zg6obq7Dtn7Zhwxc2\nYPBdgzFiwQjYnN3oZeY8VH0G57EiMkHDugbsf2Q/ji05Bg0pymaUYdB3BqHsyjKInX9pZhOzHhTc\nlWRzTX1z/DfxxEdPdNp2x7Q78OgXH+1UXB71xIwncMtnb0lr++zRsxPPE3XXIew8sTPutRk/cDwG\n/mQgWoItnfbNc+Rhw79sSDiP1Y7v7sCkpydZNg+XlULNIez6wS7UPFmD4guLMW7ZOLgGnMYjoTgP\nVa/AhzATWUxVUfdWHT598FOc/NtJ2AvtqPp2FQZ9dxDyzsrLdPOoC1ZPsgnED24/v+rnuGF5/IcY\nv3/j+7jwVxfG3b7u5nWY+PTEbm/f8b0dGPfkOPhCnR9b47a7sWfuHoz+2ei412b7d7dj5OMj0Rxo\n7rQ935mPI98/gsUbFicMflaG2p5w5Lkj2P5P2+Ea6MJ5r52H/FH5qe24fHl6dwVSVmCwIrKIquLk\nGyex9/698L7vhavShcF3DEbVzVVwlvJuvmz2zNpncPurt8cdjlp05SJTHtTb6G/sVAf1d4v/Dh8e\n/DDuPuV55QmnOxjRbwR2n9wdf3vpCOyui7/9gsEXYOORjXG/+3XnXoelW5bG337OdVi6Nf726LU7\n3Hi46xqpiK6uTbb2VHXFu9qLTVdtgjgEE96egPyzUwhX6c5jRVmBM68TWcC72ovd83aj7q06uAe7\nMepno1B5UyXsHj5SJhdYXVwepapQKMIahkKhqthTtyfhPskm6DzSeCTx9qbE25MVj2+v3Z54+/HE\n26PXrsBZgOlDp2NgwcBTdVmxCl2FpoTXTCmeWowJb0/A+kvXY8MVGzBp1SS4K92Jd+I8VH0KgxVR\nClr3tWL3Pbtx9LmjcA5wYuSikai6uYqBKseYMclmMvEmwRxaMhSHmw7H3S/ZBJ0DCwcm7LEaWDAw\nYY/VsNJhONl6Mu50CaPLR2Pz0c1xr83o/om3J5sANBeG+lJVcG4BzvvLeVh30Tp8fP3HmPDWBNgc\nnDaFDPyTQJRA2B/Gvgf3YfU5q1G7ohZn3ncmzt95PgbfNpihKgelO8lmMokmCN1zMnGP1R+u+0PC\n7YtnLU64/TfX/Cbh9idnPJlwuoT7Lrov4bV56AsPJdw+Y9QMyyZHzUZFk4ow+hej4X3fi/0/3p/p\n5lAWYbAiiqN+ZT2qJ1Vjz7/tQdmMMkzdPhXD/2M4HEXs6M1V0Uk2i1xFp4aoCpwFKHIVpTTJZjKJ\nJggNaQi2OH/lOmwO7KvfhydmdL5jEDCKv7ce3wqXves70Vx2F7bWbk24f/WhanjsXU/54bF78Pa+\ntxNem6qiqoTb/7zjz0knR+1tBt4wEOXXlmPfA/vgq+l8UwD1TfwNQdRBOBDG3h/txacLPoX7DDc+\n8/Jn0P+q/pluFpkknUk2k0lUw9XV3XhRwXAw6QSZ816fB38oziNjQv6U9k/2kOSbJt2U8NokunZ/\n2v6nHqlfyzZn/fgs1L5Yi/2P7MfIR9IfSqbcx2BFFKP101ZsmbMF3pVeVN5YiZELR8JRwv9NOrJq\ngs2ekm4Bdbzvn6iGy213Q6FdhqNUJshMtT4s3f2TXZt423uifi0b5Y3IQ8U1FTiy+AhG/PcI1loR\np1sgijr51kls+coWhH1hjP7laAz4yoBMNykr5fpcROlK9P3HDxyPykcqu5zrKc+RB7vY0RjoXGuU\nyhxa6c7BZfUcXj0xR1i2Ovr8UWz56hZM/HAiSqaVZLo5ZJFUp1tgtCYCcPi3h7Hxio1wljsxuXoy\nQ1UciYqze2OBckepfP9E/1hdOmdpt+u70q0Ps7q+zOrjZ7PiacUAgMZ1vfvPP6WGYxzU5x186iB2\n3LIDpZeVYuzSsZzkM4FExdnRAuVcnqMomWTf/5437kl459z++v1p1XelWx9mZX1ZTxw/W7nPMOax\n8h/uugaO+hYGK+rTDv3vIey4ZQf6z+yPsX8cC5ubnbiJ9NQEm8keRGz1/vFqqJJ9/2STbO48sbPL\nyUNTOXdUuvVhVk/QmesTgHYLJ02nGJYFKxH5XwBXAziqquOsOg9Rd5147QS2//N29PtiP4x9fixs\nLoaqZHqiQLnjg4RX16zG4o2LTz1vzur9E01ymez7J5tkU1Ux6NFBcSfQ7CsTbPY20akWXJWn8WBm\n6rUsK14XkYsBNAJYnGqwYvE69ZTW/a2oHl8N92A3Jn4wEY5Cdt6mwuoC5RpvDQYtHBR3+6G7DrV7\n7pzZ+yf7ftu/uz3pg4rjbS90FgKCLuvQUjl2by7+znXHlh3Dx9d9jInvT0TJ51i83ltlvHhdVd8B\ncMKq4xN1l6pi+03boQHF2KVjGapOg9UFyvPfmJ9w+z1/u8fS/ZPVUL2y45VuT6J5y9Rb4ha2R+uz\n+toEm71F7fJa2EvsKPps7kw5QtbJ+G8UEbkZwM0AMHTo0Ay3hvqC2mW1OPn6SYx8fCTyR6XwZHpq\nx8oC5W212xJu31673dL9U6kh6+4kmg+8/UDa9VmUfXyHfTj6/FFU/VMVbE6WE1AWBCtVfRrA04Ax\nFJjh5lAvp6rY+6O9yB+TjzP+NfViZmrPqgLlMeVjsLpmddzto8tHW7q/lZNopluf1Vsn2Mx1+x7Y\nBw0qBt8xONNNoSzBeE19ivcDL5o2NWHID4ZwhuQstODyBQm3P/SFhyzd38qHNCc79kOXJ37IcboP\niCbz1X9Yj5qnajDoO4PY+02n8DcL9SnHlh+DuAQVX6nIdFOoC2cUn5HwQcKJCs/N2N/KGrJkx072\nkGMWrmeXwIkAtt6wFZ4zPRj+X8Mz3RzKIlbeFfgHAJ8HUA7gCIAfquovE+3DuwLJams/txZiF0x8\nd2Kmm0IJHG483OWDhHtq/0Z/o2WTXCY7tpXnJnOEfWFs/NJG1L9fj4nvTETx+cWZbhL1gFTvCuSz\nAqlPeb/yfZR/uRyjf5G41oaIqCvhQBhb5mxB7fJajPnNGFR+I/XATrkt1WCV8eJ1op4Ubg7DXmjP\ndDOIKAeFWkLY8tUtOP7ycYx8fCRDFXWJwYr6FHuRHcH6YKabQUQ5xnfYh82zN6NhdQNGPTUKg/41\n/kS01LcxWFGfkn92Ppo2dz1XEBFRV+o/rMfH13+MYF0QY5eORcU1vPmF4uNdgdSnlFxcgoY1DfAf\n5VPoiSgxDSn2LdiHdRetg81tw6QPJjFUUVIMVtSnDPjqACAMHP7V4Uw3hYiyWPP2Zqy7ZB323LsH\nFddVYMq6KSgcz7szKTkGK+pTCsYWoN8V/bD/kf0I1AUy3RwiyjKh1hD2PrAXH43/CM1bmnHOb8/B\nuc+dC0cJK2coNQxW1OeMeGgEAscD2H337kw3hYiyhKqi9qVafDTuI+z9f3tRPqscn93yWQz8+kCI\nSKabRzmEwYr6nKJJRRjy/SE49ItDOPKHI5luDhFlmPcjLzZctgGbZ22GzWXDea+dh7FLxsJd6c50\n0ygHMVhRz1MFli83XlNZb4HhDwxHycUl2HbjNtS9XWf5+Ygo+zRuaMSm2ZuwdupaNH3chFE/G4Up\nG6ag7IqyTDeNchiDFfW8FSuAa68F7rijLUSpGu+vvdbYbjGby4Zxy8Yhb0QeNl61ESffPGn5OYko\nO3hXebFp1iZUT6hG3Vt1GPYfw3D+rvMx6NZBsDn5a5HSwz9B1PNmzwbmzgUWLWoLV3fcYbyfO9fY\n3gOc/Z0Y/8Z4eM70YOOVG3HkdxwWJOqtNKyo/VMt1l2yDmunrUX9u/UY9qNhmLZ3GobdNwyOIhan\nkzn4J4l6ngiwcKHx86JFxgIYoWrhQmN7D3FXuTHx3YnYfM1mbP3GVjRUN2DEQyNgc/PfHES9QeBk\nAIefPYyDTxxE665WuIe4cdajZ6Hqn6vgKOSvQDIfH8JMmaMK2GICTDjco6EqVtgfxq7v78LBxw+i\ncGIhxiweg8JxnLOGKBepKryrvDj0i0M4+oejCLeEUXxBMQbfMRjl15TD5uA/nOj0pfoQZv7posyI\nDv/Fig4LZqC43eayYdRjozBuxTj4DviwZtIa7Pl/exBqCZl+LiKyhv+IH/sf3Y/q86qx7oJ1OLrk\nKAZ+YyAmr52MSR9MwoCvDGCoIsvxTxj1vI41VeFw+5qr5cszVtxePqscn/34s6j4agX2PbAPH537\nEY7+8SiyqWeXiNqEmkI48vsj2DhjIz4Y9AF23bULtnwbzv6fs/G5ms9h9NOjUTSxKNPNpD6EA8zU\n81asaAtV0Zqq2Jqriy9uC1qAsa0Hi9tdFS6c+9tzUXVTFXbevhNb5mxB0U+KMPw/h6PfFf04WSBR\nhoWaQzjx6gkcXXIUx18+jnBzGO4hbgz5/hBU/mMlCs4pyHQTqQ9jjRX1PFUjXM2e3b6mKnY90Bam\nojJQ3K4hxeHFh7H3/r3wfepD0flFOPPeM9H/6v4QGwMWUU8J1AVw4i8ncGzpMZz4ywmEm8NwVjhR\ncX0FBnxtAEqml/D/SbIUa6woPenUOYVCwDXXGK+prO/q3CtWAI8+2n79o48a65P9Y8DEGi2xC6pu\nrML5n5yPUU+NQuBIAJtnbcbqc1fj4FMHEWwMpnwsIjo9LbtacOCxA9hwxQZ8UPEBtt6wFd73vaj8\nZiXG/208Lqi5AGc/eTZKLy5lqKKswWBFXUtnEs/rrze2V1a2hahQyHi/YgVw4YWJjz1/vvE6eXL7\n406enFqNlQUTkNrcNgz610GY+slUnPP7c+AocmDHLTvw4Rkf4pPvfoKG9Q2nfUwiai/UEsKJv57A\nzjt3YtWYVVg1chV2zt2J1v2tGHznYEx8fyIuOHgBzn7qbPS7vB8L0Sk7qWrWLJMnT1bKEuGw6ty5\nxj16c+d2/T6eYFC1vNz4bHl55/eBQOJjB4OqEyYY7ydMUA2FOr+3qu0pX56w1n1Yp1u+sUXfcr2l\nb+JN/WjSR7r/p/vVd9iX9vGJ+oJwMKzeaq/u++99uv6K9fqW2/h/6S33W7r+i+t1/0/3a9OOpkw3\nk0hVVQFUawpZhjVWFF/s3XtRqdY5RXuoamvb1pWXA4cPA3Z74mNHe5wmTADWr2/bHn2/bJkxpGhV\n209T4EQAR353BIefPYzGtY2ADeh3eT9UfLUC5bPL4Sp3mXo+olylIUXjpkbUv12PurfqUPd2HYIn\njeH0/HPzUfbFMvS7oh9KLymFPd+e4dYStZdqjRWDFXUtHDaG5B58EHDE3DwaDAL33APs2AEsXWqE\npKhQyBgGfP554E9/Ar78ZcDpbNseCBjro0Xr8SYITXTue+8FFixov188GZiAtGlrE4789giOLjmK\n1l2tgB0ovbgU/Wf2R/nMcuSNyLP0/ETZJNQSQsNHDah/vx7179fD+74XwTojSHmGe1B6aSn6Xd4P\npZeWwl3lznBriRJjsKL0zJsHPPyw0cvUsdcp+j62Byq2h2rqVGD1asDjAVpb2/aNvl+2zAhXvaTH\nquvTKxrXN+LYC8dQu6IWzVuaAQD55+Sj7EtlKPtiGUouKoE9j/8qp95BVdG6pxXeVV54V3rh/dCL\nxvWN0IDxOyZ/TD5Kppeg5OISlH6+FJ4hngy3mOj0MFhRemKDUjRARd+XlRk9QV1tKy8HDhwASkuN\nEOXxAA0NQFFR23uvF/jBD9rPZRU7T9UjjwBTphghasIEYM0ao3A99n2iHquOE5B2PH4PT9kAGHc3\n1f6pFif+cgJ1b9dBfQpxC0ouLEG/y/qh9LJSFE0pgs3JYlzKfqoK30EfGtc0oqG6Ad6PvGiobkDw\nuNEbZcu3oWhKEYovKEbJhSUovqCYQ+KU81INVpwglLr20kttQam2tm1ILvr++eeB73yn87bDh43h\nwGiIam1tGw6Mvp8+3ejRijdBqNvdFqLWr28bboy+f/HFxD1WySYgveSS5D1eJss7Kw9Dbh+CIbcP\nQag5hLq363Dy9ZM4+cZJ7Pn3PQCMX0bF04qNf9VfWILi84vhKOH/opRZ4WAYLZ+0oHFDIxrXR5a1\njQjUBowP2IGCsQUon1WO4qnFKDq/CAXjCnjHHvVZ7LGirkXnkpo5s3Od00svGUN54XDnbdFhwWit\nVccaq698pa0GK94EobNmGeFp1qzONVwvvth5v3htTzQBaRbNnu6v9aPurTrUv1OP+vfq0bihEQgD\nEGP4pPh845dV0eQiFHymAHYPhw/JfBpW+Pb70PRxk7Fsiixbm6A+4/eEOAUF4wpQOLEQhRMLUTSp\nCIUTClloTn0Ce6x6u3TDQ7RAvGMheHT9Aw8Y2+6/v/1+kyYZn7/hBqCgw2MjBg4EmpqAI0eAlSuB\n229vv/322431Ph9w993A1Ve3D17BoLH+qqviP6T5kkuSXppc4yp3YcD1AzDg+gEAgKA3aNSprDLq\nVI6/fByHnz0MABCHIH9sPoomGr/QCsYXoPAzhXD2dyY6BdEpoZYQWna1oGV7C5q3N6N5azOatjah\neVszwk3hU59zDXKhYFwBBl8xGAWfKUDheYXIPycfNhd7oogSYY9Vroo+qDh2uCu2tihZgXe0OD22\nZikcbqtlGjjQCEgAcN55wLp1wMSJwMaN7Y/Tv7/xuYEDgePHO5/n1luBxx8Hvvc94IknjHXRc9nt\nQEuLEa4CASAvz+iVij33bbcBP/2pEcoee8xYt3Sp8d2tujZZRlXRurcVDWsa0Li2EQ1rG9C4rhGB\no4FTn3FVuVAwtgD5Y/NRcE4B8s/JR/7ofDgHOPlswz4o2BhE6+5WI0DtakHrrla07GxB8yfN8O33\nATF/7bsHu40/L+dG/uycm4+CsQVwljGsE8Vij1VvN3t2eg8qXrAAeO01I0RNnty5QPyyy9oeKbN9\nuxGEtm/vfJzjx41QFBuqJk0C1q41fn7ySeM4Tz7Ztv03vwG++U0jROXlGeEqGqrsdiNAzZmTuWuT\nZUQEecPzkDc871SvlqrCf8SPpg1NaNrchMZNjWja3IRDzxxq1+tgL7Ej/+x85I3KQ97IPOSNyIPn\nLA/yRuTBVeniY0BykKoiWBeEb78PrZ+2wrfPh9Z9rcaytxWte1oROBZot4+jnwN5I/NQMr3E+PMw\nOs94PTsPjkL+GiAyE3usclm6UwrE9lBFRXuwAKNX6/HHjaG7KLcbuOUWYx6rl1/ufMwvf9noEVq2\nDPja19o/l08EeO45o84qGGwLU1HRHiyHw+h1evvttl4qwOi9ihaeJ/t+GZ5uIVOidTLN25qNYZ7t\nzWj5pAUtO1vQuq+1XU+FuAWeYR54hnrgOdMD9xB32zLIWOxFdvZ49aBQSwj+I374a/zwH/LDd8gH\nf40fvoM++A764D/oR+v+1nbhGQDEJfCcafx39Az3wDPCCM+eER7knZUHZz/2PhGli9Mt9BXxJsFM\ntQYrOiQXFQq1P14w2LkA3W43jnHllUB+ftu25mbg1VeN2qnp04E332xfh9XUBFx6KfD++0Z4CgQA\nV8wt2H5/+3OlO8FnBiYIzWZhXxit+4zhodY9Rs9G695IT8enrQgcCXTax1Zgg7vKDVeVC65KF1wD\nXXAOcBqvFU44K5xwVbjg6O+As58TYu+717cjDSuC3iCCJ4IIHA8gcDyA4PEgArUB+I/5EagNIHA0\ngMCxgBGmjvgR8nZ+QLk4BK4qF9yD3HANcsE92A3PUCMIe4Z64B7qhmsgex+JrMahwL4gXoF37CSb\nieqMZs3q+kHH0ZqrYNCYfypWURGweDHw1a92bk80ZI0cCezc2bm4Pfr+wguB994zeqxiRYcFnc7E\n3y2VcJTu/r2QzW1D/tn5yD87v8vtYV/Y6Bk5EFlqjB4S3yEf/If9aNzYiMCRwKmZs7viKHXA0S+y\nlEaWEgfsxXY4ih2wF9lhL7LDUeSArcAGe4H91GLLsxmLJ7K4bRCnWN5jpqrQoCLsC0N9inBrGKGW\nEMKtYYSbwwg1h4zXphBCjZGlwViCDUGEvCEEvUGE6kMI1geN5aTxinCck9oAZ3lbMC2cWAjXQCO4\nuqpcp4Ks+ww3nBVOhiaiHMJglasSTYIJGHVNieqMZs7sPOlm9P3kycCqVUBJSdeTfN5wQ/u2NDe3\n77laudKY0yqqqal9yHrttfY1VbE1Vnl5xvHuvjv+d0sWjpJdmz4crhKxuW3IG5GX9LE7YV/Y6HE5\nZvS2BGojy/GA0TtzMnAqWDRvbzYChzfYZW9MUmLc4m9zRUKWI2axC2A3atBgi3w28t9VVY1hz3Dk\n55DxnDoNxiwBRTgQhvq13RBpyk1zCOzFkaBYbARI1xku5J+bD2c/Z1vA7OeAs7+zbSk3tjEsEfVO\nDFa5KpVJMGPfR0NF9PP33NN5JvPYcHXmme1DlcPRPlzFyu/QAxIbqoDOPVdlZZ3vCowNV2eeaUw0\n2t0JPrNwgtDexOa2wTPYA8/g03skiYbV6PWJ9PaEmtqWcHMY4ZaYnqLWSO+RP2z0JAUU6o8EopAR\nijSk0LAavUJqHB8KIJpXBEZ4EUDsRhBrF8ycAnEJbE4bxC2wuY1eslM9ZpEetHa9agW2U71tNreN\n9WdE1JmqZs0yefJkpRSFw6rLlhmvidaHw6pGH46xRNeHQqp33228xoqu9/lUp05VDQTabw8EVCdO\nVM3PV21ubn/s5mZjfX29amWlalNT++1NTcb6xkbVkSNV/f72x/b7jfU+X2rfLd1rQ0RElCIA1ZpC\nlmHxem9m5Z1xyY4drc/q+BDmaO8XERFRDkm1eN3SKXRF5EoR2S4iO0XkHivPRR10rDMKh9tqru64\no/00CGYfOxBo/9DlQKDtOYFFRUboIiIi6oUs6zoQETuAJwBcAeAAgI9E5CVV3WLVOSmGlXVGyY79\n5z8nrs+68EKjOJ6IiKiXsWwoUEQuAHC/qn4x8n4+AKjqgnj7cCjQRFY+iDjZsaPzWEXnq4oKBo1Q\n1XE9ERFRlsuGeawGAdgf8/4AgPM7fkhEbgZwc+StT0Q2W9im3qwcQG2mG9GOM85sz/HWZ072Xbvc\nwWvXfbx26eH16z5eu+45M5UPZbzbQFWfBvA0AIhIdSppkDrjtes+Xrvu47XrPl679PD6dR+vnbWs\nLF4/CGBIzPvBkXVEREREvZKVweojAKNEZLiIuAB8DcBLFp6PiIiIKKMsGwpU1aCIfBfAXwHYAfyv\nqn6cZLenrWpPH8Br1328dt3Ha9d9vHbp4fXrPl47C2XVBKFEREREuczSCUKJiIiI+hIGKyIiIiKT\nZEWw4qNvuk9E/ldEjnL+r9MnIkNE5E0R2SIiH4vI3Ey3KVeIiEdEVovIhsi1+1Gm25RrRMQuIutE\n5OVMtyWXiMheEdkkIutFhDNKnwYRKRWRF0Rkm4hsjUzkTSbLeI1V5NE3nyDm0TcA/p6PvkmNiFwM\noBHAYlUdl+n25BIRqQJQpaprRaQIwBoAs/lnLzkREQAFqtooIk4A7wGYq6orM9y0nCEidwKYAqBY\nVa/OdHtyhYjsBTBFVTnB5WkSkV8DeFdVn4ncrZ+vqnWZbldvkw09VlMB7FTV3arqB/AcgFkZblPO\nUNV3AJzIdDtykaoeUtW1kZ8bAGyF8cQASkINjZG3zsjCO2FSJCKDAVwF4JlMt4X6BhEpAXAxgF8C\ngKr6GaqskQ3BqqtH3/CXG/UoERkGYCIAPh06RZGhrPUAjgJ4XVV57VL3UwB3AwhnuiE5SAG8JiJr\nIo9Eo9QMB3AMwK8iQ9DPiEhBphvVG2VDsCLKKBEpBLAUwO2q6s10e3KFqoZUdQKMpypMFREORadA\nRK4GcFRV12S6LTlquqpOAvAlALdGyiEoOQeASQCeUtWJAJoAsKbZAtkQrPjoG8qYSH3QUgC/U9Vl\nmW5PLooMJ7wJ4MpMtyVHXAhgZqRW6DkAl4nIbzPbpNyhqgcjr0cBLIdRTkLJHQBwIKZn+QUYQYtM\nlg3Bio++oYyIFGD/EsBWVX000+3JJSJSISKlkZ/zYNx8si2zrcoNqjpfVQer6jAYf9/9n6p+I8PN\nygkiUhC50QSRYay/A8A7olOgqocB7BeR0ZFVlwPgjToWsOyRNqnq5qNvKEJE/gDg8wDKReQAgB+q\n6i8z26qccSGAfwCwKVIrBAD3quorGWxTrqgC8OvIXb02AH9UVU4bQFYbCGC58W8iOAD8XlVfzWyT\ncsr3APwu0omxG8CNGW5Pr5Tx6RaIiIiIeotsGAokIiIi6hUYrIiIiIhMwmBFREREZBIGKyIiIiKT\nMFgRERERmYTBioiyhl74gv8AAAJlSURBVIjcLyLfj/z8rIhc383jDBORhPMbRT5zQ8z7b4nIz7pz\nPiKiKAYrIuqrhgG4IdmHiIhOB4MVEWWUiPybiHwiIu8BGN1h8+DIZIbx9r1fRH4jIh+KyA4R+ecO\n20dEeqbeFZG1keVzkc0PAbhIRNaLyB0d9rsqcsxyEfmyiKyKPLj2byIy0IzvTUS9E4MVEWWMiEyG\n8ViXCQBmAPhszOY8ANcCKEpymPMAXAbgAgD/T0TOiOx7FoDpAI4CuCLy4N45AB6L7HcPgHdVdYKq\nLoxp0zWRbTNUtRbAewCmRR5c+xyAu7v/jYmot8v4I22IqE+7CMByVW0GABF5KfL6FRihaK6qHk9y\njBdVtQVAi4i8CWAagH8GsEtVF4tICYCficgEACEAZyc41mUApgD4O1X1RtYNBrBERKoAuADs6c4X\nJaK+gT1WRJR1VPV5AK+n+vEO70MAvhPz/g4ARwCMhxGa4g4tAtgFo4csNnw9DuBnqvoZAP8CwJNi\nu4ioD2KwIqJMegfAbBHJE5EiAF/uxjFmiYhHRPrDeCD5Rx22lwA4pKphGA/dtkfWN6DzMOM+ANcB\nWCwiY2P2Pxj5+R+70T4i6kMYrIgoY1R1LYAlADYA+As6hyIAgIj8h4jMjHOYjQDeBLASwAOqWtNh\n+5MA/lFENgAYA6ApZr+QiGyILV5X1W0Avg7geRE5C8D9kZ/XAKg9/W9JRH2JqHbsRSciyg0icj+A\nRlX9SabbQkQEsMeKiIiIyDTssSIiIiIyCXusiIiIiEzCYEVERERkEgYrIiIiIpMwWBERERGZhMGK\niIiIyCT/H+AGYmKcYPyGAAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f8051b20250>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(Xpl, Ypl, xlabel=u'dł. płatka', ylabel=u'szer. płatka')\n",
|
||
"plot_decision_boundary(fig, theta, Xpl)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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IyMiRCgG3337g+ttvd9ena4L50EPuUsgGmh6PG6J6W7nywGLxI5WpvgzgwgsPDUfG9L1+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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f8051b4b0d0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(Xpl, Ypl, xlabel=u'dł. płatka', ylabel=u'szer. płatka')\n",
|
||
"plot_decision_boundary(fig, theta, Xpl)\n",
|
||
"plot_decision_boundary_bayes(fig, X_mean, X_std)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Kiedy naiwny Bayes nie działa?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Wczytanie danych\n",
|
||
"import pandas\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"alldata = pandas.read_csv('bayes_nasty.tsv', sep='\\t')\n",
|
||
"data = np.matrix(alldata)\n",
|
||
"\n",
|
||
"m, n_plus_1 = data.shape\n",
|
||
"n = n_plus_1 - 1\n",
|
||
"Xn = data[:, 1:]\n",
|
||
"\n",
|
||
"Xbn = np.matrix(np.concatenate((np.ones((m, 1)), Xn), axis=1)).reshape(m, n_plus_1)\n",
|
||
"Xbnp = powerme(data[:, 1], data[:, 2], n)\n",
|
||
"Ybn = np.matrix(data[:, 0]).reshape(m, 1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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Z9JF99Tlvhw9fvi3REofERSf7ytmKZi7MaOvGrRrbNkbwlnMb+jdo9027065GphDApSWe\n/C1deeIj+Rur4R78MIivjbpceSccP7405y3qTj1yJFiTlR8iSBgne/QCJvJNU7z1IkLyN1br2LHk\nW3XTCBoBoMBanciXAC5t7sHIvUitxgkPq7NcdyY/DAAgF1iJIQ+ynPzNIIv8YUoPAOgZBHBpqW8t\nqdWWToiaJmbYzyem9ACAnpCJAM7MdprZY2Y2Y2YHG2x/o5k9Y2aPhpc/iG2708ymw8udydZ8DZol\nf0dBXNoBEjPs51OWW3XTQEsygKJy91Qvkq6S9E1JL5TUL+nLkl5ct88bJf1Fg/tulPR4+Pfq8PrV\nKz3n9u3bPXW1mvvERPC3lfI01Grue/a4B6e54LJnTzbqhqXi71f0PtXf7jUTE0uPP/66TEykWz8A\nqCNpyluIn7LQAnezpBl3f9zdFyR9WNJtLd73VZJOu/sFd/++pNOSdnapnp1lFowIrO/aalaeBrrj\n8iXrrbppoCW5UKrzVY0/Mq4Dpw9o/JFxVeeraVcJSE0W5oEbkPSd2O0nJf1qg/1ea2a/Iekbku5x\n9+80ue9Aoycxs7sk3SVJW7Zs6UC1e0Cz7jiCuGwaHQ2mColP3REFccPDvRms1M9LF03Zw8CO3Jl8\nYlIjR0dU85rmFudUWlfSvlP7dPKOkz0/Iz96UxZa4Frxt5Kuc/d/q6CV7QPtPoC7P+DuQ+4+tHnz\n5o5XsHCyPsgCS+WhVTcNtCTnXnW+qpGjI6ouVDW3OCdJmlucU3UhKL+4cDHlGgLJy0IA95Sk58du\nPy8su8Tdz7v7fHhzXNL2Vu+LVaI7DkXBwI7cq5yrqOa1httqXlPlbCXhGgHpy0IA9yVJg2Z2vZn1\nS7pd0on4DmZ2bezmrZK+Hl4/JemVZna1mV0t6ZVhGdYq6o6Lt1REQVzUTQdkHS3JhTB9fvpSy1u9\nucU5zVyYSbhGQPpSz4Fz95+Y2ZsVBF5XSXqfu58zs3coGIlxQtLdZnarpJ9IuqBgVKrc/YKZ/RcF\nQaAkvcPdLyR+EEUUdbu1Wg5kEWu1FsLgpkGV1pUaBnGldSVt3bg1hVoB6WIpLQDFxVqthVCdr2rg\n0ICqC0tHnZb7y5rdP6sN/RtSqBnQeSylBQAM7CiE8vqyTt5xUuX+skrrSpKClrdyf1BO8IZelHoX\nKgAAK9mxZYdm98+qcraimQsz2rpxq8a2jRG8oWcRwAEAcmFD/wbtvml32tUAMoEuVHQXa1ECANBx\nBHDoruPHpV27rpyyIZraYdcu5pMDAGAVCOCKJIutXaxFCQBAxxHAFUkWW7vqV3Do61s6LxcAAGgL\nAVyRZLW1i7UoAQDoKAK4IslqaxdrUQIA0FEEcEWTtdYu1qIEAKDjmAeuaJq1dqUVxLEWJQD0nOp8\nVZVzFU2fn9bgpkGN3TCm8vpy2tUqFAK4Iqlv7Tp8+PJtKZ0gbnRUmpi4cs3JKIgbHmYUKgAUzOQT\nkxo5OqKa1zS3OKfSupL2ndqnk3ec1I4tO9KuXmHQhVokzVq7oi7LtEahshYl2pXFKXEArKg6X9XI\n0RFVF6qaW5yTJM0tzqm6EJRfXLiYcg2LgwCuSKLWrnhLWxTERa1gQB5kcUocACuqnKuo5rWG22pe\nU+VsJeEaFRddqEUStWq1Wg5kVXxKHOnKdAAmgAYya/r89KWWt3pzi3OauTCTcI2KixY4IA96rUsx\nq1PiAFjW4KZBldaVGm4rrStp68atCdeouAjggDzoxS7FrE2JA7ShOl/V+CPjOnD6gMYfGVd1vpp2\nlRIxdsOY+qxxaNFnfRrbNpZwjYqLAA7Ig6yustFNTACNnJp8YlIDhwa096G9uvfz92rvQ3s1cGhA\nk09Mpl21riuvL+vkHSdV7i9faokrrSup3B+Ub+jfkHINi8O8B/8ZDg0N+dTUVNrVANoTD9oiRexS\njLqFP/MZ6f77Lx/j3r3B7bvvlu67r1jHjMKozlc1cGhA1YWlLW7l/rJm98/2RBBzceGiKmcrmrkw\no60bt2ps21hPHHcnmNkZdx9aaT8GMQB5EXUpxgO4ogVvUtAd/NrXBtfvvntpN+r990svexkDc5BJ\nrYzC3H3T7oRrlbwN/Rt64jjTRBcqkBe90qU4OhoEbnH33HO59e2jHy1mlzEKgVGYoV4beJUCAjgg\nD3ppTVmzoIt0z54gaIuPQL3vvmDQRtFaHVEYjMIM9eLAq4QRwAF5kMVVNrqpWyNQaRVAlzEKM9SL\nA68SRgAH5EGvrbLRre5iWgXQZYzCDDGXY9cxChVAttT/Uq9fhWEt//y7+dhADKMwQ+5B8Bap1fiO\nrYBRqADyqVl3sRSUDw+vfgRq/WNFI3oJ3tBhjMJU85Z0vmsdQQscgGxxD4K40dEr/8k3K1/tc9Aq\nAHQPrd2r1moLXCZy4Mxsp5k9ZmYzZnawwfZ9ZvY1M/uKmT1sZi+IbfupmT0aXk4kW3MAHWcWtLDV\n/3NvVt6uXpmOBUhTrw28SkHqAZyZXSXpPZJeLenFkl5vZi+u2+1/SRpy938r6UFJ98a2/cjdbwwv\ntyZSaQD51EvTsQBp6rWBVynIQg7czZJm3P1xSTKzD0u6TdLXoh3c/VOx/b8g6Q2J1hBAMXQzvw4o\nmOp8VZVzFU2fn9bgpkGN3TCm8vpya3eOWsxbLUfbshDADUj6Tuz2k5J+dZn9d0v6+9jtZ5vZlKSf\nSHq3uzdslzWzuyTdJUlbtmxZU4UB5FTUKhDPo4uCuOFhWgWA0OQTkxo5OqKa1zS3OKfSupL2ndqn\nk3ec1I4tO9KuHpSBLtR2mNkbJA1J+r9jxS8Ik/1+R9J9ZvaLje7r7g+4+5C7D23evDmB2gLInG7n\n1wEFUJ2vauToiKoL1UvLgs0tzqm6EJRfXLiYcg0hZSOAe0rS82O3nxeWXcHMbpH0Nkm3uvt8VO7u\nT4V/H5f0aUkv6WZlAQAossq5impea7it5jVVzlYSrhEayUIA9yVJg2Z2vZn1S7pd0hWjSc3sJZL+\nSkHw9r1Y+dVmtj68fo2klyqWOwcAANozfX76UstbvbnFOc1cmEm4Rmgk9Rw4d/+Jmb1Z0ilJV0l6\nn7ufM7N3SJpy9xMKukw3SPrvFnRxPBGOOP0lSX9lZjUFwei73Z0ADgCAVRrcNKjSulLDIK60rqSt\nG7emUCvUYyJfAABwSXW+qoFDA6ouVJdsK/eXNbt/tjeXBUtIribyBQAA2VBeX9bJO06q3F9WaV1J\nUtDyVu4PygnesoEADgF36dixpROZNisHABTWji07NLt/Vkd2HtHBlx7UkZ1HNLt/trUpRDifJIIA\nDoHjx6Vdu66cjT6atX7XLpY9AYAes6F/g3bftFvvuuVd2n3T7tZb3jifJCL1QQzIiNHRy0sKSUsX\nHmaCUwAZt6aVA9A5nE8SwSAGXBZfJzISX3IIADKq0coBfdbHygHtcA9ax+IrlSxXvtJjcT5ZlVYH\nMRDA4UruUl+sZ71W48sGINMYNdkhx44FXZzxQCseiE1MtLeOKeeTVWEUKtoXfVHj4jkMAJBBrBzQ\nIfGuz+h//2q7PjmfdB0BHAL1X9RabekXGQAyiJUDOsQsaHmL/vf39V0+J7TT9cn5JBEEcAgcP770\nixr/IjNqCOia6nxV44+M68DpAxp/ZFzV+aVdgWguWjmgEVYOaFP0vz+u3by1JM4nTFVCDhxCnUxe\nBdAyku/Xjhy4DurE4IMkziedztfLEHLgsNRyv1iafanMgi8BwRvQcdX5qkaOjqi6UL3UBTi3OKfq\nQlB+ceFiyjXMB1YO6JBOdX02O2908nzSyXy9nGIeuF4STa5YwF8sQB61kny/+6bdCdcqn6KVAypn\nK5q5MKOtG7dqbNsYwVs7mnV9SkH58HB2zhH1dYtaDHtoqhICuF7C5IqdRbcz1ojk+86KVg7AKo2O\nBj/k4/+7okBpeDh754iobvHu3h4J3iS6UHtLp0YYIcByMVgjku+RKUl0fa5C00E+PT5VCYMYehGT\nK3ZGfc5FfYsmQTFWQPI9sLymg3x+5+Pacfijhfz/2+ogBrpQe02zXyw5/rCnhhwMrFGUfN9sFCrB\nG3pZfJBPJEo5GPngqzT7X3+kDXnI1+sSWuB6CS1G3UGLJtbo4sJFku+BOuOPjGvvQ3sb5omW1pV0\n5Ll3avfv/0XhcpBpgcNSeRphlBe0aKIDSL4HllpxkM8LfrZ5vl4PYBBDL4lGGMWDiyiIi0YeoXUs\nFwMAXcMgn+URwPWSjI4wyq00lh9j+RhkFMuBodPGbhhTnzUOU/qsT2PbxhKuUbYQwAGrlUaLJlOX\nIIMmn5jUwKEB7X1or+79/L3a+9BeDRwa0OQTk2lXDTmW6gobOfixzCAGIE8YiIKMYSoUdFsqg3xS\nXGuVQQxAETF1STEUaBUPlgNDt6UyyCcHKxe13IVqZq8ws/ea2Y3h7bu6Vy0ATcWDuAjBW74UqCuc\n5cBQSDlYuaidHLjfl/THkt5gZi+XdGN3qgRgWT2+fMwlOchRaSr+6z567zL2675VjBREYWX8x3I7\nAVzV3X/g7n8k6ZWSfqVLdQLQDFOXXJbnVqwc/LpvFSMFUVgZ/7HcTgD38eiKux+U9MHOVwfAstKY\nuiSr8t6KlfFf961KdaQg0C15+LHs7steJB1ROFq1WxdJOyU9JmlG0sEG29dLqoTbvyjputi2t4bl\nj0l6VSvPt337dgdyqVZzn5gI/rZSXnS1mvuePe7Bv9PgsmdPPl6HPNe9gep81cfPjPvB0wd9/My4\nV+eraVcJWL2JiaXfyfh3dmKia08tacpbiZ1W3EH6U0l/K+lnwtuvkvQ/WnnwliogXSXpm5JeKKlf\n0pclvbhun/8o6S/D67dLqoTXXxzuv17S9eHjXLXScxLAIRMIxjqjVrsyCMrD6xY/EUQniPrbANKT\n4v/nVgO4FbtQ3f3/kvQhSZ8xs/8haZ+kg+229C3jZkkz7v64uy9I+rCk2+r2uU3SB8LrD0r6LTOz\nsPzD7j7v7t9S0BJ3cwfrBnRPnnO4siLjOSpN0RUOZFsOVi5aMYAzs9+S9H9ImpN0jaS73f1zHazD\ngKTvxG4/GZY13MfdfyLpnyVtavG+koJpT8xsysymnnnmmQ5VHViDvOdwpS0POSrNsC4xgDVqZSLf\nt0n6z+4+aWa/LKliZvvc/ZNdrltHufsDkh6QgpUYUq4OwKS8a9WsFUsKyoeHuzZT+ppFv+JbLQeA\nOq10ob7c3SfD61+V9GoFeXGd8pSk58duPy8sa7iPmT1L0s9JOt/ifYHsKshIxFTQigWgh7W9mL27\nPy3ptzpYhy9JGjSz682sX8EghRN1+5yQdGd4/XWSPhkm+p2QdLuZrTez6yUNSvqfHawb0F15zeHK\nghzkqABAt7QdwEmSu/+oUxUIc9reLOmUpK9L+oi7nzOzd5jZreFufy1pk5nNKDaIwt3PSfqIpK9J\nekjSH7r7TztVN6Cr8pzDBQBIlXkPniSGhoZ8amoq7Wqg1x07Fow2jedwxYO6iYnM50NV56uqnKto\n+vy0BjcNauyGMZXXl9OuFgDklpmdcfehFfcjgANS4h4k4o+OXtnd16w8YyafmNTI0RHVvKa5xTmV\n1pXUZ306ecdJ7diyI+3qAViLnP9/yrNWA7hVdaEC6IAc53BV56saOTqi6kJVc4tzkqS5xTlVF4Ly\niwsXU64hgDVhnsrMI4AD0LbKuYpqXmu4reY1Vc5WEq4RgI5insrMa2UeOAC4wvT56Ustb/XmFuc0\nc2Em4RoB6Cjmqcw8WuAAtG1w06BK60oNt5XWlbR149aEawSg45inMtMI4AC0beyGMfVZ438ffdan\nsW1jCdcIQMcxT2WmEcABq1Cdr2r8kXEdOH1A44+MqzpfTbtKiSqvL+vkHSdV7i9faokrrSup3B+U\nb+jfkHINAawJ81RmHtOIAG1i+ozLLi5cVOVsRTMXZrR141aNbRsjeAOKoADzVOYV88AtgwAOq1Wd\nr2rg0ICqC0tb3Mr9Zc3unyWAAZB/zAOXGuaBA7qA6TMA9IQcz1PZKwjgkB3uQbN9fatws/IUMH1G\nd/V6biEAtIoADtmRg5m/mT6jeyafmNTAoQHtfWiv7v38vdr70F4NHBrQ5BOTaVetc3LwIwVAPhDA\nITtyMPM302d0R88szZWDHykA8oEADtkRTRoZBXF9fZeDt4xMHsn0Gd3RM7mFOfiRAiAfWEoL2RIF\ncdGyLVJmgrfIji07NLt/lun6MdB7AAAbhklEQVQzOqhncgtZnghIRwFH1dICh2zJyczfG/o3aPdN\nu/WuW96l3TftJnhbo0LnFtbnt7E8EZC8AqYvEMAhO5j5u2cVOrew/sThLu3de+U+fL6B7ipg+gJd\nqMiO48eX5rzFu5uGh5n5u6Ci3MJmK1zkuoUzfuKIgrT77w/+3n138DfqSqUlDuiOAqYvsBIDsqOA\nOQpoT2GX5or/2o/cfbd0333BdZYnQh4U4X+0ezBALlKrZa7OLKW1DAI4AIlb7sSRpxMgelfe10dt\n9EMqgy1wLKUFAFmx0uAclidCHuQ5j6yAOdYEcACwFiutrlCrFe7EgR6Vg7k6m2qWYx0dSw5HodKF\nCgBrsVK30lveIt17b367nYB6OcgjWyJH+Xt0oQJAElbqVnrXu4IgLd5CEf36n5jIdrcTUC8nc3Uu\n0SxNIcfpC0wjAuCS6nxVlXMVTZ+f1uCmQY3dMKby+nLa1cq2VqYnaNTC1qwcyKr6HyeHD185KCDr\n3agFQxcqAEnS5BOTTedh27FlR9rVy748disB7cj7KNScoAsVQMuq81WNHB1RdaF6aU3SucU5VReC\n8osLF1OuYcbltVsJaMfoKOkAGZJqAGdmG83stJlNh3+vbrDPjWb2j2Z2zsy+YmZjsW3vN7Nvmdmj\n4eXGZI8AKIbKuYpqXmu4reY1Vc5WEq5RjhRwegKgoQLmkeVZ2i1wByU97O6Dkh4Ob9f7F0m/6+43\nSNop6T4ze05s+x+7+43h5dHuVxkonunz05da3urNLc5p5sJMwjXKkQJOTwAg+9IexHCbpJeF1z8g\n6dOSDsR3cPdvxK7Pmtn3JG2W9INkqggU3+CmQZXWlRoGcaV1JW3duDWFWuVE1K0Un4YgCuKGh+lW\nAtAVabfAPdfdnw6v/5Ok5y63s5ndLKlf0jdjxe8Mu1YPm9n6Ze57l5lNmdnUM888s+aKA0UydsOY\n+qzxv4M+69PYtrGG2yC6lQCkousBnJl9wszONrjcFt/Pg+GwTZNFzOxaSf9N0u+5X0rWeaukF0n6\nFUkbVdd6V/f4D7j7kLsPbd68ea2HBRRKeX1ZJ+84qXJ/WaV1JUlBy1u5PygvxILyAFAgXe9Cdfdb\nmm0zs++a2bXu/nQYoH2vyX4/K+njkt7m7l+IPXbUejdvZn8j6Y86WHWgp+zYskOz+2dVOVvRzIUZ\nbd24VWPbxgjeACCD0s6BOyHpTknvDv9+rH4HM+uXdEzSB939wbptUfBnkkYlne1+lYHi2tC/Qbtv\n2p12NQAAK0g7B+7dkl5hZtOSbglvy8yGzGw83Oe3Jf2GpDc2mC7kqJl9VdJXJV0j6U+TrT4AAEDy\nWIkBAAAgI1iJAQCQHe7BUkz1jQbNygEsiwAOANB9x48H62jGV6eIVrHYtYsJj4E2EcABQDtoSVqd\n0dGlS4zFlyBjwmOgLQRwANAOWpJWp36Jsb6+pUuQAWgZARwAtIOWpNWLgrg4gjdgVQjgAKAdtCSt\nXhTsxsVbMgG0jAAOANpFS1L76lsqa7WlLZkAWkYABwDtoiWpfcePL22pjLdkkjsItIUADgDaQUvS\n6oyOShMTV7ZURkHcxAS5g0Cb0l4LFQDypVlLkhSUDw9Lr3lNunXMIrPGr0uzcgDLIoADgHZELUmj\no0tbkoaHaUkCkAi6UAGgHVGLUf2AhWblwGowYTRWQAAH5FB1vqrxR8Z14PQBjT8yrup8Ne0qAegk\nJozGCuhCBXJm8olJjRwdUc1rmlucU2ldSftO7dPJO05qx5YdaVcPQCfEJ4yWgi56JoxGDC1wQI5U\n56saOTqi6kJVc4tzkqS5xTlVF4LyiwsXU64hgI4o4oTRdAt3FAEckCOVcxXVvNZwW81rqpytJFwj\nAF1TtAmj6RbuKAI4IEemz09fanmrN7c4p5kLMwnXCEDXFG3CaNYR7igCOCBHBjcNqrSu1HBbaV1J\nWzduTbhGALqiiBNGF7FbOEXmefwQrNHQ0JBPTU2lXQ2gbdX5qgYODai6sHTUabm/rNn9s9rQvyGF\nmgHoqGPHgm7FeHATD+omJvI7AbJ7ELxFajWCtxgzO+PuQyvtRwsckCPl9WWdvOOkyv3lSy1xpXUl\nlfuDcoI3dBVJ6Mkp6tJjResWThEBHJAzO7bs0Oz+WR3ZeUQHX3pQR3Ye0ez+WaYQQfeRhJ6cIk4Y\nXcRu4RQxDxyQQxv6N2j3TbvTrgZ6DXOTYS1YR7ijyIEDALQu3ooSIQkdrXAPgrj4OsLLlfeoVnPg\nCOAAAO0hCR3oGgYxAAA6jyR0IBMI4AAArSEJHcgMAjgAncEUE8XXLAk9CuIYhQokJtUAzsw2mtlp\nM5sO/17dZL+fmtmj4eVErPx6M/uimc2YWcXM+pOrPYArMMVE8RV1bjIgh9JugTso6WF3H5T0cHi7\nkR+5+43h5dZY+Z9LOuzuWyV9XxLzKgBpYZ3D4ivi3GRATqU6CtXMHpP0Mnd/2syulfRpd/83Dfa7\n6O4b6spM0jOSft7df2Jmvy7p7e7+qpWel1GoQJcwxQQArEleRqE+192fDq//k6TnNtnv2WY2ZWZf\nMLPoZ/wmST9w95+Et5+UNNDsiczsrvAxpp555pmOVB5AnfjEnBGCNwDouK4HcGb2CTM72+ByW3w/\nD5oCmzUHviCMRn9H0n1m9ovt1sPdH3D3IXcf2rx5c/sHAmBlTDEBAInoegDn7re4+7YGl49J+m7Y\ndarw7/eaPMZT4d/HJX1a0ksknZf0HDOLlgN7nqSnunw4AJphigkASEzaXagnJN0ZXr9T0sfqdzCz\nq81sfXj9GkkvlfS1sMXuU5Jet9z9ASSEKSYAIDFpD2LYJOkjkrZI+rak33b3C2Y2JOlN7v4HZvbv\nJP2VpJqCgPM+d//r8P4vlPRhSRsl/S9Jb3D3+ZWel0EMQBewziEArBlroS6DAA4AAGRRXkahAgAA\nrF6PrgJDAAcAQI8GAYXQo6vAEMABANCjQUAh9OgqMM9aeRcAAAouHgRIwQjqHggCCiE+gfiRI5ff\nw4KvAsMgBgAAJJaCyzt3qS/WsVir5fJ9YxADAADt6OZScOTYdVcPrgJDAAcAgNTdICBPOXZ5CzZ7\ndBUYAjgAALodBOQp0T5PwabUu6vAuHvPXbZv3+5AVvzwxz/09555r7/lH97i7z3zXv/hj3+YdpWy\np1Zzn5gI/rZSDrRrYsJdct+z5/LnqVYLbkvB9rWKP150iT9fVsTrGdWv/naWFOz/g6QpbyGWYRAD\nkKLJJyY1cnRENa9pbnFOpXUl9VmfTt5xUju27Ei7etlx7Fjwyz/+CzvegjExIb3mNWnXEnmW1FJw\neUm0Z0BHahjEAGRcdb6qkaMjqi5UNbc4J0maW5xTdSEov7hwMeUaZkieup+QT2bBj4D64KRZ+Wo0\nyrHbu/fK7tms5Jl1c0AHOoIADkhJ5VxFNa813FbzmipnKx15nup8VeOPjOvA6QMaf2Rc1flqRx43\nUfU5LX19S3NegCyr/9Hx0Y8G5ffffzmIy1KeWQ+O6swbJvIFUjJ9fvpSy1u9ucU5zVyYWfNzNOqi\n3XdqXz67aKMgLt6lQ/CGvKhPtJeku+8OArj775eGh6XPfjYbrcr1wWZ8UmOJ711GEMABKRncNKjS\nulLDIK60rqStG7eu6fHjXbSR6LlGjo5odv+sNvRvWNNzJKpZiwAnE+TB6GiQqxnPpbvvvuDv/fdL\nr31tcD0LrcrNRnVKQfnwMDmnGUAXKpCSsRvG1GeNv4J91qexbWNrevykumgT0aPzPKFAGuXSmV0O\n4iJpB2/S5WAzXpcoiIuCUKSOAA5ISXl9WSfvOKlyf1mldSVJQctbuT8oX2vrWBJdtInp1XmeUGxZ\nzTNLYkAH1owuVCBFO7bs0Oz+WVXOVjRzYUZbN27V2LaxjnRtdruLNlGNup+iIG54mBYB5A95Zlgj\n5oEDCqo6X9XAoYErcuAi5f5y/nLggCJhbkM0wTxwQI/rdhctgDUgzwxrRAscUHAXFy52pYsWANB5\nrbbAkQMHrEJ1vqrKuYqmz09rcNOgxm4YU3l9Oe1qNbShf4N237Q77WoAADqIAA5oU6EmxwUA5BI5\ncEAbWL8UAJAFBHBAGwo1OS4AILfoQu0hecrbyqpCTY4LAMgtArgeQd5WZxRqclwAQG7RhdoDyNvq\nnG6vXwoAQCtSDeDMbKOZnTaz6fDv1Q32+U0zezR2+bGZjYbb3m9m34ptuzH5o8g+8rY6h8lxAQBZ\nkHYX6kFJD7v7u83sYHj7QHwHd/+UpBulIOCTNCPpH2K7/LG7P5hQfXOJvK3O6ub6pQAAtCLtAO42\nSS8Lr39A0qdVF8DVeZ2kv3f3f+lutYqlV/K2khykweS4AIA0pbqUlpn9wN2fE143Sd+PbjfZ/5OS\nDrn734W33y/p1yXNS3pY0kF3n29y37sk3SVJW7Zs2f7tb3+7k4eSab2wqHmjQRp91scgDQBArmRm\nMXsz+4SZnW1wuS2+nweRZNNo0syulfTLkk7Fit8q6UWSfkXSRi3TeufuD7j7kLsPbd68eS2HlDtF\nz9tikAYacpeOHQv+tlIOADnS9S5Ud7+l2TYz+66ZXevuT4cB2veWeajflnTM3Rdjj/10eHXezP5G\n0h91pNIFVOS8rVYGadDd2YOOH5d27ZL27JEOH5bMgqDtnnukI0ekiQnpNa9Ju5YAsCpp58CdkHSn\npHeHfz+2zL6vV9Didkks+DNJo5LOdquiRVDUvC0GaaCh0dEgeDtyJLh9+PDl4G3PnmA7AORU2vPA\nvVvSK8xsWtIt4W2Z2ZCZjUc7mdl1kp4v6TN19z9qZl+V9FVJ10j60wTqjIyJBmk0UqRBGmiTWRC0\nRUFcX9/l4C1qkQNWiy56pCzVQQxpGRoa8qmpqbSrgQ7phUEaWAP3IHiL1GoEb1i7Y8fookdXZGYQ\nQy+rzlc1/si4Dpw+oPFHxlWdXxpgYO2KPkgDaxCdUOPuuYfWEaxdvIs++kzRRY8E0QLXJUxrkbyL\nCxcLOUgDq1R/Qq3PgaMbFWsV/4xF+GxhjVptgSOA64Kku/SSnMAWyA26uJAEuujRYa0GcGmPQi2k\nJKe1aNTSt+/UPlr6gNHRIEgbHb18Qo0GNgwP08WFtWvWRU8LHBJADlwXJDWtBRPYAsswC1rY6k+k\nzcrzjlGRyarvoq/VlubEAV1EANcFSU1r0UpLH4AUJRlURRMXx4OHKMjYtSvYjs45fnxpPmV82hpe\nb3QZAVwXjN0wpj5r/NL2WZ/Gto115HmYwBbIuCSDKkZFJivqoo93l0ZBXNR1D3QROXBdEE1r0WwU\naqcGMEQtfY2COCawBTIgydUgouBBCh4/ek5GRXZH1BXfajnQYYxC7aJuT2vBBLZADiQ91QSjIoFc\nYxqRZRRpJQbmmwNyIKmginnJgNxjJYYesWPLDs3un9WRnUd08KUHdWTnEc3unyV4A7IiqdUgGBXZ\nGYzmXYrXJJvcvecu27dvdwDoulrNfc8edyn42+h2p0xMLH3c+PNNTHTuuYqM13EpXpNESZryFmKZ\n1IOpNC4EcAASkeSJr1YLHq8+KGxWjsaSDLrzgtckUa0GcOTAAUC3uAdThcRXg1iuHNng5BIuwWuS\nGAYxLIMArrexdmyOERAhKc5o3iV4TRLBIAaggcknJjVwaEB7H9qrez9/r/Y+tFcDhwY0+cRk2lVD\nK1htAEmIPlNxKw0E8YIn+rf6mhT9dcgQAjj0DNaOLQBWG0C31X+mWh3N2+0fF2kGRu28JvzISk4r\niXJFuzCIoTe998x7vfTOkuvtWnIpvbPk42fG064iWhFPoI4uJFKjU1Y78KTbif5pjgRt57kZ8LBm\nYhQqARyu9JZ/eEvD4C26HDx9MO0qFlenR0jWalcGcJwU0Clr+ax288dFmoFRu68JP7LWpNUAji5U\n9Ixo7dhGWDu2yzrZrRLdL46JatEp0Vqm9cn5zcrr94nWo410apRm9NhR12Vf3+UuzW6PBG33Nenm\n64BLCODQM8ZuGFOfNf7I91mfxraNJVyjHtKp3LX6+7HaALKk2z8u8hIY8SMrEQRw6Bnl9WWdvOOk\nyv3lSy1xpXUllfuD8g39G1KuYYF1qvXg+PGl94s/LgnSSEsSPy7yEBjxIys5rfSzFu1CDlxvq85X\nffzMuB88fdDHz4x7db6adpV6x1pz11htAFnV7UEGeRkcwLJbayZWYmiOiXyBFMR/mUeYyR1F4V2e\nZPrYsSBfNP6diX+nJiaCfLS0dft16AGsxLAMAjggYfXdKocPL73NP3WgOQKjntFqAPesJCoDoMc1\ny12TgvLh4Wy0HgBZFY34bLUchUcAB6D7RkeDLp54K0EUxA0Ps4ICALQp1VGoZvbvzeycmdXMrGlz\noZntNLPHzGzGzA7Gyq83sy+G5RUz60+m5gDaspa5tQAAS6Q9jchZSbskfbbZDmZ2laT3SHq1pBdL\ner2ZvTjc/OeSDrv7Vknfl7S7u9UFAABIX6oBnLt/3d0fW2G3myXNuPvj7r4g6cOSbjMzk/RySQ+G\n+31AEv0wAACg8NJugWvFgKTvxG4/GZZtkvQDd/9JXXlDZnaXmU2Z2dQzzzzTtcoCAAB0W9cHMZjZ\nJyT9fINNb3P3j3X7+SPu/oCkB6RgGpGknhcAAKDTuh7Aufsta3yIpyQ9P3b7eWHZeUnPMbNnha1w\nUTkAAECh5aEL9UuSBsMRp/2Sbpd0Ilxu4lOSXhfud6ekxFr0AAAA0pL2NCKvMbMnJf26pI+b2amw\n/BfM7KQkha1rb5Z0StLXJX3E3c+FD3FA0j4zm1GQE/fXSR8DAABA0lhKCwAAICNaXUorD12oAAAA\niCGAAwAAyJme7EI1s2ckfTvBp7xG0v9O8PmyhGPvTRx7b+LYexPH3lkvcPfNK+3UkwFc0sxsqpX+\n7CLi2Dn2XsOxc+y9hmNP59jpQgUAAMgZAjgAAICcIYBLxgNpVyBFHHtv4th7E8femzj2FJADBwAA\nkDO0wAEAAOQMARwAAEDOEMB1iJn9ezM7Z2Y1M2s6pNjMdprZY2Y2Y2YHY+XXm9kXw/KKmfUnU/O1\nM7ONZnbazKbDv1c32Oc3zezR2OXHZjYabnu/mX0rtu3G5I9idVo59nC/n8aO70SsvOjv+41m9o/h\nd+MrZjYW25a7973Z9ze2fX34Ps6E7+t1sW1vDcsfM7NXJVnvtWrhuPeZ2dfC9/hhM3tBbFvDz35e\ntHDsbzSzZ2LH+AexbXeG349pM7sz2ZqvXQvHfjh23N8wsx/EtuX9fX+fmX3PzM422W5mdn/42nzF\nzG6KbUvmfXd3Lh24SPolSf9G0qclDTXZ5ypJ35T0Qkn9kr4s6cXhto9Iuj28/peS/kPax9TGsd8r\n6WB4/aCkP19h/42SLkj6mfD2+yW9Lu3j6OaxS7rYpLzQ77ukfy1pMLz+C5KelvScPL7vy31/Y/v8\nR0l/GV6/XVIlvP7icP/1kq4PH+eqtI+pg8f9m7Hv83+Ijju83fCzn4dLi8f+Rkl/0eC+GyU9Hv69\nOrx+ddrH1Mljr9v/P0l6XxHe97D+vyHpJklnm2wfkfT3kkzSr0n6YtLvOy1wHeLuX3f3x1bY7WZJ\nM+7+uLsvSPqwpNvMzCS9XNKD4X4fkDTavdp23G0K6iy1VvfXSfp7d/+XrtYqGe0e+yW98L67+zfc\nfTq8Pivpe5JWnGE8oxp+f+v2ib8mD0r6rfB9vk3Sh9193t2/JWkmfLw8WPG43f1Tse/zFyQ9L+E6\ndksr73kzr5J02t0vuPv3JZ2WtLNL9eyGdo/99ZI+lEjNEuDun1XQ0NDMbZI+6IEvSHqOmV2rBN93\nArhkDUj6Tuz2k2HZJkk/cPef1JXnxXPd/enw+j9Jeu4K+9+upV/0d4bN0IfNbH3Ha9g9rR77s81s\nysy+EHUdq8fedzO7WcEv+W/GivP0vjf7/jbcJ3xf/1nB+9zKfbOq3brvVtAyEWn02c+LVo/9teHn\n+EEze36b982qlusfdplfL+mTseI8v++taPb6JPa+P6sbD1pUZvYJST/fYNPb3P1jSdcnScsde/yG\nu7uZNZ2bJvyF8suSTsWK36ogAOhXMKfOAUnvWGudO6VDx/4Cd3/KzF4o6ZNm9lUFJ/dM6/D7/t8k\n3enutbA40+872mdmb5A0JGk4Vrzks+/u32z8CLn0t5I+5O7zZvZ/KmiBfXnKdUra7ZIedPefxsqK\n/r6njgCuDe5+yxof4ilJz4/dfl5Ydl5B8+uzwl/tUXlmLHfsZvZdM7vW3Z8OT9TfW+ahflvSMXdf\njD121Iozb2Z/I+mPOlLpDunEsbv7U+Hfx83s05JeIumj6oH33cx+VtLHFfzQ+ULssTP9vjfQ7Pvb\naJ8nzexZkn5Owfe7lftmVUt1N7NbFAT2w+4+H5U3+ezn5US+4rG7+/nYzXEFuaHRfV9Wd99Pd7yG\n3dPOZ/Z2SX8YL8j5+96KZq9PYu87XajJ+pKkQQtGHvYr+NCf8CDz8VMKcsMk6U5JeWrRO6GgztLK\ndV+SJxGe/KOcsFFJDUf9ZNSKx25mV0fdg2Z2jaSXSvpaL7zv4ef8mIJckQfrtuXtfW/4/a3bJ/6a\nvE7SJ8P3+YSk2y0YpXq9pEFJ/zOheq/VisdtZi+R9FeSbnX378XKG372E6v52rVy7NfGbt4q6evh\n9VOSXhm+BldLeqWu7HnIulY+7zKzFylI1v/HWFne3/dWnJD0u+Fo1F+T9M/hj9Lk3vdujIzoxYuk\n1yjo656X9F1Jp8LyX5B0MrbfiKRvKPgl8rZY+QsV/EOfkfTfJa1P+5jaOPZNkh6WNC3pE5I2huVD\nksZj+12n4NdJX939PynpqwpO4P+fpA1pH1Mnj13SvwuP78vh39298r5LeoOkRUmPxi435vV9b/T9\nVdDte2t4/dnh+zgTvq8vjN33beH9HpP06rSPpcPH/Ynw/170Hp8Iy5t+9vNyaeHY3yXpXHiMn5L0\noth9fz/8LMxI+r20j6XTxx7efrukd9fdrwjv+4cUjJpfVHBu3y3pTZLeFG43Se8JX5uvKjb7RFLv\nO0tpAQAA5AxdqAAAADlDAAcAAJAzBHAAAAA5QwAHAACQMwRwAAAAOUMABwAAkDMEcAAAADlDAAcA\nbTCzT5nZK8Lrf2pm/2/adQLQe1gLFQDa8yeS3mFm/0rB+o63plwfAD2IlRgAoE1m9hlJGyS9zN2r\nZvZCBUtl/Zy7v275ewPA2tGFCgBtMLNflnStpAV3r0qSuz/u7rvTrRmAXkIABwAtMrNrJR2VdJuk\ni2a2M+UqAehRBHAA0AIz+xlJE5L2u/vXJf0XBflwAJA4cuAAYI3MbJOkd0p6haRxd39XylUCUHAE\ncAAAADlDFyoAAEDOEMABAADkDAEcAABAzhDAAQAA5AwBHAAAQM4QwAEAAOQMARwAAEDOEMABAADk\nzP8Pwi7E6dNciUEAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f8062365510>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(Xbn, Ybn, xlabel=r'$x_1$', ylabel=r'$x_2$')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"count: {0: 69, 1: 30}\n",
|
||
"prior prob.: {0: 0.696969696969697, 1: 0.30303030303030304}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"classes = [0, 1]\n",
|
||
"count = [sum(1 if y == c else 0 for y in Ybn.T.tolist()[0]) for c in classes]\n",
|
||
"prior_prob = [float(count[c]) / float(Ybn.shape[0]) for c in classes]\n",
|
||
"\n",
|
||
"print 'count: ', {c: count[c] for c in classes}\n",
|
||
"print 'prior prob.:', {c: prior_prob[c] for c in classes}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[matrix([[ 1. , 0.03949835, 0.02825019]]), matrix([[ 1. , 0.09929617, 0.06206227]])]\n",
|
||
"[matrix([[ 0. , 0.52318432, 0.60106092]]), matrix([[ 0. , 0.61370281, 0.6081128 ]])]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"XY = np.column_stack((Xbn, Ybn))\n",
|
||
"XY_split = [XY[np.where(XY[:,3] == c)[0]] for c in classes]\n",
|
||
"X_split = [XY_split[c][:,0:3] for c in classes]\n",
|
||
"Y_split = [XY_split[c][:,3] for c in classes]\n",
|
||
"\n",
|
||
"X_mean = [np.mean(X_split[c], axis=0) for c in classes]\n",
|
||
"X_std = [np.std(X_split[c], axis=0) for c in classes]\n",
|
||
"print X_mean\n",
|
||
"print X_std"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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jiUgkbMaNpKS8JTUVSE/PW6RSdpOVywGZjOX4AoCpU5lHV0eHFX199qBgaJizFIkAExOW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\nrkJggg==\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f80526c9fd0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(Xbn, Ybn, xlabel=r'$x_1$', ylabel=r'$x_2$')\n",
|
||
"draw_means(fig, X_mean, xmin=-1.0, xmax=1.0, ymin=-1.0, ymax=1.0)\n",
|
||
"plot_prob(fig, X_mean, X_std, classes, xmin=-1.0, xmax=1.0, ymin=-1.0, ymax=1.0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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37voxQn5f3HJS7flohWR7TgoEFWUVeOTjj2DJrCWWdsMHgCvDv/fvMoAR9cIA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\nAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f8059fbccd0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(Xbn, Ybn, xlabel=r'$x_1$', ylabel=r'$x_2$')\n",
|
||
"plot_decision_boundary_bayes(fig, X_mean, X_std, xmin=-4.0, xmax=4.0, ymin=-4.0, ymax=4.0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"theta = [[ -0.31582268]\n",
|
||
" [ 0.43496774]\n",
|
||
" [ -0.21840373]\n",
|
||
" [ -7.88802319]\n",
|
||
" [ 22.73897346]\n",
|
||
" [ -4.43682364]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Uruchomienie metody gradientu prostego dla regresji logistycznej\n",
|
||
"theta_start = np.matrix(np.zeros(Xbnp.shape[1])).reshape(Xbnp.shape[1], 1)\n",
|
||
"theta, errors = GD(h, J, dJ, theta_start, Xbnp, Ybn, \n",
|
||
" alpha=0.05, eps=10**-7, maxSteps=100000)\n",
|
||
"print(r'theta = {}'.format(theta))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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HwwBGRDREHU92AAAarm0weCRE2RPyac1N1VY1MsPV825Mc9NTK7V6rTsV2GdpdVtl1tIp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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f808fe1d8d0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(Xbnp, Ybn, xlabel=r'$x_1$', ylabel=r'$x_2$')\n",
|
||
"plot_decision_boundary(fig, theta, Xbnp, xmin=-4.0, xmax=4.0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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1wahUYnl56otykszAOYflzxbkz8+H/ryhPVx9fF4eri4sxB+amhCgxfiEpBUFMEKyRLff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\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f805269b2d0>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_data_for_classification(Xbn, Ybn, xlabel=r'$x_1$', ylabel=r'$x_2$')\n",
|
||
"plot_decision_boundary_bayes(fig, X_mean, X_std, xmin=-4.0, xmax=4.0, ymin=-4.0, ymax=4.0)\n",
|
||
"plot_decision_boundary(fig, theta, Xbnp, xmin=-4.0, xmax=4.0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "subslide"
|
||
}
|
||
},
|
||
"source": [
|
||
"* Naiwny klasyfikator Bayesa nie działa, jeżeli dane nie różnią się średnią i odchyleniem standardowym"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"celltoolbar": "Slideshow",
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.15rc1"
|
||
},
|
||
"livereveal": {
|
||
"start_slideshow_at": "selected",
|
||
"theme": "amu"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|