2732 lines
870 KiB
Plaintext
2732 lines
870 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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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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"CPython 3.5.5\n",
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"IPython 6.2.1\n",
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"\n",
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"numpy 1.14.1\n",
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"scipy 1.0.0\n",
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"sklearn 0.19.1\n",
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"pandas 0.22.0\n",
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"matplotlib 2.2.2\n"
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]
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}
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],
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"source": [
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"%load_ext watermark\n",
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"%watermark -v -p numpy,scipy,sklearn,pandas,matplotlib"
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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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"source": [
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"**5 장 – 서포트 벡터 머신**\n",
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"\n",
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"_이 노트북은 2장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._"
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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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"source": [
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"# 설정"
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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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"source": [
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"파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 파이썬 2와 파이썬 3 지원\n",
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"from __future__ import division, print_function, unicode_literals\n",
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"\n",
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"# 공통\n",
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"import numpy as np\n",
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"import os\n",
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"\n",
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"# 일관된 출력을 위해 유사난수 초기화\n",
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"np.random.seed(42)\n",
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"\n",
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"# 맷플롯립 설정\n",
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"%matplotlib inline\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams['axes.labelsize'] = 14\n",
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"plt.rcParams['xtick.labelsize'] = 12\n",
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"plt.rcParams['ytick.labelsize'] = 12\n",
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"\n",
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"# 한글출력\n",
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"plt.rcParams['font.family'] = 'NanumBarunGothic'\n",
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"plt.rcParams['axes.unicode_minus'] = False\n",
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"\n",
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"# 그림을 저장할 폴드\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"svm\"\n",
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"\n",
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"def save_fig(fig_id, tight_layout=True):\n",
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" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
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" if tight_layout:\n",
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" plt.tight_layout()\n",
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" plt.savefig(path, format='png', dpi=300)"
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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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"source": [
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"# 라지 마진 분류"
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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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"source": [
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"다음 몇 개의 코드 셀은 5장 앞부분의 그래프를 만듭니다. 실제 코드 예제는 그 이후에 나옵니다:"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n",
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" decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
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" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
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" tol=0.001, verbose=False)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.svm import SVC\n",
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"from sklearn import datasets\n",
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"\n",
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"iris = datasets.load_iris()\n",
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"X = iris[\"data\"][:, (2, 3)] # 꽃잎 길이, 꽃잎 너비\n",
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"y = iris[\"target\"]\n",
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"\n",
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"setosa_or_versicolor = (y == 0) | (y == 1)\n",
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"X = X[setosa_or_versicolor]\n",
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"y = y[setosa_or_versicolor]\n",
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"\n",
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"# SVM 분류 모델\n",
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"svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
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"svm_clf.fit(X, y)"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x194.4 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 나쁜 모델\n",
|
||
"x0 = np.linspace(0, 5.5, 200)\n",
|
||
"pred_1 = 5*x0 - 20\n",
|
||
"pred_2 = x0 - 1.8\n",
|
||
"pred_3 = 0.1 * x0 + 0.5\n",
|
||
"\n",
|
||
"def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
|
||
" w = svm_clf.coef_[0]\n",
|
||
" b = svm_clf.intercept_[0]\n",
|
||
"\n",
|
||
" # 결정 경계에서 w0*x0 + w1*x1 + b = 0 이므로\n",
|
||
" # => x1 = -w0/w1 * x0 - b/w1\n",
|
||
" x0 = np.linspace(xmin, xmax, 200)\n",
|
||
" decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
|
||
"\n",
|
||
" margin = 1/w[1]\n",
|
||
" gutter_up = decision_boundary + margin\n",
|
||
" gutter_down = decision_boundary - margin\n",
|
||
"\n",
|
||
" svs = svm_clf.support_vectors_\n",
|
||
" plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
|
||
" plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
|
||
" plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
|
||
" plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12,2.7))\n",
|
||
"\n",
|
||
"plt.subplot(121)\n",
|
||
"plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
|
||
"plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
|
||
"plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
|
||
"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
|
||
"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.ylabel(\"꽃잎 너비\", fontsize=14)\n",
|
||
"plt.legend(loc=\"upper left\", fontsize=14)\n",
|
||
"plt.axis([0, 5.5, 0, 2])\n",
|
||
"\n",
|
||
"plt.subplot(122)\n",
|
||
"plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
|
||
"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
|
||
"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.axis([0, 5.5, 0, 2])\n",
|
||
"\n",
|
||
"save_fig(\"large_margin_classification_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 특성의 스케일에 민감함"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x230.4 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
|
||
"ys = np.array([0, 0, 1, 1])\n",
|
||
"svm_clf = SVC(kernel=\"linear\", C=100)\n",
|
||
"svm_clf.fit(Xs, ys)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12,3.2))\n",
|
||
"plt.subplot(121)\n",
|
||
"plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
|
||
"plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
|
||
"plot_svc_decision_boundary(svm_clf, 0, 6)\n",
|
||
"plt.xlabel(\"$x_0$\", fontsize=20)\n",
|
||
"plt.ylabel(\"$x_1$ \", fontsize=20, rotation=0)\n",
|
||
"plt.title(\"스케일 조정 전\", fontsize=16)\n",
|
||
"plt.axis([0, 6, 0, 90])\n",
|
||
"\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_scaled = scaler.fit_transform(Xs)\n",
|
||
"svm_clf.fit(X_scaled, ys)\n",
|
||
"\n",
|
||
"plt.subplot(122)\n",
|
||
"plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
|
||
"plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
|
||
"plot_svc_decision_boundary(svm_clf, -2, 2)\n",
|
||
"plt.xlabel(\"$x_0$\", fontsize=20)\n",
|
||
"plt.title(\"스케일 조정 후\", fontsize=16)\n",
|
||
"plt.axis([-2, 2, -2, 2])\n",
|
||
"\n",
|
||
"save_fig(\"sensitivity_to_feature_scales_plot\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 이상치에 민감함"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x194.4 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
|
||
"y_outliers = np.array([0, 0])\n",
|
||
"Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
|
||
"yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
|
||
"Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
|
||
"yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
|
||
"\n",
|
||
"svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
|
||
"svm_clf2.fit(Xo2, yo2)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12,2.7))\n",
|
||
"\n",
|
||
"plt.subplot(121)\n",
|
||
"plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
|
||
"plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
|
||
"plt.text(0.3, 1.0, \"불가능!\", fontsize=24, color=\"red\")\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.ylabel(\"꽃잎 너비\", fontsize=14)\n",
|
||
"plt.annotate(\"이상치\",\n",
|
||
" xy=(X_outliers[0][0], X_outliers[0][1]),\n",
|
||
" xytext=(2.5, 1.7),\n",
|
||
" ha=\"center\",\n",
|
||
" arrowprops=dict(facecolor='black', shrink=0.1),\n",
|
||
" fontsize=16,\n",
|
||
" )\n",
|
||
"plt.axis([0, 5.5, 0, 2])\n",
|
||
"\n",
|
||
"plt.subplot(122)\n",
|
||
"plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
|
||
"plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
|
||
"plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.annotate(\"이상치\",\n",
|
||
" xy=(X_outliers[1][0], X_outliers[1][1]),\n",
|
||
" xytext=(3.2, 0.08),\n",
|
||
" ha=\"center\",\n",
|
||
" arrowprops=dict(facecolor='black', shrink=0.1),\n",
|
||
" fontsize=16,\n",
|
||
" )\n",
|
||
"plt.axis([0, 5.5, 0, 2])\n",
|
||
"\n",
|
||
"save_fig(\"sensitivity_to_outliers_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 라지 마진 *vs* 마진 오류"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"다음이 5장의 첫 번째 코드 예제입니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Pipeline(memory=None,\n",
|
||
" steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
|
||
" intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
|
||
" penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"from sklearn import datasets\n",
|
||
"from sklearn.pipeline import Pipeline\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"from sklearn.svm import LinearSVC\n",
|
||
"\n",
|
||
"iris = datasets.load_iris()\n",
|
||
"X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
|
||
"y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica\n",
|
||
"\n",
|
||
"svm_clf = Pipeline([\n",
|
||
" (\"scaler\", StandardScaler()),\n",
|
||
" (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\", random_state=42)),\n",
|
||
" ])\n",
|
||
"\n",
|
||
"svm_clf.fit(X, y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([1.])"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"svm_clf.predict([[5.5, 1.7]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"여러가지 규제 설정을 비교하는 그래프를 만들겠습니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Pipeline(memory=None,\n",
|
||
" steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
|
||
" intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
|
||
" penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"scaler = StandardScaler()\n",
|
||
"svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n",
|
||
"svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n",
|
||
"\n",
|
||
"scaled_svm_clf1 = Pipeline([\n",
|
||
" (\"scaler\", scaler),\n",
|
||
" (\"linear_svc\", svm_clf1),\n",
|
||
" ])\n",
|
||
"scaled_svm_clf2 = Pipeline([\n",
|
||
" (\"scaler\", scaler),\n",
|
||
" (\"linear_svc\", svm_clf2),\n",
|
||
" ])\n",
|
||
"\n",
|
||
"scaled_svm_clf1.fit(X, y)\n",
|
||
"scaled_svm_clf2.fit(X, y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 스케일되지 않은 파라미터로 변경\n",
|
||
"b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
|
||
"b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
|
||
"w1 = svm_clf1.coef_[0] / scaler.scale_\n",
|
||
"w2 = svm_clf2.coef_[0] / scaler.scale_\n",
|
||
"svm_clf1.intercept_ = np.array([b1])\n",
|
||
"svm_clf2.intercept_ = np.array([b2])\n",
|
||
"svm_clf1.coef_ = np.array([w1])\n",
|
||
"svm_clf2.coef_ = np.array([w2])\n",
|
||
"\n",
|
||
"# 서포트 벡터 찾기 (libsvm과 달리 liblinear 라이브러리에서 제공하지 않기 때문에 \n",
|
||
"# LinearSVC에는 서포트 벡터가 저장되어 있지 않습니다.)\n",
|
||
"t = y * 2 - 1\n",
|
||
"support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
|
||
"support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
|
||
"svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
|
||
"svm_clf2.support_vectors_ = X[support_vectors_idx2]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x230.4 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(12,3.2))\n",
|
||
"plt.subplot(121)\n",
|
||
"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris-Virginica\")\n",
|
||
"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolor\")\n",
|
||
"plot_svc_decision_boundary(svm_clf1, 4, 6)\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.ylabel(\"꽃잎 너비\", fontsize=14)\n",
|
||
"plt.legend(loc=\"upper left\", fontsize=14)\n",
|
||
"plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
|
||
"plt.axis([4, 6, 0.8, 2.8])\n",
|
||
"\n",
|
||
"plt.subplot(122)\n",
|
||
"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
|
||
"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
|
||
"plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
|
||
"plt.axis([4, 6, 0.8, 2.8])\n",
|
||
"\n",
|
||
"save_fig(\"regularization_plot\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"# 비선형 분류"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 792x288 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
|
||
"X2D = np.c_[X1D, X1D**2]\n",
|
||
"y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
|
||
"\n",
|
||
"plt.figure(figsize=(11, 4))\n",
|
||
"\n",
|
||
"plt.subplot(121)\n",
|
||
"plt.grid(True, which='both')\n",
|
||
"plt.axhline(y=0, color='k')\n",
|
||
"plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
|
||
"plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
|
||
"plt.gca().get_yaxis().set_ticks([])\n",
|
||
"plt.xlabel(r\"$x_1$\", fontsize=20)\n",
|
||
"plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
|
||
"\n",
|
||
"plt.subplot(122)\n",
|
||
"plt.grid(True, which='both')\n",
|
||
"plt.axhline(y=0, color='k')\n",
|
||
"plt.axvline(x=0, color='k')\n",
|
||
"plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
|
||
"plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
|
||
"plt.xlabel(r\"$x_1$\", fontsize=20)\n",
|
||
"plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
|
||
"plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
|
||
"plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
|
||
"plt.axis([-4.5, 4.5, -1, 17])\n",
|
||
"\n",
|
||
"plt.subplots_adjust(right=1)\n",
|
||
"\n",
|
||
"save_fig(\"higher_dimensions_plot\", tight_layout=False)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.datasets import make_moons\n",
|
||
"X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
|
||
"\n",
|
||
"def plot_dataset(X, y, axes):\n",
|
||
" plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
|
||
" plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
|
||
" plt.axis(axes)\n",
|
||
" plt.grid(True, which='both')\n",
|
||
" plt.xlabel(r\"$x_1$\", fontsize=20)\n",
|
||
" plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
|
||
"\n",
|
||
"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Pipeline(memory=None,\n",
|
||
" steps=[('poly_features', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,\n",
|
||
" intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
|
||
" penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.datasets import make_moons\n",
|
||
"from sklearn.pipeline import Pipeline\n",
|
||
"from sklearn.preprocessing import PolynomialFeatures\n",
|
||
"\n",
|
||
"polynomial_svm_clf = Pipeline([\n",
|
||
" (\"poly_features\", PolynomialFeatures(degree=3)),\n",
|
||
" (\"scaler\", StandardScaler()),\n",
|
||
" (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", random_state=42))\n",
|
||
" ])\n",
|
||
"\n",
|
||
"polynomial_svm_clf.fit(X, y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_predictions(clf, axes):\n",
|
||
" x0s = np.linspace(axes[0], axes[1], 100)\n",
|
||
" x1s = np.linspace(axes[2], axes[3], 100)\n",
|
||
" x0, x1 = np.meshgrid(x0s, x1s)\n",
|
||
" X = np.c_[x0.ravel(), x1.ravel()]\n",
|
||
" y_pred = clf.predict(X).reshape(x0.shape)\n",
|
||
" y_decision = clf.decision_function(X).reshape(x0.shape)\n",
|
||
" plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
|
||
" plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
|
||
"\n",
|
||
"plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
|
||
"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
|
||
"\n",
|
||
"save_fig(\"moons_polynomial_svc_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Pipeline(memory=None,\n",
|
||
" steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=1,\n",
|
||
" decision_function_shape='ovr', degree=3, gamma='auto', kernel='poly',\n",
|
||
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
|
||
" tol=0.001, verbose=False))])"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import SVC\n",
|
||
"\n",
|
||
"poly_kernel_svm_clf = Pipeline([\n",
|
||
" (\"scaler\", StandardScaler()),\n",
|
||
" (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
|
||
" ])\n",
|
||
"poly_kernel_svm_clf.fit(X, y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Pipeline(memory=None,\n",
|
||
" steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
|
||
" decision_function_shape='ovr', degree=10, gamma='auto', kernel='poly',\n",
|
||
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
|
||
" tol=0.001, verbose=False))])"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"poly100_kernel_svm_clf = Pipeline([\n",
|
||
" (\"scaler\", StandardScaler()),\n",
|
||
" (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
|
||
" ])\n",
|
||
"poly100_kernel_svm_clf.fit(X, y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 792x288 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(11, 4))\n",
|
||
"\n",
|
||
"plt.subplot(121)\n",
|
||
"plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
|
||
"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
|
||
"plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
|
||
"\n",
|
||
"plt.subplot(122)\n",
|
||
"plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
|
||
"plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
|
||
"plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
|
||
"\n",
|
||
"save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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oXhwsHLzK4Xh4wOzZ8O23ZicRQgghhDVIAaKAeGvNW3gX8mbcY+PMjpIvLzV4iTql6zB89XBux982O47N9egBHTrYp+O0LT36KJRPLrc6+7UIIYQQBZ30gSgANp/azLLIZXzY8kO79X2wFQ83D754/Av2nd+Hh5tr//pqDU8+aXzvjE2X0rt9G3r3hmbNYPBgs9MIIYQQIq9c+w5MALD//H4q+VZi8IOucdfWolILWlRqYXYMm1MKXnrJ7BTWU7iw0R8iKcnsJEIIIYTIDylAFACvNHqFvg/0xcvDy+woVjVjzwyi70Qz7KFhZkexujVr4OpV6NoV3FykoaFSsHixa9SmCCGEEAWZi9yaiMwkJCWw6dQmAJcrPAD8cfwPRq4dSdSNKLOjWN20afDuu673aX1K4WHLFshmgmwhhBBCODApQLiwOfvm0Hxmc9afWG92FJv4IOQD4pPiGbthrNlRrG7hQli1yhjByNVERRnDun72mdlJhBBCCJEXUoBwUXGJcby//n0al2/MIxUfMTuOTdxb8l5efOBFQneH8vfVv82OYxVxccbD3R0qVDA7jW2ULQvLl8OoUWYnEUIIIUReSAHCRc3cM5NT104x5tExKBdudP5ui3cp5FaIUWGucTf69ddQvTpcvGh2Ettq186YWVsIIYQQzkcKEC4oLjGODzd+yENBD9G6Smuz49hUuWLl+KT1J3S7v5vZUayiVi144gkICDA7ie0dPGgM6Xr0qNlJhBBCCJEbLtjCWhy+eJjbCbcZHTLapWsfUgxsPNDsCFbTsqXxKAj8/eHyZThzBqpWNTuNEEIIISwlNRAuqG6ZupwYfMLlax9Su3bnGqPWjeLoFef8ODs2FqZMgZs3zU5iP2XKwKFDEBJidhLhSJRSLZRSu5VS+5VSO5VSTTLZJlAptUgptUcptV0ptUkp1cyMvEIIURBJAcLFHL1ylISkBIp4FikQtQ8pbsXfYsKWCYzbOM7sKHmyYgW8+qoxvGlBopQxVO3OnWYnEY5AKeULLAYGaq2DgeHAMqWUT7pNPwIuAfW11o2Bz4CFdg0rhBAFmDRhciFxiXG0nN2S5hWbM6/LPLPj2FXZYmXpV78fU3dOZeQjI6nsV9nsSLnSuTPs2gUPPGB2EvsbOxbGjIFjx1x35ClhsbZApNY6HEBrHaaUigJaActTbXcWqAZ4AXeAgORlGSil+gH9AAIDAwkLC7NZ+NSio6NJTEy02/nsLSYmRq7NCcm1OSdHvDYpQLiQefvncfr6aXoF9zI7iinefPhNpu2axrhN4wjtGGp2HItpbXwSX7++2UnM8Z//GCNPlS9vdhLhAKoAx9ItO5a8PLVRQChwQSkVDVwEnsjsgFrr0ORtadiwoQ6xU5s5X19foqOjsdf57C0sLEyuzQnJtTknR7w2acLkIpJ0EuM3j6demXq0vbet2XFMUb54eV584EVm7p3J6WunzY5jkcREaNwYvvnG7CTmueceeOYZY+4LUeApIDHdsgQyvleNAMoD92itKwDTgF+UUvJbJIQQdiAFCBex7M9lRF6O5K2H3ypQfR/Se+PhN+hwXwfuJNwxO4pFoqOhYkVjRKKCbsYMmDjR7BTCZGeA9A3ZKiQvT60HMFlrfQ1Aaz0do0BR1+YJhRBCSAHCVSw8uJAqflXoVtM15kPIq0q+lVjyzBKq+VczO4pF/P3hxx/hqafMTmK+tWvh11+NJl2iwFoGBCul6gAopRoDNYC1SqnNSqmUP+wjQBellFvydo8AxYFTJmQWQogCR/pAuIh5XYz+Dx5u8pIC/HX5L45dPUa7qu3MjpKlyEgoXhzKljU7iWP4+mvw8TH6g4iCSWt9TSn1NDBDKaUxmi+1B3yAikCJ5E0HAJ8Du5VSscnLumqtL9k7sxBCFERyt+kCYhNi8fLwopJvJbOjOIzBvw9mV9QuTg45aXaULA0ebBQijh0DN6kLpEgR4+vt2/LzKMi01uuARpmsCkq1zTngObuFEkIIkYZd36YtnCDod6XU1lSPPUqp48nrGiilrqRb/7o9r8HRHI05StDnQWw6tcnsKA7ljaZvcOHmBWbvnW12lCx9+aXxqbvcLP/rzBmjT8isWWYnEUIIIURW7HbrYukEQVrrdlrrJikPYA6wInl1KeCn1Ou11gW62+WCUwuITYildunaZkdxKCGVQmhYriGfhn9Kok4/qItjqFoV2rQxO4VjKV8eevaEutIVVriIqBtRtJjVgnMx58yO4pTk5yeEY7JnEyZLJwi6SynlDQwBmiUvKgW0UUptS36+BvhYa30jk32tPnmQo03kcfb2WcIuhvF00NPs3brX7Dh3OcrPqYNvB0YfGs2aM2twD3Oc0R3/+acws2ZV4eLFLQQExJkd5y5Hed2efBLu3HGcPKlJJpFbYzaMYdOpTYxZP4YpT0wxO47TkZ+fEI7JngUISycISu0V4Detdcqg/ouB+VprrZQqCXyJUUORYQwbW0we5GgTeQz4dQDuyp3Pun9GuWLlzI5zl6P8nJonNWfRxUVc5apD5Enxww+wZUsCM2dWcKjJ0xzldQO4fBnGjYvk00+rmx0lDUf6GaVwxEzCEO8Vz8y9M0nSSczcO5N3W7xLmaJlzI7lNKJuRMnPTwgHZc/W15ZOEGRs/G/tw7iUZVrr21obgzxqra8ArwMdk7ctUC7fusyMPTNoE9jGoQoPjsTdzZ29L+/l2XueNTtKGt27w48/bnGowoOj+f57mDixOgcPmp1EiLw7X+M8SToJgESdyJj1Y0xO5FzGbBgjPz8hHJQ9CxCWThCUYgCwQmud3bje7sAdIDabbVxSSe+S/NH7D56v8LzZURxayrC2hy4eMjmJ4fp142vhwknmBnFwL7wAM2Zsp1Yts5MIkTexnrFcqXiFuESjmWJcYhwz986UtvwWSql9kJ+fEI7JngUISycIIrlj9RDgo9QHUEo9m9wZG6WUB0btxFytdYG7G1NK8XCFhynrLZMI5GTFuRXUmlqLAxcOmJrjzh2oVg3GjjU1hlMoUgQqV75ldgwh8uxkpZNo0s6KKJ+iWy517UMK+fkJ4TjsVoDQWl8DUiYI2g5MIvMJgsCoffhda51+EH9v4A+l1A5gC3AOGGbr7I7mx0M/8vIvLxMTF2N2FKfQ1L8phT0KM3nrZFNzxMfDgAEgzdUt98YbMHSo2SmEyL3rxa8bdeSpxCXGseXMFnMCOZnwM+F3ax9SyM9PCMdh14nkLJkgKHm7T7PYfyYw0wbRnIbWmvGbx3Mj9gY+hXxy3kFQolAJegf3Zva+2XzU6iMCigSYkqNYMRg1yvheBs2xTGwsJCSA1jJDtXAuDXc2JDo6mr17HWeEPGeyp/8esyMIIbIhU1g5mfAz4ez8ZyevPfgabkpePksNbjKY2MRYQneFmnL+fftgwwbjRlhYbvJkmDpVCg9CCCGEI5E7UCczedtkfAv70rtub7OjOJWaATVpe29b5h+YjzbhLn7CBOja1fhEXVgupeBw/Lj87IQQQghHIQUIJ3L62ml+OvQTLz7wIkU9i5odx+l83eFrtr+4HWXCx9nTp8OKFVC4sN1P7fT27jVm7f7hB7OTCCGEEALs3AdC5I9Sihfrv8irjV81O4pTquhbEYAknYRC2bUg4eMDDRva7XQupW5dGD8eWrY0O4kQQgghQGognEpQ8SCmdZh290ZY5N6hi4eoOaUmm05tssv5rl+H1q1hiwwckmdKwfDhyMR7QgghhIOQAoSTWPP3Gjaf2mxK+31XUsm3EhduXmDStkl2Od/x43DiBHhIXV++7dgBk+zzsgkhhBAiG1KAcAJaa15b8Rqv/f6a2VGcnk8hH/o36M/SP5dy/Opxm5+vbl2IjITGjW1+Kpe3eDGMGQM3bpidRAghhCjYpADhBFb/vZrDlw4z5MEhpnQAdjUDGw/ETbnxxfYvbHqe8+chMRHc5K/MKt5806jNKVbM7CRCCCFEwSa3Nk5g0tZJBBYJpHut7mZHcQlBxYN4uubTfLP7G67HXrfZeXr2hEcftdnhCxw/v38LD9KSTwghhDCPtMx2cJGXIllxdAXvh7yPl4eX2XFcxtvN3qZzjc42nc375Zdl7gJru34dOnaE7t1h4ECz0wghhBAFkxQgHFzk5UjKFStH/wb9zY7iUoIDgwkODLbpObp2tenhC6RixaB0aWnGJIQQQphJmjA5uCerP8mpIacILBpodhSXczv+Nh9u+JBVx1ZZ9biXL8NXX8HNm1Y9rMAY0nXRIugtE7ELIYQQppEChAM7EX2CJJ2Eu5u72VFcUiH3QoTuDmXcpnFWPe7SpTBggDGEq7CNpCTYutXsFEIIIUTBJAUIB5WQlEDIrBD6LO1jdhSX5eHmwauNXiXsRBj7z++32nH79oWICKhd22qHFOlMnQoPPQSHDpmdRAghhCh4pADhoH6O/JmT107SpUYXs6O4tBfrv4hPIR8mb51stWMqJYUHW3v2WZg3D6pWNTuJEEIIUfBIAcJBTdo6iUq+lXiy+pNmR3Fpft5+9A7uzbyIeVy8eTHfx+vUCaZMsUIwka1SpaBHD/D0NDuJEEIIUfDIKEwOaE/UHjae2sjENhOl/4MdvPbga5y+fpprsdcIKBKQ5+PcumXFUCJHWsOMGUYholcvs9MIIYQQBYcUIBzQ7H2zKVKoCH0f6Gt2lALh/oD7+aXHL/k+jo8PLFtmhUDCIkrB/Png7S0FCCGEEMKepAmTA/q0zads7rsZ38K+ZkcpUI5fPc7uqN152vfKFbiY/xZQIpd+/BGWLzc7hbAmpVQLpdRupdR+pdROpVSTLLYrpZT6USl1QCm1Syll3eHUhBBCZEkKEA5Ga42Hmwd1y9Q1O0qBorWm/fz2DPwtb9Mbf/YZVKpkFCSE/fj5GTURCQlmJxHWoJTyBRYDA7XWwcBwYJlSyifddl7AL8BkrXVtrXUD4FO7BxZCiAJKChAOJDYhlge+foDvD3xvdpQCRynFgIYD2HpmK9vPbs/1/s8/D59+CiVL2iCcyNa2bVChAuzaZXYSYQVtgUitdTiA1joMiAJapduuN7AVeC25lmIuUMieQYUQoiCTPhAOZOHBhew7vw9/b3+zoxRIL9R7gZHrRjJ522TmdZmXq33vv994CPurUQMefBDc5OMQV1AFOJZu2bHk5ak9AtQDOgKngA+AeWQsaKCU6gf0AwgMDCQsLMy6ibMQHR1NYmKi3c5nbzExMXJtTkiuzTk54rXlqwChlLpPa33EWmEKMq01k7ZOomZATR6r8pjZcQqkYl7F6FuvL1/u+JJPWn9CuWLlctxHaxg71piXoFo1O4QUGZQoAUuWmJ1CACilVgGtga5a68WplitgJtAHGK+1fjurQwCJ6ZYlkLG2vDQwS2t9Ivn444FrSqmiWuuY1BtqrUOBUICGDRvqkJCQPFxZ7vn6+hIdHY29zmdvYWFhcm1OSK7NOTniteX5MzullDtw2IpZCrRNpzax59weBj84GOO9Vphh0IOD8HDzYMvpLRZtHxlpFCA2bLBxMJGjq1dhi2Uvm7CdN4AkYGzye0SKTzEKD9OzKTwAnAEqpFtWIXl5aheA66meJ6V6CCGEsLEsCxBKqaaZLOuhlEq9j9zpWsnkbZMp6V2SnsE9zY5SoFXxq8K518/RrWY3i7avUQNOnzb6QAhz9e0LXbpAfLzZSQourfU+YC5wP9ALQCn1DjAM+AF4OYdDLAOClVJ1kvdtDNQA1iqlNiulUur5FgP9lFLFkp8PAdZqrbOfjSU+HmJjc31dQggh0squCdNGpVSxdP+Q5wJLgZRl2mbJCphXG79K5xqd8Snkk/PGwqZKFC4BwI3YGxTzKpbldlobIwCVLm2vZCI7779vvCaFpCut2UYCzwCjlVJFgQ+BlUAvrXW2NQRa62tKqaeBGUopjdF8qT3gA1QESiRvt0QpVRXYoZS6DZwEXsgx2ZkzULas0eawd2+j84zU+AohRK5l14Qps/+q8p/WRkIqhUjtgwPpt7wfzWc2R+usy8hvvAHdu0OSNJpwCMHBUFdGPzad1voMMAnjhv8LYAvQRWsdl3o7pdTA5Lkeric/wpVST2it12mtG2mtG2utm2qtw7XWp7XWQVrrnanO84nWuobpR7PgAAAgAElEQVTW+gGtdWet9T85hrt61Xh89RU89JBRhfjhh3DqlHV/CEI4sF9//RWlFEuXLs10/YEDB/Dw8GD16tV5PsfSpUvx9PTkr7/+yvMxhGPLrgCR2Z2TzmK5yKOYuBiGrxrOqWvyBuZIGpVrxL7z+9hwMuvODaVKQWCgjP7jSK5cgaFDYetWs5MUeKmnVfxvFk2LzgBvAfWBhsBaYKlSKthmqdJXTx05AiNHQsWK0LIlzJoFN27Y7PRCOIKIiAgA6tSpk+n6YcOG8fDDD9O6des8n6Nz587UqVOHt956K8/HEI4tLzUQfyul/gFO2yZSwTJn3xwmhk/k7PWzZkcRqTwf/DwlvUvyf9v/L8tt3n4bvvjCjqFEjry8YN482LHD7CQFl1LqOYxO0+eSFw3ObDut9TKt9Qqt9VGt9RGt9f+AG8BDNgtXpw6sX290mCmWrnniunXwn/9AmTLwwgtStShcVkREBD4+PlSuXDnDuvDwcFavXs2wYcPyfZ7BgwezZMkSDh48mO9jCceTl89OhwODMDqtiXxI0klM3jaZRuUa0SSoidlxRCo+hXzoV78fS/9cyonoE2nWJSbC9tzPNSfsoEgROHECBg0yO0nBpJRqD8wGDgLBwJ/Ai0qpGjns566UehYoitHkyXYeeQS+/RbOnTNKm23bpq1GvHULLlyQqkXhsiIiIqhVqxZumfyOT506FX9/f9q3b5/v83Tp0gUfHx+mTZuW72MJx5OXJkxLtNY/AT/ZJlLBseKvFRy5fIQhTYbI0K0OaECjASgU03am/ef3yy9G38uVK00KJrLlkzwOwa3sx+MRVqaUagb8iNE0qY3W+iLwLsZgHR9nsU8dpVQMEAtMA57SWkfYJbCPD/ToAb//bgylNmEC1KplrOvdO+P2U6caVY6XLtklnhDWdPDgQbp160bHjh2JiIhg586dBAUF8eGHH97dJiEhgaVLl9K6dWsKpWrut2PHDjw9PVFK4ePjQ2Rk5N11I0eORCmFUoqmTZuSkJBwd13RokVp3rw5ixYtss9FCrvKqQnTMKXUmykPe4UqKD7f+jnlipXj6ZpPmx1FZOKeEvfw83M/807zd9Isb9UKQkONr8IxTZoEVapIIcJelFJ1gV+Aa0BrrXUUgNb6R2An0Ekp1TyTXSMxZpRuAnwFzFZK1bZP6lTKlTNGRYiIgF27oFOntOsTEmDMGHjtNWMUp86djdkLZUhY4QRWrFhBo0aNiIyMvDsZWa9evShbtiwjR45k0qRJAOzatYuYmBgaN26cZv9GjRrdLWjcvn2bPn36kJiYyPbt2/n4Y+OzAV9fXxYsWICHR9rBPR966CHOnz/Pn3/+aeOrFPaWXQFiA9AKeDzVYwMZZwkVeZCQlEDFEhV5/aHXKeQu4046qvbV2lPcq3iaZUWLwksvgUe+5nEXttS4MfTqBXFxOW8r8id5ONWVGDXUbbXWx9JtMiL56yfp99VaxyX3gdiptR4B7AWG2jRwdpSC+vXB2zvt8j/+MJo8gVGYWLbMmHSkXDkYONBo05jNiG1CmOXs2bM888wz1KpVi+3bt3PvvfcCRv+ElStXUqhQobtNjA4dOgRwd5vUhg8fTps2bQDYtm0bo0ePpnfv3iQmGreEoaGhVKxYMcN+KceSfhCuJ8tbIK11iAX7S7ubPPJw8+DbTt+aHUNYYHnkcmbtm8Wipxcx8VM3ataEJ54wO5XITtOmxkPYntb6KFAmm/VrsPy9wg3wskYuq2rc2Bj6dfbstEN8XbliNG2aOtUYErZ3b+jZE+65x7ysQqTy6aefcuPGDUJDQ/H29uavv/7C09OT2rVr4+npSXBwMIcPHwbg4kVj8LSSJUtmOI5Sijlz5lC3bl3Onz/P2LFj767r168fTz+deUsKf39/AC5cuGDtSxMms7iXmFLKM90iDay3bpyC4eLNi+z8Z2fOGwqHEH0nmsWHF7PizzV8843RZFo4h927jVYpwvEopT5WSjVXSlVK7gsxDggB5pkcLSM/P3j5ZQgPh8hIY+jXChXSbvPnn/DOO/Dkk+ZkFCITP/30EzVq1OCBBx4A4MiRI9SpUwdPT+OW7tatW/j5+QHc7YuZ1fxHgYGBzJw5M82y++67724TqMykHEv6ebqe3AwzcVspdbcth9Y6SWv9qA0yubwpO6bQaHojTkafNDuKsED3Wt0JLBLIlN2TOHjQmHdKOL64OGjXDj74wOwkIgtlgO8w+kH8ATQCHtdarzA1VU7uu8/oD3H8+L9DvxYt+u/6Xr0y7nP2rDF8mxB2dOHCBU6fPk39+vUBiI2N5cSJE3efX7t2jaNHj9KgQQMAAgICALhy5UqWx0zfFOncuXOcS2nel4mUY6UcW7iO3BQgFPmcRE4p1UIptTt59tGdSqkMY5cqpRoopa4opbameryevE4ppcYopSKVUoeUUt8ppYrkJ5O93Um4w9QdU+lwXwcq+mZsLygcj5eHF/0eGMCKI7/z97UjFC+e8z7CfJ6esHQpTJ9udhKRGa31C1rrilprL611aa31Y1pr5xnbzM0NQkJgxgyjf8R33xlDwvbokXHbDh2gUiUYMcKoqRDCDi5fvgxAkSLGbVJERAQJCQl3Cww//PAD8fHxdOvWDYDatY3xC7KaPXrXrl28844xqEhKZ+nr16/z3HPPpRl9KbWjR4+mObZwHZkWIJRSEck3+XcfyavC0y9Ptz5LSilfYDEwUGsdjDGfxDKllE+6TUsBP2mtm6R6TExe1wdoD9TTWtcE4oHxub9s88yPmM/FWxcZ2sS8foIi9/wiB8OUP/l4xWyzo4hcaNoUfH3NTiFcXpEi8PzzRvvGMum6g+zfD3v3wpkz8PHHcP/9Rp+KKVMg+QZPCFsoV64cbm5ubNq0iaSkJHbt2gVA/fr1OX36NCNGjKBmzZo8++yzADzwwAMUL16cran7+SSLiYnhueeeIz4+HoA5c+bQKnkowm3btvHuu+9mmmHr1q0EBgZSvXp1W1yiMFFWnag/tcG52gKRWutwAK11mFIqCmOkp+WptisFtFFKbUt+vgb4WGt9A3gG+FprfTt53WSMqu9XbZDX6rTWTNo6ieDAYB6tJK2/nMn9VUpQr8FJ2tara3YUkUt79sCoUTB3LpQoYXYaUeAcPw6lSqWdP2LHDuMxdKgxIkOfPtC+vVFtJoSVlChRgmeffZb58+fTpUsXYmJiAFi+fDmhoaF4eXmxZMmSu3M+uLu706VLF5YtW0ZsbCxeXv+OZzBgwIC7NRM9evTgueee45FHHqFOnTpcvXqVCRMm0Lp1a1q2bHl3n5iYGDZu3Ejfvn3teNXCXjItQGits/2YVSnVCriqtd6di3NVAdIP73cseXlqi4H5WmutlCoJfAnMAZ7K5BjHgJJKqRJa62vpMvYD+oHR8ScsLCwXUTMXExOTr+OcvX2Wvy//zatVX2X9euv0P89vJltwtEzWyFO4MHw+ArhU2iF+l2zB0TJZK8+RI0UJD6/D/PkHuP/+Gw6RyZocMZNIpVMnePxxo3Zi9mxYvhySP8UlPt5oZ7d0Kfj7w6uvwujRpsYVriU0NJQiRYrw448/cvXqVZRSTJ8+nc6dO/P+++8TGBiYZvtXXnmFWbNm8csvv9C1a1cA5s+fz9y5cwEICgpiypQpAJQvX55p06bxzDPPkJSURM+ePdm/fz+lSpUCjA7ct27don///na8YmE3WutcPYDqwEVgSC73eweYlW7ZQmBoDvuVBRIAb+AIEJJqnTdGvwy/7I7RoEEDbQ3r1q3L9zFuxN7QsQmx+Q+TzBqZrM3RMuU3z4oVWt+6ZXx/9fZV/Xn45/l+DR3tZ6S142WyZp74eOscx9F+RlpbLxOwU+fy/cDZHtZ6L7BEixYtdN26dTOuuHRJ66lTtX7wQa2N2SP+fbz+ut3y5Zcj/i1YiyteW3x8vPb09NSPP/54jtu2bdtWN2vWLN/nrF+/vn7qqafyfRxLueLrlsKe12bpe0FuOlGjlGoBbAS+0lpnPW5X5s4A6ca9o0Ly8uy4A3eA2EyOUQGIAaJzmcXubsbdRGtNUc+ieLpLNbWz+Ptv48PDicm9cLac3sLQlUP54eAP5gYTueLhYdyhnT9vdhJR4Pn7wyuvGPNJpAz9mjJvRO/eGbd/7z2jg/bNm/bNKVxKZGQkcXFxVK5cOcdtJ06cSHh4OKtWrcrz+ZYuXUpERATjxztVN1WRC1kWIJRSnZVSbZRS1ZVS9yulvsEYn7u/1vq9PJxrGRCslKqTfPzGQA1grVJqs1KqWvLyZ5M7XKOU8gDGAXO11knAXODFVHNSDAIWJ5eYHNrwVcNpOL0hiUkylJ8zqVzZGKnxpZeM5+2qtqNmQE0mhk/Mcqxs4Zi6dzcKg/KyCYdRvboxLvSJE7B5MwQHp11/7hx89JExNGyZMsaQsevWQVKSKXGF8zpw4ACARQWIWrVqkZCQcHfm6bzo3LkzcXFxVKtWLc/HEI4ty5mogW+BJCBlSsIrwJM6uRN0bmmtrymlngZmKKU0RrOk9oAPUBFI6d7oDfyhlEri38nqUgosc4CqwHalVAJwCCfoQH351mVm75tNjzo9cHdzNzuOyAWljJEaU7gpN4Y1GcaLy19k7fG1tKrSyrRsInd69oToaKMAIXMaCYfi5pb51OkLFvw7f0RMDMyaZTwqVDAKFb16GYUQIXKQMn+DJQUIISyRZQ2E1tpfax2AcYPfAfgZ+F0p9Y1SqmhW+2VHa71Oa91Ia91Ya91Uax2utT6ttQ7SWu9M3mam1rpBqu3e0MmjLmmtE7XW/9Na19NaN9Ra99ZaX89LFnv6etfX3E64zZAmQ8yOInJh8mTjw8H0n1g/H/w8gUUC+TTcFoOVCVvp1MkY7MYtVw03hTBR9+4wbhzUqJF2+alTxj+nGjWgSRP46ivIZvIvIT744AO01vj7+5sdRbiIHN9KtdaxWusVWuv/AvWA+4DNSqnSNk/nAu4k3OH/tv0fbe5tQ+3SMpGKM9m9G7Zvz/hpdWGPwgxqPIgknURsQqw54USexMXBnDkQGWl2EiEsUL48vP02HDpkDPv66qtGH4rUtm2DAQOMdUIIYSe5+ixOa30c6A/8jdGnQeRg4YGFnL95nrcefsvsKCKXZs+GRYsyXzei+QhW9lyJl4dX5hsIh3T9OvTvb8wJIYTTUAoaNoQvvoB//jGGfX3qKUgevx8wmjOld+aMdPoRQthEdn0gsnIAKI4xAZzIQY86PSjpXVImjnMiCQlw7ZrxQV9W8zq5KaPsferaKXwK+VDKp5QdE4q8KlUKdu0yJgMWwil5ehrt8Tp1Mmay/v57+O03aN067Xa3b0OtWsYIT717GzNlly9vTmYhhMvJbhSmUkqp15VSbyilUs80ooDbWuufbR/P+RVyL0TH6h1R0mvTaSxebPRR3L8/++0u3bpEtS+qMXHLRPsEE1ZRs6bxga58MCucnr8/DBwIv/5qjFWc2s8/G1VuBw/CW28ZBYk2bWDePBkSVjgEGcnQuWVagFBKlQX2AUMxRjnaq5RK/dHFfUqpNA87ZHUqWms6fd+JmXtmmh1F5FLdusZ7cq1a2W9XyqcUT1Z/kq92fsWN2PzNcCzsa/ly43W+IS+bcFVRUVCkyL/PtYbVq43hyMqUgb59ISxMhoQVdqG1Zs2aNXzwwQe0atUKf39/6tWrZ3YskQ9Z1UC8B+wGKgFVgC3AqFTrDwJ/pnoctl1E5xR2IoyfI38mLjHO7Cgil6pXhwkTwN2CEXeHPzSca7HXmL57uu2DCasJDDSaM126ZHYSIWxkyBBjHonZs6FVq7SjQcTEwMyZ8OijUKWK8b0QNhQdHU3r1q0ZM2YMa9eu5cqVKxw6dIg7d+6YHU3kUVYFiObAh1rrBK11IjA+eVmKAMAv1aNkxkMUbOM3jyewSCB96vUxO4rIha++guPHLd/+waAHaVm5JZ9u+ZQ7CfKP0Fk0bgxr1xoTBQrhsooWNfo/rFljTFb30UcZ5404eVJqIYTN+fn54efnR0JCwt1lPj4+7M+prbBwWFkVICoBR1M9Pwbck+p5tNb6WuqHrQI6o73n9rLy2EoGPziYwh6FzY4jLBQVZXxoN29e7vZ7p9k7XLx1kfDTeZpjUZjo2jXYs8fsFELYQYUKMGIEHD5sDP06cCCULAmFC0O3bmm31doYGnb5coiPNyevcDl169ZN8zw+Pp5du3aZlEbkV1YFiKuAb6rnJYDUhQTp+ZKNCZsnUMyzGK80esXsKCIXypaFv//O/XDqLSu35MTgEzxaWUbacjbduhkP+QBWFBhKGVVwX35pfGqyYQOUKJF2m82bjerYJ580Rm4aMsSYGEc6vYp8aNGiBe6p2gbfvn2bjRs3mphI5EdWBYgDQLtUz9sAh1I936yU2pL6YbOETujF+i8yqd0kfAv75ryxcAgpN5Dly4NvLl82pRTlixtjDEhnaucyZowx14fMTu04lFItlFK7lVL7lVI7lVJNcth+lFIqXilVyT4JXYinJzRqlHH57Nn/fn/xIkyeDA0aQHAwfPKJMReFELnUqFEjiqTu2A9s3brVpDQiv7KaB+IzYIlSqgiQgNGp+rlU6xcB8pldFlpWbml2BJFLL79s1NTPmJFx5mlLDfptEGEnw9j38r6780QIx9Yk21tTYW9KKV9gMdBBax2ulAoBlimlKmutb2WyfUegLHDWvkld3LBhxigDc+fC2VQ/2gMH4M034e23CW7QwKiZ6NwZfHzMyyqcRoMGDYiNjU2z7PTp09y5c4fChaW5t7PJ9C5Ha70aGAQMBIYAb2itf0tZDUzWWqd52CeuYzt7/SxDfx/KhZsXzI4icqlMGaMJU36m63jonoc4cOEAyyOXWy+YsLmYGHjjDVi50uwkAmgLRGqtwwG01mFAFJlMXKqUqo7x/jTYngELhPvvh3HjjA7Wq1cbs1ynLiQkJVFyxw5jcrrPPjMvp3AqZcqUwdvbO80yb29vIiIiTEok8iPLmai11jOBzMZ2kxnRsjBh8wSm7pzK4CbyfuZsPvgg/8foXqs77657l482fcST1Z+UyQOdhJcXLFliNF1r29bsNAVeFYxBO1I7lrz8LqVUceBb4HmtdWx2f2tKqX5AP4DAwEDCwsKsmTdL0dHRJCYm2u18NuPhAX374v7cc5Rav54yq1bhu3cvKrk/xNaqVbmT7hq9Ll4kNiDAhLDWERMT4/yvWxbMvraKFSsSHR1993lcXBwLFizgphUmNzT72mzJEa8tywJENtYjnagziLoRRejuUHoH96aSbyWz4wgLRUXB6dNGn8L88nDz4K2H36L/L/1Ze3wtrapk+NBUOKBChSAiAtJ9MCbMoYDEdMsSSFVbrozSwmxgjNb6ZE4H1FqHAqEADRs21CEhIVYLmx1fX1+io6Ox1/ns4vHH4eOP4dQp/v7gA6okJdHk2WfTbnPsmDG/RNOmxhCy3buDn585efMoLCzMtV63VMy+tieffJIDBw6QmGj8mcfGxnL+/HmaN29OZGQku3btws/Pjw4dOuT62GZfmy054rXluqG21vpRLfOPZ/DJlk+IT4znnebvmB1F5MKkScb7XFSUdY7Xp24fyhUrx+Rt0qrPmaQUHs6dMzeH4AxQId2yCsnLUxQD6gHvK6W2KqW2YvSDWKKUesEuKQu6ChU41bOn0WksvTlzjK9bthidy8qWNQoRv/wiQ8IKGjdunKEj9aJFi/Dx8eHBBx/kv//9L6GhoSalE7mRlxoIkc75mPNM2zmNnsE9ubfkvWbHEbkwciQ88ojxHmcNXh5eLHlmCdX9q+e8sXAoq1fDE0/AH39A8+Y5by9sYhnwmVKqjtY6QinVGKgBrFVKbQZe0Fr/BaSZAlApdQJ4Smt9wt6BRTpXrhjNnlImDIuNNYY6W7QISpeGHj2Mmol69fLX6Uw4jaNHj7J582a2bNlCWFgYcXFxadbHJxcs4+LiKFKkCE899ZQZMUUuSQHCCuKT4ulas6vUPjihYsWMm0ZralzeaA+ltZZ+EE7k4YeNQWXulc8ATKO1vqaUehqYoZTSGM2X2gM+QEWMOYmEI/viC3jvPfj+e2M42NQThV24YFT7TpoEderAxInQurV5WYVdtGzZkvPnz2coOGQmISGB1vI74RRkrEkrCCoexNyn5nKf/31mRxEWunwZ2rWz3SzERy4foe60umw8KZPkOAsfH5gwAcqVMztJwaa1Xqe1bqS1bqy1bqq1Dtdan9ZaB2mtd2axTyWpfXAgAQEwaBDs3GkM/frWWxn/sCIioGhRc/IJu5ozZ06aCeSyU7p0aYKCgmycSFiDFCDyaX7EfPad22d2DJFLx47BoUNGB1pbCCoexIWbFxgVNso2JxA2c/gwjBolk+4KYRW1at3teM2qVdCzp9HpqFq1jBOxREdDv37G7NgyPbzLCAkJYeTIkfjkMF+IUipPnaeFOaQAkQ+Xbl2i3/J+fLz5Y7OjiFxq3Bj+/htq17bN8X0K+fB2s7dZd2IdYSfCbHMSYRNr1xpD2584YXYSIVyIu7vRXGnuXDh/Hn78MWMfiB9+gOnToUULqFrVKMkfSz+qr3BGI0aM4JFHHsHLyyvLbYoVK0bHjh3tmErkhxQg8mHiloncir/Fu4+8a3YUkQsREcaHWx427gHUv0F/yhYty6iwUcjAZc7jxRfh+HGoXDnnbYUQeVCsGAQHZ1w+e/a/3x8/bkzQU7UqNGsGoaFGDYVwSkopFi5cSKlSpbLc5s6dOzzyyCN2TCXyQwoQeXQu5hz/t/3/eKb2M9QMqGl2HGGhqCij9mGUHVoWeRfyZkSzEWw4uYENJzfY/oTCKry8IOU9zgpzGwkhLDVxojH0q69v2uWbN0P//lCmDDzzDPz227+jPAmnUbx4cX7//fcsmzLVqVMnwxCvwnFJASKPxm4YS2xCLB+EWGEKY2E3pUvDN99A3772Od9LDV5idufZNL2nqX1OKKzm5ZeN+bCk8kgIO2nSBL76ypiQ5ccfoWPHtFXFsbFGM6cnnoCffzYvp8iz2rVr8/XXX2coRHh6etK1a1eTUom8kAJEHvl7+zOo8SCq+VczO4rIBXd3eP55+zVPKexRmN51e1PI3Ua9tYXNtGgBXbrIB51C2J2XF3TtahQSzp6FyZOhfv1/1/v5ZRx/Oz5eZoJ0Ej179qRHjx54p8zgiVGAaNeunYmpRG5JASKP3n/0fT5v97nZMUQuvPsuzJ9vzrm/P/A9IbNCSEiSu1Fn8dxz8PbbthupSwhhgdKl4bXXjPkkIiLgzTeNIWLTd8ZduRLKl4f27WHhQrh925y8wiJTpkyhatWqd4d3VUpRt25dk1OJ3JACRC79eelPfj3yq3SKdTJxccYIgqnnNLInT3dP1p9cz5x9c8wJIPJszRr49VezUwghqF0bxo+H99/PuG7OHGN0jBUr4Nlnjf4SL70EmzZJO0QH5OnpyW+//UbR5LlAWrZsiZub3JI6E3m1cuntNW/TY3EPou/IaBDOxNMTwsPhww/NOf9TNZ6icfnGjAobxZ2EO+aEELmmNYwYYdyzCCEcVFISXLuWdtn160aHt+bNjZGcRo82xu4WDiMoKIjFixejlKJLly5mxxG5JAWIXNgXvY9lkct4o+kb+Hn7mR1HWOiffwpz6xa4uUHhwuZkUErxcauPOXP9DFO2TzEnhMg1pWDRIqP2SghLlClj/N6sXx/Gvn17Ucp4XqaM2clcmJub0YTp77+N2ol77027PvXy5s3h4EFzcooMWrZsyaJfFhF6O5RzMdKHxZlIAcJCSTqJr/7+ivLFyjPsoWFmxxEW0href78WrVubnQQerfwobe9ty4cbP+RWwi2z4wgLVapkFDwTEyE2Vv5liuydP5+75cKKKleG996Dv/4yhn7t1w9KlEi7zdatRr8K4TD+0H8Qfj6cMevHmB1F5IK8G1poQcQCIm9E8lGrj/AplP107MJxKAUDBx7lXQeZ6++ztp+xsNtCfDzkd8iZxMZCw4YwY4bMLieEw1MKmjaFr782RmZauNAYtcnd3ehkHRCQdvv9++H1142vwq6ibkQxc+9MknQSM/fOlFoIJyIFCAt5unvSpGQTegb3NDuKyKXg4Gs4yuhwNQNq0vpeozpEOuI7Dy8v4/6jVq1rOW8shHAchQtD9+7wyy/GkLCffJJxmxkz4LPPoG5dqFePoEWLpMrITsZsGEOSTgIgUSdKLYQTkQKEhZ6u9TTj6ozDTcmPzFl89hmMGWP0r3M0s0/MptuibmbHELkwdiw88sgls2MIIfIqMBDuuy/tsvj4tON779tH1alTjSFhO3QwJq67IwNf2EJK7UNcYhwAcYlxUgvhRORuOAfnY84zZfsU4hPjzY4icikiAvbuNfrXORpPN08WH17M6mOrzY4iciEx0RjYZd06s5MIIazCzc0YAvbZZ9OOspGYaIzf/MwzRg/4/v2NfhVSc2w1qWsfUkgthPNwwFsrx/LWmrcYunIoJ6JPmB1F5NLMmbBggdkpMtc1qCtV/KowdOVQmVzOiSQmKj7+GL77zuwkwlEFBuZuuTCZuzu0a2e8WZw7B9OnEx0cnHaba9cgNBSaNYPISHNyuqDwM+F3ax9SxCXGseXMFpMSidzwMDuAI9t8ajOz981mRLMRVPOvxlnOmh1JWODAAfDzM2qgPT3NTpM5TzdPPmn9CV1/6MrUHVN57cHXzI4kLODpqdmwAcqWNTuJcFTnkltfhISEEB0dzd69e80NJCxXogS8+CJ7q1YlpEIFmDvXqJ1ImT+iYUOoUSPtPtevG7US6Ud7Ejna03+P2RFEPkgNRBYSkhIY+NtA7il+D/9r/j+z44hceOklaN3a8Wuan6rxFG3vbcsH6z/gdvxts+MIC5UrZwzycv06XL1qdhohhE1UqQKjRsHRo7Bxo/HG8sorGbebNs1o4tSjB/z+OyRIjbIoGKQGIhKPkBIAACAASURBVAvTdk5j3/l9LHp6EUU8i5gdR+TCd98Zg20oZXaS7Cml+OqJr7idcBvvQt5mxxG5cOsW1K4NrVoZTeWEEC5KKaPpUrNmGddpDbNnG52sFywwHmXLwvPPQ+/eUKeO/fMKYSd2rYFQSrVQSu1WSu1XSu1USjXJZJtApdTXSqnDSqntSqmNSqk6yev8lVI3lVJbUz0+tUXWuoF1GdhoIF3v72qLwwsbSEw0vt57LzzyiLlZLFXZrzI1A2oCcD32uslphKV8fOB//4OBA81OIoQwzZUrUKhQ2mVRUfDppxAcDA88AJMmyZCwwiXZrQChlPIFFgMDtdbBwHBgmVIq/Yxa9YGVWuv7tdaNgaXAxOR1pYAtWusmqR7DbZG3ecXmfNn+S5Sjf4wt7urRw3lv6IavGk6Tb5pk6FAmHFf//kaTaCFEAeXvbwz1t3cvDBuWsaf83r0wdKjRIa9jR7gkw0AL12HPJkxtgUitdTiA1jpMKRUFtAKWp2yktV6Rbr8o/s1ZCghWSoUnL9sKjNVaZyjeK6X6Af0AAgMDCQsLsyjk9ivb2XZlGy9VfonC7oXTrIuJibH4OPYimQxJSeDuXoX4+ATCwk7lO8+ff/7JoEGDSEhIwMvLi9DQUCpUqADAt99+y3fJw/DUqlWLyZMn4+7unqvjp89U6kYpDl86zIDvBtCzojmTFTra75Kj5YGMmZKSjNmpvb0Tef75U1nvaMdMQgg7q1sXJk6E8eNh1Sqj4/XSpcYU9mBUj+/da4zuIYSLsGcBogpwLN2yY8nLM6WUCgQ+AP6bvGgnUE5rnaiUKpK87jelVEOdblpfrXUoEArQsGFDHRISkmPAmLgY+kztg08hHx5t8SheHl5p1oeFhWHJcexJMv2rZcuU79L+SuUlT0hICDdu3ODNN98kNjaWqVOnsnnzZnbt2sWC5LFhfX19+fXXX6lYsWKus6bPFEIIu5N2MzdyLq+3f537A+7P9THzy9F+lxwtD2Seafp0YyTIkJAs/5XZPZMQwgQeHtC+vfGIjoZFi4zCxKZN0LOn8Y8itV9+gT17oFcvqFTJlMhC5JU9+0AoIDHdsoSsMiil/IHfgNFa6/UAWutYrXVi8vc3gTeBGkBVawT83x//4/S103zT8ZsMhQfhuKZMMf4HW9vw4cNp06YNANu2bWP06NH07t2bxOTOFqGhoXkqPGTli8e/oKhnUfr+3JfEpPR/KsJRzZ1rDMQihBB3+foaIzdt3GiM5DRoUMZt/r+9O4+PosoWOP47SQgk7GsChLDIIiMIAiIiCIoK464ICCLqyOAyODyfoz5k0HHCqOCoiII6gg6LOqLi4AoKCC6AiBAERGTHQNjphEDI0n3fH7dDNpYOSXdVJ+f7+eRDakn3Kaqrqk/VvedOmgSPPw7Nm0Pv3vDGG7a8m1JhIJQJRAqQWGReon9+ISLSEFgIPGeMOd2QTYLdhlIfcct+W8ZLK17i/gvv55LES0r7cipEjhyBp5+2Y/yUNRFhxowZxPnbtY4bN46N/kGERowYwYABA8r0/eKqxTGp3yQ2HtjIxoM6WFG4iPI/x92+HT780NFQlFJudM45tv5zQSkpsGBB/vSSJXD33bYk7G23wfz5+ZVBlHKhUCYQc7H9F/IqKnXFPj1YJCLfiUgr//ym2OQhyRjzdsEXEJHr/ckFYns3/x1YfLI+ECVhjGHUvFEk1Ejg6T5Pl+alVIhVrw5r1sCECcF5/bi4ON4sUqezdevWTJw4MSjvN6T9EDY9sOlEZSYVPh55xJaJz9QhPZRSZ1KvHrz9Nvz+9xBR4KtYZqad368fNGliTyzr1jkXp1KnELIEwhiTBgwA3hCRFcBE4GogFmgK5A3j+BwQBzxcoFTrkryXAeaIyErge6ABcFtpYxMRZt08i3f6v0P1ytVL+3IqRL77zpbhrlvXJhLBsn79+kLTe/bsYU/ecLNlTESoG1sXn/Hxztp3tClTGHnxRfj+e4jRIT2UUmdSpQrceit89pl9GvHPfxYfNyI1FZ59Frp1s4PPKOUiIR0HwhjzlTHmQmNMV2NMd2PMMmPMb8aYBGPMSv86txhj6hYp1drLv+xjY8zFxpgu/tcYbow5VJqYUo+kYoyhdd3W2nQpjKxYYcf1CUbTpYJ+/PFHHnvsMQCi/G1V0tPTGTx4MLlBHHF0/ub5DJkzhGeXPhu091Blq2FDyOsSs327o6EopcJJw4bw0EP2cfrq1bb0a4MG+cv797eDzxS0f39+lSelHBDSBMJtDmUeosvrXXjky0ecDkWVUJcutr/ZsGHBe4+MjAwGDx5MTk4OADNmzKBPnz6A7VQ9duzYoL13v5b9GHjeQMZ+NZaVu1cG7X1U2Zs8Gc49F37+2elIwlNpBxxVKmyJQMeO8Pzz9qnEJ5/AwIFw113F1334YZt43HcfLF9uH8crFUIVNoEwxnDfp/ex7+g+Brcf7HQ4KkDGQFqabTJ6113BbS5y//33s2nTJgCGDBnC4MGDmT59OrX9tbwnTJjAokWLgvLeIsKr17xKfLV4hnwwhIzsjKC8jyp7AwfCX/8KrVo5HUn4KaMBR5UKf5UqwTXXwLvv2gpNBR09Cu+/D4cP2xJwF18MbdrAuHGwY4cj4aqKJ5TjQLjKG6vfYPb62fzj8n/QqWEnp8NRAXrlFXjqKVi6FBKL1vQqQ2+//TYzZ84EICEhgcmTJwPQuHFjXn31VQYNGoTP52Po0KH89NNP1KtXr8xjqB1Tm5k3zeTy6Zfz4LwHef3618v8PVTZq1/fJhAA2dkQHe1sPGGmLAYcLeRsBxUtLY/Hg9frLbeD/JXnAQzdvm1Vt2yhXfXqxBw9mj9z0yYYOxbGjuVwx47sveoq9vfqhbdI0ye3b1tp6LaFVoVMINbsWcPIz0dyRYsrePSSR50OR5VAt25www2QkBDc9xkyZAhDhgw56bKBAwcycODA4Abg17tZb5698lm6NOoSkvdTZWfXLrjqKptMDNaHnIEqiwFHCzmbQUXLQq1atfB4POV2kL/yPICh67etd2/7CP7bb+1AdbNn25rmfrWTk6mdnMy5L70EAwbY9r7+Qexcv22loNsWWhWyCdOhzEO0rtuat25+i8iIyDP/gXJcXvPOTp1sG/OICvTJfaj7Q/Rq1guAYzlaiSNcxMVB27aF+0KqMyr1gKNKVQgREXDppTB1KuzZk1/6tWhJ2N27i4+ArVQZqEBfw/Jd1vwyVt+zmgZV9coeDoyxd3CTkpyOxFnPfvcsnV7rRHqWjlQaDqKibDNlf7977eMYmGAMOKpU+RYbay+Sn38Ov/1mS7+2a2eXnazSyGuv2VGw9+8PbZyqXKlQCcTE5RN56pun8BkfEVKhNj2s5eTYduQVvb5+18Zd2XxoM3fNvQuj30bDytSp9vru8zkdieuVesBRpSq0Ro3gL3+Bn36CVavg5psLL/f5bGfrUaPsujfcAHPmaElYVWIV5lv0Z5s+46EvHmJV6ioEcTocVQLR0TB9ui2TXZH1ataL8VeMZ86GOTzz7TNOh6NKICMDPB4dpfpMymjAUaWUCFxwAVStWmh2reRkWyIWIDcXPvrIjjPRqBH86U92NEy9QaUCUCESiMzcTG59/1Y6xHVg+o3TEdEEIhxs2GCbdKam2nOh7jb434v/lyHth/DYosd4b/17ToejAjRqFHz6abFruTqJ0g44qpQ6tYzWrW3p1+7dCy84dAimTLGVStq2teUOd+50JkgVFipEArH50GaqRlflo8EfUTVar+DhYudO2LjR3iRRlogw7fpp9G7Wm6M5R8/8B8oVRGw/xiNH7M2+H35wOiKlVEWUW60a3HMPfPcd/PqrLf3atGnhlTZuhDFj4OqrnQlShYUKkUDk+nKZe+tcEmoEufanKlN9+8Ivv0CTJk5H4i5VoqqwcNhC7ux4JwA+ow3rw8WxY7B2rb0+K6WUo1q1gr//HbZuhcWLbWnYatXyl99+e/G/2b0bvEULpamKqEIkEO0btKdr465Oh6ECkJNj79B+9JGdrlw5uO+3fPlyBg0axPz58/GG0UkxrwjAf3/5L13+1YX9R7WaRjiIi7MJxNChTkeilFJ+ERHQq5cdL2LvXnjrLXsH77bbiq97443QrBmMHm3bGasKq0IkEFERFXK8vLCUkWH7d+3bF5r3S0pKYvbs2QwYMIB69erx4IMPcuDAgdC8eRmoG1OXXw78Qt9ZffEc9zgdjgpAXlL83XdwzTWFxn9SSilnxcbCkCEwb17xEVs3bLDtL1NS4Jln4He/gwsvhJdfhjC6bqqyUSESCOV+xtinorVr2y9Ww4cH/z0PHz7MwoULAThy5Agej4cpU6awcuXK4L95GenZtCdzBs1h3b51XPP2NRzN1n4R4WL/fti2DdJ1WA+lVDjYsgXq1Ss8b+VKeOABaNjQPp348EMtCVtBaAKhHGcMjBxpx7vxeu0AXKHwzjvvEFlkhM7KlSvTJ2/krzDRr2U/3un/DstTlnPjuzeS7c12OiQVgBtvhDVroHFjewxk625TSrnZtdfaPhB5pV+jo/OX5ebC3Ll23IlGjWznbFWuaQKhHCdin5Q2bmybYobKyy+/zLFjx05MR0ZGMnToUCpVqhS6IMpI/9/1543r36BtvbZUigi/+CuqvI/a6NG2OZPeuFNKuVqlSnDddfD++7bG+iuv2NKvBR06BGlpzsSnQkYTCOWY3Nz8MtOjR8P48aEb62Hjxo1s37690LzKlSszYsSI0AQQBHd0vINJv5+EiLD50GYOZx52OiQVoLZtoU2bwjf0lFLK1erUgXvvhWXL8ku/JibaZcOGFV//ySftqLAZGaGNUwWFJhDKMX/6kx3LJu9GRSgHips6dSq5RQaYiI+Pp2PHjqELIkhyvDn0m9WPy6Zfxt6MvU6HowJwxx22H6KIbSGwe7fTESmlVAm0bg3jxtmOXd9+C507F15+4AD84x9w5522HN2wYbBwoZaEDWOaQCjH/OlP9oZFzZqhfV+v18u0adPIyck5MS8mJob7778/tIEESaXISrxyzStsOrSJi6ZexLp965wOSQXIGLjlFjsCu0+H91BKhZuICLjkkuJ3BP/zH1unHeyAODNnwhVX2JKwjz1mB31SYUUTCBVSGzfCa6/Z388/H+67L/QxLFq0qNjTB5/Px9ByVJz/ynOu5Os7vybbm033ad2Zv3m+0yGpAIjApEnw4ouh7Q+klFJBdfPNtvRr27aF56ekwNNP2/ldu8LkyXDwoDMxqhLRS5QKqYkT4fHHwePgkAVTpkzhSJHi+xdffDFxcXEORRQcnRt1ZsUfV3BOnXN4btlzGGOcDkkFoEsXuOwy+/tbb8Hzz9snE0opFbYaNYJHH4X16/NLv9atW3idH36wJRnvvdeZGFWJaAKhgs7ns0UZwH4ZWrkSatUKzXvv27ePbdu2nZhOT09n3rx5hdapXr06I0eODE1AIZZQI4Fv7vqGd295FxFhb8Ze0o5rdYxw8fnntmKiNhNWSpULIrZ/xKRJtrPXf/9rn04UrH54++3F/y4lRe+kuIwmECroBgyw5aO9XoiJgSZNQvfeM2bM4A9/+AOtWrXihRde4JVXXik29oPP5+Paa68NXVAhVi26GrVjamOMYcicIXT+V2dWp652OiwVgBkz4OOP7dgo6enw449OR6SUUmUkOhpuuAE++MCWhJ08Gfr2tZ3ACsrKgg4doF07W64xJcWZeFUhmkCooLv1VvjjH51p0x0fH09MTAybN29mzJgxjBkzhqNH80drjoyMZNCgQVSuXDn0wYWYiPBk7yc5nnucbtO68dQ3T5Hryz3zHyrHRERA9er297FjoWdP2KuFtVwtPt7eZF2yZDFr1iQjYqfj452OTCkXq1sX7r8f5s0rXs/6s89sM4aff4b/+z9bKvbKK2HWLChwPS+vUo+kMip5FHsy9jgdSiGaQKgyd/RoJLfeCu++a6cHDIC77gptmdY8CQkJRPgzl8zMTLxF2oJUqVKFnj17Vpj+AT0Se5B8bzI3tLmBMYvG0G1qN7Z7tjsdlgrAE0/YwiV5XXX273c2HnVyp0rwNPFT6iylpEDVqvnTxsCCBbapU3y8/YLx1VfltnRd0tdJrE1bS9KSJKdDKUQTCFXmqlTxkpLijgtm48aN8Z3mpHL8+HFGjhxJ8+bNmTBhAvv27QthdM6oF1uP2QNmM/uW2URIBHVj6p75j5Tj6tSB/v3t76tX25tw//2vszEppVTQPfAA7Nlj23RecUXhu5EZGfDvf8Pll9NtyBCYOtWxMIMh9Ugqbya/icHwZvKbrnoKoQmEKhMrV8JNN9nyzpGR8PXX8Oc/Ox2VTSCysrJOudzr9XL06FF27NjB6NGjGT16dAijc9aA8wbw/fDvqV65Olm5WTyY/CBvr327wjyNCWeJibZZYK9edvrQoXJ7800ppaBaNfvE4csvYccOW/r13HMLrVJl7978sSbKiaSvk/AZe3L3Gq+rnkJoAqFKJe+75rFjNonYtMlOu6WGfWxsLJUKVnc4hZiYGLp3786kSZNCEJV7iP9Ozt6jeznmPcZtc26j27RuzN88XxMJF6tb1xYxqV3bHoMDB8I11zgdlVJKhUCTJrYvxM8/w4oVtvRr3br4KlWCQYMKr2uMfYLx0Udhl1zkPX3I9mYDkO3NdtVTCJd8zVPhJjfXdo4eN85OX3opbNliCyW4Tc0zDHUdGxvL5ZdfzsKFC6lasJ1lBZJYM5FXOr3CG9e/wZ6MPfR7qx893+zJgWMHnA5NBWDYMBg82P5uDHzzjVY8VEqVcyJw4YXw0kuwezerJ060bT0LWrECXn7ZVntq1AhGjbLl7MLgBFnw6UMeNz2F0ARCBczns2PAgC0rGR1duHRz0cIJblGvXr1TLouNjeWmm25i7ty5RLt1A0IkQiK464K72PTAJqZcPYU6MXVO9I/4YdcPZOWeuimYco6ITSCGDbPTX35pE/oPPnA2rorqVONRlrNxKpVyl+hojvzud8XnT5+e//uBA/bRbZcu0L49TJgAu3aFLsYSWpay7MTThzzZ3myWpix1KKLCNIFQAXviCTv+S94o8zNm2KeIbhd/ivqJsbGxDB8+nJkzZxYbG6Iii46M5r4L7+OjwR8hIqRnpdNnRh+avdiM0QtG8+vBX50OUZ1G7962T+H119vp996DMWMgO/t0f6XKyp499uZmr1696dChI8bY6T3uaHWgVMXy5z/D6NGQkFB4/vr1dmTsxEQ79sRbb9m22C6y+p7VmCcM5gnDV72+OvH76nvcMY6TJhDqlH79Fa67Dtats9NDh8K0abYvUzhp3Ljxibb+eWJjY3nkkUeYOHFisWWqsOrR1flg4Ad0adSFZ5c+S5uX29DjjR4sT1nudGjqJKKj4Y478p8I/vijbf6b97Tw++/tjTillCr3zj0XnnoKtm+3pV+HDStcEtbngy++sF9wxo93LMxwpAmEOiE7G954w7afBqhVC9auhZ077XSbNnDbbXDaMdeysuwBeZrKR6EWFxdHTEzMiemYmBjGjx/PE088oclDntPsNxHhynOu5OPBH/Pbg78x/orxHMw8SFREFAArdq1g4vKJ/Lz/Z+147ULPPAM//GCbOvl8cMstMHx4/vLUVOdiU0qpkIiMhD59bJOmPXvsv5dfXrgk7NChxf/OxU2cnBbldADKWXPn2uPn+uvt8fXoo7aiS8+e0KABbNsWwABwXi9s2ADz59uM47LL4J57bBvDvn2hbVv74g6pV6/eiUpMMTExvP7669x2222OxeMaZ7HfGlZvyCOXPMLD3R8+Me/jjR8z7hvbm75R9UZc0eIKrmh+BYPbDz6RZChnVali/xWBjz/OL/malgZNm0KSO/rkKaVU8FWrlt9xbOdO23xpwwZo1arwejt2QPPm0K2bXXfQIFv6TgGaQFQ477xjE+q//MVOT5hgmzbkJRCrV0PjxvnrnzF5yMyE116zf1itmm1PGB1t/926FZ59Fi64wH4xLfAUIJTq169PZmYmMTExzJ49m2uvvdaROFyllPut4JObpMuTuLvT3SzYuoAvt37Jp79+yhdbvmDo+fZuzvPLniczJ5OujbvSqWEn6sbqwHVOEYGOHQvPe+452+k6HPozKaXKj/j4kw84GxcXwj5DiYm2j8TJzJplOzAtW2Z/Ro2yX5aGDYN+/QpXkamANIEoZ/bts30XevSw0xMmwLx5sGiRnf7iC0hOzk8g3n3XPmnIU7Sf0Wl5vfZLaHIyNGtWONuIiID69aFePbv8tddsLWYHnkTExcXRoEEDZs2aRa+8kbcqsiDst2a1mjG803CGdxqOz/hISU85kWTM3zKfL7Z8cWLd+rH16d+2P69c+woA3+78ljoxdWhasylVoytmGV2n1Kxpd69SSoXayZKH080Puf37bcnJ3Fw7nZ0N779vf+rXhyFDbIezjh0DuNta/oS0D4SI9BKRVSLyk4isFJFuJ1lHRCRJRDaKyM8iMktEqp5pWbDEx9vPhQhcdlnvE7+forBPmcvOtk8MvF47vXEj1Khx6pheftlWYckbL6V2bZsg5H3+824650lIKEX51Q0bYNUq2wbCf/CkcoRRv/2dPWTYdUTs8tWr7fohlnoklcc2PcYPG35wTfKQeiSVUcmjnBsMJsj7LUIiSKyZeGJ6/tD5HH70MAtuX8BzVz3HDW1uoEnNJgAYY7j6ras5b8p5VHu6GvUm1KPzvzrz9yV/Z1TyKFKPpPLS9y/xn3X/YdG2Razdu5aU9BQyczLL5v8iAE6fA8IlprJS2uuEKh8KfsYL/pSHz7gKExMn2k5iL71kS78WtH8/vPgidOoE559v79RWMCF7AiEitYA5wLXGmGUi0huYKyLNjTEFa2fdAVwNdDTGZIrIm8B4YOQZlp1SVhZ4PLZTcG6uHS05Ls6ON5KTYwczTEiwo7tmZcFPP9kbs/XrnzlDTkuzT7Y6d7br79tnm5T36WPHLNm5E2bPtoOuJSTYBGDaNDtwYmKirYjyzDPwwgv2PT/91N4RXLAAWrSwT9DuvtsWEGjaFJYsgSNHTh3TkCGF+wX98Y/2J0+ZDnUwfz5Ur14o807ia9ZmbiSJakzGPzSuiG0mM38+tGtXhgGcWdLXSaxNW0vSkiQmXzM5pO99Ko7H5MB+q1WlFn1a9KFPiz6F5hsM84bOY4dnBzvSdrDDs4PtaduZv3k+a9PW8vhXjzN19dRirzem5xjGXT6OA8cO0PHVjtSsUpMalWtQo3INqlaqyl0d7+K6Ntex/+h+nvrmKapEVTnxUzmqMle0uIJ2Ddpx8NhBPt/8OVERUURKJJERkURKJF0adaFxjcYczjzM3phkaBoJvkjwRYGJhIOt2Lu3JmnH09jm2Wb/uxBEBEFoUbsFVaOr4jnuYfeR3cWWN63VlCpRVUg7nsb+Y/sRxP9fbpcn1EigUmQl0o6n4TnuObEsz959jYFIqJwGldPz52fCb2nQuEZjIiSCw5mHycjOKPb/l5fAHco8xLGcY0SIO+pplNF1QpUDrr87rSqGevXsF7aRI+2XxZkz7U/BztXr1jnWRNtJEqqqKSIyCBhljOleYF4yMNYY83GBeZ8DHxpj/uWf7ggsNMbUPd2y0793F9OixR9o0mQ2OTk1Wbp0Li1bvkjjxh+SlVWX5cs/oFWrf9Ko0SdkZsazYsV/aNPmGeLj57FkyeJTvm6vXr05cqQNq1a9Rrt2o6lbdxlpaeeRnDyZ9u0fpk6dH/B4OrJmzUQ6dPgfatVK5tChzqxf/w86dHiQGjU24PF0ZPPmkbRtO46qVbeTnt6WXbtuonnz16lSZT/HjjXB4+lIgwZfERWVQW5uVb777tPTxhQSPp9tK5XXOxPIisllxS278UUZInKFi95vTHRmgaYvx49D69a2mUwIZEVnsaLbCnyRPiK8EVy0/CKis50dLM7xmMJsv4lX6LyyMwA50TlkV8rGG+WlakZVahypQU5UDltbbiU3MhdvlJfcqFx8ET4SdiYQvzeeY7HHWNV5Fb4IHyYi/1zXekNrGu5pSHqNdFZ3Ll5Tu+36tjTY14DDtQ/zU8efigf51qew6WrO69+O9e3XF1vcYVUHaqXVYm/cXn753S/Flnf+oTPVMqqxq9EuNrfZXGx512VdiTkew87EnWw7Z1vx9392DxyNg8vGQq9xxRb3WNKDSF8km1tuZleTIlVEDPRabJ/GbWyzkT2N9hCVE0XuP3J/NMZ0KfZiIVQW14nTvX716tVN586dgxN8EcnJyeTm5tKl6J3LcsLj8VCrVq2gvf6Zrr3BFOxtc5Kbtq2s93Goti3CGC7weOi7Zw89DxzAEx3NkK5dMQVu8sTm5nLf1q182aABa2vWLLTsbIRyvy1ZsiSga0Eo+0C0ALYUmbfFP/90620B6ohIzdMtM8akFXwRERkBjACIjGxApUoL8Hg8+HxHSUx8lKioDf7pYzRr9iCRkRv908dp3vwBIiLs9Ol4PB683p9o2XIYxmzH40nH51vJuedeCxzA4zmOMd/Qrl13jDmOx+MjImIh7dsvxOezT0VgMS1bLiYnJ296GQ0bLuP4cfu9DTzExq4l48RNxDPHFDK1ahX6UpnSKRWf2C9pPjH82jmDhFUN89evUgXS04u+StCkdEjBhy0348PHrw1/JWFNSTp5lNOYwmi/GQzb6m878X8UjU22fPjw+I+FuAMnH+LXgwc80G53uxOvZSLMieTN4/PgS/dx7sFzMWIwYkDAiCHiWASeHA/eo15YvRgiciHCC+K1v++2X0LNb4ZmR5vZ3yU/Qck9kIsn24NkC00zmmLwL/NfQ47vPU5uTi6RuZE0OdIkP2j/8mMHjpGVm0UlXyWaeJoUem2AlKwa9pdfbgRPs0LLEpr8jfTD6YgRYrbEkLC/+Ocr7zxRdXNVEvYkID7hN3476f9jiJX6OnG6a0GlSpVCCo/i0QAADvhJREFUdo7Mzc3FGBPac3IIeb1ex7Yt2O/r5LYFW7hs29nEGMptWyjCwoYNiW3QgCbZ2RxOK3TaodehQ1yXmsp1qansio7mk9q1+aR2bX47bR38U3PjfgvlE4jHgNbGmDsLzHsXWG6MeaHAvF+BEcaYxf7pGOAYUAf4/lTLjDGHT/XeXbp0MStXrjzLuE+9zKmS966IKSvLVuhJTISICFI5QgsmcVxyT6wSY6LYyijiqWbvfP/2G7z66hkGkigbqUdSaTGpBcdzj+fHExXD1lFbia/mTCNaV8Sk+63EXHG8FRGMmETEDU8gSn2dCNa1oKR69+6Nx+MhOTk5JO8XaosXL6Z3795Be30nj7tgb5uT3LRtZV2FyU3bRu/ets15Ud2755eELcEThVBuW6DXglA2fE0BEovMS/TPP916iUAG9tb76ZapUKpc2Y4XcPAgYNvQ+yh8VvdiSMJ/AB08aNvRh+BLKNh+Bj7jKxyP8ZK0xLmC966ISfebcreyuE4opcLAnj02GSz6E7ISrsE0fjzcd1/xcSOWLoV777XZ08CBtuNrXtWbMBPKBGIucL6ItAcQka7AucAiEflORPJG8JgJDBeRvIbhDwBzjH1UcrplQRF38tYRp5wfCq6JqW9f26PbGJaRQrZ4Cy3OFi9LSbFnhIwMu36ILEtZRrY3u3A83myWpiwNWQxFuSYm3W8l4prjLYD3djKmMlIW1wlVDpTjz7iqCC66CKZMsVWcPvjAjh8RVaDXQFYWvPceXHstzJnjXJylELI+EMaYNBEZALwhIgbIxVbRiAWaAjX9q84AWgIrRCQX+Jn8yhqnWxYUBTNhtzwec01MbdvaEmbJyaxuOoK8BtyL27Sh98aNdh1j7GiOF1xg1w+R1ffkd4x1y35zTUy630rENcdbAW6MqSyU0XVClQPl4i60UpUrw80325/9++1ovjNmwI8/2uU1atjkoiCv15b0bNiw+Ou5SEgHkjPGfAVceJJFCQXW8QJj/D9F//6Uy5QDIiNte/qCIxrX9RdB8fls85eMjPwRjR0YRE6dhO435WKlvU4opZQr1a8Pf/6z/Vm/3iYSkZHFS8AuWABXXw1XXWUHqrvhBmfiPQMdiVqVTkyMHbhiwwY7XsC6dXDOObbjbbt2tvlL27b6JdRtdL8ppZRSzjjvPNtP4mRmzLA38+bNsz81atCmRw97Pe7RwzWjXmsCoUovMtJ+6WzXzrbr++67kFXtUaWg+00ppZRyD2PyavrnS0+n4WefwWefQfPmtorT7bfbm34Ocsfwo6r8qFzZjjGgX0LDi+43pZRSylkitjLTtm2QlAStWhVevm0bPPkktGxpn0b8dJKBTkNEEwillFJKKaXcolkz+OtfYeNGWLqUXddfX3zciGXLbL8Kh2gCoZRSSimllNuIwMUXs+nBB21J2Pfeg+uus02Q+/YtXqnp55/hoYdgzZqgh6YJhFJKKaWUUm5WpQrccgt89BHs3g0vvFB8nTffhOefh44d7c/zzwetJrImEEoppZRSSoWLBg2gTZvC83JzYdas/Ok1a+zTiIQEuOYaePddyMwssxA0gVBKKaWUUiqcidgnEIMH26cVebxeW8Hp1lttk6cRI+Dbb23Fp1LQBEIppZRSSqlwFhkJ/frB22/bZktTp8KllxZeJy0NXn8deva04z+VgiYQSimllFJKlRc1a8Ldd8OSJbB1qy39WnDciPPPh/btC//NkSM2wQiQJhBKKaWUUkqVR82bw+OPw6ZNtunSiBFw//3F15s2DeLjA35ZHYlaKaWUUkqp8kwELrnE/pzMjBlw/HjgL2dK2YkiHIjIfmBHGbxUPeBAGbxOWdKYzsxt8YDGFAi3xQPlO6amxhjnRiUKgTK8FgTKjZ+XsqLbFp5028JTKLctoGtBhUggyoqIrDTGdHE6joI0pjNzWzygMQXCbfGAxqRKpjzvG9228KTbFp7cuG3aB0IppZRSSikVME0glFJKKaWUUgHTBKJk/uV0ACehMZ2Z2+IBjSkQbosHNCZVMuV53+i2hSfdtvDkum3TPhBKKaWUUkqpgOkTCKWUUkoppVTANIFQSimllFJKBUwTCKWUUkoppVTANIE4SyJynogcEpG/uSCWJBFZIyIrRGSViNzngpju8ce0UkR+EpGTjJse0ngqichfRCRHRG51MI5e/n30k///pptTsRSIyRX/NwXicdVnxx+T644xcNd5qCIK5HgWK0lENorIzyIyS0SqOhFvSQS4bXEi8pqIbPAfG9+ISHsn4i2Jkp6HReQJ//mxWWgiPHuBbpuI1BOR90VknYj8KCJPhzrWkirBZ/I9EVnt/0x+KyI9nIi3JAK5DrvtXBLl1BuHMxGpBUwG3nE6Fr/DQBdjTI6I1Ae2icg8Y8w2J4IRkUigFXCJMSZDRBoDm0VkrjFmlxMxAX8EDLDcoffP+9zMAa41xiwTkd7AXBFpbow55lRcuOD/Jo9LPzvgsmMMXHkeqlBKcDzfAVwNdDTGZIrIm8B4YGTIgw5QCbatEzDfGHOP/+8eAp4Drgp1zIEq6XlYRK4DGgJOnn8CEui2iUhl4BPgYWPMN/55dZ2IOVAl2G9PYUdsHmiMMSJyM/Au0DjkQZdMINdhV51L9AlECYlIBDAdeAzY73A4ABhjnjfG5PgnmwEZwCEH4/EaY/5ijMnwzzoIZAORDsY0xRjzHOB1KgagL7DRGLPMH9NiIBXo42BMbvm/yYvFdZ8dcN8x5sbzUAUU6PE8CHjNGJPpn34RGByqIM9SQNtmjPncGDOnwKxU3H9jMuDzsIi0Af4HGBXKAEsh0G0bhv2i+mf/nfyZQKVQBnoWAt22XUAtoLJ/uj5hkPwFeB121bnE7Qe6I0QkGvj6JIsuBf4GfGmMWSoiIbvLcrqYjDHZItIK+Ayoi82805yOqcD0ROBdY8xOl8TjlBbAliLztvjnq5MLyWcnEE4cY6cxDgfOQ6qQQI/nouttAeqISE2HP0OnU+JzlYjEAX8H7g5iXGUhoG0TkRrANOA2Y0yWiIQovFIJdL9dCnQErgN2YvfbWzh8M+sMAt22J7BjJuwTEQ/2Bss1wQ8vJFx1LtEE4iT8XzZP1rauP5BojHnMLTEVWL4JaCUiFwCfisiVxpj1TsYEICLjsI8O+wczlkDjcZhQ/O5CLvok8KRC+dkJhBPH2Mk4eR5ShQR6PBddL9f/r5uP+xKdq/zNXz4D/maMWRLk2ErrjNsmNluYDiQZY3aEMLbSCnS/NQD+bYzZDiAi44E0EalW4Omv2wS6baOx140mxpg0Efkj8ImIXGSMcfwpeym56lzi5hOYG/0eaCsiy0VkOTAcGC4i7zkc1wnGmNXAMuAyp2MRkX8C5wH9XfIEwGkpQGKReYn++aoAN392XHCMuf48VEEEejwXXS8R2wTOE7zQSi3gc5WINAQWAs8ZY2aFILbSCmTbqmPv0D9Z4DhrCHwoIneGJMqzE+h+2wekF5j2Ffhxq0C3bQjwYt4deWPM69iEokPQIww+V51LNIEoAWPMcGNMZ2NMN2NMN2AqMNUYM8CpmESkvYgM9N8xwd/p9CJgpYMxRYjIq0ATYIDbvgA6aC5wvvirlIhIV+Bc4EtHo3IRN3523HaMufE8VEGd6nheJCLf+Zu8AczEJnjR/ukHgDnGGBPyiAMX0LaJSFNs8pBkjHnbsWhL5ozbZoxJN8Y0zzvG/MdZKnCTMebfzoV+RoF+JucAI0Skun/6f4BFDhfzOJNAt+1X4GZ/PzFE5FKgBrapVlgRkbpuPpdoE6bwtwO4F3hURHKAaGCsMcbJijpXA/dgv2B9W6Dt6F+NMQsci8ph/sepA4A3RMRgHz9ebYxx853IUHPjZ8eNx5hy2KmOZyAWaArU9K86A2gJrBCRXOBnXFyBCUq0bc8BccDDIvKwf16WMaZXqGMOVAm2LewEum3GmA9FpCXwg4hkYs9xdzoTdWBKsN/uB14AVolIln9ef2PMgVDHXAZcfS4Rd98EUUoppZRSSrmJNmFSSimllFJKBUwTCKWUUkoppVTANIFQSimllFJKBUwTCKWUUkoppVTANIFQSimllFJKBUwTCKWUUkoppVTANIFQSimllFJKBUwTCKWUUkoppVTANIFQSimllFJKBUwTCKWUUkoppVTANIFQKghE5AsRMSJyc5H5IiL/9i97xqn4lFJKBZ9eC1R5JcYYp2NQqtwRkQ7AKmAj0N4Y4/XPfw74X+B1Y8wIB0NUSikVZHotUOWVPoFQKgiMMWuAmUBb4HYAEXkMe8GYDdzrXHRKKaVCQa8FqrzSJxBKBYmIJACbgL3AP4GXgPnA9caYbCdjU0opFRp6LVDlkT6BUCpIjDEpwESgKfaCsRS4uegFQ0QuFZGPRGSXvz3snaGPVimlVDCU4FowWkR+EJF0EdkvIh+LSDsHQlbqjDSBUCq49hf4/W5jzLGTrFMNWAeMAjJDEpVSSqlQCuRa0BuYAnQHLgdygQUiUif44SlVMtqESakgEZHBwFvYx9bxwKvGmPvO8DcZwEhjzL+DH6FSSqlgO5trgf/vqgFpwI3GmI+DG6VSJaNPIJQKAhG5GpgOrAfOB34BhovIuY4GppRSKmRKeS2ojv2edjh4ESp1djSBUKqMiUgP4H0gBbjKGLMfGAtEAVrvWymlKoAyuBa8CCQDy4IWpFJnSRMIpcqQv+b3J9jHzlcaY1IBjDHvAyuBG0Skp4MhKqWUCrLSXgtE5HmgB9A/b+wIpdxEEwilyoiItMSW5jNAX2PMliKrjPb/+2xIA1NKKRUypb0WiMgLwGDgcmPM1qAFqlQpaCdqpVxEO1ErpVTFJSIvArcCvY0xG5yOR6lTiXI6AKUqOn+ljZb+yQggUUQ6AoeMMTudi0wppVSoiMhk7GjVNwKHRSTevyjDGJPhXGRKFadPIJRymIj0Br46yaLpxpg7QxuNUkopJ4jIqb6QPWmM+VsoY1HqTDSBUEoppZRSSgVMO1ErpZRSSimlAqYJhFJKKaWUUipgmkAopZRSSimlAqYJhFJKKaWUUipgmkAopZRSSimlAqYJhFJKKaWUUipgmkAopZRSSimlAqYJhFJKKaWUUipgmkAopZRSSimlAvb/19NZhdnTUToAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<Figure size 792x288 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def gaussian_rbf(x, landmark, gamma):\n",
|
||
" return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
|
||
"\n",
|
||
"gamma = 0.3\n",
|
||
"\n",
|
||
"x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
|
||
"x2s = gaussian_rbf(x1s, -2, gamma)\n",
|
||
"x3s = gaussian_rbf(x1s, 1, gamma)\n",
|
||
"\n",
|
||
"XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
|
||
"yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
|
||
"\n",
|
||
"plt.figure(figsize=(11, 4))\n",
|
||
"\n",
|
||
"plt.subplot(121)\n",
|
||
"plt.grid(True, which='both')\n",
|
||
"plt.axhline(y=0, color='k')\n",
|
||
"plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
|
||
"plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
|
||
"plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
|
||
"plt.plot(x1s, x2s, \"g--\")\n",
|
||
"plt.plot(x1s, x3s, \"b:\")\n",
|
||
"plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
|
||
"plt.xlabel(r\"$x_1$\", fontsize=20)\n",
|
||
"plt.ylabel(r\"유사도\", fontsize=14)\n",
|
||
"plt.annotate(r'$\\mathbf{x}$',\n",
|
||
" xy=(X1D[3, 0], 0),\n",
|
||
" xytext=(-0.5, 0.20),\n",
|
||
" ha=\"center\",\n",
|
||
" arrowprops=dict(facecolor='black', shrink=0.1),\n",
|
||
" fontsize=18,\n",
|
||
" )\n",
|
||
"plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
|
||
"plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
|
||
"plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
|
||
"\n",
|
||
"plt.subplot(122)\n",
|
||
"plt.grid(True, which='both')\n",
|
||
"plt.axhline(y=0, color='k')\n",
|
||
"plt.axvline(x=0, color='k')\n",
|
||
"plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
|
||
"plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
|
||
"plt.xlabel(r\"$x_2$\", fontsize=20)\n",
|
||
"plt.ylabel(r\"$x_3$ \", fontsize=20, rotation=0)\n",
|
||
"plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
|
||
" xy=(XK[3, 0], XK[3, 1]),\n",
|
||
" xytext=(0.65, 0.50),\n",
|
||
" ha=\"center\",\n",
|
||
" arrowprops=dict(facecolor='black', shrink=0.1),\n",
|
||
" fontsize=18,\n",
|
||
" )\n",
|
||
"plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
|
||
"plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
|
||
" \n",
|
||
"plt.subplots_adjust(right=1)\n",
|
||
"\n",
|
||
"save_fig(\"kernel_method_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Phi(-1.0, -2) = [0.74081822]\n",
|
||
"Phi(-1.0, 1) = [0.30119421]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"x1_example = X1D[3, 0]\n",
|
||
"for landmark in (-2, 1):\n",
|
||
" k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n",
|
||
" print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Pipeline(memory=None,\n",
|
||
" steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n",
|
||
" decision_function_shape='ovr', degree=3, gamma=5, kernel='rbf',\n",
|
||
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
|
||
" tol=0.001, verbose=False))])"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"rbf_kernel_svm_clf = Pipeline([\n",
|
||
" (\"scaler\", StandardScaler()),\n",
|
||
" (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
|
||
" ])\n",
|
||
"rbf_kernel_svm_clf.fit(X, y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 792x504 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import SVC\n",
|
||
"\n",
|
||
"gamma1, gamma2 = 0.1, 5\n",
|
||
"C1, C2 = 0.001, 1000\n",
|
||
"hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
|
||
"\n",
|
||
"svm_clfs = []\n",
|
||
"for gamma, C in hyperparams:\n",
|
||
" rbf_kernel_svm_clf = Pipeline([\n",
|
||
" (\"scaler\", StandardScaler()),\n",
|
||
" (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
|
||
" ])\n",
|
||
" rbf_kernel_svm_clf.fit(X, y)\n",
|
||
" svm_clfs.append(rbf_kernel_svm_clf)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(11, 7))\n",
|
||
"\n",
|
||
"for i, svm_clf in enumerate(svm_clfs):\n",
|
||
" plt.subplot(221 + i)\n",
|
||
" plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
|
||
" plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
|
||
" gamma, C = hyperparams[i]\n",
|
||
" plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
|
||
"\n",
|
||
"save_fig(\"moons_rbf_svc_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 회귀"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"np.random.seed(42)\n",
|
||
"m = 50\n",
|
||
"X = 2 * np.random.rand(m, 1)\n",
|
||
"y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\n",
|
||
" intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
|
||
" random_state=42, tol=0.0001, verbose=0)"
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import LinearSVR\n",
|
||
"\n",
|
||
"svm_reg = LinearSVR(epsilon=1.5, random_state=42)\n",
|
||
"svm_reg.fit(X, y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n",
|
||
"svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
|
||
"svm_reg1.fit(X, y)\n",
|
||
"svm_reg2.fit(X, y)\n",
|
||
"\n",
|
||
"def find_support_vectors(svm_reg, X, y):\n",
|
||
" y_pred = svm_reg.predict(X)\n",
|
||
" off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
|
||
" return np.argwhere(off_margin)\n",
|
||
"\n",
|
||
"svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
|
||
"svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
|
||
"\n",
|
||
"eps_x1 = 1\n",
|
||
"eps_y_pred = svm_reg1.predict([[eps_x1]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 648x288 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_svm_regression(svm_reg, X, y, axes):\n",
|
||
" x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
|
||
" y_pred = svm_reg.predict(x1s)\n",
|
||
" plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
|
||
" plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
|
||
" plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
|
||
" plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
|
||
" plt.plot(X, y, \"bo\")\n",
|
||
" plt.xlabel(r\"$x_1$\", fontsize=18)\n",
|
||
" plt.legend(loc=\"upper left\", fontsize=18)\n",
|
||
" plt.axis(axes)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(9, 4))\n",
|
||
"plt.subplot(121)\n",
|
||
"plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
|
||
"plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
|
||
"plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
|
||
"#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
|
||
"plt.annotate(\n",
|
||
" '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
|
||
" xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
|
||
" textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
|
||
" )\n",
|
||
"plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
|
||
"plt.subplot(122)\n",
|
||
"plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
|
||
"plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
|
||
"save_fig(\"svm_regression_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"np.random.seed(42)\n",
|
||
"m = 100\n",
|
||
"X = 2 * np.random.rand(m, 1) - 1\n",
|
||
"y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
|
||
" kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import SVR\n",
|
||
"\n",
|
||
"svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
|
||
"svm_poly_reg.fit(X, y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
|
||
" kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
|
||
]
|
||
},
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import SVR\n",
|
||
"\n",
|
||
"svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
|
||
"svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1)\n",
|
||
"svm_poly_reg1.fit(X, y)\n",
|
||
"svm_poly_reg2.fit(X, y)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 648x288 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(9, 4))\n",
|
||
"plt.subplot(121)\n",
|
||
"plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
|
||
"plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
|
||
"plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
|
||
"plt.subplot(122)\n",
|
||
"plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
|
||
"plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
|
||
"save_fig(\"svm_with_polynomial_kernel_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# SVM 이론"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"iris = datasets.load_iris()\n",
|
||
"X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
|
||
"y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 792x432 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||
"\n",
|
||
"def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n",
|
||
" x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n",
|
||
" X_crop = X[x1_in_bounds]\n",
|
||
" y_crop = y[x1_in_bounds]\n",
|
||
" x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n",
|
||
" x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n",
|
||
" x1, x2 = np.meshgrid(x1s, x2s)\n",
|
||
" xs = np.c_[x1.ravel(), x2.ravel()]\n",
|
||
" df = (xs.dot(w) + b).reshape(x1.shape)\n",
|
||
" m = 1 / np.linalg.norm(w)\n",
|
||
" boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n",
|
||
" margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n",
|
||
" margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n",
|
||
" ax.plot_surface(x1s, x2, np.zeros_like(x1),\n",
|
||
" color=\"b\", alpha=0.2, cstride=100, rstride=100)\n",
|
||
" ax.plot(x1s, boundary_x2s, 0, \"k-\", linewidth=2, label=r\"$h=0$\")\n",
|
||
" ax.plot(x1s, margin_x2s_1, 0, \"k--\", linewidth=2, label=r\"$h=\\pm 1$\")\n",
|
||
" ax.plot(x1s, margin_x2s_2, 0, \"k--\", linewidth=2)\n",
|
||
" ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \"g^\")\n",
|
||
" ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\"k\")\n",
|
||
" ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \"bs\")\n",
|
||
" ax.axis(x1_lim + x2_lim)\n",
|
||
" ax.text(4.5, 2.5, 3.8, \"결정 함수 $h$\", fontsize=15)\n",
|
||
" ax.set_xlabel(r\"꽃잎 길이\", fontsize=15, labelpad=15)\n",
|
||
" ax.set_ylabel(r\"꽃잎 너비\", fontsize=15, rotation=25, labelpad=15)\n",
|
||
" ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18, labelpad=10)\n",
|
||
" ax.legend(loc=\"upper left\", fontsize=16)\n",
|
||
"\n",
|
||
"fig = plt.figure(figsize=(11, 6))\n",
|
||
"ax1 = fig.add_subplot(111, projection='3d')\n",
|
||
"plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n",
|
||
"\n",
|
||
"save_fig(\"iris_3D_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 작은 가중치 벡터가 라지 마진을 만듭니다"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x230.4 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\n",
|
||
" x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\n",
|
||
" y = w * x1 + b\n",
|
||
" m = 1 / w\n",
|
||
"\n",
|
||
" plt.plot(x1, y)\n",
|
||
" plt.plot(x1_lim, [1, 1], \"k:\")\n",
|
||
" plt.plot(x1_lim, [-1, -1], \"k:\")\n",
|
||
" plt.axhline(y=0, color='k')\n",
|
||
" plt.axvline(x=0, color='k')\n",
|
||
" plt.plot([m, m], [0, 1], \"k--\")\n",
|
||
" plt.plot([-m, -m], [0, -1], \"k--\")\n",
|
||
" plt.plot([-m, m], [0, 0], \"k-o\", linewidth=3)\n",
|
||
" plt.axis(x1_lim + [-2, 2])\n",
|
||
" plt.xlabel(r\"$x_1$\", fontsize=16)\n",
|
||
" if ylabel:\n",
|
||
" plt.ylabel(r\"$w_1 x_1$ \", rotation=0, fontsize=16)\n",
|
||
" plt.title(r\"$w_1 = {}$\".format(w), fontsize=16)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12, 3.2))\n",
|
||
"plt.subplot(121)\n",
|
||
"plot_2D_decision_function(1, 0)\n",
|
||
"plt.subplot(122)\n",
|
||
"plot_2D_decision_function(0.5, 0, ylabel=False)\n",
|
||
"save_fig(\"small_w_large_margin_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([1.])"
|
||
]
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import SVC\n",
|
||
"from sklearn import datasets\n",
|
||
"\n",
|
||
"iris = datasets.load_iris()\n",
|
||
"X = iris[\"data\"][:, (2, 3)] # 꽃잎 길이, 꽃잎 너비\n",
|
||
"y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica\n",
|
||
"\n",
|
||
"svm_clf = SVC(kernel=\"linear\", C=1)\n",
|
||
"svm_clf.fit(X, y)\n",
|
||
"svm_clf.predict([[5.3, 1.3]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 힌지 손실"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 360x201.6 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"t = np.linspace(-2, 4, 200)\n",
|
||
"h = np.where(1 - t < 0, 0, 1 - t) # max(0, 1-t)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(5,2.8))\n",
|
||
"plt.plot(t, h, \"b-\", linewidth=2, label=\"$max(0, 1 - t)$\")\n",
|
||
"plt.grid(True, which='both')\n",
|
||
"plt.axhline(y=0, color='k')\n",
|
||
"plt.axvline(x=0, color='k')\n",
|
||
"plt.yticks(np.arange(-1, 2.5, 1))\n",
|
||
"plt.xlabel(\"$t$\", fontsize=16)\n",
|
||
"plt.axis([-2, 4, -1, 2.5])\n",
|
||
"plt.legend(loc=\"upper right\", fontsize=16)\n",
|
||
"save_fig(\"hinge_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 추가 내용"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 훈련 시간"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {
|
||
"scrolled": false
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[<matplotlib.lines.Line2D at 0x7f4d717f2cc0>]"
|
||
]
|
||
},
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\n",
|
||
"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
|
||
"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[LibSVM]0 0.1 0.18813824653625488\n",
|
||
"[LibSVM]1 0.01 0.1933610439300537\n",
|
||
"[LibSVM]2 0.001 0.22491121292114258\n",
|
||
"[LibSVM]3 0.0001 0.40105342864990234\n",
|
||
"[LibSVM]4 1e-05 0.6449289321899414\n",
|
||
"[LibSVM]5 1.0000000000000002e-06 0.6017529964447021\n",
|
||
"[LibSVM]6 1.0000000000000002e-07 4.703836917877197\n",
|
||
"[LibSVM]7 1.0000000000000002e-08 0.6525232791900635\n",
|
||
"[LibSVM]8 1.0000000000000003e-09 0.642498254776001\n",
|
||
"[LibSVM]9 1.0000000000000003e-10 0.6429502964019775\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import time\n",
|
||
"\n",
|
||
"tol = 0.1\n",
|
||
"tols = []\n",
|
||
"times = []\n",
|
||
"for i in range(10):\n",
|
||
" svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n",
|
||
" t1 = time.time()\n",
|
||
" svm_clf.fit(X, y)\n",
|
||
" t2 = time.time()\n",
|
||
" times.append(t2-t1)\n",
|
||
" tols.append(tol)\n",
|
||
" print(i, tol, t2-t1)\n",
|
||
" tol /= 10\n",
|
||
"plt.rcParams['font.family'] = 'stixgeneral'\n",
|
||
"plt.semilogx(tols, times)\n",
|
||
"plt.rcParams['font.family'] = 'NanumBarunGothic'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 배치 경사 하강법을 사용한 선형 SVM 분류기 구현"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 훈련 세트\n",
|
||
"X = iris[\"data\"][:, (2, 3)] # 꽃잎 길이, 꽃잎 너비\n",
|
||
"y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1, 1) # Iris-Virginica"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[1.],\n",
|
||
" [0.]])"
|
||
]
|
||
},
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.base import BaseEstimator\n",
|
||
"\n",
|
||
"class MyLinearSVC(BaseEstimator):\n",
|
||
" def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n",
|
||
" self.C = C\n",
|
||
" self.eta0 = eta0\n",
|
||
" self.n_epochs = n_epochs\n",
|
||
" self.random_state = random_state\n",
|
||
" self.eta_d = eta_d\n",
|
||
"\n",
|
||
" def eta(self, epoch):\n",
|
||
" return self.eta0 / (epoch + self.eta_d)\n",
|
||
" \n",
|
||
" def fit(self, X, y):\n",
|
||
" # Random initialization\n",
|
||
" if self.random_state:\n",
|
||
" np.random.seed(self.random_state)\n",
|
||
" w = np.random.randn(X.shape[1], 1) # n feature weights\n",
|
||
" b = 0\n",
|
||
"\n",
|
||
" m = len(X)\n",
|
||
" t = y * 2 - 1 # -1 if t==0, +1 if t==1\n",
|
||
" X_t = X * t\n",
|
||
" self.Js=[]\n",
|
||
"\n",
|
||
" # Training\n",
|
||
" for epoch in range(self.n_epochs):\n",
|
||
" support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
|
||
" X_t_sv = X_t[support_vectors_idx]\n",
|
||
" t_sv = t[support_vectors_idx]\n",
|
||
"\n",
|
||
" J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\n",
|
||
" self.Js.append(J)\n",
|
||
"\n",
|
||
" w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\n",
|
||
" b_derivative = -C * np.sum(t_sv)\n",
|
||
" \n",
|
||
" w = w - self.eta(epoch) * w_gradient_vector\n",
|
||
" b = b - self.eta(epoch) * b_derivative\n",
|
||
" \n",
|
||
"\n",
|
||
" self.intercept_ = np.array([b])\n",
|
||
" self.coef_ = np.array([w])\n",
|
||
" support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
|
||
" self.support_vectors_ = X[support_vectors_idx]\n",
|
||
" return self\n",
|
||
"\n",
|
||
" def decision_function(self, X):\n",
|
||
" return X.dot(self.coef_[0]) + self.intercept_[0]\n",
|
||
"\n",
|
||
" def predict(self, X):\n",
|
||
" return (self.decision_function(X) >= 0).astype(np.float64)\n",
|
||
"\n",
|
||
"C=2\n",
|
||
"svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\n",
|
||
"svm_clf.fit(X, y)\n",
|
||
"svm_clf.predict(np.array([[5, 2], [4, 1]]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[0, 60000, 0, 100]"
|
||
]
|
||
},
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n",
|
||
"plt.axis([0, svm_clf.n_epochs, 0, 100])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[-15.56780998] [[[2.28129013]\n",
|
||
" [2.71597487]]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(svm_clf.intercept_, svm_clf.coef_)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[-15.51721253] [[2.27128546 2.71287145]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"svm_clf2 = SVC(kernel=\"linear\", C=C)\n",
|
||
"svm_clf2.fit(X, y.ravel())\n",
|
||
"print(svm_clf2.intercept_, svm_clf2.coef_)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[4, 6, 0.8, 2.8]"
|
||
]
|
||
},
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 864x230.4 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"yr = y.ravel()\n",
|
||
"plt.figure(figsize=(12,3.2))\n",
|
||
"plt.subplot(121)\n",
|
||
"plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\", label=\"Iris-Virginica\")\n",
|
||
"plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\", label=\"Not Iris-Virginica\")\n",
|
||
"plot_svc_decision_boundary(svm_clf, 4, 6)\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.ylabel(\"꽃잎 너비\", fontsize=14)\n",
|
||
"plt.title(\"MyLinearSVC\", fontsize=14)\n",
|
||
"plt.axis([4, 6, 0.8, 2.8])\n",
|
||
"\n",
|
||
"plt.subplot(122)\n",
|
||
"plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
|
||
"plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
|
||
"plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.title(\"SVC\", fontsize=14)\n",
|
||
"plt.axis([4, 6, 0.8, 2.8])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[-14.062485 2.24179316 1.79750198]\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[4, 6, 0.8, 2.8]"
|
||
]
|
||
},
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
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Z9OjRAxvLDU6BlDLh2ePi4hg/fjxDhgyhcOHCipVpNFmHPYuNZVmYZGYfjhYmmVquXbsmO3XqlBBO2aVLF3njxg3VspTz6aefSkAWK1ZMbtq0SbUcjSbLAIJkdguT1NimUKFCrFixgnnz5uHj48OPP/6IyWRi+/btqqUppUuXLjRq1IhLly7RokULhg4dmq1LP2TXxBtVhFwKIf/k/IRdCVMtJVujDbwDIISgT58+hISEUK9ePc6fP0+zZs0SIm6ckTJlyrB9+3Y++eQT3Nzc+PLLL6lduzZhYdnzP/yEnRP48+yfDr/Y5ygErgrkdtRtuq3splpKtkYbeAeifPny/PHHH4wbNw4XFxc+++wz6tWrx19//aVamhJcXV354IMP2LNnD88++yyHDx+mdu3aHDhwQLW0NJFdE29UEXIphCNXjwBw5OoRPYvPANrAOxhubm58/PHH/PHHH5QrV46QkBBq1arFzJkz40s5OB3+/v4EBwfTv39/6tevT82aNVVLShPZNfFGFYGrAh8717P49KMNvINSv359QkJC6NOnD5GRkbz99tu0bt2ay5edc/aXO3duZs+ezaZNm3B1NapbXLhwweFLP8TP3uND4KLjovUsPhmsZ+/x6Fl8+tEG3oHJkycP8+bNY8WKFRQoUICNGzdiMplYt26damnKyJUrF2BkBvfq1YuOHTvyxhtvcPfuXcXKbJNdE29UkXj2Ho+exacPbeCzAZ07dyYsLIxmzZpx9epV2rVrx4ABA7h//75qacoQQtChQwc8PT2ZN28efn5+7N27V7WsJ8iuiTeqOHHzRJqua1LAXvGWqo/sGgefFuLi4uTUqVOlh4eHBGTFihVlUFCQallKOXz4sKxevboEpKurqxw3bpyMiYlRLUujSTfoOHjnxMXFheHDh7N//34qV67M0aNHqVevHpMnTyYuLi7lG+RAqlSpwr59+xgxYgRxcXGMGzeOJk2aOFx9n4zEwavqa4/+qlCp25HeM23gsyHVq1cnKCiIIUOGEBsby/vvv0/Tpk05c+aMamlKyJUrF5999hlbtmyhRIkSNGvWDDc3N9WyHiMjcfCq+tqjvypU6nak90xJueDMIDvWorEHmzZtonfv3ly+fJl8+fIxa9YsunVz3gWpGzdukDdv3gQDv3//fp555hkKFiyoTJN1Kdi0lq5V1dce/VWhUrc9xrZnLRo9g8/mtGjRgrCwMNq3b8/t27fp3r073bt359atW6qlKaFgwYIJxv3KlSu0adMGk8nE1q1blWnKSBy8qr726K8Klbod7T3LMQb+5s2bDhsql9kUKVKEVatWMWfOHLy9vVm6dCnVq1dn586dqqUpJX5jlQsXLtC8eXNGjBiR5aUfMhIHr6qvPfqrQqVuR3zPcoyBP3nyJDVq1GDPnj2qpShBCEG/fv04dOgQ/v7+nD17loCAAD744AOio+1fZzo7ULp0aXbu3Mn48eNxdXVl2rRp1K1blyNHjqTc2U5kJA5eVV979FeFSt2O+J7lGAPv5eXFyZMnadSoEePGjXO4KIqs4tlnn2X37t2MGTMGIQSTJk3i+eef5+jRo6qlKcHNzY2xY8fy559/Ur58eUJDQ/H392fhwoVZMn5G4uBV9bVHf1Wo1O2Q75m94i1VHzVr1pQjRoyQQggJyHr16snjx49nJBw127Nz50759NNPS0B6e3vL2bNnS7PZrFqWMu7cuSNff/11KYSQO3bsUC1Ho7EJdoyDV26Y7XXEJzpt3bpVlihRQgLSx8dHbt68OQNvdfbn1q1bMjAwMGFDkXbt2smIiAjVspRy+PDhx86PHDmiSIlG8yT2NPA5xkUTT9OmTQkLC+PVV18ld+7cVK9eXbUkpeTLl49FixaxbNky8uXLx9q1a6lWrRobN25ULU0ZVapUSfh9y5YtVK1alf79+zt16QdHQycq2YccZ+DBCJVbvnw5wcHBFClSBICYmBh273Zs/2Fm0rVrV8LCwnjhhRe4cuUKrVq14u233+bhw4eqpSnlzJkzuLu7M2fOHGrWrIkz5lI4IjpRyU7Y66uA6iOlWjQffvihBOTw4cNlZGRkKr4o5UxiY2Pl5MmTpbu7uwRk5cqV5aFDh1TLUkpoaKisWrWqBKSbm5ucOHGijI2NVS3Labl456L0nOgpGYf0muglL9295BRjx4N20aQdT0/PhFC5OnXqZGmonCPh6urKqFGj2Lt3LxUrVuSvv/6iTp06TJ06FbPZnPINciAmk4kDBw7wzjvvEBsby4cffkhAQADnz59XLc0p0YlK9sNpDPyYMWMSQuXCwsKoVasWM2bMwPjAdD5q1qxJcHAwAwcOJCYmhpEjR/Liiy86rVHz9PRk+vTpbNq0CV9fX44fP46np6dqWU6HTlSyL05j4AHq1atHSEgIb7zxBlFRUbzzzju8/PLLTpsB6+3tzaxZs1i3bh1FihRh27ZtmEwmVqxYoVqaMl566SXCw8NZt24dhQsXBoz1G2ct/ZDV6EQl++JUBh7Ax8eH77//npUrV1KwYEGklOTOnVu1LKW0adOG8PBwWrduzc2bN+nSpQu9e/fmzp07qqUpoXDhwvj7P6r1NG7cOKpXr87vv/+uUJVzoBOV7Iy9nPmqj/Rs+HHhwgV56dKjRZSLFy/Ke/fupfk+OQWz2SxnzZolvby8JCDLli0rd+3apVqWUqKjo2X9+vUlIIUQcvTo0TIqKkq1LE0OBr3Iah+KFy+Or69RyjMuLo4uXbrg5+fHgQMHFCtTgxCCgQMHcvDgQfz8/Dh16hSNGjVi7NixxMTEqJanBHd3d37//Xc+/PBDhBBMnjyZ+vXr888//6iWptGkiFMbeGsiIiK4desWx44d4/nnn+eTTz5x2l2SKlWqxN69exk1ahRSSiZMmECjRo04fvy4amlKcHd3Z8KECfz++++UKVOG4OBgatasyezZs3P8Ir3eiSp7jf0E9voqoPqwx56sDx8+lEOHDk1I62/YsKE8depUhu+bndm+fbssWbKkBGTu3Lnl999/79T1bG7duiV79OghAdmyZcsc/14MXD9Quox3kW+tfyvb9LVH/4yQ0bHRtWgyx8DHs2nTJlmsWDEJyLx588olS5bY7d7ZkRs3bsjXXnst4YOvY8eO8tq1a6plKeXHH398bP0mJybPZSTpR1Vfe/TPCPYY254GXrtobPDSSy8RFhbGK6+8wp07dzh79qxqSUopUKAAy5YtY+HCheTJk4eff/4Zk8nE5s2bVUtTxquvvpqwfhMbG0uzZs0YNGgQDx48UKzMfuidqNKOoyVK6T1Zk0FKydq1a2nTpg2urq4A3Lp1i/z589t1nOzE6dOn6dGjB3/++ScAQ4cOZdKkSY6dFBQbC+fOwcWLEB0NHh5QvDiUKgUpbc6dir579uzhhRdeICYmhueee46lS5fi5+eX8bEVYr23aDyp3WNUVV979M8I9ho70/dkFULMS+thDzGOhhCC9u3bJxj3CxcuUKFCBUaPHu20uySVKVOGHTt2MHHiRNzc3Jg+fTp16tQhPDxctbQnkRLCw2HtWjh0yDCy164ZPw8dMq6HhxvtMtC3fv367Nu3j0qVKvHPP/9Qt25dpnz6KebQ0PSN7QDonajSjiMmSiXlormfjiPHs337dm7cuMGnn37q1KFyrq6ujBkzht27d1OhQgXCw8OpXbs206dPd5x6NlLCrl1w7BjExRmHNfHXjh0z2lkb2nT09fPzIygoiEGDBhETE8Oo0aNp1r07565cSdvYDoLeiSrtOGKilHbRpJFdu3YRGBjI6dOn8fLyYtq0aQwYMAAhRKaP7Yjcu3ePYcOG8d133wHw4osvsmDBAooXL65WWHj4IwOdEq6uUKECVKuW8b7Ahlmz6PPBB0Tcvs3nPXvybps2aeqvcW4y3UWTWQgh+gshQoUQQUKIMCHEW0m0GySE+EcIcVgIsV4IUTQrdSZHgwYNCA0NpWfPnjx8+JC33nqLdu3aERERoVqaEnx8fJgzZw6rVq2iUKFCbN68GZPJxKpVq9SJio21aaAvxdzkhdMfczk2UV2Z+Nl0bGzG+lrGbuXrS/jUqYzt3Jl3WrVK6N/41NiU+9vAoeKqNdmKpHzwdYQQPydzrEzrQEIIV6AC0MDy6fQyME0IUSJRuwBgFNBISlkVCALmpnW8zCRv3rz83//9H8uXLyd//vxs3LiRkydPqpallA4dOhAeHk6LFi24fv06HTt2pG/fvty7dy/rxZw7Z/PyhGsr+fPBP0y4+lPS/TLS1+rnU/nyMb5LF1xcjP9io44t4Y/p//DWvu/SpBly2AYUmiwlqRn8bSDU6mhv+XkQCAY6CCHSNPuXUsZJKUdIKeP/x18HogHXRE1fAxZLKa9azr8EWgoh8qVlvKygS5cuhIeHs2DBAurVq5dwPTaZ2VhOplixYmzYsIEvv/ySXLlyMXfuXPz8/Ni/f3/WCrl40eYMfP6t7ZiRzL+1w/ZM+uLFjPVNZuylG/6ES7BqxgHeXfp/xFj/jVj3T0R8CVuzNGf70rWarMemkZZSHpVSjo8/AAFMklJ+IqWcaDnPKNOB5VLKxEHm5YATVlpuYnzglEl8AyHEmxZ3T9DVq1cTv5wllCxZksDAwITz9evXYzKZOHTokBI9qnFxcWHIkCEEBQVhMpk4fvw4zz//PBMmTMi6Dz4bEU4Trq3EbFlvipNm2zPxmJiM9U1mbJeXBDQEJExf/QsNx47l2KVLT/ZP3NfB4qo12YvUzsITr8RmaGVWCDERKAEMtvUykHh1KxYbWqWUc6SU/lJK//i9V1UipWTatGn8/fffRqjclClOW8+matWq7N+/n2HDhhEXF8fYsWN54YUXOHXqVOYP7uHx2Gn8DDwa4wMmmljbM3F394z1TWbsGLc4aA70BpEP9h8/jt977/H91q1GSnl8f+u+OXADCk3WkhY3i13CbYQQU4EqQCcppa1g8vNAaav23kAhy3WHRgjBL7/88ihUbtQomjdvzrlk/Ks5mVy5cjFt2jQ2b95M8eLF2b17N9WrV2fhwoVkavRW8eJGdIoF6xl4PE/MxF1djX4Z6ZuascuA2wBXKtTy5X5UFAO++46jly8/6m+FI8ZVa7IXSS2ythVCxAghooUQ0Riz6ttCiAdCiHTlYgshXIQQs4FSwKvxxl0I4SqE2CqEaGRpugjobuVzHwTssvLJOzTe3t7MnDmTX375haeeeoodO3ZgMpn44YcfVEtTRvPmzQkLC6NTp07cvXuXXr160bVrV27evJk5A5Yq9djpngf/JszA44kmlt0P/n2yX0b6pnLsGK84cnf2ZMmQIUwJDOS5kiWf6AeOGVetyV7YjIMXQhQCqibXUUqZpu1thBBtgHUYUTHWfouJwAzgXSnlGkvbIcDrQAxwERggpbxEMmRVHHxaiIiI4I033mD9+vWUKlWKf/75B29vb9WylCGlZMGCBQwZMoR79+5RsmRJFi5cSJMmTew/WEZi2cPC4OjR1I9VsSKYTI/OQ0Ph33+Tbp+YZ5+F6tVZu3YtW7du5dNPP3Xs0g+aTMWecfA60SmTkVIyZ84cKlasSEBAQMI1Z02MAjhx4gSBgYHs3bsXIQQjRoxgwoQJ5MqVy36DxGejRkQkb+RdXeGpp6BBA4j/NwkLMwx0av5vCGEYaGsDv3Ur3LiReq0FCxLVsCHlypXj4sWLVK1alSVLlmCyvqfGaciKWjQF03rYQ0xORAhB//79E4w7wHvvvceHH37otLsklS9fnj/++IOPP/4YIQSfffYZ9erV4++//7bfIEIYRrtCBcOIuyaKxnVzezRztzbusbFw/PgTxj3JRCcpjfbxEUKRkTaNe8jD0+T/pxdhkWee1HrjBrmkZM2aNTz77LMcPnyY2rVr88UXX2S49INOknJuklpkvQZcTeUR31aTCk6ePMkXX3zBJ598QoMGDTh27JhqSUpwc3Nj3Lhx/Pnnn5QrV46QkBBq1qzJ119/bb8FWCEMt0u7duDnZyxkFili/KxRw7herdoj4w4ZT3RKouha4IUZ3DY/pNv5L233Dw/H39+f4OBg+vfvT3R0NMOGDaNFixZcTCJGPjXoJCnnJikDXxYjHr0cUB5jkbWG1TXrI76tJhWUK1eObdvxnqN0AAAgAElEQVS2Ubp0aQ4cOECNGjX47rvvMjeqxIGpX78+ISEh9OnTh8jISAYPHkzr1q25fNmOM043Nyhb1pipBwQYP8uWtV2uN6OJTpeeXCoKeXiaI9FGENiR6PO2Z/GWfrlz52b27NmsWbOGwoULs2XLFtq0aZOuvw+dJKVJKtHpjNVxGiNE8oyU8gxQCWiaqI2Nv1hNUjRu3JjQ0FC6devGgwcPePPNN+nYsSPXrl1TLU0JefLkYd68eaxYsYICBQqwceNGTCYT69aty3oxGU10suHvD7ww47Fzm7P4RK6Ydu3aER4eTuvWrZk+fXq61mx0kpQmPcXGBNDL3kKcjfz587NkyRKWLFlC3rx5Wb16NaNHj1YtSymdO3cmLCyMZs2acfXqVdq1a8eAAQO4fz8Lq1FnNNEpka/fevYej81ZvMuT/xV9fX1Zv349jRs3Trg2ZcoU9u3bl+Jj6CQpDaTewEfzKNHpJEbRMI0d6NatG2FhYXTu3JnJkyerlqOckiVL8ttvvzF16lQ8PDz49ttvqVWrFgf/+AO2bYOVK2HFCuPntm2QmmJmkZFw4ICxycaqVcbPAweM64nJaKJTsWKPtU08e4/niVl8on622LlzJ6NGjaJBgwb897//Tbb0g06S0kAqDbyU0ktKeddyegN4SjhznJ+defrpp1mxYgWFCxcGICYmhp49exIWFqZYmRpcXFwYPnw4+/fvp3Llyhw9epR6TZowee5c4uKNmtkM16/Dxo2wbp3tUEiz2QhZXLcOTp+GqCgj2iUqyjhft8543do9ktFEp0R13U/EXLH5jE9cT0U9+Lp16zJixAji4uL4+OOPady4cZJVTHWSlAbSEQcvhHAHooB8VkZfOY4aB58epk6dysiRI/Hw8GDSpEkMHTo0oeysUxEXx8Plyxm1aBFf/forAI0rVWLR229T2vJhmICLC3To8Gj2bTbD+vWGMU+JXLmgTZtHbpIMbvjBli2QlizdAgWgefNUN9+2bRs9e/bkwoUL+Pj48NVXX9GrVy+nzq3ISSjd8ENKGR+8/WR1JI1dGDhwYEKo3PDhw2nRogUXLlxQLSvr2bABL3d3Zrz+Ohs/+ADf/PnZ+fffmEaMYKll0+8EzGbYsOHR+fbtqTPuYLTbvv3RedWqRvJT4tj5xMQnSVVNlPT91FOpGzed7Zs2bUpYWBivvvoq9+7do0+fPkyfPj1tY2qcgiQNvBBibFIHdio8prGNrVA5k8nEypVp3mcl+3Lv3mM+8t5ff8TlW/8C7bn94AHdZ8xAdFnNU30bPeoTGfmoXzqSjRLGS2+SFDxKlEpEkolS8HiiVCopWLAgy5cvZ8GCBVSsWJGePXumqX9WEHIphPyT8xN2Je2uRpUJWqrHpjAV7XW/5GbwTZI5dmLUidFkIvGhci+//DI3btygc+fObN68WbWsrCHRJiFXbnsCRYBVwBzAG1jK1Tt12fnXX4/3y0CyUQLpSZKCjCdKpQEhBL169eLw4cMUKlQIgOjoaD7//HOiUvvtJRMJXBXI7ajbdFvZLc19VSZoqR4bd3zsdb8kDbyUskkKh8P433Myvr6+/PLLL8ycOZNXXnmF5mnw1WZrkvRhC6AfcAjwB84SMH48HyxdSnRsrNEvg8lGj5GWJCnIeKJUOnCz0vLxxx8zfPhw6taty5EjR9J9z4wScimEI1eN8Y9cPZKmWbzKBC1HGNueOOHKXfZDCMGgQYNYuXJlwkLaqVOnmDhxYs7dHjDFGizPAruBMcZ2Y6tX8/yHH3L0/Hm7JRuli4wmSmWQ9u3bU65cOUJDQ/H39+err75SkiUduCrwsfO0zOJVJmg5ytj2Qhv4bES8cZdS0rNnTz766CMaNWrEiRMnUuiZDUlV1JA7MJEd48bxdJEiHDx5kpqjRvHt5s2PGbWMJBulmYwmSmWQevXqPVb6YciQIbRq1cq+pR9SwHr2Hk9qZ/EqE7QcaWx7oQ18NkQIwfjx4ylRogR79+6lRo0aLFiwIHUztdhYOHXKKKW7fbvx89SpNC/yZToFCqS6aaNKlQj97DMCGzXiQVQUA775hg6ffcbVO3eADCYbpfX9ymiilB2IL/3w008/UaBAAX799VeqVauWNdsl8uTsPZ7UzOJVJmg52tj2QBv4bIqtULkuXbpwI6k65FIai4hr18KhQ4bP99o14+ehQ8b18PDU1UDPCurUefw8dxKzKMv1fN7eLHr7bZbNn0++fPlYGxREteHD2XjoUPqSjdL7fmU0UcqOdOrUifDwcJo1a0adOnUoU6aM3cewxYmbtr9RJnXdGpUJWo42tj1Iakent5LpMx/4REo5zNLWBdghpWycTJ9MJyclOqUFKSULFy5k8ODB3Lt3j7Jly/L3338/vnlGRja/UMm6dbbLCSSFpye0bcvZs2fp2bYtv1sygQe3bMmUwEC8ErlPHqNgQWjWzPg9o+9XRhOl7IzZbOb+/fvkyZMHMEpWX79+ndq1a2famJr0kxWJTj2SOAIBL+AdIcSzQojyGOWCG9hDjCbtxIfKhYaGUr9+fQYOHPjkzkiHD6dsrMB4PSLCaO8ItGqVer+4i4vRHihdujRbg4KY3LMn7q6uzPz1V/xHjybk9GnbfXPlAuttAzP6fmU0UcrOuLi4JBj32NhYAgMDef755/nkk0+IS82HkCbbkp5SBa4YMfASI2YNQEopU/hrzlycdQZvTWxsLC4uLgllDTZt2kRJX1+qnDhhM3Sv64XpLC/5Lr5u+R+/kaurEeedVChgVhIXZ2SoJjeT9/KCl19+0qCazQTPmUO3CRM4evEi7q6u/O8//2FYmzaPSj8UKmSEP8afx8Ya7hfL++Xbr60lBv9xiuaL5PJ3lnLGtt4vKQ3DH7+hi/X77+ZmvF6hgmHcs/DbUlRUFKNGjeLLL431h4YNG7Jo0aIsc99oUkZpqQILEsgD5Ad87SFEk3Hc3NwSDNf58+f5z3/+g3/duny1YcMTC7CZkXiTKbi6Qtu2hgEvVMgwxEIYPwsVMq63aWN7tuziQs0BAwgOD2dgp07ExMUxcvFiXvzf/zjv5WXct2nTx78lJHpuW8bd5vXE71d6E6UymVy5cjF9+nR+/fVXfH19+fPPP6levTpLlizJUh2arCHJGbwQoi/Q3XL6g5TyW8t1VyA6fsae+FwVegb/OHfv3mXo0KHMmzcPgJY1ajBv4ECKFSjApZiblDs+mEgZg5fw4GSFmU/O4osXN3zLOYj169fz+uuvc/XqVQoUKMC3337Lq6+++nijXbseSzoSXRK9boX8ccWjk2z4fl27do1+/fqxevVqAHr37s28efN00TLFZMWm228D44G1wBpgjBBihD0G1GQNefLkYe7cuawcN46CPj78GhKCacQI1hw4kKWJN45EmzZtCA8Pp1WrVty8eZMuXbrQu3dv7ljCKQGbiUqpIhu+X4ULF+bnn3/mu+++w9vbm6pVq2rjnsNIKormb2CAlPJ3y3k9YKmUspyVD34ekAvwBDrqGbyDsmsXFw4fpvfXX7PFUmvFLcCF2IBHMbc2Z/H2npHGxhpujIsXDSPq4WGMUapUlvv6pZR88803DB8+nMjISMqWLcvixYt5/vnn7T+Dd6DnTo4zZ85QqlSpBBdfSEgIlStXxiO5yCNNppAVPviSgHXaWRhP+trvA5eBJ0vnaRyH4sUpUaQIm8aM4YtevXDzcIVnHp+lZWrijQPG3wsheOuttwgODsbPz49Tp07RqFEjxo4dS0yRIilHvyTG1vvlgM+dHE8//XSCcT979iwBAQHUr1+ff/75R7EyTUZIysAfAVpZnbcE4jMzJBAnpXxHSjkS+IhH0TQaR8OSQOPi4sLQ1q157r3ixJa0iug4BtHmTEq8iY8nj48JTxySF3/t2DGjXRYbu0qVKrF3715GjRqFlJIJEybQqEcPjlsVHSuaz3b0zhPXrd8vB3/ulLh+/ToFChQgODiYmjVr8s033yipZ6PJOEm5aF7CqMu6AYgD2gBdpZTrbd5EiKellDbK82Ud2kWTDEkk3qwNCqL9lCk0fO45Fg0eTJn42G17Jd44WMJPcuzYsYMePXpw/vx5cnt58WXv3rweEJCyT9qW7mz03Elx+/ZthgwZwsKFCwFj/WLu3Lk8ldbNTDRpxp4umuSiaGoCnTFm+T9JKR3aemoDnwxWmZm+r7eyCvHbipG/dglBXhYO6U33zp0RDRtmPHzPXvHkWcjNmzcZOHAgy5cvB6Bj3brMefNNClmShJ7AViZrNnzu5Pjxxx/p378/t27d4qmnnmLp0qU0i8/4zaFcunuJriu7srzzcnx9sj4KPEvi4KWUwVLKD6SUo6WUQUKIdvYYUKMAqx2KHjc2zTCWVzoguUOPGTPoNmsWN2/Z2HUordgrnjwLKVCgAMuWLWPhwoXkyZOHn/ftwzRyJJsTZ6omt6NTNnzu5OjSpQthYWEEBARw48YN8ubNq1pSpqNyww97k5ZEp1VJvWCZ7WscmfjEmycoDPwMGKFyP/zwA9WrVycsLO3brD2GjY0vUiSDG1/YAyEEPXr0IDQ0lAYNGnDxxg1e+u9/GbZyJZH58qWcqJRNnzs5SpUqxdatW/njjz8eq19zzkE/lDKCyg0/MoPk9mTtJoRoLYSoI4R4ikQLqUIID8vPEhix8ppsiwD6EhISQu3atRFCULp06YzdMpvHk5ctW5YdO3YwceJE3Nzc+GL5cuqMGEF43rzJ7+iUzZ87KVxcXKhXr17C+erVqylfvjxTpkzJUfVsVG74kRkkN4NfDHwGrAeCeHKj7VNCiIpAZSA0c+RpspIKFSqwa9cutm3bRv78Rkx8ZGQkR48eTfvN0hs/baeNL+yBm5sbY8aMYffu3VSoUIHw8HBq167N9OnTMSe1+1MOeO7UcPDgQWJiYhg1ahTNmzfPEbN5lRt+ZBYpuWj8pZRPYRhxAIQQvwohCgNFgR8wHLm7Mk+iJitxd3enfPnyCeejR4/Gz8+PWbNmpS1ULtHGF6nCzhtfJBAZCQcOGIufq1YZPw8cSF0p4thYahcuTPDXX9OvdWuioqJ49913admiBRdtuVUc6bkzkQkTJvDLL7/w1FNPsWPHDkwmU8LidHZF5YYfmUVyBl4ChYQQjQFrH3tjwAOIxlihexejpIEmG1C0aOqvm81mbt++zcOHDxk0aBBt27YlIiIidQMliqNPVzx5RjGbYetWo6786dMQFWVEuURFGefr1hmv25qNJ0pU8rl1izm9erFqxAgK5cnD5i1bMFWpwqqff05Wv5LnziJatWpFeHg4bdq04datW3Tt2pWePXs+XvohG6Fyw4/MIrkwSTPwGrDUcslFSukqhHgIPAOcBPyAvVJK5UvrOkwy80gcKjd//nxatWqVcsewMEiLe6diRTCZ0i/UGrMZ1q83jHlK5MplVKSMryqZwoYfl27epM+sWWwKNTyTb7z+OtO//BIfHx+jgcrnVoCUkm+//ZZhw4ZRtGhRQkNDnSLaJrPIqnLBQ4CfpZTuUkprB2H8J4KQUv4F/C2EqGUPMRrHJD5UrkmTJkRERNC6dWsGDRpETGoWBlMbT2/vIlfbt6fOuIPRbvv2R+cpbPhRrEABNrz/Pl/27k0ud3fmzpuHn58f+/fvf9RI1XMrQAjBgAEDCA4O5scff0ww7lFRUan7G9FkGinN4M3ATeAQ0CzRDP6UlNJDCDEWcJdSfpTiYEK4A+8Ak4AeUsofbLSpBWzmUWkEgBVSymnJ3TurZ/C+vnDFxlafRYtCFm5gn2aSsycpudiLFjUTETENGAO0BX4ChO1nVpnwExlpuF8suL7WGbN88sFdhCRuuVUNnrZtjbGtdMeT1AYph8+epftXXxF25gyurq58/NFHvF+5MomfINtssGJHhgwZwr59+1iyZAnPPPOMajnZhqzc8MMfaA1Ybz/fA7hudb4deD6V4/XD+AawN5k2hYGVUsp6Vkeyxl0Ftox7ctdzAhERLsBIYD8wh/jI2StXrj4ZVaIy4cdSNTMeW8bd5vXw8CTHT2qDlKqlS7Nv8mSGvfEGcXFxjB03joCxYzmVaK0i22ywYifu3LnDmjVr2L9/PzVq1OD777/X9WwUkJKB/1dKuV9K+Uv8BSnlT1LKSOCC5dIRIFWbSkopZ1mMdXKBs4WBl4QQ+yzHJ0KIJHLFNWqoARSy/B4NtKRZs2aPh8qpTPixKhaW5n42dF+Kucn8W9sxI5l/aweXYx/P9PV0dWVanz5s3ryZ4oULs+uff6g+ciQLf/8dKWWK/R090Sk95M2bl9DQUP7zn/9w//59+vXrR8eOHbl27ZpqaU5Fcgb+IyBJJ6aUsqzl5w1ghh01/QyUkVLWBV7G2NR7oa2GQog3hRBBQoigq1ev2lGCJvUcB84/GSqnMuEnvYk3ZrNN3andIKV58+aEffcdnerW5e7Dh/T6+mu6Tp/Oh2d/cMoNVvLnz8/SpUtZvHgxefPmZfXq1ZhMJn777TfV0pyG5GrRfCKltP6fcja5tvYSJKV8KC3f5SwfHsOBtkIILxtt50gp/aWU/kWKFLGXBE2aqAyE07Zt28dD5dJrsOyR8JPWOPR4XFyeSFSKn31HEwtANLG2Z+EW3YWKFGHFsGHMGzgQH09Pftyzh3mTthN9KnX9cyLdu3cnNDSURo0acenSJb7//nvVkpyGVNeiiZ+xK8AViCSZbxMa1TzFmjVrmD17Nl5eXixatIjqb7zBn//+m3JXa5JL+ImNhVOnjPDF7duNn6dOGdcTU6xY2h8hvl+iRCXr2Xs8yW6QUrw4ws2NPk2aEDJlCkXL5IM7wP8BvwGxmbzBioNSpkwZtm/fzueff87s2bMTruekMgeOSFqKjWUKQohCQohdQogKlvOuQoj8lt/dMCJuFkkpk8gNV0NaEoZyCsk9sxCC/v37c+jQIWrVqsXp8+cJO/Noi4B0J/ykZ2ekREXVXITtxb0nrler9sT4ex78mzB7jyeaZDZIsepf3tcX37754QWM9ejdwPcQfTWTNlhxcFxdXXn33XcpWLAgYIRRNmzYkC+++CLp0g+aDOEIcVnewNNAPsu5F7DVEqYpgd+BsYq0JYkjh0ImR0YCGVLzzBUrVmT37t0sXbqUXjVrwvHjEBfHqa9X4pVcnZb48rvWoYIpJBwlXDt2DG7fflS619MTChaEGzeMZsuTiFyxpmBBox8YOiwbdhwqPyX5fol1u7k91j/k2c/gWdjz4r8EfvUVJy9fwXOOO317NEWWk4j49jksRDI1/PLLL+zdu5e9e/eyYcMG/u///o/iOfybTFajZAYvpQyIj4GXUp6TUpaM31BESjlfSllLSllbSllHSjlSSvlQhU5N+vDw8KB3796IatXgqac4cfUq5QYP5rstW2yHysVvnFE1UTBWCglHCcTFGe2s67Y3aWJkqKaGXLmM9vFUrWroScmXn5RuG/3rP/ssIVOm0CcggMiYGAbPm0ebTz/lipvbk/2dhI4dO7JmzRoKFy7Mli1bqFatGj8nLv2gyRBJJjplN5ylVIGrq+3SKS4u6Q8eSS3pSu6Skk+GDOHDmTMtFzoA32FEw1qSjUIPG0bOOgvLHolSZrPhr7fM5G1SqBAEBDwqU2Clm8OHjZk4PP7murkZr1eo8KTuVPT/af9+3pw9m5v37lGkSBHmzp1L27Ztk9aYw7l8+TJ9+vTh119/BeD111/n/Ynv88amN5TtqqSSLNmyL7vhLAY+I5moKscWYikwEGPF0RdYALRIuu+pU4aP3WIYRZdXkx77xxXGL66u4Odn1Gu3JjLS8NNfumQYfRcXY0G1WrVHbpmkiI01kpAuXjRCGd3djQXRUqVS51ZJov95Iejdty9bt24FoH///kybNo3cuXOnfM8ciJSSr7/+mpEjRxIZGUmTd5vwe/7fGVBrAF+3/lq1vCxFG3gbaAPv6AYe4AxGIvQflqtDgE+R0oaR3bXrseSfVBl4MIxvgwbJi3EQzGYzX3zxBR988AHR0dFUrFiRJUuWUKuW85Z2OnLkCF99+xULnlpAVFwUXm5enHznpFPN4rOyVIFGY0eexqhs8T+M9f3ZwDHbTXPozkjWuLi4MHz4cPbv30/lypU5evQo9erVY/LkyU4bPlilShVcWrggLTUNY6/FUrtxbU6ePKlYWfZEG3hNFuMKvA/swahn8yik8bFvk06yMxJA9erVCQoK4u233yY2Npb333+fpk2bcvZskrmFOZbEuyrF/BrD+UPnMVU3sWDBAl3PJo1oA69RhD/QK+Hshx9+4KWXXuLCBUuJIyfZGSkeLy8vZsyYwcaNGylatCg7d+7EZDKxbNky1dKylCd2VWoHLlVcuH/vPn369KFLly7cSG7RXPMY2sBnMxIHe6R03Z5kJLkrOd1xcXGMHTuWLVu2YDKZWLlypVPtjGRNy5YtCQ8Pp3379ty+fZtu3boRGBjIrVu3Uu6cA3hiVyVvMHc2U7pXaXx8fPjpp58wmUxs27ZNnchshF5k1TgEiUPl+vTpw5dvvkme1FaljE84SpTFml2RUvL9998zdOhQHjx4QOnSpVm0aBGNGzdWLU0ZJ0+eJDAwkD179uDp6cnp06cpmgNTx/Uiqw0OHjQiNeKPlHB1fbx9/JFar0BG+mekr6+v7b6+qQwyyEj/jI6d/L192bBhA1999RWenp7Mnz8fv8BA9l69mv6Eo2yMEIJ+/fpx6NAh/P39OXv2LAEBAQkRN85IuXLl2LlzJ+PHj+fTTz/Nkcbd3uSYGbwQ/hIezeBTF7Znm9S8JRkPGcz6vqrHTi1//fUX3bp1IzQ0lEqVKhG+dCmu8VEUaU04ygHExMQwfvx4Jk2ahNlsplatWixZsoSKFSuqluYQLF++nIiICAYPHozIAX8DOg7eBtrAO7butBIVFcXYsWPp3LkztWvXznjCUQ7gjz/+oEePHpw5cwZvb28+//xz3nzzzRxh1NLL9evXKV++PLdv36Zly5bMnz8fX3t8pVSIPQ08UsoccUAtaZgZ40gJ67aJj9SQkf6q+qoeO6O8++67cv78+dJsNmfNgA7IrVu3ZGBgoMQoxCfbtWsnIyIiVMtSyk8//SQLFiwoAVm4cGG5Zs0a1ZIyBBAk7WUX7XUj1Yc28I6tO6Ps2bMnwah17txZXr9+PfMHdWCWLVsm8+XLJwFZtGhRuWHDBtWSlHL+/HnZvHnzhL+RN998U967d0+1rHShDbw28NlOd0Yxm81ywYIF0sfHRwKyRIkScsuWLZk/sANz5swZ+cILLyQYtcGDB8sHDx6olqWMuLg4+fnnn0sPDw8JyDZt2qiWlC60gbeDgXdxsW2oXFxS7pvR/hnpW7So7b5Fi6ZOd0b6Z3Rse3DixAlZv379BKM2fPhwGRkZmXUCHIzY2Fg5efJk6e7uLgFZuXJleejQIdWylBIaGipr1qwpg4KCVEtJF9rA2zhq1aqVsXdVk22IiYmR48ePl66urhKQY8aMUS1JOQcPHpQVK1aUgHR3d5efffaZjIuLUy1LGYnXaaZOnSpPnTqlRkwa0QZeG3iNlHLv3r3yxRdflLdv31YtxSG4f/++HDhwYMK3m6ZNm8pz586plqWcVatWSUDmzZtXLl68WLWcFNEG3taDWLlossJloMpd4QhukvSQFbofPHgg33rrLXnx4kX73TQbsm7dOlmkSBEJyAIFCsgff/xRtSSlXL16VXbo0CHhg+8///mPvHnzpmpZSaINfAoGPisW/VQtOKpc6MwIWaH7vffek/GhcqtXr7bfjbMhly9flq1atUowar169XLqbzpms1l+99130tvbWwKydOnScseOHapl2UQbeG3gtYG3wYULF+SLL76YYNT69euXbUPl7IHZbJZff/219PT0lIAsW7as3LVrl2pZSvn3339l7dq1JSCFEHLFihWqJT2BNvDawGsDnwRxcXHyiy++SAiVq1Chgty/f799B8lm/PXXX9LPz08C0sXFRY4dO1ZGR0erlqWM6Oho+eGHH8pnnnnGIb/VaAOvDbw28CkQGhoqq1atKgHp5uYmw8LCMmegbEJUVJQcNWqUFEJIQNatW1ceO3ZMtSylWOcM3L9/Xy5atMghsqTtaeBzTDVJjcYak8nEgQMHeOedd3jllVeomoMqTaYHDw8PJk+ezLZt2yhZsiT79u2jRo0azJ071zJBcj68vLwSfn/vvffo0aMH7dq1IyIiQqEq+5IjDXxWVBHNyOYX2XHcjKJCt6enJ9OnT2fZsmUJBbn+/vtvlixZknmDOjgBAQGEhYXx2muvcf/+ffr27Uvnzp25fv26amlKadSoEfnz52f9+vVUq1aNDRs2qJZkH+z1VUD1oePgNSkRHR0ta9SokS1C5TIbs9ksFy5cKPPkySMBWbx4cfnbb7+plqWUs2fPyoCAgIRF+rfeekvev38/y3WgXTQaTdpxc3Nj0KBBeHt7s2zZMkwmE7///rtqWUoQQtCjRw9CQ0Np0KABFy9e5KWXXmLYsGFERtreHjGnU6pUKbZu3cqUKVNwd3dn1qxZ+Pv7Z+s9YLWB1zgNQgj69u1LSEgIderU4dy5czRp0oTRo0c77S5JZcuWZceOHUyYMAFXV1e++OIL6tSpw+HDh1VLU4KLiwsjR45k3759VKpUiWrVqlGgQAHVstKPvb4KqD60i0aTFqKjo+VHH30kXVxcJCAbNmzo1LVbpJRy3759skKFChKQuXLlktOnT3fq9+T+/fvy1q1bCedHjx6VZ8+ezfRx0S4ajSZjuLu789///pedO3dSpkwZunfvjouLc/93qFOnDsHBwfTr14+oqCiGDh1Ky5YtuXjxomppSvD29iZfvnyAscPYq6++islkYvny5YqVpR7n/ovWOD0NGjQgPDyc/v37J1zbvHkzV65cUahKHT4+PsyZM4dVq1ZRqFAhNm/ejMlkYtWqVaqlKeXBgweULl2aW7du0bVrV3r27MW7ovIAAA/bSURBVMmdO3dUy0oRbeA1To+Pj09CGOXx48d55ZVXMJlM/PLLL4qVqaNDhw6Eh4fTokULrl+/TseOHenbty/37t1TLU0JBQoUYO3atXzzzTd4eXmxaNEiatSowa5du1RLSxZt4DUaKzw9PalTpw4RERG0adOGQYMG8eDBA9WylFCsWDE2bNjAl19+Sa5cuZg7dy5+fn7s379ftTQlCCEYMGAAwcHB1KxZk1OnTtG4cWMmTZqkWlqSaAOv0VhRsmRJtmzZ8lioXK1atTh06JBqaUpwcXFhyJAhBAUFYTKZOH78OM8//zwTJkwgNjZWtTwlPPfcc+zZs4fRo0cjpaRQoUKqJSWNvVZrVR86ikZjb4KDg2WlSpUkGLskLViwQLUkpTx8+FAOGzYsIRGoQYMG8uTJk6plKSU0NPSx+jVHjx7NcD0bsmsUjRDCXQgxQggRI4TomkQbIYSYIIQ4KoT4SwixWAiRO6V7HzwIQhiHr2/KWnx9H7W3PlLTV+Mc+Pn5ERQUxKBBg3BxccHPz0+1JKV4enoybdo0Nm/eTPHixdm1axfVq1dn4cKFGHbJ+TCZTAnrN//++y9+fn507NiRa9euKVZmkNUumn4Yn/57k2nTC2gF1JBSVgZigE/TMkhqAiCSauOkwROaJPD29mbmzJn8+++/mEymhOu7d+9WqEotzZs3JywsjE6dOnH37l169epF165duXnzpmppSjl58iRubm6sXr0ak8nEb7/9plqSGhcNsAPomsRrG4E3rc5rANdTvmfaygVn17K7GvUsWbJEArJHjx4OWU88qzCbzXLevHnSx8dHArJkyZJy27ZtqmUp5dSpU7JRo0YJbqx33nlHPnz4ME33ILu6aFJJOeCE1fkJoKAQIl/ihkKIN4UQQUKIoCxTp3F6oqOjE0Llqlev7vChcpmFEII+ffoQEhJCvXr1OH/+PM2aNeO9994jKipKtTwllClThu3bt/PJJ5/g5ubGl19+Se3atTly5IgaQfb6pEjLQfIz+H+BAKtzL4xPwwLJ31PP4DVZx99//y1r1qyZsEvShx9+6NS7JMXExMiPP/44ofRDjRo15F9//aVallIOHDggn332WZkrVy55+PDhVPcju+/olIKB3wb0tDqvCNwFRPL31AZek7VERUXJ999/P2GXpDp16shz586plqWU3bt3y3LlyklAenp6ypkzZzrELkmquHfvnty6dWvCudlsltevX0+2jz0NvHIXjRCikBBilxCiguXSIqCvEMLDcv428LPlwVNFajaRyK4bZ2gcBw8PD/73v/+xY8eOhDT2bF150A7Ur1+fkJAQevfuTWRkJIMHD6ZNmzZOW/ohd+7cNG3aNOF8yZIlVKhQgZ9//jlLxldu4AFv4Gkg3se+EPgD2G/xrefFMPLJUqvWozn45cspD3r5su35e2r6ajTWNG7cmNDQUNauXUvu3EZE77179xwmVC6ryZMnD/Pnz2fFihUUKFCADRs2UK1aNdatW6damnLWr1/PjRs36NSpE2+88Ubml36w11cB1YdOdNI4Eq+//rr09fWVv/76q2opSjl37pxs1qxZQlRJ//795b1791TLUobZbJZfffWV9PT0lIAsX7683LNnz2NtyEkuGo0mpxEZGcnx48e5fPkyLVu25J133nHaXZJKlizJb7/9xtSpU/Hw8ODbb7+lVq1aHDx4ULU0JQghGDx4MEFBQVSvXp0TJ07QsGFDxo8fnzmlH+z1SaH60DN4jSMRGxsr//e//0k3NzcJyCpVqsjQ0FDVspQSEhIiK1euLAHp5uYmJ02aJGNjY1XLUkZkZKQcMWKEFELI5557LmH/V7J7FE1mHNrAaxyR+FA5QHp4eMhp06Y5dVTJgwcP5Ntvv53gsmncuLE8c+aMallK2bp1qwwODk44t6eB1y4ajSYT8ff3Jzg4mAEDBhAdHU1YWFhC7RJnxMvLixkzZrBx40aKFi3Kzp07MZlMLFu2TLU0ZTRt2jTT6hxpA6/RZDK5c+fmm2++YcOGDcyYMSPhenbYESizaNmyJeHh4bRr147bt2/TrVs3AgMDuX37tmppOQpt4DWaLOLll18mb968gLEFXN26dXn99de5e/euYmVqKFKkCKtXr2bOnDl4e3uzZMkSTCYTO3fu/P/2zj3YqqqO45/v5SWgXQeZ1NHEXoAQkzxqGgquqfmgLNOYUG9ZzWBUQhlYjTgNpkWm1lCUoc6g4qOcppSacFIMdeIh3mG4icZoChQl4qNU5H1//bHWtc3h3Hv24Z6z97nb32dmz91rr7X2/p51fud311577d/KW1phcAfvODmwdu1aNm3axOLFixk7diyrV3cXYLW4SGL69OmsW7eOCRMmsGXLFk455RSuuOIK9uzZk7e8Xo87eMfJgZaWFtra2rKZKtcLGD58OCtXrmTu3LlIYv78+UycOJGNGzfmLa1X4w7ecXJi1KhRrFmzhjlz5tDR0cG8efOYPHkyzz77bN7ScqFfv35cc801rFixgmHDhtHW1sa4ceNYtGhRmPLnVI07eMfJkQEDBnDdddfx4IMPctxxx7Fq1aq3bPjhTiZNmsT69etpbW3ljTfeYMaMGZx77rls3749b2m9DnfwjtMAnHrqqbS3t7NgwQJaW1vfPL5///4cVeVHc3MzS5Ys4e6776a5uZmlS5cyZswYli1blre0XoU7eMdpEIYMGcKsWbPenCe/YcMGRo4cyfLly3NWlh/Tpk2jvb2dlpYWtm3bxpQpU5g5cyY7d+7MW1qvQEUZ25L0GtCIT2SGAo0aVrBRtbmu6mlUba6rekaY2RG1OFHfWpykQdhoZhPyFlGKpMcbURc0rjbXVT2Nqs11VU8tlyD1IRrHcZyC4g7ecRynoBTJwd+Ut4AuaFRd0LjaXFf1NKo211U9NdNWmIesjuM4zoEUqQfvOI7jJHAH7ziOU1B6hYOXNFrSy5LmlcmTpKslbZT0pKQ7JA2ulJeBrqMlLZL0lKTHJD0qaUzMO0rSDkmrE9v1tdKVQtv4mJe8/uyYl2eb3V+iaZ2k5ypproGmv0pamzjvQ2XKZG5nKXVlbmcpdeViYym15WVnsyU9EfU9LmlQSX7tbaxWS0PVawOOBFYAPwfmlcn/AtAGDIzpxcDCSnkZ6DobOC+Rng38Ke6PAB7Isc3OBG7uom5ubVam/GXALypproGuTUBThTKZ21lKXZnbWUpdedlYRW152BlwFfBDoG9MH1mqsx42VtMPUYdGaQLuAyYC87pwVsuASxLpk4GXKuXVW1eZOhcCD8X9DwPbgFXAWuBnwNEZttlFwGZgTdy+DxzRSG0GDIwa31FJcw20vQo8CqwD7gFObhA7q6grDztL2V6Z29ihtFkWdkZw5quBufGcDwNnZ2FjPW7Qem7AD4BL435XzmojcFoifQRhQd/m7vLqrauk/NHAM0BLTA8A+sT9wcANhP/OyqjNBvL/GVRDgLuA31Vqz4zb7JvAjWk016DNOntFTfEH/iJwQgPYWUVdedhZyvbK3MYOsc3qbmfAacBrwMUxPRZ4BXh3vW2sYcfgJZ1P+GIWVioKJEPuda6Y0FQhr966OssfBfyR4NAeBjCz3Wa2P+7vAL4FjATec6i6qtFmZjstWomZvUy4rT9H0kAao80GAt8A5qfU3CPMbGf822FmdxKc4JmlssjQzqrQFcRlaGdpdGVtY9Vo6yRDO3s7sMnMbovnXQcsB84qlUSNbayRY9GcDZwkqXMts+MhPKQzs6mJcv8ETkikTwBeB/5TIa/eupB0LOHW6kdmdlc35xThi+rpKsyptZXQB9gF7CbnNot8FVhmZltSaq41fTj4u8jaztLqysPOUukqU6aeNnYo2rKysxfKaOjgQIcN9bCxnt4SZbURb+uBo4C/AO+Nx78IPAL0j+mFwG2V8jLQNQx4Eji/TJ1PAsfGfRGGL5Zl2GbTgCPjfl9gCfE2Nc82i3mDgH8Aw0rqdKm5h1o+AIxPpKcAW/O2syp0ZWpnVejK3MbSasvazoD+wL+Bj8T0CMLQ0fh621gj9+C7YhDBqJtj+nbCLedjkvYRjP3SFHn11nUDYUz0ckmXx2O7zayFMHb2W0n9CP/J2wnjhfWiVNtAYLmkjqjlYeC7MS/PNoPQq7rfzDaXlO1Oc094HfixpGMIvbSXgTPKaMvaztLqytrO0urKw8bSaoMM7czM9kiaAtwiqYnwXVxM6NnX1cY8VIHjOE5BadiHrI7jOE7PcAfvOI5TUNzBO47jFBR38I7jOAXFHbzjOE5BcQfvvGWRdIakNZJej1H6ZpXkr5ZU1fQ9SYp/J0iqaoqapFslpXrb13HS4A7eKQSSrpRkiW1mSdoktSbKjwPuJUS2HAZ8GfiOpEtSXu9OSfsS2944d/or3dR5l6QHJO2S9HdJ03r2qR2ne9zBO0XhWkIApnGEF1R+CXwWODEe/xvw30T5qYS3Om83s5fMbAVwPelfBPoccFjcBgCjCS/X3FeusKQBhHAC/wLeB3wPuFXSpNSf0HGqpDe+yeo4B2Fme4G9kvoDr5jZXkl3Aaeb2WZJQ4HtiSpNHBwLJPWQipl1EN5IJL45eQ8h0NfWLqp8ihB0arqZ7QGekTSZEIv80bTXdZxq8B68UzSGEmKBv4mkwwkBo55LHP4N8AlJn5E0WNKHCKFjf532QpKaJF1EiPG91Myu7ab4B4GV0bl3sjwed5y64D14p9cjaRNhHD15rLM3/ufE4efjM9BPm9m9cQz8auAO4HlCAKcbU1xvMCE+yVTCghoXxyGe7ngb8FLJsRcJw0eOUxfcwTu9HjM78RDrLQWWHkK9HZKeBs4hLMSApNLf0mbggkR6Kwf31ofG445TF9zBO4VC0lRCiNckj5jZGV2U70MIpVuOjwM7y2WY2S2x/vNd1O1LCFP7q5h+ArhM0mFmtise+xiwoYv6jtNj3ME7ReMPHLxi0W5JsvKhU3cTFnXoigWEVX/KYmbHlDsu6WQOfBbwe8Jwzs2SriKsl3ohcHo313acHuEO3ikMcSz++MQhIyxt1h94QdI7E73nUMCsr6SfAoeb2ZfieQYTYouPMrOnKlwz1cybGBP8LOAmQm9+K/B5M/MZNE7dcAfvFIbSsXhJo4FvA+8HLih17gn2EOayd9L5u9hXpmw5JgDrU+h7jjAs4ziZ4A7eKQxx4enRwEcJ4+cnEV5E+glwvKRXga3JoZr4cHQfcFjiQWmnszdJfSwuXN0NfejitySpI86Zd5zM8XnwTiGQdCXwFHAVYUjma4SpieMIb7B+nTBffXyizonAXkIv/7y4v5cwVg7wNGHJtkqsITyMLbeVfbjrOFngS/Y5juMUFO/BO47jFBR38I7jOAXFHbzjOE5BcQfvOI5TUNzBO47jFBR38I7jOAXFHbzjOE5BcQfvOI5TUP4H7hxxCTT2FT4AAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<Figure size 396x230.4 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.linear_model import SGDClassifier\n",
|
||
"\n",
|
||
"sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, max_iter = 50, random_state=42)\n",
|
||
"sgd_clf.fit(X, y.ravel())\n",
|
||
"\n",
|
||
"m = len(X)\n",
|
||
"t = y * 2 - 1 # -1 if t==0, +1 if t==1\n",
|
||
"X_b = np.c_[np.ones((m, 1)), X] # 편향 x0=1을 추가\n",
|
||
"X_b_t = X_b * t\n",
|
||
"sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n",
|
||
"print(sgd_theta)\n",
|
||
"support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n",
|
||
"sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
|
||
"sgd_clf.C = C\n",
|
||
"\n",
|
||
"plt.figure(figsize=(5.5,3.2))\n",
|
||
"plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
|
||
"plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
|
||
"plot_svc_decision_boundary(sgd_clf, 4, 6)\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.ylabel(\"꽃잎 너비\", fontsize=14)\n",
|
||
"plt.title(\"SGDClassifier\", fontsize=14)\n",
|
||
"plt.axis([4, 6, 0.8, 2.8])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 연습문제 해답"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1. to 7."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"부록 A 참조."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 8."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"_문제: 선형적으로 분리되는 데이터셋에 `LinearSVC`를 훈련시켜보세요. 그런 다음 같은 데이터셋에 `SVC`와`SGDClassifier`를 적용해보세요. 거의 비슷한 모델이 만들어지는지 확인해보세요._"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Iris 데이터셋을 사용하겠습니다. Iris Setosa와 Iris Versicolor 클래스는 선형적으로 구분이 가능합니다."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn import datasets\n",
|
||
"\n",
|
||
"iris = datasets.load_iris()\n",
|
||
"X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
|
||
"y = iris[\"target\"]\n",
|
||
"\n",
|
||
"setosa_or_versicolor = (y == 0) | (y == 1)\n",
|
||
"X = X[setosa_or_versicolor]\n",
|
||
"y = y[setosa_or_versicolor]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"LinearSVC: [0.28480668] [[1.05542422 1.09851637]]\n",
|
||
"SVC: [0.31933577] [[1.1223101 1.02531081]]\n",
|
||
"SGDClassifier(alpha=0.00200): [0.32] [[1.12293103 1.02620763]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import SVC, LinearSVC\n",
|
||
"from sklearn.linear_model import SGDClassifier\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"C = 5\n",
|
||
"alpha = 1 / (C * len(X))\n",
|
||
"\n",
|
||
"lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
|
||
"svm_clf = SVC(kernel=\"linear\", C=C)\n",
|
||
"sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
|
||
" max_iter=100000, random_state=42)\n",
|
||
"\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_scaled = scaler.fit_transform(X)\n",
|
||
"\n",
|
||
"lin_clf.fit(X_scaled, y)\n",
|
||
"svm_clf.fit(X_scaled, y)\n",
|
||
"sgd_clf.fit(X_scaled, y)\n",
|
||
"\n",
|
||
"print(\"LinearSVC: \", lin_clf.intercept_, lin_clf.coef_)\n",
|
||
"print(\"SVC: \", svm_clf.intercept_, svm_clf.coef_)\n",
|
||
"print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"이 세개 모델의 결정 경계를 그려 보겠습니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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nkfIePXoQHx/P4sWLTedu3bqFi4sL69ev56WXXqJz587Ex8ezevXqR66///eUyLs/I0Kkxc0t7UlSrq75bzcrGZP6BO7/xf/ss8+yY8cOunXrBsCqVavw9vbm+PHj2X5PBwdo98GzdLk0kb82X2R27TAKq3jePdyXel09YehQSNXDK4SwbC4uLlSpUoXBgwdz6NChR8pDQ0NZs2bNA29qVqxYgbOzMy+++CI3btxg2bJlDBs2LM32JUEVIn+S7VYfJX/bpUEpRd26dXFycgLA1dWVxo0bU758eQDmzZvH119/TXJy9i7PWjOoGK/+MQCXGONEq7jAxqjx30OFCtx+uQXfNFrN7p2GfDfLT4isSGsdwfufadP+rTdtWsZ1U6tZM3P1MmvVqlXY29vj7+9P/fr1WbFihamscePGODo6snz5ctO5+fPn88orr6CU4q+//sJgMDwwhl4IIQoiSVIz4YUXXmD27NnY2NgAsGHDBhYuXGjq0YiMjOTOnTvZdr/SzsaJVt67FhrHro4axb29BxiyqTUu9Sow0Xsc88bLRCshLFX58uXZunUrERER+Pr60qFDBzp16oTBYMDa2pqQkBDmz58PwLVr1/jll18IDQ0FoCAMwRJCiMyQMalP6NatWxQtWpTExETc3d1p2bIls2fPBoz/yKhsHkh6+ngSO95fju+6Sbx4bwt3sWeZXVcudxpAy09rUb6CDFwV+Ut+Gm+4du1aWrZsybx58+jWrRt79+6lTp06REdHs2TJEiZNmsT+/fsB48Yjzs7O7Nq1i9q1a5s5csuWn35GhChIZExqDitatCgANjY2LFmyhCFDjBtfRUdHU6FCBX799ddsvV+5Srb0WNmZgLjNLB99mFXOfWmVuJS359VB1Q6E8HCka1UI8zOksfZxcHAwTk5OnD9/HoCaNWvi5+fH4sWLmT9/vqkXFaBUqVI0bdqUaanHLqRy69atnAlcCAuXU9uG5sXtSPNizE9CktSnZGVlRVBQEDVq1ADgxo0bVKpUCW9vbwAOHjzIhAkTsu0flvsTrTpfCjNNtHIrEQ99+oCnJ+c6D2X6iFNcvZottxNCZNGhQ4cIDQ3l3Dnjrs5aa3788Ufu3LlDcHCwqV737t2ZPHnyA5M07wsLC2PNmjUMGzaMy5cvA5CUlMTSpUtp3rx57j2MEBYkp7YNzYvbkebFmJ+EJKnZrHLlyqxdu5bKlSsDxgkUw4YNM/WuXLhwIdt2ubo/0arwqcOwZQs0boznku95fVwFIp1b8G2j1ez5Q3a0EiI3lStXDhcXFxo2bIi7uzteXl7MnTuXDRs2ULVqVVO90NBQDh48yIsvvoinp+cDbZQvX569e/cSGxtLzZo1cXNzo0qVKsycOZPRo0fn9iMJIYRZyJjUXHDhwgW8vLwA42u/qKgoDhw4kCP32jjnIv98Np2GJ6fiwT+cwZdVnm/iPLwP7V4rjYNDjtxWiGwn4w3F48jPSMGSU3vb51S7OSkvxpyajEm1IPcTVIB3332XDz74ADC+BmzZsiVz5szJtns16uFB6IlPiD92jjmtFxJl48PbUcNp/7YXZ17qCREReeMnWAghhBAFmiSpuaxJkyZ06dIFgNjYWBISEkhKSgIgPj6e6dOnc+PGjae+T/nKxolWNeO2sOzzw6x160uVY0uhdm0IDGR3/3DWL4snm5d6FUIIIYTIFpKkmpGjoyO//fYbffr0AeCXX36hX79+7Nu3D4Dbt2+bEtgn5eAA7T98lg7/hKH+uQhhYeg78dSe2odaHTyZ6SQTrYQQQlg+V9esnTd3uzkpL8b8JCRJtSBt2rQhMjKSoKAgAL7//nu8vLyIi4vLnhsUKwYDBnA38jDz+21mV+HG9Ir9jr7j/NjjEsy3jdcQuVsmWgkhhLA8ObVtaF7cjjQvxvwkJEm1IEopatasadrJqm7duvTv35/ixYsD8PnnnzN+/Pinvo9DYUW3qUE0j1vI5ll/M7/Cx/gn7+fdja1wquPH2YH/gytXnvo+QgghhBBPSpJUCxYUFMSnn34KGCdZ7d6927QrDRh3sbl58+YTt29tDU16etD9xCjij51jdquF3ChWBt9Jw8HLC3r2ZM0nEZw589SPIoQQQgiRJZKk5hFKKVavXm3aheb8+fO0bNmS7777DjAmsWntdJNZ5Svb8uqqzjwXuwUOH4a+fUlespSWn9XmSrlAvqsezoblMtFKCCGEELkjV5NUpZStUmqoUipJKRWSTp2aSqlrSqk/Un3eSylTSqnPlVLHlVJHlVJzlVJFcvMZzM3GxgYwLmu1Y8cO+vbtC8C2bdvw9vbmzz//fKr2lQKefRbCwji/K4rZtSZSRN1h8ME+BLT34gen92WilRBCFDCWsA1nWve//3maeHPy2ayt027b2vrp2y4Icrsn9XVAA39kUKc0sERrXSfV5+uUsp5AMFBDa/0MkASMy9GILZRSirp16+Lh4QFA4cKFqVevHhUrVgRg9erVfPPNNyQmJj7xPXyqFefV3QNxiTnMvNc3s6twI3rHfkvfcX5EugZzZ9EaeIreWyGEEHlDXtuGMyvx5uSzpff2Ud5KZk6uJqla60kpCWdGmU1poKlSanfKZ4xSqlhKWVdgqtY6PuX4e6Bbmq0UMAEBASxatIgiRYwdy+vWrWPy5MnY2toCxv3E79y580Rtl3ZWvDItiBYpE63mlf+YQNv9FO7SCvz8SB73P5ZNv8Ldu9n2OEKIPO7PP/+kbt26uLu78/PPP5s7HCFEHmSJY1KXAr5a69pAC6AsMDulrBxwKlXdU4CTUqrEw40opfoppSKVUpGXL1/O6ZgtTlhYGJGRkSilSE5Opk2bNoSEpDnCItPuT7QKPTmK4tfOwcKFUKYMViOG06KfF8sdezLhVZloJcSZM2cICQmhTJkyuLm5UblyZdNOc/fNmzePBg0a4OLigqenJ35+fvTo0YNTp4x/xc2aNQtra2vc3Nxwc3PD09OTFi1aMG/ePB7eztpgMBAWFkZAQAAuLi54e3vj7+/PmDFjABg1ahR16tTJsedNSEjAx8eHsLAw07m+fftSr149Ll68SOfOnenSpYtpIxMhhMgMi0tStdbxOuVvYK31NeA9oLVSygFQPNgLey/l10eeQ2s9TWsdoLUOcHZ2zumwLVKJEsbcXSlFeHg4I0aMAODmzZs888wzrFy58onbtnGwhc6dYcsWdk07xMrSfWmZsJS35shEK1GwGQwGmjRpgq+vLydOnCA6OprNmzfj4OAAGCc5du/enREjRjBgwACioqKIiopi+/btlClThrVr15racnZ2Jjo6mujoaE6ePEn//v355JNP6Nixo2mipMFgoH379oSFhfH1118TExPD+fPnmT17Nr/88kuuPLONjQ3+/v6m4UcAhw8fpmnTpiilsLKyomLFilSqVClX4hFC5BNa61z/AFuAkEzW9QJuYUxENwGvpiqrBNwEVEZt1KxZU4t/nT59Wjdv3lzv3LlTa631qVOn9MSJE3VsbOwTt5mcrHXEb7F6Vq2J+oh6RmvQV3DS872Gan3yZHaFLgqQo0ePmjuEJxITE6MBvWvXrjTLp02bpp2dnfXFixfTLE9MTNRaax0eHq5dXV0fKY+KitKOjo56xowZWmutJ06cqD09PfX169cfqWswGLTWWn/yySe6du3aT/Q8TwrQmzdvztF75NWfkbwo7aXjjR9LjCGn6uZkzAUJEKkzkQOavSdVKVVKKbVDKeWXchyilHJM+doGGAvM0VonA3OA15RSdimXvwUsTXlgkUlly5Zl3bp1vPDCCwCsWbOGt956y7TmakxMDAkJCVlqUykIbFScnrsH4hxtnGj1R+GGdLn4Lfj5QXAwcfNlRyuR/7m4uFClShUGDx7MoUOHHimfPn06/fv3x93dPc3r748jT4+Hhwfdu3dnwYIFAISHh9OvXz8cHR0fqXt/Y5CHjRkzhurVq+Pu7o6HhwevvfYa9+4ZX0zdunWLHj164OnpiYuLC/Xr1+f3338HYMeOHdSqVQsPDw88PT3p3bs3N27cAIwrjsyaNYsVK1bgljItukOHDri5uXHt2jVCQ0Pp1auXKYaDBw/SvHlzypYti5eXF8HBwRw5cgQwDk947rnnGDp0KG5ubjk6VEFkTl7bhjMr8ebks6XzRzDd8+JBNuYOACgM+AD3x5U6ABuVUskYVwLYCnycUjYbqABEKKXuAUeBQbkbbv7z1ltv0bJlSzw9PQF477332LlzJydPnkz3H7mMOLsYJ1oZJgeRcCYKh7nTYdo0iq9rhRNlmeDdH5fhfWjbtzSFCmX304h8bfBgSLWhRa6oUQNS1iPOrFWrVtGnTx/8/f2pV68e77//Pm3btgXgyJEjjBw58qlCevbZZ03DAo4ePcqHH36Ypev//vtvFi9ejJ+fH7GxsTz33HMsWrSIbt26ER4ezvbt2zl16hR2dnbs3LmTCxcuAPD222/TtGlTxo4dy+3btwkPDyc2NvaBBLlt27a0bdsWpRRLly41bfOc2pkzZ+jVqxfh4eFUr16d5ORkJk2aRHBwMGfPngVg//79tGvXjqioqKdapURkD0vYbjMr3VFZiTcnn00WwHk6ZsnltdZBWusFKV+f11p7aa0jU47DtdY1tdaBWutaWuv3dcpsfq21QWv9gda6hjaON31Va51NG9sXbOXKlTN93adPHz766CNTghoaGsqMGTOy3Ka1NThU8IRRo+DcOVZ0X8hF6zK8dX44bQd5scKxJxNlopXIh8qXL8/WrVuJiIjA19eXDh060KlTJwwGA1prbt++bap74MAB0+SoEiVK8MUXXzy2fa21ac3kJ3mRNHXqVKysrFi5ciWzZ8/G1taWkydPAvD8889z9epVFixYQFJSEvXr1zdNuqxduzabNm0iMjKSIkWKMGjQIHx8fLJ8/3HjxvHXX3/RrFkz3Nzc8PDw4PPPP+fq1aucP38eAE9PTz7++GOsra1N43mFEAWLJfSkCgvTsGFD09d3797ln3/+Mb3SMxgMzJkzh3bt2qX5ejFdtra0nduZO9M6s/Srw9ybMIngK3MoNmc2e+YEcKDtQNrN7wryj5HISBZ7NM0tMDCQuXPn8sorr9CyZUsWLlxIlSpVTK+1AapXr050SldO/fr1M9VruH//fvz9/QGoWLEix48fz3RMf//9Nx07dqRo0aK8/PLLeHl54enpaZqIVa9ePbZt28a4ceP48MMPCQ0NZeTIkZQoUYKwsDBmz55N//790VozbNgwunbtmpVvCQCnT5/m1VdfZdKkSenW8fLyQqW1SrsQosCQUREiQ4UKFWLjxo289957AGzfvp3evXvz22+/AcYkNikpKdPtFS4MHT6uSudLkzj2axQ/Bk6kKLdpt6I3eHnB++8Ttf00167lyOMIkePS2p44ODgYJycnzp8/T48ePZg2bRrXnvCH/NSpU8yfP5+BAwcC0K1bN2bNmpVmcpuYmPjIn8+PP/4Yb29vNm/ezKhRo3jttdews7N7oE716tWZN28eBw8e5OjRo/Ts2RMwrhTSs2dPIiMjmTRpEgMGDGD58uVZfgZPT0/27NmT5euEEAWLJKkiU+73aLz00kvs2bOHVq1aAca1HD09Pfnnn3+y2B7UalycnhEDKRV9BMNvm6FhQ/j2W9xfrECEczDfNVnD3ggZ0CPylkOHDhEaGsq5c+cA4+v4H3/8kTt37hAcHMygQYMIDAykbt26bNy4keSUddrOnj2b4Z+jmzdvMnfuXIKCgvjwww9p0KABAO+++y5OTk60a9eOY8eOmepHRETQoEEDoqKiHmgnISGBuLg47t69y71795gwYQIbN240lY8fP57t27djMBgoUaIErq6uxMbGcuPGDT755BPT63hfX1/s7OyIjY3N8vdoyJAhHDp0iFGjRnE3ZReQI0eOMGHChCy3ld9ZwnakOSmntg3NyvctqzHk998TSyKv+0WWKKUICAgwHVetWpXu3bubZvOOHz+epKQkU89rZri4KnANgkZBJJ+PYkGj6QSdmEbz31px+reyTPTuj/OIvrTtU0omWgmLV65cOVxcXGjYsCF37tzBysqKZ555hg0bNlC1alXAuG3x5MmTGTlyJGfOnMHOzo6SJUvSsWOoyvadAAAgAElEQVRHU68lwOXLl3Fzc0MpReHChalfvz5Lly4lMDDQVMfe3p6NGzfy1Vdf0bFjR65cuUKRIkVwd3enX79+eHt7PxDfZ599Rp8+fXB3d6dYsWKEhITQpk0bU7m3tzfDhw83TZysUaMGM2bMwMHBgfj4eBo2bEhcXBz29vb06tWL0NDQLH+PqlWrxsaNG/noo48ICwvD2toaT09P3n///Sy3ld/lte1Isyqntg3NyvctqzHk998TS6IKwupNAQEBOjIy0txhFAidO3cmMTGRFStWALB582YCAgIoVqzYY6580ImjSex8fxnlN0yivmErd7FnuX0IZcYNpO47gY9vQOR5x44do0qVKuYOQ1iwgvAzktGw3Pzwz3dOPV9W2s1qDPn99yQ3KKX2aq0DHldPXveLbLVo0SIWLVoEwPXr12nevDmffPKJqTyt8Xpp8XvGlp5ruvB83BaWjDrEylJ9aJmwhLqDa0FgIMyaRdRJ2dFKCCGEyK8kSRXZ7v4kDEdHRzZv3mya4HH48GG8vb3Zvn17ptsqXBg6flKVzpcncTEiCiZOhNu3oXdvilT24gen95nxH5loJYQQQuQ3kqSKHKOUom7dupQvXx6A5ORkatWqZdq/e+vWrXzzzTfEx8dnoi2oFFgcBg6EI0e4vHAzO+wa0jv2W/qMrcBu55Z833QN+/bIRCshhBAiP5AkVeQaf39/li9fjrOzMwBr167lv//9r2lR8hMnTmQqYUUpnDsH0fzmIjbNPMe8ch9RI3kf7/zaCsdafkwo8yV//3k1Jx9FCCFyRV7bjjSrcmrb0Kx837IaQ37/PbEkMnFKmNWVK1coXbo0ADVr1qRYsWJs2bIly+38dSSJXcOWUWFDGPUMv6Pt7VEhITBwILEVAylR4vFtCMtSECbFiKcjPyNC5E0ycUrkCfcTVK01X3/9NR988AEASUlJBAYG8vPPP2eqnYrPGida1YjdyuH5h1B9+sCSJVCrFqecAvnuuVn8ulImWgkhhBB5hSSpwiIopQgKCqJJkyaAcX3I0qVLm5auunTpEmFhYabtWdNTpAhUDakKkyZBVBRH3pyIQ/JtBu/vzfNtvZhZ6n1++OA016/n+COJbFAQ3vSIJyM/G0Lkf5KkCovk4eHBunXrCA4OBozjVwcNGmTaPef69eskJCRk3Ejx4jw7ybij1U99N/GHQ0N63fiW3l9UYHdp40SrpLsy0cpS2draZm6MsiiQkpKSTOPZc0pe21korVjvf9KSlZ2WcqpuVr7HOVVXWC4ZkyryjP/7v/+jcuXKALzzzjssWrSIs2fPPrLveHru3YONs6O4NGYajU9Pw51oKFsW3nwT+vQhsVgpMtmUyAVxcXHExMTg6emJg4ODaWteIZKTk4mKisLe3h4XF5ccu09eW7Q9Jxelz891Re7L7JhUSVJFnrRlyxYOHTrEW2+9BcCgQYOoUqWKaU3Wx/nrSBI2q5ZRbl0Y/P47yXb2LFQhXA0ZSKtPA/HxycnoRWbFxcVx6dIlkpKSzB2KsDBFihTBy8sLq6edBp6BvJboSJL6ZHVF7pMkNRVJUvM3g8FAcHAwNWvW5IsvvkBrzYIFC2jRogWOjo6Pb+DwYSJ6TaLK3jkU4xYRBLKrxgCe/awrDVs6PPVSKEKIvCmvJTqSpD5ZXZH7JElNRZLUgkFrjVKKAwcOUKNGDaZMmcIbb7xh6oWztbXN4FqI+C2OY/+ZQ+3IMKpwjKs4sbRkX4q+159uH5TLrccQQliIvJboSJL6ZHVF7pMlqESBc3/Mor+/PxEREYSEhACwfPlyPD09OXHiRAbXQu0mxem159+JVrsdGtD7+jd0/bACtGwJa9eiDbKGlRBCCJEbcjVJVUrZKqWGKqWSlFIh6dRxVUpNVUodU0pFKKW2KaWqpZSVUkrdVkr9kerzVW4+g7B8SikCAwMpkbKCv7e3N23btqVcOWNv6KxZsxg3bly6S9i4uCq6z2hA07jFbPzhHDcGfQT79kHLltxyq8BEny9ZNOUqj1tcQAiRt+X3nYWystNSTtXNyvc4p+oKy5Wrr/uVUgMAB6AdEKa1XpBGnRaAg9Z6acrxe0AzrXVTpVQlYKLWuklW7iuv+0VqvXr14uTJk2zfvh2AXbt2Ua1aNYoWLZr+RYmJsHw5B/qFUT32d+5iz3J740Sr1p8FUqZMLgUvhBBC5HEWPSZVKbUFmJJWkppG3VeA17TWDZVS9YClwGnABvgDGK21jsmoDUlSxcPi4+NxcHDg7t27uLm50b59e8LDw4F/x7am5fZtWPe/QySHTSb46myKcpsIAvnjuQHU/qYrtYMccvMxhBBCiDwnX4xJVUq5Ap8Bn6acigQ8tNYvAEFAIrBWpZFRKKX6KaUilVKRly9fzq2QRR7h4GBMJu3t7VmzZg3vvfceABcuXMDHx4dff/01zeuKFIFOn1aj8+VJHPnlIrNqTqAYt3j7z95Ub+UFw4bB6dO59hxCCCFEfmWxSapSqhSwFhiltd4KoLVO0FobUr6+DQwDKgMVHr5eaz1Nax2gtQ5wdnbOxchFXqKUol69elStWhWAW7duUaNGDdP41X379vHNN99w8+bNh65LmWgVOQinf44w77VN2DRpAN98AxUq8H8VWvJ987Xs3ycTrYQQQognYZFJqlLKHdgIfK21nptRVYzPEJcrgYl8r3LlyqxcuZLy5csDxu1YP/roI9Pr/3Pnzj2yVaerm+KV6Q2wWbYYzp3j3siPcDy9j3c2tKR4zQqE+cpEKyHEk7GUrUBzqm1L2L7UEmIQaUtzTKpSamZWG9Ja98n0TVONSU3pMV0J9NJan1BK+QDrgI+01kseuq4NsEdr/U/KK/4xwHNa6xYZ3U/GpIqnER0djVvK31ZNmjTh8uXL7N+/P8Nrjh9K5I/hy6jwyyTqGX4nnkIsLxTC9ZABtP8iEHf33IhcCJHXWcraoDnVtiWsZ2oJMRQ0mR2TapPO+dvZHE9GCgM+QImU468BV+B9pdT7KecStNYvAxpYqpSyBZKBg0D3XIxVFEBuqf47/cEHH3Djxg3AOMGqcePG9OzZk1dfffWBaypVs6PS2q7cvt2VRSkTrVpfnU3RWbO4HREIwwZCly7gIBOthBBCiLTIjlNCPKFr167RvXt3unfvTmhoKLdu3eLHH3+kW7duODk5PVBXa9j9axxXvplNq78nwbFj4OTEOs++/NP2TTq8V5bM7OAqhChYpCc151lCDAWNRS9BldskSRW5YdmyZXTo0IFt27ZRv359bt68iZ2dHfb29g9W1Bq2bCF2bBhFfl2OFcn8YtWCv5oM5KUvmlPjeYscKi6EMANJUnOeJcRQ0DxVkqqUqgWMyOA6rbXu+BTx5SpJUkVuOXToEFWrVkUpxaeffsqECRM4c+YMxYoVe6TuvXvwa/gFrnwxjSZnp+FGDKcox3qf/riO7EOb3qWwszPDQwghLIYkqTnPEmIoaJ52ndRY4ECqT9uUX/cC+4B2Sinp7hHiIdWqVTOtBNCwYUPeffddU4L60Ucf8e2335rq2thAi9e96HHmM24c+JtZzRfwj7UXA88No2V/L3Tv3rBnj1meQwhhGSxlK9CcatsSti+1hBhE2jL1ul8plQwU0lonpjq20VrniUUgpSdVmJvWmrZt2+Lu7s7UqVMBWL58OUFBQTimGox6+zas/d8hyq+fxPNH5sDt2yQHBDLDfiDlR3alQYtC6e6LLYQQQuQF2TomVSllABxSJakGwFaSVCGyJjk5GSsrK86ePUvZsmUZO3YsI0aMIDk5GYPBgK2t7b+VY2Nhzhxix4ZR4uL/cYVSLC/ZB+tBb9J+iEy0EkIIkTflxLaoMjJDiKdkldIN6uPjQ0REBL179wZg8+bNeHp6Prj+aokSMGgQd/ceZW7vjewuFESv69/Q8/Py7CrVivHN13Lgzzzx/0QhhBAiy9JMUpVSrZVSSUqpRKVUIsadnWKVUneUUndyN0Qh8h+lFIGBgbimDHpydHSkSZMmVKpUCTCuFDBu3DiSkpJwdVOEzmxIs5uL+XXaWX7y/ZDnkiN5e0NLStb2Q3/5FVy9as7HEUIIIbJderP7SwFVM7pQa701p4LKbvK6X+Q1gwYNYvPmzRw+fBilFH/++Sd+fn4ULVoUgGMHjDtatTgThttf26BQIW62DGFO8YG0/jQAb28zP4DIs9zcICbm0fOurhAdnfvx5EdZ+R7L74fIj2Sd1FQkSRV50a1btyhatCgGgwFfX18CAgJYtmwZYJyIdX8VAQ4dgkmTSPhhDvZJt9lNLXY/P4CqnxsnWmW0vIoQD5PleHKepSwrJYS5PNWYVKWUU1Y/2f8IQhRs93tNraysWLBgASNHjgTgxo0b+Pn5sXLlSmPFatVg8mQOrIki/PkJFCeOt/f1wr+lFz+UGkb4x2dI2clVCCGEyDPSmzh1Bbicyc/9ukKIHKCUol69etSqVQswbsf6zDPP4OHhAcCJEyf47rvvqFQLeu8dRMmLR5nT68GJVmertoJ16yBZJloJIYTIG9Ibk+qT+hA4DVQH4tJrSGt9Ltujyybyul/kZ+PHj2fIkCFERUXh6urKxYsXKVmyJLa2DqYdrbrGTcPuWgyUK8eZ5m+yr3pvWveSHa3Eo+T1cs6T1/2ioMuJdVJLaq3jlFLNAXetdXg2xJkrJEkV+d2FCxfw8vICoGvXrkRGRnLy5Ml/x60mJsKyZRAWBtu2EU8hlhcK4Xo3mWglHiRJUc6TJFUUdDmxTqqpbaDnE1wnhMgh9xNUgAEDBvDZZ5+ZEtROnToxbdYs6NoVvfV3Fn18kJVOvWh9dxEDwgO5WKY242v+yKa1d+UfPSFbROYCS9nqVAhLl9kkNZF/F/M/DfjlTDhCiKf18ssv0717dwDi4+O5desWd+/eBeDevSSue+6i8V9jOLg2ivDnxj8w0erPpsPhzBlzhi/MLDra2EP38EeWO8o+Wfkey++HKMiyvASVUsoZuAjY6TyyfpW87hfCaNOmTTRq1Ijly5fTtm1b4uPjiYlWbPtsJ84Lw2iWsAKVnAzBweypNRD7Ns3wr/EkL1yEEEKItOXk6/4bgDVQ9AmuFUKYUcOGDdm/fz8tWrQAYMaMGTxf04MmY5+h2a0lqLNn4cMP0ZGRBH4STJHn/Agr+xVLZ1wjMdG8sQshhChYspykaq2TUr60zeZYhBC5oHr16tilTOsPCAigX79+uLm5oRR8tWAB/ytalJuH/ya82QKirT0ZePZ9WrzuyZISfZjcN5ILF8z8AEIIIQqEdF/3K6U+zuC6TwBnrfW1LN1MKVvgHWAs0ENrvSCNOgr4DOgCGIB9wBta69sZlWV0X3ndL0TmhISEYDAYWLRoEQDLlv3GzR3FKTRzJsHX51KU20RQC5dPB+I7rAsUKmTmiEV+ZG2d9pK+VlZgMFhm25awfWlWY7CEmEXBlB2v+xtk8PkdSEr/0nS9jnEC1h8Z1OkJBAM1tNbPpNxnXCbKhBBPacGCBcyfPx+Aq1ev0qVLCw5bLabz1SnsX32BmTW+p5RdHL6f9AQvLxg+nM0zzxAba+bARb6S3p4T2bEXRU61nVayl9H5nJDVGCwhZiEyYpNegda6QXbfTGs9CUAp1TqDal2BqVrr+JTj74GNwKDHlAkhsoGNjfGvBScnJ3bu3ImLiwtKQRGPM4yIGs3C9csorxMgLAz99de8bPiSDf2COdl0IEFjm1Gtuky0EkII8fTSTVLNqBxwKtXxKcBJKVUiozKt9QN9OUqpfkA/gDJlyuRsxELkQ0opAgMDTcfW1tYEBQXhX70KODnxW3IyO5zq4PtLLM3+nkGLdcGcWleOSWXfxP2DPrTs4SQ7WgkhhHhiltjloTCON73vXsqvVo8pe4DWeprWOkBrHeDs7JwjgQpRkPj7+7Nw4UKcnJwA+PXXX5m29jtCT3/K9f1/M6HudKKtPRlw5n2av+bJ0pJ9uLtdxoILIYR4MpaYpF4AUnd9lgFuYVz6KqMyIUQuGjduHEeOHMHa2prK/rZMi/uekXWL8fN/DrCqZE/aJiyk0IuBULs2ybNms/032dFKCCFE5pk9SVVKlVJK7VBK3d/Fag7wmlLq/ovCt4ClKRsHZFQmhMhljo6Opq+nTJnCZ5+9T9cx/rS5+D1BFaqwr1cviIvDqndPKjfx4gfn4fw46gxxceaLWVg+q3T+ZUrvvCW0bQnbl2Y1BkuIWYiMpDkmVSk1IINrwoExWushKXWtgC1a65eeMIbCgA9QIuV4NlABiFBK3QOO8u/EqIzKhBBmopSiXr16puNr167iWtGda927w8yZrBqyAj0xnF5Xv8bq0y9ZP7olp5sNIGhsM6r6m/3/ysLCPO0yU+Zo2xKWbMpqDJYQsxAZSXOdVKXUrnTqa6AVcBmowr/jQ//SWlvnSITZQNZJFcK8wsPD6dOnHz98ugubmStpem4absRwinLs9H+THpv7QMpYVyGEEPlbZtdJTXcx/wwatsa4PqnGOJEJQEuSKoTIyKlTpyhfvjwA3Tt/jM3KGN4wHKWuYbtxU4Bu3bjVcyCxFWri6WnmYIUQQuSY7FjMPyMaKAY4Am5P2IYQogC5n6ACvPlOU+qOf55q17dxc/sBtvr6kvjTTxQNCiDKqzYTAmezdYNMtBJCiIIso21RXwO6pxwu0FpPTTlvDSTe7zl9+NgSSU+qEJbLYDDQunVrXvT3p8oeTyptmkQV/o8rlGJ5qb7YDupP+yFlKV7c3JEKIYTIDk/1ul8p9RYwAvgKY6/pEGC81vorSVKFEDlBa41SivXrDvBl8Fe8Y3OT4HursSKZDdYtuddvAK0nNsueKd5CCCHM5mmT1GNAf6311pTjOsA8rXW5VGNSZwL2QCGggySpQojsoLXmwIEDlClTnp0LYzn/4Te0vzoPN2KgfHl4802utulNMR/Z0UoIIfKipx2T6gUcTHV8kEfHnt4GooGTTxShEEKkQSlFjRo1cHIqRqv+XtRcG8LQzm9wb85P4O4OQ4dSpJIHSxx7M+X1vURFmTtiIYQQOSG9ntQ/gAla659SjjsAH2uta6Ssi5qgtbZNKbPB+LrfYt/BSU+qEPnHh23aU3a9PV2TVlOU20RQi53PvcFzY1/hpaaFUOrxbQghhDCfp33d3xRYBqzFuBZqKyBEa706nZv5aK3PPV3IOUeSVCHyl4SERCJ+jef4BzOpf3AqlTnOZUqzolRfqoX1o3bXcuYOUQghRDqeep1UpdTzQCeMQwIWa63zbJYnSaoQ+ZPWmjWr/+T0jLOU/+Unmt1dgRUGrtaujfOoUdyq25SixS32JY8QQhRI2b6Yv1KqjdZ65VNHZgaSpAqR/yUlwYqw3egpPelw9SrWV65wzrYcc4u9QrnP+tOpnye2tuaOUgghRE4kqYb0ZvArpZ7XWu/LYoy5RpJUIQqYxEQuTVnKX4MnUF/vJJ5CrHDoxvk2obT9rDYVKxYxd4RCCFFgPfWOU0qpV5RSLZVStZRSLvy7Ber9cruUXz2BFU8bsBBCZBs7O1zeDsH/xg5mvr2TlY49aR3/M+//3IhrlRoyodZs/jlz19xRCiGEyEBGY1KTgf8DSgN3Ac/UPalKqSigIVAGeEdr3Srnw30y0pMqRMGmNWxfE8v+d8NocnI2lTmOLl2aufb2OAweTLvBQ7GxMXeUQghRMGTHxKlkoKjW+o5SqigQq7W2VkqtB0IxrpF6CNiQUjY2+8LPXpKkCiHui7qgiZ63iaq/f4vNmjVYKcVvdi05HNSPS9WvMXRYK0qVKmXuMIUQIt/KjiTVAPgCZVNObU5JUu8AFTAu4r8ICAGe11ofyY7Ac4IkqUKINJ0/z7F3p1FyyXTciOEk5Vnp0Ztyn79JvdZWFC1qj4ODg7mjFEKIfOWpx6RiHINaB9iY8kl9XgHWwDiMC/tbbIIqhBDp8vamyuLPubL3b35oPJ8YK3eGXPyQZn09WefxFo0chxEbe8vcUQohRIGU0Sist4GlqXaWMqScv9/1qrTWR5VSx5RSNbXWe3MyUCGEyClVn7ej6q8hxMWFMP+LA1hNnUzHG3N4lTvQbA8MHMjIffso5enF0KHvmTtcIYQoEDLqSR0PJCilLiulfkl1/uFNB9cA7bI9MiGEyGXFi0O3/1any7Up7F15kVNvfw83bsCrrzI8bCq2H51h9mdniYuDhQsXcvXqVXOHLIQQ+dbjJk49D9gBzsDKlDGpnYDVQJzW2k4p9SIwSmvd6LE3U+pl4FuMPbiJwCCt9R8P1VkPOKY6ZQ84aq3LKqVqAr8Cf6UqX6S1/jqj+8qYVCHEE9MaNm3iYP8wnj25AoVmvXUw4w0+VBvyPF9+3ReDwYDBYMDOzs7c0QohhMXL1tn9KceGh5agOpOSODoBx7TWro8JyBE4BbTSWu9SSgUBPwNl798jneveBfy01gOUUs2ATlrr1x/3YKlJkiqEeFqJibB++nmu/28azf7+d6LVL+Xe5Gan5/jfjM5s2rSJ6tWrmztUIYSwaNkxceojICG9Qq112ZRfr2EcGvA4zYDjWutdKddtAf4B0u2BVUo5AIOB+8tblQaaKqV2p3zGKKWKZeLeQgjxVOzsoM1Ab3qeS5lo1Wgel6zcGXB6KO9+1ZJlpUpRJT4eMA4FGD16NPfu3TNz1EIIkXelm6RqrcdorQ2pTv2dUd1M3Kscxp7U1E6lnE/Pm8BarfX5lOOlgK/WujbQAuPyWLPTulAp1U8pFamUirx8+XImwhNCiMyp+rwdfX/rRtXr25g/fD+3O77KS1FR2L3wAlcq1CHirePMm3kCa2vj3NSIiAhiY2PNHLUQQuQt6b7uz/YbKfUfoKLWuleqcz8Df2itv02jvgNwHKivtU4zQVZKuQPngWJa6/j07i2v+4UQOe7GDZg9m3MjJuETf5zLlGZl6b7YDHqdYZOa8eKLNVi8eDEAWmuUengOqhBCFAzZ8bo/u13AuIVqamVSzqdlALAuvQQ1hTXGLVvTHZYghBC5wtER3n4b6+PHmBX6GxH2L9Lrypf0GOXHzKtVeO5SZ44fS+bq1av4+PiwYsUKc0cshBAWLTeT1BWAv1KqGoBSqhZQGdiklNqhlPK7X1EpVRjjWNQvUjeglApJmYCFUsoG41jVOVrr5Fx6BiGEyJCXt6LXnEY0iVvKmolnmev9HwIMEXywLQS3lyvBN9/QoHp1fH19ATh27BhffvklN27cMG/gQghhYXLtdT+AUqoB8D+MGwLcA97D2JO6C2intY5MqTcUqPTwLH6lVG9gEJCc0sZW4OOMXvWDvO4XQpjX4X2JRP5nCT3iJmG9azs4OHDg2W7srjmQaI+9jB49kIsXL1K6dGnOnz+Pk5MTRYoUMXfYQgiRI556Car8RJJUIYTFOHCApPGTSJw5lyLcYTe12fnc6wSM6079xoXo2LEDBw8e5MSJEzJuVQiRL1nimFQhhBDVq2MzYyp7V0Txg//3lOAG7/75GpWbejPTZQQ1S73DRx/9z5SgtmrViilTppg5aCGEyH2SpAohRC5TCl5q40jfA29T9O9jzOr+K3vs69PrypeMnNGA1jNmwfr13L5584HrEhMTmThxIleuXDFP4EIIkYskSRVCCDPy8lb0mtuYxnHLWDPxLOtq/IeSf+2GFi0o8vzz9L/TEI9CXbh3D37//Xfeeust9uzZA8Dt27e5e/eumZ9ACCFyhiSpQghhAe7vaNXyz9Go8+dh3jzuFHel1eb3aNzbi8Ul+nJ6sRNbthynSZMmAEydOhU3NzdkwxIhRH4kSaoQQlgaOzvo1o17m7czf/h+Vjn2oPWdBfSbWhP7oJ5MrT+f7b/d5YUX6vLOO+/g7OwMwJgxYxg7duxjGhdCiLxBklQhhLBQxYtDt/9Wp8u1qUQuj2Km/3eU5BoDd79KpSbe1Fi4gk979zbVP3z4MIcPHzYdr1q1imvXrpkjdCGEeGqyBJUQQuQhF85rfh2xkcA9YVQ9tRK0Jjm4FeGFB1L3kyZUrKyxtrbm0qVLuLu7M3z4cL744gu01iQlJWFnZ2fuRxBCFHCyBJUQQuRDXt6K3j81pupfy+DsWfjPf0jcvpu+i5pjU7US0yp/z8ofr1OypDORkZEMGDAAgMjISDw8PNi5c6d5H0AIITJJklQh8oCYmJ/YtcuXLVus2LXLl5iYn8wdkrAE3t4wejTnfv+bGQ3ncdnKlTdPvkfjXp4sdnydyGkaGxsvAAoVKkSzZs145plnAFi/fj2jR48mPj7DDfuEEMJs5HW/EBYuJuYnjh/vR3LyHdM5K6vCVKo0DVfX7maMTFiaGzdg7dgDWE+dRKtY445W+x3qUH3KAFSXzlCokKnuyJEjmTdvHmfOnMHKyoqDBw/i6+tL8eLFzfgEQoiCQLZFTUWSVJGX7drlS0LCuUfO29v78MILZ3M/IGHxtIbfV97g5Ec/0iFmEiUv/QWlSxPX5TVWevSn3Ts+FC0Kt27domjRov/f3p3HR11d/x9/nQQdwAUQMKxBQAgIWHaiqKBs2opiFZTFtSwiFkX9WSsupVWUSlF/FpSlWIUoakURLUVARWUTAUWqRAQRRNlJZQkhIef7x0xiDElIIJOZhPfz8ZgHmfvZ7p3RcPh87rkHd6dp06bUqVOH+fPnh87hKssqImGhOakiZURa2qYitYuYQacrK/O71XdQ5cevYN48uOACTn32r/R9oAEfVrmCCVfOZfN3FbOPeeGFFxg1ahQAqampNG3alFdeeSVSQxARUZAqEu0CgfgitYv8QkwMdO0Kb7zBgsnfMr3OH2mTsYzb3gomWj3baBxvT0uhTZsOdOzYEYDdu3fTrFkzatSoAcDmzZt54okn2LVrVyRHIiInGAWpIlGuQYNHiYmp+Iu2mJiKNGjwaIR6JKVVt9/Fc+PmR9i6bBNTLknKTrTqcmNtljQbCCtXAlC7dm1ef/11OnXqBMC8efO49zA8bcQAAB9+SURBVN57+emnnwDYtm0b+/fvj9g4ROTEoDmpIqXAtm1JbNgwkrS0TQQC8TRo8KiSpuS4paTAO49+RrnJE7gmLYnYgwcgMZGNvxnG9+f1puMlAbKmpW7evJm6desCMHDgQN555x02b95MuXLlIjgCESmNlDiVg4JUEZH8uQMpKdiLL8CECfD11+ygGrOqD6TCnbdy5fBgolWWxYsXk5yczM2halc33HADbdu2Zfjw4ZEZgIiUKkqcEhGRQjEDq1IZ7rgD//IrXhgwj+UnX8DNO/7KdSN/TrRa+2UmAOeff352gJqRkUFKSgr79u0DIDMzk0mTJrFjx46IjUdEygbdSRURkSOkpcGciZtIeWISl30/mTi2s46zSel7G+3G3wRVquR53PLly2nfvj3Tpk1jwIABpKWl4e6Uz7FGq4ic2KLyTqqZdTKzlWa22sw+NbPEPPZpY2a7zWxpjtfdoW1mZn8xs2Qz+9LMppvZKSU5BpFop+pUUhwCAeg1PJ6bshKtLk5iV2wc7V6+C2rXhoED+fyfq9i69ZfHtWvXjjVr1nDVVVcBMGPGDGrUqMGGDRsiMAoRKc1KLEg1s8rATGCYu58L3APMMrOKuXatBrzu7ok5Xn8LbbsR+DXQ0t3PAdKBMSU0BJGol1WdKrj4v5OW9h3JyYMVqMpx+VX7AAPf60fLfR/DqlUwYAD+8sv86ubWbKx1HuPPm86i99LIejDXrFkzTjnllOyfb7nlFurXrw/A+PHjGTVqFCfCUzwROT4leSe1B5Ds7ksA3P0D4EegS679qgHdzWxZ6PWomZ0W2nYtMNHds4pNPw30DX/XRUqHDRtG/qJ8KkBm5gE2bBgZoR5JWVK+PNCyJUyaxO7VW5jS/Cmq+G6GLb2exl3qMDXujySN/o7Q9FQA2rZty7hx47KrV61atYolS5Zkv58/fz67d++OwGhEJNqVZJDaAFifq219qD2nmcBZ7t4BuAyoD7yYzznWA2eYWaXcFzOzwaEpBZ9qAr+cKFSdSkpK1YaVGfjFHVT49ium9n2X5SdfwE2hRKuFVa5k46S5kJl5xHFTpkzh7bffBoJlWa+44gpGjvz5H1EZGRklNgYRiW4lGaQacDhXW0buPrh7qoeeA7n7buBuoKeZVcjjHFm/zY4Yh7tPcve27t62evXqxTQEkeim6lRS0uLPiuGWl7rR5ac3mP30t0yvfR8dMpdw1pBLISEBnnySFfP3kDP2zFpb9ZRTTmHRokXcddddAHzzzTfUrFmTefPmRWIoIhJlSjJI/R7I/TdlfKi9ILHAQSAtj3PEA/uAlGLqo0ippupUEilZiVY3fv8oMd9vhqQkOPNMuOsumnarzWuVBjL5tlVs2/bzMWZGq1ataNSoERC8i9qlSxeaNm0KBNdjHT16NHv37o3EkEQkwkoySJ0FnGtmLQDMrD3QBHjPzBaZWaNQ+3WhJCvMrBzwGDDN3TOBacBAMzs5dM7fAzNdM/BFAIiL609CwiQCgXqAEQjUIyFhkqpTSYk6o2YA+vWDRYtYM20Vs08fwBUHXmbQs63ZUPP8IxKtsjRp0oQZM2ZQp04dAN5//33Gjh3LSSedBEBycnJ2aVYRKftKdJ1UM7sY+CvgBB/V303w7ugSoJe7f2pmNwO3A5mh/RYCD7l7qpnFAn8GfhM6/kvgdncv8LeW1kkVEYmczExYOCuF9Q+9wIVrJpDA12ynOm/HDaTfwiGUT6iX77EpKSlUrlwZCBYRSE9PZ/ny5SXVdREJA5VFzUFBqohIdNi0MZP59y+gxusT6HHoLWJjgMsvx28bxoYGXWnYKP8HfEuXLmXv3r1069aNw4cP06FDB4YNG5Zd/UpESoeoXMxfRERObDkTrXZ+8i3cdx8sWYJd2oPDjZvwbOMneWf6LxOtsiQmJtKtWzcA9uzZQ926dakSqny1e/duxo4dq3KsImWI7qSKiEhkpaWxYNjrVHx+POdlLuYAFZh1Sj/23ziMng+1Ii7u6Kd47bXX6NOnDytWrKB169bs3r2bQCCQXVRARKKH7qSKFINwlRj97LOufPCBZb8++6xrsfUjXH1WuVUJm0CALlP60WTnIpLuCSVa7X+ZgROCiVbTekyHtLQCT9G7d2+++eYbWrVqBcCYMWOoW7cuBw4cKPA4EYleupMqko+sEqM5KzjFxFQ87mz5zz7rSkrKgiPaK1fuQsuW84+rH+Hqc7jOK5KXzEz44M0UNjz0Ty767wQasw6qV4eBA0m9YQiZdetxtBukS5cu5ZNPPmH48OEADB8+nDp16nDvvfeWwAhEpCC6kypynMJVYjSvALWg9qL0I1x9VrlVKUkxMXDJbyszcM2dBDasZc+r78L558OYMQTOacDCylfy3G/f5eu1R1a0ypKYmJgdoLo733//Pdu3b8/ePn36dM1fFYlyClJF8hEtJUaL0o9w9TlaPgs58dSrH0OV3t3gzTfh22+Z3ew+2mYs4dY3ekDTJjyX8CT/Tso70SqLmTFz5kyeeOIJANatW8f111/Pyy+/DASLCBw8eLAkhiMiRaAgVSQf0VJitCj9CFefo+WzkBNcfDxXfvEoW5ZsZnLnJHbFVOfWr++i84Da/KvyQP7z2KoCDzczABo1asQXX3zBgAEDAJgzZw41atRg9erVYR+CiBSeglSRfISrxGjlyl2K1F6UfoSrzyq3KtGkVWKAQe8HE62m372K2af354r9L3Hp/a2D0wKSkkhNObKiVU7NmzfnjDPOAKBu3bpcd9112eVYk5KSePjhhzl8+HBJDEdE8qEgVSQf4Sox2rLl/CMC0vySporaj3D1WeVWJRpVqQIDxrak957JLJv5A4fHPgk7d8KAAaTXrMvUGvfz8phN7N9f8HlatmzJc889l11+ddmyZcyZM4fY2FgAFi1axJ49e8I9HBHJRdn9IiJSdmRmkjF3AQuuHk/X1NkAzC13OZt6DuOS0V1p3KRw92bS0tIIBAKkp6dTq1YtunTpwowZM0KXyCQmRvd4RI6VsvtFROTEExNDucu60XnPm8x68lum1z4y0WrNR0e/KxoIBAAoV64c7777LiNHBley2L59O7Vr12bWrFlhHYaIKEgVEZEyKBCA394Zz43fhxKtOk3PTrQ6p3ttGDQIVq3iaNNOzYxWrVrRokULAPbt20enTp04++yzAfjiiy8YPXo0KSkp4R6SyAlHj/tFROSEsGcPfDJxFT3WT4CkJEhN5YvTzuOjFsNoPfoaOlwUILQAQKE9/fTT3Hffffzwww9UqVKFjRs3csYZZ3D66aeHZxAiZUBhH/crSBUpwNdf38YPP0wCDgOx1Ko1mMaNJ+S5b+5KUgUlQ23blsSGDSNJS9tEIBBPgwaPFlsSUjjPLVJmpKSw/a//JOWxYEWr7VTn7biBVBhxK1f+Pp6KFY9+iiw7d+6kWrVqAPTs2ZPk5GSSk5Ozl7wSkV9SkJqDglQ5FsEA9dkj2mvVGnpEoFqUUqfhLDGq8qUiRbNxQyYL7l9AzTfG0+NQMNHqP+V6svmK2+gzsStnVCvarLhly5bx448/0qtXL9ydrl27cs011zB06NBwdF+kVFLilMhxCt5BLVx7UUqdhrPEqMqXihTNWQ1i+N2MblzyvzeZNW4D02rdR7uMxdw6sweVz2sCTz0VnCdQSB06dKBXr14A7N+/n9NPPz07CevgwYOMGzfuF+VZRSR/ClJF8pVfRsXxLfAdzhKjKl8qcmzKl4ffjqjHTVuCiVZLb59OzJnVYcQIvHZtXq86iH/8/jN27Cj8OU899VTeeOMNbrnlFgA+/PBD7r777uzKVnv37uXAgQMFnULkhKYgVSRfsUVsL5xwlhhV+VKR49cqMUDiM/1h0SJYuZJv2vfnst1J/O7vrfgm7nyevSCJpQsLrmiVl+7du7N27VouvvhiACZMmECNGjXYtWtXGEYhUvqVaJBqZp3MbKWZrTazT80sMY994sxsopl9ZWafmNlHZtYitK2qme03s6U5XmNLcgxy4qhVa3Ch24tS6jScJUZVvlSkmLVqRcP3JrP41S1MPudJqvpOhi4aQIPOdXm+5v3M+OsmMjMLf7qEhITsSlaXXHIJ9913H1WrVgXg4Ycf5pFHHgnHKERKpRJLnDKzysB64HJ3X2JmnYFXgPrufiDHfpcBFdx9Zuj93UAPd+9uZgnA3929W1GurcQpOVbK7heRnL5dH0y0qvXGeHqkBxOtYq/sCbfdBl27wnFUourfvz+BQICpU6cCMHPmTC688EKqV69eLH0XiRZRl91vZtcCd7j7+TnaPgMedPfZBRzXDxjo7peYWUdgJrABKAcsBR5x920FXVtBqoiIFKeDB+GdCd/RctlEGr4/BXbsIK1eI54vfxvxD91Ej2srE3sMM4OySq5u3bqVWrVq8eCDDzJq1CjcnbS0NMqXL1/8gxEpYdGY3d+A4J3UnNaH2vNkZnHAn4FRoaZPgVrufh7QGTgE/Nu0GJ2IiJSg8uXh6rvq0fCV0bB5M0yfzpa0atyaPILO/WvxaqWiJ1oBxITuxNaoUYPPP/+cW2+9FYDFixdTs2ZNlixZUtxDEYlaJRmkGkemRWfk1wczqwr8G/iTuy8EcPc0dz8c+nk/cC/QBDg7j+MHh+a9frqjqL8lRERECisQgP79qbRmMdNGrGT2af25cv8vE62Wf5xW5NO2aNGCmjVrAlCpUiV69epF8+bNAZg9ezZ/+tOfOHjwYLEORSSalGSQ+j2QO8U4PtT+C2ZWE1gA/M3dpxdwTiM4hp9yb3D3Se7e1t3baj6PiIiEW9WqcP24VvROmcyiV7YwKUeiVUK3ujByJGw6tuXgmjdvzvPPP89pp50GBO+sJiUlZa/BumLFCvYUYT1XkdKgJOekViL4eP9id//CzNoDcwneBX0LuMnd15lZPWAOwbmqr+c6xxXAcnf/MfSI/1GglbtfVtC1NSe1dApXAlBRkqGWLWtGauqX2e8rVDiHDh3+m+e+H3xwMpCeo+UkOnc+lOe+H35YhczMlOz3MTGVueii/P+CWbSoNunpP/x85pNq0bHjljz3DdfnpoQskaL7dn0mC/44n947xlPpw7cB+LFtT2bVGUbXx7pwduNjv1eUmppKhQoVcHcaNWpEw4YNmTt3LgDurrKsErWiLnEKwMwuBv4KOMFH/XcTvJO6BOjl7p+a2b+Ai4F1OQ5Nc/dOZtYTuB84CcgEVgP3uvvugq6rILX0CVd5z6KUOs0doGbJK1A9MkDNcmSgmjtAzZJfoJo7QM0+cx6Barg+N5VbFSkG330HEyeyZ+wUqqTvIJnGvJ8wlHoP30T3PseWaAXBgHTlypVkZGTQoUMH9u/fT/PmzRk9ejR9+/Yt3jGIFINoTJzC3d9393bu3t7dz3f3Je6+2d3ruPunoX2ucfeq7p6Y49UptG22u58Xeozf3t0HHi1AldIpXOU9i1LqNK8ANf/2vALUvNvzClALas8rQM2vPVyfm8qtihSDevVg9Gg2fLCZSRdNZ49V5dbkEXTqV4tXKw9i6vCiJ1oBmBlt2rShQ4cOAKSkpNChQwfi44Mz7DZu3Mjo0aPZuXNncY5GJOxUcUqiUvjKe4an1Gm0CNfnpnKrIsWnzfkBBi/sT6Mdi3nxzlCi1b4kbnmmFRmJHSEpCdKKnmiVpXbt2syYMYOOHTsC8N577/Hggw+SmpoKwJYtW/jppyNSOUSijoJUiUrhK+8ZnlKn0SJcn5vKrYoUv6pV4YYnf060er7FOM5kOwwYAPHxrLh0JK88sYkDB45+roLccsstbNmyhbp16wJw//33k5CQwOHDZeMf51J2KUiVqBSu8p5FKXVaocI5ee6bd/tJ+VzxyPaYmMp57plf+0kn1Sp0e7g+N5VbFQmfmBjo1qcKN68eQey6ZJg7l8PtE2k593Guubc+71fuxcRr5vHN10Wov5pLjRo1sn8eNmwYTzzxRHZ51j59+vDUU08d9zhEipuCVIlKcXH9SUiYRCBQDzACgXrFkqTTuPEEatUays93TmPzTJoC6NDhv0cEpPll9weTo3IHpHln91900Z4jAtKCsvs7dtxyRECaX3Z/uD63cJ1XRHKJiYHu3Tn8+izeGLuBF2v+gXbpixnyencOJzRlYtOnmPNyCsdzE7R9+/YMGDAAgPT0dDIyMrLvqh4+fJhnnnmGrVu3FsdoRI5LiWb3R4qy+0VEpLT6dFEaK//4Gi0+nsB5voT9VGTvFf2pMeo2aNmyWK+1dOlSzjvvPGbMmMG1115Lamoq7k7FihWPfrBIIUVldr+IiIgUTduOAQZ/OIDGOxbz4h0rWBTfj7h506FVK+jYkbf7v3RMFa3ykpiYyNq1a7niiisAmD59OnFxcWzcuLFYzi9SFApSRURESoGqVeGGp1rT/bvJ2JYtMG4ch7Zs5/KX+hN/YTxTa47k1bGbCCXxH7OEhAQqVKgAQJs2bbj99tupV68eAE8++SQPPfQQJ8JTWIk8BakiIiKlTZUqMGIEuxYlM/mauaw8KZEbtz7O1f+vPu9VCiZarV937IlWWVq3bs1jjz2WXb3qyy+/ZPXq1dnv58yZw45jWdxVpBA0J1XKhGgo2VmUPhSl3KqIyNGkpsI7E75j798m8psfp3AmO/jaGhM/eijlb70JKue9esixOHz4MLGxsezdu5fq1aszaNAgnnnmGQAOHTrEySefXGzXkrJJc1LlhJFVsjMt7TvASUv7juTkwWzblhSVfcir3Gpq6pcsW9ashHorImVNhQpwzd31uPmH0Wz6eDOTLpxGbPWqlP/jCKhdm8yBg3np3s8ojqJTWUtXnXbaaSxfvpy77roLgK+++oq4uDjmz59//BcRQUGqlAHRULKzKH0oWrlVEZGiyUq0arB1MaxYAX37kjltOv2eaMW6Mzvy7IXFl2jVokUL6tevD0BMTAyXX345zZs3B2DhwoWMGjWKvXv3Fsu15MSjIFVKvWgo2RkNfRARyckMaN0apkxh1ewtTG46jmq+naEf/5xo9drfjj/RKktCQgLTpk3LLhzw8ccfM378eAKBAABr1qxhz56814MWyYuCVCn1oqFkZzT0QUQkP+26V2HQl8GKVjkTrX57T30+qdUL5s2DzONPtMpp5MiRbNiwIXuO6s0338yll16avf1EyImR46MgVUq9aCjZWZQ+FK3cqohI8WlwdgyDXutO5//NYuYTG5hW417apS+C7t2haVN++svTzH3l+Cpa5XTqqadm//zcc88xZswYADIyMjj33HOZOnVq8VxIyiQFqVLqRUPJzqL0oSjlVkVEwqFCBeh9Tz1u+vExAtu/h2nT4IwzOP2hO7ngutq8WmUwz9/5Obt2Fd8127RpQ+fOnQHYs2cPTZs2JS4uDoCdO3fy2GOPsX379uK7oJR6WoJKREREAHjjwZUcemoCPfe9REVSWWwdWX3BbbR97GradgyE7bqvvfYaffr0YfXq1bRo0YIdO3YQCAQ4/fTTw3ZNiRwtQSUiIiJFctVfWnNNyhQ+mhFMtKru27j1o/7UvSCeBYkjYVN4kkF79+7Npk2baNGiBQCjR4+mXr16pBZXVpeUSgpSRUREJFtsLPS4NphoFfN1MpOv/g8rT0rkkuWPQ/36cNVVbJo6nw3ri/dJbN26dbN/7tevH48//nh2edYhQ4bw+OOPF+v1JPopSBUREZE8NWwUw6B/9aDz/2bh69bDvffCxx8T/7tupJ/dhInnPM27rxZfolWWdu3aMWTIECC4CsDu3buz11t1d/7xj3+wdevW4r2oRJ0SnZNqZp2AJ4FywCHgdndfmmsfA/4M9AEOAyuBIe6+v6BtBV1Xc1JFRESKR2ZqGv+49DVafDSeRF/Kfiry1mn9SfvdMHo+8CuqVg3Pdd0dM2Pt2rU0bdqUCRMmMHToUNLT00lPT6dixYpHP4lEhaibk2pmlYGZwDB3Pxe4B5hlZrn/q7oR+DXQ0t3PAdKBMYXYJiIiImEWUyHAoIUDOHv7El4YvoK3T+3LlXunc9NTLUmufgHLR7wEhw4V+3WD96mgSZMmrF27ln79+gEwe/Zs4uLiWLNmTbFfUyKrJB/39wCS3X0JgLt/APwIdMm137XARHfPmi39NNC3ENtERESkhFSrBjc+HUq0enkLk5oEE63aPdUf6taFBx7g6/nFV9Eqp4SEBCpVqgRAw4YNufnmm2nSpAkAzz//PCNHjuRwcc9BkBJXYo/7zeyPwDnufn2OtteBD9396RxtycBt7r4g9P404CegMvBJftvc/X+5rjcYGBx62xzQP7FKr2rAzkh3Qo6JvrvSTd9f6aXvrnQr699fPXevfrSdypVET0KM4DzSnDI48m5u7v0yQn/GHGXbL7j7JGASgJl9Wpi5DxKd9P2VXvruSjd9f6WXvrvSTd9fUEk+7v8eyF3IPD7UXtB+8cA+IOUo20RERESkjCjJIHUWcK6ZtQAws/ZAE+A9M1tkZo1C+00DBprZyaH3vwdmenBeQkHbRERERKSMKLHH/e7+PzPrDUw1Myf4qP7XQEWgHlAptOuLwNnAJ2aWAXwJ3F6IbQWZVGwDkUjQ91d66bsr3fT9lV767ko3fX+U8DqpIiIiIiKFoYpTIiIiIhJ1FKSKiIiISNQp00GqmXUys5VmttrMPjWzxEj3SQrPzE4ys3vMLN3Mrot0f6TwzGyImX0e+v9utZndFuk+SeGZ2V9C398nod+hQyPdJykaM2tmZrvN7E+R7osUnpl9YWbLzWxp6PVepPsUSSW5TmqJylGG9XJ3X2JmnQmWYa3v7gci2zsppEGAA0sj3REpPDOLBRoBHd19n5nVBr4xs1nuviXC3ZPC2QO0dfd0M6sOfGtm/3H3byPdMTm60N9/44GXI90XKbLTgF+5e2akOxINyvKd1MKWYZUo5e4T3P1vHFkEQqKYux9293vcfV+oaRdwCIiNYLekCNx9nLunh96eRXA96t2R65EUlpnFAC8A9wM7ItwdKbozgIVmtsrMXjWzlpHuUCSV5SC1AbA+V9v6ULuIlJyngFfcfVOkOyKFZ2aNzGwdMBcYkLv0tEStR4B57r440h2RYxLn7hcCbQiuLz/fzHIXQjphlOUgtbBlWEUkTMzsEaA2hVvPWKKIu69z90YEnz69aGbNIt0nKZiZXQ3Eu/vfI90XOTbunhr6M9Pdk4AVBJ8Mn5DK7JxUgiVUu+Zqiwf+FYG+iJxwzGws0BC42t0PRbo/cmzcfZWZLQEuBv4b6f5IgS4DmppZ1jz+OhBMonL33pHrlhyHWOCnSHciUsryXcX8yrDOi2ivRMo4M4sxs+eAukBvBaili5m1MLM+Zmah97WBDsCnke2ZHI27D3T3Nu6e6O6JwBRgigLU0sHM2plZmxzvfw00JTjl5oRUZu+k5leG1d1TItw1kbLu18AQgkHNx6FYB+ABd58fsV5JYX0H3Ar8wczSgZOBB91dq2yIhNc+YJyZ1QDSCCYrdj+R4xaVRRURERGRqFOWH/eLiIiISCmlIFVEREREoo6CVBERERGJOgpSRURERCTqKEgVERERkaijIFVEJALMrLuZLTOzfWaWbGbDc21famZFqtSVY23TtqGl94py7D/NTJWKRCRqKEgVETlOZvaAmXmO1+9zvXczG5Bj/9bAm8B4oB7BdWXvM7PBhbxekpll5Hilm1kmMLSAYxqY2TwzO2hm683suuMbtYhIeClIFRE5fmOA04DWgAPPAdcCZ4Xa1wL/y7F/b2COu7/o7rvc/QNgLNC/kNe7HigfegWAZgQX/56V185mFgDmAD8AzYE/A/80swsLPUIRkRJWZitOiYiUFHdPB9LN7GRgj7unm9lLQFd3/87MqgE7chwSAxzOfZoiXC8TyAQIVad5FfiTu2/J55ArgTOBQaEytd+Y2UXACOCjwl5XRKQk6U6qiEjxqQasytlgZqcCB4FvczT/C7jczK4xs1PMLBG4C3ilsBcysxgz6w8sA95y9zEF7N4eWBwKULMsCLWLiEQl3UkVETkOZraR4LzSnG1Zd0Xfz9G8NZTXdJW7vxmaE/oXYDqwFfg78GwhrncK8BDBKQPbgBtD0wUKcjqwK1fbToJTEUREopKCVBGR4+DuZx3jcW8Bbx3DcfvNbB3QE0gGMLPcv8u/A/rmeL+FI++aVgu1i4hEJQWpIiLFxMx6A9NyNX/o7t3z2T8WsHxO9xsgNa8N7j4ldPzWfI4tB1QFZoTerwFGmFl5dz8YausG/Def40VEIk5BqohI8XkbODtXW5qZmbvnlRiVBsQWcL6ngTvz2+juNfJqN7OW/HJu7GyCUwMmm9kooCPQD+hawLVFRCJKQaqISDEIzU2tk6PJgQzgZGC7mdXPcRczuIN7OTP7/8Cp7n5L6DynAPuAc9z9q6Ncs1ArArj7ITO7FJhE8K7qFuAGd1dmv4hELQWpIiLFIPfcVDNrBvwB+BXQN3eAmsMhgmudZsn6vZxRyEu3BT4vRP++JfiIX0SkVFCQKiJSDMysKsFF9S8mOJ+0KcHF9p8E6pjZT8CWnI/9QwlPGUD5HMlPWQGrm1msu+deTzW3WPL5XW5mmaE1VUVESh2tkyoicpzM7AHgK2AUwcf7wwgu+9SaYKWpOwiuZ9omxzFnAekE77b+NvRzOsG5owDrgIWFuPwygglWeb3yTNgSESkNLO+5/CIiIiIikaM7qSIiIiISdRSkioiIiEjUUZAqIiIiIlFHQaqIiIiIRB0FqSIiIiISdRSkioiIiEjUUZAqIiIiIlFHQaqIiIiIRJ3/Awt0PGfJEKh2AAAAAElFTkSuQmCC\n",
|
||
"text/plain": [
|
||
"<Figure size 792x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 각 결정 경계의 기울기와 편향을 계산합니다\n",
|
||
"w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\n",
|
||
"b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\n",
|
||
"w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\n",
|
||
"b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\n",
|
||
"w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\n",
|
||
"b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\n",
|
||
"\n",
|
||
"# 결정 경계를 원본 스케일로 변환합니다\n",
|
||
"line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\n",
|
||
"line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\n",
|
||
"line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\n",
|
||
"\n",
|
||
"# 세 개의 결정 경계를 모두 그립니다\n",
|
||
"plt.figure(figsize=(11, 4))\n",
|
||
"plt.plot(line1[:, 0], line1[:, 1], \"k:\", label=\"LinearSVC\")\n",
|
||
"plt.plot(line2[:, 0], line2[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n",
|
||
"plt.plot(line3[:, 0], line3[:, 1], \"r-\", label=\"SGDClassifier\")\n",
|
||
"plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris-Versicolor\"\n",
|
||
"plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris-Setosa\"\n",
|
||
"plt.xlabel(\"꽃잎 길이\", fontsize=14)\n",
|
||
"plt.ylabel(\"꽃잎 너비\", fontsize=14)\n",
|
||
"plt.legend(loc=\"upper center\", fontsize=14)\n",
|
||
"plt.axis([0, 5.5, 0, 2])\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"아주 비슷하네요!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 9."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"_문제: MNIST 데이터셋에 SVM 분류기를 훈련시켜보세요. SVM 분류기는 이진 분류기라서 OvA 전략을 사용해 10개의 숫자를 분류해야 합니다. 처리 속도를 높이기 위해 작은 검증 세트로 하이퍼파라미터를 조정하는 것이 좋습니다. 어느 정도까지 정확도를 높일 수 있나요?_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"먼저 데이터셋을 로드하고 훈련 세트와 테스트 세트로 나눕니다. `train_test_split()` 함수를 사용할 수 있지만 보통 처음 60,000개의 샘플을 훈련 세트로 사용하고 나머지는 10,000개를 테스트 세트로 사용합니다(이렇게 하면 다른 사람들의 모델과 성능을 비교하기 좋습니다):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.datasets import fetch_mldata\n",
|
||
"\n",
|
||
"mnist = fetch_mldata(\"MNIST original\")\n",
|
||
"X = mnist[\"data\"]\n",
|
||
"y = mnist[\"target\"]\n",
|
||
"\n",
|
||
"X_train = X[:60000]\n",
|
||
"y_train = y[:60000]\n",
|
||
"X_test = X[60000:]\n",
|
||
"y_test = y[60000:]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"많은 훈련 알고리즘은 훈련 샘플의 순서에 민감하므로 먼저 이를 섞는 것이 좋은 습관입니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"np.random.seed(42)\n",
|
||
"rnd_idx = np.random.permutation(60000)\n",
|
||
"X_train = X_train[rnd_idx]\n",
|
||
"y_train = y_train[rnd_idx]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"선형 SVM 분류기부터 시작해보죠. 이 모델은 자동으로 OvA(또는 OvR) 전략을 사용하므로 특별히 처리해 줄 것이 없습니다. 간단하네요!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
|
||
" intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
|
||
" multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
|
||
" verbose=0)"
|
||
]
|
||
},
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"lin_clf = LinearSVC(random_state=42)\n",
|
||
"lin_clf.fit(X_train, y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"훈련 세트에 대한 예측을 만들어 정확도를 측정해 보겠습니다(최종 모델을 선택해 훈련시킨 것이 아니기 때문에 아직 테스트 세트를 사용해서는 안됩니다):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.8263333333333334"
|
||
]
|
||
},
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.metrics import accuracy_score\n",
|
||
"\n",
|
||
"y_pred = lin_clf.predict(X_train)\n",
|
||
"accuracy_score(y_train, y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"와우, MNIST에서 82% 정확도면 정말 나쁜 성능입니다. 선형 모델이 MNIST 문제에 너무 단순하기 때문이지만 먼저 데이터의 스케일을 조정할 필요가 있습니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 52,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"scaler = StandardScaler()\n",
|
||
"X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n",
|
||
"X_test_scaled = scaler.transform(X_test.astype(np.float32))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
|
||
" intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
|
||
" multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
|
||
" verbose=0)"
|
||
]
|
||
},
|
||
"execution_count": 53,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"lin_clf = LinearSVC(random_state=42)\n",
|
||
"lin_clf.fit(X_train_scaled, y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.9224"
|
||
]
|
||
},
|
||
"execution_count": 54,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred = lin_clf.predict(X_train_scaled)\n",
|
||
"accuracy_score(y_train, y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"훨씬 나아졌지만(에러율을 절반으로 줄였습니다) 여전히 MNIST에서 좋은 성능은 아닙니다. SVM을 사용한다면 커널 함수를 사용해야 합니다. RBF 커널(기본값)로 `SVC`를 적용해 보겠습니다.\n",
|
||
"\n",
|
||
"**경고**: 사이킷런 0.19버전 이하를 사용하면 기본적으로 OvO 전략을 사용할 것이므로 `decision_function_shape=\"ovr\"`로 지정해 OvR 전략으로 바꾸어 주어야 합니다(OvR은 0.19 버전부터 기본이 되었습니다)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
|
||
" decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
|
||
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
|
||
" tol=0.001, verbose=False)"
|
||
]
|
||
},
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"svm_clf = SVC(decision_function_shape=\"ovr\")\n",
|
||
"svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.94615"
|
||
]
|
||
},
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred = svm_clf.predict(X_train_scaled)\n",
|
||
"accuracy_score(y_train, y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"아주 좋네요 6배나 적은 데이터에서 모델을 훈련시켰지만 더 좋은 성능을 얻었습니다. 교차 검증을 사용한 랜덤 서치로 하이퍼파라미터 튜닝을 해보겠습니다. 진행을 빠르게 하기 위해 작은 데이터셋으로 작업하겠습니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
|
||
"[CV] gamma=0.001766074650481071, C=8.852316058423087 .................\n",
|
||
"[CV] gamma=0.001766074650481071, C=8.852316058423087 .................\n",
|
||
"[CV] gamma=0.006364737055453384, C=1.8271960104746645 ................\n",
|
||
"[CV] gamma=0.001766074650481071, C=8.852316058423087 .................\n",
|
||
"[CV] .. gamma=0.001766074650481071, C=8.852316058423087, total= 0.8s\n",
|
||
"[CV] gamma=0.006364737055453384, C=1.8271960104746645 ................\n",
|
||
"[CV] .. gamma=0.001766074650481071, C=8.852316058423087, total= 0.8s\n",
|
||
"[CV] gamma=0.006364737055453384, C=1.8271960104746645 ................\n",
|
||
"[CV] .. gamma=0.001766074650481071, C=8.852316058423087, total= 0.8s\n",
|
||
"[CV] gamma=0.051349833451870636, C=9.875199193765326 .................\n",
|
||
"[CV] . gamma=0.006364737055453384, C=1.8271960104746645, total= 0.9s\n",
|
||
"[CV] gamma=0.051349833451870636, C=9.875199193765326 .................\n",
|
||
"[CV] . gamma=0.006364737055453384, C=1.8271960104746645, total= 0.9s\n",
|
||
"[CV] gamma=0.051349833451870636, C=9.875199193765326 .................\n",
|
||
"[CV] . gamma=0.006364737055453384, C=1.8271960104746645, total= 0.9s\n",
|
||
"[CV] gamma=0.05991666578466177, C=6.59992909281409 ...................\n",
|
||
"[CV] .. gamma=0.051349833451870636, C=9.875199193765326, total= 1.0s\n",
|
||
"[CV] gamma=0.05991666578466177, C=6.59992909281409 ...................\n",
|
||
"[CV] .. gamma=0.051349833451870636, C=9.875199193765326, total= 1.0s\n",
|
||
"[CV] gamma=0.05991666578466177, C=6.59992909281409 ...................\n",
|
||
"[CV] .. gamma=0.051349833451870636, C=9.875199193765326, total= 1.0s\n",
|
||
"[CV] gamma=0.003596490522533181, C=9.053435975487119 .................\n",
|
||
"[CV] .... gamma=0.05991666578466177, C=6.59992909281409, total= 1.0s\n",
|
||
"[CV] gamma=0.003596490522533181, C=9.053435975487119 .................\n",
|
||
"[CV] .... gamma=0.05991666578466177, C=6.59992909281409, total= 1.0s\n",
|
||
"[CV] gamma=0.003596490522533181, C=9.053435975487119 .................\n",
|
||
"[CV] .... gamma=0.05991666578466177, C=6.59992909281409, total= 1.0s\n",
|
||
"[CV] gamma=0.004002330992905356, C=2.701062804458301 .................\n",
|
||
"[CV] .. gamma=0.003596490522533181, C=9.053435975487119, total= 0.9s\n",
|
||
"[CV] gamma=0.004002330992905356, C=2.701062804458301 .................\n",
|
||
"[CV] .. gamma=0.003596490522533181, C=9.053435975487119, total= 0.9s\n",
|
||
"[CV] gamma=0.004002330992905356, C=2.701062804458301 .................\n",
|
||
"[CV] .. gamma=0.003596490522533181, C=9.053435975487119, total= 0.9s\n",
|
||
"[CV] gamma=0.017596957507461645, C=3.2711787843881437 ................\n",
|
||
"[CV] .. gamma=0.004002330992905356, C=2.701062804458301, total= 0.9s\n",
|
||
"[CV] gamma=0.017596957507461645, C=3.2711787843881437 ................\n",
|
||
"[CV] .. gamma=0.004002330992905356, C=2.701062804458301, total= 0.9s\n",
|
||
"[CV] gamma=0.017596957507461645, C=3.2711787843881437 ................\n",
|
||
"[CV] .. gamma=0.004002330992905356, C=2.701062804458301, total= 0.9s\n",
|
||
"[CV] gamma=0.01573529056426603, C=6.848991127746501 ..................\n",
|
||
"[CV] . gamma=0.017596957507461645, C=3.2711787843881437, total= 1.0s\n",
|
||
"[CV] gamma=0.01573529056426603, C=6.848991127746501 ..................\n",
|
||
"[CV] . gamma=0.017596957507461645, C=3.2711787843881437, total= 1.0s\n",
|
||
"[CV] gamma=0.01573529056426603, C=6.848991127746501 ..................\n",
|
||
"[CV] . gamma=0.017596957507461645, C=3.2711787843881437, total= 1.0s\n",
|
||
"[CV] gamma=0.03834647526105027, C=2.893035364914488 ..................\n",
|
||
"[CV] ... gamma=0.01573529056426603, C=6.848991127746501, total= 1.0s\n",
|
||
"[CV] gamma=0.03834647526105027, C=2.893035364914488 ..................\n",
|
||
"[CV] ... gamma=0.01573529056426603, C=6.848991127746501, total= 1.0s\n",
|
||
"[CV] gamma=0.03834647526105027, C=2.893035364914488 ..................\n",
|
||
"[CV] ... gamma=0.01573529056426603, C=6.848991127746501, total= 1.0s\n",
|
||
"[CV] gamma=0.008808538172595842, C=5.336260835426313 .................\n",
|
||
"[CV] ... gamma=0.03834647526105027, C=2.893035364914488, total= 1.0s\n",
|
||
"[CV] gamma=0.008808538172595842, C=5.336260835426313 .................\n",
|
||
"[CV] ... gamma=0.03834647526105027, C=2.893035364914488, total= 1.0s\n",
|
||
"[CV] gamma=0.008808538172595842, C=5.336260835426313 .................\n",
|
||
"[CV] ... gamma=0.03834647526105027, C=2.893035364914488, total= 1.0s\n",
|
||
"[CV] .. gamma=0.008808538172595842, C=5.336260835426313, total= 0.9s\n",
|
||
"[CV] .. gamma=0.008808538172595842, C=5.336260835426313, total= 0.9s\n",
|
||
"[CV] .. gamma=0.008808538172595842, C=5.336260835426313, total= 0.9s\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 10.6s finished\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"RandomizedSearchCV(cv=None, error_score='raise',\n",
|
||
" estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
|
||
" decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
|
||
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
|
||
" tol=0.001, verbose=False),\n",
|
||
" fit_params=None, iid=True, n_iter=10, n_jobs=-1,\n",
|
||
" param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f4d6bfdcf60>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f4d6bfdc518>},\n",
|
||
" pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
|
||
" return_train_score='warn', scoring=None, verbose=2)"
|
||
]
|
||
},
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import RandomizedSearchCV\n",
|
||
"from scipy.stats import reciprocal, uniform\n",
|
||
"\n",
|
||
"param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
|
||
"rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, n_iter=10, verbose=2, n_jobs=-1)\n",
|
||
"rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n",
|
||
" decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
|
||
" kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
|
||
" shrinking=True, tol=0.001, verbose=False)"
|
||
]
|
||
},
|
||
"execution_count": 58,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"rnd_search_cv.best_estimator_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.856"
|
||
]
|
||
},
|
||
"execution_count": 59,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"rnd_search_cv.best_score_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"이 점수는 낮지만 1,000개의 샘플만 사용한 것을 기억해야 합니다. 전체 데이터셋으로 최선의 모델을 재훈련시켜 보겠습니다(몇 시간이 걸릴지 모르니 퇴근하기 전에 돌려 보세요):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n",
|
||
" decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
|
||
" kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
|
||
" shrinking=True, tol=0.001, verbose=False)"
|
||
]
|
||
},
|
||
"execution_count": 60,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.99965"
|
||
]
|
||
},
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
|
||
"accuracy_score(y_train, y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"아주 훌륭하네요! 이 모델을 선택하겠습니다. 이제 테스트 세트로 모델을 테스트합니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.9709"
|
||
]
|
||
},
|
||
"execution_count": 62,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
|
||
"accuracy_score(y_test, y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"아주 나쁘지 않지만 확실히 모델이 다소 과대적합되었습니다. 하이퍼파라미터를 조금 더 수정할 수 있지만(가령, `C`와/나 `gamma`를 감소시킵니다) 그렇게 하면 테스트 세트에 과대적합될 위험이 있습니다. 다른 사람들은 하이퍼파라미터 `C=5`와 `gamma=0.005`에서 더 나은 성능(98% 이상의 정확도)을 얻었습니다. 훈련 세트를 더 많이 사용해서 더 오래 랜덤 서치를 수행하면 이런 값을 얻을 수 있을지 모릅니다."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 10."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"_문제: 캘리포니아 주택 가격 데이터셋에 SVM 회귀를 훈련시켜보세요._"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"사이킷런의 `fetch_california_housing()` 함수를 사용해 데이터셋을 로드합니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.datasets import fetch_california_housing\n",
|
||
"\n",
|
||
"housing = fetch_california_housing()\n",
|
||
"X = housing[\"data\"]\n",
|
||
"y = housing[\"target\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"훈련 세트와 테스트 세트로 나눕니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"데이터의 스케일을 조정하는 것을 잊지 마세요:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_train_scaled = scaler.fit_transform(X_train)\n",
|
||
"X_test_scaled = scaler.transform(X_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"먼저 간단한 `LinearSVR`을 훈련시켜 보죠:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n",
|
||
" intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
|
||
" random_state=42, tol=0.0001, verbose=0)"
|
||
]
|
||
},
|
||
"execution_count": 66,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import LinearSVR\n",
|
||
"\n",
|
||
"lin_svr = LinearSVR(random_state=42)\n",
|
||
"lin_svr.fit(X_train_scaled, y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"훈련 세트에 대한 성능을 확인해 보겠습니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 67,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.9499688222172291"
|
||
]
|
||
},
|
||
"execution_count": 67,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.metrics import mean_squared_error\n",
|
||
"\n",
|
||
"y_pred = lin_svr.predict(X_train_scaled)\n",
|
||
"mse = mean_squared_error(y_train, y_pred)\n",
|
||
"mse"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"RMSE를 확인해 보겠습니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 68,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.9746634404845752"
|
||
]
|
||
},
|
||
"execution_count": 68,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.sqrt(mse)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"훈련 세트에서 타깃은 만달러 단위입니다. RMSE는 기대할 수 있는 에러의 정도를 대략 가늠하게 도와줍니다(에러가 클수록 큰 폭으로 증가합니다). 이 모델의 에러가 대략 $10,000 정도로 예상할 수 있습니다. 썩 훌륭하지 않네요. RBF 커널이 더 나을지 확인해 보겠습니다. 하이퍼파라미터 `C`와 `gamma`의 적절한 값을 찾기 위해 교차 검증을 사용한 랜덤 서치를 적용하겠습니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 69,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
|
||
"[CV] gamma=0.07969454818643928, C=4.745401188473625 ..................\n",
|
||
"[CV] gamma=0.07969454818643928, C=4.745401188473625 ..................\n",
|
||
"[CV] gamma=0.015751320499779724, C=8.31993941811405 ..................\n",
|
||
"[CV] gamma=0.07969454818643928, C=4.745401188473625 ..................\n",
|
||
"[CV] ... gamma=0.015751320499779724, C=8.31993941811405, total= 5.9s\n",
|
||
"[CV] gamma=0.015751320499779724, C=8.31993941811405 ..................\n",
|
||
"[CV] ... gamma=0.07969454818643928, C=4.745401188473625, total= 6.2s\n",
|
||
"[CV] gamma=0.015751320499779724, C=8.31993941811405 ..................\n",
|
||
"[CV] ... gamma=0.07969454818643928, C=4.745401188473625, total= 6.2s\n",
|
||
"[CV] gamma=0.002051110418843397, C=2.560186404424365 .................\n",
|
||
"[CV] ... gamma=0.07969454818643928, C=4.745401188473625, total= 6.2s\n",
|
||
"[CV] gamma=0.002051110418843397, C=2.560186404424365 .................\n",
|
||
"[CV] .. gamma=0.002051110418843397, C=2.560186404424365, total= 5.4s\n",
|
||
"[CV] gamma=0.002051110418843397, C=2.560186404424365 .................\n",
|
||
"[CV] ... gamma=0.015751320499779724, C=8.31993941811405, total= 5.7s\n",
|
||
"[CV] gamma=0.05399484409787431, C=1.5808361216819946 .................\n",
|
||
"[CV] .. gamma=0.002051110418843397, C=2.560186404424365, total= 5.5s\n",
|
||
"[CV] gamma=0.05399484409787431, C=1.5808361216819946 .................\n",
|
||
"[CV] ... gamma=0.015751320499779724, C=8.31993941811405, total= 5.8s\n",
|
||
"[CV] gamma=0.05399484409787431, C=1.5808361216819946 .................\n",
|
||
"[CV] .. gamma=0.05399484409787431, C=1.5808361216819946, total= 5.4s\n",
|
||
"[CV] gamma=0.026070247583707663, C=7.011150117432088 .................\n",
|
||
"[CV] .. gamma=0.002051110418843397, C=2.560186404424365, total= 5.5s\n",
|
||
"[CV] gamma=0.026070247583707663, C=7.011150117432088 .................\n",
|
||
"[CV] .. gamma=0.05399484409787431, C=1.5808361216819946, total= 5.4s\n",
|
||
"[CV] gamma=0.026070247583707663, C=7.011150117432088 .................\n",
|
||
"[CV] .. gamma=0.05399484409787431, C=1.5808361216819946, total= 5.4s\n",
|
||
"[CV] gamma=0.0870602087830485, C=1.2058449429580245 ..................\n",
|
||
"[CV] ... gamma=0.0870602087830485, C=1.2058449429580245, total= 5.3s\n",
|
||
"[CV] gamma=0.0870602087830485, C=1.2058449429580245 ..................\n",
|
||
"[CV] .. gamma=0.026070247583707663, C=7.011150117432088, total= 5.8s\n",
|
||
"[CV] gamma=0.0870602087830485, C=1.2058449429580245 ..................\n",
|
||
"[CV] .. gamma=0.026070247583707663, C=7.011150117432088, total= 5.8s\n",
|
||
"[CV] gamma=0.0026587543983272693, C=9.324426408004218 ................\n",
|
||
"[CV] .. gamma=0.026070247583707663, C=7.011150117432088, total= 6.0s\n",
|
||
"[CV] gamma=0.0026587543983272693, C=9.324426408004218 ................\n",
|
||
"[CV] ... gamma=0.0870602087830485, C=1.2058449429580245, total= 5.4s\n",
|
||
"[CV] gamma=0.0026587543983272693, C=9.324426408004218 ................\n",
|
||
"[CV] ... gamma=0.0870602087830485, C=1.2058449429580245, total= 5.3s\n",
|
||
"[CV] gamma=0.0023270677083837795, C=2.818249672071006 ................\n",
|
||
"[CV] . gamma=0.0026587543983272693, C=9.324426408004218, total= 5.6s\n",
|
||
"[CV] gamma=0.0023270677083837795, C=2.818249672071006 ................\n",
|
||
"[CV] . gamma=0.0026587543983272693, C=9.324426408004218, total= 5.5s\n",
|
||
"[CV] gamma=0.0023270677083837795, C=2.818249672071006 ................\n",
|
||
"[CV] . gamma=0.0026587543983272693, C=9.324426408004218, total= 5.5s\n",
|
||
"[CV] gamma=0.011207606211860567, C=4.042422429595377 .................\n",
|
||
"[CV] . gamma=0.0023270677083837795, C=2.818249672071006, total= 5.4s\n",
|
||
"[CV] gamma=0.011207606211860567, C=4.042422429595377 .................\n",
|
||
"[CV] . gamma=0.0023270677083837795, C=2.818249672071006, total= 5.5s\n",
|
||
"[CV] gamma=0.011207606211860567, C=4.042422429595377 .................\n",
|
||
"[CV] . gamma=0.0023270677083837795, C=2.818249672071006, total= 5.4s\n",
|
||
"[CV] gamma=0.003823475224675185, C=5.319450186421157 .................\n",
|
||
"[CV] .. gamma=0.011207606211860567, C=4.042422429595377, total= 5.4s\n",
|
||
"[CV] gamma=0.003823475224675185, C=5.319450186421157 .................\n",
|
||
"[CV] .. gamma=0.011207606211860567, C=4.042422429595377, total= 5.5s\n",
|
||
"[CV] gamma=0.003823475224675185, C=5.319450186421157 .................\n",
|
||
"[CV] .. gamma=0.011207606211860567, C=4.042422429595377, total= 5.5s\n",
|
||
"[CV] .. gamma=0.003823475224675185, C=5.319450186421157, total= 5.4s\n",
|
||
"[CV] .. gamma=0.003823475224675185, C=5.319450186421157, total= 5.3s\n",
|
||
"[CV] .. gamma=0.003823475224675185, C=5.319450186421157, total= 5.3s\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 1.1min finished\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"RandomizedSearchCV(cv=None, error_score='raise',\n",
|
||
" estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
|
||
" kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
|
||
" fit_params=None, iid=True, n_iter=10, n_jobs=-1,\n",
|
||
" param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f4d70d2cac8>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f4d70d2cb70>},\n",
|
||
" pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
|
||
" return_train_score='warn', scoring=None, verbose=2)"
|
||
]
|
||
},
|
||
"execution_count": 69,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.svm import SVR\n",
|
||
"from sklearn.model_selection import RandomizedSearchCV\n",
|
||
"from scipy.stats import reciprocal, uniform\n",
|
||
"\n",
|
||
"param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
|
||
"rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, n_iter=10, verbose=2, random_state=42, n_jobs=-1)\n",
|
||
"rnd_search_cv.fit(X_train_scaled, y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 70,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"SVR(C=4.745401188473625, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
|
||
" gamma=0.07969454818643928, kernel='rbf', max_iter=-1, shrinking=True,\n",
|
||
" tol=0.001, verbose=False)"
|
||
]
|
||
},
|
||
"execution_count": 70,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"rnd_search_cv.best_estimator_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"이제 훈련 세트에서 RMSE를 측정해 보겠습니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 71,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.5727524770785356"
|
||
]
|
||
},
|
||
"execution_count": 71,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
|
||
"mse = mean_squared_error(y_train, y_pred)\n",
|
||
"np.sqrt(mse)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"선형 모델보다 훨씬 나아졌네요. 이 모델을 선택하고 테스트 세트에서 평가해 보겠습니다:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 72,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.592916838552874"
|
||
]
|
||
},
|
||
"execution_count": 72,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
|
||
"mse = mean_squared_error(y_test, y_pred)\n",
|
||
"np.sqrt(mse)"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.5.5"
|
||
},
|
||
"nav_menu": {},
|
||
"toc": {
|
||
"navigate_menu": true,
|
||
"number_sections": true,
|
||
"sideBar": true,
|
||
"threshold": 6,
|
||
"toc_cell": false,
|
||
"toc_section_display": "block",
|
||
"toc_window_display": false
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 1
|
||
}
|