"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "w = [ 0. 0. 5.3720284 10.9138623]\n",
+ "mse = 32.75954567607067\n"
+ ]
+ }
+ ],
+ "source": [
+ "from scipy.optimize import linprog\n",
+ "\n",
+ "m, n = X.shape\n",
+ "A = np.concatenate((np.concatenate([X, -np.eye(m)], axis=1), np.concatenate([-X, -np.eye(m)], axis=1)))\n",
+ "b = np.concatenate([y, -y])\n",
+ "c = np.concatenate([np.zeros(n), np.ones(m)])\n",
+ "result = linprog(c, A, b)\n",
+ "w = result.x[:n]\n",
+ "mse = ((X.dot(w) - y)**2).mean()\n",
+ "print('w = {}'.format(w))\n",
+ "print('mse = {}'.format(mse))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Как видим, среднеквадратичная ошибка mse у ridge-регресссии меньше, зато регрессия, основанная на методе наименьших модулей позволяет отобрать наиболее информативные признаки.\n",
+ " Метод наименьших модулей из 2 модели предпочтительнее использовать в случаях, когда шум в данных подчиняется распределению Лапласа, а ridge-регрессию, когда шум гауссовский."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw2 (linear regression, feature-engineering)/main.py b/hw2 (linear regression, feature-engineering)/main.py
new file mode 100644
index 0000000..44491d4
--- /dev/null
+++ b/hw2 (linear regression, feature-engineering)/main.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+
+import argparse
+import logging
+import os
+import time
+
+import numpy as np
+import sklearn.datasets
+import sklearn.linear_model
+
+import features
+
+THRESHOLD = 0.75
+
+
+def _parse_args():
+ parser = argparse.ArgumentParser(prog='bsu 2018 / ml / hw 2')
+ parser.add_argument('--datadir', help='path to folder to cache data', default=os.getcwd())
+ return parser.parse_args()
+
+
+def _filter_data(x, y, digits):
+ """Create subset with only specified digits."""
+ rx, ry = [], []
+ for cx, cy in zip(x, y):
+ if cy in digits:
+ rx.append(cx)
+ ry.append(digits.index(cy))
+ return np.array(rx), np.array(ry)
+
+
+def _main(args):
+ sklearn_home = args.datadir
+
+ logging.info('Downloading MNIST data')
+ mnist = sklearn.datasets.fetch_mldata('MNIST original', data_home=sklearn_home)
+ logging.info('Data is ready')
+
+ solved_cases = 0
+ minimal_result = 1.
+ average_result = 0.
+ start_time = time.process_time()
+ for da in range(10):
+ for db in range(da + 1, 10):
+ logging.info('Processing case: {} vs {}'.format(da, db))
+ X, Y = _filter_data(mnist.data, mnist.target, [da, db])
+
+ logging.info('Computing features')
+ fs = features.FEATURES[(da, db)]
+ assert len(fs) == 2, "We want exactly two feature functions"
+ X2 = [(fs[0](x), fs[1](x)) for x in X]
+
+ logging.info('Training logistic regression classifier')
+ cls = sklearn.linear_model.LogisticRegression()
+ cls.fit(X2, Y)
+ logging.info('Done training')
+
+ result = cls.score(X2, Y)
+ logging.info('Case {} vs {}: {:.1f}%'.format(da, db, result * 100))
+ if result >= THRESHOLD:
+ logging.info('Case is solved')
+ solved_cases += 1
+ else:
+ logging.warning('Case is not solved!')
+ minimal_result = min(minimal_result, result)
+ average_result += result
+ elapsed_time = time.process_time() - start_time
+
+ average_result /= 45
+ print('Solved cases: {}'.format(solved_cases))
+ print('Minimal result: {:.1f}%'.format(minimal_result * 100))
+ print('Average result: {:.1f}%'.format(average_result * 100))
+ print('Elapsed time: {:.1f} second(s)'.format(elapsed_time))
+
+
+if __name__ == '__main__':
+ logging.basicConfig(level=logging.INFO)
+ _main(_parse_args())
diff --git a/hw2 (linear regression, feature-engineering)/test.py b/hw2 (linear regression, feature-engineering)/test.py
new file mode 100644
index 0000000..2f0ea78
--- /dev/null
+++ b/hw2 (linear regression, feature-engineering)/test.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+
+import argparse
+import logging
+import os
+import time
+from queue import PriorityQueue
+
+from matplotlib import pyplot as plt
+import numpy as np
+import sklearn.datasets
+import sklearn.linear_model
+
+import features as f
+
+FEATURES = [f.sum_feature, f.mean_feature,
+ f.vertical_symmetry_feature, f.horizontal_symmetry_feature,
+ f.vertical_overturn_symmetry_feature, f.horizontal_overturn_symmetry_feature,
+ f.vertical_center_feature, f.horizontal_center_feature,
+ f.uppper_part_feature, f.down_part_feature, f.left_part_feature, f.right_part_feature]
+
+THRESHOLD = 0.75
+
+
+def _parse_args():
+ parser = argparse.ArgumentParser(prog='bsu 2018 / ml / hw 2')
+ parser.add_argument('--datadir', help='path to folder to cache data', default=os.getcwd())
+ return parser.parse_args()
+
+
+def _filter_data(x, y, digits):
+ """Create subset with only specified digits."""
+ rx, ry = [], []
+ for cx, cy in zip(x, y):
+ if cy in digits:
+ rx.append(cx)
+ ry.append(digits.index(cy))
+ return np.array(rx), np.array(ry)
+
+
+def _main(args):
+ sklearn_home = args.datadir
+
+ logging.info('Downloading MNIST data')
+ mnist = sklearn.datasets.fetch_mldata('MNIST original', data_home=sklearn_home)
+ logging.info('Data is ready')
+
+ best_answer = '\n\n'
+
+ for da in range(10):
+ for db in range(da + 1, 10):
+ print('Case: {} vs {}'.format(da, db))
+ X, Y = _filter_data(mnist.data, mnist.target, [da, db])
+
+ records = PriorityQueue()
+ for di in range(len(FEATURES)):
+ for dj in range(di + 1, len(FEATURES)):
+ X2 = [(FEATURES[di](x), FEATURES[dj](x)) for x in X]
+
+ cls = sklearn.linear_model.LogisticRegression()
+ cls.fit(X2, Y)
+ result = cls.score(X2, Y)
+ records.put((-result, (FEATURES[di].__name__, FEATURES[dj].__name__)))
+
+ for i in range(10):
+ (res, (f1, f2)) = records.get()
+ print('\tfeatures: {}, {}: {:.1f}%'.format(f1, f2, - res * 100))
+ if i == 0:
+ best_answer += '({}, {}): ({}, {}),\n'.format(da, db, f1, f2)
+
+
+ print(best_answer)
+
+
+if __name__ == '__main__':
+ logging.basicConfig(level=logging.INFO)
+ _main(_parse_args())
diff --git a/hw3 (VCdim)/.ipynb_checkpoints/hw3_solution-checkpoint.ipynb b/hw3 (VCdim)/.ipynb_checkpoints/hw3_solution-checkpoint.ipynb
new file mode 100644
index 0000000..3886429
--- /dev/null
+++ b/hw3 (VCdim)/.ipynb_checkpoints/hw3_solution-checkpoint.ipynb
@@ -0,0 +1,73 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "###
Домашнее задание №3 по курсу «Машинное обучение»
\n",
+ "
*Кукуев Михаил*
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**1.** Покажем, что $VCdim(H)\\geq d$ для семейства $H$ линейных бинарных классификаторов вида $h(x)=sign(w^Tx), w \\in R^d$.\n",
+ "\n",
+ "Возьмем в качестве набора $С$ ортонормированный базис пространства $R^d$: $C=\\{x_1, x_2, ..., x_n\\}$, где $x_i=(0,...,0,1^{(i)},0,...,0), ∀i= \\overline{1,d}$ и пусть $y_i \\in \\{-1, +1\\}$ - метка класса, к которому относится элемент $x_i \\in C$.\n",
+ "\n",
+ "В качестве гипотез из $H_C$ возьмем классификаторы вида $h(x)=sign(\\sum\\limits_{i=1}^d y_i x_i^T x)$, у которых\n",
+ "вектор $w=\\sum\\limits_{i=1}^d y_i x_i, x_i \\in C$.\n",
+ "\n",
+ "Т.к. $x_i^T x_j=0$ только в случае $x_i \\neq x_j$, то выходит $h(x_j)=sign(y_j)$, и значит семейство таких гипотез разукрашивает $C$.\n",
+ "\n",
+ "Если же $|C|=d+1$, то хотя бы один из элементов набора $C$ можно представить в виде линейной комбинации остальных, т.к. пространство d-мерное. Любая гипотеза $h \\in H$ сможет вернуть для этого элемента только одно конкретное значение, причем заранее предопределенное значениями остальных элементов из $C$. Это значит, что нам не удастся получить $2^{d+1}$ различных наборов значений, возвращаемых функциями из $H$ для набора $С$, следовательно $H$ не разукрашивает $C$ размера $d+1$ и $VCdim(H) \\leq d$. Итого, $VCdim(H)=d$."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**3.**\n",
+ " Так же, как и в предыдущей задаче, возьмем в качестве $С$ базис пространства: $C=\\{x_1^C,x_2^C, ..., x_n^C\\}$, где $x_i^C=(0,...,0,1^{(i)},0,...,0), ∀i= \\overline{1,n}$.\n",
+ "Пусть $y_C=(y_1^C,y_2^C,...,y_n^C), y_j^C=\\{0,1\\}, ∀j= \\overline{1,n}$ - значение произвольной функции из $H_C$.\n",
+ "Покажем, что для получения таких значений на наборе $С$ найдется функция из $H$.\n",
+ "\n",
+ "Выберем множество $I_C=\\{i:y_i^C=1\\}$, и ему соответствующую функцию из $H$: $h_{I_C}(x)=(\\sum\\limits_{i \\in I_C} x_i) mod2$. Заметим, что $h_{I_C}(x_j^C) = 1$, только если $j \\in I_C$, что эквивалентно условию $y_j^C=1$. Следовательно, семейство $H_C$ таких функций разукрашивает $C$ и $VCdim(H)\\geq n$.\n",
+ "\n",
+ "Так как размерность элементов множества $X$ равна n, то $|H| \\leq 2^n$. И т.к. $VCdim(H) \\leq log_2(|H|)$, то $VCdim(H)=n$."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**4.**\n",
+ "1. В NFL рассматриваются все $D$, когда говорится, что найдется такое $D$, для которого $L_D(A(S)) \\geq \\frac{1}{8}$. А утверждения из определения PAC-learnability выполняются не для всех $D$, а только на тех, для которых выполняется предположение о реализуемости.\n",
+ "\n",
+ "2. Полностью согласуются, agnostic PAC-learnability утверждает только что true risk найденной алгоритмом гипотезы $h$ больше наилучшей $h'$ максимум на $ε$, а NFL утверждает, что даже для наилучшей гипотезы $h'$ найдется такое $D$, что $L_D(h') \\geq \\frac{1}{8}$."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw3 (VCdim)/hw3_solution.ipynb b/hw3 (VCdim)/hw3_solution.ipynb
new file mode 100644
index 0000000..3886429
--- /dev/null
+++ b/hw3 (VCdim)/hw3_solution.ipynb
@@ -0,0 +1,73 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "###
Домашнее задание №3 по курсу «Машинное обучение»
\n",
+ "
*Кукуев Михаил*
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**1.** Покажем, что $VCdim(H)\\geq d$ для семейства $H$ линейных бинарных классификаторов вида $h(x)=sign(w^Tx), w \\in R^d$.\n",
+ "\n",
+ "Возьмем в качестве набора $С$ ортонормированный базис пространства $R^d$: $C=\\{x_1, x_2, ..., x_n\\}$, где $x_i=(0,...,0,1^{(i)},0,...,0), ∀i= \\overline{1,d}$ и пусть $y_i \\in \\{-1, +1\\}$ - метка класса, к которому относится элемент $x_i \\in C$.\n",
+ "\n",
+ "В качестве гипотез из $H_C$ возьмем классификаторы вида $h(x)=sign(\\sum\\limits_{i=1}^d y_i x_i^T x)$, у которых\n",
+ "вектор $w=\\sum\\limits_{i=1}^d y_i x_i, x_i \\in C$.\n",
+ "\n",
+ "Т.к. $x_i^T x_j=0$ только в случае $x_i \\neq x_j$, то выходит $h(x_j)=sign(y_j)$, и значит семейство таких гипотез разукрашивает $C$.\n",
+ "\n",
+ "Если же $|C|=d+1$, то хотя бы один из элементов набора $C$ можно представить в виде линейной комбинации остальных, т.к. пространство d-мерное. Любая гипотеза $h \\in H$ сможет вернуть для этого элемента только одно конкретное значение, причем заранее предопределенное значениями остальных элементов из $C$. Это значит, что нам не удастся получить $2^{d+1}$ различных наборов значений, возвращаемых функциями из $H$ для набора $С$, следовательно $H$ не разукрашивает $C$ размера $d+1$ и $VCdim(H) \\leq d$. Итого, $VCdim(H)=d$."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**3.**\n",
+ " Так же, как и в предыдущей задаче, возьмем в качестве $С$ базис пространства: $C=\\{x_1^C,x_2^C, ..., x_n^C\\}$, где $x_i^C=(0,...,0,1^{(i)},0,...,0), ∀i= \\overline{1,n}$.\n",
+ "Пусть $y_C=(y_1^C,y_2^C,...,y_n^C), y_j^C=\\{0,1\\}, ∀j= \\overline{1,n}$ - значение произвольной функции из $H_C$.\n",
+ "Покажем, что для получения таких значений на наборе $С$ найдется функция из $H$.\n",
+ "\n",
+ "Выберем множество $I_C=\\{i:y_i^C=1\\}$, и ему соответствующую функцию из $H$: $h_{I_C}(x)=(\\sum\\limits_{i \\in I_C} x_i) mod2$. Заметим, что $h_{I_C}(x_j^C) = 1$, только если $j \\in I_C$, что эквивалентно условию $y_j^C=1$. Следовательно, семейство $H_C$ таких функций разукрашивает $C$ и $VCdim(H)\\geq n$.\n",
+ "\n",
+ "Так как размерность элементов множества $X$ равна n, то $|H| \\leq 2^n$. И т.к. $VCdim(H) \\leq log_2(|H|)$, то $VCdim(H)=n$."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**4.**\n",
+ "1. В NFL рассматриваются все $D$, когда говорится, что найдется такое $D$, для которого $L_D(A(S)) \\geq \\frac{1}{8}$. А утверждения из определения PAC-learnability выполняются не для всех $D$, а только на тех, для которых выполняется предположение о реализуемости.\n",
+ "\n",
+ "2. Полностью согласуются, agnostic PAC-learnability утверждает только что true risk найденной алгоритмом гипотезы $h$ больше наилучшей $h'$ максимум на $ε$, а NFL утверждает, что даже для наилучшей гипотезы $h'$ найдется такое $D$, что $L_D(h') \\geq \\frac{1}{8}$."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw3 (VCdim)/hw3_task.pdf b/hw3 (VCdim)/hw3_task.pdf
new file mode 100644
index 0000000..9f5091a
Binary files /dev/null and b/hw3 (VCdim)/hw3_task.pdf differ
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/encodings.xml b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/encodings.xml
new file mode 100644
index 0000000..15a15b2
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/encodings.xml
@@ -0,0 +1,4 @@
+
+
+
+
\ No newline at end of file
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/hw4.iml b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/hw4.iml
new file mode 100644
index 0000000..6711606
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/hw4.iml
@@ -0,0 +1,11 @@
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/misc.xml b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/misc.xml
new file mode 100644
index 0000000..e99195c
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/misc.xml
@@ -0,0 +1,10 @@
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/modules.xml b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/modules.xml
new file mode 100644
index 0000000..94ad33c
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/other.xml b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/other.xml
new file mode 100644
index 0000000..a708ec7
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/other.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/workspace.xml b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/workspace.xml
new file mode 100644
index 0000000..f171cd6
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/.idea/workspace.xml
@@ -0,0 +1,475 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ img_size
+ img_width, img_height
+ *area_borders
+ .apply
+ float
+ np.array(
+ self.vA
+ vApply
+ IMG_WIDTH
+ compute_threshold
+ Feature3v
+ .5
+ -1
+ location.top
+ *img_area
+ shape.width, shape.height
+ shape
+ position.x, position.y
+ position
+
+
+ img_width, img_height
+ *borders
+ min_feature_size, max_feature_size
+ position.x
+ position.y
+ img_area
+ width, height
+ x, y
+
+
+
+
+
+
+
+
+ true
+ DEFINITION_ORDER
+
+
+
+
+
+
+
+
+
+
+
+
+ Angular
+
+
+
+
+
+
+
+
+
+
+
+
+
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diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/applicator.py b/hw4 (Viola-Jones, AdaBoost, Haar features)/applicator.py
new file mode 100755
index 0000000..3f5e8ff
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/applicator.py
@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+import pickle
+import sys
+
+import learner
+from learner import (WeakClassifier, Feature, HorizontalTwoFeature, VerticalTwoFeature, HorizontalThreeFeature,
+ FourFeature)
+
+
+def run():
+ model = sys.argv[-2]
+ fname = sys.argv[-1]
+
+ img = learner.read_file(fname)
+ x = learner.to_integral(img)
+
+ classifiers = pickle.load(open(model, 'rb'))
+
+ res = 0.
+ for c in classifiers:
+ res += c.alpha * c.apply(x)
+ print(int(res >= 0))
+
+
+if __name__ == '__main__':
+ run()
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/checker.py b/hw4 (Viola-Jones, AdaBoost, Haar features)/checker.py
new file mode 100755
index 0000000..20a5a7f
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/checker.py
@@ -0,0 +1,61 @@
+#!/usr/bin/env python2
+from __future__ import print_function
+
+import argparse
+import os
+import subprocess as sp
+import logging
+
+CATEGORIES = ['cars', 'faces']
+
+def _parse_args():
+ parser = argparse.ArgumentParser(prog='checks homework #4 of bsu ml course')
+ parser.add_argument('--show-failed-examples', action='store_true', help='print file names of examples on which algo has failed')
+ parser.add_argument('executable', help='path to executable which is implementing the algorithm')
+ parser.add_argument('model', help='this file will be passed to executable as the first argument')
+ parser.add_argument('set', help='path/to/folder/with/cars/and/faces/subfolder')
+ return parser.parse_args()
+
+def _run_algo(executable, model, fname):
+ cmd = [executable, model, fname]
+ result = sp.check_output(cmd).strip()
+ if result == '0':
+ return 0
+ if result == '1':
+ return 1
+ raise RuntimeError('Expected 0/1 output, but got "{}"'.format(result))
+
+def _main(args):
+ done = 0
+ success = 0
+
+ for i, category in enumerate(CATEGORIES):
+ folder = os.path.join(args.set, category)
+ assert os.path.isdir(folder), "Folder {} doesn't exist".format(folder)
+ for fname in os.listdir(folder):
+ full_path = os.path.join(folder, fname)
+ if not full_path.endswith('.bmp'):
+ logging.warning("File {} doesn't have bmp extension".format(full_path))
+
+ try:
+ result = _run_algo(args.executable, args.model, full_path)
+ except:
+ logging.error("Running algo on %s failed", full_path)
+ raise
+
+ done += 1
+ if result == i:
+ success += 1
+ else:
+ if args.show_failed_examples:
+ print("{}\tneed {}\tgot {}".format(full_path, CATEGORIES[i], CATEGORIES[result]))
+
+ assert done > 0, "Found 0 files to check"
+ accuracy = float(success) / done * 100.
+ print('You algo got {} answers correct out of {}'.format(success, done))
+ print('Accuracy: {:.02f}'.format(accuracy))
+ return 0
+
+if __name__ == '__main__':
+ logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
+ exit(_main(_parse_args()))
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/hw4_task.pdf b/hw4 (Viola-Jones, AdaBoost, Haar features)/hw4_task.pdf
new file mode 100644
index 0000000..bc6d6da
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diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/learn_check.sh b/hw4 (Viola-Jones, AdaBoost, Haar features)/learn_check.sh
new file mode 100755
index 0000000..ab3c915
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/learn_check.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+./learner.py train model
+./checker.py ./applicator.py model train
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/learner.py b/hw4 (Viola-Jones, AdaBoost, Haar features)/learner.py
new file mode 100755
index 0000000..4a039dd
--- /dev/null
+++ b/hw4 (Viola-Jones, AdaBoost, Haar features)/learner.py
@@ -0,0 +1,229 @@
+#!/usr/bin/env python3
+from collections import namedtuple
+import pickle
+import math
+
+import imageio
+import numpy as np
+import sys
+import os
+
+
+def read_file(fname):
+ image = imageio.imread(fname)
+ assert image.shape == (26, 40, 3)
+ return image[:, :, 0].astype(np.int)
+
+
+def load_data(folder_name):
+ examples = []
+ for fname in os.listdir(os.path.join(folder_name, 'cars')):
+ img = read_file(os.path.join(folder_name, 'cars', fname))
+ examples.append([img, -1])
+ for fname in os.listdir(os.path.join(folder_name, 'faces')):
+ img = read_file(os.path.join(folder_name, 'faces', fname))
+ examples.append([img, 1])
+
+ examples = np.array(examples).T
+ return examples[0], examples[1]
+
+
+def to_integral(img):
+ integral = np.cumsum(np.cumsum(img, axis=0), axis=1)
+ return np.pad(integral, (1, 1), 'constant', constant_values=(0, 0))[:-1, :-1]
+
+
+class Feature:
+ def __init__(self, x, y, width, height):
+ self.x1 = x
+ self.y1 = y
+ self.x2 = x + width
+ self.y2 = y + height
+
+ @staticmethod
+ def _rect_sum(itg_img, x1, y1, x2, y2):
+ return itg_img[y1, x1] + itg_img[y2, x2] - itg_img[y1, x2] - itg_img[y2, x1]
+
+ def apply(self, itg_img):
+ pass
+
+ def __str__(self):
+ return f'{self.__class__.__name__}(x1={self.x1}, y1={self.y1}, x2={self.x2}, y2={self.y2})'
+
+
+class HorizontalTwoFeature(Feature):
+ def __init__(self, x, y, width, height):
+ super().__init__(x, y, width, height)
+ self.x_middle = self.x1 + (self.x2 - self.x1) // 2
+
+ def apply(self, itg_img):
+ return self._rect_sum(itg_img, self.x1, self.y1, self.x_middle, self.y2) \
+ - self._rect_sum(itg_img, self.x_middle, self.y1, self.x2, self.y2)
+
+
+class VerticalTwoFeature(Feature):
+ def __init__(self, x, y, width, height):
+ super().__init__(x, y, width, height)
+ self.y_middle = self.y1 + (self.y2 - self.y1) // 2
+
+ def apply(self, itg_img):
+ return self._rect_sum(itg_img, self.x1, self.y1, self.x2, self.y_middle) \
+ - self._rect_sum(itg_img, self.x1, self.y_middle, self.x2, self.y2)
+
+
+class HorizontalThreeFeature(Feature):
+ def __init__(self, x, y, width, height):
+ super().__init__(x, y, width, height)
+ self.x_third_1 = self.x1 + (self.x2 - self.x1) // 3
+ self.x_third_2 = self.x1 + 2 * (self.x2 - self.x1) // 3
+
+ def apply(self, itg_img):
+ return self._rect_sum(itg_img, self.x1, self.y1, self.x_third_1, self.y2) \
+ + self._rect_sum(itg_img, self.x_third_2, self.y1, self.x2, self.y2) \
+ - self._rect_sum(itg_img, self.x_third_1, self.y1, self.x_third_2, self.y2)
+
+
+class FourFeature(Feature):
+ def __init__(self, x, y, width, height):
+ super().__init__(x, y, width, height)
+ self.x_middle = self.x1 + (self.x2 - self.x1) // 2
+ self.y_middle = self.y1 + (self.y2 - self.y1) // 2
+
+ def apply(self, itg_img):
+ return self._rect_sum(itg_img, self.x1, self.y1, self.x_middle, self.y_middle) \
+ + self._rect_sum(itg_img, self.x_middle, self.y_middle, self.x2, self.y2) \
+ - self._rect_sum(itg_img, self.x_middle, self.y1, self.x2, self.y_middle) \
+ - self._rect_sum(itg_img, self.x1, self.y_middle, self.x_middle, self.y2)
+
+
+class WeakClassifier:
+ def __init__(self, feature, threshold=0., polarity=1, alpha=0.):
+ self.feature = feature
+ self.threshold = threshold
+ self.polarity = polarity
+ self.alpha = alpha
+
+ def apply(self, x):
+ return -self.polarity if self.threshold < self.feature.apply(x) else self.polarity
+
+ def __str__(self):
+ return f'feature={self.feature}, threshold={self.threshold}, polarity={self.polarity}, alpha={self.alpha}'
+
+
+ValueRange = namedtuple('ValueRange', ['min', 'max'])
+ImageArea = namedtuple('ImageArea', ['left', 'right', 'bottom', 'top'])
+
+
+def compute_positions(width, height, img_area):
+ return ((x, y)
+ for x in range(img_area.left, img_area.right - width + 1, 2)
+ for y in range(img_area.bottom, img_area.top - height + 1, 2))
+
+
+def compute_sizes(base_width, base_height, width_range, height_range):
+ return ((width, height)
+ for width in range(base_width * width_range.min, width_range.max + 1, base_width)
+ for height in range(base_height * height_range.min, height_range.max + 1, base_height))
+
+
+def generate_features(img_area, width_range, height_range):
+ horiz_2_feature = [HorizontalTwoFeature(x, y, width, height)
+ for width, height in compute_sizes(2, 1, width_range, height_range)
+ for x, y in compute_positions(width, height, img_area)]
+
+ vert_2_feature = [VerticalTwoFeature(x, y, width, height)
+ for width, height in compute_sizes(1, 2, width_range, height_range)
+ for x, y in compute_positions(width, height, img_area)]
+
+ horiz_3_feature = [HorizontalThreeFeature(x, y, width, height)
+ for width, height in compute_sizes(3, 1, width_range, height_range)
+ for x, y in compute_positions(width, height, img_area)]
+
+ four_feature = [FourFeature(x, y, width, height)
+ for width, height in compute_sizes(2, 2, width_range, height_range)
+ for x, y in compute_positions(width, height, img_area)]
+
+ return horiz_2_feature + vert_2_feature + horiz_3_feature + four_feature
+
+
+def compute_threshold(ys, weights, values, indexes):
+ negatives, positives = np.empty_like(ys), np.empty_like(ys)
+ cur_negatives, cur_positives = 0., 0.
+
+ for i in indexes:
+ if ys[i] < 0:
+ cur_negatives += weights[i]
+ else:
+ cur_positives += weights[i]
+ negatives[i] = cur_negatives
+ positives[i] = cur_positives
+
+ total_negatives = negatives[indexes[-1]]
+ total_positives = positives[indexes[-1]]
+
+ errors = np.vstack((total_negatives - negatives + positives, total_positives - positives + negatives))
+ error_type, index_min = np.unravel_index(np.argmin(errors), errors.shape)
+ threshold = values[index_min]
+ polarity = -1 if error_type == 0 else 1
+
+ return threshold, polarity
+
+
+def create_classifier(feature, xs, ys, weights):
+ values = np.array([feature.apply(x) for x in xs])
+ indexes = np.argsort(values)
+
+ threshold, polarity = compute_threshold(ys, weights, values, indexes)
+ classifier = WeakClassifier(feature, threshold, polarity)
+
+ error = 0.
+ for i in range(len(ys)):
+ res = classifier.apply(xs[i])
+ error += weights[i] * int(res != ys[i])
+
+ return classifier, error
+
+
+def ada_boost(features_count, xs, ys, features):
+ weights = np.full(ys.shape, 1. / len(ys))
+ weak_classifiers = []
+
+ for t in range(features_count):
+ best_classifier = None
+ min_e = float('inf')
+
+ for i in range(len(features)):
+ cl, e = create_classifier(features[i], xs, ys, weights)
+ if e < min_e:
+ best_classifier = cl
+ min_e = e
+
+ best_classifier.alpha = math.log((1. - min_e) / min_e)
+ z = 2 * math.sqrt(min_e * (1. - min_e))
+
+ for i in range(len(ys)):
+ weights[i] *= math.pow(math.e, - best_classifier.alpha * ys[i] * best_classifier.apply(xs[i])) / z
+
+ weak_classifiers.append(best_classifier)
+ # print(f'feature {t}, best classifier: {str(best_classifier)}')
+
+ return weak_classifiers
+
+
+def _main():
+ folder_name = sys.argv[-2]
+ model_filename = sys.argv[-1]
+
+ xs, ys = load_data(folder_name)
+ xs = np.array([to_integral(x) for x in xs])
+
+ features = generate_features(ImageArea(left=6, right=34, bottom=4, top=22),
+ width_range=ValueRange(4, 18), height_range=ValueRange(4, 12))
+ # print(f'features count: {len(features)}')
+
+ chosen_classifiers = ada_boost(10, xs, ys, features)
+ pickle.dump(chosen_classifiers, open(model_filename, 'wb'))
+
+
+if __name__ == '__main__':
+ _main()
diff --git a/hw4 (Viola-Jones, AdaBoost, Haar features)/model b/hw4 (Viola-Jones, AdaBoost, Haar features)/model
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+ pickle
+ T
+ dot
+ astyep
+ risk
+ nesterov
+ .mean
+ float
+ sigmoid
+ versicolor
+ logistic_func
+ * 2 - 1
+ Parameters(
+ .b1
+ eps
+ momentum
+ 'train.csv'
+ 'test.csv'
+ setosa
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+ TRAIN_FILENAME
+ TEST_FILENAME
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "from collections import namedtuple, defaultdict\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import itertools"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Разделим датасет случайным образом на тренировочную и тестовую выборки. Перед сохранением в соответствующие файлы проведем стандартизацию признаков."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classes = ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica')\n",
+ "TRAIN_FILENAME = 'train.csv'\n",
+ "TEST_FILENAME = 'test.csv'\n",
+ "\n",
+ "def standardize(data):\n",
+ " features = data[:, :-1].astype(float)\n",
+ " features = (features - features.mean(axis=0)) / features.std(axis=0)\n",
+ " data[:, :-1] = features\n",
+ " return data\n",
+ "\n",
+ "\n",
+ "def load_data(filename):\n",
+ " return pd.read_csv(filename, header=None).values\n",
+ "\n",
+ "\n",
+ "def split_data(data_filename, train_filename, test_filename, split_ratio=0.1):\n",
+ " data = load_data(data_filename)\n",
+ " data = standardize(data)\n",
+ " np.random.shuffle(data)\n",
+ " split_index = int(len(data) * split_ratio)\n",
+ " pd.DataFrame(data[:split_index]).to_csv(test_filename, header=None)\n",
+ " pd.DataFrame(data[split_index:]).to_csv(train_filename, header=None)\n",
+ "\n",
+ "split_data('iris_data.csv', TRAIN_FILENAME, TEST_FILENAME)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "В градиентном спуске используем сигмоид как функцию для предсказания, в качестве градиента берем производную функции максимального правдоподобия. В результате работы алгоритма возвращается набор весов, вычисленных на всех итерациях."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid(x):\n",
+ " return 1.0 / (1.0 + np.exp(-x))\n",
+ "\n",
+ "\n",
+ "def compute_gradient(x, y, w):\n",
+ " grad = np.dot(x.T, sigmoid(x.dot(w)) - y)\n",
+ " return grad if len(x.shape) == 1 else grad / len(x)\n",
+ "\n",
+ "\n",
+ "Parameters = namedtuple('Parameters', ['iterations', 'learning_rate', 'alpha', 'gamma', 'beta'])\n",
+ "ValidationResult = namedtuple('ValidationResult', ['parameters', 'accuracy'])\n",
+ "\n",
+ "\n",
+ "def gradient_descent(xs, ys, gd_type, parameters):\n",
+ " n_iter, learning_rate, alpha = parameters.iterations, parameters.learning_rate, parameters.alpha\n",
+ " gamma, beta = parameters.gamma, parameters.beta\n",
+ " eps = 1.0e-8\n",
+ "\n",
+ " ws = np.zeros((n_iter, xs.shape[1]))\n",
+ " avg_w, u, g, m, v = (np.zeros_like(ws[0]) for _ in range(5))\n",
+ "\n",
+ " x, y = xs, ys\n",
+ " for t in range(n_iter - 1):\n",
+ " if gd_type in ['stochastic', 'sgd+momentum', 'sgd+nesterov_momentum']:\n",
+ " index = random.randint(0, len(xs) - 1)\n",
+ " x, y = xs[index], ys[index]\n",
+ "\n",
+ " w = ws[t] + gamma * u if gd_type == 'nesterov_momentum' else ws[t]\n",
+ " gradient = compute_gradient(x, y, w) + alpha * w\n",
+ "\n",
+ " if gd_type in ['stochastic', 'batch']:\n",
+ " u = - learning_rate * gradient\n",
+ " elif gd_type in ['sgd+momentum', 'sgd+nesterov_momentum']:\n",
+ " u = gamma * u - learning_rate * gradient\n",
+ " elif gd_type == 'adagrad':\n",
+ " g += gradient ** 2\n",
+ " u = - learning_rate * gradient / np.sqrt(g + eps)\n",
+ " elif gd_type == 'rmsprop':\n",
+ " g = beta * g + (1 - beta) * gradient ** 2\n",
+ " u = - learning_rate * gradient / np.sqrt(g + eps)\n",
+ " elif gd_type == 'adam':\n",
+ " m = gamma * m + (1 - gamma) * gradient\n",
+ " v = beta * v + (1 - beta) * gradient ** 2\n",
+ " u = - learning_rate * m / np.sqrt(v + eps)\n",
+ " else:\n",
+ " raise (\"Undefined gradient descent type.\")\n",
+ "\n",
+ " ws[t + 1] = ws[t] + u\n",
+ " return ws"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "В качестве меры оценки качества используем точность, kfold проводим для k=10. \n",
+ "Вектор весов берем как среднее векторов? вычисленных на всех итерациях."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_accuracy(w, sample):\n",
+ " correct_count = 0\n",
+ " for x, y in zip(sample[:, :-1], sample[:, -1]):\n",
+ " y_predict = (sigmoid(x.dot(w)) >= 0.5)\n",
+ " correct_count += (y_predict == y)\n",
+ " return correct_count / len(sample)\n",
+ "\n",
+ "\n",
+ "def kfold(train, k, parameters, gd_type):\n",
+ " np.random.shuffle(train)\n",
+ " parts = np.array_split(train, k)\n",
+ " avg_accuracy = 0.0\n",
+ " for i in range(k):\n",
+ " validation_sample = parts[i]\n",
+ " train_sample = np.concatenate(np.delete(parts, i))\n",
+ "\n",
+ " xs, ys = train_sample[:, :-1], train_sample[:, -1]\n",
+ " ws = gradient_descent(xs, ys, gd_type, parameters)\n",
+ " avg_accuracy += compute_accuracy(ws.mean(axis=0), validation_sample)\n",
+ " avg_accuracy /= k\n",
+ " return avg_accuracy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для удобства добавляем признак со значением 1.0, чтобы не работать с отдельным вектором b."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def transform_sample(classname, sample):\n",
+ " sample = np.hstack((np.ones((sample.shape[0], 1)), sample))\n",
+ " sample[:, -1] = (sample[:, -1] == classname)\n",
+ " return sample.astype(float)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "При валидации будем подбирать следующие параметры: количество итераций, скорость обучения, коэффициент регуляризации alpha, коэффициент накопления импульса gamma, коэффициент накопления квадратов градиентов beta."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def search_parameters(classname, gd_type, iterations_grid=(1000,), lr_grid=(0.01,), alpha_grid=(0.0001,),\n",
+ " gamma_grid=(None,), beta_grid=(None,)):\n",
+ " train = transform_sample(classname, load_data(TRAIN_FILENAME))\n",
+ " best_result = ValidationResult(Parameters(*([None] * 5)), accuracy=0.0)\n",
+ "\n",
+ " grids = [iterations_grid, lr_grid, alpha_grid, gamma_grid, beta_grid]\n",
+ " for param_list in itertools.product(*grids):\n",
+ " parameters = Parameters(*param_list)\n",
+ " accuracy = kfold(train, 10, parameters, gd_type)\n",
+ " cur_result = ValidationResult(parameters, accuracy)\n",
+ " if accuracy > best_result.accuracy:\n",
+ " best_result = cur_result\n",
+ " return best_result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Создадим функции для запуска лучшего решения на тестовой выборке, и для сравнения различных методов градиентного спуска с уже подобранными параметрами. Аргументом в них передается название класса ирисов, для которого строим и тестируем модели."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def test(classname, gd_type, parameters):\n",
+ " train = transform_sample(classname, load_data(TRAIN_FILENAME))\n",
+ " test = transform_sample(classname, load_data(TEST_FILENAME))\n",
+ "\n",
+ " xs, ys = train[:, :-1], train[:, -1]\n",
+ " ws = gradient_descent(xs, ys, gd_type, parameters)\n",
+ " return compute_accuracy(ws.mean(axis=0), test)\n",
+ "\n",
+ "\n",
+ "def compare(classname, parameters_dict):\n",
+ " train = transform_sample(classname, load_data(TRAIN_FILENAME))\n",
+ " xs, ys = train[:, :-1], train[:, -1]\n",
+ "\n",
+ " max_iterations = max(map(lambda ps: ps.iterations, parameters_dict.values()))\n",
+ " for gd_type in parameters_dict:\n",
+ " parameters_list = list(parameters_dict[gd_type])\n",
+ " parameters_dict[gd_type] = Parameters(max_iterations, *(parameters_list[1:]))\n",
+ "\n",
+ " for gd_type, parameters in parameters_dict.items():\n",
+ " ws = gradient_descent(xs, ys, gd_type, parameters)\n",
+ " accuracy = [compute_accuracy(ws[:i + 1].mean(axis=0), train) for i in range(len(ws))]\n",
+ " plt.plot(range(parameters.iterations), accuracy)\n",
+ "\n",
+ " plt.legend(parameters_dict.keys(), loc='lower right')\n",
+ " plt.title(f\"Accuracy for '{classname}' model\")\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Задания 1-3. Проводим валидацию для sgd batch gradient descent, запускаем на тествой выборке лучшее решение, и строим графики зависмости точности от количества итераций, чтобы сравнить два метода."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-setosa' with 'stochastic': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'stochastic: 0.9186813186813187\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'stochastic: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'batch': Parameters(iterations=1600, learning_rate=0.005, alpha=0.0, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'batch: 0.9543956043956046\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'batch: 1.0\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-versicolor' with 'stochastic': Parameters(iterations=1600, learning_rate=0.005, alpha=0.01, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'stochastic: 0.6978021978021978\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'stochastic: 0.6\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'batch': Parameters(iterations=800, learning_rate=0.01, alpha=0.0, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'batch: 0.7032967032967034\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'batch: 0.4666666666666667\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-virginica' with 'stochastic': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'stochastic: 0.7571428571428572\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'stochastic: 0.7333333333333333\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'batch': Parameters(iterations=1600, learning_rate=0.001, alpha=0.001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'batch: 0.8384615384615384\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'batch: 0.7333333333333333\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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PuxQ23lYkxdIruWsspCTTtrVQOArO+wP0ODDV0Yg0Kq2Sez113JNr3tveuO2mysiCI3/QeqNFNhfDrPFQV9viTZVV7KTjuqUUdx3JpK9qgW9irrN/j3wuOLIfr325ivlrtnJ4YVcuOLJf1PZfr9rEp99s4IIj+7Jk/TZmFm9ucdwhR/Tvyughvdi4fRfjv1zFmCG9WLVxOwvWVLR424f268IpQzUCqK1Jz+QuyeMcvHUD1FQ1b/2sHDju9sTGFM2MF+Gz3yRkUz39x7+t6cULJYvjXu/UYfty/9/nAPCXqSs54+De5HXIjNj215MX8fnScrbtrOYfs9dQuqWZxziCwu55fPajk3l3Vim/nryIL5dvpGjFRrbvavk/vl6dcvjyJ0rubU1aJffdNXdJmupKL7Gf+hAcf1f86zkHj/Rs3hDC5tqxEfIL4EdLW7ypS5+fSl0dvHHjMfw0jvavfrGKH781h1XlO/aYv6WyOmpy37Sjuv5x045qrjthID85a1hLQ+fn785jQlGJv+1d9XFs31XL3acN5rZTBjV727/850LG/e9ynHMqi7Yx6ZXc9bcV2+e/h+Jpe84bPBaOuGL39KaV8NHDULtz7/VrvORAbhPHa5t5Y7znvQXlS5q2LlC2bRfFG3fQc58cCrvnRWxT52DBmq3srKkDYMDOReyyXB5+ZQY3nnQgw/t04bHJC/nhcQPZt3Mu789dyzszV3PN8QMZMaA7c0q28MwnS6ir23vb80u3ctTA+O9R2iUvG4CfTZy3x/x7J8yiY4fIb7sVG7YD8NniMiqra+u30VKdc7Op2FnDjS/PYNE6rwyzaK332CW/ZfvonJfFrto6bnh5Bhl6A8btsqP7c9LgnrEbtkBaJfcQp7GQ0X32OGDQaT9veksJbFiyZ3Jf8gHMnQAFg706eUO9D4f+o5q+70MuhmVTdn9bswl2ba4kv6qG6soMcB0jtqmuqSNz03a6ZBoZZmwhj8+zR/H+3LX06ZLHjl21PPfJMhav28a4q0by8rQV/GdJOd06dmDEgO5MnLWa9+euZVCvTnttu3eXPE4fHn/p4bDCLhxW2JVtVTUAHLRfJ3KyM1m/dScQ4Z8mXo0+w4zaOkePjjkcc2Bi7ol6/KACPpi/juUbttMhM4Pc7Az6d8+nQ1YGIwe07Ho2xx5YwPA+a1jZ4BOKNK6iqjrp+0ir5B7qOCi1R1FXB5Wb4cR74GS/uPDOLbDkoz3bVfon8m74DLJzE7f/sb9o9qr3PD+NqcvK6ZHdgRk3nxaxzdyVm7jwmc958eqRjBnSC4CBwP/7xUdsqaymts77y9i+00u4Wyqr93rs2SmHyXed2Ow4Q/p1y+edW45r8XYSYeSA7ky644SkbPvwwq784/bkbFtaJr2Su76iCrt2wMvnw/ayvZe5OsDtWVLJ6wYVa72v1IfsKPeuLZ7IxN5Mi9ZWcPv4r1le7pUsyrfvYszjHwPQMSeTJy89grvfmMWWymoq/ZODDcsZXfOzmTRnDZ8u9o7JF8s3MubxjynZ5PU2/71gPWMe/5j1W6vo2y1yyUckaNIquQuwaYVXU+9/DHTuu/fy/qPgoLN2Tx9yMVSs8xN/mD6HJzXMeH21ahOL1lXwneH78u39u7FgTQW1dY7NldV8+k0Z781aw8zizYw6oDu9OuXSOS+LYb33vGH0jScdyL8Xrgdg4qxSxg7fjw5ZGRzarwvd8juwcbt/HqFvF8YclNw6qEhrScvk3u5K7s5BrZ+gQj32k34EB54ce93eh8KFf0xebE1UW+dwzpGVmUFdnatPvL+75HDyw05ELlm/jVN/+wnLN2wD4N7vDOHb+0euH593RF/OO8L7R/fUZUck+RWItA1pldzbbc393dvhq7/sOS8//pEdyVK0YiMXPTuVj+4+iQN77hOzfUVVNaN+8RHVdY5pD5zCra9+xedLy8nJyiAve8/hg907dgDg7ZmlAHTL75D4FyASYGmV3NutNbO9kS2HXepN53aBfQ9JbUzA379eDcB/lmyIK7mv2VJV/6Wakk07mFe6lW/v340bTjxgrzHU3Tt24HeXHEbp5iq6d+zAwILII2hE2qu4kruZjQWeBDKBF5xzv2yw/HfAGH8yH+jlnEvZhavTeijkru1QtmjPedvLYP/j4IS7UxJSXZ1j0boKqmv3rNuv2ezdUq544w5ml8T+Kv3CsK/CzyzezNaqao47sAenD98vYvvzj4j+VX6R9i5mcjezTOBp4DSgBJhuZhOdc/NDbZxzd4W1vw1ISWEz1LtL49QO794Jc97Ye36nyAmwNbw/by03v/JV1OV//Gw5f/xseZO2+eA73pd/enZO/YgdkSCKp+d+FLDEObcMwMxeA84F5kdpfxnws8SE1zTtYiDklhLY92A4+f/snmfmjY5JkQ3bvC/lPHHJ4XTK3fNPqqxiJz07xX+Dik652WRmwOYd1WRlZnD0QN00WqQ54knufYHisOkS4OhIDc1sf7zvjfw7yvLrgesB+vfv36RA27XtG2Dxv7zhiptXQp8jYMjYVEdVr6raq5OfOmxf9snRaRyRtiCed2KkDnG0yselwATnXMRLzTnnngeeBxgxYkTyqifpVpf5/Cn4z5O7pw++MHWxRLCz2qu152RlpDgSEQmJJ7mXAOG3nekHlEZpeylwS0uDaq7dQyHTLLtvK4NOfeCayd5057Z1IrGqppbMDCM7U8ldpK2IJ7lPBwaZ2UBgNV4C/37DRmY2BOgGTE1ohE2Q9MsPLPonLP80ufuIZNVUyO/B5NUd+GLZRmAhAHkdMrh59LfomJPFlIXr+WzxhtaPDfhyRTm56rWLtCkxk7tzrsbMbgUm4w2FHOecm2dmDwNFzrmJftPLgNdcGxiHmLQIPnzIu1xtdn6SdtCIwWN5dNICSjZVkpedSa1z7NhVyxGF3Th12L48NnkRi9dV7PVln9YyooVXFxSRxIrr7JdzbhIwqcG8BxtMP5S4sJqnviyTrOReuQkOvxzOeSpJO2jc5un/4rKj+vPIeQezqnwHJ/56CptDVzTcsYtzD+/Lby4+LCWxiUjbkpZDG5pcc6/ZCW/f5I1Kacz2Mu+GE3H467SV/PTtuQzqtQ+9Osc/FLAxWyqr6694GHr8w5QlvPV1Cesrdibs5g4iEnxpldw7+Cf0QnfiiVv5Epj7pvcV/rxGygv9j/HuWhSHRyd5N5BevH5bwpLuyAHdGXOQd53yznlZXHhkP1aWb2dndR1H9u/GqcN6JWQ/IhJ8aZXcMzO8ukzo5gtxq9zkPZ75azhgdEJiCf8HM+GmYxOyzXBmphKMiESVVsm9Q+U6pnS4i33WVcKvY5dCdtXWsa2qhmyq6QRc/soiFllibn9V49/1J1UnOEWkfUur5N5x8zcMzFjH+7UjGTs09lURv1m9hZnFm/lWr05UZXXigP2OYoAl5pBkmLGrpo7zj4xwwwwRkSQLZnKv2uqdBG0gd4d3idnf1HyPEaOvirmZv320mPHFxXxz+xkAjE5kjCIiKRS85L5uHrXPHE8me580Pch/3Og6MeJ/Poxrc7276KqDIpJ+gpfct60jkzr+WHMm152z523kFq6t4LfTtnLayIMZ3qdzlA3saWjv+NqJiARJ4JK7c96VzP5ZexTXHXXdHstWL1jHvz4vYuLR/Tm0X8ruFSIiknKBuyDIkrKK2I1ERNq5wCX30LXDRUQkusAld+qiX1og9ZcsExFpG4KX3OOQ9Ev/ioi0cYFN7k4JXEQkqsAl98au+KiqjIiIJ3DJPR6mTr2ItHPBS+7+WVOVZUREogtechcRkZgCm9wj1dfbwO1bRUTahMAl9ybfQk9EpB0KXHIXEZHYApfcrZETqurTi4h4Apfc46GhkCLS3gU2uWsopIhIdIFL7hoRIyISW+CSe2OU90VEPIFL7q7BYyS6KqSItHeBS+6m7rmISEyBS+6NU+IXEYE4k7uZjTWzRWa2xMzuj9LmYjObb2bzzOzVxIa5m4vjwmEaCiki7V1WrAZmlgk8DZwGlADTzWyic25+WJtBwAPAcc65TWbWK1kBi4hIbPH03I8CljjnljnndgGvAec2aHMd8LRzbhOAc259YsOMRN1zEZFo4knufYHisOkSf164wcBgM/uPmU0zs7GRNmRm15tZkZkVlZWVNS/ixu7EpJK7iAgQX3KP1EVumEazgEHAaOAy4AUz67rXSs4975wb4Zwb0bNnz6bGGjfV3EWkvYsnuZcAhWHT/YDSCG3ecc5VO+eWA4vwkn3C7T6hKiIi0cST3KcDg8xsoJl1AC4FJjZo8zYwBsDMCvDKNMsSGWg8lPBFRDwxk7tzrga4FZgMLADecM7NM7OHzewcv9lkoNzM5gNTgHudc+XJChpiDIXUyVYRaediDoUEcM5NAiY1mPdg2HMH/Lf/k2Tqn4uIxJJm31AVEREIYnJv7E5M6tSLiABBTO5x0FBIEWnvApjc1T0XEYkleMm9kXHuTolfRAQIYnKPg6oyItLeBS+5x3HJXxGR9i54yV1ERGIKXHJvrKquoZAiIp7AJfcQ3YlJRCS6wCZ3ERGJLnDJPVR6iTwUUkREIIDJ3ZTCRURiClxyj++LSiq6i0j7FrjkHiq+aJy7iEh0AUzu0TmNhRQRAYKY3OPI3xoKKSLtXfCSu06oiojEFLjkrtQuIhJb4JK71Y9zV+1FRCSawCX3eCjti0h7F7jkrhtyiIjEFrjkrhtki4jEFrzkHgfTWEgRaecCl9yN6PdQFRERT+CSu4iIxBa45N5YXV0nW0VEPIFL7vFcOEwVdxFp7wKY3EVEJJa4kruZjTWzRWa2xMzuj7D8KjMrM7OZ/s+1iQ81REMhRURiyYrVwMwygaeB04ASYLqZTXTOzW/Q9HXn3K1JiLHJNBJSRNq7eHruRwFLnHPLnHO7gNeAc5MbVnTqnYuIxBZPcu8LFIdNl/jzGrrQzGab2QQzK4y0ITO73syKzKyorKysGeFqnLuISDziSe6RihwNc+u7wADn3KHAh8CfI23IOfe8c26Ec25Ez549mxZp/Taat0xEpD2JJ7mXAOE98X5AaXgD51y5c26nP/lH4NuJCS+S2BncNBhSRNq5eJL7dGCQmQ00sw7ApcDE8AZm1jts8hxgQeJCjEzXcxcRiS7maBnnXI2Z3QpMBjKBcc65eWb2MFDknJsI3G5m5wA1wEbgqiTGHD3WVOxURKQNipncAZxzk4BJDeY9GPb8AeCBxIYWNRr/sZFvqKpTLyLtXOC+oaqTpiIisQUuuYuISGyBS+7149wj9OCduvUiIkAAk7vSt4hIbIFL7tbIPVRFRMQTuOTeGPXqRUQ8aZXcQzQUUkTau+Ald5VlRERiClxyV+lFRCS2wCV3ayy9K/OLiAABTO6uwWMkpqK7iLRzgUvuIiISW/CSeyMnVJ3qMiIiQBCTexxUlBGR9i6AyV29cxGRWAKX3EPXBtM4dxGR6AKX3Buji0KKiHgCl9zrL/nbWBt16kWknQtcclfnXEQktsAl98a+oarELyLiCVxy311Xb+QG2TrZKiLtXOCSu4iIxBbA5K7ii4hILAFM7p6Ilx9Q3hcRAQKc3BujoZAi0t4FL7m72OPcRUTau+Al90boqpAiIp6sVAfQdLETuKoyIq2nurqakpISqqqqUh1KWsnNzaVfv35kZ2c3a/0AJnePLhwm0jaUlJTQqVMnBgwYoLugJYhzjvLyckpKShg4cGCzthFXWcbMxprZIjNbYmb3N9LuIjNzZjaiWdGISOBUVVXRo0cPJfYEMjN69OjRok9DMZO7mWUCTwNnAMOAy8xsWIR2nYDbgS+aHU08GjmhqqGQIqmhxJ54LT2m8fTcjwKWOOeWOed2Aa8B50Zo9wjwGJD6wpv+zkSknYsnufcFisOmS/x59czsCKDQOfdeYxsys+vNrMjMisrKypocrEfdcxGJ7YknnmDHjh3NWvehhx7i8ccfb3EML730EqWlpfXT1157LfPnz2/xduMRT3KP1A/effkuswzgd8DdsTbknHveOTfCOTeiZ8+e8Ue5xzZCAUS6QbaIiKclyT1RGib3F154gWHD9qpqJ0U8o2VKgMKw6X5Aadh0J+Bg4GO/RrQfMNHMznHOFSUq0KbQVSFFUuPawrQRAAAM9ElEQVTn785jfunWhG5zWJ/O/Oy7wxtts337di6++GJKSkqora3le9/7HqWlpYwZM4aCggKmTJnC+PHj+cUvfoFzjrPOOotf/epXALz//vv8+Mc/pra2loKCAj766CMA5s+fz+jRo1m1ahV33nknt99+OwDnnXcexcXFVFVVcccdd3D99ddTW1vLNddcQ1FREWbGD3/4QwoLCykqKuLyyy8nLy+PqVOncsYZZ/D4448zYsSIqPtNlHiS+3RgkJkNBFYDlwLfDy10zm0BCkLTZvYxcE/yEnvohKoSuIh43n//ffr06cM//vEPALZs2cKLL77IlClTKCgooLS0lPvuu48ZM2bQrVs3Tj/9dN5++22OO+44rrvuOj799FMGDhzIxo0b67e5cOFCpkyZQkVFBUOGDOGmm24iOzubcePG0b17dyorKxk5ciQXXnghK1asYPXq1cydOxeAzZs307VrV37/+9/XJ/NwZWVlUfebKDGTu3OuxsxuBSYDmcA459w8M3sYKHLOTUx4VCISSLF62MlyyCGHcM8993Dfffdx9tlnc8IJJ+yxfPr06YwePZpQOfjyyy/n008/JTMzkxNPPLF+LHn37t3r1znrrLPIyckhJyeHXr16sW7dOvr168dTTz3FW2+9BUBxcTGLFy9myJAhLFu2jNtuu42zzjqL008/vdF4p02bFnW/iRLXl5icc5OASQ3mPRil7eiWhxVdY3di0lhIkfZp8ODBzJgxg0mTJvHAAw/slVxdlNzgnIs65DAnJ6f+eWZmJjU1NXz88cd8+OGHTJ06lfz8fEaPHk1VVRXdunVj1qxZTJ48maeffpo33niDcePGRY23sf0mSuCuLbP7hGp0GnIr0r6UlpaSn5/PFVdcwT333MNXX31Fp06dqKioAODoo4/mk08+YcOGDdTW1jJ+/HhOOukkjjnmGD755BOWL18OELM8smXLFrp160Z+fj4LFy5k2rRpAGzYsIG6ujouvPBCHnnkEb766iuAPWII19T9NkdgLz8gIhIyZ84c7r33XjIyMsjOzuaZZ56pP4HZu3dvpkyZwqOPPsqYMWNwznHmmWdy7rne13Wef/55LrjgAurq6ujVqxcffPBB1P2MHTuWZ599lkMPPZQhQ4YwatQoAFavXs3VV19NXV0dAI8++igAV111FTfeeGP9CdWQnj17Nmm/zWHRPq4k24gRI1xRUdPPuU579RFGffM4h1Y9z+xfXrLHsr9MXcGD78yj6KenUrBPTuQNiEhCLViwgKFDh6Y6jLQU6dia2QznXMxLvASuLLPb3rWXXTXef83szAC/LBGRBAheFmzkk8ZOP7nnZgfvZYmIJFJgs2Ckce47q2sxgw7quYtIO5dWWXBnTR05WRm6Qp2ItHsBTO7RyzJV1bXkZme2YiwiIm1TAJO7J1KKr6yuJTdLyV1EJLDJPZItldV0yWve/QZFJLhWrFjBwQcfHHf7hldrjNbm1ltvbWloKRO8LzG53RcOq6mt4+VpK9mwbScbt1ezcG0F+3bKTXGAItLWvfTSSxx88MH06dMn1aEkTfCSe5iZxZv5+bt7Xvj+5IN6pSgaEeGf98PaOYnd5n6HwBm/jNmspqaGK6+8kq+//prBgwfzl7/8hccff5x3332XyspKjj32WJ577jnefPPNvS7FO3fuXO644w62b99OTk5O/eV3S0tLGTt2LEuXLuX888/nscceS+xrS6IAlmV2V9s3bt+1x5L/3H9yyq5KJyKptWjRIq6//npmz55N586d+cMf/sCtt97K9OnTmTt3LpWVlbz33ntcdNFFjBgxgldeeYWZM2eSmZnJJZdcwpNPPsmsWbP48MMPycvLA2DmzJm8/vrrzJkzh9dff53i4uIYUbQdge25O4yn/r14j3k9OnZIUTQiAsTVw06WwsJCjjvuOACuuOIKnnrqKQYOHMhjjz3Gjh072LhxI8OHD+e73/3uHustWrSI3r17M3LkSAA6d+5cv+yUU06hS5cuAAwbNoyVK1dSWFhIEAQ2uQPMXe3d8eXHZx7EjJWbNAxSpB1r+P0WM+Pmm2+mqKiIwsJCHnroIaqqqvZar6mX/Q2K4JVlGlx+4OfnDOf6Ew/kuf+KeR0dEUljq1atqr/y4vjx4zn++OMBKCgoYNu2bUyYMKG+bfileA866CBKS0uZPn06ABUVFYFK4tEEtuceSvEa+igiAEOHDuXPf/4zN9xwA4MGDeKmm25i06ZNHHLIIQwYMKC+7AJ7X4r39ddf57bbbqOyspK8vDw+/PDDFL6SxAjcJX9nfvAqm7/4K6/3+yl5efncf8ZB9Oqs4Y8iqaJL/iZPSy75G7ie++GnfR9O+z6jUx2IiEgbFryau4iIxKTkLiItlqrybjpr6TFVcheRFsnNzaW8vFwJPoGcc5SXl5Ob2/zziYGruYtI29KvXz9KSkooKytLdShpJTc3l379+jV7fSV3EWmR7OxsBg4cmOowpAGVZURE0pCSu4hIGlJyFxFJQyn7hqqZlQErm7l6AbAhgeEkiuJqurYam+JqGsXVNC2Ja3/nXM9YjVKW3FvCzIri+fpta1NcTddWY1NcTaO4mqY14lJZRkQkDSm5i4ikoaAm9+dTHUAUiqvp2mpsiqtpFFfTJD2uQNbcRUSkcUHtuYuISCOU3EVE0lDgkruZjTWzRWa2xMzub+V9F5rZFDNbYGbzzOwOf353M/vAzBb7j938+WZmT/mxzjazI5MYW6aZfW1m7/nTA83sCz+m182sgz8/x59e4i8fkKyY/P11NbMJZrbQP27HtJHjdZf/O5xrZuPNLDcVx8zMxpnZejObGzavycfHzK702y82syuTFNev/d/jbDN7y8y6hi17wI9rkZl9J2x+Qt+vkeIKW3aPmTkzK/CnU3q8/Pm3+a9/npk9FjY/+cfLOReYHyATWAocAHQAZgHDWnH/vYEj/eedgG+AYcBjwP3+/PuBX/nPzwT+CRgwCvgiibH9N/Aq8J4//QZwqf/8WeAm//nNwLP+80uB15N8zP4MXOs/7wB0TfXxAvoCy4G8sGN1VSqOGXAicCQwN2xek44P0B1Y5j928593S0JcpwNZ/vNfhcU1zH8v5gAD/fdoZjLer5Hi8ucXApPxvhhZ0EaO1xjgQyDHn+7VmscraW/qZPwAxwCTw6YfAB5IYTzvAKcBi4De/rzewCL/+XPAZWHt69slOI5+wEfAycB7/h/zhrA3Yv1x898Ax/jPs/x2lqTj0xkviVqD+ak+Xn2BYv/NneUfs++k6pgBAxokhSYdH+Ay4Lmw+Xu0S1RcDZadD7ziP9/jfRg6Xsl6v0aKC5gAHAasYHdyT+nxwussnBqhXascr6CVZUJvypASf16r8z+aHwF8AezrnFsD4D/28pu1VrxPAD8C6vzpHsBm51xNhP3Wx+Qv3+K3T4YDgDLgRb9k9IKZdSTFx8s5txp4HFgFrME7BjNoG8cMmn58UvG++CFerzjlcZnZOcBq59ysBotSfbwGAyf4pbxPzGxka8YVtORuEea1+lhOM9sHeBO40zm3tbGmEeYlNF4zOxtY75ybEed+W/MYZuF9VH3GOXcEsB2vzBBNq8Tm17DPxftI3AfoCJzRyL7bxN8d0eNo1fjM7CdADfBKquMys3zgJ8CDkRanKi5fFl7ZZxRwL/CGmVlrxRW05F6CV1sL6QeUtmYAZpaNl9hfcc793Z+9zsx6+8t7A+v9+a0R73HAOWa2AngNrzTzBNDVzEI3Ywnfb31M/vIuwMYExxRSApQ4577wpyfgJftUHi+AU4Hlzrky51w18HfgWNrGMYOmH59We1/4Jx/PBi53fu0gxXEdiPdPepb/HugHfGVm+6U4Lvz9/N15vsT7ZF3QWnEFLblPBwb5oxo64J3cmthaO/f/6/4JWOCc+23YoolA6Iz7lXi1+ND8H/hn7UcBW0IftxPFOfeAc66fc24A3vH4t3PucmAKcFGUmEKxXuS3T0ovzzm3Fig2syH+rFOA+aTwePlWAaPMLN//nYbiSvkxi7C/eI7PZOB0M+vmfyo53Z+XUGY2FrgPOMc5t6NBvJeaN6poIDAI+JJWeL865+Y453o55wb474ESvEEPa0nx8QLexutsYWaD8U6SbqC1jldLTyK09g/eGfBv8M4q/6SV93083sek2cBM/+dMvPrrR8Bi/7G7396Ap/1Y5wAjkhzfaHaPljnA/4NZAvyN3Wfsc/3pJf7yA5Ic0+FAkX/M3sb7mJry4wX8HFgIzAVexhu50OrHDBiPV/evxktM1zTn+ODVwJf4P1cnKa4leDXh0N/+s2Htf+LHtQg4I2x+Qt+vkeJqsHwFu0+opvp4dQD+6v+NfQWc3JrHS5cfEBFJQ0Ery4iISByU3EVE0pCSu4hIGlJyFxFJQ0ruIiJpSMldRCQNKbmLiKSh/w86XERJR774UwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gd_types = ['stochastic', 'batch']\n",
+ "for classname in classes:\n",
+ " parameters_dict = defaultdict()\n",
+ " for gd_type in gd_types:\n",
+ " best_result = search_parameters(classname, gd_type, iterations_grid=[200, 800, 1600],\n",
+ " lr_grid=[0.1, 0.05, 0.01, 0.005, 0.001],\n",
+ " alpha_grid=[0.01, 0.001, 0.0001, 0.0])\n",
+ " print(f\"Best parameters for '{classname}' with '{gd_type}': {best_result.parameters}\")\n",
+ " print(f\"Best validation accuracy for '{classname}' with '{gd_type}: {best_result.accuracy}\")\n",
+ " parameters_dict[gd_type] = best_result.parameters\n",
+ "\n",
+ " accuracy = test(classname, gd_type, best_result.parameters)\n",
+ " print(f\"Accuracy on test sample for '{classname}' with '{gd_type}: {accuracy}\\n\")\n",
+ " compare(classname, parameters_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "При валидации BGD показал немного лучшие результаты качества чем у SGD на всех классах ирисов, однако на самом 'сложном' для классификации ирисе versicolor при тестировании BGD показал результаты хуже. Возможно SGD повезло разиением валидационных выборок. Сходимость у batch происходит быстрее чем у SGD.\n",
+ "\n",
+ "Преимущества SGD:\n",
+ "1. Требует меньших вычислительных затрат по сравнению с batch GD, особенно при больших размерах тренировочной выборки.\n",
+ "2. Удобно применять для online обучения, когда необходимо быстро обновлять вектор весов w.\n",
+ "3. При очень больших наборах данных нет необходимости хранить все объекты тренировочной выборки в памяти.\n",
+ "\n",
+ "Недостатки SGD:\n",
+ "1. Временами медленная сходимость.\n",
+ "2. Может вообще не сойтись и застрять в локальном минимуме.\n",
+ "\n",
+ "Преимущества batch gradient descent:\n",
+ "1. Стабильная сходимость, более быстрая по количеству итераций чем у SGD.\n",
+ "\n",
+ "Недостатки batch gradient descent:\n",
+ "1. Требует больших вычислительных затрат при вычислении градиента по всей выборке.\n",
+ "2. Проблематично применять на больших наборах данных, из-за 1. и необходимости хранить объекты тренировочной выборки в памяти.\n",
+ "\n",
+ "У обоих недостатком является фиксированная скорость обучения, которую нужно подбирать."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Задание 4. Аналогично заданиям 1-3 проводим валидацию для методов моментов и адаптивных вариантов градиентного спуска, запускаем на тестовой выборке лучшее решение, и строим графики зависмости точности от количества итераций, для сравнения всех методов. Для адаптивных методов не подбираем скорость обучения, а берем сразу достаточно большое значение 0.1 для более быстрой сходимости."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-setosa' with 'sgd+momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.0001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'sgd+momentum: 0.9115384615384615\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'sgd+momentum: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'sgd+nesterov_momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'sgd+nesterov_momentum: 0.8879120879120878\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'sgd+nesterov_momentum: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'adagrad': Parameters(iterations=200, learning_rate=0.1, alpha=0.01, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'adagrad: 1.0\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'adagrad: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'rmsprop': Parameters(iterations=200, learning_rate=0.1, alpha=0.01, gamma=None, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'rmsprop: 1.0\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'rmsprop: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'adam': Parameters(iterations=200, learning_rate=0.1, alpha=0.01, gamma=0.9, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'adam: 1.0\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'adam: 1.0\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-versicolor' with 'sgd+momentum': Parameters(iterations=1600, learning_rate=0.05, alpha=0.0001, gamma=0.9, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'sgd+momentum: 0.7\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'sgd+momentum: 0.4666666666666667\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'sgd+nesterov_momentum': Parameters(iterations=800, learning_rate=0.001, alpha=0.0001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'sgd+nesterov_momentum: 0.7027472527472528\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'sgd+nesterov_momentum: 0.5333333333333333\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'adagrad': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'adagrad: 0.7434065934065933\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'adagrad: 0.6666666666666666\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'rmsprop': Parameters(iterations=800, learning_rate=0.1, alpha=0.001, gamma=None, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'rmsprop: 0.7543956043956045\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'rmsprop: 0.6666666666666666\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'adam': Parameters(iterations=800, learning_rate=0.1, alpha=0.01, gamma=0.999, beta=0.999)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'adam: 0.754945054945055\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'adam: 0.6\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-virginica' with 'sgd+momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.0, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'sgd+momentum: 0.8\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'sgd+momentum: 0.8\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'sgd+nesterov_momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'sgd+nesterov_momentum: 0.7857142857142857\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'sgd+nesterov_momentum: 0.7333333333333333\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'adagrad': Parameters(iterations=800, learning_rate=0.1, alpha=0.001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'adagrad: 0.9483516483516483\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'adagrad: 0.8\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'rmsprop': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0001, gamma=None, beta=0.99)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'rmsprop: 0.9703296703296704\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'rmsprop: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'adam': Parameters(iterations=800, learning_rate=0.1, alpha=0.001, gamma=0.9, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'adam: 0.9785714285714284\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'adam: 1.0\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gd_types = ['sgd+momentum', 'sgd+nesterov_momentum', 'adagrad', 'rmsprop', 'adam']\n",
+ "for classname in classes:\n",
+ " parameters_dict = defaultdict()\n",
+ " for gd_type in gd_types:\n",
+ " lr_grid = [0.1] if gd_type in ['adagrad', 'rmsprop', 'adam'] else [0.1, 0.05, 0.01, 0.005, 0.001]\n",
+ " gamma_grid = [0.9, 0.99, 0.999] if gd_type in ['sgd+momentum', 'sgd+nesterov_momentum', 'adam'] else [None]\n",
+ " beta_grid = [0.9, 0.99, 0.999] if gd_type in ['rmsprop', 'adam'] else [None]\n",
+ "\n",
+ " best_result = search_parameters(classname, gd_type, iterations_grid=[200, 800, 1600],\n",
+ " lr_grid=lr_grid, alpha_grid=[0.01, 0.001, 0.0001, 0.0],\n",
+ " gamma_grid=gamma_grid, beta_grid=beta_grid)\n",
+ "\n",
+ " print(f\"Best parameters for '{classname}' with '{gd_type}': {best_result.parameters}\")\n",
+ " print(f\"Best validation accuracy for '{classname}' with '{gd_type}: {best_result.accuracy}\")\n",
+ " parameters_dict[gd_type] = best_result.parameters\n",
+ "\n",
+ " accuracy = test(classname, gd_type, best_result.parameters)\n",
+ " print(f\"Accuracy on test sample for '{classname}' with '{gd_type}: {accuracy}\\n\")\n",
+ "\n",
+ " compare(classname, parameters_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Наиболее быструю сходимость, а также наилучшие результаты качества как на валидационной, так и на тестовой выборках, показали adam, adagrad и rmsprop. Скорее всего, благодаря тому, что adam сочетает в себе как идею накопления движения, так и идею более слабого обновления весов для типичных признаков, т.е. преимущества методов моментов и rmsprop."
+ ]
+ }
+ ],
+ "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": "3.7.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw7 (GD algorithms)/hw7.ipynb b/hw7 (GD algorithms)/hw7.ipynb
new file mode 100644
index 0000000..0602502
--- /dev/null
+++ b/hw7 (GD algorithms)/hw7.ipynb
@@ -0,0 +1,558 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "from collections import namedtuple, defaultdict\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import itertools"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Разделим датасет случайным образом на тренировочную и тестовую выборки. Перед сохранением в соответствующие файлы проведем стандартизацию признаков."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classes = ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica')\n",
+ "TRAIN_FILENAME = 'train.csv'\n",
+ "TEST_FILENAME = 'test.csv'\n",
+ "\n",
+ "def standardize(data):\n",
+ " features = data[:, :-1].astype(float)\n",
+ " features = (features - features.mean(axis=0)) / features.std(axis=0)\n",
+ " data[:, :-1] = features\n",
+ " return data\n",
+ "\n",
+ "\n",
+ "def load_data(filename):\n",
+ " return pd.read_csv(filename, header=None).values\n",
+ "\n",
+ "\n",
+ "def split_data(data_filename, train_filename, test_filename, split_ratio=0.1):\n",
+ " data = load_data(data_filename)\n",
+ " data = standardize(data)\n",
+ " np.random.shuffle(data)\n",
+ " split_index = int(len(data) * split_ratio)\n",
+ " pd.DataFrame(data[:split_index]).to_csv(test_filename, header=None)\n",
+ " pd.DataFrame(data[split_index:]).to_csv(train_filename, header=None)\n",
+ "\n",
+ "split_data('iris_data.csv', TRAIN_FILENAME, TEST_FILENAME)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "В градиентном спуске используем сигмоид как функцию для предсказания, в качестве градиента берем производную функции максимального правдоподобия. В результате работы алгоритма возвращается набор весов, вычисленных на всех итерациях."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid(x):\n",
+ " return 1.0 / (1.0 + np.exp(-x))\n",
+ "\n",
+ "\n",
+ "def compute_gradient(x, y, w):\n",
+ " grad = np.dot(x.T, sigmoid(x.dot(w)) - y)\n",
+ " return grad if len(x.shape) == 1 else grad / len(x)\n",
+ "\n",
+ "\n",
+ "Parameters = namedtuple('Parameters', ['iterations', 'learning_rate', 'alpha', 'gamma', 'beta'])\n",
+ "ValidationResult = namedtuple('ValidationResult', ['parameters', 'accuracy'])\n",
+ "\n",
+ "\n",
+ "def gradient_descent(xs, ys, gd_type, parameters):\n",
+ " n_iter, learning_rate, alpha = parameters.iterations, parameters.learning_rate, parameters.alpha\n",
+ " gamma, beta = parameters.gamma, parameters.beta\n",
+ " eps = 1.0e-8\n",
+ "\n",
+ " ws = np.zeros((n_iter, xs.shape[1]))\n",
+ " avg_w, u, g, m, v = (np.zeros_like(ws[0]) for _ in range(5))\n",
+ "\n",
+ " x, y = xs, ys\n",
+ " for t in range(n_iter - 1):\n",
+ " if gd_type in ['stochastic', 'sgd+momentum', 'sgd+nesterov_momentum']:\n",
+ " index = random.randint(0, len(xs) - 1)\n",
+ " x, y = xs[index], ys[index]\n",
+ "\n",
+ " w = ws[t] + gamma * u if gd_type == 'nesterov_momentum' else ws[t]\n",
+ " gradient = compute_gradient(x, y, w) + alpha * w\n",
+ "\n",
+ " if gd_type in ['stochastic', 'batch']:\n",
+ " u = - learning_rate * gradient\n",
+ " elif gd_type in ['sgd+momentum', 'sgd+nesterov_momentum']:\n",
+ " u = gamma * u - learning_rate * gradient\n",
+ " elif gd_type == 'adagrad':\n",
+ " g += gradient ** 2\n",
+ " u = - learning_rate * gradient / np.sqrt(g + eps)\n",
+ " elif gd_type == 'rmsprop':\n",
+ " g = beta * g + (1 - beta) * gradient ** 2\n",
+ " u = - learning_rate * gradient / np.sqrt(g + eps)\n",
+ " elif gd_type == 'adam':\n",
+ " m = gamma * m + (1 - gamma) * gradient\n",
+ " v = beta * v + (1 - beta) * gradient ** 2\n",
+ " u = - learning_rate * m / np.sqrt(v + eps)\n",
+ " else:\n",
+ " raise (\"Undefined gradient descent type.\")\n",
+ "\n",
+ " ws[t + 1] = ws[t] + u\n",
+ " return ws"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "В качестве меры оценки качества используем точность, kfold проводим для k=10. \n",
+ "Вектор весов берем как среднее векторов? вычисленных на всех итерациях."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_accuracy(w, sample):\n",
+ " correct_count = 0\n",
+ " for x, y in zip(sample[:, :-1], sample[:, -1]):\n",
+ " y_predict = (sigmoid(x.dot(w)) >= 0.5)\n",
+ " correct_count += (y_predict == y)\n",
+ " return correct_count / len(sample)\n",
+ "\n",
+ "\n",
+ "def kfold(train, k, parameters, gd_type):\n",
+ " np.random.shuffle(train)\n",
+ " parts = np.array_split(train, k)\n",
+ " avg_accuracy = 0.0\n",
+ " for i in range(k):\n",
+ " validation_sample = parts[i]\n",
+ " train_sample = np.concatenate(np.delete(parts, i))\n",
+ "\n",
+ " xs, ys = train_sample[:, :-1], train_sample[:, -1]\n",
+ " ws = gradient_descent(xs, ys, gd_type, parameters)\n",
+ " avg_accuracy += compute_accuracy(ws.mean(axis=0), validation_sample)\n",
+ " avg_accuracy /= k\n",
+ " return avg_accuracy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для удобства добавляем признак со значением 1.0, чтобы не работать с отдельным вектором b."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def transform_sample(classname, sample):\n",
+ " sample = np.hstack((np.ones((sample.shape[0], 1)), sample))\n",
+ " sample[:, -1] = (sample[:, -1] == classname)\n",
+ " return sample.astype(float)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "При валидации будем подбирать следующие параметры: количество итераций, скорость обучения, коэффициент регуляризации alpha, коэффициент накопления импульса gamma, коэффициент накопления квадратов градиентов beta."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def search_parameters(classname, gd_type, iterations_grid=(1000,), lr_grid=(0.01,), alpha_grid=(0.0001,),\n",
+ " gamma_grid=(None,), beta_grid=(None,)):\n",
+ " train = transform_sample(classname, load_data(TRAIN_FILENAME))\n",
+ " best_result = ValidationResult(Parameters(*([None] * 5)), accuracy=0.0)\n",
+ "\n",
+ " grids = [iterations_grid, lr_grid, alpha_grid, gamma_grid, beta_grid]\n",
+ " for param_list in itertools.product(*grids):\n",
+ " parameters = Parameters(*param_list)\n",
+ " accuracy = kfold(train, 10, parameters, gd_type)\n",
+ " cur_result = ValidationResult(parameters, accuracy)\n",
+ " if accuracy > best_result.accuracy:\n",
+ " best_result = cur_result\n",
+ " return best_result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Создадим функции для запуска лучшего решения на тестовой выборке, и для сравнения различных методов градиентного спуска с уже подобранными параметрами. Аргументом в них передается название класса ирисов, для которого строим и тестируем модели."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def test(classname, gd_type, parameters):\n",
+ " train = transform_sample(classname, load_data(TRAIN_FILENAME))\n",
+ " test = transform_sample(classname, load_data(TEST_FILENAME))\n",
+ "\n",
+ " xs, ys = train[:, :-1], train[:, -1]\n",
+ " ws = gradient_descent(xs, ys, gd_type, parameters)\n",
+ " return compute_accuracy(ws.mean(axis=0), test)\n",
+ "\n",
+ "\n",
+ "def compare(classname, parameters_dict):\n",
+ " train = transform_sample(classname, load_data(TRAIN_FILENAME))\n",
+ " xs, ys = train[:, :-1], train[:, -1]\n",
+ "\n",
+ " max_iterations = max(map(lambda ps: ps.iterations, parameters_dict.values()))\n",
+ " for gd_type in parameters_dict:\n",
+ " parameters_list = list(parameters_dict[gd_type])\n",
+ " parameters_dict[gd_type] = Parameters(max_iterations, *(parameters_list[1:]))\n",
+ "\n",
+ " for gd_type, parameters in parameters_dict.items():\n",
+ " ws = gradient_descent(xs, ys, gd_type, parameters)\n",
+ " accuracy = [compute_accuracy(ws[:i + 1].mean(axis=0), train) for i in range(len(ws))]\n",
+ " plt.plot(range(parameters.iterations), accuracy)\n",
+ "\n",
+ " plt.legend(parameters_dict.keys(), loc='lower right')\n",
+ " plt.title(f\"Accuracy for '{classname}' model\")\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Задания 1-3. Проводим валидацию для sgd batch gradient descent, запускаем на тествой выборке лучшее решение, и строим графики зависмости точности от количества итераций, чтобы сравнить два метода."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-setosa' with 'stochastic': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'stochastic: 0.9186813186813187\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'stochastic: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'batch': Parameters(iterations=1600, learning_rate=0.005, alpha=0.0, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'batch: 0.9543956043956046\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'batch: 1.0\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-versicolor' with 'stochastic': Parameters(iterations=1600, learning_rate=0.005, alpha=0.01, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'stochastic: 0.6978021978021978\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'stochastic: 0.6\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'batch': Parameters(iterations=800, learning_rate=0.01, alpha=0.0, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'batch: 0.7032967032967034\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'batch: 0.4666666666666667\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-virginica' with 'stochastic': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'stochastic: 0.7571428571428572\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'stochastic: 0.7333333333333333\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'batch': Parameters(iterations=1600, learning_rate=0.001, alpha=0.001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'batch: 0.8384615384615384\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'batch: 0.7333333333333333\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gd_types = ['stochastic', 'batch']\n",
+ "for classname in classes:\n",
+ " parameters_dict = defaultdict()\n",
+ " for gd_type in gd_types:\n",
+ " best_result = search_parameters(classname, gd_type, iterations_grid=[200, 800, 1600],\n",
+ " lr_grid=[0.1, 0.05, 0.01, 0.005, 0.001],\n",
+ " alpha_grid=[0.01, 0.001, 0.0001, 0.0])\n",
+ " print(f\"Best parameters for '{classname}' with '{gd_type}': {best_result.parameters}\")\n",
+ " print(f\"Best validation accuracy for '{classname}' with '{gd_type}: {best_result.accuracy}\")\n",
+ " parameters_dict[gd_type] = best_result.parameters\n",
+ "\n",
+ " accuracy = test(classname, gd_type, best_result.parameters)\n",
+ " print(f\"Accuracy on test sample for '{classname}' with '{gd_type}: {accuracy}\\n\")\n",
+ " compare(classname, parameters_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "При валидации BGD показал немного лучшие результаты качества чем у SGD на всех классах ирисов, однако на самом 'сложном' для классификации ирисе versicolor при тестировании BGD показал результаты хуже. Возможно SGD повезло разиением валидационных выборок. Сходимость у batch происходит быстрее чем у SGD.\n",
+ "\n",
+ "Преимущества SGD:\n",
+ "1. Требует меньших вычислительных затрат по сравнению с batch GD, особенно при больших размерах тренировочной выборки.\n",
+ "2. Удобно применять для online обучения, когда необходимо быстро обновлять вектор весов w.\n",
+ "3. При очень больших наборах данных нет необходимости хранить все объекты тренировочной выборки в памяти.\n",
+ "\n",
+ "Недостатки SGD:\n",
+ "1. Временами медленная сходимость.\n",
+ "2. Может вообще не сойтись и застрять в локальном минимуме.\n",
+ "\n",
+ "Преимущества batch gradient descent:\n",
+ "1. Стабильная сходимость, более быстрая по количеству итераций чем у SGD.\n",
+ "\n",
+ "Недостатки batch gradient descent:\n",
+ "1. Требует больших вычислительных затрат при вычислении градиента по всей выборке.\n",
+ "2. Проблематично применять на больших наборах данных, из-за 1. и необходимости хранить объекты тренировочной выборки в памяти.\n",
+ "\n",
+ "У обоих недостатком является фиксированная скорость обучения, которую нужно подбирать."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Задание 4. Аналогично заданиям 1-3 проводим валидацию для методов моментов и адаптивных вариантов градиентного спуска, запускаем на тестовой выборке лучшее решение, и строим графики зависмости точности от количества итераций, для сравнения всех методов. Для адаптивных методов не подбираем скорость обучения, а берем сразу достаточно большое значение 0.1 для более быстрой сходимости."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-setosa' with 'sgd+momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.0001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'sgd+momentum: 0.9115384615384615\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'sgd+momentum: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'sgd+nesterov_momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'sgd+nesterov_momentum: 0.8879120879120878\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'sgd+nesterov_momentum: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'adagrad': Parameters(iterations=200, learning_rate=0.1, alpha=0.01, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'adagrad: 1.0\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'adagrad: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'rmsprop': Parameters(iterations=200, learning_rate=0.1, alpha=0.01, gamma=None, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'rmsprop: 1.0\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'rmsprop: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'adam': Parameters(iterations=200, learning_rate=0.1, alpha=0.01, gamma=0.9, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'adam: 1.0\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'adam: 1.0\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-versicolor' with 'sgd+momentum': Parameters(iterations=1600, learning_rate=0.05, alpha=0.0001, gamma=0.9, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'sgd+momentum: 0.7\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'sgd+momentum: 0.4666666666666667\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'sgd+nesterov_momentum': Parameters(iterations=800, learning_rate=0.001, alpha=0.0001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'sgd+nesterov_momentum: 0.7027472527472528\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'sgd+nesterov_momentum: 0.5333333333333333\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'adagrad': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'adagrad: 0.7434065934065933\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'adagrad: 0.6666666666666666\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'rmsprop': Parameters(iterations=800, learning_rate=0.1, alpha=0.001, gamma=None, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'rmsprop: 0.7543956043956045\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'rmsprop: 0.6666666666666666\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'adam': Parameters(iterations=800, learning_rate=0.1, alpha=0.01, gamma=0.999, beta=0.999)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'adam: 0.754945054945055\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'adam: 0.6\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-virginica' with 'sgd+momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.0, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'sgd+momentum: 0.8\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'sgd+momentum: 0.8\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'sgd+nesterov_momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'sgd+nesterov_momentum: 0.7857142857142857\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'sgd+nesterov_momentum: 0.7333333333333333\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'adagrad': Parameters(iterations=800, learning_rate=0.1, alpha=0.001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'adagrad: 0.9483516483516483\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'adagrad: 0.8\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'rmsprop': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0001, gamma=None, beta=0.99)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'rmsprop: 0.9703296703296704\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'rmsprop: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'adam': Parameters(iterations=800, learning_rate=0.1, alpha=0.001, gamma=0.9, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'adam: 0.9785714285714284\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'adam: 1.0\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gd_types = ['sgd+momentum', 'sgd+nesterov_momentum', 'adagrad', 'rmsprop', 'adam']\n",
+ "for classname in classes:\n",
+ " parameters_dict = defaultdict()\n",
+ " for gd_type in gd_types:\n",
+ " lr_grid = [0.1] if gd_type in ['adagrad', 'rmsprop', 'adam'] else [0.1, 0.05, 0.01, 0.005, 0.001]\n",
+ " gamma_grid = [0.9, 0.99, 0.999] if gd_type in ['sgd+momentum', 'sgd+nesterov_momentum', 'adam'] else [None]\n",
+ " beta_grid = [0.9, 0.99, 0.999] if gd_type in ['rmsprop', 'adam'] else [None]\n",
+ "\n",
+ " best_result = search_parameters(classname, gd_type, iterations_grid=[200, 800, 1600],\n",
+ " lr_grid=lr_grid, alpha_grid=[0.01, 0.001, 0.0001, 0.0],\n",
+ " gamma_grid=gamma_grid, beta_grid=beta_grid)\n",
+ "\n",
+ " print(f\"Best parameters for '{classname}' with '{gd_type}': {best_result.parameters}\")\n",
+ " print(f\"Best validation accuracy for '{classname}' with '{gd_type}: {best_result.accuracy}\")\n",
+ " parameters_dict[gd_type] = best_result.parameters\n",
+ "\n",
+ " accuracy = test(classname, gd_type, best_result.parameters)\n",
+ " print(f\"Accuracy on test sample for '{classname}' with '{gd_type}: {accuracy}\\n\")\n",
+ "\n",
+ " compare(classname, parameters_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Наиболее быструю сходимость, а также наилучшие результаты качества как на валидационной, так и на тестовой выборках, показали adam, adagrad и rmsprop. Скорее всего, благодаря тому, что adam сочетает в себе как идею накопления движения, так и идею более слабого обновления весов для типичных признаков, т.е. преимущества методов моментов и rmsprop."
+ ]
+ }
+ ],
+ "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": "3.7.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw7 (GD algorithms)/hw7.py b/hw7 (GD algorithms)/hw7.py
new file mode 100644
index 0000000..14ed932
--- /dev/null
+++ b/hw7 (GD algorithms)/hw7.py
@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+import random
+from collections import namedtuple, defaultdict
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import itertools
+
+classes = ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica')
+TRAIN_FILENAME = 'train.csv'
+TEST_FILENAME = 'test.csv'
+
+
+def standardize(data):
+ features = data[:, :-1].astype(float)
+ features = (features - features.mean(axis=0)) / features.std(axis=0)
+ data[:, :-1] = features
+ return data
+
+
+def load_data(filename):
+ return pd.read_csv(filename, header=None).values
+
+
+def split_data(data_filename, train_filename, test_filename, split_ratio=0.1):
+ data = load_data(data_filename)
+ data = standardize(data)
+ np.random.shuffle(data)
+ split_index = int(len(data) * split_ratio)
+ pd.DataFrame(data[:split_index]).to_csv(test_filename, header=None)
+ pd.DataFrame(data[split_index:]).to_csv(train_filename, header=None)
+
+
+def sigmoid(x):
+ return 1.0 / (1.0 + np.exp(-x))
+
+
+def compute_gradient(x, y, w):
+ grad = np.dot(x.T, sigmoid(x.dot(w)) - y)
+ return grad if len(x.shape) == 1 else grad / len(x)
+
+
+Parameters = namedtuple('Parameters', ['iterations', 'learning_rate', 'alpha', 'gamma', 'beta'])
+ValidationResult = namedtuple('ValidationResult', ['parameters', 'accuracy'])
+
+
+def gradient_descent(xs, ys, gd_type, parameters):
+ n_iter, learning_rate, alpha = parameters.iterations, parameters.learning_rate, parameters.alpha
+ gamma, beta = parameters.gamma, parameters.beta
+ eps = 1.0e-8
+
+ ws = np.zeros((n_iter, xs.shape[1]))
+ avg_w, u, g, m, v = (np.zeros_like(ws[0]) for _ in range(5))
+
+ x, y = xs, ys
+ for t in range(n_iter - 1):
+ if gd_type in ['stochastic', 'sgd+momentum', 'sgd+nesterov_momentum']:
+ index = random.randint(0, len(xs) - 1)
+ x, y = xs[index], ys[index]
+
+ w = ws[t] + gamma * u if gd_type == 'nesterov_momentum' else ws[t]
+ gradient = compute_gradient(x, y, w) + alpha * w
+
+ if gd_type in ['stochastic', 'batch']:
+ u = - learning_rate * gradient
+ elif gd_type in ['sgd+momentum', 'sgd+nesterov_momentum']:
+ u = gamma * u - learning_rate * gradient
+ elif gd_type == 'adagrad':
+ g += gradient ** 2
+ u = - learning_rate * gradient / np.sqrt(g + eps)
+ elif gd_type == 'rmsprop':
+ g = beta * g + (1 - beta) * gradient ** 2
+ u = - learning_rate * gradient / np.sqrt(g + eps)
+ elif gd_type == 'adam':
+ m = gamma * m + (1 - gamma) * gradient
+ v = beta * v + (1 - beta) * gradient ** 2
+ u = - learning_rate * m / np.sqrt(v + eps)
+ else:
+ raise ("Undefined gradient descent type.")
+
+ ws[t + 1] = ws[t] + u
+ return ws
+
+
+def compute_accuracy(w, sample):
+ correct_count = 0
+ for x, y in zip(sample[:, :-1], sample[:, -1]):
+ y_predict = (sigmoid(x.dot(w)) >= 0.5)
+ correct_count += (y_predict == y)
+ return correct_count / len(sample)
+
+
+def kfold(train, k, parameters, gd_type):
+ np.random.shuffle(train)
+ parts = np.array_split(train, k)
+ avg_accuracy = 0.0
+ for i in range(k):
+ validation_sample = parts[i]
+ train_sample = np.concatenate(np.delete(parts, i))
+
+ xs, ys = train_sample[:, :-1], train_sample[:, -1]
+ ws = gradient_descent(xs, ys, gd_type, parameters)
+ avg_accuracy += compute_accuracy(ws.mean(axis=0), validation_sample)
+ avg_accuracy /= k
+ return avg_accuracy
+
+
+def transform_sample(classname, sample):
+ bias = np.ones((sample.shape[0], 1))
+ sample = np.hstack((bias, sample))
+ sample[:, -1] = (sample[:, -1] == classname)
+ return sample.astype(float)
+
+
+def search_parameters(classname, gd_type, iterations_grid=(1000,), lr_grid=(0.01,), alpha_grid=(0.0001,),
+ gamma_grid=(None,), beta_grid=(None,)):
+ train = transform_sample(classname, load_data(TRAIN_FILENAME))
+ best_result = ValidationResult(Parameters(*([None] * 5)), accuracy=0.0)
+
+ grids = [iterations_grid, lr_grid, alpha_grid, gamma_grid, beta_grid]
+ for param_list in itertools.product(*grids):
+ parameters = Parameters(*param_list)
+ accuracy = kfold(train, 10, parameters, gd_type)
+ cur_result = ValidationResult(parameters, accuracy)
+ if accuracy > best_result.accuracy:
+ best_result = cur_result
+ return best_result
+
+
+def test(classname, gd_type, parameters):
+ train = transform_sample(classname, load_data(TRAIN_FILENAME))
+ test = transform_sample(classname, load_data(TEST_FILENAME))
+
+ xs, ys = train[:, :-1], train[:, -1]
+ ws = gradient_descent(xs, ys, gd_type, parameters)
+ return compute_accuracy(ws.mean(axis=0), test)
+
+
+def compare(classname, parameters_dict):
+ train = transform_sample(classname, load_data(TRAIN_FILENAME))
+ xs, ys = train[:, :-1], train[:, -1]
+
+ max_iterations = max(map(lambda ps: ps.iterations, parameters_dict.values()))
+ for gd_type in parameters_dict:
+ parameters_list = list(parameters_dict[gd_type])
+ parameters_dict[gd_type] = Parameters(max_iterations, *(parameters_list[1:]))
+
+ for gd_type, parameters in parameters_dict.items():
+ ws = gradient_descent(xs, ys, gd_type, parameters)
+ accuracy = [compute_accuracy(ws[:i + 1].mean(axis=0), train) for i in range(len(ws))]
+ plt.plot(range(parameters.iterations), accuracy)
+
+ plt.legend(parameters_dict.keys(), loc='lower right')
+ plt.title(f"Accuracy for '{classname}' model")
+ plt.show()
+
+
+def compare_base_methods():
+ gd_types = ['stochastic', 'batch']
+ for classname in classes:
+ parameters_dict = defaultdict()
+ for gd_type in gd_types:
+ best_result = search_parameters(classname, gd_type, iterations_grid=[200, 800, 1600],
+ lr_grid=[0.1, 0.05, 0.01, 0.005, 0.001],
+ alpha_grid=[0.01, 0.001, 0.0001, 0.0])
+ print(f"Best parameters for '{classname}' with '{gd_type}': {best_result.parameters}")
+ print(f"Best validation accuracy for '{classname}' with '{gd_type}: {best_result.accuracy}")
+ parameters_dict[gd_type] = best_result.parameters
+
+ accuracy = test(classname, gd_type, best_result.parameters)
+ print(f"Accuracy on test sample for '{classname}' with '{gd_type}: {accuracy}\n")
+
+ compare(classname, parameters_dict)
+
+
+def compare_adaptive_methods():
+ gd_types = ['sgd+momentum', 'sgd+nesterov_momentum', 'adagrad', 'rmsprop', 'adam']
+ for classname in classes:
+ parameters_dict = defaultdict()
+ for gd_type in gd_types:
+ lr_grid = [0.1] if gd_type in ['adagrad', 'rmsprop', 'adam'] else [0.1, 0.05, 0.01, 0.005, 0.001]
+ gamma_grid = [0.9, 0.98, 0.999] if gd_type in ['sgd+momentum', 'sgd+nesterov_momentum', 'adam'] else [None]
+ beta_grid = [0.9, 0.98, 0.999] if gd_type in ['rmsprop', 'adam'] else [None]
+
+ best_result = search_parameters(classname, gd_type, iterations_grid=[200, 800, 1600],
+ lr_grid=lr_grid, alpha_grid=[0.01, 0.001, 0.0001, 0.0],
+ gamma_grid=gamma_grid, beta_grid=beta_grid)
+
+ print(f"Best parameters for '{classname}' with '{gd_type}': {best_result.parameters}")
+ print(f"Best validation accuracy for '{classname}' with '{gd_type}: {best_result.accuracy}")
+ parameters_dict[gd_type] = best_result.parameters
+
+ accuracy = test(classname, gd_type, best_result.parameters)
+ print(f"Accuracy on test sample for '{classname}' with '{gd_type}: {accuracy}\n")
+
+ compare(classname, parameters_dict)
+
+
+def main():
+ split_data('iris_data.csv', TRAIN_FILENAME, TEST_FILENAME)
+ compare_base_methods()
+ compare_adaptive_methods()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/hw7 (GD algorithms)/hw7_task.pdf b/hw7 (GD algorithms)/hw7_task.pdf
new file mode 100644
index 0000000..c427403
Binary files /dev/null and b/hw7 (GD algorithms)/hw7_task.pdf differ
diff --git a/hw7 (GD algorithms)/iris_data.csv b/hw7 (GD algorithms)/iris_data.csv
new file mode 100644
index 0000000..d4ee8db
--- /dev/null
+++ b/hw7 (GD algorithms)/iris_data.csv
@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.2,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.6,1.4,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+
diff --git a/hw8 (SVM)/.idea/encodings.xml b/hw8 (SVM)/.idea/encodings.xml
new file mode 100644
index 0000000..15a15b2
--- /dev/null
+++ b/hw8 (SVM)/.idea/encodings.xml
@@ -0,0 +1,4 @@
+
+
+
+
\ No newline at end of file
diff --git a/hw8 (SVM)/.idea/hw8.iml b/hw8 (SVM)/.idea/hw8.iml
new file mode 100644
index 0000000..2852213
--- /dev/null
+++ b/hw8 (SVM)/.idea/hw8.iml
@@ -0,0 +1,11 @@
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/hw8 (SVM)/.idea/misc.xml b/hw8 (SVM)/.idea/misc.xml
new file mode 100644
index 0000000..84fe515
--- /dev/null
+++ b/hw8 (SVM)/.idea/misc.xml
@@ -0,0 +1,4 @@
+
+
+
+
\ No newline at end of file
diff --git a/hw8 (SVM)/.idea/modules.xml b/hw8 (SVM)/.idea/modules.xml
new file mode 100644
index 0000000..1a5d9f1
--- /dev/null
+++ b/hw8 (SVM)/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/hw8 (SVM)/.idea/other.xml b/hw8 (SVM)/.idea/other.xml
new file mode 100644
index 0000000..a708ec7
--- /dev/null
+++ b/hw8 (SVM)/.idea/other.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/hw8 (SVM)/.idea/workspace.xml b/hw8 (SVM)/.idea/workspace.xml
new file mode 100644
index 0000000..1ea95e7
--- /dev/null
+++ b/hw8 (SVM)/.idea/workspace.xml
@@ -0,0 +1,528 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ argv
+ precomputed
+ scale_param = csr_find_scale_param(x, lower=0)
+ scale
+ -v
+ itertools
+ fast
+ params_str.format(degree, cost)
+ predict
+ build_options
+ os.
+ spam_tmp/
+ svm_train
+ gen_svm
+ abel = libsvm.svm_predict(m,
+ gen_svm_nodearray
+ np.std
+ kfold
+ build
+ csr_scale
+ f"
+ svm-scale
+
+
+ build_options
+ build_options(degree, cost)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
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+
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+
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+
+
+
+
+
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+
+
+
+
+
+
+ 1544651834916
+
+
+ 1544651834916
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
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+
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+
+
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+
+
+
+
+
+
+
+
+
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+
+
+
+
+
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+
+
+
+
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+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
+
+
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+
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+
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+
+
+
+
+
+
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+
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+
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+
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+
+
+
+
+
+
+
+
+
+
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+
+
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+
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+
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+
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+
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+
+
+
+
+
+
+
+
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+
+
+
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+
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+
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+
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+
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+
+
+
+
+
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+
+
+
+
+
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+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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+
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\ No newline at end of file
diff --git a/hw8 (SVM)/.ipynb_checkpoints/hw7-checkpoint.ipynb b/hw8 (SVM)/.ipynb_checkpoints/hw7-checkpoint.ipynb
new file mode 100644
index 0000000..cfdc5f2
--- /dev/null
+++ b/hw8 (SVM)/.ipynb_checkpoints/hw7-checkpoint.ipynb
@@ -0,0 +1,558 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import random\n",
+ "from collections import namedtuple, defaultdict\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import itertools"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Разделим датасет случайным образом на тренировочную и тестовую выборки. Перед сохранением в соответствующие файлы проведем стандартизацию признаков."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classes = ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica')\n",
+ "TRAIN_FILENAME = 'train.csv'\n",
+ "TEST_FILENAME = 'test.csv'\n",
+ "\n",
+ "def standardize(data):\n",
+ " features = data[:, :-1].astype(float)\n",
+ " features = (features - features.mean(axis=0)) / features.std(axis=0)\n",
+ " data[:, :-1] = features\n",
+ " return data\n",
+ "\n",
+ "\n",
+ "def load_data(filename):\n",
+ " return pd.read_csv(filename, header=None).values\n",
+ "\n",
+ "\n",
+ "def split_data(data_filename, train_filename, test_filename, split_ratio=0.1):\n",
+ " data = load_data(data_filename)\n",
+ " data = standardize(data)\n",
+ " np.random.shuffle(data)\n",
+ " split_index = int(len(data) * split_ratio)\n",
+ " pd.DataFrame(data[:split_index]).to_csv(test_filename, header=None)\n",
+ " pd.DataFrame(data[split_index:]).to_csv(train_filename, header=None)\n",
+ "\n",
+ "split_data('iris_data.csv', TRAIN_FILENAME, TEST_FILENAME)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "В градиентном спуске используем сигмоид как функцию для предсказания, в качестве градиента берем производную функции максимального правдоподобия. В результате работы алгоритма возвращается набор весов, вычисленных на всех итерациях."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sigmoid(x):\n",
+ " return 1.0 / (1.0 + np.exp(-x))\n",
+ "\n",
+ "\n",
+ "def compute_gradient(x, y, w):\n",
+ " grad = np.dot(x.T, sigmoid(x.dot(w)) - y)\n",
+ " return grad if len(x.shape) == 1 else grad / len(x)\n",
+ "\n",
+ "\n",
+ "Parameters = namedtuple('Parameters', ['iterations', 'learning_rate', 'alpha', 'gamma', 'beta'])\n",
+ "ValidationResult = namedtuple('ValidationResult', ['parameters', 'accuracy'])\n",
+ "\n",
+ "\n",
+ "def gradient_descent(xs, ys, gd_type, parameters):\n",
+ " n_iter, learning_rate, alpha = parameters.iterations, parameters.learning_rate, parameters.alpha\n",
+ " gamma, beta = parameters.gamma, parameters.beta\n",
+ " eps = 1.0e-8\n",
+ "\n",
+ " ws = np.zeros((n_iter, xs.shape[1]))\n",
+ " avg_w, u, g, m, v = (np.zeros_like(ws[0]) for _ in range(5))\n",
+ "\n",
+ " x, y = xs, ys\n",
+ " for t in range(n_iter - 1):\n",
+ " if gd_type in ['stochastic', 'sgd+momentum', 'sgd+nesterov_momentum']:\n",
+ " index = random.randint(0, len(xs) - 1)\n",
+ " x, y = xs[index], ys[index]\n",
+ "\n",
+ " w = ws[t] + gamma * u if gd_type == 'nesterov_momentum' else ws[t]\n",
+ " gradient = compute_gradient(x, y, w) + alpha * w\n",
+ "\n",
+ " if gd_type in ['stochastic', 'batch']:\n",
+ " u = - learning_rate * gradient\n",
+ " elif gd_type in ['sgd+momentum', 'sgd+nesterov_momentum']:\n",
+ " u = gamma * u - learning_rate * gradient\n",
+ " elif gd_type == 'adagrad':\n",
+ " g += gradient ** 2\n",
+ " u = - learning_rate * gradient / np.sqrt(g + eps)\n",
+ " elif gd_type == 'rmsprop':\n",
+ " g = beta * g + (1 - beta) * gradient ** 2\n",
+ " u = - learning_rate * gradient / np.sqrt(g + eps)\n",
+ " elif gd_type == 'adam':\n",
+ " m = gamma * m + (1 - gamma) * gradient\n",
+ " v = beta * v + (1 - beta) * gradient ** 2\n",
+ " u = - learning_rate * m / np.sqrt(v + eps)\n",
+ " else:\n",
+ " raise (\"Undefined gradient descent type.\")\n",
+ "\n",
+ " ws[t + 1] = ws[t] + u\n",
+ " return ws"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "В качестве меры оценки качества используем точность, kfold проводим для k=10. \n",
+ "Вектор весов берем как среднее векторов? вычисленных на всех итерациях."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_accuracy(w, sample):\n",
+ " correct_count = 0\n",
+ " for x, y in zip(sample[:, :-1], sample[:, -1]):\n",
+ " y_predict = (sigmoid(x.dot(w)) >= 0.5)\n",
+ " correct_count += (y_predict == y)\n",
+ " return correct_count / len(sample)\n",
+ "\n",
+ "\n",
+ "def kfold(train, k, parameters, gd_type):\n",
+ " np.random.shuffle(train)\n",
+ " parts = np.array_split(train, k)\n",
+ " avg_accuracy = 0.0\n",
+ " for i in range(k):\n",
+ " validation_sample = parts[i]\n",
+ " train_sample = np.concatenate(np.delete(parts, i))\n",
+ "\n",
+ " xs, ys = train_sample[:, :-1], train_sample[:, -1]\n",
+ " ws = gradient_descent(xs, ys, gd_type, parameters)\n",
+ " avg_accuracy += compute_accuracy(ws.mean(axis=0), validation_sample)\n",
+ " avg_accuracy /= k\n",
+ " return avg_accuracy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для удобства добавляем признак со значением 1.0, чтобы не работать с отдельным вектором b."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def transform_sample(classname, sample):\n",
+ " sample = np.hstack((np.ones((sample.shape[0], 1)), sample))\n",
+ " sample[:, -1] = (sample[:, -1] == classname)\n",
+ " return sample.astype(float)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "При валидации будем подбирать следующие параметры: количество итераций, скорость обучения, коэффициент регуляризации alpha, коэффициент накопления импульса gamma, коэффициент накопления квадратов градиентов beta."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def search_parameters(classname, gd_type, iterations_grid=(1000,), lr_grid=(0.01,), alpha_grid=(0.0001,),\n",
+ " gamma_grid=(None,), beta_grid=(None,)):\n",
+ " train = transform_sample(classname, load_data(TRAIN_FILENAME))\n",
+ " best_result = ValidationResult(Parameters(*([None] * 5)), accuracy=0.0)\n",
+ "\n",
+ " grids = [iterations_grid, lr_grid, alpha_grid, gamma_grid, beta_grid]\n",
+ " for param_list in itertools.product(*grids):\n",
+ " parameters = Parameters(*param_list)\n",
+ " accuracy = kfold(train, 10, parameters, gd_type)\n",
+ " cur_result = ValidationResult(parameters, accuracy)\n",
+ " if accuracy > best_result.accuracy:\n",
+ " best_result = cur_result\n",
+ " return best_result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Создадим функции для запуска лучшего решения на тестовой выборке, и для сравнения различных методов градиентного спуска с уже подобранными параметрами. Аргументом в них передается название класса ирисов, для которого строим и тестируем модели."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def test(classname, gd_type, parameters):\n",
+ " train = transform_sample(classname, load_data(TRAIN_FILENAME))\n",
+ " test = transform_sample(classname, load_data(TEST_FILENAME))\n",
+ "\n",
+ " xs, ys = train[:, :-1], train[:, -1]\n",
+ " ws = gradient_descent(xs, ys, gd_type, parameters)\n",
+ " return compute_accuracy(ws.mean(axis=0), test)\n",
+ "\n",
+ "\n",
+ "def compare(classname, parameters_dict):\n",
+ " train = transform_sample(classname, load_data(TRAIN_FILENAME))\n",
+ " xs, ys = train[:, :-1], train[:, -1]\n",
+ "\n",
+ " max_iterations = max(map(lambda ps: ps.iterations, parameters_dict.values()))\n",
+ " for gd_type in parameters_dict:\n",
+ " parameters_list = list(parameters_dict[gd_type])\n",
+ " parameters_dict[gd_type] = Parameters(max_iterations, *(parameters_list[1:]))\n",
+ "\n",
+ " for gd_type, parameters in parameters_dict.items():\n",
+ " ws = gradient_descent(xs, ys, gd_type, parameters)\n",
+ " accuracy = [compute_accuracy(ws[:i + 1].mean(axis=0), train) for i in range(len(ws))]\n",
+ " plt.plot(range(parameters.iterations), accuracy)\n",
+ "\n",
+ " plt.legend(parameters_dict.keys(), loc='lower right')\n",
+ " plt.title(f\"Accuracy for '{classname}' model\")\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Задания 1-3. Проводим валидацию для sgd batch gradient descent, запускаем на тествой выборке лучшее решение, и строим графики зависмости точности от количества итераций, чтобы сравнить два метода."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-setosa' with 'stochastic': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'stochastic: 0.9186813186813187\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'stochastic: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'batch': Parameters(iterations=1600, learning_rate=0.005, alpha=0.0, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'batch: 0.9543956043956046\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'batch: 1.0\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-versicolor' with 'stochastic': Parameters(iterations=1600, learning_rate=0.005, alpha=0.01, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'stochastic: 0.6978021978021978\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'stochastic: 0.6\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'batch': Parameters(iterations=800, learning_rate=0.01, alpha=0.0, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'batch: 0.7032967032967034\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'batch: 0.4666666666666667\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-virginica' with 'stochastic': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'stochastic: 0.7571428571428572\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'stochastic: 0.7333333333333333\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'batch': Parameters(iterations=1600, learning_rate=0.001, alpha=0.001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'batch: 0.8384615384615384\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'batch: 0.7333333333333333\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gd_types = ['stochastic', 'batch']\n",
+ "for classname in classes:\n",
+ " parameters_dict = defaultdict()\n",
+ " for gd_type in gd_types:\n",
+ " best_result = search_parameters(classname, gd_type, iterations_grid=[200, 800, 1600],\n",
+ " lr_grid=[0.1, 0.05, 0.01, 0.005, 0.001],\n",
+ " alpha_grid=[0.01, 0.001, 0.0001, 0.0])\n",
+ " print(f\"Best parameters for '{classname}' with '{gd_type}': {best_result.parameters}\")\n",
+ " print(f\"Best validation accuracy for '{classname}' with '{gd_type}': {best_result.accuracy}\")\n",
+ " parameters_dict[gd_type] = best_result.parameters\n",
+ "\n",
+ " accuracy = test(classname, gd_type, best_result.parameters)\n",
+ " print(f\"Accuracy on test sample for '{classname}' with '{gd_type}': {accuracy}\\n\")\n",
+ " compare(classname, parameters_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "При валидации BGD показал немного лучшие результаты качества чем у SGD на всех классах ирисов, однако на самом 'сложном' для классификации ирисе versicolor при тестировании BGD показал результаты хуже. Возможно SGD повезло разиением валидационных выборок. Сходимость у batch происходит быстрее чем у SGD.\n",
+ "\n",
+ "Преимущества SGD:\n",
+ "1. Требует меньших вычислительных затрат по сравнению с batch GD, особенно при больших размерах тренировочной выборки.\n",
+ "2. Удобно применять для online обучения, когда необходимо быстро обновлять вектор весов w.\n",
+ "3. При очень больших наборах данных нет необходимости хранить все объекты тренировочной выборки в памяти.\n",
+ "\n",
+ "Недостатки SGD:\n",
+ "1. Временами медленная сходимость.\n",
+ "2. Может вообще не сойтись и застрять в локальном минимуме.\n",
+ "\n",
+ "Преимущества batch gradient descent:\n",
+ "1. Стабильная сходимость, более быстрая по количеству итераций чем у SGD.\n",
+ "\n",
+ "Недостатки batch gradient descent:\n",
+ "1. Требует больших вычислительных затрат при вычислении градиента по всей выборке.\n",
+ "2. Проблематично применять на больших наборах данных, из-за 1. и необходимости хранить объекты тренировочной выборки в памяти.\n",
+ "\n",
+ "У обоих недостатком является фиксированная скорость обучения, которую нужно подбирать."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Задание 4. Аналогично заданиям 1-3 проводим валидацию для методов моментов и адаптивных вариантов градиентного спуска, запускаем на тестовой выборке лучшее решение, и строим графики зависмости точности от количества итераций, для сравнения всех методов. Для адаптивных методов не подбираем скорость обучения, а берем сразу достаточно большое значение 0.1 для более быстрой сходимости."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-setosa' with 'sgd+momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.0001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'sgd+momentum: 0.9115384615384615\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'sgd+momentum: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'sgd+nesterov_momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'sgd+nesterov_momentum: 0.8879120879120878\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'sgd+nesterov_momentum: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'adagrad': Parameters(iterations=200, learning_rate=0.1, alpha=0.01, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'adagrad: 1.0\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'adagrad: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'rmsprop': Parameters(iterations=200, learning_rate=0.1, alpha=0.01, gamma=None, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'rmsprop: 1.0\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'rmsprop: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-setosa' with 'adam': Parameters(iterations=200, learning_rate=0.1, alpha=0.01, gamma=0.9, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-setosa' with 'adam: 1.0\n",
+ "Accuracy on test sample for 'Iris-setosa' with 'adam: 1.0\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-versicolor' with 'sgd+momentum': Parameters(iterations=1600, learning_rate=0.05, alpha=0.0001, gamma=0.9, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'sgd+momentum: 0.7\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'sgd+momentum: 0.4666666666666667\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'sgd+nesterov_momentum': Parameters(iterations=800, learning_rate=0.001, alpha=0.0001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'sgd+nesterov_momentum: 0.7027472527472528\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'sgd+nesterov_momentum: 0.5333333333333333\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'adagrad': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'adagrad: 0.7434065934065933\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'adagrad: 0.6666666666666666\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'rmsprop': Parameters(iterations=800, learning_rate=0.1, alpha=0.001, gamma=None, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'rmsprop: 0.7543956043956045\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'rmsprop: 0.6666666666666666\n",
+ "\n",
+ "Best parameters for 'Iris-versicolor' with 'adam': Parameters(iterations=800, learning_rate=0.1, alpha=0.01, gamma=0.999, beta=0.999)\n",
+ "Best validation accuracy for 'Iris-versicolor' with 'adam: 0.754945054945055\n",
+ "Accuracy on test sample for 'Iris-versicolor' with 'adam: 0.6\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best parameters for 'Iris-virginica' with 'sgd+momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.0, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'sgd+momentum: 0.8\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'sgd+momentum: 0.8\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'sgd+nesterov_momentum': Parameters(iterations=1600, learning_rate=0.001, alpha=0.001, gamma=0.999, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'sgd+nesterov_momentum: 0.7857142857142857\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'sgd+nesterov_momentum: 0.7333333333333333\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'adagrad': Parameters(iterations=800, learning_rate=0.1, alpha=0.001, gamma=None, beta=None)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'adagrad: 0.9483516483516483\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'adagrad: 0.8\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'rmsprop': Parameters(iterations=1600, learning_rate=0.1, alpha=0.0001, gamma=None, beta=0.99)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'rmsprop: 0.9703296703296704\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'rmsprop: 1.0\n",
+ "\n",
+ "Best parameters for 'Iris-virginica' with 'adam': Parameters(iterations=800, learning_rate=0.1, alpha=0.001, gamma=0.9, beta=0.9)\n",
+ "Best validation accuracy for 'Iris-virginica' with 'adam: 0.9785714285714284\n",
+ "Accuracy on test sample for 'Iris-virginica' with 'adam: 1.0\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gd_types = ['sgd+momentum', 'sgd+nesterov_momentum', 'adagrad', 'rmsprop', 'adam']\n",
+ "for classname in classes:\n",
+ " parameters_dict = defaultdict()\n",
+ " for gd_type in gd_types:\n",
+ " lr_grid = [0.1] if gd_type in ['adagrad', 'rmsprop', 'adam'] else [0.1, 0.05, 0.01, 0.005, 0.001]\n",
+ " gamma_grid = [0.9, 0.99, 0.999] if gd_type in ['sgd+momentum', 'sgd+nesterov_momentum', 'adam'] else [None]\n",
+ " beta_grid = [0.9, 0.99, 0.999] if gd_type in ['rmsprop', 'adam'] else [None]\n",
+ "\n",
+ " best_result = search_parameters(classname, gd_type, iterations_grid=[200, 800, 1600],\n",
+ " lr_grid=lr_grid, alpha_grid=[0.01, 0.001, 0.0001, 0.0],\n",
+ " gamma_grid=gamma_grid, beta_grid=beta_grid)\n",
+ "\n",
+ " print(f\"Best parameters for '{classname}' with '{gd_type}': {best_result.parameters}\")\n",
+ " print(f\"Best validation accuracy for '{classname}' with '{gd_type}: {best_result.accuracy}\")\n",
+ " parameters_dict[gd_type] = best_result.parameters\n",
+ "\n",
+ " accuracy = test(classname, gd_type, best_result.parameters)\n",
+ " print(f\"Accuracy on test sample for '{classname}' with '{gd_type}: {accuracy}\\n\")\n",
+ "\n",
+ " compare(classname, parameters_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Наиболее быструю сходимость, а также наилучшие результаты качества как на валидационной, так и на тестовой выборках, показали adam, adagrad и rmsprop. Скорее всего, благодаря тому, что adam сочетает в себе как идею накопления движения, так и идею более слабого обновления весов для типичных признаков, т.е. преимущества методов моментов и rmsprop."
+ ]
+ }
+ ],
+ "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": "3.7.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw8 (SVM)/.ipynb_checkpoints/hw8-checkpoint.ipynb b/hw8 (SVM)/.ipynb_checkpoints/hw8-checkpoint.ipynb
new file mode 100644
index 0000000..2875bd0
--- /dev/null
+++ b/hw8 (SVM)/.ipynb_checkpoints/hw8-checkpoint.ipynb
@@ -0,0 +1,581 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Задание 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для выполнения кода необходимо, чтобы в текущей директории присутствовал файл 'shuffled' с исходными данными, а также распакованная и скомпилированная библиотека libsvm в папке 'libsvm'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from collections import namedtuple\n",
+ "from subprocess import call\n",
+ "from libsvm.python.svmutil import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Разбиваем данные на тренировочную и тестовую выборки, сохраняем все файлы в нужном формате в созданной папке 'spam_tmp'. Затем выполняем масштабирование с помощью svm-scale на отрезок [0,1], т.к. значения всех признаков неотрицательные."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def save_libsvm(raw_data, filename):\n",
+ " x = raw_data[:, :-1]\n",
+ " y = raw_data[:, -1].astype(int)\n",
+ " lines = []\n",
+ " for features, label in zip(x, y):\n",
+ " sparse_features = [f\"{i + 1}:{value}\" for i, value in enumerate(features) if value != 0]\n",
+ " line = ' '.join([str(label)] + sparse_features) + '\\n'\n",
+ " lines.append(line)\n",
+ " with open(filename, 'w') as f:\n",
+ " f.writelines(lines)\n",
+ "\n",
+ "spam_save_dir = 'spam_tmp'\n",
+ "os.makedirs(spam_save_dir, exist_ok=True)\n",
+ "data = pd.read_csv('shuffled', header=None).values\n",
+ "train, test = np.vsplit(data, [3450])\n",
+ "\n",
+ "save_libsvm(train, os.path.join(spam_save_dir, 'train'))\n",
+ "save_libsvm(test, os.path.join(spam_save_dir, 'test'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "cd spam_tmp\n",
+ "../libsvm/svm-scale -l 0 -s scaling_params train > train_scaled\n",
+ "../libsvm/svm-scale -r scaling_params test > test_scaled"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Функция kfold выполняет кросс-валидацию, возвращая среднюю ошибку и ее стандартное отклонение. Ошибка на каждой итерации вычисляется как 1 - accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def split(index, arr, part_size):\n",
+ " validation_part = arr[index: (index + 1) * part_size]\n",
+ " train_part = arr[:index * part_size] + arr[(index + 1) * part_size:]\n",
+ " return train_part, validation_part\n",
+ "\n",
+ "\n",
+ "def kfold(x, y, parameters, train_func, predict_func, k=10):\n",
+ " part_size = len(y) // k\n",
+ " errors = []\n",
+ " for i in range(k):\n",
+ " x_train, x_valid = split(i, x, part_size)\n",
+ " y_train, y_valid = split(i, y, part_size)\n",
+ "\n",
+ " model = train_func(y_train, x_train, parameters)\n",
+ " _, results, _ = predict_func(y_valid, x_valid, model, '-q')\n",
+ " errors.append(1 - results[0] / 100)\n",
+ " return np.mean(errors), np.std(errors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Используем полиномиальное ядро и константу равную 1 (параметр -r, значение взял из примера на сайте), т.к. со значением по умолчанию 0 по непонятным причинам все объекты выборки присваиваются одному классу.\n",
+ " Выполняем кросс-валидацию для подбора наилучших параметров и строим графики ошибки от $log_{2}C$ со стандартными отклонениями для $d \\in \\{1,2,3,4\\}$ и $С \\in \\{2^{-k}, 2^{-k+1}, ..., 2^{k}\\}$, $k=5$. Также строим график сравнения ошибок для разных степеней полинома."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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Ue5r0VaXwf72b4e3hzvO/Hcf0fBl2/4n7mk95teurVPeozsjfR3LGcsbVYaoL8Pnnn9O+fXvi4uIYNmwYubm5+Pj4MHr0aDp06MDff/9NZGQkY8aMoUuXLnz99desXbuWjh070qpVK2644QaOHz8OQLdu3XjmmWe4/PLLeeutt1y8Z65RabphUFVboK8Xj/SI5uW5m/m1Yy+6N7wCfnqewOievNL1FYb/NJxXlr/CmM5jit6Ycmz+KDi4oWS3Wb8lXD2+wNUFda1c1t0qVyZ6pq8qjcGdImkc5MOYuZvJusb6D86ch+kUfCn3tbqP75K+Y86OAod0VuWQfdfKcXFx/PLLLyQnJxe7W+UlS5b8q1xVpWf6qtLwdHfj+etjueOjFXy00cIDV70Ic5+ANZ9zf9z9rD60mpeXvUxcYBzhfqXbZ3mlVMgZeWkpqGvl1157rcS7Va4qnDrTF5HeIrJVRJJEZJSD9cNFZIOIrBWRP0Uk1rY8UkTO2JavFZEpJb0DStnrGh1Iz9h6vPNrEgejb4fIrrDwWTwyDvNK11c4YznDz3t+dnWYykndu3dn1qxZHD5s7TY9NTWV3bt3F1rHvltlwOlulauKIpO+iLgDk4GrgVjgtrNJ3c6XxpiWxpg4YALwX7t1O4wxcbZpeEkFrlRB/nNtLJY8w/gFW6HP25CbDT8+Rv0a9ahXox7bj293dYjKSfZdK7dq1YqrrrqKAwcOFFnv008/5cknn6RVq1asXbuW0aNHl0G0FYMzzTvtgSRjTDKAiEwH+gLnhqkyxtgPnFkTKF/9NasqJbxODYZd1pBJvyYxqGME8d1Hw8KnYf1MGvs3JiktydUhqmK49dZb/9UOn5GRcd78rl27zpuPi4tj2bJl/9rW4sWLSzq8CseZ5p0QYK/dfIpt2XlE5EER2YH1TP9hu1VRIrJGRH4Xka6O3kBEhopIgogkHDlypBjhK+XY/d0aEVzLm+fnJJLbbiiEtof5/0eTGiEkpyVjyaua92gr5UzS//eAjw7O5I0xk40xjYCngP/YFh8Awo0xrYHHgS9FxM9B3anGmHhjTHxgYKDz0StVgBrVPHjmmhgS959gxqr90Hcy5JwhetcysvOy2XNij6tDVMolnEn6KUCY3XwosL+Q8tOBfgDGmCxjzDHb61XADqDJhYWqVPFc1yqY9lEBTFy4hfSaUXDF0zTetRyA7Wnarq+qJmeS/kogWkSiRKQaMAA472ZnEYm2m70W2G5bHmi7EIyINASigeSSCFypoogIL1zfnPQzObzx8zbo+AANLQZ3RC/mqiqryKRvjLEAI4CFwGZgpjEmUUTGiEgfW7ERIpIoImuxNuMMti2/DFgvIuuAWcBwY0xqie+FUgWIbeDHwA4R/G/ZbrYczcKrbhPC8dSkr6ospx7OMsbMA+blWzba7vUjBdT7BvjmYgJU6mI9flUTfli/nxfnbOLLes1pnLqMrXoHj6qitBsGVen516zGyJ5N+Tv5GFtNONGn0tl7cq8OsKJKxSeffML+/Y4vey5evJjrrruujCM6nyZ9VSXc1j6cmGA/PthanSbZORgMyel6eamyKI3ulYcMGXJB9/UXlvTLA036qkpwdxOevz6WP0/Wp3F2DoC261cQjrpWBlzevXJubi5DhgyhRYsWtGzZkjfeeINZs2aRkJDAwIEDiYuL48yZMyxYsIBmzZrRpUsXvv3223P1T506xd133027du1o3bo1s2dbB/vp0KEDiYmJ58p169aNVatWXXCc+WmHa6rKaB8ZwCmvQAJMDbxx09s2i+nVFa+yJXVLiW6zWUAznmr/VIHrC+pa+c4773R598pr165l3759bNy48dw2a9euzTvvvMNrr71GfHw8mZmZ3Hffffz66680btz4vCeLx44dy5VXXsm0adNIS0ujffv29OjRgwEDBjBz5kxefPFFDhw4wP79+2nbtu1FxWpPz/RVleHmJsQE+7HbLZJGeW56pl8BFNS1MlAq3SsvXLiQuLg44uLimDNnDvfeey9xcXF06NDhX2UbNmxIcnIyDz30EAsWLMDP71/PnbJlyxaioqKIjo5GRBg0aNC5dYsWLWL8+PHExcXRrVs3MjMz2bNnD7fccgtff/01ADNnzuTmm28uzkdWJD3TV1VKTLAfaw6E0PjMKpbqHTzFUtgZeWkpqGtlsJ7Vl3T3yr169aJXr16AtU1/yJAhdOvWzWFZf39/1q1bx8KFC5k8eTIzZ85k2rRp/yon4qhTA+u+ffPNNzRt2vRf6+rUqcP69euZMWMG77//vlP75Cw901dVSmywHxssoURnnuHomaMczzzu6pBUIS6ka2Uom+6Vjx49Sl5eHjfddBMvvfQSq1evBsDX15eTJ08C0KxZM3bu3MmOHdYxmr/66qtz9Xv16sWkSZMwxtqrzZo1a86tGzBgABMmTCA9PZ2WLVuWaNya9FWVEhPsx5a8cKL1Ym6FcKFdK0Ppd6+8b98+unXrRlxcHEOGDDn3bWTIkCEMHz6cuLg4jDFMnTqVa6+9li5duhAREXGu/nPPPUdOTg6tWrWiRYsWPPfcc+fW9e/fn+nTp3PLLbeUaMwAcvYoU17Ex8ebhIQEV4ehKqnMnFzajJ7N4hr30j08hFHtRzEwZqCrwyq3Nm/eTExMjKvDUPk4+r2IyCpjTHxRdfVMX1Up3p7uNAisQ65bMLVx1zN9VeVo0ldVTmywH5tzw4i25Oltm6rK0aSvqpyYYD/WZIXQ+NQJko4nUd6aOMsb/XzKl4v9fWjSV1VObAM/tphwonOyOW05zf5T5feReVfz9vbm2LFjmvjLCWMMx44dw9vb+4K3offpqyonJtiXzSacB+3u4Anx+dcIoAoIDQ0lJSUFHca0/PD29iY0NPSC62vSV1VOkK83WTVCCLV4ApCUlkS3sG6uDaqc8vT0JCoqytVhqBKkzTuqSmrWoBaHCaeBcWfb8W2uDkepMqNJX1VJsQ38WJsdQuOsTL1tU1UpmvRVlRQb7MemvDCiM0+zK30nObk5rg5JqTKhSV9VSbHBfmy2dcdgMbnsOrHL1SEpVSY06asqKapuTXa6R+iAKqrKcSrpi0hvEdkqIkkiMsrB+uEiskFE1orInyISa7fuaVu9rSLSqySDV+pCebi7EVq/Hj55AXggJGk3y6qKKDLpi4g7MBm4GogFbrNP6jZfGmNaGmPigAnAf211Y4EBQHOgN/CubXtKuVxMfT+254YRkatn+qrqcOZMvz2QZIxJNsZkA9OBvvYFjDEn7GZrAmcf3+sLTDfGZBljdgJJtu0p5XIxwb6st4QSnXmK7XrbpqoinEn6IcBeu/kU27LziMiDIrID65n+w8Wpq5QrxDaoZe1bPyubfaf2cyrnlKtDUqrUOZP0HY319a+OOIwxk40xjYCngP8Up66IDBWRBBFJ0Me9VVlpFuxr64PHejFX2/VVVeBM0k8BwuzmQ4HCeqiaDvQrTl1jzFRjTLwxJj4wMNCJkJS6eH7enuTWjiTCdou+tuurqsCZpL8SiBaRKBGphvXC7Bz7AiISbTd7LXD2v2cOMEBEvEQkCogGVlx82EqVjKbB/mTmNqC63sGjqogiO1wzxlhEZASwEHAHphljEkVkDJBgjJkDjBCRHkAOcBwYbKubKCIzgU2ABXjQGJNbSvuiVLHFNvBjw/YwGmdv14u5qkpwqpdNY8w8YF6+ZaPtXj9SSN2xwNgLDVCp0hQT7MffeeFEZ63nt9RtGGMQcXQpSqnKQZ/IVVVabLBtQJXsHI5np3Ms85irQ1KqVGnSV1VaqH91UqpFEZ2dDejFXFX5adJXVZqI0CA4hDoWH0CTvqr8NOmrKi822I/9OWEEGGF7miZ9Vblp0ldVXmywH4m5YURnZpKkZ/qqktOkr6q8mHN962exIy2JPJPn6pCUKjWa9FWVF13Ph+0SQXR2Dmdys0g5meLqkJQqNZr0VZXn7ekOdRrTMNt6hq/t+qoy06SvFNC0QQBiCQL0Dh5VuWnSVwpru35SThihuUaTvqrUNOkrhTXpb8kLJzrzDEmpW10djlKlRpO+UtiSvgmncU4Ou0/uJTs329UhKVUqNOkrBQT6enGkRmOaZOeQSx7J6cmuDkmpUqFJXymbesHh1MvxAvRirqq8NOkrZRPToBYnMxvgYfS2TVV5adJXyia2gR/b8sJpmJPD9lQdUEVVTpr0lbKJPTtQena23sGjKi1N+krZRNX1IdktksbZORzMPMqJ7BOuDkmpEqdJXykbdzfBLagZjbMtACQd14HSVeWjSV8pO9EhdfHO9gcgKU2Tvqp8NOkrZScm2I/D2aH45MG243oxV1U+TiV9EektIltFJElERjlY/7iIbBKR9SLyi4hE2K3LFZG1tmlOSQavVEmLDfZjS14EjbOz2J66xdXhKFXiikz6IuIOTAauBmKB20QkNl+xNUC8MaYVMAuYYLfujDEmzjb1KaG4lSoVzc52x5CdTdLx7RhjXB2SUiXKmTP99kCSMSbZGJMNTAf62hcwxvxmjDltm10GhJZsmEqVDR8vD07Wakp0dg4nLKc5fPqwq0NSqkQ5k/RDgL128ym2ZQW5B5hvN+8tIgkiskxE+jmqICJDbWUSjhw54kRISpWeOsENCc1xB/TJXFX5OJP0xcEyh995RWQQEA9MtFscboyJB24H3hSRRv/amDFTjTHxxpj4wMBAJ0JSqvTEhtTCklkf0Ns2VeXjTNJPAcLs5kOB/fkLiUgP4FmgjzEm6+xyY8x+289kYDHQ+iLiVarUxQT7sdcSTmBuHtv1Dh5VyTiT9FcC0SISJSLVgAHAeXfhiEhr4H2sCf+w3XJ/EfGyva4LdAY2lVTwSpWG2AbWi7nRWVlsP6Z/rqpyKTLpG2MswAhgIbAZmGmMSRSRMSJy9m6ciYAP8HW+WzNjgAQRWQf8Bow3xuh/kSrXGtTyZq9nFI1zcthxYje5ebmuDkmpEuPhTCFjzDxgXr5lo+1e9yig3l9Ay4sJUKmyJiK41WtO9Kkcso2FPSf3EFUrytVhKVUi9IlcpRxoFFoP3yxfQAdUUZWLJn2lHIgJ9uNUVihitA8eVblo0lfKgdhgP5Lzwgm35LD92GZXh6NUidGkr5QD0fV82GYiiM7OYXuqJn1VeWjSV8oBLw93zgQ0o3F2DntOHyLTkunqkJQqEZr0lSqAf0gTIrINBtiRvsPV4ShVIjTpK1WAmAa1ccuydguid/CoykKTvlIFiAn2IzUrnGrGkKRJX1USmvSVKkBMsC9bTQSNsnPYfjTR1eEoVSI06StVgDo+Xhyu3ojo7Gxt3lGVhiZ9pQrhHtyCxjk5HMk5QVpmmqvDUeqiadJXqhCRIQ0IyKoO6IAqqnLQpK9UIWIb+JFjG1BFm3hUZaBJX6lCxAT7ccASiW9uHkmpW10djlIXTZO+UoWIrFOTnW5RROdks/3oRleHo9RF06SvVCHc3YTsOjFEZ+eQdGI3xjgcHlqpCkOTvlJFqB3WjKjsPDLysjh46qCrw1HqomjSV6oITRsEUC2rNqB38KiKT5O+UkWIDfYlMzMUgG3Ht7k4GqUujiZ9pYrQtL4fu/KiaJSdzfTNX3Lk9BFXh6TUBXMq6YtIbxHZKiJJIjLKwfrHRWSTiKwXkV9EJMJu3WAR2W6bBpdk8EqVBR8vD477RDP+yDFOZKXz8K8Pc8ZyxtVhKXVBikz6IuIOTAauBmKB20QkNl+xNUC8MaYVMAuYYKsbADwPdADaA8+LiH/Jha9U2fAIbkmz7BzG17+CxGOJPPvns+SZPFeHpVSxOXOm3x5IMsYkG2OygelAX/sCxpjfjDGnbbPLgFDb617AT8aYVGPMceAnoHfJhK5U2QkLDSfF1OXyrX/weNyD/LT7JyavnezqsJQqNmeSfgiw124+xbasIPcA84tTV0SGikiCiCQcOaLtpar8iW3gx9M59+J2bBuDN/3OTY1vZOr6qfyw4wdXh6ZUsTiT9MXBModPqIjIICAemFicusaYqcaYeGNMfGBgoBMhKVW2YoL9+COvFQlNnkC2zuXZLA/a12/P8389z+pDq10dnlJOcybppwBhdvOhwP78hUSkB/As0McYk1WcukqVd8G1vKlTsxqvHr+CvEvWLSrMAAAgAElEQVRux3PJa/y3/pU08GnAo789yt6Te4veiFLlgDNJfyUQLSJRIlINGADMsS8gIq2B97Em/MN2qxYCPUXE33YBt6dtmVIViojwzDUxJOxJY7z7MAhtT60fRzK55cPkmlxG/DKCk9knXR2mUkUqMukbYyzACKzJejMw0xiTKCJjRKSPrdhEwAf4WkTWisgcW91U4CWsB46VwBjbMqUqnJvahjKkUyRT/9rHghYTobo/ET88zpsdX2DPiT2M/H0kljyLq8NUqlBS3jqQio+PNwkJCa4OQymHcnLzGPjBctbvS2Nef18a/nATNGjDt53u4vnlL3Fr01t5tsOziDi6nKVU6RGRVcaY+KLK6RO5ShWDp7sb7wxsTa3qngxemM2p3m/Bnr+4cduf3NV8CDO2zuDLLV+6OkylCqRJX6liCvL15r1BbTmYnsnwdVHkdX4MVn/KI7k+XBF2BRNWTuCPlD9cHaZSDmnSV+oCtAn3Z0zfFvyx/SivW26GJr1xX/A048Ouo6l/U55c8qQOr6jKJU36Sl2g29qHc1v7MCb/voufmr0MdRpT49uhvN3mSWp41GDELyM4duaYq8NU6jya9JW6CC/0aU5cWG0e/X4Hu3p+CMZQf/bDTOo6ntTMVB757RGycrOK3pBSZUSTvlIXwcvDnSmD2lK9mgd3zUnlVL+P4MhWmi9+k1e6jGXdkXU8t/Q5HWZRlRua9JW6SPVrefPuwDbsTT3NI8trkdfzZdg6lx7JK3mkzSPM3zmfKeunuDpMpQBN+kqViPZRAfzn2hh+3nyYtzO6Q+tBsGQC9+BPn0Z9eHftu8xLnufqMJXSpK9USRncKZIb24Tw5i9J/NpoFIR1QGY/yPNRN9C2Xlue+fMZ5uyYU/SGlCpFmvSVKiEiwrgbWtIixI9Hvt7E7u5ToEYA1WYMZnKHF2hXvx3P/vksnyZ+6upQVRWmSV+pEuTtab2w6+Eu3PvtXk7f+BmcPkrNb4cx+fI36BnRk9cSXuPNVW/qxV3lEpr0lSphof41mHRbG3YcyWDkUsH0nQx7l1Ft3kgmdHmFW5rcwkcbP+LFv1/UDtpUmfNwdQBKVUZdousy6upmjJu3hSkhrbm/2zOweBzuWSf5z40f4O/tz/vr3yctK41XL3sVL3cvV4esqgg901eqlNzXtSHXtQpm4sItLGlwN1w9AbbMRf7XjxFNb2dU+1H8sucXHvj5ATKyM1wdrqoiNOkrVUpEhAn9WxEd5MtDX61hb/QdcPPHsH8NTOvFwOCujO86ntWHVnP3wru1ywZVJjTpK1WKalTz4P072mKMYcjHK9gb3AsGfQsnD8FHPbm2RgRvX/k2O9N3MnjBYPZl7HN1yKqS06SvVCmLrFuTD+6M5/DJLG549y/We7aEu+cDAh9fTdcc+KDnB6RmpnLnvDu1d05VqjTpK1UGOjSswzf3d8LLw41b31/GL6l14d6fwK8BfH4jcYeT+bS39f79IQuGsPbwWhdHrCorTfpKlZEm9Xz57sFONA7y4b7PEvjfJgvcNR9C2sKsu4ne+jOfXfMZ/t7+3LfoPh2IRZUKTfpKlaEgX2+mD+3IFU2DeG52IuMWHyJv4LfQ7FpYMIqQv97j054fE1Uriod/fZgfk390dciqktGkr1QZq+llvbh7R8cIpi5JZsSszWTe8DG0uxf+mkSdBc8wrccUWtdrzdN/PM0Xm79wdciqEnEq6YtIbxHZKiJJIjLKwfrLRGS1iFhEpH++dbkistY2aW9TSgEe7m6M6ducZ65pxrwNBxk4LYHUy8fBlc/Bhpn4fH0X73WdQPfw7oxfMZ63V79NnslzddiqEigy6YuIOzAZuBqIBW4Tkdh8xfYAQ4AvHWzijDEmzjb1uch4lao0RIShlzVi8u1t2LAvnRvf+4tdsfdD33dh5x94fXYDr7X5P26KvokPNnzA/T/fz9EzR10dtqrgnDnTbw8kGWOSjTHZwHSgr30BY8wuY8x6QE9FlCqma1sF8+W9HUg/k8MN7y5lVcDVcPsMOJaEx8e9eD56IKMvHc2qQ6voP6c/f+3/y9UhqwrMmaQfAuy1m0+xLXOWt4gkiMgyEennqICIDLWVSThy5EgxNq1U5RAfGcC3D3TGr7ont32wnLlnWsCQHyH7NDKtJzfXiOKra7+itldthv80nDdXvUlOXo6rw1YVkDNJXxwsK06fsOHGmHjgduBNEWn0r40ZM9UYE2+MiQ8MDCzGppWqPKLq1uTb+zvRooEfD365mqk7amPuWQTefvDJtUTv+IOvrv2SG6Nv5KONHzFkwRB9glcVmzNJPwUIs5sPBfY7+wbGmP22n8nAYqB1MeJTqkqp4+PFl/d15JqW9Rk3bwuj/ziD5a5FEH4p/PgY1b8dxgutH2Hi5RNJTkvm5jk3s2jXIleHrSoQZ5L+SiBaRKJEpBowAHDqLhwR8RcRL9vrukBnYNOFBqtUVeDt6c47t7Vh6GUN+d+y3Qz7dg+nb50JV42BrfPgvS70llp8ff3XRNaK5Infn2DM32PItGS6OnRVARSZ9I0xFmAEsBDYDMw0xiSKyBgR6QMgIu1EJAW4GXhfRBJt1WOABBFZB/wGjDfGaNJXqghubsIz18TwUt/m/Lb1MLdMXc7uZvfCPYvA3RM+uYbQVV/waa9p3NXiLr7e9jW3zb2NpONJrg5dlXNS3oZsi4+PNwkJCa4OQ6ly45fNh3hsxlryDLzUrzk3xPjB3Cdgw0yI6AI3TmVpxi6e+fMZTuecZlT7UdwYfSMiji7HqcpKRFbZrp8WSp/IVaqc6x5Tj/mPXkZMsC+PzVjHY7OTOXnNZOg3xdo3/5TOdD6ZxqzrZ3FJ0CW88PcL/N+S/+Nk9klXh67KIU36SlUAIbWr89V9HXm0RzSz1+7j2klLWVvnahi2BGqHw/TbCVw8gand3uaRNo/w0+6fuPmHm9lwZIOrQ1fljCZ9pSoID3c3Hu3RhBnDLiU3z9D/vb94dyPk3bUILh0BK6bi9mEP7q3flU96f4Ixhjvn38nHGz/WLhzUOZr0lapg2kUGMO/hrvRqXp8JC7Yy6NO1HLr0ORg4CzIOwfuXE7d3HTOvm8kV4Vfw31X/5a4Fd7H7xG5Xh67KAb2Qq1QFZYxhZsJeXpizCW9PNyb2v4QeYQa+GwbJiyG2H+a6N/l+32ImrpxITl4OD7V+iIExA3F3c3d1+KqE6YVcpSo5EeHWduH8+HAXgmtV597PEhj961EyB8yCHi/Clh+R9y/jBq8GfNf3OzoEd2BiwkQGLxhMcnqyq8NXLqJJX6kKrlGgD9892Il7u0Tx2d+76Tv5b7ZF3wN3LwSxjsNbb/kHTLrsdcZ1GcfO9J3cPOdmpm2chiXP4urwVRnTpK9UJeDl4c5/rovlk7vacexUFtdP+pP/pQRihi2BVrfCkonIh1dyfY1wZvebTZeQLryx6g3umHeHPtBVxWjSV6oS6dY0iPmPXEbHhnV47vuNDP06ieM934LbZsCpo/DBldRd/iFvXjaRCZdNICUjhVt+vIUP1n+gvXZWEZr0lapkAn29+HhIO/5zbQyLtx6m91tLWCJt4YG/ocVNsPgV5MPuXF09jO/7fs8VYVfw9pq3GTh3IFtTt7o6fFXKNOkrVQm5uQn3dm3Idw90xsfLgzunreC5Rfs5fd27cOsXcPIATO1GnZXTeL3rq7x++escOn2IAXMH8N7a98jJ1bP+ykqTvlKVWIuQWsx9uCv3doni8+W7ueatP1hVoxM8sBxi+8CvL8OH3elZw3rWf1XEVby77l0GzB3A5mObXR2+KgWa9JWq5Lw9rRd5v7qvI5Y8w81T/uaVJYfJ6vcB3PwppO+F9y/DP+ETJnQex1tXvEVqZiq3zb2Nt1e/TXZutqt3QZUgfThLqSokI8vC2Lmb+GrFXprW8+X1Wy6hRa1smPs4bJ4DIfHQ7z3S/eoxYeUE5uyYQ4hPCI+3fZyrIq7SnjvLMWcfztKkr1QV9NuWwzz1zXpST2XzSPdo7r+8IR5bvrd22Zx9Gq78D1z6IH8dWM7EhIkkpSXROqg1T8Y/ScvAlq4OXzmgSV8pVai009mMnp3InHX7uSS0Fq/fEkfj6qfgx8dg61wIbQ/93sMSEMn3Sd8zac0kUjNTuSbqGh5t8yjBPsGu3gVlR5O+Usopc9cf4D/fb+B0di7/17sZd10agVviLJj3JFgy4bIn4dIRnCKXjzZ8xGebPgPgjtg7uKfFPfhU83HxHijQpK+UKobDJzN5+psN/LLlMB2iAnjt5ksI80iHeSNhy4/WPvt7vgwxfThw6iBvrXmLuclzCfAOYETrEdzQ+AY83DxcvRtVmiZ9pVSxGGP4OiGFMT9uwhjDc9fFcmu7MGTn77DgaTi8yTo8Y+9XILgVG49uZOLKiaw+vJrGtRszMn4knUM6u3o3qixN+kqpC5Jy/DRPfr2ev5OPcWWzIMbd0JL6Ph6w+hP4dSycOQ5tB8MV/8HUrMvPe37mvwn/JSUjhc4hnRnZdiSN/Ru7ejeqnBLtWllEeovIVhFJEpFRDtZfJiKrRcQiIv3zrRssIttt02Dnd0Ep5Qqh/jX44t4OPH99LEuTjnLl64t5949dZLW+Cx5eDR2Gw5rPYVIb5O93uCrkcmb3m83I+JGsP7Kem364iTF/j+HomaOu3hXlQJFn+iLiDmwDrgJSgJXAbcaYTXZlIgE/YCQwxxgzy7Y8AEgA4gEDrALaGmOOF/R+eqavVPmxN/U0L/24iUWbDhFVtyajr4vlimZBcGQbLHwGkn6CgEbQaxw06UVaVjpT1k9hxpYZeHl4cWvTW7km6hqa+DfRe/xLWUme6bcHkowxycaYbGA60Ne+gDFmlzFmPZB/IM5ewE/GmFRbov8J6O3UHiilXC4soAZT74zns7vbIwJ3fbKSuz9ZyS4JgUGz4PavQdzgq1vh8xupfeIgo9qP4ru+33Fp8KV8mvgp/X/oT7/Z/Xhv3XvsSt/l6l2q8pw50+8P9DbG3GubvwPoYIwZ4aDsJ8CPdmf6IwFvY8zLtvnngDPGmNfy1RsKDAUIDw9vu3u3juWpVHmTbcnjk7928tbP28nJNdzbNYoHr2hMTQ8DKz6AxeMhOwPa3QvdRkGNAFIzU/l598/M3zmfVYdWYTDEBMRwddTV9I7srff6l6ASu5ArIjcDvfIl/fbGmIcclP2E85P+k4BXvqR/2hjzekHvp807SpVvh09kMn7+Fr5ds4/6ft48c20M17cKRk4fg9/GwqpPwLsWdHsG4u8Gd+utnIdOHWLhroUs2LWADUc3ABAXGMfVUVfTM7IndavXdeFeVXwlmfQvBV4wxvSyzT8NYIx5xUHZTzg/6d8GdDPGDLPNvw8sNsZ8VdD7adJXqmJYtTuV0bMTSdx/gvZRAbzYpzkxwX5wcCMsfBp2LgH/SOj4ILQeCNVqnqu79+ReFu5ayPyd89l2fBtu4ka7+u24OvJqekT0oJZXLdftWAVVkknfA+uF3O7APqwXcm83xiQ6KPsJ5yf9AKwXb9vYiqzGeiE3taD306SvVMWRm2eYvnIPry3cSvqZHAZ1jODxq5pQu7onbJ0Pf74BKSvAuza0uwfaDwXf+udtY0faDubvnM/8nfPZc3IPHuJBp5BOXBVxFR2DO1K/Zv0C3l3ZK9H79EXkGuBNwB2YZowZKyJjgARjzBwRaQd8B/gDmcBBY0xzW927gWdsmxprjPm4sPfSpK9UxZN2OpvXF23ji+W7qVXdkyd7NePWdmG4uwnsWQ5/T4LNP4K7J7S8BS59EOrFnrcNYwybUzczf+d8FuxawMFTBwGI8IugQ/0OtA9uT7v67QjwDnDFLpZ7+nCWUqrMbdp/ghfmJLJiVyotQvx44frmxEfakvSxHbDsPes9/pYz0LgHXDoCGnaDfLdz5pk8th/fzvIDy1lxcAUJhxI4lXMKgKb+TWkf3J4O9TvQtl5b7fvHRpO+UsoljDHMWbefcfM2c+hEFte0rM9TvZsRUcfWpn86FRI+guVT4dRhqNcSOo2A5jeCRzWH28zJy2HTsU2sOLCC5QeWs+bwGrLzsnEXd5rXbU6H+h3oENyBSwIvwdvDuwz3tvzQpK+UcqlTWRamLklm6pJkLHl5DL40koeujKZWDU9rgZxM2PA1/P0OHNkCvg2gwzBoOwSq1y5021m5Waw7vI5lB5ax4uAKNh7dSK7JpZpbNeKC4oivF09U7SjCfcMJ9w2vEt8GNOkrpcqFQycyeX3RVr5elYKftycPd4/mjo4RVPOwPRualwc7foG/3rbe8VPNB9rcab3fP6Dhv5p+HDmVc4pVh1adaw7akrrlvPUB3gGE+YYR7htOmF8YEb4RhPuFE+YbVmnuFNKkr5QqVzYfOMG4eZv5Y/tRIuvUYNTVzejVvP753TMcWAd/vQOJ30KeBfxCIbIzRHSGyC5OHwRO55wmJSOFvSf2sufkHnaf2M3ek9bXZy8Qn1XLq5b1YOAbRrif9ZtBXGAcob6hFarrCE36SqlyxxjD4m1HGDd3M9sPZ9Au0p9nr40lLixfc056CmyZB7v/hF1L4bSt8zbfYNsBoLO1m+e60U4dBOxlWjLZl7GPPSf2sOfknnM/957cy4FTB8gz1t5k6tWoR7v67axTvXbl/iCgSV8pVW5ZcvOYkbCXN37axtGMbPpc0oD/692UUP8a/y5sDBzdBrv+hN1LrT8zDlnX1Qw6/5tAYLNiHwTsZedms/vEblYfWs3KQytZeXAlqZnWx4rK+0FAk75SqtzLyLIwZfEOPvgjGQPc3TmKB65ohJ+3Z8GVjLHe/nn2W8DupXBin3VdjToQ0QnCO0H9llCvOdS48Pv6jTHsTN/JyoMry/1BQJO+UqrCOJB+hokLt/Lt6n0E1KzGoz2iua19OJ7uTnQEbAwc32X7FrDUejBI2/PPet8G1gfB6jWHoObWn3WbFHh7aOFvVfRBIC4wjhaBLWhSuwme7oUcvEqYJn2lVIWzcV86L8/dxLLkVOr5edEhqg7tIv2JjwygST1f6xO+zjhxAA4nwqFNcCjR+vrIVsjNtq5387Am/iDbweDs5BdSrOahwg4C1dyq0SygGS3qtjg3RfhF4CZOjV1VbJr0lVIVkjGGXzYf5vu1+1i5K5VDJ7IA8PX2oG2EP+0iA4iP8OeSsNp4e7o7v+HcHGuz0KGN1vF+D9kOCul23wq8almTf/Al0KA1hLSxDhLj5lyiNsawL2MfG49tZOORjWw8tpFNxzZxxnLGug+evsTWjaVl3ZbWA0GdFtSrWc/5fSiEJn2lVIVnjCHl+BkSdqeyctdxEnalsu1QBgCe7kLLkFrE2w4C8ZEBBNQsfpMNmelweLP1YHBok/XngfXWriIAqvlCgzjb1No6+Uc5/Y3AkmchOT2ZxKOJbDi6gY1HN7L9+HYsxgJAUPWgc98E4oLiaFe/XfH3AU36SqlKKu10Nqt2Hz93EFifkk52rvU2y0aBNWkXGUDbCH9ah/vTsG5N3JxtErKXa7HeMbR/zT/TwQ2Qa/3WgXft8w8CDVpDrTCnDwSZlky2pG4h8dg/B4LdJ3bTKrAVX1zzRfHjRZO+UqqKyMzJZcO+dFbuSiXBdiA4kWk9i/b19uCS0NrEhdmm8NrU9fG6sDfKzbF+I9i/Bvavtv48lGh9iAysdw6dPQAE274ZFOMaQXpWOqmZqUTVirqg8DTpK6WqpLw8Q9KRDNbuTbNOe9LYeugkuXnWXBfqX/2fg0BYbVqE1CretQF7OZnWi8Rnvw3sW2PtR8jkWtfXDPznAHABB4Li0KSvlFI2Z7Jz2bg/nbV70s4dDPalWdvsPdyEZsG+toOAP3FhtYisUxMPZ24XdSTnjHX0sANrYf9a68GgDA4EmvSVUqoQh09msm5vOmv3Hmft3jTW703nZJa1qcbTXYisU5PGQT40CvShUVBNGgX60DDQBx8vj+K/mbMHgqiu0PmRC9ofTfpKKVUMeXmGHUcyWJ+STtKRDJIOZ7DjSAa7j50+1zQEUN/P+9xBoFGgz7kDQz0/r+I9kevoQOAXDIO+uaD4nU36F3DIUkqpysfNTYiu50t0Pd/zlmdb8tiTepodR6wHgR2HT5F0JIPvVu87980AoGY1dxoF+dCkni/NG/jRvEEtYoJ98S2oSwnP6hDWzjqdlZdXGrt2Hk36SilViGoebjQOsp7R2zPGcORkFklHMthx5BQ7bN8MFm89zKxVKefKRdapQaztIGD96UeQbwGjezn5ENjF0KSvlFIXQEQI8vMmyM+bTo3qnltujOHwySw27T9B4v50EvefYOO+E8zb8E8//nV9vGzfBqwHg+YN/AgPqHFhzxQUk1NJX0R6A28B7sCHxpjx+dZ7AZ8BbYFjwK3GmF0iEglsBrbaii4zxgwvmdCVUqr8ERHq+XlTz8+bK5oFnVt+IjOHzftPkHhuSmdp0lEstusFPl4edGsayDu3tynV+IpM+iLiDkwGrgJSgJUiMscYs8mu2D3AcWNMYxEZALwK3Gpbt8MYE1fCcSulVIXi5+1Jh4Z16NCwzrllWZZcth/KOPeNwNe79BtfnHmH9kCSMSYZQESmA30B+6TfF3jB9noW8I6Up9EFlFKqHPLycKdFSC1ahJTdOL3OXDUIAfbazafYljksY4yxAOnA2cNZlIisEZHfRaTrRcarlFLqIjhzpu/ojD3/zf0FlTkAhBtjjolIW+B7EWlujDlxXmWRocBQgPDwcCdCUkopdSGcOdNPAcLs5kOB/QWVEREPoBaQaozJMsYcAzDGrAJ2AE3yv4ExZqoxJt4YEx8YGFj8vVBKKeUUZ5L+SiBaRKJEpBowAJiTr8wcYLDtdX/gV2OMEZFA24VgRKQhEA0kl0zoSimliqvI5h1jjEVERgALsd6yOc0YkygiY4AEY8wc4CPgfyKSBKRiPTAAXAaMERELkAsMN8aklsaOKKWUKpr2vaOUUpWAs33vlP4zv0oppcoNTfpKKVWFlLvmHRE5Auy+iE3UBY6WUDglSeMqHo2reDSu4qmMcUUYY4q8/bHcJf2LJSIJzrRrlTWNq3g0ruLRuIqnKselzTtKKVWFaNJXSqkqpDIm/amuDqAAGlfxaFzFo3EVT5WNq9K16SullCpYZTzTV0opVQBN+kopVYVUyKQvIjeLSKKI5IlIfL51T4tIkohsFZFeBdSPEpHlIrJdRGbYOpIr6RhniMha27RLRNYWUG6XiGywlSv1/idE5AUR2WcX2zUFlOtt+wyTRGRUGcQ1UUS2iMh6EflORGoXUK5MPq+i9l9EvGy/4yTb31JkacVi955hIvKbiGy2/f0/4qBMNxFJt/v9ji7tuGzvW+jvRazetn1e60WkdMcEtL5nU7vPYa2InBCRR/OVKZPPS0SmichhEdlotyxARH6y5aGfRMS/gLqDbWW2i8hgR2WKxRhT4SYgBmgKLAbi7ZbHAusALyAKa1fO7g7qzwQG2F5PAe4v5XhfB0YXsG4XULcMP7sXgJFFlHG3fXYNgWq2zzS2lOPqCXjYXr8KvOqqz8uZ/QceAKbYXg8AZpTB7y4YaGN77QtscxBXN+DHsvp7cvb3AlwDzMc69kZHYHkZx+cOHMT6AFOZf15YO59sA2y0WzYBGGV7PcrR3zwQgLVn4gDA3/ba/2JiqZBn+saYzcaYrQ5W9QWmG2s//juBJKzDPZ5jG8bxSqzDOgJ8CvQrrVht73cL8FVpvUcpODdEpjEmGzg7RGapMcYsMtZR1wCWYR23wVWc2f++WP92wPq31N32uy41xpgDxpjVttcngc38exS78qov8JmxWgbUFpHgMnz/7ljH676Yp/0vmDFmCdYeiO3Z/w0VlId6AT8ZY1KNMceBn4DeFxNLhUz6hXBmaMc6QJpdgnFUpiR1BQ4ZY7YXsN4Ai0RklW0EsbIwwvYVe1oBXymd+RxL091YzwodKYvP62KHCC11tuak1sByB6svFZF1IjJfRJqXUUhF/V5c/Tc1gIJPvFzxeQHUM8YcAOsBHQhyUKbEP7fSH3r9AonIz0B9B6ueNcbMLqiag2XODu1YbE7GeBuFn+V3NsbsF5Eg4CcR2WI7K7hghcUFvAe8hHWfX8La9HR3/k04qHvR9/Y683mJyLOABfiigM2U+OflKFQHy0rt76i4RMQH+AZ41OQbehRYjbUJI8N2veZ7rIMXlbaifi+u/LyqAX2Apx2sdtXn5awS/9zKbdI3xvS4gGrODO14FOtXSw/bGZqjMiUSo1iHjrwRaFvINvbbfh4Wke+wNi1cVBJz9rMTkQ+AHx2scuZzLPG4bBeprgO6G1uDpoNtlPjn5UBxhghNEbshQks4jn8REU+sCf8LY8y3+dfbHwSMMfNE5F0RqWuMKdXOxZz4vZTK35STrgZWG2MO5V/hqs/L5pCIBBtjDtiaug47KJOC9brDWaFYr2VesMrWvDMHGGC7syIK6xF7hX0BWzL5DeuwjmAd5rGgbw4XqwewxRiT4miliNQUEd+zr/n/9u4nxMoqjOP49+l/IVSKlNIiRESoyIX91VWLIV1EWiAtEqpZtAiJCEUiAoOCLCEiEcoIQlpIBSlDgi2yIBKUxplJwmkhBFnhQpBMQp8Wz3P1dJl75+rMe690fh+4zL3nnnfumTPvPO8757zvc2Iyc3yqurOlbRx1TYfP62WJzNlu16PAJuAxd/+rQ51+9ddlLxHaQFsuyDmDncBRd9/Woc7trbkFM7uf+Bs/2XC7evm9fAmsz6t4HgROtYY2+qDjf9uD6K9CuQ91ikP7gCEzuzWHYoey7PI1PWvdxIMIVr8CZ4HfgX3Fe68QV178DKwqykeAhfl8EXEwmAR2A9c31M6PiSUiy7KFwEjRjtF8TBDDHE333SfAGHAkd7oF7e3K16uJq0N+6VO7Jomxyx/zsaO9Xf3sr6l+fmALcVACuCH3ncnclxb1oY9WEv/aH7xPsrAAAAKHSURBVCn6aTXwfGs/A17IvhklJsQf7kO7pvy9tLXLgPezP8corrpruG03EUH85qKs7/1FHHR+A/7J2PUcMQf0NXAsv87NusuBD4ttn839bBJ4ZqZtURoGEZGK/N+Gd0REpAsFfRGRiijoi4hUREFfRKQiCvoiIhVR0JdqmNnpGWy7yyLr5nimr7i2eO/xMjujma3PehNm9pOZvZzlb5vZIzP7KURmRkFfpDe7gKXAPcCNwHDx3kZgO4CZrQJeBIbc/S4is+KprPcekU1RZGAU9KU6eVfo1jwbHzOzdVl+Vd6GP2Fme81sxMyehLhF3xNxM9Yduc0S4KxfvG1/M5G6upWW4G93/yCfHwfmmdlU+YdE+kJBX2q0FlgG3Eukytia6SnWAncSZ/PDwEPtG+awztPAV1m0gkja1XI3cKjLZx/ObUQGQkFfarQS+NTdz3kk4foGuC/Ld7v7eXc/QeRoarcdOODu3+brBcCfl/DZfxCpJUQGQkFfatRpsZOui6CY2WvAfOClovgMkYenZYIuWVWz7pke2ijSCAV9qdEBYJ2ZXW1m84ml7A4C3wFP5Nj+bRQpbc1smFjF6Cl3P198r6PA4uL1m8BbrXH7zPi6oXh/CQ1nUhXp5orNpy/SoC+I8fpRImvlRnc/YWafEcvqjRMZNn/g4pU3O4DjwPeZifdzd99CHEDeMTPLed6RPGDsz5S9DnwEF+YDFgONLeguMh1l2RQpmNkcj1WU5hFn/ytyfL/bNu8Ce9x9/zT11hALm786ey0WuTQ60xf5r71mdgtwHfD6dAE/vQE80EO9a4jlKUUGRmf6IiIV0USuiEhFFPRFRCqioC8iUhEFfRGRiijoi4hU5F92/rYtCwuHpQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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KCwtrIpxaT5O+qhUaeVqYOKwjm9OOM8dtCIT1hgUT6RfQnv/r/H/M3j6bxSmLXR2mcoFZs2bRp08fYmNjueuuu7DZbPj4+DB58mT69u3LH3/8QVRUFFOmTOGCCy7gm2++ISEhgfPOO49u3bpx1VVXcfToUQAGDhzIU089xYABA3j77bddvGWuUW+6YVB134juIXz2RwqvLErminFv0uSji2H+E9x/1QesObCGp39/ms5BnQnxCXF1qA3T/Ilw4M/qXWerrjDspXIXl9e18rnuVrk+0TN9VWuICJOHx3Ak28o7f7rDRY/Bpjl47PiZVy56hSJTxMRfJ1JY1DB/ljdEJbtWjo2NZenSpezcubPK3SovX778tHINlZ7pq1qlW1gA1/YKY+aKXYx54C5ab/4v/Pgw4fetYtJ5k5j460Q+2PABE3pMcHWoDU8FZ+Q1pbyulV977bVq71a5oXDqTF9EhorINhFJFpGJZSy/W0T+FJEEEflNRGIc86NEJNcxP0FEPqjuDVD1z+NDO+BpceOFBTtgxFQ4ngZLnuWKNlcwsu1IZmycwb7sfa4OU50Dl156KXPmzOHQoUMAZGRkkJKSUmGdkt0qA053q9xQVJr0RcQCTAOGATHADSeTeglfGGO6GmNigVeAN0os22GMiXW87q6uwFX91cLXmwmXRLMk8SC/5kVB37thzYewZyU3dLoBg2HzkbIHsFD1S8mulbt168Zll13G/v37K633ySef8Pjjj9OtWzcSEhKYPHnyOYi2bnCmeacPkGyM2QkgIl8BI4EtJwsYY0oOnNkE0Gfn1Vm59YIovlqzhyk/bGH+PU/hvvUnmHs/bW9bjJu4kZSZxGAGuzpMdQ5cf/31p7XDZ2dnnzK9e/fuU6ZjY2NZuXLlaetatmxZdYdX5zjTvBMK7C0xneqYdwoRuU9EdmA/03+gxKLWIrJeRP4nIheW9QEicqeIxItI/OHDh6sQvqqvvNwtPHV5J5IOZfP5+gwY/hYc2Y73H+8S4RtB0tEkV4eoVJ3kTNI/fcDHMs7kjTHTjDFtgSeAfzhm7wcijDE9gEeAL0TEr4y6M4wxccaYuObNmzsfvarXBse0pH+7IN5YvJ2jwRdC9xvgtzeJbtRSk75SZ8iZpJ8KhJeYDgPSKij/FTAKwBiTb4xJd7xfC+wA2p9ZqKqhEREmX9mZrLwC3lqyHYb8E7wDiN73J3uz9pJTkOPqEJWqc5xJ+muAaBFpLSKewBhgbskCIhJdYvIKIMkxv7njQjAi0gaIBnZWR+CqYejQypeb+kYya9Ueth33gEHP0D49BYNh5zH9p6RUVVWa9I0xhcAEYCGQCMw2xmwWkSkiMsJRbIKIbBaRBOzNOOMc8y8CNorIBmAOcLcxJqPat0LVa49c1h4fL3ee+3ELJuoCoq0FANrEo9QZcOrhLGPMPGBeqXmTS7x/sJx63wLfnk2ASgU28eThQdE888MWluyP5FK3RjTCje1Ht7s6NKXqHO2GQdUJN50XSbsWPjw/LxFaxNDWWPRMX9VKH3/8MWlpZV/2XLZsGVdeeeU5juhUmvRVneBhcWPSlTGkpOewxUQQnZNNUqae6Su7muheefz48Wd0X39FSb820KSv6owB7ZtzaccWzEkNJDovh4y8oxzJPeLqsFQNK6trZcDl3SvbbDbGjx9Ply5d6Nq1K2+++SZz5swhPj6em266idjYWHJzc1mwYAEdO3bkggsu4D//+U9x/RMnTnDrrbfSu3dvevTowffffw9A37592bz5ryfOBw4cyNq1a884ztK0wzVVp9x+YRte2RbKMKsVsF/MbdaomYujahheXv0yWzO2Vus6OzbtyBN9nih3eXldK99yyy0u7145ISGBffv2sWnTpuJ1BgQE8O677/Laa68RFxdHXl4ed9xxBz///DPt2rU75cniF154gUsuuYSZM2eSmZlJnz59GDRoEGPGjGH27Nk8++yz7N+/n7S0NHr16nVWsZakZ/qqTukc6sdWE047q/1nubbr12/lda0M1Ej3ygsXLiQ2NpbY2Fjmzp3L7bffTmxsLH379j2tbJs2bdi5cyf3338/CxYswM/vtOdO2bp1K61btyY6OhoRYezYscXLFi1axEsvvURsbCwDBw4kLy+PPXv2cN111/HNN98AMHv2bK699tqq7LJK6Zm+qlP8vD1o1jSQwoJggnAnKVOT/rlS0Rl5TSmva2Wwn9VXd/fKQ4YMYciQIYC9TX/8+PEMHDiwzLKBgYFs2LCBhQsXMm3aNGbPns3MmTNPKydSVqcG9m379ttv6dChw2nLgoKC2LhxI19//TXTp093apucpWf6qs7pHOzP1qJwogsK9Uy/njuTrpXh3HSvfOTIEYqKirjmmmt47rnnWLduHQC+vr5kZWUB0LFjR3bt2sWOHTsA+PLLL4vrDxkyhKlTp2KMvVeb9evXFy8bM2YMr7zyCseOHaNr167VGrcmfVXnxIT4EZ8XSnTOcXZkJmMrcu5ODVX3nGnXylDz3Svv27ePgQMHEhsby/jx44t/jYwfP567776b2NhYjDHMmDGDK664ggsuuIDIyMji+pMmTaKgoIBu3brRpUsXJk2aVLxs9OjRfPXVV1x33XXVGjOAnDzK1BZxcXEmPj7e1WGoWmzJloN8NWs6VzT9gMnNg/hh1A9E+Ue5Oqx6KTExkU6dOrk6DFVKWd+LiKw1xsRVVlfP9FWdY7+YG0H7k90xaLu+Uk7TpK/qnFZ+3pzwDibE5okbegePUlWhSV/VOSJCTKg/+ySSCO2OocbVtibghu5svw9N+qpO6hziT0J+KO3ycknSjtdqjLe3N+np6Zr4awljDOnp6Xh7e5/xOvQ+fVUnxQT7sbIogvZ5K1matZfcwlwauTdydVj1TlhYGKmpqegwprWHt7c3YWFhZ1xfk76qk2JC/PioKIJB1gL7gCqZO+ncrLOrw6p3PDw8aN26tavDUNVIm3dUndSmWRN2WyKLu2PQvvWVco4mfVUnuVvciGrVDItpiTeiSV8pJ2nSV3VWTIgfW2zhtC00eq++Uk7SpK/qrJgQfzYWhNM+N5ukDD3TV8oZmvRVnRUTbO9mOdpaQEb+UdJz010dklK1nlNJX0SGisg2EUkWkYllLL9bRP4UkQQR+U1EYkose9JRb5uIDKnO4FXD1rGVL1tNJNEnB1TRJh6lKlVp0hcRCzANGAbEADeUTOoOXxhjuhpjYoFXgDccdWOAMUBnYCjwnmN9Sp21Jl7ueAVFEFroCWh3DEo5w5kz/T5AsjFmpzHGCnwFjCxZwBhzvMRkE+Dk43sjga+MMfnGmF1AsmN9SlWLmBB/jtjCaWrcNOkr5QRnkn4osLfEdKpj3ilE5D4R2YH9TP+BKta9U0TiRSRen/xTVRET4kdCQTjR+Xl626ZSTnAm6Zc11tdpHXEYY6YZY9oCTwD/qGLdGcaYOGNMXPPmzZ0ISSm7ziH+JJoIovPz2HE0SQdUUaoSziT9VCC8xHQYkFZB+a+AUWdYV6kqiQn2Y2uRvW/9vCIrqdmprg5JqVrNmaS/BogWkdYi4on9wuzckgVEJLrE5BXAycbVucAYEfESkdZANLD67MNWyq65rxcZTdoWd8eg7fpKVazSpG+MKQQmAAuBRGC2MWaziEwRkRGOYhNEZLOIJACPAOMcdTcDs4EtwALgPmOM/v5W1apdaHO8bM0RNOkrVRmnetk0xswD5pWaN7nE+wcrqPsC8MKZBqhUZWKC/UjaGUa4LU3v1VeqEvpErqrzOof4s7kogvZ5OSRlbHV1OErVapr0VZ0XE+JHookk2lrAnqxUcgtzXR2SUrWWJn1V50U2bcwe9yiirVaKHAOqKKXKpklf1XlubkJgq9aEFngAOqCKUhXRpK/qhZhQf45bQ/E22vGaUhXRpK/qhZhgPxJtEbQtKNC+9ZWqgCZ9VS90DvFni4kkOj+fpKPbXB2OUrWWJn1VL0S39GE79jt40vMzycjLcHVIStVKmvRVveDtYcEW1FG7Y1CqEpr0Vb3RPrQZ3tYgQJO+UuXRpK/qjZgQP1ILwgksMnrbplLl0KSv6o2YYD8SiyJpn5+v3TEoVQ5N+qresHfHEE601cqOzB0UmSJXh6RUraNJX9UbAY09OerbgfbWAnKLrKRm6YAqSpWmSV/VK82Dowh2dMegF3OVOp0mfVWvdA71Jy8vGDGwPVMv5ipVmiZ9Va/EhPixwxZFeGGhdsegVBk06at6JSbYj0QTQbTVSlJ6oqvDUarW0aSv6pWwwEbs9WhtH1DlRBp5hXmuDkmpWkWTvqpXRATP4BjaWQspwrDj2A5Xh6RUreJU0heRoSKyTUSSRWRiGcsfEZEtIrJRRJaKSGSJZTYRSXC85lZn8EqVJTqkOV7WQEDv4FGqtEqTvohYgGnAMCAGuEFEYkoVWw/EGWO6AXOAV0osyzXGxDpeI6opbqXK1TnEj6P54Xgbo0lfqVKcOdPvAyQbY3YaY6zAV8DIkgWMMb8YY3IckyuBsOoNUynnxYT4sa0okjZWK0npW1wdjlK1ijNJPxTYW2I61TGvPLcB80tMe4tIvIisFJFRZVUQkTsdZeIPHz7sREhKla9dCx+SJIpoawFJ2vGaUqdwJulLGfNMmQVFxgJxwKslZkcYY+KAG4G3RKTtaSszZoYxJs4YE9e8eXMnQlKqfB4WNwqadyLaWsAR63EdUEWpEpxJ+qlAeInpMCCtdCERGQT8HRhhjMk/Od8Yk+b4uxNYBvQ4i3iVckqrkCiCrdodg1KlOZP01wDRItJaRDyBMcApd+GISA9gOvaEf6jE/EAR8XK8bwb0B7SRVdW4mBB/CvNaApr0lSqp0qRvjCkEJgALgURgtjFms4hMEZGTd+O8CvgA35S6NbMTEC8iG4BfgJeMMZr0VY3rHOpPWmEUgbYikjJ0oHSlTnJ3ppAxZh4wr9S8ySXeDyqn3u9A17MJUKkz0bGVL18VRdLeupakI5tdHY5StYY+kavqJV9vDzL92hNttZKctVsHVFHKQZO+qreahMbQxmojt6iAfVn7XB2OUrWCJn1Vb7UPbYZ3fgCgfesrdZImfVVvxYT4kZMfhmh3DEoV06Sv6q3OIf7ssEURVljI9iObXB2OUrWCJn1Vb7Xw9SLNqy3trQUkpW91dThK1Qqa9FW9JSLQqot9QJXcQzqgilJo0lf1XGh4FK2s7hRh2Hlsp6vDUcrlNOmrei0m2A+T1wLQ7hiUAk36qp7rHOLHEWtrvIqMdsegFJr0VT3XupkPO92iaFNQQNLhja4ORymX06Sv6jWLm2ANiiHaaiVJB0lXSpO+qv/8wjrTzmrjcEE2R/OOujocpVxKk76q9zqEBdE43x/Qi7lKadJX9V5MsB95+fZhnZMyNemrhk2Tvqr3OrbyI7UwikCbjaTDf7o6HKVcSpO+qvcaeVo45teRaGuBDqiiGjxN+qpB8Ajuak/62ak6oIpq0DTpqwYhIiKKVlYLuaaQfdk6oIpquDTpqwYhJsQPyWsOQMKhBBdHo5TrOJX0RWSoiGwTkWQRmVjG8kdEZIuIbBSRpSISWWLZOBFJcrzGVWfwSjkrJtiPE3lRtLUW8OKqf+qtm6rBqjTpi4gFmAYMA2KAG0QkplSx9UCcMaYbMAd4xVG3KfA00BfoAzwtIoHVF75Szgny8eKQV3veP3AIbzcP7l16LwdPHHR1WEqdc86c6fcBko0xO40xVuArYGTJAsaYX4wxOY7JlUCY4/0QYLExJsMYcxRYDAytntCVqhpbyy4E22y81+xCjucf596l95JtzXZ1WEqdU84k/VBgb4npVMe88twGzK9KXRG5U0TiRST+8OHDToSkVNX5R3RjaVFPOqx4nzc7jGNn5k4eXvYwBbYCV4em1DnjTNKXMuaZMguKjAXigFerUtcYM8MYE2eMiWvevLkTISlVdZ1D/XnQei95Ae3ot/glnu56Fyv3r+SZP57BmDL/SStV7ziT9FOB8BLTYUBa6UIiMgj4OzDCGJNflbpKnQtdQv3JpjFTWz0PFg9G/Tqdezv/H3N3zGVawjRXh6fUOeFM0l8DRItIaxHxBMYAc0sWEJEewHTsCf9QiUULgcEiEui4gDvYMU+pcy68aWNmg+P7AAAgAElEQVTuvKgN760v4OfYN+BYKndvWsrVbUcxfeN05myf4+oQlapxlSZ9Y0whMAF7sk4EZhtjNovIFBEZ4Sj2KuADfCMiCSIy11E3A3gO+4FjDTDFMU8pl/jbkA70bxfE3cs92dP/RWT3b/wjI5P+If15fuXzLE9d7uoQlapRUtvaMuPi4kx8fLyrw1D1WMYJK8On/kaRMSzt/guNV08lZ/BzjE//jd3Hd/PR0I/oHNTZ1WEqVSUistYYE1dZOX0iVzU4TZt4Mv3mXmScsHLrnmEUtb+cxouf5r02Ywj0CuS+JfeRmpXq6jCVqhGa9FWD1CXUnxev7srK3Zm83PhRaNGZZnMf5P2ej1NQVMA9S+4hMy/T1WEqVe006asG6+qeYYzvF8X0lQdZ0O0NcPeizQ+P8s75z5OWncYDvzxAvi2/8hUpVYdo0lcN2t+v6ESf1k15aEE6Oy6dAcf30+uXV/lnvymsP7SeJ399UrtiVvWKJn3VoHlY3Jh2Y08CG3tyyyLIHvoWpKxgyKZ5PBb3GItTFvNa/GuuDlOpaqNJXzV4zX29eH9sLw5n5XP3hrYUXfAorJ/FLZnHGdtpLJ9t+YzPtnzm6jCVqhaa9JUCYsMDeH5UF35LPsLL1mug03Bk8SQe8+vKoIhBvLrmVRanLHZ1mEqdNU36Sjlc1zucsedFMH35buZHPwOtumL5zx282GEc3Zt3Z+LyicQf0GdIVN2mSV+pEiZf2ZlekYE88l0yyYM+BM8meM++mal9nybUN5R7ltyjT+2qOk2TvlIleLq78f5NPfH1due279LIvuozyD5EwH/v46NB02kT0IYHfn6AuTvmVr4ypWohTfpKldLCz5v3x/YkLTOXCcuFopHvwd6VBC1+lpmD/01cqzj+/tvf+WTzJ64OVakq06SvVBl6RTbl6eGdWbbtMG+kdYEBE2HDlzT57S3eu2QagyMH81r8a7wR/4b2xa/qFHdXB6BUbXVT3wj+TD3Gu78k0+Wm8QyNTYXlr+KZfYhXhr1KoHcgH23+iIy8DJ7p9wzubvrfSdV++q9UqXKICM+O7MzWg1k8+s0G2t33Iu18W8Kvr2PJOsDfR88kyDuI9za8R2Z+Jq8OeJVG7o1cHbZSFdLmHaUq4O1h4YOxPWnkaeHOz9ZxvP+TcMUbkLwY+eRK7mk3mknnTWJ56nLuWnwXx/KPuTpkpSqkSV+pSgT7N2LajT3Zk5HD/320hoyYm2HMF3BoK/x7ENc168VrA15j05FNjF8wnoMnDro6ZKXKpUlfKSf0bRPEOzf04M99x7jqvRXsbHohjP8R8rPgw0EMtgTy/qD32X9iPzfPv5ldx3a5OmSlyqRJXyknXd41mC/vOI/svEKueu93Vllbw22LwdsfPhlO32PpzBwyk3xbPuPmj2PTkU2uDlmp02jSV6oKekUG8t29/Wnm48nYf6/iPyle9sTfohN8fRMxO3/n02Gf0tijMbcuvJXf9/3u6pCVOoUmfaWqKCKoMf+5pz9xkU15ZPYG3vzjKGbcDxA9GH56lMjVH/PZ0E8J9w3nvp/vY/6u+a4OWaliTiV9ERkqIttEJFlEJpax/CIRWScihSIyutQym4gkOF767LqqF/wbe/DJrX0Y3SuMt5cm8ch/k8kf/Sn0Gg+/vUHzhZP4aNAMujfvzhPLn+DzxM9dHbJSgBP36YuIBZgGXAakAmtEZK4xZkuJYnuA8cBjZawi1xgTWw2xKlWreLq78erobrRu1oRXF25j39Fcpo99hUC/MPjlefyyD/DB1R/yxOrneWn1SxzKOcT9Pe7Xh7iUSzlzpt8HSDbG7DTGWIGvgJElCxhjdhtjNgI6rpxqUESE+y5uxzs39CAhNZOrP/iD3Z3vhZHvwe7f8P5sFK/3fJxr21/LzE0zuW3hbRw4ccDVYasGzJmkHwrsLTGd6pjnLG8RiReRlSIyqkrRKVVHjOgewhe39yUzx8pV761gTeAwuHE2HN2N+8whTG57Hf+84J9szdjK6B9Gs2zvMleHrBooZ5K+lDGvKj1MRRhj4oAbgbdEpO1pHyByp+PAEH/48OEqrFqp2iMuqinf3dufwMae3PSvVXyf3RH+bx4UFcDMwQy3BDJ7+GxCmoRw/8/38/Lql7HarK4OWzUwziT9VCC8xHQYkObsBxhj0hx/dwLLgB5llJlhjIkzxsQ1b97c2VUrVetENWvCf+7tR2xEAA9+lcDULY0xty0Cn5bwyQgiN8xh1tBPuanTTcxKnMXYeWNJOZ7i6rBVA+JM0l8DRItIaxHxBMYATt2FIyKBIuLleN8M6A9sqbiWUnVbQGNPPrutD1f3COX1xdt5bPExrOMXQcxIWDoFzy+vZ2Kn/+Odi98h7UQa1/1wHT/u/NHVYasGotKkb4wpBCYAC4FEYLYxZrOITBGREQAi0ltEUoFrgekistlRvRMQLyIbgF+Al0rd9aNUveTlbuH167rz8KD2fLsulVu+2Mqxy6fDiKmwZxW834+L823MGT6Hjk078uSvTzJpxSRyCnJcHbqq56S2DQARFxdn4uN18GlVf3y3PpUn5vxJcIA3U2/oQTfPAzDnVji0GfrdT+HAp/hg80xmbJxBlH8Ur170Kh2adnB12KqOEZG1juunFdIncpWqYVf1COOLO/piLSzimvd/519bPSm6bQnE3Qa/T8X94yuYEHkF/xr8L7KsWdz40418vfVrHZFL1QhN+kqdA3FRTZn/4IVc3KEFL8xL5P8+38SRgS/CdZ9Cxg6YfhF901OZM3wOvYN78/yq53n0f49q//yq2mnSV+ocCWjsyfSbe/HcyM78sTOdYW//ym8e/eHu36B5R5hzK0GLn+G9C1/j0V6P8sueX7juh+tIOJTg6tBVPaJJX6lzSES4+fwovr+vP/6NPLh55ipeXplDwS0/woWPwrrPcPvXJYxv3ptPh32KiDB+wXg+/PNDbEU2V4ev6gFN+kq5QKdgP+ZO6M+Y3uG8v2wH1/4rnr09HoObv4O8TJhxMV13r+KbK2czKHIQb697m7HzxpJ0NMnVoas6Tu/eUcrFftyYxpPf/gnAP6/uyvC2HvDfuyF5CXQajhn+DgsOruLFVS+SVZDFnV3v5Paut+Nh8XBx5Ko20bt3lKojruwWwrwHL6RdSx/u/3I9ExfuJ+faL+Gy52DbfGT6RQyzNOW/o/7L4MjBvLfhPa778TodmUudEU36StUC4U0bM/uu87l3YFu+jt/L8Hd/J7HNeLh1EbhZ4KOhNP31bV4+/1mmXjKV49bj3DTvJl5b8xq5hbmuDl/VIZr0laolPCxu/G1oR2bd1pfjeYWMnLaCT/cGYe78H/QYCyvegukXMpAm/Hfkf7k6+mo+2fIJ18y9hjUH1rg6fFVHaNJXqpbp364Z8x+8kH5tg5j8/Wbu/CaZzEGvw9hvwZoDMwfju+xlno77G/8e/G8Abl14K1P+mEK2NdvF0avaTpO+UrVQMx8vZo7rzT+u6MSybYcY+tav/K+oO9z7B/QcB79PhQ8uoE9BEd+O+JZbYm7h26RvGfX9KJanLnd1+KoW06SvVC3l5ibcfmEbvr2nH028LIybuZon56WQPfg1uOV7KLTCzKE0WjKFx7vfx2fDPsPX05f7lt7HxF8ncjTvqKs3QdVCmvSVquW6hQXw0wMXcudFbfhqzR6GvrWc300XuPd36H0brHwPPuhPtxNZfH3l19zT/R4W7lrIqO9HsWDXAu3DR51Ck75SdYC3h4WnLu/EN3edj7ubcOO/VvH0ghRyLnsZxv0ARTb4+HI8F/6De2PG8fXwrwluEszjyx/ngV8eIDUr1dWboGoJfThLqTom12rj5QVb+fj33UQFNebVa7vTO8QLljwLq6dDYBSMeJfCyPOZtWUW0xKmYTM2bux4I3d0uwN/L39Xb4KqAc4+nKVJX6k66o8d6Tw+ZwP7MnO5rX9rHhvSAe99K+H7++DoLuh9Owx6lgOF2UxLmMb3yd/j6+nLXd3uYkzHMXhaPF29CaoaadJXqgE4kV/IP+cl8vmqPbRt3oTXru1Oj1Ze8PNzsPJ9CAi3j9bVZiDbMrbxxto3+D3td0J9Qnmo50MMiRqCiLh6M1Q10KSvVAPya9JhnpizkQPH87hrQFseGhSNV9oa+O+99v76u1wDg56FgHBW7FvB62tfJ+loEt2adePRuEfp2bKnqzdBnSVN+ko1MMfzCnj+xy3Mjk+lQ0tfXr+uO11aeMKvb8Dv79gL9bsf+j+EzaMRc3fM5d3173Io9xCXhF/Cw70eJso/yqXboM6cJn2lGqiftx5k4rd/kn7Cyn0Xt2PCxe3wzN4HS5+FP78Bn1Yw6GnoNobconw+3fwpMzfNxGqzMrr9aO6JvYem3k1dvRmqijTpK9WAHcsp4JkfNvPd+n3EBPvxz6u7EhseAHtXw4KJsG8thPSAIS9C5PkcyT3C+wnv823St3i7e3N719sZ22ks3u7ert4U5aRq7VpZRIaKyDYRSRaRiWUsv0hE1olIoYiMLrVsnIgkOV7jnN8EpdSZ8m/swZvXxzL95l4czs5n1LQV/G3OBg4HdIfblsDV/4Ksg/DRUPhmPM3yTjDp/En8Z+R/6N2qN2+ve5srv7uS75O/1xG76plKz/RFxAJsBy4DUoE1wA3GmC0lykQBfsBjwFxjzBzH/KZAPBAHGGAt0MsYU+7z4Xqmr1T1ys4vZOrSJGau2IW3u4UHB0Uzrl8UHrZcex8+v70Fpgj6TYALHgYvX9YcWMMb8W+wKX0TzRo1Y2jUUC5vfTldmnXRu31qqWpr3hGR84FnjDFDHNNPAhhjXiyj7MfAjyWS/g3AQGPMXY7p6cAyY8yX5X2eJn2lasaOw9lM+WEL/9t+mHYtfHhmeGcuiG4Gxxzt/Ru/Bp+WcOlk6H4jRQK/7P2FH3b8wPLU5RQUFRDuG86w1sO4ovUVtAlo4+pNUiVUZ9IfDQw1xtzumL4Z6GuMmVBG2Y85Nek/BngbY553TE8Cco0xr5WqdydwJ0BERESvlJSUyrdQKVVlxhiWJh5iyo9b2JORw9DOrfj7FZ0Ib9oYUuPt7f2payC4u729P6o/AMetx1maspR5u+ax+sBqikwRHZt2ZFjrYQyLGkawT7CLt0xVZ9K/FhhSKun3McbcX0bZjzk16T8OeJVK+jnGmNfL+zw901eq5uUV2Pj3b7t49+dkiozhrgFtuWdAWxp5uMGmb2Hx03A8FTqNgEsmQfP2xXWP5B5h4e6FzNs5j41HNgLQs0VPLm99OYOjBhPoHeiqzWrQtHlHKVWptMxcXpy/lR82pBEa0Ih/XNGJoV1aIQW58Mc0+O0NKMiB9sPs9/hH9oMSbfp7j+9l/u75zNs5jx3HduAu7pwfcj6Xt7mcS8IvobFHYxduXcNSnUnfHfuF3EuBfdgv5N5ojNlcRtmPOTXpN8V+8fbk437rsF/IzSjv8zTpK3XurdyZzjNzN7P1QBb92gbxzIjOtG/pCyeOwJoPYfUMyEmHkJ7Q/wHoOBws7sX1jTFsP7qdebvmMX/XfPaf2I+3xZuLwi7isqjLuCj0Ij0A1LBqvU9fRC4H3gIswExjzAsiMgWIN8bMFZHewHdAIJAHHDDGdHbUvRV4yrGqF4wxH1X0WZr0lXKNQlsRX6zew+uLtpOdX8gt50fy0KD2+DfygIJcSPgC/ngXMnZCQCScf5997F7PJqesp8gUseHwBn7a+RNLUpaQnpeOl8WLC0Iv4LLIyxgQNgAfTx8XbWX9pQ9nKaXOSMYJK68t2saXq/fQtLEnjwxuz/Vx4bhb3Oz99m+bZ7/Vc+8q8A6w9+bZ507wbXnaumxFNtYfWs/ilMUsSVnCodxDeLh50C+kH5dFXsbA8IHa1XM10aSvlDorm/Yd49kfNrNm91Hat/Thycs7MbB987/u09+zCv6YCok/gsUDul1vb/dv3qHM9RWZIjYe3sjilMUsTlnM/hP7cRd3+ob0ZXDkYC4Ov1gvAp8FTfpKqbNmjGHh5gO8OH8rKek5XBjdjKcu70SnYL+/CqXvsF/0TfgcCvOg/VDHRd/+p1z0Lb3ezembWZSyiEW7F7Evex8WsdC7VW8ui7yMSyIuoVmjZudoK+sHTfpKqWpjLSzis5UpvLM0iay8Aq7tFc6jg9vTwq9E3zynXfTtAb3GQ7tB4B9W7rqNMWzN2MrilMUsSllEyvEU3MSNLs26ENM0hk5BnejYtCPtAtrpwC8V0KSvlKp2mTlWpv6czKd/7MbD4sZdF7Xljota09jzrzt5KMiFDV/az/7Tk+3zmnWwJ/92l9h/AXg0KnP9xhiSMpNYtHsRaw6sYWvGVnIKcwBwd3OnrX9bOjbtWHwg6Ni0I008mpS5roZGk75SqsbsPnKClxdsZf6mA7T08+KxwR24umcYFrcSzTnGwOGtkLwEkpdCyu9gywd3b3vib3cptL3Ufg2gnGagIlPE3qy9JGYksjV9K1sztpKYkUhG3l93fUf6RRYfADo1tR8MghoF1fQuqHU06Sulatya3Rk8/1MiG/ZmEhPsx9+v6ET/duW0xVtzIGWF/QCwYykc2W6f7xdm/wXQbhC0HgCNAir8TGMMh3IOFR8AtmbYDwb7svcVl2nZuCU9W/QkrlUccS3jaO3fut53FKdJXyl1ThQVGX7YmMYrC7axLzOXSzq24KnLO9KuhW/FFTP3/HUA2Pk/yD8OYoGwuL8OAMHdym0KKu1Y/rHiA8CmI5uIPxjPkdwjADT1bkqvlr3o1bIXcS3jiA6Mxk2c6lm+ztCkr5Q6p/IKbHy0Yjfv/ZJMToGNG/qEc9dFbQkLbFT5WbatEPbF/9UUlLYeMPaDQIsYCO1hfxo4tKd92uJRaTzGGPZk7SH+QDxrD64l/mA8+0/sB8Dfy5+eLXraDwKt4ugY2BGLm6Ua9oLraNJXSrlEenY+by1J4ovVe7AVGVr5edMrKpBeEYHERQXSKdgPD0slZ9kn0mHvSti3DtLW2Q8CuY5hOCxe0Kqr/QAQ0tN+l1CzaHAiae/L3mc/AByIJ/5gPHuz9gLg4+FDjxY9ig8CMUExeLhVfmCpTTTpK6VcKiX9BMu2HSY+5SjrUo6yLzMXgEYeFmLDA+gVGUivqEB6RgTau3qoiDFwdLf9ALDPcRBIS4CCE/blnj4QHOv4RdADQnvZu4qo5BfGwRMHi38FxB+MZ9exXfYY3RvRrXk3erWwNwl1bd6VRu7ONTO5iiZ9pVStsv9YLvG7j7I2xf7asv84tiKDCLRv4XvKr4GIpo0rbxIqssGRpFMPBAf+tN8hBPYBYSLOg/DzIKIvtOpWabNQem46aw+uZd2hdaw9uJZtGdswGNzd3Okc1Ln4ukBsi1j8PP0qXNe5pklfKVWrncgvZMPeTOJTjhKfcpT1KUfJyi8EoJmPF70iA+gZEUiPiEC6hvrTyNOJNvdCKxzaYr8+sGeVvYkoc499mUdj+y+AkweC8N7gXXG/P8etx0k4lGA/EBxcx6b0TRQWFSII7QPbFx8Eerbs6fIniDXpK6XqFFuRIelQVvGvgXV7jpKSfvLBLKFTsB89Ik4eCAKc+zUAcDwN9qy0dxC3Z6X914CxAQItO0N4X8eBoC8ERFTYJJRbmFt8Z9Dag2vZeHgjuYX2Zqsovyh6tuxJqE8oTTya0Ni9MY09GtPYvbF92qMxTdyb0MijEU08muBt8a7W20g16Sul6rwj2fkk7Mlk/d6jrEvJZENqJjlWGwBBTTzpERFAD8dBoHtYAE283CtZI5Cf/dcvgT1/2IeJtGbZl/mG2JuCQuPsF4qDu5/WdXRJBUUFJKYnsu7guuJmoePW405tm5u42Q8MJw8OHo3p1LQTz/R7xqn6pWnSV0rVO7Yiw/aDWazbc5T1ezJZt+coOw/bL+a6CbRv6UvPyEB6hNsPBm2aNcHNzYlrAwc3O34J/AF7V8Mx+109iBs07+i4XdRx22jLLuBefh9AVpuVnIIccgpzOFFwovhvbkHuafNyCnLILcwtnhfmE8aTfZ88o32jSV8p1SBk5lhJ2JvJuj2ZrN9zlIS9mWTl2a8N+Hm7Extx8iAQQGx4AAGNnei0LfvQX7eLnvybk25fZvG03zJ68rmBkJ5O3zJakzTpK6UapKIiw47D2azfm8l6x4Fg+8Esihyprk3zJvQID3Q0DQXQoaWvfYCYihhjvyCctg72rYV962F/Aliz7ctL3jLqFwaeje0Xjj2blPrbGDya2P+6NwK36nsqWJO+Uko5ZOcXsjH15EEgk4S9RzmSbQXszw10DfO3HwTCA+kZEXBql9HlOe2W0XWOW0atzgfm0fjUg0FILFz1wRltoyZ9pZQqhzGG1KO5xdcG1u/NZEvaMQps9nwY1MSTDq187a+W9r/tW/pWfqHYVgD5WWA9AQU5Jf7m2B8ks+aUml9qeUAkDHnhjLZJk75SSlVBXoGNzWnH2bA3k60HjrPtYDbbD2SRW2ArLhPetBEdWvrRsZUv7Vv50rGVL62bNam8W4lzwNmk78T9TSAiQ4G3AQvwoTHmpVLLvYBPgV5AOnC9MWa3iEQBicA2R9GVxpi7nd0IpZQ6V7w9LPauISL/Gqe3qMiw92gO2w5kse1AFlsPZrH9QBa/bDuEzXGRwMMitG3uU/zLoJmPF408LHh7WBx/3fA+Oe1ZYp67pfI7i2pApUlfRCzANOAyIBVYIyJzjTFbShS7DThqjGknImOAl4HrHct2GGNiqzlupZSqcW5uQmRQEyKDmjC4c6vi+fmFNnYcOsH2g1lsPZDF9oP2h8q+T0ir0vo93d2KDwL2awsBTL2hR3VvximcOdPvAyQbY3YCiMhXwEigZNIfCTzjeD8HeFfq+4gFSqkGy8vdQkyIHzEhp/a/k5VXQGZOAfmFNnKtReQV2si12sgrsJFbYP+bV1BU/D63wEae9a95YYE136mbM0k/FNhbYjoV6FteGWNMoYgcA06OV9ZaRNYDx4F/GGN+Lf0BInIncCdARERElTZAKaVqC19vD3y9a3eXzM5cfSjrjL301d/yyuwHIowxPYBHgC9E5LSu6YwxM4wxccaYuObNmzsRklJKqTPhTNJPBcJLTIcBpRuuisuIiDvgD2QYY/KNMekAxpi1wA6g/dkGrZRS6sw4k/TXANEi0lpEPIExwNxSZeYC4xzvRwM/G2OMiDR3XAhGRNoA0cDO6gldKaVUVVXapu9oo58ALMR+y+ZMY8xmEZkCxBtj5gL/Bj4TkWQgA/uBAeAiYIqIFAI24G5jTEZNbIhSSqnK6cNZSilVDzj7cJbrHyNTSil1zmjSV0qpBkSTvlJKNSC1rk1fRA4DKWeximbAkWoKpzppXFWjcVWNxlU19TGuSGNMpQ861bqkf7ZEJN6ZixnnmsZVNRpX1WhcVdOQ49LmHaWUakA06SulVANSH5P+DFcHUA6Nq2o0rqrRuKqmwcZV79r0lVJKla8+nukrpZQqhyZ9pZRqQOpk0heRa0Vks4gUiUhcqWVPikiyiGwTkSHl1G8tIqtEJElEvnb0HlrdMX4tIgmO124RSSin3G4R+dNRrsY7HRKRZ0RkX4nYLi+n3FDHPkwWkYnnIK5XRWSriGwUke9EJKCccudkf1W2/SLi5fiOkx3/lqJqKpYSnxkuIr+ISKLj3/+DZZQZKCLHSny/k2s6LsfnVvi9iN07jv21UUR6noOYOpTYDwkiclxEHipV5pzsLxGZKSKHRGRTiXlNRWSxIw8tFpHAcuqOc5RJEpFxZZWpEmNMnXsBnYAOwDIgrsT8GGAD4AW0xt5/v6WM+rOBMY73HwD31HC8rwOTy1m2G2h2DvfdM8BjlZSxOPZdG8DTsU9jajiuwYC74/3LwMuu2l/ObD9wL/CB4/0Y4Otz8N0FAz0d732B7WXENRD48Vz9e3L2ewEuB+ZjH3DpPGDVOY7PAhzA/gDTOd9f2Hsc7glsKjHvFWCi4/3Esv7NA02xd0ffFAh0vA88m1jq5Jm+MSbRGLOtjEUjga+MffCWXUAy9jF+iznG7r0E+1i+AJ8Ao2oqVsfnXQd8WVOfUQOKx0U2xliBk+Mi1xhjzCJjTKFjciX2wXpcxZntH4n93w7Y/y1d6viua4wxZr8xZp3jfRaQiH2o0rpgJPCpsVsJBIhI8Dn8/EuBHcaYs3na/4wZY5Zj73a+pJL/hsrLQ0OAxcaYDGPMUWAxMPRsYqmTSb8CZY3nW/o/RRCQWSLBlFWmOl0IHDTGJJWz3ACLRGStY6zgc2GC4yf2zHJ+UjqzH2vSrdjPCstyLvaXM9t/yrjQQMlxoWucozmpB7CqjMXni8gGEZkvIp3PUUiVfS+u/jc1hvJPvFyxvwBaGmP2g/2ADrQoo0y17zdnBkZ3CRFZArQqY9HfjTHfl1etjHnOjudbZU7GeAMVn+X3N8akiUgLYLGIbHWcFZyxiuIC3geew77Nz2Fverq19CrKqHvW9/Y6s79E5O9AIfB5Oaup9v1VVqhlzKuxf0dVJSI+wLfAQ8aY46UWr8PehJHtuF7zX+wj1tW0yr4XV+4vT2AE8GQZi121v5xV7fut1iZ9Y8ygM6jmzHi+R7D/tHR3nKGVVaZaYhT7eMFXA70qWEea4+8hEfkOe9PCWSUxZ/ediPwL+PH/27u/0DiqKI7j36PW/+KfWjSlD1pKEVT0wf/tk0owRcRWofrQgpoHH0REpCIiQgUFq4KIoeAfBAk+FCvUslioiFUQCxbTJFYxPhQCbSo+FMRaxB4fztl2XHc3m2RnVzq/DyzZnbmTvbk7OTN778y5TVZ10o5dr1cOUt0L3OXZodnkd3S9vZqYy7zQ01aYF7rL9fgPM1tEBPxRd9/euL54EHD3mpmNmNnl7l5qcrEOPpdS9qkODQH73H2mcUW/2ivNmNmAux/Krq4jTcpME+MOdcuIscx5O926d3YAD+WVFVcTR+y9xQIZTL4g5vKFmAMG6EgAAAOBSURBVNu31TeHhbob+NHdp5utNLMLzOyi+nNiMHOiWdluaehHXdvi/TqZF7nb9boHeBa4z93/aFGmV+0173mhS6jLSTlm8B5wwN3faFHmyvrYgpndQvyP/1ZyvTr5XHYAG/MqntuAo/WujR5o+W27H+1VUNyHWsWhXcCgmV2aXbGDuWz+yh61LuNBBKtp4DgwA+wqrHueuPLiJ2CosLwGLM3ny4mDwRSwDTinpHp+QMwLXFy2FKgV6jGWj0mim6PstvsQGAf250430FivfL2GuDrklx7Va4rou/w+H1sb69XL9mr29wObiYMSwLm570zlvrS8B220mvhqv7/QTmuAx+v7GfBEts0YMSB+Rw/q1fRzaaiXAW9ne45TuOqu5LqdTwTxiwvLet5exEHnEPBXxq7HiDGgz4Gf8+dlWfYm4N3Cto/mfjYFPLLQuigNg4hIhZxu3TsiItKGgr6ISIUo6IuIVIiCvohIhSjoi4hUiIK+VIaZ/b6AbUctsm5OZPqKRYV19xezM5rZxiw3aWY/mNkzufw1M7tzYX+FyMIo6It0ZhS4BrgeOA8YLqzbBIwAmNkQ8BQw6O7XEpkVj2a5t4hsiiJ9o6AvlZN3hW7Js/FxM1ufy8/I2/AnzWynmdXM7EGIW/Q9ETdjLcttVgLH/dRt+88RqavraQn+dPd38vlBYLGZNcs/JNITCvpSReuAG4EbiFQZWzI9xTrgKuJsfhi4vXHD7NbZAHyWi1YRSbvqrgO+a/Pe+3Ibkb5Q0JcqWg185O5/eyTh+hK4OZdvc/cT7n6YyNHUaATY4+5f5esB4Nc5vPcRIrWESF8o6EsVtZrspO0kKGb2IrAEeLqw+BiRh6dukjZZVbPssQ7qKFIKBX2poj3AejM708yWEFPZ7QW+Bh7Ivv0rKKS0NbNhYhajh939ROF3HQBWFF6/Arxa77fPjK9PFtavpORMqiLt/G/z6YuU6BOiv36MyFq5yd0Pm9nHxLR6E0SGzW85deXNVuAg8E1m4t3u7puJA8jrZmY5zlvLA8buTNnrwPtwcjxgBVDahO4is1GWTZECM7vQYxalxcTZ/6rs32+3zZvAp+6+e5Zya4mJzV/oXo1F5kZn+iL/ttPMLgHOBl6aLeCnl4FbOyh3FjE9pUjf6ExfRKRCNJArIlIhCvoiIhWioC8iUiEK+iIiFaKgLyJSIf8AwDqy/Wm/3G4AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ValidationResult(degree=4, cost=1024, error=0.05838861985962853)\n"
+ ]
+ }
+ ],
+ "source": [
+ "ValidationResult = namedtuple('ValidationResult', ['degree', 'cost', 'error'])\n",
+ "\n",
+ "\n",
+ "def build_options(degree, cost):\n",
+ " return f\"-t 1 -r 1 -d {degree} -c {cost} -q\"\n",
+ "\n",
+ "\n",
+ "def plot_errors_stdevs(errors_stdevs, log_costs, d):\n",
+ " errors, stdevs = zip(*errors_stdevs)\n",
+ " errors, stdevs = np.array(errors), np.array(stdevs)\n",
+ " plt.plot(log_costs, errors - stdevs)\n",
+ " plt.plot(log_costs, errors)\n",
+ " plt.plot(log_costs, errors + stdevs)\n",
+ " plt.xlabel(\"log2(C)\")\n",
+ " plt.title(f\"Error for degree={d}\")\n",
+ " plt.legend(['error - stdev', 'error', 'error + stdev'], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def search_parameters(x, y):\n",
+ " best_result = ValidationResult(None, None, error=1.0)\n",
+ " k = 10\n",
+ " log_costs = list(range(-k, k + 1))\n",
+ " degrees = range(1, 5)\n",
+ " errors_stdevs = [[] for _ in degrees]\n",
+ "\n",
+ " for i, degree in enumerate(degrees):\n",
+ " for cost in map(lambda a: 2 ** a, log_costs):\n",
+ " error, std = kfold(x, y, build_options(degree, cost), svm_train, svm_predict)\n",
+ " if error < best_result.error:\n",
+ " best_result = ValidationResult(degree, cost, error)\n",
+ " errors_stdevs[i].append((error, std))\n",
+ " plot_errors_stdevs(errors_stdevs[i], log_costs, degree)\n",
+ "\n",
+ " for errs_stds_i in errors_stdevs:\n",
+ " errors_i, _ = zip(*errs_stds_i)\n",
+ " plt.plot(log_costs, errors_i)\n",
+ " plt.xlabel(\"log2(C)\")\n",
+ " plt.title(f\"Degrees comparison\")\n",
+ " plt.legend([f\"d={d}\" for d in degrees], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ " return best_result\n",
+ "\n",
+ "\n",
+ "y_train, x_train = svm_read_problem(os.path.join(spam_save_dir, 'train_scaled'))\n",
+ "best = search_parameters(x_train, y_train)\n",
+ "print(best)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "С параметрами d=4 и C=1024 получили ошибку $\\approx$0.058. Как видим, для С < 1 ошибка становится гораздо больше по мере приближения к 0, т.к. неправильно определенные объекты мы штрафуем все меньше, поэтому разделяющая плоскость перестает отражать основные критерии разделения данных.\n",
+ " Теперь построим графики зависимости ошибки от степени полинома d для полученного наилучшего значения С."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def error_on_degrees(x_train, y_train, x_test, y_test, cost):\n",
+ " valid_errors = []\n",
+ " test_errors = []\n",
+ " degrees = list(range(1, 21))\n",
+ " for degree in degrees:\n",
+ " valid_error, _ = kfold(x_train, y_train, build_options(degree, cost), svm_train, svm_predict)\n",
+ " valid_errors.append(valid_error)\n",
+ "\n",
+ " model = svm_train(y_train, x_train, build_options(degree, cost))\n",
+ " _, results, _ = svm_predict(y_test, x_test, model, '-q')\n",
+ " test_errors.append(1 - results[0] / 100)\n",
+ "\n",
+ " plt.plot(degrees, valid_errors)\n",
+ " plt.plot(degrees, test_errors)\n",
+ " plt.xlabel('polynom degree')\n",
+ " plt.xticks(range(2, 20, 2))\n",
+ " plt.title(f\"Error for C={cost}\")\n",
+ " plt.legend(['validation', 'test'], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "y_test, x_test = svm_read_problem(os.path.join(spam_save_dir, 'test_scaled'))\n",
+ "error_on_degrees(x_train, y_train, x_test, y_test, best.cost)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Построим график зависимости количества опорных векторов от степени полинома d."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sv_amount = []\n",
+ "degrees = list(range(1, 21))\n",
+ "for degree in degrees:\n",
+ " model = svm_train(y_train, x_train, build_options(degree, best.cost))\n",
+ " sv_amount.append(model.get_nr_sv())\n",
+ "\n",
+ "plt.plot(degrees, sv_amount)\n",
+ "plt.title(\"Support vectors amount\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Отдельно выведем количество опорных векторов и ошибку на тестовой выборке для выбранных при кросс-валидации d и C."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Support vectors amount for d=4, C=1024: 721\n",
+ "Test error = 0.07471763683753263\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = svm_train(y_train, x_train, build_options(best.degree, best.cost))\n",
+ "print(f\"Support vectors amount for d={best.degree}, C={best.cost}: {model.get_nr_sv()}\")\n",
+ "_, results, _ = svm_predict(y_test, x_test, model, '-q')\n",
+ "print(f\"Test error = {1 - results[0] / 100}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Как много опорных векторов в вашем решении? Какие выводы вы можете сделать?\n",
+ "Для выбранных параметров модели количество опорных векторов составило 721 из всех 3450 объектов, т.е. $\\approx{1/5}$ обучающей выборки. Это означает, что данные объекты попали в разделяющую полосу, которая видимо получилась достаточно широкой.\n",
+ " Из последнего графика можно сделать вывод, что при росте степени полинома уменьшается размер отступа, следовательно и количество опорных векторов."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Бонус: какое ядро подходит лучше всего для этой задачи? Почему?\n",
+ "При росте степени полинома ошибка на тестовой выборке монотонно и быстро убывает только до значения степени d=3, дальше график ведет себя неоднозначно. Однако среднее значение ошибки при валидации убывает на всем диапазоне значений степени. Это значит, что при d > 3 модель возможно начинает переобучаться, т.к. ядра более высокой степени дают более гибкую разделяющую границу.\n",
+ " Поэтому для данной задачи предпочтительнее выбирать полиномиальное ядро со степенью 3, т.к. с ним достигается почти такая же точность как с большими степенями, при этом меньше вероятность переобучения."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Задание 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для выполнения кода данного задания необходимо, чтобы в текущей директории была размещена папка 'gisette' со всеми файлами из набора gisette.\n",
+ "Аналогично первому заданию, прочитаем и сохраним данные тренировочной и валидационной выборок в нужном формате в папке 'gisette_tmp', затем выполним масштабирование на отрезок [0,1]."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def transform_gisette_data(data_dir, save_dir, base_name):\n",
+ " os.makedirs(save_dir, exist_ok=True)\n",
+ " data_path = os.path.join(data_dir, base_name)\n",
+ " x = pd.read_csv(data_path + '.data', delim_whitespace=True, header=None).values\n",
+ " y = pd.read_csv(data_path + '.labels', delim_whitespace=True, header=None).values\n",
+ " save_libsvm(np.hstack((x, y)), os.path.join(save_dir, base_name))\n",
+ "\n",
+ "\n",
+ "data_dir = 'gisette'\n",
+ "gisette_save_dir = 'gisette_tmp'\n",
+ "transform_gisette_data(data_dir, gisette_save_dir, 'gisette_train')\n",
+ "transform_gisette_data(data_dir, gisette_save_dir, 'gisette_valid')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "cd gisette_tmp\n",
+ "../libsvm/svm-scale -l 0 -s scaling_params gisette_train > train_scaled \n",
+ "../libsvm/svm-scale -r scaling_params gisette_valid 1> valid_scaled 2> /dev/null"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Объекты в данном датасете имеют большое количество признаков, и как сказано в описании, многие сформированы как произведения исходных признаков (пикселей изображения), для переведения задачи в пространство большей размерности. Поэтому будем применять SVM с линейным ядром.\n",
+ "\n",
+ " В гайде библиотеки libsvm авторы рекомендуют для решения задач с большим количеством признаков и объектов использовать вместо libsvm библиотеку liblinear, которая позволяет очень быстро обучать модели с линейным ядром. Поэтому для выполнения следующего кода необходимо, чтобы в текущей директории присутствовала распакованная и скомпилированная библиотека liblinear или liblinear-multicore в папке 'liblinear_'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from liblinear_.python.liblinearutil import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для подбора параметров будем проводить кросс-валидацию на обучающем наборе, разбивая его на 5 равных частей, т.к. при большем количестве валидация займет слишком много времени. Набор dev будем использовать в качестве тестового, для оценки качества полученной модели и контроля вероятности переобучения. Набор test никак не используем, т.к. объекты в нем не размечены."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Аналогично заданию 1 выполним кросс-валидацию методом k-fold при k = 5 на тренировочном наборе, и построим графики зависимости среднего значения ошибки от $log_{2}C$ со стандартными отклонениями для $С \\in \\{2^{-15}, 2^{-14}, ..., 2^{2}\\}$. По словам авторов библиотеки брать большие значения С особого смысла не имеет: \"Once C is larger than certain value, the obtained models have similar performances\".\n",
+ " Выберем солвера с L2-регуляризацией и L2-лоссом, решающего двойственную задачу (параметр -s). Значение параметра -e, означающего критерий остановки алгоритма, установим поменьше для более точного решения задачи оптимизации."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GisetteResult(cost=0.0078125, error=0.011400876966405949)\n",
+ "Validation accuracy: 98.85991230335941%\n"
+ ]
+ }
+ ],
+ "source": [
+ "GisetteResult = namedtuple('GisetteResult', ['cost', 'error'])\n",
+ "\n",
+ "def liblinear_options(svm_type=1, cost=1.0, eps=0.0001):\n",
+ " return f\"-s {svm_type} -c {cost} -e {eps} -q\"\n",
+ "\n",
+ "\n",
+ "def plot_gisette_errors_stdevs(errors_stdevs, log_costs):\n",
+ " errors, stdevs = zip(*errors_stdevs)\n",
+ " errors, stdevs = np.array(errors), np.array(stdevs)\n",
+ " plt.plot(log_costs, errors - stdevs)\n",
+ " plt.plot(log_costs, errors)\n",
+ " plt.plot(log_costs, errors + stdevs)\n",
+ " plt.xlabel(\"log2(C)\")\n",
+ " plt.xticks(log_costs)\n",
+ " plt.legend(['error - stdev', 'error', 'error + stdev'], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def search_gisette_parameters(x, y):\n",
+ " best_result = GisetteResult(None, error=1.0)\n",
+ " log_costs = list(range(-15, 3))\n",
+ " errors_stdevs = []\n",
+ "\n",
+ " for cost in map(lambda a: 2 ** a, log_costs):\n",
+ " error, std = kfold(x, y, liblinear_options(cost=cost), train, predict, k=5)\n",
+ " if error < best_result.error:\n",
+ " best_result = GisetteResult(cost, error)\n",
+ " errors_stdevs.append((error, std))\n",
+ "\n",
+ " plot_gisette_errors_stdevs(errors_stdevs, log_costs)\n",
+ " return best_result\n",
+ "\n",
+ "\n",
+ "y_train, x_train = svm_read_problem(os.path.join(gisette_save_dir, 'train_scaled'))\n",
+ "best = search_gisette_parameters(x_train, y_train)\n",
+ "print(best)\n",
+ "print(f\"Validation accuracy: {100 * (1 - best.error)}%\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Получили наименьшую ошибку при $С=2^{-7}$. Для $С=2^{-8}$ среднее значение ошибки практически такое же, но меньше стандартное отклонение. Проверим точность модели для двух наилучших значений С на наборе dev."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "log2(C) = -8: Test accuracy = 97.7%, error = 0.02300000000000002\n",
+ "log2(C) = -7: Test accuracy = 97.8%, error = 0.02200000000000002\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_valid, x_valid = svm_read_problem(os.path.join(gisette_save_dir, 'valid_scaled'))\n",
+ "\n",
+ "for log_c in [-8, -7]:\n",
+ " model = train(y_train, x_train, liblinear_options(cost=2**log_c))\n",
+ " _, results, _ = predict(y_valid, x_valid, model, '-q')\n",
+ " print(f\"log2(C) = {log_c}: Test accuracy = {results[0]}%, error = {1 - results[0] / 100}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Так как точность всего на 1% оказалась хуже чем при кросс-валидации, переобучение навряд ли произошло. В качестве окончательной выбираем модель с линейным ядром с параметром $С=2^{-7}$."
+ ]
+ }
+ ],
+ "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": "3.7.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw8 (SVM)/.ipynb_checkpoints/hw8_solution-checkpoint.ipynb b/hw8 (SVM)/.ipynb_checkpoints/hw8_solution-checkpoint.ipynb
new file mode 100644
index 0000000..2ffde8c
--- /dev/null
+++ b/hw8 (SVM)/.ipynb_checkpoints/hw8_solution-checkpoint.ipynb
@@ -0,0 +1,581 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Задание 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для выполнения кода необходимо, чтобы в текущей директории присутствовал файл 'spambase' с исходными данными, а также распакованная и скомпилированная библиотека libsvm в папке 'libsvm'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from collections import namedtuple\n",
+ "from subprocess import call\n",
+ "from libsvm.python.svmutil import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Разбиваем данные на тренировочную и тестовую выборки, сохраняем все файлы в нужном формате в созданной папке 'spam_tmp'. Затем выполняем масштабирование с помощью svm-scale на отрезок [0,1], т.к. значения всех признаков неотрицательные."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def save_libsvm(raw_data, filename):\n",
+ " x = raw_data[:, :-1]\n",
+ " y = raw_data[:, -1].astype(int)\n",
+ " lines = []\n",
+ " for features, label in zip(x, y):\n",
+ " sparse_features = [f\"{i + 1}:{value}\" for i, value in enumerate(features) if value != 0]\n",
+ " line = ' '.join([str(label)] + sparse_features) + '\\n'\n",
+ " lines.append(line)\n",
+ " with open(filename, 'w') as f:\n",
+ " f.writelines(lines)\n",
+ "\n",
+ "spam_save_dir = 'spam_tmp'\n",
+ "os.makedirs(spam_save_dir, exist_ok=True)\n",
+ "data = pd.read_csv('spambase', header=None).values\n",
+ "train, test = np.vsplit(data, [3450])\n",
+ "\n",
+ "save_libsvm(train, os.path.join(spam_save_dir, 'train'))\n",
+ "save_libsvm(test, os.path.join(spam_save_dir, 'test'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "cd spam_tmp\n",
+ "../libsvm/svm-scale -l 0 -s scaling_params train > train_scaled\n",
+ "../libsvm/svm-scale -r scaling_params test > test_scaled"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Функция kfold выполняет кросс-валидацию, возвращая среднюю ошибку и ее стандартное отклонение. Ошибка на каждой итерации вычисляется как 1 - accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def split(index, arr, part_size):\n",
+ " validation_part = arr[index: (index + 1) * part_size]\n",
+ " train_part = arr[:index * part_size] + arr[(index + 1) * part_size:]\n",
+ " return train_part, validation_part\n",
+ "\n",
+ "\n",
+ "def kfold(x, y, parameters, train_func, predict_func, k=10):\n",
+ " part_size = len(y) // k\n",
+ " errors = []\n",
+ " for i in range(k):\n",
+ " x_train, x_valid = split(i, x, part_size)\n",
+ " y_train, y_valid = split(i, y, part_size)\n",
+ "\n",
+ " model = train_func(y_train, x_train, parameters)\n",
+ " _, results, _ = predict_func(y_valid, x_valid, model, '-q')\n",
+ " errors.append(1 - results[0] / 100)\n",
+ " return np.mean(errors), np.std(errors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Используем полиномиальное ядро и константу равную 1 (параметр -r, значение взял из примера на сайте), т.к. со значением по умолчанию 0 по непонятным причинам все объекты выборки присваиваются одному классу.\n",
+ " Выполняем кросс-валидацию для подбора наилучших параметров и строим графики ошибки от $log_{2}C$ со стандартными отклонениями для $d \\in \\{1,2,3,4\\}$ и $С \\in \\{2^{-k}, 2^{-k+1}, ..., 2^{k}\\}$, $k=5$. Также строим график сравнения ошибок для разных степеней полинома."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ValidationResult(degree=4, cost=1024, error=0.05838861985962853)\n"
+ ]
+ }
+ ],
+ "source": [
+ "ValidationResult = namedtuple('ValidationResult', ['degree', 'cost', 'error'])\n",
+ "\n",
+ "\n",
+ "def build_options(degree, cost):\n",
+ " return f\"-t 1 -r 1 -d {degree} -c {cost} -q\"\n",
+ "\n",
+ "\n",
+ "def plot_errors_stdevs(errors_stdevs, log_costs, d):\n",
+ " errors, stdevs = zip(*errors_stdevs)\n",
+ " errors, stdevs = np.array(errors), np.array(stdevs)\n",
+ " plt.plot(log_costs, errors - stdevs)\n",
+ " plt.plot(log_costs, errors)\n",
+ " plt.plot(log_costs, errors + stdevs)\n",
+ " plt.xlabel(\"log2(C)\")\n",
+ " plt.title(f\"Error for degree={d}\")\n",
+ " plt.legend(['error - stdev', 'error', 'error + stdev'], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def search_parameters(x, y):\n",
+ " best_result = ValidationResult(None, None, error=1.0)\n",
+ " k = 10\n",
+ " log_costs = list(range(-k, k + 1))\n",
+ " degrees = range(1, 5)\n",
+ " errors_stdevs = [[] for _ in degrees]\n",
+ "\n",
+ " for i, degree in enumerate(degrees):\n",
+ " for cost in map(lambda a: 2 ** a, log_costs):\n",
+ " error, std = kfold(x, y, build_options(degree, cost), svm_train, svm_predict)\n",
+ " if error < best_result.error:\n",
+ " best_result = ValidationResult(degree, cost, error)\n",
+ " errors_stdevs[i].append((error, std))\n",
+ " plot_errors_stdevs(errors_stdevs[i], log_costs, degree)\n",
+ "\n",
+ " for errs_stds_i in errors_stdevs:\n",
+ " errors_i, _ = zip(*errs_stds_i)\n",
+ " plt.plot(log_costs, errors_i)\n",
+ " plt.xlabel(\"log2(C)\")\n",
+ " plt.title(f\"Degrees comparison\")\n",
+ " plt.legend([f\"d={d}\" for d in degrees], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ " return best_result\n",
+ "\n",
+ "\n",
+ "y_train, x_train = svm_read_problem(os.path.join(spam_save_dir, 'train_scaled'))\n",
+ "best = search_parameters(x_train, y_train)\n",
+ "print(best)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "С параметрами d=4 и C=1024 получили ошибку $\\approx$0.058. Как видим, для С < 1 ошибка становится гораздо больше по мере приближения к 0, т.к. неправильно определенные объекты мы штрафуем все меньше, поэтому разделяющая плоскость перестает отражать основные критерии разделения данных.\n",
+ " Теперь построим графики зависимости ошибки от степени полинома d для полученного наилучшего значения С."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def error_on_degrees(x_train, y_train, x_test, y_test, cost):\n",
+ " valid_errors = []\n",
+ " test_errors = []\n",
+ " degrees = list(range(1, 21))\n",
+ " for degree in degrees:\n",
+ " valid_error, _ = kfold(x_train, y_train, build_options(degree, cost), svm_train, svm_predict)\n",
+ " valid_errors.append(valid_error)\n",
+ "\n",
+ " model = svm_train(y_train, x_train, build_options(degree, cost))\n",
+ " _, results, _ = svm_predict(y_test, x_test, model, '-q')\n",
+ " test_errors.append(1 - results[0] / 100)\n",
+ "\n",
+ " plt.plot(degrees, valid_errors)\n",
+ " plt.plot(degrees, test_errors)\n",
+ " plt.xlabel('polynom degree')\n",
+ " plt.xticks(range(2, 20, 2))\n",
+ " plt.title(f\"Error for C={cost}\")\n",
+ " plt.legend(['validation', 'test'], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "y_test, x_test = svm_read_problem(os.path.join(spam_save_dir, 'test_scaled'))\n",
+ "error_on_degrees(x_train, y_train, x_test, y_test, best.cost)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Построим график зависимости количества опорных векторов от степени полинома d."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sv_amount = []\n",
+ "degrees = list(range(1, 21))\n",
+ "for degree in degrees:\n",
+ " model = svm_train(y_train, x_train, build_options(degree, best.cost))\n",
+ " sv_amount.append(model.get_nr_sv())\n",
+ "\n",
+ "plt.plot(degrees, sv_amount)\n",
+ "plt.title(\"Support vectors amount\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Отдельно выведем количество опорных векторов и ошибку на тестовой выборке для выбранных при кросс-валидации d и C."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Support vectors amount for d=4, C=1024: 721\n",
+ "Test error = 0.07471763683753263\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = svm_train(y_train, x_train, build_options(best.degree, best.cost))\n",
+ "print(f\"Support vectors amount for d={best.degree}, C={best.cost}: {model.get_nr_sv()}\")\n",
+ "_, results, _ = svm_predict(y_test, x_test, model, '-q')\n",
+ "print(f\"Test error = {1 - results[0] / 100}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Как много опорных векторов в вашем решении? Какие выводы вы можете сделать?\n",
+ "Для выбранных параметров модели количество опорных векторов составило 721 из всех 3450 объектов, т.е. $\\approx{1/5}$ обучающей выборки. Это означает, что данные объекты попали в разделяющую полосу, которая видимо получилась достаточно широкой.\n",
+ " Из последнего графика можно сделать вывод, что при росте степени полинома уменьшается размер отступа, следовательно и количество опорных векторов."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Бонус: какое ядро подходит лучше всего для этой задачи? Почему?\n",
+ "При росте степени полинома ошибка на тестовой выборке монотонно и быстро убывает только до значения степени d=3, дальше график ведет себя неоднозначно. Однако среднее значение ошибки при валидации убывает на всем диапазоне значений степени. Это значит, что при d > 3 модель возможно начинает переобучаться, т.к. ядра более высокой степени дают более гибкую разделяющую границу.\n",
+ " Поэтому для данной задачи предпочтительнее выбирать полиномиальное ядро со степенью 3, т.к. с ним достигается почти такая же точность как с большими степенями, при этом меньше вероятность переобучения."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Задание 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для выполнения кода данного задания необходимо, чтобы в текущей директории была размещена папка 'gisette' со всеми файлами из набора gisette.\n",
+ "Аналогично первому заданию, прочитаем и сохраним данные тренировочной и валидационной выборок в нужном формате в папке 'gisette_tmp', затем выполним масштабирование на отрезок [0,1]."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def transform_gisette_data(data_dir, save_dir, base_name):\n",
+ " os.makedirs(save_dir, exist_ok=True)\n",
+ " data_path = os.path.join(data_dir, base_name)\n",
+ " x = pd.read_csv(data_path + '.data', delim_whitespace=True, header=None).values\n",
+ " y = pd.read_csv(data_path + '.labels', delim_whitespace=True, header=None).values\n",
+ " save_libsvm(np.hstack((x, y)), os.path.join(save_dir, base_name))\n",
+ "\n",
+ "\n",
+ "data_dir = 'gisette'\n",
+ "gisette_save_dir = 'gisette_tmp'\n",
+ "transform_gisette_data(data_dir, gisette_save_dir, 'gisette_train')\n",
+ "transform_gisette_data(data_dir, gisette_save_dir, 'gisette_valid')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "cd gisette_tmp\n",
+ "../libsvm/svm-scale -l 0 -s scaling_params gisette_train > train_scaled \n",
+ "../libsvm/svm-scale -r scaling_params gisette_valid 1> valid_scaled 2> /dev/null"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Объекты в данном датасете имеют большое количество признаков, и как сказано в описании, многие сформированы как произведения исходных признаков (пикселей изображения), для переведения задачи в пространство большей размерности. Поэтому будем применять SVM с линейным ядром.\n",
+ "\n",
+ " В гайде библиотеки libsvm авторы рекомендуют для решения задач с большим количеством признаков и объектов использовать вместо libsvm библиотеку liblinear, которая позволяет очень быстро обучать модели с линейным ядром. Поэтому для выполнения следующего кода необходимо, чтобы в текущей директории присутствовала распакованная и скомпилированная библиотека liblinear или liblinear-multicore в папке 'liblinear_'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from liblinear_.python.liblinearutil import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для подбора параметров будем проводить кросс-валидацию на обучающем наборе, разбивая его на 5 равных частей, т.к. при большем количестве валидация займет слишком много времени. Набор dev будем использовать в качестве тестового, для оценки качества полученной модели и контроля вероятности переобучения. Набор test никак не используем, т.к. объекты в нем не размечены."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Аналогично заданию 1 выполним кросс-валидацию методом k-fold при k = 5 на тренировочном наборе, и построим графики зависимости среднего значения ошибки от $log_{2}C$ со стандартными отклонениями для $С \\in \\{2^{-15}, 2^{-14}, ..., 2^{2}\\}$. По словам авторов библиотеки брать большие значения С особого смысла не имеет: \"Once C is larger than certain value, the obtained models have similar performances\".\n",
+ " Выберем солвера с L2-регуляризацией и L2-лоссом, решающего двойственную задачу (параметр -s). Значение параметра -e, означающего критерий остановки алгоритма, установим поменьше для более точного решения задачи оптимизации."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GisetteResult(cost=0.0078125, error=0.011400876966405949)\n",
+ "Validation accuracy: 98.85991230335941%\n"
+ ]
+ }
+ ],
+ "source": [
+ "GisetteResult = namedtuple('GisetteResult', ['cost', 'error'])\n",
+ "\n",
+ "def liblinear_options(svm_type=1, cost=1.0, eps=0.0001):\n",
+ " return f\"-s {svm_type} -c {cost} -e {eps} -q\"\n",
+ "\n",
+ "\n",
+ "def plot_gisette_errors_stdevs(errors_stdevs, log_costs):\n",
+ " errors, stdevs = zip(*errors_stdevs)\n",
+ " errors, stdevs = np.array(errors), np.array(stdevs)\n",
+ " plt.plot(log_costs, errors - stdevs)\n",
+ " plt.plot(log_costs, errors)\n",
+ " plt.plot(log_costs, errors + stdevs)\n",
+ " plt.xlabel(\"log2(C)\")\n",
+ " plt.xticks(log_costs)\n",
+ " plt.legend(['error - stdev', 'error', 'error + stdev'], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def search_gisette_parameters(x, y):\n",
+ " best_result = GisetteResult(None, error=1.0)\n",
+ " log_costs = list(range(-15, 3))\n",
+ " errors_stdevs = []\n",
+ "\n",
+ " for cost in map(lambda a: 2 ** a, log_costs):\n",
+ " error, std = kfold(x, y, liblinear_options(cost=cost), train, predict, k=5)\n",
+ " if error < best_result.error:\n",
+ " best_result = GisetteResult(cost, error)\n",
+ " errors_stdevs.append((error, std))\n",
+ "\n",
+ " plot_gisette_errors_stdevs(errors_stdevs, log_costs)\n",
+ " return best_result\n",
+ "\n",
+ "\n",
+ "y_train, x_train = svm_read_problem(os.path.join(gisette_save_dir, 'train_scaled'))\n",
+ "best = search_gisette_parameters(x_train, y_train)\n",
+ "print(best)\n",
+ "print(f\"Validation accuracy: {100 * (1 - best.error)}%\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Получили наименьшую ошибку при $С=2^{-7}$. Для $С=2^{-8}$ среднее значение ошибки практически такое же, но меньше стандартное отклонение. Проверим точность модели для двух наилучших значений С на наборе dev."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "log2(C) = -8: Test accuracy = 97.7%, error = 0.02300000000000002\n",
+ "log2(C) = -7: Test accuracy = 97.8%, error = 0.02200000000000002\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_valid, x_valid = svm_read_problem(os.path.join(gisette_save_dir, 'valid_scaled'))\n",
+ "\n",
+ "for log_c in [-8, -7]:\n",
+ " model = train(y_train, x_train, liblinear_options(cost=2**log_c))\n",
+ " _, results, _ = predict(y_valid, x_valid, model, '-q')\n",
+ " print(f\"log2(C) = {log_c}: Test accuracy = {results[0]}%, error = {1 - results[0] / 100}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Так как точность всего на 1% оказалась хуже чем при кросс-валидации, переобучение навряд ли произошло. В качестве окончательной выбираем модель с линейным ядром с параметром $С=2^{-7}$."
+ ]
+ }
+ ],
+ "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": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw8 (SVM)/hw8.py b/hw8 (SVM)/hw8.py
new file mode 100755
index 0000000..c563cd4
--- /dev/null
+++ b/hw8 (SVM)/hw8.py
@@ -0,0 +1,220 @@
+#!/usr/bin/env python3
+
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from collections import namedtuple
+from subprocess import call
+from libsvm.python.svmutil import *
+from liblinear_.python.liblinearutil import *
+
+
+def save_libsvm(raw_data, filename):
+ x = raw_data[:, :-1]
+ y = raw_data[:, -1].astype(int)
+ lines = []
+ for features, label in zip(x, y):
+ sparse_features = [f"{i + 1}:{value}" for i, value in enumerate(features) if value != 0]
+ line = ' '.join([str(label)] + sparse_features) + '\n'
+ lines.append(line)
+ with open(filename, 'w') as f:
+ f.writelines(lines)
+
+
+def preprocess_spam_data(input_file, save_dir):
+ os.makedirs(save_dir, exist_ok=True)
+ data = pd.read_csv(input_file, header=None).values
+ train, test = np.vsplit(data, [3450])
+
+ save_libsvm(train, os.path.join(save_dir, 'train'))
+ save_libsvm(test, os.path.join(save_dir, 'test'))
+
+ call('./spam_scale.sh')
+
+
+def split(index, arr, part_size):
+ validation_part = arr[index: (index + 1) * part_size]
+ train_part = arr[:index * part_size] + arr[(index + 1) * part_size:]
+ return train_part, validation_part
+
+
+def kfold(x, y, parameters, train_func, predict_func, k=10):
+ part_size = len(y) // k
+ errors = []
+ for i in range(k):
+ x_train, x_valid = split(i, x, part_size)
+ y_train, y_valid = split(i, y, part_size)
+
+ model = train_func(y_train, x_train, parameters)
+ _, results, _ = predict_func(y_valid, x_valid, model, '-q')
+ errors.append(1 - results[0] / 100)
+ return np.mean(errors), np.std(errors)
+
+
+ValidationResult = namedtuple('ValidationResult', ['degree', 'cost', 'error'])
+
+
+def build_options(degree=2, cost=1):
+ return f"-t 1 -r 1 -d {degree} -c {cost} -h 0 -q"
+
+
+def plot_errors_stdevs(errors_stdevs, log_costs, d):
+ errors, stdevs = zip(*errors_stdevs)
+ errors, stdevs = np.array(errors), np.array(stdevs)
+ plt.plot(log_costs, errors - stdevs)
+ plt.plot(log_costs, errors)
+ plt.plot(log_costs, errors + stdevs)
+ plt.xlabel("log2(C)")
+ plt.title(f"Error for degree={d}")
+ plt.legend(['error - stdev', 'error', 'error + stdev'], loc='upper right')
+ plt.show()
+
+
+def search_parameters(x, y):
+ best_result = ValidationResult(None, None, error=1.0)
+ k = 10
+ log_costs = list(range(-k, k + 1))
+ degrees = range(4, 5)
+ errors_stdevs = [[] for _ in degrees]
+
+ for i, degree in enumerate(degrees):
+ for cost in map(lambda a: 2 ** a, log_costs):
+ error, std = kfold(x, y, build_options(degree, cost), svm_train, svm_predict)
+ if error < best_result.error:
+ best_result = ValidationResult(degree, cost, error)
+ errors_stdevs[i].append((error, std))
+ plot_errors_stdevs(errors_stdevs[i], log_costs, degree)
+
+ for errs_stds_i in errors_stdevs:
+ errors_i, _ = zip(*errs_stds_i)
+ plt.plot(log_costs, errors_i)
+ plt.xlabel("log2(C)")
+ plt.title(f"Degrees comparison")
+ plt.legend([f"d={d}" for d in degrees], loc='upper right')
+ plt.show()
+
+ return best_result
+
+
+def error_on_degrees(x_train, y_train, x_test, y_test, cost):
+ valid_errors = []
+ test_errors = []
+ degrees = list(range(1, 31))
+ for degree in degrees:
+ valid_error, _ = kfold(x_train, y_train, build_options(degree, cost), svm_train, svm_predict)
+ valid_errors.append(valid_error)
+
+ model = svm_train(y_train, x_train, build_options(degree, cost))
+ _, results, _ = svm_predict(y_test, x_test, model, '-q')
+ test_errors.append(1 - results[0] / 100)
+
+ plt.plot(degrees, valid_errors)
+ plt.plot(degrees, test_errors)
+ plt.xlabel('polynom degree')
+ plt.title(f"Error for C={cost}")
+ plt.legend(['validation', 'test'], loc='upper right')
+ plt.show()
+
+
+def sv_amount_on_degrees(x_train, y_train, best_degree, best_cost):
+ sv_amount = []
+ degrees = list(range(1, 51))
+ for degree in degrees:
+ model = svm_train(y_train, x_train, build_options(degree, best_cost))
+ sv_amount.append(model.get_nr_sv())
+ if degree == best_degree:
+ print(f"Support vectors amount for d={best_degree}, C={best_cost}: {model.get_nr_sv()}")
+ plt.plot(degrees, sv_amount)
+ plt.title("Support vectors amount")
+ plt.show()
+
+
+def task1():
+ save_dir = 'spam_tmp'
+ preprocess_spam_data('spambase', save_dir)
+
+ y_train, x_train = svm_read_problem(os.path.join(save_dir, 'train_scaled'))
+
+ best = search_parameters(x_train, y_train)
+ print(best)
+
+ y_test, x_test = svm_read_problem(os.path.join(save_dir, 'test_scaled'))
+ error_on_degrees(x_train, y_train, x_test, y_test, best.cost)
+
+ sv_amount_on_degrees(x_train, y_train, best.degree, best.cost)
+
+
+def transform_gisette_data(data_dir, save_dir, base_name):
+ os.makedirs(save_dir, exist_ok=True)
+ data_path = os.path.join(data_dir, base_name)
+ x = pd.read_csv(data_path + '.data', delim_whitespace=True, header=None).values
+ y = pd.read_csv(data_path + '.labels', delim_whitespace=True, header=None).values
+ save_libsvm(np.hstack((x, y)), os.path.join(save_dir, base_name))
+
+
+def preprocess_gisette(save_dir):
+ data_dir = 'gisette'
+ transform_gisette_data(data_dir, save_dir, 'gisette_train')
+ transform_gisette_data(data_dir, save_dir, 'gisette_valid')
+ call('./gisette_scale.sh')
+
+
+GisetteResult = namedtuple('GisetteResult', ['cost', 'error'])
+
+
+def liblinear_options(svm_type=1, cost=1.0, eps=0.0001):
+ return f"-s {svm_type} -c {cost} -e {eps} -q"
+
+
+def plot_gisette_errors_stdevs(errors_stdevs, log_costs):
+ errors, stdevs = zip(*errors_stdevs)
+ errors, stdevs = np.array(errors), np.array(stdevs)
+ plt.plot(log_costs, errors - stdevs)
+ plt.plot(log_costs, errors)
+ plt.plot(log_costs, errors + stdevs)
+ plt.xlabel("log2(C)")
+ plt.xticks(log_costs)
+ plt.legend(['error - stdev', 'error', 'error + stdev'], loc='upper right')
+ plt.show()
+
+
+def search_gisette_parameters(x, y):
+ best_result = GisetteResult(None, error=1.0)
+ log_costs = list(range(-15, 3))
+ errors_stdevs = []
+
+ for cost in map(lambda a: 2 ** a, log_costs):
+ error, std = kfold(x, y, liblinear_options(cost=cost), train, predict, k=5)
+ if error < best_result.error:
+ best_result = GisetteResult(cost, error)
+ errors_stdevs.append((error, std))
+
+ plot_gisette_errors_stdevs(errors_stdevs, log_costs)
+ return best_result
+
+
+def task2():
+ save_dir = 'gisette_tmp'
+ preprocess_gisette(save_dir)
+
+ y_train, x_train = svm_read_problem(os.path.join(save_dir, 'train_scaled'))
+
+ best = search_gisette_parameters(x_train, y_train)
+ print(best)
+ print(f"Validation accuracy: {100 * (1 - best.error)}%")
+
+ model = train(y_train, x_train, liblinear_options(svm_type=1, cost=best.cost, eps=0.001))
+ y_valid, x_valid = svm_read_problem(os.path.join(save_dir, 'valid_scaled'))
+ _, results, _ = predict(y_valid, x_valid, model, '-q')
+ print(f"Dev set error: {1 - results[0] / 100}")
+ print(f"Dev set accuracy: {results[0]}%")
+
+
+def main():
+ task1()
+ # task2()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/hw8 (SVM)/hw8_solution.ipynb b/hw8 (SVM)/hw8_solution.ipynb
new file mode 100644
index 0000000..2ffde8c
--- /dev/null
+++ b/hw8 (SVM)/hw8_solution.ipynb
@@ -0,0 +1,581 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Задание 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для выполнения кода необходимо, чтобы в текущей директории присутствовал файл 'spambase' с исходными данными, а также распакованная и скомпилированная библиотека libsvm в папке 'libsvm'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from collections import namedtuple\n",
+ "from subprocess import call\n",
+ "from libsvm.python.svmutil import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Разбиваем данные на тренировочную и тестовую выборки, сохраняем все файлы в нужном формате в созданной папке 'spam_tmp'. Затем выполняем масштабирование с помощью svm-scale на отрезок [0,1], т.к. значения всех признаков неотрицательные."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def save_libsvm(raw_data, filename):\n",
+ " x = raw_data[:, :-1]\n",
+ " y = raw_data[:, -1].astype(int)\n",
+ " lines = []\n",
+ " for features, label in zip(x, y):\n",
+ " sparse_features = [f\"{i + 1}:{value}\" for i, value in enumerate(features) if value != 0]\n",
+ " line = ' '.join([str(label)] + sparse_features) + '\\n'\n",
+ " lines.append(line)\n",
+ " with open(filename, 'w') as f:\n",
+ " f.writelines(lines)\n",
+ "\n",
+ "spam_save_dir = 'spam_tmp'\n",
+ "os.makedirs(spam_save_dir, exist_ok=True)\n",
+ "data = pd.read_csv('spambase', header=None).values\n",
+ "train, test = np.vsplit(data, [3450])\n",
+ "\n",
+ "save_libsvm(train, os.path.join(spam_save_dir, 'train'))\n",
+ "save_libsvm(test, os.path.join(spam_save_dir, 'test'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "cd spam_tmp\n",
+ "../libsvm/svm-scale -l 0 -s scaling_params train > train_scaled\n",
+ "../libsvm/svm-scale -r scaling_params test > test_scaled"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Функция kfold выполняет кросс-валидацию, возвращая среднюю ошибку и ее стандартное отклонение. Ошибка на каждой итерации вычисляется как 1 - accuracy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def split(index, arr, part_size):\n",
+ " validation_part = arr[index: (index + 1) * part_size]\n",
+ " train_part = arr[:index * part_size] + arr[(index + 1) * part_size:]\n",
+ " return train_part, validation_part\n",
+ "\n",
+ "\n",
+ "def kfold(x, y, parameters, train_func, predict_func, k=10):\n",
+ " part_size = len(y) // k\n",
+ " errors = []\n",
+ " for i in range(k):\n",
+ " x_train, x_valid = split(i, x, part_size)\n",
+ " y_train, y_valid = split(i, y, part_size)\n",
+ "\n",
+ " model = train_func(y_train, x_train, parameters)\n",
+ " _, results, _ = predict_func(y_valid, x_valid, model, '-q')\n",
+ " errors.append(1 - results[0] / 100)\n",
+ " return np.mean(errors), np.std(errors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Используем полиномиальное ядро и константу равную 1 (параметр -r, значение взял из примера на сайте), т.к. со значением по умолчанию 0 по непонятным причинам все объекты выборки присваиваются одному классу.\n",
+ " Выполняем кросс-валидацию для подбора наилучших параметров и строим графики ошибки от $log_{2}C$ со стандартными отклонениями для $d \\in \\{1,2,3,4\\}$ и $С \\in \\{2^{-k}, 2^{-k+1}, ..., 2^{k}\\}$, $k=5$. Также строим график сравнения ошибок для разных степеней полинома."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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J3LpSxRF5bamsa+W//vWvNd6tclPhVJu+iIwSkV0ikiwij1ewfKqIbBWRTSKyUkS62edHi0iBff4mEZlV029ANU3+Pp4cb96JmOISkjJ3uTocVUuGDRvGF198QVqaded1ZmYm+/dXfQ7HsVtlwOlulZuKao/0RcQdmAlcCaQC60VkoTHG8QLp+caYWfbyo4G/AacGiEwxxsTVbNhKgQnrSmxuCd8VZZJTnIO/l7+rQ1I1rLKulatTk90qNzbONO/0A5KNMXsARORTYAxwOukbYxwHzvQFtBcsVevC2kTTZpv1YzU5K5neYb1dHJGqDePGjTurHT43N/eM5/v27TvjeVxcHGvWrDlrXStWrKjp8BocZ5p3IoCDDs9T7fPOICL3i0gK8BfgQYdF7UVko4j8T0QGV/QCInK3iCSISEJ6evo5hK+asthWAVBkjZeq3TEo5Rxnkv7ZAz5WcCRvjJlpjOkIPAacGtniCBBljOkN/A6YLyIBFdSdbYyJN8bEh4aGOh+9atJiw/04XtwWv7Iydmu7vlJOcSbppwJtHZ5HAoerKP8pMBbAGFNkjMmwP94ApACx5xeqUmfqFObHbhNFTHExSRl6BY9SznAm6a8HYkSkvYh4AeOBhY4FRCTG4elvgCT7/FD7iWBEpAMQA+ypicCVau7lQZZfR6sPnqwUHVBFKSdUeyLXGGMTkWnAYsAdmGOM2S4iM4AEY8xCYJqIDAdKgBPAJHv1y4AZImIDSoGpxpjM2ngjqmlyC+tGTFYJOaWFHM07Smu/2h9YWqmGzKmbs4wx3wLflps33eHxQ5XU+xL48kICVKoqERERBB/zAawBVTTpK1U17XBNNWix4X6UFrUCYPeJ3S6ORin48MMPOXy44tOeK1as4JprrqnjiM6kSV81aDFh/hwojaK1rVSv4GnCaqN75cmTJ5/Xdf1VJf36QJO+atCsK3jaEltcTJKOotUoVdS1MuDy7pVLS0uZPHkyPXr0oGfPnrz22mt88cUXJCQkcOuttxIXF0dBQQGLFi2iS5cuDBo0iH/961+n6+fl5XHHHXfQt29fevfuzddffw1A//792b7916vRhg4dyoYNG847zvK0wzXVoPl4unMyIIZuxcWsyk2lpLQET3dPV4fVKL287mV2Zu6s0XV2admFx/o9VunyyrpWvu2221zevfKmTZs4dOgQ27ZtO73OFi1a8Pe//52//vWvxMfHU1hYyF133cUPP/xAp06dzriz+IUXXuCKK65gzpw5ZGVl0a9fP4YPH8748eNZsGABzz77LEeOHOHw4cNcfPHFFxSrIz3SVw2eZ3hXOhXbsJky9mTrFcGNiWPXynFxcSxbtow9e6zP+Fy7V/7xxx/PKlfe4sWLiYuLIy4ujoULF3LnnXcSFxdH//79zyrboUMH9uzZwwMPPMCiRYsICDjrvlN27txJ+/btiYmJQUSYOHHi6WVLlizhpZdeIi4ujqFDh1JYWMiBAwe4+eab+fzzzwFYsGDBGd1C1wQ90lcNXnTrUPxSrX+4pKwkOrfs7OKIGqeqjshrS2VdK4N1VF/T3SuPHDmSkSNHAlab/uTJkxk6dGiFZYOCgti8eTOLFy9m5syZLFiwgDlz5pxVTqSiTg2s9/bll1/SufPZ39fg4GC2bNnCZ599xjvvvOPUe3KWHumrBi8m3I+8ojZ4GL2Cp7E5n66VoW66Vz5+/DhlZWXccMMNPPfcc/zyyy8A+Pv7k5OTA0CXLl3Yu3cvKSkpAPzzn/88XX/kyJG89dZbp28q3Lhx4+ll48eP5y9/+QvZ2dn07NmzRuPWI33V4MWG+7PUtKVDyWGSMvQKnsaksq6V27VrV23d2u5e+dChQ9x+++2UlZUBnP41MnnyZKZOnUqzZs1YvXo1s2fP5je/+Q0hISEMGjTo9DmAp556iocffphevXphjCE6OppvvvkGgBtvvJGHHnqIp556qkZjBpD6dut6fHy8SUhIcHUYqgEpspXyf888g2/rT1gf0pbvxzXd8U9rWmJiIl27dnV1GKqcij4XEdlgjImvrq4276gGz9vDndyAGGKKizlWmEl2UbarQ1Kq3tKkrxqFZq0706HY+pmtfesrVTlN+qpR6NgqCM/CloB1BY+qOfWtCbipu9DPQ5O+ahQ6h/uTZoskoMzoFTw1yMfHh4yMDE389YQxhoyMDHx8fM57HXr1jmoUYsP9+KositiiFJIyavau0aYsMjKS1NRUdBjT+sPHx4fIyMjzrq9JXzUK0SG+pEhbYopLWJiVTJkpw030h+yF8vT0pH379q4OQ9Ug/a9QjYKnuxsFQbHElBSTV1rI4dz628uhUq6kSV81GoGtOhJVbN3yrlfwKFUxTfqq0YhpFUhpYTigV/AoVRlN+qrRiA3340BpWyJsZXoFj1KV0KSvGo2YcH9rQJWiQr2CR6lKOJX0RWSUiOwSkWQRebyC5VNFZKuIbBKRlSLSzWHZE/Z6u0RkZE0Gr5Sjdi2bkyJRxBSXsD/nIEWlRa4OSal6p9qkLyLuwEzgKqAbcItjUrebb4zpaYyJA/4C/M1etxswHugOjALetq9PqRrn4e5GcXAXYouLKaWMPVk6oIpS5TlzpN8PSDbG7DHGFAOfAmMcCxhjTjo89QVO3b43BvjUGFNkjNkLJNvXp1StCGsVSXiJN6Anc5WqiDNJPwI46PA81T7vDCJyv4ikYB3pP3iOde8WkQQRSdA7/9SFiG0VQE5ha7wM7M7Uk7lKledM0q9orK+zOuIwxsw0xnQEHgOePMe6s40x8caY+NDQUCdCUqpiMWF+JJsoOpbY9Fp9pSrgTNJPBdo6PI8Eqrrd8VNg7HnWVeqCdG7lz24TSUxRIUmZegWPUuU5k/TXAzEi0l5EvLBOzC50LCAiMQ5PfwOcOsRaCIwXEW8RaQ/EAOsuPGylKtY2qDn73KKILS4hvSiTE4UnXB2SUvVKtR2uGWNsIjINWAy4A3OMMdtFZAaQYIxZCEwTkeFACXACmGSvu11EFgA7ABtwvzGmtJbei1K4uQm2kK7EFJQAVncM/VrrtQNKneJUL5vGmG+Bb8vNm+7w+KEq6r4AvHC+ASp1rtq2Cidwtx9gXcGjSV+pX+kduarRiQn352hxBEFGtDsGpcrRpK8anc6t/EgyUcQUFpKkl20qdQZN+qrRiQnzZ2dZW2KLi0jOSqLMlLk6JKXqDU36qtGJaNGMAx7tiCkuoaC0iNScVFeHpFS9oUlfNTpuboJbaCwdi22ADqiilCNN+qpRat8qGO+SEATYnaXt+kqdoklfNUqx4f6k2CJoW6pH+ko50qSvGqWYcD92l7UlpjCfpMxdrg5HqXpDk75qlGLD/dll2hJbXMKBnIMU2ApcHZJS9YImfdUotQ70IdUzmtjiYsowOqCKUnaa9FWjJCL4hHUkutjqyVvvzFXKoklfNVqdWrWgyNYKH0RH0VLKTpO+arRiwv3ZWRpJx5JSPdJXyk6Tvmq0YsP92FXWltjCfO2DRyk7Tfqq0eoc7s9u05aY4hIyi05wvOC4q0NSyuU06atGK9Tfm8Ne7YktLgb0Ji2lQJO+asREhMDwdkSUeAKa9JUCTfqqkYtpFUBaaQQhxk1P5iqFJn3VyMWG+ZFoi6RTUbFetqkUmvRVIxfbyp9dJpLYonxSTiRTWlbq6pCUcimnkr6IjBKRXSKSLCKPV7D8dyKyQ0S2iMgyEWnnsKxURDbZp4U1GbxS1YkN92dXWRSxxSUUlRVzIOeAq0NSyqWqTfoi4g7MBK4CugG3iEi3csU2AvHGmF7AF8BfHJYVGGPi7NPoGopbKaeE+HmT3qw9MXoFj1KAc0f6/YBkY8weY0wx8CkwxrGAMWa5MSbf/nQNEFmzYSp1/kLD2xBU6ocbaLu+avKcSfoRwEGH56n2eZWZAnzn8NxHRBJEZI2IjK2ogojcbS+TkJ6e7kRISjmvcyt/9tkiaVfmzm69M1c1cR5OlJEK5pkKC4pMBOKBIQ6zo4wxh0WkA/CDiGw1xqScsTJjZgOzAeLj4ytct1LnKybcn+2lkcQUbiBRj/RVE+fMkX4q0NbheSRwuHwhERkO/AkYbYwpOjXfGHPY/ncPsALofQHxKnXOYsP82GXaElNUSGpOKvkl+dVXUqqRcibprwdiRKS9iHgB44EzrsIRkd7AO1gJP81hfpCIeNsfhwADgR01FbxSzogN92d3WSSxxSUYDMlZya4OSSmXqTbpG2NswDRgMZAILDDGbBeRGSJy6mqcVwA/4PNyl2Z2BRJEZDOwHHjJGKNJX9WpIF8vMn070sV+Bc+6o+tcHJFSruNMmz7GmG+Bb8vNm+7weHgl9X4Gel5IgErVhKhWIcjhUC5x8+eTxE/4bbff4u3u7eqwlKpzekeuahJiwvzZURrJlLwSjhcc5+vkr10dklIuoUlfNQmx4f4klkbQL20PPYO788G2D7CV2VwdllJ1TpO+ahJOjaLlVmZjStQoUnNTWbJviavDUqrOadJXTUJMuD+7jHXl8eXF0DGwI+9vex9j9LYQ1bRo0ldNQmAzT3L8OnLEuz1ua97mju63s/vEbn469JOrQ1OqTmnSV01Gp1YBfOhxMxzfxVWFxbTxbcN7W99zdVhK1SlN+qrJiA33Z272RZiQznj++CqTut3GxrSNbDi2wdWhKVVnNOmrJqNzuD8FNjje50FIT+S6Ui9a+rTUo33VpGjSV01GTLgfAAl+QyE4hmYrX2dil1tZeWglOzN3ujY4peqIJn3VZHRtHUCInzdz16TCZf8Hx7Yxzi0IX09f3t/6vqvDU6pOaNJXTYaPpzv3X96R1XsyWNVsCLTsQMCqNxkXO44l+5dw4KQOpagaP036qkm5pV8UrQN9eGVpCmbw7+HoFn7r1QYP8WDOtjmuDk+pWqdJXzUpPp7uPDgshk0Hs/jBcygERROy+m2u6zSWhSkLSctPq3YdSjVkmvRVk3PjxZG0C27OX5ftoWzQo3BkE5N9O1Jmypi7fa6rw1OqVmnSV02Op7sbjwyPJfHISb5zHwItoohc+x6jokexYPcCsouyXR2iUrVGk75qkq69qA0xYX68umwvpQN/B4c2MCWgOwW2AubvnO/q8JSqNZr0VZPk7iY8OiKWPel5/JshENiWmISPGBo5hPmJ83UcXdVoadJXTdbI7q3oERHAaz/swzbgYUhdz5SWvckqyuLLpC9dHZ5StUKTvmqyRIRHR3Qm9UQBn5UOgYAI4n5ZQHx4PB9t/4iS0hJXh6hUjdOkr5q0obGhxLcL4s0V+ym59CE4uIY7Q/txLP8Y3+z5xtXhKVXjnEr6IjJKRHaJSLKIPF7B8t+JyA4R2SIiy0SkncOySSKSZJ8m1WTwSl0oEeH3Iztz7GQR84ouA//WDNiykK4tuzJn2xxKy0pdHaJSNarapC8i7sBM4CqgG3CLiHQrV2wjEG+M6QV8AfzFXrcl8DTQH+gHPC0iQTUXvlIX7pIOwQyOCeGtn1IpuuRBZP/PTGk1kH0n97HswDJXh6dUjXLmSL8fkGyM2WOMKQY+BcY4FjDGLDfGnLrcYQ0QaX88ElhqjMk0xpwAlgKjaiZ0pWrOoyM6k5lXzPt5g8EvnOHblxAdEM17W9/TIRVVo+JM0o8ADjo8T7XPq8wU4LtzqSsid4tIgogkpKenOxGSUjUrrm0LhncN5x8/H6ag3wO471vJ7a0Hk5iZyOrDq10dnlI1xpmkLxXMq/DQR0QmAvHAK+dS1xgz2xgTb4yJDw0NdSIkpWreoyNiySm0MSt3MPiGce2ulYQ1D+O9bTrIimo8nEn6qUBbh+eRwOHyhURkOPAnYLQxpuhc6ipVH3RtHcA1vVrz7pqj5MXfh+fe/zGpzVDWH13P5vTNrg5PqRrhTNJfD8SISHsR8QLGAwsdC4hIb+AdrITv2E3hYmCEiATZT+COsM9Tql565MpYCktKeTP7Mmgewo0pGwj0DtQhFVWjUW3SN8bYgGlYyToRWGCM2S4iM0RktL3YK4Af8LmIbBKRhfa6mcBzWDuO9cAM+zyl6qWOoX7c0CeSDxLSONlnKs33LOfWNkNZcXAFSSeSXB2eUhdM6tuVCfHx8SYhIcHVYagm7GBmPle8uoJbewfzTMp4sttH159mAAAgAElEQVTEcaXbIYZFDePFwS+6OjylKiQiG4wx8dWV0ztylSqnbcvmjO8bxbxfMsiKu4fAlB+4qc1lfLf3O1JzUl0dnlIXRJO+UhWYdkUn3N2ElzMvg2ZB3HZ4DyLCn9f+meLSYleHp9R506SvVAXCA3yYNCCaz7acIKPnnYQnLeOJ2In8dOgnHl7+MEWlRdWvRKl6SJO+UpWYOqQjzTzdeeH4YPAJ5OY9G3j60qdZeWglDyx7gAJbgatDVOqcadJXqhItfb2YMqg9/9qRy7Fud8Cu/3KjfywzBs5gzZE1TFs2TQdbUQ2OJn2lqnDnZR0IbObJjPQh4B0I/76Xsa0H8+fBfybhWAL3fn8veSV5rg5TKadp0leqCgE+ntwzpAP/Tcpn99CZkJEMc8dwTasBvDz4ZTanb+aepfeQU5zj6lCVcoomfaWqMXlANCF+Xjy9NRRu+Scc3w0fjWZUeD/+OuSvbD++nXuW3kN2UbarQ1WqWpr0lapGcy8P7hvaidV7MlhZ1uvXxD93NMND+/Da5a+xM3Mndy25i6zCLFeHq1SVNOkr5YQJ/aOIDGrGw59tYk9gf7hlPqTvhrljGBrcizcuf4OUrBSmLJlCZqH2NKLqL036SjnBx9OdD2/vCxgmvLuW/UGXwvj5kL4L5o5hcMvu/H3Y3zlw8gB3LLqD4wXHXR2yUhXSpK+UkzqF+TPvzv4U2UqZ8O5aUkMG2BP/Tvh4LJe26Mzbw9/mcN5hbl90O2n5adWvVKk6pklfqXPQpVUAH0/pT05hCRPeXcvRsEFW4k9LhI/H0jewE7OGzyItP43bF93O0byjrg5ZqTNo0lfqHPWICGTulP5k5hUz4d01pLUaDOM+sRL/3LH0CWjPO1e+Q2ZhJpMXTeZQ7iFXh6zUaZr0lToPcW1b8OHtfTl6spBb311LRpshMG4epO2Aj68jzj+ad0e8y8nik9y+6HYOnjxY/UqVqgOa9JU6T/HRLXlvUjwHMvOZ+P46siIvh5s/hqPb4OOx9PCN5P0R75Nvy2fy4snsP7nf1SErpUlfqQsxoGMI794WT0paLrfNWcfJdsOsI/6j2+Dj6+javDXvj3gfW5mNSd9NIjEj0dUhqyZOk75SF+iy2FD+MbEPiUdOMnnOOnKjh8O4j+HoVph3PZ2bhfPBqA/wcvfi9sW3s+bIGleHrJowTfpK1YBhXcN565Y+bE7N5o4P15Pf/kq4eS4c2QLzrqeDdzAfX/UxrX1bc+/39/Ld3u9cHbJqojTpK1VDRvVoxevj4kjYl8ldcxMo7DgSbv7ISvwfX0e4uw8fXfURF4VexB9+/APzdsxzdciqCXIq6YvIKBHZJSLJIvJ4BcsvE5FfRMQmIjeWW1YqIpvs08KaClyp+ujai9rwyo0X8XNKBlPnbaCo0yh74t8MH11LQMFJ3rnyHYZFDePl9S/z2obXMMa4OmzVhFSb9EXEHZgJXAV0A24RkW7lih0AJgPzK1hFgTEmzj6NvsB4lar3brg4khev68mKXelMm7+RkpirrBu4MvbAO0PwPrCOV4e8yk2xNzFn2xyeXPUkJWUlrg5bNRHOHOn3A5KNMXuMMcXAp8AYxwLGmH3GmC1AWS3EqFSDM75fFDPGdGfpjmM8/OkmbB2vhLt+gGZBMHcM7utm81T/J7kv7j4WpizkwR8e1FG4VJ1wJulHAI53lqTa5znLR0QSRGSNiIw9p+iUasBuuzSaJ3/Tlf9uPcLvP99MaXCMlfhjR8Gix5F/T+XerpOYful0fj78M3cuuZMThSdcHbZq5JxJ+lLBvHNphIwyxsQDE4DXRaTjWS8gcrd9x5CQnp5+DqtWqn67c3AH/m9kZ/696TCPfLaJXGluXcd/+Z9gywKYM5KbQvvxt6F/Y/eJ3dz23W3abYOqVc4k/VSgrcPzSOCwsy9gjDls/7sHWAH0rqDMbGNMvDEmPjQ01NlVK9Ug3H95J/5vZGf+s+UwV73xI+sPZMGQP8CEz+DEfpg9lGElbsy+cjYZhRlM/HYiuzJ3uTps1Ug5k/TXAzEi0l5EvIDxgFNX4YhIkIh42x+HAAOBHecbrFIN1f2Xd2LBPZcCcPM7q3nxu0SKOgyHu5eDXxjMu54+ySuZO+oj3MWdyYsms/7oehdHrRqjapO+McYGTAMWA4nAAmPMdhGZISKjAUSkr4ikAjcB74jIdnv1rkCCiGwGlgMvGWM06asmqW90S7576DLG923LO//bw5i/ryKxOBTu/B66XANLn6LT939m3vDZhDUP456l97Bk3xJXh60aGalv1wjHx8ebhIQEV4ehVK1alniMx77cysmCEn43Ipa7BrXH/efXYdkMCOtG9vX/YNovf2Vz+mae6P8Et3S5xdUhq3pORDbYz59WSe/IVcoFhnUNZ/HDg7miSxgvfbeT8e+u4WD3qXDrF3DyEIEfjmZ2x1sYEjmEP6/9M2/88galZaWuDls1Apr0lXKRYD9v/jGxD6/edBE7j+Qw6vUf+SwrFnPXcgiIoNk/J/CaZxQ3dLqe97a+x22LbmNv9l5Xh60aOE36SrmQiHDDxZEseuQyekW24LEvt3LXNxmkj/8GelyPxw/P8/TBFF685Gn2Ze/jpv/cxEfbP9KjfnXetE1fqXqirMzwwc/7eHnRTvy8Pfjz2B6MyvkClk6HkFjSr3mVGcmfsSJ1Bb3DevPcwOdoF9DO1WGrekLb9JVqYNzchCmD2vPfBwYR0aIZUz/5hUdTLyNv3BeQn0no3Ot507cHLwx8nuSsZG5ceCPzdsyjzGjvJ8p5mvSVqmdiwv35130DePCKTvx70yFG/FtYf9V/oMMQZNEfGL32E74a/h59W/Xl5fUvc/ui2zlw8oCrw1YNhDbvKFWPbTxwgt8t2My+jDweuqITD/ovx23pdPAJxIx9m68lj5fXvUypKeWhPg9xS5dbcBM9lmuKtHlHqUagd1QQ/31wENf1juD1ZclM2t6b7ImLoXlL5JMbGZuynq+u+Yw+4X14ad1LTFk8hYM5B6tfsWqy9EhfqQbAGMOn6w/y9MLtBPt6MfPmbvTZ+Sqsfxda9cRc/z5fZW/nL+v/Qpkp49GLH+WmzjfpUX8Tokf6SjUiIsIt/aL4170D8HAXbn5/I3MC78eMnw/Zh5DZQ7g+O5uvrv0XcaFxPL/2ee5ecrf22KnOoklfqQakR0Qg3zwwmMu7hDHjmx3cv6EVuXf8D6L6wzcP0/q//8c7A19k+qXT2Xp8K9d/fT0Ldi3QK3zUaZr0lWpgApt5Mvu3F/PEVV1YvP0Y1360h51XfgQjnofdi5FZg7jJM5yvxnxFz9CePLfmOcZ9M46Vh1bqeLxKk75SDZGIcM+Qjsy/sz95RTbGvr2aL7yvs3rs9GoOH42mzZp3efeKt3lp8EvkFOdw7/f3csfiO9icvtnV4SsX0hO5SjVwaTmFPPTPTazek8H4vm15ZlQ0Psv+BL/MhTZ94Ib3KGkRxRdJXzBr8ywyCzO5ou0VPNjnQTq2OGsgO9VAOXsiV5O+Uo2ArbSM177fzczlKXRrHcA/Jvah3bHvYeGDUFoCAx+ES+4l392Tj3d8zAfbP6DAVsDojqO576L7aO3X2tVvQV0gTfpKNUE/7DzGI59tpqzM8MpNFzGqrQ0WPQ6J/wGfFlby73cPJ0wJ7219j093forBML7LeO7qeRdBPkGufgvqPGnSV6qJOpiZz/3zf2FLajZ3DW7PH0Z1wTNtKyz/M+xeBM1DYNDDED+FI8XZ/GPzP/g65WuaeTRjcvfJ3NbtNpp7Nnf121DnSJO+Uk1Yka2U579J5OM1+4lvF8SzY7rTvU0gHFwPy1+APcvBLxwGPwoXTyYlN5W3Nr7FsgPLaOnTknt63cNNsTfh6e7p6reinKRJXynF15sO8eRX28gpsjG8azgPDutEr8gWsP9n+OEF2L8SAiLgst9D3EQ2n0jkjV/eYP3R9UT4RTCt9zSubn+13tnbAGjSV0oBkF1Qwoer9vH+yj2cLLRxeedQHhgWQ5+2LWDv/6zkn7oOWkTBkMcwPcfx87F1vP7L6+zM3EmnFp2496J7Gd5uuCb/ekyTvlLqDDmFJcxdvZ93f9pDVn4Jg2NCeHBYDH3bBUHy9/DD83BkE7TsCEMfp6z7dSw5sIy3N7/N3uy9xAbFcl/cfVzR9gpExNVvR5VTo33viMgoEdklIski8ngFyy8TkV9ExCYiN5ZbNklEkuzTJOffglKqJvn7eHL/5Z1Y9dgVPHFVF3YcPslNs1Zzy7trWe3Wxxqbd/x88GwG/7oLt1mDGJVfwFfXfMmLg1+kqLSIh5c/zLhvxrHi4Aq9u7eBqvZIX0Tcgd3AlUAqsB64xRizw6FMNBAA/B5YaIz5wj6/JZAAxAMG2ABcbIw5Udnr6ZG+UnWjoLiUT9bu550f95CeU0Tf6CAeHBbDoI4tkcSFsPxFOL4L2vSGkX/G1rYf3+79ln9s+gepual0D+7O/XH3MyhikB751wM1eaTfD0g2xuwxxhQDnwJjHAsYY/YZY7YA5Xt1GgksNcZk2hP9UmCUU+9AKVWrmnm5c+fgDvz0h8t5dnR3DmYW8Nv313H9rDUsdx+IufdnGDsLctPgg6vw+Hwyo1t0Z+F1C5kxYAZZRVnct+w+Jn43kZ8P/6xH/g2EM0k/AnAclSHVPs8ZTtUVkbtFJEFEEtLT051ctVKqJvh4ujNpQDT/+8NQnh/bg7STRdz+4XpGv72GpV5XYKath8ufhOQfYGZ/PJdM57qIIfxn7H+Yful00vLTuGfpPUxeNJl1R9a5+u2oajiT9Cv63ebsLt2pusaY2caYeGNMfGhoqJOrVkrVJG8PdyZe0o7lvx/Kyzf0JLughLvmJjBuzhZ2d5kKD26EuAmwdha82RvPde9yU4cx/Pe6//Kn/n8iNTeVKUumcPui20k4qk209ZUzST8VaOvwPBI47OT6L6SuUsoFvDzcGNc3ih8eHcKL1/dkd1oOV7/xEy+uzCR/1N9g6kqI6AOLn4C3++O1ezHjO4/j2+u/5fF+j7Pv5D5uX3w7dy65k0X7FrH/5H7tz78eceZErgfWidxhwCGsE7kTjDHbKyj7IfBNuRO5G4A+9iK/YJ3Izazs9fRErlL1S2ZeMS99l8iChFTaBPrw9OjujOgWjiQvgyVPQnoiRA2AkS9ARB8KbYUs2LWA97e9T2ah9a/ezKMZsUGxdA7qTOeW1hTTIka7e6hBNXqdvohcDbwOuANzjDEviMgMIMEYs1BE+gJfAUFAIXDUGNPdXvcO4I/2Vb1gjPmgqtfSpK9U/ZSwL5Mn/72NnUdzGNYljGdGd6dtoBdsnGv165OXDj1vhmHToUVbSkpLSMpKYlfmLnad2GX9zdxFTkkOAILQLqAdsUGxdGnZxdoZBHUmrHmYXg10HvTmLKVUjSspLePDVft47fvdlBnDA1fEcNfgDnjZcmHV67B6plXw0vth0CPg7X9GfWMMR/KOsDNz5xk7gtTc1NNlWni3oHNQZ3qE9GBgxEDiwuLwdNM+gKqjSV8pVWsOZxUw4z87WLT9KJ3C/HhuTA8u7RgMWQdh2QzYugB8Q6H/PRB7FYR3hyqO3nOLc9l9Yjc7M3ee/rsrcxc2Y8PP049L21zKoIhBDGwzkHDf8Dp8pw2HJn2lVK1bvjON6Qu3cTCzgOt6R/DHq7sS6u8NhzbA0qdh309WwYBIiB0BMSOh/WXWkI7VyC3OZe2Rtfx06CdWHlrJsfxjAMQGxTIoYhCDIwZzUdhF+ivATpO+UqpOFJaUMnN5MrP+l0IzT3f+b1QXJvSLwt1N4OQRSF4KuxdDynIoyQMPHyvxx4yA2JFWR2/VMMaQlJXEykMrWXloJRuPbTzrV8CgiEGENQ+rg3dcP2nSV0rVqZT0XJ769zZ+TsngoshAXriuJz0iAn8tYCuC/atg9xJIWgyZe6z5oV2tXwGxoyCyH7h7VPtajr8Cfjr0E2n5aQB0DupsNQNFDKR7cPcmdXWQJn2lVJ0zxrBw82Ge+yaRzLwixvaOYOIl7ejdtsXZV+QcT7ZG8kpabPXvX2azhnTsNMxqBuo0DHxDnHrNU78Cfkr9iU1pm7AZG4IQHRhNt+BudGvZjW7B3ega3BVfT99aeveupUlfKeUy2QUlvPF9Ep+tP0BecSldWvlza/8oxvSOIMCngjb4wpPWaF67F0PSEuvyT7B+BbQbYJ8GQkD1A7jnFufyS9ovbM/Yzo6MHezI2HH6l8Cpy0S7BXc7PXVt2RU/L7+afPsuoUlfKeVyuUU2vt50iPlrD7D98Emaeboz+qI2TOgfRa/IwIqvxy8rgyMbYc8K6xfAgTVQnGsta9nh1x1AuwHQol2VVwWdcrzgODsydlS5I+ga3JXuwd1P7wwa2i8CTfpKqXrDGMOW1Gzmrz3Aws2HKSgppXubACb0j2JMXAR+3lW045fa4OgWawew/2frvEBhlrUsIPLMXwIhMU7tBODXHYHjdOoKIUHo2KIjPUJ60DOkJz1CehATFFOvrxTSpK+UqpdOFpbw9cZDfLL2ADuP5uDr5c7ouAhu7R915onfypSVWV0/nNoB7FsFedZRO76h1g4g6lLr3oCwbk6dFzgloyCDHRk72JaxjW3Ht7E1fSsniqzhP7zdvenSssvpnUDPkJ609W9bb+4e1qSvlKrXjDFsPJjF/LUH+GbLYQpLyugVGciEflFce1EbfKs6+j9zRZCRYu0ATu0Ish16dPcNhdAu1g4grKv9bxfwqX4HY4zhcN5hth7fyrb0bWw9vpXEzEQKbAUABHgF0DOkJ91Dup/eGYQ0c34nU5M06SulGozsghK++iWV+esOsPtYLn7eHozs3oq4qBb0jAikSyt/fDzdnV/hySPWr4G0REjbAWk7rccleb+WCYiw7wS6WieMw7pCaGfwqrot31ZmIyUrxfolcHwr245vIykr6XRPon6efoQ1DyO0eShhzex/m4cR2sz6G9Y8jJBmIXi5e53PpqqUJn2lVINjjGHD/hPMX3uA5bvSOJFfAoCHmxAb7k/PiEB6RAae346grMz6BZCWeOYOIX03lBbZCwkERUNkX4i6xGoqCukMblX3Qp9fks/OzJ1sO76NQ7mHSC9IJy0/jfT8dNIK0rCV2c6qE+QddHqHcGqn0CGwA1d3uNr59+RAk75SqkEzxnAoq4Bth7LZkprN1kPZbDuUXbM7ArBOFJ/YZ98B7ISjW+HgWsi1Turi08LaAURdak1t4sDD2+nVl5kysouySctPs3YEjjuE/DTSCqzHGYUZXBR6EXOvmntu8dtp0ldKNTrO7gi6twmgQ6gf0cHNaRfsS3RIc5p7OXmOwHohOLHXulz01GWjGUnWMg8fiLj41x1B235OnR+ojq3MRl5JHoHe57cuTfpKqSbBcUew1b4zSDySw/HcojPKhfl7Ex3ie3pH0D7El3bBzYkO9nXupHFuOhxcA/tXw4HVcGQzmFJAILyHvTnoUqs5KKANNAty+vLRmqBJXynVpOUW2dh3PI/9Gfnsy8hj3/E8629GPuk5Z+4QQv29ibbvAKJDfIlvF0TvqCC8PKpoyy/Og9T1v/4aSF0PJfm/LvfwAf9W4N/GupPYv7W1M3D8698aPGrmhK4mfaWUqkRukY39GdYOYe/xPPZn5LHvuLVzSLPvEJp5utO3fUsGdAxmYMcQurUJsHoOrUxpCRzbBpl7IeeINZ089few9ddWeHa95iH2nUIb63zB5X88u4wTnE3659DIpZRSjYOftwfd2wTSvc3Z7efZBSWs3ZPBzykZrEo+zkvf7QQgsJknl3YIZmCnYC7tGELHUN8zb8xy94Q2va2pIsZAwQmHncHhs/+m1exlnBXRI32llKpC2slCVu+xdgCrkjM4lGXdmNUqwIcBHYMZ0CmEgZ2CaR3YzKVxavOOUkrVMGMMBzLzWZWcwc8px1mdkkFGXjEA7UN8GdAxmC6tA2gV4EPrQB9aBfrQsrkXblU1C9WQGm3eEZFRwBuAO/CeMealcsu9gbnAxUAGMM4Ys09EooFEYJe96BpjzFRn34RSStUnIkK7YF/aBfsyoX8UZWWGXcdyWJV8nJ9TMvj3xkPkrT1wRh1PdyHcvhMIP70zaEarAGun0CrQhzB/bzzdq74BrKZUm/RFxB2YCVwJpALrRWShMWaHQ7EpwAljTCcRGQ+8DIyzL0sxxsTVcNxKKeVybm5C19YBdG0dwJ2DO1BaZsjILeJIdiFHTxZyNLuQI9mFHDtZyJFs67LSpTuOUWQrO2M9IhDq502/9i35+4Q+tRqzM0f6/YBkY8weKzj5FBgDOCb9McAz9sdfAH+X+tL1nFJK1RF3NyEswIewAB8uqqSMMYbsghJrx2DfOViPC6xB5WuZM0k/AnDoso5UoH9lZYwxNhHJBoLty9qLyEbgJPCkMean8i8gIncDdwNERVU/SLJSSjVUIkKL5l60aO5F19YBdf76zjQiVXTEXv7sb2VljgBRxpjewO+A+SJy1rs0xsw2xsQbY+JDQ0OdCEkppdT5cCbppwJtHZ5HAocrKyMiHkAgkGmMKTLGZAAYYzYAKUDshQatlFLq/DiT9NcDMSLSXkS8gPHAwnJlFgKT7I9vBH4wxhgRCbWfCEZEOgAxwJ6aCV0ppdS5qrZN395GPw1YjHXJ5hxjzHYRmQEkGGMWAu8DH4tIMpCJtWMAuAyYISI2oBSYaozJrI03opRSqnp6c5ZSSjUCzt6cVTd3AyillKoXNOkrpVQToklfKaWakHrXpi8i6cD+C1hFCHC8hsKpSRrXudG4zo3GdW4aY1ztjDHV3uhU75L+hRKRBGdOZtQ1jevcaFznRuM6N005Lm3eUUqpJkSTvlJKNSGNMenPdnUAldC4zo3GdW40rnPTZONqdG36SimlKtcYj/SVUkpVQpO+Uko1IQ0y6YvITSKyXUTKRCS+3LInRCRZRHaJyMhK6rcXkbUikiQin9l7D63pGD8TkU32aZ+IbKqk3D4R2WovV+udDonIMyJyyCG2qyspN8q+DZNF5PE6iOsVEdkpIltE5CsRaVFJuTrZXtW9fxHxtn/GyfbvUnRtxeLwmm1FZLmIJNq//w9VUGaoiGQ7fL7Tazsu++tW+bmI5U379toiIrU7JqD1mp0dtsMmETkpIg+XK1Mn20tE5ohImohsc5jXUkSW2vPQUhEJqqTuJHuZJBGZVFGZc2KMaXAT0BXoDKwA4h3mdwM2A95Ae6z++90rqL8AGG9/PAu4t5bjfRWYXsmyfUBIHW67Z4DfV1PG3b7tOgBe9m3arZbjGgF42B+/DLzsqu3lzPsH7gNm2R+PBz6rg8+uNdDH/tgf2F1BXEOBb+rq++Ts5wJcDXyHNeDSJcDaOo7PHTiKdQNTnW8vrB6H+wDbHOb9BXjc/vjxir7zQEus7uhbAkH2x0EXEkuDPNI3xiQaY3ZVsGgM8KmxBm/ZCyRjjfF7mn3s3iuwxvIF+AgYW1ux2l/vZuCftfUateD0uMjGmGLg1LjItcYYs8QYY7M/XYM1WI+rOPP+x2B9d8D6Lg2zf9a1xhhzxBjzi/1xDpCINVRpQzAGmGssa4AWItK6Dl9/GJBijLmQu/3PmzHmR6xu5x05focqy0MjgaXGmExjzAlgKTDqQmJpkEm/ChWN51v+nyIYyHJIMBWVqUmDgWPGmKRKlhtgiYhssI8VXBem2X9iz6nkJ6Uz27E23YF1VFiRuthezrz/M8aFBhzHha519uak3sDaChZfKiKbReQ7EeleRyFV97m4+js1nsoPvFyxvQDCjTFHwNqhA2EVlKnx7ebMwOguISLfA60qWPQnY8zXlVWrYJ6z4/meMydjvIWqj/IHGmMOi0gYsFREdtqPCs5bVXEB/wCew3rPz2E1Pd1RfhUV1L3ga3ud2V4i8ifABnxSyWpqfHtVFGoF82rte3SuRMQP+BJ42BhzstziX7CaMHLt52v+jTViXW2r7nNx5fbyAkYDT1Sw2FXby1k1vt3qbdI3xgw/j2rOjOd7HOunpYf9CK2iMjUSo1jjBV8PXFzFOg7b/6aJyFdYTQsXlMSc3XYi8i7wTQWLnNmONR6X/STVNcAwY2/QrGAdNb69KnAu40KnisO40DUcx1lExBMr4X9ijPlX+eWOOwFjzLci8raIhBhjarVzMSc+l1r5TjnpKuAXY8yx8gtctb3sjolIa2PMEXtTV1oFZVKxzjucEol1LvO8NbbmnYXAePuVFe2x9tjrHAvYk8lyrLF8wRrbt7JfDhdqOLDTGJNa0UIR8RUR/1OPsU5mbquobE0p1456XSWv58y4yDUd1yjgMWC0MSa/kjJ1tb3Oe1zoWojlNPs5g/eBRGPM3yop0+rUuQUR6Yf1P55Ry3E587ksBG6zX8VzCZB9qmmjDlT6a9sV28uB43eosjy0GBghIkH2ptgR9nnnr7bPWtfGhJWsUoEi4Biw2GHZn7CuvNgFXOUw/1ugjf1xB6ydQTLwOeBdS3F+iDUusOO8NsC3DnFstk/bsZo5anvbfQxsBbbYv3Sty8dlf3411tUhKXUUVzJW2+Um+zSrfFx1ub0qev/ADKydEoCP/buTbP8udaiDbTQI66f9FoftdDUw9dT3DJhm3zabsU6ID6iDuCr8XMrFJcBM+/bcisNVd7UcW3OsJB7oMK/OtxfWTucIUGLPXVP4//buJ7TnOI7j+PPlXykHWUvKwWEthTiQNCcH5cao5UDRzpK0cpCiKOMgtRS5LQfhYC2HORglipKNA5fdFielxsHeDp/Pz74Wv239/Pzk83pctt/n+/ns912t9+/T57vP65OeAT0E3uWvq3LfrcCNytij+e/sPXCk0XtxDIOZWUH+t+UdMzOrw0XfzKwgLvpmZgVx0TczK4iLvplZQVz0rRiSPjcwdlApdXMsx1csrVzbW01nlHQ49xuX9EbSydx+SdKuxn4Ls8a46E15aBEAAAHZSURBVJvNzyCwHtgELAd6K9f6gAEASXuA48DuiNhASlb8lPtdJaUpmrWMi74VJ+8K7c+z8deSenL7orwNf1zSkKRhSQcgbdGPjLQZa20e0wl8jZlt+6dI0dW1WIIvEXE9fz8BtEn6Vf6Q2V/hom8l6ga2AJtJURn9OZ6iG1hHms33AjtmD8zLOoeAB7mpixTaVbMReFHnvV/mMWYt4aJvJdoJ3IqIb5FCuB4B23L77YiYjohJUkbTbAPAaEQ8zq/XAB8X8N4fSNESZi3hom8l+t1hJ3UPQZF0BmgHTlSap0g5PDXj1ElVzX2n5nGPZk3hom8lGgV6JC2W1E46yu458ATYn9f2V1OJtJXUSzrF6GBETFd+1lugo/L6AnCxtm6fE1+PVa530uQkVbN6/tk8fbMmukdar39FSq3si4hJSXdIx+qNkRI2nzHznzfXgAngaU7ivRsRZ0kfIJclKT/nHc4fGCM5sjeAm/DjeUAH0LQD3c3m4pRNswpJKyKdotRGmv135fX9emOuAPcjYmSOfvtIB5uf/nN3bLYwnumb/WxI0kpgGXBuroKfnQe2z6PfEtLxlGYt45m+mVlB/CDXzKwgLvpmZgVx0TczK4iLvplZQVz0zcwK8h0DwRNAxt+vGQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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KCwtrIpxaT5O+qhUaeVqYOKwjm9OOM8dtCIT1hgUT6RfQnv/r/H/M3j6bxSmLXR2mcoFZs2bRp08fYmNjueuuu7DZbPj4+DB58mT69u3LH3/8QVRUFFOmTOGCCy7gm2++ISEhgfPOO49u3bpx1VVXcfToUQAGDhzIU089xYABA3j77bddvGWuUW+6YVB134juIXz2RwqvLErminFv0uSji2H+E9x/1QesObCGp39/ms5BnQnxCXF1qA3T/Ilw4M/qXWerrjDspXIXl9e18rnuVrk+0TN9VWuICJOHx3Ak28o7f7rDRY/Bpjl47PiZVy56hSJTxMRfJ1JY1DB/ljdEJbtWjo2NZenSpezcubPK3SovX778tHINlZ7pq1qlW1gA1/YKY+aKXYx54C5ab/4v/Pgw4fetYtJ5k5j460Q+2PABE3pMcHWoDU8FZ+Q1pbyulV977bVq71a5oXDqTF9EhorINhFJFpGJZSy/W0T+FJEEEflNRGIc86NEJNcxP0FEPqjuDVD1z+NDO+BpceOFBTtgxFQ4ngZLnuWKNlcwsu1IZmycwb7sfa4OU50Dl156KXPmzOHQoUMAZGRkkJKSUmGdkt0qA053q9xQVJr0RcQCTAOGATHADSeTeglfGGO6GmNigVeAN0os22GMiXW87q6uwFX91cLXmwmXRLMk8SC/5kVB37thzYewZyU3dLoBg2HzkbIHsFD1S8mulbt168Zll13G/v37K633ySef8Pjjj9OtWzcSEhKYPHnyOYi2bnCmeacPkGyM2QkgIl8BI4EtJwsYY0oOnNkE0Gfn1Vm59YIovlqzhyk/bGH+PU/hvvUnmHs/bW9bjJu4kZSZxGAGuzpMdQ5cf/31p7XDZ2dnnzK9e/fuU6ZjY2NZuXLlaetatmxZdYdX5zjTvBMK7C0xneqYdwoRuU9EdmA/03+gxKLWIrJeRP4nIheW9QEicqeIxItI/OHDh6sQvqqvvNwtPHV5J5IOZfP5+gwY/hYc2Y73H+8S4RtB0tEkV4eoVJ3kTNI/fcDHMs7kjTHTjDFtgSeAfzhm7wcijDE9gEeAL0TEr4y6M4wxccaYuObNmzsfvarXBse0pH+7IN5YvJ2jwRdC9xvgtzeJbtRSk75SZ8iZpJ8KhJeYDgPSKij/FTAKwBiTb4xJd7xfC+wA2p9ZqKqhEREmX9mZrLwC3lqyHYb8E7wDiN73J3uz9pJTkOPqEJWqc5xJ+muAaBFpLSKewBhgbskCIhJdYvIKIMkxv7njQjAi0gaIBnZWR+CqYejQypeb+kYya9Ueth33gEHP0D49BYNh5zH9p6RUVVWa9I0xhcAEYCGQCMw2xmwWkSkiMsJRbIKIbBaRBOzNOOMc8y8CNorIBmAOcLcxJqPat0LVa49c1h4fL3ee+3ELJuoCoq0FANrEo9QZcOrhLGPMPGBeqXmTS7x/sJx63wLfnk2ASgU28eThQdE888MWluyP5FK3RjTCje1Ht7s6NKXqHO2GQdUJN50XSbsWPjw/LxFaxNDWWPRMX9VKH3/8MWlpZV/2XLZsGVdeeeU5juhUmvRVneBhcWPSlTGkpOewxUQQnZNNUqae6Su7muheefz48Wd0X39FSb820KSv6owB7ZtzaccWzEkNJDovh4y8oxzJPeLqsFQNK6trZcDl3SvbbDbGjx9Ply5d6Nq1K2+++SZz5swhPj6em266idjYWHJzc1mwYAEdO3bkggsu4D//+U9x/RMnTnDrrbfSu3dvevTowffffw9A37592bz5ryfOBw4cyNq1a884ztK0wzVVp9x+YRte2RbKMKsVsF/MbdaomYujahheXv0yWzO2Vus6OzbtyBN9nih3eXldK99yyy0u7145ISGBffv2sWnTpuJ1BgQE8O677/Laa68RFxdHXl4ed9xxBz///DPt2rU75cniF154gUsuuYSZM2eSmZlJnz59GDRoEGPGjGH27Nk8++yz7N+/n7S0NHr16nVWsZakZ/qqTukc6sdWE047q/1nubbr12/lda0M1Ej3ygsXLiQ2NpbY2Fjmzp3L7bffTmxsLH379j2tbJs2bdi5cyf3338/CxYswM/vtOdO2bp1K61btyY6OhoRYezYscXLFi1axEsvvURsbCwDBw4kLy+PPXv2cN111/HNN98AMHv2bK699tqq7LJK6Zm+qlP8vD1o1jSQwoJggnAnKVOT/rlS0Rl5TSmva2Wwn9VXd/fKQ4YMYciQIYC9TX/8+PEMHDiwzLKBgYFs2LCBhQsXMm3aNGbPns3MmTNPKydSVqcG9m379ttv6dChw2nLgoKC2LhxI19//TXTp093apucpWf6qs7pHOzP1qJwogsK9Uy/njuTrpXh3HSvfOTIEYqKirjmmmt47rnnWLduHQC+vr5kZWUB0LFjR3bt2sWOHTsA+PLLL4vrDxkyhKlTp2KMvVeb9evXFy8bM2YMr7zyCseOHaNr167VGrcmfVXnxIT4EZ8XSnTOcXZkJmMrcu5ODVX3nGnXylDz3Svv27ePgQMHEhsby/jx44t/jYwfP567776b2NhYjDHMmDGDK664ggsuuIDIyMji+pMmTaKgoIBu3brRpUsXJk2aVLxs9OjRfPXVV1x33XXVGjOAnDzK1BZxcXEmPj7e1WGoWmzJloN8NWs6VzT9gMnNg/hh1A9E+Ue5Oqx6KTExkU6dOrk6DFVKWd+LiKw1xsRVVlfP9FWdY7+YG0H7k90xaLu+Uk7TpK/qnFZ+3pzwDibE5okbegePUlWhSV/VOSJCTKg/+ySSCO2OocbVtibghu5svw9N+qpO6hziT0J+KO3ycknSjtdqjLe3N+np6Zr4awljDOnp6Xh7e5/xOvQ+fVUnxQT7sbIogvZ5K1matZfcwlwauTdydVj1TlhYGKmpqegwprWHt7c3YWFhZ1xfk76qk2JC/PioKIJB1gL7gCqZO+ncrLOrw6p3PDw8aN26tavDUNVIm3dUndSmWRN2WyKLu2PQvvWVco4mfVUnuVvciGrVDItpiTeiSV8pJ2nSV3VWTIgfW2zhtC00eq++Uk7SpK/qrJgQfzYWhNM+N5ukDD3TV8oZmvRVnRUTbO9mOdpaQEb+UdJz010dklK1nlNJX0SGisg2EUkWkYllLL9bRP4UkQQR+U1EYkose9JRb5uIDKnO4FXD1rGVL1tNJNEnB1TRJh6lKlVp0hcRCzANGAbEADeUTOoOXxhjuhpjYoFXgDccdWOAMUBnYCjwnmN9Sp21Jl7ueAVFEFroCWh3DEo5w5kz/T5AsjFmpzHGCnwFjCxZwBhzvMRkE+Dk43sjga+MMfnGmF1AsmN9SlWLmBB/jtjCaWrcNOkr5QRnkn4osLfEdKpj3ilE5D4R2YH9TP+BKta9U0TiRSRen/xTVRET4kdCQTjR+Xl626ZSTnAm6Zc11tdpHXEYY6YZY9oCTwD/qGLdGcaYOGNMXPPmzZ0ISSm7ziH+JJoIovPz2HE0SQdUUaoSziT9VCC8xHQYkFZB+a+AUWdYV6kqiQn2Y2uRvW/9vCIrqdmprg5JqVrNmaS/BogWkdYi4on9wuzckgVEJLrE5BXAycbVucAYEfESkdZANLD67MNWyq65rxcZTdoWd8eg7fpKVazSpG+MKQQmAAuBRGC2MWaziEwRkRGOYhNEZLOIJACPAOMcdTcDs4EtwALgPmOM/v5W1apdaHO8bM0RNOkrVRmnetk0xswD5pWaN7nE+wcrqPsC8MKZBqhUZWKC/UjaGUa4LU3v1VeqEvpErqrzOof4s7kogvZ5OSRlbHV1OErVapr0VZ0XE+JHookk2lrAnqxUcgtzXR2SUrWWJn1V50U2bcwe9yiirVaKHAOqKKXKpklf1XlubkJgq9aEFngAOqCKUhXRpK/qhZhQf45bQ/E22vGaUhXRpK/qhZhgPxJtEbQtKNC+9ZWqgCZ9VS90DvFni4kkOj+fpKPbXB2OUrWWJn1VL0S39GE79jt40vMzycjLcHVIStVKmvRVveDtYcEW1FG7Y1CqEpr0Vb3RPrQZ3tYgQJO+UuXRpK/qjZgQP1ILwgksMnrbplLl0KSv6o2YYD8SiyJpn5+v3TEoVQ5N+qresHfHEE601cqOzB0UmSJXh6RUraNJX9UbAY09OerbgfbWAnKLrKRm6YAqSpWmSV/VK82Dowh2dMegF3OVOp0mfVWvdA71Jy8vGDGwPVMv5ipVmiZ9Va/EhPixwxZFeGGhdsegVBk06at6JSbYj0QTQbTVSlJ6oqvDUarW0aSv6pWwwEbs9WhtH1DlRBp5hXmuDkmpWkWTvqpXRATP4BjaWQspwrDj2A5Xh6RUreJU0heRoSKyTUSSRWRiGcsfEZEtIrJRRJaKSGSJZTYRSXC85lZn8EqVJTqkOV7WQEDv4FGqtEqTvohYgGnAMCAGuEFEYkoVWw/EGWO6AXOAV0osyzXGxDpeI6opbqXK1TnEj6P54Xgbo0lfqVKcOdPvAyQbY3YaY6zAV8DIkgWMMb8YY3IckyuBsOoNUynnxYT4sa0okjZWK0npW1wdjlK1ijNJPxTYW2I61TGvPLcB80tMe4tIvIisFJFRZVUQkTsdZeIPHz7sREhKla9dCx+SJIpoawFJ2vGaUqdwJulLGfNMmQVFxgJxwKslZkcYY+KAG4G3RKTtaSszZoYxJs4YE9e8eXMnQlKqfB4WNwqadyLaWsAR63EdUEWpEpxJ+qlAeInpMCCtdCERGQT8HRhhjMk/Od8Yk+b4uxNYBvQ4i3iVckqrkCiCrdodg1KlOZP01wDRItJaRDyBMcApd+GISA9gOvaEf6jE/EAR8XK8bwb0B7SRVdW4mBB/CvNaApr0lSqp0qRvjCkEJgALgURgtjFms4hMEZGTd+O8CvgA35S6NbMTEC8iG4BfgJeMMZr0VY3rHOpPWmEUgbYikjJ0oHSlTnJ3ppAxZh4wr9S8ySXeDyqn3u9A17MJUKkz0bGVL18VRdLeupakI5tdHY5StYY+kavqJV9vDzL92hNttZKctVsHVFHKQZO+qreahMbQxmojt6iAfVn7XB2OUrWCJn1Vb7UPbYZ3fgCgfesrdZImfVVvxYT4kZMfhmh3DEoV06Sv6q3OIf7ssEURVljI9iObXB2OUrWCJn1Vb7Xw9SLNqy3trQUkpW91dThK1Qqa9FW9JSLQqot9QJXcQzqgilJo0lf1XGh4FK2s7hRh2Hlsp6vDUcrlNOmrei0m2A+T1wLQ7hiUAk36qp7rHOLHEWtrvIqMdsegFJr0VT3XupkPO92iaFNQQNLhja4ORymX06Sv6jWLm2ANiiHaaiVJB0lXSpO+qv/8wjrTzmrjcEE2R/OOujocpVxKk76q9zqEBdE43x/Qi7lKadJX9V5MsB95+fZhnZMyNemrhk2Tvqr3OrbyI7UwikCbjaTDf7o6HKVcSpO+qvcaeVo45teRaGuBDqiiGjxN+qpB8Ajuak/62ak6oIpq0DTpqwYhIiKKVlYLuaaQfdk6oIpquDTpqwYhJsQPyWsOQMKhBBdHo5TrOJX0RWSoiGwTkWQRmVjG8kdEZIuIbBSRpSISWWLZOBFJcrzGVWfwSjkrJtiPE3lRtLUW8OKqf+qtm6rBqjTpi4gFmAYMA2KAG0QkplSx9UCcMaYbMAd4xVG3KfA00BfoAzwtIoHVF75Szgny8eKQV3veP3AIbzcP7l16LwdPHHR1WEqdc86c6fcBko0xO40xVuArYGTJAsaYX4wxOY7JlUCY4/0QYLExJsMYcxRYDAytntCVqhpbyy4E22y81+xCjucf596l95JtzXZ1WEqdU84k/VBgb4npVMe88twGzK9KXRG5U0TiRST+8OHDToSkVNX5R3RjaVFPOqx4nzc7jGNn5k4eXvYwBbYCV4em1DnjTNKXMuaZMguKjAXigFerUtcYM8MYE2eMiWvevLkTISlVdZ1D/XnQei95Ae3ot/glnu56Fyv3r+SZP57BmDL/SStV7ziT9FOB8BLTYUBa6UIiMgj4OzDCGJNflbpKnQtdQv3JpjFTWz0PFg9G/Tqdezv/H3N3zGVawjRXh6fUOeFM0l8DRItIaxHxBMYAc0sWEJEewHTsCf9QiUULgcEiEui4gDvYMU+pcy68aWNmg+P7AAAgAElEQVTuvKgN760v4OfYN+BYKndvWsrVbUcxfeN05myf4+oQlapxlSZ9Y0whMAF7sk4EZhtjNovIFBEZ4Sj2KuADfCMiCSIy11E3A3gO+4FjDTDFMU8pl/jbkA70bxfE3cs92dP/RWT3b/wjI5P+If15fuXzLE9d7uoQlapRUtvaMuPi4kx8fLyrw1D1WMYJK8On/kaRMSzt/guNV08lZ/BzjE//jd3Hd/PR0I/oHNTZ1WEqVSUistYYE1dZOX0iVzU4TZt4Mv3mXmScsHLrnmEUtb+cxouf5r02Ywj0CuS+JfeRmpXq6jCVqhGa9FWD1CXUnxev7srK3Zm83PhRaNGZZnMf5P2ej1NQVMA9S+4hMy/T1WEqVe006asG6+qeYYzvF8X0lQdZ0O0NcPeizQ+P8s75z5OWncYDvzxAvi2/8hUpVYdo0lcN2t+v6ESf1k15aEE6Oy6dAcf30+uXV/lnvymsP7SeJ399UrtiVvWKJn3VoHlY3Jh2Y08CG3tyyyLIHvoWpKxgyKZ5PBb3GItTFvNa/GuuDlOpaqNJXzV4zX29eH9sLw5n5XP3hrYUXfAorJ/FLZnHGdtpLJ9t+YzPtnzm6jCVqhaa9JUCYsMDeH5UF35LPsLL1mug03Bk8SQe8+vKoIhBvLrmVRanLHZ1mEqdNU36Sjlc1zucsedFMH35buZHPwOtumL5zx282GEc3Zt3Z+LyicQf0GdIVN2mSV+pEiZf2ZlekYE88l0yyYM+BM8meM++mal9nybUN5R7ltyjT+2qOk2TvlIleLq78f5NPfH1due279LIvuozyD5EwH/v46NB02kT0IYHfn6AuTvmVr4ypWohTfpKldLCz5v3x/YkLTOXCcuFopHvwd6VBC1+lpmD/01cqzj+/tvf+WTzJ64OVakq06SvVBl6RTbl6eGdWbbtMG+kdYEBE2HDlzT57S3eu2QagyMH81r8a7wR/4b2xa/qFHdXB6BUbXVT3wj+TD3Gu78k0+Wm8QyNTYXlr+KZfYhXhr1KoHcgH23+iIy8DJ7p9wzubvrfSdV++q9UqXKICM+O7MzWg1k8+s0G2t33Iu18W8Kvr2PJOsDfR88kyDuI9za8R2Z+Jq8OeJVG7o1cHbZSFdLmHaUq4O1h4YOxPWnkaeHOz9ZxvP+TcMUbkLwY+eRK7mk3mknnTWJ56nLuWnwXx/KPuTpkpSqkSV+pSgT7N2LajT3Zk5HD/320hoyYm2HMF3BoK/x7ENc168VrA15j05FNjF8wnoMnDro6ZKXKpUlfKSf0bRPEOzf04M99x7jqvRXsbHohjP8R8rPgw0EMtgTy/qD32X9iPzfPv5ldx3a5OmSlyqRJXyknXd41mC/vOI/svEKueu93Vllbw22LwdsfPhlO32PpzBwyk3xbPuPmj2PTkU2uDlmp02jSV6oKekUG8t29/Wnm48nYf6/iPyle9sTfohN8fRMxO3/n02Gf0tijMbcuvJXf9/3u6pCVOoUmfaWqKCKoMf+5pz9xkU15ZPYG3vzjKGbcDxA9GH56lMjVH/PZ0E8J9w3nvp/vY/6u+a4OWaliTiV9ERkqIttEJFlEJpax/CIRWScihSIyutQym4gkOF767LqqF/wbe/DJrX0Y3SuMt5cm8ch/k8kf/Sn0Gg+/vUHzhZP4aNAMujfvzhPLn+DzxM9dHbJSgBP36YuIBZgGXAakAmtEZK4xZkuJYnuA8cBjZawi1xgTWw2xKlWreLq78erobrRu1oRXF25j39Fcpo99hUC/MPjlefyyD/DB1R/yxOrneWn1SxzKOcT9Pe7Xh7iUSzlzpt8HSDbG7DTGWIGvgJElCxhjdhtjNgI6rpxqUESE+y5uxzs39CAhNZOrP/iD3Z3vhZHvwe7f8P5sFK/3fJxr21/LzE0zuW3hbRw4ccDVYasGzJmkHwrsLTGd6pjnLG8RiReRlSIyqkrRKVVHjOgewhe39yUzx8pV761gTeAwuHE2HN2N+8whTG57Hf+84J9szdjK6B9Gs2zvMleHrBooZ5K+lDGvKj1MRRhj4oAbgbdEpO1pHyByp+PAEH/48OEqrFqp2iMuqinf3dufwMae3PSvVXyf3RH+bx4UFcDMwQy3BDJ7+GxCmoRw/8/38/Lql7HarK4OWzUwziT9VCC8xHQYkObsBxhj0hx/dwLLgB5llJlhjIkzxsQ1b97c2VUrVetENWvCf+7tR2xEAA9+lcDULY0xty0Cn5bwyQgiN8xh1tBPuanTTcxKnMXYeWNJOZ7i6rBVA+JM0l8DRItIaxHxBMYATt2FIyKBIuLleN8M6A9sqbiWUnVbQGNPPrutD1f3COX1xdt5bPExrOMXQcxIWDoFzy+vZ2Kn/+Odi98h7UQa1/1wHT/u/NHVYasGotKkb4wpBCYAC4FEYLYxZrOITBGREQAi0ltEUoFrgekistlRvRMQLyIbgF+Al0rd9aNUveTlbuH167rz8KD2fLsulVu+2Mqxy6fDiKmwZxW834+L823MGT6Hjk078uSvTzJpxSRyCnJcHbqq56S2DQARFxdn4uN18GlVf3y3PpUn5vxJcIA3U2/oQTfPAzDnVji0GfrdT+HAp/hg80xmbJxBlH8Ur170Kh2adnB12KqOEZG1juunFdIncpWqYVf1COOLO/piLSzimvd/519bPSm6bQnE3Qa/T8X94yuYEHkF/xr8L7KsWdz40418vfVrHZFL1QhN+kqdA3FRTZn/4IVc3KEFL8xL5P8+38SRgS/CdZ9Cxg6YfhF901OZM3wOvYN78/yq53n0f49q//yq2mnSV+ocCWjsyfSbe/HcyM78sTOdYW//ym8e/eHu36B5R5hzK0GLn+G9C1/j0V6P8sueX7juh+tIOJTg6tBVPaJJX6lzSES4+fwovr+vP/6NPLh55ipeXplDwS0/woWPwrrPcPvXJYxv3ptPh32KiDB+wXg+/PNDbEU2V4ev6gFN+kq5QKdgP+ZO6M+Y3uG8v2wH1/4rnr09HoObv4O8TJhxMV13r+KbK2czKHIQb697m7HzxpJ0NMnVoas6Tu/eUcrFftyYxpPf/gnAP6/uyvC2HvDfuyF5CXQajhn+DgsOruLFVS+SVZDFnV3v5Paut+Nh8XBx5Ko20bt3lKojruwWwrwHL6RdSx/u/3I9ExfuJ+faL+Gy52DbfGT6RQyzNOW/o/7L4MjBvLfhPa778TodmUudEU36StUC4U0bM/uu87l3YFu+jt/L8Hd/J7HNeLh1EbhZ4KOhNP31bV4+/1mmXjKV49bj3DTvJl5b8xq5hbmuDl/VIZr0laolPCxu/G1oR2bd1pfjeYWMnLaCT/cGYe78H/QYCyvegukXMpAm/Hfkf7k6+mo+2fIJ18y9hjUH1rg6fFVHaNJXqpbp364Z8x+8kH5tg5j8/Wbu/CaZzEGvw9hvwZoDMwfju+xlno77G/8e/G8Abl14K1P+mEK2NdvF0avaTpO+UrVQMx8vZo7rzT+u6MSybYcY+tav/K+oO9z7B/QcB79PhQ8uoE9BEd+O+JZbYm7h26RvGfX9KJanLnd1+KoW06SvVC3l5ibcfmEbvr2nH028LIybuZon56WQPfg1uOV7KLTCzKE0WjKFx7vfx2fDPsPX05f7lt7HxF8ncjTvqKs3QdVCmvSVquW6hQXw0wMXcudFbfhqzR6GvrWc300XuPd36H0brHwPPuhPtxNZfH3l19zT/R4W7lrIqO9HsWDXAu3DR51Ck75SdYC3h4WnLu/EN3edj7ubcOO/VvH0ghRyLnsZxv0ARTb4+HI8F/6De2PG8fXwrwluEszjyx/ngV8eIDUr1dWboGoJfThLqTom12rj5QVb+fj33UQFNebVa7vTO8QLljwLq6dDYBSMeJfCyPOZtWUW0xKmYTM2bux4I3d0uwN/L39Xb4KqAc4+nKVJX6k66o8d6Tw+ZwP7MnO5rX9rHhvSAe99K+H7++DoLuh9Owx6lgOF2UxLmMb3yd/j6+nLXd3uYkzHMXhaPF29CaoaadJXqgE4kV/IP+cl8vmqPbRt3oTXru1Oj1Ze8PNzsPJ9CAi3j9bVZiDbMrbxxto3+D3td0J9Qnmo50MMiRqCiLh6M1Q10KSvVAPya9JhnpizkQPH87hrQFseGhSNV9oa+O+99v76u1wDg56FgHBW7FvB62tfJ+loEt2adePRuEfp2bKnqzdBnSVN+ko1MMfzCnj+xy3Mjk+lQ0tfXr+uO11aeMKvb8Dv79gL9bsf+j+EzaMRc3fM5d3173Io9xCXhF/Cw70eJso/yqXboM6cJn2lGqiftx5k4rd/kn7Cyn0Xt2PCxe3wzN4HS5+FP78Bn1Yw6GnoNobconw+3fwpMzfNxGqzMrr9aO6JvYem3k1dvRmqijTpK9WAHcsp4JkfNvPd+n3EBPvxz6u7EhseAHtXw4KJsG8thPSAIS9C5PkcyT3C+wnv823St3i7e3N719sZ22ks3u7ert4U5aRq7VpZRIaKyDYRSRaRiWUsv0hE1olIoYiMLrVsnIgkOV7jnN8EpdSZ8m/swZvXxzL95l4czs5n1LQV/G3OBg4HdIfblsDV/4Ksg/DRUPhmPM3yTjDp/En8Z+R/6N2qN2+ve5srv7uS75O/1xG76plKz/RFxAJsBy4DUoE1wA3GmC0lykQBfsBjwFxjzBzH/KZAPBAHGGAt0MsYU+7z4Xqmr1T1ys4vZOrSJGau2IW3u4UHB0Uzrl8UHrZcex8+v70Fpgj6TYALHgYvX9YcWMMb8W+wKX0TzRo1Y2jUUC5vfTldmnXRu31qqWpr3hGR84FnjDFDHNNPAhhjXiyj7MfAjyWS/g3AQGPMXY7p6cAyY8yX5X2eJn2lasaOw9lM+WEL/9t+mHYtfHhmeGcuiG4Gxxzt/Ru/Bp+WcOlk6H4jRQK/7P2FH3b8wPLU5RQUFRDuG86w1sO4ovUVtAlo4+pNUiVUZ9IfDQw1xtzumL4Z6GuMmVBG2Y85Nek/BngbY553TE8Cco0xr5WqdydwJ0BERESvlJSUyrdQKVVlxhiWJh5iyo9b2JORw9DOrfj7FZ0Ib9oYUuPt7f2payC4u729P6o/AMetx1maspR5u+ax+sBqikwRHZt2ZFjrYQyLGkawT7CLt0xVZ9K/FhhSKun3McbcX0bZjzk16T8OeJVK+jnGmNfL+zw901eq5uUV2Pj3b7t49+dkiozhrgFtuWdAWxp5uMGmb2Hx03A8FTqNgEsmQfP2xXWP5B5h4e6FzNs5j41HNgLQs0VPLm99OYOjBhPoHeiqzWrQtHlHKVWptMxcXpy/lR82pBEa0Ih/XNGJoV1aIQW58Mc0+O0NKMiB9sPs9/hH9oMSbfp7j+9l/u75zNs5jx3HduAu7pwfcj6Xt7mcS8IvobFHYxduXcNSnUnfHfuF3EuBfdgv5N5ojNlcRtmPOTXpN8V+8fbk437rsF/IzSjv8zTpK3XurdyZzjNzN7P1QBb92gbxzIjOtG/pCyeOwJoPYfUMyEmHkJ7Q/wHoOBws7sX1jTFsP7qdebvmMX/XfPaf2I+3xZuLwi7isqjLuCj0Ij0A1LBqvU9fRC4H3gIswExjzAsiMgWIN8bMFZHewHdAIJAHHDDGdHbUvRV4yrGqF4wxH1X0WZr0lXKNQlsRX6zew+uLtpOdX8gt50fy0KD2+DfygIJcSPgC/ngXMnZCQCScf5997F7PJqesp8gUseHwBn7a+RNLUpaQnpeOl8WLC0Iv4LLIyxgQNgAfTx8XbWX9pQ9nKaXOSMYJK68t2saXq/fQtLEnjwxuz/Vx4bhb3Oz99m+bZ7/Vc+8q8A6w9+bZ507wbXnaumxFNtYfWs/ilMUsSVnCodxDeLh50C+kH5dFXsbA8IHa1XM10aSvlDorm/Yd49kfNrNm91Hat/Thycs7MbB987/u09+zCv6YCok/gsUDul1vb/dv3qHM9RWZIjYe3sjilMUsTlnM/hP7cRd3+ob0ZXDkYC4Ov1gvAp8FTfpKqbNmjGHh5gO8OH8rKek5XBjdjKcu70SnYL+/CqXvsF/0TfgcCvOg/VDHRd/+p1z0Lb3ezembWZSyiEW7F7Evex8WsdC7VW8ui7yMSyIuoVmjZudoK+sHTfpKqWpjLSzis5UpvLM0iay8Aq7tFc6jg9vTwq9E3zynXfTtAb3GQ7tB4B9W7rqNMWzN2MrilMUsSllEyvEU3MSNLs26ENM0hk5BnejYtCPtAtrpwC8V0KSvlKp2mTlWpv6czKd/7MbD4sZdF7Xljota09jzrzt5KMiFDV/az/7Tk+3zmnWwJ/92l9h/AXg0KnP9xhiSMpNYtHsRaw6sYWvGVnIKcwBwd3OnrX9bOjbtWHwg6Ni0I008mpS5roZGk75SqsbsPnKClxdsZf6mA7T08+KxwR24umcYFrcSzTnGwOGtkLwEkpdCyu9gywd3b3vib3cptL3Ufg2gnGagIlPE3qy9JGYksjV9K1sztpKYkUhG3l93fUf6RRYfADo1tR8MghoF1fQuqHU06Sulatya3Rk8/1MiG/ZmEhPsx9+v6ET/duW0xVtzIGWF/QCwYykc2W6f7xdm/wXQbhC0HgCNAir8TGMMh3IOFR8AtmbYDwb7svcVl2nZuCU9W/QkrlUccS3jaO3fut53FKdJXyl1ThQVGX7YmMYrC7axLzOXSzq24KnLO9KuhW/FFTP3/HUA2Pk/yD8OYoGwuL8OAMHdym0KKu1Y/rHiA8CmI5uIPxjPkdwjADT1bkqvlr3o1bIXcS3jiA6Mxk2c6lm+ztCkr5Q6p/IKbHy0Yjfv/ZJMToGNG/qEc9dFbQkLbFT5WbatEPbF/9UUlLYeMPaDQIsYCO1hfxo4tKd92uJRaTzGGPZk7SH+QDxrD64l/mA8+0/sB8Dfy5+eLXraDwKt4ugY2BGLm6Ua9oLraNJXSrlEenY+by1J4ovVe7AVGVr5edMrKpBeEYHERQXSKdgPD0slZ9kn0mHvSti3DtLW2Q8CuY5hOCxe0Kqr/QAQ0tN+l1CzaHAiae/L3mc/AByIJ/5gPHuz9gLg4+FDjxY9ig8CMUExeLhVfmCpTTTpK6VcKiX9BMu2HSY+5SjrUo6yLzMXgEYeFmLDA+gVGUivqEB6RgTau3qoiDFwdLf9ALDPcRBIS4CCE/blnj4QHOv4RdADQnvZu4qo5BfGwRMHi38FxB+MZ9exXfYY3RvRrXk3erWwNwl1bd6VRu7ONTO5iiZ9pVStsv9YLvG7j7I2xf7asv84tiKDCLRv4XvKr4GIpo0rbxIqssGRpFMPBAf+tN8hBPYBYSLOg/DzIKIvtOpWabNQem46aw+uZd2hdaw9uJZtGdswGNzd3Okc1Ln4ukBsi1j8PP0qXNe5pklfKVWrncgvZMPeTOJTjhKfcpT1KUfJyi8EoJmPF70iA+gZEUiPiEC6hvrTyNOJNvdCKxzaYr8+sGeVvYkoc499mUdj+y+AkweC8N7gXXG/P8etx0k4lGA/EBxcx6b0TRQWFSII7QPbFx8Eerbs6fIniDXpK6XqFFuRIelQVvGvgXV7jpKSfvLBLKFTsB89Ik4eCAKc+zUAcDwN9qy0dxC3Z6X914CxAQItO0N4X8eBoC8ERFTYJJRbmFt8Z9Dag2vZeHgjuYX2Zqsovyh6tuxJqE8oTTya0Ni9MY09GtPYvbF92qMxTdyb0MijEU08muBt8a7W20g16Sul6rwj2fkk7Mlk/d6jrEvJZENqJjlWGwBBTTzpERFAD8dBoHtYAE283CtZI5Cf/dcvgT1/2IeJtGbZl/mG2JuCQuPsF4qDu5/WdXRJBUUFJKYnsu7guuJmoePW405tm5u42Q8MJw8OHo3p1LQTz/R7xqn6pWnSV0rVO7Yiw/aDWazbc5T1ezJZt+coOw/bL+a6CbRv6UvPyEB6hNsPBm2aNcHNzYlrAwc3O34J/AF7V8Mx+109iBs07+i4XdRx22jLLuBefh9AVpuVnIIccgpzOFFwovhvbkHuafNyCnLILcwtnhfmE8aTfZ88o32jSV8p1SBk5lhJ2JvJuj2ZrN9zlIS9mWTl2a8N+Hm7Extx8iAQQGx4AAGNnei0LfvQX7eLnvybk25fZvG03zJ68rmBkJ5O3zJakzTpK6UapKIiw47D2azfm8l6x4Fg+8Esihyprk3zJvQID3Q0DQXQoaWvfYCYihhjvyCctg72rYV962F/Aliz7ctL3jLqFwaeje0Xjj2blPrbGDya2P+6NwK36nsqWJO+Uko5ZOcXsjH15EEgk4S9RzmSbQXszw10DfO3HwTCA+kZEXBql9HlOe2W0XWOW0atzgfm0fjUg0FILFz1wRltoyZ9pZQqhzGG1KO5xdcG1u/NZEvaMQps9nwY1MSTDq187a+W9r/tW/pWfqHYVgD5WWA9AQU5Jf7m2B8ks+aUml9qeUAkDHnhjLZJk75SSlVBXoGNzWnH2bA3k60HjrPtYDbbD2SRW2ArLhPetBEdWvrRsZUv7Vv50rGVL62bNam8W4lzwNmk78T9TSAiQ4G3AQvwoTHmpVLLvYBPgV5AOnC9MWa3iEQBicA2R9GVxpi7nd0IpZQ6V7w9LPauISL/Gqe3qMiw92gO2w5kse1AFlsPZrH9QBa/bDuEzXGRwMMitG3uU/zLoJmPF408LHh7WBx/3fA+Oe1ZYp67pfI7i2pApUlfRCzANOAyIBVYIyJzjTFbShS7DThqjGknImOAl4HrHct2GGNiqzlupZSqcW5uQmRQEyKDmjC4c6vi+fmFNnYcOsH2g1lsPZDF9oP2h8q+T0ir0vo93d2KDwL2awsBTL2hR3VvximcOdPvAyQbY3YCiMhXwEigZNIfCTzjeD8HeFfq+4gFSqkGy8vdQkyIHzEhp/a/k5VXQGZOAfmFNnKtReQV2si12sgrsJFbYP+bV1BU/D63wEae9a95YYE136mbM0k/FNhbYjoV6FteGWNMoYgcA06OV9ZaRNYDx4F/GGN+Lf0BInIncCdARERElTZAKaVqC19vD3y9a3eXzM5cfSjrjL301d/yyuwHIowxPYBHgC9E5LSu6YwxM4wxccaYuObNmzsRklJKqTPhTNJPBcJLTIcBpRuuisuIiDvgD2QYY/KNMekAxpi1wA6g/dkGrZRS6sw4k/TXANEi0lpEPIExwNxSZeYC4xzvRwM/G2OMiDR3XAhGRNoA0cDO6gldKaVUVVXapu9oo58ALMR+y+ZMY8xmEZkCxBtj5gL/Bj4TkWQgA/uBAeAiYIqIFAI24G5jTEZNbIhSSqnK6cNZSilVDzj7cJbrHyNTSil1zmjSV0qpBkSTvlJKNSC1rk1fRA4DKWeximbAkWoKpzppXFWjcVWNxlU19TGuSGNMpQ861bqkf7ZEJN6ZixnnmsZVNRpX1WhcVdOQ49LmHaWUakA06SulVANSH5P+DFcHUA6Nq2o0rqrRuKqmwcZV79r0lVJKla8+nukrpZQqhyZ9pZRqQOpk0heRa0Vks4gUiUhcqWVPikiyiGwTkSHl1G8tIqtEJElEvnb0HlrdMX4tIgmO124RSSin3G4R+dNRrsY7HRKRZ0RkX4nYLi+n3FDHPkwWkYnnIK5XRWSriGwUke9EJKCccudkf1W2/SLi5fiOkx3/lqJqKpYSnxkuIr+ISKLj3/+DZZQZKCLHSny/k2s6LsfnVvi9iN07jv21UUR6noOYOpTYDwkiclxEHipV5pzsLxGZKSKHRGRTiXlNRWSxIw8tFpHAcuqOc5RJEpFxZZWpEmNMnXsBnYAOwDIgrsT8GGAD4AW0xt5/v6WM+rOBMY73HwD31HC8rwOTy1m2G2h2DvfdM8BjlZSxOPZdG8DTsU9jajiuwYC74/3LwMuu2l/ObD9wL/CB4/0Y4Otz8N0FAz0d732B7WXENRD48Vz9e3L2ewEuB+ZjH3DpPGDVOY7PAhzA/gDTOd9f2Hsc7glsKjHvFWCi4/3Esv7NA02xd0ffFAh0vA88m1jq5Jm+MSbRGLOtjEUjga+MffCWXUAy9jF+iznG7r0E+1i+AJ8Ao2oqVsfnXQd8WVOfUQOKx0U2xliBk+Mi1xhjzCJjTKFjciX2wXpcxZntH4n93w7Y/y1d6viua4wxZr8xZp3jfRaQiH2o0rpgJPCpsVsJBIhI8Dn8/EuBHcaYs3na/4wZY5Zj73a+pJL/hsrLQ0OAxcaYDGPMUWAxMPRsYqmTSb8CZY3nW/o/RRCQWSLBlFWmOl0IHDTGJJWz3ACLRGStY6zgc2GC4yf2zHJ+UjqzH2vSrdjPCstyLvaXM9t/yrjQQMlxoWucozmpB7CqjMXni8gGEZkvIp3PUUiVfS+u/jc1hvJPvFyxvwBaGmP2g/2ADrQoo0y17zdnBkZ3CRFZArQqY9HfjTHfl1etjHnOjudbZU7GeAMVn+X3N8akiUgLYLGIbHWcFZyxiuIC3geew77Nz2Fverq19CrKqHvW9/Y6s79E5O9AIfB5Oaup9v1VVqhlzKuxf0dVJSI+wLfAQ8aY46UWr8PehJHtuF7zX+wj1tW0yr4XV+4vT2AE8GQZi121v5xV7fut1iZ9Y8ygM6jmzHi+R7D/tHR3nKGVVaZaYhT7eMFXA70qWEea4+8hEfkOe9PCWSUxZ/ediPwL+PH/27u/0DiqKI7j36PW/+KfWjSlD1pKEVT0wf/tk0owRcRWofrQgpoHH0REpCIiQgUFq4KIoeAfBAk+FCvUslioiFUQCxbTJFYxPhQCbSo+FMRaxB4fztl2XHc3m2RnVzq/DyzZnbmTvbk7OTN778y5TVZ10o5dr1cOUt0L3OXZodnkd3S9vZqYy7zQ01aYF7rL9fgPM1tEBPxRd9/euL54EHD3mpmNmNnl7l5qcrEOPpdS9qkODQH73H2mcUW/2ivNmNmAux/Krq4jTcpME+MOdcuIscx5O926d3YAD+WVFVcTR+y9xQIZTL4g5vKFmAMG6EgAAAOBSURBVNu31TeHhbob+NHdp5utNLMLzOyi+nNiMHOiWdluaehHXdvi/TqZF7nb9boHeBa4z93/aFGmV+0173mhS6jLSTlm8B5wwN3faFHmyvrYgpndQvyP/1ZyvTr5XHYAG/MqntuAo/WujR5o+W27H+1VUNyHWsWhXcCgmV2aXbGDuWz+yh61LuNBBKtp4DgwA+wqrHueuPLiJ2CosLwGLM3ny4mDwRSwDTinpHp+QMwLXFy2FKgV6jGWj0mim6PstvsQGAf250430FivfL2GuDrklx7Va4rou/w+H1sb69XL9mr29wObiYMSwLm570zlvrS8B220mvhqv7/QTmuAx+v7GfBEts0YMSB+Rw/q1fRzaaiXAW9ne45TuOqu5LqdTwTxiwvLet5exEHnEPBXxq7HiDGgz4Gf8+dlWfYm4N3Cto/mfjYFPLLQuigNg4hIhZxu3TsiItKGgr6ISIUo6IuIVIiCvohIhSjoi4hUiIK+VIaZ/b6AbUctsm5OZPqKRYV19xezM5rZxiw3aWY/mNkzufw1M7tzYX+FyMIo6It0ZhS4BrgeOA8YLqzbBIwAmNkQ8BQw6O7XEpkVj2a5t4hsiiJ9o6AvlZN3hW7Js/FxM1ufy8/I2/AnzWynmdXM7EGIW/Q9ETdjLcttVgLH/dRt+88RqavraQn+dPd38vlBYLGZNcs/JNITCvpSReuAG4EbiFQZWzI9xTrgKuJsfhi4vXHD7NbZAHyWi1YRSbvqrgO+a/Pe+3Ibkb5Q0JcqWg185O5/eyTh+hK4OZdvc/cT7n6YyNHUaATY4+5f5esB4Nc5vPcRIrWESF8o6EsVtZrspO0kKGb2IrAEeLqw+BiRh6dukjZZVbPssQ7qKFIKBX2poj3AejM708yWEFPZ7QW+Bh7Ivv0rKKS0NbNhYhajh939ROF3HQBWFF6/Arxa77fPjK9PFtavpORMqiLt/G/z6YuU6BOiv36MyFq5yd0Pm9nHxLR6E0SGzW85deXNVuAg8E1m4t3u7puJA8jrZmY5zlvLA8buTNnrwPtwcjxgBVDahO4is1GWTZECM7vQYxalxcTZ/6rs32+3zZvAp+6+e5Zya4mJzV/oXo1F5kZn+iL/ttPMLgHOBl6aLeCnl4FbOyh3FjE9pUjf6ExfRKRCNJArIlIhCvoiIhWioC8iUiEK+iIiFaKgLyJSIf8AwDqy/Wm/3G4AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ValidationResult(degree=4, cost=1024, error=0.05838861985962853)\n"
+ ]
+ }
+ ],
+ "source": [
+ "ValidationResult = namedtuple('ValidationResult', ['degree', 'cost', 'error'])\n",
+ "\n",
+ "\n",
+ "def build_options(degree, cost):\n",
+ " return f\"-t 1 -r 1 -d {degree} -c {cost} -q\"\n",
+ "\n",
+ "\n",
+ "def plot_errors_stdevs(errors_stdevs, log_costs, d):\n",
+ " errors, stdevs = zip(*errors_stdevs)\n",
+ " errors, stdevs = np.array(errors), np.array(stdevs)\n",
+ " plt.plot(log_costs, errors - stdevs)\n",
+ " plt.plot(log_costs, errors)\n",
+ " plt.plot(log_costs, errors + stdevs)\n",
+ " plt.xlabel(\"log2(C)\")\n",
+ " plt.title(f\"Error for degree={d}\")\n",
+ " plt.legend(['error - stdev', 'error', 'error + stdev'], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def search_parameters(x, y):\n",
+ " best_result = ValidationResult(None, None, error=1.0)\n",
+ " k = 10\n",
+ " log_costs = list(range(-k, k + 1))\n",
+ " degrees = range(1, 5)\n",
+ " errors_stdevs = [[] for _ in degrees]\n",
+ "\n",
+ " for i, degree in enumerate(degrees):\n",
+ " for cost in map(lambda a: 2 ** a, log_costs):\n",
+ " error, std = kfold(x, y, build_options(degree, cost), svm_train, svm_predict)\n",
+ " if error < best_result.error:\n",
+ " best_result = ValidationResult(degree, cost, error)\n",
+ " errors_stdevs[i].append((error, std))\n",
+ " plot_errors_stdevs(errors_stdevs[i], log_costs, degree)\n",
+ "\n",
+ " for errs_stds_i in errors_stdevs:\n",
+ " errors_i, _ = zip(*errs_stds_i)\n",
+ " plt.plot(log_costs, errors_i)\n",
+ " plt.xlabel(\"log2(C)\")\n",
+ " plt.title(f\"Degrees comparison\")\n",
+ " plt.legend([f\"d={d}\" for d in degrees], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ " return best_result\n",
+ "\n",
+ "\n",
+ "y_train, x_train = svm_read_problem(os.path.join(spam_save_dir, 'train_scaled'))\n",
+ "best = search_parameters(x_train, y_train)\n",
+ "print(best)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "С параметрами d=4 и C=1024 получили ошибку $\\approx$0.058. Как видим, для С < 1 ошибка становится гораздо больше по мере приближения к 0, т.к. неправильно определенные объекты мы штрафуем все меньше, поэтому разделяющая плоскость перестает отражать основные критерии разделения данных.\n",
+ " Теперь построим графики зависимости ошибки от степени полинома d для полученного наилучшего значения С."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def error_on_degrees(x_train, y_train, x_test, y_test, cost):\n",
+ " valid_errors = []\n",
+ " test_errors = []\n",
+ " degrees = list(range(1, 21))\n",
+ " for degree in degrees:\n",
+ " valid_error, _ = kfold(x_train, y_train, build_options(degree, cost), svm_train, svm_predict)\n",
+ " valid_errors.append(valid_error)\n",
+ "\n",
+ " model = svm_train(y_train, x_train, build_options(degree, cost))\n",
+ " _, results, _ = svm_predict(y_test, x_test, model, '-q')\n",
+ " test_errors.append(1 - results[0] / 100)\n",
+ "\n",
+ " plt.plot(degrees, valid_errors)\n",
+ " plt.plot(degrees, test_errors)\n",
+ " plt.xlabel('polynom degree')\n",
+ " plt.xticks(range(2, 20, 2))\n",
+ " plt.title(f\"Error for C={cost}\")\n",
+ " plt.legend(['validation', 'test'], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "y_test, x_test = svm_read_problem(os.path.join(spam_save_dir, 'test_scaled'))\n",
+ "error_on_degrees(x_train, y_train, x_test, y_test, best.cost)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Построим график зависимости количества опорных векторов от степени полинома d."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sv_amount = []\n",
+ "degrees = list(range(1, 21))\n",
+ "for degree in degrees:\n",
+ " model = svm_train(y_train, x_train, build_options(degree, best.cost))\n",
+ " sv_amount.append(model.get_nr_sv())\n",
+ "\n",
+ "plt.plot(degrees, sv_amount)\n",
+ "plt.title(\"Support vectors amount\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Отдельно выведем количество опорных векторов и ошибку на тестовой выборке для выбранных при кросс-валидации d и C."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Support vectors amount for d=4, C=1024: 721\n",
+ "Test error = 0.07471763683753263\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = svm_train(y_train, x_train, build_options(best.degree, best.cost))\n",
+ "print(f\"Support vectors amount for d={best.degree}, C={best.cost}: {model.get_nr_sv()}\")\n",
+ "_, results, _ = svm_predict(y_test, x_test, model, '-q')\n",
+ "print(f\"Test error = {1 - results[0] / 100}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Как много опорных векторов в вашем решении? Какие выводы вы можете сделать?\n",
+ "Для выбранных параметров модели количество опорных векторов составило 721 из всех 3450 объектов, т.е. $\\approx{1/5}$ обучающей выборки. Это означает, что данные объекты попали в разделяющую полосу, которая видимо получилась достаточно широкой.\n",
+ " Из последнего графика можно сделать вывод, что при росте степени полинома уменьшается размер отступа, следовательно и количество опорных векторов."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Бонус: какое ядро подходит лучше всего для этой задачи? Почему?\n",
+ "При росте степени полинома ошибка на тестовой выборке монотонно и быстро убывает только до значения степени d=3, дальше график ведет себя неоднозначно. Однако среднее значение ошибки при валидации убывает на всем диапазоне значений степени. Это значит, что при d > 3 модель возможно начинает переобучаться, т.к. ядра более высокой степени дают более гибкую разделяющую границу.\n",
+ " Поэтому для данной задачи предпочтительнее выбирать полиномиальное ядро со степенью 3, т.к. с ним достигается почти такая же точность как с большими степенями, при этом меньше вероятность переобучения."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Задание 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для выполнения кода данного задания необходимо, чтобы в текущей директории была размещена папка 'gisette' со всеми файлами из набора gisette.\n",
+ "Аналогично первому заданию, прочитаем и сохраним данные тренировочной и валидационной выборок в нужном формате в папке 'gisette_tmp', затем выполним масштабирование на отрезок [0,1]."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def transform_gisette_data(data_dir, save_dir, base_name):\n",
+ " os.makedirs(save_dir, exist_ok=True)\n",
+ " data_path = os.path.join(data_dir, base_name)\n",
+ " x = pd.read_csv(data_path + '.data', delim_whitespace=True, header=None).values\n",
+ " y = pd.read_csv(data_path + '.labels', delim_whitespace=True, header=None).values\n",
+ " save_libsvm(np.hstack((x, y)), os.path.join(save_dir, base_name))\n",
+ "\n",
+ "\n",
+ "data_dir = 'gisette'\n",
+ "gisette_save_dir = 'gisette_tmp'\n",
+ "transform_gisette_data(data_dir, gisette_save_dir, 'gisette_train')\n",
+ "transform_gisette_data(data_dir, gisette_save_dir, 'gisette_valid')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "cd gisette_tmp\n",
+ "../libsvm/svm-scale -l 0 -s scaling_params gisette_train > train_scaled \n",
+ "../libsvm/svm-scale -r scaling_params gisette_valid 1> valid_scaled 2> /dev/null"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Объекты в данном датасете имеют большое количество признаков, и как сказано в описании, многие сформированы как произведения исходных признаков (пикселей изображения), для переведения задачи в пространство большей размерности. Поэтому будем применять SVM с линейным ядром.\n",
+ "\n",
+ " В гайде библиотеки libsvm авторы рекомендуют для решения задач с большим количеством признаков и объектов использовать вместо libsvm библиотеку liblinear, которая позволяет очень быстро обучать модели с линейным ядром. Поэтому для выполнения следующего кода необходимо, чтобы в текущей директории присутствовала распакованная и скомпилированная библиотека liblinear или liblinear-multicore в папке 'liblinear_'."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from liblinear_.python.liblinearutil import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Для подбора параметров будем проводить кросс-валидацию на обучающем наборе, разбивая его на 5 равных частей, т.к. при большем количестве валидация займет слишком много времени. Набор dev будем использовать в качестве тестового, для оценки качества полученной модели и контроля вероятности переобучения. Набор test никак не используем, т.к. объекты в нем не размечены."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Аналогично заданию 1 выполним кросс-валидацию методом k-fold при k = 5 на тренировочном наборе, и построим графики зависимости среднего значения ошибки от $log_{2}C$ со стандартными отклонениями для $С \\in \\{2^{-15}, 2^{-14}, ..., 2^{2}\\}$. По словам авторов библиотеки брать большие значения С особого смысла не имеет: \"Once C is larger than certain value, the obtained models have similar performances\".\n",
+ " Выберем солвера с L2-регуляризацией и L2-лоссом, решающего двойственную задачу (параметр -s). Значение параметра -e, означающего критерий остановки алгоритма, установим поменьше для более точного решения задачи оптимизации."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GisetteResult(cost=0.0078125, error=0.011400876966405949)\n",
+ "Validation accuracy: 98.85991230335941%\n"
+ ]
+ }
+ ],
+ "source": [
+ "GisetteResult = namedtuple('GisetteResult', ['cost', 'error'])\n",
+ "\n",
+ "def liblinear_options(svm_type=1, cost=1.0, eps=0.0001):\n",
+ " return f\"-s {svm_type} -c {cost} -e {eps} -q\"\n",
+ "\n",
+ "\n",
+ "def plot_gisette_errors_stdevs(errors_stdevs, log_costs):\n",
+ " errors, stdevs = zip(*errors_stdevs)\n",
+ " errors, stdevs = np.array(errors), np.array(stdevs)\n",
+ " plt.plot(log_costs, errors - stdevs)\n",
+ " plt.plot(log_costs, errors)\n",
+ " plt.plot(log_costs, errors + stdevs)\n",
+ " plt.xlabel(\"log2(C)\")\n",
+ " plt.xticks(log_costs)\n",
+ " plt.legend(['error - stdev', 'error', 'error + stdev'], loc='upper right')\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "def search_gisette_parameters(x, y):\n",
+ " best_result = GisetteResult(None, error=1.0)\n",
+ " log_costs = list(range(-15, 3))\n",
+ " errors_stdevs = []\n",
+ "\n",
+ " for cost in map(lambda a: 2 ** a, log_costs):\n",
+ " error, std = kfold(x, y, liblinear_options(cost=cost), train, predict, k=5)\n",
+ " if error < best_result.error:\n",
+ " best_result = GisetteResult(cost, error)\n",
+ " errors_stdevs.append((error, std))\n",
+ "\n",
+ " plot_gisette_errors_stdevs(errors_stdevs, log_costs)\n",
+ " return best_result\n",
+ "\n",
+ "\n",
+ "y_train, x_train = svm_read_problem(os.path.join(gisette_save_dir, 'train_scaled'))\n",
+ "best = search_gisette_parameters(x_train, y_train)\n",
+ "print(best)\n",
+ "print(f\"Validation accuracy: {100 * (1 - best.error)}%\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Получили наименьшую ошибку при $С=2^{-7}$. Для $С=2^{-8}$ среднее значение ошибки практически такое же, но меньше стандартное отклонение. Проверим точность модели для двух наилучших значений С на наборе dev."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "log2(C) = -8: Test accuracy = 97.7%, error = 0.02300000000000002\n",
+ "log2(C) = -7: Test accuracy = 97.8%, error = 0.02200000000000002\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_valid, x_valid = svm_read_problem(os.path.join(gisette_save_dir, 'valid_scaled'))\n",
+ "\n",
+ "for log_c in [-8, -7]:\n",
+ " model = train(y_train, x_train, liblinear_options(cost=2**log_c))\n",
+ " _, results, _ = predict(y_valid, x_valid, model, '-q')\n",
+ " print(f\"log2(C) = {log_c}: Test accuracy = {results[0]}%, error = {1 - results[0] / 100}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Так как точность всего на 1% оказалась хуже чем при кросс-валидации, переобучение навряд ли произошло. В качестве окончательной выбираем модель с линейным ядром с параметром $С=2^{-7}$."
+ ]
+ }
+ ],
+ "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": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw8 (SVM)/hw8_task.pdf b/hw8 (SVM)/hw8_task.pdf
new file mode 100644
index 0000000..a468b47
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diff --git a/hw8 (SVM)/spambase b/hw8 (SVM)/spambase
new file mode 100644
index 0000000..f2ccd23
--- /dev/null
+++ b/hw8 (SVM)/spambase
@@ -0,0 +1,4601 @@
+0.08,0.17,0.17,0,0.8,0.08,0,0.26,0,0,0,3.39,0.17,0,0,0.08,0,0,0,0,0,0,0,0,2.68,0,0,0,0,0,0,0,0,0,0,0.35,0.08,0,0,0,0,0.08,0.08,0,0,0,0,0,0.023,0.046,0,0,0.023,0,2.658,57,436,0
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diff --git a/hw9 (k-NN, t-SNE, PCA)/.idea/encodings.xml b/hw9 (k-NN, t-SNE, PCA)/.idea/encodings.xml
new file mode 100644
index 0000000..15a15b2
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/.idea/encodings.xml
@@ -0,0 +1,4 @@
+
+
+
+
\ No newline at end of file
diff --git a/hw9 (k-NN, t-SNE, PCA)/.idea/hw9.iml b/hw9 (k-NN, t-SNE, PCA)/.idea/hw9.iml
new file mode 100644
index 0000000..6711606
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/.idea/hw9.iml
@@ -0,0 +1,11 @@
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\ No newline at end of file
diff --git a/hw9 (k-NN, t-SNE, PCA)/.idea/misc.xml b/hw9 (k-NN, t-SNE, PCA)/.idea/misc.xml
new file mode 100644
index 0000000..ae42d94
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/.idea/misc.xml
@@ -0,0 +1,7 @@
+
+
+
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+
+
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\ No newline at end of file
diff --git a/hw9 (k-NN, t-SNE, PCA)/.idea/modules.xml b/hw9 (k-NN, t-SNE, PCA)/.idea/modules.xml
new file mode 100644
index 0000000..2e7e77a
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/.idea/modules.xml
@@ -0,0 +1,8 @@
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+
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\ No newline at end of file
diff --git a/hw9 (k-NN, t-SNE, PCA)/.idea/other.xml b/hw9 (k-NN, t-SNE, PCA)/.idea/other.xml
new file mode 100644
index 0000000..a708ec7
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/.idea/other.xml
@@ -0,0 +1,6 @@
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\ No newline at end of file
diff --git a/hw9 (k-NN, t-SNE, PCA)/.idea/workspace.xml b/hw9 (k-NN, t-SNE, PCA)/.idea/workspace.xml
new file mode 100644
index 0000000..612badb
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/.idea/workspace.xml
@@ -0,0 +1,244 @@
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+ split_data
+ load_data
+ knn
+ np.sum
+ astype(int)
+ plot
+ astype
+ shuffle
+ index
+ mnist
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diff --git a/hw9 (k-NN, t-SNE, PCA)/.ipynb_checkpoints/hw9-checkpoint.ipynb b/hw9 (k-NN, t-SNE, PCA)/.ipynb_checkpoints/hw9-checkpoint.ipynb
new file mode 100644
index 0000000..0caa40d
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/.ipynb_checkpoints/hw9-checkpoint.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Разобьем датасет с перемешанными данными в соотношении 9:1, и будем использовать первую часть в качестве обучающей выборки, а вторую как тестовую. Перед разбиением данных, проведем нормализацию и заменим метки классов на целочисленные значения."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "\n",
+ "classes = ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica')\n",
+ "\n",
+ "def normalize(data):\n",
+ " features = data[:, :-1].astype(float)\n",
+ " features = (features - features.min(axis=0)) / features.ptp(axis=0)\n",
+ " data[:, :-1] = features\n",
+ " return data\n",
+ "\n",
+ "\n",
+ "def to_float(sample):\n",
+ " labels_dict = dict(zip(classes, range(len(classes))))\n",
+ " ys = [labels_dict[label] for label in sample[:, -1]]\n",
+ " sample[:, -1] = np.array(ys)\n",
+ " return sample.astype(float)\n",
+ "\n",
+ "\n",
+ "def preprocess_data(data_filename, split_ratio=0.1):\n",
+ " data = pd.read_csv(data_filename, header=None).values\n",
+ " data = to_float(data)\n",
+ " data = normalize(data)\n",
+ " np.random.shuffle(data)\n",
+ " split_index = int(len(data) * split_ratio)\n",
+ " return data[split_index:], data[:split_index]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Проведем кросс-валидацию методом k-fold с разбиением на 10 частей. Подбирать будем параметр k алгоритма k-nn в диапазоне от 1 до 100."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def knn(train_data, test_data, k):\n",
+ " xs, ys = train_data[:, :-1], train_data[:, -1]\n",
+ " xs_test, ys_test = test_data[:, :-1], test_data[:, -1]\n",
+ " ys_pred = np.zeros(len(ys_test))\n",
+ "\n",
+ " for i, x_test in enumerate(xs_test):\n",
+ " distances = np.linalg.norm(xs - x_test, axis=1)\n",
+ " neighbours_indices = np.argpartition(distances, k)[:k]\n",
+ " neighbours = ys[neighbours_indices].astype(int)\n",
+ " ys_pred[i] = np.bincount(neighbours).argmax()\n",
+ "\n",
+ " error = 1 - np.sum(ys_pred == ys_test) / len(ys_test)\n",
+ " return error\n",
+ "\n",
+ "\n",
+ "def kfold(train_data, k_param, n_folds=10):\n",
+ " np.random.shuffle(train_data)\n",
+ " parts = np.array_split(train_data, n_folds)\n",
+ " sum_error = 0.0\n",
+ " for i in range(n_folds):\n",
+ " valid_sample = parts[i]\n",
+ " train_sample = np.concatenate(np.delete(parts, i))\n",
+ " sum_error += knn(train_sample, valid_sample, k_param)\n",
+ " return sum_error / n_folds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Построим графики зависимости значений средней ошибки от k при кросс-валидации и на тестовой выборке. Отдельно выведем точность на кросс-валидации и тесте для подобранного k."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best result: k=24, CV accuracy = 96.43%, test accuracy = 100.00%\n"
+ ]
+ }
+ ],
+ "source": [
+ "def search_parameters(train_data, test_data):\n",
+ " k_range = list(range(1, 101))\n",
+ "\n",
+ " valid_errors = [kfold(train_data, k) for k in k_range]\n",
+ " best_index = np.argmin(valid_errors)\n",
+ " test_errors = [knn(train_data, test_data, k) for k in k_range]\n",
+ "\n",
+ " plt.plot(k_range, valid_errors)\n",
+ " plt.plot(k_range, test_errors)\n",
+ " plt.legend(['validation errors', 'test errors'], loc='upper left')\n",
+ " plt.show()\n",
+ "\n",
+ " valid_acc = 100 * (1 - valid_errors[best_index])\n",
+ " test_acc = 100 * (1 - test_errors[best_index])\n",
+ " print(f\"Best result: k={best_index - 1}, CV accuracy = {valid_acc:0.2f}%, test accuracy = {test_acc:0.2f}%\")\n",
+ "\n",
+ "train_data, test_data = preprocess_data('iris.data.csv')\n",
+ "search_parameters(train_data, test_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "2. Загрузим данные mnist и применим t-SNE для значений perplexity 10, 50 и 100. Ограничим набор 9000 экземплярами для более быстрого выполнения."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn import metrics\n",
+ "from sklearn.datasets import fetch_mldata\n",
+ "from sklearn.utils import shuffle\n",
+ "from sklearn.manifold import TSNE\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "\n",
+ "def load_mnist(sample_size=2000):\n",
+ " mnist = fetch_mldata(\"MNIST original\")\n",
+ " x, y = shuffle(mnist.data, mnist.target)\n",
+ " x = x[:sample_size] / 255.0\n",
+ " y = y[:sample_size]\n",
+ " return train_test_split(x, y, test_size=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/mihail/bin/anaconda2/envs/py37/lib/python3.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function fetch_mldata is deprecated; fetch_mldata was deprecated in version 0.20 and will be removed in version 0.22\n",
+ " warnings.warn(msg, category=DeprecationWarning)\n",
+ "/home/mihail/bin/anaconda2/envs/py37/lib/python3.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function mldata_filename is deprecated; mldata_filename was deprecated in version 0.20 and will be removed in version 0.22\n",
+ " warnings.warn(msg, category=DeprecationWarning)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "perplexity = 10\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "perplexity = 50\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "perplexity = 100\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x_train, _, y_train, _ = load_mnist(10000)\n",
+ "\n",
+ "for perplexity in [10, 50, 100]:\n",
+ " tsne = TSNE(n_components=2, perplexity=perplexity, n_iter=300)\n",
+ " tsne_data = tsne.fit_transform(x_train)\n",
+ " \n",
+ " print(f\"perplexity = {perplexity}\")\n",
+ " plt.figure(figsize=(10, 6))\n",
+ " plt.scatter(tsne_data[:, 0], tsne_data[:, 1], c=y_train)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Как видим, t-SNE достаточно хорошо разделяет данные при perplexity = 10 даже для 1/8 части набора данных."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "3. Разделим набор mnist на обучающую и тестовую выборки в соотношении 9:1. Определим точность работы реализации алгоритма k-nn для исходной размерности данных."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/mihail/bin/anaconda2/envs/py37/lib/python3.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function fetch_mldata is deprecated; fetch_mldata was deprecated in version 0.20 and will be removed in version 0.22\n",
+ " warnings.warn(msg, category=DeprecationWarning)\n",
+ "/home/mihail/bin/anaconda2/envs/py37/lib/python3.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function mldata_filename is deprecated; mldata_filename was deprecated in version 0.20 and will be removed in version 0.22\n",
+ " warnings.warn(msg, category=DeprecationWarning)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Initial accuracy = 0.929\n"
+ ]
+ }
+ ],
+ "source": [
+ "def knn_sklearn(x_train, y_train, x_test, y_test):\n",
+ " classifier = KNeighborsClassifier(n_neighbors=10)\n",
+ " classifier.fit(x_train, y_train)\n",
+ " return metrics.accuracy_score(y_test, classifier.predict(x_test))\n",
+ "\n",
+ "\n",
+ "x_train, x_test, y_train, y_test = load_mnist(10000)\n",
+ "\n",
+ "init_acc = knn_sklearn(x_train, y_train, x_test, y_test)\n",
+ "print(f\"Initial accuracy = {init_acc:.3f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Применим PCA-преобразование для разных размерностей и построим графики точности для сжатых представлений и восстановленных. PCA реализовано через матрицу ковариаций и ее собственные вектора."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pca_transform(data, n_dims):\n",
+ " data -= np.mean(data, axis=0)\n",
+ " U, s, _ = np.linalg.svd(data.T, full_matrices=False)\n",
+ " U = U[:, :n_dims]\n",
+ " return np.dot(data, U), U\n",
+ "\n",
+ "\n",
+ "def pca_recover(pca_data, U, mean):\n",
+ " data = np.dot(pca_data, U.T)\n",
+ " data += mean\n",
+ " return data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "compressed_accs, recovered_accs = [], []\n",
+ "dims_range = [5, 10, 20] + list(range(50, 501, 50))\n",
+ "for n_dims in dims_range:\n",
+ " pca_train, U = pca_transform(x_train, n_dims)\n",
+ " pca_test = np.dot(x_test, U)\n",
+ " compressed_accs.append(knn_sklearn(pca_train, y_train, pca_test, y_test))\n",
+ "\n",
+ " recovered_train = pca_recover(pca_train, U, np.mean(x_train, axis=0))\n",
+ " recovered_test = np.dot(pca_test, U.T)\n",
+ " recovered_accs.append(knn_sklearn(recovered_train, y_train, recovered_test, y_test))\n",
+ "\n",
+ "plt.title(\"Test accuracy\")\n",
+ "plt.plot(dims_range, compressed_accs)\n",
+ "plt.plot(dims_range, recovered_accs)\n",
+ "plt.xlabel(\"dimensions\")\n",
+ "plt.legend(['compressed accuracy', 'test errors'], loc='lower right')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Точности оказались одинаковыми для сжатых и восстановленных версий изображений, точность, близкая к максимальной, достигается уже при сжатии до размерности 50."
+ ]
+ }
+ ],
+ "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": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw9 (k-NN, t-SNE, PCA)/.ipynb_checkpoints/sklearn.data_visualization-checkpoint.ipynb b/hw9 (k-NN, t-SNE, PCA)/.ipynb_checkpoints/sklearn.data_visualization-checkpoint.ipynb
new file mode 100644
index 0000000..6ca0db2
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/.ipynb_checkpoints/sklearn.data_visualization-checkpoint.ipynb
@@ -0,0 +1,478 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Sklearn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Визуализация данных"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn import datasets\n",
+ "\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pylab inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Загрузка выборки"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "digits = datasets.load_digits()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "print digits.DESCR"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print 'target:', digits.target[0]\n",
+ "print 'features: \\n', digits.data[0] \n",
+ "print 'number of features:', len(digits.data[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Визуализация объектов выборки"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#не будет работать: Invalid dimensions for image data\n",
+ "pylab.imshow(digits.data[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "digits.data[0].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print digits.data[0].reshape(8,8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "digits.data[0].reshape(8,8).shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pylab.imshow(digits.data[0].reshape(8,8))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print digits.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print digits.images[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pylab.imshow(digits.images[0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pyplot.figure(figsize(8, 8))\n",
+ "\n",
+ "pyplot.subplot(2, 2, 1)\n",
+ "pylab.imshow(digits.images[0])\n",
+ "\n",
+ "pyplot.subplot(2, 2, 2)\n",
+ "pylab.imshow(digits.images[0], cmap = 'hot')\n",
+ "\n",
+ "pyplot.subplot(2, 2, 3)\n",
+ "pylab.imshow(digits.images[0], cmap = 'gray')\n",
+ "\n",
+ "pyplot.subplot(2, 2, 4)\n",
+ "pylab.imshow(digits.images[0], cmap = 'gray', interpolation = 'nearest')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pyplot.figure(figsize(20, 8))\n",
+ "\n",
+ "for plot_number, plot in enumerate(digits.images[:10]):\n",
+ " pyplot.subplot(2, 5, plot_number + 1)\n",
+ " pylab.imshow(plot, cmap = 'gray')\n",
+ " pylab.title('digit: ' + str(digits.target[plot_number]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Уменьшение размерности"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.metrics import classification_report\n",
+ "\n",
+ "from collections import Counter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = digits.data[:1000]\n",
+ "labels = digits.target[:1000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print Counter(labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pylab.figure(figsize = (10, 6))\n",
+ "pylab.bar(Counter(labels).keys(), Counter(labels).values())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier = KNeighborsClassifier()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier.fit(data, labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print classification_report(classifier.predict(data), labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Random projection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn import random_projection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "projection = random_projection.SparseRandomProjection(n_components = 2, random_state = 0)\n",
+ "data_2d_rp = projection.fit_transform(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pylab.figure(figsize=(10, 6))\n",
+ "pylab.scatter(data_2d_rp[:, 0], data_2d_rp[:, 1], c = labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier.fit(data_2d_rp, labels)\n",
+ "print classification_report(classifier.predict(data_2d_rp), labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### PCA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.decomposition import RandomizedPCA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "pca = RandomizedPCA(n_components = 2, random_state = 0)\n",
+ "data_2d_pca = pca.fit_transform(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pylab.figure(figsize = (10, 6))\n",
+ "pylab.scatter(data_2d_pca[:, 0], data_2d_pca[:, 1], c = labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier.fit(data_2d_pca, labels)\n",
+ "print classification_report(classifier.predict(data_2d_pca), labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### MDS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn import manifold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mds = manifold.MDS(n_components = 2, n_init = 1, max_iter = 100)\n",
+ "data_2d_mds = mds.fit_transform(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pylab.figure(figsize=(10, 6))\n",
+ "pylab.scatter(data_2d_mds[:, 0], data_2d_mds[:, 1], c = labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier.fit(data_2d_mds, labels)\n",
+ "print classification_report(classifier.predict(data_2d_mds), labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### t- SNE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "tsne = manifold.TSNE(n_components = 2, init = 'pca', random_state = 0)\n",
+ "data_2d_tsne = tsne.fit_transform(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pylab.figure(figsize = (10, 6))\n",
+ "pylab.scatter(data_2d_tsne[:, 0], data_2d_tsne[:, 1], c = labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier.fit(data_2d_tsne, labels)\n",
+ "print classification_report(classifier.predict(data_2d_tsne), labels)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/hw9 (k-NN, t-SNE, PCA)/.ipynb_checkpoints/sklearn_coursera-checkpoint.ipynb b/hw9 (k-NN, t-SNE, PCA)/.ipynb_checkpoints/sklearn_coursera-checkpoint.ipynb
new file mode 100644
index 0000000..d5e69a1
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/.ipynb_checkpoints/sklearn_coursera-checkpoint.ipynb
@@ -0,0 +1,509 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Sklearn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Визуализация данных"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn import datasets\n",
+ "\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pylab inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Загрузка выборки"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "digits = datasets.load_digits()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print(digits.DESCR)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "target: 0\n",
+ "features: \n",
+ " [ 0. 0. 5. 13. 9. 1. 0. 0. 0. 0. 13. 15. 10. 15. 5. 0. 0. 3.\n",
+ " 15. 2. 0. 11. 8. 0. 0. 4. 12. 0. 0. 8. 8. 0. 0. 5. 8. 0.\n",
+ " 0. 9. 8. 0. 0. 4. 11. 0. 1. 12. 7. 0. 0. 2. 14. 5. 10. 12.\n",
+ " 0. 0. 0. 0. 6. 13. 10. 0. 0. 0.]\n",
+ "number of features: 64\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('target:', digits.target[0])\n",
+ "print('features: \\n', digits.data[0])\n",
+ "print('number of features:', len(digits.data[0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Уменьшение размерности"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.metrics import classification_report\n",
+ "\n",
+ "from collections import Counter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = digits.data[:1000]\n",
+ "labels = digits.target[:1000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Counter({3: 104, 1: 102, 6: 101, 2: 100, 5: 100, 0: 99, 7: 99, 9: 99, 4: 98, 8: 98})\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(Counter(labels))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pylab.figure(figsize = (10, 6))\n",
+ "pylab.bar(Counter(labels).keys(), Counter(labels).values())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classifier = KNeighborsClassifier()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+ " metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
+ " weights='uniform')"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "classifier.fit(data, labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 99\n",
+ " 1 1.00 0.97 0.99 105\n",
+ " 2 1.00 1.00 1.00 100\n",
+ " 3 1.00 0.98 0.99 106\n",
+ " 4 1.00 1.00 1.00 98\n",
+ " 5 0.99 1.00 0.99 99\n",
+ " 6 1.00 1.00 1.00 101\n",
+ " 7 0.99 0.99 0.99 99\n",
+ " 8 0.97 0.99 0.98 96\n",
+ " 9 0.96 0.98 0.97 97\n",
+ "\n",
+ " micro avg 0.99 0.99 0.99 1000\n",
+ " macro avg 0.99 0.99 0.99 1000\n",
+ "weighted avg 0.99 0.99 0.99 1000\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(classifier.predict(data), labels))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Random projection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn import random_projection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "projection = random_projection.SparseRandomProjection(n_components = 2, random_state = 0)\n",
+ "data_2d_rp = projection.fit_transform(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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Q4FUgC1gAnC2lbPj1XjkISKsA6X8drM0I52HgOQGMXiDcdSUmduVFOHrvpr1tSP8bYOWDYwTCeyJ57k4YwsBqkHA5NTcdPF0JR0w+X7iaH5ZtILdNKicffigdc6JFUosra3j3u1/YUFTO4G7t+c3IvniTXMOqUY4BYC4DGiSLMgxGN8qq/Lzz/S+sKyhlQNc8pozqS4on3qbg+44lLZZUzueXioWkGCmMyjqSdp6OLRpDYyzb5rsl6/ly0RpSPC5OGtOfXh1z9ndYiqIoeyRpRU2FENcBw4G0umTrdeBtKeWrQoj/AIullI831oYqanrgkeF5yPIL61b9hQEP6DmQ+TqUTY8mTGxfYWhE9y3M/hgh4ic7MvwTsvz8aHHSHe1lITPf4P5V91IY2roj4dLQyXC24bpu93DhA2+xubiSQDiCoWsYusZ9F59AmxQ3Fz/0JqZlEzYt3E6DDJ+HF286k8w0b9wYkk2am5GlJzRIPN3gmcLamuu44P7XMC2LUCQaX4rbyYs3n0VuRkqLxGdJk0fX/J1N/nWE7RAaGrowOL3ThYzI2v8jhZZtc81jM1i4ZguBUARNCByGzh+njWfauIH7OzxFUX7FWrSoqRCiI/Ab4Om6vwvgKGB78aYXgGYsn1Nas2jR1RvqVktuH7QMgLUN/E8jsl4Bz9ToZHjhBfcJiKw3EidaUiIrbwDpb9BeIaL2ca7qdRsjM8fj1jw4NRdD24zmul538cY3v7CxqJxAOJrUmZZNMGxyy3Mfc+vzn+IPRQib0dt4wbBJSVUt//kgUa2v5BNGR0Tma+AcDbhAy4KUSxFpd3LH/z6jNhgmFNkZX3lNgIff/rbF4ltQNntHogVgYxORYV7f/AwhK86t4Bb29aK1OxItAFtKQhGTB978mqra/R+foijK7iTrNuLDwJ+A1Lq/ZwEVUu6457MZ6JCkvpTWwtoCdsNK8ABhCHyMSP0TIv1uSL97z9qzi6KJWowIBD/Fk3YLp3e+kNM7X1jv7GfzV+5IVupFETHZVFwRc9y0bL5ctIY/nzlxz+JKAuHohch8od6xQDjCyk2xRWstWzJryfqWCo2F5XN2JFq70tBYV7uSvmmDWiyWeD5buGpHorUrQ9eZt3ITRw9tHeU9FEVREtnrkS0hxBSgSEq5oJnXXyyEmC+EmF9cXLy34fzqSCmR5nqkmd/yW9MIN4kLisYr+7C79lwkLPXZSHseV/w9He1GXg+XY/ffM0zbZGtgE5WR8t0+dk8EzRA/blzCupJonTND0xKuBHTushpxa2kl6wpKk17odDuXHv+1lUicWsvOHYvH63SQaMGkO8F7v7eCVoCtgY34zZqYc0XBStZWF2LaratgrqIorVcyRrYOB04UQhxPtLBSGvAvIEMIYdSNbnUEtsS7WEr5JPAkROdsJSGeXw0Z+RlZfnV0WxoAvR1k/Hu3E9CTRejZSEd/iCymftLlAe+ZTW9Py0A6hkBkAfVX77nBc0bC604dP4hVm4sJhHdOQBcC2mel07ZNCvNWbqq3JY7LYXDK2Mbn+swt/Ya3N7+ARGJJi24pvTmv69X4jObNo3p63uv8ZL+HEBIhJPLnLG4adivjBnRj1pJ1RHYpOOpy6Ew9/FA2F1dw/ZPvk19YjiY0vG4Hd58/mcP6JLcu1piso1lWtShmdMupuTjE1yupfTXH1CMO5dMFKwmG6y8w0DXBYb07JbUvKSXvb32Vb4s/RqtbkDEi8whO7XQBVZEgN/30MksrN6MLDUNo3NR/KsfkqXljiqI0bq9HtqSUN0spO0opuwJnAF9KKc8CvgKm1T3sXGDG3val7CTtSmTZuWBvIVo5PQjWBmTZdGRMxfl9R2Q8DHr7nfOycIH7SEQzkq1oew+A3nGX9tzROmC+cxNeM3lEHyYf1genQ8fjcuBzO8hO8/HQpSfyt3OPo2NOBl6XA6/LgcthMLJvZ84+ZljC9tbWrOCNTc8StAOE7CCmjLC2ejnPrHuwWc9p5uofWMQMDKeF7rDRDIlIK+EfC2/nlrMm0rVd5o743E6Dwd078PvJh3HRQ2+wZkspoYhFIByhtMrPNY/PoKCsqllxJNI7bQDjcyZjCAcuzY1L8+DVU7ik+41oraAo7KBu7bno+JE4DR2vy4HP7STF4+Rfl09Nej2yb4s/ZVbJp0RkhJAdwJQR5pfN5oOtr3Htgv+ypGIjYdskYIWpNoPcueQtllWqjbwVRWlc0lYjAgghJgDX161G7Ea09EMm8BMwXUoZOzFkF2o14p6T/peRVfcQswGz8CLS7kAke/udxmKRNoTngr0NHAMRRvcktPcj2AXgGIAweuzRdZuKK1i8diuZaV4O690ZQ9fq2pMsXL2FgrIqenfKpWeH7EbbeWrt/fxSFXtX3CGc3Nz3PrJcuU16PtfOvBErYyMN8xbL1Liw/a0Mat+bxeu2sqmogl4dc+jdKZc5y/L501PvUxusP1fJoeucN2k4l544pkkx7ImKcCmra5bh0b30SR2EobWuMnzFlTX8uGITPreT0f267NGt4Ka67ZfLqYjEzkN0CCc/FnUhaNcfXRMIJuUN5M5Bpyc9FkVRWr89XY2Y1P+tpJRfA1/X/XkdoKon7iPSKiIm0YJo7Sa7Zee+RQuKjk5ye6OafF2nnAw61dXWqt+eYFivPa8ZVREpjXtcFzpVZkWTk62wVoURZ4BI2oItVYUM7tCHwd07MLj7zjUkxZU1xNs7OmJZSR/Z2i7DmcWIzP1f6iGRnPQUfjNy3+7jWBtnjhaAKSM4NEGwwXsikWwLxC7CUBRF2dX+v0egNItwDt1lL8B6J8AxJOn92eFl2CUnY28bgF00Brv25aT3sZ0ML8YuvwK75ETsqjuRcVco7ju9Uwegi9jvIZa0yHM3fY5QW9ET24yd4a1pNod1ij/fZ1C39tgyzsbRLgeHtfBehqsrSrj22w847t3nuGHWR6yrjLcC9eDQ2dst7vE2zhxCcRYoODWDkdl7NvKqKMqvl0q2DlTOI8DoQ/3Nnt3gGBr9lUR2+GcoOxnMpUAI7BKovh274rak9gNgBz5Dlp0Noc/BXAH+V5AlU5DmpqT3lciE3OPx6F50ds4HcmouJuedglv3NLm9C4ZMx4o4sK2dCZcV0WjnH0VOSvz9G7u0bcMxQ3vhdu5M+pyGTl5mKpOGttyk9QWFWzjx/f/y3rrlrCgv5u21S5ny3gssKWnZBLilTO04HafmQuyymZVDODmt0wWc3XUsbn3n6kdD6KQ5PJzaOXmjuoqiHJySOmdrb6k5W00jZQhZ+yIE3okuv/OcivD+DiGSuxzeLp4M1tr4J3MXoWnJqcQupY0sPnzn6sodNHBPQcu4Pyn97InKSDlfbHuP5dWLSTXSOKrtFAak7/a2fEL5ZVt5ctHzlBtrEZab0RnHMn3IiY1eY9uS935YyhvfLCYYNjl2eG/OOnoovhbcauj4Gc+zrCy2Ftiw3A689ZuzWiyOllQQ2MSn295hk38dbd0dOLbdyXTx9UBKyVeFS3l5w3dUhP2Mze3D2YeMI9PVMpX+FUVpffZ0zpZKtpTdsrf1Je5GygAZT6K5JySlH2kVIIuPJbq6sgEtBy13dlL6UfaMLSXdnr8v7jlDaKw57/oWjkhRFKV12S8T5JUDg23bEHgDrA3gPg7NuZsK4cJVt4VOHEbz6xxJa1u0RpeWE51nJlJJWCRVi95uC0Zq+X7T51jSZnTHI0lx7bwNV1xRw8/rCshM8zK4e/uEBUP3PL4iiPwEWiY4hkUn7u9VeyXRGmJaG3AM3+v29jUB+AwHtWZs9fZUZ7TYqWVaLP56Kf7qAAPH9yMtMzXmsa1VTUUti79eitvnYtCE/hj7YHWjoigKqGTrV8cOL4Gy04G6Jez+Z7D1HpD1AZqW4MPffToEnotzIg2tGWUepJTI6rvA/1p0Qj82aLmIzP+C6ygIfcnOvREBPAjfRczK/5A/fLMIISRIsFjB/SO7cXyv03norW95/ZvFOAwdKSVtUr08cc002melNTO++8H/Ql18EkQGZL6AMJo3Od2ufhhqn96lvVTI/C/C6Nqs9lqCEILpfYbwwvKFBK2dJQ88usF5fYex5qf13HTcXUSCERAQCZtcfM90pl55/H6Mes988MRnPH7t8xhOAyQYTp27P/wzfQ5TW/8oipJ8rfurtZJ85WezI9HazloD1bcmvkYmKjVgJji+G8H3wf8mEAZZEx01szYiyy9HpP8dnKMAF4iU6O++86lkNBd/s4hay0GN6aTGchKwDP74wzpe/f4b3pr1M2HTojYYxh+KUFBaxXX/aWYd3dAX4H9xl/hqwS5All/SrC2RZOhr8D/XoL1CZPlFLb/FUhNdP2wsk7v2wqnppDpcuHSdk7r349L+I7jp2LuoLK7CXx3AXxUgEozw9M0vseLH1fs77EatXbyB/1z3AuFgBH9VAH91gKrSGv48+W7CcfZgVBRF2VtqZOtXxA4vTXw7MPB+4g2jgx8kaNGPHV6G5uzXpDik/3/E1gizwVwFsgot82mkVQBWIRjdEVoqn/3yHPFuClpS8NTCBQTC9b832FKSX1jBxqIKOufG1t5qPL4X48dnbY0uFNjDIqs72qt9CWKq+svoxtvmCnDs29pRe8Oh6Tw0bgp/HnEkG6sr6JrWhiy3l4Vf/EwkTmISDkb48KkvWvUI0SfPfBk3dsu0Wfj5z4yaknh3AUVRlOZQydaviV3SyMnGRqkaOSfjFwBtPI7q+MeFDnYt6CD0PNDzdpyqiQQxZWy6ZUqNgGUTb5BW1wT+UDjmePPj06LxNVXCkUE9OtJ1AMjx+Mjx+Hb8vbYqQLzsV9qSmvJmvEYtqLq8Ju6m3lJK/NUtt9WVoii/HirZOkjZgRlQdWfdB70D3L+FtNuIfkLGuXVlNDI6ZfQDc0mcExo4EtcYqg2Gefz97/n4xxVIYNKwXlx+4hh87mOh9hnqz8sCcIHRDdv/PtQ+Gh35cfRHpN7AuM6juOfnj2P6cGsWR3fqwBdrywib9VdMGrpG9/ZZiZ9XIu5jWZm/lbmVLtIyqwiFnDirvZzUvQLd0Zf/LXyXOdUfItwBRG0GJ3b4HZN6Hx4txVHzKATeilbyd09CpFwH7skQWU7sKksJjsSbGJt2hE+3vcMPpV9jygiHpg/jhPZnkOZo2kjdvjBofD8iodgk3O1zMfaUUZSFS3h/yyssr16MS3NxRPYxHNX2BHShM2dZPo/O+I78ulHHK046gtH9ujQ7llXVv/DB1tcoDG4h29WW4/NOo3964sK+h588ktnvziNYW//9ME2LIUcd2uw4FEVRElGlHw5CduBjqLw69oRzAjgHQ83DDU7okP0JmhH/A882N0DJZGLKP6Rci5ZyafxrbMlZ/3yJdQVlROqSIIeu0TEng1f/fBJ6xSlgFRNNQHTAgcj4F9LaCNUPUv82ngcyX+KIJz6iwANSI5ozWpDhh5nnXsaFD75JYUU1wbCJrgkcus5d5x/HUUOafjtr3dYvWFx1Aw5HhO1rBsIRneqirlQZ01iuf4Ju7Fw1aZkaJ6ZdxsTsJ6KrF9m+BagRXWmZ/Q6UnQvmxrrnpQFOSLsLzZu41tbja/7B2prlRGT0lpeGTqojjb/0fRCX7k54XUt56+EPeO6WVwgHIkgpcftc9BzWnds+voZ7Vt+I36pF1q0udQgnh6YPo1vNFG586kNCkZ2Jmsth8M8Lj2f8wKYvtlhetZhn1j1IRO5M3B3CyfQulzG4zci411iWxc2T72b5nFUEa0MIIXB6nEy/9RTOuPHkJsegKMqvlyr98GtWfWf84+GvIeNBcAyA6vuieygaIyDtVjQjJ2FzmtEVO/vr6EiZuSCaQKTegOZKvI/eDyvy2VRUsSPRAohYNoXl1cxaWsKRA99HBt6G8Hegd0B4zwS9MxSNJHa+VJCKwn8i1o4kxRUmlG2DAGephi/gZMGaLbz8l7P48IdlzF66gbZtUjlt/CC65TVjVAuYtf5RMtua7Lo40+mwSGu7gYVlH6E3+FejGzafFj3PxPTF7Ey0AEywKxDBLyHrTQi8hwx9GV156f0dwtEnYQyb/RtYW7NiR6IFYGMRsPzMK5vFETnHNOu5JdMp10yh3+jefPTk59RU1DKX336pAAAgAElEQVR22mjGTRvFl6XvE7KDOxItgIgMs6RyPm+/462XaAGEIiYPvfVts5KtGVterJdobe9rxtaXEiZbuq7zj4/+wqy3fuDbN+bgSfVw/EUT6T+md5P7VxRF2RMq2ToY2Y3sXRf5JZokNZIoxaMZbSHzkT1+/KpNxTEfqgD+UISVm4o4anAPhG86+KbvOCetrSDjFU+VuFiJaR6GI6ThqNqZBQUwWZZfyKRhvZk2bhDTxu2mZtge0NzF6HrsiK9laaS6aqmUvthznhri3p4lgIwsRvNOA++pCO+pexTDlkB+tE5YgybDdogNtatbRbIF0HdkT/qOrD96uK52FaaMnYBuCIMtxfHnw20qqkBK2eTaaEWhgrjHy8LF2NJGS1DLTDd0Jpx+OBNOP7xJ/SmKojSHSrYORsKbeOK13vTRA4gWQn1x5kJ+WL6RvMwULj1hDNnpibcp6ZCdjsth4G+w6svrctAhOx0pJQtXb2Hxuq1kpfmYOLQnXlcbEhU1Dcs8HIZOxKqfjHmcBp1z4+8vuDP2Cqj+V7SIq3MEeC9G06I/+gW11Xy8YSWmbTOxcw+6pWdiBdOw7Qoalh3TdRu/6WaXLRN30MKu6AT/mHzLDfohjcYXjph8tXgtm4sr6dUxmzH9u5Llyq23P992hnCQ627faHv7WztXB1bxC1aDhRU2Nm1S3ZRVxe4QkJXmbVYR2jSjDeWR2IUfPj01YaK1N/xmDYsq5lJr1tArtT+dvd13xJ2/bBNzP1yI0+1k7LRRZOU1/nOpKMqvh0q2Dka+y6Dm3tjjes9GbxcmUuMPcfwtT1MT2Hm75p3ZS7nv4ikcnWBO1PiB3dDifHhKKZkwsDuX/t9bLFm/jVDYxOUweODNb3jq2mn0TD0Ogg3rYwlSsq8g1bOcUNjCknrdURunEd0zMBE79D2Un8+OLCg8G2r+g53zFW+v28otcz5HIrGl5IGfvuPSASM5sv15rI/cjVPb5RZoRKe8pB2p4ZEUexeiO3aZsxXRGOE5GbStYIWot3pTOBDexPOACsqqOO/eV6kNhgmGTdxOg7ysNJ7542lkOLIoDm3D3mWunCEMRmcdmbC91uCI7El8Uxy7mCHDkcXFx4/h4be/JRje+Rq5nQYXHj+qWX0d2+63vL35BcJy5+1bp+bimLZTm9VeY9bULOeJtfcCEtOO8Fmhg/5pQzmn6xU8c9NLzHjkEyzTQjN0nrrpRW547jImnKZGzhRFUUVND0payoXgOYd6b6/RD7LeaFZ7Nz3zUb1Ea7s/P/NRwmtKq/0xqwMBLFvy38/n8/PaAgKhCLaUBMIRagIhbnjqA2R4VZzWNPTIfJ49/2WGdi3A0CwMzeLQjkU8c/5beI2NiYOvuIzY4aYgJduu5C9zPiNomYQsi4htE7JM/rNkLkbaeNqEz6OqyodlaURMjYriLkwb+hJ/GXsNOf5hWBEN2xKYQQf95WSmDzkJkfkSOMcQ/Q5jgHEoIvNlhJZ4hOOO/31GabUff91r4Q9F2FhYzqPvfc9VPf9Kn7SB6EJHQ6eDpwtX9vwrqY70xM+3FSgMbUYXscN/lZEyph7Rh0tPGEOqx4XD0EjxuLh0ymhOHZd4VWZjRmVNYEr7M/DqPnRh4NY8TGp7MhNyJ+/t06jHkhbPrnuQsB0kbIewsQnbIZZWLeSDH99hxqOfEgqEMSMW4UCYcCDM/ec/Rk1F6y6DoShKy1AjWwcpLf0W7NQ/g70FtCw0zdvstuatjJ/MRCybn9ZsZkiPjjHnvlq0Ju41tpTMmLOUYJz5XCUVNWwqLqZzzLx2C4Lv0DYtxOPnvE8gbGBLgc8VAXRk4CNE6lWxfZmbEhZxnbm1Gl1oNFxhGbYsPli/ghuHX4tlXUVF7Qa8rmw8PXcmOLdNuI6QGaHCX0WWLwNDrxtp03MQmU8jZQCkhdAS32aF6MTwBas2x9R8ilg2n85byc1nHMUl3f9E2A5jSxO33vz3sCX9WPZtvYn92wkEa2uXc/bEYZx51BBqAmFSPE70RNtE7QEhBONzj2NsziQClh+P7t0ntw/za9dgxplPGLZDfF/8JeE4X0Y0Q+PHj3/iqN8dkfR4FEU5sKiRrQOElCbSrom7vYuUQaQMxRzXNA3N6LRXiRbEn/a9XZzakLttK+ElsfPB4/I4zbpEq0HbUlIbDGNa22/xJdjUuq6zRH3JujO6rpOZ0g63M3ZCvFPXyE1xxU0UhPDsNtHavZ3ROTVnkxMt07QoKiyPbjqeBKZlUlZeimk1c4umOtt/fHVNI93nbnKiFbKCmHZsDJrQ8BkpSUu0pJTUmEFsGX39JDLuDgZ1j26sob2KI+gPqS2EFOUgoEa2Wjkpw8jqe8H/OhCJVlVPux3hGoc0NyIrb4bIwuhjnaMQ6f9A6O2SGsOIXp2Yszw/5rihawzpHn+y9oRBPfj3u9/FXqPBCSP78drXi2JGt7JSfXTOzQarlPofYC5wnwSBV+P0pCPcxzFnWT7/eGUmBWVVGLrO1DH9ufaUcRh4iC0lAUe193L78thExKnrTOnaBxmag6z6K1ibAQPpORmR9hfAgax9PLqptAyBloFM+ROat+lzhFwOgyE9OrBw9RbsXT6UHbrGpGG9mtweRBcy3HTJYyx8cTZELHAZTLjiWG6559xmt3ffXffz1QPzsAKg+wTH3jyGa/90TcJrRmSOZVnVIsJ2/S8AEkmP1KZt7bTdJv96Xtn4JAWBjQihMTB9OKd1uhCvEZsI7613N83j8dWfUR0J4NadnNdtPGd2PRwRJ5EzhINRWUey1DuDYG3952tFbEZMTlxctTH5yzfzwO8fZ9X8NSAEI44dzHVPX0qb3NZ9C1lRlPjUyFYrJytvrUu0goAF1mZk+RXY4R+RpadBZEH0OBaE5yBLT0PGuYWzN848Kv4HxtAeHdASjEzkZbq5cuJCnIa5Y46VyzC5cNwi/jBlCP27tsPjciCIrihMcTu556IpaG0eBJEG1I3kCC8YPSHlKhCeOD3prNjq4I9PvMfmkkosWxKKmLz7/S/c8b/Poc0jxO4r4yKn7b+4Y+REXLqBU9PRhYZbN/h9/+H0T69All8CVj7R1zYEgXeQFdcjax6Bmiejm0ljRrdAqvorMjizOS8tt58ziTapHrwuB1C3WjMnWlW9Of582RMsfGEWImQibIkIRPj6oY948M7XmtXeg/c+zMx/zsOqBkywKiWf3D6bxx55POE1/dOGMih9JE7hQiBwCAcO4eTcrlfi1JxNjqEiXMa/V/+NLYEN2NhY0uTnyvk8vvYfzXpOjfl062IeXP4B5eFaTGlTYwZ5es2XvJr/PTnO2C8xlrQYOXg0x194NC6vE93QcLgcON0Ornv6D6S2afoIZ3V5DdcccQsr5q7CMm2siMW8Txdx3bhbkzZSqShKy1IV5FsxaZcji8ZRv1AmgACjD5j5QIM5ScKHSP8nwn1s0uK47P/e5oc4I1suh85n91xCqscVG3vwE2TlzWwu1Zm5vBtSwoQ+G+iaE0ak3Qbuqfy4YiOL1xWQne7jmGG9drQj7VoIfgJ2ATgOBec4CH2FrLy+LsnZlYc3F53APe9lxdyxcRo6H919IRm+ANQ8CNZGcAwH36VodR/6m6or+XjDSiLSZlLnHvTMyMauuA6CHxF7G9IJwog/D8zog5b93h6+ovUFwyZfLlrNpqIKenXMYeyAbhh6078H2bbNpJSzEMHY22yijZfPSl9ocpuTs0/DLIv9P8KVp/HBlsYTuPzatayoXoxb8zKkzahmbzP04dbX+LLoA0xZ/3k5NRdX9fwrnbzdmtVuPKd8+wCb/LH7faYYLoZkrcWk/hcZgWBU1pGc0fki1v2czw8fLMDlcTLu1NHkdGxeUd23//Uhz/75ZUIN5oF5Ut3c/tYNDJ3YvMUEiqIkn6ogfzCwCkA4orer6pHRcw0TLYg+1tqU1DDyi8rjHtc1jZLK2rjJFmY+yCAdMy3OPXzRLvGBNDeiCcHIvl0Y2Td2iyCh+cB7Sr1j0sqP7jcYI4CDjUgZ+8HmNHS2lVeTmdYW0u+O+xw6paZz8YDDGsS+lvjzvZwgY2tEAWBtiX98D7idBscf1rfZ128X8IcgTqIFYFc1fYNl0zLjJloAoaLdj7B08XWni695dd12VRDcHJNoQTTRKQkVJjXZKgpWxj3uN8MI4YAGo8YSybbgZgC6DexCt4HN3+Nxu43Lt8QkWgC2abN1bSFDJ+51F4qitDCVbLVmemeI8yEDGhg9wFwWO8oiXGA0b9sRKSWEv0cGZgAC4TkRnGPo1zmXwrLqevOKILqysF1mavzGHH1BuGNHooS30W1qEjL61CWeDW6RCi+m1ifu9toRy6JTTjp2eDFU/T26MtMYCGl/RTMamdfmGAzmaqDhax8G4QMZ5wPZaPoejHsjYlp8vnAV3yxeS5tUL9PGDqRbXiYixQU1sYsljJwE71MjDN3A1UEntCV2FZ63a/S255tvvsnMDR9hOyN0sw/lqkuuwuOJd7u3+Q7x9WJF1c8x2/LY0qa9p3Oj124J5PN9yUxqzCoGpI9gSJuR6CLxf3tdfDmsqo6tSt/G6cWOc3teFwZdvc1/7xcsnM+Lj75G5bZqRv5mCGeffzZ9DuvBzJe+jZkDJjRBt0F7n8wpitLy1JytVkxoKeCKV8BSQsr1oOUCjl2OO0DvBM7mFVKUVbcjKy6D4LsQfAdZcRmy6g4u/s1onI76dZPcToNzJw3H43TEb8x5BOgdY+PT2oLrqKYH5xxFUXU2IXNnHGFTo6zGw4A+58ROywJG9+uCj/eg7FQwfwK7CMJfQMkE7MiKhF0J34XRpLVeox7wnk5JZErM7UopoZIzm/6cmikUMbng/te4+6WZfL5wNW9++zNn3/MKH/+4krHXTkAa9f9ZS0Pj5L/+pll9nXbX8YgGe14LF5x191Ru/uef+Kb9mzgm+HGPNdk04ieufvn3BAJNH0VrzKisI3FqrnoV9R3CSe/UQ2nr7pDwurml3/DQyr8yu2Qmiyrm8tqmp/i/VX/DtBPPabyq92RcWv2fabfm4KrexzM883AcYuecs+h8NCcTco9v1vN6/rnnufnwe/jlvxvY9HEpb9/wBdOHXcSwKQNIzUxBN3b+rDvdDroP7hqzNZKiKAcGlWy1YlJaEPohzhkDwrMRWa+D57fRCeUiAzxnRItoNmP5u4wsg8A7IHf5oJQBCLxNj9xinr7uNIb36oinbrud66dN4OJGqn4LoSEyXwbP6SDSozF6fovIei16O6aJ8gsrOOs/k3lrfj8q/G6qAk4+WtyLs586mTe+Xb6j1tWuFq7ejKy6PU5rNlRcnTh2oxMi83Vwjo2OZGl5kHoNIvUv+Ks+o2FhfFsK1m9o3gT05nh/zlLWFpQSCEfq+o8uCvj7KzNxn5pPxnU+7Gw30tCw23nIvtWHPmlDs/o659xzuODZU/D1cqB5ILWPkz+8/DuGDh1KzYSNaB6B0KMviO4VuPrY/OuJh5L1VAHwGSlc3+fvDM4YiVvzkGqkc1TbKZx/yLUJrwlZQd7Y9CwRGd6xIXbYDrE1uJH55bGrZLc7LLsHDww9m75pHfDoTg7x5XLHoNOY3GEIZ3S+iMl5p5DuyMSluemfNoQ/9r6LDGdmk5+TP+Dnlas+jM4QqBs4tANQsyHMc08+zyNz/8FRZx6BL91Lek4aJ15+HP/89NZmbWmkKMr+pybIt2IysgpZdlr8Cdl6D7ScxBXcm9xXzePImv+jYZFP0BEp1yBSLklaX83xypc/8a93ZsVUpRdARoqH8prY0ZTeeVW8eNFLMcnR9iu1diubFEO1vwRn+RE49Nj5SlUBFxmHLGlSe8118UNvMH/V5pjjPreT7hN/Jr19Vcw5h3By/+CmT5BP5IF/38/6wfPRU2Jf3OB3Bk9c+b+k9dUcK6p+5rn1DxO04/xcpA7gsh5/3g9R7fTFzM+5b+qT2HEKzGcM9vDGwv+2fFCKojTZnk6QVyNbrZnwgEwwEVlLcn0h4SP+FD5HtPxCM0kpWbu1hDVbSmIqpTeFx+VAj7NCz9A13M5o3C7DpE9eMW3Toptw1wSdcW8vRmk74ltTUcrysqKYOWmWHaIytJSAWVDXV+KyBWGz6aN1zeVzx1mQQPS5GAnCcDSj5EJjPK74PxPSkmjhODt172N+s4aN/rXUmNFE06W5dxSnbchTVyDWkhab/RsoCsbO0WoOS9qsqiogv6Y4bvHhXflSUhLW3HWmHBhTaW1bsnZ9MRs2lsQ8X78ZYlnlZkqCsYm/ovwaHRj/qn+lhNEJaXQDcwX1/2f2ILzTk9uZ+ziovi/OCQnu5u0zt3xjIdc/8T4VNdEVfKleF/deNIWB3fKa3NZRg3tw7+tfxRzXNI3pE4exOf8xLj1yNrYUGLrNL5vb8tis34Fog7TL641uSQnCdQQry4u5eOY7FAVq0QCP4eTfE05gdF5nNla9wfKy+xAIbEzSnYcytO1DrCzsS6/c5TiNne9HMKKzofpYcpv8rJrn1PEDmbsiv95mzgBpXjfjeg5gUeVcrF0WVjiEg1FZE5Iaw7lnncOffvw25rgMw9j2k5LaV2NsafP25heYU/oVhjAwpcmwNmM4tdMFuHUPIbv+6lGn5mJM9kSWVv7ES/mPYUoTW9pkuXK5sNsfyXE1ryDwDyWr+evi1wnb0T0u23kyuG/IdLqkxN/4feSIkTizdYKbrXorOzQPTLn0mGbF0JKWLN3Mbf+Ygd8fRkpok+Hlzlum0qNbLs+u/Yrn132DoWlEbIuRWT24c9DpeI34XxIU5ddAjWy1cqLNo6C3rxt58gFO8JwM7hOT24+eC+kPAh4QKXW/PIiMhxB6dpPb8wfDXPLwWxSUVRMIRwiEIxRV1HDZ/71FZW2C8gmNSPO5uf+SE/C6HPjcTnxuJy6HwW1nH8Ppo2u56pg5eF0mKe4IbofFoE7bePycr7lv/SWEbB0po1sLSQnbQj7eKL6IMz5+lfzqCgJmhFozQkmwlt9/8RYry2axvOweLOnHlLXYMkRFaDELCq+iS8/H2FqRiz/soDbkIBgxWF3chyEDb2vyc2quMf26cvbEYTgNHZ8r+lpkpXn59xVTObXzBXT0dMWpuXBpbhzCSfeUvhyfd2pSY/D5UhhfcgpmhcSqif6yQxL3N3mc+buWWywws/A9fij9GlNGCNoBTBlhYfkcPi54iz90v4kUIw2X5sGluTGEg6NzTyDTmc1z6x+m1qohZAeJyDCFwS08svrOHdvzNEVBoJw/LXyRikgtfitM0I6QX1vCH358CtOOXckJ0S8Jf3v/Rpw5GpoXNC8IJww+ryenn3763r4s+1RFpZ8bbn2D0rJaAsEIwVCEgsJKrrn5VT7K/4kX1n9DyI5Qa4YI2yZzS1dz1y9v7++wFWW/UnO2DgBS2hCZD1YROIcg9MQrsPa6L7sWwt9H/+I8HNHMfRXfn7OUf772FYEG+7q5HQbXnjKOU8cPala7gXCEH5dvxLRtRvbpTIrHhV12PoRnxz7W8jLki+mEbZPTOq6iT2oZXxV1ZlZpR9p5U6iOhKmN1C8n4NJ0pvUs4fC892Pa04SLcR3exa13YN3Gr6ipWUdW9jA6tRvarOeyt4ora1i4egsZPjfDenWqVwh1k38dRcEC8jydae/ptM9iqK2t4fkXn6cmUM3U406hd5/mbTPUXH9ZcsmOW4e7cmlu7hn4LDY2q6uX4rdq6ZHSlzRHBjO2vMQ3RR9jNZif6NLc/P6Q6+idNqBJMfxn9ef8d923MRtVe3UXdw8+g8NzEpdiiZgmH3/8EaXFZYw7cizdD9n7umT72pszFvDEc98QbjCy6vE48EyCLV2KY65xaDqfHvUXUgx3zDlFOZCpoqYHESE0cB62+wcmoy/NB+69v41RWuUnHImtERaMmJRUxZkVvIc8TgfjBzX4QLKK4j62xvLWTdnSeH1z/dpeleFQ3Hk1Idui2B9bpyraioOQVYrX0YkeXY8Gjm76E0iinPQUjh0e/4O8k7dbUot9JuLzpXD5JVfs834SCVjxf5ZCdhCJRBc6fdLqV1yviJTFJFrbVZvxi5o2pihYGZNoQbTgaVmoptFrHYbBiSckd5R6Xystq4lJtAAiEQurMn5RXR2N6khAJVvKr5ZKtg5S0tqKrHkCwj+C3gGRcgnCOSJ6LjQHWftUtAq9czQi5eLdbl5dUFvNYz//wJyCjbT3pfKHgSMZk5e4wOKQnh1wGsaO8gTbeVwOhvbs2Hjs4fnR2K3N4ByO8F2CMDoSjJjc8uknfLJ1NTaScdmHcO/xk0lxjQX/BmiwlUq200+G20Ohv/4HsgYMzWnPgqLYqu9ew8HovI5Uh1P5bGMvlpZ1IM0ZYGKnZQzKLiXV2byRm4riSl6/7z1+/GghGbnpnHLtFEafsNsvQwnVmtV8WfQhv1QuwKenMiF3MgMzRjR6jZSSj+et4LWvFxEIRThmWC/OPGooPndyJ8/vzoqyYh79eQ7Lyoro2yaXKwaNpk9m/LlN2xUVl/O3G59n1WdL0D0OJl50NNdeP43O3u6sr10V8/g8dye0BCVQ+qYO5JfKBTEbZVvS4hBf09/fkVk9mbntFwJWbNHVQW0aL0L6xOoveC3/e0JWhJ6p7bht4DQOSWlL0Arzev4cPilYjEszOLnTYUzpMDThc9qdiB1mVvFnzCv/Dl3ojMk6mlFZE5rV3uABnXjn/YUEgvX/vRm6Tt9+bfmBauwGixPcuoNct9pEW/n1UrcRD0LS3IQsnVpXM2v7N003pN0FhKHqb0Q3tgYwovspZr+H0ONPXN9SU8XxM56nNhLGrJvT4tEN/jb6GE7tGf+Wi5SSqx+bwfxVm3ZM5HY7DQZ0zeM/15ySsF6QHfgIKm/aJT49WnU+623GPP8hBdTA9sVuNqRbLuafcwx6xcnE1JB3HcdLm7tx98La6LwtNHRsPIbJC8eM4ZoPV7FZViG3t2dBqu3k/VMncuKHb1FrOrHqTjq1CBf0ruamUf+M/6I3oqq0mosH/ZHKkmrMutfC5XUx/dZTOOPGk5vcnt+s5Z4VN1JtVu6YCO/UXBydewLH5Z2S8Lq/vzyTD+cuI1AXg9Oh0zE7nZduPguXo2W+dy0o3ML0z14jZFrYSDQELl3nxWNPZ1jb+LfHKyprOLX3FdhlfjQz+h5Lh0bHyf2546WzeGTNnUTsCBI7WmhUc3JJ9xvpkRJ/C6SIHeaBlbdQHNqGWVcV3qm5GNFmLKd1/n2Tn1PENjl/zuPk1xYTsqOvrUd3MLHdQG4dkPj9uHrec8wpXV3vmEDwyuFXcfuSN1hfU7SjPbfuYHxuP+4c1PT5XLa0eXjVbWwNbNxRhd+pueibNogLGqlVlrA9W3L1Ta+wcvU2QqG6+FwGw4Z05fLrJ3D2948QtHb+X+HWHNw64BSOyVN7OioHnz29jajffvvtLRDOnnnyySdvv/jii/d3GAc8WXU3mL9Qv2aWCeG5EPqW+nsq2tFzshbhjl/Z/e4fv2RRSQHWLom5KW3mbtvEhYeOQI/z7VgIwTHDetEmxUNptZ/sdB/nTBzO9aeOj1vCAeqKuJadC1TvehQweWdZgLeLtJ2JFoCAMBaO6pUclvszMWvprS10dc/nyOxNlIU96MJmYtt8Hhj4NQVFa/ji847YQYntkGgRcG/TyNjsYENwBSsjAlPu7MySOr+UOTiv76E4jaZtR/PafTNY+MXPREI7b7FYEYul369k6hXH4WziyNJXRR+xompRvf0CLWmxoXY1R2QfgzNOmYetpZX87cXPCUV2/kxYtiQYjtC2TRp9OrXMWspLvnyHLbXVO9JiSfRnaVlpEWf2GRz3mn/e9RL5M1fsSLQAhC2pWlvMUWccx8SexxCwarGx6ZnSj7O6XEpXX4+EMehCZ0TmWAzNQa1VTbarLb/JO42JbU9sVtFQXWgc32EIbt1BRdhPB28mF/ecyAXdj0zYXmGwknuXx9+4fH7ZWrYEygnuUunelDab/WWMz+1HpiulSfH9UrWA70tmEt5lj1VLWpSFiumXNqTJG4QLIZg4oS8pPjcVFX5yclKZfvooLjl/POkuL8fmDSJoRQjZEfqnd+LmQ09mTCPz1hTlQHbHHXcU3H777U/u7nHqNuLBKDyHuEV8ZJD4haesnZPi45hdsLFeorWdadvkV1XQIyN2E2iI1sCaNm4Q08bt4WR4uyh+AVdsvtgQQIrY2xBSh6831XB5/9i5IhKBR4swpE0xTwz7ot65Wb8AUuAq0XCV7Ez+Apj8tM4knBdbK8qh2awsWcSwDvG2UEps3ieLCAdjt4hxOA3WLNrAoPH9m9TeiqrFROLs02cIg83+9XEneC9eW4ChazFFYQNhk++XbeCkMU2LobmWlsafX7e0rBApZdzkZPHnS9AisT/PUtf4/JN5XPvHaZzd9fImxeHS3UxqN5VJ7aY26bpE3LqTc7qN55xu4/fo8d8ULk14blNtGVacf78CWFy+ge6pbZsU2+rqZTElMCA6p2xd7Qo6ers2qT0Ap8Ng2knDmHbSsJhz7TwZ3Nj/pCa3qSgHs70u/SCE6CSE+EoIsUwIsfT/2Tvv+KrK+4+/n7PuyN6BhLD3DHuKiLjRurWionVVbdGfo1ZrtdZZ66ijrrZq3YO6EBG3yN5Dwg6BkISQnZvcccbz++OGkOTeCwRpf79qPn3dqufkPOd75vM93/H5CCFmNS1PFUJ8JoTY2vTPlB9ubgfaQjo+ZOBTZGB+uJMQQIlF1SCJZIhvghKObPjMIPN2bmHezi34zPCXcKYnOoGq5Tikuo9cdHhPRS3zlm9i+ebdYcJTkUAspsfsOImQQAg8uxU8uxREMPznWZ7wBO13oMRSqLRFk35h9GJdgDivhapETuyGppIUL4mUtQbTUUj3tm+iA8jITYvKYm+ZFqnZ7YsqACQbaQgEiUqIfHcVfY06FCQODgkxoi8yBKYAACAASURBVBRpidG7SjVVITslLFJdWVrNV28uZPm81VhRmhuOBhKNMNdSkhUgy99IktXEwWa4YkaBknJSkNHeVBJyuoRrvb5csZJH332LTxYvwXEO3EO2tCioW8Oq6kXUmtWHbeeifat5ZsvbfFHWeryjha5xsWvUXKqGrkQ6+4pQSHOFr9UO317mlaxhXfWuQxKoJukpaELHchQqA16qAl5sKVCFSoLW/vuvAx3oQPtxNCJbFnCTlHKVECIBWCmE+AyYCXwhpXxQCHEbcBvwm6Owvw40wfF/DLW/BaES/u61IelxRPzVyJrfAC2lSowmAeggBBcCLYt5PYi4K5m3czM3Lvi4OS1oS4fHJ53GNYPHMOvbOfitA9EUQ1GZnNOdVHf7qSEcR3Lf65/z8dKCZrqC5HgPz994DtnuEyDwWdjO/RAerh8/mTef2oa77MCs6y5TCKY73HTNRDaG/kWxLZvjdjowOn4gZaEguaIAQzkwIQUdhf4DxmDMV/EHzVZulaoo/GraUG5ctha/c+Dx0IXNoOQAXVMGtPt4z7rhVJbMXYbpPzBpK5okb2AWXfq2n8ZjcsbJZFuzmRxXgi3DR2xKhdkNk2PSPIzok4vb0GkMRkbEzp40hFfueYc3HngPzVDDdU8unYc+u5OeQ7u1276D4cLeA/hozhIC1V5swtcpNyXA9NPyY25z1a1n8/uPN0ALp0cKEAkGx50wjBOeu4OaLBuhgty7mof//iFvnn0LpqeOv267H9MxAYktbY7POp2TO50Tc18+s4EZix6kzG8iBEhWkaR/yD/H3USWJ3oE90gwJr03bkUl4Fi0jjZLLuk+mZcKv8Zs8WEkCDtho9N6cvOqV1hasRVVKEggx5PKX0f/gmQj+kfRqNRJvLRjLltqkxBCNu9vaEodg5L+b2hLOtCBnxp+cGRLSlkqpVzV9O/1QAGQA5wB7Bdjexk4OvH6DgAg7dIDheSyAaQPpB9ZMytMExF/Hc0EpRjgmohIegCR9AgYYwFXM3EpCTewzxnNDd9+jN+y8JkhfGYIv2Ux69s55Gd25n/yJ+DRdOJ1A5eqMqFzVx495tQjsn3usgLmLd9EyLJpDJo0Bk3Kquq5+bk5iMR7wXVM2GYRD7gh7moa7Ml4ysKOQMv/uStUdlTWscfWcRDYTb8AglVBP3nZ/6TE6Y4pBQFHJSQVdotj6dfpN7xw47l0yUzGbWh4XDrpSXE8cd3POKn/qfx+eBZxqkmcauJSLIan+nlu2jVHdLwZ+WVMvWcvRryNEWejuhyyh/k55dmCQ0YloiFPK2FK/D50IXErDm7FIV6xuCR5Q5iTLQps2yEYpV1fAKu+WMfbf/oAM2jirw/QWO+ntqKO20+5D9uOEQk9Qmxeswl/lQfHUpt//koPm1fH1qkcP3YA5/5lBo5XwzFUpKag5CTy5y/u4qY3n6Mm2wa3gvQo4FbwZTlc/+aTPLvtIXxWHUHHT9AJYEmTL8vnsLl+Q8x93b72WUr9Jg4KtlRwpEJNSPI/q58+qufBcRwEkY4vSHS1nkeHX0KqEY9HNXArOnlx6Tw3+kreKFrI0optBB2LRjuE3w6xs6GcP66fHXNfdabNjvp0JAqOVHGajmtjTTpB++hH7TrQgQ5E4qjWbAkhugH5wFIgS0q5X3SsDGh//qUDsRGYS7RUFwgIzEeJvwrpnQH2TlAyEOqBtIVI/RvS3gtOJWjdEcLDxxtXxtSSm1u4mSsHjWZGv3y211aR4fGS5U04YtPf/HpNc0fcfjhSUlhWRWm1Ree0p5H2PnD2gdoVocTx99mfxhzvxXkbmHFu5MTVaJVgOtX0yJ1HfXA7DaEdJLvz6aOH06w9O6fz3t0zKdpbjWU79OiUhtKUWrxw8C84s38j2yrXkuLJICcxdsH1oVBU9wb9z6qg96mVVG03cCfbJHa2UIWX+tBmEl39Dj1IC8jG11FpTTMgBAhZD+Z6MCJr5JZs2hW1XM92JO88MZdAFG4xvy9AweItDJoYvavvSLBufQBpt06RSUdh3YaDqwpcfc10Zl5+EgsWbCAlNZ4R+b0B2LS1ClxtvhkNhcoe1QSjiFCHnCCLKj6nb8KgqPtZWbkP2eYbVCLYVufHdCx05ei8MhdVrCEoIfKiKMzetZz3J5/O3Cm3scNXjq5o5HnTEELwrxXLCTqt73VLOiyu2IrfCuHRIpsjPtmzOqpTrwiFb8o3cmpOR3SrAx34d+OoOVtCiHhgNnCDlLKuZf2FlFKKcPw62nZXAVcB5OXlHS1zfvSQTiPRa5Ks5iJzoXhBiZ72EmoWqAf830bLxIpSm2I6Dg1WeGL3aDqD0n64z9yWVX4/FEUQaOLlEmoGtHAQGwKhqNsA+GOsEijYTRNugqsnCa5Idm4hBN2yU6Nu79a8DMoaF3O/hwu7qZZOc0kyBxxwagQKVtSGgEPAiUWUqTTRfUTCHzSjTriOlARjyCcJIfA3RCd4PVJIO3ow3bEVHMdBUWIH212GzvFTW6cbZQzhbUWTiBgq5H479jmPFceTgOmYR83Z8lmNMTXSg00SP4pQ6JXQmv8uaEd/dgBMaROtgrLRDmFHiXg6UkZwg3WgAx349+CovDmEEDphR+s1KeV+Eay9QohOUspSIUQnIGobkpTyeeB5CPNsHQ17/huxdnsJj/3rW7YU7yM9MY4rTxnDaWMHxCwaFq5jcBpeQNB6opQoCNcx7d7/sTndeXLNImy7tQOnKwpTcmMzkTuOw3tfv8SLn5VQ1WDQr3OAG8+ewuDek2JuM214b16avyKiM85j6ORlJvPO1vU8uXYx+xp9DEjL5PZRUzhr4mC+XLOVyEiA5MRR6SjChSNbOwaK0Ik3jiwitW1PBY/O/pY12/eQ6HVz0dThXHTccBRF8MH2jTy+ZiGlDfX0SUnn9lHHMjY7D79l8siqBcze9j2mY3N8l17cPupYOsWdTG1oE45s69RIklzRIywHhftUMNfRuiYPwI4a1QIY3bcLVpSUkcfQmXj2WD7fMYdgm+iWbdoMmnAQqZmQyWv3zubj5z4j4A8x6sShXPXwJWR3i00j0TnPYU+RQts6pc5dDu5oxUJ6iU7DO8XoXx1wQM3xcYiZ2VhpVRF/r6EzPCW2A93V66awIRhhX5oh8LaT8uNgOCZzBDAnYrmCw/iMXlQE63ly0yd8u68ATSic0nk4v+wzjUmZ/fikZE2E89Q1Lp1EPbp9kzL7M3vX0lZUEuGjkoxL/8/KK/2nUO8L8PxL3/LlNwUATJnUj6svm0xCQmwGeyklc+ev55W3FlNV3UCfnln88oopDOzX+T9ldgd+xDga3YgC+DtQIKV8tMWqD4FLm/79UuCDH7qvHys27Czjl0/MZt2OUgIhi+KKWh5480te/XxlzG2kPoAyW8dqEleWEiwJJU48qO2XaembnEF8jY6wCX/GSxA2JFTr9E6KLUT99w+f4pH3KyipiSNg6qwpSuDqJxdTsGNJzG1mHD+CTqmJuI1wWEJXFdyGxj0zT+TvG1fy+yWfsau+Br9tsbK8hIvmvYUrVSXRHaB16lTi1izOG3c2Xi0XVYQnG4GGItwMybgPRbT/e6J4Xw0zH36TJQVFBEIW5TU+nvloEX96+yte3bSa2xZ+SmFdNQHbYl1FGTPnv8vSst1c8unbvLJpNdVBPz4zxEeFBZz+0T9Jc59OvN6j2T5QUYSbQel/QBXtZ28X3rNA7w3sb05QATck3IMQ0SfclAQvv/7ZRNyGhtLkwHsMneG9c7jmN2fRK78b7vjwRKSoCi6vwa+evgJPfGwH44/nPsq7j3xEzb46Ar4AC99fznWjbqOusj7mNvdcdDqay0ZRnaZ9OWgum3sumt7u8wAwcI6O/pUPAc0/fVEDPV6UuFRXxN9bmGS7YmtF3j3kYnRFojR1xSo4qEJy5+CjK+Tt1TxkKFrTfmTzvgzVYkLSOGYuepr5ZetosILUmn5m717K9cv/wS97TyPFiMOtNj07iopXNQ5KnjogMXoTRqLmprPnx9ckbtsO19/8Gp/MX4+vIYivIcgnn6/nlze9GvWDYz9ef2cpTzz7OaVltQSDFus37uHG377J5q1l/0HrO/BjxQ9mkBdCTAQWAOs50Ld/O+G6rbeBPKAIOE9KGfmp2QI/VQb5a5+YzZKCXRHL49wGXz58DboW2QZe2vAp6/b9jmTRQKemiWuPrVAn4xme+SgZ3ontsuHrtdu548W51LlCBNPC47kqFBJDBvdffgqTh0Sm4AKhBqbe/DR+s3UuR+AwqV8Dj8/6fcz9BUIW85ZvYnFBEZ1SEzh70hCyUhPIf+PJCHFogJNdQdYtNfCHWjsnbi3EJcfFc+UZV1Da8Anl/gV41E7kJZ5LnH5wqZRYuO+1z3l/0QZsp/WzYWgqgRGSGhmZWuubksHu+hoardbRA6+m87vRU7igz0BKG+ZR3vgNLjWDvIRziDeOXHRYyhAE5iODX4CShvCcj9B7H3K7jUVlvL/wexoCIaYO783kIT1QFQXLtPjuX0tZ9MFyktITOeXKqXQfHPv87dq0h1+OuJVQmxyu4daZcec5XPjbs2JuW1xTzl8/n8u24mp65aRw7bRTyE0+MlLVaUpsJ2jAyuiOdjdvb27se0/M7fb69/HM1vfYXFdKXlwa1/Y+k67xR1f8fXv1Pi5Y/BguLYRLtZFSgABf0EWqSMWv+fC3SRl6VIMnR15Oj4RM5hSvZF3NLrrFZfCzLqMOKoUzd89qHvz+/YjIlkc1eGzEJQxP/fdraP4nsWjpNu7500f4/W2O16Nzx02nMWl85HMSMi1OP//JCAkiIWD0iB786Z7YHawd+GnjPyZELaX8juhMmfB/rdT7X4ItxRVRl9uOQ2VdI9mpkcXodcHN2NJPpVSodA4EKAUh6kJb2u1sbd2zj0DQQg8q6HUHxgsIi63FFVGdrfLKnVGvvERhU0mkg9gSbkPjZxMG8bMJB9Joe3x1ODG66YL1tVh2ZL1YwDLYWboXVXGRm/AzchN+eNPrhp1lEY4WgK6p1PgCEKXDvqguOodTo2WyrqKMn/cdRk78dHLijyyC0xZCGOA5DeE5rV3bDeiazYCukTqYmq5x7PkTOPb8CYc1TuH6XWi6SqhNJjMUMClYujX6Rk3ITc7k/nNmHq7JRwwn5KAYkcH7vYFITcyWyPJkcPeQf6+SxdKyQnAEIccg1PKWV6DarkNG6QJ1pGRrfSlDUvK4oNsELuDwrlVBXXGEowVhapetdWU/Omdr+859BAOR9ax+v8n2wvKozlZlpS9qe5CUsG1HdCLeDnSgPehgkD8Etu6p4Jt129FVlWkjetM57dBiqtKpgcBcpF2BMEaGxZ4PIgOSm5FEVX2Uol0JKQkeaoMBPiosYJ+/gZGZuUzo3JU4PQ9VeLHbFFgrwkWc3oWQZfHivOUs37KbzqmJXHfGBLJSYncQ5qYn43HpZMSXM6V/IVLCVwU9qGjIoEtmcphSIPQdMrQGoWaC+1TSknJwnOjHlZfefsqANLcHKSHVaGBGz1VkeHxsqs7izcIhaJ44dNXBbDOsWzfplJYSLv4OLUaGViDUdHCfilDC18oXrGbu1vfZ7aticHo3pnSfjq6GI2TLd+3mueVLsRyHiwbnM61fb7pnp7J1TwVOm6ivadmoHoVoZdSZ3ngq/Q0Ry92qRu/k2GnYQ6E6VMnq6sWYMsTAxHxyvd0PuU3AtPi0YCtFVdX0yUznuL49MdSw81u0bwfvL52PP2hy3NCRjO4Vrl+SUrJuRylLCopI8Lo4cWRf0hLDXqXtBClr/JwGs5AEvRdZcVPJ6ZWNbUU6xrpLo/ugI290qSqr5uu3FtFQ28iIE4bSf0zv5menqGE7BXVrcKkehqeMJUmP3tiwH9EcLYAU48ivR71Zy+qaxTRYPvokDKJHXN9m+74tX84nJUtxKToXdJtGv8SwExPwB3n5sblsWrGN3D6d+cWtZzAoLQcKI6d36UCC8GAqwQgHSRWCLnFpBEyTfyz6hvWVu+gSl8Y1E6eSGhdbwqdbXAZuRY8YTxMqXeKOnDvsu7VreXfFdyhCcNG44xjRP9xVa9s2K+atYdOybWTmpTP5vPF4E8Lp6HpfgK++3URllY9BA3IYMaxbcwdw0e5KFizaihAweUIfcnMOfn1jISc7GZdbi4xsuXVyOkdPm6Ykx4WJlaMgN8Y2HehAe9AhRH0QPPneAl7/ag2WbSOEQFUEt543hTMnRhdfBpCh1cjqy8NvTfwgvKAPRaS8EI5GRMGSgiL+59kPmwWbIRz5OW/yUKZM6s1Fn76FLR38loVX0xmcls2L06azaM8UQm26z9xKAkNT5zH9d/9sNR7Aw1edxtT86KmmoGnx8vvXMGPsQtSmxlFbCl5dPIFLf/YX9PrLwdrc1OnoAaEiUl/lnlffY94qnaB1IJXo0kwevWoI4wafdLDTGxV/W/MXuiS9gETiEC4qVB0VXXuBB/86n8p6T7M4tBAOie4Q79x5Kanyt2CubWGfgkh5ke31gnM++ZCQo9Boa3hVi7y4IO+cdgN/+GwR7+7d2Gr/oz053HvMCcx8+M1W58+laxwzuDudRqbw3Ial+K0D6zyqxhPHTuehFd+ys666WYBXEGZM/+acq0h2tb+4ekXVd7y56wUkDrZ00ITG2LQpnJ17aUznvaS2jvP+8SYNoRCNIZM4Qyctzstbl1/I56s/4s9vFiElOI6CpjpMGGrzp5m38LuX5rFgfSGBkImhh/nMHr56OiP6elhYciGW48OWjajCi6GmML7z69wy8RG2rS7EbHGePAlu/rHxcdJz2j+JL/14JX8871GklJghC5fHYPwZo7j15et5q/hvrK5ehClNVKEhEFzc9TpePnMO678tiBirx7CuJL1YGVWm5tqet0eVNDoUNtdv4G87/oyUDqY0MRQXfRMGM7PrLH618lFWV1U1SWGHyXUv7jGMs9zTuGzo/2D7AkjbQagKaCoPf/UHbtj1Fo3extafvCbc0f8cHin8FwHbZn/oWCDJcMfx/LDrOW/+Y4TcZpgR1gThCJ4ccjmje0RPS/usACfPf4Ag5oEqXRtSlHjmnnRbVG3TQ+G6vz3B8owSUEW45MyRnFDbizvOnsFNx95F8eYS/L4A7jgXmqHx2Ld/JGjo3PjbN7FtSSBo4nHr9OqZxSP3ncc776/g5dcXYTfVVKmqwhWXTOT8s0a327aQaXHh5c9TXd3QHKFWFEFykpc3X7walxE9xvDkc1/w0by1zeLaAC6XxoN3n83woUdWktCBHz86hKh/IDYWlXH/G18SNK3w5CQltiNZUlDEmRMH4XVFOk5SOsiq80DWcICWwQS7EpQUhBFd9T43I5muWSlNBfImHkPnouNH8Mvp4zh37utUBhqbaRlMx6Ey0Eii7uekhLnUS4eADL+S0xXJcMPkppcy2L0vkh7g67XbuPKUsVFtUOUu8jMeRldNVEWiKhJNleR3LUUVfgh+xYHuNwsIQWgJ95WOpId7F3U1HqQUpCc2kDmoFjV7KGOzYxcix4IveC1BaTVNV03TlnDIdRdx8ohr2LxzGRU+NwqSvtnV3HXJyfRIXQ7+2W3sMyH4LZct8LOr0Y0pNUBgSpU6U6G2fjlv7q4KTz4tqqv3mPWMTMvlvLFDWV9YSr0/iKGpnD5uILf//HjGd+4KEjZU7sWRknSPlz+Mncap3ftxavd+7KitpKiuBiEgP7MzL0w9m87xie0+D42Wjye33oMpTZymImoHm7JAMT3i+5FqRJd7uWH2x2wpryDUlIYybYeGkElJTRVvzdmOZWlIGT5ox1EoqZDUh8r4dFkx/ibaDduRWI7Dt+t2MCz/XerNzcgmXi+Jie004rfKOH/mnZQWllO8pQSAnvnduHv2LUfEih/0B/nV2NsJNAbDETMJlmlTumMv7pEOK7SvCMmwDQ4ODjbf163m/lseYsW8dVSWHEjjdh2Uy1PL7+er8o+x29CjqKgMSMqnkye3XfbZ0uLRzb8j4PibrkdYzLnGrKS4sYq5JXtwWtxMEsH66jJWz1pM7fZyhNOkbiAl0nb47tO1mPXJ2AbItPC1EtUKrs89ZKTVYmRsot7UCDkqAkh2NdIrqZSPV5ax11MfdrQE4d4IBb7ZsYmZg6LrMu4srGTeXwuw0kxkvAQHlF0a2gcepk8dhtfTviaNb1at4iVrSZjfTBFhh0sTbNcrqX+1iLVz1hFsDF8ry7QxgybrFxTw6fYKqqobm4vULcuhtraRQMDkzdnLCIUspJRIKbFth7Ubipl2bH8S4mN3EEaDqipMmdSXHUUV7C2vQ1EE+UPyeOCus0hOiq14MTK/G6GQxdbte5GOJCM9gVt+fRJjRx15bWUHfvzoEKL+gfh0xRaCUfThVEWwYH1hq1qjZlhbw0zuEfCD/18Qd1HM/R0/vA9T83vjD5q4DR1FEWyrqaQqGMmbFLAt3tm6gcsybEa7bOwmZyscjXexdkf0GjDTcthYtJcBXaNwZQU/R0RJjwkk+D8AIiMEpb46djfWsy0uC2WYg0uY7JMGiDT2btvArGHjYx5vNARCW2hwQrQtBHMQlATWc1y3/rx0+300+n1Yjk1iXDhN6FQ8GNW+mkCAgjpPBEllyNGYt8ePkHpknYaAV9av4t2fz+C9u2fiD5romtosKwTwq2HjuW7oOAKWiUfTm6NMaW4vz089i5Bt40iJWzvyx6ugbi2KUKGN4HTICbKyaiG94iOJRi3HYXHhroj0p+U4fLZ5B/FKZBQ7ZGp8sqQIfyhajZ2kwv9duC211VKbvY1fkJ/5J25/bRbWS9dhWzYuT2T33+Fi3bcFiChalYGGIEv2fk0oM7IpQUFhS/0Gnl76ILZts3dnOeld0jAMgy31G8LXpc0h29gsq/qW/JToHx2xsLNhW1SuqpATZE3NUhwZmZoUQlK6vBDR5noIoHFPBU5DLsZXXuTXHlBBWOHj31i5noTOfgalNuJIAUgUAYbipjBpB9htUoYKNCT4Ka+rIzMx0rH/6ttNOBXgmp2AVGW409gRaC6NhUu3cfrJw9p1Lt5ZtQCyoxZr8sWudcg2ReZSwu7NJdidM6BNFC0Ysvj0iw3NEa3W20kWLN7KeWeOapd9AJkZifz53vMINb3DDf3Qz6KqKlx56TFcPmMiIdPC7dIPWv7RgQ60Bz+Y+uHHCkUQgxRRxG4HiL3isCCEwOs2mmsYDvacixb/r4r9jtahsf/vQqZFjc/fguhyf3gn+p6i2+s0T2aOUPCjNxt9OC8pKSU1Pn/zCzF8Ox46rd0YAn+o5a0bg4vsYCZE59htsuLAhh6X3srR2o+gaVJe5cO2I8exTBuzbXFZOyFipHYE4uDnNsaq8OKoJcBHeNse2MgybUKB2GSbhzXawS/WIVc50oH4A8ciUGLeSS1H215TQqmv8tD2tdjKCUjsWnl4Uksxj+vAe0RI0exoQfgq7f8vRcgWz7Y86OMR62WuiBb7sgWiuc5SHpEzEYssdr+J7V51kFfqD3V2DF07LEerJVRVweM2OhytDhxVdES2YuDEUf146+u1BMy2sjIOkwfH6N7ReoNIamZwPwAPeM5rtw09ElPJ8MSxq76m9Wiqxvl9BhHOIURG30b0zmLZ5r0Ry12aSs/O6Tz4xpd8sHgDjpQkx3m49bwpHDdkGtQ/HsUKAZ5zofFlWkePBNlxaXRLSmVLdUWrF6lb1Tin18HJOr9as42H3vqKal8jihCcNnYAt5x7LAmKmzonQMvXrwLkuvNZu6OEXz/1HvVNdANuQ+OhK09jQvdzMGs2o6utaQgSDC+Dkv2srfY2pXiazoNicWqOwUvbohgm4bL82On3kGVx1aPvsq6wdP/Z4dQx/bhn5snsq/Vx18ufsmJLMQA9O6Xxh0tPpE9u9JTfwdA/cWjUzkxdGIxKiU4YqykKE3t05bvtO2npA+oKnDSgN9+UbYzYRtdtpo/rwRtfFBMyI6fDTO+x7PN/g2xxnwk0suOmUVdVzyO/eIZlc1cDks69srnp79cyYGz7iTKHHNMfJ4qCgWaoTOg0lYXKh4Sc1tEtB4fecQN54p2H2ZK1CuECtkJe8UBmnXMrShSH1VBcjEmbwosbP+eZLV8jjfA+jaDOc5NmMig9egNC17he4Fcovsem/guJlKBnQ5ffGYw9fjwr9m3GaeMySAm5Y3qx+7vNraJbUkB8Xjo+j8BsEwhXdIchGUPZIxZgylCb8Rwy6ntT7ipt/eZ2IL7OS3qUqBbAcZP78c77KyK0MR0pmTi2/aS/F4w+lqWFr4PWxhkRcFLP4Xzh/qKV8y2EoOuAXOpz0iguad2163JpnHrCEN5+bwW0iW4JAceM/3GSrnbgp4eOyFYM9OuSyaUnjsSla+iqgktXcekqd140jZSE6Hl/IQQi5akmAWUvYWfIA66xCG9s0sFYEELwzJQzSDRceDUdVQi8ms6orFwuGjAVEm4AXIDR9E8XJP6OP111FnHu1nUYAnjoylO57/XP+WDx9wRNG9Ny2FfbwJ0vz2NVoQIJt7QYr2nMhFsRCb8CfVi42B81/E+RiEj+C08fewbJLg9xLewbkp7NFQNjh/7XbN/DHS9+QnmND9NyCJo2c5YU8IdXP2NY5l/QEYSTWhIVSFQ8dE7+M7/489vNjhaEubpuePp9Xl/ckyXbs2kMaVi2oDGoUR8wuOH143ls8vmkGiHiVBMVB69q0jchwC1jr+PinGFhZrgWv2MSunJy/9is6Vc/dsDRClsIc5Zu4rHZ33DFI++wfPNuLNvBsh02F+/jikffptoXXULnYPCoXmZ0vRZdGOjCQEVFFwYTM6bRPT72BHTPlL1kxvmI00OowsGrh8hLrOG2SY0MDRShaRa6ZqIoNppm0c3YxfBe2TQkmUhFhhsTFIlUJNrAagan34lby0IVcYCKKrx4tVz6p9zKbSfey7K5q7BMC8u02VWwh9+c8EfKX8rlkAAAIABJREFUd+1r9/EabgMnSnejFbLpqvZmTOrkVudBFwYzu83in3NeZGvOKtQEUAxQ4mBX3vc8/a/H+EX3GzEUF4biQmnabmjSGIL+BP5a+CW4HYQSzmyFXCaXf/f3mMLbqlDZdbMZdrRMwAKzGApvCNGrsiedvD4ETvNPwWFwip8H37wRIyUe1KZIm6aguA3um30Lv7ntOBSXjaI7oEgU3aHLIJXrp83kuMxT0YWOKrSm49W5IO9qHj5xJp4GF4QI37MhgRJQeHTCJTHPbc/umcw4fyyGoaFpCoahYRgqN11/IinJUXhMDoEJQ4YwqSoPQk74Fwz/Tm8YwHW/m0Gv4T3wxLtRVAVPvJvEtATueOMG7rnjDBLi3XjcOqoqcLt1Bg/IYebPJ3DVzGPCdukquq5iGCrXXXEcWZntr3fsQAf+P6KjG/EQ2FVezTfrdqCrClPze5ORHLvFej+k44PAfHAqwBgJev4PCkk3mCE+2bmFcr+PUVm5jMzMaR5PWrsh+DmggPsEhNoJCMvovP7lahYXFJGTnsR10yegKDDtN89HyOQAjOmXxzOzzkZaxU3jAe5pCDVc7CylBHM5hFaHNRXdJzazlfstk3lFWyhtqCc/ozNjs7sc9Hh/9dR7LPx+Z8RyQ1OZ98CVxHuC7K19Er+1iyT3WNLiL+PJDxby8vzo94ahqYQsiyG5e8nvWkqlz8MXG3viN3Xe/t0McjO9zN/+PsW+Cgal92JC3kmoStid21i6l2eWLcFyHC4eNpzx3WN3HflDISbMejrqOk1RMHSVxja6jy5d45fTx3HJtEM2q0RFnVnD2pplmE6IAUn5ZLtjF59LKZHlozBtH18VdaOoNoneqZVMyt3NpjXduP2CTJzEIAnT4pGGSmh5PcECBfXennyfGodoCHOsSVUSSpXohsXbp5zMkPRBlDd+Q4O5k3ijFxmeiWxdWcTNU+4i0EY7UTM0zr7hVK54cEa7jvPbdxfzx/MejbquV343nln5MCX+3U3UD26GJY8hXkvk+vk/R82IfIfZtfDUsW/QaPlYU7MUv91An4TBdPF256yPH2a3qG5bPoS04bLcCVw79NSI8b4v2MCNw/9ABJ+tCjk/SyT9DpM6E6qDHhQhSXU1kKAbXNnjZrp7+vH605/y/bKtdO2bw8ybpuONC9e3VdRV8/pnn1Fd28Ck4QM5btiB7rvyQCkbaleiKTpDk0eTpIcpCCzb5tVlC1mzt4iuCelcOXEK8a5DF5EXl1SzcMk2NE3hmPF9yEg/cjF5gNWbtvDW0q9RhMLFk46nf/duQPg+XPPVBjY3UT9MOHN0cz2fPxDim4VbqKwMUz8MGZjb/K4o3VvLgkVbUIRg0vg+HY5WB/4r8B8jNf2xIy8zhYuPH9GubYQSD97YDNrtRZxucE7v6Gk5oXUB7bKI5YqiMOP4EcxoYfuO0ko0VYnqbBVX1DaNlwvazMj9CAHG6PCvDTyazpk9Bx7u4bB7X03U5bqmsq+2geT4dHJS72y1bmdpbPGB8PEI1hVns664NWHn5uIKeuX05/R+0ZsTBnTK4skzzjgsu8urI3m09sNyHDQnWm2Xxa7y6Md7OEjUk5mUccJh/rUJ0oeuOJzQfUerNXt3+hAik1CJpPLllnI6NvssGU6zxoEddyC6pAqHwtpdDMvIJzvu+FbjlRWWo0SpZbNCFrs3lxzu4TVj26rCmOvKd4drqjp7utDZ07rDVUl2iFbxoyRKTMvEq8UzPr01t3KF2YCI4ZtsqimNunzb1u0oOthtnS0barc3kiTBo4FHO+BsSympDO2jd8JALpl1StRx0xNT+PXZ0UsMMt2dOM4dSVqrqSozx7Vf/zS3cwrnn9X+YvNYyO/Xh/x+kVFWIQT5xw0m/7hIeg2P2+CkqdHfZZ2yko6oGL4DHfhvQIez9SOFdOqRje+AuRTULgjvDDqn5UYt6lWEYFC0DsUW8JlB3t6ynu9KiuiSkMQl/fPpmXRkhIiDu3eiuKI2gkTQsh1y05Moqqvm5YJVFNZVMy47j/P7DGFk31y+Wb8j6ngJHqNVerElRvZpX4v/wZCTlkS4NywSnhjcPR5DZ1D3SMb2H4qAZfHhjo3M37WVdE8cM/oOY2BaFihZ4EQ6DD2HZkRN07m8Lvp4daqFhSVbH4PpqAzNiO5E9xzWDStKE4Dh1hk4PnYaFqCieCsfPfUM21aX0mtYNtN/dS2jTsnnjQffi76voV0xbZtPNm5h3satJLgNzh8+hOFdOiPLdESXyLpFp1xF1/Qoo0F3byobA6UYi3xoC+rBqxA6KQmrn4fJnftF3WZY/jCc0KuRK3ToNDIVQ2mIqClDSHI9XamtqOPj5z9j46ItdB3YhTOuO5HMvHAd37Y1hXz0zHyqyqoZe+oIjr/4GFweFwE7xAPfv8Syyu2oCE7onM+v+54PwNaK3Tyw8hX2OLUk4+a6fmdyTPf2dRS2RJ3p5/3dy1hdvZOu3nTO7TqOHG+YULSwqIL35qyifF89Y0Z056Rpg/C4DRxHsmjpNuZ/tRFNFZx0/GBGDe/WUVTegQ7EQEca8UcIaVciK88Ep4ZwUbsGaIiUZ/j7lxr/mLesFWGnx6Xzym8upEen6M5TdcDPaR++TGWgkYBtoQqBoao8M+VnHJvbfqmPor3VXPTAa/iDZrPj4jY0Lpk2kiEjOvOLz2dj2g6WdHCrGomGi4+mX8JZt7/czAW1H5qq8MhV05n1TKTO+eDu2bx864Xttu9guOvF1/loWRmtoymS288fxWdr9rJuR2kzZYimKmQkxfHuXZfiMaJP/EeCgGVy1sevUVhXjd8yUYTAUFT+MPZ4pmauIr7xQQzlgGMVkgr+hEe4afRn7N7UWqpGMzT+su1uzvzqHRotrZkmw1AsJnS2eXFa6whjS1w17CYK17XW9FR1lTd2P0tKZnLUbXauW8gNxzxCKCgwgwq6y8EwJI8vuInfnfEae4ta13sJAc+tf5TfLV/M96Xl+E0TQTg9O2vyONy+jSyOn4vibt0tOKRyEleedl1UG7aV7+aKaXegbQ0gAhIpAEMQPD2NBW89E/N4r/j51ex6vwq5v09ECdeIPbb6Lt5qfJY6swaniT5FFzo94/tzln4F1478DYGGAKGAiWao6IbOnz7/PcVbS3n86ucwgxaO7eCOc5HVLZNHFt7DeSvuoTakNDV2SBQk3eJd3NjlXG4seAUb0XStwuuuypzI5cPbJ90EsC9Qx8WLnqLBChJ0TDShoCkqj4+YiW+Tyb0Pz8E0bRxH4nZppKUl8NzjF/Po0/NZtHQ7gaZCeLdb55Rpg5n1y+MPsccOdODHhQ5S058wZP0j4foq9kd7HMCC0FLyh9xBVkoChWVVOFIysk8XHrj8FHrlxJYweWTVAhaV7iLkhCcSSThttrC0iCsGjmr312xyvIdjBvegpLKOmgY/nVISuP6MCfx8Sj4XznuLqqAfp8kNs6RD0LZpsEyevOAMVm4rpqK2AQH06pzGizdfwKAeneifl8myzbvxh0w0ReGkUX35y7VnHPUv7cm5txII1lFQmoHtCOJdIW4+eSFnjS7nxAm/wnYku/fVYGgqJ43qx4O/OIUEb/tIGQ+F1zev5aMdBfjtsFMnCZ+nhaU7cRKK2RQIkqn50YWk2PTyVm131u3RWfP4Dpw2HV+6oTFs9BCuPHEkO2rXUhFwSDRCzOibyoPjZ6Eo0TUu6yrr+ccdb0SMZ7gNug/Ko/vg6JI99557M8XbwLbCTp1jCyxTULRhGU8sfZ5tqwopLSxHIsnqmsF9H9/OVsPkzVXr8ZsHHG3LcVixaw+9RlZRvrkMq0YidDD3SKx9kDzEw9i0KVFtePfuD9j0wfeIpu8NQZhKTNvYyMQzR5OSFekoBu0Ai3q9j/RYhIrCG8VPFHT7k4vuPbpzdu6lNFg+qkOVeLV4jsk4iXO7XM5frnmBbat2NEcBHVtihSwKlmzly9cWEGgINkebLdPG7wuwekgRO9Rgiw7aMElqTchiScUaGvYzmbZYt6GhkMt6TYt6vAfDowVz2FC7G1M22YfEkg6rKwpZ8NROGv0h9n+PW7ZDMGhSUdnAgkVbmx0tCBOU7ti5j0nj+xyUOLQDHfix4XBJTTsiWz9COOWTo6aSwI1Inxuuy2oHjnn3+Qj6CQCvpjPn9EvpkXRkGmZtUdZQz+TZLxC0I9NCWZ54ll5w7VHZz5FCOnXI8rFEo9tAJKFkLf+P2HHBJ2+wpGx3xPJ43WBo3kbSE+oj1vnmK+y7X8FfH0n+OuXCidz+2qx22bDgX0v58+VP01gX2Wk58awx3PXuzVG3O1E/B8eOUmOlSD4JvY2iRNaB/frdOXxaECluHWcY9B28icxOkSS+AsGjw16NSv8wo8e17N0ZvWPy9OtO4ldP/iJi+db67/nbjkcIOJHH2zOuH7/uc1fU8c5IviTqOVJUBZfXiHo9lA9yqdYj5Z2Upk+QtiS9EHa53hh5PT3S28fef9KX91MViiRi1it14mcnt3Ko9iMp0UNdnT8ina5rKldfPplzf3ZkzSAd6MB/Iw43svWTon6QTjUytApplx2l8WqRoZVHbbyjBhGrndsBpf1fnQl6dDkPWzrEx1h3JPBoelRuKQg3CUCYa6guuIma4AakbF0z1BgIsXZ7SdQC/BJfHSv2FlMbjJzcDhvCIDZrqLfJPsmW4n18v7OsWZZkPyy7mqr6t/H5vztyG4AkI3qkzJESV4wqTFd8dJJGRREkpLS//T8u0RO1eE0ogoTU2B27Lnf0jzvDLVEUBSklRQXFbFyyhVBTZ2eS240iBEI4JCX7iI9vZP/O3U1i00FLo9IXR8AMn4D9+okAuzbtCY8XCEd6vYmxn4GktLDt24q38PrHr7Bhx7rwflRPc7S1Lbxa+PxZlk3B5lK2F+5rjla546Kz6iuKaI4K2p11rH5upKuJpNiOVRnIQahaId7V/mfbq0Z/fqUum3nPLBeE4gX7+z/cbh1Nj4x4qpqC13v03gcd6MCPCT+JAnkpHWT9A9D4BggXyBDSdQwi+RFErLakg44nkfV/gsZXwxOwDCFd4xFJjyGOwJk56vBeAvUPcEArEEADYzhCaX8UauaAEfx+yef4rQNfuaoQDEnPJtN7aCqMw0WSy83Y7DwWl+5qFnOGsBN26YDh1Aa/Z+XeX2M69YBAFQb5mY+Q5hnNq5+v5OkPF6GpCpbt0K9LBo9eczout8b1X3/IwtIiDEUl5NhcNmAEvxkxud0pRiHcSNdxEPwSaPnF7wbvz9m2p4IbnvmA6no/ihIWLr/v8lOYMLAbO/fdymbfXAThpG684mJEp1fxGJGyO4fCJf2H823JzlbXQwCpbi+Ts4awqmoRaAcmZWEpTDt5Kq/c9VnEWLpL4ZQr219nM/TYgagxJtxTDzLetIsy+OTlfZjBA995hsth2kXplO0s587pD1JaWI6qKSBh1rNXcv6xg1lY9i39huwgzHoOgYDB1jWDOKX3CJ5eu5FdVckowsGRCjnJtVw9rDv7dldw5+kPsWdbKaqmIh3JdU9ezkW3n8W9P38M2vj1QoXps07m0gsvo/Q9X9Oj/SGpU1w8/85TeIkj6ARaUUaotsak9BNZvGw79/15DrYtcaQkNSWOB+46i9OuOYG3HnyfYIsGDt2lMf5no9m0vYjtMxTsPANsCYog4Z/VnO4ew0usjiBJRUBvGccm/LStGUyRDpkJKYdx5Vrj3K7jeGbLfALOgXtJEypjevakKs9mpbsa0ysQMkzGmlIq+Pn0MTz1wpcRYzmO7CAh7UAHYuAnEdmSja9C49tACGQ9EITgt8i6e45wvLeg8fXwOM3jLULWRU8l/KchvOeB5zTAFSZYFV7QeiCSovMYHQrn9BrE2T0H4lJV4nUDr6bTIzGVp489PMqE9uDxyafRJyUdr6YTrxu4VJVTu/Xlwj59WVp2BQF7L7ZsxJYNhJxqVuy9jgXfr+GvHy0iaFo0BEIETYvvi/Zy8/Nz+O2iT1lYUkTQtqk3QwRtm5cLVvPWlnVHZJ9Iuhe0/oCnibzWBa7jMI2ZXPX4O5RU1uEPmTQEQtQ1Brnl+Y/YsucfbPLNxSacgHSAOifAytL2cVHtx4TOXbluyBgMJXw94nSDLG8CL59wDnV/NfCvlzh+iV0vcQIS3xKHwGyD+1/fRWKqhTfexhtvY7gdrr57Dz0GRo8mHvQ8KAI9WgemDHc4xoJ15tnkDQvv29NkQ+4wC+vMs7l12j3sKigm2Biksc5PY72fR698lqIVKxgweCu6bqPrDprm4PUGGNx/Fbsr8yiuTsGRCpaj4UiF0tpktpd147aT7mXn97sJNoZorPPj9wV48rq/UZPYSNatCsIVLnBX4kBNhpxnXdz94J2UfuBDhsDxgQxB1ddBZl3zP5TdomKVS2xf+OcEJeX/sNn7TYi7HviAel+QRn+IQMCktKyGG257k3NvPp3Rp+RjuHW8iR5cXhe9h/fgxmevwnqoK3ZPF7gViFPBo+C/Ip3Ro0cxOTMXBQdFhH+qsLm8xxhO6nNc1KDX4PTDp15pifO6jmNK9kAMRSNOc+FRdXolZHHX4HPxD/FgxiugCqQWFpyuzxVoOa7oXc2KQNN+ElNKBzrQbvwkaracfVPA3hNljYHIWoUQ7Qt9O/tOAHtnjPGWN5N9/l9D2iVgbgA1G7TBP7hYvMRXx7rKMrK88QxL7/Rva/OWUrK+soyShnoGpmbRJSGJPb6P2FDxR+w2UkgKBus2HMubc7tEjKO5FKrzLUwnkqKgR2IqX559xZHbaG4Euxi0fggtj6/WbOP3L39KQ6A1BYWuKtxy+Ry8KSW0TUEqwITsJ0nwRC/kPhQqA42s2LuHZJebUVm5ICWnJ1xM0B/C1QuMLoLgdkloF0w+w+H2Z3dgmw2sWxxPoFFh8Fgf8UkKeC9GSfxtu/a99pvvufP0ByNqjhRV4bSrp/Grp6Kf25F/+iv1wSDdA2Vk+aooj09hh7sTyWV+sl4pwO+LHK/HH+PQpzYi2sjDOA2wtqw/u+ojo9NJu4PkPbw5gnRVKILUaSlk3FuHbJA0rpIID8QNF9iolN5pUj8/8p0oXKCrOiG/iXcYqEmCxjUSpxZyRvZiV2ZaRNrY6zG489bTGD+mF3u2lVK4fhedemTRc2g3dvrKuWTR060iSvtxTGZ//jz8Yoob9vLPwrm4VBdX9jyDRCOOM7/5M3v8kZxzuqLy5dTf41KPrOt1T2MVW+pK6ORJoW9iZ8rqfJz41xcJRuHk6xGXhPVtDaE28j8et86N153AiVOPzPHrQAf+G9FBatoSTm2sFSADTbU47RnvICSVTiOo/z+cLaF2BrXzURuvc3wineP//azOQgiGpHdiSHqn5mUhuxopIycmhxAONUAUZ0uP/ZVdE2y/hE4rG/UBoA9o/u9qnx87irafaTs4Stu0TxgKELKifQQcHtLcXk7s2vvAvkyruc4puA2C2w44DYarASSoGuRPalkQ7YATqaN5KNTuq4vqbDu2Q0VJbALahlDYGS10Z1PoPsA/Fqz1I6KoqTu2gx0fwmirw7d/vRIAIp0tqyaAokamOaUj8e/zoyhAgiBhcotxLVBiBOWkCVqcQqgRGldDy/BSfUU9VlpkCs+Rkpra8H2W06sTOb0O3M/VoQZURYlIZQJUBMMNDrlxWdw+qDVhcZ0Z476V4LfNI3a2crypzdxaANWNfjRFIUiks1UbDKKHIptELMuhpratLmwHOtAB+Kk4W8YoCH5NRPxd7QTiCCQrjDEQ/IyIN6WSBkdQE/XfgOKaWp5ZsIxlRbvJSojn6omjmdSz279lX8s37+bv85ZSUlHHsJ6dueKUsaQmjQwX1bRxuFThJdU9Gl0NYrYVsjUhze2hrLF1t5UiBOM65WHZDrMXrONf363HtGxOGdOfi44bjselE7DK2VbzAhX+73CpaXRPuozsuDAT+dtfr+GZOYvx+YNkJMfz2wunMrxXTtTUiselkyh6IeWKMJ9TCzhAomcaK8v38OSaReyoq2JIWidm5Y+nd3JsKg6A6vJa3nroPRZ/tJLEtATOufE0jjl3HHn9cyj6vjji7005BJgbsVziQXFNPui+Nq/Yzqv3vEPRxmJ6DO3Kxb8/lwHj+2IGTY4/p4ozflGBN97mu4+T+fDlXMacMoKC73fy26uepLZgLxgqg08eymN/m8WgTlms2VeKnWjj6BLFFKh1Kj1G9MD/bqQyuOHWyfJ1pca/A6XtN4wGWVpX6tyl9MkuI9njpy7gZktZNtnD8zCfiWSld3kMek3pTdXeLex4xotY3IB0qRjTPfS6pBZMBaI4GK48gVkauVx3aeQfP5jvautJGLmHlCHVOKZCxdJ0fOszGTo4eudv38TOWNGEt1GYmBGbFHZ4aje+Ld+EbPMuS3clkBSlg/FI0Ssj+ntMVxXG5OVS4N6Gv02noqIKhg2O/Oj5v0DItHjvo9V88tl6pJScPG0wZ50+HEP/aUx5Hfj/h59EGlFa25GV54ajWFiEYwoGIuUZhGvCEYy3E1l5Nkh/m/GeQLiOPZqm/7/A7upaznzhNRpDIeym+8Wja9x+wrGcNzxSkuOHYO6yAu599XMCTcSgihB4XBqv/ObnVIv7Kfd/gy3DX/eqcJPsGkLvuCe44L7XqGsMNksRuQ2Nm86ZTEq3OK796gOCtoUENKHg0XQ+Ov0SnnhtAUs2FTUTvLp0lW7Zqfzt5hNYXHoOplOHbKJ5UIWHXslXM+fLPrz82coIux+64lQWbdzJ/BVbmolX3bpG79x0HvzFSL7ZcxEedwBdD0+wIVOloXwsnbreyrVffUCgie5CQeDSNN45+UIGpUdnnq+rqueqITdRW1GP1WS7O87FWbNOZcQJQ7n9lPsxgyaO7aBqCobb4LEFf6REPkB+2gK8engbv6lR0pBKlx7zcRvRGzvWfLWB301/gFAT35IQAsOj89D830P93XTrvRqPt+mYgoKaSg/1nre5atwfUBpMFDt8vzi6gntIJ2a8dBm/XfJpE7kVNHEZcP/oE3nvhCep2RsZhX5s1R94uuiPaJmymbzU8UuUzQkMG3U2KwL/RFEkigApwXYE/dRzSP3Y4c2H3m9OJRpunfScNG6Z+2uun3APanWwlX2hEcnc8fB0HjvxZZwgYZ9LCQe+Z7x+Muuf2s2aLze0sk1RFZ77/mEeKf4jxDeg6E3jhRTia7rxwIn3RT2vAFMfuJu6ISFEUwBO2iBNwRN5FzF++ICo2xQ1VDBz8dMEbRNLOggELkXjofyLGJdxdIvT31m9nvs+/Rp/07NoqCqJbhfvXzmD++//iI2bSggEm+4/t864UT24+7dHv46zvZBScuPtb7FxUwnBJvtchkbfPtn85cELUaJEUDvQgSNFB6lpCwglFdynhqtdscEYjUi6H2G0T/PwwHjJ4JneFGWxwBjZNF6kbuCPAQ989g0bSvY2O1oQJpVcvquYy8aOCKdDjgJsx+GXj8/G16LuSQK2Lamsa2DGpGvxaJ0I2dW4tUx6JF1O/7Rb8bo9nDZuIIoQ+IMmA7tlcdv5xzF1eG+6J6UyOac7taEAhqJyUrc+PH7Mafiqg/z1o0WtmPRtR9IYNPEmrMKduKLZ0QrbYVEVWMPjr6UT7ftkxebd/ON/2Tvv8CrK7I9/3mm3pTcSEhJC772IKIgoICoWVLCgiIr9p6511d21rW1d3bV3XcvauytWVERBlF4ChA4hvZfbZub9/XFDSHLnoriy6m6+z5OHh5k775x5p50553u+56oZ5KQlUdPgJzXBy+kTh3HNjAn8c/5Gnn03nrApcBlhiktT+eCzMXy6uCdf2NuoDgda7Scyt1vrqmP2w3ztnndZ+skqwsG99plhiw3fbeLcO07nsJljaaqPpOVGHz2c656/FNEpjjNe3k1hVSJJ7gDVAQ8vrhvAHYsmkBqfTP8s53ZNN067g8rd1W3PU9iipriAk+d+i67vtUHVwBuvcuP1ZVSvqmtxZACELTHLGljaR1JtmHuzqs1O147txQRe2eBow9ZtW3AdHaBhjYntl4RLJWYFuAbbbKvbguEOsCejKQQoChQ3bef6M66j+5Cu1FXU40vyccwFk7jyiQu4+u5H8C8sRTHb2qeUBOh/2mimXTKWDRVrMa0wyaPdXPDELI4cfhSPXvEsVruWR5qhUe+uIjyyBFvZOxdClRDXwKCkUcRp0an3LZuKeGTpRsyAgdAk0hKEK9z4N8ex6sO1nHG8c+/DJMPL5KzBhG0Ly7YYlpLPjQOnMySlq+Pv/x30z+rE8C7Z1PgDeHSd4wb15e7jppAW5+OIw/qSlOSlri5AVqdEzjztYM4+/ZBfhSOzYvVOXnljSYsjCGBZNnV1fvr2zqJzlnN3gw504Kfgx4qa/s/EVIWWg0j8+aoFhdoZkRi7lcl/E77bvquNo7UHtpTsrK6le9rPkzotr2ls4/y03s+yTUUIoZAdP43s+GlRv0mO83DxcWO5+LjoSOXAtEwemtD2i/ur77c4Ok3+YJhlm0qY1CWaHxYIeJv7OUa/UGoaAwghmDyyN5NHtk0DLS7YQX2jzuffDOPzb4a1LPe5VcoqGsEhk72ywrkhMsCyT1cTchCb1F06m5ZvZdgRg7j2H5e2Wffe6vVoison27rzybbubdZ9vWUHJw+NjlDatu2YkgQw9A0tsietIQiwY1VtG0dmD6QqKFtVAgdFv+x2ry0iFrtw+5LddHdB3Ii2Tr2uGJjeOmf7PI2ELZPRU4cxeuqwNuuKvylBDzuQpRTBBx8u4rm7/sDYVw5ts2rZZ6vRXXrUvIeDYVZ/tp60U9p3qAYhFLY1FpLpjhYaffe9xQhLYtUZNNW15YwWeaLHao0sTzJX94u+Bw4ERublMDIvOhWqaSrHTR3KcVOH/kfs2B+sLdhN0OE5EgiEWVNQxIihXf/zRnXgfx7/M85WB346MuLi2FUb1JvsAAAgAElEQVQT/VIzLZsU7755ItKuh+CXkf+4DkUoiTF/m+B1YUsJ2HTrVYERF6a2xEvx7mTSEiPCkdX1O1lc+DhS2ozofiYZSftuehwLaYk+VIevcJeukpHkzJLW9dikek2NOAL1gToWbnuTsB1kVJejyYzPISs1gbXbSqIq9k3LRnUpIG20BoESAMsrsXwRzaxYyMhLR3y9HitNxRzoQdTbaMsaMcMmKVnJSClZ+/V6dhWWkD8wl94jupMe7yxcqikKOUmRyEt1qJLC+jV4VB99EwajCg1fopdGB9JzMJAAOBHrNXxpGtUCRPsDlqClOuvaGcmxryNvmhuFMAFLErB0BBKvHgIkZljDcEU7npalogoF0w5TUL8Sv9lIj/j+pBhp6JlupCoQVrRD2Dkv3dGG1M7JmOHoF7hQBCldktBEkMYAVJYnoig2aRm1uAxBoh5xLNcVl1FQWk5uciIjcrPp1TMbqreCkGgJIYQmMet1ZEDBHYikwgvWrefNea+hqQZnnXImmZ33EuzXl5SztqSMnKQERubloDSH9kzT4vvl26iuaWLQgByys/YS92tqm/hu2VZUVeWgEfl49yHRsQfBkMkrbyyhuLSWieP6MGJY/g9u80sjNcWHy9CiOGUut05as+Dujl2VrC3YTVpqHMMG56GqHZIVHTiw6HC2OvCDmDt2JFe8+a8W7gZE+BuH9cwneR/Olu3/CGqvZi8pxUQm3IbideZ1eN0Gk8ak8CkbKTB92FIgcwV9e+3mrO5H8unK22nwvIL0ChCwqOx9ROGRTBt5z34f06ED8nHpEn/QbtP+RBU2Mw49hsLGD7Hl3vSeQCfNN4DuWWlsLq6MGm/qqD58seVtqs2bkIpAKPBt6cOEtp3OGRNn8dWqLS08NIg4Z326pJPdJ4X331kDwT37ATtecN4ZsSkAJ/zfUczzrcd/VDyYEmSkt1+XFwSpnZO5cPg1FG0qYU/orufwbtz2r+tJ9nrwh81mhzYCXVWYMWwg7xW9zBflH6AIBYGCKhQu6nE9J1x2NK/d8w7Bpr0RLJfXxcCJ00F5BKydtCWU65xx8VTuX/QqolXKTQqw4w1OO2EMj61bimW3nfOZx4zh8zvWYYWjSehn334qr4ReJl734xURO6QUhCwfXUQfSuyv2wiNSgkp5iCKAtt5eNPtWNJCYmNLm8PSj+LCa07g4Q8ebrMPCUhD4abz2lb/7UFe3xy69u/C5hXbWvocAhgundlXn8aft/yTgoIshGgmoSEYN6aErv36MfuF11mxqxgQCAGdExN4/syT8Sz4EHVkGCEizbCFgFCJizPTh3D9XddSP347cnRkuNu2Lqff++M4d85cLnr1Xb7bUYRoHi8zPo4XzjqFhio/l1/3EoFA5Bzbts2UiQP43SWTeP+jVdz/6KeoqoJAYNuSm6+fxkEjuzseL8D3y7Zy9R9eb7lePvh4Ndmdk3nu0XN+1Xpa4w/pzYOPR4uuqorC+EN6c9tf3mfB1xtRFIEQgjifi/vvPpWszI70YgcOHP4nOFsd+PeQn5pMvMvF9zuKMFQVgWB8j3zuOn4yhkN5PYC0KqDqdCJeRLj5z4LgAvAcj1Ccq0Af3vI6hQ1eQlLDlCqmVKixXPRKK8Ad/yqGbqFpNppqo6oSS9+KHuxDSvz+fXErooFDs29g6bZONIUMdNUiLb6Je2Z+Qt/8mXj0flQGliCItH1J9YxkWMa9TBszlI+XbaSuaW+qZ0SvHG6cNZb1tXNw6Saa2myfIkFZQ4pvPMO69uO7DTtRFQUhBEN7ZnPP3GP5ZP4Gdu6uQdgCISN/uinolZjOQX3zHG1fr5XxScImLA3QFTAif/bYeHbfupLVX60n5A9hhi3MsEVNaS3+ej+/v+wkvt9RRG0ggKFqJHnc/G360WhxZby9+3nCMoQlLSxpEpZhVtV+z6XTr6K2rI6tq7bj8rgQisJxF09m1h9PQXgmQXhpRApFuEBJQCTdS5/BR7G1rpZtK7dHrgUF7E4+7njjWgoTXqU62ESt34uqRJyxrmkVdEnaQPlLYcLtohGqppJ3QS6VnnWoApRWf6YMMii9O0WB7W0yuwLon96Nj0vfotGqx5ImlrSwsdnl38a2NxooXVzaJvImACQcfuYhJCc7y5scfPxI1n+7iaqSGlxuA0+cm989eSFJg7vy+Kc7MC2QUmn5Ky1OoSkU5uP1mwiYFqZtE7Zs6gJBNldU4u+/iZBQESoIJeJsGd4wufENVPXbgOIWKEbzny4oT9nO2hVxfLhpO0HTbBmvPhhiQ2kF8579nvKKesJhC9O0sSzJ9l1VuFwajzz1BaFQZHnYtDAtm68WFXL80UNxxejxdOb5T2HZbaN/9fUBqmsaOXh0D8dtfg3QdZXRI7qxbMV2giETTVPolJHAXbecxNIV23n97e8JhsyWufAHwqxYvZNpU4f80qZ34DeIjkbUHfjZETJNtlfVkOrzkuLbd1si2fQisu4uoH0vQgMRfwXCF93st6qpmNGvPktYRjtwx+Su5oic1RhG21SOZSk0lA3j1IOf3a9jkf43kbW3AE3sroknbCnkptQihAa+81HiL8OWYRrDOzCURFxaWymGovJaCovKGdyjM8lxXt4veJKQ+hBuva2zYNmCqrojOWvovYQti51lNcR7XaQnxhE2LcZe/mCUGCZEOGif/eUCR9uvXPocX5Wvj1ruVQ20321FrI1O+8Ul+3ir8lkAimvraQqHyU9NRhGCp7fcx8raJVHbuBQ353e/lu5xfWisbaRsZyWd8tLxxreNZkqrBGQDqN0QrUJM/kCQee8sJqtzCmMOjXDCLlt+KgBhS8EfMvAYIXTVpnGJTfm1LsemzXn/SsXbqZb2sl6WraArCrZDY3AFFU1ohGQ0/2nFLC96QV1UmtM2FDpP7cHzb8auIASoLK6mobqBnF6dUTWV2z/6gue/W9EmYgiRRtlCQEMwFDWG4TNJ6F9OWEY7OvG1TfTvVhIl4mo1SrZ/lslGJdoJ1xSFzstsQv7olGqn9AQqqhqw2l1nHrfOZRcewVFHRvP1vl+2lStvfM3x+D1unQ/fvMJx3a8JUkp2l9QgJWRnJSGE4JxLnmXTlrKo3xqGxguPn0unjAOvI9iB/y50iJp24GeHoWn0zNi3/lMLZAAnvSIwkbbfsZ1zyAqgCOnc4FhIhOKg7C1sLBn9Mvth+4Ls0UnrnFTfaoXVLOkBitCJN5zTLNnpiWSn7+WfhW1/pALNwW7bjoynqyrdslJb1pm27ajNBRB2UO7eA7/lfLwCga2BU6zRbFWZlZXYNqoYsp0J2QJB2I7sy5foIz/RmfclVGeJCo/bxYkznDW8dNVG9+x1xGOYAIBUrShHq2Wdkypo83LheJUBpozmkzUj6FB40B6pWcmktuJC+cPhKEcrYoOMcnBa1onYvdIsNcZKAc73VMSxEM5BZkJhs6XpdWvYtnQkkgM0NMU+IbGO6dcGIUQbzhoQ83gVRRAM/fC570AHfio6nK1fCWRoCbLudjALI+KovgsR3pkHrCXOAYfrMKj/O20bNgMYCPfhrNlWwj2vfUHBjjISvS7OOHI4px02lCx3kG1NbS9LXVhkaJ1Aro7ajWmp9M6YzOqKEm5ZMp+V5cUkutzM6TeC8weOaiEOR9s3DrjdYYUb4Z7IB+sKuH7hx9SoQVRbcGhiHo+feCKGpvHuN2t57F+LqahtoGtmCldMH8eIzkezrPLJqNHCpkb3lKMcTfAYOn26ZLB2e1uiuSLg0IHdnO0GJmUNZk3tTgJW27m1kQxO7EKB2NDGYRWKYOTUoTQGGvjza4/yxVIL01Lo1y3IjaeezLDkg9nUsI5wO8FYS1rk+2JrN9U3NnHuw8/wvbcOWxd0roD7pk1n1ICe7Cos5uHLn2Hl52tweV1MPXciZ948A58aT6NVHzVW2sh4yhzU0Q2vQXezP7vtJZG0bCsIIekR159NDevaOF0CQTdfb3b6t0Y57obiInNoMhVbG1HaVyTakouums63HyzjiWuep2hTCek5qcy+ZQaHn3YoITvEo5vuYHNjJKroUX2clns+k/r25P01G2gKt50/07I5rGc+KyqX0rPvDny+AIGATuHGbFLsvpSL8ij7NGHRN2hEGlu0Cx4LFfKzh7OltDZKELVHeio21QTb3W8uQ2P8Ib15f9ly6ifVQnzzDqsUtPcTIum2ldt5+MnP2bq9kpRkL7NmjGHyxP4IgWPF7uAfEC59452lPPbslwSDJooiOOyQ3vzhmmNACJ5dvIynFy+l1h+gX2YG108ez+DsrH2O93Pi8HF9eOm1bwm14wbG+VzkdP7tClI3NQV59Jkv+fTzdZiWzZiR3blk7uGkp/0E0e4OHBD8elmO/0OQoeXIqnPBXAeEwS6B+juRjT+YBv7VQmjdwTsL8LBXwdIDnpPYXJbB3PteY9WWYsKmRUVdE4++t4gH3lnIvYdOwKuGMZo1i7xqmEx3kIuGX8m2Td0IhVVsG2wbQiGNoh2d8SUfxYx5L/Fd6S5CtkW5v5H7V3zNzd9+Fts+NRviLiTS6qX5NhAe8Exh0a50Lv7mPWr0IChgaZIv6rYx7cXneOnz5dz5ynyKq+oIWzaFRRVc8ci7FJdqLFp5ECGz2T4JwbBGwY48hnWaFNOOg/t3jVpmywiBPxaOzh5K74TOeNSIZIAmFFyKzh8GTGfY+UcgDRXZXF1lawqWS2PYeRM47/77+PRbSSBoYJoaqws9zL77LZLJwJLREZMsdw4u1bl6EGDS3+9nSUI9lldF6gpFGXDqF6+xYvlGLh39e77/cAWhQJj6qgbeemAet51yL2fnX+Y41rkDriDxrOFIXUE2nw7bUGjs7OOkkSfiNw0sO+I4SwmWFCgyg9Py5uLT4jCUyFzowsCtepmZO5cZXc5DFwZKc6zPUFzkertx72PXEsr2Yje3c5IiMk+hsWl4gwa3nvxXtq/bhRkyKd5Syr1zH2Pe059xx7qrWhwtAL/VyFNb7yU7I8i4Hl3x6pFWOYoQuDWN3x0+ljPGdWLwiELiE/woqsTrCzFg0HZOPzyO3vHptPW2JJZUuHzqRYS+M7CaJNKWSCvSWNz4vBN/nHkyaXFePM1K6C5NxWcY3DltMjdefTQul9ZSGetx6+R1SeW000dSP70m4mjtuRVTbJrOrKOsrJbrbnqDws1lmKZFWXk9Dz0xnzfeW8a5Z7aVvwAwdJUbrjo65jXxr49Wcf9jn7WIidq2ZP6C9Vx5w6vc89lX3P/lN5Q3NBKyLFYUFXPW86+zobQi5ng/N2acOJKsrCTc7si50nUFt0vnhquO/lVohP0USCm54vev8MHHq2lsChEMmiz4ZiPnX/4c/sBPiPp34ICgg7P1K4BddSaEFkevED5Exrf73Sj71wQZWob0vwtIhOcY0Edw3VMf8NmywqjUi0tX+eSu82k0i3m54C2219UzKjOHab1PYmVhNVc//h4Duq2jX+9tCEWyaVMXvl/fn+zxaSyt2x2lBeZSVZbMuJhEV2yHQYZXIZveBsII91FgjGHyP55io10VLadlQ856H00N0Q+wblkpFFfVk5BWzJCBheiaxdoN+RRvz+V308dz0rjB0fuWkolXP0pNY3teG/TpksE/rz89pt2mbbGgrICF5etJNuKYljOCXG8qk697gsqyGlzry1CrmjAz4gj1SqdfH8HGHZKw2S5qqJlMOqmMpsQSTNk2xaIJnev73kOqKyNq/18uW8vs795DGm2/10TIZvhbNfgX7IjSpDI8Bo8t/wuePJ03d/2D4sBOstxdODFnFvUNBtMefwGruI64ZaUofgt/nxSsvmlMPxzqPEupCQtsKUCCoVok6ApX9r6NJCOVbyu/ZFfTVjp78hidOh6fFinxLw3sZnHlFzSYdQxIHM7AxOH8c9vXPLruY8yXa9CW+ZGGwJwch2tiAvlXVLN7XbTGWUJGHNkf+B0jzdnurlzd53YWbtnORwWFeHSdEwf3o29mBvesvz4SYWsHjxrHwpJMQu2apCsITugyiqv6HMMTTz/OyrolKJbKhG5TOOmkk4BIf8l3VxWwoqiY/NRkThoygLS4SIq3uKSG9z9aRUVlAyOHdWX82N7ctf5t3i2K7noAkFeYSdm86OvP6zV47+VLWbuhmCee/ZLKygZGDs/ngrMPw+uN/Tw6dsb91NVHjwdQPcYg2C6UpwjBpD49+PtJx8Qc8+dGKGwyf8F6lq/YTkZGAsdOGUxG+m+Xq7VyzU6u/ePrUVIXbrfOJXMP59gp0c+eDvx86OBs/ZZgFjovlxbYlZEejr9RCGMYwmgrKrlhZ5kjx0VTVXZX1tErpyuXjWpLwN28u5CwabOkoCdLCnq2WmOzqb7KUXTVUFS211czyBV7/oQ+CJE4qM2yXcE6cHifCAmNSsiRCVRUUYcASovS+KioNa/NomBHNCEXoCkYpt7v/OW5vbTacfkeaIrK4ZkDODxzr8p8KGxSWd+EdOsEhrQV0mwMlSGU6GbJYVOj2q5GldFcFk1olAR2OTpbSzZsRljtO/RF5BOqtlWgOnCfNF1l29qdHNJrNOd1v6rNuuXbNqOrCsF0LzWTW0X1pE15eBeaJ0x8ux7LinBTHNhFlqcLh2U4p2o7uTtzXPZpbZYV1O4ipFpwejyh0/emWRQEpZvLHcepr2hEBhWEg99eESpBCMGh3btG9QstDTqL0wYtP25ViXK2bCTranehqioXnHchcGHUtj7D4NQRgzl1RPRLNCszifPOaqs+X1AXu9l5iV6NQrR8i9XcVHpw/xwe/Etsp7896htic70MvyDoaXvF2FKyrsT5/jhQMHSNKRMHMGWic4eG3xq2bq9oFltui0AgTOGm/W8y34EDgw5n6wcgw2uQgU8AA+GZitAOgKif2jXiVEVBgJKCtKuQ/vfAKkUYI8E1DhGLDdsM07ZYWL6B1TXbyfIkMylrMAn/RqNay7ZZuHorKzbvplNKPEeN7EOiL3bEaA9kuBAZmAdIhHsyQu9Dt6xUdpbVRL2ow6ZFVoozxyC3UzK6ppCYWMnAPltQhGRtYVdqazLJ8CVSUx/AbjdiyLbIiUuk3h/ko+83sKu8hv55mRw2pDt6s2RFQ3gbxQ0fYssQmb4jSHT1o5MRx1a7JirJLgW4LY2gQ/VbRlIclXWNUcvdhkb3zs5FBR5Dx+vWqXcgI3dOjXxpL/uukOcf+ICgP8jU0w7lmBPGRGyRkhWfr2HpJ6tITItnwqmHkJqVTKLXRU1NI67NFag1fsxUH6H8VNxaoiP/RlNNEkQCAYJY7Y7Lkhbpriz8oTCfLN3Ilt2V9MhJ48hhvRjcNRdZuzZqPBGySchJIrStkXA7MrJlWnTp7awT3zUlybEqU1dVkrXONFFK/QqT+q9sFB8kHqVgdJZkuLIIWmHml66hsL6EfF8GR2YNxK3Gjr70iM/ky7ICQnZb+ySQmptCWWH0y9+b5Ea4nB3jZD3VcTlAqpHO7vod1M2XBNZLjFxBwiSBK94g4HC8qlDoER8pOHhq/ge8t2YxmlA5++DJHD38oJj72Re6x2VSWF/ibF84geooXmWEMJ6Y4KU61MCHu1dSEaxjWEo3xqT1RBGx2Sder0Fjo7PDFXI5FJAQ4Zt14KejS3YKihr9Ceh26eTn/ciCpg4ccHSkEfcBu+7P0PQKECLy5lUh/joU34//0vsxkMHFyOq5tJVJ8IDvLIRrArJ6TiTKRQDwgt4bkfIcQjgrQDeZQeZ++zg7myrxWyFcio6mKDw66jx6J8RqihIb/lCY8/76GttKq2gKhnHpEV7IY5dPp1+ecyUagN3wODQ8QKRZtwQM8J3D+qoTOfevrxAI73UYXbrJ0SNzuXHWDMexLNvmj69eyqhhC1EUGyEkpqWyYvVgjht7J6d9/DJ+a+/L061qHJPfh4t6jGbOPa8QMi0CIROPSyczOZ5nr55BZfhN1lf/FSlNJDaKcJEbfwrFlSdw7oK3aKNAYUGeksiZnYfy1Lxv27QVchsad517NA+8vZDtpdWEm1+iQkC8x817t55NvNfZMX3+k+955P1FUePdPmcqi19bxKd3vRchKdkSNIWsQ3rx9Id/5Obp97Dyi3UEGgPoLh1FVfjT61eyorqGl899AhG2EKaNrSng0bnktct57tt57Cr1YFl7DszG7TJ58veTeWzrX8C19+VvB6Gz3ZXZfX7PWXe9RGMwjD8YxuvSife6eO7aUzni7/dRl6kj9T0kK4kStHlxyLHccdTd+Ov3Et51l06/g3txz2c3xbxeZj//Okt37iZk7Y32eA2dV845jhtnX0PN52akyFWL6FINuSObay68lbMXPUx92E+TFcKjGnhUg2fGXEiWJzqSB1AdamD6gntpNAMt7rkuVHomZDGnaCh3z34wSsR1zu2nsmzCe9Sb0Y2yL+pxPb3jnRuyf7t1IbeMvx+zSmI3RWiBigvO+dfxLHMLFpavJ2i3vm51nhtzMRc++Tcqu9qgi0jRrC0ZWJrGU3OvctzPvlARqOfoL+6I+rjRhMK9aXP4421vt3CsANwujVNOHMmIY3K57PtnsKRN0DbxqAa94rN4cOQcXGq7MGMzXn/nex54LFpQtG+vLDpN6szbK9cRMFvtS9N44ayTGdg59nOkA/uGbUumz3qIquq2ki+GrvLWPy8hzvfDnQI68NPR0Yj634QMrYS624g4OJEWMmBFuFWekxCKcxn8T4HQckDrDeGVIOtBxEPcXPBdDFUzQVZDS9Qh3Cwi6Y1Kz+3BU5s/Z0FZAUE78sVqSZuwbbGsaiun5I3Zb/ue/eg75q8obHEILDsiBrhk/U5mThjiyGOR5g6ouYyIqOkeVW0TwqtJi5cM6DSftbtTqW1y4zXCzBy1isuOmI8Sd5bjeEGrBDv+LhTVRGkWgFRVSU5WFYMzjmFs9mBWVRRTFfDj1XRm9R3KH0dP5P8efJvdlXUtURPTsmkMBGkM1qOm3IZNiD0kZYlJfWgDI3OOoYuaz6KdOzAVG2HDEHcmb86cxajeXTBUlYIdZYTCJlkp8Vx/6kQmDOnBpBG92VVRy/aySApweM8c7rtwGhlJsSuCBnXLwmvorNteSjBs0ik5jmtnTKB3ahL3zfgbwrIRe3jNtqR+VxVFJdUsfW8pgeYIgm3ZWGGLxe8vRRbXUr65FJpb0QhbolqSeKlw65/PYUPJtxRXKICgW5cm/jL3KCoWNvHpDd+hd7PQ0iLtDmvel2y+uoGVSS4KiytbpCjClk0gFGZXaTX+2xcQilcJZXlAgHtLA7lPbWfi+GGccNlUNq/YRuXuanSXzhFnjOOaZy9GN5xf0gCT+vakrL6RTeWV2FIyODuTB046ltrvi/nsr4sxm5qdsOZbsXpBkMKJYQr8xS1RKlNaBK0w2xrLOaqzs0ilRzU4NKMPhXUllAXq0IXKEVkD+fPgmfQcmE9GXjoblmzC3+AnMS2e2bfM4PhLpzImdQLr6pbTYEZaV+nC4JQu5zAkeXTMY3r12g8o/HobdrDZ1TGBEATXw5+vu5i6UBOb6kuxpaRnfCa3Dz6VTxd9x6K4neBWIqWpqgBNUOZpZJieS+fk/YsEeTUXw5Lz+aq8oMWxS3fF89RBFzCwaxe65qZSsLGYxsYgcXEuTjvlIM6YcRDnffsotWE/lrRb5rY21ESc7mJgUq7jvvr16QxSsnpdUYusydBBXfjbnacyvmc+IdOioLQM07LplprM3cdPYURudO/FDvx4VFU38uJr30alElVNZcyo7mR0VCQeUHSImv6bsOvuhKZniBZ98iASbkB4Tzkg+5UyDGgIIZDmJmTl9BbdpzbQeqGkve84xvFf/oXd/mjOj6FovDHuSjq5Y/cndMIJf3q2xYFoDZeu8dofZpGTHt3mQjY+g6z/K5GoYGuoIJJARtKmpi1QhWzWUfIg0t5CaNGyB9vqXmJ91T3YUSKVCj0Sz6NXyqXN49moItKGo6bBz+TrHm+JNLVGcrzC1Rc936YlTwSC/ITZ9E29EoBA2MRQFRSlbepESolp2y3pyNawbYlEoio/vti3/Xj33voKH9z2FqJdiboEXMleQtXRwqWeeDeBxiDSgb/hiXPzbt3zAFiWhSUtDC2Sarvx2Dv49l/LIj9UaZFy8iR42H3aUByGQ1MEWS8uo6nOH7lDFBDN03zYjLHc8NLlkX2ZFoqq7JeEiZQSS0q05vm7/fS/8/lLC6N+503wUHN5Kv6DotPjihB8M+nWfaa8IJJuV4Rw/J0ZNtH0aKaFbduYmC0VkPvCCamzaaiOTi+rmsLbNc/h9rqaj9dGUyLnfurfbqCilx1xtFrDbzO8KpNHzrn8B/cbCyHbRAE0Jfq4TNOKtPMRgs31pcxZ/Iijpluv+CxeGHtp1PL2CARCGIbmeO+0Pr8d+Pfwr49Xcf+jnxFox5MUAk46bgSXzD38F7LsfwM/NrJ1wK92IcQUIcQGIcQmIcR1B3p/PxuESnQ5GpEr+ABS3YTQW72YVEeBzwhi26Du4wWjxhJ63Af2VRK9p4GrZfsJmGXIFgmBGPOHoHUjO02RrQQrJc6SnCBQHMeL9IdTm22wKG7cRkMoEnlwajTd+picRS8FSis+nFuPfllARDDRydHaM7aTo2XbNgUlpZTXN0StC5kW1fV+ws0pNE2PwckTEd0saI4VJurY7mYJA0lMp0Zp1WhXVdUWRwsiX8AtaKcAEUsYtLV+WSTqtnedqu2xR1LdGKDRofzclmECZim2jOYLCSHavIjVffThE3uOy5SIChOatbMEe89vWVU1hVt3YNvRTremqDEdMidHCyDQGKS+tNFRkLYq2EB9K70wJWaDY9FyX/mtEDWhveMpiJj3vdrskJmWTXlFvaNIZ2lVKSsKVxI2o+dWyjpsGe38AWia2nL9qELEFNxt/XzZsbOSouLoDzHLsqlvCBJ26HXZ/vz+GFiWTUVlfZRD8VNh2zLmeDnJzH0AACAASURBVMFgmPKK+t+MeKuqKDGeZKKjwfavCAeUIC8ib8GHgCOBXcB3Qoh3pZTrDuR+fw4I97HIxueJajcjLXBP+M8YoXYFJR3sHe1W6OCJHVmblj2CJzd/1oYLIhDk+zJIc+9/ifPxYwfwyLvftGmkLICc9EQ6JXtYXXEzRQ3vRkwWHvqmXEO2dxLU3+0wmgDPDGh8nKiol9oJVOf0RKZ3Iusq73JYo5Dlm8K8rc9RF3oQtxoEISn3D2Zmr4fo3zWTVVuK21Q/unSVaQf1d1QfV4RBVtzUH5iR/cdDC7/hvnVfYzarzOcQz5unzCLV5+XBd77mlS9WICXomsIFx4xh5tkTefePDu1SVIWJsyfwzueL2DUzGyteRwrwraqh57sVjJrQn+/mrcBqpUCvGRoTZo6NaduUOYez7NNVLWnJll2pCuMHdWP+qk2R6oBmCCE5clhPVj4fLSfg9rmYNHsCSwt3cdNzH1Ne04CUkoP65nHzWZNJ9LnZXPM4m2ufalZ5V+iWOIceSefHdBQnz57AF699gxlo61jYls34SSOY/+iXuF6tiqQXBQSPT2bMlYdSWlHJxafcTO3Xpc21JjpnPzyLU0+YEnMu9oXGuibumfMw376/FBRBUnoCv3viQkZMGsy62l3ctOo1ipqqABic3JWbB53MEaeP4+1HPsQOtXI6VMGQw/sT1iQ3rXiJL0vXIYQgUfdy/YATOGXIOB6snR9JH7aGAv83+QTefn85T/xjQUtq97ipQzh/zmFU1VVw0kN/Y2dGAkhQA+9xbnoq1512IXWhDaws+z0N4YgMRZJrIEPS78SjO3M483zppLriKfJXtVnuVnWOzxnJwkWF3HzXe4SanT2PW+eum6czeGAu8z5ZzcNPfk4waCKBqUcO4JK5E9FjfUD8AD6ev5YHH59PIBBGIpl8+AD+78KJGDGc4R/CZ18WcP9jn9HUFAIpOWJCPy6/6EgUIXjg8c+Y98kaBOBy61x0zmGO7Yx+TTh4dHf++uDHUct1Q+WIw/r+AhZ1wAkH2u0dBWySUm6RUoaAl4HjDvA+fxYIvQ/EXQK4mv88kX8T73IsoT8gNggBqpNaswWacxsZgFPzxzIoKQ+PaqALFa/qItnw8echM3+SHTMPG8LQntl4DB1dVfG6dJLiPNx93jGsqbiFoob3sGUQWwYJ2zWsrbyFisB6cNQH00AfBA4Vfai5MV+4upqI6lAQIITCqoq1BK2/Em80oasWumKT7l7FyxvO4bazp5Ca4MXritjucUVU28+bOp5BabehCBeKcDf/66Jn0oUkGLFV038KPlhXwF82LMTUZeSOU2AX9Rz18jM89v5iXvliBYGQSTBs0uAP8eA7X/Pd1mJm/PUMpKZE/lSBVBX6nDCSo35/FLsu6IaZ6oroXOkKTYOSMO8ewxWPnU9GbhqeeA+aoeGJc5PbJ5tz74xd1HHQMcOZeMahuDwGuiuyjSfOzc1vXUODZznu+CCqbiEUG0W3cCcGqNWX86fXr8TldeH2uTDcOobH4Oi5R5A2MIdLH3yLoopaQqZF2LJZVLCdix94k621z7O59gks2YQtA1iyiS21T7GtOcXpBJnppXxsCrYusDWB7VKwDYWiGdkUX74M10tVCL9EBCUiIHG9WU3xWd9yzhHXUft1CcKUiLBEloZ46rSnWbRs1U86j3867i6+/ddSwiGTcCBM+c5KbjrxbpYvK+CiJU+xrbGcsLQIS4vlVVs4f8kTdL6gL2aejnQLpAbSI5CpGhk3DOC65f/ky9J1hKVFyDYpD9bx++X/pF9+V9yvVkPQhoANfhuCNomPVrNzYx0PP/U5DY1BgkGTYNDknQ9W8PizX3LMQ39nZ3pCRBjWUDATDB6rq+Hlz19k8e7Z1Ic3IgkjCVMTXMGi4lmOkUWIPHvuHnY6CboHb/NzxK3ojE7twShXT2649a0WRwvAHwhz2XUv88XCDdz38CfU1QcIhkxCIZN5n6zh749++pPm/Ltl27jngY+orfM3j2fx8fy13PtAtHPxY7B81Q7u+ts8amqaCIVMQmGLT78o4O6/zePvj3zKh5+uIRQyCYZM6ur83PfwJyz+bstP2td/CgnxHq6/ciouQ8Pt0nAZGoahMWvGGHp27/RLm9eBZhxQzpYQ4iRgipTy3Ob/zwJGSykvcfr9r4mztQfS3AXBz0Ho4J6EUP5zLR2kXYUsG0c07wkwxqGkRLeHadlWSlbX7GBN7U4y3ImMy+iL4cDT+NG2SMnqrSWs2lpMRpKP8YO6o6gBPts+rplk3hbdXbn0VIshKmXhBTUbLCdtMQORscBxjosbP2FV+Y1Y7cZThYfKQAIJRmkUxSVsq4zIeJcMTw4LVm+huKqevrkZDOuR3eLUBa1KShvnYxOmk+ewmF/6/w4Oe/oxtsna6E8bGzLWuzAboiNsXdKTeOeWs9m5vYznHplHoCnI8bMOY/jIXlyzcB5vbFoTpS3mVjU+OG42eb5EvvtwBbs27qbboDyGHD7AMRXaHtvW7mTZp6uIS/Ix9oRR+BK8XLj4DHTdonpXIk01HnzJfpJyagn6dR4b+xwNNY0sfGsJTXVNjJwyhC69s7nntS949cuVUVIOHkNn7mkfk54R/fIylBSOyFvgaNep9zzEosR69Iog3jV1SJdCw7Bk0ATdLl2OsBz6EgrAEIhg23VSgZzpPXn2FadWTbGxq7CYC4ZcRbCdLpqiKuQd34t154koKQmvahCnuSkL1KKu9KNuDWJn6Zgjfbh0A5Btos8QiUD3rkmh8pxlBBJswiN9iLBEW9SIT+p4DhlAiYz+IIlLbWDdod69laF7YEumxS9n6qhNUfxEVfgYkn4nnXyxI/UBK8yXpeuoDNUzJLkr/RJzuPXu9/j0iwLH36cke6Oq4iBSGffOy5fi9eyfQPP/XfNPVq7Z5TjeT6m0+931r7B0xfao5bqugpSEzeh7sX+fzjx87xn7tZ9fAjW1TSz4ZiOmaTNmVHeyOu0fN7cDPw2/GVFTIcRcYC5Abq5zCumXRKRScNYvs3OrJBIdcmq0bLVPLbaFEIJByXkMSs77WUwRQjCoWxaDuu0VCG0MV0X4Ug7+umqXg+KkJN0EVgyhPaGDVQYOzpbf3I3tMA+W9ONSZZSjBWDaCsVNW8mOz2Pi0J7RPwBcaiq5CSc72/MzocJsAociPCEhoFhoDoyLitqIU9klL4Mb7jyrzbqttc4irrqiUNRQR7fEFA46ZjgwfL/s7Nq/C137742kBkJBDFckVZWSW0tK7l7ZA8MdiYjEJfmYcnbbl/X20mpHzSxFEZTX2KRHa6QSsqsjzZQdIpu7Qo2gKYQzPdRm7iXDK34LR/Y+xKT/CRuqNu9/e5iy7eVohhblbNmWTcWmckJ29DUrpaQ61AhCYA3xYg3Z2+zQtE3cqhGl2SaRlMl6LH8IxQ+u9/fOue1RqS2phk4O+/I0IUx3tLOlCHyJfodCkEgxjt+MLXgKkbTh5M5txVOLimti/r6+3lljS1EUauv8++1sFZdGS21AJMVdXd24385WcYmz7aqqYFt7qs7boqSsbr/28UshKdHLtKOcK3A78MvjQDtbRUDrPFhO87IWSCkfBx6HSGTrANvz24LaFRyUvUEFY/9epAcCbi0T50y0gq31AbEaZLuvXOEFvR+ElhD1YJM2aHlsLanipfnL2FFWw/BeOZw8bjBJRn8UoWG1S3uowovfzMZQNkU1KlaFTY+kQTSEtrKt7kUaw1tJcY8gN2EGLnXfEUrTbmJn/RuUNS3ArWXQNeE0El39f9zEtEMvXxrLAsWOIqlpioea9rxAoFdOeszxRmV2YWVFSZT6eNCy6JMSe7v9hdtw0VRn4E2IdnKbamML2g7rmcP3G3cSbEeODpsWPXKchXV9en7MFPLQxAx2BYuRrrbek1SbZRFMB7FMBZwyZFKD/LHdqKip5ea/3UZT5i6kX9CNkdx4xRXRGzQjf1BeVPshAN2l0fOQHlSq9fjbNQaXQH5cButKSwlsj8fyayiGhbtLAynpbhrM6POuCZW+RhabfJuiOHRCEXQdmEdBWXQjbz2YjlQlRjXEF4LqB38WNOTaNJQnofb2YrW7F4VQf/CaLtxcypvvLaO0rI5Rw/M59qjBDB7QhYINzsr4OdnJbN0e7cyqqkJ6ahybt5bxxrtLKSmtY+Swrhx71JB9Okz9enemrHxD9AoBnTrtP/+0f99sSsrqohXXJWiaINTuFAsB/fr8djt4/BC211XzzLqlFNZUMCwjmzP7DiPd8/PJGnVgLw40Z+s7oKcQIl9EGvzNBN49wPv8r4FQvBB3QUQJsQUKCA/CF93G4z8NVRj0Sr4UtY19AlW4yUr+I2g9ifDd9sCIEODjb2o+plaXn/BA3AV8u76M0+94kbe+XsOSDTt5+sMlnHTLc5iB3iQYfVFajadg4NGyOCjzVsK2Rutis6ClURuagmVv4uvdJ7Oj/jUqA9+yueYJvtp1HH7T+WUBELYbWFh0Ehuq/05lYBFFDe+xqPgsdtW/95Pm6bbDJ6HYtIkACguGuDK59sTDcRttv3nchsblJ0Y3Ad6D2f2G49ONNhWBHk1jZq9BP/uDMrNkNJbZ1gmyTEG36kNibnPsmL6Ysm0rHykgPzeVxMRphKy2TlPIUrGJHV28fsY0tKCEVikeEbTpVqlyzDkTHbcZN30Mif1T27V5jkS2zj5nGte9dQEcuZO40RA3zqZ09LfM+dO5MW1Izkhk6Kzebdv1KCA9NhddM5NkIw6tVRWrS9EYkdqNoxPG0Lg2BatBB0vB9ms0bUxipBzCybkH4Vb0VsMJPKrB1UdMJyUzCa3VdeHyGPQ/uDeXXX88Llfb68Xl0rhw1tH03RCk0+cS33bwlEHSGknWZ4ITe59DXZPEapV+tGxBVY2HJFfsSMjnC9Zz8VUv8uGna1i6YjtPP7+QORc9w0nThjmS3b0eg1nTR0TKYltHXqVkSM8MvlmymQt/9wLzPomM98wLX3P2hU9TUxuddtyD6dOcPyoPH9fnJxHkZ592MG6XTmu/3u3SOPO0gznvrPG4W82tEBEV9jlnxL7Wf8tYWlrElHee5YX1K/i6eAePr17CkW8+xc762JHLDvx0HFBR05tuusm++eabC4EXgUuBF6SUb8T6/a9J1PRXA30EQs0Ga2uzyNKhiKT7EFrXX9oyAJLdg/DpXWkMbwNpk+oZzdCMu4l39QTPsYAF1i4QPvCegki8E0XrBO4jIylDuxq0HET8NeA5lbn3vU5tY6DlWW3ZkpAZIY7PHHMJCGgKF6EqXrrEn8DgjNtJ93ZBEWNYV70RIeqoCyXi1s7i5B7XsqT0PEJ2FXuFSy0sGSZsV5Ppc35Rb615hjL/glaaXhKJSYV/EfmJZ7WRhvgxSI+P4+D0XJZs2Um9FcKwVE7I6seTJ06nZ046fXI7sbOshpBpMSg/i1tmT2FQt9jcMZ9ucGx+X6oDfsr8jWR647hsyFguHXLwfulZ/Rg8tmQzm5b78SSEUDRJXZmHggXZEJ/P8YP6OW4zr6CQz3dvxbIkworwpMJxUK75+aqpkpXlnUjz1GMoJjvqU3lxw8F8tMvinH7ODmac18OUrG4sX7KRahnEaLI4SunMPy6dyyHHjaKprokN322OpCEVwdRzJ3LJ/XN46fevYbd20ADdrbPeXIN7QhOqNzJXQgiELtDzQ3i2ZZOfG12UErQCfJT7HCLRIrQTUCBhgqDrHS6ycjtxQc+TaLQClPrrSDJ8nNb1EK7seyyXvPQBjaEwe2VLBCDYuLuGfxx/OqmuOHY0VaIIwbiMvvx5yEyy41OZeMahBBoClO2sJCE1nhMum8qlD55Lp4xERg3Pp6SslqamIHm5aVx+4RGMHdODl55ahRmy9+5JClxSIRQOsCWzEk23cSthwrbK9lAqqxs60bmhB5np0ZEb07T4v2tewu8P770XLZtgyMQwNG64ciorV++kuroJoQgG9svmkXtnceefXqcmaEJrjqAQFO2qYsmKbVHjhUIWtpSMGNrV8dy//MYSNm4ujZKhqKxq5JQTRu739Z6Q4OGQMT2pqGqgrs5P56xkLjjnMI4/eih9e2eRm5PCzqJqbNtm6KA8/nDNsXTP//mixb8mnPnxa5T5G1ranFlSErItShrrOTq/zy9s3W8HHaKmHfjNobS6nuP+9AwhB22e9EQfH925f4540Krk8x1HOhL4DSWZI/K+ctxuYdEp1IWi1Uk04WNU5hMkuQc5bPXfB1tK+t32N0fJJ11RWHPDZY7bXfDyO3xeGE2Cj3MZVMc1YjtmEiVbzrr6RxH5fwy++2gFt824l6a6aEHgzrepJB0V/ZK2GiXa/Hz+9sc7otYV1q/lyS1/JWBHj9fd14f/6/UnRzt633pfTBsXXHYenRLi9nUYPxoFG4r53Q2vROQM2iEu2aT771a16LPtgR2APrtHc/H0aJHUws2lXHrNS/gdGqXn56Xx7CNzHO2YcOTt2E4RJ8vGJSDocH67ZCfzwhPnOY43Y/ajjpwpt0vn6Ydnk531n6kM/29DfSjI0H8+gCmjOWrxusHqM366cO7/Gn41oqYd+N+GlBYytAIZWoZ05J/thdelO6qfA8R59r+/lyo8yBjqkJoS+yVnKM5VPDYWmrLv1hembfJ+0VJe27GYpnacnPJAHS9v+5ovStZGCWw2hYuo9C8haP548nZjKMSS7bvYUFoR9eW/rWojX219n1212370eO0hAJfmnKrxNLfdkVKyq2krmxoKCNmRF3Oy190mxbkHUkoU1VkoUlPsH3S0QnaITQ0F7GzaGlNwcw/iknyO15IQYDcIpAPPCwnx7sj53bRiK6/f+x5rv14PgFv1YjuQpwF8WuxrwmkeWmx07x9ZfF+I87liinC6XArSdLDDgjjD2fa4OHfM8eLjY/P11FjnRYAdYy7i4mKPF2udZdv7Tbb/pVBUXM2yldv3mS79T8NQ1ZjXpk//z8+rPxBixeodbN5a9oP39m8Vv3g1Ygf+eyFDS5HVFxPpjwigQ9L9CNdBjr+P97oZ1SeXJet3tGmx4zY0Tp2w/1U2muIlwzuOsqYFSPYyX1XhpmtCbN2prolnUB1cgdWqTZJAwad1Ic7Ij7ndJ8Ur+cPKV1vC8n9Z9y5zuh3GBb0mcc2yF/iibG+0TBcqj4+eS5+ENJaXXUVFYDEKBrYMkh03jQFpf2hRxnfCP79fyV2fLEBTFSxb0jkxnidOPYFkn8rLq2eTmrAR01KpKrf4eNMwzhjyGIa6fw9RIQQnDunPGyvWEGwlkurWNE4ZOpDSwG4e23wX9WYtCgo2klO6zOHU4YP5YN3GKBHcOJfBgO4+Fu5qxJZ7HStF2Bycs+8Iz/dVC3ll51MoCGxs4rVEzu9+LZ3czunWPqN6kJAWH2lf1OrhbXhcDEo4mB3mAkS7p58Mw6VzLmB270spKixpWZ7cKZEn1tyHYXkJ2gFEa7HRsGBc+uSYdh/UtQvfbI2uHO6cEI/P+Pleal1yUsjpHCGntyZ/u906px4/jAWsjN5IgePGn+g4XlanRLp1TaNwUylW6/FcOifF4FEBTBiZz8fLd7RNI0pJooDc/jms27Aby2pr38nHxw4KnHz8CO576BMCwb33r6YqDOyXTXLSr5vI3dgU5MZb32JNwW50XSUUMjlu6hAumXv4z57u31+4VI0peb34cPvGNoU2blXjzL7OPXcPFN75YAUPPzEftflZlpEez923nPxfJ13REdnqwAGBtOuR1eeCrIpobclGkDXImvORdlXM7W47+yh6d8nAbWjEuQ0MTeWY0f048ZCflroblHYria7+KMKNJuJQMMjyTSUv4bSY22R4x9Mt8WwU4UITcajCg1fLZUTmwzG3qQs1ccPKV1ocrT14essX3L32nTaOFkBYWpy/5AnWVt5BRWAxtgxiynpsQhQ1vs+W2n/E3Nf3O4q4+9MFBEyThmAIfzjM1spqzn3pLV5Z9TtS4jeiqxYeI4SuWqQkLOe11bf8yBlri2uPGMfovC64NJU4l4FLUzm0ex6XHnYQD236M1WhckJ2kIDtJ2QHeGXHk6SmBLn2iHG4NI04l4HP0OmUEMfTp0/n4fHn0D1ZQRE2umKhCJtuyQoPjzsnpg27/Tt4eccThOxA836CVIbKeGjTbdgOaRCIOIp3fnhjROA1zo03wYPh1pnz55lcfd1FiI+6YPslVkPkL1whGVx0DH897cE2jhZAdWktvxv/B9ZcVEm4OJJutOoldkBS+pjJV2+uiWn7QXlOosQwODvzR8z+/uHOm6aT0zkZj1vH5zUwdJWZJ47iyCkDHds/GW4Db5zXYaQIbrvxBLrkpOBuNd5Jxw9n3NjYor+Dx/dr62g1o/vgPG6+/jjyuqS1Ge/4o4dw+LjY/KDJE/tz9ORB6LqKz2vgdmnkd03jT9dN+4HZ+OVx533zWL2uiFDIpLEx0rro/Q9X8f5HP01U9+fGnw+exLCMzrhVjXjdwKWqTMnrxdwBo/5jNqwpKOLhJ+YTCJo0NoUIBMLsKqrmqhtf/a+LcHVwtjpwQCCbXkPW3Qa057i4EfFXIXxn7nP7wqIKSqrq6NMlg/Skf5/XUh8qxG/uJt7ojUf7cS+6kFVNTXA1hppCotF/n1+jD6yfx/PbnDlgmlAcuREAp3ZeRu/4aNFGl9qJibmfOW5z+ev/4sOCjVEJUq+ucdP0J9G1aM5bQ8DDKX2/i2n/D2FbZTXbqqrpnpZCl+QkCuvX8cSWvxC026ZKFRTGpB7OKbnn0BAMsnTnbv6fvfOOs6I63/h32i3b7vbCVhaW3ntRRFBUBMSKnaix16hRkxhFoyYaYzexxB419oZdUFBAQKX3vuyyve/eMndmzu+Puyx7uXMXlvDTGPfhs38w954zZ+bOnPOe933e5413OhmSkxUWtvihejOrqncyOLWAYakdK/a/sftZFlXPiyiv5JRd/LrwBnrFD4jaVgjBhqVbaK5rod/YXsS184Zs37Wb5998lVhXDFdd+Gti3S6mqGdEDWXjBALgHgBKgoR3jcBqhoRxMbz1tb1xfNTDT1PeGFkL06kqfH/TlVHrax4qhBBs2lJOfYOXPr2ySPTE8Hn5e3xS/ibGfmF8p+xiVu6vGZ4cvZSTEILNWyuoq29p668j/OryZ22lHzRN4Z2XryQu1smW7ZXU1DTTuyiT5KSD807V1DazeWsFaanx9Cy0EWr7L4PXG2DGmY+1lVRqj7ycZF56Knrm64+NrfU1FDfV0zspjey4zstp/Ce4894PmL9wA/ubIW6XxsP3nkXvosO/KTnc+NmImnbhfxRWPWBXCiSAsOoPWA67KDuVouzUwzaceEcR8Q57YdNocChJpMdMOKjv1uiRC+pemFEMLYD6oH0oybAitZT2orqlxZ60rggUJXJyB3CqNsK4nUBBShIFKfvIyC1mk22RaguLJiMkRBnndHJUT/uw67DUXgc0svaiKdhgW8cSJFqM6PcdQh6ufmPsz1OYn8udN9wUdiyqoQVtcQDfWmiv4xGsj14cuclvL/JpWBYBwzzsxpYkSfTpFZ5d2GQ0RhhaAKYwaTEPfP86s+A1NdsJGYe4a16vTnyci149MqCTZWRSkuMYO+rwJBP8GPD5g0Tbm0W7Rz8Veiam0DMx5Sc5d219S4ShBSEB5MamyGSUnzO6jK0utMGvG/xr3vd8sCQU8po+th/nHTMc56EUfHWOgWaNiBqIkhvJOZbtlRtYuO0vuGM3E9Dj6eY6n2MGdFwSI2iYvDJ/Be8uWospLKaO7MPsKSNxO23k2dthafUWnt32JXt8dfTz5HBp0TEUxnU82fuMMrbU/YNq3xIcShKFngvIij0+qnfrhG5D+GjPCtvPMl2JlPnttWtGJNstthLJrhGYlsUbC1bzxsJVBIIGxw7vxYXHjWRSr0JWVZUSSPFhyDISAtUv4at1UduURmpCVVhvloCa5gJMy+Ktr9fw+oJQLcZJQ3py0Qmj8cS62FRdxXVfzGVzSw0aMjO79+OeiVOiktYLY3vbLuAO2Ul/T8ecj43LtvDinNfZuW43+f1yOH/OLPqOjm4ID/AMY8XaFey4xUdgS0iwNG4y5N+pUxjXm03fbePFOa+zY80ucvtkc/7tZ9B/XO8Ox9AY9PHcti/5smIdTlnjtLzRnJI3mviUOJpqIg0Q1aFiWkaEkSs5BXnjole+GJWfa5uZmZeUSIym8umyp6i2XkNzetEb+jOx3+/ISe9BizfAK28sZd6CDWiawvTjB3PKjOGoSueZH30TBvJN1Xx2NLuo9schIchwN5Eb66cozl6+Yy8WLtrMy298S22dl2GD8/jVOeM75NKMHNadT+etjRANjY11kpbacXJJNJSU1vLcy4tYva6EtNR4zj1jDONG9zykvn4sJCfF4vHEUFUdvmmSZYmRwwp+mkEdJlR4m3h4xWIWlO4g0eni1wNGMrOw3yHx0MaP7sn6jXsIBMLnkqBhRWwafu7oCiN2AQDLElz4t9fYtLuKQCux2akp9MlN59kbZx3Si2TVXQf6l9BGNHeDcwy7gtexovYcNC3YpvquB1WC9adz+qg/2PYlhODyR95i1baytvE5VIXuWcm8dPPZURehT/as4J617+K3Qt4HCQmXovHMmMvoGW+/Y/cbVXxdOpOg1QyEPEWK5KbQcwFFSVdEvd6Jn8/Ba4Z7kCTglSOu5dxFj0Z4uCZnDOC3fXqxvOIyLBFEYCKhokguxnV7hT+9uIGv12zH31rwV1NlspITuP2yYzn1039hCYl9+k2C/IQYpsY56NX9H6iKiSILDFPGMBWqKv7A7m2xzF+5dV9/ikx6Yhx/u+Ykjv/gOSxJ7OvOgn5x6Xw061dRr/fDPa/zVdVH6FbIe6NJDtJdWfym151osr3HbuWXa7l12p/Dyt44Yxz86f1bGDppoG2byj1VnJN/xd6fog2e7nHcKsT4swAAIABJREFU/vxN/O6Eu9B9etsO2Rnj4Pa3fsvI4+yTKvymzlnfPEKlv4GgCHXqUjQmpvfjhOJCbjvpvog2Vz/+a7btfJZ5jxkEfKH77nCZJGca3PfNfWR1s+cdfbJ+M9e+9WFkfxPGkCbexpn5OQ5n6PcwDQk94GR02hv84fb57Cmrb5NBcTpVRgwt4J7b7AntHUE3g0xfMIcG3cRqdc/JWOTFxvD6kbdFbffKm0t54eXFbeR0WZaIjXHwzGMXkJFuH26qrGrk11e/gNerEzRMZFnCoSncdssMxh+CgVRSWsvF176I3x9sM+BcTo0rfj2Rk04c2un+fkx8u3w7t93zLsGgiWUJNE3G5XTw9KOzf7bk7xq/lynvPEtDwN9GjXCrGrP7DuWWERM73Z/Xp3PJtS9SWdlIoHVecjk1Ljh3PGee+uNxx/4THGwY8f9V1LSz6BI1/enw7YZiXvtqZdtCDCFB0UZvgAHds8hJPYTJwTUFSckF0QRyDlL85Uhx1/P+mt8TF1+Mouwz9BXFAm0jufHn4lAjF+rV28t47pPlYRlupiXwBYL0zE6lIDOy/I4lLK767rmIsiimMKnwN0TUfNuLzXWPU+dfQXuvnMCgPrCa/IRzUKTI8W1uLOOd4mWY+/k+nJJK/8Rcruszle3NFdTpzXi0GC4vmsK1fafi1rqRGTMZU/iRJY3M2GMYnP5nKqpjefCthW2GJYQMYj1oskjZSqU/AGFhPImGQJANWw1W7OyOQzExLZk1u/N5ZclRLNtoULyjJqyEjiUEhmkxv2k7VcIbni4jQZXewpTsnqTF2odvesX3J8ddgM/0Eqd6OCrtBE7PvQCHHF2m47aT76W6NDxBwgyabF2xg+mX22f1/e3CJyheF8lrC9TrrF28kbry8Pp5ZtBk03fbmHnVCbb9fVDyPfMr1qK388wZwmK3t4bZRx3PpKlj2Pz9dnwtATK7p3PzS1dx9Kz+jB70V/qNbKClUSE+0eTE82q47v5KklM8SM7Rtue64+P5lDZE6kTtqqllwrB/t9WehBCvXJIsPv6wiY1r1LbFB0ICoJVVjYwZWUhKcufCaV9Vrmde+SaC7Yx9gYTfkhidWkSaK9Jw8vl1brn9rTCPgxBgmYJA0GDsyB6254qNdXLc5AEhiQ1LMHhALjddezxDBh5a3duHn/iCLdsqwzxlhmmxel0pZ5w8EuUQPH0/FnKykxg7qgc+n47TqTFpQh/+cOOJpKf9uLyow4m/r/qWJWXFYc+SYVmsqSnn3N5DcasdRxn2h6YpHH/MAGJiHPj9QYp6ZnD1pZOZMunQSqP9FDhYUdOuMGIXAFi3sxxfIJJ74teDrNlRxug+nZ8sJUkG93Qk9/Sw45p7U5ihtRcCiR2V6xmQG7mjWbOz3La4sTcQZOW2UiYOjpz8a/UWvEYkZ0YAa+qjF/Ku9S8Pk4rYCxmNZn0rSa5Ij8n6hpJQyG3/eoXC4IfaHRzfbQiPjrQXgoxz9GBQWni24Nqd62x1cHx6kNqWFrDhS0kSGLEGVfWJvL40vMSIEjCIkTX2dw/59CA13jqw4T1LAr7YsY1+6dFDrv08Q+jnOXhZjp1rd0c9Hq0Q9brFNrXxWrFni33ZpZJNezBNE8WGE/V97fY2T2d7KJLM+obdnDB+KE+uvD/sMxFYDJKDweOaGDyuJbyhvhi4xnYca8vsi65Xt3jx+p14HOHaS6pmsaesGZ/fXqxzw6YyijrJd1pVtwufGcnZs4RgTX0x/Tw5EZ/tLq2zNWQM02LFqujvDoRCaJdccFSnxhgNq9eVRtYxBCzLoqKygZzsjmuc/tToWZjOH26c9lMP47BhSdmuiJqsAA5ZYUNtJeO65Xe6zxi3gzNPHfWz8WQdKrqMrZ85hFkDgQWhldZ5NJKceMA2QctkYekOyluaGZbejb7JoYw/l0PFp4fHzl0OjYz/IBuw3lvG5so3QVj0zDiV5Jgc9EASUBfxXUW2SIu3101KT4xFU2WCZviL7tJUspLtd4pxanSxxFRniD8SNBup9C3AEkHS3RNwqqm41W406hthPy+VRRCXYp8JleZKQJYiFyeHrJLlPvBvsj8yEuOQJFA0k+S8OmRVUFfigYAbRdXwGe1LwLRCgBSQbMnzNkMDQqHEeFnDL7zEOAOkxTURNBXKGz1YyPRMSkEIwbflu9nWUEOvxFRGZuQcsk5QQnIc9dVNmANcmHlOlGIdZa2PhOQ4JEnCFwzy5ebtNAd0xnbPIzfJQ2p2ErVlkc8LgDs+Bm9jpFhkrCfG1tAC6BaThCYpbSHE9kh3RvHgKhkgggQsmUWNWTQaDobFV5Hn9IISkneoqm5i2fc7cDhUxo3uQWyMk9TYGIr1hojuVEXG6bAxgCyIi5VwOBR0PXx8iiwfEu8py52EU9YI7GdgqpJCusv+elOSYgnaVHIAyGwtAF1X38K3y7cjyRLjRvUgId6+yHh7GIbJ0u93UFPTTP++2QcshZOeFk9lVaRn0DAtPAfIjGxo9LFk2TYAxowsPGAmZRcOjNz4RFZUlUVI3AQti8zYQ+Pk/VLQZWz9jGF534DGO0FSWu2C2xGePyO7T4zaZmdjHWd89AotRhCzVcX86JxC7htzPA++tSDM2JIILcbHDDu4rLH9sXzXk/RXH6KvGlqYlbonWFpxKT0Tf0118DYc2r5zBQ2FupreZBRF7rIBjhrUA6fjS3x6MCx7RVFkThhpz5dxKRrTsoczt/SHsIXGpWhc2ONoylvms7LqJiRkQLCOu+iTdAOFnguo8i3CEvvCjxIaSc6huDV7Y3B0Sk/iVCd+Uw+biFRJZnrOAcP5ERjRO5esQi8ZY1YhRMiWRhLsWZnH6UNO4/dL5+2XxSOIdykMjs9lmXdPuB0m4Lj+Pdm2sZpATUOYSKWiyFw/5gherHiW7mnVoRrCSCAkftjWm/H5+Zzw3vPsbqrHFAJZkujhSeGV42cR7+i8qv+Mm6fyhGsJRpYWCltaoJYbnNsyhh927+HiV99BCIHV+nfeyKFcct/53DhpTkRfqkPlzFtO4rlb/x2WRShJEtMuPTbqGE7OHcW/tn0Tfo8siFFdDE0usG0jqT3YoA/m6k0ZmELCFBICiRnJxdw4+Fe88uZSnnvpG2RZQpIl7n8U/vSHmZw/ZjD3fPoVltVOxFW2mDaoN03VWagZpajaPo+taSjMOHYaa5evo70XUpYlYmIcjBweXVQ3Gk7oNoQnt3wRfj2t3MUj0uwTCVKS4xg4OJMVK3cjjH1jVx1w5mmjeP/jVTz65BfIsowEPPDoZ/z+xqlMPCK6ZlZJaS1X3/Qqfn/r3CNg3Oge/PGm6VHDgefNGsPt97yHv1040+FQOXJsEfEdKM9//uV67nv4E5TWEkWmJbj+ymM54Vh7XmAXDg6/HjCST3dtxmfu+z00WWZASgaFnv9uL+NPjf/egHcXOoQwdocMLQIgvIAX8EPDLQizKmq7y+e/S7XPS0tQx28a+E2Dr0q28+6u9Tx9/RkUZibj1JQ28vk/rz/jgNl+dqhq3kF/9SFcikmsahCrGrgUk4GOJynMLEJpuRi/34GuqxiGQl1NX04b/M+o/Tk1lWdvmEVRt1QcqoJTU8hLT+Sp35xGQmz0Sff6vicyJWsQDlnFrTiIURxcVnQsR6Xns7LqJizhxxReTOHDEjob6x5Ek+MZlPonNDkRRXIj4yDNPZ5hGdHr3KmywpOjL6FPQjccsopT1ujmTuKRERe2edE6A0Po5E9Yj6JZqA4LRbNQVEHBiD0ckZ/MtcNHocgCSRKAIC3OwfvTLuCZi06lf1JqyPhu/RuTlc39Z57I09efzoCCTDRVwaWpZCTF8ciVMxnUA4rS61BkgaoINMVCU00m9i3mtm8/Y1tDDS1GEL9p4DWCbKqr4p7lX3X6mgB2HgtWdxfEyOCSIUbGKnCw41iLy/79Hs0BnRY9iC9oEDBMXv5uJS15cVx877nI7Wr7xSS4eXjx3VTsqo70sklQsSv6O1Bd7kcs8yC8MsIAYYJVp9LwuTuM09YelrC4YWsfmkwHXksjIFR0ofBhXQ9eW9HI8y8vQg+a+AMGPl8Qvz/IH+9+lzWBz+nesxRFMVEUE1m2yM6pojbpc973jWVncQZGUEYPqHibncxdOwo9O4u/3T2LrEwPToeKpikU9cjgsb9GTwTpCImOWP4+6iJy3Mk4ZRWHrFIUn8nToy9Fk+3320IIEmf+QEKvRiTFQtZMFLdB3kklVMau49En56HrJn5/EJ8/SEA3uOf+j6irb7HtD+DWu96lrr4Fr08nEDAI6AZLlm3jww5EPseM7MEVF08iNsaJy6WhaQpHji3i5uuOj9qmqrqJ+x7+BF038LWOT9cNHnj8cyps6ix24eAxICWDhyZMI8UVg1vVcMgK47Ly+ecxnU/c+KWhKxvxZwrR/BSi+WEitaxcSAk3I8VElqMpbW5k0tv/JGBGpuz3SUrjk5kXAFBeG0pXzkw+dLfwt9v/zEDni7j3030KmDIrfWcwtuedBAw/xdVbSYlNJzn+4IUKK+ubMS2LzKT4gw5nNRt+6gItZLg9OGSVkqZ3WVdzD6bYPwSl0MNzEb2Tr8ESBj5jD5qcgEM5+FBgtb8R3TLJcicecrhtZd1SXil+IkI0VELiyNQpnJr7K3TTYEXNbtJd8XRPCNckq25sZnNFDf26ZZC4nzFa09iCXzfolpKAJEk8s/1BVjcsixiDU3axYEseFU2RwpMxqsb6837T6es68rPbCFg2NTKbXBjbU2jRI0NrU/v14sFTT8SyLNYs3EBCajzdB4Q4hDM85+FritQtUjWFj/yv2t7/u1/5gne+WRtSn4+xwJQgIBPrcnDn7OM4ekhk1tya+mKuXv4cXjOSA5i7LIuapb4IvaAYt4OkaVtJG1KDaUr4/Q6cziCqamEJWF1dhM8ycOtB3KZBndOFkCVGJvfg8VEXIYSgvLIRh6Z0mhRvByEE5f56VEmxJcW3R6W/jPs23kJQ6Bg+BdOr4EjUkRSI9WWw9M85ERxKp1PlqosnMWNqJIevrLye2Zc9G0b634uehek889ivOhxPMGhSUdWIJ8HdoUcL4M33vufJ576KCMNqmsLFsycw65SRHbbvwoFhWhYlzQ3EO5wku37Z4dkuUdP/cQgRICIXHgALhL2ApW4aUV2Z7Q2w/8TIahufpSPbsIcUSSBaw3NO1UVRZnT172hIPwQOWZzqCuNwWUKPIpRpYYnQgipLKrFa5xMDUg+wkB0MDGEvlCkQbQWfHYrK6HT7sFJqQhypCfb3KSUh3HgKWvbPS0i01F6Q1bCiC7V2hGhK+qYloopA+o3QsynLMoMnhmcpmdE8UZaISrgPBE0sIQAJvPt4XUIIdBvFb4CgZUQdnxE0bYUZhRBtr6iiCGJjww01uTWO6XNo+NjnPd4b8pYk6bBKBEiSRJbbnni/PwwRDN07AarbRHXvuy8mRhsFoT32ZsvaQQ+aSLL9DYzGDWsPTVPI6XZwYw8GDSzTjlQv0IM2hn4XOg1FlslPOLjfowshdBlbP1NIrsmIlqeB/Xf1Ejgn2bYpSEgiyeXG1xIutOdUFE4q7FjcsLPITjkJ4f13xPGgkMlIOumwnqsjCCF47auVPPvJMmqbfORnJHHD6UcxtNeRUPuXiO8rkovM2GNYVVXG7Uu/YHV1OfGak9l9h3LNkPGoUUQ+D4TSprmsrbmzzZOW6BjMqKynUWX7XWGf+EGYNgRuh+xkaJJ9Ie9DxYjk8WxpXhshUmph0Su+L1VNpWE8NFmSmJhTiM8I8ufvFvDWljX4TYMxmXncOfYYenhSEMZWROOdoC8HyQXuU5Hib2RMahHL3vwO1/PVSJUGIl3FPzuVUTOL+GZHpJioW9OYPiA6D2jU1GEsfm85VjsviyRLDJ7YHyEZbKh5hN1Nb2IKH0nOofRL/T1Thvdi3g9b8OnhBq1pWYzpm89Tb3zMu49+g1VtISVKHH3RMK67YOb++RIAuGSNY47sx9x16/D79+vPtEgqTMSyavavy4yue5BlBfYjrbtkjRO6DWH17mJu/vpVajwNYEF+SwaPnvArMj0eln28gidueIGSzXtIykjknFtPZfplUzr0olZWN/HwPz7n2+XbkWWZo4/szdWXTCY+3t5LlOnKwSm72jTU9kKTHAzxjOUHrTzCcwSCcaPsJSHycpKJj3VG3COHQ+GYiX2jjhtgxepiHn1yHjt2VZMQ7+LMU0cx65RRYWHl9hg3uifP/msR7Od5UxX5kLS+utCFw4EuztbPFJLWD2LOAtyEfkYZcEHcJUiqffqtJEk8fNR0YlQNpxza0ceoGgUJSVw84PC61vOShrDSOxWfqWKK0LznNVRWtRxNj9TotdgON57/bDmPvPsN1Y1eLCHYUV7LDU98wLrtBkWJVyFLLkL3TkKR3HSLnUaNP5ezPvk3K6vKsISgQffz9Nrl/G7xp4c0hmrfUlZV3xIWsqzXV/F1yclR28RpCZycfS6a5EBufU0dspOBnhH0jj+8JN8UJZ14uR6l1Q0jYSFjkUEzd4+bisfpatPPiVE1Ulxu5oyezMVfvM1rm1eFki2EYHHZLk6e+y+qmnchamaBvhQwQ0XIva8h6q4k8StwP1SBXGEgCZArDNwPV5DyjcSfph2LS1XbDNoYTWNUfg7H9Y2uLn/FQxeQmJaAKzZE1nfFOIlPiuO6Jy5hZeVv2dX4KoZoRmBSG/iOJXvOY1hvF0cM6N7GRVRkCaemcuPpE3nrs4W8/aeFiCoLSQB1gvmPfM9fn3yTOYNOxylrqFLo3XErDvol5nDZ5EkcObYIl0sLZZAqEk6HyhW/PpobR15B0HRimKFrMkwJw1K4sPBK7hw8C5esobXrr3dCN8Ym9OaS756iJrEBFECDXXEVnPHJQ3z3+SruPO1+dm8sRViC2rI6nvrtS7z5wAdR75HPr3PZdS+yeOk2DMNC1w3mLdjANTe/YiurACBLMucXXIVDdqJKoT25U3aS4cpmWo+ZKDabjhi3M6rYqSRJ/PGm6bhcGo7Wothul0ZudjKnzYwegdmwuYybb3+TbTuqsCxBfYOP519ezNMvLIjaJj83hVmnjMDpVJGk1kRtp8op04dRWNBx9mMXuvD/hS7O1s8cQl+J8H8EyEjuaUjagcNyld5m3tiyhtKWJsZm5nJcfi8ch7lG215srvqSmrrXEQiSPafSJyN6ltjhRtA0mXTjE7T4I8NkQ3p049kbZ9EY2Ehp84dY6GTFHkeScyg3ffMxb29bh7nfu+FUFBadfhmp7oMrnrsXC0tm0ByMLNkCML7b63ic0b2K5b4Sltd+TcAKMDhxJD3jDq0sRkd4bt11JDnn02DFUhWMR5MMsh31qJj0T3mZjNgi3t22jg21VQxISeekHv3Y09zEjA9exL8f/8+pKFzeW3BN/vPA/vfdxbGDByNXRXKsrFQX8ypfYldtPe+sWkeDL8DRvQo5oke+rd5Ye/iafcx7+Ru2rthO94F5HHPuBKSYWhaWzmwLCe+FhEZBwjn0Sb6BZZt289XKrcS4HJw4ui+FWSmcOOlWrLJIj6KIk/ho6V2U+xv4sPR7avQWxqf1YlxabxRJRgjBitXFfL14Cy6XxpRJ/emeH+LRNejNvLjlHXa2bCXN2Y3zi06hW0xo0S/z1TG39AdqAs2MTS3iiPQ+zPn4LT41V8D+eSk6FPxbpu7fkbpjsZ4Y3qp6FkWNfI8//Gw1jzwxL8Kr5HZr3HXryYwYWhD13tbrtSytXUC9XkPv+IEMTBzOvC838cBjn+Gz6W/OLTMYE0XwFKC6pomPv1hLZWUTQwfnMWFcEarNmPfiptveZOl3ke+O06ny/qtX43JFT97ZsLmM+Qs2IIRg0oS+9Otjn0nchS78J+jibP1CIDmGIDkOXlQSID0mjisHj/1/GlE4eqUdDWlH/yjn2h/1zT5bIVSAHeUhFfMEZx8SnOFhqnW1lRGGFoQ0s3Y11nfa2PIa9sKbALW+7zo0tjLdOUzPPqtT5+ssLLEVh2KRpjSRpu0LMfsMjZ2N6yhKGsD5fcPrHW6p3xHyQO1nlwRMk7W1zZBvwwOTVKRq+yK8UrUfy7LIT07kuqM75/l0x7kjpB4qvd8jo2ERbmwJgjTo65AkidF98iLEes1q075IeougyeenW2wSFxcdEzl+SWLY4HyGDY70KnsccVzd/zzbsWe5k7i45+SwY1say8EuH0OCKpffdtLW/UGa61vwpEZ6lrZtr4wwtABMw2JncU2HxlaiI5njMsM9sNt3VkUYWgBB3WTX7poOja3UlHjOm3Xwc8+OnfZZpbIsUVXdRG5OdLmBvr2y6Ps/Vl+vCz9fdBlbXfifRWKcu01nZ38UZEQnd/ZNTmdTXVWEwRUwDfISEmkM+vikdAU7vdUM8OQyOXMATiW0w27Wt1Ha/CGmCJAZOzmkzaVk0WLYe7aSXR0Xbd5ZXstHyzbg0w0mDu7BsJ7Zh92zJUuFGNYuVDn8ehXJIj/B3hDs4UmxJUk7ZYV+SfGAgwjPljAQKU6kahtV/xQnsiyzqmoBL22YS1PQZFJuEaf1uARFURBCsHzTbhau2U6s28GJo/qRlx6ySLx6kI/Wb2J9WSVFaSlMH9iXWK0Ay6YKgISGx9EvRF7XlyEC80CKRXLPQFK7o6Qqtp4tYiHe3XEWXDQEAkG+/GYTGzeVkZebzLFH9+8wo64oPoPtwbJIz5aQSPU5sStprjlV4hLtNwE9uqfjcmkRBpeiyuTnpnQ49oZgHctrv6Zer6Uovj8DPMMoyE/F7dIiDC6HQyEvp+P+AmY1JU3v4zfKSXaPJCNmIrIU3TtVkJ9K5X7FnCFEdk9NjWPHriqefG4BFZVNDB+Sz6/PPwKXy74u5y8VpmXxVel2vi7dSZo7llN6DiDrIARIt9bX8O72dfgNgyl5Rf+RmHEXusKIXfgfxz8/XsqznywLq/no1FQeuXImI3vn2rbZWl/D9A9ebFVpD8GlqEwt6M2VQ0dwydInCVomfiuIW3GQ5Ijl+bFXUO97j411D2CJIGChSC6yYqeSFTuF5RWXRpzHpWQxKe/zqGN/6+vV3P/GAgwzVMjW5dCYPKyIO87vmAzdWWytW8+GujNCzLXWboWAan8as/t9GbXdWR+/yvdVe9DNvVwviNOczJt5Eqktp4BoT3h3gmME9z1/LJ///j1kfZ+hZjlkjrn7JJKOKeZvKy0sIWEKGads0CepltePv4s/vjCPRWt34tODqIqMIsvcevYxjOiXy2nPvEJzQMcbDOLWVFyaxhsXnkmFfivV3iVh3i1FiuHI7HdwtTwAgS9A+AkRoxRIuJ2/vS3xxV++Q25nb1kK9D2vgIdujvwND4T6Bi+XXPsijY0+fP4gLqeKpqk8/rdzoho6FQ0NzJh/H8Ip9rFqDdAaNR5Kmckdp9wfVsjbFevk3D+exqybZtr25/PrnH3RU9Q3+No4Wqoqk9Mtief+fmFUovnWpvU8uf0+LGFhiCBO2UWGK5tL837H7EteoLauZV9/ikxWpocXnrgoqkBprf8HlpdfihAWFgEUKYZYLZ8xWS9ETRRZv3EP1/3u32E1Gl1OlZOmDSUvO5m/PhLOo9Q0hdeeu/SwSGX8L0A3Tc799DXW1lTgNYI4ZAVFknhi8skclR1dIPeljSu4e9mXGJaFKSxcqsa0gj7cd8TxXQbXfjjYMGIXQb4L/9O46PhRXDljPMmtpUTyM5L46yXTohpaAD0TQwrpg1IyWw0IBxf2G869RxzPnWvepNnwt9XW85k6lf4Gnt7yNhvr7m9VnTcBgSl8lLV8hCI5GZR6N4q0r5yJxzGQCTnvRR1DXbOP+9/4ikDQwLQEglAdw3k/bGHZxo5r03UWW5q/whJKmLSBJIHHWUtLMNKrsBfPHHMqZ/QciFtRkZEYk5nHO9POJT2uACn5NdBGAjJIbnCfjpT0d265/myOveckrBQnArBSnBxz90lcesUkHlhpErRUTKEAEgFLY0NdMrfNe7TN0IJQqZZA0OCuV77gjo/mUdPixRts/T2CBg0+P7d/NI+haX8jL+H01vsukeQcytisF3Fb2yAwD4SPUIqhAQSg8Q6Wip1UjY0nGBsSLjHcMjXD4liVEFly52DwxLMLqK5pbvMC+QMGzS1+7n3w46htync3Evd2InKxGlLeMEDZouF6O5bCkUXc+tr1ZBdlgQRJGR4uuudszvht9Axft8vBEw+ez+gRhSiKhKYpTDyiD4/+9eyohpYlLF7Y+Si6FWiTIQlYfsp8u1nW+CVPPHgeY0f1QFFkVFVmwvhePHb/OVENLSEEKytvCokHtxq/pvDSrG9nZ+O/oo69X59u/OX2U9v4bwnxLs47cyyXzJ7AA49/FvH9YNBkzl/ej9rfLw2vb17NmupyvK0bR90y8ZkG13z1QVT5lmpfC3ctm4/fNDBEKA/ZZwT5cOdGlpQf3rnnl4SuMGIX/qchSRLnTB7GOZOHRdVdssPQtG68P+P8sDbNhp/NTWURCgCGsChp/pJBsZFEX1P4KWv5jP6pvycn/iQsywoVrD4Alqzf2ZrxFVk4+rPvNzO6b+cLvkZDpfcT0hw2pHAh8UPNJxyZebptuxjNwV3jpnDXuCkR91bSipBSXra957dcfza3XH922L14eeOjyFKkl123NObt9mPpkaEmVZFZuG1nRLjXEoIlO3YjSQ76pdxCv5RbwsZhNTzXWnVhP0gqm0os9Dw3LXlu9tVJgqYagTcYIEbrXImirxdvxtyPNygEbNxShs+v47YJeS1YtBmzVuB8Pw7R+rRJSKgujW+/286J04YzZtrwTj3PGekJ/GXOqeyNZByoXbm/FL/lizgeFDrL675hUp9p3HPbKQfdX0twJ0Er0mC1CFDaPJeeiZdEbTtsSD7P/+PCsOtdtaYY00ZLC2Ddhj0djuWXhHe2rw8rrbMXprBYU1PO0LTZGTn6AAAgAElEQVTIpIGFpTtRJJu5xwjy0Y5NjMs6fHPPLwldnq0uRECYlQiz8icdQ4uvjvKq9QQNe0K1HUzLYk9NA02+SE4QRF8QhFmNMMuxC6m3b6O0yVDaIeSNsTkrsrRvQT0YQwtAUxSwGa8sSW2p8x3BtHx4gyWYlv29CEf0PZcmh4wLvxFkd1N9m8Do/rC7t0IIqnZX01gT6R0LBIJUVDYSCARbz7NvDG6fTnyzP1SVGYEiiaj3PVqmoixJbW2EVQ/WHkSboKoTkBAC9vhiqdX3GVDSXoPPEigBwNxr7GArd7D/9fqMcgJmbdsxRY3WRor6LGiq0nY/pdZ/obGFPoOQB6e8ogGfTaZtR5Akyfa3CgZNysrr8baGJxVJsX0fgDYpCICa2mZq66KX6NkLWdKiiAgTxtmqqW2mpjZSb23v2PfC0UEJsfbPREf9/RKgRXnGLCHQWuV/fH6dsvL6NsFXVZZtnxEJqS1r3RKCkuYGGgIHPz//0tHl2epCG4SxFVH/GzB2hP6vdkdKfBBJ/fGEAHXdy5o1V9AvYynxloSvRWZV/fmMGHJTh+0+Xb6Je1+bj7817HbUoELmnDeFmA7IssIobr3eTYAESjdI/FtU+Qy36mBoUneW12wLs6sUZHonnoglfohoI6GRHTftoK69Pcb3LwgrrrwXDlVh2ujo2YtCmGysfYBdTa+1LtISPTwX0SPxkqjGZn7CadR778Uhh+9kLWSGJE/hL999xfPrf0CWJARwyYCRXDdkfIfejB/mreH+Cx6noaYJYVoMOLIvv3v5WhLTEnjmpW94453lIetBCE6bOYKzzzqHP8//G6Myd+EZ2IIQYPhUNv7QjROG5vD+WhFWJH0vpg/sw/trNrbxxiC0WBzbpyeIeqy6G0H/FlBATgDPPUjuk1lWuogbVo+jKuBGCInBiZU8MvRbhha6WDs/QEzJPiPLmwHp45W2JAg71PlXsqrqd/jNSoSwSHQOYEj6fRw3qT9vvPcd+wvnDxuSh9NhP/1OmdSPd+b+EMZTghApfNzonrz+znKe+9eiULFuSzD12AFcfenkDiUUOsLb7//AP19ciGkKLMviuMkDuOayySQ6kqkKlId91yE7GZ86ma3bK7nz3g8oK69HAN3zUrn9lunkZNtnB8ZoOcSoOa0SKO0Fcl3kxZ3B9p1V3HnvB5TsqQNCIqi33zIjKq+tb68sNE2xVZ8fPbKQncU13Hnv+xSXhAzf3Owkbrt5Rls48peCs3sPYXW7MOJeJDpd9Pak8vATXzD3k9WhAuoSzD5rPNNmDG6tsBAOh6JwSo/+fFG8ld8t/pQmPYAlBEd0K+DBCSficR5aAskvBcqcOXN+6jG04amnnppzySXR3cld+P+DsLyI6mlg7SHkPjbBqgXfhxBzDlIHGUOHEytXXEzfjKW4NBNNsXCoJomO1Wwq85CROsi2zYqtpdz0z7k0+3UM08KyBCVVDWwqqeL4kfbq40IEETUzwNxFiLNjgqgH/4cQcwZSO35V2LmWVLLJLNm3XFgg6lVOjDmCd9b66Jm5laCpYFoyQkh8umY4RxbMIsbRuQwpTVXok5fOlyu34lAVFFlGliUumjo66jUBbKn/Bzsb/4Ul/AgMBEHqA2twKIl4nP1t2+TF9uf72lWolGIJGcNSMIVMrucvzN1ex9PrluM3DYKWhWFZrK4uI1Zz2oYgAEq2lHHDxNtprGnCNEws06KqpIbvP11FS2oiL7++FH/AwDAsDNNi89ZynA4ngawvicvwIisgy6A6LdKymzjKMZNuKRms21WBqoSKkKuqwoOXn8TJw/uzYOtOWnQdWZJwqSo5iR4eOW0azsYLILiC0O9rhMRV/Z+xR5zKqV9Cre7EFDImMhX+WD6vHsZZjlGsWrAL2QJJhP40L0ztNZCxw+03HX6jksV7zkK3ahCYgEXArKTcO481W3uxqbwe2QgZqkIBwyUQuTqnTxht219yUiyaprB67W40TUHTFBRF5rabprOzuJrHnpqPzx/EMCxM02JHcTVNzQFGDY9OeI6GBd9s4qG/fx7qz7QwLcGu3TXU1bcwe9JJrKj7FlmSkZBQJJUBnuFMiJ/BRVe/QFV1E6YVMvhq61uYv3Ajp8wYFpW3leIaQ1nLp4QKFclIkkaaezy5riu46KoXqahqxGrtr77By/wFGzll+rCoRuQPq4spr4gMTV503hHcds97lFc2tBqQof7mLdjAzGlD0Q7CM/y/gl6JqexqrGN7Qy2KLONSFGJUB89POZ13X/uO9z9eha63vouGxdr1peRkJjJj+AA+27UFh6KgSjKKLHHN4PF09yRxwedv0qAHMISF2erhWl5RwulFh1ds+eeCO+64o2zOnDlPHeh7XdmIXQBAeN8OlVdhfy5LDJLndiR3dLXzwwWfvwGqxuDUIner26u60XPgV7btrn38Xb5euyPiuENV+OBPF5JmU0tR+L9ANPw2tACHwQXx1yPH/iqiTdAwOeqGv+PXg0jpOsRYiAYV6lRSM+OpdulYNNE/uxhNMVm/Jxe/nsBVE8ZyyfhDU+hv8gVYsHobft1gfP8CspKj110UQvD5rjEYEdd04MxHgC0NK1lX9zFOJZEjM84mzuFh8MuP0KBHhgrS3bEsO/NK234ev/ZZPvjHZ5j71Rl0xToRx4+kqSUytOkp9JN3/iYULdwFZJkQ2JLDU2f9ldLqBpas30mMy8FRg3oQ2+q1FEKwbFcJW6tq6J6SxJjueUjGFkTN6cD+vCOFB7afzVNbYtCt8PHFqg56fqZSXxPJ54pxO/jwjWttCeVb6p5gW/1TWPtJXShSLI8vHMVqIxdnDWj1YMSBPwNk3WTDhTfidETfxFRWN/Ht8m04NJXxo3sSH+9i9mXPsLO4JuK7TqfKh69f22lD4qKrnmfr9kjKgMOhMve1q1E0WNe4gsZgPYVxvcl25/Pehyv4+z+/xL+f5y3G7eCm647n6COjbwYsEaTSu4CAWU2iczAeZ18+/HQ1jzxpL7p6w1VTOPboyE1CU7Ofk8953NazlZudTHVNU6ToqkvjmsuOYeqUX55RsKW+mm/Ld5PsdDM5tweqpHDiaQ/jD0TKo+RmJ/Gvpy+mIeDni91bCZgGE7ML6RaXwG8WzuW97RsiPF8uReXjk35Fd0903bP/VXSJmnahc7DKiFyYAPxgltscP/xobKkgPgo7JzHGTl0ohNKaRtvjmqpQ1dBia2xhloGw4x/5wdht21+LX2/VlpIQleFE6VqfD9Mp4Q86+W5H+/IyJrvrDi2TDSDe7ewwbNgeFkEMO+I3oFuRC/T+KPIMocizTyB3b6kiO9T67Z6VEPZsLY8wtABkRabBxtACsFzeiFBbqA2YMSHOTXaqh9MmDI74jiRJjC7IZXTBvgxTYZWBpNjUMzTZ2eRDt+yI7oKmRvvr9QeCBA3TNvTnNXZHGFqtV0Vcog9qIJAa+mv7RJGob2ohI8VOvTSE9NR4ZpwQLlhcXWPPP7IsQYs3QKLHXkIhGqpsNKz2orklQEpyHIMTR4UdL6tsjDC0AHTdoLLK/l3cC1nSyIwNF4WtrGq0FV0NBAwqq+zHV1ffgqrItsZWXX1LRAgWwO8PUlnd8fj+V1GUmEpR4r4HsKnZj2Ha6MkBNbWhzZrH6eLUnuGUil2N9bYhRk2WKWtp+kUaWweLLmML2LBiF+++tJjaykZGTezD1DNHE9uB6OCBsHFVMe+9tJjq8gZGTOjNiWeOJi7BPiz1XwNtUChFf//FWnKFPvsRkOIpoLlRwaWFT5SmBaUNhaQIwSe7NvPvzasxhcUpPQYwo7Avw4uyKa6si1CLN0wrunipNgj7/JAYJIe90Kgn1oUnxkV1Y6RB0zstlbXBSIMmRtMYntcNw2qhuPF1Krxf4VRSKfCcc0BB085CkRy41Wx8RknEZwlayNuwcOtOXvluFV5d54R+vTh1SH8cqv00IEsShQlJNOjFTM5ZT05cLcVNKcwv6UdWbCEAz33+LW9+8D26z2DE6AJ+d/ZxDD56AN8v3kDjoBxITEAIC3VHOY5tNeR2S6K4lZfTHvGBDGQlMq3cDErEt2R3/mao/UDYGUBOxmSmM7/SwtJacLmDWAL8XieGoZJfkEKldzvDTl5HdlY1lTWJfPduf9zN+VETE5JdIyhv+QxThBugAkF9SUqofOl+cLRYpCUlsL15EwuqPqEpWM8Az3DGpU7GpbixRJDS5rnsaZ6LLDnJiz+N9Jij6dMrk+9W7IroLz7ORUJ85+eYfn268e3ybey/frpdGomeGNY3lPDKzkVU+hsYm1rEaXljGNCnm62oqaYp9DmAYntDYB07Gl7CZ+wh1T2O/ISz6Ns7C7dbw+cL78/pUKP2l5nhseUMyrJEUc8MNm4qixify6UdsqK8EIIFizbz4aersSzBcZP7M/movlFDpv/tiIt1kpgQQ5neTGMvCCaAowYStkCvnhlR243NymNtTUWEV1i3TPokd9Wd7Ai/eGPrkzeW8cQ9c9EDQYSAzWtL+Oi1pTz61tWHZCB98d4PPDbnXfSAgRCCzWtL+PDf3/L429cQn9i5XeePCsd4UIsguBHaRCCdoPYCx49T2kdVHSzacwYT817F3WpwmRb4TRUSruWmbz7mw52b2sieP1TuYe6ODdw15Vg+Wb6JFr/etutyOVQuPH5UdIK8Nggcw0H/DtjrzXCESPIu+/qNkiRx/WlHcedLn+NvzdyRCC0Kt54+mYcXL2HJjt1tGXuaIpMeH8eUPt34pvR0/EZFq8aQRJVvAX2Sf0t+wqzDceva0C/lFlZU3tiq9xWCLLnok3IjD85fxAvLfsDXOvZVe8p5Z/V6Xp59Rij70Qa3jiqgIfA4imSiyoKChGrGZW0jPeYhLrv/VdYv2I3UOu9+894mTlq4jX/ecRaPfLMBxZCQgyBQEPm5KEO7c91VU/jdHW+FeR6cTpUbLjyZJzeX4Oxe3hZKtEwwdZUbjr6w0/dBUtIQMbPA92arnhaACnI803vN4tGSJ7EkHal1rXQ6DLppHo49x4Mr+QPU1uvN9tQx6LpdFJdcFjUhIMM5haXV9+NICKC2jj3oVzCqe3BM0hGs961vHdQ+xdgjSGVJ7XzeKXmJYKtRWOzdxqLqL7ix959YVXUtDYE1bQZcrX85OXEzufSCi1i7/lUCerDNQHI6Va68eFJUzayOcPHsI1mxuphAILy/yy+ayOcVq7h77bvoloFAsKGhhLd3L+P50VeS3S2J4t016K2eJadDpXdRJoP650Q9157mT1ld/QcsoQMWDfo6ipteZ+zg18jLSWHHrip0PdSfw6HSo3sawwbn2fbl0FQunn0kTz63oM3LJssSLqfG9VdM4S8PfcSWbZXorUkVDodCfl4KI4d1ntcG8OcHPmLBos1tHri1G0r58utN3HPbyT9LkU9Jkjj2/MHcu2sRQgZkCCRDSyHMGBN9E3hBv+G8umkVDbq/TXLFraqc23soya7/4vXtvwC/aIJ8wB/kdxc+Q6Ddjso0LQIBA4dDZeDIzr2Yum5w8+yn8bdTdzZNi6BuIisyg0dHrxn2U0OSJHBPByQw94DsCRHjPXf9aOR4IQQXvb2J78sSyIlvQJJgyZ4cbv5yClt9yXxasSVMMyZoWVR4m5mQ152LJo6iodlHfbOP3PRErjtlAmccFRly2gtJksB1AkgOMEtBigsR4z33IsnRjeye2an0L8iktLoB07IY3iuXuy88gT656RzXtwi3plJa30iMQ+O0IQO4b+ZxVPheo9L3VZiSucCg1r+cgoRzOyxX0lnEaQUkOYfiM/ZgiSBJrmEMTrsbXe/J9W9/hL9deM+wLJr8AfKSE+mVbp+ltaf5VgxRzt51XJZAlS30lmreedqB1M6ZKAkwAxZLtu/GW+lHbv1Mav3M77M455yxHD2+NxWVjei6Sb/eWdzym6kMGZTH8b0nsWhRMU1yLZYhYezqxlV9bqB33iHq+jgmgJwC5m7AAa6pSIkPsLCmgsXVGzDbSRFIEuiSj8Kkd0h1NrLXYSFLoMiCoGszQ9Musj3Nt/M289wfGoEgsSl+vHVOvn+rB/MfK+K7zAaaYgkx/tudrKK5CSv9qzZDC8DCImjpqGIXAX1BmKdMYNAU3MKA3NOZMGYYVa2cpMLuadxw1RSOHNs+dH3wSE6KZfzontTUttDi1SnMT+W6K47liPE9uXzZM/jMdnOZsNBNA1mW+P1pM7AsQWV1EwkJbk47aRi/uWoKahSj3RIGS8t/hSla2BvbFZhYQkeSTM6eejEAlZWNJMS7OGX6cG64ekqHGZZ9e3ejIC+VPeUhisGo4YXcdvM08nJTOGZiX2RZpqKygbhYFydPH8Zvrzn+kMjxW7ZV8Pd/fhm2QTAMi6qaJgb2zyYzw9PpPv8bcNOqz6gL+vZlVsuAAn7NYnphX9s2MZqDGYX9aNT91Pi9ZMd5uHHYBC4dOOpnaXQeDnQR5A8CG1bs4tZLnsPbHMkjKeqfzSNvXtWp/rasK+WW2U/jteGldO+Vyd/fu/aQx/pLQHljE1Mef46ADd/HlaLgiw1GuK8BLh84mptHHPVjDPGQsHjP2dQHVkccV6U4RmQ8RrL7gNzK/xhz127ktg+/oEWP5MZM7d+LB085MeK4EBYf7xyMDfGJTSsKePv5Ccg2tDfLISHrkW2EArNmj+XK0448pGs4XPjjqtf4tGxVxPEYxcF1he9ESGAAWAKOzFmMxxGZoPDgH97is7cj5y2X28Gqi91YLptQkyk4qc9miI0MSY+KbyZJiUz4UCQXfZNvJi/BXmT2cGJz4x4uWfo0XjNyLiuMS+ffR1zXqf6a9K0s3nM2pg2nMEbNZ2Luh4c81v9vvPb2Mp5+fiFBI5JUeO6sMVw8e8JPMKr/DE16gKGvPIphQ5SM1xysObdzv+8vGV3leg4C8YkxmDYvEIAn2b6oa0eIS3Bj2BgKAAmH0N8vDbEOhy35EiBWcaDKMgiQAhKSXwIRKnyc5Pzv5sM5ZHvSqMBEU0IE6eKWapZUbabCH06mN4XF6rpdLKvZit/snIBle3jcLkJbWEFiYhMpqQ0oiokiSaTGhp7NZl+AJet3sXbnXoFXCUWyV0x3x+69BjAdEoZLQkggJJAdko15FkJa0qG/B7WNXp7/cCnvLlwdwc8raW7gq5LtFDeFJ1IIIdjVso2Njavxm6GFPtkRh2I79UkELHtmhSlkXLL9vUhMibUVL5VkCSUQGqfaZBG7PYhW21pH0hIoDgMhwFvqpnFLPIYv5HWRpXikiCrUAAqa0rEXRQjB1vV7+P6bzTTV2ydLHAziNTeGsJ/LErVQuEg3DL7duZvlu0oiSr/U1rWw9Lvt7CyuBkCTE1plMSLhUKInCfwU2FJfzVcl26nyhYji8XFuFBsPm8Oh4Plv5+JGgVNRowoCxzk6VyWhCweHXzRnK6d7GtkFqezcUoHVbvJ2ujVmzh7f6f6ycpMp6JXJ1vWlWO1KSTjdGief3/n+fmmIdzk5orCAb7bvJNju93BrKpcPG82fV32Fo0INc7RIaTCzx8Fl6/1UKPCcR43/W8x2PCoJGbeagyLnctXyZ1lVtwtNVtAtg2MyB3LrgFPY1lzBb75/Aa+hIwEWglsHnMKxWZ1PWBjbPY+kRD8jB65CVU0EEpIk2LyukFnDBvLagpU89NbXqIqMEIKk+Bgev/pkcuJPZXfjm2EhUFlycuSwyfzLHcSboIY4H61wNpicPGMwc19ZQXvBcAFImsQpE8Oz6w4Wf3rmEz5/Z3VbyOPBBz/ljttmMm5QIb9ZOJcvdm/DISvolskR3fJ5fOIMmswa/rH1LzQbDUhImMJkRvZZnJQ7grd3L2vNLG03dkWluHkYRQlLccj7PgtaMluae3GSar8ITTllBG8/93XEcUmSmCTSWf1BMQmbgwgVJBN83RTc4xOJD2ax5LEYgo0aSAJhymQfU8XA86ZS0nAzQgT3608m3R3di1JV3sAfL36OitI6ZEXGCBqcednRnHXZpIO9zW3IcifRKz6LDQ2lYeFWl6JxVsERLNi6g+vf/qjtXVQVmcfPmMHw3G488uQ85n68Cs2hYBgWPQvT+cucU0l0DKQusBLBPneoIrnp7pnd6fH9f6A+4OOCz99iY20lqiwTME3O7j2EG8eP59En50V8X5IkJh9lH277b4dDUTguv4i5OzaGbYwUSeJXfQ5v4k4XQvhFe7YA5vxjNrmFabjcGjFxLhxOlXOumMzw8b0Oqb/bHzuP/J4ZON0aMXFONIf6f+ydd3hUZdrGf6dOSe8VCIQaem9KVUCwoigu9rrqZ1t31V13Leta17WvbS1rX7ErCooiINJ77ySkkIT0MuXU748JgTAzkYRg2c19XbkuOGfOe945c8pznud+7psLrh7H8PG/zovyp8ajZ0+mX3oaTlkmyqGiShIzBvbl7L69UMpkBEtAsA//KeUyYV7AfzFIdA2nW9yNiIIDWYhEElxEKFkMTX2Ov2/7jPWV+/BbOnWGD80y+LZ4M6/vWcj/rXqVMn8tHtNPvenHa2r8ddMH5NUdbPEcBMFi6IjtOJw6smKhKCaybNGnfy4Flbk89dH3+HWDep+Gx69TVF7N9U9/RI/YW0lyj26YexSioJLiHk/XuN9ipAvYEgFiU8OfliAyfEgiE87qgy0GSoe2BIJL5MH7zkVuRffW/JU7mP/xRgQrEKwIJth+m7vv+4TH13zPt/l78JsGtbofv2mwpCiXv636jud3P0iFVorf8uGzvOi2xmdF7wJV/LnPObgklQjZgVtSSXbG8M+hV3JaZj921aagWyJeU0a3RHLrExiRGP5+YB/hoXjUGk52ZBG7U0c0QfKDaIC7wOTkyjT2vpmNv9yBpUlYfhnbEClZkIp3XzYDkx5FFiIazpcIHFIiw1L/hSSG75K+7/o3yN93EJ9Xw1PnQ/MbvPfSQlYu3N7iYw7w6KCLyI5KwSkpRMgOVFHmks5j6OnuyE3vz6HOr1GnBf6qvD6uefdjPvpiLV9+tRFNN6mv1/D7DXbsKuaBv3/BwJR/EK32QBKcyEIkIipZ0ZeQ6g7dkPJT45ZFc9hcXozXNKjVNTTL5L1dG/micCeP/vU8YqJduF0qbrdKZISDB/5yDgnxIWRlfiUIVUSwbTDD5qXbcTz4n+ZsHYJt2+TuLKaqop7ufTKJiDp+24F9O4upKquja+8MomJ+nanmnxN7yyoorqmlR0oSCRFuPt6whb/O/Q6P3vRtX5Ukbh43iqtGnXje0/FCt2qp9m9BFWOJUntg2hZj59+LHiJajJJdmLYVxJmRBJHfZI3mxh6ntWjf22rW89q+p/EfZS4sIiKVdue7T6KDbrFuh8JzN51Lvy5pePRC6o08IpXOuOQ0Plq3mIdeW4mpH1VeESwG9Hfw6rX/R0WNhy+WbiEhJoIpw3sesy/k0bjw9lcp3FwWpMBmS1A7UqEyLZiHlhLpY2z3ffitYN2swXGjuCTrRnymzuaq/bgklV4xGYiCyDd5Y9CsCoo80ZT6oolX6+kYWYmIyuSsVQhCcDnp1X/M5aN/LwmiJLgiHMiKFLKcJysSWpd4fCEsiEYO7cLD952HaWtU+TYgCiqxjr4IQvjjV7DvIDdMfwYthF7VwFFdefCV0OT+Y8Ge2mLK/XX0jMkgWnHx6rI1PPHdD00skiAgc5K1XaSiNFgLTFEkPn7rBqKinNRqu/GbB4lWe/1iSohVfi/D/vNcSE5o99hEvj7nCgzTYtv2IizLpnev9FbbI/0S4DcN+r71VMjvm+aOYtkF1/0Ms/p1ol3UtAUQBIHOPVqnvxIOnbunQuuSY+0AuiTG0yXxMNep2ucP4oUAaKZJlTe8wOYvCYoYRaJrROP/ddPECvMW6Tc1JDH4Zm7aFpXajxv/Hg2PcbgL7EhYWBjUYRNM/BYEgVpPIFhxKxm4lcN6VxX1Ycx9bZGa+gC3LD7azcVTWqecfyTqav1hjagNb+i0pi1oCGG2qtUDwpZOSWFIQtMO4UOisOnuGtLdhwUwLQwsTCSCf5PqCk9I7qdtWfjDzM+yLMIpNVTVBM5nSVBJcB3b8auv9SHLYkhp1ZrK1nO3ALKjUsmOOmJ+Xm9QoAWgWya+MN9XFAQ8Xo2oKCdRalei+On8Vo8FdboWlsNU0yDsK0sifZuRtvg1QTPNsPzYOv1YzOvb0VIcV7AlCMLfgTMADdgDXG7bdlXDuj8CVxIw2rvJtu2vjnOu7fgfxsisjiFvhm5F4aTsLJbv288fP/+aAzW1KKLEeQP68Jcp41qdTfHoBeysfIZy3wpUMY4uMZeTHnlGs+3N9ZrGLR98wQ/78rBt6JmSyNPnnUGHuNCkZpes0tGdyL76pnYpAjAgvjPrK3ODt5FUTk4Kb4cSDl2jeqFbwVkPGYUukQPZEbef0cNW0iM7H79fZenq3mzc2pt+XUK/hEzKGcQL1rag5ZJsMqZf6yRObNsir+Y98mrfwbDqSHaNpVvcDYwcns28/PVNZCYAsKB7n2TWeIIdDtKdnTHtYJFURXTQLzZ8AJPgHMJB7w8cHZhGKd0QUZj3wSo+/vcS6mq8DD6pGxffeCrDxvZg0bwN+D1Nj69p2vQZkkXhwTUMv2gbSdk11BS7WfF2D6jOIS/EuaSqEieP6kZxbTl3zZ/Nmr21SCJM6J3MfRMuxK042FFTxAu75rOtupB0VxxXdZ3AkB5dCFWlUB0yIyc2T2Eo9ObxZdFs8jx7iFeTmJR6Dn1iwvN2RnfpxJsr1wdlmSVBZNCADixbuhvzqAaGqCgnSYlRtDWef2UhH3++Fk03SYiP4Lb/m8yoVkjspEdEE6068HmbZhplQWR8Zjam5WNv9WsU1H2CbVukR06ja+zVyGLbNz4VHajilTe/Z+2G/cTGurnw3GGcOj6nTaUVolQHnaJj2VNd0WS5iMDo9Cx0y+SVLat5d8cG/KbJaVnduXnAKAbpY3gAACAASURBVGJ/4c1Iv2QcVxlREIRJwALbtg1BEB4BsG37DkEQcoB3gWFAOvAN0N22m2fXtHsjtqM53PX513y5ZWfjTd6lyIzs3JErRwxm1hvvB31+eKdM3rik5W3yPqOExYXnYFh1HGJ5S4KLrOiL6RF/U9jtRv7jBSo8TbNssiiy7LZriXaGLk2/unsBL+z+Jmj5g/1nsq2miPf3L8NnBr6vU1ToGZPBc0OvRA6R9fox3LbuQowAVb1hSeDav7XLn1hTeBWq6kWSAss0TcbyjmH6gKfDjnfH7Nf49vtyLEMEBETZJDbe4vO7bsbVio6mjQfv5kD93EZ9KQEJVYpjcOIHnHv1W+jVGoLVMGsR+ozqxC3XTWT6F2/hN010y0QRRBRJ4r3TLqTcWsecov80alkpgkqiI4Xf9bgfNUxnYZ22j6VFF2LaGjYaAjKioDA89RXee2I/c99f2ajLJ0oCkVEunvjoeq669kmsPAOhIbVkq5A4KYHLL8umTrwXWTUbBVR1n4Sn8mJW7Upj9YcFYICAgC3bCJE2Lz1+ERe9+w619RJ2Q/eBKFp0TLF5csaFXLvyJfym3hgOOkWFP/U5G3mlzbP3HRZUVp0y8YlRzQo0F3ryeHLXPWjW4WyGIqic1+FyRiSMC7mNbdtcP/szlu3Lx9t4LSqcltOd20aP5KobX6fe40fTTCRRQFYk7r/rbIYP6XIMZ8Gx48/3f8z3y3YFLf/7/TNaZcr9XcFerlvwCbplYto2DkkiSnEw58xL2Vd9A9X+LY2NIqKgEiFnMTpjNqLQdgWi0oM1XH79a3g8Rwg0OxQuOHcoV1x0UpvtB2BVSQGXfP0+mmlg2jaqKOGSZT474xLuX/kdS4py8TXoGiqiRFpEFF+ffTlO+afRXfy14FjLiG3G2RIE4RzgPNu2ZzVktbBt+6GGdV8B99q2vay5MdqDrXY0B9u2+XbHHj5YvxndtDinfw6n5XTnjBffZE9ZRchtFt98NSnRLSOxbi1/mLya/zTpmoJAF97EjgtRxOA39DmbtnPbJ3NDjnf+wD7cf3owCdi2bU5f+AgH/cF+bf1jO/HS8GtYXraLj/JX4DU1JqcNYEp6fxSx5Tf3NeXzeGP/vyGY+URHh0pv15YmHYcQeKCMy5yHU04OO+5H6xbz7sI1eLwGYwd24voJpxPpaLmStNc4wKKCaQ3q4kfOwUG32OtIcVzE39/4luXL96A6Fc4/czAXnxbw7Cuqq+G1rWvYUHaA3gkpXJEzmA5RAS7QnrrtfH/wK+qMGvrHDmd4wtiwgdYh+IwScqvfpsq/kUi1G51jLkavjeeS8Y+gH8WxUlSZvudls2ToXqyVOuqaQKDlHwVijsKVnZaR7DgQtI86w8VTeydjFAnIGxwIHgGzs47Yx6SnlMSy9Rqm2TSgliST/ifJ7PEEG0fHq5F8Of5Odm0u5NM3l1JeUs3QsT057fxhzVqPvbjnEbbWrA9a7pYieaDvi4hheGKmZTF3604+3rgVWRQ5b0AfTumRjSAIVFV7+GTOOtZt3E9Geiwzzh5K506hRXNbizqPj2nnhX4RSEmKZvbrv23VuDsqD/LqltXk1lYxKrUjF/caiG1vYXXJ9UF2TJLgZkDSw6REtLzbMxyefH4+n3+5IUjaRFVlPnnnBiLcbSvLsK+6gle2rGZXdTmDk9O5rNdgyn0eps95q4mANIBbVrh3xCmc3+1/z8i7OfwcnK0rgPca/p0BLD9iXUHDsna0o9UQBIFTenbllJ5N+R77K8ObVC/Py+esvi3rBK3wrQ4KtABEFOq0PcQ5g+ULFu0JFqE8hNX7i0Iu95gaFVpo7tPO2gMIgsDIpO6MTDp+8t/GqqVh1giUal56uYJ5GiIqNdr2ZoOt6QPHMH3g8Ys61vi3IaIGGTpbtp8K32qyY6/ivmunwbXB26ZHRnPXsPEhx82O7El2ZMvKrk45hZ4Jv2uybN323aiqHBRs6ZrBzjUFeIcaMEzAf4Rns0sQiFFCd466JB+RElSmmehphzlVJrDnQBWmGRzQ27bAfk9ZyPFqdS81upcefTtw+6PHbgGVV78n5HLd8lNrVBOjhPYWlUSR0/v05PQ+wcc2NsbNZbNGc9msEyd3s2VrYdh1B8vDm2v/GHrEJfHISU2bT/ZWb8ayg0vwpu2hyr+pTYOtDZsKggItAEUW2Z9fQa+25hbHxPO3UZOaLPuuYG/wOxngMXRWlRS0B1utxI8GW4IgfAOkhlh1l23bnzZ85i7AAN5u6QQEQbgGuAagY8fQPljt+GVD1wxWLtxOWWkNPfpm0qNfh0Z+QWVZLcsXbMO2bYaP70VCcjARuyUor/ewYOceTMtmfLcupERHEu10UF4fmiTfLSkB3TRZvDuXwuoaclKTGdwhvVn+g1vuQI22g6N5OxY6TjkF0zBZ9f1OSgoq6NIznT5DsugcH97tPiM28ODcWVrGyrwCYl1OJvbIxikrqKLcxBLlEBIcbdtSnubqwPqanSHW2LglCZDgKNFJGwOnHOrSPz7YtkWZbzn12l4ilM4kukbilNOwMdE8MnuXpaJ5ZDoMPEh8ph+3cmLuC/mVVSzek4dLkTmlR3Zjqde2bXbXbaXIm0+SI5We0f1ISosJKVgsigIJmdEcEDU0q2kgJggiXtOFQwx++Ju2iMcMrioICMRFqZSIFpbVNKskijZRkosKM7hBQhJEImQHPlPn+9JtVGh1DIzrTPfo5h/OsWo89d5QwYmAS4rAb+osbhhvQFwWPaLTGz+xYMka3nzjW0RR4OprpjJiUO9m99WW6NghfKbM5WzbMpdLSkMUHFT5ZTaWZWIh0DehgHgnuOTA8di5u5hNWwuJj4tg1PCuONTW5THS02LZl3cwSJZB100SE34amYn0iOiQGU2HJNEp6pfRPfprxI+eEbZtn9LcekEQLgNOBybah2uShUCHIz6W2bAs1PgvAS9BoIz441Nuxy8JRXll/P6iF/F5dQzdRJIEeg/uzD3/vJhFX27kmXs/Rmhou3rhwTn89k+nM/WC4a3a1+ebt3PX518jCgGF8ge/XsgfJp7MTWNHcc+XwaKDiRFuYl1OTnnmVWr9fnTTQhJF+qQl88qs6Tjk0Kd/l9grKPV+39TMGZV451Dqy9389jePUVvtxdANJFmic49U7n3xUp5ZvLSBZ9E0kLvjlDHc+dlXzN2yExsbSRS598sFvHbRdGZ2GsW7uT/gO4K87pQUruzSdm/LAKemXMG8km+xCZ7fWRkzKa75G9YRlEoBhUi1G9Fq27bU6mY1yw5citcowrYNBEHGJacxPPV1Cjd25ZN7MkGwsUwByCFnSj5/+euFbToHgCe/W8qry1cjAKIg8te5C3hmxhkMzUrh2V33U+o/gGmbSIJElBzLzd3voVufDLZvyMfQDx8nRZW59tqp3Fr8VpOcnCSIxKkRxDguRDNeaWIBpFkSHnsM/eK7sL4it4n0h0OUuWXkVK7f9TVNmm8FC1WxuLr7BB7Z9nnQ9xmbnMO+uoNct/JlTNvCsE0EBMam9OKv/c4PWw6cnDqdt3KfQ7ObcrZGJIwjv76C3658Gd0yMWwTEYFRSd15YMCFXH7FI5SuOFz+vnfxW3SelMzzT9567D/CcSAtJYa01BgOFFcHrZt1/ogQW7QeyRETWLnldd7Y3pdD3gjv7x7KjK6bGZcxmb888AkrVu/FtmxkWUKWJZ5+9MJWlU5/M2M4q9bua+LDqCgSgwZ0OiENBqEwKq0j8Q4XPkNvNJuGQLNAe1ar9TguUVNBEKYAtwNn2nYT06vPgJmCIDgEQegMdANWHs++2vHLxIO/e5eq8nq89X50zcDn1dm8eh/vPL+AZ+79GM1v4Pfq+L06umbw4kNzOJAfml/VHMrq6rnr8/n4DROvbuDTDfyGyd+/XcLwrA5cOnxgkxAiLTqKz665mNs/mUdpXT31mo5mmnh1nY1Fxby4JPzpGOvoy4DEh3FIiYiCExGVZPc4BiX/g3/88X3KSqobvq+Jz6Oxe0sRH742j5snz8Mh6wQyYjaSaDJr1A/sKlvFvK278BmBOXs0nVq/n+vf+4yruk7k3I7DcYgKTlHBLTm4puspTM0Y2OJj1BwkSeKWrn9EEYTG+QnYTE05lQHxZzM45WmcUiqi4EBAIdE1gqEpL7TpHAC2VDxMvZ6HaXuw0DBtD/V6HhuKH2L2Xzqj+2R0r4KpyZiaxKa5WXz0aUGbzmFtfhH/XrEGv2HiM0w8uo5XN7jp/Tl8mPcuB3z5+C0fhq3jt3xUaAd5L/9l7vnnJQw5uTuyIqE6ZBKSo7nrqVkM7NuVZ4deSUd3AqooowgS/WI78uLwq5nW6SYM8Wz8loxmSeiWRJ01kvO7PM4jA2cxOrkniiDhEGUSHFE8MGAmYzP68fTMCURH6YiihSBYpCZYvH3ZBXxRFMyvAlhatoPfr32TWsOLx/SjWQZ+S2dxyTbmhtkGoH/sMM7K+A0uyY0iqCiCwvCEsZydcRG3r3ubat3TOJ7P0llatpN7Pnib0hU1CDZN/vZ9XcrS1Zva9LdqDv96+lIy0g+XOQVg2qS+bR5slXs13toxBN2S0SwFzVIwLJmP9gzinXmbWbk6EBxpuonHq1FT6+XP938csjv0x9C7Zzp33TaNuFg3DlVGUSROHtmNe+48s02/U3OQRJHZU3/D4OQMFFHCIUl0jo7j7SkzSXb/ekVcf24cbzfibsABlDcsWm7b9m8b1t1FgMdlALfYth2aPXwE2gnyvy6Ul9Zw+al/D+KxQMBb0lvvR/Mf1UqtiFx84yTOv7plxtH/WbORh+cvwqsfNZ4ocv3Jw7lhzAgMy2JLUQmp0VGkREdS4/Mx6vEXm1j/HEJqdCSLbr662X3atoXPLEEWo1DESHwejfOG3xdSU2ngGWWMvX4Dpu3hYE0Ufl0mM6ESENlRMJwXFgZbCkWoCq9ffB5901PxmTpVWj0JjshWEeBbgiLPbjxGJZ0jBiFJh0nYtm0Hvq/gRpGOr9wbDvNyB2PZwfyw/WtT+fi+YYh68P0oqk8is9+/rc3mcPcX3zB77aYg1bEIVaFP/93EpZQEbSMi8diA15EEifpaH556PwnJUU2kRWzbpsxfiyLKxKpNmwT8ppcSbx4JjgwilKYZijrDR73uI8kZXL7ZU1mIKil0iA7w5obPu6shOxkMhyDjt4OvxX6xnXh5RAiy2xEwbYNqvYpIOQpVdJBXd5CLlz3b2Al7JKI+B2WBHdxqIUDa6Fhe+9cdze6rrVFeUUdxSTXdslNQW1m+aw5vblvHA6u+a+zMOwRZEMkqdKP/EMy7dDpkXn7mMjpkhqcXNAfLsikrryUiwtHmpPiWoNLnRbMMkl2RbSo98d+En4Qgb9t2WGU627YfAB44nvF/7bAsq9U6T78GmIYV2qUEsEw7pGiebYEZxqy7ORiWFXI8y7bRGwQWZVGkf+ZhjopphX+ROJKEeuiF4+ibiSCIuOQjxwttWg6AaHKI45UUfSQHxg5JtocAP+dQIOiUFFJdbcuHsGw7pDZZujv0ZSsIAq5WcLQCx89uVuH88GfDiHw2c0q05nxpDpoZzpBEIERcDgR+xYAtD0REOUO6TAiCQJIzdJDqkFx0DEPUj5SdRMqhOwa7xKYftST8OR1W+PWIYx7uXJcEmXg1sck24URhaea6OvJF5Ke6/yXER4a1zQn3fX8MR147hm2FzFJZ2Bh2mBNGEEIS3Y8VoiiQnHRiXnhCIdy9Is4ZXlfLsgMBd3sQdmxoV5BvYxi6yZvPzGfOO8vx1Pvp3COV6/9yFn0GZ/3cU2tzJKXFkJgaQ1FeeZPliiozdlo/vv4wOEspKRIjJ7bcOHp8ty48+s3ioOWqJDGpZ7eQ28S5XXRNTGBbSdOOMEkQmNq7B5UeL3+dt4D523Zj2jaju3Tk3qkTyYwNLUIaEekku1c6uzYXNCGwyrJIh7gJ2ASXayTBSaeYqbiU4qCsnCQK9MtoWwK6bdu8vWMDT63/gYPeetIjorhzyDjO7NL23pyG5WFb+aMU1n+OZWvEOQbSJ/FuotTw6uBJ7pMp9SykadAg0Hdodz4N8Qy3ZIFxp7fOvDocpvXuwbytuxo1og7BsEzGde3K1voyLI7krwlkR/ZAPsEZxyNR6Kng4S2fsKp8D5IgMiG1D3/IOZPOEcnsrQ+WfnCKCk5JxXfUOeYUFaamDwyUQve/ws7aTQiCQL+YYczocDkRcmgeUOfIZNyyI6h5QxUlcoZmsGtRXsi4b8ZvxrL6+x288OAcCnPLiIx2cd5VY5hx5Zif9MWzvKKOJ/45n6Ur9wA2I4Zm87sbTiUxITzvybJtnt+4nJc2r6Ja89ElOo57RpzCKR2yeXjVwqDPK6LEmd1z+GLlGgyj6cFwuxQ6dUho42/VtrBtm9e3reWZDcso93nIjIzhrqHjOC2rR7PbbTh4gL8sm8+m8mKcssLM7v24c8hYHFJ7ONEc/nvTLj8Tnr73Yz59cyme+kCpZN+OYv581avs2xmscv1rhyAI3PHYTFwRDtSGDiCnWyWzcyJX/G4KF143AYdTQRQFBFHA4VQ455LRZHVveYCRERvNzWNH4ZRlJEFABJyKzKyh/clJCy9NMCw+PaA8fugl0wJbtxmemsGs12fz9bbd6A1Zsx/27mfGK+9Sr4UyPQngtodmEBHlwuE6/H2T0mOZdc059Iq/vYHzJAMCkuAiI/J0zsg5hwEZabiVwDaqJOFUZB6fPg25jR9Ab25fxwOrFnDQG+hYK6qv5Y4lc5mXG6oT8fiwuuR6Cus+aygL2lT617Gs6CL8Rmh5AoB4xzCCn9I2GXHDkSelY0lgiYFPWDL40lTOmd66hopwOKlLJyb17IpLkREIZESdssx9UydyQeeLiFFicTQYPquCA7cUyYUdmy/DtSXqDB+XL3uOleV7sLDRbZNvizdz7YqXuLBTaDmF8Sm9eXDATFySitoQFLollR4x6ZyW3pd/7PgLO2o3YmFh2iYbqlby1M57scJkZkRBZGJqBKJgITRcPKJg4ZA83HDWOMT0QNkwkNMM/FvuBJ1iUvjbTW9TmBs4B+pqvLz7/AJef/LrNj9O4WAYJtf/7i2Wrgwo2ZumzbKVe7ju1rfQ9NBZZoBH1yzi2Q3LqG6w59lbU8m1335MYV0NkV5H4B5y6AtboNRLDOumBqnlg016pxrEcH5MvxC8tHkVj6xZTLkvQLcuqKvm1sVf8F1+aDkQgNyaSi6c9x82lhdjA15D590dG7hl0ZyfaNa/XrQbUbchqivruXjcw0EcJlEUGHNaP+54bObPNLMTi5rKehZ8vp6Sokp6D8pixPheyEqAC7RvxwEWzd0INpw0uS9dc44ui7QMu0rL+GLLDgzLZkpON/qkpYT9rG6YjP/989RrOroLbBkkDSQfdOoUz36hlnqtaXbDpSj88dQxXDC4X9hx62t9fDdnHYW55XTvm8noSX0auSJ12j6K6r/Esv2kuCcS5+wPBN6av9+Ty7J9+4l3uzmrXy9SotqWbGrbNoPffZYKf7AMRpeYeBZMv6rN9lWj7WBZ0SxMu6nRs4hKl9ir6B53fcjtFuw/FZ8ZLPJZVZfNI3NOwaj2E7nXi6jbeDIcWGlOLhsxmNsmtq16tm3brMkvZMHOvUSoKmf06UnH+EAZV7c01lUup8CbS6ozg0Fxo3BKP51Nyft5y3hm57wgvpRbUkl0RIfU2oqQHcyf8GeqdA9zC9dR5q9lSEI2o5K6s7J8IR8VvtFEJR7AITq5ovOt9IwOPtfrjTru3nw9HsOkzBeJ35KJUXzEO7yke7qz/GYbLdaL1WDMLRomjjoXKRnx5IZ4sXQ4Fd5b9hccbSzLEAqLluzgoSe+xOs96tp2Kdx+0xQmjA3O8voMnQHvPBPEywLoFZNEya56ai0N0x3IeEpeiUhUepVWU7hDArvpS5Mkm8x+4woSY8Pfn35OmJbFwHefoUYL5k/mxCfz5VmXhdzuz0u/5t2dG5p0KUJAFmLB9KvJiPzpSp+/FLQbUf8MKC6oQFGloGDLsuz/yszWIUTHRXD2JaHfuDv3SGtTk+9uyYncknxsLdVV9V5My0awQT3Ki7eopgYjKvhFw6vr7DwYPjMDAc7O6ReODLkuUu1Md/WGoOWiIDC2a2fGdm25jcixQrNMqvy+kOsKaoNb5I8H9XouhDBlttCo0YI9Ew/BZ4a+DvIrDWRJxBchUdX3iCDUsthaElw2O14IgsCQjpkM6RhsLKyIKsMSxjCM4xdrbQ1215aEJKabtk2JL7SAr26Z1Bk+Eh1RXNyl6byLvPuDAq3AeCYlvqKQwVaFdhBZkFElnfSIpg4H5RTj88SB5/DvbyGhySYlhZUh5ycIUFVeR0pGaJHUtsT+wgp8vuDj5/Xq7C8I3Ql90Fsf1og6v64a2xAQLQGx5vAj04NO2QGCAi0ASbbYf2DfLzbYqtM1vEbwMQLIqw0vEr21ojQo0AJQRZncmsr/yWDrWNEebLUh0jLj0fXQwofHm9FpR/PQTZPv1u9m+db9pMRFctaoPiTEuAMPcJPGzJaogeyFDrGx7LWrOVrI060o9EoNX5ZsLQzLYsGOPSzek0tSZATT+/cOa1DdWqiiRLzTRZnPE7SuU3Qga7O+cB3vrv2Oek1nSq8+TO152o9yaWo9Pj5fvo2dBQfp1TGZacN7EalkYxPiXMdBjBpe3NIlZ+A1gqUcOsWrIbtGVUmib1oKhmny5gc/8N28jbgiHVxyyVhGDw1ogNX5NT7btJVNRaX0TEnknP45jQKluTWVzN61kUqfjwkdujAhMxtJFLFtmxXlu1lYvAW37GBaxiCyo1r/YLRtm+W5+Xy1bRcuReHsfjn0SGmdRU336DScotJEew0CAXuqMy7IuBwCD7soxcWWtbn8+4mvqCqvY+iYnlxy06lkuLNQRUdQwCUJEqmu4GATIEFNwgjR2SggkEg6TndA9qTJHFSJtA7x7N0enLkEgbjESIrra5m9axOFddWMSuvEaVk9UBs6YteUFPLZvm2ICJyZ3YuBSa27Z2Z1SMTpVEJmtrLCaF8luSLDth5kRcVRJNdiaE3PT7eqkJwBBTVWo4flIVimSOf01hmy/xSIUh1EyCpVWvDLWZfo8AFx38RUNpYVBzUGaJZBdkzrOi//V9BeRmxjPHvfJ3zzyVr8R7xZOV0KT73/f3TMbvuHeDvAq+lc+dhs8kor8fp1FElEkkQe/+2ZLNy+l9c2NhDXRcAC0Ybnzj2Dp5YtZ3dZeeNDXhQEEiLcfH3D5bjVtit3aIbBJW9+wI6SMjy6jiyKyKLIE+dOZUL3tr0hv7djA/eu/BavcfhB6ZRknh13JjvzF/PconJMS8SyBVTZoH+mxuuz/ogUxth6f2kVlz76Ln7dwKcZOFUZt0PljTsupED7PRW+NUd4KgrIYhRjM+fgkELfeIvq5rKx7C9NBWMFJ/0S7+evc/ws3ZePv2HuAhDpUPn82ou54dLnqd9TjWjYAa6QCGOuHM7lV0xo5Nl5dQOnLONUZN67fCaba0r4/fdfYlgWhm3hlhUGJKXx2qnncs/G2Swr24XX1JAQkEWZW3pO5dyOLeeH2bbN7z+ey4Kde/HoOqIgoEoSt008iUuGtVwvrV73MeWrh/CL+mFWrQHJQgx3Dz+X29a+if8oEdzfdjsVYa6fN55uamrucCm8PP8WHtx2K4ZD45BnsqVBpC+GB8Y8F1bw9PkVd7PV3sWRVpK2Bjdm3cX9M+dQcbC2ka8kKxLpHRO46a/TuevKV5rc/xwuhRlXjiH73O5cMf9DDMtCs0zcskKHqBg+mnYRT6xbwts7NuAzdAQEHJLE5TmDuX1IyyRiINBpfMalz1Bf6UdoeLzZArhjHMx54/+Q5dDn+hPrlvDS5pVB187bky/gLx99Q15FFUZDR7IsCiRFRvL45H7cfuc36NrhvIWsGpw0VuS+W+9s8dx/Svx76xoeWb2oiQeiU5L518TpnJyRFXKb/Npqpnz6GvW61mSbSR278fS4M070lH+RONYyonTvvff+BNM5Nrz00kv3XnPNNT/3NI4Lg0/ujiBA7s4SDMOkZ78O3Pn4hWT3bM9snSi8/c1avlm3C19D+daybQzTYumWXIoUDxVe7+G+eAFESSA60smjZ0+hrM5DbnklgiAwsXsXnplxBnHutuXnzF67iU83bWvsRrRsG8OyWLx7H5eNGIzUhiT5PomppEdEsaWilHpdo3N0HA+Mmkz/+ChumL0cw5RpaNjGtCTK6gTS4yrolRJaluCOl+eQW1zZGJAapoVfNygoq+aysf+HYddTp+3BxibRNYJBKU/ilsOXjaPUbrjljtRo2zCsetxyJjkJfyI98jRO7dkVj66z+2AFtm0zonMHnp5xBnM+W82mL7YjNnR8CQRENPPWF7ImzsPOiorD87MsNNNkR2kZb+SvxWsaWA05C92yKPd58At1LDi4obHTzgZM22JV+R6mdxiOU2pZoP3D3v089/0KPA3djXbDPFbm5TNjYB/cqtqi8bZsKeK7l3ajRxjY0RYYIG1XEec4uOr0cQxO7czO2gPU6F6SnNFc330SZyUP4o9Xvho0lmlY7Fidx95n9yMmmSgZgYCpep7N/ts1TrlgHJGxEUHbGYbOEzO/xO+RUTtoCLKNb7eDgy/GE+ct5oZ7r6ekqIri/ApkVWLs1H788fHfkNk5kV4DO7JvxwFqqjzEJURy0Q0TmX7FGGZ8+Q5Vfl9jGUq3LOp0jQq/h//s3NjIl7IJyC1sKi9hSqfuxDtbZmy+p6ac5+vXQb2NXBcIzD2ZUDYSJmV3J9EV/H0BRqR2wC2rbKsoxW+a9IpP4vEx0xiW2oFpfXpysLae3IpKJFHglB5deWbGGXRN70yPHJvte3ZQVyPjitA4/cwY/nD1Lb942Z/+iWkkuSLYWlGK1zDoEhPPI6OnMDazS9htYhxOxmd2HTX+YgAAIABJREFUYXdVOSXeOqIVB5flDOLuERORjkH65b8R991334F77733pR/7XHtmqx2/esx84C12FgQb/jqdMtWJVuPb6JFIiHCz9Hc/TYfZb/79Hmvyg82oI1WVl35zNoM7nHiP9nfWzObhr3PxG8GBxMndvLw8809Byy3LZtj/PRVS30yRJVY8c9MJmevROP/8f1C7KZhHZykC5SNjqM0MDmYEQMqC+hC8lK5pAjUEc4vckspdfaZzalr45ohQuPuLb3hvbbByultRuGfqBM7u1zKpk6de+IaPP18b5I/ndCrcdO1Epk0Ont+3n63jsTtmhxxPtC20XXuDmkAdLpVrH7uEM66bHLTN3i3Lue3iD/F5g5kmnbO9PDfnyWP/QsDuqnLO/PwNPCF+j3iHi2rNF8QFUkSRPwwawzV9hwVt0xxe3LSCx9Z+j37UdS8JAr8beBI39A/Nt2xHO1qDY81s/W+Gou34r4JDCV0WCBUkHIIqhd7mWFHr87OnrCJI1wiguKaW/RVVjUKI4TwYLeyw69oaDkVtTO5Fu+pJjKwhYNlj4ZRD3wYEIaAFFgqKdOJuHX6jjHo9D7uBF6I45LB8GiHM3EVRCLuNKshhBDuFRtkEzfJT4ivEZ4Y2OD8SDlkOSa4WhPC/fbPjqXJIoUhBCPCiQsHpCp+NE0WhiVNA43iigOIMBKq6pVHiK8RjBCRDFIeDcHqdquPHJQ00zSB/bym11YHj55DksNejIkqBrIgNUn3gDwLyE63RblKl0L+H1MrxjkRhXQ35tVWtsuJpK9RofnZXleMzgu89pWW1FB1ou/kV19eSV1MZNJ5pWeytrqDMG2yK3o7QaCfIt+NXjxlj+rO7sAzvEV2gApAWG033jm5W5RVg2YdvvqoM5w/q06p9aabJfV8u4LNN21AkEcuG604axjWjh1JQVc1NH3zBnrJyBEEg3u3isXNO48LB/VhXcCBIRDPG6aT3CSDjh8KUHqfw5HdruXbk92TEV2DbAh5N5b3lJzFz0DkhtxEEgVMHd2f+mp1NyOuKLDF1WOiy4/HAb5SxtvQ2qv2bEAQJSXDTL+l+zp01mhfXfYRgHvUAEWDSqf34bOsONPMIc2hRZFKvrizy7wvKpLhlhZmdR/Livi/RjyKA27bNsISufFk0mwWlXyAKIqZtMiJhHNMzL0USQgc65/TLYfbaTUEPP9uGMV2zWnwcJk3ozUefrcV/VFezbcGo4aEFY0dOzEGSxBCaT3DSpL4szssPUuK3LZtRZw1hQckc5hZ/gICAaZsMjB3B+VlXkpRiUJgvYR9x7TicBtMuaF4g99M3f2jU1TIMi9GT+nDr36aTFR3HzsqyxrIugEtWuLTXIP658AcSfwDJC9hgREDNaJvTslpuhD41qzsPr14UtFwQYOqPCHaGw+6qcq777lP211YhACnuSP457kz6JLatKHFz0EyTu5Z9zad7tqJIErZtc2P/UVzXbziFByq5+4FP2V8QuPfExUbwl9tPp0+v1mXN82uruP67T9lZVYaIQJzTxZNjTmdYagfm5u7gT0u/xm8aGJbFsNRMnhl7ZrNq8+1oz2y1478AU4f14pRB3XEoMk5VJsKpEBfl5vHfnsmVY7YTF1GHQ9ZQJR1F0slOLmJav/IfHzgEHpm/iDmbt6OZJvWajlfXeX7JCj7csIVZr7/P9pKDAYNj3aCoupar3v6Y/hmpnNM/B4cs4VJkIlSVWJeTF2ae9ZNZXbhVN388YxEdEspQJBNVNoh1e7hq3AIGdQjP0bjjgvF0SUvA5VBwqjIuVaF7ZiI3T29bWQTbtllRfDWV/vUNBtVeNKuctaW/Y9KEOHpM7YYtBhTlLSXwd/WDZ/OnqePplZqEW1FwKTJuVSE7MZ57TpvIa6eeS7TqIFJRcckKDkliVs8BjE/qibfQhW0FbIJsE2xTQNifyKqKhXxX+iW6rTWaUa8sX8SXRaFLdAA5acncPG4kqiThVhQiVAW3ovDs+WcQ0UK+FkCXrCSuuXwsqiLhciq4XApOh8J9fzorrE+eKIrc9fSsIPusjE6J/OHxC7nu8UtRHArOSCeuKCcOt8pd/7mV3cJm5hZ/gGb5G7/v+qoVfFT0Bnc/ezExcTout4HDaaCqJqPHi0w494qwc/9h/hZee+IrvB4Nr0dD1wyWzt/M0/d8zAsTzibR5W74PWSckswpHbK5KLs/6YsF5LqA65VogVILmYtFosSWH78UdxR/P+k0nJJMhKzglhUcksxDo6aQ3gppAp+hM+PLd9hdVYbfNPCZBnm1Vcyc9x+qw0itnAjcv3IBn+/dhmaZ1OsaHkPn6Q0/8OHOzdz4h3fYs+8gmmbi9xsUl1Tz+7tmU1HZ8syTaVmcP/ddtjRw17ymQVF9LZfN/4CF+Xu5dfEXVPq9eAwdzTJZUZzPld98eAK+8X8X2jlb7fivwb7iCtbvLiQhJoKROZ0QBZ35+0djWH52FadTWRdJRnw5HRLKiVS6MibzkxaNr5kmQx99LmT6PiUqkjq/P0gkVZUkbhgzgt+eNIy8iipW5hUQ53YxJrsT6k9UQgQo965idckNmHZTWQgBhc4xl9Az/taw29q2zdrdheSVVJKdlkC/LmltHiRW+Tez4sDlmPbRZTuJjlEz6JP4Z7bsLOSzL9cQEengkvNPIjY6onF+6wsPsKu0nM4JcQzpmNE4P59hsLBgL9Waj1FpHekQFcvjC5bw6vI1GKKOHKODCXp1oBV+8uQteOxgnSGH6OThfq+E7dwDKK2tY8nePJyyzNhunVsVaB2Jisp6Vq7Zh6JIjBzaBfcxGBJ76n28/69FHCyuZty0AQw5+XBmqLK0mlVz1yErEsOnDSIiJoJHtt1BkW9/0DiyoPBQv5cRDIs1Cz+j8mAZvYcOp2P3wc3u/6bznmXXlsKg5Yoq85+lf0Z1KXxfuI8Sbx2DkzPoFpvI5/M28OyLC/D5j5JqcCrcesMkJk8MLyXSHKr8Xr4r2Ittw/jMLq3OvHy6dyt/+uGrIP6fS5L507DxXNyz5R2nLYXfNOj/9tMhRVfT1ShiPvLj8QZLcVw+6yR+M6NlHbYLC/Zyw8LPmnQcQkBapktMHDsry5tkJyHQkfjFWZeSHfPLtig6EWgXNW3H/xw6p8bTOfWw5IDfCJCgRQF6pDUlqPvN5oVLQ8Gj6ZhhiCxVXl9IFpBmmhRVB0QhO8XH0im+bc2mjxU+M7QwqI2OJ4Tu1ZEQBIHB3TIZ3C20JlNbwG8cRAghkgpmoy5X7+4Z9O4eXBYRBIGBmekMzAzu+HXKMlOOKkUVVNUEyqKmhF52eJ824LPrQs5Ps/yYtoEohA+gkqMimd6/dYFBKMTHRTDllJaVu90RTi69JZjwDhCXHMOkS8c1WVZjhBew9Jr1xKhxjJh0/jHvv7y0JuRyURSorfKQEhHH+A5N5U7Ky+uCAi0Av2ZQVl4btPxYEetwcU728f8eJfV1+M1gTTmvaXCgrvXzawnqdC0s561K9xIZwqtd00xKSlsuZlzqqQu5L80yOejxBAVaEODdldTX/U8GW8eK9mCrHScMpmmxcM56vvpwNbZtc+o5g5l45kCkMDo3h7DlQAmvLFvD/soqhnbK5PLhg0huhbWNKsUjCxFo9tHq2QJxjubNjau9Pt5ctZ5Fu/aSHBXJpcMHMbRjBnFuF6W1wan5nNQkthUHd0S6FYWhIVTKTyTqtD3srf43tdouYhx96BJzGbGOviFFSCXBRaJzRLPjlfiKWFAyhyJvHpnuLkxMOZ1ERwq2bbNw1z7eXr2eGp+f03K6M3NwP1yKgs+j8cV7K1jy1SYio12cMWskw8aG53nFOHKw7GBPSlFwkvAj82spRmR1YMGWnUjLConYUo6lStQNS8XsnUiq2pEibW/QNvFqEkozJa1aj4fHX/mEtV/vQHJITD5/CFefN6XV7f91mp/7Fs5l4Y48JFFg+sAcfj9iAqIoUuKt4p3cJayvzCMrIomLupxMt6jWuTRkRXRjc/WaoOVOyUWUHF5019BNvv10LfM/WYskiUw+bwjjpvUnZ1Anfpi/Bds6qrNQlUlMCV3C69UjDadLpizZoK4zYEPkPkgslcj5BUjmDExOR5UkDKPpi1aErDAk5cR3EgPEOVzEOJyNnqdHok9sCpUEOzM4HTL9+3Sg2u/j9W1rWVCwhyRXBFfkDGFkWsew+xqQlB6SYO+WFQYlp7O4cB9+q+m9RLMMchLadSSbQ3uw1Y4Thkdue5eVi3fgb1By3rWlkCVfbea+Fy4NW4ZasHMPt374JZppYtk224sP8uG6LXx89SwyYlvGtxAEkZyEO9lYdvcRIpoikuCkR/zNYber8vo466U3qfB40QwTKGHJ3jzuPHUsMwb05p/frwza5upRQ/lk41a+35PbqKflkCTSY6KY3Cs0qflEoMK3llXF12DZOjYmNdp2Cus+Z2T6m6RFTKG4/uvGUp2AgkNKJCPy9LDj5dbv5p+7/4Zh6VhYFHhzWVO5hJu63c37y4t4feXaxu+7o6SMjzds5a1Z53H7hS9yIL8CrSFjsWn1Ps69/GQuvvHUkPtxyil0iDqXgrpPmsxPFWPpEH1uWx4ipvToyr/OfhKruA6x4QHqyK8hpU5m5BVnM7vwSQTBQhQCJHfLFukbMS3seD6/xqwZj6DlawgNCZqPH/ieVct38vLjt7R4fpppMP7FF6ipMcEKXCevfLeRxXv28dz0c7hs2XP4TB3DNtlRU8R3JVv4+6CLGJ7YrcX7Oj3tAnbVbkGz/NgNGQtFVDkn4+KwJVPLsrjnutfZsja38dreuSmfVYt2cOnNk1izZBd+r4bVEHA5nApX3zEt7EvWkIFZVIyRqHAb2A1PJC0eVI9C/z4/7YtKKAxJzmBgUjprSgsby3gOSaZrbAJjM06c/daREAWBC7r15dmNy4PWXTtoOC98/A2lB4Ozin0GZjL1039T5qtvzM4tKczlzqHjuLTXoJD76h6XyMQO2SzI39MoeOoQJdIiorh/5Kmc/vnrmD5fo4q8S1a4ImcwsY52gnxzaBc1bccJwY5N+bz+1NeNN2MICCxWlNaSM6hTSI80y7a55M0PqPH5GxPVlm2jmyZVXh+n9mx50BKldiPW0R+vUQzYJLlGMSDpEaLU8GO99MNKfti7v0mHm2FZrMgtILeimlp/00yZQEAl/snzTifa6eRAdS2RDpWZg/vx4JmTcbWhGv2PYXXx9Q0lw8YjiI1Ovb6fQcmPo0rxeI0DyKKbDlHT6Z/0ALIUPmv48t7HqNTLGh/ENjambVBQv5/XvqnGbzQ9Rh5No2ZtKbuW7kPzNf3tt2/I57Tzh+F0h84QJblOximn4DWKEAUnmZFnMSD5YVSpbW2Nlry/nNUfrMD0H9G9atoY+yvZnBPFqkoJp2IgiRZVngjW7e/I1oMCF4d5OL34wTy2zd2HcERiTjChJreePqd0ITWhZX6Az65azA/bihoDLQBsgYpqP3ulvRTq5VgEHnSHBEA3VOZxQadRLebSRSkx9IsdSr1Rh8/0kunO4vwOV9IvNjwFZf2yPXzw6uIm17ZhWBzIr2DCmYM4c9ZIaqo8eOp8dOmRxg13n8VJk8OXQ1eVFjD7wBZ0jsgciWC64aSMLNIiolr0ndoagiBwRudeuGWFovoaYh1OLssZxEOjJqMep5TEscK2be74YV6QcbQA1JV5KVlVHtSJKisS+5Qa1mrFTcqghm2xong/V+QMRgkjgTOlU3diHE6K6muIVFRm9ujPYydPJcHl5uwuOXgNnQqfl05RcdwxZCyX5wz+yZp9fmk4VlHT9sxWO04INq7YixHCJ9Ln09iwYg/9hgV3wJXW1lHjCzbMtWybH/bmtXouia7hJLqOnSS6cNe+JoHWIYiCQElNMEfDBpbnFiCLIhcNHcBFQ5svUZ4oWLZOrb475LpK/1oEQaRj9Aw6Rs84xvEs8r37Qq7L9+1BlVKCjpNXN1i/bGeTB/EhKKrEtvX7GTkxtMinIAhkRp1FZtRZxzS/1mLl3LX46oPPM0mW2P7DTmqGx7Eqt+n5We2twGcE7ICCxlu8HSF4OBBh/vfrGNA9fLdnKCzYtQfbCv3g2lhYipgYXOI56KuhWvcSq7ZMbR0gxZnBpZ1vPObPb1i+O8gXEQK0gY0r9zD9spO5/e8XHPN4y4v34wshdqpbFssP5LfaI7EtoUoSV/cdxtUtFFhtK1T6vZR6gkuINrBpcwGxIU4Xn09n7dpc/DnB9zJZENlaUcqQlNCZQ0kUuaTXIC4J8YKR7I7kryNDZ6jbER7twdYJgG3b7NxUQGV5HT36ZhKX+PO8mR0srmbPtiISU2LI7nVsHWS19V4++HYtAOdNHERUROtSw9FxESiKjGkc3SGjEBMf6CIrra1j84ESEiMj6JuWQqRDxQpDQI91OVs1j9YgISL0A8u0LERBCOl6H+MKdIpZtk6Fby2WrRHvHIwstvzh11oISIiCihXEUQNZDGSvNFNj0e5FeHQfY7JHE+cKbx4rIIQ0MAZQBGdjtutIiIJARJwbn1jdWEY6BNu2iY4LHI86PZd6bR8RamcilazGz5imydalO/HUeOg9umdIK5njRXxaHJIsBelOCYKAo2F+kmQiyRamKWIaEqoohRXCjU5wUyIFsllNB4SE+Oavfdu2qfJvQLOqiHP0R5XiiI9wgVAdMPRrMj9wOST8gFAjIpSJ2NEWdqIFArh+xGbIZ2qsq8hFEkUGxmWhiK27/cckRKI6ZDR/0844WZGIbsXvFedw4ZDkoE47VZKIazAULyupZvfWIhKTo8nOST/uLEpFaQ07txQSnxRFt94ZxzSe3zRYVVKAadsMT8nEKR8+3jsKS/l23Q4yk2I5fWjvJly93XtLKSmtplt2CslJLZeegABfKhxckSqSGNylqCgikTFOIFiY17Csxu7MHZUH2V9bRc+4JDpE/TwNPP8LaA+22hgHD1Txxyteoby0BlEU0DWTcy4dzWW3Tv7J0qyWZfHPv37G/I/XoKgSlmmRnpXIAy9fQWx8+JLRK58s4bG1h/lIj65ewR8GD+eKs0a3eA4nTe7Diw/NCVouijDmtH48PH8Rb6/agCpLWJZNWkwUr110LuO6dWHRUZkllyJz5cjmW87bEpeNGMSq/QWNXCQIWH10S06gY1wMX27Z2STMkASBS4YOoNK3gdUlN2DbOiBgY9An4T4yosLzfdoSgiDSIXI6+bUfHWEODZLgJCv6IlbuX8H17y1AMyQEwca0tnPjuCSuHXVZmPEEhseNZ3Hp1wjSEUGwKXFy6iRWOmvwaHqTY6FKEpdeNZFnNvyniRmxIApEx0bQvV8Kq4qvo9y3EhEFC50E5zAGJT9B/raD3Dnlb3hqvAgCGJrB1Y9cxNk3Tm3T4zT1qol8/txXTYItQQBHhIOZ547n3wXfIDs0bDtQpjENhUkJw0OqkgNcftEk/vzpS9jmkRwnG1GBmaeF1yPz6PmsLL4av1mBgIiFRnbMNdw0chpLt75/lL2OjSDClf1H8uoLS2CXDJIdKDUmWZx0TRaOZoKtBcWbuW/TB4gNPbOiIPLYoIsYGN9yztG4af15/amvg5aLosDoU1ve/XdG516hRUgROK1TD/55/6d89cHqhnuZTVrHeB54+YpWvcTats1LD3/BF/9ZERjPsknJiOPBl68gPjl8ILS0KI9rv/ukkThu2zZPjT2DCZlduOKht9i7rBhbAmz4R+RX/PPhWXSMi+UPf36f3P3lSJKArptMHJfD7TdPQQzjzBAOTllhalYP5ubuaEJOd0kyl00awYcrlwZtI4oiV581mtvWz2tiri0JAtmxCSS7Ijnvi7fZUl6CLIpolsWkjl15YszpyL9wX8dfI9o5W22MOy79F/l7S9E1E/3/2TvvMCuq+41/pt26vbCFhaW3pVdBOgiICAL2ECNq/FmSqImJJTExsWssMTHGGnuNHVFUQJTee1vKwvbeb5tyfn9c2nLvXdh1XVB5n+c+8MzdOXNm7syZ7/me9/u+ARPLtNi7o5B2ndvQvnPrVGsseG8t7z6/hIDfQA+YGIZFTZWH7G35TJwRnneyL7eUG+d/htAkhHL0szw/j/O6dSE+pmkzVptNpe+Qjqz6emfQFsSm4oqy8+d/XcFWXxVPLlmO3zAJmCa6ZVHj9bEuN59/XjSNjflFlNbV49RUJAnmDBnAVcNbjxPQPj4Oh6ax9kA+Dk1FliW6tUniP5dewKJde9lX3tBXT5YlhnZIpl76DYaowULHIoDAoNS7lDT3ZGxK68wYEx3DqNOz8Rh5KFJw5poedR4dYq5n1vNvU+d3YFoKhqVgCZm1B+sY1tFGekz4qqoP55dwoO4gjmgfph7M7JTtT8RZOIQ7po/nm705BAwTh6aiKTL3TDuHKYN7kpwey4ble7DZVRRFJrVtPPc+fxUF1r8pqv8SC/+ha2TiM4rxG9X8ZfQnlOdXYAQMdL+BaVhs/mY7/cf3ITmj5UrKY5NiaNejLWsWbESzayiqQnJGIg98/if2OQvYWrsfJIEkHbIsUgQ9EhMZlxo+kIiOqaI+8QUOrE1C0SxkReBO8HPh/Rvp32kOshQ6pxVCsKJwDh4jD0HgyLWo9m+he8JZxMSms2p/PpIc7INqg8cvmoK+yWLD13lggGRKSJaE7JVp709hwujwyu6F3kpuXPMiPktHFya6MAlYBouKtnFR5vAjFkUnC6fLTo9+7VmzZCeyIqNpCjFxLv72zJWktoucKY3YnqoxOCWDRbl7kWUZm6wQa7Pz4sTZZC/cx9vPfn3MWGZSW+1h95Y8zpnZ9AnY4nkbeeOphUfb003qqr1s33CQybPD89Sq/T5mzHuVej1AwDIJWMEx64uD2VTtqmHt/L3IFkiHPsIvWLBqO/t2lLJ1Rz4B3UTXTUxLkJdfgdttp1f3pi+Njm7bgc1lRRR66nCpwbHx0m79uXng2Qwd1JEVa/YhLIFNU3E4VP5y+wzGDehGlGZjVVEuzkO2Ut3iknjxnNn8ZeVXLC88iP/QOZnCIre2CkWSGJrarsn9+6nijBH1KUDBgTKuv+DJBsTgw+g7tBMPvfzLVunHdec/wYE9xSHbNZvCq1/fQWx8aOB027/f56PiHIR63NKFIZiZ2pEHrg9v6XIimKbFnu35IKBLVlsURebiF99kU35oqbJdVVhww1zSYqPJraymuLaWLslJrbqEeCzqAwF2FJUQ73LROSkBn24w+JGnGljXHMbY7oXMGroEUzTkVUiodIq9iu4JrWPafBheowivnofb1gG7ksTH2+bxp0+249cbZj8kLKb0Nnli5h9C2hBCMOKmf+HXDWyuAI4YH95qB7rXRrTTzpLHbkAIwa6SMuoDAXqnpTTwAgz4dbK35eOKctChawqSJLEgZ2iIsCpAwZp4Pv5lB7y1DRW5JUli4s9H84eXftVCV+YoAn6d7HX7cLjtdOqbiSRJTFp4H1V6KDdGkxW+OeevQQ+/47Cj/FFyal7F0E1K9sSh2kySOtWgym76Jd9PqntCyD61gWyWF1weRsQ1WCgwJPVpvHqAednbcGka53buhSzLXHzlfygOo2WlqQqfvvsb7PbQ7NYLexfx4p7F6KLhOqdLsXFb1gzOTW+eKKdpmGRvy0dWZLr0Sm+2zMWR9iyLLeXFSEDvxBQUWeaGC/7B/l2hY4VmU3hl0e3EJTZNEuami55i99ZQXTnNrvLigltJSgktxnh792buXrUQ73G8MpuskLoYpJJQTpRQQHJKUBf6fs1Ij+f155v/Lsirq6agroYucYkkOI7SFIQQZO8tRjcsundJQT2m+rNeD7C9ooR4u5MucYn4TYPer/0D3Qrte6oripWX3NDs/v3UcEbU9BTAU+dHiWDQe9iQtVX6UR/eQkKWZXyeQNhgq9YXQITpupAJS1o/WSiKTPc+DWdJtRHaU2SZOr8fiKZdfCzt4lu2Cq2pcNtsDD5GIytgGkScm0geBKFBmMBAt5ouLPhd4VRTcapHfdtqfHWEoVghkKn2hv89hAhWWQIEPDYCnqNVhN5DSvmSJNEjJTns/ja7RtbADg22heOTAfhqjbCZSyEENd9B2LIx2OwaWSMaeuX5rFDiNwQDAVNYYYMt3apGYKBokNbz2KynhWGFF0nVrdoIIq4cuV+cmo2LejUMhLze8P0TCPwBI2ywVat7QwItAFNY1OnNt5tRVIUe/SLrNTW5PVmmf3JDvTBPXfj7RVZkPPX+Jgdb9bXhx2FFkYPHSgn9rjbgxwgTlOiWieUP/ysKKRhwhXsbHK/03lRkRMWSERU6NkqSRLcu4b0a3ZqNIceQ4XXLjGhWfbxy/Bm0DM4EWy2IzK4pYdfibXaVsye1nLL0iTBsXE8+e2dNCAE4KtZJm/Twy1lTB/dk0cKCkIBLsuD8Ib3J2V3Efx9fwM6NB4lLiuLS/xvHuGmNV90Zloc9Vc+QX/cJYJHmnkrX+BuY2L0LL61aH1LJZlMUOiY1fRmiufAbBv9Zupr3N27DsCym9OrKb8aMIDZCJi3G4aB9fGzIMqIEJDnPAlaE7KNITtq4xpBbWcWji5axMieXGIeducMGcumgvkiSxJLs/fxjyXLyq2ro3iaJ344fSf+M5olUAixf8zY5nmdwxFfhLU+ie8JNjOt8Ng9YoTN6m6ozqWdXSgureOnxBaxduhtXlIMZc0Ywfc5w+nRM46B/J5mD83DG+vBUOjmwJoOe8c0z8o53DKTCt5bjI7+s4d34LBCaEXa47Yyc1bio6fqK/Tyd/QU5daVkuBK5rutEhiV1RQiLAzVvsL/mNUyrniTnCLon3IxTTaOyrJZX/vElKxZtx+7QmHrxMGZfNYrBCZ1ZVrorhPzfNSYt4nJbimssBfWfYR2XpRLCJNEZvnot1tYrbHAuYyfVFbnSa+igjiz6ZmdI8UFaahzRUQ4+yl3DaznfUhXwMDChIzd2m8yMma4/AAAgAElEQVSI5O68f3A1Pqvh9RXA0Ea0uYQQfP7VVt54dxXVNV76ZmVw7dzRtM9IpCbg54kNS/lk/04USWJWlyx+3W8EzkaI3M3BsPE9mf/WSgz9OEHRKAepGU2T1AA4a0IvPnp1eUiltN2u0bZDUth9RrXtwN/Xf9tQmoLg8mdGViz5K8uQj/8pZUi1uymhYbCtKDLDw1RitzaiNDudYhPYXdXQSUOWJEafQDsst7aKh9d9w7KCA0Tb7FydNZg5PQZE5DSeQRBnOFstCEWRSc1IYM2SXQhLIERQ0C8xJYbf3nchNnvrxLZds9qy6JMNmKaFaVgoiozNrnLb3y8lPTP8gNItM5XPFm+iUuhBfxuCS4hdhIsrRw7m5kv/zcE9xfh9OtUV9axduhtFkUMyF4chhMXKwiso8S7GsGowhYca/w5KPIuY1O0GPt26G900MSwLRZKwqyoPXTCFLsmtY/cghGDua+/z2Y5sqn0+PLrOjuJSPt+ezSUD+6BEWBIpra1jbW5ByPZfjZ5A+wQ7NYGdCILZIEVykuAYRIz8cy547nV2FJXi1XWqvT5WHcil0uOl2ufj9x99TmF1LT7DIL+6hnlbdzIkM4P02KZXLi1a9jyVcY/iTqxDtZs442qpYjGitAfOuHp2FAUIvqclbKpOxyQ/twy9lF/N/Be7t+Th8+rU1XjZunY/RfmVjL88icq2X+GMCaCoAkd0gOTOFVw4cCwd45qe1Yi196agbt6hYMZCQkOVnAzLfILY2HS2fLsD65DQqMNtJ7NXBtc9dmVEQczVZXv47bpXyPdW4Ld0Sv01LC7aRqeoFGq9/2Z/zSvoVgWm8FGr7yW/7mMSlXP59cxn2b7hAN76APW1PnZsPMie7QVcf9l5zC/YAAQzP6qkYFc0HhxwOcmO8L+HLKXyTfEHOBUvihR87nWhkOMdxNmpP4uwj4pDaUOpd/mhoEsgSw6cWlv6JN2NLIUPWnp0TWPBwm0IITBNgarI2Gwqf7tzBu9WruD5PYsoD9Tht3QO1Jcxv2ADF7cbzmt71iDJ1hGjassC3a9xa9aUiFzI/76+jBdeWUp5ZT1+v0FufgWff7WNUaO68rNF7/BNwX5q9QB1eoDNpUV8W5DDxV37tCi3smtWWxZ9vBHTMDFNC1mRsNk1/vDIJWR0CJ9RbQxdeqWzeN5GTCM4NspysL1bH76Y9p3Cc2qTnG4K6mvYU12BYQXvTZeqMS6jE/ecN4X3v9gAJkjiUAGpArPmDmbO1GEsWboLQXC8sdlUoqPs/OX26Sflc/l9o1diG+bt3wkiqMZnVxSiNBv/HncBsfbwE84STx3nfvQSW8uL8Zg61QEfK4sOUub1MC7j1AeRpwJnOFunEPt2FvLJ6ysoLapi8OjuTJ41GKe7dR+uuhovn72zmk2r9pLWLpHpc4bTLsJgchiWZfH0e9/wwcZtIMGsfllcN3s0D9/6Nt8u2BIym3a4bLy9/E/YwixdlHqXs7745hB+jiK56J/8IG71bN7dsJWl+w7QNjaGOUP6061N+EDw+8D63AKuev19vHrD2b7LpvG3qRM5v0+otYwlBGc/9gwVntCliKGZGbx6xUWUeVeQW/sepuUjPWoqae7JPPTVUl5bszGE62VXFKIddsrqQzlM/TPSeHvupU06J8uyeG/9WbgTQ9urzk3istFf89Xur3hj3Wrq/RZTenXk8oGzefc/S3nn+SXox5XyazaVga9WURnGRzLN0Y7bez7cpP4dhs8oJqfmTWr824mx96JDzGU41OD6zbblu/jkP19QW1HHqNlnMf7ykWHvr8O4fOmT7KkL5fSkO2O4rv07WDRcEpGxox88hxdusYVogdkcGk++eyNR7Vz87+BKtlbl0SU6hYszh5PmjJxF+eDgap7Y+Qld3AfoHZNPwFJZX92eQl86z591Hd1iImcpq/3bOFDzJn6zjGTnWNpFz0CRG5dbqar28NGnG9myPZ/MdgnMmj6QmCQH5y5+gIB1nByDJJMip7Mxvwab3cDuDICQ8Hlt6AGFB0ZO5NIuoXQTj8fPBZc/hT/QsD1Fkcga1o6FmYV4juMwuVSNFybObtQKpjmoq/Hy2bur2bRyL6kZCUyfM+I7FRvV1/n4/N01bFieTZv0eGbMGUFm1zDrh8dACMHivH28m70FU1jM6tybSZldkSWJkupaHn7zK7ZsyiM23sn/XTKKCf2Cy9N5+RW89/F6cvMr6NenHTOm9icm+vRRWs+treKlHevJripnUHI6c3oOINERWa7mgTVf8+L2tehW6Fi27KLrSHK2vFTL6Y4znK1TiE490rjpnlmntA9RMU4uumYMF10z5qT3kWWZGy8ay40XjW2wfefm3JBAC4LLZ8X5lWGDuBr/dsww/BxTeKj2byfFPZ65Zw1i7lmtJ+lwLLYXlYTV9PIEdDblF4YNtqq9Pur84fkMO4qCRs9JzuEkOYc3+G7dwfywpHpNVagME7gB7C5uulF2QPfhjA8NtADcKRUATOw2kYndJjb4btu6nJBAC0Czy2EDLQj6JTYXDjWFHgnhbWyyRnQP4VE1hpz68AbbBd4awAbHBVsWfjzSFvzeviH7KLLE3u0FjO8ygP/revKijZurD+C1TLbUZrCl9igvxinDrpqCRoOtWHsWfZPvPeljAcTFuvjF5SMabNtSdRBNVkKCLUNY7KuqRggFv8+G33eser9geVFO2GArr6ASVZU5/nY3TcHe7FI8bcOJkJpsLS9q8WArKsbJRVeP4aKrT34sawzuKAez545i9txRJ72PJEmMb9eZ8ceZaAO0iY3m79eFLyDKaJvATddPDPvd6YB20XHcNXT8Sf/9muK8kEALgsUCuyvLfpLB1sniTLD1E4Tfp7P8q22Ul9TQa0AmPfu3bzT1n5oRT3F+Zch2w7Aiat041bYokj1sZsuptY55a2NoGxuDKsv4jzNndqgq7RPC89qi7DYUWUYRAbLaHiQxupb8ikSyi9JJjYms+dMhMZ6thSVYx2WRddPEpioN9LwOo030iQetHUUlrNifS6zTweSeXXBpDnSvht0d+iL0VUeerWZ0TGbLmv3YunuQsywkH3i/sWH6NRy48BEawEVrJy5eqA3sodS7DE12k+o6B+0kbHd0s5oiz1foVh3JzhFE245yiuoCeynxLkWV3KS6z8GmxJJoj6bYF1qAEK3aQTLCFAUoaGYGmk1BDzT87QXQJoyN1InQ3pWEXVbxHxfoSJJEqjMOyxKs23iAPfuKaZsWz4hhnY9Uink8fr5Znk11jYcBfds3IDjvrt7Cl4WL0GSV8zOmkebKDPZTCDau3Mve7QWktktg2NgepDhiw1aWSUgkOOzU1OuolRZRe3RQoKarDStaonNs+GX75KRoAroJlkDxBJBMgWVXEQ6FpDZROFWDQLWOqyC4dOZtC7YYlYyoWIQQbFmzn91b8khOi2P4hJ6NZidbG5ZlMW/+BlYs3UVKWhxz544jNiaYbQr4dVYs3EFpYRXd+7aj9+AOP1kbmnDoFJvAprJC1CId9wED0ylR282Grli0jWqeYOtPBWeCrZ8YDmQX8/srnsUIGAQCJpqm0Gtge+7+9y/QbOFvh8uuH8/OTbkNRCptdpXR5/YlKiZ8SjzFNZ7t8oOYpg+OEEslZMlGmntyC59V0zGqSwfsQqbeEkc4agiBMC1m9AmvV6QpClee1Y6U5PuxqwFUxcCwFMpq42nnfCLiseaeNYh5W3eFbO/RJpn+GWm8vHpDg+0ScPXwyFlpIQS3f7yAz3dkY1oCTZa5b8Finrt8JiJ/Enr7z9EcR1+8uk8huv7iiO2dP+csFji/ZbeejiUkFIcF02GMx8Pk9Jl8VvS/BiryNtnOpJTIUiBCCLaV30te3UcIYSJLKtvLH2JQypMhWb9jUeZdxbrioMSDJQx2S/8kI2o6vRL+xM7KRzhY+y4IE0lS2VHxIAPbPMHcTmN5Yuf8BuRvh6JxZedxuNSN1OnZx/eOwd1v4DX1/QbBlqLKtEmLI2tgZsT+RcL0jMG8vO8b4BjhSGQS7dH0dLTl2pteJi+/Et0wsWkqUW47Tz02h7KyWn73p3cQlkA3TBRFZsTQLvz5tvN5ZNtDfFQQnOBIwEv7/83N3bKYnnIpt135HAf3lmAEDDS7istt59E3rqdXTAYbq3Ia9E0C/jRoErf97T0Sl/qObGyz2EvJVCf/N2dk2HOKj3PTv2sqWz/bFixLFcH9hNvG9ZePYcd7H+DYePQg8ZvAOAtGzcrkDz9/lj07CtADBja7ytP3aTz6+nURCeitCa8vwM+mP4qnoBYOPfufv7CMP/3n53RIS+TWOc8Q8OsE/AaaTaVLr7bc9/zc0ypYPJW4utdgVv1jNa5dfrCCFZepC73EXteFzJimT1R+SjgjE/sTw303v05dtQevJ4BpmPi8AbatO8Anb4S6yR9Gv2GdueW+2cQlRqHZVGx2lYkzBvLrv0Z+4SqynRFprxFn74uEioRGrC2L4WmvtqqFTSTUVXmI/6gYR6kOpgBTYKs0SP+ykoqDoVm8wxjW7QOinV4cNh1VETg0g7bxlXRI+TLiPntLK9DCEO7zqmvILi3n+HmzLEnsKimN2N7nO7JZsGMPPt1AN008uk59QOfGtz/mvPH3oB+YiO5VMfwy/noN8mYyeUz4ZTuA1bXL2a+3QTdVTEshYGoETI2l0U5GJExgQpvp2GUHmqThkJ1MSZ3N2Umh2lGHUepdSn7dx1jCh0DHFF5M4T3E4YsgrSACrC++6cjfCnQs4SO/7hP2VT9Pbu3/sIQP60h7PtaX/JbpbftybdcJRKl27LKKS7FxRcfRXJ55Fj4jlMslSxq22DIe+O81tOvcBlVTUDWF/md14aGXf9msLEaiPZqnh15Dp6gUNElBlRQGJXbimaG/5MVXl5JzsByvT8cwLDzeAGUVdTz42Hz+eM8HeDyBI9/5/QYrVu/llaVv8XFBJYZQMISCfujfJ3Zv44Xn3mf/riJ8ngCGYeGtD1BZVsff73iHfG9FSN9UWSF3bwlpK3Rkk+DHCH4yPg/gq44k+yEo25iPZAmkQ0r6kgB7wGTPyv3EbgHZOvQxg/+615i89fRidm/Nw+cJYB7qX02lhwd/92aTr+v3gfvvfR9Pfg2Seei8TAG6xf03v8GDv3uLmkoP3vpg332eALu35PLef7891d0+bVCyqoj4vWbwHrJA0UEOCKRX80OMsM+gIc5ktn5CKM6vpDi/MkQryu/T+eK9Ncy6MvwsF2DM1H6MmtKH6op63NGOk5rpubR2jEh/Dd2sAcRJLSO1FlYt3ondD+kLq7A0CSGBEhDIssTXn26iY/dQno1heajyb0SWGg4qkmSQX/cxPRNvDXus/23cgj+MsbU3EGBVTm7ISpcpBJ9s3cVdU8JzKd7buC2E2A8QMC22FpVy4ZRH8fu9VFYVk5iQhta78eKM9zdtI2CGLltaAr7Z9y1Tus/inNTp1Bt1uNVoFCl8ZeBh5Nd9FFasUwAV3jUku0Ltn8LJQQCYwsvB2nfDtichUe5fxZyOY7k082yqdA9xmgtVVij3rg4rrWAJP3m1HzC47794dt4tVFfWB90NvmMBS8/Ytrw18iaqAh5UWSZKDVZzfbl4B/pxMgOWJdiw+SC2MJlkn1/n6+qV6FrohERGsNZaih5ouGRtWYItRQfw6KHPZMAy+PTjVUcqPI+FosisXLSDKRcNCfkub38pVRWhGmF6wOSzd9dguUP7LssSX7y/LsQzUQjBgT3FVJbVnjKf2MNY/9VOjnt8kQCrTidnT3GI9lTAb/Dl++u47LqT5zX9mLHgvTXovlDagx4w2L05l54Dmp4Z/qngTLD1E4JpWhFn7uEI8MdDluVmDZaacvqt5VumdURLSTLEkeySoLFr0dg1ivydGbE9KeJejVUJm2EIqhC0dTncd5vNQXJyGrJkC/u3x+J4LtnR3gXlDwAUSSVGOznLIRFGQDPYnoQg/HcI69ARw3wVJmg6/liKJBOD44joqEBEaI0GfQgn8PtdEKNpSMcsGBzx0oPgOoJ19CyPveeQgpkjACEJjqzbHd936Zj2VIK2PQR5U1Kk62eGswwPwjp0LwkhCFgGNllFkiQsU0RuzxJh708hGrtvJcRJjDGNIRAwUFU5RKneEgLdMrErJ36dNfZcRbpfTmZsbAyWZVHnDxBlt4X03bCC45AmNz6BOV1gRcheSZLUyDh3BnAm2PpJIa1dAvHJ0RTlNlxusNlVJkwP75n4Y8WwsT3459/nUTQyBk9GMKvhKNFpu8nLqCl9wu6jym5i7VlU+TdzbHAloZHqnhLxWDP7ZbG1sDiECG9TFXqmJrM+tyDE2Hpyz8hikzP79WJDXmFIdktCom/bVPZXv8qeqmfQrRrsSgLd428mI/qCiO1N792dbQU5BIyGmREhJMZ0OvmKrcNIj5pGqXdpSDZKYJLoCM2iACQ4BoUNqhTJSUbUDHJqXg1pz8IkyTmcBe+t5eUnFlBdUU9UjJOf3TiBqZeHr3JVJCdto6Y3+ZxOhLrAXraU/ZVK/0YkZFJcE+mddBdjR3bnnZ2bKM8C0wmyDrE7YYSSRk5+BdWpJt42MsigeiApT2K4eyB7AjvQj3t3WUj0F8OYN3AnddMEVixIPnAukuhXns4utQZvoOEyrUPWOOe8gXy2eHkDziWAZQqGjevJ4qKtPL5zPiW+alyqnZ91GMkvOo3GFW3Hd5zauaIqTJgxgHeWbA+RhQDB2PP6seCdNejHfZfePrFRo+fGsHrJTp6+7xNK8iuxO23MmDOcOb8+ByHB4xuW8tKO9XgNnXZRsdw9bALjwlQMHkbv0V3ZPH9baHbLqZKWFk/u3obL95pNZfz5jYs3R4JhWlzz+tt848vFUsHml/i/joP53ZRxlHrruXPZAhbn7UMgGJrSjgfPnnza854mXjCIXZtz8R0nmyLLMj36nvFTbAxnOFs/IUiSxB2PXYYryo7dEXyxOl02OnZP44JfhC7t/JgRmxRFzay0YKAlSyBL+NpoFExOIL1rZLHEvsn3ocmxR0yeFcmFS8uge/yvI+4zo29PhrTPwKUFr7ldVXBqKk/MPo8BGWkhWQdLCAa1i1yxeV5Wd4Z3bNegPceh9grq32J35ZPoVhVg4TfL2Fp+LwV1n0Vs75IBs+mXEcCm6oBAlQ00xeBv5/fAZWt65ifFNY42rrGHrpGEhA1ZctA/+aGIGlKK7KR/8kPIkgMJGyAdUd/vGncjqe5zjrQnoyFLdvom3cc383bx9L0fU1lWh2UJaqo8vPjY53z+1nr6t/k7suRAJhhMK5KTJOfZpLknNfmcGkPArGJ54Rwq/RsIykMaFHsWsqpwLl0mtaVsIJguQALLBlW9IOOCtiRMSMGbooASdLw23BKl3STG9ZrGuGQnmmQiYSFjokom13TqSO+Z/am9FKz44D0rXBLeSZD520we6H8ZTsWG/ZDSvVOx0SeuPVeNm8j5c4Zjd2jIsoSiBkWOf3nbVPbIxfx587sU+aqwENQZPl7ev4Tn9i4ikBIVtJ2RglMLIUFAgbZZ6Vw8awh2W9CkXTkkrHrtlWO46ubJtOuUjNMVzKg6nBruGAe3Pdo0zbjD2LY+h/tvfoOi3AosS+Ct9/PBy8t49sF53LN6ES9uW0u9HsASggO1VVy/+CPWFedHbO+Pd81GS3QhlGAeS8gSQpG4+cELuePRy3BHO3A4j46N7Tu3aZJ8zrG44pU3WOLPxdIACQIOwb9y1/DckhVc+OnrLM7bhyEsTCFYVZTLzHmvnfZWOeOm9aPv0E44Dv2+NruK3alx5+OXo2o/jOzcqcIZUdOfIGqrvSyZv4myomp6Dcxk8Khu39lE9oeGb/fm8Jv/zcNznEWMU1O5beJoLhvcL+K+huWhsP4zPHoeMfZepLjGRlT8PgwhBCtzclm5P5cEt5NpvXsQ73Iy9JGnqfWHkpS7JCfw6XW/aLS9NQfzWbb3APFuJ9OyupPodvHVwVGHAq2GcKmZjG33acT2LMtiyb4lLNy9kTink0sGnEu7uObzL4QQVPk3UuL5Fk2JId197hHh0sbgM0ooqJ+PbtbQxjWKOHt/JElCCEG1fwslniWospu0qHNxqmlcec7DFOeFFjTEJbp5c+mf8Btl5Nd/im5WkewaSbx9YIuX8u+r+i+7q57CEg19BhXJxSPrf8Ge6lBj6yjJhlQgh1hWKZLEjL49eWD6ZNaVLeOLwm+wSQoz2p9Hl5gsfr7sX+yqDdU4cyk2vppwF3WGjwWFmyj31zIooRNDEjsjH1pa3bezkOVfbUXVVEZP6UN6ZhJXrXiardW5Ie3ZJQ3pXy6kgECtDyAZFqZDw3KqJCVF896rN7Avp5Qly3ajKjJjR3anXUbQass0LdYs2cnOTbm0SY9jzHn9cEc1z0z+zqtfYMPyPSHbbXaVXb+Kw6eELkuPSu/Aq5MjV98ahslrbyxl7YpsklPj+OW1E0hPC2aU6ut8LPl0EyUFVfTo144hY3pE9LttDHU+H31efRIRZu0oyqcgopWQwMqlatw1dDyXdY889pwOOCztsX55NrHxbsae1++Uc/FOJc6Imp5BRETHOpl2WeN+cz92HKioCst98uoGe8rKG91XlV20i57dpONJksTwju0Z3vGo4GOdPxCW6A6QX9W4+bIkSQzNzGBo5lERTVMEIppe+4zCRtuTZZlxXcYxrsu4Rv/uZCFJEvGOAcQ7Bpz4j4+BQ21Dp9grw7YX5+hLnKOhGGlZUfjzrSqvxzQt7GoSnWIjB60tgVp9T0igFYQgP4w7AIDfbxCjOkOCLVMIdpcE779BSWczKKlhxjlcxSGALkzqDB9xNjeXZI4I+zedeqTRqUfDwo88T/h73RQWqkOAKWPENAyUqqqD59SpQzKdwljmKIrMWeN7cdb4XmHbbgry9oWvypVkCXudhS9Mzc2+6vDX6DBUVeHKK8Zw5RWhGSt3lIOplwxrVl+PxYHSyBXNHtVEDUNd9Bg6e6qaLmbc2pAkib5DO9H3NPB4/CGhRYItSZJ+B/wdSBZClEnBqeM/gKmAB7hSCLG+JY51Bj8sbF27n0WfbMSygnyOfsM6fafMwvYNB1j40XpMw2LM1H70H965We11TU485H/YcNRzaRpZaSfOwLQE3DaNaIc9rIp8p6TGuRuGbrL8q22s/nonsYluplw4hIyOydiVJPxm6AvKrXUAoNq/g7zaDzBEPamuibRxjUGSZAzL4qude1i0ex/xLicX9s+iayvaJzUXae0Sydsfer7JqbHNykg0B7G2LAqlL0KMqAE6xkSzvaImZHuU00agPPSNq8oyfdNTQ7YfRqeoNmyuOhiy3SHbiNaabgPTOTqVLZW76RuTS3tnBeWBKNZXZ2KKGCxv+OcquRWzGJ16plFWXB1SQY0APTr0+ZUIev4JIdi6NofF8zYihGDctP70GdKx1QRKO6UkBr0Sj/9CQLShYTggcJwIrUvVyEqM/Nv/EFBVXsfn/1vDgexiuvfN4JyZg3FHNy+r+WPDdw62JElqB0wCjh0BzgW6HvoMA54+9O8Z/ITw/CPzmffmSgI+HSFgyacbmTBjIL/6S2SydmN46fEFfPjKMgJ+AyEES+ZvYszUftx0z6wmD6JDMzPolJjA7pKyI9kFVZaIcdiZ2uvk7WK+CyRJ4rfjzua+L77Gdwx53qGq3Do+sgxHIGBw+y+eY//uoN6Sosh8+uYqbr5vNt1H3cy28nswj8m0yJKD7gm3kFP9OjsrH8cSAcCiqP4LEh1D6Zv0BFe9/gHbCkvw6DqKJPHWus389bwJXND3u2cnvk/MuWUof//9xxj+o3wR1W5y2U2tV/DRNno6e6qfIWD6OSzgK0s2Ymw9uXPwFK5Z+D4+8+jv61RUbh06mu3xpczbuhOfcfQ7u6pw1fDIFlZzUkdzW9nrCPWY17gOM5OGHqnEbAqu7TyUneXP4FQC2GQT3ZI5O3EvXvl3LOxokr031A6pNe1n5vzqHDau2NuA3G93asyeO4rSAQ6e27YWr3GsoK3Kzf3P5rmHPmX+O6sJHNpv8byNTJo5iBvumtEq/XbabEyJ6sRn9fsaLCVKJvxp4FheKtpEdlX5kYBLlWTi7A6mdmidsef7QM7uIm6d8wx6wCDgN1ixcDtvP7uEJ9+9keS0k6tk/jGjJaZ+jwN/oGEQPwN4RQSxEoiTJCmyQdgZ/OhwcG8Jn7y+Ar9XPzIr9Xl1vvpwPXu2RSawRkLBgTI+eHkpfp9+pHzb59X5ev4mdm4K5ZycCJIk8fLPL+SiAb2Jtttx2TSm9OrG/665HIfWeqvrFw/sw33TziEzIQ67qtAjJZl/XzKdEZ0i86UWfriefbsK8XmCnA/TtPD7dP5x1/ska1Ppm3wfLjUTWbIRrXVjYJvHibP3ZmflY4eWu4IBgSm8lPvW8Mb6j9haWIzn0JKmKQQ+w+Du+QtDOG2nG9x93mLKHetJyKxB0UziM2o553cbiB/6Rqv1QZOjODv9bVJdQRK/JsfSPvpShqY+w8i2HXhuwix6JbTBrqhkRsfx4NlT+FmP/vztvAlcP2oYSW4XDlVlRMf2vDX3UtrFR9ajW/nBAezzopBK5KDsQ7WMttjJuheb51WpiQ+J1YKBVvBcLGyyQbr6Dv95/ApGDOuMfGgi43bZ+dPvz2P4kMjVfi2NLr3SeeC/19Czf3tsdpXktFiuvvVc5vxqIrcMGMmdg8eQ5o7GrqgMapPO61MuxV0umP/26iNjjxDg9+p88f469u1sfDm9JfGvS2dxeWJvbF4JyYQYj8p9WRO4aNgA3jr3Mi7u1odozY5L1Ti/Yw8+Ov8KHOoPl9nzjz+/T32t74jOmt+nU1NZz3MPzz/FPTs98J1+WUmSZgD5QohNx2UW2gLHvgHzDm1rvTv9DE4p1nyzK6w+TSBgsOrrHXTJapo/4ppvQu1uAAI+nbLNUEQAACAASURBVFWLd9Czf9PNb6PsNv587nj+fO6pFSyc1rsH03qHGl9HwpL5m/B7Q4MgWZbYuekg/YZNDrFEKqj7DAkVaEjGN4WH+duz8eqhqX5FllmXm8+ozh1Oum+tjTLvcjoODdBxaEO1+Ap/MUJYSM3I9jQHTjWVgSmPhv1uVNsOjGp7Zch2RZa5buRQrhs59KSPs2zlHqRaBcfBhjIKB9Vyaut8RDeRiF7kWcTxS3EAXiMPU6rkgb80jZv4faBn//Y89ub1Yb/7ec+B/Lxnwyzmux8vwTBCz0nXDVZ9vTOEt/Z9QZZl7ps1lfuYGvJdtM3OvcMnce/wlq2MPVUIBAx2b8kL2W5ZIuLY/VPDCUciSZK+kiRpa5jPDOBO4M/fpQOSJF0rSdJaSZLWlpZGtig5g5ZFZVkt+3cXEfB/P5kLu0NDDsOZUVUZh+vEat0V9R52FpceIZDbHTZkOXSpUFHlI2XILQ09YLB/dxHlJaGcm9ZEUV4FB/YUHxGgdEa4fkKII5Iex0ORnRFEG2VcEUq2hRA4TyLLV1JQxYHs4ibZdRRW1/D59t0UVn+3axtJtFVCIbJM5YlR5w+ws7iUam844nvLorykhpzdRSHaVOFgswV/K6EJrEQT4Th6zVW16YGlIkW4l4h8bb8vCCHI219Kfk5Zo+KjJ4LdqaEoMgIIxEgEYoLiwYqiHJF18BkGOytKKfWGVoo21r+CA2Xk7S/9Tv07GfjNYP9KPKEq/qcjFFmKWNEezinhp4gTXgUhRNgFekmS+gAdgcNZrQxgvSRJQ4F84FiFs4xD28K1/yzwLASlH5rS+TNoOjz1fh7+/VusX7YHVVMQQnDlLZOZMSd8BVNzMXJSb54Pkz6WJInREURDAby6zu0fLWDR7n1oioIlLG4cfRaXTOzFf+7/JOTvZVlm7HktXyr9xfvreOaBTxBCYOgWWQMzufPxy4mOaz1fx4KD5dz7m9fIzylDlmWcbhu/f/gSpl46jLVLd2HoDYMbp9NGtz4ZYdtKcgwnXPAhSzYuHzyMjbmbQ0RXnZrGgIz0iP0rLazint+8xoHsYmRFxm7X+O39FzJ0bOQsXcAwuPCFN9lVcrTqqltyIu9dczm2ZiyhtI2efsg38WgZvYRGetS5zSJDCyH4+8KlvLpmA6qsoJsm0/v05O6p49GUltURqq6s5/6b32DHxoOomowkydxw1/mNCgyff24/Xti7CH9/b3A1WAF1r42hlb1wOpoeHLWPvpjsqqcbVFNKqCQ4BqPJrUeE3701j/tvfiNoESQgoU00f3ziZ3TuGfn+i4SRk/rwz+c+R3gkVE/wlWJES5guGDWlDy/vWM9Da5cgSRK6aTIyvQNPjp1GlBZ5Erh/dxH3/uY1youDk4PYBDd3Pn453b8HIc+3d2/mntWLEAIMYTIkJYOnxs4g1n76Es0VVWH4xF6sWLgd4xh7KptdZdLsE6oi/CTQ7By7EGKLEKKNEKKDEKIDwaXCgUKIIuBj4AopiLOAaiHEmSXE0wCP/OFt1i/bgx4w8Nb78XkC/Pexz1n99c4WPU5cYhR/eOQS7A4Np9uO023HZle55b7ZjZIl75r3FYuz9xEwTeoDAby6wVPfrGRpQR53Pn45dufh9mzY7Cq/vnsmqYf0fVoKW9fu59/3fISnzo+3PoAeMNi6Lod7fvNaix6nMZimxW1XPEdOdjEBv4HPGzQc/uuNr2CaJiJMEskd44w4u1RkO4NT/40qRaFIbhTJhSzZ6Bb/KyZ1H8OVwwZhUxRcNg23zUac08Fzl808VLEZCiEEt1/5PHu3FwT75wkEg4db3ghbHXgYv3zzwwaBFsDu0nKuefODk784x6BH/C3E2vugSE4UyYUiOYmxdadX4h3Nau+V1Rt5fe1G/Ebw/guYJvO27uSxRcua1V5j+OsNr7Btfc6hZzGAp87HP//yITs2HIi4T+wIDX2gDzTADqhgdtaJmt68obxj7BUkO0cgS45D18+FS82gf/IDzTupZqCuxssdVz5PcX4lfq+O36dTeLCC2658Dm99eKPsxmBqArnKQK0VR0y3tVqBXGnyTekBHly7BI+hU68HCFgmSwtyuGnJvIjt+bwBbrviWQoOlOP3BftXUlDFHVe9QG1VeGmP5mJF4UHuXvkVdXqAeiOA3zRZVZTL/y1q3vPRmvj13TPJ7NIGh8uGw2XD7tToNSCTn/+69QoqTmd8X/m9+QRlH/YQlH6Y+z0d5wyagOrKetYtzQ5ZrvB7dd55fkmjGYnmYMTELF7/9k7WL81GCMHAs7sSFRO5PL3OH2DBjuwQ7SGvbvDssjV8eO0c3vz2j6xblo1pWAw8uyvRsU0vdz8R3nvx2xBrE0M32bU5l6K8ihYP7sJh44o91Nf5QvzkTMPipce/COtneHg5L7NreOmKBMdAJrRfQpl3GYbwkuQchl0JyjvcPG4Elw3qy8qcXGIcds7unImtkUzO9g0HqCyrDeHlGbrJvDdXct2d54fdb1VO+GKG1TmhfI+TgSI7GZ72MtX+bdQG9hCldSTW3qfZJf4vrFgbkuHzGQZvrtvE7yeOOkIW/67Izylj785CzOMMogN+nfdeWsqfIhj6vn5wKZbS8JoLVfBl2WbusC7AJjdtSJcljUEpT1Ib2EO1fxsutS3xjkGtJpEA8M1nm48skR8L07BY+uVWzrkgcnVmODz75iKwwnhjCsFTr32Bt/tx19wy+TY/hzJvPUnOUMeE5V9ta5CtOQzLtPj6002c/7PhTepfo33fuhqv2fD+0y2LDaWF5NVVkxEVuXjiVCM61sk/3/s1OzYepOBAOR27pzYrM/ljRYsFW4eyW4f/L4AbW6rtM2gZVFfUo6oy4Rwhvi9ekjvKEdFr8HjU+vwRX2ZldUFuhdNtZ+Sk3i3Wv3AojSCUqWoKlaW1rRJsVZTWhuWFGLpJdXldWFNfRZWpKK2NGGxBMMOV4g5fEJASE8WMvj1Pun/hXsimaVFSEKpgfxiNWXxbltVsJ4NYexax9qxm7XssInG0/IaJbprYW6harLKsFk1VCNAwqBciuDwbcb9AeA6PEAKP4W82Pyba1oVoW5dm7ftdUVlWG+K1B8HAs7K0cXHfcCgrqUEKQ3+TdPBV+gmmBRtCk2UqfN6wwVZFaS2BQGiw5ffpLT5uFtaHP19Nlinzek7rYAuCNJFeAzLpFWGy8FPGGebaaYLSwirefX4JW9flkNYukYuuGU2Pfk2vsGsMae0SkMKRzBWZfqeBGnCbaDdOm9ZAdwhAliQGZ2ZgmhaLPt7AZ++sxjQtJl4wkCkXDkFrYQLmgBFdOLCnOGQ2axoWHbq1vOjgjqISnl22hr1lFfRrm8YvRwymZ//2WGEI5w6XjaxBHVjzza4jJdaHYegmnXs2r9KqwlPOv759g2/3VBHlgJ8P7cusPpE1iXr0bYcepuLL7tQYeHbkl7ZdVfCH209VTgvLqN7pKaw9GEovbRcX22KBFkDHHmnoemhEoNkUBo6IfP36xGWyomxXSNAaZ3MTqzWPT1jkreKVfUvYWHmAdu5EftFpDL1iw3P/TgalRdXBsWztflIzErjomjFHqoVXFeXy7NbVFNTVcHZ6Jtf2HkrP/pnYnVpIha1mU+nZv+kv7WHDurH9vR1Ix8VvQpPI7JNGsVSBcdw6vCRJZMaEpzdkDchE0xTM4+7bw89iS2JkeiZ7q8vRj8v0mZZFt7jEFj3WGbQuTv3odgYU5lZw/Yx/MP+d1ezfVcSKhdu4/crnWfblthY9jmZTueYPUxtUrCmqjNNt57LrT638AQRL4f84eWwDnStFknDZNG4eO4KHfvcmT93zETs2HmT3ljxeeOQz/njNi2GXIL4LZs8dRVSMA0U9uoxmd2r87FcTcbpPXEnZFCzfd4BLX3qbz3dks6ukjPc2buWC517D45YYNaUPdufR38pmV0lJj+eGu2YQHedqYPzqcGpceNVoYuKbbhxd7a1k+rP/4e11Pg5WRLG9IIq/fLKbuz9/MuI+yWlxTJ49GIfzKClbs6kkJEczcUbkZZ/bJo4Ou/0PE0c1ud/fB+6YNAanph7JsEqAQ1NbXB7EHeXg0uvGNfh9VU0mKtrJBb+ILGj76+6TcSg25GMWyRyyxu97nd+spb/c+nIuX/YkH+atYU9dEV8Xb+O6Vc/xTcmOJrcFwcrZG2b8g8/eXsX+XUWsXLSdO+Y+z7cLtvBu9hau/OJdFubuZUdlKS/vWM+UD/9Lat82YU2M7Q6NrEFND7YunjQUW4corGNiY0sFrb2Lh66YhVvTUI+RBHGqKncOGYddCR9M9+jfnt5DOjYYN+0OlU7d0xg0smuT+9cYru09lGjNflz/NG4ZOBKX1rrVoWfQslDuvvvuU92HI3j22Wfvvvbaa091N1od/77nI/ZuL8Ayj85XTcNiy5p9zLxyZIvyJ7pmtaVbnwzKS2pQVYWzz+nNHx65hJS2jdvDtBa6t0liULu2lNbWI0sS47t15tGZ5xIo9PDS4wsacKlMw6KmykPXrLakZ7actYzDZWP8+QPQAwb1tV7ad2nDtbdPY/Kslq2qEUJwzRsfUF7vOZKpEIBuWuRWVvPAzbNIbBNDeXENUTFOpl46jJvvnU1MnIvx0wdgmRa1VR4yOiZx1a1TmXbZWc26V55a9gor9xkY5tGXjSlkdhYZXDigI1H28FVpQ0Z3p03bOMqLa3C57Uy5cAi/vf8iXI1oPfVtm0qXpEQ25BXgN0yS3C7umTaRC/uf3FLz94020VFM6NaZKq+PgGkysF0690+fxJDM5md6IqHP4I5kdkmhrLgGu0Nl3LT+/P7hS4hLjIq4T4I9ivGpvak1vARMnd5x7bmz90zOSmreS//BbR+yu7YQ65hcmSEs1lfs57IOZzf5fnr6vo/ZvTU/ZCzbvHof/0vJp948+vxaQqBbFqXbyqhcWRKqjSVBv2Gdm6w+LkkSM2YOI9tfSVFhJSJKof8FvfjnY1eTHBXFjE698Bg6dbqfXglt+OtZE5nWMTJf9XAFdVSsk4qSWmLj3Vzwi5HceNd01BYWQHZrNmZ2zsJvGlQHfHSPS+auYeO4qOvp8XycQSj++te/Ft59993PnujvpO9bL6QpGDx4sFi7du2p7kar4/JR91FZFsrFsNlVnv/8VpJTT+91+tbABy8t5b+PfY4ehqh60dWjuerWc09Br74b6gMBhjz8b8wwz6BL09hw+69apR+zXriHbQWhL3iHFuBv03oxo3d4svsZ/PAxedF9VAZCtaZsssqHo28lyRETZq/I+NmY+6koCeUdaXaVnGviqHGHPr9d1oD2dWUIoU9RZK646Rwu/uXYJvXhDM6gNSFJ0johxAln4mc4W6cBYuLdYYMtIcQZE89DiE10o2pKSLBls6vEJwczL2XF1ezYeJD4xCh6Dcz8zhyg8pIatm84QFxCFFmDjrYnhGBbUQm5ldX0TEmmQ2LzsoJ2VUVV5BAuCECss/V+96QoFQkLcRyrwBISbaK+/2KA0xF1gX3U6tm41Uxi7C1bpXssLMti69ocaqo89BqQSULyd9O2MgyTT95bS3FRFROm9KFr96PVYPWGn7Xle1FlhcEJnbArGrGaK2ywJRC4VQeWZbF9w0Eqy2rp0a/9CSd+sfHusMGWsAQBW/jl/qgEJ9jr8Pl1TIeGBMg+Hc2uEpsQnATkV9WwpaCIlJgo+rdNO5JxE0KwbWcBZeV19OiaSmrKd5uYCiHYWFZIQV0NfZNSaRd9clm10qJqdm46SHxSNFkDM1u1mvPHgKryOrauyyEqxkmfIR1bzUS+NXEm2DoNcNHVo/nnXz9sQBDVbCojJvTC1cIcoR8qRkzM4ul7Pw7ZfljU9NkH5zHvzVWHuB+C2Hg3D770y2YtjwohePHRz/no1eVotqDwa3Ssiwf+ew2uZDdXv/4+e8sqkCUJw7IY27UDj86c2mTRS1WWmdUvi/c3bcd/TFGAQ1OZe1brGSlfddZYlu9dim4eHeAkySLe5WdY+5Yra/8hwBQBNhTfQplv5SF7I4toW3eGpP4HTY68vNcc5OeUcfvc56mv9SFJoAdMZs8dyS9unnzincNg07oc7rjqecQhOYkPn/maLoMzefKla/myaDP3bv3gKBdIgocHzOHnHUfzyPaP8VlHxx6brDIuJYua4npun/s81eV1IEkYusnUS4byf3dMixhMXHTNGP7x5/dDxrJh43oQl6mxsuhgA/K3U9W4+tLxPLPoPbwpbo4YqUoSWo2fEZOy+OMnX/LJ1h1osoKFIC0mmpfmzEYOCG65421Ky4KVsYZhMmlCFr/71eSwbhMnQqm3np99/hZ5dTXIkoRumUzr2JNHRp4bsUpaCMEz989j/jurj449CVE89NIvaZN+xnz5ZPDmfxbx5tOL0TQFAThdNu5/8Woyu0Suqv4h4gxn6zRAx+6p6P6gt5TdqSFJEgOGd+bWBy9u8Uq7HypUTWHg2d1Y++1uLNNCsylExTj5879+zoE9xbz6zy8J+A0M3cTQTTx1fjau3Mt5l57V5GOtWLidlx77nIDfQA8caq/ez/rle/jaWcm6g/n4zaAUgGlZ5FfVIMsSQ9o3ndczvGN79pZVcLCiCpdNQ5Lg0oF9uXF08/hXzYHTXoSXN9hVlIaqmMiSIDW2khsnriIr+Wc/qVl6duU/Kaibj4UfgY7AwG+W4zUKSHW3nDijEIJbLnuakoIq9EDwPrNMi+zt+XTslkpGx+QmtWdZFr+c/jimz0ASgAiS+ysKqykNeHmWhfgtA12YwY9lsrh4G3f2nomFxY7qfByKhoTEsMQu/KXPRfxx7ovk55QdeQYs0+JAdjFp7RPpEEFepEPXFEzDYteWPOyO4FjWb1gnfv/wJUzp1J31JQWUeutxqcEihFsGjGRy26689eXmoHacLAU/koSItqN2cfPq2g34DJOAaaKbFjVeH5vyi1j1/g727i9F1010w8S0BAdzy0mId9OtS9Orhq/56n22lZfgt0wClokpBDk1lcTa7PRLDl/lu2T+Jl5/auFxY4+Pzav3MfWSYU3uw08NG1fu5am/fRQcaw9dP68nwKrFO5hxxYgfxNhzspytM2/y0wCSJHHFTZOYfdVoDu4tITktlqTvmA7/MaJLr3ReXvgHcrKLMQ2LTj1SkWWZW+c8E6LTY1mCggPlFBwoazJ5/uPXV4S0JyxBUWEle7P3YxxX/egzDN5au5nrRzZ9cLWrKk/MPo/Sunryq2rokBhPXCsuIQIcrH2bQR2z6dd+LwWVCThtAZJjalAkN5X+jSQ4Wi/LdqpxsPZ/WMeZdQt0CusX0E/chyS1jGVPzu4iKkpCtdT8Xp1P3ljJsHEnp3d2GGtW7MHw6iFCnpKARf9bj5kVnpv7bclObug2mSs6jmF/fQkpjljaOGIpzK0gb39ZiGitzxvg49eXM2Zq37DtSZLEnF9NZOaVI8ndW0JiSmyDpcc3z72UvLpqSr31dItLwq3ZePfDtWFdLCVZ4r/L14WIzJpCsLmgiKT9emj//Abvf7KeaVOaZuFV6fOytiQ/RBLCa+i8smM9V/QM/wx8/FroWGFZgtx9Ja0mgPxDxrw3V4ZIfkDQVWDX5rwjkiE/BpwJtk4juKMdP6qb6/uAJEl0PE7rylMbXohSVmQ89WEUXE+AiO1pckQD2sOG2c1FcpSb5Kimyza0BHQzKMyoKhbtk45a6UhImFbL2pGc7rBEeHsYgYmFiULLBFueej+yEn7WXl/rbXJ7VVWeYBYgzP1p6iamCKOALgT1RvBej9Ic9Ik7OvZ46/0oqgRhLkek5+NYuKMcEXUCM/6/vfsOc6pKHzj+PenJ9GEYGOrQO1KliSLSUQQFu2vB7trdte3Pruu67rrq7qqrsjZsyNoLCIqAgNK79Dq0gWGGKek5vz8SgSHJwLRkyvt5nnnI3OQmbw53bt6ce857ElNKFecsKfHg84WP5/L5Arj8kRfnVgqUKXL7lTgr8Dfv82JUikh/xcVl/G2XFEVuC6PRUKGlhuqb4iORj3VlUHWu/ereKDRRZxV7PDzz3Y8M+Nsr9HvuZR75ehb5TheDRnaNeLnVaDTQqn35r/sPHtUNizXC8/khOz18DJhBwVntWuF2eXnz+RlcNvgpJvV/nOcf+pj8Q5ErftckWYkjMarwZY8C+Eiz9azy11s6fyO3TXyJC/s8ym0XvsTS+Rur/DUqKsM+gEinxRRLZ4wqep2jvNxC/vbgNCb1f5zLBj/F2y/MxOOO/iHdrkvTiIm7xWZi8KjIvUZlGRylJ0wraN27GTZjeOwaTf+M9hH3a9k2E5MpPLE0W02cMbJqV3A4vU8rzJbw1zKZDAxp0wpzhMHSKTYbKRHqTplMBs4cGPk9laVJQhLptvC/AbPBwIiW0ctqDBrRNeK5wmQx0aJNZtT9nD4PL/z6NSO/f4phs5/gz2s+Id9Tv77YAGG1BH/j9wXqXMeDJFuiVtBac9U7HzN18UrySpzkO11MX76Wi6a8z9jL+9OoaerRP1qj0YDVZubupyeWKkx6qs69tD9ZzdOxhZ7PYFRYbWbufOIC7hhmwmryYjQEv3GbjT4cVieTB6by8I1v8slb8zl8sIiiAiezP1/O7ZP+iasC37RjqUnieSRa2h2XcBkwKBud0+/DZKja3rZFP6zn8dveZfPaPZQUu9m8bg9P3PYui36oWBHNqtYp/Y+YDUkYVHBiigEzRpVAt4xHo+7jLHZz+6R/8sMXKygqcHL4YBHT/zuPR29+O+o+FquZ2x6dgNVmPjqY22o307RlBmMuPr3ccTscVs6//ky0OlZBQSsw2c089uSlDMpoj/24hMtmNHNxywE0T4hcldxoMnL30xOx2sxHZ4ZZ7WYys1IZ/7voRVcrolP7LIYO7ojtuKKhNpuZUed05f6xQ2iYmHC00LHJYMBmNvHM+SN58O5zsVqDM3oBrFYTGemJXH5R+cdpKqV4bvAYHCYz5tAkApvRRIY9gdt7DIy63wVXD6Zh45SjBU9P5dyjtebWxW8wbeciDnuKOeJ18kXOUq5Z+G88gcg9eXXVsPG9aNm20dHiyAZD8Fx785/GVXkB6XiTOluiVli4bSe3fPQ5JZ7SvQUOi5mnzh3O0Nat+P7z5SyZt5GMximce2n/Mr9Znozb5eWHL1bwy4+/ktEombGX9KdF24bM2nkmBwq9/LSxI/sL0shuuJ8BbTfAnp5Mvbdp2PgNm8PCTQ+cy8iJfSscSywEtJe9xd+yr3gWFkMaLZIvIsXaucpf57rRfyNn+8Gw7U1aNuCNb++t8terCI//MDsLp5HvXk2SuT0tky/CZoreQ/rVB4t4/dmvw/7vrXYzz71zI227NI2677aN+/jq/UXk5RbS7+xOnH3uaVis4d/0T9XCeRt485/fUZBXTI8BbbjlnjEkpzgI6ADzczfw7Z4VmA1Gzmvamz4N2pz0+XZtPcAX7y3i4L4Cep/RjnPO71Vq1YCqorXm5yXbmPn9GpRSjBrWlT49s1FKUeT28MnKtSzctpPmaalc1uc0WqYHZ/rt3nOYT79czp59+fTu0ZLRw7ricFT8Q3pnYT7vrF/O9iOHGZDVgkntupFkKfv5XE4Psz9bxpJ5G8lsmsa5l/Sjeevo555leVu5a+nbOP2lv4TZjRYe6DKeUU16VDj+2sjr8TH3m1UsmLWOlPQExlzcj7ada88C1qdaZ0uSLVEr/HfRUv72/U94/eFjTyYP6M0foywDU5U8/ny+3zmUAOE9Veu+acf817qVqnD/m1GTTueOxydUe3y1wZjOD0a8fKYUfLX26Vox++hEf3tgGrM+XRa23WYPfkMfUcUrD4ja7YPtP/HSxm/xBsLPZZe2HMRdncbGISpRUVLUNI6KC13Mn7Gaw4eK6No7my69s2vlh0h10lqzevE21i3fQVpGEoNHdSuzpljztFQsRkNYsuUwmyOOo6oOJkMCSplAhydbmc0cGCKMLbHYzDRrVXVLCdV2aQ0TIxa9TG2QVC1/I0cOFzP329UUF7roMaANHbo1P+k+Ae0jt2Qehd6NJJhakpkwtMzxWs1bN8RiNYUtDK6UqlGz0XYU5TI3dz0mZWRooy40sp+8DlRxkYv53wbPZV16ZdO1T806lxV7PXy9fQMHSoro2bAJA7Ja1Kj4ImnqaIBZmQhs92GfDcoPzsFg6mqhRULdPVfkHTjCvJlrcDu99D2rQ9hEp7pOkq0qtmH1Lh685g0CgQAetw+LzUzX3tk88q/fRVxstT7yenw8fOOb/LpqF26XF6vVzGt/+Ypn3rw+avfxWW2zSbJZcXp9wXo8BOsIWUxGxnTpEJO4DcpMdvKVbDvyFgHtOm67jWFnX823f/0lbAaNDgQYPr7+lE44mctuHsprz35darq31W7mspvPrvLXWr5wM4/f+jZag8/r5/1XvmfgsC7c+8ykqKsLeP0FLNh7BS7ffvzaiVE5MOf9lQFNpmI3Rf5wGHFhHz78z5xSyZbRZCCjcQrd+raq8vdVEa9vns1bW3/ErwMYUPx74wz+0Hkc45pF/0K+aW0O91/9GgG/xu0O/p127tWSx16+qkacy37Ny+Xib97HG/Dj8vmwmUx0SW/EOyMvwmaquR9tAzLaYX/Ph23RsW3m9aDbexk5vXwlK2qL+TNX89f7poHW+P0Bpv57NqMn9S2zQG5dIwPkq5DWmidvn0pJsRuXM1gDxlXiYfWSbcyYvjje4dUYX32wiPUrduIq8aADGpfTQ3Ghi6funBq1tILZaOTDay6hX3YzTAYDRoOBHs2y+OCaS0i0Vv0Ykmjap91K6+SrMCoHCjMWQxpdG/yJRN9AiiJOY1bs3ZUXs/hqujEX9+Oq20eQmGzDZDaSkGTjytuGM/bS8g9qLovX4+OpO6bicnpxu7z4/QHcTi8LZ61jwax1Ufdbn/d3Sry78esSQOPXxbj8uaw++EjUfVLTE3nu3RtpQ6KmSQAAIABJREFU27kJRqMBo8lA7zPa8+zbN9SID5KNR/by1ta5uAM+fDqAR/txB3w8u+5zDrnDexnht3PZu5QUuXE5j/2drl26nW+m/RLjdxDZ7+d8ToHHRYnPSwBNic/L6kP7+O+6mj0UZefGA7DIh4JSP4aNmuWzN8U3uGpQXOTiufum4XF58bh9+H0BPC4vMz5ezOrF2+IdXszU3PS/FtqxaT9FBeEfuG6nl5n/W1qhauZ10cz/LY04tulwbiF7dhyiaXbkrvTGyUm8ecVEnF4vAa1JsMQuyfqNUgbap99G27Sb8QdKMBkSUcrA7M+WYTQZ8XpKX+b0eX3M+WolHbqf/PJVfaCUYsLVZzDuyoGUFLlxJFqrZR20dct3oAPhibvL6eG7T5ZyxojI5Qv2Fc9Ah1VbCnDIuYiA9mJQkQevt+qQxUvTb6Ok2H10RlpNMWvfKrwRZrkZUMw78Cvjm4dP3ti1NZeCw+FrJrpdXmZOX8J5l8V3GaecoiPsKioI2+7y+5i2aQ03d6+559qPp8yNet8nb81n8MhuMYym+i2bvyniEAu3y8sPX66g++mt4xBV7EnPVhXSQMRSyBC1x6Y+KqspTqWd7GZzxERLa01xkQufN3zgaVk8bm+5yzMYlAmzMRmlflucOsoDlarX//e+QAkBHZ5YG40GklLs1bvgbIX+Fiv3f+VIsNaoRAvKfr86yvvVWqOiNODxe7icnoj1xAKBAHuLj+Dxla+UgcvvxeU/eYHgaHGHRxjZb/G5yhlflajH54MT1aemkJ6tKtSybSYJSTZcJaU/uK02s8xIOs7wCb15+4WZYb1baRmJUXu1TmbpTxv552Ofkbs3H6PRwIgL+nD9/WOxlLG2ZF5uIc8/9DHLF24GDe27NeOupy4sc9p2NH3P6kDg0fAq2BariSFj6+Y4jLLku1ezOvdhirxbAQONE4bRNeNhzIakmLx+554tI17Cs9ktDJ/QO+p+jRNGkFP0BZpjH8IKIw3s/aL2atV0w7O688GOhbgDJywrg2ZwZuRiqC3aZJKUag/7EmK1mxkxoTd7dhzk7w9N59cVO0HBaae35q6nJ5LRKIUnf57NlHXLCISSnp4ZWXw4+lIsZYyjyinJ44nV01mZvyO4T1orHu52IY2jDOIPVqFPZktB6Uv0NqOJC9uVXXT178vm8a9Vi/CHPum7pGcybexlOEyx6Sm/8Noz+eHLlRHvG3/loJjEEEu9BrUj4A8/N1ptFoaeV3/KXEjPVhUyGAz86YXLcSRYsdmDiwrb7Ba69M5mVA2vsxRL513Wn/bdm2FzWEJtZMaRaOWhf1Rs0eNNa3N44vfvsm9XXnA8gNvHzP8t4fkHP466j98f4J7LX2H5gs34fQH8/gC/rtzFPZe9QvEpLEdyopS0BG57bAIWqwmzxYTRaMBiM3PeZQOiLltSVzm9e/h572QKvZvQ+NF42Vc8iyX7bo5ZDGaLiQefvwyrzYzFZkYZFFa7mX5nd2TQ8C5R9+uYfi8OUzOMygEojMqBxdiAbhmPxSz2qtY+uQlXZJ+B1WDCqAyYDUYsBhP3djqPDGvk5FcpxUOhc5n1t3OZw0KnHi0Zcu5p3HXpy6xbvgO/P4DfF2DFoi3cfenL/HvFAl5ft/RoogWw/OBeJnz1btT4XH4vkxe9worD2/HrAH4dYFneViYvernMIp//HDKOZIsVh8mMAhwmM10aZDK5c/Rz7dRfV/DiyoVHEy2AtXkHGPvZW2W0YNVq06kJQ8eFJxld+2Rz5ujyrx5Q0yUk2bjnzxNLnRutNjPDJ/SqMRNIYkHqbFWDoiNO5n67mvyDhXTt04pufVvViIGyNYnWmhWLtrB++Q7SGyYzeHQ3EhIrtgDzk7e/y4JZ68Iul5gtJt7+/j5SGySG7bN47gb+fPd7OIvDeyEn/2F0hcek5O7NZ/6MNXg8PvoN6Uh2PZveDLD+0HNsPzI1bOyTUdkY0OQ9ki3lX06lovLzipj3zWqKjjjpObAtHbo3P+nfYkD7OFAyh0LPJhLMLWmUMKzM0g+1xbaiA8w9sB6TMjC0cVey7CcvmVJc6GLuN6s4fLCQLr2z6X56a2ZOX8IrT38Z1utlT7Cy4fpEii3hvRgAyy79Pek2R9j2r3OW85d1n4UV+XQYLTzU9QKGZ0VPQIq8br7etoF9odIPg5q0xFDG/2/P917isDvyenzzJt5I86SUiPdVhzVLtzPt9R/x+/ycd/kA+g0p3+Ljtc2hA0eY+80q3C4vp5/VkdYds+IdUpWQOltxlJhsZ8xF5V9yoz5RStFzQFt6Dmhb6efatS034rgUs8VI7r6CiMnW3l2H8EdY/Nbt8rJ7W26FY2mYlcqEq6t2OZPapsi7JcIgc1CYKPHuimmylZqeyHmXly9xNigTjROG0ThhWDVFFR+tEjNplVi+S+QJSTZGn3Auy9lxMOIYR4/bhxM/0QbLbS3Ii5hs7S45FJZoQbDHa4/zcJnxJZqtXNT+1HuDCj3Re63X5x2IabLVtXc2XXtnx+z14q1BZjITrqq/50ZJtkSt16Fbc3ZvzSVwwuwzn9dPkxaR135r3SErYq0lm8NCuy7NKhzL0l05TF++FpfPy5guHRnavnWZ37Rru9V79vHRsjUUutyM6NSWEZ3akWo9jUPOn8Mq7QfwkmSJvqhvTeHxF7Cr8GPy3atIsrSnRdJF2EwN4x1WpQQCmkWLtzBrznrMZiNjhnfjtFMo8BpJm05NsTksYWNTzRYjyQYL+RESbYCOaZETvXbJWdiNlrCEy2Y00zapanuGMx2J7CmOXO6iV2btWSImkjVLtzNz+hK8Hh9njenO6UM6Rq0nV13yDhzhqw9/ZvvG/XQ8rTmjJvYlKTU8wa5OPq+f+TNXs2DWOpJTHYyadHqNWP5Hki1R6118wxDmzVhd6uRvtZs599L+JCRFvjTZpXc22e0bsWX9Xrye4LgQo8lAcqqDM0dXbOr1Sz8u5I2FS3B5fWjgh43bGNi6BS9NOq9OJlzv/LKc576fj8fnJ6A1P27exgdLV/HKpZPYfuRdAgEfEOw9NCgbmfYzSTDX7PFrJd4cftpzMX7tJKDd5JbMY3vB2/TPeotka8d4h1chWmue+OsXLPh5Cy6XF6VgzrwNXDCuFzdec1a5n6//0I54Hgoe478d1RoImDWPDBnOXfO/Dtvn7GatSYxSqmVww440sqWQU5KHVwdnEpuVkSx7Gv0zqjY5f7z/MK6b/UnY9tMzm5Fhr9pF12PpnRe/Y/p/5+Fxe9E6uOB7nzPa8+A/LovZEJatv+7l3itexef14fX4WTJ/Ix9PmcuL035Po6axWeXD6/Fx/zWvs/XXvbhKPBgMilmfLuPGB8Yy+qJ+MYkhGhkgL2q9ptkZ/P39m+l9RjvsDguNmqZx7T2jmHzv6Kj7KKX485TrOO+y/qSkJZCQZOOccb144aNbK7QQ8J6CI7y2YDHOUKIFUOL1smDrThZs3VnBd1Zz5TtdPDtrHq7jKvqXeL2s2rOfORsPMqjJhzR2DMekErAaG9Im5Tp6ZP4lzlGf3Pq8Z/EGjhDQwZUAAnjw6WJWH6y9A+RXrtl1NNGC4HR7l9vLx58tJWdv2ZfpIpnxy3L8/kCpi4UKcBd7yXKb+MugUSSZg0tvmZSBi9t1441zLoj6fCaDkdf738R5zfqQbLaTbLYzvnlfXut3I0ZVtR9Rw1q044WzziXFEvwSZlSK81t34oPRl1Tp68TS/pzDTHtjLm6X92gpBVeJhyXzNrJi0ZaYxfGP/5uOs9h9tNagx+WlsMDJa89+FbMY5ny9kq3r9xz94h0IaNwuL688/SXFReWf+FSVpGdL1Amt2jfmydeuLdc+NoeF6+8by/X3VX7h15+27sSgDEDpGl8lXi+zN2zhjDYtK/0aNcniHbuxmIx4Tlir0un18u36TYzpci69Gv0tTtFV3EHnAn7rjTtegWctfu2plQPlF/y8BXeEWlgK+GXpdiacW75eh1mzlhPlSiFffbeYJ+66kovLMY4KINls5/4u53N/l/PLtV9FnN+6M+e37lztrxMry37ahMEY3nvlcnpY9P26KhkXezIet5ct6/aEbdcBzdL5sauKP+/b1bic4QenyWxk7dLtnH5W/HqnpWdLiCqQYDFjiNBbbzKomC4nFCt2szliQUIFJNXi92tQkRdDVxgxUPZ6gFr7OeL+lSLP1hpVyNbhsGCMMHbHYFDY7SfvxT2wJ5+Na3YfrYtnd1iI2BQGKjyj+GR8gRLy3Wtw+fZXy/NXp7wDR9iwehclJ66bqjVbi/az8cheAjryDM5TYXNYMEQ4+RhNBhzV9P8R9lpGQ8Qq8QDWClwpqKiERBuRrppqDXZH5L/tWJGeLSGqwJB2kZecMBmMjD+t7nyL/k2/7GaYI5xcrWYTF/WqvcuNNEucwJa8dzCYjtV3CviMNEkajlLRk63ckp9YkXt/6PJjAJspi96ZL5JoiX8doRFnd+G9j37Gd0JhSa3hjP7Rx0QV5pfw5B1T+XXlTkxmIwF/gKvvGsUVk4ay8r1NJ3bigoYrxw+t8vi35L/OpvxXMGAigId02+n0zHwOsyF8lnFN4irx8Jc/fMDS+ZswW4z4vH4mXnsmV9w2jM1F+/jDsnfJ8xShUNiNFp7ucQm90su/dE2/szvx4iPh49CMRgPnjOtZFW/lpIwmI4OGd+Wn79aUWsHDYjUxalLsakyOubgfC2evCyuYbbMHF1GPJ+nZEqIKOCxmXr1kPIlWC4kWCwkWC1aTkYdHn02bjPR4h1flzEYjb1x+Aal2W6n3e+eQgZzWtPbWz1n3eU92r2yA12XAXWzC6zSSuyWFJe9GL+VS4s1h6YE78AYO49cl+LWLYu92ft53TcSlimKtSVYqf7hjFFaLCYfdgsNhwWG38PQjF5CYEP3b/lN3TmXd8u143L7QgtRe/vv8t3j2ejjvgYFoM2hb6McKl/95BE0bRp79W1F7i79jc/6rBLQLny4ioD0ccv7CqtwHq/R1qsMLj3zC0p824fUE28/j9jH9zXnM+HQJN//yOnuch3H5vTj9HvI8Rdy19O2oC4OXxZFg5bGXr8KRaA3+JFixWE38/pHxNGsVu1m0tz06nradm2CzW7AnWLHYzJzWrw2X3XpOzGLo1rcVl90yFLPFhD0h2BYp6Qk8+dq11bs02CmQoqZCVCGPz8eCbTtx+/wMaNWcZFtsuvHjxev3s2j7LorcHvplNyfdYY93SJVyxVl/5tCBI6S3KKRB9hHy9ySQuzkVq83M/5Y+GnEq/YbDL7E1f0qEIq4J9Mz8K5mOM2MVfpmKS9wsXb4Dk8lA7x4ty7y8k7uvgMmjnsPrDq/g3nNgW55+YzIHC44w/bufMBuNXDh8ICmJVd/T9FPOJRR41oRtN2BhaIsfsBhjVxerPFwlHi4a8MTRmc7Ha5Cdwu673ZT4S19WtBhM3NRuOFe0Glyh1/S4vSxfsBmf10+PAW2jzsSubpvW5pCz/SCtOjSmZdtGcYkh/1ARqxdvw5FkpUe/NhhNZQ8BqAwpaipEHFhMpqiXFOsis9HI4DbZ8Q6jyhQeCVYXz9uZRN7OY0vZeD0+fF4/Fmt4suX27Y9YxBUCuP2HqivUcktwWDlz0KkVlC3IK8ZkMkZMtg7tPwJARkoyN06MPuO3KkRrP6WMeAP5NTbZKi5yRRw7BFB82IkvQieHJ+DjoPtIhV/TYjXT7+z4V6Fv16Up7bo0jWsMqQ0SGTyqZg1nkGSrHlq+cDMfv/4jufsKOK1/Gy66fggNG9fMk5YQsdSxe3NW/bI1bHuTlg2ilgTJsA9kb/FM/Lqk1HZNgHRbrzJfb8Phl9hRMBW/dpNkac9pDZ8iyVL9s8dOpkWbhhEH+ZvMBnqfEbsVADLs/dld9DknDhAzKAt2U3w/0MuSlpFIQpINj7uo1HaDQdGxb0sOsSNsH7vRQu/0NuTlFjLt9R9ZOn8j6Q2TuPDaM+l7ZodYhX5SBYeLmfb6jyz+cQPJaQ4mXHUGA4dFX29UBMmYrXpmxvQlPHbL2yxbsJldW3P55qNfuGX8CxzYkx/v0ISIuxvuHxuc3RUa36EMCqvNzK0Pj4+6T+OE4SSYW2Dg2Pgno7LTJGEsCebog3J/2XsjW/JfxaeL0Hg54lnLvJwJFHm2Vd0bqiCL1cx1fxiN1XYswTSZjSQk2Zk4OXaXRdul3YLZkIg6rl/AqGx0Tr8fg6q5fQUGg4FbHz4fq818tIfLaDJgc1i57d5xDGrYAZvxWNtaDWbaJTWmk2rCLeNf4Mv3F7Jray4rf97KU3dO5ZO35sfpnZRWmF/CrRNe5LN3FrBzywHWLNnOs3/8kPdf/j7eodV4MmarHvF6fFwy6ClKTijuZjQaGH5BH+54fEKcIhOi5ti9LZcPX5vDptU5tGibycU3DKFNp7KX+/AHnGw/8j57i7/BqGy0SL6YJgljo1bvdvr288OuyAOH06y9GdDkrUq/j6qwfOFmpk+Zx6H9BfQa1I6Jk88kLSPp5DtWIadvH1sLpnDIuRi7KYs2qZNJt/WOaQwVtX7FTj56bQ57d+bRpXc2F11/Fo2apuHXAb7KWcanuxbj037GNOnJBS368fZzM/ns3QWlZvQBWG1mPvjpT9gc8S2rMvXfs/noP3PwnHB52WI1MXXugyQm1+4xmxUhY7ZEmL278tCB8Houfn+AFYs2xyEiIWqeZq0acs/Tk8q1j9Fgp03qtbRJPbXCuvtLZke9r8CzrlyvXZ2qarH4yrCbGtOlQc2ffRhJpx4teORfvwvbblQGxjXrw7hmpT+jly3YFJZoQbBXbMfm/XToXrH1LKvK0vkbwxItCPZ6bl63hx7928Qhqtqh0pcRlVK3KaV+VUqtVUo9e9z2B5RSm5VSG5RSIyv7OqLyktMc+HyRi+elN4ztt1UhqtKW9Xv4/ovlbFi9q0YVFI0mwZQd9T6zqtn1o2qLYp+bH/at4ft9ayjyxXeplkicPg9z9q9j1r7VFHqDEzMyooyd9Xn9pDaI/3HRsHFqxN5avy9AesMktNb8unIn33+xnG0b9sYhwpqrUj1bSqmzgfOB07TWbqVUZmh7Z+ASoAvQBJillGqvtQ5P2UXMpKYn0mtQW5b9tOno+lUQXLT5ouvKvyCtEPHmcnp45Ka32LB6Fwal0FqT3b4xT75+bbVVM68KDR0DMSpH2KB6gHZpN8Uhorplzr61PLz6I4wY0IBfB3i020TOyaoZM9QW5m7k/hXvoUIrTPq1n/u7jOfCawaz6uetpYpymsxGOnRvHrPFnMsy4apB/PzD+lLxGU0GWrTNJC0jiTsm/YtdW3NRKrguYZfe2TzyrysrtN5sXVPZnq2bgWe0Dq7aqrU+ENp+PvCB1tqttd4GbAaiVwUUMfOHZy+mR/+2R4u+2ewWrrpjRI2YMixEeb35/AzWr9iJ2+nFWeLB5fSyed0eXnnqi3iHdlIDm3yA6YRerGaJF9Ai+eI4RVQ3HHIX8n+rPsLl91LsD9azcge8PLp6GrmuipdWqCqFXif3rZiK0++h5Gh8Pp5Z+ykZ3dK48cFzsSeECoNaTXTtnc3/vXhFvMMGoONpLbj9sQk4Em1H4+vQvTmPvXwV/3rsU7Zt3IfL6cFZ4sHt8rJmyTbe/eeseIddI1R2zFZ7YLBS6inABdyrtV4MNAUWHfe43aFtIs4SEm08/urV5B04wuFDRTRr1bDUjCMhapPvPlkaVjjS5/Xz49crufvpiVEHqNcESZbWjMhexGHXSpy+vTR0nFHjl6CpDWbvWwOEX0rWwKx9q7k0e1DMYzrej/vXHe3ROp5fa77ds4LJk4Zyzrie7NqaS0p6AhmNalZZnqHjejJ4VDd2bc0lMdlOZpNU/D4/P323Fp+v9MUrj9vHjOlLuPae6q3HVhucNNlSSs0CGke466HQ/ulAf6Av8JFSqlwVHZVSNwA3ALRo0aI8u4pKSM9MJj0zOd5hCFEpx18OP57PG0BrXaOTrd+k2U4jjdPiHUad4fJ78UVY2Nkf8OP0e+IQUWlOvyfiwtN+7ac4VFXeYjWfdAZsPJktJlp3PLYslz+gCUSYfAVEHFBfH5002dJaD4t2n1LqZuB/Ojgi9RelVADIAHKA46dNNAtti/T8/wH+A8HSD6ceuhCivus5sC2L525AB46dOpSCLr1bRlxaR1SNfE8xL274lh/2r8GAYmTWadzSYSSJpviPkxvYsD2vbZ6N/4SExmQwcUbD2BUH1Vrz3SdLef/lH8g7WEir9o25/r4xDOjYnhdWfo3jS411CRAAT3fwjzdzZmbtHM5hsZho17UpG1btLrVdGRR9BseuCG5NVtmz0afA2QBKqfaABTgIfA5copSyKqVaAe2AXyr5WkIIUcpND55HYrIdS+hSuMVqwpFo47ZHpWZcdfEEfFy98N98u2c5xT43hT4Xn+1ewk0/vxaxxybW2iY1ZnyzPtiM5qMX6+xGC+c27UX75Nj1Fv3vzfn8+8nP2bc7D4/Ly4ZVu3ho8hSObCii2X/s2BaAwQkGN1iXQto/FJ3sNbc362TuePwCHIlWzNZgH47VZiY51cH1fxwb58hqhkoVNVVKWYApQA/AQ3DM1veh+x4CrgV8wJ1a629O9nxS1FQIUV6F+SXMmL6EjWt207pjFqMm9SU1XcY+VZeZe1fy9JpPKDnhkpzDaOEvPS+nX0a7OEV2jNaaZXnb+HrPcjQwukkP+qS3jtllZZ/Xz8UDn6CkyB12X5vOTcjZfhBXSen2szks3PbIeIaO6xmTGKvD4YOFfDttMds27qPjac0ZcUGfOl/oNCZFTbXWHiDiNAmt9VPAU5V5fiGEOJmkVEdMl5Cp7zYe2RuWaAF4An42F+6rEcmWUoreDVrTu0F8FoXPzyvCH6WmYc72g/h94WMNXSUeNq3LqdXJVlpGEpfePDTeYdRIUkFeCIHWmsPu5RwsWYjZmEyThNFYTRnxDkvUQC0SMrAbLWGDzS0GE80cDeIUVc2SnJZAhAmHAGQ0SuHQgSNhkztsdjPNW2fGIDoRDzKCVIh6TusAKw7cy+J9N7K54BU25D3PD7tHcaBkXrxDEzXQ8MbdsRnNGI7LJowYSLHYGRTDAeg1mcViYtwVA7HaS5fVsdrM3PjAWMwWY9g+AQ1Dxsqs1LpKki0h6rl9Jd9xwDkXv3YCmgAeAtrFigP34tfxnyovaha7ycKU/jfTMy0bozJgVAb6ZbTjjX43YTKEJxH11dV3juDCawbjSLBiMCoaNk7hnmcm0apDFs7i8LFcBDR7dhyKfaAiJuQyohD1XE7h56FEK9xh1zIy7P1jHJGo6Zo60nm53/V4Aj4UYDbIR8mJDAYDV942nMtvPQePy4fVbkYpxdcf/hwqS1L6MqLX52f+jNW07Vx7ZySK6KRnS4h6r6zTwMlnb7lKPBTmh6/xJ+o+i8FUpYlWUbGb4pIIvT41RLHPTZG3fItaGwwGbA7LsZmQUWZEKgUGQ80vwisqRr6OCFHPNUsazyHXovDeLWUg3dYr6n5HDhfz/J+ms2TeRgCymqdz11MT6dRDVoIQ5bM7J4+n/vYVGzftB6BjhyweumcsTbJS4xxZ0F7nYR5dNY3V+TsBaJ/chMe6TaJlYsNyP9eAoZ149enwtTtNZhODR3evdKyiZpKeLSHquUaOoWQljMSgbChMGJUdo7LTO/MFDCryuplaax68bgpL5m3A5/Xj8/rZtTWXBye/wYE9+TF+B6I2c7m83HrvVNZv2IvPH8DnD7Du1z3ceu9UPN74L/XiDfi4btErrDy8A58O4NMB1hfkcN3Pr1LiK38vXFpGEr9/ZDwWqwmL1YTZYsRiNXHZzWfTqn2klfFEXSA9W0LUc0opujd8kuzkKzjoXIDJkExWwnDMxugL4G5am0POtoP4vKVrCfm8fr58f6EsPCtO2Zz5G3C7fRxfXzsQ0DhdHuYt2MQ5Z8V3CZt5B36l2OcmcNzi1hqNJ+Dju32rOb/ZSetZhhk+oTc9B7Zlway1+L0B+g3tRJMWUjajLpNkSwgBQLK1I8nWjqf02P27D0ccX+Lz+tm17WBVhybqsL378nG6vGHb3W4f+/YXxCGi0nKceXgC4T1sTr+HnJKKzx7MaJTCuMsHViY0UYtIsiWEKLc2nbLwRaiCbbGZ6NKrZRwiEhVV4HTx4bLVLN65m1YN0riiTw9apMdurFS7No2w2804naUTLqvVRLs2jWIWRzQdkptgNpjwRVieqGNy0zhFJWobGbMlhCi3Ji0z6Hd2J6y2Y2O6jEYDjgQbIy/sG8fIRHnsLyxizMtv8c+5C5m7eTtTF69k3H/eYfGO3TGLof/pbWicmYLZfKxGl8VspFmTNPr0zI5ZHNH0TW9Dq8RMLMfNujQrI5m2FM7MjO8lTlF7VGoh6qomC1ELUXv4fX6mT5nHlx8swuX00m9IR666cwQZjaKP9RI1y4NfzOTTlevwn/A50Dwthe9uvSZmCzcXl7iZ8u58Zs9Zj1KK4Wd35urLB+GwW2Ly+ifj9Hl4fctsvs5ZQYAAI7K6c0PbYSSZ6/Yiy+LkTnUhakm2hBCinhr491c5VBxeI81iNPLD7ZPJSEyIQ1RC1B6nmmzJZUQhhKinEixRSnugsZkj3yeEKD9JtoQQop66vG8P7ObS86RMBgODWrck0VozLuEJURdIsiWEEPXUlX17MLJTO6wmI4kWC3azmQ6NMnhm3Mh4hyZEnSJjtoQQop7bnV/A+n25NE1JpnNWZrzDEaLWONUxW1JnSwgh6rlmqSk0S5VZpEJUF7mMKIQQQghRjSTZEkIIIYSoRpJsCSGEEEJUI0m2hBBCCCGqkSRbQgghhBDVSJItIYQQQohqJMmWEEIIIUQ1kmRLCCGEEKIaSbIlhBBCCFGNpIK8EELUAX4d4O2tP/LBjgUUeV10SW3G3R3PpWNK03iHJkS9Jz1bQghRBzy77nMgqS2rAAAIyklEQVT+u2UOhz3FeLWfFYd3cOMvr7Gj+GC8QxOi3pNkSwgharl8TzFf5SzDFfCW2u72e3l7649xikoI8RtJtoQQopbbVXIIs8EYtj2A5tcjOXGISAhxPBmzJYQQtVwTexregD9suwFFm8TGcYhI1AYlxW7mfLWSnO0HadelCQOHd8VikbSgOkirCiFELdfAmsTQRl35Yf8a3AHf0e0Wg4mrWp8Zx8hETbV7Wy53X/YKHrcXt9OLzWHh7Re/4x8f3EJyWkK8w6tz5DKiEELUAf/X7QImtuiP3WhGAW0SG/Fi32tokyQ9WyLc3x/8mKICJ25ncJyfq8RD7p58/vv8zDhHVjcprXW8YziqT58+esmSJfEOQwghai2tNX4dwBRhDJcQEEysJvZ7DL8vEHZfYoqdaYsejkNUtZNSaqnWus/JHlepni2lVA+l1CKl1Aql1BKl1Omh7Uop9aJSarNSapVSqldlXkcIIcSpUUpJoiXKpAwq6n1Gg1zwqg6VbdVngce01j2Ah0O/A4wG2oV+bgBeruTrCCGEEKIKWG1mup/eGoOxdNJltpgYOq5nnKKq2yqbbGkgOXQ7BdgTun0+8LYOWgSkKqWyKvlaQgghhKgCdz89kYxGKdgTrJgtRmwOC606NOZ3tw+Pd2h1UmVnI94JzFBKPUcwcRsY2t4U2HXc43aHtu2t5OsJIYQQopIyGqUwZca9LJ67gb278mjdMYvup7dGqeiXGEXFnTTZUkrNAiJNZ3kIOAe4S2s9XSl1EfAGMKw8ASilbiB4qZEWLVqUZ1chhBBCVJDRZKT/0M7xDqNeqNRsRKVUAZCqtdYqmA4XaK2TlVKvAnO01u+HHrcBGKK1LrNnS2YjCiGEEKK2iMlsRIJjtM4K3R4KbArd/hz4XWhWYn+CSZhcQhRCCCFEvVPZMVvXAy8opUyAi9DlQOBrYAywGSgBrqnk6wghhBBC1EqVSra01vOB3hG2a+DWyjy3EEIIIURdINXLhBBCCCGqkSRbQgghhBDVSJItIYQQQohqJMmWEEIIIUQ1kmRLCCGEEKIaSbIlhBBCCFGNKlVBvqoppXKBHfGOowplAAfjHUQNIW1xjLTFMdIWQdIOx0hbHCNtEVST26Gl1rrhyR5Uo5KtukYpteRUyvjXB9IWx0hbHCNtESTtcIy0xTHSFkF1oR3kMqIQQgghRDWSZEsIIYQQohpJslW9/hPvAGoQaYtjpC2OkbYIknY4RtriGGmLoFrfDjJmSwghhBCiGknPlhBCCCFENZJkq4oopSYppdYqpQJKqT4n3PeAUmqzUmqDUmrkcdtHhbZtVkrdH/uoq59SqodSapFSaoVSaolS6vTQdqWUejH03lcppXrFO9bqppS6TSn1a+g4efa47RGPj7pOKXWPUkorpTJCv9fHY+KvoWNilVLqE6VU6nH31avjoj6cD6NRSjVXSv2glFoXOj/cEdqerpT6Tim1KfRvWrxjjRWllFEptVwp9WXo91ZKqZ9Dx8eHSilLvGMsF621/FTBD9AJ6ADMAfoct70zsBKwAq2ALYAx9LMFaA1YQo/pHO/3UQ3tMhMYHbo9Bphz3O1vAAX0B36Od6zV3A5nA7MAa+j3zLKOj3jHG4P2aA7MIFhXL6M+HhOh9zwCMIVu/wX4S308LurL+bCM958F9ArdTgI2ho6BZ4H7Q9vv/+34qA8/wN3Ae8CXod8/Ai4J3X4FuDneMZbnR3q2qojWer3WekOEu84HPtBau7XW24DNwOmhn81a661aaw/wQeixdY0GkkO3U4A9odvnA2/roEVAqlIqKx4BxsjNwDNaazeA1vpAaHu046Ouex74I8Hj4zf17ZhAaz1Ta+0L/boIaBa6Xd+Oi/pyPoxIa71Xa70sdLsQWA80JdgGb4Ue9hYwPj4RxpZSqhkwFng99LsChgIfhx5S69pCkq3q1xTYddzvu0Pbom2va+4E/qqU2gU8BzwQ2l5f3v9v2gODQ93gPyql+oa217d2QCl1PpCjtV55wl31ri1OcC3Bnj2of21R395vVEqpbKAn8DPQSGu9N3TXPqBRnMKKtX8Q/DIWCP3eAMg/7otJrTs+TPEOoDZRSs0CGke46yGt9WexjqemKKtdgHOAu7TW05VSFwFvAMNiGV+snKQdTEA6wctjfYGPlFKtYxheTJ2kLR4kePmsXjiV84ZS6iHAB0yNZWyiZlFKJQLTgTu11keCHTpBWmutlKrz5QOUUucCB7TWS5VSQ+IdT1WRZKsctNYVSRJyCI5P+U2z0DbK2F6rlNUuSqm3gTtCv04j1C1M2e1SK52kHW4G/qeDAw5+UUoFCK73VefaAaK3hVKqG8ExSCtDHyTNgGWhiRP1qi1+o5S6GjgXOCd0fEAdbYsy1Lf3G0YpZSaYaE3VWv8vtHm/UipLa703dEn9QPRnqDMGAeOUUmMAG8FhKC8QHFZgCvVu1brjQy4jVr/PgUuUUlalVCugHfALsBhoF5phYQEuCT22rtkDnBW6PRTYFLr9OfC70Ay0/kDBcd3lddGnBAfJo5RqT3AQ8EGiHx91ktZ6tdY6U2udrbXOJng5oJfWeh/175hAKTWK4OWScVrrkuPuqlfHBfXnfBhRaEzSG8B6rfXfj7vrc+Cq0O2rgDp/BUVr/YDWulno/HAJ8L3W+nLgB2Bi6GG1ri2kZ6uKKKUmAC8BDYGvlFIrtNYjtdZrlVIfAesIXia4VWvtD+3ze4IzsozAFK312jiFX52uB15QSpkAF3BDaPvXBGefbQZKgGviE17MTAGmKKXWAB7gqlAvRtTjox6qb8cEwD8Jzjj8LtTTt0hrfVNZ5426SGvtqyfnw2gGAVcCq5VSK0LbHgSeITjkYDLBmbsXxSm+muA+4AOl1JPAcoLJaa0hFeSFEEIIIaqRXEYUQgghhKhGkmwJIYQQQlQjSbaEEEIIIaqRJFtCCCGEENVIki0hhBBCiGokyZYQQgghRDWSZEsIIYQQohpJsiWEEEIIUY3+H+x98j8xNFTYAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pylab.figure(figsize=(10, 6))\n",
+ "pylab.scatter(data_2d_rp[:, 0], data_2d_rp[:, 1], c = labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.74 0.47 0.58 154\n",
+ " 1 0.75 0.58 0.65 131\n",
+ " 2 0.67 0.59 0.63 113\n",
+ " 3 0.71 0.51 0.60 144\n",
+ " 4 0.38 0.56 0.45 66\n",
+ " 5 0.42 0.53 0.47 79\n",
+ " 6 0.52 0.57 0.55 93\n",
+ " 7 0.35 0.51 0.42 69\n",
+ " 8 0.37 0.58 0.45 62\n",
+ " 9 0.62 0.69 0.65 89\n",
+ "\n",
+ " micro avg 0.55 0.55 0.55 1000\n",
+ " macro avg 0.55 0.56 0.54 1000\n",
+ "weighted avg 0.60 0.55 0.56 1000\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "classifier.fit(data_2d_rp, labels)\n",
+ "print(classification_report(classifier.predict(data_2d_rp), labels))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### PCA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.decomposition import PCA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pca = PCA(n_components = 2, random_state = 0)\n",
+ "data_2d_pca = pca.fit_transform(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pylab.figure(figsize = (10, 6))\n",
+ "pylab.scatter(data_2d_pca[:, 0], data_2d_pca[:, 1], c = labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.83 0.73 0.77 113\n",
+ " 1 0.56 0.54 0.55 105\n",
+ " 2 0.59 0.57 0.58 104\n",
+ " 3 0.77 0.79 0.78 101\n",
+ " 4 0.95 0.93 0.94 100\n",
+ " 5 0.56 0.54 0.55 104\n",
+ " 6 0.92 0.93 0.93 100\n",
+ " 7 0.76 0.71 0.74 105\n",
+ " 8 0.62 0.66 0.64 92\n",
+ " 9 0.52 0.67 0.58 76\n",
+ "\n",
+ " micro avg 0.71 0.71 0.71 1000\n",
+ " macro avg 0.71 0.71 0.71 1000\n",
+ "weighted avg 0.71 0.71 0.71 1000\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "classifier.fit(data_2d_pca, labels)\n",
+ "print(classification_report(classifier.predict(data_2d_pca), labels))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### t- SNE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tsne = manifold.TSNE(n_components = 2, init = 'pca', random_state = 0)\n",
+ "data_2d_tsne = tsne.fit_transform(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pylab.figure(figsize = (10, 6))\n",
+ "pylab.scatter(data_2d_tsne[:, 0], data_2d_tsne[:, 1], c = labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 1.00 1.00 1.00 99\n",
+ " 1 1.00 0.98 0.99 104\n",
+ " 2 0.99 1.00 0.99 99\n",
+ " 3 1.00 0.97 0.99 107\n",
+ " 4 1.00 0.99 0.99 99\n",
+ " 5 0.98 1.00 0.99 98\n",
+ " 6 0.99 1.00 1.00 100\n",
+ " 7 0.99 0.99 0.99 99\n",
+ " 8 0.96 0.98 0.97 96\n",
+ " 9 0.98 0.98 0.98 99\n",
+ "\n",
+ " micro avg 0.99 0.99 0.99 1000\n",
+ " macro avg 0.99 0.99 0.99 1000\n",
+ "weighted avg 0.99 0.99 0.99 1000\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "classifier.fit(data_2d_tsne, labels)\n",
+ "print(classification_report(classifier.predict(data_2d_tsne), labels))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/hw9 (k-NN, t-SNE, PCA)/hw9.ipynb b/hw9 (k-NN, t-SNE, PCA)/hw9.ipynb
new file mode 100644
index 0000000..0caa40d
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/hw9.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Разобьем датасет с перемешанными данными в соотношении 9:1, и будем использовать первую часть в качестве обучающей выборки, а вторую как тестовую. Перед разбиением данных, проведем нормализацию и заменим метки классов на целочисленные значения."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "\n",
+ "classes = ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica')\n",
+ "\n",
+ "def normalize(data):\n",
+ " features = data[:, :-1].astype(float)\n",
+ " features = (features - features.min(axis=0)) / features.ptp(axis=0)\n",
+ " data[:, :-1] = features\n",
+ " return data\n",
+ "\n",
+ "\n",
+ "def to_float(sample):\n",
+ " labels_dict = dict(zip(classes, range(len(classes))))\n",
+ " ys = [labels_dict[label] for label in sample[:, -1]]\n",
+ " sample[:, -1] = np.array(ys)\n",
+ " return sample.astype(float)\n",
+ "\n",
+ "\n",
+ "def preprocess_data(data_filename, split_ratio=0.1):\n",
+ " data = pd.read_csv(data_filename, header=None).values\n",
+ " data = to_float(data)\n",
+ " data = normalize(data)\n",
+ " np.random.shuffle(data)\n",
+ " split_index = int(len(data) * split_ratio)\n",
+ " return data[split_index:], data[:split_index]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Проведем кросс-валидацию методом k-fold с разбиением на 10 частей. Подбирать будем параметр k алгоритма k-nn в диапазоне от 1 до 100."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def knn(train_data, test_data, k):\n",
+ " xs, ys = train_data[:, :-1], train_data[:, -1]\n",
+ " xs_test, ys_test = test_data[:, :-1], test_data[:, -1]\n",
+ " ys_pred = np.zeros(len(ys_test))\n",
+ "\n",
+ " for i, x_test in enumerate(xs_test):\n",
+ " distances = np.linalg.norm(xs - x_test, axis=1)\n",
+ " neighbours_indices = np.argpartition(distances, k)[:k]\n",
+ " neighbours = ys[neighbours_indices].astype(int)\n",
+ " ys_pred[i] = np.bincount(neighbours).argmax()\n",
+ "\n",
+ " error = 1 - np.sum(ys_pred == ys_test) / len(ys_test)\n",
+ " return error\n",
+ "\n",
+ "\n",
+ "def kfold(train_data, k_param, n_folds=10):\n",
+ " np.random.shuffle(train_data)\n",
+ " parts = np.array_split(train_data, n_folds)\n",
+ " sum_error = 0.0\n",
+ " for i in range(n_folds):\n",
+ " valid_sample = parts[i]\n",
+ " train_sample = np.concatenate(np.delete(parts, i))\n",
+ " sum_error += knn(train_sample, valid_sample, k_param)\n",
+ " return sum_error / n_folds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Построим графики зависимости значений средней ошибки от k при кросс-валидации и на тестовой выборке. Отдельно выведем точность на кросс-валидации и тесте для подобранного k."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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u/HdXDj/uzWvyNcvTjjD1r98xY8Easo+XNfu9zpYGd6WUV1q//xgAx0oqePSDtPqzzZ5mcC8qt/KvH/YzaWAMv718AF06BvDMip2nnMW2rNLG05/tIC4iiJ1ZJ7j0pdWsc9arpWlwV0p5pY0H8gn2t/DwJf34cns2SzfWWYbiNIP7wrUHOV5mZda4XgT6Wbjnwt5sOJDP1z8dbfQ1C1btJbOglGevGsRHd48mNNCX6xas4aNNjS2J4Toa3JVSbY4xhh/35p0yj73+QD6D48OZNa4XwxMieXLZNjLyS04WOI3gXmmz84/V+xiREMmQ7hEAXHtePD06BfPsip0UllTWe83hglL+9k06U5JiGHVOJ/p0CeWju0czKTGGAd1afmy9BnelVJvzw948rl2whrve3thggC8ut7LjyHFSekRg8RH+dPUg7MYwb9m2k4WamMu90mZn7b5j/OuH/dy7cBNHCsu484Jzqo/7WXx4dHJ/dmaf4ILnVvLWmgNYbfbq47///CeMgV9PObk4fFiQH3+9fih9uoSe3S+gGZo1n7tSSnmS79PzEIGvfzrK7Hc3Mf/6ofj7nmyrbj5UgN3A0B6OVnZ8ZDC/Gn8uz67YyZq9eYzs1ckR3P1DwdJwGLxv4WY+SzsCQMdAX64aFscFfWovDzopMYZPZo/hqU+38/hHW3nlmz34WYS8ogpOlFuZM6E3cRHBLfRbOLVmBXcRmQS8iGMlpteMMX9ooMw1wDzAAFuMMde7sJ5KKVVtzd48BsWFc8XQWJ74eBtz3tvEX64fgp/FEeA3HMhHhOoUCsBtYxJ4e80B/m/5Dj761Wh8yo5DYMOt9tW7c/ks7QizxvXiltE9iekYiIg0WDYxNoxFs0by+dYs3l9/iJBAPzp18KdHp2CuH9Hd9RffTE0GdxGxAPOBiUAGsE5Elhljttco0xt4FBhtjMkXkc4tVWGlVPtWWmFjS0YBt43pxU2jemK1GZ76dDv/WL2PO893pE3WH8inT+dQwoL8ql8X6Gfhfy7uy4Pvb+GT1MNMKyvABIbx/rpDhAT6MiWpKwBWm50nP9lG98hgHpjYh0C/pldpEhGmJHWtPocnaE7OfTiQbozZa4ypABYC0+qUuR2Yb4zJBzDGNN59rJRSZ2HjwXwqbYYRvSIBuHVMAhf0jeaV/+7hRFkldrth04F8hvWMqPfa6UNiGdC1I8/8eyfHC/LYfkx4eGkqv3pnI3/+chfGGN5ec4DdR4v4zaX9mxXYPVVzgnsscKjGdoZzX019gD4i8p2IrHGmcZRSyuXW7M3D4iOk9DgZvB+c2JeCkkpeX72fXUdPcKLcWut4FR8f4TeX9iezoJSDh4+QZwvkhWsHc/WwOF78ajcPLt7C81/uYsy5UVw8oEtrXpbLuapD1RfoDVwAxAGrRCTJGFNQs5CIzAJmAXTv7r5clFKq7VqzN4/E2DBCA0+mXJLiwrhkYBde+3YvvhZHbnxYA8EdYPS5Udw2JoHY1Ar69O6F/5BYpg3uRrfwIF78ajcWH+GJywc0mmNvK5oT3DOB+Brbcc59NWUAPxpjKoF9IrILR7BfV7OQMWYBsAAgJSWl8du6lFKqAaUVNjYfKuDW0Qn1jt0/sQ9fbP+WF7/aTVRIAN0jGx+l8vhlA2BrCXRwfAGICPdP7EPvLiFUWO2tMlSxpTUnLbMO6C0iCSLiD8wAltUp8xGOVjsiEoUjTbPXhfVUSik2OfPtI3t1qnesX0xHLkvuRoXVzrAe4aduedvtUF5/LvfLkrtxxdA4V1fbLZoM7sYYKzAbWAHsABYbY7aJyFMiMtVZbAWQJyLbgZXAQ8aYpmfUUUqp07Bmbx4+AikNdJYC3HdRb/wtPow+N+rUJ6ooAmP32oU6oJk5d2PMcmB5nX1P1HhugAecP0op1SLW7D1WL99e0znRIXz7yHg6dfA/9YnOcC73tkSnH1BKtQlllY58e0MpmZq6dAzE19JEaNPgrpRSnmHTwQIqbHZGJESe/ck0uCullGfYfMgxsrrmlAJnTIO7Ukp5hrTMAuIjg4hsKp/eHE3MCOkNNLgrpdqELYcKSY4Ld83Jyo87HgNddD4PpMFdKeXx8orKySwoZVCci9Io1WkZbbkrpZTbpGY6gnFSrIta2mWF4NcBLA0PqfQGGtyVUh4v9VAhIo45ZFyirMCrO1NBg7tSqg1IzSjgnOgQQgJcNNdhWaEGd6WUcidjDFsyCkl2VasdNLgrpZS7ZR0vI7eonORYDe6nQ4O7UsqjbTnk6ExNjnfhsMXyIvDv4LrzeSAN7kopj5aaUYCvjzCgqwuHLdoqwTfAdefzQBrclVIeLS2zkL4xoa5dz9RWDhYX3OnqwTS4K6U8ljGGVFd3pgJYy7XlrpRS7nIgr4TC0krXTTtQxVahLXcAEZkkIjtFJF1E5jZw/GYRyRGRzc6fX7q+qkqp9mbR+kMADHJ1cLd6f1qmyTsCRMQCzAcm4lgIe52ILDPGbK9TdJExZnYL1FEp1Q699u1eXv5mD1cNi6N/VxcuWG23gbFpWgYYDqQbY/YaYyqAhcC0lq2WUqo9W7TuIP/72Q4mJ8bwhyuSTr3Y9emyVTgevbzl3pzgHgscqrGd4dxX15UikioiS0QkvqETicgsEVkvIutzcnLOoLpKKW/3w5485n6Qxrg+0bwwY3DTS+adLmu541Fb7s3yCdDTGJMMfAm82VAhY8wCY0yKMSYlOjraRW+tlPIm/92Vg0WEV2YOJcDXhcMfq2jLvVomULMlHufcV80Yk2eMcX4d8howzDXVU0q1N/tzi+keGUywv4smCatLW+7V1gG9RSRBRPyBGcCymgVEpGuNzanADtdVUSnVnuzPKyYhqgWnBmgnLfcmvxqNMVYRmQ2sACzA68aYbSLyFLDeGLMMmCMiUwErcAy4uQXrrJTyUna7YX9eMaPPjWq5N6lqubf34A5gjFkOLK+z74kazx8FHnVt1ZRS7U32iTLKKu30bI2Wu6ZllFKqdezLLQYgoVNrpGU0uCulVKuoCu49o4Jb7k2qO1S9Oy2jwV0p5TH25xbj7+tDt7CglnsTW1XOXVvuSinVKvblltCzUzA+Pi68I7Uua1Vaxq/l3sMDaHBXSrWajzdn8tJXuxs9vj+vmJ4tmW8H7VBVSilXe399Bi9/swerzV7vmM1uOJhX0rJj3EE7VJVSytUy8ksorbSRnlNU79jhglIqbC08DBK0Q1UppVzJbjdkFpQCkOpc9Lqm/XnOkTItnpbRDlWllHKZoyfKqbQZAFIzC+odrxoG2Su6pVvuVTl3bbkrpdRZy8gvAcDf14fUjPot9325xQT7W+gc2sItalv7mH5Ag7tSqlVk5DtSMuf3iWbHkeOUW221ju/PLaZHpw6uXZijIbZKx6OmZZRS6uxVtdwnJ8ZQaTPszDpR6/j+vBISWvLO1CrWchAfsLTQlMIeQoO7UqpVZOSXEhUSwPCESAC21EjNWG12Dh0rafnOVHCkZby81Q4a3JVSrSQjv5S4iCBiw4Po1MGf1EMFtY5Z7ablx7iDo0PVyztTQYO7UqqVZOSXEBcRhIiQFBdWq1N1n3MYZKsEd1u513emQjODu4hMEpGdIpIuInNPUe5KETEikuK6Kiql2rqqMe5xEY6cenJcOLuPnqCkwgrAnqOOm5pa/AYmcLTcNS0DImIB5gOTgQHAdSIyoIFyocC9wI+urqRSqm2rGuMeF+GY7XFQXBh2A9sOH+dgXgl/XZlOv5hQOnVohRa1TdMyVYYD6caYvcaYCmAhMK2Bcr8D/giUubB+SikvUDVSpiq4J8WFAbBmTx6z3lqP3W54Zeawlh8GCdqhWkMscKjGdoZzXzURGQrEG2M+c2HdlFJeomqMe1VapnNoIF3DAnnhq93syj7BX64f2jopGdAO1eYSER/geeDBZpSdJSLrRWR9Tk7O2b61UqqNqNtyB0iKDcNmNzwyqR/n94luvcq0k5Z7c0bxZwLxNbbjnPuqhAKJwDfOP6ligGUiMtUYs77miYwxC4AFACkpKeYs6q2UakOqxrgH+lmq991xfi+G9ohg1rherVsZa0W7GC3TnOC+DugtIgk4gvoM4Pqqg8aYQiCqaltEvgH+p25gV0q1X1Vj3Gsa1iOSYT0iW78ytgoI7Nj679vKmkzLGGOswGxgBbADWGyM2SYiT4nI1JauoFKq7asa4+4RNC1zkjFmObC8zr4nGil7wdlXSynlLarGuE9K7Oruqjhoh6pSSp29umPc3a6dtNw1uCulWlRDI2XcSlvuSil19uqOcXc7nVtGKaXOnse13G2VmpZRSqmz1dAYd7eylmtaRimlztYhTxoGaYx2qCqllCscOlZKfKSn5Nud66dqy10ppc6c1WYns6CU7pEe0nK3lTseteWulFJn7khhGTa7Id5TRspYKxyPOlpGKaXO3KFjjpEy3T0mLeMM7pqWUUqpM3fIOQzSc3LumpZRSqmzdvBYCRYfoWtYoLur4lCVlvHV4K6UUmfs0LFSYsOD8LV4SKipbrlrWkYppc7YwWMlxHvKSBnQDlWllHKFjPwSz+lMBe1QVUqps1VcbiW3qMJzJgwD7VCtS0QmichOEUkXkbkNHL9TRNJEZLOIrBaRAa6vqlKqLamaDdKjWu7aoXqSiFiA+cBkYABwXQPB+11jTJIxZjDwDPC8y2uqlGpTDh7zsGGQoB2qdQwH0o0xe40xFcBCYFrNAsaY4zU2OwDGdVVUSrVFHncDEzhmhIR20XJvzhqqscChGtsZwIi6hUTkbuABwB+40CW1U0q1WQePldDB30JEsJ+7q3JSVYeqxYPq1EJc1qFqjJlvjDkHeAR4rKEyIjJLRNaLyPqcnBxXvbVSygNl5JcQHxmMiLi7KidVB3fvb7k3J7hnAvE1tuOc+xqzEPh5QweMMQuMMSnGmJTo6Ojm11Ip1eY4xrh7UEoGtEO1jnVAbxFJEBF/YAawrGYBEeldY/NSYLfrqqiUamuMMRw6VupZ+XZoVx2qTebcjTFWEZkNrAAswOvGmG0i8hSw3hizDJgtIhcBlUA+8IuWrLRSyrPlFlVQWmkj3lNWYKqiHaq1GWOWA8vr7HuixvN7XVwvpVQbVjUbZPdOntZy1+kHlFLqjFUNg/SYRTqqWMvBxw88qZO3hWhwV0q53ME8R3D3qKkHwLGGajtIyYAGd6VUCziUX0J0aABB/hZ3V6U2W3m7SMmABnelVAs4eMzDZoOsYi3XlrtSSp0JYww7s05wTnQHd1elPluFttyVUqop6/YfI+dEea19Gfml5JdUkhwX7qZanYK23JVS6tQqbXZmvvYjz63YWWv/5kMFAAzyxOBuq9SWu1JKncqBvBLKrXbW7MurtT81owB/Xx/6xoS6qWanoB2qSil1aulHiwBHkM8qLKvevyWjkAFdO+Lv64HhRdMySil1aulHT1Q//9HZerfZDVszCxkUF+auap2adqgqpdSp7T5aRNewQEIDfFmz9xgAe3KKKKmweWZnKrSrlnuz5pZRSqm6dmcX0TcmFOFky726MzVeW+7upi13pdRps9kNe3KKODc6hBG9OrE3p5ijJ8pIzSggJMCXXlEh7q5iw9pRcNeWu1LqtGXml1JutdO7Swh9ujhGxazdd4zUjEKSYsPw8fHQibmsFe0mLaMtd6XUadvt7Ew9t3MoibFhBPtbWL07lx1HjpPsqSkZ0KGQSil1KlXDIM/tHIKfxYdhPSL4aHMmlTbjmTcvVWlHHarNCu4iMklEdopIuojMbeD4AyKyXURSReQrEenh+qoqpTzF7qNFdA4NICzID4CRvTpRVmkHYFC8Bwf3dpRzbzK4i4gFmA9MBgYA14nIgDrFNgEpxphkYAnwjKsrqpRyn2VbDlNYWlm9vftoEb27nOw0HZEQCUBUiD/dwgJbvX7NZtW0TE3DgXRjzF5jTAWwEJhWs4AxZqUxpsS5uQaIc201lVLusiv7BHPe28Sfv9wFOGZ93HPUMVKmSnJcOIF+PiTHhSOeusqR3QbGpmmZGmKBQzW2M5z7GnMb8HlDB0RkloisF5H1OTk5za+lUsptNhzIB2Dx+kMUllaSdbyMonIr53Y5OXeMv68Pf75mMA9M7OOuajatHa2fCi4eCikxDlEHAAAbFElEQVQiM4EU4PyGjhtjFgALAFJSUowr31sp1TI2HMgnwNeHkgobC9cepH/XjgD07lx7LPvkpK7uqF7zWZ1TE2vLvVomEF9jO865rxYRuQj4DTDVGFNe97hSqm3aeDCfsb2jGdWrE29+v58dR44DjpEybUo7a7k3J7ivA3qLSIKI+AMzgGU1C4jIEOBVHIH9qOurqZRyh/ziCvbmFDO0Rzi3jUngcGEZr3+3j4hgPzp1aGNBUlvutRljrMBsYAWwA1hsjNkmIk+JyFRnsWeBEOB9EdksIssaOZ1Sqg3ZdMiRbx/aPYIL+3UmIaoD2cfL6d051HM7ThvTzlruzcq5G2OWA8vr7HuixvOLXFwvpZQH2HAgH4uPMCguHB8f4dYxCTz+0VbOaWspGWh3wV3vUFVKNWrjgQIGdO1IkL8FgKuGxjEoLozxfaPdXLMz0M7SMjpxmFKqQVabnc2HCrj2vJPjKYL8LXw8e4wba3UWqlvu7SO4a8tdKdWgn7JOUFppY0h3D55O4HRUt9w1LaOUasc2HnR0pg7rEeHmmriIzRncteWulGrPNh7Ip3NoALHhQe6uimvYnHPjWPzcW49WosFdKdWgjQcLGNo9ou0NeWxMO+tQ1eCulKons6CUg8dKvCclA9qhqpRq3+x2wyNLUgn082HigC7uro7raIeqUqo9e3XVXlan5/LEZQPpGdXB3dVxHe1QVUq1V5sO5vOnL3ZyaVJXrhse3/QL2hJrVVqmfXSo6k1MSjXg/fWHEBGuGuY56858vDmT79JzySuqILe4gvN7R3H/xD4u6/A8eryMOQs30aVjIP93RVLb6UitLIWPZ8P4X0OncxovV5VzbycdqhrclapjT04Rv/4wjWB/X6YN7oaf5ez+wLXbDT4+Zxco/75qL08v30GnDv506RiIn0V46et0Avws3D3+3LM6N8CKbVnMXZpKaaWNd345snpt1DYhYz1sXQKd+8O4/2m8XDvrUNXgrlQNxhjmLduG1W4oLK3ku/RcLujb+YzPt27/Me58awN/vnYw4/qc2Xwsr6/ex9PLd3BpUldenDEYX4sPxhjuW7SZZ1fsJC4iiGmDT7U4WuPKKm3MW7aNhesOkRjbkReuHdL25mnP3lr7sTHWchAfsLSPsKc5d6VqWLEtm2935/LIpH6EBPiyPO1IreNWm52icmuzz7dg1V7yiiu4d+EmMvJLqvdX2uws23KYY8UVjb7WGMM/Vu/jqU+3MzkxhhecgR1ARHjmqmRGJETy0PupfL8nt97rK6x2tmYW8t9dOSzdkMHnaUew208ugFZWaeOXb65n0fpD3HXBOXxw1+i2F9gBstJqPzbGVt5uWu2gLXelqpVW2Pjdp9vpFxPKL8cksDPrBCu2ZfP0dHt1auaRpWksTzvC3Mn9uHFkj1OmWzILSvlqRzZTB3Vj5U9H+dU7G1l8xyjyiiu4592NbDxYQFSIP09NS2RKnSXqsgrL+PWHaXz901EuHtCFl64bUi89FOBrYcGNKVzx8nfc8NqPTB8Sy/0X9SE6NIDF6w/xyjd7OFxYVus1Y86N4rmrBxEe7Mcdb23guz25PHvVII/qWzhtWamOx7w9UF4EAY18QVkr2s0wSNDgrrxYWaWNQD9Ls8u//E06mQWlLJo1El+LD1OSuvLhpszq1MzWzEKWbswgpmMgv122jc/SjvDQJX05XlrJwWMlBPlZuCYlvjrgL1x7EAM8PKkvlyV3ZdZbG7jjrQ1sySjAajM8cdkAPtiUwa/e2ciUpBjG9nakbfJLKnj5mz1U2uw8ftkAbv5ZTyyNfImEBfux9K6f8fI3e3jj+/18suUwHQP9yCuuYFiPCB6Z3I/Y8CCiQgL4fk8ev/t0O5NeXEXvziGs25/PH69MatuB3VoBR3+CqL6QuxOObof44Q2XtZW3m7ncoZnBXUQmAS8CFuA1Y8wf6hwfB7wAJAMzjDFLXF1RpU7HruwT/Hz+d1x7XjxPXDagyZEfJRVW/vndfqYkxTCiVycAxvaOqk7NXNC3M3/890+EB/ux4v5xrNiaxe8+287Vr/xQ6zy5ReXMvrA3FVY77609xIV9OxMXEUxcRDB3jz+H+Sv3MKBrR+bfMJSEqA7cNKoHr67ay4v/2c3ytKzq8wzvGckzVyU3a5x5eLA/j07pzy2jE/jryt0cPV7OLaMTGNkrstZ194zqwMhekdy3aDPr9ufzf9OTuPa87qfza/U8ubvAXgmDr4P/zHOkZhoN7pWalqlJRCzAfGAikAGsE5FlxpjtNYodBG4GTtFVrVTr+d/PdlBWaeOf3+3HR4THLu2PiFBSYeXT1COMTOhE907B1eU/TT3CiXIrt4xOqN4X6Gdh4oAurNiWzeSko3y7O5fHLu1PWJAf15wXz/l9o9lwIJ+uYYHERwbzu0+386cvd5EcF87xskpyi8qZObJH9fkemNiX4QmdGJEQWf0Xha/Fh7vHn8tNo3pQXG4DwEcgOjTgtIcixoQF8r8/TzplmV7RISy962ccLiilRycvuEGpKs/eZzKsfuHUeXdruaZl6hgOpBtj9gKIyEJgGlAd3I0x+53H7C1QR6VOy8qdR1m1K4fHLu1PRn4p/1i9DwEiQ/z5x7f7qlMWS+4cVR1A31t7kHM7h5BSZy6VqtTMfQs3ExsexI2jTgbrLh0Da+XKf39FEj8dOcG9CzfRLTyI+MigWiNkLD7C+Y2MmAkN9CM0sHWGH/pZfLwjsIMjmPsGQVRviEk6dXBvZx2qzRktEwscqrGd4dx32kRkloisF5H1OTk5Z3IKpU6p0mbn6c920LNTMDeN6slvLx/AzJHdeW31Pp75904SY8O4fWwCGw7k8+X2bAB+yjrOpoMFzDgvvl5ruSo1U1hayYMX9yHAt/EcfrC/L6/cOAyrzbDt8HGuH96j0Vy5cpGsVOgyAHwsjuCevQ3stobLaodqyzHGLAAWAKSkpJgmiit12t5be5D0o0W8euMw/H0dbZenpiaSHBdO3y6hDIoPx2qz89VPR3lmxU4u7NeZhWsP4W/x4cqh9TsWA/0sXDk0lrTMwmaNJU+I6sBL1w3h5f/uqbU8nWoBxjha6gN/7tiOSQJrqWPUTHSf+uXbWcu9OcE9E6j5rzTOuU8pl1u77xir03O5b0Lv076rc/vh4/z5y12M7BXJxTVmM/TxEa5JOflP2Nfiw8OX9OPOtzfw9poDfLAxg8lJMUR0aLhV9+S0RIwxzc6Bj+/XmfH9zvzGJ9VMxzOhrMAR1OHkY3ZaI8G9sl2NlmlOWmYd0FtEEkTEH5gBLGvZaqn2qKTCypz3NvHSV7v568r0WseOnihj6YYMrLb63TrF5Vae/mw7l/91NT4iPDk1sclAfMnALgzpHs7vPtvB8TIr1w0/9aiRNjPPSntSlV/v4gzqUX3Bx6/xvHs761BtMrgbY6zAbGAFsANYbIzZJiJPichUABE5T0QygKuBV0VkW0tWWnmnl7/ZQ9bxMob3jOTP/9nFNzuPArA7+wTT53/Pg+9vYeY/fiS3yDF1qzGGT1MPM/H5//L3b/dx7XnxfP3gBfSNCW3yvUSEuZP6YbMbekV1YERCZItem2oBWWmAOHLu4Ajc0f0aD+6alqnPGLMcWF5n3xM1nq/Dka5R6owcOlbCq6v2Mm1wN/5wRTLT//Yd9y3azG8vH8BvP95GgJ+F/7m4D3/5Op2pf1nNI5P78faaA6zbn8+Arh35y/VDT3vVoBG9OvHgxD4MjO2oLfO2KCsVIntBQI0v85gk2PNVw+XbWYeqzi2jWt2mg/lMm/8dE/70DR9tysRuNzz92Q4sIsyd3I8gfwuvzByGzW64f9EWokMD+OCunzH7wt4sufNniAj3LtzMvtxi/nBFEp/cM+aMl4O7Z0JvLuznRasNtSdZW0/m2avEJEFRNhQdrV9eW+6qLckqLMPPInQKObN/tKd7i/7peuqT7Rw8VsLQHuEMigvn861HeOfHg3QODSAi2J/7Fm3mxa92sy+3mAcn9qFrWBDguJvylZnD+HhzJr+e0p/wYEeLKykujE/uHsmWVR8zonsHgv22wq4mZgNsa8QHeo5pfI6UlmIM7PsvVJQ0XbY1BUdC95G195Udh/x9MGRm7f1VwX7jv6DzgPqvaUctdw3ubdienCKu+Nv3VNrs3D3+XG4bk3BagfrJT7axcO0h3rx1OMNbIOe8dt8xXv9uH1Eh/vxnh2NMuY/ALT9L4IGL+xDsZ+HTtCP86Yud9IrqwO3jetV6/ehzoxh9blS980Ye+Jzx6+50dPV7q3EPwYWPte577lsF/5rWuu/ZXLPXO25UqpLt7NZrqOVuCYCvf9fweYLr/3vyVl4T3HNOlPP1T9lcMTTurBdX8DR5ReW8++NB+sSEcvGALogIuUXl3PzPtfj6CMMTonh2xU7e/fEgf7gyqXoCqlN564f9/PO7/QT7W/jlm+tYfOco+sV0BOBwQSkfbz6MxQeC/H0JD/Ljov5dCPI/+cWxJ6eIP32xk0sGxjQ4/tsYw5++2ElUSADfPjyeskobWzIKiA0PoneXkznSqYO6cVlSV6x2Uz0uvUmZGx3/gW/9t6OV620+mAWHN7X++x7e6Hi85d/gF9T679+QggOw+CY4vLl2cK/qNK0b3IPCYc5GKK4/BTIiEN2/5erqYbwiuH+1I5uHl6SSV1zB8VJrvRZgW1VhtfOvH/bz4le7OVHmmEN8Qr/OPDqlHw++n0rOiXIWzhrF4Phwvk/P5bGPtzL73U2semg8YcGN38r+7e4c5n2ynQn9OjNv6kCueuV7fvH6Wt755Qg+TT3CK//dQ1ll7SGH3cICmTulP5cldeXdtQf538+2U2G1szwti69/OspT0xJrrd7z/Z48ftx3jHmXDyDI30KQv6XRRS98fAT/0xnTnpXmGCERO7T5r2lLYoc13inYkrLSIKw79BjV+u/dmC4DHWPTs9NwDMZzykqF4E4Q2rX+a8LiHD/tXJsL7qt25fDF9iyiQgKICglg2+HjvLf2IP27duScziG89NVufj4kluhQRw7aarPzWdoRxvfrTMeznLvjrTUHKCm3csf5p1in8SzkF1ewbMth0o8WcfBYCTuzTpB1vIxxfaJ5dHI/vkvP5fkvd3HR86sQgVdnDmNwfDgAPzs3ivnXD2XKS9/y15W7+c2lAxp8j/SjRfzqnY307hzCi9cNISTAlzdvHc7Vr/zARc+vAuDS5K7MndSPiA7+lFRY2ZVVxO8/38Gc9zbxf5/tIOt4GWN7R/HHK5NZsiGDF7/azfr9+Tw9PZEL+nbGGMNzX+ykW1gg141w8ayDxjhW3Ol3qWvP60likmDLu45OwZBWvBkqayvEJLbe+zWHxc+xfF7d4Y3Zzs5UHeXUqDYX3PfnFfNp6hEKSiqr980a14sHL+5DZn4pF/95Fc+u+IlnrhqE3W54eEkqH2zKZGj3cN66bQQdAs7skj/alMnjHzk67sKC/JjRxE0vzVVps7Mnp4h//eC4U7Ks0k5YkB/dI4MZ1iOCK4fFMr5vZ0SE/l07MiWpK89/uYvhCZFcPDCm1rn6d+3IVUPjePP7A9w0qifxkcG1jheWVnL7v9YT4OvDa79IIcT5u+gX05F/3nwef/92L7eN6VUr/x4S4Evn0ECWnTOGJRsO8frq/cwa14ubf9YTHx9hzoTejOsTzQOLNnPzP9cxvm804/pEs+lgAb+/IumUc7GckRNHoCTv5I0r3qgqwGalwbkTWuc9K0ogb/fJW/k9SZck2PVvxxe7CNiskL0dRsxyd808WpsL7jeN6slNo3pSabNXL1HWpWMg4JjO9JbRPXlt9T5mjuzB++sz+GBTJpcld+XzrVnMems9//jFeQT4+rBqdy5v/bCfId0jmDWu1ynz9BsO5PPw0lSGJ0QS4OvD4x9vpXeXEIb1aF4npDGGFduyWboxgxNllZRU2Cgut5JXXFH9JeXv68P0wbHcOibhlDfhdAsP4rmrBzV6/MGL+/JJ6mGeWbGTv1w3pHq/zW6ql3p79/aRxEXUDvwpPSNJ6dn49Vh8hGvP697g/N+D48P5933jePP7/bz01W5W7syhe2RwyywC0Viu1Zt0cUNwP7oDjN0zf68xSbD5bccQx9AYx5eQrdy7v+BdoM0F9yp+Fp/qoF7TPRN68+GmTG78x1oKSyu54/xezJ3Ujw83ZfLA4i3MemsDFVYba/YeIzzYj//sOMrnW4/w7FWD6N+1I2WVNjLyS6m02eng70tJpZU73lpPTMdAXpk5DIsIU+ev5s63N/LJ7DHEhNWvAzgCernVzoYD+TyzYidbDjk6E2PDg4gI9icuIohOHRyppc4dA5g4oAtRZzicsaaYsEBuH9uLv3ydzm1jEqrTNs+u2Mk3O3N4enoi550iiJ8pf18fbh/Xi+lDY3nt231c0De6ZTq2q5ZU6zLQ9ef2FMGREBbf9JqgrlT1e/XU4A6OtFFoTPv4gneBNhvcG9Mx0I+HJ/Xj4SWp3DCiO3Mn9UNEuGJoHMXlVh7/eBtRIf48OXUg1w3vzlc7snn8461M/etqIjv4k328vN45QwN8WTgrhUjnxFJ/vymF6c6bcCI6+BPsb8Hi40NphZWSCpvzx0rVWsRdwwJ55spkrhgaW73AcUu64/xzeG/tQW587UfiI4PpGOTLmr3HuH5Ed24Y0aPpE5yFqJAA5k7u13JvkLUVIhIgsGPLvYcniEly5JVbS/ZWCOgI4S377+OMVKepUqH3RY5HS0Dt0TOqHq8L7gBXD4sjOS6MPp1Da91WfuOongzpHkFCVIfq3PvkpK6M6NWJl77aTVG5le6RwcRHBhHoa6G4wkZphZURvTpxbueTqZI+XUJ589bhfLgpk1JnMLfaDcH+FoKdI0M6+PsS5G+hS8dALkvu2qI3CtUVEuDLqzcOY/G6DHKLysktKueKIbHMu9wLWrtZae2jxRbjzDNXlrbOsMSsNEc6yBM7KAPDHF86VS32rDRHJ6uldRY3aau8MriLSPWY7boSY8Pq7Yvs4M+8qacX+JrKUbvbsB6Rze4TaDPKT8CxvTBohrtr0vK6JDpy4Ee3O4ZGtiS73fEXUd27PT1J1SpLxjjq2neSu2vk8bzwDhDltbK3A6b9tNyhdfLu+fugstjzhkHWFJMEeemOL/eSXIhJdneNPJ4Gd9V2eHKnn6uF93DkwFsjuLeF32tMEmAgdVGNbXUqGtxV25G9FYIioOMZLeHbtvj4OFIzWa3QqZq1FcTi2bfmVwXzze85Hr15tJSLNCu4i8gkEdkpIukiMreB4wEissh5/EcR6enqiipV3ZnqiZ1+LaFqxIy9/upTLpWVBtF9wa/hYb0eISze0bFaeBAiejqeq1NqMriLiAWYD0wGBgDXiUjde9tvA/KNMecCfwb+6OqKqnbOZnXMBNieblyJSYSKIkdOvCVVjZTxZCInP3tPr6uHaE7LfTiQbozZa4ypABYCdecFnQa86Xy+BJggurSNcqVje8Ba1r5yra3RqVqcBycOt43fa/VC2NqZ2hzNGQoZCxyqsZ0BjGisjDHGKiKFQCeggXk3z9LGt+CHv7r8tMrDVRQ7HttCEHKV6P6OXPjyh+Cb37fMe1jLHI9t4fdaHdzbQF09QKuOcxeRWcAsgO7dz3DireBIR35QtT99pzhuXmkv/AJhwuMtP7d7wrj6Kx15on5T4Ohs6HWBu2vSJjQnuGcC8TW245z7GiqTISK+QBiQV/dExpgFwAKAlJQUcyYVpt+l3j3dq1I1jbnf3TXwHEERcMnT7q5Fm9GcnPs6oLeIJIiIPzADWFanzDLgF87nVwFfG2POLHgrpZQ6a0223J059NnACsACvG6M2SYiTwHrjTHLgH8Ab4lIOnAMxxeAUkopN2lWzt0YsxxYXmffEzWel1FrDSyllFLupHeoKqWUF9LgrpRSXkiDu1JKeSEN7kop5YU0uCullBcSdw1HF5Ec4MBpvCSKlpjOwPO1x+tuj9cM7fO62+M1w9lddw9jTHRThdwW3E+XiKw3xqS4ux6trT1ed3u8Zmif190erxla57o1LaOUUl5Ig7tSSnmhthTcF7i7Am7SHq+7PV4ztM/rbo/XDK1w3W0m566UUqr52lLLXSmlVDO1ieDe1ALd3kBE4kVkpYhsF5FtInKvc3+kiHwpIrudjxHurquriYhFRDaJyKfO7QTnQuvpzoXX/d1dR1cTkXARWSIiP4nIDhEZ1U4+6/ud/763ish7IhLobZ+3iLwuIkdFZGuNfQ1+tuLwkvPaU0VkqKvq4fHBvZkLdHsDK/CgMWYAMBK423mdc4GvjDG9ga+c297mXmBHje0/An92Lriej2MBdm/zIvBvY0w/YBCO6/fqz1pEYoE5QIoxJhHHFOIz8L7P+w1gUp19jX22k4Hezp9ZwMuuqoTHB3eat0B3m2eMOWKM2eh8fgLHf/ZYai8+/ibwc/fUsGWISBxwKfCac1uAC3EstA7eec1hwDgc6yBgjKkwxhTg5Z+1ky8Q5FyxLRg4gpd93saYVTjWtaipsc92GvAv47AGCBeRrq6oR1sI7g0t0B3rprq0ChHpCQwBfgS6GGOOOA9lAV3cVK2W8gLwMGB3bncCCowxVue2N37eCUAO8E9nOuo1EemAl3/WxphM4DngII6gXghswPs/b2j8s22x+NYWgnu7IiIhwFLgPmPM8ZrHnEsXes3wJhG5DDhqjNng7rq0Ml9gKPCyMWYIUEydFIy3fdYAzjzzNBxfbt2ADtRPX3i91vps20Jwb84C3V5BRPxwBPZ3jDEfOHdnV/2Z5nw86q76tYDRwFQR2Y8j3XYhjlx0uPPPdvDOzzsDyDDG/OjcXoIj2HvzZw1wEbDPGJNjjKkEPsDxb8DbP29o/LNtsfjWFoJ7cxbobvOcueZ/ADuMMc/XOFRz8fFfAB+3dt1aijHmUWNMnDGmJ47P9WtjzA3AShwLrYOXXTOAMSYLOCQifZ27JgDb8eLP2ukgMFJEgp3/3quu26s/b6fGPttlwE3OUTMjgcIa6ZuzY4zx+B9gCrAL2AP8xt31aaFrHIPjT7VUYLPzZwqOHPRXwG7gP0Cku+vaQtd/AfCp83kvYC2QDrwPBLi7fi1wvYOB9c7P+yMgoj181sCTwE/AVuAtIMDbPm/gPRx9CpU4/kq7rbHPFhAcowH3AGk4RhK5pB56h6pSSnmhtpCWUUopdZo0uCullBfS4K6UUl5Ig7tSSnkhDe5KKeWFNLgrpZQX0uCulFJeSIO7Ukp5of8HKmdkfETbO9kAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best result: k=24, CV accuracy = 96.43%, test accuracy = 100.00%\n"
+ ]
+ }
+ ],
+ "source": [
+ "def search_parameters(train_data, test_data):\n",
+ " k_range = list(range(1, 101))\n",
+ "\n",
+ " valid_errors = [kfold(train_data, k) for k in k_range]\n",
+ " best_index = np.argmin(valid_errors)\n",
+ " test_errors = [knn(train_data, test_data, k) for k in k_range]\n",
+ "\n",
+ " plt.plot(k_range, valid_errors)\n",
+ " plt.plot(k_range, test_errors)\n",
+ " plt.legend(['validation errors', 'test errors'], loc='upper left')\n",
+ " plt.show()\n",
+ "\n",
+ " valid_acc = 100 * (1 - valid_errors[best_index])\n",
+ " test_acc = 100 * (1 - test_errors[best_index])\n",
+ " print(f\"Best result: k={best_index - 1}, CV accuracy = {valid_acc:0.2f}%, test accuracy = {test_acc:0.2f}%\")\n",
+ "\n",
+ "train_data, test_data = preprocess_data('iris.data.csv')\n",
+ "search_parameters(train_data, test_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "2. Загрузим данные mnist и применим t-SNE для значений perplexity 10, 50 и 100. Ограничим набор 9000 экземплярами для более быстрого выполнения."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn import metrics\n",
+ "from sklearn.datasets import fetch_mldata\n",
+ "from sklearn.utils import shuffle\n",
+ "from sklearn.manifold import TSNE\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "\n",
+ "def load_mnist(sample_size=2000):\n",
+ " mnist = fetch_mldata(\"MNIST original\")\n",
+ " x, y = shuffle(mnist.data, mnist.target)\n",
+ " x = x[:sample_size] / 255.0\n",
+ " y = y[:sample_size]\n",
+ " return train_test_split(x, y, test_size=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/mihail/bin/anaconda2/envs/py37/lib/python3.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function fetch_mldata is deprecated; fetch_mldata was deprecated in version 0.20 and will be removed in version 0.22\n",
+ " warnings.warn(msg, category=DeprecationWarning)\n",
+ "/home/mihail/bin/anaconda2/envs/py37/lib/python3.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function mldata_filename is deprecated; mldata_filename was deprecated in version 0.20 and will be removed in version 0.22\n",
+ " warnings.warn(msg, category=DeprecationWarning)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "perplexity = 10\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "perplexity = 50\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "perplexity = 100\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x_train, _, y_train, _ = load_mnist(10000)\n",
+ "\n",
+ "for perplexity in [10, 50, 100]:\n",
+ " tsne = TSNE(n_components=2, perplexity=perplexity, n_iter=300)\n",
+ " tsne_data = tsne.fit_transform(x_train)\n",
+ " \n",
+ " print(f\"perplexity = {perplexity}\")\n",
+ " plt.figure(figsize=(10, 6))\n",
+ " plt.scatter(tsne_data[:, 0], tsne_data[:, 1], c=y_train)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Как видим, t-SNE достаточно хорошо разделяет данные при perplexity = 10 даже для 1/8 части набора данных."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "3. Разделим набор mnist на обучающую и тестовую выборки в соотношении 9:1. Определим точность работы реализации алгоритма k-nn для исходной размерности данных."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/mihail/bin/anaconda2/envs/py37/lib/python3.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function fetch_mldata is deprecated; fetch_mldata was deprecated in version 0.20 and will be removed in version 0.22\n",
+ " warnings.warn(msg, category=DeprecationWarning)\n",
+ "/home/mihail/bin/anaconda2/envs/py37/lib/python3.7/site-packages/sklearn/utils/deprecation.py:77: DeprecationWarning: Function mldata_filename is deprecated; mldata_filename was deprecated in version 0.20 and will be removed in version 0.22\n",
+ " warnings.warn(msg, category=DeprecationWarning)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Initial accuracy = 0.929\n"
+ ]
+ }
+ ],
+ "source": [
+ "def knn_sklearn(x_train, y_train, x_test, y_test):\n",
+ " classifier = KNeighborsClassifier(n_neighbors=10)\n",
+ " classifier.fit(x_train, y_train)\n",
+ " return metrics.accuracy_score(y_test, classifier.predict(x_test))\n",
+ "\n",
+ "\n",
+ "x_train, x_test, y_train, y_test = load_mnist(10000)\n",
+ "\n",
+ "init_acc = knn_sklearn(x_train, y_train, x_test, y_test)\n",
+ "print(f\"Initial accuracy = {init_acc:.3f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Применим PCA-преобразование для разных размерностей и построим графики точности для сжатых представлений и восстановленных. PCA реализовано через матрицу ковариаций и ее собственные вектора."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pca_transform(data, n_dims):\n",
+ " data -= np.mean(data, axis=0)\n",
+ " U, s, _ = np.linalg.svd(data.T, full_matrices=False)\n",
+ " U = U[:, :n_dims]\n",
+ " return np.dot(data, U), U\n",
+ "\n",
+ "\n",
+ "def pca_recover(pca_data, U, mean):\n",
+ " data = np.dot(pca_data, U.T)\n",
+ " data += mean\n",
+ " return data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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Suoou3DPhQh2VWqhDREpY0YV7tqUOgOoBA/NcExGR/Cm6cKdlMymPkqjQxGEiUrqKLtwjyXoarFILdYhISSu6BIwm62iyynxXQ0Qkr4ou3OOpepojmhFSREpb0YV7WbqBVi3UISIlrujCPZFpJBlTz11ESlvRhXuwUIem+xWR0lZ04V5JI5kyLdQhIqWtqMI9m8lQ7c2ghTpEpMQVVbg31G8iYg4JhbuIlLaiCvemtoU6tAqTiJS4ogr35vog3KMVmu5XREpbUYV7S0M4l3uVeu4iUtqKKtyTjcESe+X9NN2viJS2ogr3VGMwl3tCqzCJSIkrqnDPhKswaaEOESl1RRXu2Zag516tcBeREldU4U5LPUmPUp7QlL8iUtqKKtwjyToarUoLdYhIySuqFIwm62nUQh0iIsUV7vG0FuoQEYEiC/fydAOtmstdRKS4wj2RaSClcBcR6V64m9lJZva6ma02s6s62X+LmS0LL2+Y2aaer2rXgoU6NJe7iEisqwPMLArcDkwAaoFFZvaYu69qO8bdL8s5fhowthfq2qUqb9JCHSIidK/n/mlgtbuvdfckMAs4dRvHnw38ricqtz0y6TTV1oxroQ4RkW6F+z7AmznbtWHZR5jZfsAIYN5W9k8xs8VmtnjDhg3bW9dtaqgPRoIsoRkhRUR6+gPVs4CH3D3T2U53n+7u49x9XE1NTY/ecVPdRgCiWqhDRKRb4b4eGJazPTQs68xZ5GFIBqCpLpjuN1qpcBcR6U64LwIOMLMRZlZGEOCPdTzIzA4EdgcW9mwVu6e1IQj3eOXu+bh7EZE+pctwd/c0MBWYA7wKPODuK83sh2Y2MefQs4BZ7u69U9VtSzYGY+7lmstdRKTrr0ICuPtsYHaHsus6bF/fc9XafulwLveKfgp3EZGi+YVqpjmYy71CS+yJiBRPuHtzHQDVAxTuIiLFE+6tm2n1OImKqnxXRUQk74om3COtdTRoLncREaCIwj2WqqfJ1GsXEYEiCvd4qoGWqMJdRASKKNzL0g20KtxFRIAiCvdEtoFkTNP9iohAEYV7pRbqEBFpVzThXu2NZDWXu4gIUCThnk4lqbRWLdQhIhIqinBvDKf7tYTCXUQEiizcI1qoQ0QEKJJwb65/H4C4FuoQEQGKJNxbGoLpfuOVmu5XRASKJNxTbQt1aLpfERGgaMI9GHOvqNYSeyIiUCTh3rZQR2V/hbuICBRJuHtLEO7VAwbmuSYiIn1DUYS7tdbT4nHKyhP5roqISJ9QFOEeSdbRoLncRUTaFUW4R5P1NGsVJhGRdkUR7vF0Pc3R6nxXQ0SkzyiKcC9PN9CqcBcRaVcU4Z7INpKKK9xFRNoURbhroQ4RkS11K9zN7CQze93MVpvZVVs55gwzW2VmK83stz1bzW2r8iayZZruV0SkTayrA8wsCtwOTABqgUVm9pi7r8o55gDgamC8u39gZoN7q8IdpZKtWqhDRKSD7vTcPw2sdve17p4EZgGndjjmQuB2d/8AwN3f7dlqbp0W6hAR+ajuhPs+wJs527VhWa5PAJ8wsz+b2V/M7KSeqmBXGus2AhDVdL8iIu26HJbZjvMcABwLDAUWmNkod9+Ue5CZTQGmAOy77749csfN9UHPPaZwFxFp152e+3pgWM720LAsVy3wmLun3P1/gTcIwn4L7j7d3ce5+7iampodrfMWWhqCcI9XaRUmEZE23Qn3RcABZjbCzMqAs4DHOhzzCEGvHTMbRDBMs7YH67lVqcZgRsiE5nIXEWnXZbi7exqYCswBXgUecPeVZvZDM5sYHjYH2Ghmq4D5wJXuvrG3Kp0r3RSM/FT0U7iLiLTp1pi7u88GZncouy7nugOXh5ddqn2hDi2xJyLSruB/oeotdQBUD1C4i4i0Kfhwt5bNNHk58bLyfFdFRKTPKPhwjyTraNRc7iIiWyj4cI+l6mmKaBUmEZFcBR/u8VQDLQp3EZEtFHy4l2caaI1pLncRkVwFH+6JbCNphbuIyBYKPtwrs42kNZe7iMgWCj7cq71RC3WIiHRQ0OGebG0hYSm8XEvsiYjkKuhwb9gcTF9jCc0IKSKSq6DDvSlchSlaoXAXEclV0OHe3NC2UIfCXUQkV0GHe2sY7mVVmu5XRCRXQYd7qjEI94TmchcR2UJhh3tTMN2vVmESEdlSQYe7tzYAUFGt77mLiOQq6HDPJpsASFRq+gERkVwFHe6WbAQgUaFwFxHJVdDhTqqJJi8nEo3muyYiIn1KQYe7pZtoMS2vJyLSUUGHezTVRIsl8l0NEZE+p7DDPdNMUuEuIvIRhR/uEYW7iEhHBR3u8UwLqUhFvqshItLnFHa4Z1tIRRXuIiIdFXS4l2ebyUQ1LCMi0lG3wt3MTjKz181stZld1cn+SWa2wcyWhZcLer6qH1XuLWRilbvirkRECkqsqwPMLArcDkwAaoFFZvaYu6/qcOj97j61F+q4VQla8ZiGZUREOupOz/3TwGp3X+vuSWAWcGrvVqt7Et5CNl6V72qIiPQ53Qn3fYA3c7Zrw7KOvmJmL5vZQ2Y2rLMTmdkUM1tsZos3bNiwA9X9UCrZSplloEzDMiIiHfXUB6p/AIa7+2jgKeDuzg5y9+nuPs7dx9XU1OzUHTY11gNgCncRkY/oTrivB3J74kPDsnbuvtHdW8PN/wYO75nqbV1rU1u4a1hGRKSj7oT7IuAAMxthZmXAWcBjuQeY2ZCczYnAqz1Xxc61NgcLdUTLFe4iIh11+W0Zd0+b2VRgDhAF7nT3lWb2Q2Cxuz8GXGxmE4E08D4wqRfrDEAy7LlHyjWXu4hIR12GO4C7zwZmdyi7Luf61cDVPVu1bUuFPfd4Qj13EZGOCvYXqqmWINxjCncRkY8o4HAPltiLJzQsIyLSUcGGe7Y16LmXa3FsEZGPKNhwz7QGPffyyv55romISN9TsOHuybZw75fnmoiI9D0FHO5NAFRWKdxFRDoq2HAn1UTSo8TLyvNdExGRPqdgwz2SaqJFi2OLiHSqYMPdUk20oF67iEhnCjbco5lmWtVzFxHpVOGGe7qZZEThLiLSmYIN93hG4S4isjWFG+7ZZlJRrZ8qItKZgg33smwLGYW7iEinCjfcXeEuIrI1BRvu5d5KJq71U0VEOlOw4V7hLXhM4S4i0pmCDHfPZkmQBPXcRUQ6VZDh3tLcSMQcj2vMXUSkMwUZ7s2NdQBYmZbYExHpTEGGe0tTsApTtFzhLiLSmYIM92RzPQARhbuISKcKNNzDnrsWxxYR6VSBhnvQc48l1HMXEelMLN8V2BHplmD91LIKLbEnfVcqlaK2tpaWlpZ8V0UKUCKRYOjQocTj8R26fUGGe6a1Ldw1LCN9V21tLf369WP48OGYWb6rIwXE3dm4cSO1tbWMGDFih87RrWEZMzvJzF43s9VmdtU2jvuKmbmZjduh2nTTh+Gunrv0XS0tLQwcOFDBLtvNzBg4cOBOvevrMtzNLArcDpwMjATONrORnRzXD7gE+OsO16abPAz3RKV67tK3KdhlR+3sc6c7PfdPA6vdfa27J4FZwKmdHPcj4KdArw8wZpNNgMJdRGRruhPu+wBv5mzXhmXtzOwwYJi7P76tE5nZFDNbbGaLN2zYsN2VbT9PMuy5a8xdpOQ9++yznHLKKfmuRp+z01+FNLMI8DPgiq6Odffp7j7O3cfV1NTs+J2mmmjyciLR6I6fQ0R6TTqdzncVdqlMJpPvKnxEd74tsx4YlrM9NCxr0w84BHg2HCPaC3jMzCa6++KeqmguSzfRYuVoTkgpFDf8YSWr3qrr0XOO3Ls/P/jSwds85p577uHmm2/GzBg9ejQzZ85k3bp1TJ48mffee4+amhruuusu9t13XyZNmkRFRQVLly7l3Xff5c477+See+5h4cKFHHHEEcyYMQOA6upqLrzwQubOnctee+3FrFmzqKmp4dhjj2XMmDE8//zznH322Zx33nlcdNFF/OMf/wDg5z//OePHj+e5557jkksuAYJx5QULFtDQ0MCZZ55JXV0d6XSaO+64g6OPPpq5c+fygx/8gNbWVj7+8Y9z1113UV1dzZNPPsmll15KZWUln/3sZztt+7p16/jGN75BY2PwTv+Xv/wlRx11FAA//elPuffee4lEIpx88snceOONrF69mosuuogNGzYQjUZ58MEHefPNN7n55pv54x//CMDUqVMZN24ckyZNYvjw4Zx55pk89dRTfO9736O+vp7p06eTTCbZf//9mTlzJpWVlbzzzjtcdNFFrF27FoA77riDJ598kj322INLL70UgGuuuYbBgwe3Py49oTs990XAAWY2wszKgLOAx9p2uvtmdx/k7sPdfTjwF6DXgh0gmmqixbQ4tsi2rFy5kh//+MfMmzeP5cuXc+uttwIwbdo0vvnNb/Lyyy9zzjnncPHFF7ff5oMPPmDhwoXccsstTJw4kcsuu4yVK1fyyiuvsGzZMgAaGxsZN24cK1eu5JhjjuGGG25ov30ymWTx4sVcccUVXHLJJVx22WUsWrSIhx9+mAsuuACAm2++mdtvv51ly5bxpz/9iYqKCn77299y4oknsmzZMpYvX86YMWN47733+PGPf8zTTz/NkiVLGDduHD/72c9oaWnhwgsv5A9/+AMvvfQSb7/9dqftHzx4ME899RRLlizh/vvvb2/nE088waOPPspf//pXli9fzve+9z0AzjnnHL773e+yfPlyXnjhBYYMGdLlYzxw4ECWLFnCWWedxemnn86iRYtYvnw5Bx10EL/5zW8AuPjiiznmmGNYvnw5S5Ys4eCDD2by5Mncc889AGSzWWbNmsW55567Xf++Xemy5+7uaTObCswBosCd7r7SzH4ILHb3x7Z9hp4XzTSTVLhLAemqh90b5s2bx9e+9jUGDRoEwB577AHAwoUL+f3vfw/AN77xjfZwA/jSl76EmTFq1Cj23HNPRo0aBcDBBx/MunXrGDNmDJFIhDPPPBOAc889l9NPP7399m3lAE8//TSrVq1q366rq6OhoYHx48dz+eWXc84553D66aczdOhQPvWpTzF58mRSqRRf/vKXGTNmDM899xyrVq1i/PjxQPDCceSRR/Laa68xYsQIDjjggPY6TJ8+/SPtT6VSTJ06lWXLlhGNRnnjjTfa63X++edTWVnZ/rjU19ezfv16TjvtNCD4AVF35LZ3xYoVXHvttWzatImGhgZOPPHE9n+HtiCPRqMMGDCAAQMGMHDgQJYuXco777zD2LFjGThwYLfus7u69SMmd58NzO5Qdt1Wjj1256u1bdFMC8mIwl2kp5WXlwMQiUTar7dtb20cPfcre1VVH04Jks1m+ctf/vKRoLzqqqv44he/yOzZsxk/fjxz5szhc5/7HAsWLODxxx9n0qRJXH755ey+++5MmDCB3/3ud1vcvu0dRFduueUW9txzT5YvX042m+12YOeKxWJks9n27Y7fO89t76RJk3jkkUc49NBDmTFjBs8+++w2z33BBRcwY8YM3n77bSZPnrzddetKQc4tE880k4pooQ6RbTnuuON48MEH2bhxIwDvv/8+AEcddRSzZs0C4L777uPoo4/ervNms1keeughAH77299udcz7hBNO4Be/+EX7dlsor1mzhlGjRvH973+fT33qU7z22mv8/e9/Z8899+TCCy/kggsuYMmSJXzmM5/hz3/+M6tXrwaC4aA33niDAw88kHXr1rFmzRqAj4R/m82bNzNkyBAikQgzZ85s/9BzwoQJ3HXXXTQ1NbU/Lv369WPo0KE88sgjALS2ttLU1MR+++3HqlWraG1tZdOmTTzzzDNbfVzq6+sZMmQIqVSK++67r738+OOP54477gCCD143b94MwGmnncaTTz7JokWL2nv5Pakwwz3bQiqqcBfZloMPPphrrrmGY445hkMPPZTLL78cgF/84hfcdddd7R+wto3Fd1dVVRUvvvgihxxyCPPmzeO66zp9E89tt93G4sWLGT16NCNHjuTXv/41EHywesghhzB69Gji8Tgnn3wyzz77LIceeihjx47l/vvv55JLLqGmpoYZM2Zw9tlnM3r06PYhmUQiwfTp0/niF7/IYYcdxuDBgzu9/+985zvcfffdHHroobz22mvtvexHoQ8KAAAMCUlEQVSTTjqJiRMnMm7cOMaMGcPNN98MwMyZM7ntttsYPXo0Rx11FG+//TbDhg3jjDPO4JBDDuGMM85g7NixW31cfvSjH3HEEUcwfvx4DjzwwPbyW2+9lfnz5zNq1CgOP/zw9qGqsrIyPv/5z3PGGWcQ7YVv/pm79/hJu2PcuHG+ePGOfeb65g0H8W7VJzj8nx/t4VqJ9JxXX32Vgw46KN/V6HHV1dU0NDTkuxoFL5vNcthhh/Hggw+2f37QUWfPITN7yd27nOKlIHvu5d5CJqYvQopIYVq1ahX7778/xx9//FaDfWcV5KyQCVrxmIZlRPJBvfadN3LkyPbvvfeWguy5J7yFbFwLdYiIbE3BhXsq2UqZZaBMwzIiIltTcOHe1BgssWcKdxGRrSq4cG9bHNvKNCwjIrI1BRfuLU1Bzz1arnAX2ZZNmzbxq1/9aodv//Of/7z9hz5SeAou3JNhuEfKNZe7yLbkO9w7TlfQ3WmAS2264N5ScF+FTIXDMvGEeu5SQJ64Ct5+pWfPudcoOPnGre6+6qqrWLNmDWPGjGHChAncdNNN3HTTTTzwwAO0trZy2mmnccMNN9DY2MgZZ5xBbW0tmUyGf/3Xf+Wdd97hrbfe4vOf/zyDBg1i/vz5W5z7pZde4vLLL6ehoYFBgwYxY8YMhgwZ8pFpf1955RUSiQRLly5l/PjxXHvttUyePJm1a9dSWVnJ9OnTGT16NNdffz1r1qxh7dq17Lvvvlx77bWcf/75JJNJstksDz/8cK99H7xYFV64twThHlO4i2zTjTfeyIoVK9rndJk7dy5/+9vfePHFF3F3Jk6cyIIFC9iwYQN77703jz8eLKS2efNmBgwYwM9+9jPmz5/fPqtkm1QqxbRp03j00Uepqanh/vvv55prruHOO+8EPpz2F4LJtGpra3nhhReIRqNMmzaNsWPH8sgjjzBv3jzOO++89vqtWrWK559/noqKCqZNm8Yll1zCOeecQzKZ7JOLYfR1BRjuwcT78YSGZaSAbKOHvavMnTuXuXPnts+P0tDQwN/+9jeOPvporrjiCr7//e9zyimndDmR2Ouvv86KFSuYMGECEEyGlTv3ee40uABf+9rX2udOef7553n44YeBYGKzjRs3UlcXLGIyceJEKiqCHyceeeSR/OQnP6G2tpbTTz9dvfYdUHDhnm0Neu7lWhxbZLu4O1dffTXf/va3P7JvyZIlzJ49m2uvvZbjjz9+q5OBtZ3n4IMPZuHChZ3uz50Gt7Ptrck97utf/zpHHHEEjz/+OF/4whf4r//6L4477rhunUcCBfeBaqY16LmXV/bPc01E+rZ+/fpRX1/fvn3iiSdy5513tk8fsH79et59913eeustKisrOffcc7nyyitZsmRJp7dv88lPfpINGza0h3sqlWLlypXdqtPRRx/dPh3us88+y6BBg+jf/6P/l9euXcvHPvYxLr74Yk499VRefvnl7Wu8FF7P3ZNt4d4vzzUR6dsGDhzI+PHjOeSQQzj55JO56aabePXVVznyyCOBYHbHe++9l9WrV3PllVcSiUSIx+Ptc49PmTKFk046ib333nuLD1TLysp46KGHuPjii9m8eTPpdJpLL72Ugw/uerWp66+/nsmTJzN69GgqKyu5++67Oz3ugQceYObMmcTjcfbaay/+5V/+pQcekdJScFP+Lp17L758FqMueZh4WXnXNxDJk2Kd8ld2nZ2Z8rfgeu5jTzgXTujZhWRFRIpNwY25i4hI1xTuIr0oX8OeUvh29rmjcBfpJYlEgo0bNyrgZbu5Oxs3biSRSOzwOQpuzF2kUAwdOpTa2lo2bNiQ76pIAUokEgwdOnSHb69wF+kl8XicESNG5LsaUqI0LCMiUoQU7iIiRUjhLiJShPL2C1Uz2wD8fTtuMgh4r5eq05eVYrtLsc1Qmu0uxTbDzrV7P3ev6eqgvIX79jKzxd35yW2xKcV2l2KboTTbXYpthl3Tbg3LiIgUIYW7iEgRKqRwn57vCuRJKba7FNsMpdnuUmwz7IJ2F8yYu4iIdF8h9dxFRKSbFO4iIkWoIMLdzE4ys9fNbLWZXZXv+vQUM7vTzN41sxU5ZXuY2VNm9rfw7+5huZnZbeFj8LKZHZa/mu8cMxtmZvPNbJWZrTSzS8Lyom27mSXM7EUzWx62+YawfISZ/TVs2/1mVhaWl4fbq8P9w/NZ/51hZlEzW2pmfwy3S6HN68zsFTNbZmaLw7Jd+vzu8+FuZlHgduBkYCRwtpmNzG+teswM4KQOZVcBz7j7AcAz4TYE7T8gvEwB7thFdewNaeAKdx8JfAb4bvhvWsxtbwWOc/dDgTHASWb2GeCnwC3uvj/wAfCt8PhvAR+E5beExxWqS4BXc7ZLoc0An3f3MTnfZ9+1z29379MX4EhgTs721cDV+a5XD7ZvOLAiZ/t1YEh4fQjwenj9v4CzOzuu0C/Ao8CEUmk7UAksAY4g+JViLCxvf64Dc4Ajw+ux8DjLd913oK1DCYLsOOCPgBV7m8P6rwMGdSjbpc/vPt9zB/YB3szZrg3LitWe7v5/4fW3gT3D60X5OIRvvccCf6XI2x4OTywD3gWeAtYAm9w9HR6S2672Nof7NwMDd22Ne8TPge8B2XB7IMXfZgAH5prZS2Y2JSzbpc9vzefeh7m7m1nRflfVzKqBh4FL3b3OzNr3FWPb3T0DjDGz3YD/AQ7Mc5V6lZmdArzr7i+Z2bH5rs8u9ll3X29mg4GnzOy13J274vldCD339cCwnO2hYVmxesfMhgCEf98Ny4vqcTCzOEGw3+fuvw+LS6Lt7r4JmE8wJLGbmbV1snLb1d7mcP8AYOMururOGg9MNLN1wCyCoZlbKe42A+Du68O/7xK8kH+aXfz8LoRwXwQcEH7CXgacBTyW5zr1pseAb4bXv0kwHt1Wfl74yfpngM05b/EKigVd9N8Ar7r7z3J2FW3bzawm7LFjZhUEnzG8ShDyXw0P69jmtsfiq8A8DwdkC4W7X+3uQ919OMH/23nufg5F3GYAM6sys35t14ETgBXs6ud3vj946OaHE18A3iAYo7wm3/XpwXb9Dvg/IEUwzvYtgjHGZ4C/AU8De4THGsG3htYArwDj8l3/nWj3ZwnGJF8GloWXLxRz24HRwNKwzSuA68LyjwEvAquBB4HysDwRbq8O938s323YyfYfC/yxFNoctm95eFnZllm7+vmt6QdERIpQIQzLiIjIdlK4i4gUIYW7iEgRUriLiBQhhbuISBHSL1Sl4JjZ9UAD0B9Y4O5P7+L7nwiMdPcbd+X9imwPfRVSCk5buLv7zfmui0hfpWEZKQhmdo2ZvWFmzwOfDMtmmNlXw+vrzOzf2+bPNrPDzGyOma0xs4tyznOlmS0K581um1N9uJm9amb/L5xrfW74K1LM7GIL5p1/2cxmhWWTzOyXObedF+5/xsz2zanbbWb2gpmtzannEDNbENZzhZkdvQsfRikhCnfp88zscIKfr48h+CXrp7Zy6D/cfQzwJ4K58r9KMF98W4ifQDBn9qfDcx1uZp8Lb3sAcLu7HwxsAr4Sll8FjHX30UD7i0SOXwB3h/vvA27L2TeE4Ne4pwBtQzhfJ5jidgxwKMGvc0V6nMbcpRAcDfyPuzcBmNnW5hZqK38FqHb3eqDezFrDeV1OCC9Lw+OqCUL9H8D/untb0L5EMM8+BNMF3GdmjwCPdHKfRwKnh9dnAv+Rs+8Rd88Cq8ysbXrXRcCd4cRpj+Tcp0iPUs9diklr+Debc71tO0Ywh8e/e7A6zhh339/df9PhtgAZPuz4fJFg3o/DgEU5sxluT30I7xt3XwB8jmDWvxlmdt52nE+k2xTuUggWAF82s4pwtr0v7eB55gCTw3nkMbN9wvm2O2VmEWCYu88Hvk8wBW11h8NeIBgyAjiHYEhoq8xsP+Add/9/wH8TvGiI9DgNy0if5+5LzOx+gln23iUY2tiR88w1s4OAheHCIA3AuQQ99c5EgXvNbABBz/s2d9+Uu6gIMA24y8yuBDYA53dRjWOBK80sFd6/eu7SK/RVSBGRIqRhGRGRIqRwFxEpQgp3EZEipHAXESlCCncRkSKkcBcRKUIKdxGRIvT/A+W6aIvjjF8TAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "compressed_accs, recovered_accs = [], []\n",
+ "dims_range = [5, 10, 20] + list(range(50, 501, 50))\n",
+ "for n_dims in dims_range:\n",
+ " pca_train, U = pca_transform(x_train, n_dims)\n",
+ " pca_test = np.dot(x_test, U)\n",
+ " compressed_accs.append(knn_sklearn(pca_train, y_train, pca_test, y_test))\n",
+ "\n",
+ " recovered_train = pca_recover(pca_train, U, np.mean(x_train, axis=0))\n",
+ " recovered_test = np.dot(pca_test, U.T)\n",
+ " recovered_accs.append(knn_sklearn(recovered_train, y_train, recovered_test, y_test))\n",
+ "\n",
+ "plt.title(\"Test accuracy\")\n",
+ "plt.plot(dims_range, compressed_accs)\n",
+ "plt.plot(dims_range, recovered_accs)\n",
+ "plt.xlabel(\"dimensions\")\n",
+ "plt.legend(['compressed accuracy', 'test errors'], loc='lower right')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Точности оказались одинаковыми для сжатых и восстановленных версий изображений, точность, близкая к максимальной, достигается уже при сжатии до размерности 50."
+ ]
+ }
+ ],
+ "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": "3.7.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hw9 (k-NN, t-SNE, PCA)/hw9.py b/hw9 (k-NN, t-SNE, PCA)/hw9.py
new file mode 100755
index 0000000..e755cf4
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/hw9.py
@@ -0,0 +1,160 @@
+#!/usr/bin/env python3
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from sklearn import metrics
+from sklearn.manifold import TSNE
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.datasets import fetch_mldata
+
+from sklearn.model_selection import train_test_split
+from sklearn.utils import shuffle
+
+classes = ('Iris-setosa', 'Iris-versicolor', 'Iris-virginica')
+
+
+def normalize(data):
+ features = data[:, :-1].astype(float)
+ features = (features - features.min(axis=0)) / features.ptp(axis=0)
+ data[:, :-1] = features
+ return data
+
+
+def to_float(sample):
+ labels_dict = dict(zip(classes, range(len(classes))))
+ ys = [labels_dict[label] for label in sample[:, -1]]
+ sample[:, -1] = np.array(ys)
+ return sample.astype(float)
+
+
+def preprocess_data(data_filename, split_ratio=0.1):
+ data = pd.read_csv(data_filename, header=None).values
+ data = to_float(data)
+ data = normalize(data)
+ np.random.shuffle(data)
+ split_index = int(len(data) * split_ratio)
+ return data[split_index:], data[:split_index]
+
+
+def knn(train_data, test_data, k):
+ xs, ys = train_data[:, :-1], train_data[:, -1]
+ xs_test, ys_test = test_data[:, :-1], test_data[:, -1]
+ ys_pred = np.zeros(len(ys_test))
+
+ for i, x_test in enumerate(xs_test):
+ distances = np.linalg.norm(xs - x_test, axis=1)
+ neighbours_indices = np.argpartition(distances, k)[:k]
+ neighbours = ys[neighbours_indices].astype(int)
+ ys_pred[i] = np.bincount(neighbours).argmax()
+
+ error = 1 - np.sum(ys_pred == ys_test) / len(ys_test)
+ return error
+
+
+def kfold(train_data, k_param, n_folds=10):
+ np.random.shuffle(train_data)
+ parts = np.array_split(train_data, n_folds)
+ sum_error = 0.0
+ for i in range(n_folds):
+ valid_sample = parts[i]
+ train_sample = np.concatenate(np.delete(parts, i))
+ sum_error += knn(train_sample, valid_sample, k_param)
+ return sum_error / n_folds
+
+
+def search_parameters(train_data, test_data):
+ k_range = list(range(1, 101))
+
+ valid_errors = [kfold(train_data, k) for k in k_range]
+ best_index = np.argmin(valid_errors)
+ test_errors = [knn(train_data, test_data, k) for k in k_range]
+
+ plt.plot(k_range, valid_errors)
+ plt.plot(k_range, test_errors)
+ plt.legend(['validation errors', 'test errors'], loc='upper right')
+ plt.show()
+
+ valid_acc = 100 * (1 - valid_errors[best_index])
+ test_acc = 100 * (1 - test_errors[best_index])
+ print(f"Best result: k={best_index - 1}, CV accuracy = {valid_acc:0.2f}%, test accuracy = {test_acc:0.2f}%")
+
+
+def task1():
+ train_data, test_data = preprocess_data('iris.data.csv')
+ search_parameters(train_data, test_data)
+
+
+def knn_sklearn(x_train, y_train, x_test, y_test):
+ classifier = KNeighborsClassifier(n_neighbors=10)
+ classifier.fit(x_train, y_train)
+ return metrics.accuracy_score(y_test, classifier.predict(x_test))
+
+
+def load_mnist(sample_size=2000):
+ mnist = fetch_mldata("MNIST original")
+ x, y = shuffle(mnist.data, mnist.target)
+ x = x[:sample_size] / 255.0
+ y = y[:sample_size]
+ return train_test_split(x, y, test_size=0.1)
+
+
+def task2():
+ x_train, _, y_train, _ = load_mnist(5000)
+
+ for perplexity in [10, 50, 100]:
+ tsne = TSNE(n_components=2, perplexity=perplexity, n_iter=300)
+ tsne_data = tsne.fit_transform(x_train)
+
+ plt.figure(figsize=(10, 6))
+ plt.scatter(tsne_data[:, 0], tsne_data[:, 1], c=y_train)
+ plt.show()
+
+
+def pca_transform(data, n_dims):
+ data -= np.mean(data, axis=0)
+ U, s, _ = np.linalg.svd(data.T, full_matrices=False)
+ U = U[:, :n_dims]
+ return np.dot(data, U), U
+
+
+def pca_recover(pca_data, U, mean):
+ data = np.dot(pca_data, U.T)
+ data += mean
+ return data
+
+
+def task3():
+ x_train, x_test, y_train, y_test = load_mnist(10000)
+
+ init_acc = knn_sklearn(x_train, y_train, x_test, y_test)
+ print(f"Initial accuracy = {init_acc:.3f}")
+
+ compressed_accs, recovered_accs = [], []
+ dims_range = [5, 10, 20, 50, 100]
+ for n_dims in dims_range:
+ pca_train, U = pca_transform(x_train, n_dims)
+ pca_test = np.dot(x_test, U)
+ compressed_accs.append(knn_sklearn(pca_train, y_train, pca_test, y_test))
+
+ recovered_train = pca_recover(pca_train, U, np.mean(x_train, axis=0))
+ recovered_test = np.dot(pca_test, U.T)
+ recovered_accs.append(knn_sklearn(recovered_train, y_train, recovered_test, y_test))
+
+ plt.title("Test accuracy")
+ plt.plot(dims_range, compressed_accs)
+ plt.plot(dims_range, recovered_accs)
+ plt.xlabel("dimensions")
+ plt.legend(['compressed accuracy', 'test errors'], loc='lower right')
+ plt.show()
+
+
+def main():
+ # task1()
+ # task2()
+ task3()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/hw9 (k-NN, t-SNE, PCA)/hw9_task.pdf b/hw9 (k-NN, t-SNE, PCA)/hw9_task.pdf
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diff --git a/hw9 (k-NN, t-SNE, PCA)/iris.data.csv b/hw9 (k-NN, t-SNE, PCA)/iris.data.csv
new file mode 100644
index 0000000..5c4316c
--- /dev/null
+++ b/hw9 (k-NN, t-SNE, PCA)/iris.data.csv
@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+
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