diff --git a/.DS_Store b/.DS_Store index 2511b44..2c8a94b 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/README.md b/README.md index e69de29..8aa5aa5 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,88 @@ +# Week 4 — Scikit-learn Classification + +## Тема проекта + +Classification — Linear Discriminant Analysis (LDA) + +Используемый пример из официальной документации scikit-learn: + +https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html + + + +## Цель работы + +Изучить применение алгоритма Linear Discriminant Analysis (LDA) для решения задачи классификации с использованием библиотеки scikit-learn. + +В ходе работы были: +- изучены основы классификации; +- выполнен препроцессинг данных; +- обучена модель LDA; +- проведена визуализация результатов; +- выполнен анализ качества модели. + +--- + +## Используемые технологии + +- Python +- JupyterLab +- scikit-learn +- pandas +- matplotlib +- seaborn +- openml + +--- + +## Структура проекта + +```text +project/ +│ +├── week4_scikit_learn.ipynb +├── requirements.txt +├── README.md +├── .gitignore +└── data/ + └── wine.csv + +## Используемые датасеты + +### 1. Встроенный датасет +Wine Dataset из scikit-learn. + +### 2. Внешний датасет +CSV-версия Wine Dataset, загружаемая через pandas.read_csv(). + + +## Этапы выполнения работы + +1. Импорт библиотек +2. Загрузка данных +3. Анализ данных +4. Масштабирование признаков +5. Разделение выборки +6. Обучение модели LDA +7. Предсказание классов +8. Оценка качества модели +9. Визуализация результатов +10. Интерпретация результатов + + +## Результаты + +Модель Linear Discriminant Analysis показала высокую точность классификации на датасете Wine. + +Было установлено, что: +- масштабирование признаков улучшает качество модели; +- LDA эффективно разделяет классы; +- метод хорошо подходит для задач классификации и снижения размерности. + +--- + +## Автор + +Студент: Борончук Савелий Сергеевич + +Группа: ИНБб-2302-02-00 \ No newline at end of file diff --git a/data/wine.csv b/data/wine.csv new file mode 100644 index 0000000..5104c4e --- /dev/null +++ b/data/wine.csv @@ -0,0 +1,179 @@ +alcohol,malic_acid,ash,alcalinity_of_ash,magnesium,total_phenols,flavanoids,nonflavanoid_phenols,proanthocyanins,color_intensity,hue,od280/od315_of_diluted_wines,proline,target +14.23,1.71,2.43,15.6,127.0,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065.0,0 +13.2,1.78,2.14,11.2,100.0,2.65,2.76,0.26,1.28,4.38,1.05,3.4,1050.0,0 +13.16,2.36,2.67,18.6,101.0,2.8,3.24,0.3,2.81,5.68,1.03,3.17,1185.0,0 +14.37,1.95,2.5,16.8,113.0,3.85,3.49,0.24,2.18,7.8,0.86,3.45,1480.0,0 +13.24,2.59,2.87,21.0,118.0,2.8,2.69,0.39,1.82,4.32,1.04,2.93,735.0,0 +14.2,1.76,2.45,15.2,112.0,3.27,3.39,0.34,1.97,6.75,1.05,2.85,1450.0,0 +14.39,1.87,2.45,14.6,96.0,2.5,2.52,0.3,1.98,5.25,1.02,3.58,1290.0,0 +14.06,2.15,2.61,17.6,121.0,2.6,2.51,0.31,1.25,5.05,1.06,3.58,1295.0,0 +14.83,1.64,2.17,14.0,97.0,2.8,2.98,0.29,1.98,5.2,1.08,2.85,1045.0,0 +13.86,1.35,2.27,16.0,98.0,2.98,3.15,0.22,1.85,7.22,1.01,3.55,1045.0,0 +14.1,2.16,2.3,18.0,105.0,2.95,3.32,0.22,2.38,5.75,1.25,3.17,1510.0,0 +14.12,1.48,2.32,16.8,95.0,2.2,2.43,0.26,1.57,5.0,1.17,2.82,1280.0,0 +13.75,1.73,2.41,16.0,89.0,2.6,2.76,0.29,1.81,5.6,1.15,2.9,1320.0,0 +14.75,1.73,2.39,11.4,91.0,3.1,3.69,0.43,2.81,5.4,1.25,2.73,1150.0,0 +14.38,1.87,2.38,12.0,102.0,3.3,3.64,0.29,2.96,7.5,1.2,3.0,1547.0,0 +13.63,1.81,2.7,17.2,112.0,2.85,2.91,0.3,1.46,7.3,1.28,2.88,1310.0,0 +14.3,1.92,2.72,20.0,120.0,2.8,3.14,0.33,1.97,6.2,1.07,2.65,1280.0,0 +13.83,1.57,2.62,20.0,115.0,2.95,3.4,0.4,1.72,6.6,1.13,2.57,1130.0,0 +14.19,1.59,2.48,16.5,108.0,3.3,3.93,0.32,1.86,8.7,1.23,2.82,1680.0,0 +13.64,3.1,2.56,15.2,116.0,2.7,3.03,0.17,1.66,5.1,0.96,3.36,845.0,0 +14.06,1.63,2.28,16.0,126.0,3.0,3.17,0.24,2.1,5.65,1.09,3.71,780.0,0 +12.93,3.8,2.65,18.6,102.0,2.41,2.41,0.25,1.98,4.5,1.03,3.52,770.0,0 +13.71,1.86,2.36,16.6,101.0,2.61,2.88,0.27,1.69,3.8,1.11,4.0,1035.0,0 +12.85,1.6,2.52,17.8,95.0,2.48,2.37,0.26,1.46,3.93,1.09,3.63,1015.0,0 +13.5,1.81,2.61,20.0,96.0,2.53,2.61,0.28,1.66,3.52,1.12,3.82,845.0,0 +13.05,2.05,3.22,25.0,124.0,2.63,2.68,0.47,1.92,3.58,1.13,3.2,830.0,0 +13.39,1.77,2.62,16.1,93.0,2.85,2.94,0.34,1.45,4.8,0.92,3.22,1195.0,0 +13.3,1.72,2.14,17.0,94.0,2.4,2.19,0.27,1.35,3.95,1.02,2.77,1285.0,0 +13.87,1.9,2.8,19.4,107.0,2.95,2.97,0.37,1.76,4.5,1.25,3.4,915.0,0 +14.02,1.68,2.21,16.0,96.0,2.65,2.33,0.26,1.98,4.7,1.04,3.59,1035.0,0 +13.73,1.5,2.7,22.5,101.0,3.0,3.25,0.29,2.38,5.7,1.19,2.71,1285.0,0 +13.58,1.66,2.36,19.1,106.0,2.86,3.19,0.22,1.95,6.9,1.09,2.88,1515.0,0 +13.68,1.83,2.36,17.2,104.0,2.42,2.69,0.42,1.97,3.84,1.23,2.87,990.0,0 +13.76,1.53,2.7,19.5,132.0,2.95,2.74,0.5,1.35,5.4,1.25,3.0,1235.0,0 +13.51,1.8,2.65,19.0,110.0,2.35,2.53,0.29,1.54,4.2,1.1,2.87,1095.0,0 +13.48,1.81,2.41,20.5,100.0,2.7,2.98,0.26,1.86,5.1,1.04,3.47,920.0,0 +13.28,1.64,2.84,15.5,110.0,2.6,2.68,0.34,1.36,4.6,1.09,2.78,880.0,0 +13.05,1.65,2.55,18.0,98.0,2.45,2.43,0.29,1.44,4.25,1.12,2.51,1105.0,0 +13.07,1.5,2.1,15.5,98.0,2.4,2.64,0.28,1.37,3.7,1.18,2.69,1020.0,0 +14.22,3.99,2.51,13.2,128.0,3.0,3.04,0.2,2.08,5.1,0.89,3.53,760.0,0 +13.56,1.71,2.31,16.2,117.0,3.15,3.29,0.34,2.34,6.13,0.95,3.38,795.0,0 +13.41,3.84,2.12,18.8,90.0,2.45,2.68,0.27,1.48,4.28,0.91,3.0,1035.0,0 +13.88,1.89,2.59,15.0,101.0,3.25,3.56,0.17,1.7,5.43,0.88,3.56,1095.0,0 +13.24,3.98,2.29,17.5,103.0,2.64,2.63,0.32,1.66,4.36,0.82,3.0,680.0,0 +13.05,1.77,2.1,17.0,107.0,3.0,3.0,0.28,2.03,5.04,0.88,3.35,885.0,0 +14.21,4.04,2.44,18.9,111.0,2.85,2.65,0.3,1.25,5.24,0.87,3.33,1080.0,0 +14.38,3.59,2.28,16.0,102.0,3.25,3.17,0.27,2.19,4.9,1.04,3.44,1065.0,0 +13.9,1.68,2.12,16.0,101.0,3.1,3.39,0.21,2.14,6.1,0.91,3.33,985.0,0 +14.1,2.02,2.4,18.8,103.0,2.75,2.92,0.32,2.38,6.2,1.07,2.75,1060.0,0 +13.94,1.73,2.27,17.4,108.0,2.88,3.54,0.32,2.08,8.9,1.12,3.1,1260.0,0 +13.05,1.73,2.04,12.4,92.0,2.72,3.27,0.17,2.91,7.2,1.12,2.91,1150.0,0 +13.83,1.65,2.6,17.2,94.0,2.45,2.99,0.22,2.29,5.6,1.24,3.37,1265.0,0 +13.82,1.75,2.42,14.0,111.0,3.88,3.74,0.32,1.87,7.05,1.01,3.26,1190.0,0 +13.77,1.9,2.68,17.1,115.0,3.0,2.79,0.39,1.68,6.3,1.13,2.93,1375.0,0 +13.74,1.67,2.25,16.4,118.0,2.6,2.9,0.21,1.62,5.85,0.92,3.2,1060.0,0 +13.56,1.73,2.46,20.5,116.0,2.96,2.78,0.2,2.45,6.25,0.98,3.03,1120.0,0 +14.22,1.7,2.3,16.3,118.0,3.2,3.0,0.26,2.03,6.38,0.94,3.31,970.0,0 +13.29,1.97,2.68,16.8,102.0,3.0,3.23,0.31,1.66,6.0,1.07,2.84,1270.0,0 +13.72,1.43,2.5,16.7,108.0,3.4,3.67,0.19,2.04,6.8,0.89,2.87,1285.0,0 +12.37,0.94,1.36,10.6,88.0,1.98,0.57,0.28,0.42,1.95,1.05,1.82,520.0,1 +12.33,1.1,2.28,16.0,101.0,2.05,1.09,0.63,0.41,3.27,1.25,1.67,680.0,1 +12.64,1.36,2.02,16.8,100.0,2.02,1.41,0.53,0.62,5.75,0.98,1.59,450.0,1 +13.67,1.25,1.92,18.0,94.0,2.1,1.79,0.32,0.73,3.8,1.23,2.46,630.0,1 +12.37,1.13,2.16,19.0,87.0,3.5,3.1,0.19,1.87,4.45,1.22,2.87,420.0,1 +12.17,1.45,2.53,19.0,104.0,1.89,1.75,0.45,1.03,2.95,1.45,2.23,355.0,1 +12.37,1.21,2.56,18.1,98.0,2.42,2.65,0.37,2.08,4.6,1.19,2.3,678.0,1 +13.11,1.01,1.7,15.0,78.0,2.98,3.18,0.26,2.28,5.3,1.12,3.18,502.0,1 +12.37,1.17,1.92,19.6,78.0,2.11,2.0,0.27,1.04,4.68,1.12,3.48,510.0,1 +13.34,0.94,2.36,17.0,110.0,2.53,1.3,0.55,0.42,3.17,1.02,1.93,750.0,1 +12.21,1.19,1.75,16.8,151.0,1.85,1.28,0.14,2.5,2.85,1.28,3.07,718.0,1 +12.29,1.61,2.21,20.4,103.0,1.1,1.02,0.37,1.46,3.05,0.906,1.82,870.0,1 +13.86,1.51,2.67,25.0,86.0,2.95,2.86,0.21,1.87,3.38,1.36,3.16,410.0,1 +13.49,1.66,2.24,24.0,87.0,1.88,1.84,0.27,1.03,3.74,0.98,2.78,472.0,1 +12.99,1.67,2.6,30.0,139.0,3.3,2.89,0.21,1.96,3.35,1.31,3.5,985.0,1 +11.96,1.09,2.3,21.0,101.0,3.38,2.14,0.13,1.65,3.21,0.99,3.13,886.0,1 +11.66,1.88,1.92,16.0,97.0,1.61,1.57,0.34,1.15,3.8,1.23,2.14,428.0,1 +13.03,0.9,1.71,16.0,86.0,1.95,2.03,0.24,1.46,4.6,1.19,2.48,392.0,1 +11.84,2.89,2.23,18.0,112.0,1.72,1.32,0.43,0.95,2.65,0.96,2.52,500.0,1 +12.33,0.99,1.95,14.8,136.0,1.9,1.85,0.35,2.76,3.4,1.06,2.31,750.0,1 +12.7,3.87,2.4,23.0,101.0,2.83,2.55,0.43,1.95,2.57,1.19,3.13,463.0,1 +12.0,0.92,2.0,19.0,86.0,2.42,2.26,0.3,1.43,2.5,1.38,3.12,278.0,1 +12.72,1.81,2.2,18.8,86.0,2.2,2.53,0.26,1.77,3.9,1.16,3.14,714.0,1 +12.08,1.13,2.51,24.0,78.0,2.0,1.58,0.4,1.4,2.2,1.31,2.72,630.0,1 +13.05,3.86,2.32,22.5,85.0,1.65,1.59,0.61,1.62,4.8,0.84,2.01,515.0,1 +11.84,0.89,2.58,18.0,94.0,2.2,2.21,0.22,2.35,3.05,0.79,3.08,520.0,1 +12.67,0.98,2.24,18.0,99.0,2.2,1.94,0.3,1.46,2.62,1.23,3.16,450.0,1 +12.16,1.61,2.31,22.8,90.0,1.78,1.69,0.43,1.56,2.45,1.33,2.26,495.0,1 +11.65,1.67,2.62,26.0,88.0,1.92,1.61,0.4,1.34,2.6,1.36,3.21,562.0,1 +11.64,2.06,2.46,21.6,84.0,1.95,1.69,0.48,1.35,2.8,1.0,2.75,680.0,1 +12.08,1.33,2.3,23.6,70.0,2.2,1.59,0.42,1.38,1.74,1.07,3.21,625.0,1 +12.08,1.83,2.32,18.5,81.0,1.6,1.5,0.52,1.64,2.4,1.08,2.27,480.0,1 +12.0,1.51,2.42,22.0,86.0,1.45,1.25,0.5,1.63,3.6,1.05,2.65,450.0,1 +12.69,1.53,2.26,20.7,80.0,1.38,1.46,0.58,1.62,3.05,0.96,2.06,495.0,1 +12.29,2.83,2.22,18.0,88.0,2.45,2.25,0.25,1.99,2.15,1.15,3.3,290.0,1 +11.62,1.99,2.28,18.0,98.0,3.02,2.26,0.17,1.35,3.25,1.16,2.96,345.0,1 +12.47,1.52,2.2,19.0,162.0,2.5,2.27,0.32,3.28,2.6,1.16,2.63,937.0,1 +11.81,2.12,2.74,21.5,134.0,1.6,0.99,0.14,1.56,2.5,0.95,2.26,625.0,1 +12.29,1.41,1.98,16.0,85.0,2.55,2.5,0.29,1.77,2.9,1.23,2.74,428.0,1 +12.37,1.07,2.1,18.5,88.0,3.52,3.75,0.24,1.95,4.5,1.04,2.77,660.0,1 +12.29,3.17,2.21,18.0,88.0,2.85,2.99,0.45,2.81,2.3,1.42,2.83,406.0,1 +12.08,2.08,1.7,17.5,97.0,2.23,2.17,0.26,1.4,3.3,1.27,2.96,710.0,1 +12.6,1.34,1.9,18.5,88.0,1.45,1.36,0.29,1.35,2.45,1.04,2.77,562.0,1 +12.34,2.45,2.46,21.0,98.0,2.56,2.11,0.34,1.31,2.8,0.8,3.38,438.0,1 +11.82,1.72,1.88,19.5,86.0,2.5,1.64,0.37,1.42,2.06,0.94,2.44,415.0,1 +12.51,1.73,1.98,20.5,85.0,2.2,1.92,0.32,1.48,2.94,1.04,3.57,672.0,1 +12.42,2.55,2.27,22.0,90.0,1.68,1.84,0.66,1.42,2.7,0.86,3.3,315.0,1 +12.25,1.73,2.12,19.0,80.0,1.65,2.03,0.37,1.63,3.4,1.0,3.17,510.0,1 +12.72,1.75,2.28,22.5,84.0,1.38,1.76,0.48,1.63,3.3,0.88,2.42,488.0,1 +12.22,1.29,1.94,19.0,92.0,2.36,2.04,0.39,2.08,2.7,0.86,3.02,312.0,1 +11.61,1.35,2.7,20.0,94.0,2.74,2.92,0.29,2.49,2.65,0.96,3.26,680.0,1 +11.46,3.74,1.82,19.5,107.0,3.18,2.58,0.24,3.58,2.9,0.75,2.81,562.0,1 +12.52,2.43,2.17,21.0,88.0,2.55,2.27,0.26,1.22,2.0,0.9,2.78,325.0,1 +11.76,2.68,2.92,20.0,103.0,1.75,2.03,0.6,1.05,3.8,1.23,2.5,607.0,1 +11.41,0.74,2.5,21.0,88.0,2.48,2.01,0.42,1.44,3.08,1.1,2.31,434.0,1 +12.08,1.39,2.5,22.5,84.0,2.56,2.29,0.43,1.04,2.9,0.93,3.19,385.0,1 +11.03,1.51,2.2,21.5,85.0,2.46,2.17,0.52,2.01,1.9,1.71,2.87,407.0,1 +11.82,1.47,1.99,20.8,86.0,1.98,1.6,0.3,1.53,1.95,0.95,3.33,495.0,1 +12.42,1.61,2.19,22.5,108.0,2.0,2.09,0.34,1.61,2.06,1.06,2.96,345.0,1 +12.77,3.43,1.98,16.0,80.0,1.63,1.25,0.43,0.83,3.4,0.7,2.12,372.0,1 +12.0,3.43,2.0,19.0,87.0,2.0,1.64,0.37,1.87,1.28,0.93,3.05,564.0,1 +11.45,2.4,2.42,20.0,96.0,2.9,2.79,0.32,1.83,3.25,0.8,3.39,625.0,1 +11.56,2.05,3.23,28.5,119.0,3.18,5.08,0.47,1.87,6.0,0.93,3.69,465.0,1 +12.42,4.43,2.73,26.5,102.0,2.2,2.13,0.43,1.71,2.08,0.92,3.12,365.0,1 +13.05,5.8,2.13,21.5,86.0,2.62,2.65,0.3,2.01,2.6,0.73,3.1,380.0,1 +11.87,4.31,2.39,21.0,82.0,2.86,3.03,0.21,2.91,2.8,0.75,3.64,380.0,1 +12.07,2.16,2.17,21.0,85.0,2.6,2.65,0.37,1.35,2.76,0.86,3.28,378.0,1 +12.43,1.53,2.29,21.5,86.0,2.74,3.15,0.39,1.77,3.94,0.69,2.84,352.0,1 +11.79,2.13,2.78,28.5,92.0,2.13,2.24,0.58,1.76,3.0,0.97,2.44,466.0,1 +12.37,1.63,2.3,24.5,88.0,2.22,2.45,0.4,1.9,2.12,0.89,2.78,342.0,1 +12.04,4.3,2.38,22.0,80.0,2.1,1.75,0.42,1.35,2.6,0.79,2.57,580.0,1 +12.86,1.35,2.32,18.0,122.0,1.51,1.25,0.21,0.94,4.1,0.76,1.29,630.0,2 +12.88,2.99,2.4,20.0,104.0,1.3,1.22,0.24,0.83,5.4,0.74,1.42,530.0,2 +12.81,2.31,2.4,24.0,98.0,1.15,1.09,0.27,0.83,5.7,0.66,1.36,560.0,2 +12.7,3.55,2.36,21.5,106.0,1.7,1.2,0.17,0.84,5.0,0.78,1.29,600.0,2 +12.51,1.24,2.25,17.5,85.0,2.0,0.58,0.6,1.25,5.45,0.75,1.51,650.0,2 +12.6,2.46,2.2,18.5,94.0,1.62,0.66,0.63,0.94,7.1,0.73,1.58,695.0,2 +12.25,4.72,2.54,21.0,89.0,1.38,0.47,0.53,0.8,3.85,0.75,1.27,720.0,2 +12.53,5.51,2.64,25.0,96.0,1.79,0.6,0.63,1.1,5.0,0.82,1.69,515.0,2 +13.49,3.59,2.19,19.5,88.0,1.62,0.48,0.58,0.88,5.7,0.81,1.82,580.0,2 +12.84,2.96,2.61,24.0,101.0,2.32,0.6,0.53,0.81,4.92,0.89,2.15,590.0,2 +12.93,2.81,2.7,21.0,96.0,1.54,0.5,0.53,0.75,4.6,0.77,2.31,600.0,2 +13.36,2.56,2.35,20.0,89.0,1.4,0.5,0.37,0.64,5.6,0.7,2.47,780.0,2 +13.52,3.17,2.72,23.5,97.0,1.55,0.52,0.5,0.55,4.35,0.89,2.06,520.0,2 +13.62,4.95,2.35,20.0,92.0,2.0,0.8,0.47,1.02,4.4,0.91,2.05,550.0,2 +12.25,3.88,2.2,18.5,112.0,1.38,0.78,0.29,1.14,8.21,0.65,2.0,855.0,2 +13.16,3.57,2.15,21.0,102.0,1.5,0.55,0.43,1.3,4.0,0.6,1.68,830.0,2 +13.88,5.04,2.23,20.0,80.0,0.98,0.34,0.4,0.68,4.9,0.58,1.33,415.0,2 +12.87,4.61,2.48,21.5,86.0,1.7,0.65,0.47,0.86,7.65,0.54,1.86,625.0,2 +13.32,3.24,2.38,21.5,92.0,1.93,0.76,0.45,1.25,8.42,0.55,1.62,650.0,2 +13.08,3.9,2.36,21.5,113.0,1.41,1.39,0.34,1.14,9.4,0.57,1.33,550.0,2 +13.5,3.12,2.62,24.0,123.0,1.4,1.57,0.22,1.25,8.6,0.59,1.3,500.0,2 +12.79,2.67,2.48,22.0,112.0,1.48,1.36,0.24,1.26,10.8,0.48,1.47,480.0,2 +13.11,1.9,2.75,25.5,116.0,2.2,1.28,0.26,1.56,7.1,0.61,1.33,425.0,2 +13.23,3.3,2.28,18.5,98.0,1.8,0.83,0.61,1.87,10.52,0.56,1.51,675.0,2 +12.58,1.29,2.1,20.0,103.0,1.48,0.58,0.53,1.4,7.6,0.58,1.55,640.0,2 +13.17,5.19,2.32,22.0,93.0,1.74,0.63,0.61,1.55,7.9,0.6,1.48,725.0,2 +13.84,4.12,2.38,19.5,89.0,1.8,0.83,0.48,1.56,9.01,0.57,1.64,480.0,2 +12.45,3.03,2.64,27.0,97.0,1.9,0.58,0.63,1.14,7.5,0.67,1.73,880.0,2 +14.34,1.68,2.7,25.0,98.0,2.8,1.31,0.53,2.7,13.0,0.57,1.96,660.0,2 +13.48,1.67,2.64,22.5,89.0,2.6,1.1,0.52,2.29,11.75,0.57,1.78,620.0,2 +12.36,3.83,2.38,21.0,88.0,2.3,0.92,0.5,1.04,7.65,0.56,1.58,520.0,2 +13.69,3.26,2.54,20.0,107.0,1.83,0.56,0.5,0.8,5.88,0.96,1.82,680.0,2 +12.85,3.27,2.58,22.0,106.0,1.65,0.6,0.6,0.96,5.58,0.87,2.11,570.0,2 +12.96,3.45,2.35,18.5,106.0,1.39,0.7,0.4,0.94,5.28,0.68,1.75,675.0,2 +13.78,2.76,2.3,22.0,90.0,1.35,0.68,0.41,1.03,9.58,0.7,1.68,615.0,2 +13.73,4.36,2.26,22.5,88.0,1.28,0.47,0.52,1.15,6.62,0.78,1.75,520.0,2 +13.45,3.7,2.6,23.0,111.0,1.7,0.92,0.43,1.46,10.68,0.85,1.56,695.0,2 +12.82,3.37,2.3,19.5,88.0,1.48,0.66,0.4,0.97,10.26,0.72,1.75,685.0,2 +13.58,2.58,2.69,24.5,105.0,1.55,0.84,0.39,1.54,8.66,0.74,1.8,750.0,2 +13.4,4.6,2.86,25.0,112.0,1.98,0.96,0.27,1.11,8.5,0.67,1.92,630.0,2 +12.2,3.03,2.32,19.0,96.0,1.25,0.49,0.4,0.73,5.5,0.66,1.83,510.0,2 +12.77,2.39,2.28,19.5,86.0,1.39,0.51,0.48,0.64,9.899999,0.57,1.63,470.0,2 +14.16,2.51,2.48,20.0,91.0,1.68,0.7,0.44,1.24,9.7,0.62,1.71,660.0,2 +13.71,5.65,2.45,20.5,95.0,1.68,0.61,0.52,1.06,7.7,0.64,1.74,740.0,2 +13.4,3.91,2.48,23.0,102.0,1.8,0.75,0.43,1.41,7.3,0.7,1.56,750.0,2 +13.27,4.28,2.26,20.0,120.0,1.59,0.69,0.43,1.35,10.2,0.59,1.56,835.0,2 +13.17,2.59,2.37,20.0,120.0,1.65,0.68,0.53,1.46,9.3,0.6,1.62,840.0,2 +14.13,4.1,2.74,24.5,96.0,2.05,0.76,0.56,1.35,9.2,0.61,1.6,560.0,2 diff --git a/week4_scikit_learn.ipynb b/week4_scikit_learn.ipynb new file mode 100644 index 0000000..727d800 --- /dev/null +++ b/week4_scikit_learn.ipynb @@ -0,0 +1,1708 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c4a86232-387a-495c-8fcc-a287df21bf21", + "metadata": {}, + "source": [ + "# Linear Discriminant Analysis (LDA)\n", + "\n", + "## Цель работы\n", + "\n", + "Изучить применение алгоритма Linear Discriminant Analysis (LDA)\n", + "для задачи классификации.\n", + "\n", + "В работе используется:\n", + "\n", + "1. Встроенный датасет Wine из scikit-learn\n", + "2. Пользовательский CSV-датасет\n", + "\n", + "Будут выполнены:\n", + "\n", + "- препроцессинг данных;\n", + "- обучение модели;\n", + "- визуализация результатов;\n", + "- анализ качества классификации." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cd23e4e4-05dc-4344-b167-d4f2f94c76e6", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sklearn.datasets import load_wine\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.metrics import classification_report, confusion_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "69e1629a-11de-451b-ae2e-cd604ae16743", + "metadata": {}, + "outputs": [], + "source": [ + "wine = load_wine()\n", + "\n", + "X = wine.data\n", + "y = wine.target\n", + "\n", + "feature_names = wine.feature_names\n", + "target_names = wine.target_names" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b48c2a39-473f-486b-be01-6413fac421cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
014.231.712.4315.6127.02.803.060.282.295.641.043.921065.0
113.201.782.1411.2100.02.652.760.261.284.381.053.401050.0
213.162.362.6718.6101.02.803.240.302.815.681.033.171185.0
314.371.952.5016.8113.03.853.490.242.187.800.863.451480.0
413.242.592.8721.0118.02.802.690.391.824.321.042.93735.0
\n", + "
" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + "\n", + " od280/od315_of_diluted_wines proline \n", + "0 3.92 1065.0 \n", + "1 3.40 1050.0 \n", + "2 3.17 1185.0 \n", + "3 3.45 1480.0 \n", + "4 2.93 735.0 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(X, columns=feature_names)\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6def62cc-7b85-44c5-b1aa-94254447464f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['class_0' 'class_1' 'class_2']\n" + ] + } + ], + "source": [ + "print(target_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1b373b87-8f54-40b3-a934-45455d233f54", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "\n", + "sns.boxplot(data=df)\n", + "\n", + "plt.xticks(rotation=90)\n", + "plt.title(\"Распределение признаков\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4fee8db7-038c-4a3c-a02e-e2e144c59964", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X,\n", + " y,\n", + " test_size=0.2,\n", + " random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2afba4f5-8146-40f6-9db7-bd398c74e9ee", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5c9b30c1-a0f0-45b0-89ed-346a6bf3e986", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearDiscriminantAnalysis()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearDiscriminantAnalysis()" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lda = LinearDiscriminantAnalysis()\n", + "\n", + "lda.fit(X_train_scaled, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ea2c200d-ccdd-43d0-8ec5-6a527cb73c05", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = lda.predict(X_test_scaled)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7bef8a3e-d2fc-477e-9160-68888cdbc2f3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 14\n", + " 1 1.00 1.00 1.00 14\n", + " 2 1.00 1.00 1.00 8\n", + "\n", + " accuracy 1.00 36\n", + " macro avg 1.00 1.00 1.00 36\n", + "weighted avg 1.00 1.00 1.00 36\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2e337622-7433-40e7-b545-1d545c796102", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cm = confusion_matrix(y_test, y_pred)\n", + "\n", + "plt.figure(figsize=(6, 5))\n", + "\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + "\n", + "plt.xlabel(\"Предсказанные\")\n", + "plt.ylabel(\"Истинные\")\n", + "plt.title(\"Confusion Matrix\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ebac437d-71b0-49ca-9238-fad83a4d744f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAArEAAAIjCAYAAAAUdENlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8ekN5oAAAACXBIWXMAAA9hAAAPYQGoP6dpAACiAElEQVR4nOzdBZhUZRcH8P+d2U6W7u5uJKSkEQVUsFBQsbu7PhUVE8UOMDBAEAUFaZHu7m6W2s6Z+z3n3Z1ld5lamJ7/73vm292Zu3Pf3ZXdM+ee9xxN13UdRERERER+xODtBRARERERlRSDWCIiIiLyOwxiiYiIiMjvMIglIiIiIr/DIJaIiIiI/A6DWCIiIiLyOwxiiYiIiMjvMIglIiIiIr/DIJaIiIiI/A6DWCKiADRhwgRomob9+/d7/Nw1a9bEyJEjPX5eIgouDGKJKGADuNWrV9s8RoI7OcZyCw0NRdmyZdGpUyc8++yzOHjwoN1zPPnkk+rzhg8fXuIAr/B5y5cvj8svvxzTpk2DP1m6dClefvllnDt3zttLIaIgpem6rnt7EURErg5iR40ahVWrVqFt27Y2g9hatWrhhhtuwIABA2A2m3H27Fn1OVOnTlUB5tdff43rr7/+gs+VX5vVq1dHSEgITpw4oW6xsbFOB7EJCQl47LHH1MdHjx7F559/jr179+LTTz/F3XffDVcwmUzIyclBeHi4+lpc7Z133sETTzyBffv2qa+psKysLBgMBvXCgIjIXULc9sxERH6gdevWuPnmm4vcd+DAAfTp0we33norGjVqhBYtWhR5fOHChTh8+DDmz5+Pvn37qqBXjnVWlSpVipzzlltuQd26dfH+++/bDGJzc3NVoB0WFubUOYxGo7p5gwTORETuxnICIqJiatSoobK52dnZePvtty94/Mcff0Tjxo3Ro0cP9OrVS318KSpWrKiCZclqFi51kGznBx98gDp16qjAcOvWrepxCZ6lBCE6OhqlSpXC1VdfjW3btjlVE/v3338XfK5kjwcOHIgtW7ZcsKbt27dj2LBhKFeuHCIjI9GgQQM899xz6jEpI5AsrJBstqU0wnIuazWxkmm+7rrrULp0aURFReGyyy7DzJkzL3hxIM/z66+/4vXXX0fVqlURERGBK664Art3776k7zERBR5mYomIrOjYsaMKHufMmXPBpfLffvutoBxAyhGkdOH48eMqGL0Yctn/0KFDKFOmTJH7v/32W2RmZuLOO+9UQawEgHPnzkX//v1Ru3ZtFUxmZGTgo48+QufOnbF27doLLu0X9v3336uMsWSP33rrLaSnp6sShi5dumDdunUFn7tx40YV6Eo5gJxb7t+zZw/+/PNPFVwOHToUO3fuxE8//aSyx1JLLCTgtUbKLaTWWM734IMPqq9z4sSJuOqqqzBlyhQMGTKkyPFvvvmmKkd4/PHHkZSUpF5I3HTTTVixYsVFfX+JKEBJTSwRUSD59ttvpdZfX7Vqlc1j9u3bp44ZO3aszWOuvvpqdUxSUlLBfVOmTFH37dq1S32cnJysR0RE6O+//75Ta6tRo4bep08fPTExUd02bNigX3/99eo5H3jggSJri4uL00+ePFnk81u2bKmXL19eP336dMF98hwGg0G/5ZZbLvgeyHOJlJQUvVSpUvro0aOLPN/x48f1+Pj4Ivd37dpVj42N1Q8cOFDkWLPZXPC+fN8KP3/xr/HWW28t+Pjhhx9Wxy5evLjgPllPrVq19Jo1a+omk0ndt2DBAnVco0aN9KysrIJjP/zwQ3X/pk2bnPoeE1FwYDkBEZENMTEx6m1KSkrBfVI6IJvFpIZVWC7Jl6Sk4J9//lFZS7lJve3kyZMxYsQIlR0t7JprrimS3Tx27BjWr1+vLtVLVtaiefPm6N27N/766y+b55SMsnQSkMzxqVOnCm5SN9uhQwcsWLBAHZeYmIh///0Xt912m9q8VtjFbhCTdbVv315lfAt/byXLKyUIljIJC8lsF679laywpSSBiMiC5QRERDakpqaqt5bOAxIESkB2//33F6nRlEv5UmIgl9jr16/v8HklaHzttddUUCj1oVIPK7WtxUm9afENZ0LqU4uT55g9ezbS0tJUvWtxu3btUm979uxpdU1xcXFFAsWmTZvCVWTd8jVbW7Pl8cLnKx48SzcHId0jiIgsGMQSEdmwefNm1cfVEuBJxlRqYt999111K06ysa+88orD55UaUtkQ5ohsqHIV6WxgqYu1Vrsr7cJ8ha2uCuwISUSF+c5vLSIiH7Js2TK1malwKywJUiVj+NJLL11wvPR6nTRpklNB7KV0TRA7duyw2k1AgmNrWVghm9SEBOX2AmjZMGYJ4O0pSWmBrNvWmi2PExGVFGtiiYiKkcvbUncqdZmWVlLSPUBqRaXt1LXXXnvBTeo4pcTAnTvoK1WqhJYtW6qd/YUnZUnAKXW2MrTBFulIIBnlN954Q3VDKE5qYYXU4Hbt2hXffPPNBVPLCmdCLcGyMxO7ZF0rV65ULwwspOzhiy++UJ0PpF0ZEVFJMRNLRAFLArFZs2ZdcP9DDz1U8L60pfrhhx/U5XYJyGRil9S3SqZRLr3LpikhWVYJ4qQtlK1ATS7JS7bWWv2nq4wdO1a12JIWYLfffntBi634+HjVcssWCWClnZZsIJMBDzKJTAJWCVSlX6vU9X788cfq2HHjxqlNWHKcbL6S2lzZgCXHycYy0aZNG/VWesfKc0k7rkGDBlnNBD/99NOqHZesW1psyaY0CcSlL658r6WdFhFRiXm7PQIRkatZ2kvZuh06dKigjZXlFhISopcuXVrv0KGD/swzz1zQXqpZs2Z69erV7Z63e/fuqv1VTk6OzWOk/dTAgQPtPo+j9l9z587VO3furEdGRqo2XIMGDdK3bt1q9XtQvAWWtLHq27evaqslrcHq1Kmjjxw5Ul+9enWR4zZv3qwPGTJEteWS4xo0aKC/8MILRY753//+p1epUkW19yp8ruIttsSePXv0a6+9tuD52rdvr8+YMeOCtcnzTJ482er3Q74mIiILTf6v5KEvERH5sq+//hp33HGHKoOQyVdERIGG13CIiAKQ9JSVkojC/WSJiAIJa2KJiAKIjHiVUa6fffaZqpuVPrRERIGImVgiogCybds21VFBJopNmDDB28shInIb1sQSERERkd9hJpaIiIiI/A6DWCIiIiLyO0G1sUuamR89ehSxsbElGplIRERERJ4hla4pKSmoXLmy3WEoQRXESgBbrVo1by+DiIiIiBxw1Oc6qIJYycBavikygpGIiIiIfEtycrJKOlriNluCKoi1lBBIAMsgloiIiMh3OSr95MYuIiIiIvI7DGKJiIiIyO8wiCUiIiIiv8MgloiIiIj8DoNYIiIiIvI7DGKJiIiIyO8wiCUiIiIiv8MgloiIiIj8DoNYIiIiIvI7DGKJiIiIyO8wiCUiIiIiv8MgloiIiIj8DoNY8qrsrBycOJCIpFPJ3l4KERER+ZEQby+AglPK2VT88OoU/P31PGSkZqr7mnRugBEvXoc2vVt4e3lERETk45iJJY9LPZeGhzo/j98//rsggBXblu3C0/1ew9wf/vXq+oiIiMj3+W0Q++abb0LTNDz88MPeXgqV0A//m4Iju47BbDIXud9sNgM68P6dnyEtKc1r6yMiIiLf55dB7KpVq/D555+jefPm3l4KlVBOdo4qISgewBY5JisXc39Y7NF1ERERkX/xuyA2NTUVN910E7788kskJCTYPTYrKwvJyclFbr5gz4b96pL54qkrgi7jeO5kMtKTM+weYwwx4OC2wx5bExEREfkfv9vYdd9992HgwIHo1asXXnvtNbvHjhkzBq+88gp8xYFthzF25HjsWLW74L7QiFAMeWAAbnv9BhhDjAh0EdHhDo/RdR1RsZEeWQ8RERH5J7/KxP78889Yu3atCk6d8cwzzyApKangdujQIXjLsX0n8HCX57Fr7d4i9+dk5mDyO9Pxwd2fIxjEJsSgebfGMBht/6dnyjXj8msv8+i6iIiIyL/4TRArAehDDz2EH3/8EREREU59Tnh4OOLi4orcvOWnN6YiPSXDai2orgOzvlmA/Vu8F2R7krTR0s06oF34mAS3bfu2QP02dbyxNCIiIvITfhPErlmzBidPnkTr1q0REhKibosWLcK4cePU+yaTCb7KlGvCvB8Xw5xrtlsHOue7RQgGLXs0xTM/PoTwiDAVyIaEGtXXL9r0bo7nf3nU20skIiIiH+c3NbFXXHEFNm3aVOS+UaNGoWHDhnjqqadgNPpuPalkYLMzc+wfpANnT5xDsOhxfWe0H9AK8yf9pzZxRcZE4PJrLkO91rW9vTQiIiLyA34TxMbGxqJp06ZF7ouOjkaZMmUuuN/XyCalsMgwZGdk2z5IA8pUst9tIdBEx0Vh0N19vL0MIiIi8kN+U07gz6TrQJ9busGQf8nc1mamPiO7e3RdRERERP7KbzKx1ixcuBD+4oZnh2Lxb8uRcjbN6uauQff0QbUGVbyyNiIiIiJ/w0ysh5SvVhYfLHkdTbs0LHK/1ILKbv37P7rda2sjIiIi8jeaLp3lg4RM7IqPj1c9Y73ZbuvQjiPYv/mQqpOVnqmR0c61DCMiIiIKdMlOxmt+XU7gr6RsgKUDRERERBeP5QRERERE5HcYxBIRERGR32EQS0RERER+h0EsEREREfkdBrFERERE5HcYxBIRERGR32EQS0RERER+h0EsEREREfkdDjugSyID3zb/tx0zv5yDwzuPIb5MLHrc0AVdr+uIsPBQby+PiIiIAhTHztJFM5vNeG/0Z5j97QIYQwww5ZqhGTToZh01GlfF2HkvIaFCKW8vk4iIiAIwXmM5AV20ye/8qQJYIQGskABWHN55FK9e965X10dERESBi0EsXZTcnFxMee9Pm49LUCtlBrvW7vXouoiIiCg4MIili3Jw2xGcO5lk9xiD0YA1czZ6bE1EREQUPBjE0kUxm/LKB+zRNMnImjyyHiIiIgouDGLpolRtUBmRMRF2j5GSgsYd63tsTURERBQ8GMTSRYmICsfAO3vDYNBslhJIoNuyR1OPr42IiIgCH4NYumgj/zccTTo3VO9rUjtQKICNTYjGy789XuR+IiIiIlfhsAO6aOGR4Xhrzgv4Z8JCzPh8Do7tPYHo+Cj0HtENg+7tizKVEry9RCIiIgpQHHZAQU/+CRzZfRwZKRmoULMc4krHentJREREQSvZyXiNmViySrKqB7cfQUR0uNqcFRoWmCNkF09dgYkv/owDWw+rj2XyWNdrO2L02yNQrmoZby+PiIi8TDcdBzKmQjcdBgyloEUMghbayNvLImZiqbgju4/hw3u+xLp5mwruiysTi5uevwZDHhwQUDWuM7+Ygw/u/kJ9TYX/GUhNb6ny8Ri/cgzKVmEgS0QUjNTfhbTPoKd+WGgbkfytMAHh/aCVGgtNC/fyKgMTx85SiZ04kIgHOz6HDQu3FLk/+XQKPn1kAia++AsCReq5NIx/6Bv1fvHXcdID91xiEia+9KuXVkdERF6XMRl66vvyVyH/lpsXwIqsf6AnveztFQY9BrFU4PtXfkVqUprNQQaT3piKk4dOIRDMn/QfcrNtD2Iw55ox78d/kZGW6dF1ERGR9+m6CXrqeDtHmIFMKTE47sFVUXEMYknJysjCvEn/qeDNFs2gYe73/yIQHN19TNW/2pOTlYszx856bE1EROQjcncA5mOOj8ua74nVkA3c2EVKyplU5GbLpRLYDWIT3ZCJzcnOwemjZxEaHorSFUt5pO42tnQszGbH5eDSMoyIiIKMnu7EQRqgZ3hgMWQLM7GkRJeKVhua7NHNutrw5CqZ6Vn45rlJGFZxNEbUvg/XV7kTd7d6AosmL4O7dRvW0WbZhJDvhUwbK1XOdV8vERH5CWMtJ0IkMxBSz0MLImsYxJISGR2BLkPbw2DnErsEfVfcdLnLyhee7P0qfnl7utpkZbFv00G8Nvw9/Dp2Otypav3K6HVzV5VdLs6SCL7l5WFuXQMREfkmzVgGCO8r0ayNIwyAoRIQ1tnDK6PCGMRSgREvDlP9YK1lZCWwGzD6ChX8ucL0j2dh+4pdF2RDLZ0Cvnz6BxzbdwLu9MiXd6PPrd1V+YIEs8bQvF9WMaVi8NJvj6PZ5ewDSEQUrLS4ZwFNJk8WT3bI38gQaKXegabZCnLJE9gnlorYsWo33rzlIxzecbTgvpCwEAx+oD/uGHMTjCGu+Qd7U617cPKA7fpaCaSHP3k1bnv9RniitdiSaSuRlpyO6g2roOPV7RAWHpjDHYiIyDl6xl/Qkx7Lb69VLFSKeQSGmHvgTbo5BchZK+8AoY2hGSsgUHBiF12UBu3q4putH2DLku1qilVkTATa9mvp0lGsplyT3QBW6GYzDhUKpN2pQo1yGPrwQI+ci4iIfJ+euz8/gLXRijH1Q+jhV0ALre/ppUHXs6GnvAOk/yTFefn3GqCH94UW/xI0Q2kECwaxdAG5vN60SyN1cwfJsoaGh6gWVvaOiYyNcMv5iYiI7NHTJzk4QoOe/gO0+FfhSbpuhn7uQSBrYX6G2MKcN4Dh9E6gzGRohhgEA9bEkleC5K7XdrTbp9WUa0bXazp6dF1ERERK1n+2s7CKCcheAo/LXprfm9Zadx0TYNoLZATPtEkGseQVUu+qGQxWuwNIh4Q6LWuiXf+WXlkbEREFO2e2C3l+S5GePsVOxwShQ08PnBHxjjCIJa+o1awGXp/xTMEwgZBQY8GmsQZt62LMrOdhNHLXJxEReUG4XAm09zfICIR54WqhmiJmcnDMCQQL1sSS17Tu1Ry/HPlCDTfYs26fmth12aC2aNyxvkemdhEREVmjRd3koC7WDC3qZnicoXx+cG0nkDWUQbBgEEteFRYRht4juqkbERGRL9BC6gDxb0JPejq/T6wlaJQA0gwt7n/QQj3fS1yLvBp61mw7RxigRV6LYMEgloiIiKgYLXIwENJYdSHI28SlqxICydJqoY29s6jwHkBo27z+sCi+ucsIGCoAUTcgWDCIJSIiIrJC+sB6uo2WPWpCWMKX0JNfBDJnFg1kQ9tAKzUWmqEUggWDWCIiIiI/oRmioZV6F7rpCSB7BaCbgLDm0ELqItgwiCUiIiLyM5qxIhB5NYIZg1giIiKiIKbrOpCzGnrGNMB8WtXWapHXAKHNfbpbEINYIiIioiCl61nQzz2UPwnM0r7LCD3jZyBiEBD/FjTNN8NFDjsgIiIiClJ68utA1sL8j0xF32bOgJ76AXyVb4bWRJfgwNZDmP3tAhw/kIhSZePQ86bL0aRTA5ddEslMz8Lq2euRciYVFWqWR8seTWAw8PUgERH5F910GsiYYqVdV8ERQNr30KPvURvKfA2DWHK73JxcJB46DWOIAeWqlXVbfY3U9Hz26ERM/XCmOpfZrKvg8s/P/kH7Aa3w3M+PYN/Ggzhz/BzKVE5Aow71SrQWef5pH/6FiS//gvTkjIL7y1cvi4c/vwvt+rZ0y9dFRETkFtnL5K+0g4MygJw1QHhX+BoGseQ22Vk5+HnMNEwfPwvJp1PUfZXrVsT1Tw1Gv9t6ujyYnfLeDBXAClNu3qtKkznvksiqv9dheKXRyEzLKji+cp0KuPfD29BhQGvnnv/dP/HFk99fcL8E6M9fOQZvz3kRLbo3cdFXQ0RE5G7Zzh2mO3mch/EaKLkt+/rCoDH44bUpBQGsOLrnON4b/Rm+fuZHl54vJzsHP781zebjsvGycAArju09iRcGvYkVM9c4fP60pDRMePFnG8+tq5u1AJeIiMhnhTiTeNEAL4zYdQaDWHKLfyYsxNq5m6Cb9aIP5H/4y9vTsXvdPpedb9eavUg+dT5YdrqlCIDxD30Ls9lWPVCeJb+vQnZmju3nMuvYuXoPDu86VqI1EBERuZOeexDm5P/BfKI9zMebwpw4EHr6j9D1bGihDYDQ1vldCawxAmHdoBmrwBcxiCW3+OPT2XbLBaRmdeYXc1x2vpwsRzU9tgPZY3tPYOuynXaPO3siCQaj438u506cu6h1EBERuZqevQH66auA9EmALn+fsgHTbujJr0I/cxt0PRNa/NuAIcFKIGvI6xcb/z/4Kgax5BaHdx4tyHRaIzWrB7cdcdn5qjeu6lSQacvpI2fsPl62SmmYTfaztaJMldIXvQYiIiJX0fVc6OfuBfTMQq2z1CN5t5xV0FPehRZSHVqZ34Ho2wCtVF75gKEMEH03tLJToRkrwFcxiCW3iIyOsPu4ZtAQFR/psvMllI/H5ddedtGBbEJF+YdrW6fB7RAZY/trkvM2u7wRKtXy3X/sREQURLLmA+ZE++2z0ifCfHoEYDoCQ+wTMFRYCa3CdhjKL4Mh9mFoBt9OzDCIJbfofn1nuwGl1JB2H9bZpee874NRqt1VSQPZctXKoEnnBg6D8rvfvdXqYwaDpsoj7nrnlhKdl4iIyF30nE3ONaHKWQn9zE3Qs/5TH/rymNniGMS6iVxKT0/JQHamb7alcLehDw9EeGSY1YBSAr6q9SupzKkrJVQohfEr38TwJ69GXJlYdZ+soWXPpnY/T4JTo9FWUft5A0b3wlPfPaBKCwqr1bwG3lnwChq0q3uJXwEREZGrhJzfTW2XHGOCnvSUKkHwJ5pur3AxwCQnJyM+Ph5JSUmIi4tzW2up3z/6G9M++hsnD0gaH2jZoymuf3ow2vRugWCybcUuvDzkbTVcwBhqVP9OTLkm1G1VC//74ymUrVLGbeeW/6xzsnIQGh6qXlXO+3ExPntsIs6dTCpSQiDZ227DOpXouU0mE7Yt24nk06moWKs8ajev4YavgIiI6OLp2Wuhn7m+RJ+jlfocWkQP+Eu8xiDW1b1Rr3oTa/7ZWGRTk2QjZVPQw5/diYF39kYwke/J0umrsGPlbhhCjGjXr6WqHfXG5QpZy7p5m/IndpVGq55NYQxxnIElIiLyN7r0MD9zHZCzpdjGLlsM0GKfhCYbvLyMQawXgtjfP/4bnzz0jWqsb40Esz/s+wTlqrovA+nvpF+rBLj+VJNDRETki3RTIvSztwK5u504WoMW9z9oUcPgL/Eaa2JdHMSq1hR2/P3VPI+tx19IicEfn8zGbY0fQt+Q4RgQcQP+N+xd7FjlzD86IiIiskYzlstrnxX/PgD7XYNUSBh+BfyJE9vWyNk6ySM77U9r0s1m7Nt80GNr8pcA9qWhY9XoV0v4n5tjwpLfV+K/aSvx/C+P4vKhHby8SiIiIv+kaWHQIgdCV5u3Hrd1FBB1MzSjf10pZibWRQwGA0Jk85IdmsGgdsv7ejC+afE2/DdtBXat3Wt3YIErSAZ25cw1atNX4VPJMARpw/XmzR8i+UzJxskSERFRUVrkVdBiXwAQmn/VOCQ/DNSAyOuhxT4Ff8NMrItIDWfHq9ph6fSVKgCzRjZ3dbq6HXyV7OD/6ukfcKrQ9KqaTavhwfGj1WYsV5MAedq4mTYbgOR1GMjFnImLcM0jV7r8/ERERL5Kzz0EPeM3wHQAMMRDixgIhLa9pD0jWvQIIHIQkDEDuukINEMpIGIgtJCq8EcMYl1I+pPKZXD5D6x4BlN6o1asVcFng9hZ38zHu3d8esH9B7YexpO9XlF9UJt0sj8QoKQy07NwbO9Jh5O9dq3bC086svsY5v2wGOcSk9UmvN63dHVrOzAiIiILFT+kjYee+lF+plTiCQ16+iQg7DKg1CfQDDEX/fwqcI2+2cEOHv/AcgIXkmb3UsMZGh6iAllDiKGghVPlOhXx9pwXEBLqe68bsjKyVA9Va+SSvmSQP3t0gtXHTx87i32bDiDpVHKJzyvfG0cvKOX7GBomlz48U5/74b1fYGT9B/Hj67/hr6/mYsKLP+PGGvfgu5d/dXtpBREReZ6u50DXnWlB5SEZv0FPHVcwhCBvbGz++rJXQk960tsr9Bm+F1H5OdmE1PLIF+oS+O71+xAaFoLLBrVF+wGtnJoK5Q3L/1yDtKR0m4+bzTq2r9yNwzuPomr9yuq+LUt34JvnJmHjoq0FGdNOV7XD7WNuRLUGVZw6b1h4KFr2bIYNC7eoQNlWYHnZlW3gCVJKMePzOep9tZ5Cv9O+f3UyYkvHYMiDAzyyFiIich8VtGZMhp42ETDtyct0hnWBFn0ntHDvbSbWdTP0tPH5NavWEidmIGsuzEkvAubTUugKLaIPEN4TmhZ8IV3wfcUeEJsQo8au+gupgbUMZHB0nASxa+duxLMD3iiSmZSM7bI/V2Pd/E0Yt/R11GhczalzX//0EDWAwBrJZFeqVcEjQey0cX9hynsz7B7z42tTMOiePj6ZTSciIucDWP3cI0DWrEJtMXUgeyn07P8Ab/ZKNe0FTEccH5fxS/47BuiZfwAhdYGEb6EZKyCYsJyAkFAh3mEAazlOuheMve0TNZSg+OfIx5lpWfj4gW+cPnfrK5rhkc/vUkG03OT3iXoLoGKNcnhz9vNun6o19cOZ+OThbx0el3QqBVuX7XTrWoiIyM0ypuUHsCiW7ZTLbzr05Behm456Z216prMH4ny5gfSm3Af97J1BV/bGlBKh41VtEREdrgJQa6RUoHbzGiq7umr2epw6fNrmc0kgu37BZhzbewKVajv3inDA6F5o17+VGgSxd9MBhEWEovPV7dF5SHu3Zz1Tz6Xhq2d+dPr4jFRnf8EQEZEv0tO/t3O53nLMr9BiH/bsuswp0LOWOFybdSYgdxuQvQwI74RgwSCWEBkTidtev9FqNtLSyuPOsbeot0d2HbPafaG4o3uOOx3ECukCcMvLnr988+/kZcjJynH6+OoNnav3JSIiH5UrV9Ts/Q0zAznbPDwe9ij00zcCZhmadLHZ1BDoWQugBVEQy3ICUmTD0gMf34GYUtFF7i9XrQxe+/MZddlfRMdHOXW5Qo7zB4mHTztVriAlDq2uaFaiwJyIiPwxfyelbeHwJP3sg4D5xCUEsPn0bAQTZmKpwFX39kW/23pgzZyNSD6dggo1yqF5t8ZqGpmFbLKSFmIyhMAWCXzrt60DTzq865hac/lqZUrU07VU+XiYbQynKCwqNhIPfjL6EldJREReF34FkDX7fD3pBczQInp4bDl6zkYgd6MTR8rET3tBai60UNcPJvJlDGKpiLCIMHQc1NZu54XrHrsKk96YavOYUf+7oUjg606r/9mAr5/5EbvX7cu7QwPa9m2Ju8begppNHHdI6DasIz59ZIJq5WVvQ9u4ZW+gYs3yrlw6ERF5gRZzO/SCzgTFM59GwFAeiPBgO8Xs1fkXxh0kVCKHqLZg1o/TVLstRAxCMGE5AZXYra8Ox7DHr1KX2GXTV0ioDC3QVIb2vg9vQ+9burl9DVLSMPndP/BMv9dUP97zDwBr52zEg52exf4thxw+T6ly8bjhmSHWH8zvlPDy1CcYwBIRBQgttBm0Uh9Yz+NpUUCpD6C5uZxAN5+DnvY1zKevh54m+1EcXxFExNVAaLv84LvwpCApiTOqr0kzFC0JDHSaHkT9GJKTkxEfH4+kpCTExcV5ezl+79TRM/j312U4l5iE8tXLofvwThfU1LqDDFr48J4vsG/TQZvHSPDZonsTvD3nRYfPJ/8EfhozDT+NmVqkQ0OFmuXw2Ff3oFXPvHpgIiIKHOaM34GkZ4qVFUhwGA4t4VNo4Z3dcl49Zxv0M7cCepLzNbBaDLTyS/Nyj+mToKf/AJjkb2AoENEPWvTt0EIbI9jiNQax5Fe2rdiFR7u9CFOOyakNZj/u/0QF2M7ISM3AqlnrkXouHZXrVLigHpiIiAKDLn1VTw0s6A1blASyYdDKzYZmrOza8+pZ0BN7AOYzzmVfLeuJvhOG2MeKPZdJBbWWLkJOryF7DfT0HwGpxZWMc3gvaFE3QDNWhL/Fa6yJJb/yxRPfwZzrXAArju076XQQK63Gul7b8RJXSEREvk5lMgsGBlzwKIAc6Ok/Q4t91LUnzvwLMJ9y8mApEzABYd2gxTxwwaOaVvJBQOaUDwE11jb/uUXuHuhpE4DSX0ELk3IF/+E3aaYxY8agXbt2iI2NRfny5TF48GDs2LHD28siDzq27wQ2/7cdZrPzFw/iSse4dU1EROSHshba6U4gzPnHuJaetSw/gHTAUB0I6wyt1Md5pQ1a2KWfO3NufgCLYl+7ZISzoJ+9C7o5Ff7Eb4LYRYsW4b777sPy5csxZ84c5OTkoE+fPkhLS/P20shDTh+Ryy/OkasrVRtURs2m1Ut0jh2r92DmF3Mwe8ICnD529iJWSUREPk/Pdc0xJSYBo+NEjFZuJgySGY3oc1EZV2v0tG/shH1mOQCQOmE/4jflBLNmWeYc55kwYYLKyK5ZswZdu3b12rr8nWzK2rl6r+oy0PiyeoiO992djQkVSzl9rFQb3P7GjU7XCh3cfgRv3vwhdq3dV2RzWK8RXfHg+DsQHumenarS2mvl3+vUJDQZECEjgKVjAhERFa0lReYs6DlbpL0AtPDuQGjbEteDFghrrZ7PdjbWCIS1gatpYa2gZ/5p7wggpL7LuyPo8kcxZ62DOlwNevZKaNE3w1/4TRBbnBT7itKlS9s8JisrS90KFwpTntRzaWrM7PxJ/xX0SA2NCMWA26/AnWNHqH6xvqZK3Upo0K4udq7ZA91OSUFkbISaPtZlSAenp3Y93OV5pCWlF7nfbDJjzneLcC4xGa/98fTF/7K0YdWsdRg7ajzOnkhSAbOcz3iPEUMeGoA73rwJRqNrXn0TEfkzuQSvn3swfze/hC069LQvgdDmQKnPoBnLlvg5tagR0DNn2jnCDC3qRrictMlKeQfQM2wElDq0qFHwDh3+xm/KCQozm814+OGH0blzZzRt2tRuHa3sbrPcqlVz3Pw+GGRlZOGJK17BvB8XF2nyn5OZgz8+nY0Xr34LJpO9WiHvGf32zTAYNJsBZc8bu2Dy8a/Qe4TzvWonv/MH0pPTVRBZnATLK2euVbW4rrRp8TY8P+hNnDuZ98LKcm75efz23p/4/LHvXHo+IqKLpZtOQU/9FObTN6m+puaUd6DnHvbMuXN2QT87GtAtSSi5xJ//9ylnC/SzI6FfxGV/Law1tBjLbv/CCYO897XY590y/UozxEAr9Wleaywr50Xk8LyhBq4+r6YBoa0dhn3c2OUBUhu7efNm/Pzzz3aPe+aZZ1TG1nI7dMhx8/tg8M/ERWpAgK2gTcbOrpgplx18T4tuTfD6zGdRvkbRV95yKf7eD0bh6e8fLPGl/38mLoTJzuhZY4gRc79fBFf69oWf1FtrXRbkrt8/+ltliImIvEnPWg498QroqR8COavyLkmnfQX9VG/oGTPcf/60r2y0wRImIHfnRW/A0mLugpYwEQjvCmjRqherjKTVSv8ILXrEJa/d5nnDL4NWdiYQNQIwVAK00kDYZdAkqxz3qsuv+llo0ZLhtfW3zpD3PXBDAO1OfldOcP/992PGjBn4999/UbVqVbvHhoeHqxsV9deXc60O27OQS9t/fz0Pna7yzVdkbXq3wHe7P1bZzGN7TyKuTAza9mlxUSUQknEuXkZwwTG5JlVS4MohEZv+3Wb/IA1Y9OtSXPtocI0QJCLfoZtOqh3rQGaxvxh5gZCe9DgQUhdaaEP3LUJaUtntImCEnjkLWkSvi3p6LbyjunmaFlIdWtyzgNwKkayynns07wNjFZubuvJqhOfkDTzQYoGIvtCM9idLahG9oUffC6R9UrTFlspnyoCHz1Wm2J/4TRArGasHHngA06ZNw8KFC1GrVi1vL8lvJR46pbJ9tkiG9uQBZ/vYeYcMIZCsrNwuhdSdliofj3Mnk2wfE2JAuapl4CopZxy3MDEaDUg+neKycxIRlVjGr6r1ku2UhwY97Ttopd5wy+l1Pa/1k32mvF31fk6VRKR9DT194vk+soaKQPRtQNQt0LTzF871jL+gJ78A6Cn5YZwJSHkdeuRN0OKegabZDu0MsQ9DD+8CPX0SkLNBDXWAdEDwsWEHARfESgnBpEmTMH36dNUr9vjx4+p+qXWNjIz09vL8igRtSadsB0hSc1q6BJ0A/N3A0b3w05vTrJZXCCk16Duqh8vOV6ZyQsFGLltyc02oUMO5IQ1ERO6gZy1ysJvdBGS7ttSqMAncdGM1wHTYTiBtBEJqw59JsK6fexjImlP06zQfh57yBpCzHYgfo8oM9KzF0JMeKfTZuZZnATJ+UJ+txb9g93xaWFt1CwR+UxP76aefqrrW7t27o1KlSgW3X375xdtL8zt9R/VULbVskWECfUa6LmjzdUMfGYjy1cvCEGLln4MG9L/jCtRr7bpfknGlY9FlaHsVyNoSFh6K7sM7ueycREQlpsaauuCYS6BFOWr3ZIYWOQx+TYLXrH9sB+qZU4HspepdPeW9/DttTBrL+BG66QSChcGfygms3UaOHOntpfmdAXf0RMWa5a0GbRJY1W9TWwVZwUKCyg+XvIaOg9oWCe6j4qJwy4vD8NCno11+ztvfuAlRcZE2A9m73xvp0z17iSgIqGydvVZ/0ktVdry7kbS5srqrPu93tRbzKLSQGvBn6tK+g3BMT3oK5oxZQO4WB62wdCBzNoKFpjs7hD4ASJ9YKT+QjG5cXByCmUyjeuuWj7Bu3qbzd2pA58Ht8fjX9yKmVHAGUKeOnMa+TQcRGh6KRpfVc9uQg5zsHEwfPxu/vv276hNrUal2BYz83/XoeUMXt5yXiMhZeu4+6Kf62y0p0BK+hxbewb3r0DOhp34GSLCnn8u7UwYCRN8DLXIg/J35ZBf5Pxc9Wwi0mHugxTyAYIjX/KYmllyrTKUEvD3nRTWpauuynaoOtnm3xipDG8zKVimjbu505vhZPNrtJTWlqziZ2NXj+s5uPT8RkTO0kFpA3OvQk5/NzxRaSgfydrZrMQ+7PYBV69AioMmGpJj7ALlUroUBhnJua0XlcdLaC64KYnMBqSN2Ed2cCmTNzdtsJhvNIq6ApvnOPiRmYok8SP653dXiMezbbLtn8f3jbsPV90v2g4jI+/ScjdDTJgJZi/MC2bB20GTHfDjr9l1BTx0PPfUjB5voLCyBu43QTYuGVn7pJQeaKjRM/xZ6ygf5LdbyW3LJ88c+BS3qevhCvOY3NbFEgWDL0h12A1jx7fM/Wx2CQETkDVpocxhKvQtDhZUwVFgDQ8JnDGBdKfJ6aSngoP7YQs8P3YofK8GtBi3uFddkStO/h57yZn4Ai/NZeD0NevKL0DOmwRcwiCVykcO7jmHSG1Px5VM/4K+v5iE9RWZjFzXjM9mBal9acjoObOV0OSKiYKAZy0Ar8yNgrOzcJ4TUB8Klg1ChEE5qhGXiV+RVl7weGaSgJrTZOyblXehu7kzhDNbEEl2i7KwcvD/6M8z94V/VbUDqi6XP6ycPf6s6G/Qe0a3gWGdHyR7ecQw1m1R346qJiMhXaCF1gbJzoJ8dDWT/Z2+mJjSZzhVzL3TTacB8BNDiAGMN19UIZy3JH6Rgh2xEy1kDhHm3kxEzsUSX6P07P8O8SYsLpp3l5sgEGSArPQtvj/wYK/5aW3Cs9KN1Rrnq7t1cRkREnqPagubuhp6zBbo5xeZwB6k3zQvNrAWkBpmTC0Redz6DG9ocWkhN125yM59z8riz8DYGsUSX4Oie4yoDq5utv2qWXywTXzo/kGPIAwMcPmd4VDjqtuRYZSKiQKBnTIV+qhf0UwOgnx4C/WRHmM89C9185oJjtVApC3g/v+a1cIimAdKlIeEraEY3T3M0VnHyONd1QbhYLCegoLNj1W5M/2QWtvy3A8ZQIzpe2QZX3tMHlWpVKPFzLf5tBQwG2yNkJbjdtWYvTh5MRPnq5VCtURUVpEqW1pabnhsKY4gzBf5EROTLpL+tnmqZsmWRDWROg56zGigzGZpBNnWdp0X0A8q1hJ7+S94leykhCOsMRF0DzVDa/YsOawcYKgNmaQNpLUFjAELqASGN4G0MYimoTH7nD3zx5Pcwhhhgys0LPKVf67SP/sIr055Eu36tSvR8aUlpqgbW7KC+PS05b5PX10//iOzMbJvHxSREY9iTV5doDURE5Ht001HoqZJVtcYEmA5CT/sCWuwTFzyqGStCi30I3qBpBiD+Nehn78gvayicpMnrjKDFvewTfXpZTkBuaeb/75RlWDR5GU4eOgVfsX7BZhXACksAW1DHmm3Cy9e8g7MnnKwFyletQZW8Glg7QkKNKF+tjOpW8PfX82yWHojUs2nYvmJ3idZAREQ+KOM3G7WtFmYg/RfoujP9YT1LC+8CLWEiENKk6AOhLaGV/hFaWBv4AmZiyWUkSBt375dY8POSgsvr8kqt85D2eOSLuxBXOtar6/vtgxkwhBhgLhTAFi66z8nKwd9fz8eNzw51+jkvv/YyfPTAV8hIzbR61UUyvt2v74zo+GhsXbYD2Zk5dp9PsroyQa1JpwZwpZSzqdi0eJsK3uu3qY0KNdxcU0VEFOT03IMOglg5KFn1XoXm3b+P1sg0Ni38NzV+OG9iVwVoIb7VNYdBLLlEbk4unun3Grav3F2kPlSCw6XTV6lL9uOWvYGIqHCvrXHjwq1WA1gLyZBKtrYkQax8PY99eQ9ev+EDyBUYc6EsqwSwCRVK4fYxN6mPpf2WI3r+57my/dcXT3yHv76ci5ys3Lw7NeCygW3UC4vSFRNcdi4iIiqkWK2rdUa1Ycvnxw/DNzcbs5yAXOLfKctVBtHaBie5b//mg5jz3SIEom7DOuHN2c+jYYd6BfeFhIXgipu64qMVY1C2cl4hfu0WNRFTKtruc0kg3bpXc5esS15AvHrdu/jjk9nnA1j1ALBy1jo8cvkLSD2X5pJzERFRUVqEdKOxV25mBML7QtNCPbiqwMIgllxi9rfz1aVwe6Qe1Juad2+sygls0QwaWnQvVv/jJAk8P1zyOn46/Dm+3voBfkv8Bk98e19BACvCwkMx9KGBsFULL2tr1asZajZxTduSdfM2YcWMNVZrcCUjfWzfSacmiBER0UUIbQWEdbERauVvkIq52wsLCxwMYsklTh0+U+RSenG6Dpw+emFPPE+65uErbZYTSO1uaFgIBtxxxSWdQ4LW6g2rICrW+uzqG58biu7DO6v3LWUDEjyLGo2q4tkfXbcbdfaEBXaDdgluZ34512XnIyL/JQ349fSp0FO/hJ7xJ3T9wrHZVDLyd0Ur9REQ3jv/HsP5Kk5DGWilv4EW2tCbS/R7rIkllyhbtTQO7zxqM5CV7GOZQllJb2jZoynufHvEBS22pFZVPn556hOqhtWdpP/rMz8+hP53XIG/vpqHo7uPI75cHHrddLnaJBYa5rrLSjLi1l4NsDhzzPsTV4jIe6TsCGlfQk8dl9e/VDXZNwHJ0UDss9Ci8qZD0cXRDNHQEj7K2xyVNR/QM4GQ+kB4D2gaQ7BLxe8guUTfUT2xdu4mm49LaNv/9kvLcrrCdY9fhebdGmP6+FnYsuTShx1c7KvzVj2bqZs7SVZYAnRbgxiEu4N2IvJx6d9AT32n0B35NZx6GvTk5/KmREUO8tbqAobaHBVyu7eXEXAYxJJLdL32Mkz/+O8LuhMICaRqNK6K3rd0gy9o0K4unpxwPwKdfL+l3ZktUsbgCy8siMg7dHM69NSP7B+T8g4QIbX8rD4k38P/KsklQkJDMGbW8+h5Q5ciraRUn9jB7fDOgpe92l7LH+1auxefPjIBb9z0gWqTtW+z9Bx0Xps+LdTN2oY7KZ8oX60sBt3bx4UrJiK/kv0voKfbP0ZGj+Zs8NSKiEpE01VBTHBITk5GfHw8kpKSEBcX5+3lBPTErs3/bVebuRpdVk8FS1SynrtjR43H/En/qWBTvo9SUyw1vFfe1Rv3f3w7jEapW3MsKyMLHz/4DeZMXART7vlWL617NcvrnlCljBu/EiLyZXr6z9CTX3R4nFbqc2gRPTyyJqKSxGsMYol8zCePfIvfx/2dt+HCihEvXodbXh5Wouc8ezIJGxduUYFs/XZ1UbVeJexevw+/vT9DDaPIzc5FnZa1MPiB/uhxfWefmIlNRO6lZ/0H/extDo/TysyEFnq+DzaRuzGItYJBLPm65NMpGF7lThVU2hIZG4Ffj311SeUZi6euwOvXv6feL+jSYNBUdwmppX38m3thMLDaiCiQ6boJemIPwHwif/ttcQYgpDEMZad6YXUUzJKdjNf4V4qCyp4N+7Fo8jKs/meDGsnqa9bO3Wg3gBUZKZnYtHjbRZ/jXGISxtz0IcwmvSCAFZb2aDJZLVCnqxHReZpmhBb/at4s6gvCgbyeplrcS15aHZFj7E5AQbNJ6v07P1dvLWITonHzi9dhyIMD7F4+N5vNavrVrrX71ECE9gNaoVqDKm5ZZ1aG9Gl0LCfz4gPw2d8uVHW3ti7CSNeCaR/ORN+RrIEjCnRaeHcg4VvoKWOB3M3nHwhtAy3uaWih7m0FSHQpGMRSwJNd/Y90fRE5xTKvKWfT1O5/yWze9Pw1Vj9397p9ePW6d3Fs7wnVdUECv88em4jLrmyDp79/ANHx0S5da50WNZ06rlaz6hd9jh2rd1u/clhokteeDQdgMpmc3kBGRP5LC+8ILXxqXkN+82nAUBFaSFVvL4vIIZYTUMD75rlJKoC11fT/+1cnq0vsxR3bdwKP9XgJJw4kqo/l8yXAEyv/XodnB45RWVpXqtuqFuq1qV2kTVlh0q1AOgtUql3hkqaGOdq3JednTSxR8DXk18LaMoAlv8G/UhTwG6VWzFhrd2qVBKILfrpwKMCUd/9EVnqW1c+V+7Yu3YE1/7i+f+JT3z2A6LhIGEIMFwSW8WXj8MgXd1/S87fr29LmeGDLeaS/LDsUEBGRL2MQSwHt7IlzNms/LYxGA04fPXPB/fN+XFxk41NxEmTam4h1sWo0qopP147FwDt6ITwyTN0XGROBq+/rh/Gr30LFmuUv6fm7DeuI0pUSbGZ7JUAf9vhVl3QOIvIvup4LPXsN9KzF0HMPe3s5RE5hTSwFtPhycXkbb+3EsSaTGaUrJhS5TwLftCT7k2zMuWaV6XWHCjXK4cFPRqvBBpINDo8Kd9nl/fDIcLz1zwt4stcrqn+sBk19vZaa3wfHj0bLHk1dci4i8m3qRX7GJOipH+fVw1ruD+sMLe5laCE1vLo+InsYxFJAK1UuHu37t8Lq2RtslhRIf9Tu13cqcp9cSi9fvSxOHjxl87mlPvVSalOdIYFrZEyky5+3ZpNqmLjrIzUVbNmfq5GdmYN6rWuriWDu/pqIyIekfQI99cML789eDv30MKDsVGhG93RjIbpUDGIp4I167Qasn78FuXqO1VrQ658eogLcDQu3ICImAnVb1VS78q+8qw++feGngs1cxUmpQf/br4C/kuB44J291Y2Igo9uOgE99SMbj5oAPRl6ykfQSr3p4ZUROYdBLAW8ui1r4Z0FL+Pd2z/Bga2Hi0y+GnR3X+xevx83Vr+noHa2bNUyuPXlYbj6/n5Y8PN/6nOsZXGHPjQQtZvzUhsR+amM6Q4OMAGZf0LXX4Kmuf6KENGlYhBLQaFRh3r4ctN72L5yN47sOoaouEhUa1gFj3Z9UdW1Ft78derwabx7x6dIOpWC9xa9ii+f+gFzvl9UMGBANkUNf/JqNSSBiMhf6eZj+fu77bUKzAHMZwGjbwaxeu5u6Om/Arl7AUMMtIh+QPgV0LRQby+NPEDTHW3dDsJZvBQc3rn9E8z9fpHNDgRS8/rToc+RUKEU0pLScHD7UYSGh6BW0+qq1yrZd/LQKbU5rny1Mi4fCkFEl05KCfTUT/IyrjYZoJVfDc0QA18ioYsqhUj7WH5b538N+QF5SD1oCROgGct5aC25QNYC6NlL5JUBtNCWQKRMgozwyPmDOV5jEBvg9mzYj1nfzFcN++NKx6LnjV3QsmfToG9kn5mehSGlRyI3O9fmMTJ+dfRbI3DdY4M8ujZ/J4MgJr70C3au3qM+Dgk1ovv1nXH7GzeibJUy3l4eEeXTc/dDP9XHzhFGILwnDAnj4Wv0jGnQk56y8agRCGkCrcxkt/e7Vt/Ds7cDpkOFLm7nAlo8tIRPoIW1c+v5gz1eYzlBgJLXJp88/C1+/+hvlVGUbKO8nT1hAVp0b4JXpz+FqFjfvDzkCUmJyXYDWEv/2BP7T3psTYFg/qTFGDNiXJE/HLk5JtUFYd28Tfh4xRgGskQ+QgupCT1yGJAx2UofQkl0hECLuR++Ji8L+xls9080AbkbgZy1QFgb963DnAb9zAjAbOliU+hvip4C/cwdQNk/oYVc/Jhwsi+403EBbMp7M1QAKyyXyy1vNy3epi6lB7OYUlEq02qPdDKQCVnknIzUDLx/1+fqb0rxjg6yMe7cySR8+/zPXlsfEV1IesEiatSFOS1jVWilv4MW2gi6boKe9R/0tO+gp0+Fbr5wOIxHmY8Dpn32G4DDCD1rkXvXkfknYD5hoxxD/t5mQ0//oci9um6GnrkA5rP3wnxqIMxnboGe/ht0PdO9aw1QzMQGoNycXPzy9u82H5eAYvFvy3Fs74mg7QkqNZodBrRWl75t9Y+V+3vc2MXja/NXi35dpso0bJEXUZKpvffDUYiOi/Lo2ojIOk0LgRb3NPSYuwAJ+vR0wFgHCGuvrqjoWSugJz0JqE1g+ZnP5BDoUSOgxT6hPt/j9LxNtvZpTh53CcvInGUnG2zp7jATiHs273g9B/q5B4GseYXqeHdDz14OpH8NJHwPzcgrVSXBTGwA2r1un7pcbo9MaZIALpjd8vIwVWIhww6Kk1/e/W7rgar1Knllbf5Iuj6EONjwJqUF0v2BiHyLZkiAFjkYWtSN0MI75AWwOZugn70tP9uIQsFaLpA+AXry/7yzWGNFVXNqXy60UDdPHtTTHGSDVaR7/l3ZiJY1P/8jS/Y2//Nz90FPetRNCw1cDGIDkKNaTyElizlZ7n2V6utkQtWYWc+jdOXS6mNLMCuB7VX39cVDn97p5RX6l+j4KKvDJIqLYhaWyC/oKR/mB1vWrlbJuNqfoeceKvnzSnlC5jyYU8bCnPIu9Kwl6jK7szQtDIi60U4IYwAMpYEINw9yCWmUn1G1xaA6JQhdzwDSv7eftc1eBj1np1uWGqhYThCAqjeuqnaES9bLFgk2JIgLdi26NcEP+8Zj7dxNOLTtiJrY1XFQG9VWi0rm8msvw9fPTrL5uNQgN2hbB+Wq8nIZka/TzeeA7MUOMo0akDkDiLnH+efN2Q797N2A+WhBCKKnfQ4Y6wIJn0MLqebU82gx90DPXgXkrLE8c/5bCSpDoZX6OC/YdSMt6nroGfbq/M3Qom7Oezdne37m1u4zqnG/CK3v0s1nkIEVOZsBLRRaWFcgvCs0LTDaRDITG4CklVaPG7rAYLT+45X7qzaojObdGnt8bb5IRsy269sSQx8eiAF3XMEA9iJVqVsJvUZ0tb5hLr9s7NZXr/fG0oiopMxSkuboyooBugxCcJJuOpm/m99SniBXDfOvHJr2qcd0c6pTzyU9WLXSE6DFPgcYa+YFr1oMEHkdtLK/QwtrC3fTQhsD0fdZPir8SN6b8P5AhGUojrOZZtd1PdWzFkNP7AI9+UUgYyqQ/gv0c3dBP3UldNMRBAJmYgPU3e/emjedaufRIpd4DSEGREZH4PmfH3F7/zwKPo98cbd6O/f7f9WLJSnRkCsC8t+cPNa2TwtvL5GInCGX41WIYK88zQzNWNnpp9TTf1Ktp6wHdKa8zWOZf+SXCjimMq3Rt0CLvgXeYoh9CHpIHehpXwK52/LuNFaGFjUSiLoZmpafTAppAECGH9jrQqADYa1dsi49Z2dexrvg51fo52jaD/3MrUDZv9yerXY3DjsIYDJlatq4vzHj839w+uhZNWq194huuPaxQahYs7y3l0cB7Oie4/h3ynKkJ6ejSr1K6HpdRxXIEpH/MJ97Iq9cwOZErxBo5f+DpgJeuXSdlNcRwFD6fPBW+PkSewGmg3bOqAGhbWEo8yP8ka6y1zLoIMFqksic/BqgWm6ZbQxoaAxD2d9cshbzuaeBzOl2p7Fp8e9Ci/TNYT6c2GVFsAWxhZnN5qCf0uUK6SkZ2LVmr2q2Xa91LY5TJaKApecehn56aH729MJgSIt9Clr07dAzZ0NP/RzI3Zz3gKECtKhbgOiR0LTQguPNJzsCZgfdSUIawFD2TwQi2dyln7ktv463cGsuDTBUhFZmEjRjFZecy3yilYMaXAMQ3geGhHHwRZzYRUUwgL002ZnZ+PqZSZj5xRxkZWSr+0IjQtFvZA+MHjuCWUYiCjhaSFWgzGToya8C2f+dD7ok4Ip5EFrUtSp41VPfLbrFxnwCeuo7QPZKIOHT871kjbUAVUNrqz7UWLCbPxBpWiRQeiKQMQN6xk+A6TCglYIWORSIGgbN4KhtWAnojoYnmPN6Avs5BrFEDphMJrx49VtYO29TkUlUOZk5mPnlXOzbfBBvz30RoWHnMw5ERIFAC6kBrfTX0E1Hgdz9gBYNhDZVu9v13L35ASysBKY6kL0IyPgNiBqe91xRN0FPWm3nbCa14z+Q5bUHGwotaqh7TxRSG8jdbWejmNGlXRC8hek5IgeWTl+NNXM2XjBK1TLVa/N/27Hw56VeWRsRkSfIBi4tvBO0sBYF7Zn09F8c9EnVio5djegHhPcqtpO/EEN5NUDBfPbB/N6xQVPt6HJa1AgHnQ7M0CKHwd8xiCVy4K8v59psVyakpZSUGRARBZXcnXY3DqkgKndvwUcS/GqlxkGLeRQwlC10XH4gbE4EcncAWXOgnx2lJljJYAS6CJHXAmHdrLxgyPtbpsU+CS1EWpP5N5YTUNDYsXoPZn09D8f3n0R82Tj0uL4z2vZrqfrE2iPHS8bVFsnQHt+f6IYVB1fJRlZ6NiKiw1m/TeQvpLRABUV2eqBq4UU/lPrYmLuA6NsB01Ho5x4CcrfnP2rJHOYHrpl/ASH1SzRMgQp9nxM+AdK/g542ETAfz3sgtBm06LugRUhG3P8xiKWg6Mzw0X1fYcbnc2AMMcKUa1KZ1Xk/LkaTzg3w+oxn7HYZSCgfjyO7jlktJyg4poILC/KDiLxA+PnN3zHn+0XIzshGZEwE+o7sgeFPD0bZ/HHAROSbtIg+0LP+sXOEEYjob/1ztRDo0vUgd4udz9ehp32rAl5/72fqDZp0hpAXC1GjAP0cgDBohhgEEqY8KOD9OvYPFcAKCWCFJbO6bfkuvD1yvN3P731LN7sBrPQD7HNrd5euORgc2HoI97R5ErO+macCWJGRmok/P5uNe9s8iWP7LFN9iMgnSY2rsbqNuli5jG2EFj3K9udnLXUchkjwpTYoOaabTkFP/QTmU0NgPjUQ5nPP5I1bDXKaZlC9fAMtgBUMYimg5WTnYPK7f9h8XILZpdNX4cjuYzaP6XljF9RoXFVNOyvOGGJApdrl0Wckg9iSeuvWj5GenAFTbtFLkfJx0ukUvH/n5yV+TnmRIqUJROR+kh3VpGWUGvtqubibf4FXi4GW8Dm0kLp2nkH+7TszOdLxBi89ex30U72hp47Ly+7m7gIyf1d9bvXUT537gsjvMIilgLZn/X4kn5JG3bCbSV01a73Nx8Mjw/HOgpfRqmezQp+U96ZJ54Z4d9GriI6Lctmag8HudfvU0AhbtcbmXDPWzdukJn85IjuYpTTk3nZPoV/Y9egfdj0e7/kyVvy11g0rJ6LCpDm/VnYmtISv8sbFRl4HLe6NvEle4Z3tf7IasWpyXHcbUsfuIbo5FfrZ0YCeUaw+N++59dT3oWcucPprIv/BmlgKaLnZ9uZ+nw9iHR1Xqlw83pz1PA5uP4INC7dI5ISmlzdCraZyKY1Kau/GA04dt2/TQVSuU9FuAPvxA1/jj09mqy4RefcBmxZvUz+nO968GcOfvNpl6yYiK/RUwHQof/JUaSC0eV5jf0dC2wLGeoBpr41g1gBEXg9NczBMJvMPQJeRr7YYoad/Cy2ih+M1kV9hEEsBrUaTaggJC7EbpMrGrwbt7F3yOq96wyrqRpcmPNK5TRphDo5bMXOtCmBF4bplS4b3q6d/QLt+LVG7eY1LWi8RWaenT4We/JLMNcyvjZXNWOOhh/eBVmqs3WBWEghI+Bj6mZsA85lCWdT8jgdh7aHFPux4DVnLi41xLc6kpofJi151TgoYLCeggBabEINeN3e12edV7q/eqAqadmno8bUFs9a9myM03P5r6KjYSDS7vJHdY37/+G+7PXylZvnPT/OCXCJyLblEryc/LTu08gPI3PMZ1ay50M895fA5tJBa0MrOgBbzUN5YWi0/kxv3JrSEr6EVa9FlYyVOHsPhCYGGQSwFvDvHjlCBquVys4UEP9FxkXjh18f46twLLy4G3dPX9vddA659dBAiouz/Adu11nZdrWWTmPQHJiLX01M/shNGmIGsWdBzHf/7UzvnY+6BodxsGCosh6HMr2osq2oR5QQtrI2DIwxAaEu1S58CC8sJKCgCpnFLX8f08bMx8/M5OHnoFGJKRavWWUMfHojy1fImx5w9mYTZ38zH+gWb1WWn5l2boP8dPVG6YoJLakAtQxYaXVaPDf0BjH7rZiQlJqtNWdK/N+9SX17geeVdfXDTC9c4fI6w8FCXlS4QkfN003Eg11H7KiOQ+Y/7hxVEDgFSP8jf2GUt22q23+qL/JamB9Fw4uTkZMTHxyMpKQlxcXHeXg75kLVzN+LFwW8hOzOnoLZSMrehYSF4ccrj6DBAdtGW3JalO/DR/V+pLgkW5aqVwei3RqiJYQTsXr8Pc7//F2dPnFMDDqRdWY3G1Zz6XNnU9edn/9jMxsrPUL7X1z02yMWrJgpueu5u6KcGODgqBIgeBUPsE+5fT9ZS6GfvKlrSoGp0TUD0HdBingiKK266enGxUyZR5GefwwI6XmMQS0FPMrOjGj6EHAlgi/1zkN95xtAQfLX5PVSpW6lEz7t1+U481v0l1bvU2rCEx7+5V02noot3eNcx3Nn8UeTmXPg9zisXicKEXeMQVzrWa2skCkS6OQ36yQ75G7ps0+LfhBY51DNryj0IPX0SkDVHGsfmjViNuhlaeCcEQ/CqJ78MZEkrsfzfhVo8tOjReUG8n5VSOBuv+ddXReQGMz77R3UvsPZ6Tu6SLN+f+TvgS+KzxybCbCOAFZ8+MgHZmfb/AJB9VetVwqvTn0Z4RJjKshgMcjMUlJG8NecFBrBEbqAZooHIwTamdakj8nq82hg765Y1hVSHIe5pGMrNg6H8YhgSPgmSAPYU9NPDgKxFRcsp9CToqe9AT3kDgYpBLAW95TPW2N0cJI8tm7GmRM8pE8C2LdsJs51xtWlJ6Vj2x+oSPS9dqG2fFvjp8Oe4+71bcfm1l6Hb8I549Mu78cP+T1CvdW1vL48oYGkxjwDGilYCWQktNGjxY5zrF+sCuuko9Ow10HOl52xw0dO+AsyJtgdHpH/n1AY7f8SNXRT0TDkmlwxNKOzUYel5aJ9c7k48fLpEz0vWyUa9oQ8NVDc6Lzt/BG+Y0Va2jOjiacYyQOkpeV0KMqbK1IG8B0LbQot5AFq4lBu4l56zDXrKGCB7+fn7QhpBi30CWngXt5/flXTplZv1X34pRENooU0df45uBjJ+dTD5zAg9Y6r6ngQaBrEU9Bp3aqAyp7Ir3hrZOd+kc4MSPWep8o5rriXDm1ChVImel8gRKYuZuWsHvlq7GhtPnlD3NS9fAbe3bosr6zUIis0tviqvZMkETQsJqEBWi38ZetzTgCkRMESrllmeoOdshX76hvw+tYXkbod+9nag1HhoEb3g63Q9G3ryG/nB6PmEiR7SJG9gRIidYTx6Rt7ENPtnAEzHEIhYTkBB76p7+9oMYIVszBp8f8nquqo3qqqmRNkLGMKjwtHp6rbYuWYPJr0xFd+/OhmrZq1TE8SILtZbSxfjwVkzsTnxZMF98v5Ds2birSX/enVtwUrP3gDz2fuhn2gC/URjmBN7QU/7Drqeg0Aho2G1kGoeC2CFnvy//AC2+O/MvDIuPfkFn/8eywsb/dzjQMZPRQLYgmD89A2qVMImTUbyOhoIkT8OOAAFzstBooskdZN3v3ur2oglE54sAa1c7pds6W2v34jGHUuWiZXgVYYsPNP/9bxhiFZKY4c9cRWeHfAGNv+3XZ3L0iO1Uu0KeOm3x1GnRU24Q1pSGuZ8/y+2r9ylssxterfA5dd0QGiYc43FyXetOHwIX6xZpd43F/qPzvL+F2tXo0fN2uhQ1bkWZnTp9MzZ0M89lD8WNf+Sr+kQ9JTXgayFQMJnftsGyZv03ANAjr29CrIr9zSQtRiI6AmflbNRDYWwzqSyrHral9DiZLTvhTTNCD3yaiDjNzslBSZockwAYiaWCMA1j1yJd+a/jHb9WyEiJgIR0eFo27cF3vrnBdzwzJCLek4JDv83/SmUqVz0FXBUXCTueucWLP5tObYu26nuk2DZEjyfOJCIx3u8rFp/udqq2etxfdW78MlD32LBT0sw74d/MeamD3FL3QdwYOshl5+PPOv7TethtJP9l8e+27jeo2sKZro5KS/LpjKDpgtHoGYvAdK/9+IK/ZjpsBMHaU4e5z165u92OjwIk6o3ttcNVYu+E1Ab6KyFdAYgvA+00GYIRMzEEuVr0b2JurlSh4Ft1C75DQu24Pj+RMSXjVXB8dLpq7F/s/WgUQLa9JQMTP/ob4x+e4TL1nJw+xG8NPgt5Gab8i5hmc7/Ujxz7Cye7PUqvt0xDlGxntlNTK63+eQJmOz8sZPH5BjykAwJUKSNnq2fia7KChB1G2uVS8rgzH4CHTDEw6fJZq4LyiGKUZPIsm2WDWgh1YHSk6Cfewww7Sr0iBGIvBZa3AsIVAxiidzMaDSida/mRe5b+MsS1dPUVgsuCWTn/rjYpUHs1A9mque19ope7j9z4pwaATvo7j4uOyd5VmSI45KQiBD+2vcUPXdbfnbMzs5x87G8jTka+xmXSEhjwFhdlWbYfpEQDoRfAZ9mqOD4vxH134b9khMttCFQdgaQsx7I3QFIiUpYV2jGvLHqgYrlBERekHw6xW4PWZGenO7Sc/43dYXdDWySB1oybYVLz0me1adOXRjsZPTksX5163l0TcHNyVpXjfXoNnftZ/4NPfXTvI1whXbYS+Y6r2WUncvsMfdAM8TAl+VNM7PfHgtRw53K1GvyPQlrBS3qevW8gR7ACgaxRF5QtX5ltYnMFvl9JRu8XMnRdDBJ0GZlcIKYP7uxaQuVjbUWyMp98pgcQ56hhfdwHKCEXaZ29lNReuZ86Ce7qE1xeuo4tRFOT+wOc9LzKrgVWkRfaPFvSbSa/1mW2tJQaDEPAtH3wNepDGrkcBuPGgFDWWhRt3t4Vf6DQSyRm5w6choTXvgZd7V8HLc1fgjv3DYeO1bnTU0ZMLqX3ayouPIu117Wr92ipuqCYIsE1XVb1nLpOcmzKsTE4LvB1yA2LLxgI5dlo5fcN3HwNeoY8pDwroCxjp2NOyZo0Xd5eFG+T89eBf3cvWpsah7T+c1wGZOhJ71YcKwWOQRa+aXQ4t9TE8S0uNfzPo6532/qjLW4l9VwCDWm9/y9QFgXaGUm5w2VIKs03d6WtwCTnJyM+Ph4JCUlIS7OcTN6oou1YdEWPDdwDHKycgpG2lrad93x5s0Y/uTVGHffl/jz038u+FyplW3UsT7envsSwsJdd5lR6nBfv+EDu8d8tfk91GjM9kv+Lj0nB3/u3K5abokOVapiUINGiArlZWtPk0vg+pmRgGlffjArvw/ygist7hVoUbaycMHLfPpmIEdGctspfyo7N29DUwDRZQNX9tr8iV31oRmrIFglOxmvMYglcrGUs6m4qcY9yEzPgm6j7vXN2c+rzV7Txv2FX8dOx+mjZ9X9kbERuPLO3rjlleGIiHLUwLpkZIjC27d+rDZvSYLC8i/f0g/3zrdH4LrHr3LpOYlI/q3lAFnzoWfOBfQsaKH1gMjroBkrentpPkc3nYae2NHBUUZVLiA1rxTc8Rq3qRK52OxvFyAzLctmXz8JGqe8+6fqIzv0oYG4+v5+OLzjqMrSVqlXEeGRrg1eC85rMODJifej2eWNMPXDmTi47Yi6X0bqDnv8alx2ZRu3nJco2GmycUvqNyP6enspvk9PduIgDbo5OT+fbeepzMlAzjp5BwhtCs1YzkWLJF/hd0Hs+PHjMXbsWBw/fhwtWrTARx99hPbt23t7WUQFNizcIt0fbT4uWU8pNyjcgstTl/AlkB14Z291y8rIUgE1J3URkc8wlMsPTYqNYC3CpEbc2qLrWdCT3wYyfsnvr6qeGHpEfzX5SnOqxyz5A7/a2PXLL7/g0UcfxUsvvYS1a9eqILZv3744efL8jHAib1MZWAdFOr5QxCMZXwawRORLVEusiEEOpliFARFXWn1E183Qz94LZPxYKIAVZiBzFvQzN0M3u7Z9IXmPXwWx7733HkaPHo1Ro0ahcePG+OyzzxAVFYVvvvnG20sjKtDs8sbQDHZ6dRoNaNaloUfXRETkL7TYRwBDgpVA1rIh7jloBht1klmLgOzFNjaFmYDcXUDGFNcvmrzCb4LY7OxsrFmzBr169SpyaVQ+XrZsmdXPycrKUsXBhW9E7tbvth4Iiwi1GchKOcE1j1jPIhARBTvZ8KaVmQKE9ykaphhrQyv1kWrmb4ue8ZuDLK4c86srl0te5DdB7KlTp2AymVChQtEG8PKx1MdaM2bMGLW7zXKrVo2tg8j94svG4eWpTyIkLKRIX1bLcIMRL16HDgO5iYqIyBbNWBmGhA/zer6WmQKt7CxoZf9yvDnOdMTBgAkdMFmPGcj/+N3GrpJ45plnVA2thWRiGciSJ7Tt0wJfb34ff3wyG0v/WIWcrFw0vqwerr6/v+oOQETkj3RzKpA5A3rubpk0AC2iN7TQ5m47n2YoDcjNWcbyQO42uz1mYeDwgEDhN0Fs2bJl1S7uEydOFLlfPq5Y0XqvvfDwcHUj8gYZG3vXO7eoGxGRv9MzZ0M/9ySAjPzwQYee9jn0sI55l/lt1al6kBY5FHrWAntHQIu6zu5z6DlbAdMhQIsHwtpC0/wmVAo6flNOEBYWhjZt2mDevHlFmrfLxx07OmqMTERERJc2CvYhAJn59+Sev2yfvVJ1BPCJ2UnhVwChLW2EN0bAWBWIHGb1U/XsDTCfuhr66cHQzz0A/ewt0BO7Qk+f7PZlU4AHsUJKA7788ktMnDgR27Ztwz333IO0tDTVrYCIiIjcQ0/9JL87gLVA1QTkrMwbLOBlkjXVEr4BIvpfGOKEtYdWepLVjLGesxn6mZuA3B1FHzCfgp78HPS079y8croYfpUjHz58OBITE/Hiiy+qzVwtW7bErFmzLtjsRURERK6hm9OA7CUOjjJCz5wFLaw1fKHXrFbqfeimp4Ds5XkTu8JaQgupbfNz9JS387PL1mtp9ZR3gMiheX1syWdouk/k/31rFi8RERHl0U2noSc6KtsLUUGeIf41+BvddAx6YjeHx2lxb0KLGuqRNQW7ZCfjNb/KxBIFq9ycXOxZvx852bmo3qgK4krHentJRBQsDPESwQF6soNRsHXgl0zOTP0MAcxszeVrGMQS+TC5UDLlvRn4dex0nDuZpO4LCTWix41dcPc7tyKuDINZInJ/nakuAwbSvrLTukoysYPhl5xquWUCDGU9sBgK2I1dRMHm00cm4IsnvisIYEVujgnzfliMhy9/AWlJaV5dHxEFBy36biCkrpWwIe9jLe4VaGpUrP/RQqoCoa0dhEShQEQ/D66KnMEglshH7d14ANPG/WVzdO2RXccw9UPrjxMRuXyzVOmfgKhRgFZoc1NoC2gJX0GLuhb+TIt9Ij8ksh4WaTH3+0QfXCqKQSyRj5r1zfyCUbW2AtmZX8zx6JqIKHhphlgY4p6CVn45tHIL1VtDmV+ghXeFv9PC2uS15jJWKfZADLTYZ4Dou7y1NLKDNbFEPurEgUSYcu2MTgRw+uhZNfTDYODrUSLyDE0LA4yVEWi08MuAsnOBnNXnJ3aFd4amRXh7aWQDg1giHxVXOkZlYu0FspGxEQxgiYhcRNM0IKwdALmRr+NfPyIf1eOGLnYDWAlwe49w3NuQiIgoEDGIJfJRra5ohhY9msBgvPCfqdwXER2Bax8b5JW1EREReRuDWCIfvqz16u9PofPg9mpkuXxsKR2oXKcC3lnwMirV4shlIiIKThw7S+QHju09gRV/rUVOVi7qt6mN5t0a59VuERERBRiOnSUKIJVqV8Dg+/t7exlEREQ+g+UEREREROR3GMQSERERkd9hEEtEREREfoc1sUREFyHbZMLkrZvxw8b12H/uLKJCw3BVg4YY1bI1qseX8vbyiIgCHrsTEBGVUFZuLm77YyqWHT4k3c9g+SVq1DSEh4TghyHXoWXFSl5eJRFRYMdrJS4n+OSTT9CrVy8MGzYM8+bNK/LYqVOnULt27YtbMRGRn/hk9QqsOHJYvV84C2DSdWTm5uKuGdORYzJ5bX1ERMGgREHsuHHj8MQTT6Bhw4YIDw/HgAEDMGbMmILHTSYTDhw44I51EhH5BAlOv9+4HmYbF7Hk/sT0NMzdt8fjayMiCiYlqon9/PPP8eWXX+LGG29UH99zzz0YPHgwMjIy8Oqrr7prjUREPuNoSgrOZWbaPSbEYMCGE8fRv259j62LiCjYlCiI3bdvHzp16lTwsbw/f/58VV6Qk5ODhx9+2B1rJCLyGRKgOqQDoc4cRz7tRGoqpu/YhuNpqSgbGYWrGzRCFe6nIPLPILZs2bI4dOgQatasWXBf06ZNVSDbs2dPHD161B1rJCLyGZVjY1EjvhQOJp0rUg9bWK5uRrcatTy8MnIV2e/84Ypl+HjV8oINe1Lv/O6y/zCqZRs8e3k3GDj2mcjrSpQq6NKlC6ZOnXrB/Y0bN1abvP7++29Xro2IyOdomoa727a3GcBKwNOsfAW0qVTZwysjV/l2/VqMW7lM1TfLLcdsVm/lZ/7N+jUYt2KZt5dIRCUNYp9++mk0b97c6mNNmjRRGdkXX3zRVWsjIvJJwxo3xV1t2hUErcKSmatZKgFfXDlYBbvkn+3TPl6Zl4G15Yu1q5CanV3kvj1nTmPlkcMqQ09EnsE+sUREF2lr4kn8tHkj9pw9g7iwcAyoVx9969RTvWLJPy05dAAjpk1xeNz4AYPUxr3/Dh7Am/8twtZTiQWPSRb++a490KJCRTevlii447WL+k27a9cuTJ8+Hfv371fZhlq1aqkuBewRS0TBpHG58vhfj17eXga5UFqxDKu94+bv24s7Z/yuamgLW3f8GIZP+Rk/DR2GViwrIXKbEm+flb6wUgP71FNP4bfffsPkyZMLese+88477lklERGRB9RJKO3UcTVLlcJz8+eoALb45Uypn801m/HSwqIDgYjIi0HsggUL8Pzzz+O5555T07mOHTuG48ePIzExUdXLyu3ff/918RKJiIg8o07pMqocwFLrXJzUPtctXQYZObk4kZZqc4OfBLKbE09ix+lTbl0vUTArURD72Wef4Y477sDLL7+MhISEgvtLly6thh3cdttt+PTTT92xTiIiIo94vWdvRIWGXhDIysdhRiPG9u6HIynJTj3X4aQkN62SiEoUxK5cuRIjRoyw+bg8tny5/V2dREREvqx+mbL4/fqb1catwt0nrqhVB1OH3ag2bJWKiHTquRIinTuOiEquRBu7Tpw4UWTQQXGywUvKC4iIiOyRWtJVR49g3r49yMzNRcOy5TCofkPEhIXBF9QqlYBx/a9UrbTOZKSroDUuPLzg8W41aiI6NBRpOTl2B2O0rFjJQysmCj4lCmIzMzMRZucXTGhoKLKd3NlJRETB6XR6Ou6aMR1rjx8tGOMrG6FeX7wQ7/cZgN516sJXSFBtLbCODA3Fw5d1Vmu25clOl3OyF5EblbjF1ldffYWYmBirj6WkpLhiTUREFKBkw9Ptf0zFlsSTBcGrRUZODu796w9Mvu4Gv8hg3tayNUxmMz5YsVRlkyVgla8vNixM9Ym9qkEjby+RKKCVaNiBlBI4mkIjj+/duxe+iMMOiIi8a/HB/bj1999sPm7Mrz397Mqr4S+k5GDOnt04lZGGSjGx6FW7DiJCQr29LCK/5ZZhBzLcwJ7Dhw+rLgVERETWzNq9CyGaAbn6+QxsYSZdx9x9e5BjMiHUaIQ/kHKDIY0ae3sZREGnxMMO7Dl9+jS+/vprVz4lEVFAkMvMh5KScODcuSKX0INNek4OzDa7q57/XmWZTB5bExH5Jw74JiJyIwnIvt+4Dl+sWY1jqXn7BspERmFky1a4s3U7v8k2uooMCnCkfFS02vlPRGQPg1giIjeRLQfPz5+Dn7dsKnL/6Yx0vLdsCTYcP45PB14FY/4O/WBwXeMm+GD5EpuPy+aoES1aOtx/EahSsrLwx87t2HEqUXVAkPratpWqBO33g8geBrFERG6y4sjhCwJYC7mgLrWff+3eqfqjBovy0TF4ufsVeGHB3ILd/BbycdPyFXBbyzYI1nrhx/75S3U6sLyw+XLtarSuWBlfDLoapSOjvL1EIv8NYocOHWr38XPnzl3qeoiIAsakTRvUbnvZrGSNBG0/bNwQVEGsuKlZC7WLf/yq5Vh3/Ji6r1REhLr/nrYdVAYy2Kw5dgT3//2nyt7Lfy2F66Y3nDiGO/78Hb9ddwMzskQXG8RKuwNHj99yyy0leUoiooC1+8xpmwGskCzk3rNnEIx61qqtbmczMpBlylV1wsFWH1zYJ6tWQsJTa1v+5L+h9cePYdnhQ+hUrboXVkcUAEHst99+676VEBEFmNjwcBWY2NuL7ytjVr0lITISwS4rNxcL9++1+9+JtCWbvWcXg1iiQoJnNwERkYdJmYC9wETKCa5qEFylBHQhyUQ7mjokRQbSnoyIzuPGLiIiNxnSsDE+W7MSJ1JTLygrkFpZydTe3KwlgpnUgP6zdze+WbdG1cdK5rpj1eq4o3VbdKleA8EgJixclVNI1wpb5L+euqVLe3RdRL6OmVgiIjeJDgvDT0OHo3ZCXvARYjCom5CNTZOGDkO56GgEs7eWLsY9M//AmmNH1WamHLMZSw4dwC2/T1GBbTCQjPzNzVuot/aOuaZRU4+ui8jXMRNLRORG1eLjMeumW9WmnKWHDqrNXG0qV0b3GrWCqj+sNYsP7scXa1ap9wu32rJkrV9bvFDVgDYsWw6BbnTrdpi/by+2JJ68oO2YfPy/7legbBRbbBEVxiCWiMjNpC2SBGPclFPUdxvW2W1BJo/9sGkDXuvRC4EuKjRUZeal/OTHTRtwLjNT3S89Yu9t1wHda9by9hKJfA6DWCIi8or1x4/bbUGW11rqKIKp/OSxjl3wUIdOOJ2ejoiQEMRHRHh7WUQ+i0EsEV3UZpylhw/i9+3bVJ/PyrGxGNakmZq2ROSsUKPjcoowY/D9mZK66QoxMd5eBpHPC77fDkR0SaTNz10zfseSQwcLLgVbLvve0LQ5/tejl90NKkQWfWrXVZfO7U0061WrjsfXRUT+Ibh3FRBRiT0z7x+1SUlYgg/L2582b8Snq1d6dX3kP25p0UoFqtZe8sj9UicqGX4iImsYxBKR046kJGPGzu1Fdk8X99XaVWoCEZEj0nrssyuvViUDllBWy7/JJLMJV1/DHflEZBPLCYjIaf8e2O9wslBSVhY2nTyBtpWreGhV5M961KyN/0aNxpRtm7Hm6FFIJYp0cRjSsAniwsO9vTwi8mEMYonIaTkmk8qSOQpks00mBPOmN2ncP3XbFiSmp6F8dAyuadQErSpWUq223Gn3mdPYfPIkwowGNfUqITIS/qBMVBTuatMeaOPtlRCRP2EQS0ROk+4DjgJY2eRVv0xZBCMJ3h+aNROz9+wqsulNaoUH1muA9/r0R6jR6PLzHkpKwhNzZmHl0cNFdrjf0KQZnr28O8JD+KueiAIPa2KJyGmSTWxQpqwKzKyR+wfUaxC0dYxj/luEf/bssrrp7a9dOzB26WKXn1OyvddN/glrjh0pcr+McP1x00Y8OGumyg4TEQUaBrFE5DS5HP5hv4GIDQ+/IJCV3eTV40vhxa49EIzOZWZg0qYNNjPVcv/3G9cjOSvLpef9Zt0anM5It9qmygwdc/buxupiAS4RUSBgEEtEJSKlAjNuGKHaI8nGGwllK0TH4KEOHTFt+I2qvjEYLT10CDlms91jskwmrDpy/pK/K0zeutnu1CujZsDUbVtdek4iIl/AQikiKrHKsXF4oWsPdZNL1e7esOQPcszObWaTQNZV5Ht/JiPD7jEm3YyTaakuOycRka9gJpaILgkD2DxNy5V36rgmTh7n7PfeUf2xZGIrxsS67JxERL6CQSwRkQvUKV0GHapUtbvprUv1GqhRqpRLzzu8STO7Y34lE3ttoybwNJPZrFp+7Th9isMviMgtWE5AROQiY3v3w7W//nTBRisJYMtFRePNK/q4/JyjWrZWNa9SMlC8NlZCW+kW0bJipUs+z/x9e/HNutVYdfSIygBLwH5Hq7a4vEbNIsfJNLcJ69fii7WrcDItTd0XGxaGm5q1VHXTbPdFRK6i6UHUeyU5ORnx8fFISkpCXFyct5dDRG6WkpWFv3bvxNGUZCRERGJAvfpq+IA7Scsr6Rjw65bNOJuZgYSICDW9LC07R31cOTYWwxo3Q89atWE0uOZi2LGUFDwz7x8sPnh+olq40ag23z3escsl96b9cMVSfLhiWUHvW2F5/6nOl+cNKsiv0X1hwVxM2rzxgueQbHH7ylUxYfA1CHNDr1wiCr54jUEsEQUkaXf1v38XItuUq4JFk1k2oAF3tGqDJzt3tXsJ3lWSMzMxcvpvWH/iuDqfZCktwd/l1WvgiysHuzQzeTDpHLYkysQuowoYpRXapVp55DCu/+0Xu8f8PvwmNK9QUfWqvW7yz3aPfaNnb1zftPklr4uIApez8RprYoko4EzfsQ3PL5iLLFOuykxK438dugoiv1i7Gu8tW+KRdTw7fw42nTyh3pdzC0smc8mhg3jjv0UuPZ/06e1ftz6uqFXHJQGs+G7jOpt1vkIe+2HjevX+T5s22j1WHvlx0waXrIuIiEEsEQUUCRbfXfqf3WO+XLsKSZmZbl3HkZRk/L17p80errLOX7Zscvs6LtX6Y8fs9qGVx9YeP6be33v2rN1j5ZEDSefcsk4iCj4MYokooMjl9MMpyXaPkaEEc/ftces6Vh4+bHN6l0W2yYR1+QGgr3KmntZS4xofEeHwj4ps8iIicgUGsUQUUJKzHGc2pT5VNn25k7S2coalzMBX9apdx26JgHwve9euo96/qn5D2Puq5djBDRu7YZVEFIwYxBJRQKkRX8qpwFHqR92pdaXKDo+R4LBZhQrwZSOat0SIwaDqWa0FpdIF4Yb8jVrS/aFOQmmrQa/cFxsWrp6PiMgVGMQSUUCpGhePTlWr2cweyr3lo6LRtVh/U1ernVAanatVtzv84Mr6DVT/WF8mwb6li0LhUFbeiwwJwTdXDS2YCCbH/Dj0OtWpwPI1SgAsqsTG4adrhnF6GBG5DFtsEVHA2XTiOK6d/JOqfS3MEoJJ4NWtZi23r0MGEAyb8gsOJZ0rqI+1rKFh2XIqqIsLj4A/OJuRgclbN2PFkcOqVVnHqtXVJDCpg7Vmw/FjWHzwgOoMIVlpmVbmibZmROT/2CfWCgaxRIEvMzcH1//2KzafPGG13rRnzdr46qohHltPclYWft2ySQWAp9LTVCZS+qRKABgZGuqxdRAR+Qtn4zXO/yOigCJtqyQTa+vV+fz9e7Hu2FG0cqJm1RXiwsNxR+u26kZERK7DmlgiCiiOmukbNQN+3rLJY+shIiL3YBBLRAHlcHKy3f6s0vpq/7mzHlwRERG5A8sJiMgrTqWn43ByEqJDw1C3dGloLtr0I22cMnNzbT4um4tKR0bCn5zJSMfPmzfhjx3bkJydhbqly+CmZi3Qu3ZdbpYioqDFIJaIPEoC19cXL8ScvXsKNl7VLpWARzt2UX1GL9WQRo3x9drVdse9DqrfCP5iz5nTaqPa2cyMgu9XYloa/jt4AP3q1MO4/lcWtLEiIgom/M1HRB5zJCUZQ375EXMLBbBi37mzuP/vP/HT5o022zutPnoEG08cVy2b7BnZohViw8NtNtxvXK58wYQpXyffo9Ezfse5QgGssATos/fswhdrVnlxhURE3uMXQez+/ftx++23o1atWoiMjESdOnXw0ksvITs729tLIyI7ZLTr0ZRkZOVf3n9n6X84l5l5QZbU8tGri+YXGRsrl9Ef++dvdPj6Mwyb8jMG//IjOn3zOb5dvxa2ugNKC6ufrhleMJFLAlfLJffLqlbDd4OvQajRqPqYPvD3n2jyyYdo8PH7KrievmObzef1Bsm27j93zmZWWe6dsGGtw8CeiCgQ+UU5wfbt22E2m/H555+jbt262Lx5M0aPHo20tDS888473l4eERWz6eQJjFuxFPP37VWBlowmHVS/IWbs3G4zIBPZJhP+3LlD1XtKMHvd5J9xMKloECe1tP/7dwGOp6bgmS7drD5PgzJlMXfEKCw7fAgbThxTl9svr15TDRgQUlv66D9/q8EDlueWNT8y+y8sOXgAb/Xq67Ia3Uux6uhhtXZ7Qap8P+R7JBPCiIiCiV8Esf369VM3i9q1a2PHjh349NNPGcQS+Zhlhw5i5PTf1OVvS+iZZTJh6rYtcJQvNBoMarqV+HrdGhxIOmd1YIH4cu1qXNe4qdrkZI0EoZ2qVVe3wk6kpuLxObMueF7Lx1O2bUGnajUwuKH362a1QmNe7R7nAwE3EZGn+UU5gTUyxaF0afuZh6ysLDX1ofCNiNzHZDarDKdkN4tnXJ254C2BpGWMqfR7tRXAWsoEZApWSf26dZPd55XSA7lE7ws6VK3qsFSgfHQ0qsfFu3UdR5KT8daSf9H3hwm44rtv8MScWSpzTUTkTX6RiS1u9+7d+OijjxxmYceMGYNXXnnFY+siCnaLDuzHibTUi/58qUcdWK+BKis4k5Fh91gJRA8lJZX4HLI5zF4QK49tOXlCrcXbGc5OVaujbkJptfHNVhnG7a3aqAy2uyw+sB93zvhdBdOWNUj5wm/btuDZLt04iYyIgjMT+/TTT6s/EvZuUg9b2JEjR1RpwXXXXafqYu155plnVMbWcjt06JCbvyKi4Lbn7OmL7lsqnzWsSTO1ISvUYEBEiP3X2AbNgISL6PcaajA6vEgvQaG3A1gha/hy0BCUjYousmZL54XBDRrhtpZt3HZ+qbe9a+Z09aKicBBtef+N/xap8hFfJrXTv2/fpspZ9p494+3lEFGgZGIfe+wxjBw50u4xUv9qcfToUfTo0QOdOnXCF1984fD5w8PD1Y2IPCMyJNSp3f1x4eFIzspSI2DNulkFazc0bY4Xu/ZQj8vHEqBJuYCtDKRM3rqqfsMSr7FbjZqYtWeXzcclQOxRsxZ8RY1SpTD75pEq86mGHWTlDTu4sWlzdK1R063B9q9bNqkAVrfzvfpm/Rp0LFZ37AtSs7Px/II5mLFzR5HMu9RIv9u7PyrExHh1fUR06TTdl/rJ2CEZWAlg27Rpgx9++AFGo7HEzyE1sfHx8SorGxcX55Z1EgUzyXp1+fZLu5frq8TGYc6IUViwfy/2nT2LmLAw9K1T74KgQkbDDvrpB2Tk5lzwfJLt7VS1GiYOvrbEQVx6Tg66T/xK9Z61FiDLs/163fVoU6kKgt0tv09Rbb7sCTMa1YuPdpWrol4Z65vsPE1KH2787VesPX70gv92JPCuHBuHP2+4GXHhefXXRORbnI3XDP4SwHbv3h3Vq1dXdbCJiYk4fvy4uhGR75AercMaN7V7uf6hDh1VqUD/uvVxb7sOuKVFK6tZsZqlEvDTNcNQNf8XmASu8rxy61e3Hj67cvBFZSGjQkPx3eBrC0oRLM8gzy8Bztu9+zGAzedMjkMytc8vmIu+P05QgePJS6iJdpX5+/Zg9bEjVl9MyQuXw8nJNgdrEJH/8ItM7IQJEzBq1Cirj5Vk+czEErmfBDVPz52N33dsU0GhBJoSTEiw+ESny3Fnm3Ylej753GWHD2JbYqLK+vWoWRvV4i99N35adjb+3Lld9bLNNpvQvHxFDG/aTGWKg7GrxM7Tp5BtNqsRwDLxTIxftRzvL19qN7NemPy8q8bF488bRqgMu7fcNWO6CmTt9SSWF0nzb7nNo+siItfGa34RxLoKg1giz9l95rQKEuWyvQSdgxs2RrmoaG8viwqRX//fbVyHT1evxMm0NHWfvFAY2rAxnu7SVfX37frtl3brYouTFysvdeupMuzeMviXH7DxhP0WYBJkb7z7AY+tiYhcH6/5ZYstInI/qUldf/yYuszeoUq1Em+Ekc1Hj1zW2W3ro0s35r9F+GrdmiL3ScAqG+rWHj+GKdfdgE8HXo27Z05X2Vp7mc3C5PO9GcRKNn3LyZM21yuBdkVu7CLyewxiiaiIxLQ0PDl3lur5aiGB7JX1GuC1nr29epmYXGfH6VMXBLAWEvxJJn3ihnW4r10H/HPzSDV8Yt7ePTiYnGR3AIOEjacz0uFN1zZuir932+5AIa5v0txj6yEi9/CLjV1E5BkpWVkY/tvPF+xIl5rIGbt2qHGyjiZIedr2U4l4ccFcDPnlR1w/5Rc1rjYpM9Pby/L5Fyoydcse+ZlP2rRBvS+9e5/p0g1zb7kNHatWs9sLWB6r5uYJYo50q1FLtVKztk6p25WrBMObNPPK2ojIdRjEElGBX7ZswoFz56xehpWgZu2xo/hnz274ii/WrMKASd+pneYbThzHyqOH8cbihejx3dfYmnjS28vzSTJta+BP32GzE2Njj6WmXLCpS4I/RxPPvJ3llOD1s4FX46ZmLVSNb+H7+9ath5+vGY5oXlEg8nssJyCiAr9u3Wz3cQkCpOn+gHr14W3SZ/bNJf+q9wsH3Xp+RvnW33/D4lF3ICIk1CPrkaB55q4d6tyy831Iw8YXNVHM3R6Z/ZfabOeM6NCwC7KZferUQ+dq1bHs8CGr/XtbV6yMgfUbwNvCQ0LwSvcr8OhlnVW/WKnpbVa+IoccEAUQBrFEVOBUeprdXegStJxIS4Ev+HLNahU02eoFKnWZf+7cgesaN3XrOmR4wsOzZmLuvj0FLcUkYHpryb94ufsVahKZr9h2KhHrjh9z6lj5WoY0bHTB/SEGA74cNBhvL1mMnzZvQpYpV90vGU/5XkvZQeHsp7fFR0SotmxEFHgYxBJRgYrRMaqe1N6Y0coxcV5vC7Xm6BEsP3LI7nES4C45dKAgiN179gx+374NielpKB8drTKlkjG9VI//8zfm7997PiOcH1TnmM14bv4clI2MQu86dZ1+vszcHPy1a6cKOGUoRK9addC8QkWXjJd1poTA8r2TDPYdrdtafVwee7FbT9V9YtPJE9Cho1n5CpyARUQexSCW6CKcSE1VbYR2nJZAIxR9atdFz1q1YTT4d5m5NPt/ddECm49LkObuzKY9h5OTcO/MP7DZmXpXXYfZrKus6KuL5uP7TRtUEJ7XYEnHRyuXY2SLVni+aw+7G5XskR38s/bY3gUvM8Y+XLHU6SD23wP78eCsGUjOylIZTwnYx69agfZVquLTAVddcnlCqMG5DGnpyEh8fdVQtaHLHhmK0Kla9UtaExHRxWIQS1RCv2zeqMZs6oUCFakTlR3P3w2+Ro1e9VfXNW6mLhHvOXP6gs1dEujJznQJ1r1BArvrf/tFvYBwhqy+daXKGLdymQpgRd7XdP7rmrBhHUpFROLBDh0vak2z9+xSgbGtfqSSodx6KhFHUpIdTgLbcvIE7vhzmgq6ReEuEJJ5vv3Paapv68UG3EJqWW2VYFiEGYyYN+K2gqldRES+yr/TRkQetvjgfjwzf44KWsz5N5OeF2zsO3sGo6ZPdXpEpy+KCg3Fz9cMQ7+69YsES6EGA65v0kzVQnor2yyZ72MpKU413JeVy6X4vnXq4qu1q+0eK5nOFxbMxUcrl6kBDyWth3XmMn96do7DYz5ZvVJlXq19dfI1y+CJpYcO4lKUi47GNY2a2AyE5d7bWrVhAEtEfoGZWKIS+HTVSrubiaSBvPRY7VqjJvyVZCY/6n+lynhuOHEMRs2gMpre3mk/bftWp0afSmZUAm1psbT55Elk5OZtPLIlx2zCz5s3qvffX75UtZD6X49e6nK+I3UTyjjsmxtuNKJyrP3svGRf/9mzy26ALuv5e/dOdKlew+rj8t/kHzu2qQEFO0+fUrvz+9etj9tatkad0mUKjnule0+cTEtVwywsWWTL20H1G+LRjpyyRkT+gUEsUQmybo42E0mgMXfvbr8OYi2kFVGfmHrwFecyHbeFkhcYNzZrgVtbtELthNKYvmObU89dOHj8dcsmlcV9qVtPh5/Xv149vLxoHlKzs60G2BIcDm3UxGFPUhn16ijDLEGqnMfq+s1mPDRrJv7avbPgRZYE7/K1SKmLZNAvr57336TUcH9z1VAsP3wIU7dvVR0pKsXEqlrnlhUruWQD2aU4k5GOhfv3qa9VfoZSc3spJRREFLgYxBI5KcdkcnyQriPLmeOoxKSTwPHUVJvlGhIwdq5WQ/UGtahfKAPpLHn2Hzaux33tLkPZqCi7x0pA+G6f/rh75h+qNqtwICrrkTpY6VPqiATN5aKikJhuf1xr7QTr3RRkJKxkaUXh748qezGZcO/MP7Hs9rsKRgZLoNqxWnV18xW5+W3JJJMs7+dtvwOqxsbhnT791eY2IqLCWBNL5KS48HBUiI62e4wEDY3KlvPYmoLJjU2b2603lu/9jc2K9mRtVK68av2U15XAefJc8/btcerYXrXrqglQhXfpR4WEYkTzlpg6/EaUcRAIW4LKEc1bOcw4WusMIXW036xfY/Nz5DuWlpPtdFbaW15eNB/frFtTUJ5h+UkfTU3BLb9Pcbo9GBEFD2ZiiZwkgcYtLVrh3aVLYLZy8VjCjzBjCIY2auyV9QW6vnXqoUfNWupSs27le39FrToqoCxubO9+uG7yT6ocxJlNYUKCyTQbl+6taVu5CiYOvlZ1UJDPkxZVUpNaErKhSupipZtB4WBdul/Idq9nu3RDZSsdDuSy+8GkJLvPLUG81DfLGFZrJBBeevggJm3aqNqGxYdHYFCDhqqXriV76+5RuD9t2mC1JENtnjSb8cHypfjqqiFuXwsR+Q9mYolK4LaWbdC+alUVNBVmmdT0Xt/+bPjuJrJZ69OBV+Oeth0QWyiwkgz5/e0vw/gBg6xmMuuXKYs/rh+BwQ0bqy4LzpDAqU5C6RKvUdZSKTa2xAGspTPET9cMVwMGCn99DcuWVV+bBLnWONMtQgLhEM1g82t9at5sjJg2RQXRu86cxppjR/Dywnno9+MEHHIQILvCHzu2281Cy4sPGTOcnJXp9rUQkf/QdHkJHiSSk5MRHx+PpKQkxMV5d+oQ+a+s3Fx8v3E9vtu4DoeTk1UA26t2HdzVpr3aGEOe+RnsPHNavZioV7qM00GjfF5ydhaemjNbtUuzlpmVYEomei0eOdpr7cRknSfSUhFuDFEb7By55tdJ2HDiuN1yCwmEpVtBcV+vW4PXFy+0+jny37Z0Nvj7xlvcuuHr1X8XqDpkR50eFt56u8MBDEQUPPEaywmISkgCJsmWyU2CjVCjkbunvfAzkFrXi/m8ciEhavPXkF9+RFJW5gWbseRn+W7v/l6dvibrLEmwdneb9rhr5nSrj8nXJN0HZHxtcXKZ3l4fXfneSLuuFUcO47Kq1eAulWNiHfZXls4fZSId1xcTUfBgOQHRJQYbDGD9T7X4eEy/4WZc3aBRQYmB/BSlDdWv193gU7v2nSFjbZ/qfLl637KJzfJfZbmoaEwccq16sVWcXEmQjK+j4PFShyw4cnXDRheU6BQmX5P0sHXUqoyIggszsUQUlKrkt256tUcvnE5PV/Ws8RH+W88s5Sw9a9bBT1s2YlviSUSGhKqJZYMaNFL1ttZY26Bo9Tg3V51JoC1DJiblD52w1srskcs6uXUNROR/GMQSUVCTAC8qPt6pQE6msa09dhRGg4au1WuieYWKXh8OUFi9MmXwYtceTh8vPVilk8KZDNuDJKROVbovuJNMEPt9x7aC3rDFZZtyHdbLElHwYRBLREHhcHISpmzdot5KxvWqBo3QokJFpz5326lE3D1jOg4lJ6nL67IfVkbUtqpYCZ8OvArlox1vvvJFUmIwskVrvL98ic2JY5KxdvcEup82b0Rmbq7NvLC8gPhh4wY837W7W9dBRP6FNbFEFNAk4PxwxVJ0m/AVxq9arpr+f79hvdrYNfrP35GZm2P384+npuDG337F0ZRk9bFkBC2bwTaeOI6bpk5WG/z81V1t2qkeu6Jwfbe8L/1ivxg02O1133P37nE4yGLO3t1uXQMR+R8GsUQU0CTL9+GKZSrLJ8GQ3HL1vEvT0nv0mXlzbH7usZQU3P/XjAu6GFjIfXvOnikY+So7+aVl1ZdrV2H10SMqgPaHbKxkk8f1G4h2lauo8be1SiXgwfYd8ffNt6o+u+6W7cSoZmeOIaLgwnICIgpY0kLq41XLbT4u2b8/dmzD4x27oEqxXoQS4N4z8w+HwZNkAqRMYeq2rfjv0IGCrKU8twSAnwwYhNoXMTjBk6Sd2JX1G6qbN0hZxt6zZ2xOVJOyBjmGiKgwZmKJKGDtOH0Kx1Ptt5AS8/btKfLxkZRkFcDmOJH9k5zuuuNHsezwwYLg1XJpfM+Z0xg+5Rckpqdd9NcQDG5u3tLuSGB5bETzlh5dExH5PgaxRBSwMhzUuwrJnMqmosImbdqgal+dKQaQvGtGbq7NcoOzmRlqGhXZ1rR8BTzduWuRPrfCktW+r10Hv+vdS0TuxyCWiAKW1HYWDoqskUCzQbG6z0UH9jvdG1WOsncGeR4pNZD62ENJSSo7nJqd7dRzB5M727TDxMHXoFO16moAhXSBkBrdL668Go917OLt5RGRD2JNLBEFrNKRURhYrwFm7tphNVMqmb6KMTHoUr3GBbW0zpJRqKcz0u0eI8MUBkz6TgWwIsxoxOAGjfBEp8tRJoqjVC1kYprciIicwUwsEQW057p2R6WY2AsysvJxqMGID/sNVBubCrusajWHGdyIkBA8f3l3dK5e3eGxmaZc1bnAQjaL/bZtC675dRLOOAiAiYjIOgaxRBSQzmZkYP3xY2oa1bThN2JUy9aIDQtTj8mlatmJP/36m9Cm0oXTqG5u1sJhOcF3g6/Bba3a4IYmze1uSrIofoR8jmwg+3il7e4JRERkG8sJiCigyAjTN/5bhJk7z5cQVIuLx0MdOmLtnfepetTI0FB1Sd+WOqXLYMwVffDMvH9UyYHleYz57z/bpRvaVq6q7mtfpaoqDZAhCsUDVVtjVC3kuX7dshlPde6K8BD+OiYiKgn+1iSigCGtrIb8MkkFsoWzozIu9vE5s1Tt6ujW7Zx6rmFNmqFRufKYsH4tFh/cD3m6y6pWxa0tWqNt5fPZW03TMLZ3P9QpXVoNOjiXmVlQblA9Ph57ztjufyrSc3NUB4OKMbGX9LUTEQUbBrFEFDDGr1x+QQBb2NtLFmNwg8YoFx3t1PM1K18B7/bp7/A4qam9r91lKkDefipRbQyrV6Ys3lv2H3afPm33cyVbGxWaV+ZARETOY00sEQUE2Sw1eetmu1lPeUQ2VLmLlCg0r1ARjcuVx9gl/+KHTRvUMARbpDxBduPHhYe7bU1ERIGKmVgiCgjnMjPU0AF7pL5VSgvcSfrB3vfXn2psrTOdZh/ocJlb10NEFKiYiSWigBATFm536IAlwIwPj3DrOpYdPoT5TgSwElB/OvAqq90RiIjIMWZiiSggRIWG4opadVQG1FZJgdw/qH4Dq4/tP3dWjZvdcOK4KgvoUbM2rm3cBHElDHqnbtuisgOOxiVIC6+SPjcREZ3HIJaIAsaDHTpi0YF9KgtavM+rZD771qmrOg4U9/PmjXhu/pwi7bSWHjqIj1Yux/dDrkXT8hWcXsPx1FSHAaylHla6HkiLLiIiKjmWExBRwJBg89urr1GjYC2BYt7/gKsbNMJ7fQZc8DkrjxxWAayEroUzuPJeSnYWbv19iuot6ywZY+vML1ZpzZVbgvG2JSVrluB8zH+L1ECFvWfPuO1cRETewEwsEQWUTtWqY8ltd2Lh/r3Ycfo0okND0at2HVSNi7d6/FdrVxfJwBYm2dyzmZlqkMFNzVo4df5rGjXB1O1bHR4nAWyLCpXgDr9v36oC88zcXDWdTL6O95YvwaD6DfF2r74crEBEAYG/yYgo4Ejg1qt2XXVzRC7p22vLJVncfw/sczqIvaxqNVxRqzbm7dtr8xgJmstGRang2tUW7t+Hx/75u2BjWU6hbO/MXTvU1/NBv4EuPy8RkaexnICIgpq9AFbIo7lmZ5plnS8T+Lj/INzYtLnVbglS4hAZEorPrxysgm1X+2D5EpuPSUb2j53bse/cWfiKxLQ0rDt2FHvOnFbdI4iInMVMLBEFteblK2L9iWMXbAQrnDVtVbFkl/3lcv1rPXvjocs6YtyK5So7eio9HbFhYbiqQSOMbNnKZnnDpTiakoyNJ0/YPUa+nlm7d+Keth0u6VwScEo98caTxxFqMKJrjZqonVDa6c+XbhBvLF6Eefv2FGSN65Yugyc6dkHvOo4z6EREDGKJKKiNatkaD8yaYfUxLT9zOrxJs4t67nJRMfhfj17wlLTsHIfHSBBbko1q1uw4fQr3//Un9pw9o55PAloJRKXF2bt9+jlsHXbg3DkM+WUSUrOzivTTlWzsXTOn453e/TC0UZNLWiMRBT6WExBRUBtQrz5GNG+p3peAzEKCV/lY6kfLRUfDH0hnhFAHJQqyoaxWqYRLyvYOn/KzyqQKyWBbAlHZTHfbH9NgctB14e2l/6oAtngph+WjFxfOQ3qO44CciIIbg1giCmpSw/pyt574bOBVqmerdDOQqV6DGzbGH9ffjP5168NfxIaHq1ZiEoBbo+UPhRhQz/rAB2d8s24t0rKzrdYSy31rjx3FogP7bX7+mYx0zN6z224tsgSwf+/eedFrJKLgwHICIgp6Esj2qVNP3fzd45264L9DB9SGqcKBouWy/1tX9FWB7MWaun2L3QBUAug/dm5Dz1q1rT5+LCXFZv2xhWx4O5h07qLXSETBgZlYIqIAUj46Br8Pv0n1q5XxuRayOW3i4Gsx0MbYXWelZGXZfVwC3DMZGTYfLxUR6fAcEuRKNpyIyB5mYomIAjCQfbNXX7zQtQdOpKUiJixM3ecKlWJjcTg52W4mtnp8KZuPV4mLQ/MKFbH5xHG743mlVpmIyB5mYomIAlR0WJhqe+WqAFbI0IfCG+CsZWIddXN4vGMXtYnL2rPIfbLRrmJMrAtWS0SBjEEsEbmV1GEuO3QQby75F68vXogZO7cj22Ty9rLoIt3crCXqlylrc/OYDHloVr6C3efoUr0Gxg+4Sm1EE/Jc8mwSHI9s2RrPXd7dLWsnosCi6UE0IiU5ORnx8fFISkpCXFyct5dDFPCkHdMdf07D9lOnCqZTSYunMpFR+PzKq9G6UmVvL5EuQnJWFsb8twjTtm8teEFSOjISd7Zuhztat7WbqS0sKzcXc/fuwYGkc4gLD0ffOvX8pp0ZEXk/XmMQS0RuIQFK/0kTcSgp6YLd7BLkRBhD8NdNt9itnyTflpyViV1nTquJXY3KlkNooY1kRETujtdYTkBEbvHXrp3Yf+6c1XZMsvs8y5SLCevXemVt5BoymatNpSpqoxYDWCLyNAaxROQW0qze3i8YCW7/2LndgysiIqJAwiCWiNwiJTvLbgslwdGiRER0sdgnlojcom7pMlh99IjN6U6y9ad2qQSPr8tXyYa3RQf2qRKM2LAw9K5dFwmRjgcDEBEFKwaxROQWNzRtjh83bbD5uJ7fD5SARfv34cm5s5GYnqY2vUnN8PML5uK2Vm3wRMcuMOZ3diAiovP4m5GI3KJxufK4p2179X7xhkvSFVR6hQ5t1ATBbtXRw6oN2an0NPWxBLCWzOwXa1apVlZERHQhBrFE5DYymentXn1Rq1DZgPSIffiyjvhq0BDuaAfwztL/VFbaVq/DCRvW4XhqiodXRUTk+1hOQERuo2karm3cFNc0aoLjqakw6WY1TtQy+CDYnUhNxaqjRxweN3PXTtzeqo1H1kRE5C8YxBKRR4LZSrGx3l6GzzmbmeHwGKmRPZvh+DhPkild+8+dVWurWSrBIy9KjiQnY+WRwzBDR6uKlVA7obTbz0lEvo1BLBGRl1SIjinYyGWLyWxGVR+ZMCjB6/hVy/HdhnVIyspS95WNilJZ4jtatXXLBjSZCvb03H8we8+uIiUXl1evgXd69+eYWqIgxmt6REReIi20+tSuC6NWfOvbeWHGEAyo1wDeJhvN7p4xHR+vXFEQwIpT6el4a8liPDlnFlw9xVyC5hHTpmDO3t0X1AwvPXQQw6b8jJRCayGi4MIglojIi57sfDmiw8JsBrLPd+2OuPBweNvMXTuw8MA+6Da2oE3bsQ1LDh10+ejiTSdPWO01LPcdTDqHyVs3u/ScROQ/GMQSEXmR1JROHXYjOlWrXqQVmZQQfNB3AG5q1gK+YNKmDar0wRYJwn/abLsv8MWYun2L3XMKBrFEwYs1sUREXiablCYOvhZHUpJxKClJTexqVK68wwDOk/adO2u/dlfXsefsGZee82Raqt1z6vnlDEQUnBjEEhH5iCqxcepWXI7JpOpCp23fhtPp6agWH4dhTZqhU9XqqvODJ8SFhdsNGGUV8eERLj1n5Zg47D5zxmYgK+dk1wui4MUglojIhyVlZuLW36dg48kTBZ0MNp08jj937kD/uvVVyYEnhkYMbtgYH6xYajOglHuvbtDIpeeUQF3qcG2Rc17fpJlLz0lE/oM1sUREPuzxObOwJfGket8SQFo2Os3avRPjVi7zyDpuaNocCRGRVjegyX3V4uJVoOtKvWvXQaeq1ayWVcg5m5Qrj6GNXHtOIvIfDGKJiHyUDBSYt2+P1d35Qu6dsH4tMnJy3L6WMlFR+PmaYageX0p9bNQMBQFtw7LlMOmaYYgKDXXpOaXv7FdXDcGNTZsjrFC2WYYrXFW/IX4cOgwRIa49JxH5D5YTFGM2m5GdnY1gERoaCiPn1xP5JGlZJWGive6raTk5qg1V+ypV3b6eOqXLYM6IUapH65pjR1SG9LKq1dC2UhW31eZKkPpqj154rGMXrDt+TGWjm1eoqIYsEFFwYxBbiASv+/btU4FsMClVqhQqVqzosQ0iROQcmdblyuNcQQLXLtVrqJsnxUdEoHvNWh49JxH5Ngax+WTSzLFjx1RWslq1ajB4YBa4L3zN6enpOHkyr96uUqVK3l4SERXSqlJlu1lYEWowoFG5ch5aERGR72AQmy83N1cFdJUrV0ZUEF2mioyMVG8lkC1fvjxLC4h8SLPyFdSl8y02plZJTeqQho1RKiLv3zERUTAJ/HSjk0wmk3obFhaGYGMJ2nM8sDmEiEpmXL+Bqv6z8A59Lf8mG6qeu7y7V9dHROQtfhfEZmVloWXLlqp+c/369S5//mCsCw3Gr5nIX0g3gBk33IIH23dUo2ijQ8NQr3QZvNStJ3699nrEhod7e4lERF7hd+UETz75pLrkv2GDa2d0ExH5Kmlv9WCHjupGRER+mIn9+++/8c8//+Cdd96Br2Y0f//9d28vg4iIiCjg+U0Qe+LECYwePRrff/+90xuvpPQgOTm5yO1SHD9+HA888ABq166N8PBw1cVg0KBBmDdv3iU9LxEREREFYBArraBGjhyJu+++G23btnX688aMGYP4+PiCmwSdF2v//v1o06YN5s+fj7Fjx2LTpk2YNWsWevTogfvuu++in5eIiIiI/CyIffrpp9UleHu37du346OPPkJKSgqeeeaZEj2/HJ+UlFRwO3To0EWv9d5771XrWblyJa655hrUr18fTZo0waOPPorly5db/ZynnnpKHSeZY8nevvDCC0U6AEhdrwTBsbGxiIuLU0Hy6tWr1WMHDhxQWd6EhARER0erc/31118Fn7t582b0798fMTExqFChAkaMGIFTp04VPD5lyhQ0a9ZMtdAqU6YMevXqhbS0tIv++omIiIh8iVc3dj322GMqw2qPBH+S/Vy2bJm6hF+YZGVvuukmTJw40ernyvHFP+dinDlzRmVdX3/9dRVQWpt4ZY0EpxMmTFAb0SRzK+UQcp9sThOy9latWuHTTz9V/Vml24KMgRWS3ZUJYv/++68659atW1XAKs6dO4eePXvijjvuwPvvv4+MjAwVMA8bNkx9r2Roww033IC3334bQ4YMUS8AFi9erDLaRERERIHAq0FsuXLl1M2RcePG4bXXXiv4+OjRo+jbty9++eUXdOjQwc2rBHbv3q0CwIYNG5bo855//vmC92vWrInHH38cP//8c0EQe/DgQTzxxBMFz1uvXr2C4+UxyfhKNtUSzFt8/PHHKvh94403Cu775ptvVLnEzp07kZqaqoY3DB06FDVq5I2GtDwPERERUSDwixZb1atXL/KxJSNZp04dVK1a1e3nv9gMpgTZEoDv2bOnILCUsgELKUWQbKpsVpPL/dddd536msSDDz6Ie+65R3VjkMckoG3evHlBGcKCBQsKvg+Fybn69OmDK664QgWuEuzLx9dee60qTSAiIiIKBH6xscvbJENqqc91lpQ/SLnAgAEDMGPGDKxbtw7PPfecKhGwePnll7FlyxYMHDhQlQE0btwY06ZNU49JcLt3715V6yqlCFI6IbXBQgJiqZeV8oPCt127dqFr166qNGHOnDmqJZk8p3xegwYNsG/fPjd8d4iIiIg8T9ODqFBSWmxJlwLZ5FU4IyoyMzNVkFerVi1ERERc8LmyiUqCyR07dlxQFys1qlIXK4GuBKGDBw/Gu+++i08++URlRi0kMJUNV3K8NVLHKpuv/vjjD6ub1GbOnImNGzeqYPi3335Tm7tCQkKcGqkrZQWS+ZVbcY6+diIiIiJfiNcKYybWSePHj1fBYPv27VUAKVnPbdu2qXKBjh07Ws3eSl2r1MBKICvHWbKsQjZj3X///Vi4cKHqRLBkyRKsWrUKjRo1Uo8//PDDmD17tgou165dq8oHLI/Jpi/ZbCZBr3yOPL8cO2rUKLXGFStWqHpZ6XQga5g6dSoSExMLPp+IiIjI3/lFTawvkI1VEkxKhwLpqiAdAGRTmrTFku4CxV111VV45JFHVKAqQxekZEBabEkJgZBL/qdPn8Ytt9yiBjmULVtWbcR65ZVX1OMSjEqwevjwYfUqpF+/fqoTgZBuBxL0SkcCqXeV55dMqxxjMBjU8dLV4IMPPlCvZuQxyQxLNpmIiIgoELCcIF8wX1IP5q+diIiIfAvLCYiIiIgoYLGcgIjICXLRasauHZiwfi22Jp5EiMGAK2rXwR2t2qJp+QreXh4RUdBhEEtE5EQA+9Tc2ZiybQsMmgazriPLZMLMnTswY+cOjOs3EAPqNfD2MomIggrLCYiIHPh9+zYVwAoJYC1Muq4C3Edm/43E9DQvrpCIKPgwiCUicuDbDWtVBtYaCWlNuhm/btns8XUREQUzlhMQEdkhmdYtJ0+oYNXeMZtOHPfgqoiIiJlYIiIHZBOXPTKtz9ExRETkWvytS0TkIEC9vHpNGG2UE1jqZLvWqOnRdRERBTsGsUREDtzZpl2RDV2FSXBbNioKg+o39Pi6iIiCGYNYN5GxsRsWbsH8n/5Tb+VjIvJP7atUxRs9e6vNXZaMrCUvWyoiEt8NvhaRoaFeXSMRUbDhxi43WDx1BT55+FucOny64L6yVcvg3g9G4fKhHdx67vHjx2Ps2LE4fvw4WrRogY8++gjt27d36zmJgsHwps3RqVoN/LR5IzadPI5wYwh61qqNqxs0QnRYmLeXR0QUdBjEuiGAffW6d/L67hRy6shpdf+Lkx93WyD7yy+/4NFHH8Vnn32GDh064IMPPkDfvn2xY8cOlC9f3i3nJAom1eLj8WTny729DCIiYjmBa0nJgGRgrfbiyb/v00e+dVtpwXvvvYfRo0dj1KhRaNy4sQpmo6Ki8M0337jlfERERETewiDWhTYv3l6khOACOpB46LQ6ztWys7OxZs0a9OrVq+A+g8GgPl62bJnLz0dERETkTQxiXej0sbMuPa4kTp06pTK8FSpUKHK/fCz1sURERESBhEGsC5WplODS44iIiIjIOgaxLtT08oaqC0FB753iNKBctTLqOFcrW7YsjEYjTpw4UeR++bhixYouPx8RERGRNzGIdSEJIqWNllI8kM3/+J73R6njXC0sLAxt2rTBvHnzCu4zm83q444dO7r8fERERETexCDWxaR9lrTRKlulTJH7y1Ut49b2WkLaa3355ZeYOHEitm3bhnvuuQdpaWmqWwERERFRIGGfWDeQQLXT1W1VFwLZxCU1sFJC4I4MbGHDhw9HYmIiXnzxRbWZq2XLlpg1a9YFm72IiIiI/B2DWDeRgLVF9yYeP+/999+vbkRERESBjOUEREREROR3GMQSERERkd9hEEtEREREfodBLBERERH5HQaxREREROR3GMQSERERkd9hEEtEREREfodBLBERERH5HQaxREREROR3OLHLTUxmM1YdPYKTaakoHx2DdpWrwGjgawYiIiIiV2AQ6wazdu/Cq//Ox/HU1IL7KsbE4MWuPdGvbj23nffff//F2LFjsWbNGhw7dgzTpk3D4MGD3XY+IiIiIm9hatANAex9f/1RJIAVJ1JT1f3yuLukpaWhRYsWGD9+vNvOQUREROQLmIl1cQmBZGB1K4/JfRqA//27AL1r13FLaUH//v3VjYiIiCjQMRPrQlIDWzwDWzyQPZaaoo4jIiIioovHINaFZBOXK48jIiIiIusYxLqQdCFw5XFEREREZB2DWBeSNlrShUBqX62R+yvFxKrjiIiIiOjiMYh1IdmsJW20RPFA1vLxC117sF8sERER0SViNOVi0gd2/ICrUCGmaMlAxZhYdb87+8SmpqZi/fr16ib27dun3j948KDbzklERETkDWyx5QYSqEobLU9P7Fq9ejV69OhR8PGjjz6q3t56662YMGGCW89NRERE5EkMYt1EAtbLqlbz6Dm7d+8OXbfWpZaIiIgosLCcgIiIiIj8DoNYIiIiIvI7DGKJiIiIyO8wiCUiIiIiv8MgloiIiIj8DoNYIiIiIvI7DGKJiIiIyO8wiCUiIiIiv8MglojIDbJyc3E4OQnnMjO8vRQiooDEiV1uousmIHs1YE4EDOWAsLbQNKO3l0VEbnYqPR3jVizFlG1bkJmbq+7rWLUaHurQCe2rVPX28oiIAgYzsW6gZ86GntgD+tkR0JMezXsrH2fOdut5x4wZg3bt2iE2Nhbly5fH4MGDsWPHDreek4iKBrBDf/kRP23eWBDAihVHDuPGqb9i9p5dXl0fEVEgYRDrjgD23IOA+XjRB8wn1P3uDGQXLVqE++67D8uXL8ecOXOQk5ODPn36IC0tzW3nJKLz3lm6GMdSU2DS9SL3m3Uduq7jyTmzkJmb47X1EREFEpYTuLiEQE9+Xd6z9igADXryG0B4L7eUFsyaNavIxxMmTFAZ2TVr1qBr164uPx8RnZeanY3fd2y7IIC1kHtTsrPx965dGNKoscfXR0QUaJiJdSVVA1ssA1uEDpiP5R3nAUlJSept6dKlPXI+omB2LCUF2SaT3WNCDAbsPnvaY2siIgpkDGJdSTZxufK4S2A2m/Hwww+jc+fOaNq0qdvPRxTsosJCHR4jZQXRoWEeWQ8RUaBjOYErSRcCVx53CaQ2dvPmzfjvv//cfi4iAqrExqFxuXLYlngKutWSorwgtl/deh5fGxFRIGIm1pXC2gKGiqr21ToNMFTKO86N7r//fsyYMQMLFixA1aps6UPkKQ936GQzgDVoGgbUq4/aCSzvISJyBQaxLiSbtbS45ywfFX807//jnnVbv1jZ/SwB7LRp0zB//nzUqlXLLechIut61a6LMVf0QZjRqP7FSw2sUcv7t9+7dl2M7dXP20skIgoYLCdwMS2iL1BqXF6XgsKbvAwV8wJYedyNJQSTJk3C9OnTVa/Y48fzzh8fH4/IyEi3nZeIzhvepBn61amH6Tu2Yf+5s4gJC1cZ2IZl3V9GREQUTDRd0ndBIjk5WQV0sms/Li6uyGOZmZnYt2+fyl5GRET45cQuLT/jU9y3336LkSNH2vw8V3/tRERERO6I1wpjJtZNVMAa3sGj5wyi1yNEREQU5FgTS0RERER+h0EsEREREfkdBrFERERE5HcYxBIRERGR32EQW0wwbo4Kxq+ZiIiI/BuD2HxGY177q+zsbASb9PR09TY01PHsdyIiIiJfwBZb+UJCQhAVFYXExEQVzBkMhqDIwEoAe/LkSZQqVaogkCciIiLydQxiCw0KqFSpkmr6f+DAAQQTCWArVqzo7WUQEREROY1BbCFhYWGoV69eUJUUSNaZGVgiIiLyNwxii5EyAo5eJSIiIvJtgV/4SUREREQBh0EsEREREfkdBrFERERE5HdCgrGpf3JysreXQkRERERWWOI0R8OYgiqITUlJUW+rVavm7aUQERERkYO4LT4+3ubjmh5EM0fNZjOOHj2K2NhY1RfW2VcDEvQeOnQIcXFxbl8j2cafhe/gz8K38OfhO/iz8B38Wfjvz0NCUwlgK1eubHf4VFBlYuUbUbVq1Yv6XPmG8x+Bb+DPwnfwZ+Fb+PPwHfxZ+A7+LPzz52EvA2vBjV1ERERE5HcYxBIRERGR32EQ60B4eDheeukl9Za8iz8L38GfhW/hz8N38GfhO/izCPyfR1Bt7CIiIiKiwMBMLBERERH5HQaxREREROR3GMQSERERkd9hEEtEREREfodBbAnNnDkTHTp0QGRkJBISEjB48GBvLymoZWVloWXLlmoC2/r16729nKC0f/9+3H777ahVq5b6d1GnTh21AzU7O9vbSwsK48ePR82aNREREaF+N61cudLbSwpKY8aMQbt27dREyPLly6u/DTt27PD2sgjAm2++qf5GPPzww95eSlA6cuQIbr75ZpQpU0b9jWjWrBlWr17tkudmEFsCv/32G0aMGIFRo0Zhw4YNWLJkCW688UZvLyuoPfnkk2osHXnP9u3b1Ujnzz//HFu2bMH777+Pzz77DM8++6y3lxbwfvnlFzz66KPqRcPatWvRokUL9O3bFydPnvT20oLOokWLcN9992H58uWYM2cOcnJy0KdPH6SlpXl7aUFt1apV6ndT8+bNvb2UoHT27Fl07twZoaGh+Pvvv7F161a8++67KgnoEtJiixzLycnRq1Spon/11VfeXgrl++uvv/SGDRvqW7ZskTZx+rp167y9JMr39ttv67Vq1fL2MgJe+/bt9fvuu6/gY5PJpFeuXFkfM2aMV9dFun7y5En1e2nRokXeXkrQSklJ0evVq6fPmTNH79atm/7QQw95e0lB56mnntK7dOnitudnJtZJkuWQlLjBYECrVq1QqVIl9O/fH5s3b/b20oLSiRMnMHr0aHz//feIiory9nKomKSkJJQuXdrbywhoUq6xZs0a9OrVq+A++f0kHy9btsyra6O8fwOC/w68RzLjAwcOLPJvhDzrjz/+QNu2bXHdddepMhuJn7788kuXPT+DWCft3btXvX355Zfx/PPPY8aMGSod3r17d5w5c8bbywsqMp9j5MiRuPvuu9U/DvItu3fvxkcffYS77rrL20sJaKdOnYLJZEKFChWK3C8fHz9+3GvrIqjyGqm/lMuoTZs29fZygtLPP/+skk9Sq0zejZ0+/fRT1KtXD7Nnz8Y999yDBx98EBMnTnTJ8wd9EPv000+rgm97N0vNn3juuedwzTXXoE2bNvj222/V45MnT/b2lxFUPwsJkFJSUvDMM894e8kBzdmfR2FytaJfv37qVbdkyomCNQMoV+kkkCLPO3ToEB566CH8+OOPasMjeY/ETq1bt8Ybb7yhsrB33nmn+tsg+yZcIQRB7rHHHlNZPXtq166NY8eOqfcbN25ccL/M/5XHDh486PZ1BgNnfxbz589Xl0uLz1+WrOxNN93ksld4wc7Zn4fF0aNH0aNHD3Tq1AlffPGFB1YY3MqWLQuj0ahKawqTjytWrOi1dQW7+++/X12p+/fff1G1alVvLycoSZmNbG6U4MlCrlrIz+Tjjz9WXW3k3w65n5ReFo6bRKNGjdRGeVcI+iC2XLly6uaIZF4laJKWKV26dFH3ye5TaS9Uo0YND6w08Dn7sxg3bhxee+21IsGT7MiWndrSYog8+/OwZGAlgLVcoZDaTHKvsLAw9f2eN29eQas/yXrIxxJIkefLnB544AFMmzYNCxcuVC3nyDuuuOIKbNq0qch90lWoYcOGeOqppxjAepCU1BRvNbdz506XxU1BH8Q6Ky4uTtVgSiubatWqqR/A2LFj1WNy6ZQ8p3r16kU+jomJUW+lPykzH54nAazUhsu/iXfeeQeJiYkFjzEj6F7SXuvWW29VVyHat2+PDz74QLV0kj/Y5PkSgkmTJmH6/9u7g1Z4tzgO4L+RSBJRYiNKKVspK1FegIXMno0VeQOzULJmbUPewOwsbLwD2SmFFSULUbOwcDun7r/7595ut8aM434+NY2ZeXqcmdX3ec7v/E69nnvF/lmX3N/fn3tj0jrp9/9Yi9zb25v7lKpRbq3t7e08O5fKCVZXV3Mf6zRT16zZOiH2P0ihtbOzM/eKbTQa+a5fmtpuWr8zKFDqiZkWc6XHx4uIdHeKr1OtVvNFQ61Wy6Epbfxxenr6abEXXy8tXknSBd1fpZmJfyvLgZ9qdnY2z06kNSw7Ozt5hiJdbKfSv2aopD5bTTkTAAC0iMI1AACKI8QCAFAcIRYAgOIIsQAAFEeIBQCgOEIsAADFEWIBACiOEAsAQHGEWAAAiiPEAnwjaYvS5eXlv/1sfHw8KpVKfvT09OTXaT/ytP31R5ubmzEzMxPd3d15O1qAn0aIBShI2n/8/v4+rq6u4vj4OAYGBmJpaSl2d3c/Hbu2thbVarUt4wT4ap1f/h8AaJq+vr4YGRnJf4+NjcX8/HyMjo5GrVaLlZWVmJqayp8dHBzk58fHx7i8vGzrmAG+gjuxAIXb2tqK9/f3qNfr7R4KQMsIsQCFGxwcjOHh4bi9vW33UABaRogF+AHSndi04Avg/0KIBSjc09NTrn2dmJho91AAWkaIBSjc/v5+dHR0/GNrLoCfSHcCgG/m+fk5Li4ufntvaGgoP7+8vMTDw0O8vb3Fzc1NnJycxOHhYezt7cXk5OSv46+vr+P19TUf22g0fp1veno6urq6WvyNAJqv8p4KqQD4NpsdHB0dfXp/fX09zs7O4u7uLr9OQTS12pqbm4uNjY1YXFz87fiFhYU4Pz//dJ4UfNMmCQClE2IBACiOmlgAAIojxAIAUBwhFgCA4gixAAAUR4gFAKA4QiwAAMURYgEAKI4QCwBAcYRYAACKI8QCAFAcIRYAgCjNH3oQlufDqtCgAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lda_vis = LinearDiscriminantAnalysis(n_components=2)\n", + "\n", + "X_lda = lda_vis.fit_transform(X_train_scaled, y_train)\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "\n", + "scatter = plt.scatter(\n", + " X_lda[:, 0],\n", + " X_lda[:, 1],\n", + " c=y_train,\n", + " cmap='viridis'\n", + ")\n", + "\n", + "plt.xlabel(\"LD1\")\n", + "plt.ylabel(\"LD2\")\n", + "plt.title(\"LDA Projection\")\n", + "\n", + "plt.legend(*scatter.legend_elements(), title=\"Classes\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "67acfd2f-d2d4-48c3-a7aa-0209069e1b41", + "metadata": {}, + "source": [ + "# Интерпретация результатов\n", + "\n", + "## Наблюдения\n", + "\n", + "- Алгоритм LDA успешно разделяет классы вин.\n", + "- После масштабирования качество классификации улучшилось.\n", + "- Матрица ошибок показывает небольшое количество ошибок классификации.\n", + "\n", + "## Особенности LDA\n", + "\n", + "Linear Discriminant Analysis:\n", + "- уменьшает размерность;\n", + "- максимизирует разделение классов;\n", + "- хорошо работает на линейно разделимых данных.\n", + "\n", + "## Вывод\n", + "\n", + "LDA показал высокую точность классификации на датасете Wine.\n", + "Метод эффективен для задач, где классы имеют различимые статистические характеристики." + ] + }, + { + "cell_type": "markdown", + "id": "f01fc2f8-bad8-4c07-8c63-945d514a3252", + "metadata": {}, + "source": [ + "# Работа с CSV-файлом" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "f80bc1fa-117e-4c2f-99e8-0705ac120a29", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.makedirs(\"data\", exist_ok=True)\n", + "\n", + "df['target'] = y\n", + "\n", + "df.to_csv(\"data/wine.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "c511727f-06a7-4551-97f6-f98449bd927a", + "metadata": {}, + "outputs": [], + "source": [ + "df['target'] = y\n", + "\n", + "df.to_csv(\"data/wine.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "39a90fe8-65ac-4307-897e-049f216c4fba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolinetarget
014.231.712.4315.6127.02.803.060.282.295.641.043.921065.00
113.201.782.1411.2100.02.652.760.261.284.381.053.401050.00
213.162.362.6718.6101.02.803.240.302.815.681.033.171185.00
314.371.952.5016.8113.03.853.490.242.187.800.863.451480.00
413.242.592.8721.0118.02.802.690.391.824.321.042.93735.00
\n", + "
" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + "\n", + " od280/od315_of_diluted_wines proline target \n", + "0 3.92 1065.0 0 \n", + "1 3.40 1050.0 0 \n", + "2 3.17 1185.0 0 \n", + "3 3.45 1480.0 0 \n", + "4 2.93 735.0 0 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_csv = pd.read_csv(\"data/wine.csv\")\n", + "\n", + "df_csv.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1f921259-75cd-41ac-a748-c6b6a6b7350e", + "metadata": {}, + "outputs": [], + "source": [ + "X = df_csv.drop(\"target\", axis=1)\n", + "y = df_csv[\"target\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "5459dbb8-ab4e-41aa-87c3-0b4183899522", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alcohol 0\n", + "malic_acid 0\n", + "ash 0\n", + "alcalinity_of_ash 0\n", + "magnesium 0\n", + "total_phenols 0\n", + "flavanoids 0\n", + "nonflavanoid_phenols 0\n", + "proanthocyanins 0\n", + "color_intensity 0\n", + "hue 0\n", + "od280/od315_of_diluted_wines 0\n", + "proline 0\n", + "target 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(df_csv.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "15aabc65-0aef-4352-b376-e4e2cacefacb", + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "\n", + "X_scaled = scaler.fit_transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "8c587ff0-8969-4713-bb51-5ecdc274eb25", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X_scaled,\n", + " y,\n", + " test_size=0.2,\n", + " random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e6e87cd8-011a-4561-8586-988944354e00", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LinearDiscriminantAnalysis()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LinearDiscriminantAnalysis()" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lda = LinearDiscriminantAnalysis()\n", + "\n", + "lda.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "39fcce7c-ac0b-45ff-bd31-0e94dcbfb026", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = lda.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "034a8356-95e6-44b0-9912-6a5284f37447", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 14\n", + " 1 1.00 1.00 1.00 14\n", + " 2 1.00 1.00 1.00 8\n", + "\n", + " accuracy 1.00 36\n", + " macro avg 1.00 1.00 1.00 36\n", + "weighted avg 1.00 1.00 1.00 36\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "4e6d7cd6-cf45-423f-9125-b919aa0ea1fe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cm = confusion_matrix(y_test, y_pred)\n", + "\n", + "plt.figure(figsize=(6, 5))\n", + "\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Greens')\n", + "\n", + "plt.title(\"Confusion Matrix (CSV Dataset)\")\n", + "plt.xlabel(\"Предсказанные\")\n", + "plt.ylabel(\"Истинные\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e1729881-b33a-49e0-86f1-edc618a62258", + "metadata": {}, + "source": [ + "# Итоговый вывод\n", + "\n", + "В ходе работы был изучен алгоритм Linear Discriminant Analysis (LDA)\n", + "из библиотеки scikit-learn.\n", + "\n", + "Были выполнены:\n", + "- обучение модели;\n", + "- масштабирование признаков;\n", + "- работа со встроенным и внешним датасетами;\n", + "- визуализация результатов.\n", + "\n", + "LDA показал высокую точность классификации и хорошее разделение классов.\n", + "\n", + "Алгоритм особенно эффективен:\n", + "- при небольшом количестве классов;\n", + "- при линейной разделимости данных;\n", + "- для снижения размерности." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd87bba9-da3e-435a-b4e5-4aff5465419e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}