Работа с данными: https://snap.berkeley.edu/project?username=avsmolkina&projectname=work%20on%20fruit%20data Создаются клоны со случайным положением, костюмом (яблоко или банан), для каждого записывается переменная "ripe" со случайным значением "yes" или "no", случайное значение переменной "dirty" от 0 до 100. Собираем данные: costume, ripe, x, y, dirty. 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Работа с данными: https://snap.berkeley.edu/project?username=avsmolkina&projectname=work%20on%20fruit%20data Создаются клоны со случайным положением, костюмом (яблоко или банан), для каждого записывается переменная "ripe" со случайным значением "yes" или "no", случайное значение переменной "dirty" от 0 до 100. Собираем данные: costume, ripe, x, y, dirty.

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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg==1486