11

#encoding=utf8
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#encoding=utf8
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import pandas as pd
from sklearn.linear_model import LinearRegression
train_data = pd.read_csv('./step3/train_data.csv')
train_label = pd.read_csv('./step3/train_label.csv')
train_label = train_label['target']
test_data = pd.read_csv('./step3/test_data.csv')
lr = LinearRegression()
lr.fit(train_data,train_label)
predict = lr.predict(test_data)
df = pd.DataFrame({
    'result':predict})
df.to_csv('./step3/result.csv', index=False)
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第一题

 import numpy as np

'''
arr为一个ndarray类型的数组,line为花式索引的索引数组
'''
def advanced_index(arr, line):
    # ********** Begin *********** #

    # 利用花式索引获取 arr 数组的 line 行
    a = arr[line, :]

    # 获取数组 a 的四个角的元素
    b = np.array([a[0, 0], a[0, -1], a[-1, 0], a[-1, -1]])

    # 利用布尔索引获取 b 中大于 10 的元素
    c = b[b > 10]

    # *********** End ************ #
    return c

def main():
    line = list(map(lambda x: int(x), input().split(",")))
    arr = np.arange(24).reshape(6, 4)
    print(advanced_index(arr, line))

if __name__ == '__main__':
    main()