第二周神经网络基础2.1 二分分类2.2 logistic回归2.3 logistic 回归损失函数2.4 梯度下降2.5 导数2.14 向量化logistic 回归的输出2.15 Python中的广

import numpy as np
A=np.array([
    [56.0,0.0,4.4,68.0],
    [1.2,104.0,52.0,8.0],
    [1.8,135.0,99.0,0.9]
])
print(A)

[[  56.     0.     4.4   68. ]
 [   1.2  104.    52.     8. ]
 [   1.8  135.    99.     0.9]]

cal=A.sum(axis=0)
print(cal)

[  59.   239.   155.4   76.9]

percentage=100*A/cal.reshape(1,4)
print(percentage)

[[ 94.91525424   0.           2.83140283  88.42652796]
 [  2.03389831  43.51464435  33.46203346  10.40312094]
 [  3.05084746  56.48535565  63.70656371   1.17035111]]