python中的一些函数

1、tile 元素重复函数

第二个参数是一维

>>> a=[[1,2,3],[4,5,5]]
>>> b=np.tile(a,3)
>>> print(b)
[[1 2 3 1 2 3 1 2 3]
 [4 5 5 4 5 5 4 5 5]]

第二个参数是二维

>>> c=np.tile(a,[2,3])
>>> print(c)
[[1 2 3 1 2 3 1 2 3]
 [4 5 5 4 5 5 4 5 5]
 [1 2 3 1 2 3 1 2 3]
 [4 5 5 4 5 5 4 5 5]]

第二个参数是三维

d=np.tile(a,[2,3,4])
>>> print(d)
[[[1 2 3 1 2 3 1 2 3 1 2 3]
  [4 5 5 4 5 5 4 5 5 4 5 5]
  [1 2 3 1 2 3 1 2 3 1 2 3]
  [4 5 5 4 5 5 4 5 5 4 5 5]
  [1 2 3 1 2 3 1 2 3 1 2 3]
  [4 5 5 4 5 5 4 5 5 4 5 5]]

 [[1 2 3 1 2 3 1 2 3 1 2 3]
  [4 5 5 4 5 5 4 5 5 4 5 5]
  [1 2 3 1 2 3 1 2 3 1 2 3]
  [4 5 5 4 5 5 4 5 5 4 5 5]
  [1 2 3 1 2 3 1 2 3 1 2 3]
  [4 5 5 4 5 5 4 5 5 4 5 5]]]

2、expand_dims 元素增加维度

>>> a=[[1,2,3],[4,5,5]]
>>> b=np.expand_dims(a,0)
>>> print(b)
[[[1 2 3]
  [4 5 5]]]
>>> c=np.expand_dims(a,1)
>>> print(c)
[[[1 2 3]]

[[4 5 5]]]

>>> d=np.expand_dims(a,2)
>>> print(d)
[[[1]
[2]
[3]]

[[4]
[5]
[5]]]

3、求accuracy  precision  recall

import numpy as np
from sklearn import metrics


def get_precision(lable, pre):
    # ((lable == pre) & lable).sum() / (pre.sum() + 0.00001)
    return metrics.precision_score(lable, pre)


def get_recall(lable, pre):
    # ((lable == pre) & lable).sum() / (lable.sum() + 0.00001)
    return metrics.recall_score(lable, pre)


def get_accuracy(label, pre):
    return (label == pre).mean()


def get_auc(label, pre):
    return metrics.roc_auc_score(label, pre)


if __name__ == '__main__':
    print get_recall(np.array([True, False, False]), np.array([True, False, True]))

 4、不使用科学计数法

np.set_printoptions(suppress=True)