Python的Numpy库中的 nonzero函数，及应用在 hardlim硬限幅函数中numpy.nonzero

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我是靠谱客的博主丰富哈密瓜，最近开发中收集的这篇文章主要介绍Python的Numpy库中的 nonzero函数，及应用在 hardlim硬限幅函数中numpy.nonzero，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

Python的Numpy库中的 nonzero函数，及应用在 hardlim硬限幅函数中

在学习邓捷的《机器学习算法原理与编程实践》中 p168页的激活函数 hardlim的时候，看不懂。

查询相关的知识点，学习了下。

先看 python代码

#! /usr/bin/python
from
numpy import *
'''
print a
[[-4 -3 -2]
[-1
0
1]
[ 2
3
4]]
print dataset.A
[[-4 -3 -2]
[-1
0
1]
[ 2
3
4]]
print
dataset.A > 0
[[False False False]
[False False
True]
[ True
True
True]]
print
nonzero(
dataset.A > 0 )
(array([1, 2, 2, 2]), array([2, 0, 1, 2]))
print hardlim( a )
[[0 0 0]
[1 1 1]
[1 1 1]]
'''
def hardlim(dataset):
dataset[ nonzero( dataset.A > 0 )[0] ] = 1
dataset[ nonzero( dataset.A <=0 )[0] ] = 0
return dataset
if __name__ == "__main__":
a = arange(9) - 4
#print a
#[-4 -3 -2 -1
0
1
2
3
4]
a = a.reshape(3, 3)
#print a
#[[-4 -3 -2]
# [-1
0
1]
# [ 2
3
4]]
a = mat(a)
#print a
#[[-4 -3 -2]
# [-1
0
1]
# [ 2
3
4]]
print a
print hardlim( a )

numpy是Python用来科学计算的一个非常重要的库，numpy主要用来处理一些矩阵对象

numpy.nonzero()函数，简记如下：

官方文档链接如下：http://docs.scipy.org/doc/numpy/reference/generated/numpy.nonzero.html

内容如下：

numpy.nonzero

numpy. nonzero ( a )

Returnthe indices of the elements that are non-zero.

Returnsa tuple of arrays, one for each dimension of a ,containing the indices of the non-zero elements in thatdimension. The values in a arealways tested and returned in row-major, C-style order. Thecorresponding non-zero values can be obtained with:

a[nonzero(a)]

Togroup the indices by element, rather than dimension, use:

transpose(nonzero(a))

Theresult of this is always a 2-D array, with a row for eachnon-zero element.

Parameters:

a :array_like

Inputarray.

Returns:

tuple_of_arrays :tuple

Indicesof elements that are non-zero.

Seealso

flatnonzero

Returnindices that are non-zero in the flattened version of theinput array.

ndarray.nonzero

Equivalentndarray method.

count_nonzero

Countsthe number of non-zero elements in the input array.

举些使用的例子

>>> b1 = np.array([True, False, True, False])
>>> np.nonzero(b1)

(array([0, 2]),)

可以看到，对于一维的array，输出的是非零值的“下标”。

>>> import numpy as np
>>> a = np.array([[1,2,3],[4,5,6],[7,8,9]])
>>> a > 3
array([[False, False, False],

[ True,
True,
True],

[ True,
True,
True]], dtype=bool)
>>> np.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
>>> np.transpose(
np.nonzero(a > 3)
)

array([[1, 0],
这就是非零元素的坐标

[1, 1],


[1, 2],


[2, 0],


[2, 1],


[2, 2]])