How to use the np.random.permutation function in python

1: Function introduction

np.random.permutation() Generally speaking, it is a random permutation function, which is to The input data is randomly arranged. The official document states that this function can only randomly arrange one-dimensional data, and for multi-dimensional data, it can only randomly arrange the data in the first dimension.

In short: the function of np.random.permutation function is to generate a scrambled random list according to the given list

When processing the data When setting up a data set, you can usually use this function to shuffle the internal order of the data set and shuffle the label sequence in the same order.

2: Example

2.1 Directly process array or list numbers

import numpy as np

data = np.array([1,2,3,4,5,6,7])
a = np.random.permutation(data)
b = np.random.permutation([5,0,9,0,1,1,1])
print(a)
print( "data:", data )
print(b)

2.2 Indirect processing: do not change the original data (for arrays Index processing)

label = np.array([1,2,3,4,5,6,7])
a = np.random.permutation(np.arange(len(label)))
print("Label[a] :" ,label[a] )

##Supplement: Generally it can only be used for N-dimensional arrays and can only convert integer scalar arrays to scalar indexes

why?label1[a1] label1 is a list, a1 is a random arrangement of list subscripts but! The list structure does not have a scalar index label1[a1] error

label1=[1,2,3,4,5,6,7]
print(len(label1))

a1 = np.random.permutation(np.arange(len(label1)))#有结果

print(a1)

print("Label1[a1] :" ,label1[a1] )#这列表结构没有标量索引 所以会报错

2.3 Example: random shuffling of iris flowers in iris data (can be used directly)

from sklearn import svm
from sklearn import datasets #sklearn 的数据集
iris = datasets.load_iris()
iris_x = iris.data
iris_y = iris.target
indices = np.random.permutation(len(iris_x))

#此时 打乱的是数组的下标的排序
print(indices)
print(indices[:-10])#到倒数第10个为止
print(indices[-10:])#最后10个

# print(type(iris_x))   <class &#39;numpy.ndarray&#39;>

#9:1分类
#iris_x_train = iris_x[indices[:-10]]#使用的数组打乱后的下标
#iris_y_train = iris_y[indices[:-10]]
#iris_x_test= iris_x[indices[-10:]]
#iris_y_test= iris_y[indices[-10:]]

The array subscript is the redistribution of scalar index: The subscript starts from 0

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