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Shuffle arrays or sparse matrices in a consistent way
This is a convenience alias to resample(*arrays, replace=False) to do random permutations of the collections.
| Parameters: |
*arrays : sequence of indexable data-structures
random_state : int or RandomState instance
n_samples : int, None by default
|
|---|---|
| Returns: |
shuffled_arrays : sequence of indexable data-structures
|
# -*- coding: utf-8 -*- """ Spyder Editor This is a temporary script file. """ import numpy as np X = np.array([[1., 0.], [2., 1.], [0., 0.]]) y = np.array([0, 1, 2]) from scipy.sparse import coo_matrix X_sparse = coo_matrix(X) print ‘稀疏矩阵%s\n‘,X_sparse from sklearn.utils import shuffle X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0) print ‘X值\n‘, X print ‘X_sparse值\n‘, X_sparse print ‘y值\n‘, y ‘‘‘ 稀疏矩阵%s (0, 0) 1.0 (1, 0) 2.0 (1, 1) 1.0 X值 [[ 0. 0.] [ 2. 1.] [ 1. 0.]] X_sparse值 (1, 1) 1.0 (1, 0) 2.0 (2, 0) 1.0 y值 [2 1 0] ‘‘‘
随机打乱工具sklearn.utils.shuffle，将原有的序列打乱，返回一个全新的错乱顺序的值
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原文地址：http://www.cnblogs.com/qqhfeng/p/5762471.html
