Numpy：如何随机地将一个矩阵分成n个不同的矩阵

Question

• 我有一个形状为（4601,58）的numpy矩阵。
• 我想基于行数的矩阵分裂随机按60％，20％，20％分
• 这是机器学习任务，我需要
• 有随机选取行的numpy的功能？

Answer 1

可以使用numpy.random.shuffle

import numpy as np 

N = 4601 
data = np.arange(N*58).reshape(-1, 58) 
np.random.shuffle(data) 

a = data[:int(N*0.6)] 
b = data[int(N*0.6):int(N*0.8)] 
c = data[int(N*0.8):] 

Answer 2

如果你想随机选择行，你可以只使用random.sample从标准Python库：

import random 

population = range(4601) # Your number of rows 
choice = random.sample(population, k) # k being the number of samples you require 

random.sample样品无需更换，所以你不必担心重复行结束了在choice。给定一个叫做matrix的数组，可以通过切片来选择行，如下所示：matrix[choice]。

当然，k可以等于总体中元素的总数，然后choice将包含行的索引的随机排序。那么你可以根据你的需要划分choice，如果这就是你需要的。

Answer 3

一种补充HYRY的答案，如果你想一直洗牌多个阵列X，Y，Z，使用相同的第一维：x.shape[0] == y.shape[0] == z.shape[0] == n_samples。

你可以这样做：

rng = np.random.RandomState(42) # reproducible results with a fixed seed 
indices = np.arange(n_samples) 
rng.shuffle(indices) 
x_shuffled = x[indices] 
y_shuffled = y[indices] 
z_shuffled = z[indices] 

然后每个洗牌阵列的分裂在HYRY的回答进行。

Answer 4

既然你需要它的机器学习，这里是我写的一个方法：

import numpy as np 

def split_random(matrix, percent_train=70, percent_test=15): 
    """ 
    Splits matrix data into randomly ordered sets 
    grouped by provided percentages. 

    Usage: 
    rows = 100 
    columns = 2 
    matrix = np.random.rand(rows, columns) 
    training, testing, validation = \ 
    split_random(matrix, percent_train=80, percent_test=10) 

    percent_validation 10 
    training (80, 2) 
    testing (10, 2) 
    validation (10, 2) 

    Returns: 
    - training_data: percentage_train e.g. 70% 
    - testing_data: percent_test e.g. 15% 
    - validation_data: reminder from 100% e.g. 15% 
    Created by Uki D. Lucas on Feb. 4, 2017 
    """ 

    percent_validation = 100 - percent_train - percent_test 

    if percent_validation < 0: 
     print("Make sure that the provided sum of " + \ 
     "training and testing percentages is equal, " + \ 
     "or less than 100%.") 
     percent_validation = 0 
    else: 
     print("percent_validation", percent_validation) 

    #print(matrix) 
    rows = matrix.shape[0] 
    np.random.shuffle(matrix) 

    end_training = int(rows*percent_train/100)  
    end_testing = end_training + int((rows * percent_test/100)) 

    training = matrix[:end_training] 
    testing = matrix[end_training:end_testing] 
    validation = matrix[end_testing:] 
    return training, testing, validation 

# TEST: 
rows = 100 
columns = 2 
matrix = np.random.rand(rows, columns) 
training, testing, validation = split_random(matrix, percent_train=80, percent_test=10) 

print("training",training.shape) 
print("testing",testing.shape) 
print("validation",validation.shape) 

print(split_random.__doc__) 

• 训练（80,2）
• 测试（10,2）
• 验证（10， 2）