Numpy 将二维数组与一维数组连接起来 [英] Numpy concatenate 2D arrays with 1D array

问题描述

我正在尝试连接 4 个数组，一个一维形状数组 (78427,) 和 3 个二维形状数组 (78427, 375/81/103).基本上这是 4 个具有 78427 张图像特征的数组，其中一维数组每个图像只有 1 个值.

我尝试按如下方式连接数组:

<预><代码>>>>打印 X_Cscores.shape(78427, 375)>>>打印 X_Mscores.shape(78427, 81)>>>打印 X_Tscores.shape(78427, 103)>>>打印 X_Yscores.shape(78427,)>>>np.concatenate((X_Cscores, X_Mscores, X_Tscores, X_Yscores),axis=1)

这会导致以下错误:

<块引用>

回溯(最近一次调用最后一次):文件"，第 1 行，在ValueError:所有输入数组必须具有相同的维数

问题似乎是一维数组，但我真的不明白为什么(它也有 78427 个值).我试图在连接它之前转置一维数组，但这也不起作用.

有关连接这些数组的正确方法的任何帮助将不胜感激！

解决方案

尝试连接 X_Yscores[:, None](或 X_Yscores[:, np.newaxis] 作为imaluengo 建议).这将从一维数组中创建一个二维数组.

示例:

A = np.array([1, 2, 3])打印 A.shape打印 A[:, None].shape

输出:

(3,)(3,1)

I am trying to concatenate 4 arrays, one 1D array of shape (78427,) and 3 2D array of shape (78427, 375/81/103). Basically this are 4 arrays with features for 78427 images, in which the 1D array only has 1 value for each image.

I tried concatenating the arrays as follows:

>>> print X_Cscores.shape
(78427, 375)
>>> print X_Mscores.shape
(78427, 81)
>>> print X_Tscores.shape
(78427, 103)
>>> print X_Yscores.shape
(78427,)
>>> np.concatenate((X_Cscores, X_Mscores, X_Tscores, X_Yscores), axis=1)

This results in the following error:

Traceback (most recent call last): File "", line 1, in ValueError: all the input arrays must have same number of dimensions

The problem seems to be the 1D array, but I can't really see why (it also has 78427 values). I tried to transpose the 1D array before concatenating it, but that also didn't work.

Any help on what's the right method to concatenate these arrays would be appreciated!

解决方案

Try concatenating X_Yscores[:, None] (or X_Yscores[:, np.newaxis] as imaluengo suggests). This creates a 2D array out of a 1D array.

Example:

A = np.array([1, 2, 3])
print A.shape
print A[:, None].shape

Output:

(3,)
(3,1)

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