从列表创建numpy数组会产生错误的形状 [英] Creating numpy array from list gives wrong shape

问题描述

我正在从numpy数组列表中创建多个numpy数组，如下所示:

I'm creating several numpy arrays from a list of numpy arrays, like so:

seq_length = 1500
seq_diff = 200  # difference between start of two sequences
# x and y are 2D numpy arrays
x_seqs = [x[i:i+seq_length,:] for i in range(0, seq_diff*(len(x) // seq_diff), seq_diff)]
y_seqs = [y[i:i+seq_length,:] for i in range(0, seq_diff*(len(y) // seq_diff), seq_diff)]
boundary1 = int(0.7 * len(x_seqs))   # 70% is training set
boundary2 = int(0.85 * len(x_seqs))  # 15% validation, 15% test
x_train = np.array(x_seqs[:boundary1])
y_train = np.array(y_seqs[:boundary1])
x_valid = np.array(x_seqs[boundary1:boundary2])
y_valid = np.array(y_seqs[boundary1:boundary2])
x_test = np.array(x_seqs[boundary2:])
y_test = np.array(y_seqs[boundary2:])

我想最终得到6个形状数组(n，1500、300)，其中n分别是我用于训练，验证和测试数组的数据的70％，15％或15％.

I'd like to end up with 6 arrays of shape (n, 1500, 300) where n is either 70%, 15% or 15% of my data for the training, validation and test arrays, respectively.

这是出问题的地方:_train和_valid数组结果很好，但是_test数组是数组的一维数组.那是:

This is where it goes wrong: the _train and _valid arrays turn out fine, but the _test arrays are one-dimensional arrays of arrays. That is:

• x_train.shape是(459, 1500, 300)
• x_valid.shape是(99, 1500, 300)
• x_test.shape是(99,)

• x_train.shape is (459, 1500, 300)
• x_valid.shape is (99, 1500, 300)
• x_test.shape is (99,)

但是打印x_test会验证它包含正确的元素-即，它是一个99个元素的(1500, 300)数组长数组.

But printing x_test verifies that it contains the correct elements - i.e. it's a 99-element long array of (1500, 300) arrays.

为什么_test矩阵的形状错误，而_train和_valid矩阵却没有?

Why do the _test matrices get the wrong shape, while the _train and _valid matrices don't?

推荐答案

x_seqs中的项目长度不同.当它们都具有相同的长度时，np.array可以从它们组成一个3d数组.当它们不同时，它将构成一个对象列表数组.查看x_test的dtype.看看[len(i) for i in x_test].

The items in x_seqs vary in length. When they are all the same length, np.array can make a 3d array from them; when they differ it makes an object array of lists. Look at the dtype of x_test. Look at the [len(i) for i in x_test].

我接受了您的代码，并补充:

I took your code, added:

x=np.zeros((2000,10))
y=x.copy()
...
print([len(i) for i in x_seqs])
print(x_train.shape)
print(x_valid.shape)
print(x_test.shape)

并得到:

1520:~/mypy$ python3 stack40643639.py 
[1500, 1500, 1500, 1400, 1200, 1000, 800, 600, 400, 200]
(7,)
(1, 600, 10)
(2,)

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