实例化numpy数组时出现内存错误 [英] Memory error In instantiating the numpy array

问题描述

我有一个50,000个元素的列表 A ，每个元素都是一个形状数组(102400)

I have a list A of a 50,000 elements and each element is an array of shape (102400)

我尝试实例化数组 B .

     B=numpy.array(A)

但这会引发MemoryError异常.我知道内存和大小非常大吗，但是有没有一种方法可以避免numpy中的ndarrays导致此MemoryError ??

But this throws an exception MemoryError.I know that the memory and size is very huge?But is there a way to avoid this MemoryError with ndarrays in numpy ??

推荐答案

作为一种解决方法，您可以避免使用以下方式复制数组:

As a workaround, you could avoid copying the arrays with something like:

B = np.empty(len(A), dtype=object)
for i, a in enumerate(A):
    B[i] = a

这将允许创建dtype object的ndarray，其元素将是A的原始元素.然后，您可以对其执行 some 数组操作.

This would allow to create a ndarray of dtype object whose elements would be the original elements of A. You could then perform some array operations on it.

但是我不确定您是否可以通过这种方式赢得胜利.

But I am not sure you have something to win in going this way.

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