Numpy Matrix内存大小比Numpy Array低 [英] Numpy Matrix Memory size low compared to Numpy Array

问题描述

我有一个.npz文件，我想将其加载到RAM中.压缩文件大小为30MB.我正在执行以下操作以将数据加载到RAM.

I have a .npz file which I want to load into RAM . The compressed file size is 30MB . I am doing the following operation to load the data into RAM.

import numpy as np
from scipy import sparse
from sys import getsizeof

a = sparse.load_npz('compressed/CRS.npz').todense()
getsizeof(a)
# 136
type(a)
# numpy.matrixlib.defmatrix.matrix
b = np.array(a)
getsizeof(b)
# 64000112
type(b)
# numpy.ndarray

与numpy.arrray相比，为什么numpy.matrix对象占用的内存很小? a和b都具有相同的维度和数据.

Why numpy.matrix object occupy very low memory size compared to numpy.arrray ? Both a and b have same dimension and data.

推荐答案

您的a矩阵是另一个数组的视图，因此基础数据不计入其getsizeof中.您可以通过检查a.base is not None或查看OWNDATA标志在a.flags中是False来看到此情况.

Your a matrix is a view of another array, so the underlying data is not counted towards its getsizeof. You can see this by checking that a.base is not None, or by seeing that the OWNDATA flag is False in a.flags.

您的b数组不是视图，因此基础数据计入其getsizeof.

Your b array is not a view, so the underlying data is counted towards its getsizeof.

numpy.matrix不能节省任何内存.

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