使用 SciPy/Numpy 在 Python 中连接稀疏矩阵
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Concatenate sparse matrices in Python using SciPy/Numpy
提问by PascalVKooten
What would be the most efficient way to concatenate sparse matrices in Python using SciPy/Numpy?
使用 SciPy/Numpy 在 Python 中连接稀疏矩阵的最有效方法是什么？
Here I used the following:
在这里我使用了以下内容：
>>> np.hstack((X, X2))
array([ <49998x70000 sparse matrix of type '<class 'numpy.float64'>'
with 1135520 stored elements in Compressed Sparse Row format>,
<49998x70000 sparse matrix of type '<class 'numpy.int64'>'
with 1135520 stored elements in Compressed Sparse Row format>],
dtype=object)
I would like to use both predictors in a regression, but the current format is obviously not what I'm looking for. Would it be possible to get the following:
我想在回归中使用这两个预测变量，但当前的格式显然不是我想要的。是否有可能获得以下内容：
<49998x1400000 sparse matrix of type '<class 'numpy.float64'>'
with 2271040 stored elements in Compressed Sparse Row format>
It is too large to be converted to a deep format.
它太大而无法转换为深度格式。
采纳答案by Saullo G. P. Castro
You can use the scipy.sparse.hstack:
您可以使用scipy.sparse.hstack：
from scipy.sparse import hstack
hstack((X, X2))
Using the numpy.hstackwill create an array with two sparse matrix objects.
使用numpy.hstack将创建一个包含两个稀疏矩阵对象的数组。
