scipy.sparse點在Python

Question

下面的代碼非常緩慢甚至不能完成我的系統上：

import numpy as np 
from scipy import sparse 
p = 100 
n = 50 
X = np.random.randn(p,n) 
L = sparse.eye(p,p, format='csc') 
X.T.dot(L).dot(X) 

有爲什麼這個矩陣乘法掛任何解釋？

Answer 1

X.T.dot(L)不是，因爲你可能會認爲，一個50×矩陣，但100×100

>>> X.T.dot(L).shape 
(50, 100) 
>>> X.T.dot(L)[0,0] 
<100x100 sparse matrix of type '<type 'numpy.float64'>' 
    with 100 stored elements in Compressed Sparse Column format> 

50×稀疏矩陣陣列看來這個問題是X的dot方法，它是一個數組，不知道稀疏矩陣。因此，您必須使用其todense或toarray方法將稀疏矩陣轉換爲密度。前者返回一個matrix對象，後者的array：

>>> X.T.dot(L.todense()).dot(X) 
matrix([[ 81.85399873, 3.75640482, 1.62443625, ..., 6.47522251, 
      3.42719396, 2.78630873], 
     [ 3.75640482, 109.45428475, -2.62737229, ..., -0.31310651, 
      2.87871548, 8.27537382], 
     [ 1.62443625, -2.62737229, 101.58919604, ..., 3.95235372, 
      1.080478 , -0.16478654], 
     ..., 
     [ 6.47522251, -0.31310651, 3.95235372, ..., 95.72988689, 
      -18.99209596, 17.31774553], 
     [ 3.42719396, 2.87871548, 1.080478 , ..., -18.99209596, 
      108.90045569, -16.20312682], 
     [ 2.78630873, 8.27537382, -0.16478654, ..., 17.31774553, 
      -16.20312682, 105.37102461]]) 

另外，稀疏矩陣有dot方法，它知道數組：

>>> X.T.dot(L.dot(X)) 
array([[ 81.85399873, 3.75640482, 1.62443625, ..., 6.47522251, 
      3.42719396, 2.78630873], 
     [ 3.75640482, 109.45428475, -2.62737229, ..., -0.31310651, 
      2.87871548, 8.27537382], 
     [ 1.62443625, -2.62737229, 101.58919604, ..., 3.95235372, 
      1.080478 , -0.16478654], 
     ..., 
     [ 6.47522251, -0.31310651, 3.95235372, ..., 95.72988689, 
     -18.99209596, 17.31774553], 
     [ 3.42719396, 2.87871548, 1.080478 , ..., -18.99209596, 
     108.90045569, -16.20312682], 
     [ 2.78630873, 8.27537382, -0.16478654, ..., 17.31774553, 
     -16.20312682, 105.37102461]])