修改scipy稀疏矩陣到位

基本上，我只是試圖做一個簡單的矩陣乘法，具體來說，提取它的每一列，並通過用它的長度來分割它。修改scipy稀疏矩陣到位

#csc sparse matrix 
    self.__WeightMatrix__ = self.__WeightMatrix__.tocsc() 
    #iterate through columns 
    for Col in xrange(self.__WeightMatrix__.shape[1]): 
     Column = self.__WeightMatrix__[:,Col].data 
     List = [x**2 for x in Column] 
     #get the column length 
     Len = math.sqrt(sum(List)) 
     #here I assumed dot(number,Column) would do a basic scalar product 
     dot((1/Len),Column) 
     #now what? how do I update the original column of the matrix, everything that have been returned are copies, which drove me nuts and missed pointers so much

我已經通過scipy稀疏矩陣文檔搜索，沒有得到有用的信息。我希望函數能夠返回一個指向矩陣的指針/引用，以便我可以直接修改它的值。謝謝

來源

2013-03-04 Cong Hui

你有沒有試過'self.__ WeightMatrix __ [：，Col] = ...'？ – Blender 2013-03-04 07:09:12

我這樣認爲，原始值並沒有改變，導致我相信[：Col]返回了一個副本，並且據我所知，似乎csc稀疏矩陣不支持直接索引，如果發生錯誤這樣做。 – 2013-03-04 07:10:10

在CSC格式中，您有兩個可寫屬性，data和indices，它們包含矩陣的非零條目和相應的行索引。

def sparse_row_normalize(sps_mat) : 
    if sps_mat.format != 'csc' : 
     msg = 'Can only row-normalize in place with csc format, not {0}.' 
     msg = msg.format(sps_mat.format) 
     raise ValueError(msg) 
    row_norm = np.sqrt(np.bincount(sps_mat.indices, weights=mat.data * mat_data)) 
    sps_mat.data /= np.take(row_norm, sps_mat.indices)

一看就知道它的實際工作：您可以按如下方式使用這些你的優勢

>>> mat = scipy.sparse.rand(4, 4, density=0.5, format='csc') 
>>> mat.toarray() 
array([[ 0.  , 0.  , 0.58931687, 0.31070526], 
     [ 0.24024639, 0.02767106, 0.22635696, 0.85971295], 
     [ 0.  , 0.  , 0.13613897, 0.  ], 
     [ 0.  , 0.13766507, 0.  , 0.  ]]) 
>>> mat.toarray()/np.sqrt(np.sum(mat.toarray()**2, axis=1))[:, None] 
array([[ 0.  , 0.  , 0.88458487, 0.46637926], 
     [ 0.26076366, 0.03003419, 0.24568806, 0.93313324], 
     [ 0.  , 0.  , 1.  , 0.  ], 
     [ 0.  , 1.  , 0.  , 0.  ]]) 
>>> sparse_row_normalize(mat) 
>>> mat.toarray() 
array([[ 0.  , 0.  , 0.88458487, 0.46637926], 
     [ 0.26076366, 0.03003419, 0.24568806, 0.93313324], 
     [ 0.  , 0.  , 1.  , 0.  ], 
     [ 0.  , 1.  , 0.  , 0.  ]])

同時，它也numpy的快，沒有Python的循環破壞的樂趣：

In [2]: mat = scipy.sparse.rand(10000, 10000, density=0.005, format='csc') 

In [3]: mat 
Out[3]: 
<10000x10000 sparse matrix of type '<type 'numpy.float64'>' 
    with 500000 stored elements in Compressed Sparse Column format> 

In [4]: %timeit sparse_row_normalize(mat) 
100 loops, best of 3: 14.1 ms per loop

來源

2013-03-04 08:21:55 Jaime

修改scipy稀疏矩陣到位

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