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In another post regarding resizing of a sparse matrix in SciPy the accepted answer works when more rows or columns are to be added, using scipy.sparse.vstack or hstack, respectively. In SciPy 0.12 the reshape or set_shape methods are still not implemented.

Are there some stabilished good practices to reshape a sparse matrix in SciPy 0.12? It would be nice to have some timing comparisons.

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3 回答 3

8

我不知道任何既定的良好做法，所以这里有一个相当直接的 coo_matrix 重塑函数。它将其参数转换为 coo_matrix，因此它将实际适用于其他稀疏格式（但它返回一个 coo_matrix）。

from scipy.sparse import coo_matrix


def reshape(a, shape):
    """Reshape the sparse matrix `a`.

    Returns a coo_matrix with shape `shape`.
    """
    if not hasattr(shape, '__len__') or len(shape) != 2:
        raise ValueError('`shape` must be a sequence of two integers')

    c = a.tocoo()
    nrows, ncols = c.shape
    size = nrows * ncols

    new_size =  shape[0] * shape[1]
    if new_size != size:
        raise ValueError('total size of new array must be unchanged')

    flat_indices = ncols * c.row + c.col
    new_row, new_col = divmod(flat_indices, shape[1])

    b = coo_matrix((c.data, (new_row, new_col)), shape=shape)
    return b

例子：

In [43]: a = coo_matrix([[0,10,0,0],[0,0,0,0],[0,20,30,40]])

In [44]: a.A
Out[44]: 
array([[ 0, 10,  0,  0],
       [ 0,  0,  0,  0],
       [ 0, 20, 30, 40]])

In [45]: b = reshape(a, (2,6))

In [46]: b.A
Out[46]: 
array([[ 0, 10,  0,  0,  0,  0],
       [ 0,  0,  0, 20, 30, 40]])

现在，我敢肯定这里有几个定期贡献者可以提出更好的东西（更快，更高效的内存，更少的填充...... :)

于 2013-05-13T23:02:28.070 回答
2

从SciPy 1.1.0开始，所有稀疏矩阵类型都实现了reshape和方法。set_shape签名是您所期望的，并且与 NumPy 中的等效方法尽可能相同（例如，您不能重塑为向量或张量）。

签名：

reshape(self, shape: Tuple[int, int], order: 'C'|'F' = 'C', copy: bool = False) -> spmatrix

例子：

>>> from scipy.sparse import csr_matrix
>>> A = csr_matrix([[0,0,2,0], [0,1,0,3]])
>>> print(A)
  (0, 2)    2
  (1, 1)    1
  (1, 3)    3
>>> B = A.reshape((4,2))
>>> print(B)
  (1, 0)    2
  (2, 1)    1
  (3, 1)    3
>>> C = A.reshape((4,2), order='F')
>>> print(C)
  (0, 1)    2
  (3, 0)    1
  (3, 1)    3

完全披露：我编写了实现。

于 2019-02-04T13:37:36.590 回答
0

我有一个 CSR 矩阵的工作示例，但我不能保证它总是有效

展平矩阵 A：

    indices = zeros_like(A.indices)
    indices[A.indptr[1:-1]] = A.shape[1]
    indices = cumsum( indices)+A.indices
    A_flat = sparse.csc_matrix((T_rot.data, indices,[0,size(A)]),shape=(prod(A.shape),1))

重塑矩阵 A

    indices = zeros_like(A.indices)
    indices[A.indptr[1:-1]] = A.shape[1]
    indices = cumsum( indices)+A.indices

    indices %= N*A.shape[1]
    indptr = r_[0, where(diff(indices)<0)[0]+1, size(A)]
    A_reshaped = sparse.csc_matrix((A.data, indices,indptr),shape=(N*A.shape[1],A.shape[0]/N ))
于 2015-07-03T15:36:49.650 回答