def tobsr(self, blocksize=None, copy=True):
from bsr import bsr_matrix
if blocksize is None:
from spfuncs import estimate_blocksize
return self.tobsr(blocksize=estimate_blocksize(self))
elif blocksize == (1,1):
arg1 = (self.data.reshape(-1,1,1),self.indices,self.indptr)
return bsr_matrix(arg1, shape=self.shape, copy=copy )
else:
R,C = blocksize
M,N = self.shape
if R < 1 or C < 1 or M % R != 0 or N % C != 0:
raise ValueError('invalid blocksize %s' % blocksize)
blks = csr_count_blocks(M,N,R,C,self.indptr,self.indices)
indptr = np.empty(M/R + 1, dtype=np.intc)
indices = np.empty(blks, dtype=np.intc)
data = np.zeros((blks,R,C), dtype=self.dtype)
csr_tobsr(M, N, R, C, self.indptr, self.indices, self.data, \
indptr, indices, data.ravel() )
return bsr_matrix((data,indices,indptr), shape=self.shape)
def tobsr(self, blocksize=None, copy=True):
from bsr import bsr_matrix
if blocksize is None:
from spfuncs import estimate_blocksize
return self.tobsr(blocksize=estimate_blocksize(self))
elif blocksize == (1, 1):
arg1 = (self.data.reshape(-1, 1, 1), self.indices, self.indptr)
return bsr_matrix(arg1, shape=self.shape, copy=copy)
else:
R, C = blocksize
M, N = self.shape
if R < 1 or C < 1 or M % R != 0 or N % C != 0:
raise ValueError('invalid blocksize %s' % blocksize)
blks = csr_count_blocks(M, N, R, C, self.indptr, self.indices)
indptr = np.empty(M // R + 1, dtype=np.intc)
indices = np.empty(blks, dtype=np.intc)
data = np.zeros((blks, R, C), dtype=self.dtype)
csr_tobsr(M, N, R, C, self.indptr, self.indices, self.data, \
indptr, indices, data.ravel() )
return bsr_matrix((data, indices, indptr), shape=self.shape)
def kron(A, B, format=None):
"""kronecker product of sparse matrices A and B
Parameters
----------
A : sparse or dense matrix
first matrix of the product
B : sparse or dense matrix
second matrix of the product
format : string
format of the result (e.g. "csr")
Returns
-------
kronecker product in a sparse matrix format
Examples
--------
>>> A = csr_matrix(array([[0,2],[5,0]]))
>>> B = csr_matrix(array([[1,2],[3,4]]))
>>> kron(A,B).todense()
matrix([[ 0, 0, 2, 4],
[ 0, 0, 6, 8],
[ 5, 10, 0, 0],
[15, 20, 0, 0]])
>>> kron(A,[[1,2],[3,4]]).todense()
matrix([[ 0, 0, 2, 4],
[ 0, 0, 6, 8],
[ 5, 10, 0, 0],
[15, 20, 0, 0]])
"""
B = coo_matrix(B)
if (format is None
or format == "bsr") and 2 * B.nnz >= B.shape[0] * B.shape[1]:
#B is fairly dense, use BSR
A = csr_matrix(A, copy=True)
output_shape = (A.shape[0] * B.shape[0], A.shape[1] * B.shape[1])
if A.nnz == 0 or B.nnz == 0:
# kronecker product is the zero matrix
return coo_matrix(output_shape)
B = B.toarray()
data = A.data.repeat(B.size).reshape(-1, B.shape[0], B.shape[1])
data = data * B
return bsr_matrix((data, A.indices, A.indptr), shape=output_shape)
else:
#use COO
A = coo_matrix(A)
output_shape = (A.shape[0] * B.shape[0], A.shape[1] * B.shape[1])
if A.nnz == 0 or B.nnz == 0:
# kronecker product is the zero matrix
return coo_matrix(output_shape)
# expand entries of a into blocks
row = A.row.repeat(B.nnz)
col = A.col.repeat(B.nnz)
data = A.data.repeat(B.nnz)
row *= B.shape[0]
col *= B.shape[1]
# increment block indices
row, col = row.reshape(-1, B.nnz), col.reshape(-1, B.nnz)
row += B.row
col += B.col
row, col = row.reshape(-1), col.reshape(-1)
# compute block entries
data = data.reshape(-1, B.nnz) * B.data
data = data.reshape(-1)
return coo_matrix((data, (row, col)),
shape=output_shape).asformat(format)
def kron(A, B, format=None):
"""kronecker product of sparse matrices A and B
Parameters
----------
A : sparse or dense matrix
first matrix of the product
B : sparse or dense matrix
second matrix of the product
format : string
format of the result (e.g. "csr")
Returns
-------
kronecker product in a sparse matrix format
Examples
--------
>>> A = csr_matrix(array([[0,2],[5,0]]))
>>> B = csr_matrix(array([[1,2],[3,4]]))
>>> kron(A,B).todense()
matrix([[ 0, 0, 2, 4],
[ 0, 0, 6, 8],
[ 5, 10, 0, 0],
[15, 20, 0, 0]])
>>> kron(A,[[1,2],[3,4]]).todense()
matrix([[ 0, 0, 2, 4],
[ 0, 0, 6, 8],
[ 5, 10, 0, 0],
[15, 20, 0, 0]])
"""
B = coo_matrix(B)
if (format is None or format == "bsr") and 2*B.nnz >= B.shape[0] * B.shape[1]:
#B is fairly dense, use BSR
A = csr_matrix(A,copy=True)
output_shape = (A.shape[0]*B.shape[0], A.shape[1]*B.shape[1])
if A.nnz == 0 or B.nnz == 0:
# kronecker product is the zero matrix
return coo_matrix( output_shape )
B = B.toarray()
data = A.data.repeat(B.size).reshape(-1,B.shape[0],B.shape[1])
data = data * B
return bsr_matrix((data,A.indices,A.indptr), shape=output_shape)
else:
#use COO
A = coo_matrix(A)
output_shape = (A.shape[0]*B.shape[0], A.shape[1]*B.shape[1])
if A.nnz == 0 or B.nnz == 0:
# kronecker product is the zero matrix
return coo_matrix( output_shape )
# expand entries of a into blocks
row = A.row.repeat(B.nnz)
col = A.col.repeat(B.nnz)
data = A.data.repeat(B.nnz)
row *= B.shape[0]
col *= B.shape[1]
# increment block indices
row,col = row.reshape(-1,B.nnz),col.reshape(-1,B.nnz)
row += B.row
col += B.col
row,col = row.reshape(-1),col.reshape(-1)
# compute block entries
data = data.reshape(-1,B.nnz) * B.data
data = data.reshape(-1)
return coo_matrix((data,(row,col)), shape=output_shape).asformat(format)
