def tocsc(self):
indptr = np.empty(self.shape[1] + 1, dtype=np.intc)
indices = np.empty(self.nnz, dtype=np.intc)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
csr_tocsc(self.shape[0], self.shape[1], self.indptr, self.indices, self.data, indptr, indices, data)
from csc import csc_matrix
A = csc_matrix((data, indices, indptr), shape=self.shape)
A.has_sorted_indices = True
return A
def getcol(self, j):
"""Returns a copy of column j of the matrix, as an (m x 1) sparse
matrix (column vector).
"""
# Spmatrix subclasses should override this method for efficiency.
# Post-multiply by a (n x 1) column vector 'a' containing all zeros
# except for a_j = 1
from csc import csc_matrix
n = self.shape[1]
a = csc_matrix((n, 1), dtype=self.dtype)
a[j, 0] = 1
return self * a
def tocsc(self):
indptr = np.empty(self.shape[1] + 1, dtype=np.intc)
indices = np.empty(self.nnz, dtype=np.intc)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
csr_tocsc(self.shape[0], self.shape[1], \
self.indptr, self.indices, self.data, \
indptr, indices, data)
from csc import csc_matrix
A = csc_matrix((data, indices, indptr), shape=self.shape)
A.has_sorted_indices = True
return A
def tocsc(self):
"""Return a copy of this matrix in Compressed Sparse Column format
Duplicate entries will be summed together.
Example
-------
>>> from numpy import array
>>> from scipy.sparse import coo_matrix
>>> row = array([0,0,1,3,1,0,0])
>>> col = array([0,2,1,3,1,0,0])
>>> data = array([1,1,1,1,1,1,1])
>>> A = coo_matrix( (data,(row,col)), shape=(4,4)).tocsc()
>>> A.todense()
matrix([[3, 0, 1, 0],
[0, 2, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 1]])
"""
from csc import csc_matrix
if self.nnz == 0:
return csc_matrix(self.shape, dtype=self.dtype)
else:
M,N = self.shape
indptr = np.empty(N + 1, dtype=np.intc)
indices = np.empty(self.nnz, dtype=np.intc)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
coo_tocsr(N, M, self.nnz, \
self.col, self.row, self.data, \
indptr, indices, data)
A = csc_matrix((data, indices, indptr), shape=self.shape)
A.sum_duplicates()
return A
def tocsc(self):
"""Return a copy of this matrix in Compressed Sparse Column format
Duplicate entries will be summed together.
Examples
--------
>>> from numpy import array
>>> from scipy.sparse import coo_matrix
>>> row = array([0,0,1,3,1,0,0])
>>> col = array([0,2,1,3,1,0,0])
>>> data = array([1,1,1,1,1,1,1])
>>> A = coo_matrix( (data,(row,col)), shape=(4,4)).tocsc()
>>> A.todense()
matrix([[3, 0, 1, 0],
[0, 2, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 1]])
"""
from csc import csc_matrix
if self.nnz == 0:
return csc_matrix(self.shape, dtype=self.dtype)
else:
M, N = self.shape
indptr = np.empty(N + 1, dtype=np.intc)
indices = np.empty(self.nnz, dtype=np.intc)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
coo_tocsr(N, M, self.nnz, \
self.col, self.row, self.data, \
indptr, indices, data)
A = csc_matrix((data, indices, indptr), shape=self.shape)
A.sum_duplicates()
return A
def getcol(self, j):
"""Returns a copy of column j of the matrix, as an (m x 1) sparse
matrix (column vector).
"""
# Spmatrix subclasses should override this method for efficiency.
# Post-multiply by a (n x 1) column vector 'a' containing all zeros
# except for a_j = 1
from csc import csc_matrix
n = self.shape[1]
if j < 0:
j += n
if j < 0 or j >= n:
raise IndexError("index out of bounds")
col_selector = csc_matrix(([1], [[j], [0]]), shape=(n,1), dtype=self.dtype)
return self * col_selector
def getcol(self, j):
"""Returns a copy of column j of the matrix, as an (m x 1) sparse
matrix (column vector).
"""
# Spmatrix subclasses should override this method for efficiency.
# Post-multiply by a (n x 1) column vector 'a' containing all zeros
# except for a_j = 1
from csc import csc_matrix
n = self.shape[1]
if j < 0:
j += n
if j < 0 or j >= n:
raise IndexError("index out of bounds")
col_selector = csc_matrix(([1], [[j], [0]]), shape=(n,1), dtype=self.dtype)
return self * col_selector
def transpose(self, copy=False):
from csc import csc_matrix
M,N = self.shape
return csc_matrix((self.data,self.indices,self.indptr), shape=(N,M), copy=copy)
def transpose(self, copy=False):
from csc import csc_matrix
M, N = self.shape
return csc_matrix((self.data, self.indices, self.indptr),
shape=(N, M),
copy=copy)
