232 lines (182 sloc) 7.14 KB
"""Compressed Sparse Column matrix format"""
from __future__ import division, print_function, absolute_import
__docformat__ = "restructuredtext en"
__all__ = ['csc_matrix', 'isspmatrix_csc']
import numpy as np
from .base import spmatrix
from ._sparsetools import csc_tocsr
from . import _sparsetools
from .sputils import upcast, isintlike, IndexMixin, get_index_dtype
from .compressed import _cs_matrix
class csc_matrix(_cs_matrix, IndexMixin):
Compressed Sparse Column matrix
This can be instantiated in several ways:
with a dense matrix or rank-2 ndarray D
with another sparse matrix S (equivalent to S.tocsc())
csc_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N)
dtype is optional, defaulting to dtype='d'.
csc_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
where ``data``, ``row_ind`` and ``col_ind`` satisfy the
relationship ``a[row_ind[k], col_ind[k]] = data[k]``.
csc_matrix((data, indices, indptr), [shape=(M, N)])
is the standard CSC representation where the row indices for
column i are stored in ``indices[indptr[i]:indptr[i+1]]``
and their corresponding values are stored in
``data[indptr[i]:indptr[i+1]]``. If the shape parameter is
not supplied, the matrix dimensions are inferred from
the index arrays.
dtype : dtype
Data type of the matrix
shape : 2-tuple
Shape of the matrix
ndim : int
Number of dimensions (this is always 2)
Number of nonzero elements
Data array of the matrix
CSC format index array
CSC format index pointer array
Whether indices are sorted
Sparse matrices can be used in arithmetic operations: they support
addition, subtraction, multiplication, division, and matrix power.
Advantages of the CSC format
- efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
- efficient column slicing
- fast matrix vector products (CSR, BSR may be faster)
Disadvantages of the CSC format
- slow row slicing operations (consider CSR)
- changes to the sparsity structure are expensive (consider LIL or DOK)
>>> import numpy as np
>>> from scipy.sparse import csc_matrix
>>> csc_matrix((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=int8)
>>> row = np.array([0, 2, 2, 0, 1, 2])
>>> col = np.array([0, 0, 1, 2, 2, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
>>> indptr = np.array([0, 2, 3, 6])
>>> indices = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csc_matrix((data, indices, indptr), shape=(3, 3)).toarray()
array([[1, 0, 4],
[0, 0, 5],
[2, 3, 6]])
format = 'csc'
def transpose(self, axes=None, copy=False):
if axes is not None:
raise ValueError(("Sparse matrices do not support "
"an 'axes' parameter because swapping "
"dimensions is the only logical permutation."))
M, N = self.shape
from .csr import csr_matrix
return csr_matrix((, self.indices,
self.indptr), (N, M), copy=copy)
transpose.__doc__ = spmatrix.transpose.__doc__
def __iter__(self):
for r in self.tocsr():
yield r
def tocsc(self, copy=False):
if copy:
return self.copy()
return self
tocsc.__doc__ = spmatrix.tocsc.__doc__
def tocsr(self, copy=False):
M,N = self.shape
idx_dtype = get_index_dtype((self.indptr, self.indices),
maxval=max(self.nnz, N))
indptr = np.empty(M + 1, dtype=idx_dtype)
indices = np.empty(self.nnz, dtype=idx_dtype)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
csc_tocsr(M, N,
from .csr import csr_matrix
A = csr_matrix((data, indices, indptr), shape=self.shape, copy=False)
A.has_sorted_indices = True
return A
tocsr.__doc__ = spmatrix.tocsr.__doc__
def __getitem__(self, key):
# Use CSR to implement fancy indexing.
row, col = self._unpack_index(key)
# Things that return submatrices. row or col is a int or slice.
if (isinstance(row, slice) or isinstance(col, slice) or
isintlike(row) or isintlike(col)):
return self.T[col, row].T
# Things that return a sequence of values.
return self.T[col, row]
def nonzero(self):
# CSC can't use _cs_matrix's .nonzero method because it
# returns the indices sorted for self transposed.
# Get row and col indices, from _cs_matrix.tocoo
major_dim, minor_dim = self._swap(self.shape)
minor_indices = self.indices
major_indices = np.empty(len(minor_indices), dtype=self.indices.dtype)
_sparsetools.expandptr(major_dim, self.indptr, major_indices)
row, col = self._swap((major_indices, minor_indices))
# Remove explicit zeros
nz_mask = != 0
row = row[nz_mask]
col = col[nz_mask]
# Sort them to be in C-style order
ind = np.argsort(row, kind='mergesort')
row = row[ind]
col = col[ind]
return row, col
nonzero.__doc__ = _cs_matrix.nonzero.__doc__
def getrow(self, i):
"""Returns a copy of row i of the matrix, as a (1 x n)
CSR matrix (row vector).
# we convert to CSR to maintain compatibility with old impl.
# in spmatrix.getrow()
return self._get_submatrix(i, slice(None)).tocsr()
def getcol(self, i):
"""Returns a copy of column i of the matrix, as a (m x 1)
CSC matrix (column vector).
M, N = self.shape
i = int(i)
if i < 0:
i += N
if i < 0 or i >= N:
raise IndexError('index (%d) out of range' % i)
idx = slice(*self.indptr[i:i+2])
data =[idx].copy()
indices = self.indices[idx].copy()
indptr = np.array([0, len(indices)], dtype=self.indptr.dtype)
return csc_matrix((data, indices, indptr), shape=(M, 1),
dtype=self.dtype, copy=False)
# these functions are used by the parent class (_cs_matrix)
# to remove redudancy between csc_matrix and csr_matrix
def _swap(self, x):
"""swap the members of x if this is a column-oriented matrix
return x[1], x[0]
def isspmatrix_csc(x):
return isinstance(x, csc_matrix)