def __radd__(self, other):
# First check if argument is a scalar
if isscalarlike(other):
new = dok_matrix(self.shape, dtype=self.dtype)
# Add this scalar to every element.
M, N = self.shape
for i in xrange(M):
for j in xrange(N):
aij = self.get((i, j), 0) + other
if aij != 0:
new[i, j] = aij
elif isinstance(other, dok_matrix):
if other.shape != self.shape:
raise ValueError("matrix dimensions are not equal")
new = dok_matrix(self.shape, dtype=self.dtype)
new.update(self)
for key in other:
new[key] += other[key]
elif isspmatrix(other):
csc = self.tocsc()
new = csc + other
elif isdense(other):
new = other + self.todense()
else:
raise TypeError("data type not understood")
return new
def __add__(self, other):
# First check if argument is a scalar
if isscalarlike(other):
new = dok_matrix(self.shape, dtype=self.dtype)
# Add this scalar to every element.
M, N = self.shape
for i in xrange(M):
for j in xrange(N):
aij = self.get((i, j), 0) + other
if aij != 0:
new[i, j] = aij
#new.dtype.char = self.dtype.char
elif isinstance(other, dok_matrix):
if other.shape != self.shape:
raise ValueError("matrix dimensions are not equal")
# We could alternatively set the dimensions to the the largest of
# the two matrices to be summed. Would this be a good idea?
new = dok_matrix(self.shape, dtype=self.dtype)
new.update(self)
for key in other.keys():
new[key] += other[key]
elif isspmatrix(other):
csc = self.tocsc()
new = csc + other
elif isdense(other):
new = self.todense() + other
else:
raise TypeError("data type not understood")
return new
def __radd__(self, other):
# First check if argument is a scalar
if isscalarlike(other):
new = dok_matrix(self.shape, dtype=self.dtype)
# Add this scalar to every element.
M, N = self.shape
for i in xrange(M):
for j in xrange(N):
aij = self.get((i, j), 0) + other
if aij != 0:
new[i, j] = aij
elif isinstance(other, dok_matrix):
if other.shape != self.shape:
raise ValueError("matrix dimensions are not equal")
new = dok_matrix(self.shape, dtype=self.dtype)
new.update(self)
for key in other:
new[key] += other[key]
elif isspmatrix(other):
csc = self.tocsc()
new = csc + other
elif isdense(other):
new = other + self.todense()
else:
raise TypeError("data type not understood")
return new
def __init__(self, arg1, shape=None, dtype=None, copy=False):
dict.__init__(self)
spmatrix.__init__(self)
self.dtype = getdtype(dtype, default=float)
if isinstance(arg1, tuple) and isshape(arg1): # (M,N)
M, N = arg1
self.shape = (M, N)
elif isspmatrix(arg1): # Sparse ctor
if isspmatrix_dok(arg1) and copy:
arg1 = arg1.copy()
else:
arg1 = arg1.todok()
if dtype is not None:
arg1 = arg1.astype(dtype)
self.update(arg1)
self.shape = arg1.shape
self.dtype = arg1.dtype
else: # Dense ctor
try:
arg1 = np.asarray(arg1)
except:
raise TypeError('invalid input format')
if len(arg1.shape)!=2:
raise TypeError('expected rank <=2 dense array or matrix')
from coo import coo_matrix
self.update( coo_matrix(arg1, dtype=dtype).todok() )
self.shape = arg1.shape
self.dtype = arg1.dtype
def __add__(self, other):
# First check if argument is a scalar
if isscalarlike(other):
new = dok_matrix(self.shape, dtype=self.dtype)
# Add this scalar to every element.
M, N = self.shape
for i in xrange(M):
for j in xrange(N):
aij = self.get((i, j), 0) + other
if aij != 0:
new[i, j] = aij
#new.dtype.char = self.dtype.char
elif isinstance(other, dok_matrix):
if other.shape != self.shape:
raise ValueError("matrix dimensions are not equal")
# We could alternatively set the dimensions to the the largest of
# the two matrices to be summed. Would this be a good idea?
new = dok_matrix(self.shape, dtype=self.dtype)
new.update(self)
for key in other.keys():
new[key] += other[key]
elif isspmatrix(other):
csc = self.tocsc()
new = csc + other
elif isdense(other):
new = self.todense() + other
else:
raise TypeError("data type not understood")
return new
def __init__(self, arg1, shape=None, dtype=None, copy=False):
dict.__init__(self)
spmatrix.__init__(self)
self.dtype = getdtype(dtype, default=float)
if isinstance(arg1, tuple) and isshape(arg1): # (M,N)
M, N = arg1
self.shape = (M, N)
elif isspmatrix(arg1): # Sparse ctor
if isspmatrix_dok(arg1) and copy:
arg1 = arg1.copy()
else:
arg1 = arg1.todok()
if dtype is not None:
arg1 = arg1.astype(dtype)
self.update(arg1)
self.shape = arg1.shape
self.dtype = arg1.dtype
else: # Dense ctor
try:
arg1 = np.asarray(arg1)
except:
raise TypeError('invalid input format')
if len(arg1.shape) != 2:
raise TypeError('expected rank <=2 dense array or matrix')
from coo import coo_matrix
self.update(coo_matrix(arg1, dtype=dtype).todok())
self.shape = arg1.shape
self.dtype = arg1.dtype
def __truediv__(self,other):
if isscalarlike(other):
return self * (1./other)
elif isspmatrix(other):
if other.shape != self.shape:
raise ValueError('inconsistent shapes')
return self._binopt(other,'_eldiv_')
else:
raise NotImplementedError
def __truediv__(self, other):
if isscalarlike(other):
return self * (1. / other)
elif isspmatrix(other):
if other.shape != self.shape:
raise ValueError('inconsistent shapes')
return self._binopt(other, '_eldiv_')
else:
raise NotImplementedError
def __sub__(self,other):
# First check if argument is a scalar
if isscalarlike(other):
# Now we would add this scalar to every element.
raise NotImplementedError, 'adding a scalar to a sparse ' \
'matrix is not supported'
elif isspmatrix(other):
if (other.shape != self.shape):
raise ValueError, "inconsistent shapes"
return self._binopt(other,'_minus_')
elif isdense(other):
# Convert this matrix to a dense matrix and subtract them
return self.todense() - other
else:
raise NotImplementedError
def __sub__(self,other):
# First check if argument is a scalar
if isscalarlike(other):
# Now we would add this scalar to every element.
raise NotImplementedError, 'adding a scalar to a sparse ' \
'matrix is not supported'
elif isspmatrix(other):
if (other.shape != self.shape):
raise ValueError, "inconsistent shapes"
return self._binopt(other,'_minus_')
elif isdense(other):
# Convert this matrix to a dense matrix and subtract them
return self.todense() - other
else:
raise NotImplementedError
def __add__(self,other):
# First check if argument is a scalar
if isscalarlike(other):
# Now we would add this scalar to every element.
raise NotImplementedError('adding a scalar to a CSC or CSR '
'matrix is not supported')
elif isspmatrix(other):
if (other.shape != self.shape):
raise ValueError("inconsistent shapes")
return self._binopt(other,'_plus_')
elif isdense(other):
# Convert this matrix to a dense matrix and add them
return self.todense() + other
else:
raise NotImplementedError
def __setitem__(self, index, x):
try:
i, j = index
except (ValueError, TypeError):
raise IndexError('invalid index')
# shortcut for common case of single entry assign:
if np.isscalar(x) and np.isscalar(i) and np.isscalar(j):
self._insertat2(self.rows[i], self.data[i], j, x)
return
# shortcut for common case of full matrix assign:
if isspmatrix(x):
if isinstance(i, slice) and i == slice(None) and \
isinstance(j, slice) and j == slice(None):
x = lil_matrix(x, dtype=self.dtype)
self.rows = x.rows
self.data = x.data
return
if isinstance(i, tuple): # can't index lists with tuple
i = list(i)
if np.isscalar(i):
rows = [self.rows[i]]
datas = [self.data[i]]
else:
rows = self.rows[i]
datas = self.data[i]
x = lil_matrix(x, copy=False)
xrows, xcols = x.shape
if xrows == len(rows): # normal rectangular copy
for row, data, xrow, xdata in zip(rows, datas, x.rows, x.data):
self._setitem_setrow(row, data, j, xrow, xdata, xcols)
elif xrows == 1: # OK, broadcast down column
for row, data in zip(rows, datas):
self._setitem_setrow(row, data, j, x.rows[0], x.data[0], xcols)
# needed to pass 'test_lil_sequence_assignement' unit test:
# -- set row from column of entries --
elif xcols == len(rows):
x = x.T
for row, data, xrow, xdata in zip(rows, datas, x.rows, x.data):
self._setitem_setrow(row, data, j, xrow, xdata, xrows)
else:
raise IndexError('invalid index')
def __setitem__(self, index, x):
try:
i, j = index
except (ValueError, TypeError):
raise IndexError('invalid index')
# shortcut for common case of single entry assign:
if np.isscalar(x) and np.isscalar(i) and np.isscalar(j):
self._insertat2(self.rows[i], self.data[i], j, x)
return
# shortcut for common case of full matrix assign:
if isspmatrix(x):
if isinstance(i, slice) and i == slice(None) and \
isinstance(j, slice) and j == slice(None):
x = lil_matrix(x, dtype=self.dtype)
self.rows = x.rows
self.data = x.data
return
if isinstance(i, tuple): # can't index lists with tuple
i = list(i)
if np.isscalar(i):
rows = [self.rows[i]]
datas = [self.data[i]]
else:
rows = self.rows[i]
datas = self.data[i]
x = lil_matrix(x, copy=False)
xrows, xcols = x.shape
if xrows == len(rows): # normal rectangular copy
for row, data, xrow, xdata in zip(rows, datas, x.rows, x.data):
self._setitem_setrow(row, data, j, xrow, xdata, xcols)
elif xrows == 1: # OK, broadcast down column
for row, data in zip(rows, datas):
self._setitem_setrow(row, data, j, x.rows[0], x.data[0], xcols)
# needed to pass 'test_lil_sequence_assignement' unit test:
# -- set row from column of entries --
elif xcols == len(rows):
x = x.T
for row, data, xrow, xdata in zip(rows, datas, x.rows, x.data):
self._setitem_setrow(row, data, j, xrow, xdata, xrows)
else:
raise IndexError('invalid index')
def __sub__(self, other):
# First check if argument is a scalar
if isscalarlike(other):
if other == 0:
return self.copy()
else: # Now we would add this scalar to every element.
raise NotImplementedError("adding a nonzero scalar to a " "sparse matrix is not supported")
elif isspmatrix(other):
if other.shape != self.shape:
raise ValueError("inconsistent shapes")
return self._binopt(other, "_minus_")
elif isdense(other):
# Convert this matrix to a dense matrix and subtract them
return self.todense() - other
else:
raise NotImplementedError
def __init__(self, arg1, shape=None, dtype=None, copy=False):
spmatrix.__init__(self)
self.dtype = getdtype(dtype, arg1, default=float)
# First get the shape
if isspmatrix(arg1):
if isspmatrix_lil(arg1) and copy:
A = arg1.copy()
else:
A = arg1.tolil()
if dtype is not None:
A = A.astype(dtype)
self.shape = A.shape
self.dtype = A.dtype
self.rows = A.rows
self.data = A.data
elif isinstance(arg1,tuple):
if isshape(arg1):
if shape is not None:
raise ValueError('invalid use of shape parameter')
M, N = arg1
self.shape = (M,N)
self.rows = np.empty((M,), dtype=object)
self.data = np.empty((M,), dtype=object)
for i in range(M):
self.rows[i] = []
self.data[i] = []
else:
raise TypeError('unrecognized lil_matrix constructor usage')
else:
#assume A is dense
try:
A = np.asmatrix(arg1)
except TypeError:
raise TypeError('unsupported matrix type')
else:
from csr import csr_matrix
A = csr_matrix(A, dtype=dtype).tolil()
self.shape = A.shape
self.dtype = A.dtype
self.rows = A.rows
self.data = A.data
def __init__(self, arg1, shape=None, dtype=None, copy=False):
spmatrix.__init__(self)
self.dtype = getdtype(dtype, arg1, default=float)
# First get the shape
if isspmatrix(arg1):
if isspmatrix_lil(arg1) and copy:
A = arg1.copy()
else:
A = arg1.tolil()
if dtype is not None:
A = A.astype(dtype)
self.shape = A.shape
self.dtype = A.dtype
self.rows = A.rows
self.data = A.data
elif isinstance(arg1, tuple):
if isshape(arg1):
if shape is not None:
raise ValueError('invalid use of shape parameter')
M, N = arg1
self.shape = (M, N)
self.rows = np.empty((M, ), dtype=object)
self.data = np.empty((M, ), dtype=object)
for i in range(M):
self.rows[i] = []
self.data[i] = []
else:
raise TypeError('unrecognized lil_matrix constructor usage')
else:
#assume A is dense
try:
A = np.asmatrix(arg1)
except TypeError:
raise TypeError('unsupported matrix type')
else:
from csr import csr_matrix
A = csr_matrix(A, dtype=dtype).tolil()
self.shape = A.shape
self.dtype = A.dtype
self.rows = A.rows
self.data = A.data
def __setitem__(self, index, x):
if np.isscalar(x):
x = self.dtype.type(x)
elif not isinstance(x, spmatrix):
x = lil_matrix(x)
try:
i, j = index
except (ValueError, TypeError):
raise IndexError('invalid index')
if isspmatrix(x):
if (isinstance(i, slice) and (i == slice(None))) and \
(isinstance(j, slice) and (j == slice(None))):
# self[:,:] = other_sparse
x = lil_matrix(x)
self.rows = x.rows
self.data = x.data
return
if np.isscalar(i):
row = self.rows[i]
data = self.data[i]
self._insertat3(row, data, j, x)
elif issequence(i) and issequence(j):
if np.isscalar(x):
for ii, jj in zip(i, j):
self._insertat(ii, jj, x)
else:
for ii, jj, xx in zip(i, j, x):
self._insertat(ii, jj, xx)
elif isinstance(i, slice) or issequence(i):
rows = self.rows[i]
datas = self.data[i]
if np.isscalar(x):
for row, data in zip(rows, datas):
self._insertat3(row, data, j, x)
else:
for row, data, xx in zip(rows, datas, x):
self._insertat3(row, data, j, xx)
else:
raise ValueError('invalid index value: %s' % str((i, j)))
def __setitem__(self, index, x):
if np.isscalar(x):
x = self.dtype.type(x)
elif not isinstance(x, spmatrix):
x = lil_matrix(x)
try:
i, j = index
except (ValueError, TypeError):
raise IndexError('invalid index')
if isspmatrix(x):
if (isinstance(i, slice) and (i == slice(None))) and \
(isinstance(j, slice) and (j == slice(None))):
# self[:,:] = other_sparse
x = lil_matrix(x)
self.rows = x.rows
self.data = x.data
return
if np.isscalar(i):
row = self.rows[i]
data = self.data[i]
self._insertat3(row, data, j, x)
elif issequence(i) and issequence(j):
if np.isscalar(x):
for ii, jj in zip(i, j):
self._insertat(ii, jj, x)
else:
for ii, jj, xx in zip(i, j, x):
self._insertat(ii, jj, xx)
elif isinstance(i, slice) or issequence(i):
rows = self.rows[i]
datas = self.data[i]
if np.isscalar(x):
for row, data in zip(rows, datas):
self._insertat3(row, data, j, x)
else:
for row, data, xx in zip(rows, datas, x):
self._insertat3(row, data, j, xx)
else:
raise ValueError('invalid index value: %s' % str((i, j)))
def __init__(self, arg1, shape=None, dtype=None, copy=False):
_data_matrix.__init__(self)
if isspmatrix(arg1):
if arg1.format == self.format and copy:
arg1 = arg1.copy()
else:
arg1 = arg1.asformat(self.format)
self._set_self(arg1)
elif isinstance(arg1, tuple):
if isshape(arg1):
# It's a tuple of matrix dimensions (M, N)
# create empty matrix
self.shape = arg1 #spmatrix checks for errors here
M, N = self.shape
self.data = np.zeros(0, getdtype(dtype, default=float))
self.indices = np.zeros(0, np.intc)
self.indptr = np.zeros(self._swap((M, N))[0] + 1,
dtype=np.intc)
else:
if len(arg1) == 2:
# (data, ij) format
from coo import coo_matrix
other = self.__class__(coo_matrix(arg1, shape=shape))
self._set_self(other)
elif len(arg1) == 3:
# (data, indices, indptr) format
(data, indices, indptr) = arg1
self.indices = np.array(indices, copy=copy)
self.indptr = np.array(indptr, copy=copy)
self.data = np.array(data,
copy=copy,
dtype=getdtype(dtype, data))
else:
raise ValueError(
"unrecognized %s_matrix constructor usage" %
self.format)
else:
#must be dense
try:
arg1 = np.asarray(arg1)
except:
raise ValueError("unrecognized %s_matrix constructor usage" %
self.format)
from coo import coo_matrix
self._set_self(self.__class__(coo_matrix(arg1, dtype=dtype)))
# Read matrix dimensions given, if any
if shape is not None:
self.shape = shape # spmatrix will check for errors
else:
if self.shape is None:
# shape not already set, try to infer dimensions
try:
major_dim = len(self.indptr) - 1
minor_dim = self.indices.max() + 1
except:
raise ValueError('unable to infer matrix dimensions')
else:
self.shape = self._swap((major_dim, minor_dim))
if dtype is not None:
self.data = self.data.astype(dtype)
self.check_format(full_check=False)
def __init__(self, arg1, shape=None, dtype=None, copy=False):
_data_matrix.__init__(self)
if isspmatrix_dia(arg1):
if copy:
arg1 = arg1.copy()
self.data = arg1.data
self.offsets = arg1.offsets
self.shape = arg1.shape
elif isspmatrix(arg1):
if isspmatrix_dia(arg1) and copy:
A = arg1.copy()
else:
A = arg1.todia()
self.data = A.data
self.offsets = A.offsets
self.shape = A.shape
elif isinstance(arg1, tuple):
if isshape(arg1):
# It's a tuple of matrix dimensions (M, N)
# create empty matrix
self.shape = arg1 #spmatrix checks for errors here
self.data = np.zeros((0, 0), getdtype(dtype, default=float))
self.offsets = np.zeros((0), dtype=np.intc)
else:
try:
# Try interpreting it as (data, offsets)
data, offsets = arg1
except:
raise ValueError(
'unrecognized form for dia_matrix constructor')
else:
if shape is None:
raise ValueError('expected a shape argument')
self.data = np.atleast_2d(
np.array(arg1[0], dtype=dtype, copy=copy))
self.offsets = np.atleast_1d(
np.array(arg1[1], dtype=np.intc, copy=copy))
self.shape = shape
else:
#must be dense, convert to COO first, then to DIA
try:
arg1 = np.asarray(arg1)
except:
raise ValueError("unrecognized form for" \
" %s_matrix constructor" % self.format)
from coo import coo_matrix
A = coo_matrix(arg1, dtype=dtype).todia()
self.data = A.data
self.offsets = A.offsets
self.shape = A.shape
if dtype is not None:
self.data = self.data.astype(dtype)
#check format
if self.offsets.ndim != 1:
raise ValueError('offsets array must have rank 1')
if self.data.ndim != 2:
raise ValueError('data array must have rank 2')
if self.data.shape[0] != len(self.offsets):
raise ValueError('number of diagonals (%d) ' \
'does not match the number of offsets (%d)' \
% (self.data.shape[0], len(self.offsets)))
if len(np.unique(self.offsets)) != len(self.offsets):
raise ValueError('offset array contains duplicate values')
def __init__(self,
arg1,
shape=None,
dtype=None,
copy=False,
blocksize=None):
_data_matrix.__init__(self)
if isspmatrix(arg1):
if isspmatrix_bsr(arg1) and copy:
arg1 = arg1.copy()
else:
arg1 = arg1.tobsr(blocksize=blocksize)
self._set_self(arg1)
elif isinstance(arg1, tuple):
if isshape(arg1):
#it's a tuple of matrix dimensions (M,N)
self.shape = arg1
M, N = self.shape
#process blocksize
if blocksize is None:
blocksize = (1, 1)
else:
if not isshape(blocksize):
raise ValueError('invalid blocksize=%s' % blocksize)
blocksize = tuple(blocksize)
self.data = np.zeros((0, ) + blocksize,
getdtype(dtype, default=float))
self.indices = np.zeros(0, dtype=np.intc)
R, C = blocksize
if (M % R) != 0 or (N % C) != 0:
raise ValueError, 'shape must be multiple of blocksize'
self.indptr = np.zeros(M / R + 1, dtype=np.intc)
elif len(arg1) == 2:
# (data,(row,col)) format
from coo import coo_matrix
self._set_self(
coo_matrix(arg1, dtype=dtype).tobsr(blocksize=blocksize))
elif len(arg1) == 3:
# (data,indices,indptr) format
(data, indices, indptr) = arg1
self.indices = np.array(indices, copy=copy)
self.indptr = np.array(indptr, copy=copy)
self.data = np.array(data,
copy=copy,
dtype=getdtype(dtype, data))
else:
raise ValueError('unrecognized bsr_matrix constructor usage')
else:
#must be dense
try:
arg1 = np.asarray(arg1)
except:
raise ValueError("unrecognized form for" \
" %s_matrix constructor" % self.format)
from coo import coo_matrix
arg1 = coo_matrix(arg1, dtype=dtype).tobsr(blocksize=blocksize)
self._set_self(arg1)
if shape is not None:
self.shape = shape # spmatrix will check for errors
else:
if self.shape is None:
# shape not already set, try to infer dimensions
try:
M = len(self.indptr) - 1
N = self.indices.max() + 1
except:
raise ValueError('unable to infer matrix dimensions')
else:
R, C = self.blocksize
self.shape = (M * R, N * C)
if self.shape is None:
if shape is None:
#TODO infer shape here
raise ValueError('need to infer shape')
else:
self.shape = shape
if dtype is not None:
self.data = self.data.astype(dtype)
self.check_format(full_check=False)
def cs_graph_components(x):
"""
Determine connected compoments of a graph stored as a compressed
sparse row or column matrix. For speed reasons, the symmetry of the
matrix x is not checked. A nonzero at index `(i, j)` means that node
`i` is connected to node `j` by an edge. The number of rows/columns
of the matrix thus corresponds to the number of nodes in the graph.
Parameters
-----------
x: ndarray-like, 2 dimensions, or sparse matrix
The adjacency matrix of the graph. Only the upper triangular part
is used.
Returns
--------
n_comp: int
The number of connected components.
label: ndarray (ints, 1 dimension):
The label array of each connected component (-2 is used to
indicate empty rows in the matrix: 0 everywhere, including
diagonal). This array has the length of the number of nodes,
i.e. one label for each node of the graph. Nodes having the same
label belong to the same connected component.
Notes
------
The matrix is assumed to be symmetric and the upper triangular part
of the matrix is used. The matrix is converted to a CSR matrix unless
it is already a CSR.
Example
-------
>>> from scipy.sparse import cs_graph_components
>>> import numpy as np
>>> D = np.eye(4)
>>> D[0,1] = D[1,0] = 1
>>> cs_graph_components(D)
(3, array([0, 0, 1, 2]))
>>> from scipy.sparse import dok_matrix
>>> cs_graph_components(dok_matrix(D))
(3, array([0, 0, 1, 2]))
"""
try:
shape = x.shape
except AttributeError:
raise ValueError(_msg0)
if not ((len(x.shape) == 2) and (x.shape[0] == x.shape[1])):
raise ValueError(_msg1 % x.shape)
if isspmatrix(x):
x = x.tocsr()
else:
x = csr_matrix(x)
label = np.empty((shape[0],), dtype=x.indptr.dtype)
n_comp = _cs_graph_components(shape[0], x.indptr, x.indices, label)
return n_comp, label
def cs_graph_components(x):
"""
Determine connected compoments of a graph stored as a compressed
sparse row or column matrix. For speed reasons, the symmetry of the
matrix x is not checked. A nonzero at index `(i, j)` means that node
`i` is connected to node `j` by an edge. The number of rows/columns
of the matrix thus corresponds to the number of nodes in the graph.
Parameters
-----------
x: ndarray-like, 2 dimensions, or sparse matrix
The adjacency matrix of the graph. Only the upper triangular part
is used.
Returns
--------
n_comp: int
The number of connected components.
label: ndarray (ints, 1 dimension):
The label array of each connected component (-2 is used to
indicate empty rows in the matrix: 0 everywhere, including
diagonal). This array has the length of the number of nodes,
i.e. one label for each node of the graph. Nodes having the same
label belong to the same connected component.
Notes
------
The matrix is assumed to be symmetric and the upper triangular part
of the matrix is used. The matrix is converted to a CSR matrix unless
it is already a CSR.
Example
-------
>>> from scipy.sparse import cs_graph_components
>>> import numpy as np
>>> D = np.eye(4)
>>> D[0,1] = D[1,0] = 1
>>> cs_graph_components(D)
(3, array([0, 0, 1, 2]))
>>> from scipy.sparse import dok_matrix
>>> cs_graph_components(dok_matrix(D))
(3, array([0, 0, 1, 2]))
"""
try:
shape = x.shape
except AttributeError:
raise ValueError(_msg0)
if not ((len(x.shape) == 2) and (x.shape[0] == x.shape[1])):
raise ValueError(_msg1 % x.shape)
if isspmatrix(x):
x = x.tocsr()
else:
x = csr_matrix(x)
label = np.empty((shape[0], ), dtype=x.indptr.dtype)
n_comp = _cs_graph_components(shape[0], x.indptr, x.indices, label)
return n_comp, label
def __init__(self, arg1, shape=None, dtype=None, copy=False):
_data_matrix.__init__(self)
if isinstance(arg1, tuple):
if isshape(arg1):
M, N = arg1
self.shape = (M, N)
self.row = np.array([], dtype=np.intc)
self.col = np.array([], dtype=np.intc)
self.data = np.array([], getdtype(dtype, default=float))
else:
try:
obj, ij = arg1
except:
raise TypeError('invalid input format')
try:
if len(ij) != 2:
raise TypeError
except TypeError:
raise TypeError('invalid input format')
self.row = np.array(ij[0], copy=copy, dtype=np.intc)
self.col = np.array(ij[1], copy=copy, dtype=np.intc)
self.data = np.array(obj, copy=copy)
if shape is None:
if len(self.row) == 0 or len(self.col) == 0:
raise ValueError(
'cannot infer dimensions from zero sized index arrays'
)
M = self.row.max() + 1
N = self.col.max() + 1
self.shape = (M, N)
else:
# Use 2 steps to ensure shape has length 2.
M, N = shape
self.shape = (M, N)
elif arg1 is None:
# Initialize an empty matrix.
if not isinstance(shape, tuple) or not isintlike(shape[0]):
raise TypeError('dimensions not understood')
warn('coo_matrix(None, shape=(M,N)) is deprecated, ' \
'use coo_matrix( (M,N) ) instead', DeprecationWarning)
self.shape = shape
self.data = np.array([], getdtype(dtype, default=float))
self.row = np.array([], dtype=np.intc)
self.col = np.array([], dtype=np.intc)
else:
if isspmatrix(arg1):
if isspmatrix_coo(arg1) and copy:
self.row = arg1.row.copy()
self.col = arg1.col.copy()
self.data = arg1.data.copy()
self.shape = arg1.shape
else:
coo = arg1.tocoo()
self.row = coo.row
self.col = coo.col
self.data = coo.data
self.shape = coo.shape
else:
#dense argument
try:
M = np.atleast_2d(np.asarray(arg1))
except:
raise TypeError('invalid input format')
if np.rank(M) != 2:
raise TypeError('expected rank <= 2 array or matrix')
self.shape = M.shape
self.row, self.col = M.nonzero()
self.data = M[self.row, self.col]
if dtype is not None:
self.data = self.data.astype(dtype)
self._check()
def __init__(self, arg1, shape=None, dtype=None, copy=False):
_data_matrix.__init__(self)
if isspmatrix_dia(arg1):
if copy:
arg1 = arg1.copy()
self.data = arg1.data
self.offsets = arg1.offsets
self.shape = arg1.shape
elif isspmatrix(arg1):
if isspmatrix_dia(arg1) and copy:
A = arg1.copy()
else:
A = arg1.todia()
self.data = A.data
self.offsets = A.offsets
self.shape = A.shape
elif isinstance(arg1, tuple):
if isshape(arg1):
# It's a tuple of matrix dimensions (M, N)
# create empty matrix
self.shape = arg1 #spmatrix checks for errors here
self.data = np.zeros( (0,0), getdtype(dtype, default=float))
self.offsets = np.zeros( (0), dtype=np.intc)
else:
try:
# Try interpreting it as (data, offsets)
data, offsets = arg1
except:
raise ValueError('unrecognized form for dia_matrix constructor')
else:
if shape is None:
raise ValueError('expected a shape argument')
self.data = np.atleast_2d(np.array(arg1[0], dtype=dtype, copy=copy))
self.offsets = np.atleast_1d(np.array(arg1[1], dtype=np.intc, copy=copy))
self.shape = shape
else:
#must be dense, convert to COO first, then to DIA
try:
arg1 = np.asarray(arg1)
except:
raise ValueError("unrecognized form for" \
" %s_matrix constructor" % self.format)
from coo import coo_matrix
A = coo_matrix(arg1, dtype=dtype).todia()
self.data = A.data
self.offsets = A.offsets
self.shape = A.shape
if dtype is not None:
self.data = self.data.astype(dtype)
#check format
if self.offsets.ndim != 1:
raise ValueError('offsets array must have rank 1')
if self.data.ndim != 2:
raise ValueError('data array must have rank 2')
if self.data.shape[0] != len(self.offsets):
raise ValueError('number of diagonals (%d) ' \
'does not match the number of offsets (%d)' \
% (self.data.shape[0], len(self.offsets)))
if len(np.unique(self.offsets)) != len(self.offsets):
raise ValueError('offset array contains duplicate values')
def __init__(self, arg1, shape=None, dtype=None, copy=False):
_data_matrix.__init__(self)
if isspmatrix(arg1):
if arg1.format == self.format and copy:
arg1 = arg1.copy()
else:
arg1 = arg1.asformat(self.format)
self._set_self( arg1 )
elif isinstance(arg1, tuple):
if isshape(arg1):
# It's a tuple of matrix dimensions (M, N)
# create empty matrix
self.shape = arg1 #spmatrix checks for errors here
M, N = self.shape
self.data = np.zeros(0, getdtype(dtype, default=float))
self.indices = np.zeros(0, np.intc)
self.indptr = np.zeros(self._swap((M,N))[0] + 1, dtype=np.intc)
else:
if len(arg1) == 2:
# (data, ij) format
from coo import coo_matrix
other = self.__class__( coo_matrix(arg1, shape=shape) )
self._set_self( other )
elif len(arg1) == 3:
# (data, indices, indptr) format
(data, indices, indptr) = arg1
self.indices = np.array(indices, copy=copy)
self.indptr = np.array(indptr, copy=copy)
self.data = np.array(data, copy=copy, dtype=getdtype(dtype, data))
else:
raise ValueError("unrecognized %s_matrix constructor usage" %
self.format)
else:
#must be dense
try:
arg1 = np.asarray(arg1)
except:
raise ValueError("unrecognized %s_matrix constructor usage" %
self.format)
from coo import coo_matrix
self._set_self( self.__class__(coo_matrix(arg1, dtype=dtype)) )
# Read matrix dimensions given, if any
if shape is not None:
self.shape = shape # spmatrix will check for errors
else:
if self.shape is None:
# shape not already set, try to infer dimensions
try:
major_dim = len(self.indptr) - 1
minor_dim = self.indices.max() + 1
except:
raise ValueError('unable to infer matrix dimensions')
else:
self.shape = self._swap((major_dim,minor_dim))
if dtype is not None:
self.data = self.data.astype(dtype)
self.check_format(full_check=False)
def __init__(self, arg1, shape=None, dtype=None, copy=False):
_data_matrix.__init__(self)
if isinstance(arg1, tuple):
if isshape(arg1):
M, N = arg1
self.shape = (M,N)
self.row = np.array([], dtype=np.intc)
self.col = np.array([], dtype=np.intc)
self.data = np.array([], getdtype(dtype, default=float))
else:
try:
obj, ij = arg1
except:
raise TypeError('invalid input format')
try:
if len(ij) != 2:
raise TypeError
except TypeError:
raise TypeError('invalid input format')
self.row = np.array(ij[0], copy=copy, dtype=np.intc)
self.col = np.array(ij[1], copy=copy, dtype=np.intc)
self.data = np.array( obj, copy=copy)
if shape is None:
if len(self.row) == 0 or len(self.col) == 0:
raise ValueError('cannot infer dimensions from zero sized index arrays')
M = self.row.max() + 1
N = self.col.max() + 1
self.shape = (M, N)
else:
# Use 2 steps to ensure shape has length 2.
M, N = shape
self.shape = (M, N)
elif arg1 is None:
# Initialize an empty matrix.
if not isinstance(shape, tuple) or not isintlike(shape[0]):
raise TypeError('dimensions not understood')
warn('coo_matrix(None, shape=(M,N)) is deprecated, ' \
'use coo_matrix( (M,N) ) instead', DeprecationWarning)
self.shape = shape
self.data = np.array([], getdtype(dtype, default=float))
self.row = np.array([], dtype=np.intc)
self.col = np.array([], dtype=np.intc)
else:
if isspmatrix(arg1):
if isspmatrix_coo(arg1) and copy:
self.row = arg1.row.copy()
self.col = arg1.col.copy()
self.data = arg1.data.copy()
self.shape = arg1.shape
else:
coo = arg1.tocoo()
self.row = coo.row
self.col = coo.col
self.data = coo.data
self.shape = coo.shape
else:
#dense argument
try:
M = np.atleast_2d(np.asarray(arg1))
except:
raise TypeError('invalid input format')
if np.rank(M) != 2:
raise TypeError('expected rank <= 2 array or matrix')
self.shape = M.shape
self.row,self.col = (M != 0).nonzero()
self.data = M[self.row,self.col]
if dtype is not None:
self.data = self.data.astype(dtype)
self._check()
def __init__(self, arg1, shape=None, dtype=None, copy=False, blocksize=None):
_data_matrix.__init__(self)
if isspmatrix(arg1):
if isspmatrix_bsr(arg1) and copy:
arg1 = arg1.copy()
else:
arg1 = arg1.tobsr(blocksize=blocksize)
self._set_self( arg1 )
elif isinstance(arg1,tuple):
if isshape(arg1):
#it's a tuple of matrix dimensions (M,N)
self.shape = arg1
M,N = self.shape
#process blocksize
if blocksize is None:
blocksize = (1,1)
else:
if not isshape(blocksize):
raise ValueError('invalid blocksize=%s' % blocksize)
blocksize = tuple(blocksize)
self.data = np.zeros( (0,) + blocksize, getdtype(dtype, default=float) )
self.indices = np.zeros( 0, dtype=np.intc )
R,C = blocksize
if (M % R) != 0 or (N % C) != 0:
raise ValueError('shape must be multiple of blocksize')
self.indptr = np.zeros(M//R + 1, dtype=np.intc )
elif len(arg1) == 2:
# (data,(row,col)) format
from coo import coo_matrix
self._set_self( coo_matrix(arg1, dtype=dtype).tobsr(blocksize=blocksize) )
elif len(arg1) == 3:
# (data,indices,indptr) format
(data, indices, indptr) = arg1
self.indices = np.array(indices, copy=copy)
self.indptr = np.array(indptr, copy=copy)
self.data = np.array(data, copy=copy, dtype=getdtype(dtype, data))
else:
raise ValueError('unrecognized bsr_matrix constructor usage')
else:
#must be dense
try:
arg1 = np.asarray(arg1)
except:
raise ValueError("unrecognized form for" \
" %s_matrix constructor" % self.format)
from coo import coo_matrix
arg1 = coo_matrix(arg1, dtype=dtype).tobsr(blocksize=blocksize)
self._set_self( arg1 )
if shape is not None:
self.shape = shape # spmatrix will check for errors
else:
if self.shape is None:
# shape not already set, try to infer dimensions
try:
M = len(self.indptr) - 1
N = self.indices.max() + 1
except:
raise ValueError('unable to infer matrix dimensions')
else:
R,C = self.blocksize
self.shape = (M*R,N*C)
if self.shape is None:
if shape is None:
#TODO infer shape here
raise ValueError('need to infer shape')
else:
self.shape = shape
if dtype is not None:
self.data = self.data.astype(dtype)
self.check_format(full_check=False)