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 __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 __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 __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 __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):
_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, 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 __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')
