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):
spmatrix.__init__(self)
def __init__(self):
spmatrix.__init__(self)
