def resize(self, *shape):
shape = check_shape(shape)
if hasattr(self, 'blocksize'):
bm, bn = self.blocksize
new_M, rm = divmod(shape[0], bm)
new_N, rn = divmod(shape[1], bn)
if rm or rn:
raise ValueError("shape must be divisible into %s blocks. "
"Got %s" % (self.blocksize, shape))
M, N = self.shape[0] // bm, self.shape[1] // bn
else:
new_M, new_N = self._swap(shape)
M, N = self._swap(self.shape)
if new_M < M:
self.indices = self.indices[:self.indptr[new_M]]
self.data = self.data[:self.indptr[new_M]]
self.indptr = self.indptr[:new_M + 1]
elif new_M > M:
self.indptr = np.resize(self.indptr, new_M + 1)
self.indptr[M + 1:].fill(self.indptr[M])
if new_N < N:
mask = self.indices < new_N
if not np.all(mask):
self.indices = self.indices[mask]
self.data = self.data[mask]
major_index, val = self._minor_reduce(np.add, mask)
self.indptr.fill(0)
self.indptr[1:][major_index] = val
np.cumsum(self.indptr, out=self.indptr)
self._shape = shape
def _set_self(self, other, copy=False):
"""take the member variables of other and assign them to self"""
if copy:
other = other.copy()
self.data = other.data
self.indices = other.indices
self.indptr = other.indptr
self._shape = check_shape(other.shape)
def test_check_shape_overflow(self):
new_shape = sputils.check_shape([(10, -1)], (65535, 131070))
assert_equal(new_shape, (10, 858967245))
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 = check_shape(arg1)
M, N = self.shape
# Select index dtype large enough to pass array and
# scalar parameters to sparsetools
idx_dtype = get_index_dtype(maxval=max(M, N))
self.data = np.zeros(0, getdtype(dtype, default=float))
self.indices = np.zeros(0, idx_dtype)
self.indptr = np.zeros(self._swap((M, N))[0] + 1,
dtype=idx_dtype)
else:
if len(arg1) == 2:
# (data, ij) format
from scipy.sparse.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
# Select index dtype large enough to pass array and
# scalar parameters to sparsetools
maxval = None
if shape is not None:
maxval = max(shape)
idx_dtype = get_index_dtype((indices, indptr),
maxval=maxval,
check_contents=True)
self.indices = np.array(indices,
copy=copy,
dtype=idx_dtype)
self.indptr = np.array(indptr, copy=copy, dtype=idx_dtype)
self.data = np.array(data, copy=copy, dtype=dtype)
else:
raise ValueError("unrecognized {}_matrix "
"constructor usage".format(self.format))
else:
# must be dense
try:
arg1 = np.asarray(arg1)
except Exception:
raise ValueError("unrecognized {}_matrix constructor usage"
"".format(self.format))
from scipy.sparse.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 = check_shape(shape)
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 Exception:
raise ValueError('unable to infer matrix dimensions')
else:
self._shape = check_shape(
self._swap((major_dim, minor_dim)))
if dtype is not None:
self.data = self.data.astype(dtype, copy=False)
self.check_format(full_check=False)