def _set_many(self, i, j, x):
"""\
Sets value at each (i, j) to x
Here (i,j) index major and minor respectively, and must not contain
duplicate entries.
"""
i, j, M, N = self._prepare_indices(i, j)
if np.isscalar(x): # Scipy 1.3+ compat
n_samples = 1
else:
n_samples = len(x)
offsets = np.empty(n_samples, dtype=self.indices.dtype)
ret = _sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
if ret == 1:
# rinse and repeat
self.sum_duplicates()
_sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
if -1 not in offsets:
# make a list for interaction with h5py
offsets = list(offsets)
# only affects existing non-zero cells
self.data[offsets] = x
return
else:
raise ValueError(
'Currently, you cannot change the sparsity structure of a SparseDataset.'
)
def _offsets(self, i: Iterable[int], j: Iterable[int],
n_samples: int) -> np.ndarray:
i, j, M, N = self._prepare_indices(i, j)
offsets = np.empty(n_samples, dtype=self.indices.dtype)
ret = _sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
if ret == 1:
# rinse and repeat
self.sum_duplicates()
_sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
return offsets
def _set_many(self, i, j, x):
"""Sets value at each (i, j) to x
Here (i,j) index major and minor respectively.
"""
M, N = self._swap(self.shape)
def check_bounds(indices, bound):
idx = indices.max()
if idx >= bound:
raise IndexError('index (%d) out of range (>= %d)' %
(idx, bound))
idx = indices.min()
if idx < -bound:
raise IndexError('index (%d) out of range (< -%d)' %
(idx, bound))
check_bounds(i, M)
check_bounds(j, N)
i = np.asarray(i, dtype=self.indices.dtype)
j = np.asarray(j, dtype=self.indices.dtype)
n_samples = len(x)
offsets = np.empty(n_samples, dtype=self.indices.dtype)
ret = _sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
if ret == 1:
# rinse and repeat
self.sum_duplicates()
_sparsetools.csr_sample_offsets(M, N, self.indptr,
self.indices, n_samples, i, j,
offsets)
if -1 not in offsets:
# only affects existing non-zero cells
self.data[offsets] = x
return
else:
warn("Changing the sparsity structure of a %s_matrix is expensive. "
"lil_matrix is more efficient." % self.format,
SparseEfficiencyWarning)
# replace where possible
mask = offsets > -1
self.data[offsets[mask]] = x[mask]
# only insertions remain
mask = ~mask
i = i[mask]
i[i < 0] += M
j = j[mask]
j[j < 0] += N
self._insert_many(i, j, x[mask])
def _zero_many(self, i, j):
"""Sets value at each (i, j) to zero, preserving sparsity structure.
Here (i,j) index major and minor respectively.
"""
i, j, M, N = self._prepare_indices(i, j)
n_samples = len(i)
offsets = np.empty(n_samples, dtype=self.indices.dtype)
ret = _sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
if ret == 1:
# rinse and repeat
self.sum_duplicates()
_sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
# only assign zeros to the existing sparsity structure
self.data[list(offsets[offsets > -1])] = 0
def _zero_many(self, i, j):
"""Sets value at each (i, j) to zero, preserving sparsity structure.
Here (i,j) index major and minor respectively.
"""
i, j, M, N = self._prepare_indices(i, j)
n_samples = len(i)
offsets = np.empty(n_samples, dtype=self.indices.dtype)
ret = _sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
if ret == 1:
# rinse and repeat
self.sum_duplicates()
_sparsetools.csr_sample_offsets(M, N, self.indptr,
self.indices, n_samples, i, j,
offsets)
# only assign zeros to the existing sparsity structure
self.data[list(offsets[offsets > -1])] = 0
def _set_many(self, i, j, x):
"""Sets value at each (i, j) to x
Here (i,j) index major and minor respectively, and must not contain
duplicate entries.
"""
i, j, M, N = self._prepare_indices(i, j)
x = np.array(x, dtype=self.dtype, copy=False, ndmin=1).ravel()
n_samples = x.size
offsets = np.empty(n_samples, dtype=self.indices.dtype)
ret = csr_sample_offsets(M, N, self.indptr, self.indices, n_samples, i,
j, offsets)
if ret == 1:
# rinse and repeat
self.sum_duplicates()
csr_sample_offsets(M, N, self.indptr, self.indices, n_samples, i,
j, offsets)
if -1 not in offsets:
# only affects existing non-zero cells
self.data[offsets] = x
return
else:
warn("Changing the sparsity structure of a {}_matrix is expensive."
" lil_matrix is more efficient.".format(self.format),
SparseEfficiencyWarning,
stacklevel=3)
# replace where possible
mask = offsets > -1
self.data[offsets[mask]] = x[mask]
# only insertions remain
mask = ~mask
i = i[mask]
i[i < 0] += M
j = j[mask]
j[j < 0] += N
self._insert_many(i, j, x[mask])
def _set_many(self, i, j, x):
"""Sets value at each (i, j) to x
Here (i,j) index major and minor respectively, and must not contain
duplicate entries.
"""
i, j, M, N = self._prepare_indices(i, j)
n_samples = len(x)
offsets = np.empty(n_samples, dtype=self.indices.dtype)
ret = _sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
if ret == 1:
# rinse and repeat
self.sum_duplicates()
_sparsetools.csr_sample_offsets(M, N, self.indptr,
self.indices, n_samples, i, j,
offsets)
if -1 not in offsets:
# make a list for interaction with h5py
offsets = list(offsets)
# only affects existing non-zero cells
self.data[offsets] = x
return
else:
# raise ValueError(
# 'Currently, you cannot change the sparsity structure of a SparseDataset.')
# replace where possible
mask = offsets > -1
self.data[offsets[mask]] = x[mask]
# only insertions remain
mask = ~mask
i = i[mask]
i[i < 0] += M
j = j[mask]
j[j < 0] += N
self._insert_many(i, j, x[mask])
def _set_many(self, i, j, x):
"""Sets value at each (i, j) to x
Here (i,j) index major and minor respectively, and must not contain
duplicate entries.
"""
i, j, M, N = self._prepare_indices(i, j)
n_samples = len(x)
offsets = np.empty(n_samples, dtype=self.indices.dtype)
ret = _sparsetools.csr_sample_offsets(M, N, self.indptr, self.indices,
n_samples, i, j, offsets)
if ret == 1:
# rinse and repeat
self.sum_duplicates()
_sparsetools.csr_sample_offsets(M, N, self.indptr,
self.indices, n_samples, i, j,
offsets)
if -1 not in offsets:
# make a list for interaction with h5py
offsets = list(offsets)
# only affects existing non-zero cells
self.data[offsets] = x
return
else:
# raise ValueError(
# 'Currently, you cannot change the sparsity structure of a SparseDataset.')
# replace where possible
mask = offsets > -1
self.data[offsets[mask]] = x[mask]
# only insertions remain
mask = ~mask
i = i[mask]
i[i < 0] += M
j = j[mask]
j[j < 0] += N
self._insert_many(i, j, x[mask])
