def _major_slice(self, idx, copy=False):
"""Index along the major axis where idx is a slice object.
"""
if idx == slice(None):
return self.copy() if copy else self
M, N = self._swap(*self.shape)
start, stop, step = idx.indices(M)
M = len(range(start, stop, step))
new_shape = self._swap(M, N)
if M == 0 or self.nnz == 0:
return self.__class__(new_shape)
if step == 1:
indptr, indices, data = _index._get_csr_submatrix_major_axis(
self.indptr, self.indices, self.data, start, stop)
else:
rows = cupy.arange(start,
start + M * step,
step,
dtype=self.indptr.dtype)
indptr, indices, data = _index._csr_row_index(
rows, self.indptr, self.indices, self.data)
return self.__class__((data, indices, indptr),
shape=new_shape,
copy=False)
def _major_index_fancy(self, idx):
"""Index along the major axis where idx is an array of ints.
"""
_, N = self._swap(*self.shape)
M = idx.size
new_shape = self._swap(M, N)
if self.nnz == 0 or M == 0:
return self.__class__(new_shape)
return self.__class__(
_index._csr_row_index(self.data, self.indices, self.indptr, idx),
shape=new_shape, copy=False)
def _major_index_fancy(self, idx):
"""Index along the major axis where idx is an array of ints.
"""
_, N = self._swap(*self.shape)
M = len(idx)
new_shape = self._swap(M, N)
if M == 0:
return self.__class__(new_shape)
row_nnz = cupy.diff(self.indptr)
idx_dtype = self.indices.dtype
res_indptr = cupy.zeros(M + 1, dtype=idx_dtype)
cupy.cumsum(row_nnz[idx], out=res_indptr[1:])
res_indices, res_data = _index._csr_row_index(idx, self.indptr,
self.indices, self.data,
res_indptr)
return self.__class__((res_data, res_indices, res_indptr),
shape=new_shape,
copy=False)
def _major_slice(self, idx, copy=False):
"""Index along the major axis where idx is a slice object.
"""
if idx == slice(None):
return self.copy() if copy else self
M, N = self._swap(*self.shape)
start, stop, step = idx.indices(M)
M = len(range(start, stop, step))
new_shape = self._swap(M, N)
if M == 0:
return self.__class__(new_shape)
row_nnz = cupy.diff(self.indptr)
idx_dtype = self.indices.dtype
res_indptr = cupy.zeros(M + 1, dtype=idx_dtype)
cupy.cumsum(row_nnz[idx], out=res_indptr[1:])
if step == 1:
idx_start = self.indptr[start]
idx_stop = self.indptr[stop]
res_indices = cupy.array(self.indices[idx_start:idx_stop],
copy=copy)
res_data = cupy.array(self.data[idx_start:idx_stop], copy=copy)
else:
rows = cupy.arange(start,
start + (res_indptr.size - 1) * step,
step,
dtype=res_indptr.dtype)
res_indices, res_data = _index._csr_row_index(
rows, self.indptr, self.indices, self.data, res_indptr)
return self.__class__((res_data, res_indices, res_indptr),
shape=new_shape,
copy=False)