def _rvs(self, low, high):
"""An array of *size* random integers >= ``low`` and < ``high``."""
if self._size is not None:
# Numpy's RandomState.randint() doesn't broadcast its arguments.
# Use `broadcast_to()` to extend the shapes of low and high
# up to self._size. Then we can use the numpy.vectorize'd
# randint without needing to pass it a `size` argument.
low = broadcast_to(low, self._size)
high = broadcast_to(high, self._size)
randint = np.vectorize(self._random_state.randint, otypes=[np.int_])
return randint(low, high)
def _rvs(self, low, high):
"""An array of *size* random integers >= ``low`` and < ``high``."""
if self._size is not None:
# Numpy's RandomState.randint() doesn't broadcast its arguments.
# Use `broadcast_to()` to extend the shapes of low and high
# up to self._size. Then we can use the numpy.vectorize'd
# randint without needing to pass it a `size` argument.
low = broadcast_to(low, self._size)
high = broadcast_to(high, self._size)
randint = np.vectorize(self._random_state.randint, otypes=[np.int_])
return randint(low, high)
def __rsub__(self, other): # other - self
if isscalarlike(other):
if other == 0:
return -self.copy()
raise NotImplementedError('subtracting a sparse matrix from a '
'nonzero scalar is not supported')
elif isdense(other):
other = broadcast_to(other, self.shape)
return self._rsub_dense(other)
else:
return NotImplemented
def __rsub__(self,other): # other - self
if isscalarlike(other):
if other == 0:
return -self.copy()
raise NotImplementedError('subtracting a sparse matrix from a '
'nonzero scalar is not supported')
elif isdense(other):
other = broadcast_to(other, self.shape)
return self._rsub_dense(other)
else:
return NotImplemented
def __sub__(self, other): # self - other
if isscalarlike(other):
if other == 0:
return self.copy()
raise NotImplementedError('subtracting a nonzero scalar from a '
'sparse matrix is not supported')
elif isspmatrix(other):
if other.shape != self.shape:
raise ValueError("inconsistent shapes")
return self._sub_sparse(other)
elif isdense(other):
other = broadcast_to(other, self.shape)
return self._sub_dense(other)
else:
return NotImplemented
def __sub__(self, other): # self - other
if isscalarlike(other):
if other == 0:
return self.copy()
raise NotImplementedError('subtracting a nonzero scalar from a '
'sparse matrix is not supported')
elif isspmatrix(other):
if other.shape != self.shape:
raise ValueError("inconsistent shapes")
return self._sub_sparse(other)
elif isdense(other):
other = broadcast_to(other, self.shape)
return self._sub_dense(other)
else:
return NotImplemented
def __add__(self, other): # self + other
if isscalarlike(other):
if other == 0:
return self.copy()
# Now we would add this scalar to every element.
raise NotImplementedError('adding a nonzero scalar to a '
'sparse matrix is not supported')
elif isspmatrix(other):
if other.shape != self.shape:
raise ValueError("inconsistent shapes")
return self._add_sparse(other)
elif isdense(other):
other = broadcast_to(other, self.shape)
return self._add_dense(other)
else:
return NotImplemented
def __add__(self, other): # self + other
if isscalarlike(other):
if other == 0:
return self.copy()
# Now we would add this scalar to every element.
raise NotImplementedError('adding a nonzero scalar to a '
'sparse matrix is not supported')
elif isspmatrix(other):
if other.shape != self.shape:
raise ValueError("inconsistent shapes")
return self._add_sparse(other)
elif isdense(other):
other = broadcast_to(other, self.shape)
return self._add_dense(other)
else:
return NotImplemented
