def _mul_scalar(self, other):
res_dtype = upcast_scalar(self.dtype, other)
# Multiply this scalar by every element.
new = dok_matrix(self.shape, dtype=res_dtype)
for (key, val) in iteritems(self):
new[key] = val * other
return new
def conjtransp(self):
"""Return the conjugate transpose."""
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype)
dict.update(new, (((right, left), np.conj(val))
for (left, right), val in iteritems(self)))
return new
def __truediv__(self, other):
if isscalarlike(other):
res_dtype = upcast_scalar(self.dtype, other)
new = dok_matrix(self.shape, dtype=res_dtype)
dict.update(new, ((k, v / other) for k, v in iteritems(self)))
return new
return self.tocsr() / other
def conjtransp(self):
""" Return the conjugate transpose
"""
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype)
for key, value in iteritems(self):
new[key[1], key[0]] = np.conj(value)
return new
def _mul_multivector(self, other):
# matrix * multivector
M,N = self.shape
n_vecs = other.shape[1] # number of column vectors
result = np.zeros((M,n_vecs), dtype=upcast(self.dtype,other.dtype))
for (i,j),v in iteritems(self):
result[i,:] += v * other[j,:]
return result
def _mul_multivector(self, other):
# matrix * multivector
result_shape = (self.shape[0], other.shape[1])
result_dtype = upcast(self.dtype, other.dtype)
result = np.zeros(result_shape, dtype=result_dtype)
for (i, j), v in iteritems(self):
result[i,:] += v * other[j,:]
return result
def _mul_multivector(self, other):
# matrix * multivector
M,N = self.shape
n_vecs = other.shape[1] # number of column vectors
result = np.zeros((M,n_vecs), dtype=upcast(self.dtype,other.dtype))
for (i,j),v in iteritems(self):
result[i,:] += v * other[j,:]
return result
def transpose(self):
""" Return the transpose
"""
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype)
for key, value in iteritems(self):
new[key[1], key[0]] = value
return new
def transpose(self):
""" Return the transpose
"""
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype)
for key, value in iteritems(self):
new[key[1], key[0]] = value
return new
def _mul_multivector(self, other):
# matrix * multivector
result_shape = (self.shape[0], other.shape[1])
result_dtype = upcast(self.dtype, other.dtype)
result = np.zeros(result_shape, dtype=result_dtype)
for (i, j), v in iteritems(self):
result[i,:] += v * other[j,:]
return result
def conjtransp(self):
""" Return the conjugate transpose
"""
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype)
for key, value in iteritems(self):
new[key[1], key[0]] = np.conj(value)
return new
def __itruediv__(self, other):
if isscalarlike(other):
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
self[key] = val / other
return self
else:
return NotImplemented
def __itruediv__(self, other):
if isscalarlike(other):
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
self[key] = val / other
return self
else:
return NotImplemented
def __imul__(self, other):
if isscalarlike(other):
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
self[key] = val * other
# new.dtype.char = self.dtype.char
return self
else:
return NotImplemented
def __imul__(self, other):
if isscalarlike(other):
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
self[key] = val * other
# new.dtype.char = self.dtype.char
return self
else:
return NotImplemented
def __truediv__(self, other):
if isscalarlike(other):
res_dtype = upcast_scalar(self.dtype, other)
new = dok_matrix(self.shape, dtype=res_dtype)
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
new[key] = val / other
# new.dtype.char = self.dtype.char
return new
else:
return self.tocsr() / other
def transpose(self, axes=None, copy=False):
if axes is not None:
raise ValueError("Sparse matrices do not support "
"an 'axes' parameter because swapping "
"dimensions is the only logical permutation.")
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype, copy=copy)
dict.update(new, (((right, left), val)
for (left, right), val in iteritems(self)))
return new
def __truediv__(self, other):
if isscalarlike(other):
res_dtype = upcast_scalar(self.dtype, other)
new = dok_matrix(self.shape, dtype=res_dtype)
# Multiply this scalar by every element.
for (key, val) in iteritems(self):
new[key] = val / other
# new.dtype.char = self.dtype.char
return new
else:
return self.tocsr() / other
def transpose(self, axes=None, copy=False):
if axes is not None:
raise ValueError("Sparse matrices do not support "
"an 'axes' parameter because swapping "
"dimensions is the only logical permutation.")
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype, copy=copy)
dict.update(new, (((right, left), val)
for (left, right), val in iteritems(self)))
return new
def transpose(self, axes=None, copy=False):
if axes is not None:
raise ValueError(("Sparse matrices do not support "
"an 'axes' parameter because swapping "
"dimensions is the only logical permutation."))
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype, copy=copy)
for key, value in iteritems(self):
new[key[1], key[0]] = value
return new
def transpose(self, axes=None, copy=False):
if axes is not None:
raise ValueError(("Sparse matrices do not support "
"an 'axes' parameter because swapping "
"dimensions is the only logical permutation."))
M, N = self.shape
new = dok_matrix((N, M), dtype=self.dtype, copy=copy)
for key, value in iteritems(self):
new[key[1], key[0]] = value
return new
def _mul_scalar(self, other):
res_dtype = upcast_scalar(self.dtype, other)
# Multiply this scalar by every element.
new = dok_matrix(self.shape, dtype=res_dtype)
dict.update(new, ((k, v * other) for k, v in iteritems(self)))
return new
def __itruediv__(self, other):
if isscalarlike(other):
dict.update(self, ((k, v / other) for k, v in iteritems(self)))
return self
return NotImplemented
def _mul_vector(self, other):
# matrix * vector
result = np.zeros(self.shape[0], dtype=upcast(self.dtype, other.dtype))
for (i, j), v in iteritems(self):
result[i] += v * other[j]
return result
def _mul_vector(self, other):
# matrix * vector
result = np.zeros(self.shape[0], dtype=upcast(self.dtype, other.dtype))
for (i, j), v in iteritems(self):
result[i] += v * other[j]
return result
def _mul_scalar(self, other):
res_dtype = upcast_scalar(self.dtype, other)
# Multiply this scalar by every element.
new = dok_matrix(self.shape, dtype=res_dtype)
dict.update(new, ((k, v * other) for k, v in iteritems(self)))
return new
def __itruediv__(self, other):
if isscalarlike(other):
dict.update(self, ((k, v / other) for k, v in iteritems(self)))
return self
return NotImplemented
