def multiply(self, other):
"""Point-wise multiplication by another matrix, vector, or
scalar.
"""
# Scalar multiplication.
if isscalarlike(other):
return self.__mul__(other)
# Sparse matrix or vector.
if isspmatrix(other):
if self.shape == other.shape:
other = self.__class__(other)
return self._binopt(other, '_elmul_')
# Single element.
elif other.shape == (1,1):
return self.__mul__(other.tocsc().data[0])
elif self.shape == (1,1):
return other.__mul__(self.tocsc().data[0])
# A row times a column.
elif self.shape[1] == other.shape[0] and self.shape[1] == 1:
return self._mul_sparse_matrix(other.tocsc())
elif self.shape[0] == other.shape[1] and self.shape[0] == 1:
return other._mul_sparse_matrix(self.tocsc())
# Row vector times matrix. other is a row.
elif other.shape[0] == 1 and self.shape[1] == other.shape[1]:
other = dia_matrix((other.toarray().ravel(), [0]),
shape=(other.shape[1], other.shape[1]))
return self._mul_sparse_matrix(other)
# self is a row.
elif self.shape[0] == 1 and self.shape[1] == other.shape[1]:
copy = dia_matrix((self.toarray().ravel(), [0]),
shape=(self.shape[1], self.shape[1]))
return other._mul_sparse_matrix(copy)
# Column vector times matrix. other is a column.
elif other.shape[1] == 1 and self.shape[0] == other.shape[0]:
other = dia_matrix((other.toarray().ravel(), [0]),
shape=(other.shape[0], other.shape[0]))
return other._mul_sparse_matrix(self)
# self is a column.
elif self.shape[1] == 1 and self.shape[0] == other.shape[0]:
copy = dia_matrix((self.toarray().ravel(), [0]),
shape=(self.shape[0], self.shape[0]))
return copy._mul_sparse_matrix(other)
else:
raise ValueError("inconsistent shapes")
# Dense matrix.
if isdense(other):
if self.shape == other.shape:
ret = self.tocoo()
ret.data = np.multiply(ret.data, other[ret.row, ret.col]
).view(np.ndarray).ravel()
# Current tests expect dense output.
return ret.todense()
# Single element.
elif other.size == 1:
return self.__mul__(other.flat[0])
# Anything else.
return np.multiply(self.todense(), other)
def create_sparse_rate_matrix(self):
"""
constructs the following rate matrix for a M/M/1 queue
TODO: fix math string
:math: `
Q = \begin{pmatrix}
-\lambda & \lambda \\
\mu & -(\mu+\lambda) & \lambda \\
&\mu & -(\mu+\lambda) & \lambda \\
&&\mu & -(\mu+\lambda) & \lambda &\\
&&&&\ddots
\end{pmatrix}`
taken from: https://en.wikipedia.org/wiki/Transition_rate_matrix
"""
lambda_ = 5
mu = 3
dim = self.dim
diag = np.empty((3, dim))
# main diagonal
diag[0, 0] = (-lambda_)
diag[0, 1:dim - 1] = -(mu + lambda_)
diag[0, dim - 1] = lambda_
# lower diag
diag[1, :] = mu
diag[1, -2:] = -mu
diag[1, -2:] = lambda_
diag[0, dim - 1] = -lambda_
# upper diag
diag[2, :] = lambda_
offsets = [0, -1, 1]
return dia_matrix((diag, offsets), shape=(dim, dim))
def create_sparse_rate_matrix(self):
"""
constructs the following rate matrix for a M/M/1 queue
TODO: fix math string
:math: `
Q = \begin{pmatrix}
-\lambda & \lambda \\
\mu & -(\mu+\lambda) & \lambda \\
&\mu & -(\mu+\lambda) & \lambda \\
&&\mu & -(\mu+\lambda) & \lambda &\\
&&&&\ddots
\end{pmatrix}`
taken from: https://en.wikipedia.org/wiki/Transition_rate_matrix
"""
lambda_ = 5
mu = 3
dim = self.dim
diag = np.empty((3, dim))
# main diagonal
diag[0, 0] = (-lambda_)
diag[0, 1:dim - 1] = -(mu + lambda_)
diag[0, dim - 1] = lambda_
# lower diag
diag[1, :] = mu
diag[1, -2:] = -mu
diag[1, -2:] = lambda_
diag[0, dim - 1] = -lambda_
# upper diag
diag[2, :] = lambda_
offsets = [0, -1, 1]
return dia_matrix((diag, offsets), shape=(dim, dim))
def _n_theta_operator(self) -> csc_matrix:
"""
Returns
-------
`n_theta` operator in the charge basis"""
diag_elements = np.arange(-self.ncut, self.ncut + 1)
return dia_matrix(
(diag_elements, [0]),
shape=(self._dim_theta(), self._dim_theta())).tocsc()
def create_rev_t(self):
dim = self.dim
diag = np.zeros((3, dim))
# forward_p = 4 / 5.
forward_p = 0.6
backward_p = 1 - forward_p
# main diagonal
diag[0, 0] = backward_p
diag[0, -1] = backward_p
# lower diag
diag[1, :] = backward_p
diag[1, 1] = forward_p
# upper diag
diag[2, :] = forward_p
return dia_matrix((diag, [0, 1, -1]), shape=(dim, dim))
def create_rev_t(self):
dim = self.dim
diag = np.zeros((3, dim))
# forward_p = 4 / 5.
forward_p = 0.6
backward_p = 1 - forward_p
# main diagonal
diag[0, 0] = backward_p
diag[0, -1] = backward_p
# lower diag
diag[1, :] = backward_p
diag[1, 1] = forward_p
# upper diag
diag[2, :] = forward_p
return dia_matrix((diag, [0, 1, -1]), shape=(dim, dim))
def partial_fit(self, x):
max_idx = max(self.vocabulary_.values())
for a in x:
# update vocabulary_
if self.lowercase:
a = a.lower()
tokens = re.findall(self.token_pattern, a)
for w in tokens:
if w not in self.vocabulary_:
max_idx += 1
self.vocabulary_[w] = max_idx
# update idf_
df = (self.n_docs + self.smooth_idf) / np.exp(self.idf_ -
1) - self.smooth_idf
self.n_docs += 1
df.resize(len(self.vocabulary_))
for w in tokens:
df[self.vocabulary_[w]] += 1
idf = np.log(
(self.n_docs + self.smooth_idf) / (df + self.smooth_idf)) + 1
self._tfidf._idf_diag = dia_matrix((idf, 0),
shape=(len(idf), len(idf)))
def multiply(self, other):
"""Point-wise multiplication by another matrix, vector, or
scalar.
"""
# print("multiply")
# Scalar multiplication.
if isscalarlike(other):
return self._mul_scalar(other)
# Sparse matrix or vector.
if isspmatrix(other):
if self.shape == other.shape:
other = self.__class__(other)
return self._binopt(other, '_elmul_')
# Single element.
elif other.shape == (1, 1):
return self._mul_scalar(other.toarray()[0, 0])
elif self.shape == (1, 1):
return other._mul_scalar(self.toarray()[0, 0])
# A row times a column.
elif self.shape[1] == 1 and other.shape[0] == 1:
return self._mul_sparse_matrix(other.tocsc())
elif self.shape[0] == 1 and other.shape[1] == 1:
return other._mul_sparse_matrix(self.tocsc())
# Row vector times matrix. other is a row.
elif other.shape[0] == 1 and self.shape[1] == other.shape[1]:
other = dia_matrix((other.toarray().ravel(), [0]),
shape=(other.shape[1], other.shape[1]))
return self._mul_sparse_matrix(other)
# self is a row.
elif self.shape[0] == 1 and self.shape[1] == other.shape[1]:
copy = dia_matrix((self.toarray().ravel(), [0]),
shape=(self.shape[1], self.shape[1]))
return other._mul_sparse_matrix(copy)
# Column vector times matrix. other is a column.
elif other.shape[1] == 1 and self.shape[0] == other.shape[0]:
other = dia_matrix((other.toarray().ravel(), [0]),
shape=(other.shape[0], other.shape[0]))
return other._mul_sparse_matrix(self)
# self is a column.
elif self.shape[1] == 1 and self.shape[0] == other.shape[0]:
copy = dia_matrix((self.toarray().ravel(), [0]),
shape=(self.shape[0], self.shape[0]))
return copy._mul_sparse_matrix(other)
else:
raise ValueError("inconsistent shapes")
# Assume other is a dense matrix/array, which produces a single-item
# object array if other isn't convertible to ndarray.
other = np.atleast_2d(other)
if other.ndim != 2:
return np.multiply(self.toarray(), other)
# Single element / wrapped object.
if other.size == 1:
return self._mul_scalar(other.flat[0])
# Fast case for trivial sparse matrix.
elif self.shape == (1, 1):
return np.multiply(self.toarray()[0, 0], other)
from scipy.sparse.coo import coo_matrix
ret = self.tocoo()
# Matching shapes.
if self.shape == other.shape:
data = np.multiply(ret.data, other[ret.row, ret.col])
# Sparse row vector times...
elif self.shape[0] == 1:
if other.shape[1] == 1: # Dense column vector.
data = np.multiply(ret.data, other)
elif other.shape[1] == self.shape[1]: # Dense matrix.
data = np.multiply(ret.data, other[:, ret.col])
else:
raise ValueError("inconsistent shapes")
row = np.repeat(np.arange(other.shape[0]), len(ret.row))
col = np.tile(ret.col, other.shape[0])
return coo_matrix((data.view(np.ndarray).ravel(), (row, col)),
shape=(other.shape[0], self.shape[1]),
copy=False)
# Sparse column vector times...
elif self.shape[1] == 1:
if other.shape[0] == 1: # Dense row vector.
data = np.multiply(ret.data[:, None], other)
elif other.shape[0] == self.shape[0]: # Dense matrix.
data = np.multiply(ret.data[:, None], other[ret.row])
else:
raise ValueError("inconsistent shapes")
row = np.repeat(ret.row, other.shape[1])
col = np.tile(np.arange(other.shape[1]), len(ret.col))
return coo_matrix((data.view(np.ndarray).ravel(), (row, col)),
shape=(self.shape[0], other.shape[1]),
copy=False)
# Sparse matrix times dense row vector.
elif other.shape[0] == 1 and self.shape[1] == other.shape[1]:
data = np.multiply(ret.data, other[:, ret.col].ravel())
# Sparse matrix times dense column vector.
elif other.shape[1] == 1 and self.shape[0] == other.shape[0]:
data = np.multiply(ret.data, other[ret.row].ravel())
else:
raise ValueError("inconsistent shapes")
ret.data = data.view(np.ndarray).ravel()
return ret
