def run(self, X, Y, start_vector=None):
"""Find the right-singular vector of the product of two matrices.
Parameters
----------
X : Numpy array with shape (n, p). The first matrix of the product.
Y : Numpy array with shape (p, m). The second matrix of the product.
start_vector : BaseStartVector. A start vector generator. Default is to
use a random start vector.
"""
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = utils.time()
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, False)
M, N = X.shape
if start_vector is None:
start_vector = start_vectors.RandomStartVector(normalise=True)
v = start_vector.get_vector(Y.shape[1])
for it in xrange(1, self.max_iter + 1):
v_ = v
v = np.dot(X, np.dot(Y, v_))
v = np.dot(Y.T, np.dot(X.T, v))
v *= 1.0 / maths.norm(v)
if maths.norm(v_ - v) / maths.norm(v) < self.eps \
and it >= self.min_iter:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, utils.time() - _t)
if self.info_requested(utils.Info.func_val):
_f = maths.norm(np.dot(X, np.dot(Y, v))) # Largest singular value.
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return utils.direct_vector(v)
def run(self, X, Y, start_vector=None):
"""Find the right-singular vector of the product of two matrices.
Parameters
----------
X : Numpy array with shape (n, p). The first matrix of the product.
Y : Numpy array with shape (p, m). The second matrix of the product.
start_vector : BaseStartVector. A start vector generator. Default is to
use a random start vector.
"""
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = utils.time()
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, False)
M, N = X.shape
if start_vector is None:
start_vector = weights.RandomUniformWeights(normalise=True)
v = start_vector.get_weights(Y.shape[1])
for it in range(1, self.max_iter + 1):
v_ = v
v = np.dot(X, np.dot(Y, v_))
v = np.dot(Y.T, np.dot(X.T, v))
v *= 1.0 / maths.norm(v)
if maths.norm(v_ - v) / maths.norm(v) < self.eps \
and it >= self.min_iter:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, utils.time() - _t)
if self.info_requested(utils.Info.func_val):
_f = maths.norm(np.dot(X, np.dot(Y, v))) # Largest singular value.
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return utils.direct_vector(v)
def run(self, X, start_vector=None):
"""Find the right-singular vector of the given sparse matrix.
Parameters
----------
X : Scipy sparse array. The sparse matrix to decompose.
start_vector : BaseStartVector. A start vector generator. Default is
to use a random start vector.
"""
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = utils.time()
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, False)
if start_vector is None:
start_vector = start_vectors.RandomStartVector(normalise=True)
v0 = start_vector.get_vector(np.min(X.shape))
# determine when to use power method or scipy_sparse
use_power = True if X.shape[1] >= 10 ** 3 else False
if not use_power:
try:
if not sp.sparse.issparse(X):
X = sp.sparse.csr_matrix(X)
try:
[_, _, v] = sparse_linalg.svds(X, k=1, v0=v0,
tol=self.eps,
maxiter=self.max_iter,
return_singular_vectors=True)
except TypeError: # For scipy 0.9.0.
[_, _, v] = sparse_linalg.svds(X, k=1, tol=self.eps)
v = v.T
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
except ArpackNoConvergence:
use_power = True
if use_power: # Use the power method if scipy failed or if determined.
M, N = X.shape
if M < N:
K = X.dot(X.T)
t = v0
for it in xrange(self.max_iter):
t_ = t
t = K.dot(t_)
t *= 1.0 / maths.norm(t)
crit = float(maths.norm(t_ - t)) / float(maths.norm(t))
if crit < consts.TOLERANCE:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
v = X.T.dot(t)
v *= 1.0 / maths.norm(v)
else:
K = X.T.dot(X)
v = v0
for it in xrange(self.max_iter):
v_ = v
v = K.dot(v_)
v *= 1.0 / maths.norm(v)
crit = float(maths.norm(v_ - v)) / float(maths.norm(v))
if crit < consts.TOLERANCE:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, utils.time() - _t)
if self.info_requested(utils.Info.func_val):
_f = maths.norm(X.dot(v)) # Largest singular value.
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return utils.direct_vector(v)
def run(self, X, start_vector=None):
"""Find the right-singular vector of the given matrix.
Parameters
----------
X : Numpy array. The matrix to decompose.
start_vector : BaseStartVector. A start vector generator. Default is
to use a random start vector.
"""
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = utils.time()
if start_vector is None:
start_vector = start_vectors.RandomStartVector(normalise=True)
v0 = start_vector.get_vector(np.min(X.shape))
arpack_failed = False
try:
try:
[_, _, v] = sparse_linalg.svds(X, k=1, v0=v0,
tol=self.eps,
maxiter=self.max_iter,
return_singular_vectors=True)
except TypeError: # For scipy 0.9.0.
[_, _, v] = sparse_linalg.svds(X, k=1, tol=self.eps)
v = v.T
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
except ArpackNoConvergence:
arpack_failed = True
if arpack_failed: # Use the power method if this happens.
M, N = X.shape
if M < 80 and N < 80: # Very arbitrary threshold from one computer
_, _, V = scipy.linalg.svd(X, full_matrices=True)
v = V[[0], :].T
elif M < N:
K = np.dot(X, X.T)
t = v0
for it in xrange(self.max_iter):
t_ = t
t = np.dot(K, t_)
t *= 1.0 / maths.norm(t)
if maths.norm(t_ - t) / maths.norm(t) < self.eps:
break
v = np.dot(X.T, t)
v *= 1.0 / maths.norm(v)
else:
K = np.dot(X.T, X)
v = v0
for it in xrange(self.max_iter):
v_ = v
v = np.dot(K, v_)
v *= 1.0 / maths.norm(v)
if maths.norm(v_ - v) / maths.norm(v) < self.eps:
break
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, utils.time() - _t)
if self.info_requested(utils.Info.func_val):
_f = maths.norm(np.dot(X, v)) # Largest singular value.
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return utils.direct_vector(v)
def run(self, g, x):
"""Find the best separating margin for the samples in X.
Parameters
----------
g : MajoriserFunction
The function that majorises self.function.
x : array_like
The point at which to start the minimisation process.
"""
x = check_arrays(x)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = []
if self.info_requested(utils.Info.func_val):
_f = []
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, False)
xnew = xold = x
for i in range(1, self.max_iter + 1):
if self.info_requested(utils.Info.time):
tm = utils.time()
xold = xnew
if isinstance(g, properties.MajoriserFunction):
maj_f = g(self.function, xold)
else:
g.at_point(xold)
maj_f = g
xnew = self.algorithm.run(maj_f, xold)
if self.info_requested(utils.Info.time):
_t.append(utils.time() - tm)
if self.info_requested(utils.Info.func_val):
_f.append(g.f(xnew))
if self.callback is not None:
self.callback(locals())
if i >= self.min_iter and np.linalg.norm(xnew - xold) < self.eps:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
self.num_iter = i
if self.info_requested(utils.Info.num_iter):
self.info_set(utils.Info.num_iter, self.num_iter)
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, _t)
if self.info_requested(utils.Info.func_val):
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return xnew
def run(self, X, start_vector=None):
"""Find the right-singular vector of the given sparse matrix.
Parameters
----------
X : Scipy sparse array. The sparse matrix to decompose.
start_vector : BaseStartVector. A start vector generator. Default is
to use a random start vector.
"""
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = utils.time()
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, False)
if start_vector is None:
start_vector = weights.RandomUniformWeights(normalise=True)
v0 = start_vector.get_weights(np.min(X.shape))
# determine when to use power method or scipy_sparse
use_power = True if X.shape[1] >= 10 ** 3 else False
if not use_power:
try:
if not sp.sparse.issparse(X):
X = sp.sparse.csr_matrix(X)
try:
[_, _, v] = sparse_linalg.svds(X, k=1, v0=v0,
tol=self.eps,
maxiter=self.max_iter,
return_singular_vectors=True)
except TypeError: # For scipy 0.9.0.
[_, _, v] = sparse_linalg.svds(X, k=1, tol=self.eps)
v = v.T
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
except ArpackNoConvergence:
use_power = True
if use_power: # Use the power method if scipy failed or if determined.
# TODO: Use estimators for this!
M, N = X.shape
if M < N:
K = X.dot(X.T)
t = v0
for it in range(self.max_iter):
t_ = t
t = K.dot(t_)
t *= 1.0 / maths.norm(t)
crit = float(maths.norm(t_ - t)) / float(maths.norm(t))
if crit < consts.TOLERANCE:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
v = X.T.dot(t)
v *= 1.0 / maths.norm(v)
else:
K = X.T.dot(X)
v = v0
for it in range(self.max_iter):
v_ = v
v = K.dot(v_)
v *= 1.0 / maths.norm(v)
crit = float(maths.norm(v_ - v)) / float(maths.norm(v))
if crit < consts.TOLERANCE:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, utils.time() - _t)
if self.info_requested(utils.Info.func_val):
_f = maths.norm(X.dot(v)) # Largest singular value.
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return utils.direct_vector(v)
def run(self, X, start_vector=None):
"""Find the right-singular vector of the given matrix.
Parameters
----------
X : Numpy array. The matrix to decompose.
start_vector : BaseStartVector. A start vector generator. Default is
to use a random start vector.
"""
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = utils.time()
if start_vector is None:
start_vector = weights.RandomUniformWeights(normalise=True)
v0 = start_vector.get_weights(np.min(X.shape))
arpack_failed = False
try:
try:
[_, _, v] = sparse_linalg.svds(X, k=1, v0=v0,
tol=self.eps,
maxiter=self.max_iter,
return_singular_vectors=True)
except TypeError: # For scipy 0.9.0.
[_, _, v] = sparse_linalg.svds(X, k=1, tol=self.eps)
v = v.T
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
except ArpackNoConvergence:
arpack_failed = True
if arpack_failed: # Use the power method if this happens.
M, N = X.shape
if M < 80 and N < 80: # Very arbitrary threshold from one computer
_, _, V = scipy.linalg.svd(X, full_matrices=True)
v = V[[0], :].T
elif M < N:
K = np.dot(X, X.T)
t = v0
for it in range(self.max_iter):
t_ = t
t = np.dot(K, t_)
t *= 1.0 / maths.norm(t)
if maths.norm(t_ - t) / maths.norm(t) < self.eps:
break
v = np.dot(X.T, t)
v *= 1.0 / maths.norm(v)
else:
K = np.dot(X.T, X)
v = v0
for it in range(self.max_iter):
v_ = v
v = np.dot(K, v_)
v *= 1.0 / maths.norm(v)
if maths.norm(v_ - v) / maths.norm(v) < self.eps:
break
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, utils.time() - _t)
if self.info_requested(utils.Info.func_val):
_f = maths.norm(np.dot(X, v)) # Largest singular value.
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return utils.direct_vector(v)
def run(self, g, x):
"""Run the optimiser using the majoriser function starting at the given
point.
Parameters
----------
g : MajoriserFunction
The function that majorises self.function.
x : array_like
The point at which to start the minimisation process.
"""
# x = check_arrays(x)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = []
if self.info_requested(utils.Info.func_val):
_f = []
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, False)
xnew = xold = x
for i in range(1, self.max_iter + 1):
if self.info_requested(utils.Info.time):
tm = utils.time()
xold = xnew
if isinstance(g, properties.MajoriserFunction):
maj_f = g(self.function, xold)
else:
g.at_point(xold)
maj_f = g
xnew = self.algorithm.run(maj_f, xold)
if self.info_requested(utils.Info.time):
_t.append(utils.time() - tm)
if self.info_requested(utils.Info.func_val):
_f.append(g.f(xnew))
if self.callback is not None:
self.callback(locals())
if isinstance(xnew, list):
val = list_op((xnew, xold),
lambda new, old: np.linalg.norm(new - old),
aggregator=np.max)
else:
val = np.linalg.norm(xnew - xold)
if i >= self.min_iter and val < self.eps:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
self.num_iter = i
if self.info_requested(utils.Info.num_iter):
self.info_set(utils.Info.num_iter, self.num_iter)
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, _t)
if self.info_requested(utils.Info.func_val):
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return xnew
def run(self, g, x):
"""Run the optimiser using the majoriser function starting at the given
point.
Parameters
----------
g : MajoriserFunction
The function that majorises self.function.
x : array_like
The point at which to start the minimisation process.
"""
# x = check_arrays(x)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = []
if self.info_requested(utils.Info.func_val):
_f = []
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, False)
xnew = xold = x
for i in range(1, self.max_iter + 1):
if self.info_requested(utils.Info.time):
tm = utils.time()
xold = xnew
if isinstance(g, properties.MajoriserFunction):
maj_f = g(self.function, xold)
else:
g.at_point(xold)
maj_f = g
xnew = self.algorithm.run(maj_f, xold)
if self.info_requested(utils.Info.time):
_t.append(utils.time() - tm)
if self.info_requested(utils.Info.func_val):
_f.append(g.f(xnew))
if self.callback is not None:
self.callback(locals())
if isinstance(xnew, list):
val = list_op((xnew, xold),
lambda new, old: np.linalg.norm(new - old),
aggregator=np.max)
else:
val = np.linalg.norm(xnew - xold)
if i >= self.min_iter and val < self.eps:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
self.num_iter = i
if self.info_requested(utils.Info.num_iter):
self.info_set(utils.Info.num_iter, self.num_iter)
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, _t)
if self.info_requested(utils.Info.func_val):
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return xnew
def run(self, g, x):
"""Find the best separating margin for the samples in X.
Parameters
----------
g : MajoriserFunction
The function that majorises self.function.
x : array_like
The point at which to start the minimisation process.
"""
x = check_arrays(x)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, False)
if self.info_requested(utils.Info.time):
_t = []
if self.info_requested(utils.Info.func_val):
_f = []
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, False)
xnew = xold = x
for i in range(1, self.max_iter + 1):
if self.info_requested(utils.Info.time):
tm = utils.time()
xold = xnew
if isinstance(g, properties.MajoriserFunction):
maj_f = g(self.function, xold)
else:
g.at_point(xold)
maj_f = g
xnew = self.algorithm.run(maj_f, xold)
if self.info_requested(utils.Info.time):
_t.append(utils.time() - tm)
if self.info_requested(utils.Info.func_val):
_f.append(g.f(xnew))
if self.callback is not None:
self.callback(locals())
if i >= self.min_iter and np.linalg.norm(xnew - xold) < self.eps:
if self.info_requested(utils.Info.converged):
self.info_set(utils.Info.converged, True)
break
self.num_iter = i
if self.info_requested(utils.Info.num_iter):
self.info_set(utils.Info.num_iter, self.num_iter)
if self.info_requested(utils.Info.time):
self.info_set(utils.Info.time, _t)
if self.info_requested(utils.Info.func_val):
self.info_set(utils.Info.func_val, _f)
if self.info_requested(utils.Info.ok):
self.info_set(utils.Info.ok, True)
return xnew
