def get_err_fast_sparse_svd(self, nrow, ncol, density):
X = generate_sparse_matrix(shape=(nrow, ncol), density=density)
# For debug
np.save("/tmp/X_%d_%d.npy" % (nrow, ncol), X)
# svd from parsimony
fast_sparse_svd = RankOneSparseSVD(max_iter=1000)
parsimony_v = fast_sparse_svd.run(X)
# return self.get_err_by_np_linalg_svd(parsimony_v, X)
return self.get_err_by_sp_sparse_linalg_svds(parsimony_v, X)
def get_err_fast_sparse_svd(self, nrow, ncol, density):
X = generate_sparse_matrix(shape=(nrow, ncol),
density=density)
# For debug
np.save("/tmp/X_%d_%d.npy" % (nrow, ncol), X)
# svd from parsimony
fast_sparse_svd = RankOneSparseSVD(max_iter=1000)
parsimony_v = fast_sparse_svd.run(X)
# return self.get_err_by_np_linalg_svd(parsimony_v, X)
return self.get_err_by_sp_sparse_linalg_svds(parsimony_v, X)
def lambda_max(self):
""" Largest eigenvalue of the corresponding covariance matrix.
From the interface "Eigenvalues".
"""
if self._lambda_max is None:
try:
self._lambda_max = self.A().get_singular_values(0)
except:
pass
if self._lambda_max is None:
# Note that we can save the state here since lmax(A) does not change.
# TODO: This only work if the elements of self._A are scipy.sparse. We
# should allow dense matrices as well.
if len(self._A) == 3 \
and self._A[1].nnz == 0 and self._A[2].nnz == 0:
# TODO: Instead of p, this should really be the number of non-zero
# rows of A.
self._lambda_max = 2.0 * (1.0 - math.cos(float(self._p - 1)
* math.pi
/ float(self._p)))
else:
from parsimony.algorithms.nipals import RankOneSparseSVD
A = sparse.vstack(self.A())
# TODO: Add max_iter here!
v = RankOneSparseSVD().run(A) # , max_iter=max_iter)
us = A.dot(v)
self._lambda_max = np.sum(us ** 2.0)
return self._lambda_max
def lambda_max(self):
""" Largest eigenvalue of the corresponding covariance matrix.
From the interface "Eigenvalues".
"""
# Note that we can save the state here since lmax(A) does not change.
if len(self._A) == 4 and self._A[2].nnz == 0 and self._A[3].nnz == 0:
# if len(self._shape) == 3 \
# and self._shape[0] == 1 and self._shape[1] == 1:
# TODO: Instead of p, this should really be the number of non-zero
# rows of A.
p = self._A[1].shape[0]
lmaxTV = 2.0 * (1.0 - math.cos(float(p - 1) * math.pi
/ float(p)))
self._lambda_max = lmaxTV * self.g ** 2.0 + self.l ** 2.0
elif self._lambda_max is None:
from parsimony.algorithms.nipals import RankOneSparseSVD
A = sparse.vstack(self.A()[1:])
# TODO: Add max_iter here!!
v = RankOneSparseSVD().run(A) # , max_iter=max_iter)
us = A.dot(v)
self._lambda_max = np.sum(us ** 2.0) + self.l ** 2.0
return self._lambda_max
def get_err_fast_sparse_svd(self, nrow, ncol, density):
X = generate_sparse_matrix(shape=(nrow, ncol), density=density)
# For debug
# np.save("/tmp/X_%d_%d.npy" % (nrow, ncol), X)
fd = None
try:
fd, tmpfilename = tempfile.mkstemp(suffix=".npy",
prefix="X_%d_%d" % (nrow, ncol))
np.save(tmpfilename, X)
finally:
if fd is not None:
os.close(fd)
# svd from parsimony
fast_sparse_svd = RankOneSparseSVD(max_iter=1000)
parsimony_v = fast_sparse_svd.run(X)
# return self.get_err_by_np_linalg_svd(parsimony_v, X)
return self.get_err_by_sp_sparse_linalg_svds(parsimony_v, X)
def get_err_fast_sparse_svd(self, nrow, ncol, density):
X = generate_sparse_matrix(shape=(nrow, ncol),
density=density)
# For debug
# np.save("/tmp/X_%d_%d.npy" % (nrow, ncol), X)
fd = None
try:
fd, tmpfilename = tempfile.mkstemp(suffix=".npy",
prefix="X_%d_%d" % (nrow, ncol))
np.save(tmpfilename, X)
finally:
if fd is not None:
os.close(fd)
# svd from parsimony
fast_sparse_svd = RankOneSparseSVD(max_iter=1000)
parsimony_v = fast_sparse_svd.run(X)
# return self.get_err_by_np_linalg_svd(parsimony_v, X)
return self.get_err_by_sp_sparse_linalg_svds(parsimony_v, X)
def lambda_max(self):
""" Largest eigenvalue of the corresponding covariance matrix.
From the interface "Eigenvalues".
"""
# Note that we can save the state here since lmax(A) does not change.
# TODO: This only work if the elements of self._A are scipy.sparse. We
# should allow dense matrices as well.
if self._lambda_max is None:
from parsimony.algorithms.nipals import RankOneSparseSVD
A = sparse.vstack(self.A())
# TODO: Add max_iter here!
v = RankOneSparseSVD().run(A) # , max_iter=max_iter)
us = A.dot(v)
self._lambda_max = np.sum(us**2.0)
return self._lambda_max
