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 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)
