def _dense_svd(matrix_, reduced_dimension):
print "Running dense svd"
u, s, vt = np.linalg.svd(matrix_.mat, False, True)
rank = len(s[s > Linalg._SVD_TOL])
no_cols = min(u.shape[1], reduced_dimension, rank)
u = DenseMatrix(u[:,0:no_cols])
s = s[0:no_cols]
v = DenseMatrix(vt[0:no_cols,:].transpose())
Linalg._check_reduced_dim(matrix_.shape[1], u.shape[1], reduced_dimension)
if not u.is_mostly_positive():
u = -u
v = -v
return u, s, v
def _dense_svd(matrix_, reduced_dimension):
print "Running dense svd"
u, s, vt = np.linalg.svd(matrix_.mat, False, True)
rank = len(s[s > Linalg._SVD_TOL])
no_cols = min(u.shape[1], reduced_dimension, rank)
u = DenseMatrix(u[:, 0:no_cols])
s = s[0:no_cols]
v = DenseMatrix(vt[0:no_cols, :].transpose())
Linalg._check_reduced_dim(matrix_.shape[1], u.shape[1],
reduced_dimension)
if not u.is_mostly_positive():
u = -u
v = -v
return u, s, v
