def export(self):
# http://tedlab.mit.edu/~dr/SVDLIBC/SVD_F_DT.html
# only importing default 'dt' S, Ut and Vt (dense text output matrices)
PREFIX = self._svd_prefix
file_Ut = PREFIX + '-Ut'
file_Vt = PREFIX + '-Vt'
file_S = PREFIX + '-S'
# Not really used:
file_U = PREFIX + '-U'
file_V = PREFIX + '-V'
# Read matrices files (U, S, Vt), using CSV (it's much faster than numpy.loadtxt()!)
try:
Ut = array(list(csv.reader(open(file_Ut),
delimiter=' '))[1:]).astype('float')
U = Ut.transpose()
except:
U = array(list(csv.reader(open(file_U),
delimiter=' '))[1:]).astype('float')
try:
Vt = array(list(csv.reader(open(file_Vt),
delimiter=' '))[1:]).astype('float')
V = Vt.transpose()
except:
V = array(list(csv.reader(open(file_V),
delimiter=' '))[1:]).astype('float')
#Vt = V.transpose()
_S = array(list(csv.reader(open(file_S),
delimiter=' '))[1:]).astype('float')
S = _S.reshape(_S.shape[0], )
PREFIX_INDEXES = PREFIX + '.ids.'
file_U_idx = PREFIX_INDEXES + 'rows'
file_V_idx = PREFIX_INDEXES + 'cols'
try:
U_idx = [int(idx.strip()) for idx in open(file_U_idx)]
except:
U_idx = [idx.strip() for idx in open(file_U_idx)]
try:
V_idx = [int(idx.strip()) for idx in open(file_V_idx)]
except:
V_idx = [idx.strip() for idx in open(file_V_idx)]
#Check no duplicated IDs!!!
assert (len(U_idx) == len(OrderedSet(U_idx)))
assert (len(V_idx) == len(OrderedSet(V_idx)))
# Create SVD
svd = SVD()
svd._U = DenseMatrix(U, OrderedSet(U_idx), None)
svd._S = S
svd._V = DenseMatrix(V, OrderedSet(V_idx), None)
svd._matrix_similarity = svd._reconstruct_similarity()
svd._matrix_reconstructed = svd._reconstruct_matrix()
return svd
def export(self):
# http://tedlab.mit.edu/~dr/SVDLIBC/SVD_F_DT.html
# only importing default 'dt' S, Ut and Vt (dense text output matrices)
PREFIX = self._svd_prefix
file_Ut = PREFIX + '-Ut'
file_Vt = PREFIX + '-Vt'
file_S = PREFIX + '-S'
# Not really used:
file_U = PREFIX + '-U'
file_V = PREFIX + '-V'
# Read matrices files (U, S, Vt), using CSV (it's much faster than numpy.loadtxt()!)
try:
Ut = array(list(csv.reader(open(file_Ut),delimiter=' '))[1:]).astype('float')
U = Ut.transpose()
except:
U = array(list(csv.reader(open(file_U),delimiter=' '))[1:]).astype('float')
try:
Vt = array(list(csv.reader(open(file_Vt),delimiter=' '))[1:]).astype('float')
V = Vt.transpose()
except:
V = array(list(csv.reader(open(file_V),delimiter=' '))[1:]).astype('float')
#Vt = V.transpose()
_S = array(list(csv.reader(open(file_S),delimiter=' '))[1:]).astype('float')
S = _S.reshape(_S.shape[0], )
PREFIX_INDEXES = PREFIX + '.ids.'
file_U_idx = PREFIX_INDEXES + 'rows'
file_V_idx = PREFIX_INDEXES + 'cols'
try:
U_idx = [ int(idx.strip()) for idx in open(file_U_idx)]
except:
U_idx = [ idx.strip() for idx in open(file_U_idx)]
try:
V_idx = [ int(idx.strip()) for idx in open(file_V_idx)]
except:
V_idx = [ idx.strip() for idx in open(file_V_idx)]
#Check no duplicated IDs!!!
assert(len(U_idx) == len(OrderedSet(U_idx)))
assert(len(V_idx) == len(OrderedSet(V_idx)))
# Create SVD
svd = SVD()
svd._U = DenseMatrix(U, OrderedSet(U_idx), None)
svd._S = S
svd._V = DenseMatrix(V, OrderedSet(V_idx), None)
svd._matrix_similarity = svd._reconstruct_similarity()
svd._matrix_reconstructed = svd._reconstruct_matrix()
return svd
def export(self):
# http://tedlab.mit.edu/~dr/SVDLIBC/SVD_F_DT.html
# only importing default 'dt' S, Ut and Vt (dense text output matrices)
PREFIX = self._svd_prefix
file_Ut = PREFIX + '-Ut'
file_Vt = PREFIX + '-Vt'
file_S = PREFIX + '-S'
# Not really used:
file_U = PREFIX + '-U'
file_V = PREFIX + '-V'
# Read matrices files (U, S, Vt), using CSV (it's much faster than numpy.loadtxt()!)
if VERBOSE:
sys.stdout.write('Reading files: %s, %s, %s\n' %
(file_Ut, file_Vt, file_S))
try:
Ut = array(list(csv.reader(open(file_Ut),
delimiter=' '))[1:]).astype('float')
U = Ut.transpose()
except:
U = array(list(csv.reader(open(file_U),
delimiter=' '))[1:]).astype('float')
try:
Vt = array(list(csv.reader(open(file_Vt),
delimiter=' '))[1:]).astype('float')
V = Vt.transpose()
except:
V = array(list(csv.reader(open(file_V),
delimiter=' '))[1:]).astype('float')
#Vt = V.transpose()
_S = array(list(csv.reader(open(file_S),
delimiter=' '))[1:]).astype('float')
S = _S.reshape(_S.shape[0], )
PREFIX_INDEXES = PREFIX + '.ids.'
file_U_idx = PREFIX_INDEXES + 'rows'
file_V_idx = PREFIX_INDEXES + 'cols'
if VERBOSE:
sys.stdout.write('Reading index files: %s, %s\n' %
(file_U_idx, file_V_idx))
try:
U_idx = [int(idx.strip()) for idx in open(file_U_idx)]
except:
U_idx = [idx.strip() for idx in open(file_U_idx)]
try:
V_idx = [int(idx.strip()) for idx in open(file_V_idx)]
except:
V_idx = [idx.strip() for idx in open(file_V_idx)]
#Check no duplicated IDs!!!
assert (len(U_idx) == len(
OrderedSet(U_idx))), 'There are duplicated row IDs!'
assert (len(U_idx) == U.shape[0]
), 'There are duplicated (or empty) row IDs!'
assert (len(V_idx) == len(
OrderedSet(V_idx))), 'There are duplicated col IDs!'
assert (len(V_idx) == V.shape[0]
), 'There are duplicated (or empty) col IDs'
# Create SVD
if VERBOSE:
sys.stdout.write('Creating SVD() class\n')
svd = SVD()
svd._U = DenseMatrix(U, OrderedSet(U_idx), None)
svd._S = S
svd._V = DenseMatrix(V, OrderedSet(V_idx), None)
svd._matrix_similarity = svd._reconstruct_similarity()
svd._matrix_reconstructed = svd._reconstruct_matrix()
# If save_model, then use row and col ids from SVDLIBC
MAX_VECTORS = 2**21
if len(svd._U) > MAX_VECTORS:
svd._file_row_ids = file_U_idx
if len(svd._V) > MAX_VECTORS:
svd._file_col_ids = file_V_idx
return svd
def export(self):
# http://tedlab.mit.edu/~dr/SVDLIBC/SVD_F_DT.html
# only importing default 'dt' S, Ut and Vt (dense text output matrices)
PREFIX = self._svd_prefix
file_Ut = PREFIX + '-Ut'
file_Vt = PREFIX + '-Vt'
file_S = PREFIX + '-S'
# Not really used:
file_U = PREFIX + '-U'
file_V = PREFIX + '-V'
# Read matrices files (U, S, Vt), using CSV (it's much faster than numpy.loadtxt()!)
if VERBOSE:
sys.stdout.write('Reading files: %s, %s, %s\n' % (file_Ut, file_Vt, file_S))
try:
Ut = array(list(csv.reader(open(file_Ut),delimiter=' '))[1:]).astype('float')
U = Ut.transpose()
except:
U = array(list(csv.reader(open(file_U),delimiter=' '))[1:]).astype('float')
try:
Vt = array(list(csv.reader(open(file_Vt),delimiter=' '))[1:]).astype('float')
V = Vt.transpose()
except:
V = array(list(csv.reader(open(file_V),delimiter=' '))[1:]).astype('float')
#Vt = V.transpose()
_S = array(list(csv.reader(open(file_S),delimiter=' '))[1:]).astype('float')
S = _S.reshape(_S.shape[0], )
PREFIX_INDEXES = PREFIX + '.ids.'
file_U_idx = PREFIX_INDEXES + 'rows'
file_V_idx = PREFIX_INDEXES + 'cols'
if VERBOSE:
sys.stdout.write('Reading index files: %s, %s\n' % (file_U_idx, file_V_idx))
try:
U_idx = [ int(idx.strip()) for idx in open(file_U_idx)]
except:
U_idx = [ idx.strip() for idx in open(file_U_idx)]
try:
V_idx = [ int(idx.strip()) for idx in open(file_V_idx)]
except:
V_idx = [ idx.strip() for idx in open(file_V_idx)]
#Check no duplicated IDs!!!
assert(len(U_idx) == len(OrderedSet(U_idx))), 'There are duplicated row IDs!'
assert(len(U_idx) == U.shape[0]), 'There are duplicated (or empty) row IDs!'
assert(len(V_idx) == len(OrderedSet(V_idx))), 'There are duplicated col IDs!'
assert(len(V_idx) == V.shape[0]), 'There are duplicated (or empty) col IDs'
# Create SVD
if VERBOSE:
sys.stdout.write('Creating SVD() class\n')
svd = SVD()
svd._U = DenseMatrix(U, OrderedSet(U_idx), None)
svd._S = S
svd._V = DenseMatrix(V, OrderedSet(V_idx), None)
svd._matrix_similarity = svd._reconstruct_similarity()
svd._matrix_reconstructed = svd._reconstruct_matrix()
# If save_model, then use row and col ids from SVDLIBC
MAX_VECTORS = 2**21
if len(svd._U) > MAX_VECTORS:
svd._file_row_ids = file_U_idx
if len(svd._V) > MAX_VECTORS:
svd._file_col_ids = file_V_idx
return svd