def fit(self, w0=None, verbose=0, fnpy=None):
N, U, D = self.N, self.U, self.D
if verbose > 0:
t0 = time.time()
print('\nC: %g, %g, %g' % (self.C1, self.C2, self.C3))
num_vars = (U + N + 1) * D
if w0 is None:
if fnpy is None:
w0 = self._init_vars()
else:
try:
assert type(fnpy) == str
assert fnpy.endswith('.npy')
w0 = np.load(fnpy, allow_pickle=False)
print('Restore from %s' % fnpy)
except (IOError, ValueError):
w0 = self._init_vars()
assert w0.shape == (num_vars, )
try:
# f: callable(x, g, *args)
# LBFGS().minimize(f, x0, progress=progress, args=args)
optim = LBFGS()
optim.linesearch = 'wolfe'
optim.max_linesearch = 100
param_dict = {'N': self.N, 'C': (self.C1, self.C2, self.C3)}
res = optim.minimize(objective,
w0,
progress,
args=(self.X, self.cliques, self.data_helper,
param_dict, verbose, fnpy))
self.mu = res[:D]
self.V = res[D:(U + 1) * D].reshape(U, D)
self.W = res[(U + 1) * D:].reshape(N, D)
self.trained = True
except (LBFGSError, MemoryError) as err:
self.trained = False
sys.stderr.write('LBFGS failed: {0}\n'.format(err))
sys.stderr.flush()
if verbose > 0:
print('Training finished in %.1f seconds' % (time.time() - t0))
