def callback(w):
W = w.reshape((-1, 1), order='F')
u, v, c = W[:size_u], W[size_u:size_u + size_v], W[size_u + size_v:]
uv0 = he.khatri_rao(v, u)
loss.append(.5 * (linalg.norm(y_train - X_train.dot(uv0)) **
2))
timings.append((datetime.now() - start).total_seconds())
# verbose=False, plot=False, n_jobs=1)
#print datetime.now() - start
#u_obo, v = out
print('RANK ONE CLASSIC SETTING')
u = u0
v = v0[:, 0]
for i, y_train in enumerate(Y_train.T):
pl.figure()
y_train = y_train[:, None]
# compute optimal point
u0, v0 = u, v # the one from previous voxel
u, v = he.rank_one(
X_train, y_train, fir_length, u0=u0, v0=v0,
verbose=1, method='TNC', rtol=1e-32)
uv0 = he.khatri_rao(v, u)
min_loss = .5 * (linalg.norm(y_train - X_train.dot(uv0)) ** 2)
for solver in ('TNC', 'Newton-CG', 'L-BFGS-B', 'trust-ncg', 'CG'):
loss = []
timings = []
def callback(w):
W = w.reshape((-1, 1), order='F')
u, v, c = W[:size_u], W[size_u:size_u + size_v], W[size_u + size_v:]
uv0 = he.khatri_rao(v, u)
loss.append(.5 * (linalg.norm(y_train - X_train.dot(uv0)) **
2))
timings.append((datetime.now() - start).total_seconds())
start = datetime.now()
u0 = canonical
out = he.rank_one(
X_train, y_train, fir_length, u0=u0, v0=v0,