def gen_zero_energy_guess(H, rank):
"""
Generate psi such that <psi|H|psi> = 0
Parameters:
-----------
H: tt.matrix
hamiltonian in the TT-matrix format
rank: int
Rank of the guess
"""
v = 1.0
while v > 1e-12:
# Create two random TT vectors and normalize them
psi1 = tt.rand(H.n, r=rank)
psi2 = tt.rand(H.n, r=1)
psi1 = psi1 * (1.0 / psi1.norm())
psi2 = psi2 * (1.0 / psi2.norm())
# Calculate coefficients of the quadratic equation
h22 = tt.dot(tt.matvec(H, psi2), psi2)
h21 = tt.dot(tt.matvec(H, psi2), psi1)
h11 = tt.dot(tt.matvec(H, psi1), psi1)
# find vectors such that <psi|H|psi> = 0
rs = np.roots([h22, 2 * h21, h11])
v = np.linalg.norm(np.imag(rs))
psi = psi1 + rs[0] * psi2
psi = psi * (1.0 / psi.norm())
return psi
def test_gradient_wrt_core(self):
w = tt.rand([2, 2, 2], 3, [1, 2, 2, 1])
x_1 = all_subsets.subset_tensor([3, 4, 5])
x_2 = all_subsets.subset_tensor([-1, 12, 5])
X = np.array([[3, 4, 5], [-1, 12, 5]])
eps = 1e-8
def loss(core):
new_w = w.copy()
new_w.core = copy.copy(core)
res = (tt.dot(new_w, x_1))**2 # Quadratic.
res += tt.dot(new_w, x_2) # Linear.
return res
# Derivatives of the quadratic and linear functions in the loss.
dfdz = [2 * tt.dot(w, x_1), 1]
core = w.core
value = loss(core)
numerical_grad = np.zeros(len(core))
for i in range(len(core)):
new_core = copy.copy(core)
# print(new_core)
new_core[i] += eps
numerical_grad[i] = (loss(new_core) - value) / eps
w_cores = tt.tensor.to_list(w)
gradient = all_subsets.gradient_wrt_cores(w_cores, X, dfdz)
np.testing.assert_array_almost_equal(numerical_grad, gradient, decimal=3)
def test_gradient_wrt_core(self):
w = tt.rand([2, 2, 2], 3, [1, 2, 2, 1])
x_1 = all_subsets.subset_tensor([3, 4, 5])
x_2 = all_subsets.subset_tensor([-1, 12, 5])
X = np.array([[3, 4, 5], [-1, 12, 5]])
eps = 1e-8
def loss(core):
new_w = w.copy()
new_w.core = copy.copy(core)
res = (tt.dot(new_w, x_1))**2 # Quadratic.
res += tt.dot(new_w, x_2) # Linear.
return res
# Derivatives of the quadratic and linear functions in the loss.
dfdz = [2 * tt.dot(w, x_1), 1]
core = w.core
value = loss(core)
numerical_grad = np.zeros(len(core))
for i in range(len(core)):
new_core = copy.copy(core)
# print(new_core)
new_core[i] += eps
numerical_grad[i] = (loss(new_core) - value) / eps
w_cores = tt.tensor.to_list(w)
gradient = all_subsets.gradient_wrt_cores(w_cores, X, dfdz)
np.testing.assert_array_almost_equal(numerical_grad,
gradient,
decimal=3)
def eval_post(Atts,
params,
P0,
time_observation,
observations,
obs_operator,
eps=1e-7,
method='cheby',
dtmax=0.1,
Nmax=16):
Att = Atts[0] * params[0]
for i in range(1, params.size):
Att += Atts[i] * params[i]
Att = Att.round(1e-12)
qtt = True
if qtt:
A_qtt = ttm2qttm(Att)
integrator = ttInt(A_qtt,
epsilon=eps,
N_max=Nmax,
dt_max=1.0,
method=method)
P = tt2qtt(P0)
else:
integrator = ttInt(Att,
epsilon=eps,
N_max=Nmax,
dt_max=1.0,
method=method)
P = P0
ps = []
for i in range(1, time_observation.size):
dt = time_observation[i] - time_observation[i - 1]
# print(i,int(np.ceil(dt/dtmax)))
# tme = timeit.time.time()
P = integrator.solve(P,
dt,
intervals=int(np.ceil(dt / dtmax)),
qtt=True)
Po_tt = obs_operator(observations[i, :], P, time_observation[i])
Po_tt = tt2qtt(Po_tt)
ps.append(tt.dot(Po_tt, P))
P = (P * Po_tt).round(1e-9)
P = P * (1 / ps[-1])
# tme = timeit.time.time() - tme
# print(i,' time ',tme,' ',P.r)
ps = np.array(ps)
return ps
def test_guess():
# Set up the parameters of the script
d = 4
r = 5
H = dyn.gen_heisenberg_hamiltonian(d)
# guess
np.random.seed(10)
psi1 = dyn.gen_rounded_gaussian_guess(H, r)
np.random.seed(10)
psi2 = dyn.gen_implicit_gaussian_guess(H, r)
assert (np.allclose(tt.dot(psi1, psi2), -0.18779753088022963))
def GMRES(A, u_0, b, eps=1E-6, restart=20, verb=0):
"""GMRES linear systems solver based on TT techniques.
A = A(x[, eps]) is a function that multiplies x by matrix.
"""
do_restart = True
while do_restart:
r0 = b + A((-1) * u_0)
r0 = r0.round(eps)
beta = r0.norm()
bnorm = b.norm()
curr_beta = beta
if verb:
print("/ Initial residual norm: %lf; mean rank:" % beta,
r0.rmean())
m = restart
V = np.zeros(m + 1, dtype=object) # Krylov basis
V[0] = r0 * (1.0 / beta)
H = np.mat(np.zeros((m + 1, m), dtype=np.complex128, order='F'))
j = 0
while j < m and curr_beta / bnorm > eps:
delta = eps / (curr_beta / beta)
# print("Calculating new Krylov vector")
w = A(V[j], delta)
#w = w.round(delta)
for i in range(j + 1):
H[i, j] = tt.dot(w, V[i])
w = w + (-H[i, j]) * V[i]
w = w.round(delta)
if verb > 1:
print("|% 3d. New Krylov vector mean rank:" % (j + 1),
w.rmean())
H[j + 1, j] = w.norm()
V[j + 1] = w * (1 / H[j + 1, j])
Hj = H[:j + 2, :j + 1]
betae = np.zeros(j + 2, dtype=np.complex128)
betae[0] = beta
# solving Hj * y = beta e_1
y, curr_beta, rank, s = np.linalg.lstsq(Hj, betae)
curr_beta = curr_beta[0]
if verb:
print("|% 3d. LSTSQ residual norm:" % (j + 1), curr_beta)
j += 1
x = u_0
for i in range(j):
x = x + V[i] * y[i]
x = x.round(eps)
if verb:
print("\\ Solution mean rank:", x.rmean())
u_0 = x
do_restart = (curr_beta / bnorm > eps)
return x
def GMRES(A, u_0, b, eps=1E-6, restart=20, verb=0):
"""GMRES linear systems solver based on TT techniques.
A = A(x[, eps]) is a function that multiplies x by matrix.
"""
do_restart = True
while do_restart:
r0 = b + A((-1) * u_0)
r0 = r0.round(eps)
beta = r0.norm()
bnorm = b.norm()
curr_beta = beta
if verb:
print "/ Initial residual norm: %lf; mean rank:" % beta, r0.rmean()
m = restart
V = np.zeros(m + 1, dtype=object) # Krylov basis
V[0] = r0 * (1.0 / beta)
H = np.mat(np.zeros((m+1, m), dtype=np.complex128, order='F'))
j = 0
while j < m and curr_beta / bnorm > eps:
delta = eps / (curr_beta / beta)
#print "Calculating new Krylov vector"
w = A(V[j], delta)
#w = w.round(delta)
for i in range(j + 1):
H[i, j] = tt.dot(w, V[i])
w = w + (-H[i, j]) * V[i]
w = w.round(delta)
if verb > 1:
print "|% 3d. New Krylov vector mean rank:" % (j + 1), w.rmean()
H[j+1, j] = w.norm()
V[j+1] = w * (1 / H[j+1, j])
Hj = H[:j+2, :j+1]
betae = np.zeros(j+2, dtype=np.complex128)
betae[0] = beta
# solving Hj * y = beta e_1
y, curr_beta, rank, s = np.linalg.lstsq(Hj, betae)
curr_beta = curr_beta[0]
if verb:
print "|% 3d. LSTSQ residual norm:" % (j + 1), curr_beta
j += 1
x = u_0
for i in range(j):
x = x + V[i] * y[i]
x = x.round(eps)
if verb:
print "\\ Solution mean rank:", x.rmean()
u_0 = x
do_restart = (curr_beta / bnorm > eps)
return x
def goal_function(thetuta):
L1 = np.array([lagrange(thetuta[0], i, pts1) for i in range(pts1.size)])
L2 = np.array([lagrange(thetuta[1], i, pts2) for i in range(pts2.size)])
L3 = np.array([lagrange(thetuta[2], i, pts3) for i in range(pts3.size)])
L4 = np.array([lagrange(thetuta[3], i, pts4) for i in range(pts4.size)])
L5 = np.array([lagrange(thetuta[4], i, pts5) for i in range(pts5.size)])
val = tt.dot(
Post,
tt.mkron(tt.tensor(L1.flatten()), tt.tensor(L2.flatten()),
tt.tensor(L3.flatten()), tt.tensor(L4.flatten()),
tt.tensor(L5.flatten())))
return -val
def increase_rank(w0, rank, train_x, train_y, vectorized_tt_dot_h, loss_h,
loss_grad_h, project_h, object_tensor_h, reg):
"""Implements the idea from the paper
Riemannian Pursuit for Big Matrix Recovery
That is, to init the tensor with the desired rank, we add orthogonal
component of the current gradient to our current low-rank estimate w0.
"""
w = w0
if rank > max(w.r):
# Choose not too many objects, so that the rank is reasonable.
num_objects_used = rank * 5
w_x = vectorized_tt_dot_h(w, train_x[:num_objects_used, :])
grad_coef = loss_grad_h(w_x, train_y[:num_objects_used])
proj_grad = project_h(w,
train_x[:num_objects_used, :],
grad_coef,
reg=reg)
grad = reg * w
for i in range(0, num_objects_used):
grad = grad + grad_coef[i] * object_tensor_h(train_x[i, :])
orth = grad - proj_grad
orth = orth.round(eps=0, rmax=rank - max(w0.r))
batch_w_x = vectorized_tt_dot_h(w, train_x)
w_w = w.norm()**2
batch_orth_x = vectorized_tt_dot_h(orth, train_x)
orth_orth = orth.norm()**2
w_orth = tt.dot(w, orth)
def w_step_objective(w_step):
steps = np.array([w_step, 0])
obj = _regularized_loss_step(steps, loss_h, train_y, batch_w_x,
batch_orth_x, 0, 0, reg, w_w, w_orth,
orth_orth)
return obj
step_w = minimize_scalar(w_step_objective).x
w = (w - step_w * orth).round(eps=0)
return w
def increase_rank(w0, rank, train_x, train_y, vectorized_tt_dot_h,
loss_h, loss_grad_h, project_h, object_tensor_h, reg):
"""Implements the idea from the paper
Riemannian Pursuit for Big Matrix Recovery
That is, to init the tensor with the desired rank, we add orthogonal
component of the current gradient to our current low-rank estimate w0.
"""
w = w0
if rank > max(w.r):
# Choose not too many objects, so that the rank is reasonable.
num_objects_used = rank * 5
w_x = vectorized_tt_dot_h(w, train_x[:num_objects_used, :])
grad_coef = loss_grad_h(w_x, train_y[:num_objects_used])
proj_grad = project_h(w, train_x[:num_objects_used, :], grad_coef, reg=reg)
grad = reg * w
for i in range(0, num_objects_used):
grad = grad + grad_coef[i] * object_tensor_h(train_x[i, :])
orth = grad - proj_grad
orth = orth.round(eps=0, rmax=rank-max(w0.r))
batch_w_x = vectorized_tt_dot_h(w, train_x)
w_w = w.norm()**2
batch_orth_x = vectorized_tt_dot_h(orth, train_x)
orth_orth = orth.norm()**2
w_orth = tt.dot(w, orth)
def w_step_objective(w_step):
steps = np.array([w_step, 0])
obj = _regularized_loss_step(steps, loss_h, train_y,
batch_w_x, batch_orth_x, 0,
0, reg, w_w,
w_orth, orth_orth)
return obj
step_w = minimize_scalar(w_step_objective).x
w = (w - step_w * orth).round(eps=0)
return w
#Generate the initial Gaussian, which is just shifted
gs = np.exp(-0.5*(x-2)**2)
gs = tt.tensor(gs,1e-8)
start = None
for i in xrange(f):
start = tt.kron(start,gs)
radd = 20
start = start+0*tt.rand(start.n,start.d,radd)
y = start.copy()
print 'initial value norm:', start.norm()
cf = []
tf = 150.0
nit = 5000
tau = (tf/nit)
i = 0
t = 0
import time
t1 = time.time()
while t <= tf:
print '%f/%f' % (t,tf)
y = ksl(H,y,tau)
cf.append(tt.dot(y,start))
t += tau
t2 = time.time()
print("Elapsed time: %f" % (t2-t1))
zz = np.abs(fft(np.conj(cf)))
lls = np.arange(zz.size)*pi/(0.5*tf)
def loss(core):
new_w = w.copy()
new_w.core = copy.copy(core)
res = (tt.dot(new_w, x_1))**2 # Quadratic.
res += tt.dot(new_w, x_2) # Linear.
return res
tme_total = datetime.datetime.now() - tme_total
#%% show
print()
print('Total time ', tme_total)
print('Maximum size ', tensor_size * 8 / 1e6, ' MB')
Post = Pt_fwd
Prior = Pt_prior
nburn = 20000
xs_tt = tt_meshgrid([pts1, pts2, pts3, pts4, pts5])
Pt = Pt_fwd
E = np.array([tt.dot(x_tt * Pt, WS) for x_tt in xs_tt])
V = np.array(
[tt.dot(Pt * xs_tt[i], xs_tt[i] * WS) - E[i]**2 for i in range(5)])
import pyswarm
def goal_function(thetuta):
L1 = np.array([lagrange(thetuta[0], i, pts1) for i in range(pts1.size)])
L2 = np.array([lagrange(thetuta[1], i, pts2) for i in range(pts2.size)])
L3 = np.array([lagrange(thetuta[2], i, pts3) for i in range(pts3.size)])
L4 = np.array([lagrange(thetuta[3], i, pts4) for i in range(pts4.size)])
L5 = np.array([lagrange(thetuta[4], i, pts5) for i in range(pts5.size)])
def parallel_worker(task_id,
H,
operators,
guess_generator,
rank,
tau,
n_steps,
callbacks=[],
**kwargs):
"""
Parallel worker for the calculation of the expectation value
of an operator
Parameters:
-----------
H: tt.matrix
hamiltonain matrix in the TT format
operators: iterable of tt.matrix
matrix of the operator in the TT format
guess_generator: function
initial vector generator
rank: int
TT rank of the initial vector
tau: float
time step
n_steps:
number of steps of the dynamics
callbacks: list, default []
list of extra callbacks. The callback has to have a signature
(tt.vector) -> Scalar. The callback will receive the wavefunction,
and the result will be collected. The results of the
callbacks are stored in the matrix along with mean values of
the operators.
Returns:
--------
(time, evs) : (np.array, np.array)
time array and array of expectation values
"""
# np.random.seed(seed)
psi = guess_generator(H, rank)
time = []
evs = []
t = 0
psi = ksl(A=-1j * H, y0=psi, tau=1e-10, **kwargs)
for i in range(n_steps):
ev = []
for operator in operators:
ev.append(tt.dot(tt.matvec(operator, psi), psi))
for func in callbacks:
ev.append(func(psi))
time.append(t)
evs.append(ev)
# update
psi = ksl(A=-1j * H, y0=psi, tau=tau, **kwargs)
t += tau
evs = np.array(evs).real
time = np.array(time)
return time, evs
def GMRES(A,
u_0,
b,
eps=1e-6,
maxit=100,
m=20,
_iteration=0,
callback=None,
verbose=0):
"""
Flexible TT GMRES
:param A: matvec(x[, eps])
:param u_0: initial vector
:param b: answer
:param maxit: max number of iterations
:param eps: required accuracy
:param m: number of iteration without restart
:param _iteration: iteration counter
:param callback:
:param verbose: to print debug info or not
:return: answer, residual
>>> from tt import GMRES
>>> def matvec(x, eps):
>>> return tt.matvec(S, x).round(eps)
>>> answer, res = GMRES(matvec, u_0, b, eps=1e-8)
"""
maxitexceeded = False
converged = False
if verbose:
print('GMRES(m=%d, _iteration=%d, maxit=%d)' % (m, _iteration, maxit))
v = np.ones((m + 1), dtype=object) * np.nan
R = np.ones((m, m)) * np.nan
g = np.zeros(m)
s = np.ones(m) * np.nan
c = np.ones(m) * np.nan
v[0] = b - A(u_0, eps=eps)
v[0] = v[0].round(eps)
resnorm = v[0].norm()
curr_beta = resnorm
bnorm = b.norm()
wlen = resnorm
q = m
for j in range(m):
_iteration += 1
delta = eps / (curr_beta / resnorm)
if verbose:
print("it = %d delta = " % _iteration, delta)
v[j] *= 1.0 / wlen
v[j + 1] = A(v[j], eps=delta)
for i in range(j + 1):
R[i, j] = tt.dot(v[j + 1], v[i])
v[j + 1] = v[j + 1] - R[i, j] * v[i]
v[j + 1] = v[j + 1].round(delta)
wlen = v[j + 1].norm()
for i in range(j):
r1 = R[i, j]
r2 = R[i + 1, j]
R[i, j] = c[i] * r1 - s[i] * r2
R[i + 1, j] = c[i] * r2 + s[i] * r1
denom = np.hypot(wlen, R[j, j])
s[j] = wlen / denom
c[j] = -R[j, j] / denom
R[j, j] = -denom
g[j] = c[j] * curr_beta
curr_beta *= s[j]
if verbose:
print("it = {}, ||r|| = {}".format(_iteration, curr_beta / bnorm))
converged = (curr_beta / bnorm) < eps or (curr_beta / resnorm) < eps
maxitexceeded = _iteration >= maxit
if converged or maxitexceeded:
q = j + 1
break
y = la.solve_triangular(R[:q, :q], g[:q], check_finite=False)
for idx in range(q):
u_0 += v[idx] * y[idx]
u_0 = u_0.round(eps)
if callback is not None:
callback(u_0)
if converged or maxitexceeded:
return u_0, resnorm / bnorm
return GMRES(A,
u_0,
b,
eps,
maxit,
m,
_iteration,
callback=callback,
verbose=verbose)
from __future__ import print_function, absolute_import, division
import sys
sys.path.append('../')
import numpy as np
import tt
d = 30
n = 2**d
b = 1E3
h = b / (n + 1)
#x = np.arange(n)
#x = np.reshape(x, [2] * d, order = 'F')
#x = tt.tensor(x, 1e-12)
x = tt.xfun(2, d)
e = tt.ones(2, d)
x = x + e
x = x * h
sf = lambda x: np.sin(x) / x #Should be rank 2
y = tt.multifuncrs([x], sf, 1e-6, y0=tt.ones(2, d))
#y1 = tt.tensor(sf(x.full()), 1e-8)
print("pi / 2 ~ ", tt.dot(y, tt.ones(2, d)) * h)
#print (y - y1).norm() / y.norm()
def collect_ev_sequential(H,
operators,
guess_generator,
rank,
n_samples,
tau,
n_steps,
filename=None,
append_file=True,
dump_every=0,
callbacks=[],
**kwargs):
"""
Generate the expectation value of a provided operator
in the dynamical process generated by the hamiltonian H.
The dynamics starts from the initial vector, which is
generated by the guess_generator
Parameters:
-----------
H: tt.matrix
hamiltonain matrix in the TT format
operators: iterable of tt.matrix or tt.matrix
matrices of the operators in the TT format
guess_generator: function
initial vector generator
rank: int
TT rank of the initial vector
n_samples: int
number of sample trajectories
tau: float
time step
n_steps:
number of steps of the dynamics
filename: str, default None
filename to output results. The file is appended if exists
append_file: bool, default True
if we append to the existing file instead of replacing it
dump_every: int, default 0
dump current results every n parallel rounds. Default is 0.
callbacks: list, default []
list of extra callbacks. The callback has to have a signature
(tt.vector) -> Scalar. The callback will receive the wavefunction,
and the result will be collected. The results of the
callbacks are stored in the matrix along with mean values of
the operators.
Returns:
--------
(time, evs) : (np.array, np.array)
time array and array of expectation values
"""
# ensure that operators is iterable
if not isinstance(operators, Iterable):
operators = [operators]
evs_all_l = []
for s in tqdm(range(n_samples),
desc="guess={}, n_steps={}".format(guess_generator.__name__,
n_steps)):
# np.random.seed(s)
psi = guess_generator(H, rank)
time_l = []
evs = []
t = 0
psi = ksl(A=-1j * H, y0=psi, tau=1e-10, **kwargs)
for i in range(n_steps):
ev = []
for operator in operators:
ev.append(tt.dot(tt.matvec(operator, psi), psi))
for func in callbacks:
ev.append(func(psi))
time_l.append(t)
evs.append(ev)
# update
psi = ksl(A=-1j * H, y0=psi, tau=tau, **kwargs)
t += tau
evs_all_l.append(evs)
if ((dump_every > 0) and (s // dump_every == 0) and (s != 0)
and (filename is not None)):
# time to dump results
evs_all = np.array(evs_all_l).real
time = np.array(time_l)
if (s == dump_every) and (not os.path.isfile(filename)
or not append_file):
# rewrite old file with the first batch
np.savez(filename, t=time, evs=evs_all)
else:
time_old = np.load(filename)['t']
evs_old = np.load(filename)['evs']
assert (np.allclose(time_old, time))
evs_updated = np.vstack((evs_old, evs_all))
np.savez(filename, t=time, evs=evs_updated)
evs_all = np.array(evs_all_l).real
time = np.array(time_l)
if filename is not None:
if not os.path.isfile(filename) or not append_file:
np.savez(filename, t=time, evs=evs_all)
else:
time_old = np.load(filename)['t']
evs_old = np.load(filename)['evs']
assert (np.allclose(time_old, time))
evs_updated = np.vstack((evs_old, evs_all))
np.savez(filename, t=time, evs=evs_updated)
return time, evs_all
from __future__ import print_function, absolute_import, division
import sys
sys.path.append('../')
import numpy as np
import tt
d = 30
n = 2 ** d
b = 1E3
h = b / (n + 1)
#x = np.arange(n)
#x = np.reshape(x, [2] * d, order = 'F')
#x = tt.tensor(x, 1e-12)
x = tt.xfun(2, d)
e = tt.ones(2, d)
x = x + e
x = x * h
sf = lambda x : np.sin(x) / x #Should be rank 2
y = tt.multifuncrs([x], sf, 1e-6, y0=tt.ones(2, d))
#y1 = tt.tensor(sf(x.full()), 1e-8)
print("pi / 2 ~ ", tt.dot(y, tt.ones(2, d)) * h)
#print (y - y1).norm() / y.norm()
tme = datetime.datetime.now() - tme
print('',flush=True)
print('\tmax rank ',max(P.r))
Ppred = P
Ppost = PO * Ppred
Ppost = Ppost.round(1e-10)
print('\tmax rank (after observation) ',max(Ppost.r))
if tensor_size<tt_size(Ppost): tensor_size = tt_size(Ppost)
if not qtt:
# Ppost = Ppost * (1/tt.sum(Ppost * tt.kron(tt.ones(N),WS)))
Pt = tt.sum(tt.sum(tt.sum(tt.sum(Ppost,0),0),0),0)
Z = tt.dot(Pt,WS)
Pt = Pt * (1/Z)
Pt = Pt.round(1e-10)
else:
# Ppost = Ppost * (1/tt.sum(Ppost * tt.kron(tt.ones(int(np.sum(np.log2(N)))*[2]),ws_qtt)))
Pt = Ppost
for i in range(int(np.sum(np.log2(N)))): Pt = tt.sum(Pt,0)
Z = tt.dot(Pt,ws_qtt)
Pt = Pt * (1/Z)
Pt = Pt.round(1e-10)
Ppost = Ppost*(1/tt.sum(Ppost))
def loss(core):
new_w = w.copy()
new_w.core = copy.copy(core)
res = (tt.dot(new_w, x_1))**2 # Quadratic.
res += tt.dot(new_w, x_2) # Linear.
return res
for j in xrange(d):
if j % 2:
e0 = v0#np.random.rand(2)
else:
e0 = v1#e0 = tt.tensor(e0,1e-12)
e1 = tt.kron(e1,e0)
r = [1]*(d+1)
r[0] = 1
r[d] = 1
x0 = tt.rand(n,d,r)
tau = 1e-2
tf = 100
t = 0
start = e1
psi = start + 0 * x0
psi1 = start + 0 * x0
cf = []
while t <= tf:
print '%f/%f' % (t,tf)
psi = ksl(-1.0j*A,psi,tau)
#psi1 = kls(-1.0j*A,psi1,tau)
cf.append(tt.dot(psi,start))
#import ipdb; ipdb.set_trace()
t += tau
#x0 = tt.rand(n,d,r)
#t1 = time.time()
#print 'Matrices are done'
#y, lam = eigb(A,x0,1e-3)
#lm.append(d)
#t2 = time.time()
def riemannian_sgd(train_x, train_y, vectorized_tt_dot_h, loss_h,
loss_grad_h, project_h, w0, intercept0=0,
fit_intercept=True, val_x=None, val_y=None,
reg=0., exp_reg=1., dropout=None, batch_size=-1,
num_passes=30, seed=None, logger=None, verbose_period=1,
debug=False, beta=0.5, rho=0.1):
"""Riemannian SGD method optimization for a linear model with weights in TT.
The objective function is
reg <w, w> + \sum_i f(d(w, x_i) + b, y_i)
* where f(o, y) is the loss w.r.t. one object (this function is from R^2 to R);
* d(w, x_i) is the dot product between the tensor w and the tensor build
from the vector x_i.
"""
num_objects, num_features = train_x.shape
is_val_set_provided = False
if val_x is not None and val_y is not None:
is_val_set_provided = True
if seed is not None:
np.random.seed(seed)
if batch_size == -1:
# Full gradient learning.
batch_size = num_objects
# TODO: correctly process the last batch.
num_batches = num_objects // batch_size
w = w0
b = intercept0
# TODO: start not from zero in case we are resuming the learning.
start_epoch = 0
if logger is not None:
logger.before_first_iter(train_x, train_y, w, lambda w, x: vectorized_tt_dot_h(w, x) + b, num_passes, num_objects)
reg_tens = build_reg_tens(w.n, exp_reg)
for e in xrange(start_epoch, num_passes):
idx_perm = np.random.permutation(num_objects)
for batch_idx in xrange(num_batches):
start = batch_idx * batch_size
end = (batch_idx + 1) * batch_size
curr_idx = idx_perm[start:end]
curr_batch = train_x[curr_idx, :]
if dropout is not None:
dropout_mask = np.random.binomial(1, dropout,
size=curr_batch.shape)
# To make the expected value of <W, dropout(X)> equals to <W, X>.
dropout_mask = dropout_mask / dropout
curr_batch = dropout_mask * curr_batch
batch_y = train_y[curr_idx]
batch_w_x = vectorized_tt_dot_h(w, curr_batch)
batch_linear_o = batch_w_x + b
batch_loss_arr = loss_h(batch_linear_o, batch_y)
wreg = w * reg_tens
wregreg = w * reg_tens * reg_tens
wreg_wreg = wreg.norm()**2
batch_loss = np.sum(batch_loss_arr) + reg * wreg_wreg / 2.0
batch_grad_coef = loss_grad_h(batch_linear_o, batch_y)
batch_gradient_b = np.sum(batch_grad_coef)
direction = project_h(w, curr_batch, batch_grad_coef, reg=0)
direction = riemannian.project(w, [direction, reg * wregreg])
batch_dir_x = vectorized_tt_dot_h(direction, curr_batch)
dir_dir = direction.norm()**2
wreg_dir = tt.dot(wreg, direction)
if fit_intercept:
# TODO: Use classical Newton-Raphson (with hessian).
step_objective = lambda s: _regularized_loss_step(s, loss_h, batch_y, batch_w_x, batch_dir_x, b, batch_gradient_b, reg, wreg_wreg, wreg_dir, dir_dir)
step_gradient = lambda s: _regularized_loss_step_grad(s, loss_grad_h, batch_y, batch_w_x, batch_dir_x, b, batch_gradient_b, reg, wreg_dir, dir_dir)
step0_w, step0_b = fmin_bfgs(step_objective, np.ones(2), fprime=step_gradient, gtol=1e-10, disp=logger.disp())
else:
def w_step_objective(w_step):
steps = np.array([w_step, 0])
obj = _regularized_loss_step(steps, loss_h, batch_y,
batch_w_x, batch_dir_x, b,
batch_gradient_b, reg, wreg_wreg,
wreg_dir, dir_dir)
return obj
step0_w = minimize_scalar(w_step_objective).x
# TODO: consider using Probabilistic Line Searches for Stochastic Optimization.
# Armiho step choosing.
step_w = step0_w
# <gradient, direction> =
# = <(\sum_i coef[i] * x_i + reg * w), direction> =
# = \sum_i coef[i] <x_i, direction> + reg * <w, direction>
grad_times_direction = batch_dir_x.dot(batch_grad_coef) + reg * wreg_dir
while step_w > 1e-10:
new_w = (w - step_w * direction).round(eps=0, rmax=max(w.r))
new_w_x = vectorized_tt_dot_h(new_w, curr_batch)
if fit_intercept:
b_objective = lambda b: np.sum(loss_h(new_w_x + b, batch_y))
m = minimize_scalar(b_objective)
b = m.x
new_loss = m.fun
else:
new_loss = np.sum(loss_h(new_w_x + b, batch_y))
new_wreg = new_w * reg_tens
new_loss += reg * new_wreg.norm()**2 / 2.0
if new_loss <= batch_loss - rho * step_w * grad_times_direction:
break
step_w *= beta
w = new_w
if (logger is not None) and e % verbose_period == 0:
logger.after_each_iter(e, train_x, train_y, w, lambda w, x: vectorized_tt_dot_h(w, x) + b, stage='train')
if is_val_set_provided:
logger.after_each_iter(e, val_x, val_y, w, lambda w, x: vectorized_tt_dot_h(w, x) + b, stage='valid')
return w, b
if qtt:
x1 = tt2qtt(x1)
x2 = tt2qtt(x2)
x3 = tt2qtt(x3)
x4 = tt2qtt(x4)
for i in range(len(P_bck)):
print(i)
Pf = P_fwd[i]
Pb = P_bck[i]
# if qtt:
# Pf = qtt2tt(Pf,N)
# Pb = qtt2tt(Pb,N)
Z = tt.dot(Pf, Pb)
mean = [
tt.dot(Pf, Pb * x1) / Z,
tt.dot(Pf, Pb * x2) / Z,
tt.dot(Pf, Pb * x3) / Z,
tt.dot(Pf, Pb * x4) / Z
]
var = [
tt.dot(Pf * x1, Pb * x1) / Z - mean[0]**2,
tt.dot(Pf * x2, Pb * x2) / Z - mean[1]**2,
tt.dot(Pf * x3, Pb * x3) / Z - mean[2]**2,
tt.dot(Pf * x4, Pb * x4) / Z - mean[3]**2
]
Es.append(mean)
def riemannian_sgd(train_x,
train_y,
vectorized_tt_dot_h,
loss_h,
loss_grad_h,
project_h,
w0,
intercept0=0,
fit_intercept=True,
val_x=None,
val_y=None,
reg=0.,
exp_reg=1.,
dropout=None,
batch_size=-1,
num_passes=30,
seed=None,
logger=None,
verbose_period=1,
debug=False,
beta=0.5,
rho=0.1):
"""Riemannian SGD method optimization for a linear model with weights in TT.
The objective function is
reg <w, w> + \sum_i f(d(w, x_i) + b, y_i)
* where f(o, y) is the loss w.r.t. one object (this function is from R^2 to R);
* d(w, x_i) is the dot product between the tensor w and the tensor build
from the vector x_i.
"""
num_objects, num_features = train_x.shape
is_val_set_provided = False
if val_x is not None and val_y is not None:
is_val_set_provided = True
if seed is not None:
np.random.seed(seed)
if batch_size == -1:
# Full gradient learning.
batch_size = num_objects
# TODO: correctly process the last batch.
num_batches = num_objects // batch_size
w = w0
b = intercept0
# TODO: start not from zero in case we are resuming the learning.
start_epoch = 0
if logger is not None:
logger.before_first_iter(train_x, train_y, w,
lambda w, x: vectorized_tt_dot_h(w, x) + b,
num_passes, num_objects)
reg_tens = build_reg_tens(w.n, exp_reg)
for e in xrange(start_epoch, num_passes):
idx_perm = np.random.permutation(num_objects)
for batch_idx in xrange(num_batches):
start = batch_idx * batch_size
end = (batch_idx + 1) * batch_size
curr_idx = idx_perm[start:end]
curr_batch = train_x[curr_idx, :]
if dropout is not None:
dropout_mask = np.random.binomial(1,
dropout,
size=curr_batch.shape)
# To make the expected value of <W, dropout(X)> equals to <W, X>.
dropout_mask = dropout_mask / dropout
curr_batch = dropout_mask * curr_batch
batch_y = train_y[curr_idx]
batch_w_x = vectorized_tt_dot_h(w, curr_batch)
batch_linear_o = batch_w_x + b
batch_loss_arr = loss_h(batch_linear_o, batch_y)
wreg = w * reg_tens
wregreg = w * reg_tens * reg_tens
wreg_wreg = wreg.norm()**2
batch_loss = np.sum(batch_loss_arr) + reg * wreg_wreg / 2.0
batch_grad_coef = loss_grad_h(batch_linear_o, batch_y)
batch_gradient_b = np.sum(batch_grad_coef)
direction = project_h(w, curr_batch, batch_grad_coef, reg=0)
direction = riemannian.project(w, [direction, reg * wregreg])
batch_dir_x = vectorized_tt_dot_h(direction, curr_batch)
dir_dir = direction.norm()**2
wreg_dir = tt.dot(wreg, direction)
if fit_intercept:
# TODO: Use classical Newton-Raphson (with hessian).
step_objective = lambda s: _regularized_loss_step(
s, loss_h, batch_y, batch_w_x, batch_dir_x, b,
batch_gradient_b, reg, wreg_wreg, wreg_dir, dir_dir)
step_gradient = lambda s: _regularized_loss_step_grad(
s, loss_grad_h, batch_y, batch_w_x, batch_dir_x, b,
batch_gradient_b, reg, wreg_dir, dir_dir)
step0_w, step0_b = fmin_bfgs(step_objective,
np.ones(2),
fprime=step_gradient,
gtol=1e-10,
disp=logger.disp())
else:
def w_step_objective(w_step):
steps = np.array([w_step, 0])
obj = _regularized_loss_step(steps, loss_h, batch_y,
batch_w_x, batch_dir_x, b,
batch_gradient_b, reg,
wreg_wreg, wreg_dir, dir_dir)
return obj
step0_w = minimize_scalar(w_step_objective).x
# TODO: consider using Probabilistic Line Searches for Stochastic Optimization.
# Armiho step choosing.
step_w = step0_w
# <gradient, direction> =
# = <(\sum_i coef[i] * x_i + reg * w), direction> =
# = \sum_i coef[i] <x_i, direction> + reg * <w, direction>
grad_times_direction = batch_dir_x.dot(
batch_grad_coef) + reg * wreg_dir
while step_w > 1e-10:
new_w = (w - step_w * direction).round(eps=0, rmax=max(w.r))
new_w_x = vectorized_tt_dot_h(new_w, curr_batch)
if fit_intercept:
b_objective = lambda b: np.sum(loss_h(
new_w_x + b, batch_y))
m = minimize_scalar(b_objective)
b = m.x
new_loss = m.fun
else:
new_loss = np.sum(loss_h(new_w_x + b, batch_y))
new_wreg = new_w * reg_tens
new_loss += reg * new_wreg.norm()**2 / 2.0
if new_loss <= batch_loss - rho * step_w * grad_times_direction:
break
step_w *= beta
w = new_w
if (logger is not None) and e % verbose_period == 0:
logger.after_each_iter(e,
train_x,
train_y,
w,
lambda w, x: vectorized_tt_dot_h(w, x) + b,
stage='train')
if is_val_set_provided:
logger.after_each_iter(
e,
val_x,
val_y,
w,
lambda w, x: vectorized_tt_dot_h(w, x) + b,
stage='valid')
return w, b
