def save_vec(vec):
fname = 'data/peps_vec1'
np.save(fname, vec)
e = bound_energy_fn(vec)
elst.append(e)
print ' --- save vec into fname=', fname, ' e=', e
return 0
def test_save():
print '\n[test_gen]'
shape = (nr, nc)
# simple peps
peps0 = peps.random(shape, pdim, bond, fac=rfac)
vec = peps.flatten(peps0)
np.save('data/peps_vec3by3', vec)
dumpVcoeff.dump(peps0)
return 0
def test_load():
print '[test_load]'
shape = (nr, nc)
# simple peps
vec = np.load('data/peps_vec2by2.npy')
peps0 = peps.aspeps(vec, (nr, nc), pdim, bond)
ovlp, etot = energy_fpeps2by2(peps0)
ovlp, php = nAverage_fpeps2by2(peps0)
print 'etot=', ovlp, etot, etot / ovlp
print 'nAverage=', ovlp, php, php / ovlp
exit()
def energy_fn(vec, pdim, bond):
P = peps.aspeps(vec, (nr, nc), pdim, bond)
PP, PHP = energy_fpeps2by2(P)
e = PHP / PP
print ' PHP,PP,PHP/PP,eav=', PHP, PP, e, e / (nr * nc)
return PHP / PP
def bound_energy_fn(vec):
return energy_fn(vec, pdim, bond)
vec = peps.flatten(peps0)
import scipy.optimize
import autograd
deriv = autograd.grad(bound_energy_fn)
print 'nparams=', len(vec)
elst = []
def save_vec(vec):
fname = 'data/peps_vec1'
np.save(fname, vec)
e = bound_energy_fn(vec)
elst.append(e)
print ' --- save vec into fname=', fname, ' e=', e
return 0
# Optimize
result = scipy.optimize.minimize(bound_energy_fn, jac=deriv, x0=vec,\
tol=1.e-4, callback=save_vec)
P0 = peps.aspeps(result.x, (nr, nc), pdim, bond)
print "final =", energy_fn(peps.flatten(P0), pdim, bond)
np.save('data/energy', elst)
return 0
def dump(peps0):
shape = peps0.shape
if shape == (2, 2):
vcoeff = dump2by2(peps0)
np.save('data/vcoeff2by2', vcoeff)
elif shape == (2, 3):
vcoeff = dump2by3(peps0)
np.save('data/vcoeff2by3', vcoeff)
elif shape == (3, 3):
vcoeff = dump3by3(peps0)
np.save('data/vcoeff3by3', vcoeff)
print '\n[dumpVcoeff.dump] shape=', shape
print 'shape=', vcoeff.shape
print 'vcoeff<P|P>=', vcoeff.dot(vcoeff)
print '<P|P>=', vcoeff.dot(vcoeff)
return 0
def save_vec(vec):
fname = 'peps_vec1_3by3'
np.save(fname, vec)
print ' --- save vec into fname=', fname
return 0