n2.init_lt(state)
n1.init_lt(state)
flips = np.array(np.random.choice(np.arange(n1.nv), 2))
lv_match = np.all(np.isclose(n1.log_val(state), n2.log_val(state)))
lp_match = np.all(np.isclose(n1.log_pop(state, flips), n2.log_pop(state, flips)))
print("Log_val matches: {}".format(lv_match))
print("Log_pop matches: {}".format(lp_match))
if not (lv_match and lp_match):
exit()
nruns = 1000
h = ising1d.Ising1d(40, 0.5)
print("Sampling n1 ...")
start_time = time.time()
s1 = sampler.Sampler(n1, h)
s1.run(nruns)
print("time elapsed: {:.2f}s".format(time.time() - start_time))
print("Sampling n2 ...")
start_time = time.time()
s2 = sampler.Sampler(n2, h)
s2.run(nruns)
print("time elapsed: {:.2f}s".format(time.time() - start_time))
alpha = 2
m = True
wf = nqs.NqsTI(nspins, alpha) # A translation invariant NQS instance
wf.Wreduced = 0.1 * np.random.uniform(
-1, 1, wf.Wreduced.shape) + 0j # Fill in with starting values
if type(wf.a) == int:
wf.a = 0.1 * np.random.uniform(-1, 1) + 0j
else:
wf.a = 0.1 * np.random.uniform(-1, 1) * np.ones(wf.a.shape) + 0j
wf.breduced = 0.1 * np.random.uniform(-1, 1, wf.breduced.shape) + 0j
#wf.load_parameters('../Outputs/'+str(nspins)+'_alpha='+str(alpha)+'_Ising10_ti_100.npz')
h = ising1d.Ising1d(nspins, 1.0)
#h = heisenberg1d.Heisenberg1d(10,1)
#h = xyz.XYZ(10,(-1,-1,0))
#h = fermionhop1d.FermionHop(nspins,-2)
base_array = -1 * np.ones(nspins)
base_array[0:nspins // 2] *= -1
state = np.random.permutation(
base_array) # return a random permutation of the half 1, half-1 array
wf.init_lt(state)
s = sampler.Sampler(wf, h, mag0=m)
s.run(nruns, init_state=state)
state = s.state
wf.init_lt(state)
import nqs
import numpy as np
import sampler
import observables
import time
import matplotlib.pyplot as plt
import distances
import ising1d
#Script to generate the data -- sigmax(t) for all our time-evolved wave functions
nsteps = 400
data_rate = 5
nruns = 1000
talk_rate = 25
h = ising1d.Ising1d(10, 1.0)
## Now that we have all the wavefunctions generated, find the <sigma(x)> at each one
#Fully connected translation-invariant
print("Fully connected ANNQS")
sxnonloc = []
nonlocerr = []
wf = nqs.NqsTI(10, 1)
start_time = time.time()
for t in np.arange(0, nsteps, data_rate):
if t % talk_rate == 0:
print('t = {}'.format(t))
wf.load_parameters('../Outputs/10SpinEvolve/evolution_ti_' + str(t) +
'.npz')
s = sampler.Sampler(wf,
observables.Sigmax(10, 1),
import ising1d
import fermionhop1d
import nqs
import trainer
import sampler
import numpy as np
import matplotlib.pyplot as plt
import time
start = time.time()
nruns = 1000
k = 2
gam = .01
nspins = 10
#h = heisenberg1d.Heisenberg1d(nspins,1)
h = ising1d.Ising1d(10, 0.5)
#h = fermionhop1d.FermionHop(nspins,1)
wf = nqs.NqsLocal(nspins, 1, k) # A local NQS instance
wf.W = 0.1 * np.random.random(wf.W.shape) + 0j # Fill in with starting values
wf.a = 0.1 * np.random.random(wf.a.shape) + 0j
wf.b = 0.1 * np.random.random(wf.b.shape) + 0j
base_array = np.concatenate(
(np.ones(int(1)),
-1 * np.ones(int(nspins - 1)))) # make an array of half 1, half -1
state = np.random.permutation(
base_array) # return a random permutation of the half 1, half-1 array
wf.init_lt(state)
