def __sqa(self, v_0, params):
h0_sampled, h0_prob = self.__forward(v_0)
sol = sq.cpu
if sq.is_cuda_available():
import sqaod.cuda
sol = sqaod.cuda
ann = sol.bipartite_graph_annealer()
ann.seed(13255)
ann.set_qubo(self.b, self.c, self.W.T, sq.maximize)
ann.set_preferences(n_trotters=params.get("trotter", 10))
ann.prepare()
ann.randomize_spin()
Ginit = params.get("Ginit", 5.)
Gfin = params.get("Gfin", 0.001)
beta = params.get("beta", 50)
tau = params.get("tau", 0.99)
v_sampled = np.zeros(self.n_visible)
h_sampled = np.zeros(self.n_hidden)
n_sample = params.get("n_sample", 1)
for _ in range(n_sample):
G = Ginit
while Gfin <= G:
ann.anneal_one_step(G, beta)
G *= tau
xlist = ann.get_x()
best_index = np.argmax(ann.get_E())
v_sampled += xlist[best_index][0]
h_sampled += xlist[best_index][1]
v_sampled = v_sampled / n_sample
h_sampled = h_sampled / n_sample
return v_0.mean(axis=0), h0_prob.mean(axis=0), v_sampled, h_sampled
def solver(arr): W = arr output = [] sol = sq.cpu if sq.is_cuda_available() : import sqaod.cuda sol = sqaod.cuda ann = sol.dense_graph_annealer() ann.seed(1) ann.set_qubo(W, sq.minimize) ann.set_preferences(n_trotters = W.shape[0]) h, J, c = ann.get_hamiltonian() output.append(h); output.append(J); output.append(c) ann.prepare() ann.randomize_spin() Ginit = 5. Gfin = 0.01 beta = 1. / 0.02 tau = 0.99 G = Ginit while Gfin <= G : ann.anneal_one_step(G, beta) G *= tau E = ann.get_E() q = ann.get_q() x = ann.get_x() summary = sq.make_summary(ann) out_summary = [] nToShow = min(len(summary.xlist), 4) for idx in range(nToShow) : out_summary.append(summary.xlist[idx]) output.append(out_summary) return output, out_summary
from __future__ import print_function
import sqaod
import numpy as np
if sqaod.is_cuda_available():
import sqaod.cuda
else:
print('cuda not available')
def output(searcher):
summary = sqaod.make_summary(searcher)
print('E {}'.format(summary.E))
print('Number of solutions : {}'.format(len(summary.xlist)))
for x in summary.xlist:
print(x)
def anneal(ann):
print(ann.__class__)
Ginit = 5.
Gfin = 0.01
nRepeat = 1
beta = 1. / 0.02
tau = 0.99
for loop in range(0, nRepeat):
sqaod.anneal(ann, Ginit, Gfin, beta)
output(ann)
import numpy as np
import sqaod as sq
N = 8
# 1. set problem. As an example, a matrix filled with 1. is used.
W = np.ones((N, N))
# 2. choosing solver .
sol = sq.cpu # use CPU searchealer
# If you want to use CUDA, check CUDA availability with sq.is_cuda_available().
if sq.is_cuda_available() :
import sqaod.cuda
sol = sqaod.cuda
# 3. instanciate solver
search = sol.dense_graph_bf_searcher()
# 4. Setting problem
# Setting W and optimize direction (minimize or maxminize)
# n_trotters is implicitly set to N/4 by dfault.
search.set_qubo(W, sq.maximize)
# 5. (optional) set preferences,
# Set tile_size. Typical not required.
search.set_preferences(tile_size = W.shape[0])
# altternative for 4. and 5.
# W, optimize direction and tile_size are able to be set on instantiation.