def save_coef_file(self, hq, Jq, fname):
''' '''
try:
fp = open(fname, 'w')
except:
print('Failed to open file: {0}'.format(fname))
raise IOError
# chimera size
M, N, L = self.chimera_widget.M, self.chimera_widget.N, 4
Nqbits = 2*M*N*L
fp.write('{0}\n'.format(Nqbits))
# h parameters
for qb in sorted(hq):
qbl = tuple_to_linear(qb, M, N, L=L, index0=False)
if hq[qb] != 0:
fp.write('{0} {1} {2}\n'.format(qbl, qbl, round(hq[qb],3)))
# J parameters
for qb1, qb2 in sorted(Jq):
qbl1 = tuple_to_linear(qb1, M, N, L=L, index0=False)
qbl2 = tuple_to_linear(qb2, M, N, L=L, index0=False)
if Jq[(qb1, qb2)] != 0:
fp.write('{0} {1} {2}\n'.format(qbl1, qbl2,
round(Jq[(qb1, qb2)], 3)))
fp.close()
def save_coef_file(self, hq, Jq, fname):
''' '''
try:
fp = open(fname, 'w')
except:
print('Failed to open file: {0}'.format(fname))
raise IOError
# chimera size
M, N, L = self.chimera_widget.M, self.chimera_widget.N, 4
Nqbits = 2 * M * N * L
fp.write('{0}\n'.format(Nqbits))
# h parameters
for qb in sorted(hq):
qbl = tuple_to_linear(qb, M, N, L=L, index0=False)
if hq[qb] != 0:
fp.write('{0} {1} {2}\n'.format(qbl, qbl, round(hq[qb], 3)))
# J parameters
for qb1, qb2 in sorted(Jq):
qbl1 = tuple_to_linear(qb1, M, N, L=L, index0=False)
qbl2 = tuple_to_linear(qb2, M, N, L=L, index0=False)
if Jq[(qb1, qb2)] != 0:
fp.write('{0} {1} {2}\n'.format(qbl1, qbl2,
round(Jq[(qb1, qb2)], 3)))
fp.close()
def run_heur_embedding(self, full_adj=True):
'''Setup and run the Heuristic algorithm'''
# update embedding type in case direct call
self.use_dense = False
active_cells, qca_adj = self.get_reduced_qca_adj()
S_size = len(qca_adj)
A_size = len(self.chimera_adj)
# construct S
S = {}
for i in range(S_size):
c1 = active_cells[i]
for j in range(S_size):
c2 = active_cells[j]
v = 1 if c2 in qca_adj[c1] else 0
S[(i, j)] = v
S[(j, i)] = v
# construct A
A = set()
for qb1 in self.chimera_adj:
for qb2 in self.chimera_adj[qb1]:
l1 = tuple_to_linear(qb1,
self.M,
self.N,
L=self.L,
index0=True)
l2 = tuple_to_linear(qb2,
self.M,
self.N,
L=self.L,
index0=True)
A.add((l1, l2))
try:
print 'Running heuristic embedding'
models = find_embedding(S, S_size, A, A_size)
except Exception as e:
print(e.message())
print 'Embedding finished'
self.good = len(models) == S_size
# map models to standard format
mapper = lambda ind: linear_to_tuple(
ind, self.M, self.N, L=self.L, index0=True)
self.models = {
active_cells[i]: [mapper(c) for c in models[i]]
for i in xrange(S_size)
}
def run_heur_embedding(self, full_adj=True):
'''Setup and run the Heuristic algorithm'''
# update embedding type in case direct call
self.embed_method = 'heur'
active_cells, qca_adj = self.get_reduced_qca_adj()
S_size = len(qca_adj)
A_size = len(self.chimera_adj)
# construct S, the problem adjacency edge list
S = set()
smap = {c: i for i, c in enumerate(active_cells)}
for c1, adj in qca_adj.items():
for c2 in adj:
if c1 < c2:
S.add((smap[c1], smap[c2]))
# construct A, the chimera adjacency edge list
A = set()
for qb1 in self.chimera_adj:
for qb2 in self.chimera_adj[qb1]:
l1 = tuple_to_linear(qb1,
self.M,
self.N,
L=self.L,
index0=True)
l2 = tuple_to_linear(qb2,
self.M,
self.N,
L=self.L,
index0=True)
A.add((l1, l2))
try:
print 'Running heuristic embedding'
#models = find_embedding(S, S_size, A, A_size)
models = find_embedding(S, A)
except Exception as e:
print(e.message())
print 'Embedding finished'
self.good = len(models) == S_size
# map models to standard format
mapper = lambda ind: linear_to_tuple(
ind, self.M, self.N, L=self.L, index0=True)
self.models = {
active_cells[i]: [mapper(c) for c in model]
for i, model in enumerate(models)
}
def process_solution(self, fname, coef_data, pol_data, embeddings):
'''Process a single solution file. It is assumed that the solution
corresponds to the configurations set by the pol_data and embeddings.
The filename should be of format [name][pind]_[rt]us.json, where pind
and rt are integers. If there is only one element of pol_data there is
no pind.'''
fprint(os.path.basename(fname))
NP = len(pol_data) # number of possible polarization comfigurations
print(NP)
pind = None
if NP == 1:
fn_regex = re.compile('.*[a-zA-Z_]+_[0-9]+us\.json')
val_regex = re.compile('_[0-9]+us')
else:
fn_regex = re.compile('.*[0-{0}]+_[0-9]+us\.json'.format(NP))
val_regex = re.compile('[0-{0}]+_[0-9]+us'.format(NP))
if not fn_regex.match(fname):
try:
fn_regex = re.compile('.*_[0-9]+us\.json')
assert fn_regex.match(fname)
val_regex = re.compile('_[0-9]+us')
pind = 0
except:
print('Given filename does not match the required pattern: {0}...'.format(fname))
return
# extract pind and rt
val_str = val_regex.findall(fname)[-1] # will work if this far
vals = [int(x) for x in re.findall('[0-9]+', val_str)]
if pind is None:
pind = 0 if NP==1 else vals[0]
rt = vals[-1]
# print('{0}: pind={1}, rt={2}'.format(fname, pind, rt))
# get solution object
try:
solution = DWAVE_Sol(fname)
sol_name = os.path.basename(fname)
except IOError:
return
h = coef_data[pind]['h']
J = coef_data[pind]['J']
efunc = lambda s: compute_E(h, J, s)
for key in embeddings:
embed = embeddings[key]
pols = pol_data[pind][key]
qbits = list(reduce(lambda x,y:x+y, embed['models'].values()))
qbits = [tuple_to_linear(qb, M=12, N=12, L=4, index0=False) for qb in qbits]
sol = solution.get_reduced_solution(qbits, efunc)
self.save_subsol(embed, pols, sol, h, J, rt, pind, sol_name)
def run_heur_embedding(self, full_adj=True):
'''Setup and run the Heuristic algorithm'''
# update embedding type in case direct call
self.use_dense = False
active_cells, qca_adj = self.get_reduced_qca_adj()
S_size = len(qca_adj)
A_size = len(self.chimera_adj)
# construct S
S = {}
for i in range(S_size):
c1 = active_cells[i]
for j in range(S_size):
c2 = active_cells[j]
v = 1 if c2 in qca_adj[c1] else 0
S[(i, j)] = v
S[(j, i)] = v
# construct A
A = set()
for qb1 in self.chimera_adj:
for qb2 in self.chimera_adj[qb1]:
l1 = tuple_to_linear(qb1, self.M, self.N, L=self.L, index0=True)
l2 = tuple_to_linear(qb2, self.M, self.N, L=self.L, index0=True)
A.add((l1, l2))
try:
print 'Running heuristic embedding'
models = find_embedding(S, S_size, A, A_size)
except Exception as e:
print(e.message())
print 'Embedding finished'
self.good = len(models) == S_size
# map models to standard format
mapper = lambda ind: linear_to_tuple(ind, self.M, self.N,
L=self.L, index0=True)
self.models = {active_cells[i]: [mapper(c) for c in models[i]]
for i in xrange(S_size)}
def run_processing(self):
''' '''
sol_fname = os.path.normpath(str(self.i1.text()))
coef_fname = os.path.normpath(str(self.i2.text()))
summ_fname = os.path.normpath(str(self.i3.text()))
# confirm fname format
if not re.match('.+[.]json', sol_fname):
print('Invalid filename format...')
return
if not coef_fname:
print('Missing coef file')
return
try:
sol = DWAVE_Sol(sol_fname)
except IOError:
print('Failed to read solution file')
return
try:
# load coef file
h, J = self.load_coef_file(coef_fname)
efunc = lambda s: compute_E(h, J, s)
except:
print('Invalid coef file given...')
return
try:
# load summary file
embeds = self.process_summ_file(summ_fname)
subsols = {}
params = {}
for k, embed in embeds.items():
qca_file = os.path.basename(embed['qca_file'])
if not qca_file == EMBED_FILTER:
continue
qbits = list(
reduce(lambda x, y: x + y, embed['models'].values()))
qbits = [
tuple_to_linear(qb, M=12, N=12, L=4, index0=False)
for qb in qbits
]
subsols[k] = sol.get_reduced_solution(qbits, efunc)
h_, J_ = reduce_coefs(h, J, qbits)
params[k] = {'h': h_, 'J': J_, 'qca_file': qca_file}
except:
print('Failed to read embed summary...')
return
for k, subsol in subsols.items():
self.export_subsol(subsol, params[k])
return
for k, subsol in subsols.items():
print(k)
keys, spins, cell_occ, occ = subsol.model_reduction(embeds[k]['models'], \
ind_map=lambda qb: tuple_to_linear(qb, 12, 12, index0=False))
# outcome statistics
# cell_occ = sol.cell_occ
# cell_occ = subsols[k].cell_occ
M = max(cell_occ) # number fo gauge transformation
N = max(max(x) for k, x in cell_occ.items()) # number of states
D = np.zeros([M, N], dtype=int)
for g, co in cell_occ.items():
for s, o in co.items():
D[g - 1, s - 1] = o
# reject rare outcomes
if False:
inds = np.nonzero(np.max(D, axis=0) > 2)[0]
D = D[:, inds]
if False:
if True:
stack_plot = stackPlot(label='GT')
stack_plot.set_data(D)
stack_plot.plot(block=True)
else:
plt.figure('GT')
plt.clf()
plt.plot(D.transpose(), 'x')
plt.show(block=True)
# statistical distance
SD = np.zeros([M, M], dtype=float)
print('Computing statistical distances...')
k = 0
for i in range(M - 1):
for j in range(i + 1, M):
k += 1
sys.stdout.write('\r{0}%'.format(k * 100. / (.5 * M *
(M - 1))))
sys.stdout.flush()
SD[i, j] = SD[j, i] = stat_dist(D[i, :], D[j, :])
print('\n')
# seriate SD matrix if at least one pair of GTs have SD overlap
if True:
mask = np.ones(SD.shape, dtype=bool)
np.fill_diagonal(mask, 0)
if np.min(SD[mask]) > 0:
print('seriating SD matrix...')
try:
print('\tattempting {0}'.format(self.i4.text()))
new_inds = seriate(SD, method=str(self.i4.text()))
except:
print('\tfailed, using default method')
new_inds = seriate(SD, method='MDS')
print(new_inds)
SD = SD[new_inds, :][:, new_inds]
else:
print(
'No overlap between GT distributions. Seriation not possible.'
)
plt.imshow(SD, aspect='auto', interpolation='none')
plt.colorbar()
plt.show(block=True)
if False:
avg_pdf = np.sum(D, axis=0) * 1. / np.sum(D)
# look at number of random GT needed to estimate avg_pdf
sd_est = {k: match_dist(D, avg_pdf, k) for k in range(1, M)}
pprint(sd_est)
K = sorted(sd_est.keys())
plt.figure('SD v K')
plt.plot(K, [sd_est[x][0] for x in K], 'x')
plt.xlabel('Number of samples', fontsize=FS)
plt.ylabel('Statistical Distance', fontsize=FS)
plt.show(block=False)
plt.figure('stdev(SD) v K')
plt.plot(K, [sd_est[x][1] for x in K])
plt.xlabel('Number of samples', fontsize=FS)
plt.ylabel('$\sigma_{SD}$', fontsize=FS)
plt.show(block=True)
def run_processing(self):
''' '''
sol_fname = os.path.normpath(str(self.i1.text()))
coef_fname = os.path.normpath(str(self.i2.text()))
summ_fname = os.path.normpath(str(self.i3.text()))
# confirm fname format
if not re.match('.+[.]json', sol_fname):
print('Invalid filename format...')
return
if not coef_fname:
print('Missing coef file')
return
try:
sol = DWAVE_Sol(sol_fname)
except IOError:
print('Failed to read solution file')
return
try:
# load coef file
h, J = self.load_coef_file(coef_fname)
efunc = lambda s: compute_E(h, J, s)
except:
print('Invalid coef file given...')
return
try:
# load summary file
embeds = self.process_summ_file(summ_fname)
subsols = {}
params = {}
for k, embed in embeds.items():
qca_file = os.path.basename(embed['qca_file'])
if not qca_file == EMBED_FILTER:
continue
qbits = list(reduce(lambda x,y:x+y, embed['models'].values()))
qbits = [tuple_to_linear(qb, M=12, N=12, L=4, index0=False) for qb in qbits]
subsols[k] = sol.get_reduced_solution(qbits, efunc)
h_, J_ = reduce_coefs(h, J, qbits)
params[k] = {'h': h_, 'J': J_, 'qca_file': qca_file}
except:
print('Failed to read embed summary...')
return
for k, subsol in subsols.items():
self.export_subsol(subsol, params[k])
return
for k, subsol in subsols.items():
print(k)
keys, spins, cell_occ, occ = subsol.model_reduction(embeds[k]['models'], \
ind_map=lambda qb: tuple_to_linear(qb, 12, 12, index0=False))
# outcome statistics
# cell_occ = sol.cell_occ
# cell_occ = subsols[k].cell_occ
M = max(cell_occ) # number fo gauge transformation
N = max(max(x) for k, x in cell_occ.items()) # number of states
D = np.zeros([M, N], dtype=int)
for g, co in cell_occ.items():
for s, o in co.items():
D[g-1,s-1] = o
# reject rare outcomes
if False:
inds = np.nonzero(np.max(D, axis=0) > 2)[0]
D = D[:, inds]
if False:
if True:
stack_plot = stackPlot(label='GT')
stack_plot.set_data(D)
stack_plot.plot(block=True)
else:
plt.figure('GT')
plt.clf()
plt.plot(D.transpose(), 'x')
plt.show(block=True)
# statistical distance
SD = np.zeros([M, M], dtype=float)
print('Computing statistical distances...')
k = 0
for i in range(M-1):
for j in range(i+1, M):
k += 1
sys.stdout.write('\r{0}%'.format(k*100./(.5*M*(M-1))))
sys.stdout.flush()
SD[i,j] = SD[j,i] = stat_dist(D[i,:], D[j,:])
print('\n')
# seriate SD matrix if at least one pair of GTs have SD overlap
if True:
mask = np.ones(SD.shape, dtype=bool)
np.fill_diagonal(mask, 0)
if np.min(SD[mask])>0:
print('seriating SD matrix...')
try:
print('\tattempting {0}'.format(self.i4.text()))
new_inds = seriate(SD, method=str(self.i4.text()))
except:
print('\tfailed, using default method')
new_inds = seriate(SD, method='MDS')
print(new_inds)
SD = SD[new_inds, :][:, new_inds]
else:
print('No overlap between GT distributions. Seriation not possible.')
plt.imshow(SD, aspect='auto', interpolation='none')
plt.colorbar()
plt.show(block=True)
if False:
avg_pdf = np.sum(D,axis=0)*1./np.sum(D)
# look at number of random GT needed to estimate avg_pdf
sd_est = {k: match_dist(D, avg_pdf, k) for k in range(1, M)}
pprint(sd_est)
K = sorted(sd_est.keys())
plt.figure('SD v K')
plt.plot(K, [sd_est[x][0] for x in K], 'x')
plt.xlabel('Number of samples', fontsize=FS)
plt.ylabel('Statistical Distance', fontsize=FS)
plt.show(block=False)
plt.figure('stdev(SD) v K')
plt.plot(K, [sd_est[x][1] for x in K])
plt.xlabel('Number of samples', fontsize=FS)
plt.ylabel('$\sigma_{SD}$', fontsize=FS)
plt.show(block=True)
