def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--MC','-Q', help='Quantum training file', nargs='+')
parser.add_argument('--ED','-C', help='Classical training file', nargs='+')
parser.add_argument('--clamped', help='Clamped simulation', default=False, action='store_true')
args = vars(parser.parse_args())
if 'MC' in args.keys():
skip = 3 # skip estimators we don't care about
for filename in args['MC']:
fparams = ssexyhelp.getReduceParamMap(filename)
data = np.loadtxt(filename)
headers = Hfile.getHeaders(filename)
if not(args['clamped']): (first, last) = (headers.index('X0'), headers.index('sX0'))
else: (first, last) = (headers.index('sX0'), len(headers))
aves = np.zeros(last-first)
stds = np.zeros(last-first)
for i, c in enumerate(range(first, last)):
cdata = data[~np.isnan(data[:,c]),c]
aves[i] = np.mean(cdata)
stds[i] = np.amax(MCstat.bin(cdata[np.newaxis].T))
if 'ED' in args.keys():
for filename in args['ED']:
fparams = ssexyhelp.getReduceParamMap(filename)
ED = np.loadtxt(filename)
print ED
print aves
print stds
colors = ["#66CAAE", "#CF6BDD", "#E27844", "#7ACF57", "#92A1D6", "#E17597", "#C1B546"]
fig = pl.figure(1, figsize=(10,5))
pl.connect('key_press_event',kevent.press)
ax = pl.subplot(111)
ax.plot((ED-aves)/stds, color=colors[0])#, lw=2, m='o', ls='')#, label=r'$data$')
pl.ylabel(r'$(ED-MC)/\Delta_{MC}$')
pl.xlabel(r'$Averages$')
lgd = pl.legend(loc = 'best')
lheaders = []
for head in Hfile.getHeaders(args['ED'][0]):
lheaders += [r'$%s$' %head]
pl.xticks(range(len(lheaders)), lheaders, rotation='vertical')
#lgd.draggable(state=True)
#lgd.draw_frame(False)
pl.tight_layout()
pl.show()
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--data', '-D', help='Data file', type=str)
parser.add_argument('--inter','-I', help='Interactions file', type=str)
parser.add_argument('--train','-T', help='Training file', type=str)
args = vars(parser.parse_args())
# ----------------------------------------------------------------------
data = np.loadtxt(args['data'], dtype='int32')
udata = []
cdata = collections.OrderedDict()
for i,a in enumerate(data):
d = a.tolist()
if not(d in udata): udata += [d]; cdata[repr(d)] = 1
else: cdata[repr(d)] += 1
weights = np.array(cdata.values())/float(data.shape[0])
data = udata
eweights = np.zeros(2**len(udata[0]))
for i,cbits in enumerate(data):
d = toDecimal(cbits)
eweights[d] = weights[i]
#sortind = [i[0] for i in sorted(enumerate(data), key=HammingDistance1)]
#sortind = np.argsort(weights)
#weights = weights[sortind]
#data = (np.array(data)[sortind]).tolist()
#for v in data:
# print v, HammingDistance(v)
#colors = ["#66CAAE", "#CF6BDD", "#E27844", "#7ACF57", "#92A1D6", "#E17597", "#C1B546"]
fig = pl.figure(1, figsize=(13,6))
pl.connect('key_press_event',kevent.press)
ax = pl.subplot(111)
#ax.plot( np.sum(-(data*2-1), axis=0)/(1.0*data.shape[0]))
if ((args['inter'] != None) and (args['train'] != None)):
# ----------------------------------------------------------------------
Hs, Ds, Js, bonds = Hfile.LoadInters(args['inter'])
# ----------------------------------------------------------------------
headers = Hfile.getHeaders(args['train'])
tdata = Hfile.cleanData(np.loadtxt(args['train']))
Hs = tdata[:, headers.index('H0') :headers.index('D0')]
Ds = tdata[:, headers.index('D0') :headers.index('J(0,1)')]
Js = tdata[:, headers.index('J(0,1)'):headers.index('<Z0>')]
# ----------------------------------------------------------------------
(Ns, Nb) = (Hs.shape[1], len(bonds))
norm = mpl.colors.Normalize(vmin=0, vmax=tdata.shape[0])
c_m = mpl.cm.cool
s_m = mpl.cm.ScalarMappable(cmap=c_m, norm=norm)
s_m.set_array([])
for i in range(tdata.shape[0]):
ds = np.transpose(np.vstack((np.arange(Ns), Ds[i,:])))
hs = np.transpose(np.vstack((np.arange(Ns), Hs[i,:])))
js = np.hstack((bonds, Js[i,:].reshape(Nb,1)))
#Ps = []
#kwargs = {'X': ds, 'Z1': hs, 'Z2': js}
#BM = bmachine.BoltzmannMachine(Ns, 1.0, **kwargs)
#for cbits in data:
# BM.setProjector(cbits)
# Ps += [BM.evaluateProjector()]
#del BM
#ax.plot(-weights*np.log(Ps), color=s_m.to_rgba(i), lw=1, ls='-')# Ps = []
kwargs = {'X': ds, 'Z1': hs, 'Z2': js}
BM = bmachine.BoltzmannMachine(Ns, 1.0, **kwargs)
Ps = np.zeros(2**len(udata[0]))
for cbits in data:
BM.setProjector(cbits)
d = toDecimal(cbits)
Ps[d] = BM.evaluateProjector()
del BM
ax.plot(-eweights*np.log(Ps), color=s_m.to_rgba(i), lw=1, ls='-')#, label = r'$epoch \, %d$' %i)
plt.colorbar(s_m)
ax.plot(-eweights*np.log(eweights), color='black', lw=2, ls='-', label = r'$data$')
#ax.plot(-weights*np.log(weights), color='black', lw=2, ls='-', label = r'$data$')
pl.xlabel(r'$Data$')
pl.ylabel(r'$P_{data}\log P_{model}$')
lgd = pl.legend(loc = 'best')
lgd.draggable(state=True)
pl.tight_layout()
pl.show()
