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('--quant', '-Q', help='Quantum training file', nargs='+')
parser.add_argument('--squant1', '-S', help='Fixed delta quantum training file', nargs='+')
parser.add_argument('--aquant1', help='Approximate quantum training file', nargs='+')
parser.add_argument('--squant2', help='Fixed delta quantum training file', nargs='+')
parser.add_argument('--squant3', help='Fixed delta quantum training file', nargs='+')
parser.add_argument('--aquant2', help='Approximate quantum training file', nargs='+')
parser.add_argument('--aquant3', help='Approximate quantum training file', nargs='+')
parser.add_argument('--class','-C', help='Classical training file', nargs='+')
args = vars(parser.parse_args())
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)
# ----------------------------------------------------------------------
if 'class' in args.keys():
cdata = {}
i = -1
for filename in args['class']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in cdata.keys()): cdata[N] = []
cdata[N] += [LL]
(Ns, vLLs, eLLs) = (cdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(cdata[N]))]
eLLs += [np.std(np.array(cdata[N]))/float(np.sqrt(len(cdata[N])))]
cLLs = unumpy.uarray((np.array(vLLs), np.array(eLLs)))
#ax.errorbar(Ns, unumpy.nominal_values(cLLs), unumpy.std_devs(cLLs), color=colors[1], lw=1, marker='o', ms=4, ls='', label=r'$classical$')
# ----------------------------------------------------------------------
if 'quant' in args.keys():
qdata = {}
i = -1
for filename in args['quant']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in qdata.keys()): qdata[N] = []
qdata[N] += [LL]
(Ns, vLLs, eLLs) = (qdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(qdata[N]))]
eLLs += [np.std(np.array(qdata[N]))/float(np.sqrt(len(qdata[N])))]
eqLLs = cLLs - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(eqLLs), unumpy.std_devs(eqLLs), color=colors[0], lw=1, ls=':', marker='s', ms=4, label=r'$exact \, quantum$')
#ax.plot(Ns, unumpy.nominal_values(eqLLs), color=colors[2], lw=1, ls='-', marker='s', ms=4, label=r'$exact \, quantum$')
# ----------------------------------------------------------------------
if 'aquant1' in args.keys():
aqdata = {}
i = -1
for filename in args['aquant1']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in aqdata.keys()): aqdata[N] = []
aqdata[N] += [LL]
(Ns, vLLs, eLLs) = (aqdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(aqdata[N]))]
eLLs += [np.std(np.array(aqdata[N]))/float(np.sqrt(len(aqdata[N])))]
aqLLs = cLLs - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(aqLLs), unumpy.std_devs(aqLLs), color=colors[1], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=1$')
#ax.plot(Ns, unumpy.nominal_values(aqLLs), color=colors[3], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=2$')
# ----------------------------------------------------------------------
if 'squant1' in args.keys():
qdata = {}
i = -1
for filename in args['squant1']:
print filename
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in qdata.keys()): qdata[N] = []
qdata[N] += [LL]
(Ns, vLLs, eLLs) = (qdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(qdata[N]))]
eLLs += [np.std(np.array(qdata[N]))/float(np.sqrt(len(qdata[N])))]
eqLLs = cLLs[:-1] - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(eqLLs), unumpy.std_devs(eqLLs), color=colors[1], lw=1, ls='--', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta=1$')
#ax.plot(Ns, unumpy.nominal_values(eqLLs), color=colors[5], lw=1, ls='-', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta =1$')
# ----------------------------------------------------------------------
if 'aquant2' in args.keys():
aqdata = {}
i = -1
for filename in args['aquant2']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in aqdata.keys()): aqdata[N] = []
aqdata[N] += [LL]
(Ns, vLLs, eLLs) = (aqdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(aqdata[N]))]
eLLs += [np.std(np.array(aqdata[N]))/float(np.sqrt(len(aqdata[N])))]
aqLLs = cLLs - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(aqLLs), unumpy.std_devs(aqLLs), color=colors[2], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=2$')
#ax.plot(Ns, unumpy.nominal_values(aqLLs), color=colors[3], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=2$')
# ----------------------------------------------------------------------
if 'squant2' in args.keys():
qdata = {}
i = -1
for filename in args['squant2']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in qdata.keys()): qdata[N] = []
qdata[N] += [LL]
(Ns, vLLs, eLLs) = (qdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(qdata[N]))]
eLLs += [np.std(np.array(qdata[N]))/float(np.sqrt(len(qdata[N])))]
eqLLs = cLLs[:-1] - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(eqLLs), unumpy.std_devs(eqLLs), color=colors[2], lw=1, ls='--', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta=3$')
#ax.plot(Ns, unumpy.nominal_values(eqLLs), color=colors[5], lw=1, ls='-', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta =1$')
# ----------------------------------------------------------------------
if 'aquant3' in args.keys():
aqdata = {}
i = -1
for filename in args['aquant3']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in aqdata.keys()): aqdata[N] = []
aqdata[N] += [LL]
(Ns, vLLs, eLLs) = (aqdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(aqdata[N]))]
eLLs += [np.std(np.array(aqdata[N]))/float(np.sqrt(len(aqdata[N])))]
aqLLs = cLLs - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(aqLLs), unumpy.std_devs(aqLLs), color=colors[3], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=3$')
#ax.plot(Ns, unumpy.nominal_values(aqLLs), color=colors[3], lw=1, ls='-', marker='p', ms=4, label=r'$appr. \,quantum \, \Delta=2$')
# ----------------------------------------------------------------------
if 'squant3' in args.keys():
qdata = {}
i = -1
for filename in args['squant3']:
data = Hfile.cleanData(np.loadtxt(filename))
LL = data[-1,0]
fparams = ssexyhelp.getReduceParamMap(filename)
N = int(fparams['N'])
if not(N in qdata.keys()): qdata[N] = []
qdata[N] += [LL]
(Ns, vLLs, eLLs) = (qdata.keys(), [], [])
Ns.sort()
for N in Ns:
vLLs += [np.mean(np.array(qdata[N]))]
eLLs += [np.std(np.array(qdata[N]))/float(np.sqrt(len(qdata[N])))]
eqLLs = cLLs[:-1] - unumpy.uarray((np.array(vLLs), np.array(eLLs)))
ax.errorbar(Ns, unumpy.nominal_values(eqLLs), unumpy.std_devs(eqLLs), color=colors[3], lw=1, ls='--', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta=3$',capthick=2.0, capsize=3.0, markersize='5')
#ax.plot(Ns, unumpy.nominal_values(eqLLs), color=colors[5], lw=1, ls='-', marker='s', ms=4, label=r'$fixed \, quantum \, \Delta =1$')
pl.xlabel(r'$System \, size$')
pl.ylabel(r'$\Delta LL$')
ax.set_xlim([3,11])
ax.set_yscale('log')
lgd = pl.legend(loc = 'best')
lgd.draggable(state=True)
lgd.draw_frame(False)
pl.tight_layout()
pl.show()
def main():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--quant','-Q', help='Quantum training file', nargs='+')
parser.add_argument('--class','-C', help='Classical training file', nargs='+')
args = vars(parser.parse_args())
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)
# ----------------------------------------------------------------------
if 'class' in args.keys():
cdata = np.zeros((5, len(args['class'])//5))
ssizes = []
i = -1
for filename in args['class']:
data = Hfile.cleanData(np.loadtxt(filename))
cKL = np.amin(data[:,1])
fparams = ssexyhelp.getReduceParamMap(filename)
(bsize, seed) = (int(fparams['ssise']), int(filename[:-4].split('_')[-1]))
if not(bsize in ssizes):
i += 1
ssizes += [bsize]
cdata[seed, i] = cKL
cKLs = np.sum(cdata, axis = 0)
ax.plot(ssizes, cKLs, color=colors[1], lw=1, ls='-', label=r'$classical$')
# ----------------------------------------------------------------------
if 'quant' in args.keys():
qdata = np.zeros((5, len(args['quant'])//5))
ssizes = []
i = -1
for filename in args['quant']:
data = cleanData(np.loadtxt(filename))
qKL = np.amin(data[:,1])
fparams = ssexyhelp.getReduceParamMap(filename)
(bsize, seed) = (int(fparams['ssise']), int(filename[:-4].split('_')[-1]))
if not(bsize in ssizes):
i += 1
ssizes += [bsize]
qdata[seed, i] = qKL
qKLs = np.sum(qdata, axis = 0)
ax.plot(ssizes, qKLs, color=colors[2], lw=1, ls='-', label=r'$quantum$')
pl.xlabel(r'$Training \, sample \, size$')
pl.ylabel(r'$KL-divergence$')
lgd = pl.legend(loc = 'best')
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()
