def run_test(argsin):
n_rows = args["num_rows"]
n_iters = args["num_iters"]
n_chains = args["num_chains"]
ct_kernel = args["ct_kernel"]
fig = pylab.figure(num=None, facecolor='w', edgecolor='k',frameon=False, tight_layout=True)
plt = 0
data = {'x':[], 'sin':[], 'ring':[], 'dots':[]}
xlims = dict()
ylims = dict()
for shape in shapes:
plt += 1
data[shape] = gen_function[shape](n_rows)
ax = pylab.subplot(n_chains+1,4,plt)
pylab.scatter( data[shape][0], data[shape][1], s=10, color='blue', edgecolor='none', alpha=.2 )
# pylab.ylabel("X")
# pylab.ylabel("Y")
# pylab.title("%s original" % shape)
ax.set_xticks([])
ax.set_yticks([])
pylab.suptitle( "Kernel %i" % ct_kernel)
xlims[shape] = ax.get_xlim()
ylims[shape] = ax.get_ylim()
States = []
for chain in range(n_chains):
print("chain %i of %i." % (chain+1, n_chains))
plt = 0
for shape in shapes:
print("\tWorking on %s." % shape)
plt += 1
T = data[shape]
S = cc_state.cc_state(T, cctypes, ct_kernel=ct_kernel, distargs=distargs)
S.transition(N=n_iters)
T_chain = numpy.array(su.simple_predictive_sample(S, n_rows, [0,1], N=n_rows))
ax = pylab.subplot(n_chains+1,4,chain*4+4+plt)
ax.set_xticks([])
ax.set_yticks([])
pylab.scatter( T_chain[:,0], T_chain[:,1], s=10, color='red', edgecolor='none', alpha=.2 )
pylab.xlim(xlims[shape])
pylab.ylim(ylims[shape])
# pylab.title("%s simulated (%i)" % (shape, chain))
print("Done.")
pylab.show()
def run_test(argsin):
n_rows = args["num_rows"]
n_iters = args["num_iters"]
n_chains = args["num_chains"]
n_per_chain = int(float(n_rows) / n_chains)
plt = 0
for shape in shapes:
print "Shape: %s" % shape
plt += 1
T_o = gen_function[shape](n_rows)
T_i = []
for chain in range(n_chains):
print "chain %i of %i" % (chain + 1, n_chains)
S = cc_state.cc_state(T_o, cctypes, ct_kernel=1, distargs=distargs)
S.transition(N=n_iters)
T_i.extend(
su.simple_predictive_sample(S, n_rows, [0, 1], N=n_per_chain))
T_i = numpy.array(T_i)
ax = pylab.subplot(2, 4, plt)
pylab.scatter(T_o[0], T_o[1], color='blue', edgecolor='none')
pylab.ylabel("X")
pylab.ylabel("Y")
pylab.title("%s original" % shape)
pylab.subplot(2, 4, plt + 4)
pylab.scatter(T_i[:, 0], T_i[:, 1], color='red', edgecolor='none')
pylab.ylabel("X")
pylab.ylabel("Y")
pylab.xlim(ax.get_xlim())
pylab.ylim(ax.get_ylim())
pylab.title("%s simulated" % shape)
print "Done."
pylab.show()
def run_test(argsin):
n_rows = args["num_rows"]
n_iters = args["num_iters"]
n_chains = args["num_chains"]
n_per_chain = int(float(n_rows) / n_chains)
plt = 0
for shape in shapes:
print "Shape: %s" % shape
plt += 1
T_o = gen_function[shape](n_rows)
T_i = []
for chain in range(n_chains):
print "chain %i of %i" % (chain + 1, n_chains)
S = cc_state.cc_state(T_o, cctypes, ct_kernel=1, distargs=distargs)
S.transition(N=n_iters)
T_i.extend(su.simple_predictive_sample(S, n_rows, [0, 1], N=n_per_chain))
T_i = numpy.array(T_i)
ax = pylab.subplot(2, 4, plt)
pylab.scatter(T_o[0], T_o[1], color="blue", edgecolor="none")
pylab.ylabel("X")
pylab.ylabel("Y")
pylab.title("%s original" % shape)
pylab.subplot(2, 4, plt + 4)
pylab.scatter(T_i[:, 0], T_i[:, 1], color="red", edgecolor="none")
pylab.ylabel("X")
pylab.ylabel("Y")
pylab.xlim(ax.get_xlim())
pylab.ylim(ax.get_ylim())
pylab.title("%s simulated" % shape)
print "Done."
pylab.show()
def run_test(argsin):
n_rows = args["num_rows"]
n_iters = args["num_iters"]
n_chains = args["num_chains"]
ct_kernel = args["ct_kernel"]
fig = pylab.figure(num=None,
facecolor='w',
edgecolor='k',
frameon=False,
tight_layout=True)
plt = 0
data = {'x': [], 'sin': [], 'ring': [], 'dots': []}
xlims = dict()
ylims = dict()
for shape in shapes:
plt += 1
data[shape] = gen_function[shape](n_rows)
ax = pylab.subplot(n_chains + 1, 4, plt)
pylab.scatter(data[shape][0],
data[shape][1],
s=10,
color='blue',
edgecolor='none',
alpha=.2)
# pylab.ylabel("X")
# pylab.ylabel("Y")
# pylab.title("%s original" % shape)
ax.set_xticks([])
ax.set_yticks([])
pylab.suptitle("Kernel %i" % ct_kernel)
xlims[shape] = ax.get_xlim()
ylims[shape] = ax.get_ylim()
States = []
for chain in range(n_chains):
print("chain %i of %i." % (chain + 1, n_chains))
plt = 0
for shape in shapes:
print("\tWorking on %s." % shape)
plt += 1
T = data[shape]
S = cc_state.cc_state(T,
cctypes,
ct_kernel=ct_kernel,
distargs=distargs)
S.transition(N=n_iters)
T_chain = numpy.array(
su.simple_predictive_sample(S, n_rows, [0, 1], N=n_rows))
ax = pylab.subplot(n_chains + 1, 4, chain * 4 + 4 + plt)
ax.set_xticks([])
ax.set_yticks([])
pylab.scatter(T_chain[:, 0],
T_chain[:, 1],
s=10,
color='red',
edgecolor='none',
alpha=.2)
pylab.xlim(xlims[shape])
pylab.ylim(ylims[shape])
# pylab.title("%s simulated (%i)" % (shape, chain))
print("Done.")
pylab.show()