def fit_beta_by_pt(nsteps):
"""Fit a beta distribution by parallel tempering."""
num_dimensions = 1
# Create the dummy model
b = BetaFit(0.5, 0.5)
# Create the options
opts = MCMCOpts()
opts.model = b
opts.estimate_params = b.parameters
opts.initial_values = [10 ** 0.5]
opts.nsteps = nsteps
opts.anneal_length = 0
opts.T_init = 1
opts.use_hessian = False
opts.seed = 1
opts.norm_step_size = 0.5
opts.likelihood_fn = b.likelihood
opts.step_fn = step
# Create the MCMC object
num_temps = 8
pt = PT_MCMC(opts, num_temps, 10)
pt.estimate()
plt.ion()
for chain in pt.chains:
fig = plt.figure()
chain.prune(nsteps/10, 1)
(heights, points, lines) = plt.hist(chain.positions, bins=100,
normed=True)
plt.plot(points, beta.pdf(points, b.a, b.b), 'r')
plt.ylim((0,10))
plt.xlim((0, 1))
return pt
def fit_twod_gaussians_by_pt(nsteps):
means_x = [ 0.1, 0.5, 0.9,
0.1, 0.5, 0.9,
0.1, 0.5, 0.9]
means_y = [0.1, 0.1, 0.1,
0.5, 0.5, 0.5,
0.9, 0.9, 0.9]
sd = 0.01
tdg = TwoDGaussianFit(means_x, means_y, sd ** 2)
# Create the options
opts = MCMCOpts()
opts.model = tdg
opts.estimate_params = tdg.parameters
opts.initial_values = [1.001, 1.001]
opts.nsteps = nsteps
opts.anneal_length = 0 # necessary so cooling does not occur
opts.T_init = 1
opts.use_hessian = False
opts.seed = 1
opts.norm_step_size = 0.1
opts.likelihood_fn = tdg.likelihood
opts.step_fn = step
# Create the PT object
num_temps = 8
pt = PT_MCMC(opts, num_temps, 100)
pt.estimate()
plt.ion()
for chain in pt.chains:
fig = plt.figure()
chain.prune(nsteps/10, 1)
plt.scatter(chain.positions[:,0], chain.positions[:,1])
ax = fig.gca()
for x, y in zip(means_x, means_y):
circ = plt.Circle((x, y), radius=2*sd, color='r', fill=False)
ax.add_patch(circ)
plt.xlim((-0.5, 1.5))
plt.ylim((-0.5, 1.5))
plt.title('Temp = %.2f' % chain.options.T_init)
plt.show()
return pt
