min_temp = 1
max_temp = 1e5
# Create temperature array based on number of workers (excluding master)
temps = np.logspace(np.log10(min_temp), np.log10(max_temp), num_chains-1)
rate_step_sizes = np.logspace(np.log10(5e-4), np.log10(5e-2),
num_chains-1)
scaling_step_sizes = np.logspace(np.log10(1e-4), np.log10(1e-2),
num_chains-1)
# Initialize the MCMC arguments
b = args['builder']
opts = MCMCOpts()
opts.model = b.model
opts.tspan = args['time']
opts.estimate_params = b.estimate_params
opts.initial_values = b.random_initial_values()
opts.nsteps = args['nsteps']
#opts.norm_step_size = np.array([0.01] + \
# ([0.05] * (len(opts.estimate_params)-1)))
# FIXME not correct ordering
#opts.norm_step_size = np.array([scaling_step_sizes[rank-1]] + \
# ([rate_step_sizes[rank-1]] *
# (len(opts.estimate_params)-1)))
#print "Step size: %g" % (opts.norm_step_size[1])
opts.norm_step_size = 0.05
opts.sigma_step = 0 # Don't adjust step size
#opts.sigma_max = 50
# Frequency for proposing swaps
swap_period = 5
# Temperature range
min_temp = 1
max_temp = 1e5
# Create temperature array based on number of workers (excluding master)
temps = np.logspace(np.log10(min_temp), np.log10(max_temp), num_chains-1)
rate_step_sizes = np.logspace(np.log10(5e-4), np.log10(5e-2), num_chains-1)
scaling_step_sizes = np.logspace(np.log10(1e-4), np.log10(1e-2), num_chains-1)
# Initialize the MCMC arguments
opts = MCMCOpts()
opts.model = model
opts.tspan = nbd.time_other
opts.estimate_params = builder.estimate_params
opts.initial_values = builder.random_initial_values()
opts.nsteps = nsteps
#opts.norm_step_size = np.array([0.01] + \
# ([0.05] * (len(opts.estimate_params)-1)))
opts.norm_step_size = np.array([scaling_step_sizes[rank-1]] + \
([rate_step_sizes[rank-1]] * (len(opts.estimate_params)-1)))
print "Step size: %g" % (opts.norm_step_size[1])
opts.sigma_step = 0 # Don't adjust step size
opts.sigma_max = 50
opts.sigma_min = 0.01
opts.accept_rate_target = 0.23
opts.accept_window = 100
