fixed_delta = mod.particles[-1, :, ind].mean()
mod._particles[-1][:, ind] = fixed_delta
mod._params[ind].prior = fixed_delta
mod._log_likes[-1], temp_posts = mod._calc_log_likes(mod._particles[-1])
mod._weights[-1][:] = mod._log_likes[-1][:] + \
mod.calc_log_prior(mod._particles[-1])
# run for more iterations to map posterior
mod(1000, burnin=False)
# joint plot
pl.figure(1)
pl.clf()
ax = joint_plot(mod.particles[burnin:, :, :-1],
mod.weights[burnin:],
burnin=burnin,
names=mod.param_display_names[:-1],
rot=45,
sep=.02)
pl.show()
# ppd plot
pl.figure(2)
violin_plot([d.ravel() for d in mod.posts[burnin:].T], positions=xData)
pl.plot(xData, yData, 'xr', markersize=10)
pl.show()
# show best fit
burnin = 400
print("Best fitting params:")
best_ind = mod.weights[burnin:].argmax()
indiv = [
proposal_gen=DE(gamma_best=None, rand_base=True),
migration_prob=0.0,
initial_zeros_ok=False,
use_priors=True,
save_posts=False,
)
abc_rand(burnin)
abc_rand.set_proposal_gen(DE(gamma_best=0.0, rand_base=True))
abc_rand(iterations)
# joint plot
pl.figure(1)
pl.clf()
ax = joint_plot(
abc_true.particles, abc_true.weights, burnin=burnin, names=abc_true.param_display_names, rot=45, sep=0.02
)
pl.show()
pl.figure(2)
pl.clf()
b = pl.hist(abc.particles[(burnin + 100) :, :, 0].flat, 50, alpha=0.3, normed=True)
b = pl.hist(abc_true.particles[(burnin + 100) :, :, 0].flat, 50, alpha=0.3, normed=True)
b = pl.hist(abc_rand.particles[(burnin + 100) :, :, 0].flat, 50, alpha=0.3, normed=True)
pl.legend(["pda", "true", "rand"])
pl.show()
pl.figure(3)
pl.clf()
b = pl.hist(abc.particles[(burnin + 100) :, :, 1].flat, 50, alpha=0.3, normed=True)
b = pl.hist(abc_true.particles[(burnin + 100) :, :, 1].flat, 50, alpha=0.3, normed=True)
group_size=25,
proposal_gen=DE(gamma_best=None, rand_base=True),
migration_prob=0.0,
initial_zeros_ok=False,
use_priors=True,
save_posts=False)
abc_rand(burnin)
abc_rand.set_proposal_gen(DE(gamma_best=0.0, rand_base=True))
abc_rand(iterations)
# joint plot
pl.figure(1)
pl.clf()
ax = joint_plot(abc_true.particles,
abc_true.weights,
burnin=burnin,
names=abc_true.param_display_names,
rot=45,
sep=.02)
pl.show()
pl.figure(2)
pl.clf()
b = pl.hist(abc.particles[(burnin + 100):, :, 0].flat,
50,
alpha=.3,
normed=True)
b = pl.hist(abc_true.particles[(burnin + 100):, :, 0].flat,
50,
alpha=.3,
normed=True)
b = pl.hist(abc_rand.particles[(burnin + 100):, :, 0].flat,
abc(burnin)
# fix the delta error term based on the mean
ind = abc.param_names.index('delta')
abc.fix_delta(ind, abc.particles[-1,:,ind].mean(),
proposal_gen=DE_LOCAL(),
recalc_weights=True)
# run for more iterations to map prior
abc(100)
# joint plot
pl.figure(1)
pl.clf()
ax = joint_plot(abc.particles[:,:,:-1],abc.weights,
burnin=burnin,
names=abc.param_display_names[:-1],
rot=45,sep=.02)
pl.show()
# ppd plot
pl.figure(2)
violin_plot([d.ravel() for d in abc.posts[burnin:].T],
positions=xData)
pl.plot(xData,yData,'xr',markersize=10)
pl.show()
# show best fit
print "Best fitting params:"
best_ind = abc.weights[burnin:].argmax()
indiv = [abc.particles[burnin:,:,i].ravel()[best_ind] for i in range(abc.particles.shape[-1])]
for p,v in zip(abc.param_names,indiv):
