def good_sigma_evaluator_1(nodes=20, observations=10, numObservations=10, trials=10000, size=10):
results = numpy.zeros(size);
for i in range(0, size):
G, sig, D = mcmc.generate_graph_and_paths(nodes, observations, numObservations);
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc(G, D, trials);
bar1 = sig_ind_prob/samples;
results[i] = mcmc.real_distribution_error(bar1, sig);
return results, numpy.mean(results), numpy.var(results);
def good_sigma_evaluator_1(nodes=20,
observations=10,
numObservations=10,
trials=10000,
size=10):
results = numpy.zeros(size)
for i in range(0, size):
G, sig, D = mcmc.generate_graph_and_paths(nodes, observations,
numObservations)
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc(G, D, trials)
bar1 = sig_ind_prob / samples
results[i] = mcmc.real_distribution_error(bar1, sig)
return results, numpy.mean(results), numpy.var(results)
def run_sigma_experiment_1(nodes=20, observations=10, numObservations=10, trials=10000, fileIndex=0):
G, sig, D = mcmc.generate_graph_and_paths(nodes, observations, numObservations);
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc(G, D, trials);
print(sig);
bar1 = sig_ind_prob/samples;
bar2 = numpy.ones(G.shape[0]) - bar1;
ind = numpy.arange(G.shape[0]);
p1 = plot.bar(ind, bar1, 0.35, color='r');
p2 = plot.bar(ind, bar2, 0.35, color='b', bottom=bar1);
plot.ylabel('L/R')
plot.title('Switch index')
plot.legend((p1[0], p2[0]), ('L', 'R'))
plot.show();
bar = numpy.zeros(len(sigmas));
label = numpy.zeros(len(sigmas));
ind = numpy.arange(len(sigmas));
i = 0;
for sigma, n in sigmas.items():
bar[i] = n/float(samples);
label[i] = mcmc.sigma_hash(sigma);
i += 1;
p1 = plot.bar(ind, bar, 0.1, color='black');
#plot.xticks(ind+0.1/2., tuple(label), rotation=90);
plot.ylabel('Probability of sigma')
plot.xlabel('Sigma index')
plot.title('Joint probability of sigma')
plot.axis([0, len(sigmas), 0, max(bar)+0.05]);
plot.show();
true_sig_prob = 1;
for i in range(0, nodes):
if(sig[i] == 1):
true_sig_prob *= bar1[i];
else:
true_sig_prob *= bar2[i];
numpy.savetxt('test_results/sigma_experiment_1_ind_prob_L_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,fileIndex), bar1);
numpy.savetxt('test_results/simga_experiment_1_search_space_index_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,fileIndex), label);
numpy.savetxt('test_results/sigma_experiment_1_search_space_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,fileIndex), bar);
print('Distribution error: {}'.format(mcmc.real_distribution_error(bar1, sig)));
print('The probability of guessing the true sigma is {}'.format(true_sig_prob));
print('posterior/prior = {}'.format(true_sig_prob/pow(0.5, nodes)));
def run_sigma_experiment_1(nodes=20,
observations=10,
numObservations=10,
trials=10000,
fileIndex=0):
G, sig, D = mcmc.generate_graph_and_paths(nodes, observations,
numObservations)
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc(G, D, trials)
print(sig)
bar1 = sig_ind_prob / samples
bar2 = numpy.ones(G.shape[0]) - bar1
ind = numpy.arange(G.shape[0])
p1 = plot.bar(ind, bar1, 0.35, color='r')
p2 = plot.bar(ind, bar2, 0.35, color='b', bottom=bar1)
plot.ylabel('L/R')
plot.title('Switch index')
plot.legend((p1[0], p2[0]), ('L', 'R'))
plot.show()
bar = numpy.zeros(len(sigmas))
label = numpy.zeros(len(sigmas))
ind = numpy.arange(len(sigmas))
i = 0
for sigma, n in sigmas.items():
bar[i] = n / float(samples)
label[i] = mcmc.sigma_hash(sigma)
i += 1
p1 = plot.bar(ind, bar, 0.1, color='black')
#plot.xticks(ind+0.1/2., tuple(label), rotation=90);
plot.ylabel('Probability of sigma')
plot.xlabel('Sigma index')
plot.title('Joint probability of sigma')
plot.axis([0, len(sigmas), 0, max(bar) + 0.05])
plot.show()
true_sig_prob = 1
for i in range(0, nodes):
if (sig[i] == 1):
true_sig_prob *= bar1[i]
else:
true_sig_prob *= bar2[i]
numpy.savetxt(
'test_results/sigma_experiment_1_ind_prob_L_{}_{}_{}_{}.{}.txt'.format(
nodes, observations, numObservations, trials, fileIndex), bar1)
numpy.savetxt(
'test_results/simga_experiment_1_search_space_index_{}_{}_{}_{}.{}.txt'
.format(nodes, observations, numObservations, trials,
fileIndex), label)
numpy.savetxt(
'test_results/sigma_experiment_1_search_space_{}_{}_{}_{}.{}.txt'.
format(nodes, observations, numObservations, trials, fileIndex), bar)
print('Distribution error: {}'.format(
mcmc.real_distribution_error(bar1, sig)))
print('The probability of guessing the true sigma is {}'.format(
true_sig_prob))
print('posterior/prior = {}'.format(true_sig_prob / pow(0.5, nodes)))