def good_sigma_evaluator_2(nodes=20, observations=10, numObservations=10, trials=10000, init=100, 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_2(G, D, trials, init);
bar1 = sig_ind_prob/samples;
results[i] = mcmc.real_distribution_error(bar1, sig);
return results, numpy.mean(results), numpy.var(results);
def run_convergence_experiment(nodes=20, observations=10, numObservations=10, trials=10000, init=100, chainSize=3):
G, sig, D = mcmc.generate_graph_and_paths(nodes, observations, numObservations);
chain_results_dict_array = [];
sig_ind_prob_array = numpy.zeros((chainSize, nodes));
for i in range(0, chainSize):
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc_2(G, D, trials, init);
chain_results_dict_array.append(sigmas);
sig_ind_prob_array[i,:] = sig_ind_prob;
print('Chain {} processed...'.format(i+1));
print('Calculating R...');
result = mcmc.simple_convergence_checker(nodes, chain_results_dict_array, sig_ind_prob_array, samples);
return result;
def good_sigma_evaluator_2(nodes=20,
observations=10,
numObservations=10,
trials=10000,
init=100,
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_2(G, D, trials, init)
bar1 = sig_ind_prob / samples
results[i] = mcmc.real_distribution_error(bar1, sig)
return results, numpy.mean(results), numpy.var(results)
def evaluate_sigma_mcmc(nodes=20, observations=10, numObservations=10, trials=10000, init=100, 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_2(G, D, trials, init);
bar1 = sig_ind_prob/samples;
bar2 = numpy.ones(G.shape[0]) - bar1;
true_sig_prob = 1;
for j in range(0, nodes):
if(sig[j] == 1):
true_sig_prob *= bar1[j];
else:
true_sig_prob *= bar2[j];
results[i] = true_sig_prob;
return results, numpy.mean(results), sum(numpy.power(results - numpy.mean(results), 2));
def run_sigma_experiment_2(nodes=20, observations=10, numObservations=10, trials=10000, init=100, fileIndex=0):
G, sig, D = mcmc.generate_graph_and_paths(nodes, observations, numObservations);
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc_2(G, D, trials, init);
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)]);
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_2_ind_prob_L_{}_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,init,fileIndex), bar1);
numpy.savetxt('test_results/sigma_experiment_2_search_space_{}_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,init,fileIndex), bar);
numpy.savetxt('test_results/sigma_experiment_2_search_space_index_{}_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,init,fileIndex), label);
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 calculate_final_distribution(G, O):
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc_2(G, [O], 10000, 100)
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(sigmas);
i += 1;
p1 = plot.bar(ind, bar, 0.1, color='black');
#plot.xticks(ind+0.1/2., tuple(label));
plot.ylabel('Probability of sigma')
plot.xlabel('Sigma index')
plot.title('Joint probability of sigma')
plot.show();
s = numpy.zeros(G.shape)
for sigma, n in sigmas.items():
sigma = numpy.array(sigma)
# s2 is already normalized, i.e. it is
# p(s, O | G, sigma)/p(O | G, sigma) = p(s | G, O, sigma)
s2, _ = state_probabilities.state_probabilities(G, sigma, O)
# multiply s2 by p(sigma | G, O)
s2 *= n / samples
# sum over all sigmas
s += s2
return s
def run_convergence_experiment(nodes=20,
observations=10,
numObservations=10,
trials=10000,
init=100,
chainSize=3):
G, sig, D = mcmc.generate_graph_and_paths(nodes, observations,
numObservations)
chain_results_dict_array = []
sig_ind_prob_array = numpy.zeros((chainSize, nodes))
for i in range(0, chainSize):
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc_2(G, D, trials, init)
chain_results_dict_array.append(sigmas)
sig_ind_prob_array[i, :] = sig_ind_prob
print('Chain {} processed...'.format(i + 1))
print('Calculating R...')
result = mcmc.simple_convergence_checker(nodes, chain_results_dict_array,
sig_ind_prob_array, samples)
return result
def calculate_final_distribution(G, O):
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc_2(G, [O], 10000, 100)
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(sigmas)
i += 1
p1 = plot.bar(ind, bar, 0.1, color='black')
#plot.xticks(ind+0.1/2., tuple(label));
plot.ylabel('Probability of sigma')
plot.xlabel('Sigma index')
plot.title('Joint probability of sigma')
plot.show()
s = numpy.zeros(G.shape)
for sigma, n in sigmas.items():
sigma = numpy.array(sigma)
# s2 is already normalized, i.e. it is
# p(s, O | G, sigma)/p(O | G, sigma) = p(s | G, O, sigma)
s2, _ = state_probabilities.state_probabilities(G, sigma, O)
# multiply s2 by p(sigma | G, O)
s2 *= n / samples
# sum over all sigmas
s += s2
return s
def evaluate_sigma_mcmc(nodes=20,
observations=10,
numObservations=10,
trials=10000,
init=100,
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_2(G, D, trials, init)
bar1 = sig_ind_prob / samples
bar2 = numpy.ones(G.shape[0]) - bar1
true_sig_prob = 1
for j in range(0, nodes):
if (sig[j] == 1):
true_sig_prob *= bar1[j]
else:
true_sig_prob *= bar2[j]
results[i] = true_sig_prob
return results, numpy.mean(results), sum(
numpy.power(results - numpy.mean(results), 2))
def run_sigma_experiment_2(nodes=20,
observations=10,
numObservations=10,
trials=10000,
init=100,
fileIndex=0):
G, sig, D = mcmc.generate_graph_and_paths(nodes, observations,
numObservations)
sigmas, sig_ind_prob, samples = mcmc.sig_mcmc_2(G, D, trials, init)
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)])
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_2_ind_prob_L_{}_{}_{}_{}_{}.{}.txt'.
format(nodes, observations, numObservations, trials, init,
fileIndex), bar1)
numpy.savetxt(
'test_results/sigma_experiment_2_search_space_{}_{}_{}_{}_{}.{}.txt'.
format(nodes, observations, numObservations, trials, init,
fileIndex), bar)
numpy.savetxt(
'test_results/sigma_experiment_2_search_space_index_{}_{}_{}_{}_{}.{}.txt'
.format(nodes, observations, numObservations, trials, init,
fileIndex), label)
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)))
