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main.py
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main.py
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import collections
import numpy
import generate_g
import mcmc
import state_probabilities
import matplotlib.pyplot as plot
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_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 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 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 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_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 plot_sigma_observation_development(nodes=20, observations=10, numObservations=10, trials=10000, init=100, size=10, fileIndex=0):
mean_1 = numpy.zeros(numObservations);
var_1 = numpy.zeros(numObservations);
mean_2 = numpy.zeros(numObservations);
var_2 = numpy.zeros(numObservations);
for i in range(0, numObservations):
_, m, v = good_sigma_evaluator_1(nodes, observations, i, trials, size);
mean_1[i] = m;
var_1[i] = v;
_, m, v = good_sigma_evaluator_2(nodes, observations, i, trials, init, size);
mean_2[i] = m;
var_2[i] = v;
print('Simulations done!');
plot.plot(range(1,numObservations+1), mean_1);
plot.plot(range(1,numObservations+1), mean_2);
plot.axis([1, numObservations, 0, 1]);
#plot.legend((mean_1[0], mean_2[0]), ('mcmc_1', 'mcmc_2'))
plot.ylabel('Mean Distribution error')
plot.xlabel('Number of simulations')
plot.show();
plot.plot(range(1,numObservations+1), var_1);
plot.plot(range(1,numObservations+1), var_2);
#plot.legend((var_1[0], var_2[0]), ('mcmc_1', 'mcmc_2'))
plot.ylabel('Distribution error Variance')
plot.xlabel('Number of simulations')
plot.show();
numpy.savetxt('test_results/mcmc_mean_1_{}_{}_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,init,size, fileIndex), mean_1);
numpy.savetxt('test_results/mcmc_mean_2_{}_{}_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,init,size, fileIndex), mean_2);
numpy.savetxt('test_results/mcmc_var_1_{}_{}_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,init,size, fileIndex), var_1);
numpy.savetxt('test_results/mcmc_var_2_{}_{}_{}_{}_{}_{}.{}.txt'.format(nodes,observations,numObservations,trials,init,size, fileIndex), var_2);
return mean_1, var_1, mean_2, var_2;
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 run_experiment(nodes=20, observations=10):
G, sigma = generate_g.generate_graph_and_settings(nodes)
O, actual_path = generate_g.simulate_train(G, sigma, observations + 1)
last_node = actual_path[-2]
last_label = numpy.where(G[last_node] == actual_path[-1])[0][0]
s = calculate_final_distribution(G, O)
print("Guessed the correct stop position with probability {}.".format(
s[last_node, last_label]))
def observations_needed(max_nodes=100, runs=50):
"""Calculates the number of observations needed
to get a good estimate of the stop position."""
failure_probabilities = []
with open("observations_needed.dat", 'w') as obs_file:
for nodes in range(6, max_nodes + 1, 2):
total_observations_needed = 0
failed_runs = 0
i = 0
while True:
G, sigma = generate_g.generate_graph_and_settings(nodes)
O, actual_path = generate_g.simulate_train(G, sigma, 200)
for observations in range(1, len(O)):
s, _ = state_probabilities.state_probabilities(
G, sigma, O[:observations])
if s.max() > 0.90:
break
else:
print(s.max())
failed_runs += 1
continue
total_observations_needed += observations
i += 1
if i >= runs:
break
obs_file.write("{}\t{}\n".format(nodes, total_observations_needed / runs))
print(nodes, total_observations_needed / runs)
failure_probability = failed_runs / (runs + failed_runs)
print("Failure probability: {}".format(failure_probability))
failure_probabilities.append(failure_probability)
print("Average failure probability: {}".format(sum(failure_probabilities) / len(failure_probabilities)))
#if __name__ == '__main__':
#run_experiment()
#observations_needed()