def generate_graph_and_paths(n, t, d):
print('Generating graph and data...');
G, sig = generate_g.generate_graph_and_settings(n)
D = [];
for i in range(0,d):
a, _ = generate_g.simulate_train(G, sig, t);
D.append(a);
print('Done!');
return G, sig, D;
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 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 setup(nodes, chain_specification, burn_in=1000, calculate_actual_distribution=False):
G, sigma = generate_g.generate_graph_and_settings(nodes)
if G.min() < 0:
print("Erroneous graph!")
return
O, path = generate_g.simulate_train(G, sigma, 10)
chains = [[itertools.islice(chain, burn_in, None)
for chain in chains]
for chains in build_chains(chain_specification, G, O)]
if calculate_actual_distribution:
sigma_dist = calculate_sigma(G, O)
return chains, sigma_dist
else:
return chains
def setup(nodes,
chain_specification,
burn_in=1000,
calculate_actual_distribution=False):
G, sigma = generate_g.generate_graph_and_settings(nodes)
if G.min() < 0:
print("Erroneous graph!")
return
O, path = generate_g.simulate_train(G, sigma, 10)
chains = [[itertools.islice(chain, burn_in, None) for chain in chains]
for chains in build_chains(chain_specification, G, O)]
if calculate_actual_distribution:
sigma_dist = calculate_sigma(G, O)
return chains, sigma_dist
else:
return chains
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)))
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)))