def thomas_tests():
graph = thomas()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'Cross')
print("P(Cross | Diesel=true, Diesel10=true) = <", mean, ", ", f_mean, ">")
graph = thomas()
graph.node_dict['Diesel'].observed = False
graph.node_dict['Diesel10'].observed = False
graph.node_dict['Thomas'].observed = True
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'Cross')
print("P(Cross | Thomas=true) = <", mean, ", ", f_mean, ">")
graph = thomas()
graph.node_dict['Diesel'].observed = False
graph.node_dict['Diesel10'].observed = False
graph.node_dict['Cross'].observed = True
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'Diesel')
print("P(Diesel | Cross=true) = <", mean, ", ", f_mean, ">")
print()
def alarm_tests():
mcmc = MCMC()
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'B')
print("P(Burglary | JohnCalls=true, MaryCalls=true) = <", mean, ", ", f_mean, ">")
mean, f_mean = Tests.sample_dim(samples, 'A')
print("P(Alarm | JohnCalls=true, MaryCalls=true) = <", mean, ", ", f_mean, ">")
mean, f_mean = Tests.sample_dim(samples, 'E')
print("P(Earthquake | JohnCalls=true, MaryCalls=true) = <", mean, ", ", f_mean, ">")
mcmc = MCMC({'J': 1., 'M': 0.})
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'B')
print("P(Burglary | JohnCalls=true, MaryCalls=false) = <", mean, ", ", f_mean, ">")
mcmc = MCMC({'J': 1.})
mcmc.graph.node_dict['M'].observed = False
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'B')
print("P(Burglary | JohnCalls=true) = <", mean, ", ", f_mean, ">")
mcmc = MCMC({'M': 1.})
mcmc.graph.node_dict['J'].observed = False
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'B')
print("P(Burglary | MaryCalls=true) = <", mean, ", ", f_mean, ">")
print()
def beta_bernoulli_tests():
graph = beta_bernoulli()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(5000, 100000)
Tests.plotposterior([s['A'] for s in samples], beta_expected_t, 'beta-bernoulli', 0, 1)
graph = beta_bernoulli(b=0)
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(13000, 100000)
Tests.plotposterior([s['A'] for s in samples], beta_expected_f, 'beta-bernoulli', 0, 1)
def home_or_school_tests():
graph = home_or_school()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'AS')
print("P(AS | IA=false) = <", mean, ", ", f_mean, ">")
graph = home_or_school()
mcmc = MCMC({'IA': 1}, graph=graph)
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'AS')
print("P(AS | IA=false) = <", mean, ", ", f_mean, ">")
print()
def dirty_roommates_tests():
graph = dirty_roommates()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'CK')
print("P(CK | DR=false) = <", mean, ", ", f_mean, ">")
graph = dirty_roommates()
graph.node_dict['DR'].observed = False
graph.node_dict['CK'].observed = True
mcmc = MCMC({'CK': 1}, graph=graph)
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'IT')
print("P(IT | CK=true) = <", mean, ", ", f_mean, ">")
def burn_tests():
graph = burn()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(10000, 10000)
mean, f_mean = Tests.sample_dim(samples, 'M')
print("P(MapoDoufu | Burn=false) = <", mean, ", ", f_mean, ">")
print()
def progress_tests():
graph = progress()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(2000, 50000)
Tests.plot_distribution(samples, 'Encounters')
encounters = [s['Encounters'] for s in samples]
median = np.median(encounters)
print('median', median)
def tanks_tests():
graph = tanks()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(50, 100000)
Tests.mixing_plot(samples, 'num_tanks')
Tests.plot_distribution(samples, 'num_tanks')
Tests.mixing_plot(samples, 'A')
Tests.plot_distribution(samples, 'A')
def faculty_evaluation_tests():
graph = faculty_evals()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(500, 10000)
Tests.mixing_plot(samples, 'mu')
Tests.mixing_plot(samples, 'sigma2')
Tests.plotposterior([s['mu'] for s in samples], faculty_mean_prior, 'mean', 5.0, 6.5)
Tests.plotposterior([s['sigma2'] for s in samples], faculty_var_prior, 'var', 0.0001, 1.0)
def golfer_network_tests():
graph = golf()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(1000, 100000)
print('woohoo! the golfers finished!')
with open('golf_samples.pickle', 'wb') as gs:
pickle.dump(samples, gs)
with open('golf_graph.pickle', 'wb') as gg:
pickle.dump(graph, gg)
def hyper_alarm_inference():
legs = [10, 25, 50, 100, 1000]
model = 'lab'
for n in legs:
name = model + '-inf_' + str(n)
graph = hyper_alarm(val_dict=name, inference=True)
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(2000, 100000)
mean, f_mean = Tests.sample_dim(samples, 'B')
print(mean)
def pareto_poisson_tests():
graph = pareto()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(2000, 100000)
for node in graph.hidden_nodes:
#Tests.mixing_plot(samples, node.name)
Tests.plot_distribution(samples, node.name)
encounters = [s[node.name] for s in samples]
median = np.median(encounters)
print('median', median)
def wacky_network_tests():
graph = wacky()
# graph.node_dict['G'].observed = True
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(20000, 2000000)
with open('wacky_G_samples.pickle', 'wb') as ws:
pickle.dump(samples, ws)
with open('wacky_G_graph.pickle', 'wb') as wg:
pickle.dump(graph, wg)
for node in graph.nodes:
Tests.mixing_plot(samples, node.name)
Tests.plot_distribution(samples, node.name)
def hyper_faculty_tests():
meta_samples = []
for n in range(5):
graph = faculty_evals_1hyper(n)
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(10000, 100000)
print(n)
Tests.plot_multi(samples, ['mu', 'sigma2'])
print(graph.nodes)
for node in graph.hidden_nodes:
if node.name != 'mu' and node.name != 'sigma2':
Tests.mixing_plot(samples, node.name)
Tests.plot_distribution(samples, node.name)
def hyper_alarm_learning_tests():
legs = [1, 10, 25, 50, 75, 100, 250, 500, 750, 1000]
prior = 'lab'
model = '01_50'
for n in legs:
graph = hyper_alarm_learn('alarm-gen-' + model + '.json', n=n, val_dict=prior)
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(1000, 1000)
mean, f_mean = Tests.sample_dim(samples, 'b_B')
print(mean, f_mean)
#mean, f_mean = Tests.sample_dim(samples, 'b_E')
#print(mean, f_mean)
name = 'alarm-' + model + '_' + str(n) + '_samples.pickle'
with open(name, 'wb') as f:
pickle.dump(samples, f)
def hyper_alarm_generate():
val_dict = 'orig'
graph = hyper_alarm(val_dict)
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(10000, 30000)
mean, f_mean = Tests.sample_dim(samples, 'B')
print(mean, f_mean)
mean, f_mean = Tests.sample_dim(samples, 'E')
print(mean, f_mean)
saved_samples = []
for i, sample in enumerate(samples):
if i < 3001 and i % 3 == 0:
#print(sample)
saved_samples.append(sample)
with open('alarm-gen-' + val_dict + '.json', 'w') as f:
json.dump(saved_samples,f)
def normal_normal_tests():
graph = normal_normal()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(500, 10000)
Tests.plotposterior([s['A'] for s in samples], normal_expected, 'normal-normal', -3, 3)
def gamma_poisson_tests():
graph = gamma_poisson()
mcmc = MCMC(graph=graph)
samples = mcmc.gibbs(50000, 10000)
Tests.plotposterior([s['L'] for s in samples], gamma_expected, 'gamma-poisson', 0, 12)
