is_observed=True)
labels = EmpiricalVariable(output_labels,
indices=minibatch_indices,
name="labels",
is_observed=True)
weights = NormalVariable(np.zeros((1, number_regressors)), 0.5 * np.ones(
(1, number_regressors)), "weights")
logit_p = BF.matmul(weights, x)
k = BinomialVariable(1, logit_p=logit_p, name="k")
model = ProbabilisticModel([k])
#samples = model._get_sample(300)
#model.calculate_log_probability(samples)
# Observations
k.observe(labels)
#observed_model = inference.get_observed_model(model)
#observed_samples = observed_model._get_sample(number_samples=1, observed=True)
# Variational Model
Qweights = NormalVariable(np.zeros((1, number_regressors)),
np.ones((1, number_regressors)),
"weights",
learnable=True)
model.set_posterior_model(ProbabilisticModel([Qweights]))
# Inference
inference.perform_inference(model,
number_iterations=200,
number_samples=100,
from brancher.standard_variables import BetaVariable, BinomialVariable
from brancher import inference
from brancher.visualizations import plot_posterior
# betaNormal/Binomial model
number_tosses = 1
p = BetaVariable(1., 1., "p")
k = BinomialVariable(number_tosses, probs=p, name="k")
model = ProbabilisticModel([k, p])
# Generate data
p_real = 0.8
data = model.get_sample(number_samples=30, input_values={p: p_real})
# Observe data
k.observe(data)
# Inference
inference.perform_inference(model,
number_iterations=1000,
number_samples=500,
lr=0.1,
optimizer='SGD')
loss_list = model.diagnostics["loss curve"]
#Plot loss
plt.plot(loss_list)
plt.title("Loss (negative ELBO)")
plt.show()
#Plot posterior
#Real model
number_samples = 1
p_real = 0.8
k_real = BinomialVariable(number_samples, probs=p_real, name="k")
# betaNormal/Binomial model
p = BetaVariable(1., 1., "p")
k = BinomialVariable(number_samples, probs=p, name="k")
model = ProbabilisticModel([k])
# Generate data
data = k_real._get_sample(number_samples=50)
# Observe data
k.observe(data[k_real][:, 0, :])
# Variational distribution
Qp = BetaVariable(1., 1., "p", learnable=True)
model.set_posterior_model(ProbabilisticModel([Qp]))
# Inference
inference.perform_inference(model,
number_iterations=3000,
number_samples=100,
lr=0.01,
optimizer='Adam')
loss_list = model.diagnostics["loss curve"]
# Statistics
p_posterior_samples = model._get_posterior_sample(