def model_statistics(dic):
num_samples = list(dic.values())[0].shape[0]
reassigned_particles = [reassign_samples(p._get_sample(num_samples), source_model=p, target_model=dic)
for p in particles]
statistics = [self.deviation_statistics([self.cost_function(value_pair[0].detach().cpu().numpy(),
value_pair[1].detach().cpu().numpy())
for var, value_pair in zip_dict(dic, p).items()])
for p in reassigned_particles]
return np.array(statistics).transpose()
def get_particle_loss(self, joint_model, particle_list, sampler_model,
number_samples, input_values):
samples_list = [
sampler._get_sample(number_samples,
input_values=input_values,
max_itr=1) for sampler in sampler_model
]
if self.biased:
importance_weights = [1. / number_samples for _ in sampler_model]
else:
importance_weights = [
joint_model.get_importance_weights(
q_samples=samples, q_model=sampler,
for_gradient=False).flatten()
for samples, sampler in zip(samples_list, sampler_model)
]
reassigned_samples_list = [
reassign_samples(samples,
source_model=sampler,
target_model=particle) for samples, sampler,
particle in zip(samples_list, sampler_model, particle_list)
]
pair_list = [
zip_dict(particle._get_sample(1),
samples) for particle, samples in zip(
particle_list, reassigned_samples_list)
]
if not self.biased:
particle_loss = sum([
torch.sum(
to_tensor(w) * self.deviation_statistics([
self.cost_function(
value_pair[0],
value_pair[1].detach()) #TODO: numpy()
for var, value_pair in particle.items()
])) for particle, w in zip(pair_list, importance_weights)
])
else:
particle_loss = sum([
torch.sum(
self.deviation_statistics([
self.cost_function(value_pair[0],
value_pair[1].detach())
for var, value_pair in particle.items()
])) for particle in pair_list
])
return particle_loss