target_values=[parameters.A, parameters.Q,
parameters.C, parameters.R],
metric_names = ['mse', 'mse', 'mse', 'mse'],
)
]
sample_functions = sample_function_parameters(
['A', 'Q', 'LQinv', 'C', 'R', 'LRinv'],
)
sampler = SLDSSampler(**parameters.dim)
sampler.setup(data['observations'], prior)
sampler.init_sample_latent() ## THIS IS IMPORTANT
evaluator = SamplerEvaluator(
sampler=sampler,
metric_functions=metric_functions,
sample_functions=sample_functions,
)
print(evaluator.metrics)
print(evaluator.samples)
## Run a few Gibbs Sampler steps
for _ in range(10):
evaluator.evaluate_sampler_step(['sample_gibbs', 'project_parameters'])
print(evaluator.metrics)
print(evaluator.samples)
## Run a few ADA_GRAD sampler steps
for _ in range(10):
evaluator.evaluate_sampler_step(
['step_adagrad', 'project_parameters'],
metric_function_parameters(
parameter_names=['A', 'LQinv', 'LRinv'],
target_values=[parameters.A, parameters.LQinv, parameters.LRinv],
metric_names = ['mse', 'mse', 'mse'],
)
]
sample_functions = sample_function_parameters(
['A', 'Q', 'LQinv', 'R', 'LRinv'],
)
sampler = SVMSampler(**parameters.dim)
sampler.setup(data['observations'], prior)
evaluator = SamplerEvaluator(
sampler=sampler,
metric_functions=metric_functions,
sample_functions=sample_functions,
)
print(evaluator.metrics)
print(evaluator.samples)
## Run a few ADA_GRAD sampler steps
for _ in tqdm(range(100)):
evaluator.evaluate_sampler_step(
['step_adagrad', 'project_parameters'],
[dict(kind='pf', N=1000,
epsilon=0.1, subsequence_length=10, buffer_length=5), {}],
)
print(evaluator.metrics)
print(evaluator.samples)