print(sampler.sample_sgrld(epsilon=0.1, preconditioner=preconditioner).project_parameters())
## Using Evaluator
from sgmcmc_ssm import SamplerEvaluator
from sgmcmc_ssm.metric_functions import (
sample_function_parameters,
noisy_logjoint_loglike_metric,
metric_function_parameters,
)
metric_functions = [
noisy_logjoint_loglike_metric(),
metric_function_parameters(
parameter_names=['A', 'Q', 'C', 'R'],
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,
).project_parameters())
## Using Evaluator
from sgmcmc_ssm import SamplerEvaluator
from sgmcmc_ssm.metric_functions import (
sample_function_parameters,
noisy_logjoint_loglike_metric,
metric_function_parameters,
)
metric_functions = [
noisy_logjoint_loglike_metric(kind='pf', N=1000),
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,
)
# Setup my_evaluators
from sgmcmc_ssm.evaluator import SamplerEvaluator
from sgmcmc_ssm.metric_functions import (
metric_function_from_sampler,
metric_function_parameters,
metric_compare_x,
noisy_logjoint_loglike_metric,
sample_function_parameters,
)
parameter_names2 = ['A', 'C', 'Q', 'R']
my_metric_functions = [
metric_function_parameters(
parameter_names2,
target_values=[
getattr(my_data['parameters'], parameter_name)
for parameter_name in parameter_names2
],
metric_names=['mse' for parameter_name in parameter_names2],
),
metric_compare_x(my_data['latent_vars']),
metric_function_from_sampler("predictive_loglikelihood"),
noisy_logjoint_loglike_metric(),
]
my_sample_functions = [
sample_function_parameters(parameter_names2 + ['LRinv', 'LQinv']),
]
my_evaluators = {
"{0}_{1}".format(*key):
SamplerEvaluator(sampler,
my_metric_functions,
my_sample_functions,
metric_function_from_sampler,
metric_function_parameters,
metric_compare_x,
metric_compare_z,
noisy_logjoint_loglike_metric,
sample_function_parameters,
)
parameter_names = ['pi']
parameter_names2 = ['A', 'Q']
parameter_names3 = ['C', 'R']
my_metric_functions = [
metric_function_parameters(
parameter_names,
target_values=[
getattr(my_data['parameters'], parameter_name)
for parameter_name in parameter_names
],
metric_names=['logmse' for parameter_name in parameter_names],
criteria=[min for parameter_name in parameter_names],
double_permutation_flag=True,
),
metric_function_parameters(
parameter_names,
target_values=[
getattr(my_data['parameters'], parameter_name)
for parameter_name in parameter_names
],
metric_names=['mse' for parameter_name in parameter_names],
criteria=[min for parameter_name in parameter_names],
double_permutation_flag=True,
),
metric_function_parameters(
).project_parameters())
## Using Evaluator
from sgmcmc_ssm import SamplerEvaluator
from sgmcmc_ssm.metric_functions import (
sample_function_parameters,
noisy_logjoint_loglike_metric,
metric_function_parameters,
)
metric_functions = [
noisy_logjoint_loglike_metric(kind='pf', N=1000),
metric_function_parameters(
parameter_names=['alpha', 'beta', 'gamma', 'tau'],
target_values=[parameters.alpha, parameters.beta,
parameters.gamma, parameters.tau],
metric_names = ['mse', 'mse', 'mse', 'mse'],
)
]
sample_functions = sample_function_parameters(
['alpha', 'beta', 'gamma', 'tau'],
)
sampler = GARCHSampler(**parameters.dim)
sampler.setup(data['observations'], prior)
evaluator = SamplerEvaluator(
sampler=sampler,
metric_functions=metric_functions,
sample_functions=sample_functions,
)