# Path: group_testing/bayes.py
# def log_likelihood(particles: np.ndarray, test_results: np.ndarray,
#                    groups: np.ndarray, log_specificity: np.ndarray,
#                    log_1msensitivity: np.ndarray) -> float:
# def log_prior(particles: np.ndarray,
#               base_infection_rate: np.ndarray) -> np.ndarray:
# def log_probability(particles: np.ndarray,
#                     test_results: np.ndarray,
#                     groups: np.ndarray,
#                     log_prior_specificity: np.ndarray,
#                     log_prior_1msensitivity: np.ndarray,
#                     prior_infection_rate: np.ndarray):
# def tempered_logpos_logbase(particles: np.ndarray,
#                             log_posterior_params: Dict[str, np.ndarray],
#                             log_base_measure_params: Dict[str, np.ndarray],
#                             temperature: float):
# 
# Path: group_testing/utils.py
# def unique(rng, binary_vectors: np.ndarray) -> int:
# def select_from_sizes(values: np.ndarray, sizes: np.ndarray) -> np.ndarray:
# 
# Path: group_testing/samplers/kernels.py
# def gibbs_kernel(rng: np.ndarray,
#                  particles: np.ndarray,
#                  rho: float,
#                  log_posterior_params: Dict[str, np.ndarray],
#                  log_base_measure_params: Dict[str, np.ndarray],
#                  cycles: int = 2,
#                  liu_modification: bool = True):
#   def gibbs_loop(i, rng_particles_log_posteriors):
#   def __init__(self,
#                cycles: int = 2,
#                liu_modification: bool = False):
#   def __call__(self, rng, particles, rho,
#                log_posterior_params,
#                log_base_measure_params):
#   def fit_model(self, particle_weights, particles):
#   def __init__(self, sparse_model_lr=True):
#   def __call__(self,
#                rng: np.ndarray,
#                particles: np.ndarray,
#                rho: float,
#                log_posterior_params: Dict[str, np.ndarray],
#                log_base_measure_params: Dict[str, np.ndarray]):
#   def sample_from_model(self, rng, particles):
#   def fit_model(self, particle_weights: np.ndarray,
#                 particles: np.ndarray) -> np.ndarray:
#   def __call__(self, rng, particles, rho,
#                log_posterior_params, log_base_measure_params):
# class Gibbs:
# class Chopin:
# class ChopinGibbs(Chopin):
# 
# Path: group_testing/samplers/sampler.py
# class Sampler:
#   def __init__(self):
#   def reset(self):
#   def is_cheap(self):
#   def marginal(self):
#   def reset_convergence_metric(self):
# 
# Path: group_testing/samplers/temperature.py
# def effective_sample_size(alpha: float,
#                           log_posterior: np.ndarray):
# def find_step_length(rho: float,
#                      log_posterior: np.ndarray,
#                      tolerance=0.02,
#                      effective_sample_size_target=0.9):
# def importance_weights(log_unnormalized_probabilities):


from typing import Dict
from group_testing import bayes
from group_testing import utils
from group_testing.samplers import kernels
from group_testing.samplers import sampler
from group_testing.samplers import temperature
import gin
import jax
import jax.numpy as np
import numpy as onp
# coding=utf-8
# Copyright 2020 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""Sequential Monte-Carlo Sampler in jax."""




@gin.configurable
class SmcSampler(sampler.Sampler):
  """Sequential monte carlo sampler."""

  NAME = 'smc'

  def __init__(self,

# Path: group_testing/bayes.py
# def log_likelihood(particles: np.ndarray, test_results: np.ndarray,
#                    groups: np.ndarray, log_specificity: np.ndarray,
#                    log_1msensitivity: np.ndarray) -> float:
# def log_prior(particles: np.ndarray,
#               base_infection_rate: np.ndarray) -> np.ndarray:
# def log_probability(particles: np.ndarray,
#                     test_results: np.ndarray,
#                     groups: np.ndarray,
#                     log_prior_specificity: np.ndarray,
#                     log_prior_1msensitivity: np.ndarray,
#                     prior_infection_rate: np.ndarray):
# def tempered_logpos_logbase(particles: np.ndarray,
#                             log_posterior_params: Dict[str, np.ndarray],
#                             log_base_measure_params: Dict[str, np.ndarray],
#                             temperature: float):
# 
# Path: group_testing/utils.py
# def unique(rng, binary_vectors: np.ndarray) -> int:
# def select_from_sizes(values: np.ndarray, sizes: np.ndarray) -> np.ndarray:
# 
# Path: group_testing/samplers/kernels.py
# def gibbs_kernel(rng: np.ndarray,
#                  particles: np.ndarray,
#                  rho: float,
#                  log_posterior_params: Dict[str, np.ndarray],
#                  log_base_measure_params: Dict[str, np.ndarray],
#                  cycles: int = 2,
#                  liu_modification: bool = True):
#   def gibbs_loop(i, rng_particles_log_posteriors):
#   def __init__(self,
#                cycles: int = 2,
#                liu_modification: bool = False):
#   def __call__(self, rng, particles, rho,
#                log_posterior_params,
#                log_base_measure_params):
#   def fit_model(self, particle_weights, particles):
#   def __init__(self, sparse_model_lr=True):
#   def __call__(self,
#                rng: np.ndarray,
#                particles: np.ndarray,
#                rho: float,
#                log_posterior_params: Dict[str, np.ndarray],
#                log_base_measure_params: Dict[str, np.ndarray]):
#   def sample_from_model(self, rng, particles):
#   def fit_model(self, particle_weights: np.ndarray,
#                 particles: np.ndarray) -> np.ndarray:
#   def __call__(self, rng, particles, rho,
#                log_posterior_params, log_base_measure_params):
# class Gibbs:
# class Chopin:
# class ChopinGibbs(Chopin):
# 
# Path: group_testing/samplers/sampler.py
# class Sampler:
#   def __init__(self):
#   def reset(self):
#   def is_cheap(self):
#   def marginal(self):
#   def reset_convergence_metric(self):
# 
# Path: group_testing/samplers/temperature.py
# def effective_sample_size(alpha: float,
#                           log_posterior: np.ndarray):
# def find_step_length(rho: float,
#                      log_posterior: np.ndarray,
#                      tolerance=0.02,
#                      effective_sample_size_target=0.9):
# def importance_weights(log_unnormalized_probabilities):


# Path: group_testing/samplers/sequential_monte_carlo.py
from typing import Dict
from group_testing import bayes
from group_testing import utils
from group_testing.samplers import kernels
from group_testing.samplers import sampler
from group_testing.samplers import temperature
import gin
import jax
import jax.numpy as np
import numpy as onp

# coding=utf-8
# Copyright 2020 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""Sequential Monte-Carlo Sampler in jax."""




@gin.configurable
class SmcSampler(sampler.Sampler):
  """Sequential monte carlo sampler."""

  NAME = 'smc'

  def __init__(self,

               kernel=kernels.Gibbs(),