# Path: amdtk/io/persistent_model.py
# class PersistentModel(metaclass=abc.ABCMeta):
#     """Base class for classes that can be serialized."""
# 
#     @abc.abstractmethod
#     def to_dict(self):
#         """Get the data of the model into a dictionary.
# 
#         Returns
#         -------
#         dict : dictionary
#             Dictionary containing the model's data.
# 
#         """
#         pass
# 
#     @abc.abstractclassmethod
#     def load_from_dict(cls, model_data):
#         """Create and initialize the model from a dictionary.
# 
#         Parameters
#         ----------
#         model_data : dictionary
#             Dictionary containing the model's data.
# 
#         Returns
#         -------
#         model : :class:`PersistentModel`
#             Model initialized from the data.
# 
#         """
#         pass
# 
#     def save(self, fname):
#         """Store the model.
# 
#         Parameters
#         ----------
#         fname : string
#             String where to store the model.
# 
#         """
#         retval = self.to_dict()
#         retval['class'] = self.__class__
#         with open(fname, 'wb') as fid:
#             pickle.dump(retval, fid)
# 
#     @staticmethod
#     def load(file_obj):
#         """Load a model.
# 
#         Parameters
#         ----------
#         file_obj : file object
#             File-like object  where to store the model.
# 
#         Returns
#         -------
#         model : :class:`PersistentModel`
#             Model initialized from the stored data.
# 
#         """
#         model_data = pickle.load(file_obj)
#         cls = model_data['class']
#         return cls.load_from_dict(model_data)
# 
# Path: amdtk/models/mlp_utils.py
# class GaussianNeuralNetwork(NeuralNetwork):
# 
#     def __init__(self, structure, inputs=None, n_samples=10):
#         NeuralNetwork.__init__(self, structure, inputs)
#         self.mean = self.layers[-1].outputs
#         self.var = self.layers[-1].var
#         self.n_samples = n_samples
# 
#         # Noise variable for the reparameterization trick.
#         if "gpu" in theano.config.device:
#             srng = theano.sandbox.cuda.rng_curand.CURAND_RandomStreams(123)
#         else:
#             srng = T.shared_randomstreams.RandomStreams()
#             self.eps = srng.normal((n_samples, self.mean.shape[0],
#                                     self.mean.shape[1]))
# 
#         # Latent variable.
#         self.sample = self.mean + T.sqrt(self.var) * self.eps
# 
#         self.sample = T.reshape(self.sample,
#             (n_samples * self.mean.shape[0], -1))
# 
#         # Build the functions.
#         self.forward = theano.function(
#             inputs=[self.inputs],
#             outputs=[self.mean, self.var]
#         )
# 
# Path: amdtk/models/mlp_utils.py
# class NeuralNetwork(object):
# 
#     def __init__(self, structure, inputs=None):
#         # Build the neural network.
#         self.layers = []
#         self.params = []
#         current_inputs = inputs
#         for layer_struct in structure:
#             layer_type = layer_struct[0]
#             layer_params = layer_struct[1:]
# 
#             logger.debug('create nnet layer type={layer_type} '
#                 'params={params}'.format(layer_type=layer_type,
#                                          params=layer_params))
# 
#             self.layers.append(
#                 LAYER_TYPES[layer_type](
#                     current_inputs,
#                     *layer_params
#                 )
#             )
#             self.params += self.layers[-1].params
#             current_inputs = self.layers[-1].outputs
# 
#         self.inputs = self.layers[0].inputs
#         self.outputs = self.layers[-1].outputs


import abc
import pickle
import numpy as np
import theano
import theano.tensor as T
from ..io import PersistentModel
from .mlp_utils import GaussianNeuralNetwork
from .mlp_utils import NeuralNetwork
    def to_dict(self):
        return {
            'prior_latent_class': self.prior_latent.__class__,
            'prior_latent_data': self.prior_latent.to_dict(),
            'encoder_struct': self.encoder_struct,
            'decoder_struct': self.decoder_struct,
            'n_samples': self.n_samples,
            'params': [param.get_value() for param in self.params]
        }

    @classmethod
    def load_from_dict(cls, model_data):
        encoder_struct = model_data['encoder_struct']
        decoder_struct = model_data['decoder_struct']
        prior_latent_cls = model_data['prior_latent_class']
        prior_latent = prior_latent_cls.load_from_dict(
            model_data['prior_latent_data']
        )
        n_samples = model_data['n_samples']
        model = SVAE(encoder_struct, decoder_struct, prior_latent, n_samples)
        params = model_data['params']
        for i, param in enumerate(model.params):
            param.set_value(params[i])

        return model


class MLPClassifier(object):

    def __init__(self, structure):

# Path: amdtk/io/persistent_model.py
# class PersistentModel(metaclass=abc.ABCMeta):
#     """Base class for classes that can be serialized."""
# 
#     @abc.abstractmethod
#     def to_dict(self):
#         """Get the data of the model into a dictionary.
# 
#         Returns
#         -------
#         dict : dictionary
#             Dictionary containing the model's data.
# 
#         """
#         pass
# 
#     @abc.abstractclassmethod
#     def load_from_dict(cls, model_data):
#         """Create and initialize the model from a dictionary.
# 
#         Parameters
#         ----------
#         model_data : dictionary
#             Dictionary containing the model's data.
# 
#         Returns
#         -------
#         model : :class:`PersistentModel`
#             Model initialized from the data.
# 
#         """
#         pass
# 
#     def save(self, fname):
#         """Store the model.
# 
#         Parameters
#         ----------
#         fname : string
#             String where to store the model.
# 
#         """
#         retval = self.to_dict()
#         retval['class'] = self.__class__
#         with open(fname, 'wb') as fid:
#             pickle.dump(retval, fid)
# 
#     @staticmethod
#     def load(file_obj):
#         """Load a model.
# 
#         Parameters
#         ----------
#         file_obj : file object
#             File-like object  where to store the model.
# 
#         Returns
#         -------
#         model : :class:`PersistentModel`
#             Model initialized from the stored data.
# 
#         """
#         model_data = pickle.load(file_obj)
#         cls = model_data['class']
#         return cls.load_from_dict(model_data)
# 
# Path: amdtk/models/mlp_utils.py
# class GaussianNeuralNetwork(NeuralNetwork):
# 
#     def __init__(self, structure, inputs=None, n_samples=10):
#         NeuralNetwork.__init__(self, structure, inputs)
#         self.mean = self.layers[-1].outputs
#         self.var = self.layers[-1].var
#         self.n_samples = n_samples
# 
#         # Noise variable for the reparameterization trick.
#         if "gpu" in theano.config.device:
#             srng = theano.sandbox.cuda.rng_curand.CURAND_RandomStreams(123)
#         else:
#             srng = T.shared_randomstreams.RandomStreams()
#             self.eps = srng.normal((n_samples, self.mean.shape[0],
#                                     self.mean.shape[1]))
# 
#         # Latent variable.
#         self.sample = self.mean + T.sqrt(self.var) * self.eps
# 
#         self.sample = T.reshape(self.sample,
#             (n_samples * self.mean.shape[0], -1))
# 
#         # Build the functions.
#         self.forward = theano.function(
#             inputs=[self.inputs],
#             outputs=[self.mean, self.var]
#         )
# 
# Path: amdtk/models/mlp_utils.py
# class NeuralNetwork(object):
# 
#     def __init__(self, structure, inputs=None):
#         # Build the neural network.
#         self.layers = []
#         self.params = []
#         current_inputs = inputs
#         for layer_struct in structure:
#             layer_type = layer_struct[0]
#             layer_params = layer_struct[1:]
# 
#             logger.debug('create nnet layer type={layer_type} '
#                 'params={params}'.format(layer_type=layer_type,
#                                          params=layer_params))
# 
#             self.layers.append(
#                 LAYER_TYPES[layer_type](
#                     current_inputs,
#                     *layer_params
#                 )
#             )
#             self.params += self.layers[-1].params
#             current_inputs = self.layers[-1].outputs
# 
#         self.inputs = self.layers[0].inputs
#         self.outputs = self.layers[-1].outputs


# Path: amdtk/models/vae.py
import abc
import pickle
import numpy as np
import theano
import theano.tensor as T
from ..io import PersistentModel
from .mlp_utils import GaussianNeuralNetwork
from .mlp_utils import NeuralNetwork

    def to_dict(self):
        return {
            'prior_latent_class': self.prior_latent.__class__,
            'prior_latent_data': self.prior_latent.to_dict(),
            'encoder_struct': self.encoder_struct,
            'decoder_struct': self.decoder_struct,
            'n_samples': self.n_samples,
            'params': [param.get_value() for param in self.params]
        }

    @classmethod
    def load_from_dict(cls, model_data):
        encoder_struct = model_data['encoder_struct']
        decoder_struct = model_data['decoder_struct']
        prior_latent_cls = model_data['prior_latent_class']
        prior_latent = prior_latent_cls.load_from_dict(
            model_data['prior_latent_data']
        )
        n_samples = model_data['n_samples']
        model = SVAE(encoder_struct, decoder_struct, prior_latent, n_samples)
        params = model_data['params']
        for i, param in enumerate(model.params):
            param.set_value(params[i])

        return model


class MLPClassifier(object):

    def __init__(self, structure):

        self.nnet = NeuralNetwork(structure, [])