# Path: mjhmc/misc/utils.py
# def overrides(interface_class):
#     """
#     override decorator. need to specify the super class, unfortunately
#     """
#     def overrider(method):
#         assert(method.__name__ in dir(interface_class))
#         return method
#     return overrider
# 
# def package_path():
#     """ Returns the absolute path to this package base directory
# 
#     :returns: absolute path to MJHMC/
#     :rtype: string
# 
#     """
#     import sys
#     mjhmc_path = [path for path in sys.path if 'MJHMC' in path][0]
#     if mjhmc_path is None:
#         raise Exception('You must include MJHMC in your PYTHON_PATH')
#     prefix = mjhmc_path.split('MJHMC')[0]
#     return "{}MJHMC".format(prefix)


import numpy as np
import os
import pickle
            import theano.tensor as T
            import theano
from .utils import overrides, package_path
from scipy import stats
            from mjhmc.misc.gen_mj_init import MAX_N_PARTICLES, cache_initialization
        """
        resets the object. returns self for convenience
        """
        self.E_count = 0
        self.dEdX_count = 0
        if not self.generation_instance:
            self.init_X()
        return self

    def __call__(self, X):
        """
        Convenience method for NUTS compatibility
        returns -E, -dEdX
        """
        rshp_X = X.reshape(len(X), 1)
        E = float(self.E(rshp_X))
        dEdX = self.dEdX(rshp_X).T[0]
        return -E, -dEdX

    def load_cache(self):
        """ Loads and returns the cached fair initializations and
         estimated variances associated with this
         distribution. Throws an error if the cache does not exist

        :returns: the loaded cache: (fair_initialization, emc_var_estimate, true_var_estimate)
        :rtype: (np.ndarray, float, float)
        """
        distr_name = type(self).__name__
        distr_hash = hash(self)
        file_name = '{}_{}.pickle'.format(distr_name, distr_hash)

# Path: mjhmc/misc/utils.py
# def overrides(interface_class):
#     """
#     override decorator. need to specify the super class, unfortunately
#     """
#     def overrider(method):
#         assert(method.__name__ in dir(interface_class))
#         return method
#     return overrider
# 
# def package_path():
#     """ Returns the absolute path to this package base directory
# 
#     :returns: absolute path to MJHMC/
#     :rtype: string
# 
#     """
#     import sys
#     mjhmc_path = [path for path in sys.path if 'MJHMC' in path][0]
#     if mjhmc_path is None:
#         raise Exception('You must include MJHMC in your PYTHON_PATH')
#     prefix = mjhmc_path.split('MJHMC')[0]
#     return "{}MJHMC".format(prefix)


# Path: mjhmc/misc/distributions.py
import numpy as np
import os
import pickle
            import theano.tensor as T
            import theano
from .utils import overrides, package_path
from scipy import stats
            from mjhmc.misc.gen_mj_init import MAX_N_PARTICLES, cache_initialization

        """
        resets the object. returns self for convenience
        """
        self.E_count = 0
        self.dEdX_count = 0
        if not self.generation_instance:
            self.init_X()
        return self

    def __call__(self, X):
        """
        Convenience method for NUTS compatibility
        returns -E, -dEdX
        """
        rshp_X = X.reshape(len(X), 1)
        E = float(self.E(rshp_X))
        dEdX = self.dEdX(rshp_X).T[0]
        return -E, -dEdX

    def load_cache(self):
        """ Loads and returns the cached fair initializations and
         estimated variances associated with this
         distribution. Throws an error if the cache does not exist

        :returns: the loaded cache: (fair_initialization, emc_var_estimate, true_var_estimate)
        :rtype: (np.ndarray, float, float)
        """
        distr_name = type(self).__name__
        distr_hash = hash(self)
        file_name = '{}_{}.pickle'.format(distr_name, distr_hash)

        file_prefix = '{}/initializations'.format(package_path())