# 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
        self.Xinit = self.scale1 * np.random.randn(self.ndims, self.nbatch)

    @overrides(Distribution)
    def __hash__(self):
        return hash((self.ndims, self.scale1, self.scale2))

class MultimodalGaussian(Distribution):
    def __init__(self, ndims=2, nbatch=100, separation=3):
        self.sep_vec = np.array([separation] * nbatch +
                                [0] * (ndims - 1) * nbatch).reshape(ndims, nbatch)
        # separated along first axis
        self.sep_vec[0] += separation
        super(MultimodalGaussian, self).__init__(ndims, nbatch)

    @overrides(Distribution)
    def E_val(self, X):
        trim_sep_vec = self.sep_vec[:, :X.shape[1]]
        return -np.log(np.exp(-np.sum((X + trim_sep_vec)**2, axis=0)) +
                       np.exp(-np.sum((X - trim_sep_vec)**2, axis=0)))

    @overrides(Distribution)
    def dEdX_val(self, X):
        # allows for partial batch size
        trim_sep_vec = self.sep_vec[:, :X.shape[1]]
        common_exp = np.exp(np.sum(4 * trim_sep_vec * X, axis=0))
        # floating point hax
        return ((2 * ((X - trim_sep_vec) * common_exp + trim_sep_vec + X)) /
                (common_exp + 1))



# 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

        self.Xinit = self.scale1 * np.random.randn(self.ndims, self.nbatch)

    @overrides(Distribution)
    def __hash__(self):
        return hash((self.ndims, self.scale1, self.scale2))

class MultimodalGaussian(Distribution):
    def __init__(self, ndims=2, nbatch=100, separation=3):
        self.sep_vec = np.array([separation] * nbatch +
                                [0] * (ndims - 1) * nbatch).reshape(ndims, nbatch)
        # separated along first axis
        self.sep_vec[0] += separation
        super(MultimodalGaussian, self).__init__(ndims, nbatch)

    @overrides(Distribution)
    def E_val(self, X):
        trim_sep_vec = self.sep_vec[:, :X.shape[1]]
        return -np.log(np.exp(-np.sum((X + trim_sep_vec)**2, axis=0)) +
                       np.exp(-np.sum((X - trim_sep_vec)**2, axis=0)))

    @overrides(Distribution)
    def dEdX_val(self, X):
        # allows for partial batch size
        trim_sep_vec = self.sep_vec[:, :X.shape[1]]
        common_exp = np.exp(np.sum(4 * trim_sep_vec * X, axis=0))
        # floating point hax
        return ((2 * ((X - trim_sep_vec) * common_exp + trim_sep_vec + X)) /
                (common_exp + 1))



    @overrides(Distribution)