# Path: mjhmc/fast/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


import numpy as np
import theano.tensor as T
import theano
from .utils import overrides
from scipy.sparse import rand
        X = T.matrix()
        E = self.E_def(X)
        dEdX = T.grad(T.sum(E),X)
        #@overrides(Distribution)
        self.E_val=theano.function([X],E,allow_input_downcast=True)
        #@overrides(Distribution)
        self.dEdX_val = theano.function([X],dEdX,allow_input_downcast=True)
        super(ProductOfT,self).__init__(ndims,nbatch)

    def E_def(self,X):
        """
        energy for a POE with student's-t expert in terms of:
                samples [# samples]x[# dimensions] X
                receptive fields [# dimensions]x[# experts] W
                biases [# experts] b
                expert weighting [# experts] alpha
        """
        self.b = self.b.reshape((1,-1))
        alpha = T.exp(self.logalpha).reshape((1,-1))
        E_perexpert = alpha*T.log(1 + (T.dot(X.T,self.W) + self.b)**2)
        E = T.sum(E_perexpert, axis=1).reshape((1,-1))
        return E

    """
    @overrides(Distribution)
    def dEdX_val(self,X):
        dEdX = np.sum(((2*self.alpha*(self.W.T,X))/(1+(self.W.T,X)**2))*self.W,axis=0)
        return dEdX
    """


# Path: mjhmc/fast/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


# Path: mjhmc/fast/distributions_T.py
import numpy as np
import theano.tensor as T
import theano
from .utils import overrides
from scipy.sparse import rand

        X = T.matrix()
        E = self.E_def(X)
        dEdX = T.grad(T.sum(E),X)
        #@overrides(Distribution)
        self.E_val=theano.function([X],E,allow_input_downcast=True)
        #@overrides(Distribution)
        self.dEdX_val = theano.function([X],dEdX,allow_input_downcast=True)
        super(ProductOfT,self).__init__(ndims,nbatch)

    def E_def(self,X):
        """
        energy for a POE with student's-t expert in terms of:
                samples [# samples]x[# dimensions] X
                receptive fields [# dimensions]x[# experts] W
                biases [# experts] b
                expert weighting [# experts] alpha
        """
        self.b = self.b.reshape((1,-1))
        alpha = T.exp(self.logalpha).reshape((1,-1))
        E_perexpert = alpha*T.log(1 + (T.dot(X.T,self.W) + self.b)**2)
        E = T.sum(E_perexpert, axis=1).reshape((1,-1))
        return E

    """
    @overrides(Distribution)
    def dEdX_val(self,X):
        dEdX = np.sum(((2*self.alpha*(self.W.T,X))/(1+(self.W.T,X)**2))*self.W,axis=0)
        return dEdX
    """


    @overrides(Distribution)