def log_prob_v_given_hs(self, V, H, S):

        half = as_floatX(0.5)
        two = as_floatX(2.)
        pi = as_floatX(np.pi)
        N = as_floatX(self.nhid)

        term1 = half * T.sum(T.log(self.B))
        term2 = - half * N * T.log(two * pi)

        mean_HS = H * S
        recons = T.dot(H*S, self.W.T)
        residuals = V - recons


        term3 = - half * T.dot(T.sqr(residuals), self.B)

        rval = term1 + term2 + term3

        assert len(rval.type.broadcastable) == 1

        return rval
Minimize a function over a hyperparameter space.
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/models/s3c.py/S3C.log_prob_v_given_hs--crossover with--dataset/ETHPy150Open hyperopt/hyperopt/hyperopt/base.py/Trials.fmin