def infer_H_hat(self, V, H_hat, S_hat, count = None):

        arg_to_sigmoid = self.infer_H_hat_presigmoid(V, H_hat, S_hat)

        H = T.nnet.sigmoid(arg_to_sigmoid)

        V_name = make_name(V, anon = 'anon_V')

        if count is not None:
            H.name = 'H_hat(%s, %d)' % ( V_name, count)

        return H
.. todo::
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/models/s3c.py/E_Step.infer_H_hat