def _make_phi(self, F):
        """
        A helper function for making predictions. Constructs a probability
        matrix where each row output the probability of the corresponding
        label, and the rows match the entries of F.

        Note that a matrix of F values is flattened.
        """
        scaled_bins_left = tf.concat([self.bin_edges / self.sigma, np.array([np.inf])], 0)
        scaled_bins_right = tf.concat([np.array([-np.inf]), self.bin_edges / self.sigma], 0)
        return inv_probit(scaled_bins_left - tf.reshape(F, (-1, 1)) / self.sigma) \
               - inv_probit(scaled_bins_right - tf.reshape(F, (-1, 1)) / self.sigma)
def _make_phi ( self , F ) : scaled_bins_left = tf . concat ( [ self . bin_edges / self . sigma , np . array ( [ np . inf ] ) ] , 0 ) scaled_bins_right = tf . concat ( [ np . array ( [ - np . inf ] ) , self . bin_edges / self . sigma ] , 0 ) return inv_probit ( scaled_bins_left - tf . reshape ( F , ( - 1 , 1 ) ) / self . sigma ) - inv_probit ( scaled_bins_right - tf . reshape ( F , ( - 1 , 1 ) ) / self . sigma )
A helper function for making predictions. Constructs a probability
        matrix where each row output the probability of the corresponding
        label, and the rows match the entries of F.

        Note that a matrix of F values is flattened.