def phi_nonTerminal(self, s):
        F_s = np.zeros(self.features_num)
        # From IndependentDiscretization
        F_s[self.activeInitialFeatures(s)] = 1
        bebf_features_num = self.features_num - self.initial_features_num
        for features_ind, F_s_ind in enumerate(np.arange(bebf_features_num) + self.initial_features_num):
            F_s[F_s_ind] = self.features[features_ind].predict(s)
#        print 's,F_s',s,F_s
#        shout('F_s:',F_s)
        return F_s

    # Adds new features based on the Bellman Error in batch setting.
    # @param td_errors: p-by-1 (How much error observed for each sample)
    # @param all_phi_s: n-by-p features corresponding to all samples (each column corresponds to one sample)
    # @param s: List of states corresponding to each td_error in td_errors (note that the same state may appear multiple times because of different actions taken while there)
    # self.batchThreshold is threshold below which no more BEBFs are added.
dataset/ETHPy150Open rlpy/rlpy/rlpy/Representations/BEBF.py/BEBF.phi_nonTerminal/original