4461f2d4c1f140667b8fe9305efffb4351e54460,snntoolbox/simulation/target_simulators/INI_target_sim.py,SNN,simulate,#SNN#,112

Before Change

                        output_b_l_t, 2) == 0)] = self._num_timesteps
                    guesses_b = np.argmin(first_spiketimes_b_l, 1)
                else:
                    guesses_b = np.argmax(np.sum(output_b_l_t, 2), 1)
                echo("{:.2%}_".format(np.mean(kwargs[str("truth_b")] ==
                                              guesses_b)))

            if self.config.getboolean("conversion", "use_isi_code") and \
                    all(np.count_nonzero(output_b_l_t, (1, 2)) >= self.top_k):
                print("Finished early.")
                break

        if self.config.getboolean("conversion", "use_isi_code"):
            for b in range(self.batch_size):
                for l in range(self.num_classes):
                    spike = 0
                    for t in range(self._num_timesteps):
                        if output_b_l_t[b, l, t] != 0:
                            spike = 1
                        output_b_l_t[b, l, t] = spike

        return np.cumsum(np.asarray(output_b_l_t, bool), 2)

    def reset(self, sample_idx):

After Change

                else:
                    spike_sums_b_l = np.sum(output_b_l_t, 2)
                    undecided_b = np.sum(spike_sums_b_l, 1) == 0
                    guesses_b = np.argmax(spike_sums_b_l, 1)
                none_class_b = -1 * np.ones(self.batch_size)
                clean_guesses_b = np.where(undecided_b, none_class_b, guesses_b)
                echo("{:.2%}_".format(np.mean(kwargs[str("truth_b")] ==
                                              clean_guesses_b)))

Instances