htm.py 文件源码

def stabilize(self, prior_columns, percent):
        """
        This activates prior columns to force active in order to maintain
        the given percent of column overlap between time steps.  Always call
        this between compute and learn!
        """
        # num_active      = (len(self.columns) + len(prior_columns)) / 2
        num_active      = len(self.columns)
        overlap         = self.columns.overlap(prior_columns)
        stabile_columns = int(round(num_active * overlap))
        target_columns  = int(round(num_active * percent))
        add_columns     = target_columns - stabile_columns
        if add_columns <= 0:
            return

        eligable_columns  = np.setdiff1d(prior_columns.flat_index, self.columns.flat_index)
        eligable_excite   = self.raw_excitment[eligable_columns]
        selected_col_nums = np.argpartition(-eligable_excite, add_columns-1)[:add_columns]
        selected_columns  = eligable_columns[selected_col_nums]
        selected_index    = np.unravel_index(selected_columns, self.columns.dimensions)
        # Learn.  Note: selected columns will learn twice.  The previously
        # active segments learn now, the current most excited segments in the
        # method SP.learn().
        # Or learn not at all if theres a bug in my code...
        # if self.multisegment:
        #     if hasattr(self, 'prior_segment_excitement'):
        #         segment_excitement = self.prior_segment_excitement[selected_index]
        #         seg_idx = np.argmax(segment_excitement, axis=-1)
        #         self.proximal.learn_outputs(input_sdr=input_sdr,
        #                                     output_sdr=selected_index + (seg_idx,))
        #     self.prev_segment_excitement = self.segment_excitement
        # else:
        #     1/0
        self.columns.flat_index = np.concatenate([self.columns.flat_index, selected_columns])