neighbour_sets.py

    def _nearest_neighbours(self, codes_data, indices):
        '''Ascertain indices in memory bank of the k-nearest neighbours to given codes
        
        Returns:
            indices_nearest (numpy.ndarray): Boolean array of k-nearest neighbours for the batch of codes

        '''
        self.neighbour_finder.fit(self.memory_bank.vectors)
        indices_nearest = self.neighbour_finder.kneighbors(codes_data, return_distance=False)

        return self.memory_bank.mask(indices_nearest)

    def _close_grouper(self, indices):
        '''Ascertain indices in memory bank of vectors that are in the same cluster as vectors of given indices

        Returns:
            indices_close (numpy.ndarray): Boolean array of close neighbours for the batch of codes

        '''
        memberships = [[]] * len(indices)
        for clusterer in self.clusterer:
            clusterer.fit(self.memory_bank.vectors)
            for k_index, cluster_index in enumerate(clusterer.labels_[indices]):
                other_members = np.where(clusterer.labels_ == cluster_index)[0]
                other_members_union = np.union1d(memberships[k_index], other_members)
                memberships[k_index] = other_members_union.astype(int)

        return self.memory_bank.mask(np.array(memberships, dtype=object))

    def _intersecter(self, n1, n2):
        '''Compute set intersection of two boolean arrays'''
        return np.array([[v1 and v2 for v1, v2 in zip(n1_x, n2_x)] for n1_x, n2_x in zip(n1, n2)])