def _sample_kmc2_chain(): """Returns previous centers as well as a new center sampled using k-MC2.""" # Extract the subset from the underlying batch. start = i * self._kmc2_chain_length end = start + self._kmc2_chain_length subset = first_shard[start:end] # Compute the distances from points in the subset to previous centers. _, distances = gen_clustering_ops.nearest_neighbors( subset, self._cluster_centers, 1) # Sample index of new center using k-MC2 Markov chain. new_center_index = gen_clustering_ops.kmc2_chain_initialization( array_ops.squeeze(distances), self._seed) # Extract actual new center. newly_sampled_center = array_ops.reshape( subset[new_center_index], [1, -1]) # Return concatenation with previously sampled centers. if self._distance_metric == COSINE_DISTANCE: newly_sampled_center = nn_impl.l2_normalize( newly_sampled_center, dim=1) return array_ops.concat( [self._cluster_centers, newly_sampled_center], 0)
def _sample_kmc2_chain(): """Returns previous centers as well as a new center sampled using k-MC2. """ # Extract the subset from the underlying batch. start = i * self._kmc2_chain_length end = start + self._kmc2_chain_length subset = first_shard[start:end] # Compute the distances from points in the subset to previous centers. _, distances = gen_clustering_ops.nearest_neighbors( subset, self._cluster_centers, 1) # Sample index of new center using k-MC2 Markov chain. new_center_index = gen_clustering_ops.kmc2_chain_initialization( array_ops.squeeze(distances), self._seed) # Extract actual new center. newly_sampled_center = array_ops.reshape(subset[new_center_index], [1, -1]) # Return concatenation with previously sampled centers. if self._distance_metric == COSINE_DISTANCE: newly_sampled_center = nn_impl.l2_normalize( newly_sampled_center, dim=1) return array_ops.concat([self._cluster_centers, newly_sampled_center], 0)