def evaluate(self, eval_features, return_realism=False, return_neighbors=False):
        """Evaluate if new feature vectors are in the estimated manifold."""
        num_eval_images = eval_features.shape[0]
        num_ref_images = self.D.shape[0]
        distance_batch = np.zeros([self.row_batch_size, num_ref_images], dtype=np.float16)
        batch_predictions = np.zeros([num_eval_images, self.num_nhoods], dtype=np.int32)
        #max_realism_score = np.zeros([num_eval_images,], dtype=np.float32)
        realism_score = np.zeros([num_eval_images,], dtype=np.float32)
        nearest_indices = np.zeros([num_eval_images,], dtype=np.int32)

        for begin1 in range(0, num_eval_images, self.row_batch_size):
            end1 = min(begin1 + self.row_batch_size, num_eval_images)
            feature_batch = eval_features[begin1:end1]

            for begin2 in range(0, num_ref_images, self.col_batch_size):
                end2 = min(begin2 + self.col_batch_size, num_ref_images)
                ref_batch = self._ref_features[begin2:end2]

                distance_batch[0:end1-begin1, begin2:end2] = self._distance_block.pairwise_distances(feature_batch, ref_batch)

            # From the minibatch of new feature vectors, determine if they are in the estimated manifold.
            # If a feature vector is inside a hypersphere of some reference sample, then the new sample lies on the estimated manifold.
            # The radii of the hyperspheres are determined from distances of neighborhood size k.
            samples_in_manifold = distance_batch[0:end1-begin1, :, None] <= self.D
            batch_predictions[begin1:end1] = np.any(samples_in_manifold, axis=1).astype(np.int32)

            #max_realism_score[begin1:end1] = np.max(self.D[:, 0] / (distance_batch[0:end1-begin1, :] + 1e-18), axis=1)
            #nearest_indices[begin1:end1] = np.argmax(self.D[:, 0] / (distance_batch[0:end1-begin1, :] + 1e-18), axis=1)
            nearest_indices[begin1:end1] = np.argmin(distance_batch[0:end1-begin1, :], axis=1)
            realism_score[begin1:end1] = self.D[nearest_indices[begin1:end1], 0] / np.min(distance_batch[0:end1-begin1, :], axis=1)

        if return_realism and return_neighbors:
            return batch_predictions, realism_score, nearest_indices
        elif return_realism:
            return batch_predictions, realism_score
        elif return_neighbors:
            return batch_predictions, nearest_indices

        return batch_predictions
Evaluate if new feature vectors are in the estimated manifold.
converters/stylegan2ada_tf_official/metrics/precision_recall.py