/*
 * Copyright (c) 2019-2021, NVIDIA CORPORATION.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include "knn_opg_common.cuh"

namespace ML {
namespace KNN {
namespace opg {

using namespace knn_common;

template struct KNN_CL_params<float, int64_t, float, int>;

void knn_classify(raft::handle_t &handle, std::vector<Matrix::Data<int> *> *out,
                  std::vector<std::vector<float *>> *probas,
                  std::vector<Matrix::floatData_t *> &idx_data,
                  Matrix::PartDescriptor &idx_desc,
                  std::vector<Matrix::floatData_t *> &query_data,
                  Matrix::PartDescriptor &query_desc,
                  std::vector<std::vector<int *>> &y,
                  std::vector<int *> &uniq_labels, std::vector<int> &n_unique,
                  bool rowMajorIndex, bool rowMajorQuery, bool probas_only,
                  int k, size_t batch_size, bool verbose) {
  knn_operation knn_op =
    probas_only ? knn_operation::class_proba : knn_operation::classification;
  KNN_CL_params<float, int64_t, float, int> params(
    knn_op, &idx_data, &idx_desc, &query_data, &query_desc, rowMajorIndex,
    rowMajorQuery, k, batch_size, verbose, n_unique.size(), &y, &n_unique,
    &uniq_labels, out, probas);

  cuda_utils cutils(handle);
  opg_knn(params, cutils);
}
};  // namespace opg
};  // namespace KNN
};  // namespace ML
