Developer Guide and Reference

  • 2021.6
  • 04/11/2022
  • Public Content
Contents

knn_reg_brute_force_dense_batch.cpp

/******************************************************************************* * Copyright 2021 Intel 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. *******************************************************************************/ #ifndef ONEDAL_DATA_PARALLEL #define ONEDAL_DATA_PARALLEL #endif #include "oneapi/dal/algo/knn.hpp" #include "oneapi/dal/io/csv.hpp" #include "oneapi/dal/exceptions.hpp" #include "example_util/utils.hpp" namespace dal = oneapi::dal; void run(sycl::queue& q) { const auto train_data_file_name = get_data_path("knn_regression_train_data.csv"); const auto train_response_file_name = get_data_path("knn_regression_train_responses.csv"); const auto test_data_file_name = get_data_path("knn_regression_test_data.csv"); const auto test_response_file_name = get_data_path("knn_regression_test_responses.csv"); const auto x_train = dal::read<dal::table>(q, dal::csv::data_source{ train_data_file_name }); const auto y_train = dal::read<dal::table>(q, dal::csv::data_source{ train_response_file_name }); using float_t = float; using method_t = dal::knn::method::by_default; using task_t = dal::knn::task::regression; using descriptor_t = dal::knn::descriptor<float_t, method_t, task_t>; const auto knn_desc_uniform = descriptor_t(5); const auto knn_desc_distance = descriptor_t(5).set_voting_mode(dal::knn::voting_mode::distance); const auto x_test = dal::read<dal::table>(q, dal::csv::data_source{ test_data_file_name }); const auto y_test = dal::read<dal::table>(q, dal::csv::data_source{ test_response_file_name }); const auto train_result_uniform = dal::train(q, knn_desc_uniform, x_train, y_train); const auto train_result_distance = dal::train(q, knn_desc_distance, x_train, y_train); const auto test_result_uniform = dal::infer(q, knn_desc_uniform, x_test, train_result_uniform.get_model()); const auto test_result_distance = dal::infer(q, knn_desc_distance, x_test, train_result_distance.get_model()); std::cout << "Test results (uniform regression):\n" << test_result_uniform.get_responses() << std::endl; std::cout << "Test results (distance regression):\n" << test_result_distance.get_responses() << std::endl; std::cout << "True responses:\n" << y_test << std::endl; } int main(int argc, char const* argv[]) { for (auto d : list_devices()) { std::cout << "Running on " << d.get_info<sycl::info::device::name>() << "\n" << std::endl; auto q = sycl::queue{ d }; // TODO: Should be deleted after regression algorithm introduction on CPU try { run(q); } catch (const dal::unimplemented& e) { std::cout << e.what() << std::endl; } } return 0; }

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