Developer Guide and Reference

  • 2021.2
  • 03/26/2021
  • Public Content
Contents

pca_dense_batch.cpp

/******************************************************************************* * Copyright 2020-2021 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. *******************************************************************************/ #include "oneapi/dal/algo/pca.hpp" #include "oneapi/dal/io/csv.hpp" #include "example_util/utils.hpp" namespace dal = oneapi::dal; template <typename Method> void run(const dal::table& x_train, const std::string& method_name) { const auto pca_desc = dal::pca::descriptor<float, Method>().set_component_count(5).set_deterministic(true); const auto result_train = dal::train(pca_desc, x_train); std::cout << method_name << "\n" << std::endl; std::cout << "Eigenvectors:\n" << result_train.get_eigenvectors() << std::endl; std::cout << "Eigenvalues:\n" << result_train.get_eigenvalues() << std::endl; const auto result_infer = dal::infer(pca_desc, result_train.get_model(), x_train); std::cout << "Transformed data:\n" << result_infer.get_transformed_data() << std::endl; } int main(int argc, char const* argv[]) { const auto train_data_file_name = get_data_path("pca_normalized.csv"); const auto x_train = dal::read<dal::table>(dal::csv::data_source{ train_data_file_name }); run<dal::pca::method::cov>(x_train, "Training method: Covariance"); run<dal::pca::method::svd>(x_train, "Training method: SVD"); return 0; }

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