/*******************************************************************************
* Copyright 2023 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 <iomanip>
#include <iostream>
#include <sycl/sycl.hpp>
#ifndef ONEDAL_DATA_PARALLEL
#define ONEDAL_DATA_PARALLEL
#endif
#include "oneapi/dal/algo/pca.hpp"
#include "oneapi/dal/io/csv.hpp"
#include "example_util/utils.hpp"
namespace dal = oneapi::dal;
namespace pca = dal::pca;
template <typename Method>
void run(sycl::queue& q, const dal::table& x_train, const std::string& method_name, bool whiten) {
const std::int64_t nBlocks = 10;
pca::partial_train_result<> partial_result;
const auto pca_desc = pca::descriptor<>()
.set_component_count(5)
.set_deterministic(true)
.set_normalization_mode(pca::normalization::mean_center)
.set_whiten(whiten);
auto input_table = split_table_by_rows<double>(x_train, nBlocks);
for (std::int64_t i = 0; i < nBlocks; i++) {
partial_result = dal::partial_train(q, pca_desc, partial_result, input_table[i]);
}
auto result_train = dal::finalize_train(q, pca_desc, partial_result);
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;
std::cout << "Singular Values:\n" << result_train.get_singular_values() << std::endl;
std::cout << "Variances:\n" << result_train.get_variances() << std::endl;
std::cout << "Means:\n" << result_train.get_means() << std::endl;
std::cout << "Explained variances ratio:\n"
<< result_train.get_explained_variances_ratio() << std::endl;
const auto result_infer = dal::infer(q, 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_non_normalized.csv");
const auto x_train = dal::read<dal::table>(dal::csv::data_source{ train_data_file_name });
for (auto d : list_devices()) {
std::cout << "Running on " << d.get_platform().get_info<sycl::info::platform::name>()
<< ", " << d.get_info<sycl::info::device::name>() << "\n"
<< std::endl;
auto q = sycl::queue{ d };
run<pca::method::cov>(q, x_train, "Training method: Online Covariance Whiten:false", false);
run<pca::method::cov>(q, x_train, "Training method: Online Covariance Whiten:true", true);
}
return 0;
}
