/*******************************************************************************
* 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 <iomanip>
#include <iostream>
#include "example_util/utils.hpp"
#include "oneapi/dal/algo/kmeans.hpp"
#include "oneapi/dal/algo/kmeans_init.hpp"
#include "oneapi/dal/io/csv.hpp"
namespace dal = oneapi::dal;
template <typename Method>
void run(const dal::table& x_train, const std::string& method_name) {
constexpr std::int64_t cluster_count = 20;
constexpr std::int64_t max_iteration_count = 1000;
constexpr double accuracy_threshold = 0.01;
const auto kmeans_init_desc =
dal::kmeans_init::descriptor<float, Method>().set_cluster_count(cluster_count);
const auto result_init = dal::compute(kmeans_init_desc, x_train);
const auto kmeans_desc = dal::kmeans::descriptor<>()
.set_cluster_count(cluster_count)
.set_max_iteration_count(max_iteration_count)
.set_accuracy_threshold(accuracy_threshold);
const auto result_train = dal::train(kmeans_desc, x_train, result_init.get_centroids());
std::cout << "Method: " << method_name << std::endl;
std::cout << "Max iteration count: " << max_iteration_count
<< ", Accuracy threshold: " << accuracy_threshold << std::endl;
std::cout << "Iteration count: " << result_train.get_iteration_count()
<< ", Objective function value: " << result_train.get_objective_function_value()
<< '\n'
<< std::endl;
}
int main(int argc, char const* argv[]) {
const auto train_data_file_name = get_data_path("kmeans_init_dense.csv");
const auto x_train = dal::read<dal::table>(dal::csv::data_source{ train_data_file_name });
run<dal::kmeans_init::method::dense>(x_train, "dense");
run<dal::kmeans_init::method::random_dense>(x_train, "random_dense");
run<dal::kmeans_init::method::plus_plus_dense>(x_train, "plus_plus_dense");
run<dal::kmeans_init::method::parallel_plus_dense>(x_train, "parallel_plus_dense");
return 0;
}
