single_variate_multi_optima_function.h

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/*
 * Copyright 2023 MusicScience37 (Kenta Kabashima)
 *
 * 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.
 */
#pragma once
 
#include <algorithm>
#include <random>
 
#include <Eigen/Core>
 
#include "num_collect/base/exception.h"
#include "num_collect/base/index_type.h"
#include "num_collect/base/precondition.h"
#include "num_collect/logging/logging_macros.h"
 
namespace num_prob_collect::opt {
 
class single_variate_multi_optima_function {
public:
    using variable_type = double;
    using value_type = double;
 
    single_variate_multi_optima_function(Eigen::ArrayXd optimal_variables,
        Eigen::ArrayXd optimal_values, Eigen::ArrayXd coefficients)
        : optimal_variables_{std::move(optimal_variables)},
          optimal_values_(std::move(optimal_values)),
          coefficients_(std::move(coefficients)) {}
 
    void evaluate_on(double variable) noexcept {
        value_ = ((optimal_variables_ - variable).square() * coefficients_ +
            optimal_values_)
                     .minCoeff();
    }
 
    [[nodiscard]] auto value() const noexcept -> const double& {
        return value_;
    }
 
    [[nodiscard]] auto optimal_variables() const noexcept
        -> const Eigen::ArrayXd& {
        return optimal_variables_;
    }
 
    [[nodiscard]] auto optimal_values() const noexcept
        -> const Eigen::ArrayXd& {
        return optimal_values_;
    }
 
    [[nodiscard]] auto coefficients() const noexcept -> const Eigen::ArrayXd& {
        return coefficients_;
    }
 
private:
    Eigen::ArrayXd optimal_variables_;
 
    Eigen::ArrayXd optimal_values_;
 
    Eigen::ArrayXd coefficients_;
 
    double value_{0.0};
};
 
class random_single_variate_multi_optima_function_generator {
public:
    static constexpr double min_variable = -10.0;
 
    static constexpr double max_variable = 10.0;
 
    static constexpr double global_optimal_value = 0.0;
 
    static constexpr double min_non_global_optimal_value = 1.0;
 
    static constexpr double max_non_global_optimal_value = 5.0;
 
    static constexpr num_collect::index_type default_num_local_optima = 5;
 
    explicit random_single_variate_multi_optima_function_generator(
        num_collect::index_type num_local_optima = default_num_local_optima)
        : num_local_optima_(num_local_optima) {
        NUM_COLLECT_PRECONDITION(
            num_local_optima >= 2, "num_local_optima must be at least 2.");
    }
 
    [[nodiscard]] auto operator()() noexcept
        -> single_variate_multi_optima_function {
        Eigen::ArrayXd optimal_variables;
        optimal_variables.resize(num_local_optima_);
        std::generate(optimal_variables.begin(), optimal_variables.end(),
            [this] { return optimal_variable_dist_(generator_); });
 
        Eigen::ArrayXd optimal_values;
        optimal_values.resize(num_local_optima_);
        optimal_values[0] = global_optimal_value;
        std::generate(optimal_values.begin() + 1, optimal_values.end(),
            [this] { return non_global_optimal_value_dist_(generator_); });
 
        Eigen::ArrayXd coefficients;
        coefficients.resize(num_local_optima_);
        std::generate(coefficients.begin(), coefficients.end(),
            [this] { return coefficients_dist_(generator_); });
 
        return single_variate_multi_optima_function{
            std::move(optimal_variables), std::move(optimal_values),
            std::move(coefficients)};
    }
 
private:
    std::mt19937
        generator_{};  // NOLINT(cert-msc32-c,cert-msc51-cpp): For reproducibility.
 
    std::uniform_real_distribution<double> optimal_variable_dist_{
        min_variable, max_variable};
 
    std::uniform_real_distribution<double> non_global_optimal_value_dist_{
        min_non_global_optimal_value, max_non_global_optimal_value};
 
    std::uniform_real_distribution<double> coefficients_dist_{
        0.5, 3.0};  // NOLINT
 
    num_collect::index_type num_local_optima_;
};
 
}  // namespace num_prob_collect::opt