chore: import upstream snapshot with attribution

math/integral_approximation2.cpp

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+/**
+ * @file
+ * @brief [Monte Carlo
+ * Integration](https://en.wikipedia.org/wiki/Monte_Carlo_integration)
+ *
+ * @details
+ * In mathematics, Monte Carlo integration is a technique for numerical
+ * integration using random numbers. It is a particular Monte Carlo method that
+ * numerically computes a definite integral. While other algorithms usually
+ * evaluate the integrand at a regular grid, Monte Carlo randomly chooses points
+ * at which the integrand is evaluated. This method is particularly useful for
+ * higher-dimensional integrals.
+ *
+ * This implementation supports arbitrary pdfs.
+ * These pdfs are sampled using the [Metropolis-Hastings
+ * algorithm](https://en.wikipedia.org/wiki/Metropolis–Hastings_algorithm). This
+ * can be swapped out by every other sampling techniques for example the inverse
+ * method. Metropolis-Hastings was chosen because it is the most general and can
+ * also be extended for a higher dimensional sampling space.
+ *
+ * @author [Domenic Zingsheim](https://github.com/DerAndereDomenic)
+ */
+
+#define _USE_MATH_DEFINES  /// for M_PI on windows
+#include <cmath>           /// for math functions
+#include <cstdint>         /// for fixed size data types
+#include <ctime>           /// for time to initialize rng
+#include <functional>      /// for function pointers
+#include <iostream>        /// for std::cout
+#include <random>          /// for random number generation
+#include <vector>          /// for std::vector
+
+/**
+ * @namespace math
+ * @brief Math algorithms
+ */
+namespace math {
+/**
+ * @namespace monte_carlo
+ * @brief Functions for the [Monte Carlo
+ * Integration](https://en.wikipedia.org/wiki/Monte_Carlo_integration)
+ * implementation
+ */
+namespace monte_carlo {
+
+using Function = std::function<double(
+    double&)>;  /// short-hand for std::functions used in this implementation
+
+/**
+ * @brief Generate samples according to some pdf
+ * @details This function uses Metropolis-Hastings to generate random numbers.
+ * It generates a sequence of random numbers by using a markov chain. Therefore,
+ * we need to define a start_point and the number of samples we want to
+ * generate. Because the first samples generated by the markov chain may not be
+ * distributed according to the given pdf, one can specify how many samples
+ * should be discarded before storing samples.
+ * @param start_point The starting point of the markov chain
+ * @param pdf The pdf to sample
+ * @param num_samples The number of samples to generate
+ * @param discard How many samples should be discarded at the start
+ * @returns A vector of size num_samples with samples distributed according to
+ * the pdf
+ */
+std::vector<double> generate_samples(const double& start_point,
+                                     const Function& pdf,
+                                     const uint32_t& num_samples,
+                                     const uint32_t& discard = 100000) {
+    std::vector<double> samples;
+    samples.reserve(num_samples);
+
+    double x_t = start_point;
+
+    std::default_random_engine generator;
+    std::uniform_real_distribution<double> uniform(0.0, 1.0);
+    std::normal_distribution<double> normal(0.0, 1.0);
+    generator.seed(time(nullptr));
+
+    for (uint32_t t = 0; t < num_samples + discard; ++t) {
+        // Generate a new proposal according to some mutation strategy.
+        // This is arbitrary and can be swapped.
+        double x_dash = normal(generator) + x_t;
+        double acceptance_probability = std::min(pdf(x_dash) / pdf(x_t), 1.0);
+        double u = uniform(generator);
+
+        // Accept "new state" according to the acceptance_probability
+        if (u <= acceptance_probability) {
+            x_t = x_dash;
+        }
+
+        if (t >= discard) {
+            samples.push_back(x_t);
+        }
+    }
+
+    return samples;
+}
+
+/**
+ * @brief Compute an approximation of an integral using Monte Carlo integration
+ * @details The integration domain [a,b] is given by the pdf.
+ * The pdf has to fulfill the following conditions:
+ * 1) for all x \in [a,b] : p(x) > 0
+ * 2) for all x \not\in [a,b] : p(x) = 0
+ * 3) \int_a^b p(x) dx = 1
+ * @param start_point The start point of the Markov Chain (see generate_samples)
+ * @param function The function to integrate
+ * @param pdf The pdf to sample
+ * @param num_samples The number of samples used to approximate the integral
+ * @returns The approximation of the integral according to 1/N \sum_{i}^N f(x_i)
+ * / p(x_i)
+ */
+double integral_monte_carlo(const double& start_point, const Function& function,
+                            const Function& pdf,
+                            const uint32_t& num_samples = 1000000) {
+    double integral = 0.0;
+    std::vector<double> samples =
+        generate_samples(start_point, pdf, num_samples);
+
+    for (double sample : samples) {
+        integral += function(sample) / pdf(sample);
+    }
+
+    return integral / static_cast<double>(samples.size());
+}
+
+}  // namespace monte_carlo
+}  // namespace math
+
+/**
+ * @brief Self-test implementations
+ * @returns void
+ */
+static void test() {
+    std::cout << "Disclaimer: Because this is a randomized algorithm,"
+              << std::endl;
+    std::cout
+        << "it may happen that singular samples deviate from the true result."
+        << std::endl
+        << std::endl;
+    ;
+
+    math::monte_carlo::Function f;
+    math::monte_carlo::Function pdf;
+    double integral = 0;
+    double lower_bound = 0, upper_bound = 0;
+
+    /* \int_{-2}^{2} -x^2 + 4 dx */
+    f = [&](double& x) { return -x * x + 4.0; };
+
+    lower_bound = -2.0;
+    upper_bound = 2.0;
+    pdf = [&](double& x) {
+        if (x >= lower_bound && x <= -1.0) {
+            return 0.1;
+        }
+        if (x <= upper_bound && x >= 1.0) {
+            return 0.1;
+        }
+        if (x > -1.0 && x < 1.0) {
+            return 0.4;
+        }
+        return 0.0;
+    };
+
+    integral = math::monte_carlo::integral_monte_carlo(
+        (upper_bound - lower_bound) / 2.0, f, pdf);
+
+    std::cout << "This number should be close to 10.666666: " << integral
+              << std::endl;
+
+    /* \int_{0}^{1} e^x dx */
+    f = [&](double& x) { return std::exp(x); };
+
+    lower_bound = 0.0;
+    upper_bound = 1.0;
+    pdf = [&](double& x) {
+        if (x >= lower_bound && x <= 0.2) {
+            return 0.1;
+        }
+        if (x > 0.2 && x <= 0.4) {
+            return 0.4;
+        }
+        if (x > 0.4 && x < upper_bound) {
+            return 1.5;
+        }
+        return 0.0;
+    };
+
+    integral = math::monte_carlo::integral_monte_carlo(
+        (upper_bound - lower_bound) / 2.0, f, pdf);
+
+    std::cout << "This number should be close to 1.7182818: " << integral
+              << std::endl;
+
+    /* \int_{-\infty}^{\infty} sinc(x) dx, sinc(x) = sin(pi * x) / (pi * x)
+       This is a difficult integral because of its infinite domain.
+       Therefore, it may deviate largely from the expected result.
+    */
+    f = [&](double& x) { return std::sin(M_PI * x) / (M_PI * x); };
+
+    pdf = [&](double& x) {
+        return 1.0 / std::sqrt(2.0 * M_PI) * std::exp(-x * x / 2.0);
+    };
+
+    integral = math::monte_carlo::integral_monte_carlo(0.0, f, pdf, 10000000);
+
+    std::cout << "This number should be close to 1.0: " << integral
+              << std::endl;
+}
+
+/**
+ * @brief Main function
+ * @returns 0 on exit
+ */
+int main() {
+    test();  // run self-test implementations
+    return 0;
+}