broken img calc; change dir struct

1 files changed, 0 insertions, 174 deletions

diff --git a/genetic_algos.h b/genetic_algos.h
deleted file mode 100644
index 8dea732..0000000
--- a/genetic_algos.h
+++ /dev/null
@@ -1,174 +0,0 @@
-#pragma once
-
-// #define _USE_MATH_DEFINES
-#include <algorithm>
-#include <cmath>
-#include <numbers>
-#include <random>
-// TODO: <execution>
-
-/*
-
-a, sigma
-
-*/
-
-// #define POP_SIZE 30
-// #define NUM_IT 25
-// #define COL_SIZE 16
-
-// using Column = double;
-
-static std::random_device random_device;
-static std::mt19937 gen(random_device());
-static std::uniform_real_distribution<> dis01(0., 1.);
-static std::uniform_real_distribution<> disDelta2(-2., 2.);
-
-template<typename T, size_t COL_SIZE = 16>
-struct Item {
-    double a, sigma; // E, sigma = sqrt(D)
-    T* column;
-
-    double W;
-
-    Item() {}
-
-    Item(double a_, double s, T * column_) : a(a_), sigma(s), column(column_) {
-        update();
-    }
-
-    double gauss(double t) {
-        return std::exp(std::pow((t - a) / sigma,
-                                 2) /
-                        2) /
-               std::sqrt(2 * std::numbers::pi) /
-               sigma;
-    }
-
-    double F() { // objective function
-        double s = 0;
-        int x0 = std::floor(.5 + a - 3 * sigma);
-        double x1 = std::round(a + 3 * sigma);
-
-        for (int j = x0; j <= x1; j++)
-            s += std::pow(gauss(j) - column[j], 2);
-
-        return s;
-    }
-
-    void update() {
-        W = F();
-    }
-
-    // action
-
-    Item move() {
-        double a1 = a + disDelta2(gen);
-        double sigma1 = sigma * (1 + disDelta2(gen)); // a: ~  +- 2 pixel
-
-        return Item(a1, sigma1, column);
-    }
-
-    // a = q * a1 + (1 - q) * a2,   sigma = q * s1 + (1 - q) * s2
-    Item crossover(const Item& other) {
-        double q = dis01(gen);
-        double a_ = q * a + (1 - q) * other.a;
-        double sigma_ = q * sigma + (1 - q) * other.sigma;
-
-        return Item(a_, sigma_, column);
-    }
-};
-
-template <typename T = uint16_t,
-         size_t POP_SIZE = 30,
-         size_t COL_SIZE = 16,
-         size_t NUM_IT = 25,
-         double maxW = .01>
-struct Algo {
-    T * column;
-    using I = Item<T, COL_SIZE>;
-    std::vector<I> population;
-    std::uniform_real_distribution<double> disA { 0., double(1.) };
-
-    double targetW;
-
-    Algo(T * column_): column(column_) {
-        init();
-    }
-
-    I getNewItem() {
-        double a = rand() % (COL_SIZE * 1000) / 1000.;
-        double sigma = rand() % (COL_SIZE * 100) / 1000.;
-
-        return I(a, sigma, column);
-    }
-
-    void init() {
-        for (size_t i = 0; i < POP_SIZE; i++) {
-            population.push_back(getNewItem());
-        }
-    }
-
-    bool stopCondition() {
-        // return population[0].W <= targetW;
-        return population[0].W <= maxW;
-    }
-
-    I run() {
-        for (int it = 0; it < NUM_IT; it++) {
-            work();
-
-            // if (stopCondition())
-                // break;
-        }
-
-        return population[0];
-    }
-
-    void work() {
-        move();
-        crossover();
-        addNew();
-
-        sort();
-
-        remove();
-    }
-
-    void sort() {
-        // sort vector ASC
-        std::sort(population.begin(), population.end(),
-                  [](const auto & a, const auto & b)
-                  {
-                      return a.W < b.W;
-                  });
-    }
-
-    void remove() {
-        population.erase(population.begin() + POP_SIZE, population.end());
-    }
-
-    void move() {
-        for (I& item : population) {
-            population.push_back(item.move());
-        }
-    }
-
-    void addNew() {
-        for (int i = 0; i < 5; i++) {
-            population.push_back(getNewItem());
-        }
-    }
-
-    void crossover() {
-        for (int i1 = 0; i1 < 4; i1++) {
-
-            for (int i2 = i1 + 1; i2 < 4; i2++) {
-                I& x1 = population[i1];
-                I& x2 = population[i2];
-                 // a = q * a1 + (1 - q) * a2,   sigma = q * s1 + (1 - q) * s2
-                population.push_back(x1.crossover(x2));
-            }
-        }
-    }
-};