diff --git a/15/main.cpp b/15/main.cpp
index 9ae02b8..88d4a1c 100644
--- a/15/main.cpp
+++ b/15/main.cpp
@@ -11,3 +11,256 @@
 В решении требуется разумно разделить код на файлы. Каждому классу - свой заголовочный файл и файл с реализацией.
 */
 
+
+#include <iostream>
+#include <fstream>
+#include <algorithm>
+#include <queue>
+#include <unordered_map>
+#include <assert.h>
+#include <list>
+#include <vector>
+#include <cmath>
+
+using std::vector;
+using std::queue;
+using std::cout;
+using std::pair;
+
+std::string PATH_TO_EXCEL_TABLE = "example1.csv";
+
+struct Point {
+    Point(double x, double y): x(x), y(y) {};
+    const double x;
+    const double y;
+};
+
+struct QuasiNode {
+    int vertex;
+    int parent;
+    double dist;
+};
+
+class Graph {
+public:
+    Graph( vector<Point> vertices ): vertices( vertices ) { adj.assign(vertices.size(), {}); };
+    void add_edge( int u, int v );
+    double MST();
+    void self_test() const;
+    void __get_optimal_path( int num, vector<bool> visited, int start, double result, int prev );
+    double get_optimal_path( int num );
+    double main_DFS();
+
+private:
+    const vector<Point> vertices;
+    vector< vector<int> > adj;
+    double get_distance( int v1, int v2 ) const;
+    void one_test( vector<Point> vertices, int dist ) const;
+    void DFS( vector<bool>& visited, double& dist, int prev );
+    double min_distance = INT_MAX;
+    int last_vertex = -1;
+};
+
+double Graph::get_distance( int v1, int v2 ) const {
+    Point point1 = vertices[v1];
+    Point point2 = vertices[v2];
+    int x = point1.x - point2.x;
+    int y = point1.y - point2.y;
+//    cout << "x: " << x << ", y: " << y << ", dist: " << sqrt( x*x + y*y ) << std::endl;
+    return sqrt( x*x + y*y );
+}
+
+void Graph::add_edge( int u, int v ) {
+    adj[u].push_back( v );
+    adj[v].push_back( u );
+}
+
+struct Less {
+    const bool operator()( QuasiNode& left, QuasiNode& right ) const {
+        return left.dist > right.dist;
+    };
+};
+
+double Graph::MST () {
+    if ( vertices.size() == 0 ) { return 0; }
+    double result = 0;
+    std::priority_queue< QuasiNode, vector< QuasiNode >, Less> positions_queue;
+    vector<bool> visited( vertices.size(), false );
+//Начинаем обход с вершины 0
+    positions_queue.push( { 0, 0 } );
+    while (!positions_queue.empty()) {
+        QuasiNode current = positions_queue.top();
+        positions_queue.pop();
+        if ( visited[current.vertex] ) { continue; }
+        visited[current.vertex] = true;
+        add_edge( current.vertex, current.parent );
+        result += current.dist;
+        for ( int sibling = 0; sibling < vertices.size(); ++sibling ) {
+            if ( visited[sibling] ) { continue; }
+            double dist = get_distance( current.vertex, sibling );
+            positions_queue.push( {sibling, current.vertex, dist} );
+        }
+    }
+    return result;
+}
+
+void Graph::one_test( vector<Point> vertices, int dist ) const {
+    Graph g( vertices );
+    assert( g.MST() == dist );
+}
+
+void Graph::self_test() const {
+    vector<Point> vertices{{1,2}, {2,4}, {2,6}, {2,2}, {4,3}};
+    one_test(vertices, 14);
+    vertices = vector<Point>{ {-2, -3}, {-2, 3}, {2, 3}, {2, -3}, {-4, 0}, {7, 0} };
+    one_test(vertices, 92);
+    vertices = vector<Point> { {0,17}, {27,17}, {12,11}, {10,5}, {13,5}, {15,8}, {9,2}, {15,1}, {19,7}, {30,0} };
+    one_test(vertices, 601);
+}
+
+// int cnt = 0;
+void Graph::__get_optimal_path( int num, vector<bool> visited, int start, double result, int prev ) {
+    bool indicator = true;
+    for ( int i = 0; i < num; ++i ) {
+        if ( visited[i] ) { continue; }
+        int curr = i;
+        indicator = false;
+        visited[i] = true;
+        __get_optimal_path( num, visited, start, result + get_distance( prev, curr ), curr );
+        visited[i] = false;
+    }
+    if ( indicator ) {
+//         cnt++;
+        result += get_distance( start, prev );
+        if ( result < min_distance ) { min_distance = result; }
+        return;
+    }
+}
+
+double Graph::get_optimal_path( int num ) {
+    double result = 0;
+    int start = 0;
+    vector<bool> visited( num, false );
+    visited[start] = true;
+    __get_optimal_path( num, visited, start, result, 0 );
+    return min_distance;
+}
+
+double Graph::main_DFS() {
+    vector<bool> visited( adj.size(), false );
+    int start = 0;
+    last_vertex = start;
+    double dist = 0;
+    visited[start] = true;
+    DFS( visited, dist, start );
+    dist += get_distance( start, last_vertex );
+    return dist;
+}
+
+void Graph::DFS( vector<bool>& visited, double& dist, int prev ) {
+    for ( int curr: adj[prev] ) {
+        if ( visited[curr] ) { continue; }
+        visited[curr] = true;
+        dist += get_distance( last_vertex, curr );
+        last_vertex = curr;
+        DFS( visited, dist, curr );
+    }
+}
+
+double generate_gaussian_noise(double mu, double sigma) {
+
+    static const double epsilon = std::numeric_limits<double>::min();
+    static const double two_pi = 2.0*3.14159265358979323846;
+
+    thread_local double z1;
+    thread_local bool generate;
+    generate = !generate;
+
+    if (!generate) {
+       return z1 * sigma + mu;
+    }
+
+    double u1, u2;
+    do {
+       u1 = rand() * (1.0 / RAND_MAX);
+       u2 = rand() * (1.0 / RAND_MAX);
+     }
+    while ( u1 <= epsilon );
+
+    double z0;
+    z0 = sqrt(-2.0 * log(u1)) * cos(two_pi * u2);
+    z1 = sqrt(-2.0 * log(u1)) * sin(two_pi * u2);
+    return z0 * sigma + mu;
+}
+
+vector<Point> generate_points( double mu, double sigma, int size ) {
+    vector<Point> result;
+    for ( int i = 0; i < size; ++i ) {
+        Point point( generate_gaussian_noise( mu, sigma ), generate_gaussian_noise( mu, sigma ) );
+        result.push_back( point );
+    }
+    return result;
+}
+
+void make_experiment( int N_start, int N_end, int n_each ) {
+    std::ofstream myfile;
+    myfile.open (PATH_TO_EXCEL_TABLE);
+    myfile << "n,absolute opt,absolute,mean,min,max,median,percentile(98)\n";
+    for ( int N = N_start; N <= N_end; ++ N ) {
+        double sum = 0;
+        double sum_opt = 0;
+        double squares = 0;
+        double squares_opt = 0;
+        double min = 2;
+        double max = -1;
+        vector<double> values;
+        for ( int i = 0; i < n_each; ++i ) {
+            vector<Point> points = generate_points( 0 , 1, N );
+            Graph g( points );
+            g.MST();
+            double current = g.main_DFS();
+            sum += current;
+            squares += ( current * current );
+            auto start = std::chrono::high_resolution_clock::now();
+            double current_opt = g.get_optimal_path( N );
+            auto finish = std::chrono::high_resolution_clock::now();
+            std::chrono::duration<double> elapsed = finish - start;
+//            std::cout << "Elapsed time: " << elapsed.count() << " s\n";
+            sum_opt += current_opt;
+            squares_opt += ( current_opt * current_opt );
+            if ( (current_opt / current < min) && (current_opt / current > 0 ) ) { min = current_opt / current; }
+            if ( current_opt / current > max ) { max = current_opt / current; }
+            values.push_back( current_opt / current );
+
+
+        }
+        double mean = sum / n_each;
+        //Вначале сигма будет дисперсией
+        double sigma = ( squares - 2 * mean * sum + n_each * mean * mean ) / n_each;
+        sigma = sqrt( sigma );
+        cout << "        N = " << N << "         " << std::endl;
+//        cout << "mean = " << mean << ", sigma = " << sigma << std::endl;
+        //t квантиль для n_each = 100 будет 1.6602
+//        cout << "Доверительный интервал для приближенного решения\n" << mean << " ± " << 1.6602 * sigma / sqrt(n_each) << std::endl;
+
+        double mean_opt = sum_opt / n_each;
+        double sigma_opt = ( squares_opt - 2 * mean_opt + n_each * mean_opt * mean_opt ) / n_each;
+        sigma_opt = sqrt( sigma_opt );
+//        cout << "mean_opt = " << mean_opt << ", sigma_opt = " << sigma_opt << std::endl;
+//        cout << "Доверительный интервал для точного решения\n" << mean_opt << " ± " << 1.6602 * sigma_opt / sqrt(n_each) << std::endl;
+        cout << std::endl;
+        sort( values.begin(), values.end() );
+        double median = values[values.size() / 2];
+        double percentile = values[n_each * 0.98];
+        myfile << N << ',' << mean_opt << ',' << mean << ',' << mean_opt/mean << ',' << min << ',' << max << ',' << median << ',' << percentile << '\n';
+
+    }
+    myfile.close();
+}
+
+int main() {
+//    make_experiment( 2, 10, 100 );
+    cout << "Вывод: \n1)В среднем результат приближенного решения удовлетворителен для всех рассматриваемых n. Меньше, чем 0.65 отношение оптимального и приближенного решения не опускалось. Однако есть тенденция. С увеличением n приближенное решение становится монотонно хуже. Необходимо дальнейшее исследование.\n\n2)Если в среднем приближенное решение показывает себя хорошо, то в худшем случае результат значительно хуже. Для n = 10 соотношение равно 0.2\n\n";
+    return 0;
+}
+