kohonen_som_topology.cpp File Reference

Kohonen self organizing map (topological map) More...

#include <algorithm>
#include <cmath>
#include <cstdlib>
#include <ctime>
#include <fstream>
#include <iostream>
#include <valarray>
#include <vector>

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Namespaces
	machine_learning
	Machine learning algorithms.

Macros
#define	_USE_MATH_DEFINES
#define	MIN_DISTANCE   1e-4
	Minimum average distance of image nodes.

Functions
double	_random (double a, double b)
int	save_2d_data (const char *fname, const std::vector< std::valarray< double >> &X)
void	get_min_2d (const std::vector< std::valarray< double >> &X, double *val, int *x_idx, int *y_idx)
int	machine_learning::save_u_matrix (const char *fname, const std::vector< std::vector< std::valarray< double >>> &W)
double	machine_learning::update_weights (const std::valarray< double > &X, std::vector< std::vector< std::valarray< double >>> *W, std::vector< std::valarray< double >> *D, double alpha, int R)
void	machine_learning::kohonen_som (const std::vector< std::valarray< double >> &X, std::vector< std::vector< std::valarray< double >>> *W, double alpha_min)
void	test_2d_classes (std::vector< std::valarray< double >> *data)
void	test1 ()
void	test_3d_classes1 (std::vector< std::valarray< double >> *data)
void	test2 ()
void	test_3d_classes2 (std::vector< std::valarray< double >> *data)
void	test3 ()
double	get_clock_diff (clock_t start_t, clock_t end_t)
int	main (int argc, char **argv)

Detailed Description

Kohonen self organizing map (topological map)

Author

Krishna Vedala This example implements a powerful unsupervised learning algorithm called as a self organizing map. The algorithm creates a connected network of weights that closely follows the given data points. This thus creates a topological map of the given data i.e., it maintains the relationship between varipus data points in a much higher dimesional space by creating an equivalent in a 2-dimensional space.

Note

This C++ version of the program is considerable slower than its C counterpart

The compiled code is much slower when compiled with MS Visual C++ 2019 than with GCC on windows

See also

kohonen_som_trace.cpp

Function Documentation

get_clock_diff()

double get_clock_diff	(	clock_t	start_t,
		clock_t	end_t
	)

Convert clock cycle difference to time in seconds

Parameters

[in]	start_t	start clock
[in]	end_t	end clock

Returns

time difference in seconds

                                                      {
    return static_cast<double>(end_t - start_t) / CLOCKS_PER_SEC;
}

main()

int main	(	int	argc,
		char **	argv
	)

Main function

                                {
#ifdef _OPENMP
    std::cout << "Using OpenMP based parallelization\n";
#else
    std::cout << "NOT using OpenMP based parallelization\n";
#endif
 
    std::srand(std::time(nullptr));
 
    std::clock_t start_clk = std::clock();
    test1();
    auto end_clk = std::clock();
    std::cout << "Test 1 completed in " << get_clock_diff(start_clk, end_clk)
              << " sec\n";
 
    start_clk = std::clock();
    test2();
    end_clk = std::clock();
    std::cout << "Test 2 completed in " << get_clock_diff(start_clk, end_clk)
              << " sec\n";
 
    start_clk = std::clock();
    test3();
    end_clk = std::clock();
    std::cout << "Test 3 completed in " << get_clock_diff(start_clk, end_clk)
              << " sec\n";
 
    std::cout
        << "(Note: Calculated times include: creating test sets, training "
           "model and writing files to disk.)\n\n";
    return 0;
}

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test1()

void test1

(

)

Test that creates a random set of points distributed in four clusters in circumference of a circle and trains an SOM that finds that circular pattern. The following CSV files are created to validate the execution:

• test1.csv: random test samples points with a circular pattern
• w11.csv: initial random map
• w12.csv: trained SOM map

             {
    int j, N = 300;
    int features = 2;
    int num_out = 30;
    std::vector<std::valarray<double>> X(N);
    std::vector<std::vector<std::valarray<double>>> W(num_out);
    for (int i = 0; i < std::max(num_out, N); i++) {
        // loop till max(N, num_out)
        if (i < N)  // only add new arrays if i < N
            X[i] = std::valarray<double>(features);
        if (i < num_out) {  // only add new arrays if i < num_out
            W[i] = std::vector<std::valarray<double>>(num_out);
            for (int k = 0; k < num_out; k++) {
                W[i][k] = std::valarray<double>(features);
#ifdef _OPENMP
#pragma omp for
#endif
                for (j = 0; j < features; j++)
                    // preallocate with random initial weights
                    W[i][k][j] = _random(-10, 10);
            }
        }
    }
 
    test_2d_classes(&X);  // create test data around circumference of a circle
    save_2d_data("test1.csv", X);  // save test data points
    save_u_matrix("w11.csv", W);   // save initial random weights
    kohonen_som(X, &W, 1e-4);      // train the SOM
    save_u_matrix("w12.csv", W);   // save the resultant weights
}

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test2()

void test2

(

)

Test that creates a random set of points distributed in 4 clusters in 3D space and trains an SOM that finds the topological pattern. The following CSV files are created to validate the execution:

• test2.csv: random test samples points with a lamniscate pattern
• w21.csv: initial random map
• w22.csv: trained SOM map

             {
    int j, N = 300;
    int features = 3;
    int num_out = 30;
    std::vector<std::valarray<double>> X(N);
    std::vector<std::vector<std::valarray<double>>> W(num_out);
    for (int i = 0; i < std::max(num_out, N); i++) {
        // loop till max(N, num_out)
        if (i < N)  // only add new arrays if i < N
            X[i] = std::valarray<double>(features);
        if (i < num_out) {  // only add new arrays if i < num_out
            W[i] = std::vector<std::valarray<double>>(num_out);
            for (int k = 0; k < num_out; k++) {
                W[i][k] = std::valarray<double>(features);
#ifdef _OPENMP
#pragma omp for
#endif
                for (j = 0; j < features; j++)
                    // preallocate with random initial weights
                    W[i][k][j] = _random(-10, 10);
            }
        }
    }
 
    test_3d_classes1(&X);  // create test data around circumference of a circle
    save_2d_data("test2.csv", X);  // save test data points
    save_u_matrix("w21.csv", W);   // save initial random weights
    kohonen_som(X, &W, 1e-4);      // train the SOM
    save_u_matrix("w22.csv", W);   // save the resultant weights
}

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test3()

void test3

(

)

Test that creates a random set of points distributed in eight clusters in 3D space and trains an SOM that finds the topological pattern. The following CSV files are created to validate the execution:

• test3.csv: random test samples points with a circular pattern
• w31.csv: initial random map
• w32.csv: trained SOM map

             {
    int j, N = 500;
    int features = 3;
    int num_out = 30;
    std::vector<std::valarray<double>> X(N);
    std::vector<std::vector<std::valarray<double>>> W(num_out);
    for (int i = 0; i < std::max(num_out, N); i++) {
        // loop till max(N, num_out)
        if (i < N)  // only add new arrays if i < N
            X[i] = std::valarray<double>(features);
        if (i < num_out) {  // only add new arrays if i < num_out
            W[i] = std::vector<std::valarray<double>>(num_out);
            for (int k = 0; k < num_out; k++) {
                W[i][k] = std::valarray<double>(features);
#ifdef _OPENMP
#pragma omp for
#endif
                for (j = 0; j < features; j++)
                    // preallocate with random initial weights
                    W[i][k][j] = _random(-10, 10);
            }
        }
    }
 
    test_3d_classes2(&X);  // create test data around circumference of a circle
    save_2d_data("test3.csv", X);  // save test data points
    save_u_matrix("w31.csv", W);   // save initial random weights
    kohonen_som(X, &W, 1e-4);      // train the SOM
    save_u_matrix("w32.csv", W);   // save the resultant weights
}

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test_2d_classes()

void test_2d_classes

(

std::vector< std::valarray< double >> *

data

)

Creates a random set of points distributed in four clusters in 3D space with centroids at the points

• \((0,5, 0.5, 0.5)\)
• \((0,5,-0.5, -0.5)\)
• \((-0,5, 0.5, 0.5)\)
• \((-0,5,-0.5, -0.5)\)

Parameters

[out]

data

matrix to store data in

                                                           {
    const int N = data->size();
    const double R = 0.3;  // radius of cluster
    int i;
    const int num_classes = 4;
    const double centres[][2] = {
        // centres of each class cluster
        {.5, .5},   // centre of class 1
        {.5, -.5},  // centre of class 2
        {-.5, .5},  // centre of class 3
        {-.5, -.5}  // centre of class 4
    };
 
#ifdef _OPENMP
#pragma omp for
#endif
    for (i = 0; i < N; i++) {
        // select a random class for the point
        int cls = std::rand() % num_classes;
 
        // create random coordinates (x,y,z) around the centre of the class
        data[0][i][0] = _random(centres[cls][0] - R, centres[cls][0] + R);
        data[0][i][1] = _random(centres[cls][1] - R, centres[cls][1] + R);
 
        /* The follosing can also be used
        for (int j = 0; j < 2; j++)
            data[i][j] = _random(centres[class][j] - R, centres[class][j] + R);
        */
    }
}

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test_3d_classes1()

void test_3d_classes1

(

std::vector< std::valarray< double >> *

data

)

Creates a random set of points distributed in four clusters in 3D space with centroids at the points

• \((0,5, 0.5, 0.5)\)
• \((0,5,-0.5, -0.5)\)
• \((-0,5, 0.5, 0.5)\)
• \((-0,5,-0.5, -0.5)\)

Parameters

[out]

data

matrix to store data in

                                                            {
    const int N = data->size();
    const double R = 0.3;  // radius of cluster
    int i;
    const int num_classes = 4;
    const double centres[][3] = {
        // centres of each class cluster
        {.5, .5, .5},    // centre of class 1
        {.5, -.5, -.5},  // centre of class 2
        {-.5, .5, .5},   // centre of class 3
        {-.5, -.5 - .5}  // centre of class 4
    };
 
#ifdef _OPENMP
#pragma omp for
#endif
    for (i = 0; i < N; i++) {
        // select a random class for the point
        int cls = std::rand() % num_classes;
 
        // create random coordinates (x,y,z) around the centre of the class
        data[0][i][0] = _random(centres[cls][0] - R, centres[cls][0] + R);
        data[0][i][1] = _random(centres[cls][1] - R, centres[cls][1] + R);
        data[0][i][2] = _random(centres[cls][2] - R, centres[cls][2] + R);
 
        /* The follosing can also be used
        for (int j = 0; j < 3; j++)
            data[i][j] = _random(centres[class][j] - R, centres[class][j] + R);
        */
    }
}

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test_3d_classes2()

void test_3d_classes2

(

std::vector< std::valarray< double >> *

data

)

Creates a random set of points distributed in four clusters in 3D space with centroids at the points

• \((0,5, 0.5, 0.5)\)
• \((0,5,-0.5, -0.5)\)
• \((-0,5, 0.5, 0.5)\)
• \((-0,5,-0.5, -0.5)\)

Parameters

[out]

data

matrix to store data in

                                                            {
    const int N = data->size();
    const double R = 0.2;  // radius of cluster
    int i;
    const int num_classes = 8;
    const double centres[][3] = {
        // centres of each class cluster
        {.5, .5, .5},    // centre of class 1
        {.5, .5, -.5},   // centre of class 2
        {.5, -.5, .5},   // centre of class 3
        {.5, -.5, -.5},  // centre of class 4
        {-.5, .5, .5},   // centre of class 5
        {-.5, .5, -.5},  // centre of class 6
        {-.5, -.5, .5},  // centre of class 7
        {-.5, -.5, -.5}  // centre of class 8
    };
 
#ifdef _OPENMP
#pragma omp for
#endif
    for (i = 0; i < N; i++) {
        // select a random class for the point
        int cls = std::rand() % num_classes;
 
        // create random coordinates (x,y,z) around the centre of the class
        data[0][i][0] = _random(centres[cls][0] - R, centres[cls][0] + R);
        data[0][i][1] = _random(centres[cls][1] - R, centres[cls][1] + R);
        data[0][i][2] = _random(centres[cls][2] - R, centres[cls][2] + R);
 
        /* The follosing can also be used
        for (int j = 0; j < 3; j++)
            data[i][j] = _random(centres[class][j] - R, centres[class][j] + R);
        */
    }
}

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