kohonen_som_trace.c File Reference

Kohonen self organizing map (data tracing) More...

#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>

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Macros
#define	_USE_MATH_DEFINES
#define	max(a, b)   (((a) > (b)) ? (a) : (b))
#define	min(a, b)   (((a) < (b)) ? (a) : (b))

Functions
double	_random (double a, double b)
int	save_nd_data (const char *fname, double **X, int num_points, int num_features)
void	get_min_1d (double const *X, int N, double *val, int *idx)
void	update_weights (double const *x, double *const *W, double *D, int num_out, int num_features, double alpha, int R)
void	kohonen_som_tracer (double **X, double *const *W, int num_samples, int num_features, int num_out, double alpha_min)
void	test_circle (double *const *data, int N)
void	test1 ()
void	test_lamniscate (double *const *data, int N)
void	test2 ()
void	test_3d_classes (double *const *data, int N)
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 (data tracing)

Author

Krishna Vedala

This example implements a powerful self organizing map algorithm. The algorithm creates a connected network of weights that closely follows the given data points. This this creates a chain of nodes that resembles the given input shape.

See also

kohonen_som_topology.c

Macro Definition Documentation

_USE_MATH_DEFINES

#define _USE_MATH_DEFINES

required for MS Visual C

max

#define max	(		a,
			b
	)		(((a) > (b)) ? (a) : (b))

shorthand for maximum value \

min

#define min	(		a,
			b
	)		(((a) < (b)) ? (a) : (b))

shorthand for minimum value \

Function Documentation

_random()

double _random	(	double	a,
		double	b
	)

Helper function to generate a random number in a given interval.
Steps:

1. r1 = rand() % 100 gets a random number between 0 and 99
2. r2 = r1 / 100 converts random number to be between 0 and 0.99
3. scale and offset the random number to given range of \([a,b)\)

   \[ y = (b - a) \times \frac{\text{(random number between 0 and RAND_MAX)} \; \text{mod}\; 100}{100} + a \]

Parameters

[in]	a	lower limit
[in]	b	upper limit

Returns

random number in the range \([a,b)\)

{

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

{

get_min_1d()

void get_min_1d	(	double const *	X,
		int	N,
		double *	val,
		int *	idx
	)

Get minimum value and index of the value in a vector

Parameters

[in]	x	vector to search
[in]	N	number of points in the vector
[out]	val	minimum value found
[out]	idx	index where minimum value was found

    {
        if (X[i] < val[0])  // if a lower value is found
        {                   // save the value and its index
            idx[0] = i;
            val[0] = X[i];
        }
    }
}
 
/**

kohonen_som_tracer()

void kohonen_som_tracer	(	double **	X,
		double *const *	W,
		int	num_samples,
		int	num_features,
		int	num_out,
		double	alpha_min
	)

Apply incremental algorithm with updating neighborhood and learning rates on all samples in the given datset.

Parameters

[in]	X	data set
[in,out]	W	weights matrix
[in]	D	temporary vector to store distances
[in]	num_samples	number of output points
[in]	num_features	number of features per input sample
[in]	num_out	number of output points
[in]	alpha_min	terminal value of alpha

{
    int R = num_out >> 2, iter = 0;
    double alpha = 1.f;
    double *D = (double *)malloc(num_out * sizeof(double));
 
    // Loop alpha from 1 to slpha_min
    for (; alpha > alpha_min; alpha -= 0.01, iter++)
    {
        // Loop for each sample pattern in the data set
        for (int sample = 0; sample < num_samples; sample++)
        {
            const double *x = X[sample];
            // update weights for the current input pattern sample
            update_weights(x, W, D, num_out, num_features, alpha, R);
        }
 
        // every 10th iteration, reduce the neighborhood range
        if (iter % 10 == 0 && R > 1)
            R--;
    }
 
    free(D);
}
 
/** Creates a random set of points distributed *near* the circumference

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main()

int main	(	int	argc,
		char **	argv
	)

Main function

              : Calculated times include: creating test sets, training "
        "model and writing files to disk.)\n\n");
    return 0;
}

save_nd_data()

int save_nd_data	(	const char *	fname,
		double **	X,
		int	num_points,
		int	num_features
	)

Save a given n-dimensional data martix to file.

Parameters

[in]	fname	filename to save in (gets overwriten without confirmation)
[in]	X	matrix to save
[in]	num_points	rows in the matrix = number of points
[in]	num_features	columns in the matrix = dimensions of points

Returns

0 if all ok

-1 if file creation failed

{
    FILE *fp = fopen(fname, "wt");
    if (!fp)  // error with fopen
    {
        char msg[120];
        sprintf(msg, "File error (%s): ", fname);
        perror(msg);
        return -1;
    }
 
    for (int i = 0; i < num_points; i++)  // for each point in the array
    {
        for (int j = 0; j < num_features; j++)  // for each feature in the array
        {
            fprintf(fp, "%.4g", X[i][j]);  // print the feature value
            if (j < num_features - 1)      // if not the last feature
                fprintf(fp, ",");          // suffix comma
        }
        if (i < num_points - 1)  // if not the last row
            fprintf(fp, "\n");   // start a new line
    }
    fclose(fp);
    return 0;
}
 
/**

test1()

void test1

(

)

Test that creates a random set of points distributed near the 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

The outputs can be readily plotted in gnuplot using the following snippet

set datafile separator ','
plot "test1.csv" title "original", \
     "w11.csv" title "w1", \
     "w12.csv" title "w2"

    {
        if (i < N)  // only add new arrays if i < N
            X[i] = (double *)malloc(features * sizeof(double));
        if (i < num_out)  // only add new arrays if i < num_out
        {
            W[i] = (double *)malloc(features * sizeof(double));
#ifdef _OPENMP
#pragma omp for
#endif
            // preallocate with random initial weights
            for (j = 0; j < features; j++) W[i][j] = _random(-1, 1);
        }
    }
 
    test_circle(X, N);  // create test data around circumference of a circle
    save_nd_data("test1.csv", X, N, features);  // save test data points
    save_nd_data("w11.csv", W, num_out,
                 features);  // save initial random weights
    kohonen_som_tracer(X, W, N, features, num_out, 0.1);  // train the SOM
    save_nd_data("w12.csv", W, num_out,
                 features);  // save the resultant weights
 
    for (int i = 0; i < max(num_out, N); i++)
    {
        if (i < N)
            free(X[i]);
        if (i < num_out)
            free(W[i]);
    }
}
 
/** Creates a random set of points distributed *near* the locus

test2()

void test2

(

)

Test that creates a random set of points distributed near the locus of the Lamniscate of Gerono and trains an SOM that finds that circular 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

The outputs can be readily plotted in gnuplot using the following snippet

set datafile separator ','
plot "test2.csv" title "original", \
     "w21.csv" title "w1", \
     "w22.csv" title "w2"

    {
        if (i < N)  // only add new arrays if i < N
            X[i] = (double *)malloc(features * sizeof(double));
        if (i < num_out)  // only add new arrays if i < num_out
        {
            W[i] = (double *)malloc(features * sizeof(double));
 
#ifdef _OPENMP
#pragma omp for
#endif
            // preallocate with random initial weights
            for (j = 0; j < features; j++) W[i][j] = _random(-1, 1);
        }
    }
 
    test_lamniscate(X, N);  // create test data around the lamniscate
    save_nd_data("test2.csv", X, N, features);  // save test data points
    save_nd_data("w21.csv", W, num_out,
                 features);  // save initial random weights
    kohonen_som_tracer(X, W, N, features, num_out, 0.01);  // train the SOM
    save_nd_data("w22.csv", W, num_out,
                 features);  // save the resultant weights
 
    for (int i = 0; i < max(num_out, N); i++)
    {
        if (i < N)
            free(X[i]);
        if (i < num_out)
            free(W[i]);
    }
    free(X);
    free(W);
}
 
/** Creates a random set of points distributed in four clusters in

test3()

void test3

(

)

Test that creates a random set of points distributed in six clusters in 3D space. 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

The outputs can be readily plotted in gnuplot using the following snippet

set datafile separator ','
plot "test3.csv" title "original", \
     "w31.csv" title "w1", \
     "w32.csv" title "w2"

    {
        if (i < N)  // only add new arrays if i < N
            X[i] = (double *)malloc(features * sizeof(double));
        if (i < num_out)  // only add new arrays if i < num_out
        {
            W[i] = (double *)malloc(features * sizeof(double));
 
#ifdef _OPENMP
#pragma omp for
#endif
            // preallocate with random initial weights
            for (j = 0; j < features; j++) W[i][j] = _random(-1, 1);
        }
    }
 
    test_3d_classes(X, N);  // create test data around the lamniscate
    save_nd_data("test3.csv", X, N, features);  // save test data points
    save_nd_data("w31.csv", W, num_out,
                 features);  // save initial random weights
    kohonen_som_tracer(X, W, N, features, num_out, 0.01);  // train the SOM
    save_nd_data("w32.csv", W, num_out,
                 features);  // save the resultant weights
 
    for (int i = 0; i < max(num_out, N); i++)
    {
        if (i < N)
            free(X[i]);
        if (i < num_out)
            free(W[i]);
    }
    free(X);
    free(W);
}
 
/**

test_3d_classes()

void test_3d_classes	(	double *const *	data,
		int	N
	)

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
[in]	N	number of points required

                                {
        // 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++)
    {
        int class =
            rand() % num_classes;  // select a random class for the point
 
        // create random coordinates (x,y,z) around the centre of the class
        data[i][0] = _random(centres[class][0] - R, centres[class][0] + R);
        data[i][1] = _random(centres[class][1] - R, centres[class][1] + R);
        data[i][2] = _random(centres[class][2] - R, centres[class][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);
        */
    }
}
 
/** Test that creates a random set of points distributed in six clusters in

test_circle()

void test_circle	(	double *const *	data,
		int	N
	)

Creates a random set of points distributed near the circumference of a circle and trains an SOM that finds that circular pattern. The generating function is

\begin{eqnarray*} r &\in& [1-\delta r, 1+\delta r)\\ \theta &\in& [0, 2\pi)\\ x &=& r\cos\theta\\ y &=& r\sin\theta \end{eqnarray*}

Parameters

[out]	data	matrix to store data in
[in]	N	number of points required

    {
        double r = _random(a_r, b_r);      // random radius
        double theta = _random(a_t, b_t);  // random theta
        data[i][0] = r * cos(theta);       // convert from polar to cartesian
        data[i][1] = r * sin(theta);
    }
}
 
/** Test that creates a random set of points distributed *near* the

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test_lamniscate()

void test_lamniscate	(	double *const *	data,
		int	N
	)

Creates a random set of points distributed near the locus of the Lamniscate of Gerono.

\begin{eqnarray*} \delta r &=& 0.2\\ \delta x &\in& [-\delta r, \delta r)\\ \delta y &\in& [-\delta r, \delta r)\\ \theta &\in& [0, \pi)\\ x &=& \delta x + \cos\theta\\ y &=& \delta y + \frac{\sin(2\theta)}{2} \end{eqnarray*}

Parameters

[out]	data	matrix to store data in
[in]	N	number of points required

    {
        double dx = _random(-dr, dr);     // random change in x
        double dy = _random(-dr, dr);     // random change in y
        double theta = _random(0, M_PI);  // random theta
        data[i][0] = dx + cos(theta);     // convert from polar to cartesian
        data[i][1] = dy + sin(2. * theta) / 2.f;
    }
}
 
/** Test that creates a random set of points distributed *near* the locus

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update_weights()

void update_weights	(	double const *	x,
		double *const *	W,
		double *	D,
		int	num_out,
		int	num_features,
		double	alpha,
		int	R
	)

Update weights of the SOM using Kohonen algorithm

Parameters

[in]	X	data point
[in,out]	W	weights matrix
[in,out]	D	temporary vector to store distances
[in]	num_out	number of output points
[in]	num_features	number of features per input sample
[in]	alpha	learning rate \(0<\alpha\le1\)
[in]	R	neighborhood range

{
    int j, k;
 
#ifdef _OPENMP
#pragma omp for
#endif
    // step 1: for each output point
    for (j = 0; j < num_out; j++)
    {
        D[j] = 0.f;
        // compute Euclidian distance of each output
        // point from the current sample
        for (k = 0; k < num_features; k++)
            D[j] += (W[j][k] - x[k]) * (W[j][k] - x[k]);
    }
 
    // step 2:  get closest node i.e., node with snallest Euclidian distance to
    // the current pattern
    int d_min_idx;
    double d_min;
    get_min_1d(D, num_out, &d_min, &d_min_idx);
 
    // step 3a: get the neighborhood range
    int from_node = max(0, d_min_idx - R);
    int to_node = min(num_out, d_min_idx + R + 1);
 
    // step 3b: update the weights of nodes in the
    // neighborhood
#ifdef _OPENMP
#pragma omp for
#endif
    for (j = from_node; j < to_node; j++)
        for (k = 0; k < num_features; k++)
            // update weights of nodes in the neighborhood
            W[j][k] += alpha * (x[k] - W[j][k]);
}
 
/**

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