mirror of
https://github.com/TheAlgorithms/C
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544 lines
17 KiB
C
544 lines
17 KiB
C
/**
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* \file
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* \brief [Kohonen self organizing
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* map](https://en.wikipedia.org/wiki/Self-organizing_map) (data tracing)
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*
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* \details
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* This example implements a powerful self organizing map algorithm.
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* The algorithm creates a connected network of weights that closely
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* follows the given data points. This creates a chain of nodes that
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* resembles the given input shape.
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* \author [Krishna Vedala](https://github.com/kvedala)
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* \see kohonen_som_topology.c
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*/
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#define _USE_MATH_DEFINES /**< required for MS Visual C */
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <time.h>
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#ifdef _OPENMP // check if OpenMP based parallelization is available
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#include <omp.h>
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#endif
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/**
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* @addtogroup machine_learning Machine learning algorithms
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* @{
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* @addtogroup kohonen_1d Kohonen SOM trace/chain algorithm
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* @{
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*/
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#ifndef max
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/** shorthand for maximum value */
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#define max(a, b) (((a) > (b)) ? (a) : (b))
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#endif
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#ifndef min
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/** shorthand for minimum value */
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#define min(a, b) (((a) < (b)) ? (a) : (b))
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#endif
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/**
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* \brief Helper function to generate a random number in a given interval.
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* \details
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* \n Steps:
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* 1. `r1 = rand() % 100` gets a random number between 0 and 99
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* 2. `r2 = r1 / 100` converts random number to be between 0 and 0.99
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* 3. scale and offset the random number to given range of \f$[a,b)\f$
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* \f[
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* y = (b - a) \times \frac{\text{(random number between 0 and RAND_MAX)} \;
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* \text{mod}\; 100}{100} + a \f]
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*
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* \param a lower limit
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* \param b upper limit
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* \returns random number in the range \f$[a,b)\f$
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*/
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double _random(double a, double b)
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{
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int r = rand() % 100;
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return ((b - a) * r / 100.f) + a;
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}
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/**
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* Save a given n-dimensional data martix to file.
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*
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* \param [in] fname filename to save in (gets overwriten without confirmation)
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* \param [in] X matrix to save
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* \param [in] num_points rows in the matrix = number of points
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* \param [in] num_features columns in the matrix = dimensions of points
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* \returns 0 if all ok
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* \returns -1 if file creation failed
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*/
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int save_nd_data(const char *fname, double **X, int num_points,
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int num_features)
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{
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FILE *fp = fopen(fname, "wt");
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if (!fp) // error with fopen
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{
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char msg[120];
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sprintf(msg, "File error (%s): ", fname);
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perror(msg);
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return -1;
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}
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for (int i = 0; i < num_points; i++) // for each point in the array
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{
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for (int j = 0; j < num_features; j++) // for each feature in the array
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{
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fprintf(fp, "%.4g", X[i][j]); // print the feature value
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if (j < num_features - 1) // if not the last feature
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fprintf(fp, ","); // suffix comma
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}
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if (i < num_points - 1) // if not the last row
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fprintf(fp, "\n"); // start a new line
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}
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fclose(fp);
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return 0;
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}
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/**
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* Get minimum value and index of the value in a vector
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* \param[in] X vector to search
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* \param[in] N number of points in the vector
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* \param[out] val minimum value found
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* \param[out] idx index where minimum value was found
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*/
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void kohonen_get_min_1d(double const *X, int N, double *val, int *idx)
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{
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val[0] = INFINITY; // initial min value
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for (int i = 0; i < N; i++) // check each value
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{
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if (X[i] < val[0]) // if a lower value is found
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{ // save the value and its index
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idx[0] = i;
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val[0] = X[i];
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}
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}
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}
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/**
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* Update weights of the SOM using Kohonen algorithm
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*
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* \param[in] x data point
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* \param[in,out] W weights matrix
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* \param[in,out] D temporary vector to store distances
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* \param[in] num_out number of output points
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* \param[in] num_features number of features per input sample
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* \param[in] alpha learning rate \f$0<\alpha\le1\f$
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* \param[in] R neighborhood range
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*/
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void kohonen_update_weights(double const *x, double *const *W, double *D,
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int num_out, int num_features, double alpha, int R)
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{
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int j, k;
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#ifdef _OPENMP
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#pragma omp for
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#endif
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// step 1: for each output point
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for (j = 0; j < num_out; j++)
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{
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D[j] = 0.f;
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// compute Euclidian distance of each output
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// point from the current sample
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for (k = 0; k < num_features; k++)
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D[j] += (W[j][k] - x[k]) * (W[j][k] - x[k]);
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}
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// step 2: get closest node i.e., node with smallest Euclidian distance to
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// the current pattern
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int d_min_idx;
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double d_min;
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kohonen_get_min_1d(D, num_out, &d_min, &d_min_idx);
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// step 3a: get the neighborhood range
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int from_node = max(0, d_min_idx - R);
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int to_node = min(num_out, d_min_idx + R + 1);
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// step 3b: update the weights of nodes in the
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// neighborhood
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#ifdef _OPENMP
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#pragma omp for
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#endif
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for (j = from_node; j < to_node; j++)
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for (k = 0; k < num_features; k++)
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// update weights of nodes in the neighborhood
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W[j][k] += alpha * (x[k] - W[j][k]);
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}
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/**
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* Apply incremental algorithm with updating neighborhood and learning rates
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* on all samples in the given datset.
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*
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* \param[in] X data set
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* \param[in,out] W weights matrix
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* \param[in] num_samples number of output points
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* \param[in] num_features number of features per input sample
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* \param[in] num_out number of output points
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* \param[in] alpha_min terminal value of alpha
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*/
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void kohonen_som_tracer(double **X, double *const *W, int num_samples,
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int num_features, int num_out, double alpha_min)
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{
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int R = num_out >> 2, iter = 0;
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double alpha = 1.f;
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double *D = (double *)malloc(num_out * sizeof(double));
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// Loop alpha from 1 to alpha_min
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for (; alpha > alpha_min; alpha -= 0.01, iter++)
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{
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// Loop for each sample pattern in the data set
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for (int sample = 0; sample < num_samples; sample++)
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{
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const double *x = X[sample];
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// update weights for the current input pattern sample
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kohonen_update_weights(x, W, D, num_out, num_features, alpha, R);
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}
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// every 10th iteration, reduce the neighborhood range
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if (iter % 10 == 0 && R > 1)
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R--;
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}
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free(D);
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}
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/**
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* @}
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* @}
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*/
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/** Creates a random set of points distributed *near* the circumference
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* of a circle and trains an SOM that finds that circular pattern. The
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* generating function is
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* \f{eqnarray*}{
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* r &\in& [1-\delta r, 1+\delta r)\\
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* \theta &\in& [0, 2\pi)\\
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* x &=& r\cos\theta\\
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* y &=& r\sin\theta
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* \f}
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*
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* \param[out] data matrix to store data in
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* \param[in] N number of points required
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*/
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void test_circle(double *const *data, int N)
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{
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const double R = 0.75, dr = 0.3;
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double a_t = 0., b_t = 2.f * M_PI; // theta random between 0 and 2*pi
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double a_r = R - dr, b_r = R + dr; // radius random between R-dr and R+dr
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int i;
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#ifdef _OPENMP
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#pragma omp for
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#endif
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for (i = 0; i < N; i++)
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{
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double r = _random(a_r, b_r); // random radius
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double theta = _random(a_t, b_t); // random theta
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data[i][0] = r * cos(theta); // convert from polar to cartesian
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data[i][1] = r * sin(theta);
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}
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}
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/** Test that creates a random set of points distributed *near* the
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* circumference of a circle and trains an SOM that finds that circular pattern.
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* The following [CSV](https://en.wikipedia.org/wiki/Comma-separated_values)
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* files are created to validate the execution:
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* * `test1.csv`: random test samples points with a circular pattern
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* * `w11.csv`: initial random map
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* * `w12.csv`: trained SOM map
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*
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* The outputs can be readily plotted in [gnuplot](https:://gnuplot.info) using
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* the following snippet
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* ```gnuplot
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* set datafile separator ','
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* plot "test1.csv" title "original", \
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* "w11.csv" title "w1", \
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* "w12.csv" title "w2"
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* ```
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* 
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*/
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void test1()
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{
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int j, N = 500;
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int features = 2;
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int num_out = 50;
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// 2D space, hence size = number of rows * 2
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double **X = (double **)malloc(N * sizeof(double *));
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// number of clusters nodes * 2
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double **W = (double **)malloc(num_out * sizeof(double *));
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for (int i = 0; i < max(num_out, N); i++) // loop till max(N, num_out)
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{
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if (i < N) // only add new arrays if i < N
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X[i] = (double *)malloc(features * sizeof(double));
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if (i < num_out) // only add new arrays if i < num_out
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{
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W[i] = (double *)malloc(features * sizeof(double));
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#ifdef _OPENMP
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#pragma omp for
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#endif
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// preallocate with random initial weights
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for (j = 0; j < features; j++) W[i][j] = _random(-1, 1);
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}
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}
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test_circle(X, N); // create test data around circumference of a circle
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save_nd_data("test1.csv", X, N, features); // save test data points
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save_nd_data("w11.csv", W, num_out,
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features); // save initial random weights
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kohonen_som_tracer(X, W, N, features, num_out, 0.1); // train the SOM
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save_nd_data("w12.csv", W, num_out,
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features); // save the resultant weights
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for (int i = 0; i < max(num_out, N); i++)
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{
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if (i < N)
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free(X[i]);
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if (i < num_out)
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free(W[i]);
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}
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}
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/** Creates a random set of points distributed *near* the locus
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* of the [Lamniscate of
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* Gerono](https://en.wikipedia.org/wiki/Lemniscate_of_Gerono).
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* \f{eqnarray*}{
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* \delta r &=& 0.2\\
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* \delta x &\in& [-\delta r, \delta r)\\
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* \delta y &\in& [-\delta r, \delta r)\\
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* \theta &\in& [0, \pi)\\
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* x &=& \delta x + \cos\theta\\
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* y &=& \delta y + \frac{\sin(2\theta)}{2}
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* \f}
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* \param[out] data matrix to store data in
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* \param[in] N number of points required
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*/
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void test_lamniscate(double *const *data, int N)
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{
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const double dr = 0.2;
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int i;
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#ifdef _OPENMP
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#pragma omp for
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#endif
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for (i = 0; i < N; i++)
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{
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double dx = _random(-dr, dr); // random change in x
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double dy = _random(-dr, dr); // random change in y
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double theta = _random(0, M_PI); // random theta
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data[i][0] = dx + cos(theta); // convert from polar to cartesian
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data[i][1] = dy + sin(2. * theta) / 2.f;
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}
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}
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/** Test that creates a random set of points distributed *near* the locus
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* of the [Lamniscate of
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* Gerono](https://en.wikipedia.org/wiki/Lemniscate_of_Gerono) and trains an SOM
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* that finds that circular pattern. The following
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* [CSV](https://en.wikipedia.org/wiki/Comma-separated_values) files are created
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* to validate the execution:
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* * `test2.csv`: random test samples points with a lamniscate pattern
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* * `w21.csv`: initial random map
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* * `w22.csv`: trained SOM map
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*
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* The outputs can be readily plotted in [gnuplot](https:://gnuplot.info) using
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* the following snippet
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* ```gnuplot
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* set datafile separator ','
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* plot "test2.csv" title "original", \
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* "w21.csv" title "w1", \
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* "w22.csv" title "w2"
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* ```
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* 
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*/
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void test2()
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{
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int j, N = 500;
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int features = 2;
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int num_out = 20;
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double **X = (double **)malloc(N * sizeof(double *));
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double **W = (double **)malloc(num_out * sizeof(double *));
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for (int i = 0; i < max(num_out, N); i++)
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{
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if (i < N) // only add new arrays if i < N
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X[i] = (double *)malloc(features * sizeof(double));
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if (i < num_out) // only add new arrays if i < num_out
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{
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W[i] = (double *)malloc(features * sizeof(double));
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#ifdef _OPENMP
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#pragma omp for
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#endif
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// preallocate with random initial weights
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for (j = 0; j < features; j++) W[i][j] = _random(-1, 1);
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}
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}
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test_lamniscate(X, N); // create test data around the lamniscate
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save_nd_data("test2.csv", X, N, features); // save test data points
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save_nd_data("w21.csv", W, num_out,
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features); // save initial random weights
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kohonen_som_tracer(X, W, N, features, num_out, 0.01); // train the SOM
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save_nd_data("w22.csv", W, num_out,
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features); // save the resultant weights
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for (int i = 0; i < max(num_out, N); i++)
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{
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if (i < N)
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free(X[i]);
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if (i < num_out)
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free(W[i]);
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}
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free(X);
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free(W);
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}
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/** Creates a random set of points distributed in four clusters in
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* 3D space with centroids at the points
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* * \f$(0,5, 0.5, 0.5)\f$
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* * \f$(0,5,-0.5, -0.5)\f$
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* * \f$(-0,5, 0.5, 0.5)\f$
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* * \f$(-0,5,-0.5, -0.5)\f$
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*
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* \param[out] data matrix to store data in
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* \param[in] N number of points required
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*/
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void test_3d_classes(double *const *data, int N)
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{
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const double R = 0.1; // radius of cluster
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int i;
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const int num_classes = 4;
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const double centres[][3] = {
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// centres of each class cluster
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{.5, .5, .5}, // centre of class 1
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{.5, -.5, -.5}, // centre of class 2
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{-.5, .5, .5}, // centre of class 3
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{-.5, -.5 - .5} // centre of class 4
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};
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#ifdef _OPENMP
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#pragma omp for
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#endif
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for (i = 0; i < N; i++)
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{
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int class =
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rand() % num_classes; // select a random class for the point
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// create random coordinates (x,y,z) around the centre of the class
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data[i][0] = _random(centres[class][0] - R, centres[class][0] + R);
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data[i][1] = _random(centres[class][1] - R, centres[class][1] + R);
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data[i][2] = _random(centres[class][2] - R, centres[class][2] + R);
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/* The follosing can also be used
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for (int j = 0; j < 3; j++)
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data[i][j] = _random(centres[class][j] - R, centres[class][j] + R);
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*/
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}
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}
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/** Test that creates a random set of points distributed in six clusters in
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* 3D space. The following
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* [CSV](https://en.wikipedia.org/wiki/Comma-separated_values) files are created
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* to validate the execution:
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* * `test3.csv`: random test samples points with a circular pattern
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* * `w31.csv`: initial random map
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* * `w32.csv`: trained SOM map
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*
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* The outputs can be readily plotted in [gnuplot](https:://gnuplot.info) using
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* the following snippet
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* ```gnuplot
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* set datafile separator ','
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* plot "test3.csv" title "original", \
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* "w31.csv" title "w1", \
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* "w32.csv" title "w2"
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* ```
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* 
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*/
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void test3()
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{
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int j, N = 200;
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int features = 3;
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int num_out = 20;
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double **X = (double **)malloc(N * sizeof(double *));
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double **W = (double **)malloc(num_out * sizeof(double *));
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for (int i = 0; i < max(num_out, N); i++)
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{
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if (i < N) // only add new arrays if i < N
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X[i] = (double *)malloc(features * sizeof(double));
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if (i < num_out) // only add new arrays if i < num_out
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{
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W[i] = (double *)malloc(features * sizeof(double));
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#ifdef _OPENMP
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#pragma omp for
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#endif
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// preallocate with random initial weights
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for (j = 0; j < features; j++) W[i][j] = _random(-1, 1);
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}
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}
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test_3d_classes(X, N); // create test data around the lamniscate
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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);
|
|
}
|
|
|
|
/**
|
|
* Convert clock cycle difference to time in seconds
|
|
*
|
|
* \param[in] start_t start clock
|
|
* \param[in] end_t end clock
|
|
* \returns time difference in seconds
|
|
*/
|
|
double get_clock_diff(clock_t start_t, clock_t end_t)
|
|
{
|
|
return (double)(end_t - start_t) / (double)CLOCKS_PER_SEC;
|
|
}
|
|
|
|
/** Main function */
|
|
int main(int argc, char **argv)
|
|
{
|
|
#ifdef _OPENMP
|
|
printf("Using OpenMP based parallelization\n");
|
|
#else
|
|
printf("NOT using OpenMP based parallelization\n");
|
|
#endif
|
|
clock_t start_clk = clock();
|
|
test1();
|
|
clock_t end_clk = clock();
|
|
printf("Test 1 completed in %.4g sec\n",
|
|
get_clock_diff(start_clk, end_clk));
|
|
start_clk = clock();
|
|
test2();
|
|
end_clk = clock();
|
|
printf("Test 2 completed in %.4g sec\n",
|
|
get_clock_diff(start_clk, end_clk));
|
|
start_clk = clock();
|
|
test3();
|
|
end_clk = clock();
|
|
printf("Test 3 completed in %.4g sec\n",
|
|
get_clock_diff(start_clk, end_clk));
|
|
printf(
|
|
"(Note: Calculated times include: creating test sets, training "
|
|
"model and writing files to disk.)\n\n");
|
|
return 0;
|
|
}
|