diff --git a/machine_learning/kohonen_som_image.c b/machine_learning/kohonen_som_image.c
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+++ b/machine_learning/kohonen_som_image.c
@@ -0,0 +1,691 @@
+/**
+ * \file
+ * \brief [Kohonen self organizing
+ * map](https://en.wikipedia.org/wiki/Self-organizing_map) (topological map)
+ *
+ * \author [Krishna Vedala](https://github.com/kvedala)
+ *
+ * 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.
+ * 
+ */
+#define _USE_MATH_DEFINES // required for MS Visual C
+#include
+#include
+#include
+#include
+#ifdef _OPENMP // check if OpenMP based parallellization is available
+#include
+#endif
+
+#define max(a, b) (a > b ? a : b) // shorthand for maximum value
+#define min(a, b) (a < b ? a : b) // shorthand for minimum value
+
+/** to store info regarding 3D arrays */
+struct array_3d
+{
+ int dim1, dim2, dim3; /**< lengths of each dimension */
+ double *data; /**< pointer to data */
+};
+
+/** Function that returns the pointer to (x, y, z) ^th location in the
+ * linear 3D array given by:
+ * \f[
+ * X_{i,j,k} = i\times M\times N + j\times N + k
+ * \f]
+ * where \f$L\f$, \f$M\f$ and \f$N\f$ are the 3D matrix dimensions.
+ * \param[in] arr pointer to ::array_3d structure
+ * \param[in] x first index
+ * \param[in] y second index
+ * \param[in] z third index
+ * \returns pointer to (x,y,z)^th location of data
+ */
+double *data_3d(const struct array_3d *arr, int x, int y, int z)
+{
+ int offset = (x * arr->dim2 * arr->dim3) + (y * arr->dim3) + z;
+ return arr->data + offset;
+}
+
+/**
+ * Helper function to generate a random number in a given interval.
+ * \n 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 \f$[a,b)\f$
+ * \f[
+ * y = (b - a) \times \frac{\text{(random number between 0 and RAND_MAX)} \;
+ * \text{mod}\; 100}{100} + a \f]
+ *
+ * \param[in] a lower limit
+ * \param[in] b upper limit
+ * \returns random number in the range \f$[a,b)\f$
+ */
+double _random(double a, double b)
+{
+ return ((b - a) * (rand() % 100) / 100.f) + a;
+}
+
+/**
+ * Save a given n-dimensional data martix to file.
+ *
+ * \param[in] fname filename to save in (gets overwriten without confirmation)
+ * \param[in] X matrix to save
+ * \param[in] num_points rows in the matrix = number of points
+ * \param[in] num_features columns in the matrix = dimensions of points
+ * \returns 0 if all ok
+ * \returns -1 if file creation failed
+ */
+int save_2d_data(const char *fname, double **X, int num_points,
+ int num_features)
+{
+ 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
+ fputc(',', fp); // suffix comma
+ }
+ if (i < num_points - 1) // if not the last row
+ fputc('\n', fp); // start a new line
+ }
+ fclose(fp);
+ return 0;
+}
+
+/**
+ * Create the distance matrix or U-matrix from the trained weights and save to
+ * disk.
+ *
+ * \param[in] fname filename to save in (gets overwriten without confirmation)
+ * \param[in] W model matrix to save
+ * \returns 0 if all ok
+ * \returns -1 if file creation failed
+ */
+int save_u_matrix(const char *fname, struct array_3d *W)
+{
+ FILE *fp = fopen(fname, "wt");
+ if (!fp) // error with fopen
+ {
+ char msg[120];
+ sprintf(msg, "File error (%s): ", fname);
+ perror(msg);
+ return -1;
+ }
+
+ int R = max(W->dim1 >> 3, 2); /* neighborhood range */
+
+ for (int i = 0; i < W->dim1; i++) // for each x
+ {
+ for (int j = 0; j < W->dim2; j++) // for each y
+ {
+ double distance = 0.f;
+ int k;
+
+ int from_x = max(0, i - R);
+ int to_x = min(W->dim1, i + R + 1);
+ int from_y = max(0, j - R);
+ int to_y = min(W->dim2, j + R + 1);
+
+#ifdef _OPENMP
+#pragma omp parallel for reduction(+ : distance)
+#endif
+ for (int l = from_x; l < to_x; l++)
+ {
+ for (int m = from_y; m < to_y; m++)
+ {
+ double d = 0.f;
+ for (k = 0; k < W->dim3; k++) // for each feature
+ {
+ double *w1 = data_3d(W, i, j, k);
+ double *w2 = data_3d(W, l, m, k);
+ d += (w1[0] - w2[0]) * (w1[0] - w2[0]);
+ // distance += w1[0] * w1[0];
+ }
+ distance += sqrt(d);
+ // distance += d;
+ }
+ }
+
+ distance /= R * R; // mean disntance from neighbors
+ fprintf(fp, "%.4g", distance); // print the mean separation
+ if (j < W->dim2 - 1) // if not the last column
+ fputc(',', fp); // suffix comma
+ }
+ if (i < W->dim1 - 1) // if not the last row
+ fputc('\n', fp); // start a new line
+ }
+ fclose(fp);
+ return 0;
+}
+
+/**
+ * Get minimum value and index of the value in a matrix
+ * \param[in] X matrix to search
+ * \param[in] N number of points in the vector
+ * \param[out] val minimum value found
+ * \param[out] idx index where minimum value was found
+ */
+void get_min_2d(double **X, int N, double *val, int *x_idx, int *y_idx)
+{
+ val[0] = INFINITY; // initial min value
+
+ for (int i = 0; i < N; i++) // traverse each x-index
+ {
+ for (int j = 0; j < N; j++) // traverse each y-index
+ {
+ if (X[i][j] < val[0]) // if a lower value is found
+ { // save the value and its index
+ x_idx[0] = i;
+ y_idx[0] = j;
+ val[0] = X[i][j];
+ }
+ }
+ }
+}
+
+/**
+ * Update weights of the SOM using Kohonen algorithm
+ *
+ * \param[in] X data point
+ * \param[in,out] W weights matrix
+ * \param[in,out] D temporary vector to store distances
+ * \param[in] num_out number of output points
+ * \param[in] num_features number of features per input sample
+ * \param[in] alpha learning rate \f$0<\alpha\le1\f$
+ * \param[in] R neighborhood range
+ */
+double update_weights(const double *X, struct array_3d *W, double **D,
+ int num_out, int num_features, double alpha, int R)
+{
+ int x, y, k;
+ double d_min = 0.f;
+
+#ifdef _OPENMP
+#pragma omp for
+#endif
+ // step 1: for each 2D output point
+ for (x = 0; x < num_out; x++)
+ {
+ for (y = 0; y < num_out; y++)
+ {
+ D[x][y] = 0.f;
+ // compute Euclidian distance of each output
+ // point from the current sample
+ for (k = 0; k < num_features; k++)
+ {
+ double *w = data_3d(W, x, y, k);
+ D[x][y] += (w[0] - X[k]) * (w[0] - X[k]);
+ }
+ D[x][y] = sqrt(D[x][y]);
+ }
+ }
+
+ // step 2: get closest node i.e., node with smallest Euclidian distance to
+ // the current pattern
+ int d_min_x, d_min_y;
+ get_min_2d(D, num_out, &d_min, &d_min_x, &d_min_y);
+
+ // step 3a: get the neighborhood range
+ int from_x = max(0, d_min_x - R);
+ int to_x = min(num_out, d_min_x + R + 1);
+ int from_y = max(0, d_min_y - R);
+ int to_y = min(num_out, d_min_y + R + 1);
+
+ // step 3b: update the weights of nodes in the
+ // neighborhood
+#ifdef _OPENMP
+#pragma omp for
+#endif
+ for (x = from_x; x < to_x; x++)
+ {
+ for (y = from_y; y < to_y; y++)
+ {
+ for (k = 0; k < num_features; k++)
+ {
+ // apply scaling inversely proportional to distance from the
+ // current node
+ double d2 = (d_min_x - x) * (d_min_x - x) +
+ (d_min_y - y) * (d_min_y - y);
+ double scale_factor = exp(-d2 * 0.5 / (alpha * alpha));
+
+ double *w = data_3d(W, x, y, k);
+
+ // update weights of nodes in the neighborhood
+ w[0] += alpha * scale_factor * (X[k] - w[0]);
+ }
+ }
+ }
+ return d_min;
+}
+
+/**
+ * Apply incremental algorithm with updating neighborhood and learning rates
+ * on all samples in the given datset.
+ *
+ * \param[in] X data set
+ * \param[in,out] W weights matrix
+ * \param[in] D temporary vector to store distances
+ * \param[in] num_samples number of output points
+ * \param[in] num_features number of features per input sample
+ * \param[in] num_out number of output points
+ * \param[in] alpha_min terminal value of alpha
+ */
+void kohonen_som(double **X, struct array_3d *W, int num_samples,
+ int num_features, int num_out, double alpha_min)
+{
+ int R = num_out >> 2, iter = 0;
+ double **D = (double **)malloc(num_out * sizeof(double *));
+ for (int i = 0; i < num_out; i++)
+ D[i] = (double *)malloc(num_out * sizeof(double));
+
+ double dmin = 1.f;
+ // Loop alpha from 1 to slpha_min
+ for (double alpha = 1.f; alpha > alpha_min && dmin > 1e-9;
+ alpha -= 0.005, iter++)
+ {
+ dmin = 0.f;
+ // 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
+ dmin = update_weights(x, W, D, num_out, num_features, alpha, R);
+ }
+
+ // every 20th iteration, reduce the neighborhood range
+ if (iter % 20 == 0 && R > 0)
+ R--;
+
+ dmin /= num_samples;
+ printf("alpha: %.4g\t R: %d\td_min: %.4g\n", alpha, R, dmin);
+ }
+
+ for (int i = 0; i < num_out; i++)
+ free(D[i]);
+ free(D);
+}
+
+/** Creates a random set of points distributed in four clusters in
+ * 3D space with centroids at the points
+ * * \f$(0,5, 0.5, 0.5)\f$
+ * * \f$(0,5,-0.5, -0.5)\f$
+ * * \f$(-0,5, 0.5, 0.5)\f$
+ * * \f$(-0,5,-0.5, -0.5)\f$
+ *
+ * \param[out] data matrix to store data in
+ * \param[in] N number of points required
+ */
+void test_2d_classes(double *const *data, int N)
+{
+ 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++)
+ {
+ 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);
+
+ /* 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);
+ */
+ }
+}
+
+/** Test that creates a random set of points distributed in four clusters in
+ * 2D space and trains an SOM that finds the topological pattern.
+ * The following [CSV](https://en.wikipedia.org/wiki/Comma-separated_values)
+ * 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](https:://gnuplot.info) using
+ * the following snippet
+ * ```gnuplot
+ * set datafile separator ','
+ * plot "test1.csv" title "original", \
+ * "w11.csv" title "w1", \
+ * "w12.csv" title "w2"
+ * ```
+ * 
+ */
+void test1()
+{
+ int j, N = 300;
+ int features = 2;
+ int num_out = 30; // image size - N x N
+
+ // 2D space, hence size = number of rows * 2
+ double **X = (double **)malloc(N * sizeof(double *));
+
+ // cluster nodex in 'x' * cluster nodes in 'y' * 2
+ struct array_3d W = {.dim1 = num_out, .dim2 = num_out, .dim3 = features};
+ W.data = (double *)malloc(num_out * num_out * features *
+ sizeof(double)); // assign rows
+
+ for (int i = 0; i < max(num_out, N); i++) // loop till max(N, num_out)
+ {
+ 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
+ {
+ for (int k = 0; k < num_out; k++)
+ {
+#ifdef _OPENMP
+#pragma omp for
+#endif
+ // preallocate with random initial weights
+ for (j = 0; j < features; j++)
+ {
+ double *w = data_3d(&W, i, k, j);
+ w[0] = _random(-5, 5);
+ }
+ }
+ }
+ }
+
+ test_2d_classes(X, N); // create test data around circumference of a circle
+ save_2d_data("test1.csv", X, N, features); // save test data points
+ save_u_matrix("w11.csv", &W); // save initial random weights
+ kohonen_som(X, &W, N, features, num_out, 1e-4); // train the SOM
+ save_u_matrix("w12.csv", &W); // save the resultant weights
+
+ for (int i = 0; i < N; i++)
+ free(X[i]);
+ free(X);
+ free(W.data);
+}
+
+/** Creates a random set of points distributed in four clusters in
+ * 3D space with centroids at the points
+ * * \f$(0,5, 0.5, 0.5)\f$
+ * * \f$(0,5,-0.5, -0.5)\f$
+ * * \f$(-0,5, 0.5, 0.5)\f$
+ * * \f$(-0,5,-0.5, -0.5)\f$
+ *
+ * \param[out] data matrix to store data in
+ * \param[in] N number of points required
+ */
+void test_3d_classes1(double *const *data, int N)
+{
+ const double R = 0.2; // 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++)
+ {
+ 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 4 clusters in
+ * 3D space and trains an SOM that finds the topological pattern. The following
+ * [CSV](https://en.wikipedia.org/wiki/Comma-separated_values) 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](https:://gnuplot.info) using
+ * the following snippet
+ * ```gnuplot
+ * set datafile separator ','
+ * plot "test2.csv" title "original", \
+ * "w21.csv" title "w1", \
+ * "w22.csv" title "w2"
+ * ```
+ * 
+ */
+void test2()
+{
+ int j, N = 500;
+ int features = 3;
+ int num_out = 30; // image size - N x N
+
+ // 3D space, hence size = number of rows * 3
+ double **X = (double **)malloc(N * sizeof(double *));
+
+ // cluster nodex in 'x' * cluster nodes in 'y' * 2
+ struct array_3d W = {.dim1 = num_out, .dim2 = num_out, .dim3 = features};
+ W.data = (double *)malloc(num_out * num_out * features *
+ sizeof(double)); // assign rows
+
+ for (int i = 0; i < max(num_out, N); i++) // loop till max(N, num_out)
+ {
+ 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
+ {
+ for (int k = 0; k < num_out; k++)
+ {
+#ifdef _OPENMP
+#pragma omp for
+#endif
+ for (j = 0; j < features; j++)
+ { // preallocate with random initial weights
+ double *w = data_3d(&W, i, k, j);
+ w[0] = _random(-5, 5);
+ }
+ }
+ }
+ }
+
+ test_3d_classes1(X, N); // create test data
+ save_2d_data("test2.csv", X, N, features); // save test data points
+ save_u_matrix("w21.csv", &W); // save initial random weights
+ kohonen_som(X, &W, N, features, num_out, 1e-4); // train the SOM
+ save_u_matrix("w22.csv", &W); // save the resultant weights
+
+ for (int i = 0; i < N; i++)
+ free(X[i]);
+ free(X);
+ free(W.data);
+}
+
+/** Creates a random set of points distributed in four clusters in
+ * 3D space with centroids at the points
+ * * \f$(0,5, 0.5, 0.5)\f$
+ * * \f$(0,5,-0.5, -0.5)\f$
+ * * \f$(-0,5, 0.5, 0.5)\f$
+ * * \f$(-0,5,-0.5, -0.5)\f$
+ *
+ * \param[out] data matrix to store data in
+ * \param[in] N number of points required
+ */
+void test_3d_classes2(double *const *data, int N)
+{
+ 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++)
+ {
+ 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 eight clusters in
+ * 3D space and trains an SOM that finds the topological pattern. The following
+ * [CSV](https://en.wikipedia.org/wiki/Comma-separated_values) 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](https:://gnuplot.info) using
+ * the following snippet
+ * ```gnuplot
+ * set datafile separator ','
+ * plot "test3.csv" title "original", \
+ * "w31.csv" title "w1", \
+ * "w32.csv" title "w2"
+ * ```
+ * 
+ */
+void test3()
+{
+ int j, N = 500;
+ int features = 3;
+ int num_out = 30;
+ double **X = (double **)malloc(N * sizeof(double *));
+
+ // cluster nodex in 'x' * cluster nodes in 'y' * 2
+ struct array_3d W = {.dim1 = num_out, .dim2 = num_out, .dim3 = features};
+ W.data = (double *)malloc(num_out * num_out * features *
+ sizeof(double)); // assign rows
+
+ for (int i = 0; i < max(num_out, N); i++) // loop till max(N, num_out)
+ {
+ 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
+ {
+ for (int k = 0; k < num_out; k++)
+ {
+#ifdef _OPENMP
+#pragma omp for
+#endif
+ // preallocate with random initial weights
+ for (j = 0; j < features; j++)
+ {
+ double *w = data_3d(&W, i, k, j);
+ w[0] = _random(-5, 5);
+ }
+ }
+ }
+ }
+
+ test_3d_classes2(X, N); // create test data around the lamniscate
+ save_2d_data("test3.csv", X, N, features); // save test data points
+ save_u_matrix("w31.csv", &W); // save initial random weights
+ kohonen_som(X, &W, N, features, num_out, 0.01); // train the SOM
+ save_u_matrix("w32.csv", &W); // save the resultant weights
+
+ for (int i = 0; i < N; i++)
+ free(X[i]);
+ free(X);
+ free(W.data);
+}
+
+/**
+ * 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, end_clk;
+
+ start_clk = clock();
+ test1();
+ 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;
+}