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https://github.com/TheAlgorithms/C
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kohonen2d: update ML documentation and add grouping
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Krishna Vedala
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797a7d4c73
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b33bd37623
@ -7,8 +7,8 @@
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* This example implements a powerful unsupervised learning algorithm called as
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* a self organizing map. The algorithm creates a connected network of weights
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* that closely follows the given data points. This thus creates a topological
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* map of the given data i.e., it maintains the relationship between varipus
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* data points in a much higher dimesional space by creating an equivalent in a
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* map of the given data i.e., it maintains the relationship between various
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* data points in a much higher dimensional space by creating an equivalent in a
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* 2-dimensional space.
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* <img alt="Trained topological maps for the test cases in the program"
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* src="https://raw.githubusercontent.com/TheAlgorithms/C/docs/images/machine_learning/kohonen/2D_Kohonen_SOM.svg"
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@ -27,6 +27,13 @@
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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_2d Kohonen SOM topology 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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@ -37,7 +44,7 @@
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#endif
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/** to store info regarding 3D arrays */
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struct array_3d
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struct kohonen_array_3d
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{
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int dim1; /**< lengths of first dimension */
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int dim2; /**< lengths of second dimension */
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@ -51,13 +58,13 @@ struct array_3d
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* X_{i,j,k} = i\times M\times N + j\times N + k
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* \f]
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* where \f$L\f$, \f$M\f$ and \f$N\f$ are the 3D matrix dimensions.
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* \param[in] arr pointer to ::array_3d structure
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* \param[in] arr pointer to ::kohonen_array_3d structure
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* \param[in] x first index
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* \param[in] y second index
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* \param[in] z third index
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* \returns pointer to (x,y,z)^th location of data
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*/
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double *data_3d(const struct array_3d *arr, int x, int y, int z)
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double *kohonen_data_3d(const struct kohonen_array_3d *arr, int x, int y, int z)
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{
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int offset = (x * arr->dim2 * arr->dim3) + (y * arr->dim3) + z;
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return arr->data + offset;
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@ -85,7 +92,7 @@ double _random(double a, double b)
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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] fname filename to save in (gets overwritten 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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@ -129,7 +136,7 @@ int save_2d_data(const char *fname, double **X, int num_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_u_matrix(const char *fname, struct array_3d *W)
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int save_u_matrix(const char *fname, struct kohonen_array_3d *W)
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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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@ -164,8 +171,8 @@ int save_u_matrix(const char *fname, struct array_3d *W)
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double d = 0.f;
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for (k = 0; k < W->dim3; k++) // for each feature
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{
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double *w1 = data_3d(W, i, j, k);
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double *w2 = data_3d(W, l, m, k);
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double *w1 = kohonen_data_3d(W, i, j, k);
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double *w2 = kohonen_data_3d(W, l, m, k);
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d += (w1[0] - w2[0]) * (w1[0] - w2[0]);
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// distance += w1[0] * w1[0];
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}
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@ -224,8 +231,9 @@ void get_min_2d(double **X, int N, double *val, int *x_idx, int *y_idx)
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* \param[in] R neighborhood range
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* \returns minimum distance of sample and trained weights
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*/
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double update_weights(const double *X, struct array_3d *W, double **D,
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int num_out, int num_features, double alpha, int R)
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double kohonen_update_weights(const double *X, struct kohonen_array_3d *W,
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double **D, int num_out, int num_features,
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double alpha, int R)
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{
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int x, y, k;
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double d_min = 0.f;
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@ -243,7 +251,7 @@ double update_weights(const double *X, struct array_3d *W, double **D,
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// point from the current sample
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for (k = 0; k < num_features; k++)
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{
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double *w = data_3d(W, x, y, k);
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double *w = kohonen_data_3d(W, x, y, k);
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D[x][y] += (w[0] - X[k]) * (w[0] - X[k]);
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}
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D[x][y] = sqrt(D[x][y]);
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@ -283,7 +291,7 @@ double update_weights(const double *X, struct array_3d *W, double **D,
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for (k = 0; k < num_features; k++)
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{
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double *w = data_3d(W, x, y, k);
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double *w = kohonen_data_3d(W, x, y, k);
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// update weights of nodes in the neighborhood
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w[0] += alpha * scale_factor * (X[k] - w[0]);
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}
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@ -303,7 +311,7 @@ double update_weights(const double *X, struct array_3d *W, double **D,
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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(double **X, struct array_3d *W, int num_samples,
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void kohonen_som(double **X, struct kohonen_array_3d *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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@ -322,8 +330,8 @@ void kohonen_som(double **X, struct array_3d *W, int num_samples,
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for (int sample = 0; sample < num_samples; sample++)
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{
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// update weights for the current input pattern sample
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dmin += update_weights(X[sample], W, D, num_out, num_features,
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alpha, R);
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dmin += kohonen_update_weights(X[sample], W, D, num_out,
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num_features, alpha, R);
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}
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// every 20th iteration, reduce the neighborhood range
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@ -340,6 +348,11 @@ void kohonen_som(double **X, struct array_3d *W, int num_samples,
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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 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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@ -400,7 +413,7 @@ void test1()
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double **X = (double **)malloc(N * sizeof(double *));
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// cluster nodex in 'x' * cluster nodes in 'y' * 2
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struct array_3d W;
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struct kohonen_array_3d W;
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W.dim1 = num_out;
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W.dim2 = num_out;
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W.dim3 = features;
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@ -421,7 +434,7 @@ void test1()
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// preallocate with random initial weights
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for (j = 0; j < features; j++)
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{
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double *w = data_3d(&W, i, k, j);
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double *w = kohonen_data_3d(&W, i, k, j);
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w[0] = _random(-5, 5);
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}
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}
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@ -500,7 +513,7 @@ void test2()
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double **X = (double **)malloc(N * sizeof(double *));
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// cluster nodex in 'x' * cluster nodes in 'y' * 2
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struct array_3d W;
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struct kohonen_array_3d W;
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W.dim1 = num_out;
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W.dim2 = num_out;
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W.dim3 = features;
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@ -520,7 +533,7 @@ void test2()
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#endif
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for (j = 0; j < features; j++)
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{ // preallocate with random initial weights
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double *w = data_3d(&W, i, k, j);
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double *w = kohonen_data_3d(&W, i, k, j);
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w[0] = _random(-5, 5);
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}
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}
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@ -601,7 +614,7 @@ void test3()
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double **X = (double **)malloc(N * sizeof(double *));
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// cluster nodex in 'x' * cluster nodes in 'y' * 2
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struct array_3d W;
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struct kohonen_array_3d W;
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W.dim1 = num_out;
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W.dim2 = num_out;
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W.dim3 = features;
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@ -622,7 +635,7 @@ void test3()
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// preallocate with random initial weights
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for (j = 0; j < features; j++)
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{
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double *w = data_3d(&W, i, k, j);
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double *w = kohonen_data_3d(&W, i, k, j);
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w[0] = _random(-5, 5);
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}
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}
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