mirror of
https://github.moeyy.xyz/https://github.com/TheAlgorithms/C.git
synced 2023-10-11 15:56:24 +08:00
bug fixes, optimization and more documentation
Signed-off-by: Krishna Vedala <7001608+kvedala@users.noreply.github.com>
This commit is contained in:
Krishna Vedala
parent
8d2ca00459
commit
c532a38ce1
@ -1,10 +1,9 @@
|
||||
/**
|
||||
* \file
|
||||
* \author [Krishna Vedala](https://github.com/kvedala)
|
||||
* \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
|
||||
@ -14,6 +13,9 @@
|
||||
* <img alt="Trained topological maps for the test cases in the program"
|
||||
* src="https://raw.githubusercontent.com/kvedala/C/docs/images/machine_learning/kohonen/2D_Kohonen_SOM.svg"
|
||||
* />
|
||||
* \warning MSVC 2019 compiler generates code that does not execute as expected.
|
||||
* However, MinGW, Clang for GCC and Clang for MSVC compilers on windows perform
|
||||
* as expected. Any insights and suggestions should be directed to the author.
|
||||
*/
|
||||
#define _USE_MATH_DEFINES /**< required for MS Visual C */
|
||||
#include <math.h>
|
||||
@ -24,8 +26,12 @@
|
||||
#include <omp.h>
|
||||
#endif
|
||||
|
||||
#ifndef max
|
||||
#define max(a, b) (a > b ? a : b) /**< shorthand for maximum value */
|
||||
#endif
|
||||
#ifndef min
|
||||
#define min(a, b) (a < b ? a : b) /**< shorthand for minimum value */
|
||||
#endif
|
||||
|
||||
/** to store info regarding 3D arrays */
|
||||
struct array_3d
|
||||
@ -111,11 +117,12 @@ int save_2d_data(const char *fname, double **X, int num_points,
|
||||
}
|
||||
|
||||
/**
|
||||
* Create the distance matrix or U-matrix from the trained weights and save to
|
||||
* disk.
|
||||
* Create the distance matrix or
|
||||
* [U-matrix](https://en.wikipedia.org/wiki/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
|
||||
* \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
|
||||
*/
|
||||
@ -164,7 +171,7 @@ int save_u_matrix(const char *fname, struct array_3d *W)
|
||||
}
|
||||
}
|
||||
|
||||
distance /= R * R; // mean disntance from neighbors
|
||||
distance /= R * R; // mean distance from neighbors
|
||||
fprintf(fp, "%.4g", distance); // print the mean separation
|
||||
if (j < W->dim2 - 1) // if not the last column
|
||||
fputc(',', fp); // suffix comma
|
||||
@ -259,16 +266,20 @@ double update_weights(const double *X, struct array_3d *W, double **D,
|
||||
{
|
||||
for (y = from_y; y < to_y; y++)
|
||||
{
|
||||
/* you can enable the following normalization if needed.
|
||||
personally, I found it detrimental to convergence */
|
||||
// const double s2pi = sqrt(2.f * M_PI);
|
||||
// double normalize = 1.f / (alpha * s2pi);
|
||||
|
||||
/* 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 / (2.f * alpha * alpha));
|
||||
|
||||
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]);
|
||||
}
|
||||
@ -299,25 +310,27 @@ void kohonen_som(double **X, struct array_3d *W, int num_samples,
|
||||
|
||||
double dmin = 1.f;
|
||||
// Loop alpha from 1 to slpha_min
|
||||
for (double alpha = 1.f; alpha > alpha_min && dmin > 1e-10;
|
||||
for (double alpha = 1.f; alpha > alpha_min && dmin > 1e-3;
|
||||
alpha -= 0.001, 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);
|
||||
dmin += update_weights(X[sample], W, D, num_out, num_features,
|
||||
alpha, R);
|
||||
}
|
||||
|
||||
// every 20th iteration, reduce the neighborhood range
|
||||
if (iter % 50 == 0 && R > 1)
|
||||
if (iter % 100 == 0 && R > 1)
|
||||
R--;
|
||||
|
||||
dmin /= num_samples;
|
||||
printf("alpha: %.4g\t R: %d\td_min: %.4g\n", alpha, R, dmin);
|
||||
printf("iter: %5d\t alpha: %.4g\t R: %d\td_min: %.4g\r", iter, alpha, R,
|
||||
dmin);
|
||||
}
|
||||
putchar('\n');
|
||||
|
||||
for (int i = 0; i < num_out; i++)
|
||||
free(D[i]);
|
||||
@ -697,7 +710,6 @@ int main(int argc, char **argv)
|
||||
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");
|
||||
printf("(Note: Calculated times include: writing files to disk.)\n\n");
|
||||
return 0;
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user