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updated documentations
(cherry picked from commit 05b3dbdab9b6df041ac17af89610c5b018adeed2)
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@ -16,6 +16,7 @@
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* \warning MSVC 2019 compiler generates code that does not execute as expected.
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* However, MinGW, Clang for GCC and Clang for MSVC compilers on windows perform
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* as expected. Any insights and suggestions should be directed to the author.
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* \see kohonen_som_trace.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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@ -219,6 +220,7 @@ void get_min_2d(double **X, int N, double *val, int *x_idx, int *y_idx)
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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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* \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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@ -308,7 +310,8 @@ void kohonen_som(double **X, struct array_3d *W, int num_samples,
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for (int i = 0; i < num_out; i++)
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D[i] = (double *)malloc(num_out * sizeof(double));
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double dmin = 1.f;
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double dmin = 1.f; // average minimum distance of all samples
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// Loop alpha from 1 to slpha_min
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for (double alpha = 1.f; alpha > alpha_min && dmin > 1e-3;
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alpha -= 0.001, iter++)
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@ -385,17 +388,6 @@ void test_2d_classes(double *const *data, int N)
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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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* ![Sample execution
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* output](https://raw.githubusercontent.com/kvedala/C/docs/images/machine_learning/kohonen/test1.svg)
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*/
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void test1()
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{
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@ -496,17 +488,6 @@ void test_3d_classes1(double *const *data, int N)
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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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* ![Sample execution
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* output](https://raw.githubusercontent.com/kvedala/C/docs/images/machine_learning/kohonen/test2.svg)
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*/
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void test2()
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{
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@ -610,17 +591,6 @@ void test_3d_classes2(double *const *data, int N)
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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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* ![Sample execution
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* output](https://raw.githubusercontent.com/kvedala/C/docs/images/machine_learning/kohonen/test3.svg)
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*/
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void test3()
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{
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