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added convenient min, max definitions

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Signed-off-by: Krishna Vedala <7001608+kvedala@users.noreply.github.com>
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Krishna Vedala 2020-06-13 14:01:53 -04:00
parent 72c9b26a13
commit 352bd8a588
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1 changed files with 16 additions and 9 deletions
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machine_learning/kohonen_som_trace.c
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@ -19,6 +19,9 @@
#include <omp.h> #include <omp.h>
#endif #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
/** /**
* Helper function to generate a random number in a given interval. * Helper function to generate a random number in a given interval.
* \n Steps: * \n Steps:
@ -132,8 +135,8 @@ void update_weights(double const *x, double *const *W, double *D, int num_out,
get_min_1d(D, num_out, &d_min, &d_min_idx); get_min_1d(D, num_out, &d_min, &d_min_idx);
// step 3a: get the neighborhood range // step 3a: get the neighborhood range
int from_node = 0 > (d_min_idx - R) ? 0 : d_min_idx - R; int from_node = max(0, d_min_idx - R);
int to_node = num_out < (d_min_idx + R + 1) ? num_out : d_min_idx + R + 1; int to_node = min(num_out, d_min_idx + R + 1);
// step 3b: update the weights of nodes in the // step 3b: update the weights of nodes in the
// neighborhood // neighborhood
@ -240,10 +243,14 @@ void test1()
int j, N = 500; int j, N = 500;
int features = 2; int features = 2;
int num_out = 50; int num_out = 50;
// 2D space, hence size = number of rows * 2
double **X = (double **)malloc(N * sizeof(double *)); double **X = (double **)malloc(N * sizeof(double *));
// number of clusters nodes * 2
double **W = (double **)malloc(num_out * sizeof(double *)); double **W = (double **)malloc(num_out * sizeof(double *));
for (int i = 0; i < (num_out > N ? num_out : N);
i++) // loop till max(N, num_out) 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 if (i < N) // only add new arrays if i < N
X[i] = (double *)malloc(features * sizeof(double)); X[i] = (double *)malloc(features * sizeof(double));
@ -266,7 +273,7 @@ void test1()
kohonen_som_tracer(X, W, N, features, num_out, 0.1); // train the SOM kohonen_som_tracer(X, W, N, features, num_out, 0.1); // train the SOM
save_nd_data("w12.csv", W, num_out, features); // save the resultant weights save_nd_data("w12.csv", W, num_out, features); // save the resultant weights
for (int i = 0; i < (num_out > N ? num_out : N); i++) for (int i = 0; i < max(num_out, N); i++)
{ {
if (i < N) if (i < N)
free(X[i]); free(X[i]);
@ -335,7 +342,7 @@ void test2()
int num_out = 20; int num_out = 20;
double **X = (double **)malloc(N * sizeof(double *)); double **X = (double **)malloc(N * sizeof(double *));
double **W = (double **)malloc(num_out * sizeof(double *)); double **W = (double **)malloc(num_out * sizeof(double *));
for (int i = 0; i < (num_out > N ? num_out : N); i++) for (int i = 0; i < max(num_out, N); i++)
{ {
if (i < N) // only add new arrays if i < N if (i < N) // only add new arrays if i < N
X[i] = (double *)malloc(features * sizeof(double)); X[i] = (double *)malloc(features * sizeof(double));
@ -359,7 +366,7 @@ void test2()
kohonen_som_tracer(X, W, N, features, num_out, 0.01); // train the SOM kohonen_som_tracer(X, W, N, features, num_out, 0.01); // train the SOM
save_nd_data("w22.csv", W, num_out, features); // save the resultant weights save_nd_data("w22.csv", W, num_out, features); // save the resultant weights
for (int i = 0; i < (num_out > N ? num_out : N); i++) for (int i = 0; i < max(num_out, N); i++)
{ {
if (i < N) if (i < N)
free(X[i]); free(X[i]);
@ -438,7 +445,7 @@ void test3()
int num_out = 20; int num_out = 20;
double **X = (double **)malloc(N * sizeof(double *)); double **X = (double **)malloc(N * sizeof(double *));
double **W = (double **)malloc(num_out * sizeof(double *)); double **W = (double **)malloc(num_out * sizeof(double *));
for (int i = 0; i < (num_out > N ? num_out : N); i++) for (int i = 0; i < max(num_out, N); i++)
{ {
if (i < N) // only add new arrays if i < N if (i < N) // only add new arrays if i < N
X[i] = (double *)malloc(features * sizeof(double)); X[i] = (double *)malloc(features * sizeof(double));
@ -462,7 +469,7 @@ void test3()
kohonen_som_tracer(X, W, N, features, num_out, 0.01); // train the SOM 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 save_nd_data("w32.csv", W, num_out, features); // save the resultant weights
for (int i = 0; i < (num_out > N ? num_out : N); i++) for (int i = 0; i < max(num_out, N); i++)
{ {
if (i < N) if (i < N)
free(X[i]); free(X[i]);