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fix const pointer parameters

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This commit is contained in:
Krishna Vedala 2020-06-03 12:29:54 -04:00
parent 6af5c96573
commit 0650630aa9
1 changed files with 7 additions and 7 deletions
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14
machine_learning/kohonen_som.c
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@ -39,7 +39,7 @@ double random(double a, double b)
* \param[in] num_points number of rows in the matrix * \param[in] num_points number of rows in the matrix
* \param[in] num_features number of columns in the matrix * \param[in] num_features number of columns in the matrix
*/ */
void save_2d_data(const char *fname, const double **X, int num_points, void save_2d_data(const char *fname, double const *const *X, int num_points,
int num_features) int num_features)
{ {
FILE *fp = fopen(fname, "wt"); FILE *fp = fopen(fname, "wt");
@ -83,13 +83,13 @@ void get_min_1d(double *X, int N, double *val, int *idx)
* *
* \param[in] X data point * \param[in] X data point
* \param[in,out] W weights matrix * \param[in,out] W weights matrix
* \param[in] D temporary vector to store distances * \param[in,out] D temporary vector to store distances
* \param[in] num_out number of output points * \param[in] num_out number of output points
* \param[in] num_features number of features per input sample * \param[in] num_features number of features per input sample
* \param[in] alpha learning rate \f$0<\alpha\le1\f$ * \param[in] alpha learning rate \f$0<\alpha\le1\f$
* \param[in] R neighborhood range * \param[in] R neighborhood range
*/ */
void update_weights(double *x, double **W, double *D, int num_out, void update_weights(double const *x, double **W, double *D, int num_out,
int num_features, double alpha, int R) int num_features, double alpha, int R)
{ {
int j, k; int j, k;
@ -140,7 +140,7 @@ void update_weights(double *x, double **W, double *D, int num_out,
* \param[in] num_out number of output points * \param[in] num_out number of output points
* \param[in] alpha_min terminal value of alpha * \param[in] alpha_min terminal value of alpha
*/ */
void kohonen_som_tracer(double **X, double **W, int num_samples, void kohonen_som_tracer(double const *const *X, double **W, int num_samples,
int num_features, int num_out, double alpha_min) int num_features, int num_out, double alpha_min)
{ {
int R = num_out >> 2, iter = 0; int R = num_out >> 2, iter = 0;
@ -153,7 +153,7 @@ void kohonen_som_tracer(double **X, double **W, int num_samples,
// Loop for each sample pattern in the data set // Loop for each sample pattern in the data set
for (int sample = 0; sample < num_samples; sample++) for (int sample = 0; sample < num_samples; sample++)
{ {
double *x = X[sample]; const double *x = X[sample];
// update weights for the current input pattern sample // update weights for the current input pattern sample
update_weights(x, W, D, num_out, num_features, alpha, R); update_weights(x, W, D, num_out, num_features, alpha, R);
} }
@ -294,9 +294,9 @@ void test_lamniscate(double **data, int N)
*/ */
void test2() void test2()
{ {
int j, N = 200; int j, N = 500;
int features = 2; int features = 2;
int num_out = 30; 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 < (num_out > N ? num_out : N); i++)