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const 2d pointer access attempt 2

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This commit is contained in:
Krishna Vedala 2020-06-03 12:38:10 -04:00
parent 0650630aa9
commit 31c145b773
1 changed files with 8 additions and 7 deletions
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15
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, double const *const *X, int num_points, void save_2d_data(const char *fname, const double *const *X, int num_points,
int num_features) int num_features)
{ {
FILE *fp = fopen(fname, "wt"); FILE *fp = fopen(fname, "wt");
@ -64,7 +64,7 @@ void save_2d_data(const char *fname, double const *const *X, int num_points,
* \param[out] val minimum value found * \param[out] val minimum value found
* \param[out] idx index where minimum value was found * \param[out] idx index where minimum value was found
*/ */
void get_min_1d(double *X, int N, double *val, int *idx) void get_min_1d(double const *X, int N, double *val, int *idx)
{ {
val[0] = INFINITY; // initial min value val[0] = INFINITY; // initial min value
@ -89,7 +89,7 @@ void get_min_1d(double *X, int N, double *val, int *idx)
* \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 const *x, double **W, double *D, int num_out, void update_weights(double const *x, double *const *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;
@ -140,8 +140,9 @@ void update_weights(double const *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 const *const *X, double **W, int num_samples, void kohonen_som_tracer(const double *const *X, double *const *W,
int num_features, int num_out, double alpha_min) int num_samples, int num_features, int num_out,
double alpha_min)
{ {
int R = num_out >> 2, iter = 0; int R = num_out >> 2, iter = 0;
double alpha = 1.f; double alpha = 1.f;
@ -174,7 +175,7 @@ void kohonen_som_tracer(double const *const *X, double **W, int num_samples,
* \param[out] data matrix to store data in * \param[out] data matrix to store data in
* \param[in] N number of points required * \param[in] N number of points required
*/ */
void test_circle(double **data, int N) void test_circle(double *const *data, int N)
{ {
const double R = 0.75, dr = 0.3; const double R = 0.75, dr = 0.3;
double a_t = 0., b_t = 2.f * M_PI; // theta random between 0 and 2*pi double a_t = 0., b_t = 2.f * M_PI; // theta random between 0 and 2*pi
@ -255,7 +256,7 @@ void test1()
* that finds that circular pattern. \param[out] data matrix to store data in * that finds that circular pattern. \param[out] data matrix to store data in
* \param[in] N number of points required * \param[in] N number of points required
*/ */
void test_lamniscate(double **data, int N) void test_lamniscate(double *const *data, int N)
{ {
const double dr = 0.2; const double dr = 0.2;
int i; int i;