diff options
Diffstat (limited to 'include')
| -rw-r--r-- | include/gensvm_base.h | 16 | ||||
| -rw-r--r-- | include/gensvm_globals.h | 35 | ||||
| -rw-r--r-- | include/gensvm_grid.h | 24 | ||||
| -rw-r--r-- | include/gensvm_memory.h | 20 | ||||
| -rw-r--r-- | include/gensvm_predict.h | 4 | ||||
| -rw-r--r-- | include/gensvm_queue.h | 5 | ||||
| -rw-r--r-- | include/gensvm_task.h | 22 |
7 files changed, 104 insertions, 22 deletions
diff --git a/include/gensvm_base.h b/include/gensvm_base.h index e97fab9..999bf2f 100644 --- a/include/gensvm_base.h +++ b/include/gensvm_base.h @@ -42,11 +42,12 @@ * @param K number of classes * @param n number of instances * @param m number of predictors - * @param *y pointer to vector of class labels - * @param *Z pointer to augmented data matrix - * @param *spZ pointer to the sparse augmented data matrix - * @param *RAW pointer to augmented raw data matrix - * @param *J pointer to regularization vector + * @param y pointer to vector of class labels + * @param Z pointer to augmented data matrix + * @param spZ pointer to the sparse augmented data matrix + * @param RAW pointer to augmented raw data matrix + * @param J pointer to regularization vector + * @param Sigma eigenvalues from the reduced eigendecomposition * @param kerneltype kerneltype used in GenData::Z * @param *kernelparam kernel parameters used in GenData::Z * @@ -70,8 +71,13 @@ struct GenData { double *RAW; ///< augmented raw data matrix double *Sigma; + ///< eigenvalues from the reduced eigendecomposition KernelType kerneltype; + ///< kerneltype used to generate the kernel corresponding to the data + ///< in Z double *kernelparam; + ///< kernelparameters used to generate the kernel corresponding to the + ///< data in Z }; /** diff --git a/include/gensvm_globals.h b/include/gensvm_globals.h index 7c3b68e..75b13d5 100644 --- a/include/gensvm_globals.h +++ b/include/gensvm_globals.h @@ -48,24 +48,45 @@ #include <time.h> #include <cblas.h> +/** + * Maximum line length of files that are read into GenSVM. + */ #define MAX_LINE_LENGTH 1024 #ifndef MIN_MAX_DEFINE -#define MIN_MAX_DEFINE -#define maximum(a, b) (a) > (b) ? (a) : (b) -#define minimum(a, b) (a) < (b) ? (a) : (b) + /** + * Flag to check if minimum/maximum macro's are already defined. This can be + * useful when linking. + */ + #define MIN_MAX_DEFINE + /** + * Macro for taking the maximum of two arguments. + */ + #define maximum(a, b) (a) > (b) ? (a) : (b) + /** + * Macro for taking the minimum of two arguments. + */ + #define minimum(a, b) (a) < (b) ? (a) : (b) #endif -// Set a matrix element (RowMajor) +/** + * Macro for setting a matrix element (RowMajor order) + */ #define matrix_set(M, cols, i, j, val) M[(i)*(cols)+j] = val -// Get a matrix element (RowMajor) +/** + * Macro for getting a matrix element (RowMajor order) + */ #define matrix_get(M, cols, i, j) M[(i)*(cols)+j] -// Add to a matrix element (RowMajor) +/** + * Macro for adding to a matrix element (RowMajor order) + */ #define matrix_add(M, cols, i, j, val) M[(i)*(cols)+j] += val -// Multiply a matrix element (RowMajor) +/** + * Macro for multiplying a matrix element (RowMajor order) + */ #define matrix_mul(M, cols, i, j, val) M[(i)*(cols)+j] *= val #endif diff --git a/include/gensvm_grid.h b/include/gensvm_grid.h index 24a6e9a..900a62a 100644 --- a/include/gensvm_grid.h +++ b/include/gensvm_grid.h @@ -66,28 +66,52 @@ */ struct GenGrid { TrainType traintype; + ///< type of training to use KernelType kerneltype; + ///< type of kernel to use throughout training long folds; + ///< number of folds in cross validation long repeats; + ///< number of repeats to be done after the grid search to find the + ///< parameter set with the most consistent high performance double percentile; + ///< percentile to use for the consistency repeats long Np; + ///< size of the array of p values long Nl; + ///< size of the array of lambda values long Nk; + ///< size of the array of kappa values long Ne; + ///< size of the array of epsilon values long Nw; + ///< size of the array of weight_idx values long Ng; + ///< size of the array of gamma values long Nc; + ///< size of the array of coef values long Nd; + ///< size of the array of degree values int *weight_idxs; + ///< array of weight_idxs double *ps; + ///< array of p values double *lambdas; + ///< array of lambda values double *kappas; + ///< array of kappa values double *epsilons; + ///< array of epsilon values double *gammas; + ///< array of gamma values double *coefs; + ///< array of coef values double *degrees; + ///< array of degree values char *train_data_file; + ///< filename of train data file char *test_data_file; + ///< filename of test data file }; // function declarations diff --git a/include/gensvm_memory.h b/include/gensvm_memory.h index 085fcec..e5982f6 100644 --- a/include/gensvm_memory.h +++ b/include/gensvm_memory.h @@ -32,12 +32,32 @@ #include <stddef.h> +/** + * Wrapper macro for mycalloc(). This macro uses the __FILE__ and __LINE__ + * standard macros to fill in some of the arguments to mycalloc(). This macro + * should be used when writing code, not mycalloc(). + */ #define Calloc(type, size) \ mycalloc(__FILE__, __LINE__, size, sizeof(type)) + +/** + * Wrapper macro for mymalloc(). This macro uses the __FILE__ and __LINE__ + * standard macros to fill in some of the arguments to mymalloc(). This macro + * should be used when writing code, not mymalloc(). + */ #define Malloc(type, size) \ mymalloc(__FILE__, __LINE__, (size)*sizeof(type)) +/** + * Wrapper macro for myrealloc(). This macro uses the __FILE__ and __LINE__ + * standard macros to fill in some of the arguments to myrealloc(). This macro + * should be used when writing code, not myrealloc(). + */ #define Realloc(var, type, size) \ myrealloc(__FILE__, __LINE__, (size)*sizeof(type), var) +/** + * Wrapper macro for memset(). Since memset is only used to zero a matrix, + * this macro is defined. + */ #define Memset(var, type, size) \ memset(var, 0, (size)*sizeof(type)) diff --git a/include/gensvm_predict.h b/include/gensvm_predict.h index ec644bb..069e8d3 100644 --- a/include/gensvm_predict.h +++ b/include/gensvm_predict.h @@ -38,10 +38,6 @@ // function declarations void gensvm_predict_labels(struct GenData *testdata, struct GenModel *model, long *predy); -void gensvm_predict_labels_dense(struct GenData *testdata, - struct GenModel *model, long *predy); -void gensvm_predict_labels_sparse(struct GenData *testdata, - struct GenModel *model, long *predy); double gensvm_prediction_perf(struct GenData *data, long *perdy); #endif diff --git a/include/gensvm_queue.h b/include/gensvm_queue.h index f8faa87..c680ebf 100644 --- a/include/gensvm_queue.h +++ b/include/gensvm_queue.h @@ -40,14 +40,17 @@ * This struct is basically just an array of pointers to Task instances, * with a length and an index of the current task. * - * @param **tasks array of pointers to Task structs + * @param tasks array of pointers to Task structs * @param N size of task array * @param i index used for keeping track of the queue */ struct GenQueue { struct GenTask **tasks; + ///< array of pointers to Task structs long N; + ///< size of task array long i; + ///< index used for keeping track of the queue }; // function declarations diff --git a/include/gensvm_task.h b/include/gensvm_task.h index 803a2ec..03fcbb5 100644 --- a/include/gensvm_task.h +++ b/include/gensvm_task.h @@ -45,29 +45,41 @@ * @param lambda parameter for the GenModel * @param epsilon parameter for the GenModel * @param kerneltype parameter for the GenModel - * @param *kernelparam parameters for the GenModel - * @param *train_data pointer to the training data - * @param *test_data pointer to the test data (if any) + * @param kernelparam kernel parameters for the GenModel + * @param train_data pointer to the training data + * @param test_data pointer to the test data (if any) * @param performance performance after cross validation */ struct GenTask { KernelType kerneltype; + ///< kerneltype parameter for the GenModel int weight_idx; + ///< weight_idx parameter for the GenModel long folds; + ///< number of folds in cross validation long ID; + ///< numeric id of the task in the queue double p; + ///< p parameter for the GenModel double kappa; + ///< kappa parameter for the GenModel double lambda; + ///< lambda parameter for the GenModel double epsilon; + ///< epsilon parameter for the GenModel double *kernelparam; + ///< kernelparam parameters for the GenModel struct GenData *train_data; + ///< pointer to the training data struct GenData *test_data; + ///< pointer to the test data (if any) double performance; + ///< performance after cross validation }; struct GenTask *gensvm_init_task(); -struct GenTask *gensvm_copy_task(); -void gensvm_free_task(struct GenTask *task); +struct GenTask *gensvm_copy_task(struct GenTask *t); +void gensvm_free_task(struct GenTask *t); void gensvm_task_to_model(struct GenTask *task, struct GenModel *model); #endif |