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/**
* @file gensvm_base.h
* @author Gertjan van den Burg
* @date May, 2016
* @brief Header file for gensvm_base.c
*
* @details
* Contains documentation and declarations of GenModel and GenData, and
* function declarations for the functions in gensvm_base.c.
*
*/
#ifndef GENSVM_BASE_H
#define GENSVM_BASE_H
// includes
#include "gensvm_globals.h"
// type declarations
/**
* @brief A structure to represent the data.
*
* @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 *RAW pointer to augmented raw data matrix
* @param *J pointer to regularization vector
* @param kerneltype kerneltype used in GenData::Z
* @param *kernelparam kernel parameters used in GenData::Z
*
*/
struct GenData {
long K;
///< number of classes
long n;
///< number of instances
long m;
///< number of predictors (width of RAW)
long r;
///< number of eigenvalues (width of Z)
long *y;
///< array of class labels, 1..K
double *Z;
///< augmented data matrix (either equal to RAW or to the eigenvectors
///< of the kernel matrix)
double *RAW;
///< augmented raw data matrix
double *Sigma;
KernelType kerneltype;
double *kernelparam;
};
/**
* @brief A structure to represent a single GenSVM model.
*
*/
struct GenModel {
int weight_idx;
///< which weights to use (1 = unit, 2 = group)
long K;
///< number of classes in the dataset
long n;
///< number of instances in the dataset
long m;
///< number of predictor variables in the dataset
double epsilon;
///< stopping criterion for the IM algorithm.
double p;
///< parameter for the L-p norm in the loss function
double kappa;
///< parameter for the Huber hinge function
double lambda;
///< regularization parameter in the loss function
double *V;
///< augmented weight matrix
double *Vbar;
///< augmented weight matrix from the previous iteration of the IM
///< algorithm
double *U;
///< simplex matrix
double *UU;
///< simplex difference matrix
double *Q;
///< error matrix
double *H;
///< Huber weighted error matrix
double *rho;
///< vector of instance weights
double training_error;
///< loss function value after training has finished
char *data_file;
///< filename of the data
KernelType kerneltype;
///< type of kernel used in the model
double *kernelparam;
///< array of kernel parameters, size depends on kernel type
};
/**
* @brief A structure to hold the GenSVM workspace
*
*/
struct GenWork {
long n;
///< number of instances for the workspace
long m;
///< number of features for the workspace
long K;
///< number of classes for the workspace
double *LZ;
///< n x (m+1) working matrix for the Z'*A*Z calculation
double *ZB;
///< (m+1) x (K-1) working matrix for the Z'*B calculation
double *ZBc;
///< (K-1) x (m+1) working matrix for the Z'*B calculation
double *ZAZ;
///< (m+1) x (m+1) working matrix for the Z'*A*Z calculation
double *ZV;
///< n x (K-1) working matrix for the Z * V calculation
double *beta;
///< K-1 working vector for a row of the B matrix
};
// function declarations
struct GenModel *gensvm_init_model();
void gensvm_allocate_model(struct GenModel *model);
void gensvm_reallocate_model(struct GenModel *model, long n, long m);
void gensvm_free_model(struct GenModel *model);
struct GenData *gensvm_init_data();
void gensvm_free_data(struct GenData *data);
struct GenWork *gensvm_init_work(struct GenModel *model);
void gensvm_free_work(struct GenWork *work);
void gensvm_reset_work(struct GenWork *work);
#endif
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