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/**
* @file gensvm_base.h
* @author Gertjan van den Burg
* @date May, 2016
* @brief Definitions for GenData and GenModel structures
*
* @details
* Contains documentation and declarations of GenModel and GenData.
*
*/
#ifndef GENSVM_BASE_H
#define GENSVM_BASE_H
// includes
#include "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 *W;
///< weight matrix
double *t;
///< translation vector
double *V;
///< augmented weight matrix
double *Vbar;
///< augmented weight matrix from the previous iteration of the IM
///< algorithm
double *U;
///< simplex matrix
double *UU;
///< 3D simplex difference matrix
double *Q;
///< error matrix
double *H;
///< Huber weighted error matrix
double *R;
///< 0-1 auixiliary matrix, this matrix is n x K, with for row i a 0 on
///< column y[i]-1, and 1 everywhere else.
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
};
// 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);
#endif
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