major refactor of the code

1 files changed, 116 insertions, 0 deletions

diff --git a/include/gensvm_base.h b/include/gensvm_base.h
new file mode 100644
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+++ b/include/gensvm_base.h
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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