rename msvmmaj to gensvm

1 files changed, 0 insertions, 326 deletions

diff --git a/src/libMSVMMaj.c b/src/libMSVMMaj.c
deleted file mode 100644
index df422c0..0000000
--- a/src/libMSVMMaj.c
+++ /dev/null
@@ -1,326 +0,0 @@
-/**
- * @file libMSVMMaj.c
- * @author Gertjan van den Burg
- * @date August 8, 2013
- * @brief Main functions for the MSVMMaj algorithm
- *
- * @details
- * The functions in this file are all functions needed
- * to calculate the optimal separation boundaries for 
- * a multiclass classification problem, using the 
- * MSVMMaj algorithm.
- *
- */
-
-#include <cblas.h>
-#include <math.h>
-
-#include "libMSVMMaj.h"
-#include "msvmmaj.h"
-#include "msvmmaj_matrix.h"
-
-inline double rnd() { return (double) rand()/0x7FFFFFFF; }
-
-/**
- * @brief Generate matrix of simplex vertex coordinates
- * 
- * @details
- * Generate the simplex matrix. Each row of the created 
- * matrix contains the coordinate vector of a single 
- * vertex of the K-simplex in K-1 dimensions. The simplex
- * generated is a special simplex with edges of length 1.
- * The simplex matrix U must already have been allocated.
- *
- * @param[in] 		K 	number of classes
- * @param[in,out] 	U 	simplex matrix of size K * (K-1)
- */
-void msvmmaj_simplex_gen(long K, double *U)
-{
-	long i, j;
-	for (i=0; i<K; i++) {
-		for (j=0; j<K-1; j++) {
-			if (i <= j) {
-				matrix_set(U, K-1, i, j, -1.0/sqrt(2.0*(j+1)*(j+2)));
-			} else if (i == j+1) {
-				matrix_set(U, K-1, i, j, sqrt((j+1)/(2.0*(j+2))));
-			} else {
-				matrix_set(U, K-1, i, j, 0.0);
-			}
-		}
-	}
-}
-
-/**
- * @brief Generate the category matrix
- *
- * @details
- * Generate the category matrix R. The category matrix has 1's everywhere
- * except at the column corresponding to the label of instance i, there the
- * element is 0.
- *
- * @param[in,out] 	model 		corresponding MajModel
- * @param[in] 		dataset 	corresponding MajData
- *
- */
-void msvmmaj_category_matrix(struct MajModel *model, struct MajData *dataset)
-{
-	long i, j;
-	long n = model->n;
-	long K = model->K;
-
-	for (i=0; i<n; i++) {
-		for (j=0; j<K; j++) {
-			if (dataset->y[i] != j+1)
-				matrix_set(model->R, K, i, j, 1.0);
-			else
-				matrix_set(model->R, K, i, j, 0.0);
-		}
-	}
-}
-
-/**
- * @brief Generate the simplex difference matrix
- *
- * @details
- * The simplex difference matrix is a 3D matrix which is constructed
- * as follows. For each instance i, the difference vectors between the row of 
- * the simplex matrix corresponding to the class label of instance i and the
- * other rows of the simplex matrix are calculated. These difference vectors 
- * are stored in a matrix, which is one horizontal slice of the 3D matrix.
- *
- * @param[in,out] 	model 	the corresponding MajModel
- * @param[in] 		data 	the corresponding MajData
- *
- */
-void msvmmaj_simplex_diff(struct MajModel *model, struct MajData *data)
-{
-	long i, j, k;
-	double value;
-
-	long n = model->n;
-	long K = model->K;
-
-	for (i=0; i<n; i++) {
-		for (j=0; j<K-1; j++) {
-			for (k=0; k<K; k++) {
-				value = matrix_get(model->U, K-1, data->y[i]-1, j);
-				value -= matrix_get(model->U, K-1, k, j);
-				matrix3_set(model->UU, K-1, K, i, j, k, value);
-			}
-		}
-	}
-}
-
-/**
- * @brief Calculate the scalar errors
- * 
- * @details
- * Calculate the scalar errors q based on the current estimate of V, and
- * store these in Q. It is assumed that the memory for Q has already been 
- * allocated. In addition, the matrix ZV is calculated here. It is assigned 
- * to a pre-allocated block of memory, which is passed to this function.
- *
- * @param[in,out] 	model 	the corresponding MajModel
- * @param[in] 		data 	the corresponding MajData
- * @param[in,out] 	ZV 	a pointer to a memory block for ZV. On exit
- * 				this block is updated with the new ZV matrix
- * 				calculated with MajModel::V.
- *
- */
-void msvmmaj_calculate_errors(struct MajModel *model, struct MajData *data,
-	       	double *ZV)
-{
-	long i, j, k;
-	double a, value;
-
-	long n = model->n;
-	long m = model->m;
-	long K = model->K;
-
-	cblas_dgemm(
-			CblasRowMajor,
-			CblasNoTrans,
-			CblasNoTrans,
-			n,
-			K-1,
-			m+1,
-			1.0,
-			data->Z,
-			m+1,
-			model->V,
-			K-1,
-			0.0,
-			ZV,
-			K-1);
-
-	Memset(model->Q, double, n*K);
-	for (i=0; i<n; i++) {
-		for (j=0; j<K-1; j++) {
-			a = matrix_get(ZV, K-1, i, j);
-			for (k=0; k<K; k++) {
-				value = a * matrix3_get(model->UU, K-1, K, i,
-					       	j, k);
-				matrix_add(model->Q, K, i, k, value);
-			}
-		}
-	}
-}	
-
-/**
- * @brief Calculate the Huber hinge errors
- *
- * @details
- * For each of the scalar errors in Q the Huber hinge errors are 
- * calculated. The Huber hinge is here defined as 
- * @f[
- * 	h(q) = 
- * 		\begin{dcases}
- * 			1 - q - \frac{\kappa + 1}{2} & \text{if } q \leq -\kappa \\
- * 			\frac{1}{2(\kappa + 1)} ( 1 - q)^2 & \text{if } q \in (-\kappa, 1] \\
- * 			0 & \text{if } q > 1
- * 		\end{dcases}
- * @f]
- *
- * @param[in,out] model 	the corresponding MajModel
- */
-void msvmmaj_calculate_huber(struct MajModel *model) 
-{
-	long i, j;
-	double q, value;
-
-	for (i=0; i<model->n; i++) {
-		for (j=0; j<model->K; j++) {
-			q = matrix_get(model->Q, model->K, i, j);
-			value = 0.0;
-			if (q <= -model->kappa) {
-				value = 1.0 - q - (model->kappa+1.0)/2.0;
-			} else if (q <= 1.0) {
-				value = 1.0/(2.0*model->kappa+2.0)*pow(1.0 - q, 2.0);
-			}
-			matrix_set(model->H, model->K, i, j, value);
-		}
-	}
-}
-
-/**
- * @brief seed the matrix V from an existing model or using rand
- *
- * @details
- * The matrix V must be seeded before the main_loop() can start. 
- * This can be done by either seeding it with random numbers or 
- * using the solution from a previous model on the same dataset
- * as initial seed. The latter option usually allows for a 
- * significant improvement in the number of iterations necessary
- * because the seeded model V is closer to the optimal V.
- *
- * @param[in] 		from_model 	MajModel from which to copy V
- * @param[in,out] 	to_model 	MajModel to which V will be copied
- */
-void msvmmaj_seed_model_V(struct MajModel *from_model,
-	       	struct MajModel *to_model, struct MajData *data)
-{
-	long i, j, k;
-	double cmin, cmax, value;
-	
-	long n = data->n;
-	long m = data->m;
-	long K = data->K;
-	
-	if (from_model == NULL) {
-		for (i=0; i<m+1; i++) {
-			cmin = 1e100;
-			cmax = -1e100;
-			for (k=0; k<n; k++) {
-				value = matrix_get(data->Z, m+1, k, i);
-				cmin = minimum(cmin, value);
-				cmax = maximum(cmax, value);
-			}
-			for (j=0; j<K-1; j++) {
-				cmin = (abs(cmin) < 1e-10) ? -1 : cmin;
-				cmax = (abs(cmax) < 1e-10) ? 1 : cmax;
-				value = 1.0/cmin + (1.0/cmax - 1.0/cmin)*rnd();
-				matrix_set(to_model->V, K-1, i, j, value);
-			}
-		}
-	} else {
-		for (i=0; i<m+1; i++) 
-			for (j=0; j<K-1; j++) {
-				value = matrix_get(from_model->V, K-1, i, j);
-				matrix_set(to_model->V, K-1, i, j, value);
-			}
-	}
-}
-
-/**
- * @brief Use step doubling
- *
- * @details
- * Step doubling can be used to speed up the Majorization algorithm. Instead 
- * of using the value at the minimimum of the majorization function, the value
- * ``opposite'' the majorization point is used. This can essentially cut the
- * number of iterations necessary to reach the minimum in half.
- *
- * @param[in] 	model	MajModel containing the augmented parameters
- */
-void msvmmaj_step_doubling(struct MajModel *model)
-{
-	long i, j;
-	double value;
-
-	long m = model->m;
-	long K = model->K;
-
-	for (i=0; i<m+1; i++) {
-		for (j=0; j<K-1; j++) {
-			matrix_mul(model->V, K-1, i, j, 2.0);
-			value = - matrix_get(model->Vbar, K-1, i, j);
-			matrix_add(model->V, K-1, i, j, value);
-		}
-	}
-}
-
-/**
- * @brief Initialize instance weights
- *
- * @details
- * Instance weights can for instance be used to add additional weights to 
- * instances of certain classes. Two default weight possibilities are
- * implemented here. The first is unit weights, where each instance gets 
- * weight 1. 
- *
- * The second are group size correction weights, which are calculated as
- * @f[
- * 	\rho_i = \frac{n}{Kn_k} ,
- * @f]
- * where @f$ n_k @f$ is the number of instances in group @f$ k @f$ and
- * @f$ y_i = k @f$.
- *
- * @param[in] 		data 	MajData with the dataset
- * @param[in,out] 	model 	MajModel with the weight specification. On 
- * 				exit MajModel::rho contains the instance 
- * 				weights.
- */
-void msvmmaj_initialize_weights(struct MajData *data, struct MajModel *model)
-{
-	long *groups;
-	long i;
-
-	long n = model->n;
-	long K = model->K;
-
-	if (model->weight_idx == 1) {
-		for (i=0; i<n; i++)
-			model->rho[i] = 1.0;
-	} 
-	else if (model->weight_idx == 2) {
-		groups = Calloc(long, K);
-		for (i=0; i<n; i++) 
-			groups[data->y[i]-1]++;
-		for (i=0; i<n; i++) 
-			model->rho[i] = ((double) n)/((double) (groups[data->y[i]-1]*K));
-	} else {
-		fprintf(stderr, "Unknown weight specification.\n");
-		exit(1);
-	}
-}
-