1 files changed, 0 insertions, 215 deletions
diff --git a/src/msvmmaj_pred.c b/src/msvmmaj_pred.c
deleted file mode 100644
index ea1ebfe..0000000
--- a/src/msvmmaj_pred.c
+++ /dev/null
@@ -1,215 +0,0 @@
-/**
- * @file msvmmaj_pred.c
- * @author Gertjan van den Burg
- * @date August 9, 2013
- * @brief Main functions for predicting class labels..
- *
- * @details
- * This file contains functions for predicting the class labels of instances
- * and a function for calculating the predictive performance (hitrate) of 
- * a prediction given true class labels.
- *
- */
-
-#include <cblas.h>
-
-#include "libMSVMMaj.h"
-#include "msvmmaj.h"
-#include "msvmmaj_kernel.h"
-#include "msvmmaj_matrix.h"
-#include "msvmmaj_pred.h"
-
-#include "util.h" // testing
-
-void msvmmaj_predict_labels(struct MajData *data_test,
-	       	struct MajData *data_train, struct MajModel *model,
-	       	long *predy)
-{
-	if (model->kerneltype == K_LINEAR)
-		msvmmaj_predict_labels_linear(data_test, model, predy);
-	else
-		msvmmaj_predict_labels_kernel(data_test, data_train, model,
-			       	predy);
-}
-
-/**
- * @brief Predict class labels of data given and output in predy
- *
- * @details
- * The labels are predicted by mapping each instance in data to the 
- * simplex space using the matrix V in the given model. Next, for each
- * instance the nearest simplex vertex is determined using an Euclidean
- * norm. The nearest simplex vertex determines the predicted class label,
- * which is recorded in predy.
- *
- * @param[in] 	data 		MajData to predict labels for
- * @param[in] 	model 		MajModel with optimized V
- * @param[out] 	predy 		pre-allocated vector to record predictions in
- */
-void msvmmaj_predict_labels_linear(struct MajData *data,
-	       	struct MajModel *model, long *predy)
-{
-	long i, j, k, label;
-	double norm, min_dist;
-
-	long n = data->n; // note that model->n is the size of the training sample.
-	long m = data->m;
-	long K = model->K; //data->K does not necessarily equal the original K.
-
-	double *S = Calloc(double, K-1);
-	double *ZV = Calloc(double, n*(K-1));
-	double *U = Calloc(double, K*(K-1));
-
-	// Get the simplex matrix
-	msvmmaj_simplex_gen(K, U);
-
-	// Generate the simplex-space vectors
-	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);
-
-	// Calculate the distance to each of the vertices of the simplex.
-	// The closest vertex defines the class label.
-	for (i=0; i<n; i++) {
-		label = 0;
-		min_dist = 1000000000.0;
-		for (j=0; j<K; j++) {
-			for (k=0; k<K-1; k++) {
-				S[k] = matrix_get(ZV, K-1, i, k) -
-				       	matrix_get(U, K-1, j, k);
-			}
-			norm = cblas_dnrm2(K-1, S, 1);
-			if (norm < min_dist) {
-				label = j+1; // labels start counting from 1
-				min_dist = norm;
-			}
-		}
-		predy[i] = label;
-	}
-
-	free(ZV);
-	free(U);
-	free(S);
-}
-
-void msvmmaj_predict_labels_kernel(struct MajData *data_test,
-	       	struct MajData *data_train, struct MajModel *model,
-	       	long *predy)
-{
-	long i, j, k, label;
-	double norm, min_dist;
-
-	long n_train = data_train->n;
-	long n_test = data_test->n;
-	long r = model->m;
-	long K = model->K;
-
-	double *K2 = NULL;
-	msvmmaj_make_crosskernel(model, data_train, data_test, &K2);
-
-	double *S = Calloc(double, K-1);
-	double *ZV = Calloc(double, n_test*(r+1));
-	double *KPS = Calloc(double, n_test*(r+1));
-	double *U = Calloc(double, K*(K-1));
-
-	msvmmaj_simplex_gen(K, U);
-	
-	// were doing the computations explicitly since P is included in 
-	// data_train->Z. Might want to look at this some more if it turns out 
-	// to be slow.
-
-	double value, rowvalue;
-	for (i=0; i<n_test; i++) {
-		for (j=1; j<r+1; j++) {
-			value = 0.0;
-			for (k=0; k<n_train; k++) {
-				rowvalue = matrix_get(K2, n_train, i, k);
-				rowvalue *= matrix_get(data_train->Z, r+1, k,
-					       	j);
-				value += rowvalue;
-			}
-			value *= matrix_get(data_train->J, 1, j, 0);
-			matrix_set(KPS, r+1, i, j, value);
-		}
-		matrix_set(KPS, r+1, i, 0, 1.0);
-	}
-	
-	cblas_dgemm(
-			CblasRowMajor,
-			CblasNoTrans,
-			CblasNoTrans,
-			n_test,
-			K-1,
-			r+1,
-			1.0,
-			KPS,
-			r+1,
-			model->V,
-			K-1,
-			0.0,
-			ZV,
-			K-1);
-
-	for (i=0; i<n_test; i++) {
-		label = 0;
-		min_dist = 1e10;
-		for (j=0; j<K; j++) {
-			for (k=0; k<K-1; k++) {
-				S[k] = matrix_get(ZV, K-1, i, k) -
-					matrix_get(U, K-1, j, k);
-			}
-			norm = cblas_dnrm2(K, S, 1);
-			if (norm < min_dist) {
-				label = j+1;
-				min_dist = norm;
-			}
-		}
-		predy[i] = label;
-	}
-
-	free(ZV);
-	free(U);
-	free(S);
-	free(KPS);
-	free(K2);
-
-}
-
-/**
- * @brief Calculate the predictive performance (percentage correct)
- *
- * @details
- * The predictive performance is calculated by simply counting the number
- * of correctly classified samples and dividing by the total number of 
- * samples, multiplying by 100.
- *
- * @param[in] 	data 	the MajData dataset with known labels
- * @param[in] 	predy 	the predicted class labels
- *
- * @returns percentage correctly classified.
- */
-double msvmmaj_prediction_perf(struct MajData *data, long *predy)
-{
-	long i, correct = 0;
-	double performance;
-
-	for (i=0; i<data->n; i++)
-		if (data->y[i] == predy[i])
-			correct++;
-
-	performance = ((double) correct)/((double) data->n)* 100.0;
-
-	return performance;
-}