refactor gensvm_pred to gensvm_predict

1 files changed, 0 insertions, 99 deletions

diff --git a/src/gensvm_pred.c b/src/gensvm_pred.c
deleted file mode 100644
index 31b591c..0000000
--- a/src/gensvm_pred.c
+++ /dev/null
@@ -1,99 +0,0 @@
-/**
- * @file gensvm_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 "gensvm_pred.h"
-
-/**
- * @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] 	testdata 	GenData to predict labels for
- * @param[in] 	model 		GenModel with optimized V
- * @param[out] 	predy 		pre-allocated vector to record predictions in
- */
-void gensvm_predict_labels(struct GenData *testdata, struct GenModel *model,
-		long *predy)
-{
-	long i, j, k, n, K, label;
-	double norm, min_dist,
-	       *S = NULL,
-	       *ZV = NULL;
-
-	n = testdata->n;
-	K = model->K;
-
-	// allocate necessary memory
-	S = Calloc(double, K-1);
-	ZV = Calloc(double, n*(K-1));
-
-	// Generate the simplex matrix
-	gensvm_simplex(model);
-
-	// Generate the simplex space vectors
-	gensvm_calculate_ZV(model, testdata, ZV);
-
-	// 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 = INFINITY;
-		for (j=0; j<K; j++) {
-			for (k=0; k<K-1; k++) {
-				S[k] = matrix_get(ZV, K-1, i, k) -
-					matrix_get(model->U, K-1, j, k);
-			}
-			norm = cblas_dnrm2(K-1, S, 1);
-			if (norm < min_dist) {
-				label = j+1;
-				min_dist = norm;
-			}
-		}
-		predy[i] = label;
-	}
-
-	free(ZV);
-	free(S);
-}
-
-/**
- * @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 GenData dataset with known labels
- * @param[in] 	predy 	the predicted class labels
- *
- * @returns percentage correctly classified.
- */
-double gensvm_prediction_perf(struct GenData *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;
-}