refactor gensvm_pred to gensvm_predict

1 files changed, 99 insertions, 0 deletions

diff --git a/src/gensvm_predict.c b/src/gensvm_predict.c
new file mode 100644
index 0000000..1112e55
--- /dev/null
+++ b/src/gensvm_predict.c
@@ -0,0 +1,99 @@
+/**
+ * @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_predict.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;
+}