rename msvmmaj to gensvm

1 files changed, 215 insertions, 0 deletions

diff --git a/src/gensvm_pred.c b/src/gensvm_pred.c
new file mode 100644
index 0000000..f331116
--- /dev/null
+++ b/src/gensvm_pred.c
@@ -0,0 +1,215 @@
+/**
+ * @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 <cblas.h>
+
+#include "libGenSVM.h"
+#include "gensvm.h"
+#include "gensvm_kernel.h"
+#include "gensvm_matrix.h"
+#include "gensvm_pred.h"
+
+#include "util.h" // testing
+
+void gensvm_predict_labels(struct GenData *data_test,
+	       	struct GenData *data_train, struct GenModel *model,
+	       	long *predy)
+{
+	if (model->kerneltype == K_LINEAR)
+		gensvm_predict_labels_linear(data_test, model, predy);
+	else
+		gensvm_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 		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_linear(struct GenData *data,
+	       	struct GenModel *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
+	gensvm_simplex_gen(K, U);
+
+	// Generate the simplex-space vectors
+	cblas_dgemm(
+			CblasRowGenor,
+			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 gensvm_predict_labels_kernel(struct GenData *data_test,
+	       	struct GenData *data_train, struct GenModel *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;
+	gensvm_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));
+
+	gensvm_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(
+			CblasRowGenor,
+			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 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;
+}