first working version of new kernel GenSVM

1 files changed, 35 insertions, 114 deletions

diff --git a/src/gensvm_pred.c b/src/gensvm_pred.c
index 88678d7..7baae07 100644
--- a/src/gensvm_pred.c
+++ b/src/gensvm_pred.c
@@ -12,6 +12,7 @@
  */
 
 #include <cblas.h>
+#include <math.h>
 
 #include "libGenSVM.h"
 #include "gensvm.h"
@@ -19,19 +20,6 @@
 #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
  *
@@ -46,24 +34,40 @@ void gensvm_predict_labels(struct GenData *data_test,
  * @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)
+void gensvm_predict_labels(struct GenData *testdata, 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));
+	long i, j, k, n, m, K, label;
+	double norm, min_dist, *S, *ZV, *U;
+
+	n = testdata->n;
+	m = testdata->r;
+	K = model->K;
+
+	// allocate necessary memory
+	S = Calloc(double, K-1);
+	if (S == NULL) {
+		fprintf(stderr, "Failed to allocate memory for S in "
+				"gensvm_predict_labels.\n");
+		exit(1);
+	}
+	ZV = Calloc(double, n*(K-1));
+	if (ZV == NULL) {
+		fprintf(stderr, "Failed to allocate memory for ZV in "
+				"gensvm_predict_labels.\n");
+		exit(1);
+	}
+	U = Calloc(double, K*(K-1));
+	if (U == NULL) {
+		fprintf(stderr, "Failed to allocate memory for U in "
+				"gensvm_predict_labels.\n");
+		exit(1);
+	}
 
-	// Get the simplex matrix
+	// Generate the simplex matrix
 	gensvm_simplex_gen(K, U);
 
-	// Generate the simplex-space vectors
+	// Generate the simplex space vectors
 	cblas_dgemm(
 			CblasRowMajor,
 			CblasNoTrans,
@@ -72,7 +76,7 @@ void gensvm_predict_labels_linear(struct GenData *data,
 			K-1,
 			m+1,
 			1.0,
-			data->Z,
+			testdata->Z,
 			m+1,
 			model->V,
 			K-1,
@@ -81,96 +85,16 @@ void gensvm_predict_labels_linear(struct GenData *data,
 			K-1);
 
 	// Calculate the distance to each of the vertices of the simplex.
-	// The closest vertex defines the class label.
+	// 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(
-			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;
+		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(U, K-1, j, k);
 			}
-			norm = cblas_dnrm2(K, S, 1);
+			norm = cblas_dnrm2(K-1, S, 1);
 			if (norm < min_dist) {
 				label = j+1;
 				min_dist = norm;
@@ -182,9 +106,6 @@ void gensvm_predict_labels_kernel(struct GenData *data_test,
 	free(ZV);
 	free(U);
 	free(S);
-	free(KPS);
-	free(K2);
-
 }
 
 /**