Print training accuracyHEADmaster

4 files changed, 20 insertions, 5 deletions

diff --git a/include/gensvm_base.h b/include/gensvm_base.h
index f5f205f..16b7508 100644
--- a/include/gensvm_base.h
+++ b/include/gensvm_base.h
@@ -172,6 +172,8 @@ struct GenWork {
 	///< n x (K-1) working matrix for the Z * V calculation
 	double *beta;
 	///< K-1 working vector for a row of the B matrix
+	long *yhat;
+	///< n vector of predicted classes
 };
 
 // function declarations
diff --git a/include/gensvm_optimize.h b/include/gensvm_optimize.h
index d7a8248..e12f0f5 100644
--- a/include/gensvm_optimize.h
+++ b/include/gensvm_optimize.h
@@ -33,6 +33,7 @@
 
 #include "gensvm_sv.h"
 #include "gensvm_simplex.h"
+#include "gensvm_predict.h"
 #include "gensvm_update.h"
 #include "gensvm_zv.h"
 
diff --git a/src/gensvm_base.c b/src/gensvm_base.c
index 52e1e82..c5d036b 100644
--- a/src/gensvm_base.c
+++ b/src/gensvm_base.c
@@ -265,6 +265,7 @@ struct GenWork *gensvm_init_work(struct GenModel *model)
 	work->tmpZAZ = Calloc(double, (m+1)*(m+1)),
 	work->ZV = Calloc(double, n*(K-1));
 	work->beta = Calloc(double, K-1);
+	work->yhat = Calloc(long, n);
 
 	return work;
 }
@@ -288,6 +289,7 @@ void gensvm_free_work(struct GenWork *work)
 	free(work->tmpZAZ);
 	free(work->ZV);
 	free(work->beta);
+	free(work->yhat);
 	free(work);
 	work = NULL;
 }
@@ -317,4 +319,5 @@ void gensvm_reset_work(struct GenWork *work)
 	Memset(work->tmpZAZ, double, (m+1)*(m+1)),
 	Memset(work->ZV, double, n*(K-1));
 	Memset(work->beta, double, K-1);
+	Memset(work->yhat, long, n);
 }
diff --git a/src/gensvm_optimize.c b/src/gensvm_optimize.c
index df46ec4..c6c8538 100644
--- a/src/gensvm_optimize.c
+++ b/src/gensvm_optimize.c
@@ -56,7 +56,7 @@
 void gensvm_optimize(struct GenModel *model, struct GenData *data)
 {
 	long it = 0;
-	double L, Lbar;
+	double L, Lbar, acc;
 
 	long n = model->n;
 	long m = model->m;
@@ -98,9 +98,14 @@ void gensvm_optimize(struct GenModel *model, struct GenData *data)
 		Lbar = L;
 		L = gensvm_get_loss(model, data, work);
 
-		if (it % GENSVM_PRINT_ITER == 0)
+		if (it % GENSVM_PRINT_ITER == 0) {
+			gensvm_predict_labels(data, model, work->yhat);
+			acc = gensvm_prediction_perf(data, work->yhat);
 			note("iter = %li, L = %15.16f, Lbar = %15.16f, "
-			     "reldiff = %15.16f\n", it, L, Lbar, (Lbar - L)/L);
+			     "reldiff = %15.16f, acc = %.2f\n", it, L, Lbar,
+			     (Lbar - L)/L, acc);
+		}
+
 		it++;
 	}
 
@@ -120,10 +125,14 @@ void gensvm_optimize(struct GenModel *model, struct GenData *data)
 		model->status = 2;
 	}
 
+	// compute final training accuracy
+	gensvm_predict_labels(data, model, work->yhat);
+	acc = gensvm_prediction_perf(data, work->yhat);
+
 	// print final iteration count and loss
 	note("Optimization finished, iter = %li, loss = %15.16f, "
-			"rel. diff. = %15.16f\n", it-1, L,
-			(Lbar - L)/L);
+			"rel. diff. = %15.16f, acc = %.2f\n", it-1, L,
+			(Lbar - L)/L, acc);
 
 	// compute and print the number of SVs in the model
 	note("Number of support vectors: %li\n", gensvm_num_sv(model));