Merge branch 'slep' of git://github.com/lisitsyn/shogun

shogun-toolbox · Jun 17, 2012 · c3bfbbe · c3bfbbe
2 parents afe730c + d48a682
commit c3bfbbe
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diff --git a/examples/undocumented/python_modular/transfer_multitask_group_regression.py b/examples/undocumented/python_modular/transfer_multitask_group_regression.py
@@ -0,0 +1,34 @@
+from numpy import array
+from numpy.random import seed, rand
+from tools.load import LoadMatrix
+lm=LoadMatrix()
+
+traindat = lm.load_numbers('../data/fm_train_real.dat')
+testdat = lm.load_numbers('../data/fm_test_real.dat')
+label_traindat = lm.load_labels('../data/label_train_twoclass.dat')
+
+parameter_list = [[traindat,testdat,label_traindat]]
+
+def transfer_multitask_group_regression(fm_train=traindat,fm_test=testdat,label_train=label_traindat):
+
+	from modshogun import RegressionLabels, RealFeatures, Task, TaskGroup, MultitaskLSRegression
+
+	features = RealFeatures(traindat)
+	labels = RegressionLabels(label_train)
+
+	n_vectors = features.get_num_vectors()
+	task_one = Task(0,n_vectors/2)
+	task_two = Task(n_vectors/2,n_vectors)
+	task_group = TaskGroup()
+	task_group.add_task(task_one)
+	task_group.add_task(task_two)
+
+	mtlsr = MultitaskLSRegression(0.1,features,labels,task_group)
+	mtlsr.train()
+	mtlsr.set_current_task(0)
+	out = mtlsr.apply_regression().get_labels()
+	return out
+
+if __name__=='__main__':
+	print('TransferMultitaskGroupRegression')
+	transfer_multitask_group_regression(*parameter_list[0])
diff --git a/src/shogun/lib/slep/slep_mt_lsr.cpp b/src/shogun/lib/slep/slep_mt_lsr.cpp
@@ -79,8 +79,8 @@ SGMatrix<double> slep_mt_lsr(
 	}
 
 	double* s = SG_CALLOC(double, n_feats*n_tasks);
-	double* g = SG_CALLOC(double, n_feats*n_tasks*2);
-	double* v = SG_CALLOC(double, n_feats*n_tasks*2);
+	double* g = SG_CALLOC(double, n_feats*n_tasks);
+	double* v = SG_CALLOC(double, n_feats*n_tasks);
 
 	double* Aw = SG_CALLOC(double, n_vecs);
 	for (t=0; t<n_tasks; t++)
@@ -165,7 +165,6 @@ SGMatrix<double> slep_mt_lsr(
 			for (i=0; i<n_vecs; i++)
 				Av[i] = Aw[i] - As[i];
 
-			// squared frobenius norm of r
 			double r_sum = SGVector<float64_t>::dot(v,v,n_feats*n_tasks);
 
 			double l_sum = SGVector<float64_t>::dot(Av,Av,n_vecs);
@@ -192,17 +191,17 @@ SGMatrix<double> slep_mt_lsr(
 		for (i=0; i<n_vecs; i++)
 			resid[i] = Aw[i] - y[i];
 
-		double w_norm = 0.0;
+		double regularizer = 0.0;
 		for (i=0; i<n_feats; i++)
 		{
 			double w_row_norm = 0.0;
 			for (t=0; t<n_tasks; t++)
 				w_row_norm += CMath::pow(w(i,t),options.q);
-			w_norm += CMath::pow(w_row_norm,1.0/options.q);
+			regularizer += CMath::pow(w_row_norm,1.0/options.q);
 		}
 
 		funcp = func;
-		func = 0.5*SGVector<float64_t>::dot(resid,resid,n_vecs) + lambda*w_norm;
+		func = 0.5*SGVector<float64_t>::dot(resid,resid,n_vecs) + lambda*regularizer;
 
 		if (gradient_break)
 			break;
@@ -251,27 +250,9 @@ SGMatrix<double> slep_mt_lsr(
 				done = true;
 		}
 
-		if (iter%options.restart_num==0)
-		{
-			alphap = 0.0;
-			alpha = 1.0;
-			L = 0.5*L;
-			for (i=0; i<n_feats; i++)
-				wp[i] = w[i];
-
-			for (i=0; i<n_vecs; i++)
-				Awp[i] = Aw[i];
-
-			for (i=0; i<n_feats; i++)
-				wwp[i] = 0.0;
-		}
-
 		iter++;
 	}
 
-	// SG_SPRINT("Iteration = %d\n", iter);
-	// TODO Nemirovsky, Nesterov methods
-
 	SG_FREE(ATy);
 	SG_FREE(wp);
 	SG_FREE(wwp);