Fixed least squares multitask L1/Lq regressor and made it fit with SL…

…EPs analogous method
shogun-toolbox · Jul 1, 2012 · 6375108 · 6375108
1 parent a1b9e16
commit 6375108
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Showing 3 changed files with 14 additions and 28 deletions.
diff --git a/src/shogun/lib/slep/slep_mt_lr.cpp b/src/shogun/lib/slep/slep_mt_lr.cpp
@@ -29,7 +29,6 @@ slep_result_t slep_mt_lr(
 	double funcp = 0.0, func = 0.0;
 
 	int n_tasks = options.n_tasks;
-	//SG_SPRINT("N tasks = %d \n", n_tasks);
 
 	int iter = 1;
 	bool done = false;
@@ -77,10 +76,10 @@ slep_result_t slep_mt_lr(
 		double q_bar = 0.0;
 		if (options.q==1)
 			q_bar = CMath::ALMOST_INFTY;
-		if (options.q>1e-6)
+		else if (options.q>1e-6)
 			q_bar = 1;
-
-		q_bar = options.q/(options.q-1);
+		else 
+			q_bar = options.q/(options.q-1);
 		lambda_max = 0.0;
 
 		for (i=0; i<n_feats; i++)
@@ -155,7 +154,6 @@ slep_result_t slep_mt_lr(
 	while (!done && iter <= options.max_iter) 
 	{
 		beta = (alphap-1.0)/alpha;
-		//SG_SPRINT("beta = %f \n", beta);
 
 		for (i=0; i<n_feats*n_tasks; i++)
 			s[i] = w[i] + beta*wwp[i];
@@ -165,8 +163,6 @@ slep_result_t slep_mt_lr(
 		for (i=0; i<n_vecs; i++)
 			As[i] = Aw[i] + beta*(Aw[i]-Awp[i]);
 
-		//SG_SPRINT("As = %f\n",SGVector<float64_t>::dot(As,As,n_vecs));
-
 		double fun_s = 0.0;
 		for (i=0; i<n_tasks*n_feats; i++)
 			g[i] = 0.0;
@@ -192,10 +188,6 @@ slep_result_t slep_mt_lr(
 		}
 		fun_s /= n_vecs;
 
-		//SG_SPRINT("fun_s = %f\n", fun_s);
-
-		//SG_SPRINT("g = %f\n", SGVector<float64_t>::dot(g,g,n_feats*n_tasks));
-
 		for (i=0; i<n_feats*n_tasks; i++)
 			wp[i] = w[i];
 
@@ -217,11 +209,6 @@ slep_result_t slep_mt_lr(
 
 			eppMatrix(w.matrix, v, n_feats, n_tasks, lambda/L, options.q);
 
-			//SG_SPRINT("params [%d,%d,%f,%f]\n", n_feats, n_tasks, lambda/L, options.q);
-
-			//w.display_matrix();
-			//SG_SPRINT("w = %f \n", SGVector<float64_t>::dot(w.matrix,w.matrix,n_feats*n_tasks));
-
 			// v = x - s
 			for (i=0; i<n_feats*n_tasks; i++)
 				v[i] = w[i] - s[i];
@@ -242,9 +229,6 @@ slep_result_t slep_mt_lr(
 			}
 			fun_x /= n_vecs;
 
-			//SG_SPRINT("Aw = %f\n", SGVector<float64_t>::dot(Aw,Aw,n_vecs));
-			//c.display_vector();
-
 			double r_sum = SGVector<float64_t>::dot(v,v,n_feats*n_tasks);
 			double l_sum = fun_x - fun_s - SGVector<float64_t>::dot(v,g,n_feats*n_tasks);
 
@@ -255,8 +239,6 @@ slep_result_t slep_mt_lr(
 			}
 			r_sum /= 2.0;
 
-			//SG_SPRINT("sums = [%f, %f, %f]\n", r_sum, l_sum, fun_x);
-
 			if (r_sum <= 1e-20)
 			{
 				gradient_break = true;

diff --git a/src/shogun/lib/slep/slep_mt_lsr.cpp b/src/shogun/lib/slep/slep_mt_lsr.cpp
@@ -28,7 +28,7 @@ SGMatrix<double> slep_mt_lsr(
 	double lambda, lambda_max, beta;
 	double funcp = 0.0, func = 0.0;
 
-	int n_tasks = options.n_nodes;
+	int n_tasks = options.n_tasks;
 
 	int iter = 1;
 	bool done = false;
@@ -51,17 +51,20 @@ SGMatrix<double> slep_mt_lsr(
 		double q_bar = 0.0;
 		if (options.q==1)
 			q_bar = CMath::ALMOST_INFTY;
-		else if (options.q>1e-6)
+		else if (options.q>1e6)
 			q_bar = 1;
 		else
 			q_bar = options.q/(options.q-1);
+
 		lambda_max = 0.0;
 
-		for (t=0; t<n_tasks; t++)
+		for (i=0; i<n_feats; i++)
 		{
+			double sum = 0.0;
+			for (t=0; t<n_tasks; t++)
+				sum += CMath::pow(fabs(ATy[t*n_feats+i]),q_bar);
 			lambda_max = 
-				CMath::max(lambda_max, 
-						SGVector<float64_t>::qnorm(ATy+t*n_feats, n_feats, q_bar));
+				CMath::max(lambda_max, CMath::pow(sum,1.0/q_bar));
 		}
 
 		lambda = z*lambda_max;
@@ -110,7 +113,7 @@ SGMatrix<double> slep_mt_lsr(
 	double alphap = 0.0;
 	double alpha = 1.0;
 
-	while (!done && iter < options.max_iter) 
+	while (!done && iter <= options.max_iter) 
 	{
 		beta = (alphap-1.0)/alpha;
 

diff --git a/src/shogun/transfer/multitask/MultitaskLSRegression.cpp b/src/shogun/transfer/multitask/MultitaskLSRegression.cpp
@@ -89,6 +89,7 @@ bool CMultitaskLSRegression::train_machine(CFeatures* data)
 	options.regularization = m_regularization;
 	options.termination = m_termination;
 	options.tolerance = m_tolerance;
+	options.max_iter = m_max_iter;
 
 	ETaskRelationType relation_type = m_task_relation->get_relation_type();
 	switch (relation_type)
@@ -98,7 +99,7 @@ bool CMultitaskLSRegression::train_machine(CFeatures* data)
 			CTaskGroup* task_group = (CTaskGroup*)m_task_relation;
 			SGVector<index_t> ind = task_group->get_SLEP_ind();
 			options.ind = ind.vector;
-			options.n_nodes = ind.vlen-1;
+			options.n_tasks = ind.vlen-1;
 
 			m_tasks_w = slep_mt_lsr(features, y.vector, m_z, options);
 		}