From e16cbac1fe51ec243d04a7e2475336b9aebdb4cb Mon Sep 17 00:00:00 2001
From: Jiayu Zhou <dearjiayu@gmail.com>
Date: Mon, 27 Feb 2017 15:07:47 -0500
Subject: [PATCH] Formatting issue and variable names.

---
 MALSAR/functions/dirty/Logistic_Dirty.m | 67 +++++++++++++++++++------
 examples/example_Dirty.m                |  2 +-
 examples/example_Dirty_Classify.m       | 10 ++--
 3 files changed, 58 insertions(+), 21 deletions(-)

diff --git a/MALSAR/functions/dirty/Logistic_Dirty.m b/MALSAR/functions/dirty/Logistic_Dirty.m
index f2c1a1d..ad8b549 100644
--- a/MALSAR/functions/dirty/Logistic_Dirty.m
+++ b/MALSAR/functions/dirty/Logistic_Dirty.m
@@ -1,11 +1,48 @@
-function [W, C, P, Q, L, F] = Logistic_Dirty(X, Y, rho1, rho2, opts)
-%W: model parameters
-%C: constant parameters
-%P: shared structure
-%Q: non_shared structure
-%L: objective value for every iteration 
-%F: loss value for every iteration 
+%% FUNCTION Least_Dirty
+%   Dirty Multi-Task Learning with Least Squares Loss.
+%
+%% INPUT
+%   X: {n * d} * t - input matrix
+%   Y: {n * 1} * t - output matrix
+%   rho1: group sparsity regularization parameter
+%   rho2: elementwise sparsity regularization parameter
+%
+%% OUTPUT
+%   W: model: d * t
+%   C: constant parameters
+%   P: group sparsity structure (joint feature selection)
+%   Q: elementwise sparsity component
+%   funcVal: function (objective) value vector.
+%   lossVal: loss value for every iteration.
+%
+%% LICENSE
+%   This program is free software: you can redistribute it and/or modify
+%   it under the terms of the GNU General Public License as published by
+%   the Free Software Foundation, either version 3 of the License, or
+%   (at your option) any later version.
+%
+%   This program is distributed in the hope that it will be useful,
+%   but WITHOUT ANY WARRANTY; without even the implied warranty of
+%   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+%   GNU General Public License for more details.
+%
+%   You should have received a copy of the GNU General Public License
+%   along with this program.  If not, see <http://www.gnu.org/licenses/>.
+%
+%   Copyright (C) 2011 - 2012 Jiayu Zhou, Pinghua Gong and Jieping Ye 
+%
+%   You are suggested to first read the Manual.
+%   For any problem, please contact with Jiayu Zhou via jiayu.zhou@asu.edu
+%
+%   Last modified on June 3, 2012.
+%
+%% RELATED PAPERS
+%
+%   [1] Jalali, A. and Ravikumar, P. and Sanghavi, S. and Ruan, C. A dirty
+%       model for multi-task learning, NIPS 2010.
+%
 
+function [W, C, P, Q, funcVal, lossVal] = Logistic_Dirty(X, Y, rho1, rho2, opts)
 
 if nargin <4
     error('\n Inputs: X, Y, rho1, should be specified!\n');
@@ -20,8 +57,8 @@
 
 task_num  = length (X);
 dimension = size(X{1}, 1);
-L = [];
-F = [];
+funcVal = [];
+lossVal = [];
 
 %initialize a starting point
 C0_prep = zeros(1, task_num);
@@ -141,27 +178,27 @@
     Qz = Qzp;
     Cz = Czp;
     
-    L = cat(1, L, Fzp + rho1*L1infnorm(Pzp) + rho2*L11norm(Qzp));
-    F = cat(1, F, Fzp);
+    funcVal = cat(1, funcVal, Fzp + rho1*L1infnorm(Pzp) + rho2*L11norm(Qzp));
+    lossVal = cat(1, lossVal, Fzp);
 
     
     % test stop condition.
     switch(opts.tFlag)
         case 0
             if iter>=2
-                if (abs( L(end) - L(end-1) ) <= opts.tol)
+                if (abs( funcVal(end) - funcVal(end-1) ) <= opts.tol)
                     break;
                 end
             end
         case 1
             if iter>=2
-                if (abs( L(end) - L(end-1) ) <=...
-                        opts.tol* L(end-1))
+                if (abs( funcVal(end) - funcVal(end-1) ) <=...
+                        opts.tol* funcVal(end-1))
                     break;
                 end
             end
         case 2
-            if ( L(end)<= opts.tol)
+            if ( funcVal(end)<= opts.tol)
                 break;
             end
         case 3
diff --git a/examples/example_Dirty.m b/examples/example_Dirty.m
index 71a5299..8f85cf7 100644
--- a/examples/example_Dirty.m
+++ b/examples/example_Dirty.m
@@ -62,7 +62,7 @@
 rho_1 = 350;%   rho1: group sparsity regularization parameter
 rho_2 = 10;%   rho2: elementwise sparsity regularization parameter
 
-[W funcVal P Q] = Least_Dirty(X, Y, rho_1, rho_2, opts);
+[W, funcVal, P, Q] = Least_Dirty(X, Y, rho_1, rho_2, opts);
 
 
 
diff --git a/examples/example_Dirty_Classify.m b/examples/example_Dirty_Classify.m
index d771f85..f5fd06d 100644
--- a/examples/example_Dirty_Classify.m
+++ b/examples/example_Dirty_Classify.m
@@ -41,8 +41,8 @@
 opts.maxIter = 60000; 
 
 % lambda range
-lambda1_range = [1:-0.01:0.01];
-lambda2_range = [2:-0.05:0.05];
+lambda1_range = 1:-0.01:0.01;
+lambda2_range = 2:-0.05:0.05;
 
 %container for holding the results
 r_acc=cell(1,3);
@@ -77,20 +77,20 @@
     
     %inner cv
     fprintf('inner CV started\n')
-    [best_lambda1 best_lambda2 accuracy_mat] = CrossValidationDirty( Xtr, Ytr, ...
+    [best_lambda1, best_lambda2, accuracy_mat] = CrossValidationDirty( Xtr, Ytr, ...
         'Logistic_Dirty', opts, lambda1_range,lambda2_range, in_cv_fold, ...
         'eval_MTL_accuracy');
     
     %train
     %warm start for one turn
-    [W C P Q L F] = Logistic_Dirty(Xtr, Ytr, best_lambda1, best_lambda2, opts);
+    [W, C, P, Q, funcVal, lossVal] = Logistic_Dirty(Xtr, Ytr, best_lambda1, best_lambda2, opts);
     opts2=opts;
     opts2.init=1;
     opts2.C0=C;
     opts2.P0=P;
     opts2.Q0=Q;
     opts2.tol = 10^-10;
-    [W2 C2 P2 Q2 L2 F2] = Logistic_Dirty(Xtr, Ytr, best_lambda1, best_lambda2, opts2);
+    [W2, C2, P2, Q2, funcVal2, lossVal2] = Logistic_Dirty(Xtr, Ytr, best_lambda1, best_lambda2, opts2);
     
 
      %test