modified brief

liblinear_test_multicore.m

@@ -0,0 +1,67 @@
+function [predicted_label, accuracy, scores] =liblinear_test_multicore ( labels_test, feat_test, svmmodel, settings )
+%
+% BRIEF
+%   A simple wrapper to provide testing of 1-vs-all-classification for LIBLINEAR. 
+%   No further settings are adjustable currently.
+% 
+% INPUT
+%  labels_test   -- multi-class labels (#samples x 1)
+%  feat_test     -- features for test images (#samples x # dimensions)
+%  svmmodel      -- cell ( #classes x 1 ), every model entry is obtained via
+%                   svmtrain of the corresponding 1-vs-all-problem
+%  settings      -- struct for configuring the svm classification, e.g., via
+%                   'b_verbose' ...
+% 
+% OUTPUT:
+% 
+%
+% date: 30-04-2014 ( dd-mm-yyyy )
+% last modified: 22-10-2015
+% author: Alexander Freytag, Chirstoph Käding
+
+    if ( nargin < 4 ) 
+        settings = [];
+    end
+    
+    libsvm_options = '';
+    
+    % outputs for training
+    if ( ~ getFieldWithDefault ( settings, 'b_verbose', false ) )
+        libsvm_options = sprintf('%s -q', libsvm_options);
+    end    
+  
+    % add multithreading
+    i_numThreads = getFieldWithDefault ( settings, 'i_numThreads', 2);
+    libsvm_options = sprintf('%s -n %d', libsvm_options, i_numThreads);    
+    
+    i_numClasses = size ( svmmodel,1);
+    i_numSamples = size( labels_test,1);
+    
+    b_weightBalancing = getFieldWithDefault ( settings, 'b_weightBalancing', false);
+    
+    if ( ~b_weightBalancing )
+        [predicted_label,~,scores] = predict( labels_test, feat_test, svmmodel, libsvm_options );
+    else
+        scores = zeros( i_numSamples, i_numClasses );
+        
+        % classify with everyone-against-all model    
+        for k=1:i_numClasses
+            yBin              = 2*double( labels_test == svmmodel{k}.uniqueLabel )-1;
+            [~,~,scores(:,k)] = predict( yBin, feat_test, svmmodel{k}, libsvm_options );
+            
+            %Q: Why the sign of predicted labels and decision values are sometimes reversed?
+            %Please see the answer in LIBSVM faq.
+            %To correctly obtain decision values, you need to check the array
+            %label
+            %in the model.
+            scores(:,k)       = scores(:,k) .* repmat( svmmodel{k}.Label(1), [i_numSamples,1] ) ;
+        end 
+
+        %# predict the class with the highest score
+        [~,predicted_label] = max(scores,[],2);        
+    end
+
+    % accuracy
+    accuracy = sum(predicted_label == labels_test) ./ numel(labels_test) ;   
+end
+