Export results

p-values, confusion matrix and transition probabilities are now exported
to txt files.

Author: avouros

gui.m

@@ -772,7 +772,7 @@ function confusion_matrix_Callback(hObject, eventdata, handles)
         segmentation_configs = res{1};
         set(handles.seg_path,'String',res{2});
     end  
-    % see if we have the path for the segmentation_config file
+    % see if we have the path for the classification_config file
     rpath = get(handles.class_path,'String');
     [error, classification_configs] = select_files_default(3,rpath);
     if error

results/results_confusion_matrix.m

@@ -4,6 +4,7 @@ function results_confusion_matrix(segmentation_configs,classification_configs,fo
 % cross-validation of the clustering algorithm
 
     fn = fullfile(strcat(segmentation_configs.OUTPUT_DIR,'/'), 'confusion_matrix.mat');
+    fn2 = fullfile(strcat(segmentation_configs.OUTPUT_DIR,'/'), 'Confusion_matrix.txt');
 
     % get classifier object
     classif = classification_configs.SEMISUPERVISED_CLUSTERING;
@@ -41,15 +42,35 @@ function results_confusion_matrix(segmentation_configs,classification_configs,fo
         cm = cm + res.results(i).confusion_matrix;
     end
     tags = res.results(1).classes;
+    
+    % NaN values
+    for i = 1:size(cm,1)
+        for j = 1:size(cm,2)
+            if isnan(cm)
+                cm(i,j) = 0;
+            end
+        end
+    end    
 
     % save data
     save(fn, 'tags', 'cm');   
-    
     disp('Tags:');
     for i = 1:length(tags)
         fprintf('%s\n', tags{1,i}{1,2});
-    end
-        
+    end  
     fprintf('\nConfusion matrix:\n');    
     cm        
+    
+    % save to file
+    fileID = fopen(fn2,'wt');
+    for i = 1:length(tags)
+        fprintf(fileID,'%d. %s\n', i, tags{1,i}{1,2});
+    end
+    fprintf(fileID, '\n');
+    for i=1:size(cm,1)
+        fprintf(fileID, '%d ', cm(i,:));
+        fprintf(fileID, '\n');
+    end
+    fclose(fileID);
+    
 end

results/results_latency_speed_length.m

@@ -5,6 +5,9 @@ function results_latency_speed_length(segmentation_configs,varargin)
 % 2. The average movement speed.
 % 3. The average path length.
 
+    fn = strcat(segmentation_configs.OUTPUT_DIR,'/','metrics_p.txt');
+    fileID = fopen(fn,'wt');
+
     % get the configurations from the configs file
     [FontName, FontSize, LineWidth, Export, ExportStyle] = parse_configs;
 
@@ -152,14 +155,16 @@ function results_latency_speed_length(segmentation_configs,varargin)
         set(f,'papersize',[8,8], 'paperposition',[0,0,8,8]);
         export_figure(f, strcat(segmentation_configs.OUTPUT_DIR,'/'), sprintf('animals_%s', names{i}),Export, ExportStyle);
 
-        % run friedman test            
+        % run friedman test  
         try
             p = friedman(fried, n);
             str = sprintf('Friedman p-value (%s): %g', ylabels{i}, p);
+            fprintf(fileID,'%s\n',str);
             disp(str);          
         catch
             disp('Error on Friedman test. Friedman test is skipped');
-        end    
+        end        
     end
+    fclose(fileID);
 end
 

results/results_strategies_distributions_length.m

@@ -4,6 +4,9 @@ function results_strategies_distributions_length(segmentation_configs,classifica
 % show the average length in meters that the animals spent in one strategy 
 % during each trial (for S total strategies, were S is defined by the user)
 
+    fn = strcat(segmentation_configs.OUTPUT_DIR,'/','strategies_p.txt');
+    fileID = fopen(fn,'wt');
+
     % get the configurations from the configs file
     [FontName, FontSize, LineWidth, Export, ExportStyle] = parse_configs;
 
@@ -113,6 +116,7 @@ function results_strategies_distributions_length(segmentation_configs,classifica
         try
             p = friedman(mfried, nanimals);
             str = sprintf('Class: %s\tp_frdm: %g', segments_classification.classes{1,c}{1,2}, p);            
+            fprintf(fileID,'%s\n',str);
             disp(str);        
         catch
             disp('Error on Friedman test. Friedman test is skipped');
@@ -165,6 +169,6 @@ function results_strategies_distributions_length(segmentation_configs,classifica
         export_figure(gcf, strcat(segmentation_configs.OUTPUT_DIR,'/'), sprintf('segment_length_strategy_%d', c), Export, ExportStyle);
 
     end    
-    
+    fclose(fileID);
 end
 

results/results_strategies_transition_prob.m

@@ -91,8 +91,27 @@ function results_strategies_transition_prob(segmentation_configs,classification_
     if length(groups) ~= 1
         trans_prob1 = trans_prob1 ./ repmat(sum(trans_prob1, 2), 1, nc);
         trans_prob2 = trans_prob2 ./ repmat(sum(trans_prob2, 2), 1, nc);
+        % NaN values
+        for i = 1:size(trans_prob1,1)
+            for j = 1:size(trans_prob1,2)
+                if isnan(trans_prob1)
+                    trans_prob1(i,j) = 0;
+                end
+                if isnan(trans_prob2)
+                    trans_prob2(i,j) = 0;
+                end
+            end
+        end    
     else
         trans_prob1 = trans_prob1 ./ repmat(sum(trans_prob1, 2), 1, nc);
+        % NaN values
+        for i = 1:size(trans_prob1,1)
+            for j = 1:size(trans_prob1,2)
+                if isnan(trans_prob1)
+                    trans_prob1(i,j) = 0;
+                end
+            end
+        end
     end
 
     for i = 1:segments_classification.nclasses
@@ -108,5 +127,32 @@ function results_strategies_transition_prob(segmentation_configs,classification_
         save(fn, 'trans_prob1');
         trans_prob1
     end    
+    
+    % save to txt
+    fn2 = fullfile(strcat(segmentation_configs.OUTPUT_DIR,'/'), 'transition_probabilities.txt');
+    fileID = fopen(fn2,'wt');
+    for i = 1:segments_classification.nclasses
+        fprintf(fileID,'%d. %s\n', i, segments_classification.classes{1,i}{1,2});
+    end
+    
+    fprintf(fileID, '\n');
+    if length(groups) ~= 1
+        for i=1:size(trans_prob1,1)
+            fprintf(fileID, '%6.4f ', trans_prob1(i,:));
+            fprintf(fileID, '\n');
+        end
+        fprintf(fileID, '\n');
+        for i=1:size(trans_prob2,1)
+            fprintf(fileID, '%6.4f ', trans_prob2(i,:));
+            fprintf(fileID, '\n');
+        end      
+    else
+        for i=1:size(trans_prob1,1)
+            fprintf(fileID, '%6.4f ', trans_prob1(i,:));
+            fprintf(fileID, '\n');
+        end
+    end   
+    fclose(fileID);
+    
 end
 

results/results_transition_counts.m

@@ -2,6 +2,9 @@ function results_transition_counts(segmentation_configs,classification_configs,v
 % Computes and presents the number of transitions between strategies 
 % for two groups of N animals.
 
+    fn = strcat(segmentation_configs.OUTPUT_DIR,'/','transitions_p.txt');
+    fileID = fopen(fn,'wt');
+    
     % get the configurations from the configs file
     [FontName, FontSize, LineWidth, Export, ExportStyle] = parse_configs;
 
@@ -160,8 +163,12 @@ function results_transition_counts(segmentation_configs,classification_configs,v
 
     try
         p = friedman(mfried, nanimals, 'off');
-        str = sprintf('p_frdm: %g', p);            
+        str = sprintf('p_frdm: %g', p); 
+        fprintf(fileID,'%s\n',str);
         disp(str);
     catch
         disp('Error on Friedman test. Friedman test is skipped');
     end
+    
+    fclose(fileID);
+