script update

Author: avouros

results/class_statistics.m

@@ -62,7 +62,7 @@
     for i = 1:length(strats)
         rows{i+1} = strats{i}{2};
     end 
-    count_ = num2cell(count);
+    count_ = num2cell(round(count));
     count_ = [cols;count_];
     count_ = [rows',count_];
     count_ = cell2table(count_);
@@ -73,8 +73,9 @@
     % Export the tables
     writetable(count_,fullfile(rpath,'numeric.csv'),'WriteVariableNames',0);
     writetable(percentage_per_classifier_,fullfile(rpath,'percentage.csv'),'WriteVariableNames',0);
-    
-    error = 0;
     delete(h);
+ 
+    error = 0;
+
 end
 

generate/generate_results.m renamed to results/generate_results.m

@@ -1,6 +1,6 @@
-segmentation_path = 'C:\Users\Avgoustinos\Desktop\MWMGEN\tiago_original\segmentation\segmentation_configs_29476_250_09.mat';
-classification_folder = 'C:\Users\Avgoustinos\Desktop\MWMGEN\tiago_original\Mclassification\class_1664_29476_250_09_10_10_mr0';
-animals_trajectories_map_file = 'C:\Users\Avgoustinos\Desktop\MWMGEN\animals_trajectories_map.mat';
+segmentation_path = 'C:\Users\Avgoustinos\Desktop\tiago_original\segmentation\segmentation_configs_29476_250_09.mat';
+classification_folder = 'C:\Users\Avgoustinos\Desktop\tiago_original\Mclassification\class_2322_29476_250_09_10_10_mr0-correct2';
+animals_trajectories_map_file = 'C:\Users\Avgoustinos\Desktop\traj_map.mat';
 
 % Output Folder
 output_folder = char(getSpecialFolder('Desktop'));

unused/test_scripts/test_sigma.m

@@ -11,12 +11,14 @@
 sigma = 4;
 discard_undefined = 0;
 %w = 'defined';
-w = 'defined';
-tiny_num = 1e-6;
-% data smoothing
+w = 'computed';
+norm_method = 'off';
+hard_bounds = 'on';
+%tiny_num = 1e-6;
+tiny_num = realmin;
 min_seg = 1;
 
-sig = 1:50;
+sig = 0.05:0.05:10;
 h = waitbar(0,'Generating distr_strats');
 for ss = 1:length(sig)
 sigma = sig(ss);
@@ -65,7 +67,30 @@
             end
         case 'computed'
             class_w = computed_weights(segmentation_configs, classification_configs);           
-    end      
+    end   
+    
+    %weights normalization
+    if ~isequal(norm_method,'off') && ~isequal(norm_method,'OFF') && ~isequal(norm_method,0)
+        class_w = normalizations(class_w,norm_method);
+    end
+    %hard bounds
+    if ~isequal(hard_bounds,'off') && ~isequal(hard_bounds,'OFF') && ~isequal(hard_bounds,0);
+        avg_w = max(class_w)/2;
+        avg_w = avg_w-1;
+        avg_w = avg_w/2;
+        for i = 1:length(class_w)
+            if class_w(i) < avg_w
+                class_w(i) = 1;
+            else
+                class_w(i) = max(class_w);
+            end
+%             if class_w(i) < 5
+%                 class_w(i) = 1;
+%             else
+%                 class_w(i) = 10;
+%             end
+        end
+    end
 
     %strategies distribution
     class_distr_traj = [];