Changeset 34797 for main/trunk/model-sites-dev/mars/src/java

Timestamp:

2021-02-02T22:28:26+13:00 (3 years ago)

Author:

davidb

Message:

Fixed up spelling mixup valance -> valence

Location:

main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars

Files:

• WekaApplyArousalModel.java (modified) (1 diff)
• WekaApplyValenceModel.java (moved) (moved from main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaApplyValanceModel.java ) (2 diffs)
• WekaTrainArousalModel.java (modified) (2 diffs)
• WekaTrainValenceModel.java (moved) (moved from main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaTrainValanceModel.java ) (1 diff)
• WekaUtil.java (modified) (6 diffs)

main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaApplyArousalModel.java

@@ -87 +87 @@
         // Dealing with ground-truth data:
         //   => already has 'arousal' attribute
-        //   => in fact has 'valance' attribute too, which we want to remove
+        //   => in fact has 'valence' attribute too, which we want to remove
         
-        unlabeled_instances = WekaUtil.applyFilter(unlabeled_instances,"472"); // top-up with removal of 'valance'
+        unlabeled_instances = WekaUtil.applyFilter(unlabeled_instances,"472"); // top-up with removal of 'valence'
         
         // reference share this as 'groundtruth_instances' to trigger error calculation and output

main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaApplyValenceModel.java

@@ -26 +26 @@
 //   https://waikato.github.io/weka-wiki/use_weka_in_your_java_code/
 
-class WekaApplyValanceModel
+class WekaApplyValenceModel
 {
     
@@ -47 +47 @@
     //      to trigger calculating the error on the predicted vaues
 
-    boolean has_groundtruth_data = WekaUtil.instancesHavePredictAttribute(unlabeled_instances,WekaUtil.VALANCE_ATTRIBUTE_NAME); 
+    boolean has_groundtruth_data = WekaUtil.instancesHavePredictAttribute(unlabeled_instances,WekaUtil.VALENCE_ATTRIBUTE_NAME); 
 
     // The following deals with (i) internally, ensuring that what is returned is suitable for making predictions on
     Instances filtered_unlabeled_instances
         = WekaUtil.filterInstancesForApplying(unlabeled_instances,has_groundtruth_data,
-                          WekaUtil.VALANCE_ATTRIBUTE_NAME,"471");
+                          WekaUtil.VALENCE_ATTRIBUTE_NAME,"471");
 
     // The following deals with (ii)

main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaTrainArousalModel.java

@@ -48 +48 @@
     String output_model_filename = args[1];
 
-    // knock out 472 (valance) from dataset
+    // knock out 472 (valence) from dataset
     Instances filtered_data_instances = WekaUtil.loadAndFilterDataInstances(input_data_filename, "472");
     Classifier classifier = WekaUtil.trainREPTree(filtered_data_instances);
@@ -73 +73 @@
 
         // *********
-        Instances filtered_data_instances = WekaUtil.applyFilter(data_instances,"472"); // top-up with removal of 'valance'
+        Instances filtered_data_instances = WekaUtil.applyFilter(data_instances,"472"); // top-up with removal of 'valence'
 
             

main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaTrainValenceModel.java

@@ -15 +15 @@
 
 
-class WekaTrainValanceModel
+class WekaTrainValenceModel
 {
     // Scheme:       weka.classifiers.trees.REPTree -M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0   -- (i.e., default vals)

main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaUtil.java

@@ -31 +31 @@
 {
     public final static String AROUSAL_ATTRIBUTE_NAME = "arousal_sample_26500ms";
-    public final static String VALANCE_ATTRIBUTE_NAME = "valence_sample_26500ms";
+    public final static String VALENCE_ATTRIBUTE_NAME = "valence_sample_26500ms";
 
 
@@ -76 +76 @@
         // top up,
         //   e.g. '471' representing arousal in ground-truth files
-        //   e.g. '472' representing valance in ground-truth files
+        //   e.g. '472' representing valence in ground-truth files
         remove_option_args += "," + additional_remove;
     }
@@ -84 +84 @@
     try {
         // remove ordinal attributes and any additional topups,
-        // such as 'valance' (when predicting 'arousal') and vice versa
+        // such as 'valence' (when predicting 'arousal') and vice versa
         
         String[] filter_options = weka.core.Utils.splitOptions(remove_option_args); 
@@ -109 +109 @@
         DataSource gt_data_source = new DataSource(gt_datasource_filename);
         Instances gt_instances = gt_data_source.getDataSet();
-        gt_instances = applyFilter(gt_instances,additional_remove); // remove 'valance' or 'arousal'
+        gt_instances = applyFilter(gt_instances,additional_remove); // remove 'valence' or 'arousal'
         
         gt_instances.setClassIndex(gt_instances.numAttributes() - 1);
@@ -138 +138 @@
         Instances data_instances = data_source.getDataSet();
 
-        // Training dataset has two ground-truth attributes: 'arousal' and 'valance'.
+        // Training dataset has two ground-truth attributes: 'arousal' and 'valence'.
         // When training for one, need to knock out the other.  This is the purpose
         // of 'additional_attribute_remove'
@@ -265 +265 @@
     else {
         // Dealing with ground-truth data:
-        //   => already has 'arousal' and 'valance' attributes
+        //   => already has 'arousal' and 'valence' attributes
         //   => need to keep the 'predict_attribute_name' and remove the other one
         //   => (its -R value of which is specified in 'additional_attribute_remove')