Ignore:
Timestamp:
2021-02-02T22:28:26+13:00 (3 years ago)
Author:
davidb
Message:

Fixed up spelling mixup valance -> valence

File:
1 edited

Legend:

Unmodified
Added
Removed
  • main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaUtil.java

    r34788 r34797  
    3131{
    3232    public final static String AROUSAL_ATTRIBUTE_NAME = "arousal_sample_26500ms";
    33     public final static String VALANCE_ATTRIBUTE_NAME = "valence_sample_26500ms";
     33    public final static String VALENCE_ATTRIBUTE_NAME = "valence_sample_26500ms";
    3434
    3535
     
    7676        // top up,
    7777        //   e.g. '471' representing arousal in ground-truth files
    78         //   e.g. '472' representing valance in ground-truth files
     78        //   e.g. '472' representing valence in ground-truth files
    7979        remove_option_args += "," + additional_remove;
    8080    }
     
    8484    try {
    8585        // remove ordinal attributes and any additional topups,
    86         // such as 'valance' (when predicting 'arousal') and vice versa
     86        // such as 'valence' (when predicting 'arousal') and vice versa
    8787       
    8888        String[] filter_options = weka.core.Utils.splitOptions(remove_option_args);
     
    109109        DataSource gt_data_source = new DataSource(gt_datasource_filename);
    110110        Instances gt_instances = gt_data_source.getDataSet();
    111         gt_instances = applyFilter(gt_instances,additional_remove); // remove 'valance' or 'arousal'
     111        gt_instances = applyFilter(gt_instances,additional_remove); // remove 'valence' or 'arousal'
    112112       
    113113        gt_instances.setClassIndex(gt_instances.numAttributes() - 1);
     
    138138        Instances data_instances = data_source.getDataSet();
    139139
    140         // Training dataset has two ground-truth attributes: 'arousal' and 'valance'.
     140        // Training dataset has two ground-truth attributes: 'arousal' and 'valence'.
    141141        // When training for one, need to knock out the other.  This is the purpose
    142142        // of 'additional_attribute_remove'
     
    265265    else {
    266266        // Dealing with ground-truth data:
    267         //   => already has 'arousal' and 'valance' attributes
     267        //   => already has 'arousal' and 'valence' attributes
    268268        //   => need to keep the 'predict_attribute_name' and remove the other one
    269269        //   => (its -R value of which is specified in 'additional_attribute_remove')
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