Changeset 129 for trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier

Timestamp:

06/22/16 11:25:46 (8 years ago)

Author:

sherbold

Message:

• added the same workaround for the problem with Discretize to the TopMetricFilter?. We slightly refactored the implementation within the AbstractCODEP by putting the rescaling of sets to the WekaUtils? to facilitate better re-use.

File:

• trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/AbstractCODEP.java (modified) (5 diffs)

trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/AbstractCODEP.java

@@ -23 +23 @@
 import java.util.regex.Pattern;
 
+import de.ugoe.cs.cpdp.util.WekaUtils;
 import de.ugoe.cs.util.console.Console;
 import weka.classifiers.AbstractClassifier;
@@ -71 +72 @@
     private Map<Integer, Integer> upscaleIndex = null;
 
-    /**
-     * Scaling value that moves the decimal point by 5 digets.
-     */
-    private final double SCALER = 10000.0d;
-
     /*
      * (non-Javadoc)
@@ -136 +132 @@
                     }
                     else {
-                        traindataCopy = upscaleAttribute(traindata, attrIndex);
+                        traindataCopy = WekaUtils.upscaleAttribute(traindata, attrIndex);
                     }
 
@@ -182 +178 @@
                 // instance value must be upscaled
                 int attrIndex = upscaleIndex.get(j);
-                double upscaledVal = instance.value(attrIndex) * SCALER;
+                double upscaledVal = instance.value(attrIndex) * WekaUtils.SCALER;
                 traindataCopy = new Instances(instance.dataset());
                 instance = new DenseInstance(instance.weight(), instance.toDoubleArray());
@@ -231 +227 @@
     /**
      * <p>
-     * Upscales the value of a single attribute. This is a workaround to get BayesNet running for
-     * all data. Works on a copy of the training data, i.e., leaves the original data untouched.
-     * </p>
-     *
-     * @param traindata
-     *            data from which the attribute is upscaled.
-     * @param attributeIndex
-     *            index of the attribute
-     * @return data with upscaled attribute
-     */
-    private Instances upscaleAttribute(Instances traindata, int attributeIndex) {
-        Instances traindataCopy = new Instances(traindata);
-        for (int i = 0; i < traindata.size(); i++) {
-            traindataCopy.get(i).setValue(attributeIndex,
-                                          traindata.get(i).value(attributeIndex) * SCALER);
-        }
-        return traindataCopy;
-    }
-
-    /**
-     * <p>
      * Abstract method through which implementing classes define which classifier is used for the
      * CODEP.