Improve regression diff and update some test fixtures

.gitignore

@@ -0,0 +1,7 @@
+.classpath
+.project
+.settings/
+build/
+dist/
+doc/
+

weka/src/test/java/weka/core/OptionHandlersTest.java

@@ -43,7 +43,7 @@ public class OptionHandlersTest extends TestCase {
   /**
    * tests a specific OptionHandler
    */
-  public static class OptionHandlerTest extends TestCase {
+  static class OptionHandlerTestInternal extends TestCase {
 
     /** the class to test */
     protected String m_Classname;
@@ -57,7 +57,7 @@ public static class OptionHandlerTest extends TestCase {
      * @param name the name of the test class
      * @param classname the actual classname
      */
-    public OptionHandlerTest(String name, String classname) {
+    public OptionHandlerTestInternal(String name, String classname) {
       super(name);
 
       m_Classname = classname;
@@ -215,7 +215,7 @@ public static Test suite() {
     try {
       // determine all test methods in the OptionHandlerTest class
       Vector<String> testMethods = new Vector<String>();
-      Method[] methods = OptionHandlerTest.class.getDeclaredMethods();
+      Method[] methods = OptionHandlerTestInternal.class.getDeclaredMethods();
       for (Method method : methods) {
         if (method.getName().startsWith("test")) {
           testMethods.add(method.getName());
@@ -251,7 +251,7 @@ public static Test suite() {
 
           // add tests for this class
           for (int i = 0; i < testMethods.size(); i++) {
-            suite.addTest(new OptionHandlerTest(testMethods.get(i), classname));
+            suite.addTest(new OptionHandlerTestInternal(testMethods.get(i), classname));
           }
         }
       }

weka/src/test/java/weka/test/Regression.java

@@ -27,6 +27,7 @@
 import java.io.FileWriter;
 import java.io.IOException;
 import java.io.Reader;
+import java.io.StringReader;
 import java.io.Writer;
 import java.util.Properties;
 
@@ -141,19 +142,31 @@ public String diff() throws IOException {
    *         differences.
    */
   protected String diff(String reference, String current) {
-
-    if (reference.equals(current)) {
-      return "";
-    } else {
-      // Should do something more cunning here, like try to isolate the
-      // actual differences. We could also try calling unix diff utility
-      // if it exists.
-      StringBuffer diff = new StringBuffer();
-      diff.append("+++ Reference: ").append(m_RefFile).append(" +++\n")
-        .append(reference).append("+++ Current +++\n").append(current)
-        .append("+++\n");
-      return diff.toString();
+    StringBuffer diff = new StringBuffer();
+    try {
+      BufferedReader brExp = new BufferedReader(new StringReader(reference));
+      BufferedReader brWas = new BufferedReader(new StringReader(current));
+      String expLine = brExp.readLine();
+      String wasLine = brWas.readLine();
+      while (expLine != null || wasLine != null) {
+        if (!("" + expLine).equals("" + wasLine)) {
+          if (diff.length() == 0) {
+            diff.append("+++ Reference: ").append(m_RefFile).append(" +++\n")
+            .append(reference)
+            .append("+++ Current +++\n")
+            .append(current)
+            .append("+++\n");
+          }
+          diff.append("Exp: " + expLine + "\n");
+          diff.append("Was: " + wasLine + "\n");
+        }
+        expLine = brExp.readLine();
+        wasLine = brWas.readLine();
+      }
+    } catch (IOException ex) {
+      throw new IllegalStateException(ex);
     }
+    return diff.toString();
   }
 
   /**

weka/src/test/resources/wekarefs/weka/classifiers/functions/LinearRegressionTest.ref

@@ -12,13 +12,13 @@ NUM: 1.0427293479442596 1.1169759757532756 1.0
 
 10 predictions
 NUM: 2.80855806E12 2.8085580597055015E12 1.0
-NUM: 2.58763326E12 2.5876332599510005E12 1.0
-NUM: 1.54625406E12 1.5462540611080254E12 1.0
+NUM: 2.58763326E12 2.587633259951001E12 1.0
+NUM: 1.54625406E12 1.546254061108026E12 1.0
 NUM: 2.61925566E12 2.619255659915878E12 1.0
 NUM: 2.84009406E12 2.8400940596704585E12 1.0
-NUM: 1.04133246E12 1.0413324616691511E12 1.0
+NUM: 1.04133246E12 1.0413324616691517E12 1.0
 NUM: 3.43971006E12 3.439710059004154E12 1.0
 NUM: 2.80855806E12 2.8085580597055015E12 1.0
-NUM: 1.83025086E12 1.8302508607925725E12 1.0
-NUM: 1.29379326E12 1.2937932613885295E12 1.0
+NUM: 1.83025086E12 1.8302508607925728E12 1.0
+NUM: 1.29379326E12 1.29379326138853E12 1.0
 

weka/src/test/resources/wekarefs/weka/classifiers/meta/ClassificationViaRegressionTest.ref

@@ -1,12 +1,12 @@
 10 predictions
-NOM: 0.0 0.0 1.0 0.973341367583095 0.02665863241690505
-NOM: 0.0 0.0 1.0 0.9524951020531026 0.047504897946897384
-NOM: 0.0 0.0 1.0 0.993490469203576 0.006509530796424058
-NOM: 0.0 0.0 1.0 0.9429297606330429 0.05707023936695715
-NOM: 1.0 1.0 1.0 0.0022872290965505475 0.9977127709034495
+NOM: 0.0 0.0 1.0 0.9733413675830949 0.026658632416905144
+NOM: 0.0 0.0 1.0 0.9524951020531025 0.04750489794689747
+NOM: 0.0 0.0 1.0 0.9934904692035759 0.006509530796424155
+NOM: 0.0 0.0 1.0 0.9429297606330428 0.05707023936695722
+NOM: 1.0 1.0 1.0 0.002287229096550769 0.9977127709034492
 NOM: 1.0 1.0 1.0 0.0 1.0
-NOM: 1.0 1.0 1.0 0.05165225921391215 0.9483477407860879
-NOM: 0.0 0.0 1.0 0.9732051524144633 0.026794847585536737
+NOM: 1.0 1.0 1.0 0.051652259213912366 0.9483477407860876
+NOM: 0.0 0.0 1.0 0.9732051524144631 0.026794847585536827
 NOM: 1.0 1.0 1.0 0.0 1.0
-NOM: 1.0 1.0 1.0 0.024988999313463548 0.9750110006865365
+NOM: 1.0 1.0 1.0 0.024988999313463767 0.9750110006865362
 

weka/src/test/resources/wekarefs/weka/classifiers/rules/M5RulesTest.ref

@@ -11,14 +11,14 @@ NUM: 0.09358982741832733 0.07144234520918284 1.0
 NUM: 1.0427293479442596 1.116975975753276 1.0
 
 10 predictions
-NUM: 2.80855806E12 2.8085580597055024E12 1.0
+NUM: 2.80855806E12 2.808558059705502E12 1.0
 NUM: 2.58763326E12 2.5876332599510015E12 1.0
-NUM: 1.54625406E12 1.546254061108025E12 1.0
+NUM: 1.54625406E12 1.5462540611080254E12 1.0
 NUM: 2.61925566E12 2.619255659915879E12 1.0
 NUM: 2.84009406E12 2.8400940596704595E12 1.0
-NUM: 1.04133246E12 1.0413324616691499E12 1.0
-NUM: 3.43971006E12 3.439710059004156E12 1.0
-NUM: 2.80855806E12 2.8085580597055024E12 1.0
-NUM: 1.83025086E12 1.8302508607925723E12 1.0
-NUM: 1.29379326E12 1.2937932613885286E12 1.0
+NUM: 1.04133246E12 1.0413324616691508E12 1.0
+NUM: 3.43971006E12 3.4397100590041553E12 1.0
+NUM: 2.80855806E12 2.808558059705502E12 1.0
+NUM: 1.83025086E12 1.8302508607925728E12 1.0
+NUM: 1.29379326E12 1.2937932613885293E12 1.0
 

weka/src/test/resources/wekarefs/weka/classifiers/trees/M5PTest.ref

@@ -11,14 +11,14 @@ NUM: 0.09358982741832733 0.07144234520918284 1.0
 NUM: 1.0427293479442596 1.116975975753276 1.0
 
 10 predictions
-NUM: 2.80855806E12 2.8085580597055024E12 1.0
+NUM: 2.80855806E12 2.808558059705502E12 1.0
 NUM: 2.58763326E12 2.5876332599510015E12 1.0
-NUM: 1.54625406E12 1.546254061108025E12 1.0
+NUM: 1.54625406E12 1.5462540611080254E12 1.0
 NUM: 2.61925566E12 2.619255659915879E12 1.0
 NUM: 2.84009406E12 2.8400940596704595E12 1.0
-NUM: 1.04133246E12 1.0413324616691499E12 1.0
-NUM: 3.43971006E12 3.439710059004156E12 1.0
-NUM: 2.80855806E12 2.8085580597055024E12 1.0
-NUM: 1.83025086E12 1.8302508607925723E12 1.0
-NUM: 1.29379326E12 1.2937932613885286E12 1.0
+NUM: 1.04133246E12 1.0413324616691508E12 1.0
+NUM: 3.43971006E12 3.4397100590041553E12 1.0
+NUM: 2.80855806E12 2.808558059705502E12 1.0
+NUM: 1.83025086E12 1.8302508607925728E12 1.0
+NUM: 1.29379326E12 1.2937932613885293E12 1.0
 

weka/src/test/resources/wekarefs/weka/filters/unsupervised/attribute/NumericToDateTest.ref

@@ -0,0 +1,32 @@
+@relation FilterTest-weka.filters.unsupervised.attribute.NumericToNominal-Rfirst-last
+
+@attribute StringAtt1 string
+@attribute NominalAtt1 {r,g,b}
+@attribute NumericAtt1 {0,1,1.4,2,2.3,3,3.3,4,4.3,5,5.3,6,6.5,7,7.5,8,8.5,9,9.4}
+@attribute StringAtt2 string
+@attribute NominalAtt2 {a,b,c,d}
+@attribute NumericAtt2 {-9.3,-9,-8.3,-7.3,-5.6,-5.3,-4.3,-3.8,-3.3,-2.8,-2.6,-2.4,-2.3,-2,-1,0,3.4}
+@attribute DateAtt1 {1970-01-01,2001-04-03,2001-04-04,2001-05-04,2001-05-05,2001-05-06,2001-06-06,2001-06-07}
+
+@data
+humpty,g,1,the,a,-2.3,2001-04-03
+dumpty,b,2,quick,b,-3.3,2001-04-03
+sat,r,3,brown,c,-2.4,2001-04-03
+on,r,4,fox,d,-5.3,2001-04-03
+a,b,5,jumped,a,-2.6,2001-04-03
+wall,r,6,over,b,-7.3,2001-04-04
+humpty,r,7,the,c,-2.8,2001-04-04
+dumpty,g,8,lazy,d,-9.3,2001-04-04
+had,b,9,dog,?,-2,2001-05-04
+a,r,9.4,?,?,-9,2001-05-04
+great,r,1.4,the,a,-8.3,2001-05-05
+fall,b,2.3,quick,b,-7.3,2001-05-05
+all,r,3.3,brown,c,?,2001-05-05
+the,r,4.3,fox,d,-5.3,2001-05-05
+kings,g,5.3,jumped,?,-5.6,2001-05-06
+horses,b,6.5,over,b,-4.3,2001-05-06
+and,r,7.5,the,c,-3.8,2001-06-06
+all,r,8.5,lazy,d,-2.3,2001-06-06
+the,r,9.4,?,a,-1,2001-06-07
+{0 wall,2 4.3,3 the,4 d,5 0}
+{0 humpty,1 b,3 lazy,5 3.4}