Finally all float values

jplag/src/main/java/de/jplag/JPlagComparison.java

@@ -130,7 +130,7 @@ public class JPlagComparison { // FIXME TS: contains a lot of code duplication
      */
     public final double similarityOfFirst() {
         int divisor = firstSubmission.getSimilarityDivisor(true);
-        return (divisor == 0 ? 0f : (getNumberOfMatchedTokens() * 100 / (double) divisor));
+        return (divisor == 0 ? 0 : (getNumberOfMatchedTokens() * 100 / (double) divisor));
     }
 
     /**
@@ -139,7 +139,7 @@ public class JPlagComparison { // FIXME TS: contains a lot of code duplication
      */
     public final double similarityOfSecond() {
         int divisor = secondSubmission.getSimilarityDivisor(true);
-        return (divisor == 0 ? 0f : (getNumberOfMatchedTokens() * 100 / (double) divisor));
+        return (divisor == 0 ? 0 : (getNumberOfMatchedTokens() * 100 / (double) divisor));
     }
 
     /**

jplag/src/main/java/de/jplag/clustering/ClusteringResult.java

@@ -101,7 +101,7 @@ public class ClusteringResult<T> {
     }
 
     private static double calculateAverageSimilarityFor(Collection<Integer> cluster, RealMatrix similarityMatrix) {
-        var sumOfSimilarities = 0f;
+        double sumOfSimilarities = 0;
         List<Integer> indices = List.copyOf(cluster);
         for (int i = 1; i < cluster.size(); i++) {
             int indexOfSubmission1 = indices.get(i);
@@ -111,16 +111,16 @@ public class ClusteringResult<T> {
             }
         }
         int nMinusOne = cluster.size() - 1;
-        double numberOfComparisons = (nMinusOne * (nMinusOne + 1))
-                / 2f; /*
-                       * Use Gauss sum to calculate number of comparisons in cluster: Given cluster of size n we need Gauss sum of n-1
-                       * comparisons: compare first element of cluster to all other except itself: n-1 comparisons. compare second element to
-                       * all other except itself and first element (as these two were already compared when we processed the first element),
-                       * n-2 comparisons. compare third element to all other but itself and all previously compared: n-3 comparisons and so
-                       * on. when we reach the second to last element we have n-(n-1)=1 comparisons left. when we reach the last element it
-                       * has already been compared to all other. adding up all comparisons we get: (n-1) + (n-2) + (n-3) + ... + (n-(n-1)) =
-                       * Gauss sum of (n-1)
-                       */
+        double numberOfComparisons = (nMinusOne * (nMinusOne + 1)) / 2.0;
+        /*
+         * Use Gauss sum to calculate number of comparisons in cluster: Given cluster of size n we need Gauss sum of n-1
+         * comparisons: compare first element of cluster to all other except itself: n-1 comparisons. compare second element to
+         * all other except itself and first element (as these two were already compared when we processed the first element),
+         * n-2 comparisons. compare third element to all other but itself and all previously compared: n-3 comparisons and so
+         * on. when we reach the second to last element we have n-(n-1)=1 comparisons left. when we reach the last element it
+         * has already been compared to all other. adding up all comparisons we get: (n-1) + (n-2) + (n-3) + ... + (n-(n-1)) =
+         * Gauss sum of (n-1)
+         */
         return sumOfSimilarities / numberOfComparisons;
     }
 

jplag/src/test/java/de/jplag/BaseCodeTest.java

@@ -42,7 +42,7 @@ public class BaseCodeTest extends TestBase {
         assertEquals(1, result.getAllComparisons().size());
         assertEquals(1, result.getAllComparisons().get(0).getMatches().size());
         assertEquals(1, result.getSimilarityDistribution()[1]);
-        assertEquals(85f, result.getAllComparisons().get(0).similarity(), DELTA);
+        assertEquals(85, result.getAllComparisons().get(0).similarity(), DELTA);
     }
 
     @Test

jplag/src/test/java/de/jplag/ParallelComparisonTest.java

@@ -41,7 +41,7 @@ public class ParallelComparisonTest extends TestBase {
         assertEquals(3, result.getNumberOfSubmissions());
         assertEquals(3, result.getAllComparisons().size());
 
-        result.getAllComparisons().forEach(comparison -> assertEquals(0f, comparison.similarity(), DELTA));
+        result.getAllComparisons().forEach(comparison -> assertEquals(0, comparison.similarity(), DELTA));
     }
 
     /**
@@ -59,7 +59,7 @@ public class ParallelComparisonTest extends TestBase {
         // All comparisons with E shall have no matches
         result.getAllComparisons().stream()
                 .filter(comparison -> comparison.getSecondSubmission().getName().equals("E") || comparison.getFirstSubmission().getName().equals("E"))
-                .forEach(comparison -> assertEquals(0f, comparison.similarity(), DELTA));
+                .forEach(comparison -> assertEquals(0, comparison.similarity(), DELTA));
 
         // Hard coded assertions on selected comparisons
         assertEquals(24.6, getSelectedPercent(result, "A", "B"), DELTA);

jplag/src/test/java/de/jplag/clustering/ClusteringResultTest.java

@@ -16,8 +16,8 @@ class ClusteringResultTest {
         RealMatrix similarity = new Array2DRowRealMatrix(4, 4);
 
         // These are similar
-        setEntries(similarity, 0, 1, 1f);
-        setEntries(similarity, 2, 3, 1f);
+        setEntries(similarity, 0, 1, 1.0);
+        setEntries(similarity, 2, 3, 1.0);
 
         // Others are dissimilar
 
@@ -95,14 +95,14 @@ class ClusteringResultTest {
         RealMatrix similarity = new Array2DRowRealMatrix(4, 4);
 
         // These are similar
-        setEntries(similarity, 0, 1, 1f);
-        setEntries(similarity, 2, 3, 1f);
+        setEntries(similarity, 0, 1, 1.0);
+        setEntries(similarity, 2, 3, 1.0);
 
         // Others are dissimilar
 
         ClusteringResult<Integer> result = ClusteringResult.fromIntegerCollections(List.of(List.of(0, 1), List.of(2, 3)), similarity);
         var cluster = result.getClusters().stream().findFirst().orElseThrow();
-        assertEquals(1f, cluster.getAverageSimilarity(), 0.00001);
+        assertEquals(1.0, cluster.getAverageSimilarity(), 0.00001);
     }
 
     private static void setEntries(RealMatrix matrix, int i, int j, double similarity) {