OPENNLP-1285: Changing Math to StrictMath for Java 8 vs 11 test incon…

…sistencies. (#377)
apache · Jun 10, 2020 · 7c10174 · 7c10174
1 parent e4b331d
commit 7c10174
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Showing 41 changed files with 114 additions and 114 deletions.
diff --git a/opennlp-morfologik-addon/src/main/java/opennlp/morfologik/cmdline/CLI.java b/opennlp-morfologik-addon/src/main/java/opennlp/morfologik/cmdline/CLI.java
@@ -80,7 +80,7 @@ private static void usage() {
 
       System.out.print("  " + tool.getName());
 
-      for (int i = 0; i < Math.abs(tool.getName().length()
+      for (int i = 0; i < StrictMath.abs(tool.getName().length()
           - numberOfSpaces); i++) {
         System.out.print(" ");
       }

diff --git a/opennlp-tools/src/main/java/opennlp/tools/cmdline/CLI.java b/opennlp-tools/src/main/java/opennlp/tools/cmdline/CLI.java
@@ -203,7 +203,7 @@ private static void usage() {
 
       System.out.print("  " + tool.getName());
 
-      for (int i = 0; i < Math.abs(tool.getName().length() - numberOfSpaces); i++) {
+      for (int i = 0; i < StrictMath.abs(tool.getName().length() - numberOfSpaces); i++) {
         System.out.print(" ");
       }
 

diff --git a/opennlp-tools/src/main/java/opennlp/tools/cmdline/FineGrainedReportListener.java b/opennlp-tools/src/main/java/opennlp/tools/cmdline/FineGrainedReportListener.java
@@ -723,7 +723,7 @@ private void increment(String column) {
      */
     public double getAccuracy() {
       // we save the accuracy because it is frequently used by the comparator
-      if (Math.abs(acc - 1.0d) < 0.0000000001) {
+      if (StrictMath.abs(acc - 1.0d) < 0.0000000001) {
         if (total == 0)
           acc = 0.0d;
         acc = (double) correct / (double) total;

diff --git a/opennlp-tools/src/main/java/opennlp/tools/dictionary/Dictionary.java b/opennlp-tools/src/main/java/opennlp/tools/dictionary/Dictionary.java
@@ -124,8 +124,8 @@ public Dictionary(InputStream in) throws IOException {
    */
   public void put(StringList tokens) {
     entrySet.add(new StringListWrapper(tokens));
-    minTokenCount = Math.min(minTokenCount, tokens.size());
-    maxTokenCount = Math.max(maxTokenCount, tokens.size());
+    minTokenCount = StrictMath.min(minTokenCount, tokens.size());
+    maxTokenCount = StrictMath.max(maxTokenCount, tokens.size());
   }
 
   /**

diff --git a/opennlp-tools/src/main/java/opennlp/tools/languagemodel/NGramLanguageModel.java b/opennlp-tools/src/main/java/opennlp/tools/languagemodel/NGramLanguageModel.java
@@ -62,13 +62,13 @@ public double calculateProbability(StringList tokens) {
     if (size() > 0) {
       for (StringList ngram : NGramUtils.getNGrams(tokens, n)) {
         double score = stupidBackoff(ngram);
-        probability += Math.log(score);
+        probability += StrictMath.log(score);
         if (Double.isNaN(probability)) {
           probability = 0d;
           break;
         }
       }
-      probability = Math.exp(probability);
+      probability = StrictMath.exp(probability);
     }
     return probability;
   }
@@ -79,13 +79,13 @@ public double calculateProbability(String... tokens) {
     if (size() > 0) {
       for (String[] ngram : NGramUtils.getNGrams(tokens, n)) {
         double score = stupidBackoff(new StringList(ngram));
-        probability += Math.log(score);
+        probability += StrictMath.log(score);
         if (Double.isNaN(probability)) {
           probability = 0d;
           break;
         }
       }
-      probability = Math.exp(probability);
+      probability = StrictMath.exp(probability);
     }
     return probability;
   }

diff --git a/opennlp-tools/src/main/java/opennlp/tools/lemmatizer/DefaultLemmatizerContextGenerator.java b/opennlp-tools/src/main/java/opennlp/tools/lemmatizer/DefaultLemmatizerContextGenerator.java
@@ -42,15 +42,15 @@ public DefaultLemmatizerContextGenerator() {
   protected static String[] getPrefixes(String lex) {
     String[] prefs = new String[PREFIX_LENGTH];
     for (int li = 1; li < PREFIX_LENGTH; li++) {
-      prefs[li] = lex.substring(0, Math.min(li + 1, lex.length()));
+      prefs[li] = lex.substring(0, StrictMath.min(li + 1, lex.length()));
     }
     return prefs;
   }
 
   protected static String[] getSuffixes(String lex) {
     String[] suffs = new String[SUFFIX_LENGTH];
     for (int li = 1; li < SUFFIX_LENGTH; li++) {
-      suffs[li] = lex.substring(Math.max(lex.length() - li - 1, 0));
+      suffs[li] = lex.substring(StrictMath.max(lex.length() - li - 1, 0));
     }
     return suffs;
   }

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/ArrayMath.java b/opennlp-tools/src/main/java/opennlp/tools/ml/ArrayMath.java
@@ -43,15 +43,15 @@ public static double innerProduct(double[] vecA, double[] vecB) {
   public static double l1norm(double[] v) {
     double norm = 0;
     for (int i = 0; i < v.length; i++)
-      norm += Math.abs(v[i]);
+      norm += StrictMath.abs(v[i]);
     return norm;
   }
 
   /**
    * L2-norm
    */
   public static double l2norm(double[] v) {
-    return Math.sqrt(innerProduct(v, v));
+    return StrictMath.sqrt(innerProduct(v, v));
   }
 
   /**
@@ -73,9 +73,9 @@ public static double logSumOfExps(double[] x) {
     double sum = 0.0;
     for (int i = 0; i < x.length; i++) {
       if (x[i] != Double.NEGATIVE_INFINITY)
-        sum += Math.exp(x[i] - max);
+        sum += StrictMath.exp(x[i] - max);
     }
-    return max + Math.log(sum);
+    return max + StrictMath.log(sum);
   }
 
   public static double max(double[] x) {

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/BeamSearch.java b/opennlp-tools/src/main/java/opennlp/tools/ml/BeamSearch.java
@@ -96,7 +96,7 @@ public Sequence[] bestSequences(int numSequences, T[] sequence,
     }
 
     for (int i = 0; i < sequence.length; i++) {
-      int sz = Math.min(size, prev.size());
+      int sz = StrictMath.min(size, prev.size());
 
       for (int sc = 0; prev.size() > 0 && sc < sz; sc++) {
         Sequence top = prev.remove();
@@ -115,7 +115,7 @@ public Sequence[] bestSequences(int numSequences, T[] sequence,
 
         Arrays.sort(temp_scores);
 
-        double min = temp_scores[Math.max(0,scores.length - size)];
+        double min = temp_scores[StrictMath.max(0,scores.length - size)];
 
         for (int p = 0; p < scores.length; p++) {
           if (scores[p] >= min) {
@@ -149,7 +149,7 @@ public Sequence[] bestSequences(int numSequences, T[] sequence,
       next = tmp;
     }
 
-    int numSeq = Math.min(numSequences, prev.size());
+    int numSeq = StrictMath.min(numSequences, prev.size());
     Sequence[] topSequences = new Sequence[numSeq];
 
     for (int seqIndex = 0; seqIndex < numSeq; seqIndex++) {

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/GISModel.java b/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/GISModel.java
@@ -188,7 +188,7 @@ static double[] eval(Context[] context, float[] values, double[] prior,
 
     double normal = 0.0;
     for (int oid = 0; oid < model.getNumOutcomes(); oid++) {
-      prior[oid] = Math.exp(prior[oid]);
+      prior[oid] = StrictMath.exp(prior[oid]);
       normal += prior[oid];
     }
 

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/GISTrainer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/GISTrainer.java
@@ -530,14 +530,14 @@ private double gaussianUpdate(int predicate, int oid, double correctionConstant)
     double modelValue = modelExpects[0][predicate].getParameters()[oid];
     double observedValue = observedExpects[predicate].getParameters()[oid];
     for (int i = 0; i < 50; i++) {
-      double tmp = modelValue * Math.exp(correctionConstant * x0);
+      double tmp = modelValue * StrictMath.exp(correctionConstant * x0);
       double f = tmp + (param + x0) / sigma - observedValue;
       double fp = tmp * correctionConstant + 1 / sigma;
       if (fp == 0) {
         break;
       }
       double x = x0 - f / fp;
-      if (Math.abs(x - x0) < 0.000001) {
+      if (StrictMath.abs(x - x0) < 0.000001) {
         x0 = x;
         break;
       }
@@ -623,8 +623,8 @@ private double nextIteration(double correctionConstant,
           if (model[aoi] == 0) {
             System.err.println("Model expects == 0 for " + predLabels[pi] + " " + outcomeLabels[aoi]);
           }
-          //params[pi].updateParameter(aoi,(Math.log(observed[aoi]) - Math.log(model[aoi])));
-          params[pi].updateParameter(aoi, ((Math.log(observed[aoi]) - Math.log(model[aoi]))
+          //params[pi].updateParameter(aoi,(StrictMath.log(observed[aoi]) - StrictMath.log(model[aoi])));
+          params[pi].updateParameter(aoi, ((StrictMath.log(observed[aoi]) - StrictMath.log(model[aoi]))
               / correctionConstant));
         }
 
@@ -695,7 +695,7 @@ public ModelExpectationComputeTask call() {
           }
         }
 
-        loglikelihood += Math.log(modelDistribution[outcomeList[ei]]) * numTimesEventsSeen[ei];
+        loglikelihood += StrictMath.log(modelDistribution[outcomeList[ei]]) * numTimesEventsSeen[ei];
 
         numEvents += numTimesEventsSeen[ei];
         if (printMessages) {

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihood.java b/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/quasinewton/NegLogLikelihood.java
@@ -137,7 +137,7 @@ public double[] gradientAt(double[] x) {
       logSumOfExps = ArrayMath.logSumOfExps(expectation);
 
       for (oi = 0; oi < numOutcomes; oi++) {
-        expectation[oi] = Math.exp(expectation[oi] - logSumOfExps);
+        expectation[oi] = StrictMath.exp(expectation[oi] - logSumOfExps);
       }
 
       for (oi = 0; oi < numOutcomes; oi++) {

diff --git a/...nlp-tools/src/main/java/opennlp/tools/ml/maxent/quasinewton/ParallelNegLogLikelihood.java b/...nlp-tools/src/main/java/opennlp/tools/ml/maxent/quasinewton/ParallelNegLogLikelihood.java
@@ -237,7 +237,7 @@ public GradientComputeTask call() {
         logSumOfExps = ArrayMath.logSumOfExps(expectation);
 
         for (oi = 0; oi < numOutcomes; oi++) {
-          expectation[oi] = Math.exp(expectation[oi] - logSumOfExps);
+          expectation[oi] = StrictMath.exp(expectation[oi] - logSumOfExps);
         }
 
         for (oi = 0; oi < numOutcomes; oi++) {

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/quasinewton/QNMinimizer.java b/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/quasinewton/QNMinimizer.java
@@ -36,7 +36,7 @@
  *
  *    {@literal @}Override
  *    public double valueAt(double[] x) {
- *      return Math.pow(x[0]-1, 2) + 10;
+ *      return StrictMath.pow(x[0]-1, 2) + 10;
  *    }
  *
  *    {@literal @}Override
@@ -279,7 +279,7 @@ else if (iter < 100)
     if (l1Cost > 0 && l2Cost > 0) {
       double[] x = lsr.getNextPoint();
       for (int i = 0; i < dimension; i++) {
-        x[i] = Math.sqrt(1 + l2Cost) * x[i];
+        x[i] = StrictMath.sqrt(1 + l2Cost) * x[i];
       }
     }
 
@@ -375,7 +375,7 @@ private boolean isConverged(LineSearchResult lsr) {
     }
 
     // Check gradient's norm using the criteria: ||g(x)|| / max(1, ||x||) < threshold
-    double xNorm = Math.max(1, ArrayMath.l2norm(lsr.getNextPoint()));
+    double xNorm = StrictMath.max(1, ArrayMath.l2norm(lsr.getNextPoint()));
     double gradNorm = l1Cost > 0 ?
         ArrayMath.l2norm(lsr.getPseudoGradAtNext()) : ArrayMath.l2norm(lsr.getGradAtNext());
     if (gradNorm / xNorm < REL_GRAD_NORM_TOL) {

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/quasinewton/QNModel.java b/opennlp-tools/src/main/java/opennlp/tools/ml/maxent/quasinewton/QNModel.java
@@ -80,7 +80,7 @@ private double[] eval(String[] context, float[] values, double[] probs) {
 
     double logSumExp = ArrayMath.logSumOfExps(probs);
     for (int oi = 0; oi < outcomeNames.length; oi++) {
-      probs[oi] = Math.exp(probs[oi] - logSumExp);
+      probs[oi] = StrictMath.exp(probs[oi] - logSumExp);
     }
     return probs;
   }
@@ -117,7 +117,7 @@ static double[] eval(int[] context, float[] values, double[] probs,
     double logSumExp = ArrayMath.logSumOfExps(probs);
 
     for (int oi = 0; oi < nOutcomes; oi++) {
-      probs[oi] = Math.exp(probs[oi] - logSumExp);
+      probs[oi] = StrictMath.exp(probs[oi] - logSumExp);
     }
 
     return probs;

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/model/ComparableEvent.java b/opennlp-tools/src/main/java/opennlp/tools/ml/model/ComparableEvent.java
@@ -48,7 +48,7 @@ public int compareTo(ComparableEvent ce) {
       return compareOutcome;
     }
 
-    int smallerLength = Math.min(predIndexes.length, ce.predIndexes.length);
+    int smallerLength = StrictMath.min(predIndexes.length, ce.predIndexes.length);
 
     for (int i = 0; i < smallerLength; i++) {
       int comparePredIndexes = Integer.compare(predIndexes[i], ce.predIndexes[i]);

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/model/ComparablePredicate.java b/opennlp-tools/src/main/java/opennlp/tools/ml/model/ComparablePredicate.java
@@ -37,7 +37,7 @@ public ComparablePredicate(String n, int[] ocs, double[] ps) {
   }
 
   public int compareTo(ComparablePredicate cp) {
-    int smallerLength = Math.min(outcomes.length, cp.outcomes.length);
+    int smallerLength = StrictMath.min(outcomes.length, cp.outcomes.length);
 
     for (int i = 0; i < smallerLength; i++) {
       int compareOutcomes = Integer.compare(outcomes[i], cp.outcomes[i]);

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/model/UniformPrior.java b/opennlp-tools/src/main/java/opennlp/tools/ml/model/UniformPrior.java
@@ -44,7 +44,7 @@ public void logPrior(double[] dist, int[] context) {
 
   public void setLabels(String[] outcomeLabels, String[] contextLabels) {
     this.numOutcomes = outcomeLabels.length;
-    r = Math.log(1.0 / numOutcomes);
+    r = StrictMath.log(1.0 / numOutcomes);
   }
 
   @Override

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/naivebayes/LogProbabilities.java b/opennlp-tools/src/main/java/opennlp/tools/ml/naivebayes/LogProbabilities.java
@@ -113,7 +113,7 @@ private Map<T, Double> normalize() {
       T t = entry.getKey();
       Double p = entry.getValue();
       if (p != null) {
-        double temp_p = Math.exp(p - highestLogProbability);
+        double temp_p = StrictMath.exp(p - highestLogProbability);
         if (!Double.isNaN(temp_p)) {
           sum += temp_p;
           temp.put(t, temp_p);
@@ -133,7 +133,7 @@ private Map<T, Double> normalize() {
   }
 
   private double log(double prob) {
-    return Math.log(prob);
+    return StrictMath.log(prob);
   }
 
   /**
@@ -163,7 +163,7 @@ public Double getLog(T t) {
   }
 
   public void discardCountsBelow(double i) {
-    i = Math.log(i);
+    i = StrictMath.log(i);
     ArrayList<T> labelsToRemove = new ArrayList<>();
     for (Entry<T, Double> entry : map.entrySet()) {
       final T label = entry.getKey();

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/naivebayes/LogProbability.java b/opennlp-tools/src/main/java/opennlp/tools/ml/naivebayes/LogProbability.java
@@ -36,7 +36,7 @@ public LogProbability(T label) {
    * @param probability the probability to assign
    */
   public void set(double probability) {
-    this.probability = Math.log(probability);
+    this.probability = StrictMath.log(probability);
   }
 
   /**
@@ -55,7 +55,7 @@ public void set(Probability probability) {
    * @param probability the probability to assign
    */
   public void setIfLarger(double probability) {
-    double logP = Math.log(probability);
+    double logP = StrictMath.log(probability);
     if (this.probability < logP) {
       this.probability = logP;
     }
@@ -98,7 +98,7 @@ public void setLog(double probability) {
    * @param probability the probability weight to add
    */
   public void addIn(double probability) {
-    setLog(this.probability + Math.log(probability));
+    setLog(this.probability + StrictMath.log(probability));
   }
 
   /**
@@ -107,7 +107,7 @@ public void addIn(double probability) {
    * @return the probability associated with the label
    */
   public Double get() {
-    return Math.exp(probability);
+    return StrictMath.exp(probability);
   }
 
   /**

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/naivebayes/Probabilities.java b/opennlp-tools/src/main/java/opennlp/tools/ml/naivebayes/Probabilities.java
@@ -82,7 +82,7 @@ public void setIfLarger(T t, double probability) {
    * @param probability the log probability to assign
    */
   public void setLog(T t, double probability) {
-    set(t, Math.exp(probability));
+    set(t, StrictMath.exp(probability));
   }
 
   /**
@@ -97,7 +97,7 @@ public void addIn(T t, double probability, int count) {
     Double p = map.get(t);
     if (p == null)
       p = 1.0;
-    probability = Math.pow(probability, count);
+    probability = StrictMath.pow(probability, count);
     map.put(t, p * probability);
   }
 
@@ -121,7 +121,7 @@ public Double get(T t) {
    * @return the log probability associated with the label
    */
   public Double getLog(T t) {
-    return Math.log(get(t));
+    return StrictMath.log(get(t));
   }
 
   /**

diff --git a/opennlp-tools/src/main/java/opennlp/tools/ml/naivebayes/Probability.java b/opennlp-tools/src/main/java/opennlp/tools/ml/naivebayes/Probability.java
@@ -88,7 +88,7 @@ public boolean isLarger(Probability probability) {
    * @param probability the log probability to assign
    */
   public void setLog(double probability) {
-    set(Math.exp(probability));
+    set(StrictMath.exp(probability));
   }
 
   /**
@@ -115,7 +115,7 @@ public Double get() {
    * @return the log probability associated with the label
    */
   public Double getLog() {
-    return Math.log(get());
+    return StrictMath.log(get());
   }
 
   /**