Merge "Fix Sonar Issues in apex examples-adaptive"

4 files changed, 28 insertions, 125 deletions

diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AnomalyDetection.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AnomalyDetection.java
index 6ff5ebccc..b0cff91d0 100644
--- a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AnomalyDetection.java
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AnomalyDetection.java
@@ -1,6 +1,7 @@
 /*-
  * ============LICENSE_START=======================================================
  *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ *  Modifications Copyright (c) 2021 Nordix Foundation.
  * ================================================================================
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -24,17 +25,15 @@ import java.io.Serializable;
 import java.util.ArrayList;
 import java.util.LinkedList;
 import java.util.List;
+import lombok.EqualsAndHashCode;
 
 /**
  * The Class AnomalyDetection is used as a Java context for Adaptive anomaly detection in the adaptive domain.
  */
+@EqualsAndHashCode
 public class AnomalyDetection implements Serializable {
     private static final long serialVersionUID = -823013127095523727L;
 
-    private static final int HASH_PRIME_1 = 31;
-    private static final int HASH_PRIME_2 = 1231;
-    private static final int HASH_PRIME_3 = 1237;
-
     private boolean firstRound = true;
     private int frequency = 0;
 
@@ -65,7 +64,7 @@ public class AnomalyDetection implements Serializable {
      */
     public void init(final int incomingFrequency) {
         frequencyForecasted = new ArrayList<>(incomingFrequency);
-        for (int i = 0; i < incomingFrequency; i++) {
+        for (var i = 0; i < incomingFrequency; i++) {
             frequencyForecasted.add(null);
         }
     }
@@ -182,56 +181,4 @@ public class AnomalyDetection implements Serializable {
         return "AnomalyDetection [firstRound=" + firstRound + ", frequency=" + frequency + ", anomalyScores="
                 + anomalyScores + ", frequencyForecasted=" + frequencyForecasted + "]";
     }
-
-    /**
-     * {@inheritDoc}.
-     */
-    @Override
-    public int hashCode() {
-        final int prime = HASH_PRIME_1;
-        int result = 1;
-        result = prime * result + ((anomalyScores == null) ? 0 : anomalyScores.hashCode());
-        result = prime * result + (firstRound ? HASH_PRIME_2 : HASH_PRIME_3);
-        result = prime * result + frequency;
-        result = prime * result + ((frequencyForecasted == null) ? 0 : frequencyForecasted.hashCode());
-        return result;
-    }
-
-    /**
-     * {@inheritDoc}.
-     */
-    @Override
-    public boolean equals(final Object obj) {
-        if (this == obj) {
-            return true;
-        }
-        if (obj == null) {
-            return false;
-        }
-        if (getClass() != obj.getClass()) {
-            return false;
-        }
-        final AnomalyDetection other = (AnomalyDetection) obj;
-        if (anomalyScores == null) {
-            if (other.anomalyScores != null) {
-                return false;
-            }
-        } else if (!anomalyScores.equals(other.anomalyScores)) {
-            return false;
-        }
-        if (firstRound != other.firstRound) {
-            return false;
-        }
-        if (frequency != other.frequency) {
-            return false;
-        }
-        if (frequencyForecasted == null) {
-            if (other.frequencyForecasted != null) {
-                return false;
-            }
-        } else if (!frequencyForecasted.equals(other.frequencyForecasted)) {
-            return false;
-        }
-        return true;
-    }
 }
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AutoLearn.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AutoLearn.java
index dd5bf0c45..60c4d96d9 100644
--- a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AutoLearn.java
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AutoLearn.java
@@ -1,6 +1,7 @@
 /*-
  * ============LICENSE_START=======================================================
  *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
+ *  Modifications Copyright (c) 2021 Nordix Foundation.
  * ================================================================================
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -23,11 +24,15 @@ package org.onap.policy.apex.examples.adaptive.concepts;
 import java.io.Serializable;
 import java.util.ArrayList;
 import java.util.List;
+import lombok.EqualsAndHashCode;
+import lombok.ToString;
 
 /**
  * The Class AutoLearn is used as a Java context for Adaptive auto-learning of trends towards a fixed value in the
  * adaptive domain.
  */
+@EqualsAndHashCode
+@ToString
 public class AutoLearn implements Serializable {
     private static final long serialVersionUID = 3825970380434170754L;
 
@@ -52,14 +57,14 @@ public class AutoLearn implements Serializable {
     public void init(final int size) {
         if (avDiffs == null || avDiffs.isEmpty()) {
             avDiffs = new ArrayList<>(size);
-            for (int i = 0; i < size; i++) {
+            for (var i = 0; i < size; i++) {
                 avDiffs.add(i, Double.NaN);
             }
         }
 
         if (counts == null || counts.isEmpty()) {
             counts = new ArrayList<>(size);
-            for (int i = 0; i < size; i++) {
+            for (var i = 0; i < size; i++) {
                 counts.add(i, 0L);
             }
         }
@@ -133,55 +138,5 @@ public class AutoLearn implements Serializable {
         counts = null;
     }
 
-    /**
-     * {@inheritDoc}.
-     */
-    @Override
-    public String toString() {
-        return "AutoLearn [avDiffs=" + avDiffs + ", counts=" + counts + "]";
-    }
 
-    /**
-     * {@inheritDoc}.
-     */
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        result = prime * result + ((avDiffs == null) ? 0 : avDiffs.hashCode());
-        result = prime * result + ((counts == null) ? 0 : counts.hashCode());
-        return result;
-    }
-
-    /**
-     * {@inheritDoc}.
-     */
-    @Override
-    public boolean equals(final Object obj) {
-        if (this == obj) {
-            return true;
-        }
-        if (obj == null) {
-            return false;
-        }
-        if (getClass() != obj.getClass()) {
-            return false;
-        }
-        final AutoLearn other = (AutoLearn) obj;
-        if (avDiffs == null) {
-            if (other.avDiffs != null) {
-                return false;
-            }
-        } else if (!avDiffs.equals(other.avDiffs)) {
-            return false;
-        }
-        if (counts == null) {
-            if (other.counts != null) {
-                return false;
-            }
-        } else if (!counts.equals(other.counts)) {
-            return false;
-        }
-        return true;
-    }
 }
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicyDecideTaskSelectionLogic.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicyDecideTaskSelectionLogic.java
index 6c1998108..6b61e822b 100644
--- a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicyDecideTaskSelectionLogic.java
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicyDecideTaskSelectionLogic.java
@@ -1,7 +1,7 @@
 /*-
  * ============LICENSE_START=======================================================
  *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
- *  Modifications Copyright (C) 2020 Nordix Foundation.
+ *  Modifications Copyright (C) 2020-2021 Nordix Foundation.
  * ================================================================================
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -78,7 +78,7 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
     public boolean getTask(final TaskSelectionExecutionContext executor) {
         String id = executor.subject.getId();
         executor.logger.debug(id);
-        String inFields = executor.inFields.toString();
+        var inFields = executor.inFields.toString();
         executor.logger.debug(inFields);
         final double now = (Double) (executor.inFields.get("MonitoredValue"));
         final Integer iteration = (Integer) (executor.inFields.get("Iteration"));
@@ -120,7 +120,7 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
         }
 
         // Get the context object
-        AnomalyDetection anomalyDetection =
+        var anomalyDetection =
                 (AnomalyDetection) executor.getContextAlbum(ANOMALY_DETECTION_ALBUM).get(ANOMALY_DETECTION);
         if (anomalyDetection == null) {
             anomalyDetection = new AnomalyDetection();
@@ -132,7 +132,7 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
             anomalyDetection.init(FREQUENCY);
         }
 
-        boolean unsetfirstround = false;
+        var unsetfirstround = false;
 
         int frequency = anomalyDetection.getFrequency();
         frequency = frequency + 1;
@@ -169,7 +169,7 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
         listSizeControl(anomalyDetection.getAnomalyScores(), FREQUENCY * 4);
 
         // ---------- calculate the anomaly probability
-        double anomalyProbability = 0.0;
+        var anomalyProbability = 0.0;
         if (anomalyDetection.getAnomalyScores().size() > 30) {
             // 0.5
             anomalyProbability = getStatsTest(anomalyDetection.getAnomalyScores(), ANOMALY_SENSITIVITY);
@@ -282,10 +282,10 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
         final double s = (z * z * n * (2.0 - n)) / (z * z * n - (n - 1.0) * (n - 1.0));
         final double t = FastMath.sqrt(s);
         // default p value = 0
-        double pvalue = 0.0;
+        var pvalue = 0.0;
         if (!Double.isNaN(t)) {
             // t distribution with n-2 degrees of freedom
-            final TDistribution tDist = new TDistribution(n - 2);
+            final var tDist = new TDistribution(n - 2);
             pvalue = n * (1.0 - tDist.cumulativeProbability(t));
             // set max pvalue = 1
             pvalue = pvalue > 1.0 ? 1.0 : pvalue;
@@ -312,9 +312,9 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
      * @return the double[]
      */
     private static Double[] removevalue(final Double[] lvalues, final double val) {
-        for (int i = 0; i < lvalues.length; i++) {
+        for (var i = 0; i < lvalues.length; i++) {
             if (Double.compare(lvalues[i], val) == 0) {
-                final Double[] ret = new Double[lvalues.length - 1];
+                final var ret = new Double[lvalues.length - 1];
                 System.arraycopy(lvalues, 0, ret, 0, i);
                 System.arraycopy(lvalues, i + 1, ret, i, lvalues.length - i - 1);
                 return ret;
@@ -330,7 +330,7 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
      * @return the mean
      */
     private static double getMean(final Double[] lvalues) {
-        double sum = 0.0;
+        var sum = 0.0;
         for (final double d : lvalues) {
 
             sum += d;
@@ -346,7 +346,7 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
      * @return stddev
      */
     private static double getStdDev(final Double[] lvalues, final double mean) {
-        double temp = 0.0;
+        var temp = 0.0;
         for (final double d : lvalues) {
             temp += (mean - d) * (mean - d);
         }
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicyDecideTaskSelectionLogic.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicyDecideTaskSelectionLogic.java
index 1805d81a2..317a86349 100644
--- a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicyDecideTaskSelectionLogic.java
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicyDecideTaskSelectionLogic.java
@@ -2,6 +2,7 @@
  * ============LICENSE_START=======================================================
  *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
  *  Modifications Copyright (C) 2021 AT&T Intellectual Property. All rights reserved.
+ *  Modifications Copyright (c) 2021 Nordix Foundation.
  * ================================================================================
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -51,10 +52,10 @@ public class AutoLearnPolicyDecideTaskSelectionLogic {
      * @return the task
      */
     public boolean getTask(final TaskSelectionExecutionContext executor) {
-        String idString = executor.subject.getId();
+        var idString = executor.subject.getId();
         executor.logger.debug(idString);
 
-        String inFieldsString = executor.inFields.toString();
+        var inFieldsString = executor.inFields.toString();
         executor.logger.debug(inFieldsString);
 
         final List<String> tasks = executor.subject.getTaskNames();
@@ -68,7 +69,7 @@ public class AutoLearnPolicyDecideTaskSelectionLogic {
         }
 
         // Get the context object
-        AutoLearn autoLearn = (AutoLearn) executor.getContextAlbum(AUTO_LEARN_ALBUM).get(AUTO_LEARN);
+        var autoLearn = (AutoLearn) executor.getContextAlbum(AUTO_LEARN_ALBUM).get(AUTO_LEARN);
         if (autoLearn == null) {
             autoLearn = new AutoLearn();
         }
@@ -105,12 +106,12 @@ public class AutoLearnPolicyDecideTaskSelectionLogic {
      */
     private int getOption(final double diff, final AutoLearn autoLearn) {
         final Double[] avdiffs = autoLearn.getAvDiffs().toArray(new Double[autoLearn.getAvDiffs().size()]);
-        final int r = RAND.nextInt(size);
+        final var r = RAND.nextInt(size);
         int closestupi = -1;
         int closestdowni = -1;
         double closestup = Double.MAX_VALUE;
         double closestdown = Double.MIN_VALUE;
-        for (int i = 0; i < size; i++) {
+        for (var i = 0; i < size; i++) {
             if (Double.isNaN(avdiffs[i])) {
                 return r;
             }