Use lombok in apex-pdp #5

Updated examples thru integration-executor-test.

Issue-ID: POLICY-3391
Change-Id: Idc707146a646d05720675a787761c199484048e9
Signed-off-by: Jim Hahn <jrh3@att.com>

4 files changed, 19 insertions, 126 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 b0cff91d0..10c3610ba 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
@@ -2,6 +2,7 @@
  * ============LICENSE_START=======================================================
  *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
  *  Modifications Copyright (c) 2021 Nordix Foundation.
+ *  Modifications Copyright (C) 2021 AT&T Intellectual Property. All rights reserved.
  * ================================================================================
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -26,11 +27,17 @@ import java.util.ArrayList;
 import java.util.LinkedList;
 import java.util.List;
 import lombok.EqualsAndHashCode;
+import lombok.Getter;
+import lombok.Setter;
+import lombok.ToString;
 
 /**
  * The Class AnomalyDetection is used as a Java context for Adaptive anomaly detection in the adaptive domain.
  */
+@Getter
+@Setter
 @EqualsAndHashCode
+@ToString
 public class AnomalyDetection implements Serializable {
     private static final long serialVersionUID = -823013127095523727L;
 
@@ -70,60 +77,6 @@ public class AnomalyDetection implements Serializable {
     }
 
     /**
-     * Indicates if this is the first round of the algorithm.
-     *
-     * @return true if this is the first round of the algorithm
-     */
-    public boolean getFirstRound() {
-        return firstRound;
-    }
-
-    /**
-     * Sets the first round indicator of the algorithm.
-     *
-     * @param firstRound the first round indicator of the algorithm
-     */
-    public void setFirstRound(final boolean firstRound) {
-        this.firstRound = firstRound;
-    }
-
-    /**
-     * Gets the frequency value of the algorithm.
-     *
-     * @return the frequency value of the algorithm
-     */
-    public int getFrequency() {
-        return frequency;
-    }
-
-    /**
-     * Sets the frequency value of the algorithm.
-     *
-     * @param frequency the frequency value of the algorithm
-     */
-    public void setFrequency(final int frequency) {
-        this.frequency = frequency;
-    }
-
-    /**
-     * Gets the anomaly score values of the algorithm.
-     *
-     * @return the anomaly score values of the algorithm
-     */
-    public List<Double> getAnomalyScores() {
-        return anomalyScores;
-    }
-
-    /**
-     * Sets the anomaly score values of the algorithm.
-     *
-     * @param anomalyScores the anomaly score values of the algorithm
-     */
-    public void setAnomalyScores(final List<Double> anomalyScores) {
-        this.anomalyScores = anomalyScores;
-    }
-
-    /**
      * Check if the anomaly score values of the algorithm are set.
      *
      * @return true, if the anomaly score values of the algorithm are set
@@ -140,24 +93,6 @@ public class AnomalyDetection implements Serializable {
     }
 
     /**
-     * Gets the frequency forecasted by the algorithm.
-     *
-     * @return the frequency forecasted by the algorithm
-     */
-    public List<Double> getFrequencyForecasted() {
-        return frequencyForecasted;
-    }
-
-    /**
-     * Sets the frequency forecasted by the algorithm.
-     *
-     * @param frequencyForecasted the frequency forecasted by the algorithm
-     */
-    public void setFrequencyForecasted(final List<Double> frequencyForecasted) {
-        this.frequencyForecasted = frequencyForecasted;
-    }
-
-    /**
      * Check if the frequency forecasted by the algorithm is set.
      *
      * @return true, if the frequency forecasted by the algorithm is set
@@ -172,13 +107,4 @@ public class AnomalyDetection implements Serializable {
     public void unsetFrequencyForecasted() {
         frequencyForecasted = null;
     }
-
-    /**
-     * {@inheritDoc}.
-     */
-    @Override
-    public String toString() {
-        return "AnomalyDetection [firstRound=" + firstRound + ", frequency=" + frequency + ", anomalyScores="
-                + anomalyScores + ", frequencyForecasted=" + frequencyForecasted + "]";
-    }
 }
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 60c4d96d9..1760f910d 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
@@ -2,6 +2,7 @@
  * ============LICENSE_START=======================================================
  *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
  *  Modifications Copyright (c) 2021 Nordix Foundation.
+ *  Modifications Copyright (C) 2021 AT&T Intellectual Property. All rights reserved.
  * ================================================================================
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -25,12 +26,16 @@ import java.io.Serializable;
 import java.util.ArrayList;
 import java.util.List;
 import lombok.EqualsAndHashCode;
+import lombok.Getter;
+import lombok.Setter;
 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.
  */
+@Getter
+@Setter
 @EqualsAndHashCode
 @ToString
 public class AutoLearn implements Serializable {
@@ -71,24 +76,6 @@ public class AutoLearn implements Serializable {
     }
 
     /**
-     * Gets the average difference values of the algorithm.
-     *
-     * @return the average difference values of the algorithm
-     */
-    public List<Double> getAvDiffs() {
-        return avDiffs;
-    }
-
-    /**
-     * Sets the average difference values of the algorithm.
-     *
-     * @param avDiffs the average difference values of the algorithm
-     */
-    public void setAvDiffs(final List<Double> avDiffs) {
-        this.avDiffs = avDiffs;
-    }
-
-    /**
      * Check if the average difference values of the algorithm are set.
      *
      * @return true, if check set av diffs
@@ -105,24 +92,6 @@ public class AutoLearn implements Serializable {
     }
 
     /**
-     * Gets the count values of the algorithm.
-     *
-     * @return the count values of the algorithm
-     */
-    public List<Long> getCounts() {
-        return counts;
-    }
-
-    /**
-     * Sets the count values of the algorithm.
-     *
-     * @param counts the count values of the algorithm
-     */
-    public void setCounts(final List<Long> counts) {
-        this.counts = counts;
-    }
-
-    /**
      * Check if the count values of the algorithm are set.
      *
      * @return true, if the count values of the algorithm are set
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelSaver.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelSaver.java
index 83a42ac8d..0ebff497c 100644
--- a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelSaver.java
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelSaver.java
@@ -2,6 +2,7 @@
  * ============LICENSE_START=======================================================
  *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
  *  Modifications Copyright (C) 2019 Nordix Foundation.
+ *  Modifications Copyright (C) 2021 AT&T Intellectual Property. All rights reserved.
  * ================================================================================
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -21,6 +22,8 @@
 
 package org.onap.policy.apex.examples.adaptive.model;
 
+import lombok.AccessLevel;
+import lombok.NoArgsConstructor;
 import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
 import org.onap.policy.apex.model.basicmodel.handling.ApexModelSaver;
 import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
@@ -32,18 +35,12 @@ import org.slf4j.ext.XLoggerFactory;
  *
  * @author Liam Fallon (liam.fallon@ericsson.com)
  */
+@NoArgsConstructor(access = AccessLevel.PRIVATE)
 public final class AdaptiveDomainModelSaver {
     // Logger for this class
     private static final XLogger LOGGER = XLoggerFactory.getXLogger(AdaptiveDomainModelSaver.class);
 
     /**
-     * Private default constructor to prevent subclassing.
-     */
-    private AdaptiveDomainModelSaver() {
-        // Prevent subclassing
-    }
-
-    /**
      * Write the AADM model to args[0].
      *
      * @param args Not used
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 6b61e822b..e059c9810 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
@@ -2,6 +2,7 @@
  * ============LICENSE_START=======================================================
  *  Copyright (C) 2016-2018 Ericsson. All rights reserved.
  *  Modifications Copyright (C) 2020-2021 Nordix Foundation.
+ *  Modifications Copyright (C) 2021 AT&T Intellectual Property. All rights reserved.
  * ================================================================================
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -144,7 +145,7 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
         }
         anomalyDetection.setFrequency(frequency);
 
-        if (unsetfirstround && anomalyDetection.getFirstRound()) {
+        if (unsetfirstround && anomalyDetection.isFirstRound()) {
             anomalyDetection.setFirstRound(false);
         }
 
@@ -160,7 +161,7 @@ public class AnomalyDetectionPolicyDecideTaskSelectionLogic {
         anomalyDetection.getFrequencyForecasted().set(frequency, forecastedValue);
 
         // anomaly score is ignored in the first frequency period
-        if (!anomalyDetection.getFirstRound()) {
+        if (!anomalyDetection.isFirstRound()) {
             ((LinkedList<Double>) anomalyDetection.getAnomalyScores()).addLast(anomalyScore);
         }