/*-
* ============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.
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;
*/
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);
}
}
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;
- }
}
/*-
* ============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.
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;
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);
}
}
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;
- }
}
/*-
* ============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.
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"));
}
// Get the context object
- AnomalyDetection anomalyDetection =
+ var anomalyDetection =
(AnomalyDetection) executor.getContextAlbum(ANOMALY_DETECTION_ALBUM).get(ANOMALY_DETECTION);
if (anomalyDetection == null) {
anomalyDetection = new AnomalyDetection();
anomalyDetection.init(FREQUENCY);
}
- boolean unsetfirstround = false;
+ var unsetfirstround = false;
int frequency = anomalyDetection.getFrequency();
frequency = frequency + 1;
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);
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;
* @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;
* @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;
* @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);
}
* ============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.
* @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();
}
// 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();
}
*/
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;
}
Code Review / policy / apex-pdp.git / commitdiff