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-rw-r--r--examples/examples-adaptive/pom.xml102
-rw-r--r--examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AnomalyDetection.java243
-rw-r--r--examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AutoLearn.java198
-rw-r--r--examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/package-info.java27
-rw-r--r--examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelFactory.java558
-rw-r--r--examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelSaver.java64
-rw-r--r--examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java414
-rw-r--r--examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicy_Decide_TaskSelectionLogic.java151
-rw-r--r--examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/package-info.java27
-rw-r--r--examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/package-info.java27
-rw-r--r--examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AnomalyDetectionEvents.json200
-rw-r--r--examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AutoLearnEvents.json10000
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask0_TaskLogic.mvel40
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask1_TaskLogic.mvel40
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask2_TaskLogic.mvel40
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask3_TaskLogic.mvel40
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask4_TaskLogic.mvel40
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask5_TaskLogic.mvel40
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask6_TaskLogic.mvel40
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAnomalyDetectionTask_Logic.mvel25
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAutoLearnTask_Logic.mvel26
-rw-r--r--examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultState_Logic.mvel23
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDBWrite.java60
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java81
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java56
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTSLUseCase.java164
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestApexActionListener.java91
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDBWrite.java60
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java81
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java56
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTSLUseCase.java187
-rw-r--r--examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/package-info.java27
-rw-r--r--examples/examples-adaptive/src/test/resources/META-INF/persistence.xml69
33 files changed, 13297 insertions, 0 deletions
diff --git a/examples/examples-adaptive/pom.xml b/examples/examples-adaptive/pom.xml
new file mode 100644
index 000000000..66f991e88
--- /dev/null
+++ b/examples/examples-adaptive/pom.xml
@@ -0,0 +1,102 @@
+<!--
+ ============LICENSE_START=======================================================
+ Copyright (C) 2018 Ericsson. 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.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+
+ SPDX-License-Identifier: Apache-2.0
+ ============LICENSE_END=========================================================
+-->
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+ xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+ <modelVersion>4.0.0</modelVersion>
+ <parent>
+ <groupId>org.onap.policy.apex-pdp.examples</groupId>
+ <artifactId>examples</artifactId>
+ <version>2.0.0-SNAPSHOT</version>
+ </parent>
+
+ <artifactId>examples-adaptive</artifactId>
+ <name>${project.artifactId}</name>
+ <description>Specific code for the Apex Adaptive Example</description>
+
+ <dependencies>
+ <dependency>
+ <groupId>org.onap.policy.apex-pdp.model</groupId>
+ <artifactId>policy-model</artifactId>
+ <version>${project.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.onap.policy.apex-pdp.core</groupId>
+ <artifactId>core-engine</artifactId>
+ <version>${project.version}</version>
+ </dependency>
+ <dependency>
+ <groupId>org.onap.policy.apex-pdp.core</groupId>
+ <artifactId>core-infrastructure</artifactId>
+ <version>${project.version}</version>
+ <scope>test</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.onap.policy.apex-pdp.plugins.plugins-executor</groupId>
+ <artifactId>plugins-executor-mvel</artifactId>
+ <version>${project.version}</version>
+ <scope>test</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.onap.policy.apex-pdp.plugins.plugins-executor</groupId>
+ <artifactId>plugins-executor-java</artifactId>
+ <version>${project.version}</version>
+ <scope>test</scope>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.commons</groupId>
+ <artifactId>commons-math3</artifactId>
+ <version>3.2</version>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.derby</groupId>
+ <artifactId>derby</artifactId>
+ <version>${version.derby}</version>
+ <scope>test</scope>
+ </dependency>
+ </dependencies>
+
+ <build>
+ <plugins>
+ <plugin>
+ <groupId>org.codehaus.mojo</groupId>
+ <artifactId>exec-maven-plugin</artifactId>
+ <executions>
+ <execution>
+ <id>generate-models</id>
+ <phase>process-classes</phase>
+ <goals>
+ <goal>exec</goal>
+ </goals>
+ <configuration>
+ <executable>java</executable>
+ <arguments>
+ <argument>-classpath</argument>
+ <!-- automatically creates the classpath using all project dependencies, also adding the project build directory -->
+ <classpath />
+ <argument>org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelSaver</argument>
+ <argument>${project.build.directory}/classes/examples/models/Adaptive</argument>
+ </arguments>
+ </configuration>
+ </execution>
+ </executions>
+ </plugin>
+ </plugins>
+ </build>
+</project> \ No newline at end of file
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
new file mode 100644
index 000000000..f574c74ec
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AnomalyDetection.java
@@ -0,0 +1,243 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.concepts;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.LinkedList;
+import java.util.List;
+
+/**
+ * The Class AnomalyDetection is used as a Java context for Adaptive anomaly detection in the adaptive domain.
+ */
+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;
+
+ /**
+ * The Constructor creates an AnomalyDetection instance.
+ */
+ public AnomalyDetection() {
+ firstRound = true;
+ frequency = 0;
+ }
+
+ private List<Double> anomalyScores = new LinkedList<>();
+ private List<Double> frequencyForecasted;
+
+ /**
+ * Checks if the AnomalyDetection instance is initialized.
+ *
+ * @return true, if the AnomalyDetection instance is initialized
+ */
+ public boolean isInitialized() {
+ return (frequencyForecasted != null);
+ }
+
+ /**
+ * Initializes the AnomalyDetection instance.
+ *
+ * @param incomingFrequency the frequency
+ */
+ public void init(final int incomingFrequency) {
+ frequencyForecasted = new ArrayList<>(incomingFrequency);
+ for (int i = 0; i < incomingFrequency; i++) {
+ frequencyForecasted.add(null);
+ }
+ }
+
+ /**
+ * 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 LinkedList<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
+ */
+ public boolean checkSetAnomalyScores() {
+ return ((anomalyScores != null) && (!anomalyScores.isEmpty()));
+ }
+
+ /**
+ * Unset the anomaly score values of the algorithm.
+ */
+ public void unsetAnomalyScores() {
+ anomalyScores = null;
+ }
+
+ /**
+ * 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
+ */
+ public boolean checkSetFrequencyForecasted() {
+ return ((frequencyForecasted != null) && (!frequencyForecasted.isEmpty()));
+ }
+
+ /**
+ * Unset the frequency forecasted by the algorithm.
+ */
+ public void unsetFrequencyForecasted() {
+ frequencyForecasted = null;
+ }
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see java.lang.Object#toString()
+ */
+ @Override
+ public String toString() {
+ return "AnomalyDetection [firstRound=" + firstRound + ", frequency=" + frequency + ", anomalyScores="
+ + anomalyScores + ", frequencyForecasted=" + frequencyForecasted + "]";
+ }
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see java.lang.Object#hashCode()
+ */
+ @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;
+ }
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see java.lang.Object#equals(java.lang.Object)
+ */
+ @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
new file mode 100644
index 000000000..f2e27725b
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/AutoLearn.java
@@ -0,0 +1,198 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.concepts;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * The Class AutoLearn is used as a Java context for Adaptive auto-learning of trends towards a fixed value in the
+ * adaptive domain.
+ */
+public class AutoLearn implements Serializable {
+ private static final long serialVersionUID = 3825970380434170754L;
+
+ private List<Double> avDiffs = null;
+
+ private List<Long> counts = null;
+
+ /**
+ * The Constructor creates an AutoLearn concept.
+ */
+ public AutoLearn() {}
+
+ /**
+ * Checks if the Autolearn instance is initialized.
+ *
+ * @return true, if the Autolearn instance is initialized
+ */
+ public boolean isInitialized() {
+ return (avDiffs != null && counts != null);
+ }
+
+ /**
+ * initializes the auto learning algorithm with the number of convergent variables to use.
+ *
+ * @param size the number of convergent variables to use
+ */
+ public void init(final int size) {
+ if (avDiffs == null || avDiffs.size() == 0) {
+ avDiffs = new ArrayList<>(size);
+ for (int i = 0; i < size; i++) {
+ avDiffs.add(i, Double.NaN);
+ }
+ }
+
+ if (counts == null || counts.size() == 0) {
+ counts = new ArrayList<>(size);
+ for (int i = 0; i < size; i++) {
+ counts.add(i, 0L);
+ }
+ }
+ }
+
+ /**
+ * 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
+ */
+ public boolean checkSetAvDiffs() {
+ return ((avDiffs != null) && (!avDiffs.isEmpty()));
+ }
+
+ /**
+ * Unset the average difference values of the algorithm.
+ */
+ public void unsetAvDiffs() {
+ avDiffs = null;
+ }
+
+ /**
+ * 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
+ */
+ public boolean checkSetCounts() {
+ return ((counts != null) && (!counts.isEmpty()));
+ }
+
+ /**
+ * Unset the count values of the algorithm.
+ */
+ public void unsetCounts() {
+ counts = null;
+ }
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see java.lang.Object#toString()
+ */
+ @Override
+ public String toString() {
+ return "AutoLearn [avDiffs=" + avDiffs + ", counts=" + counts + "]";
+ }
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see java.lang.Object#hashCode()
+ */
+ @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;
+ }
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see java.lang.Object#equals(java.lang.Object)
+ */
+ @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/concepts/package-info.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/package-info.java
new file mode 100644
index 000000000..8561ed074
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/concepts/package-info.java
@@ -0,0 +1,27 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+/**
+ * Provides Java types that are used in the Adaptive domain.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+
+package org.onap.policy.apex.examples.adaptive.concepts;
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelFactory.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelFactory.java
new file mode 100644
index 000000000..6242b1618
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelFactory.java
@@ -0,0 +1,558 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.model;
+
+import org.onap.policy.apex.model.basicmodel.concepts.ApexRuntimeException;
+import org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey;
+import org.onap.policy.apex.model.basicmodel.concepts.AxKeyInformation;
+import org.onap.policy.apex.model.basicmodel.concepts.AxReferenceKey;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.contextmodel.concepts.AxContextAlbum;
+import org.onap.policy.apex.model.contextmodel.concepts.AxContextAlbums;
+import org.onap.policy.apex.model.contextmodel.concepts.AxContextSchema;
+import org.onap.policy.apex.model.contextmodel.concepts.AxContextSchemas;
+import org.onap.policy.apex.model.eventmodel.concepts.AxEvent;
+import org.onap.policy.apex.model.eventmodel.concepts.AxEvents;
+import org.onap.policy.apex.model.eventmodel.concepts.AxField;
+import org.onap.policy.apex.model.policymodel.concepts.AxLogicReader;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicies;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicy;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxState;
+import org.onap.policy.apex.model.policymodel.concepts.AxStateOutput;
+import org.onap.policy.apex.model.policymodel.concepts.AxStateTaskOutputType;
+import org.onap.policy.apex.model.policymodel.concepts.AxStateTaskReference;
+import org.onap.policy.apex.model.policymodel.concepts.AxTask;
+import org.onap.policy.apex.model.policymodel.concepts.AxTaskLogic;
+import org.onap.policy.apex.model.policymodel.concepts.AxTaskSelectionLogic;
+import org.onap.policy.apex.model.policymodel.concepts.AxTasks;
+import org.onap.policy.apex.model.policymodel.handling.PolicyLogicReader;
+
+/**
+ * The Class AdaptiveDomainModelFactory.
+ */
+public class AdaptiveDomainModelFactory {
+
+ /**
+ * Gets the anomaly detection policy model.
+ *
+ * @return the anomaly detection policy model
+ */
+ // CHECKSTYLE:OFF: checkstyle:maximumMethodLength
+ public AxPolicyModel getAnomalyDetectionPolicyModel() {
+ // CHECKSTYLE:ON: checkstyle:maximumMethodLength
+ // Data types for event parameters
+ final AxContextSchema monitoredValue =
+ new AxContextSchema(new AxArtifactKey("MonitoredValue", "0.0.1"), "Java", "java.lang.Double");
+ final AxContextSchema iteration =
+ new AxContextSchema(new AxArtifactKey("Iteration", "0.0.1"), "Java", "java.lang.Integer");
+
+ final AxContextSchemas adContextSchemas = new AxContextSchemas(new AxArtifactKey("AADMDatatypes", "0.0.1"));
+ adContextSchemas.getSchemasMap().put(monitoredValue.getKey(), monitoredValue);
+ adContextSchemas.getSchemasMap().put(iteration.getKey(), iteration);
+
+ final AxEvent anomalyDetectionTriggerEvent =
+ new AxEvent(new AxArtifactKey("AnomalyDetectionTriggerEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ anomalyDetectionTriggerEvent.setSource("External");
+ anomalyDetectionTriggerEvent.setTarget("Match");
+ anomalyDetectionTriggerEvent.getParameterMap().put("MonitoredValue", new AxField(
+ new AxReferenceKey(anomalyDetectionTriggerEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+ anomalyDetectionTriggerEvent.getParameterMap().put("Iteration", new AxField(
+ new AxReferenceKey(anomalyDetectionTriggerEvent.getKey(), "Iteration"), iteration.getKey()));
+
+ final AxEvent anomalyDetectionMatchEvent = new AxEvent(new AxArtifactKey("AnomalyDetectionMatchEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ anomalyDetectionMatchEvent.setSource("Match");
+ anomalyDetectionMatchEvent.setTarget("Establish");
+ anomalyDetectionMatchEvent.getParameterMap().put("MonitoredValue", new AxField(
+ new AxReferenceKey(anomalyDetectionMatchEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+ anomalyDetectionMatchEvent.getParameterMap().put("Iteration",
+ new AxField(new AxReferenceKey(anomalyDetectionMatchEvent.getKey(), "Iteration"), iteration.getKey()));
+
+ final AxEvent anomalyDetectionEstablishEvent =
+ new AxEvent(new AxArtifactKey("AnomalyDetectionEstablishEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ anomalyDetectionEstablishEvent.setSource("Establish");
+ anomalyDetectionEstablishEvent.setTarget("Decide");
+ anomalyDetectionEstablishEvent.getParameterMap().put("MonitoredValue",
+ new AxField(new AxReferenceKey(anomalyDetectionEstablishEvent.getKey(), "MonitoredValue"),
+ monitoredValue.getKey()));
+ anomalyDetectionEstablishEvent.getParameterMap().put("Iteration", new AxField(
+ new AxReferenceKey(anomalyDetectionEstablishEvent.getKey(), "Iteration"), iteration.getKey()));
+
+ final AxEvent anomalyDetectionDecideEvent =
+ new AxEvent(new AxArtifactKey("AnomalyDetectionDecideEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ anomalyDetectionDecideEvent.setSource("Decide");
+ anomalyDetectionDecideEvent.setTarget("Act");
+ anomalyDetectionDecideEvent.getParameterMap().put("MonitoredValue", new AxField(
+ new AxReferenceKey(anomalyDetectionDecideEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+ anomalyDetectionDecideEvent.getParameterMap().put("Iteration",
+ new AxField(new AxReferenceKey(anomalyDetectionDecideEvent.getKey(), "Iteration"), iteration.getKey()));
+
+ final AxEvent anomalyDetectionActEvent = new AxEvent(new AxArtifactKey("AnomalyDetectionActEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ anomalyDetectionActEvent.setSource("Act");
+ anomalyDetectionActEvent.setTarget("External");
+ anomalyDetectionActEvent.getParameterMap().put("MonitoredValue", new AxField(
+ new AxReferenceKey(anomalyDetectionActEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+ anomalyDetectionActEvent.getParameterMap().put("Iteration",
+ new AxField(new AxReferenceKey(anomalyDetectionActEvent.getKey(), "Iteration"), iteration.getKey()));
+
+ final AxEvents anomalyDetectionEvents = new AxEvents(new AxArtifactKey("AnomalyDetectionEvents", "0.0.1"));
+ anomalyDetectionEvents.getEventMap().put(anomalyDetectionTriggerEvent.getKey(), anomalyDetectionTriggerEvent);
+ anomalyDetectionEvents.getEventMap().put(anomalyDetectionMatchEvent.getKey(), anomalyDetectionMatchEvent);
+ anomalyDetectionEvents.getEventMap().put(anomalyDetectionEstablishEvent.getKey(),
+ anomalyDetectionEstablishEvent);
+ anomalyDetectionEvents.getEventMap().put(anomalyDetectionDecideEvent.getKey(), anomalyDetectionDecideEvent);
+ anomalyDetectionEvents.getEventMap().put(anomalyDetectionActEvent.getKey(), anomalyDetectionActEvent);
+
+ // Data types for context
+ final AxContextSchema anomalyDetection = new AxContextSchema(new AxArtifactKey("AnomalyDetection", "0.0.1"),
+ "Java", "org.onap.policy.apex.examples.adaptive.concepts.AnomalyDetection");
+ adContextSchemas.getSchemasMap().put(anomalyDetection.getKey(), anomalyDetection);
+
+ // One context map
+ final AxContextAlbum anomalyDetectionAlbum = new AxContextAlbum(
+ new AxArtifactKey("AnomalyDetectionAlbum", "0.0.1"), "APPLICATION", true, anomalyDetection.getKey());
+ final AxContextAlbums anomalyDetectionAlbums =
+ new AxContextAlbums(new AxArtifactKey("AnomalyDetectionAlbums", "0.0.1"));
+ anomalyDetectionAlbums.getAlbumsMap().put(anomalyDetectionAlbum.getKey(), anomalyDetectionAlbum);
+
+ // Tasks
+ final AxLogicReader logicReader =
+ new PolicyLogicReader().setLogicPackage(this.getClass().getPackage().getName())
+ .setDefaultLogic("DefaultAnomalyDetectionTask_Logic");
+
+ final AxTask anomalyDetectionMatchTask = new AxTask(new AxArtifactKey("AnomalyDetectionMatchTask", "0.0.1"));
+ anomalyDetectionMatchTask.duplicateInputFields(anomalyDetectionTriggerEvent.getParameterMap());
+ anomalyDetectionMatchTask.duplicateOutputFields(anomalyDetectionMatchEvent.getParameterMap());
+ anomalyDetectionMatchTask
+ .setTaskLogic(new AxTaskLogic(anomalyDetectionMatchTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask anomalyDetectionEstablishTask =
+ new AxTask(new AxArtifactKey("AnomalyDetectionEstablishTask", "0.0.1"));
+ anomalyDetectionEstablishTask.duplicateInputFields(anomalyDetectionMatchEvent.getParameterMap());
+ anomalyDetectionEstablishTask.duplicateOutputFields(anomalyDetectionEstablishEvent.getParameterMap());
+ anomalyDetectionEstablishTask.setTaskLogic(
+ new AxTaskLogic(anomalyDetectionEstablishTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask anomalyDetectionDecideTask0 =
+ new AxTask(new AxArtifactKey("AnomalyDetectionDecideTask0", "0.0.1"));
+ anomalyDetectionDecideTask0.duplicateInputFields(anomalyDetectionEstablishEvent.getParameterMap());
+ anomalyDetectionDecideTask0.duplicateOutputFields(anomalyDetectionDecideEvent.getParameterMap());
+ anomalyDetectionDecideTask0
+ .setTaskLogic(new AxTaskLogic(anomalyDetectionDecideTask0.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask anomalyDetectionDecideTask1 =
+ new AxTask(new AxArtifactKey("AnomalyDetectionDecideTask1", "0.0.1"));
+ anomalyDetectionDecideTask1.duplicateInputFields(anomalyDetectionEstablishEvent.getParameterMap());
+ anomalyDetectionDecideTask1.duplicateOutputFields(anomalyDetectionDecideEvent.getParameterMap());
+ anomalyDetectionDecideTask1
+ .setTaskLogic(new AxTaskLogic(anomalyDetectionDecideTask1.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask anomalyDetectionDecideTask2 =
+ new AxTask(new AxArtifactKey("AnomalyDetectionDecideTask2", "0.0.1"));
+ anomalyDetectionDecideTask2.duplicateInputFields(anomalyDetectionEstablishEvent.getParameterMap());
+ anomalyDetectionDecideTask2.duplicateOutputFields(anomalyDetectionDecideEvent.getParameterMap());
+ anomalyDetectionDecideTask2
+ .setTaskLogic(new AxTaskLogic(anomalyDetectionDecideTask2.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask anomalyDetectionActTask = new AxTask(new AxArtifactKey("AnomalyDetectionActTask", "0.0.1"));
+ anomalyDetectionActTask.duplicateInputFields(anomalyDetectionDecideEvent.getParameterMap());
+ anomalyDetectionActTask.duplicateOutputFields(anomalyDetectionActEvent.getParameterMap());
+ anomalyDetectionActTask
+ .setTaskLogic(new AxTaskLogic(anomalyDetectionActTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTasks anomalyDetectionTasks = new AxTasks(new AxArtifactKey("AnomalyDetectionTasks", "0.0.1"));
+ anomalyDetectionTasks.getTaskMap().put(anomalyDetectionMatchTask.getKey(), anomalyDetectionMatchTask);
+ anomalyDetectionTasks.getTaskMap().put(anomalyDetectionEstablishTask.getKey(), anomalyDetectionEstablishTask);
+ anomalyDetectionTasks.getTaskMap().put(anomalyDetectionDecideTask0.getKey(), anomalyDetectionDecideTask0);
+ anomalyDetectionTasks.getTaskMap().put(anomalyDetectionDecideTask1.getKey(), anomalyDetectionDecideTask1);
+ anomalyDetectionTasks.getTaskMap().put(anomalyDetectionDecideTask2.getKey(), anomalyDetectionDecideTask2);
+ anomalyDetectionTasks.getTaskMap().put(anomalyDetectionActTask.getKey(), anomalyDetectionActTask);
+
+ // Policies
+ logicReader.setDefaultLogic("DefaultState_Logic");
+
+ final AxPolicy anomalyDetectionPolicy = new AxPolicy(new AxArtifactKey("AnomalyDetectionPolicy", "0.0.1"));
+ anomalyDetectionPolicy.setTemplate("MEDA");
+
+ final AxState anomalyDetectionActState =
+ new AxState(new AxReferenceKey(anomalyDetectionPolicy.getKey(), "Act"));
+ anomalyDetectionActState.setTrigger(anomalyDetectionDecideEvent.getKey());
+ final AxStateOutput adAct2Out = new AxStateOutput(anomalyDetectionActState.getKey(),
+ AxReferenceKey.getNullKey(), anomalyDetectionActEvent.getKey());
+ anomalyDetectionActState.getStateOutputs().put(adAct2Out.getKey().getLocalName(), adAct2Out);
+ anomalyDetectionActState.setTaskSelectionLogic(
+ new AxTaskSelectionLogic(anomalyDetectionActState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+ anomalyDetectionActState.setDefaultTask(anomalyDetectionActTask.getKey());
+ anomalyDetectionActState.getTaskReferences().put(anomalyDetectionActTask.getKey(),
+ new AxStateTaskReference(anomalyDetectionActState.getKey(), anomalyDetectionActTask.getKey(),
+ AxStateTaskOutputType.DIRECT, adAct2Out.getKey()));
+
+ logicReader.setDefaultLogic(null);
+
+ final AxState anomalyDetectionDecideState =
+ new AxState(new AxReferenceKey(anomalyDetectionPolicy.getKey(), "Decide"));
+ anomalyDetectionDecideState.setTrigger(anomalyDetectionEstablishEvent.getKey());
+ final AxStateOutput adDec2Act = new AxStateOutput(anomalyDetectionDecideState.getKey(),
+ anomalyDetectionActState.getKey(), anomalyDetectionDecideEvent.getKey());
+ anomalyDetectionDecideState.getStateOutputs().put(adDec2Act.getKey().getLocalName(), adDec2Act);
+ anomalyDetectionDecideState.setTaskSelectionLogic(new AxTaskSelectionLogic(anomalyDetectionDecideState.getKey(),
+ "TaskSelectionLogic", "JAVA", logicReader));
+ anomalyDetectionDecideState.setDefaultTask(anomalyDetectionDecideTask0.getKey());
+ anomalyDetectionDecideState.getContextAlbumReferences().add(anomalyDetectionAlbum.getKey());
+ anomalyDetectionDecideState.getTaskReferences().put(anomalyDetectionDecideTask0.getKey(),
+ new AxStateTaskReference(anomalyDetectionDecideState.getKey(), anomalyDetectionDecideTask0.getKey(),
+ AxStateTaskOutputType.DIRECT, adDec2Act.getKey()));
+ anomalyDetectionDecideState.getTaskReferences().put(anomalyDetectionDecideTask1.getKey(),
+ new AxStateTaskReference(anomalyDetectionDecideState.getKey(), anomalyDetectionDecideTask1.getKey(),
+ AxStateTaskOutputType.DIRECT, adDec2Act.getKey()));
+ anomalyDetectionDecideState.getTaskReferences().put(anomalyDetectionDecideTask2.getKey(),
+ new AxStateTaskReference(anomalyDetectionDecideState.getKey(), anomalyDetectionDecideTask2.getKey(),
+ AxStateTaskOutputType.DIRECT, adDec2Act.getKey()));
+
+ logicReader.setDefaultLogic("DefaultState_Logic");
+
+ final AxState anomalyDetectionEstablishState =
+ new AxState(new AxReferenceKey(anomalyDetectionPolicy.getKey(), "Establish"));
+ anomalyDetectionEstablishState.setTrigger(anomalyDetectionMatchEvent.getKey());
+ final AxStateOutput adEst2Dec = new AxStateOutput(anomalyDetectionEstablishState.getKey(),
+ anomalyDetectionDecideState.getKey(), anomalyDetectionEstablishEvent.getKey());
+ anomalyDetectionEstablishState.getStateOutputs().put(adEst2Dec.getKey().getLocalName(), adEst2Dec);
+ anomalyDetectionEstablishState.setTaskSelectionLogic(new AxTaskSelectionLogic(
+ anomalyDetectionEstablishState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+ anomalyDetectionEstablishState.setDefaultTask(anomalyDetectionEstablishTask.getKey());
+ anomalyDetectionEstablishState.getTaskReferences().put(anomalyDetectionEstablishTask.getKey(),
+ new AxStateTaskReference(anomalyDetectionEstablishState.getKey(),
+ anomalyDetectionEstablishTask.getKey(), AxStateTaskOutputType.DIRECT, adEst2Dec.getKey()));
+
+ final AxState anomalyDetectionMatchState =
+ new AxState(new AxReferenceKey(anomalyDetectionPolicy.getKey(), "Match"));
+ anomalyDetectionMatchState.setTrigger(anomalyDetectionTriggerEvent.getKey());
+ final AxStateOutput adMat2Est = new AxStateOutput(anomalyDetectionMatchState.getKey(),
+ anomalyDetectionEstablishState.getKey(), anomalyDetectionMatchEvent.getKey());
+ anomalyDetectionMatchState.getStateOutputs().put(adMat2Est.getKey().getLocalName(), adMat2Est);
+ anomalyDetectionMatchState.setTaskSelectionLogic(new AxTaskSelectionLogic(anomalyDetectionMatchState.getKey(),
+ "TaskSelectionLogic", "MVEL", logicReader));
+ anomalyDetectionMatchState.setDefaultTask(anomalyDetectionMatchTask.getKey());
+ anomalyDetectionMatchState.getTaskReferences().put(anomalyDetectionMatchTask.getKey(),
+ new AxStateTaskReference(anomalyDetectionMatchState.getKey(), anomalyDetectionMatchTask.getKey(),
+ AxStateTaskOutputType.DIRECT, adMat2Est.getKey()));
+
+ anomalyDetectionPolicy.setFirstState(anomalyDetectionMatchState.getKey().getLocalName());
+ anomalyDetectionPolicy.getStateMap().put(anomalyDetectionMatchState.getKey().getLocalName(),
+ anomalyDetectionMatchState);
+ anomalyDetectionPolicy.getStateMap().put(anomalyDetectionEstablishState.getKey().getLocalName(),
+ anomalyDetectionEstablishState);
+ anomalyDetectionPolicy.getStateMap().put(anomalyDetectionDecideState.getKey().getLocalName(),
+ anomalyDetectionDecideState);
+ anomalyDetectionPolicy.getStateMap().put(anomalyDetectionActState.getKey().getLocalName(),
+ anomalyDetectionActState);
+
+ final AxPolicies anomalyDetectionPolicies =
+ new AxPolicies(new AxArtifactKey("AnomalyDetectionPolicies", "0.0.1"));
+ anomalyDetectionPolicies.getPolicyMap().put(anomalyDetectionPolicy.getKey(), anomalyDetectionPolicy);
+
+ final AxKeyInformation keyInformation =
+ new AxKeyInformation(new AxArtifactKey("AnomalyDetectionKeyInformation", "0.0.1"));
+ final AxPolicyModel anomalyDetectionPolicyModel =
+ new AxPolicyModel(new AxArtifactKey("AnomalyDetectionPolicyModel", "0.0.1"));
+ anomalyDetectionPolicyModel.setPolicies(anomalyDetectionPolicies);
+ anomalyDetectionPolicyModel.setEvents(anomalyDetectionEvents);
+ anomalyDetectionPolicyModel.setTasks(anomalyDetectionTasks);
+ anomalyDetectionPolicyModel.setAlbums(anomalyDetectionAlbums);
+ anomalyDetectionPolicyModel.setSchemas(adContextSchemas);
+ anomalyDetectionPolicyModel.setKeyInformation(keyInformation);
+ anomalyDetectionPolicyModel.getKeyInformation().generateKeyInfo(anomalyDetectionPolicyModel);
+
+ final AxValidationResult result = anomalyDetectionPolicyModel.validate(new AxValidationResult());
+ if (!result.getValidationResult().equals(AxValidationResult.ValidationResult.VALID)) {
+ throw new ApexRuntimeException("model " + anomalyDetectionPolicyModel.getID() + " is not valid" + result);
+ }
+ return anomalyDetectionPolicyModel;
+ }
+
+ /**
+ * Gets the auto learn policy model.
+ *
+ * @return the auto learn policy model
+ */
+ // CHECKSTYLE:OFF: checkstyle:maximumMethodLength
+ public AxPolicyModel getAutoLearnPolicyModel() {
+ // CHECKSTYLE:ON: checkstyle:maximumMethodLength
+ // Data types for event parameters
+ final AxContextSchema monitoredValue =
+ new AxContextSchema(new AxArtifactKey("MonitoredValue", "0.0.1"), "Java", "java.lang.Double");
+
+ final AxContextSchemas alContextSchemas = new AxContextSchemas(new AxArtifactKey("ALDatatypes", "0.0.1"));
+ alContextSchemas.getSchemasMap().put(monitoredValue.getKey(), monitoredValue);
+
+ final AxEvent autoLearnTriggerEvent = new AxEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ autoLearnTriggerEvent.setSource("External");
+ autoLearnTriggerEvent.setTarget("Match");
+ autoLearnTriggerEvent.getParameterMap().put("MonitoredValue", new AxField(
+ new AxReferenceKey(autoLearnTriggerEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+ autoLearnTriggerEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+ new AxReferenceKey(autoLearnTriggerEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+ final AxEvent autoLearnMatchEvent = new AxEvent(new AxArtifactKey("AutoLearnMatchEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ autoLearnMatchEvent.setSource("Match");
+ autoLearnMatchEvent.setTarget("Establish");
+ autoLearnMatchEvent.getParameterMap().put("MonitoredValue", new AxField(
+ new AxReferenceKey(autoLearnMatchEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+ autoLearnMatchEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+ new AxReferenceKey(autoLearnMatchEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+ final AxEvent autoLearnEstablishEvent = new AxEvent(new AxArtifactKey("AutoLearnEstablishEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ autoLearnEstablishEvent.setSource("Establish");
+ autoLearnEstablishEvent.setTarget("Decide");
+ autoLearnEstablishEvent.getParameterMap().put("MonitoredValue", new AxField(
+ new AxReferenceKey(autoLearnEstablishEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+ autoLearnEstablishEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+ new AxReferenceKey(autoLearnEstablishEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+ final AxEvent autoLearnDecideEvent = new AxEvent(new AxArtifactKey("AutoLearnDecideEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ autoLearnDecideEvent.setSource("Decide");
+ autoLearnDecideEvent.setTarget("Act");
+ autoLearnDecideEvent.getParameterMap().put("MonitoredValue", new AxField(
+ new AxReferenceKey(autoLearnDecideEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+ autoLearnDecideEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+ new AxReferenceKey(autoLearnDecideEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+ final AxEvent autoLearnActEvent = new AxEvent(new AxArtifactKey("AutoLearnActEvent", "0.0.1"),
+ "org.onap.policy.apex.examples.adaptive.events");
+ autoLearnActEvent.setSource("Act");
+ autoLearnActEvent.setTarget("External");
+ autoLearnActEvent.getParameterMap().put("MonitoredValue",
+ new AxField(new AxReferenceKey(autoLearnActEvent.getKey(), "MonitoredValue"), monitoredValue.getKey()));
+ autoLearnActEvent.getParameterMap().put("LastMonitoredValue", new AxField(
+ new AxReferenceKey(autoLearnActEvent.getKey(), "LastMonitoredValue"), monitoredValue.getKey()));
+
+ final AxEvents autoLearnEvents = new AxEvents(new AxArtifactKey("AutoLearnEvents", "0.0.1"));
+ autoLearnEvents.getEventMap().put(autoLearnTriggerEvent.getKey(), autoLearnTriggerEvent);
+ autoLearnEvents.getEventMap().put(autoLearnMatchEvent.getKey(), autoLearnMatchEvent);
+ autoLearnEvents.getEventMap().put(autoLearnEstablishEvent.getKey(), autoLearnEstablishEvent);
+ autoLearnEvents.getEventMap().put(autoLearnDecideEvent.getKey(), autoLearnDecideEvent);
+ autoLearnEvents.getEventMap().put(autoLearnActEvent.getKey(), autoLearnActEvent);
+
+ // Data types for context
+ final AxContextSchema autoLearn = new AxContextSchema(new AxArtifactKey("AutoLearn", "0.0.1"), "Java",
+ "org.onap.policy.apex.examples.adaptive.concepts.AutoLearn");
+ alContextSchemas.getSchemasMap().put(autoLearn.getKey(), autoLearn);
+
+ // One context map
+ final AxContextAlbum autoLearnAlbum = new AxContextAlbum(new AxArtifactKey("AutoLearnAlbum", "0.0.1"),
+ "APPLICATION", true, autoLearn.getKey());
+
+ final AxContextAlbums autoLearnAlbums = new AxContextAlbums(new AxArtifactKey("AutoLearnContext", "0.0.1"));
+ autoLearnAlbums.getAlbumsMap().put(autoLearnAlbum.getKey(), autoLearnAlbum);
+
+ // Tasks
+ final AxLogicReader logicReader = new PolicyLogicReader()
+ .setLogicPackage(this.getClass().getPackage().getName()).setDefaultLogic("DefaultAutoLearnTask_Logic");
+
+ final AxTask autoLearnMatchTask = new AxTask(new AxArtifactKey("AutoLearnMatchTask", "0.0.1"));
+ autoLearnMatchTask.duplicateInputFields(autoLearnTriggerEvent.getParameterMap());
+ autoLearnMatchTask.duplicateOutputFields(autoLearnMatchEvent.getParameterMap());
+ autoLearnMatchTask.setTaskLogic(new AxTaskLogic(autoLearnMatchTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask autoLearnEstablishTask = new AxTask(new AxArtifactKey("AutoLearnEstablishTask", "0.0.1"));
+ autoLearnEstablishTask.duplicateInputFields(autoLearnMatchEvent.getParameterMap());
+ autoLearnEstablishTask.duplicateOutputFields(autoLearnEstablishEvent.getParameterMap());
+ autoLearnEstablishTask
+ .setTaskLogic(new AxTaskLogic(autoLearnEstablishTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ logicReader.setDefaultLogic(null);
+
+ final AxTask autoLearnDecideTask0 = new AxTask(new AxArtifactKey("AutoLearnDecideTask0", "0.0.1"));
+ autoLearnDecideTask0.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+ autoLearnDecideTask0.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+ autoLearnDecideTask0
+ .setTaskLogic(new AxTaskLogic(autoLearnDecideTask0.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask autoLearnDecideTask1 = new AxTask(new AxArtifactKey("AutoLearnDecideTask1", "0.0.1"));
+ autoLearnDecideTask1.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+ autoLearnDecideTask1.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+ autoLearnDecideTask1
+ .setTaskLogic(new AxTaskLogic(autoLearnDecideTask1.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask autoLearnDecideTask2 = new AxTask(new AxArtifactKey("AutoLearnDecideTask2", "0.0.1"));
+ autoLearnDecideTask2.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+ autoLearnDecideTask2.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+ autoLearnDecideTask2
+ .setTaskLogic(new AxTaskLogic(autoLearnDecideTask2.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask autoLearnDecideTask3 = new AxTask(new AxArtifactKey("AutoLearnDecideTask3", "0.0.1"));
+ autoLearnDecideTask3.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+ autoLearnDecideTask3.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+ autoLearnDecideTask3
+ .setTaskLogic(new AxTaskLogic(autoLearnDecideTask3.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask autoLearnDecideTask4 = new AxTask(new AxArtifactKey("AutoLearnDecideTask4", "0.0.1"));
+ autoLearnDecideTask4.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+ autoLearnDecideTask4.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+ autoLearnDecideTask4
+ .setTaskLogic(new AxTaskLogic(autoLearnDecideTask4.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask autoLearnDecideTask5 = new AxTask(new AxArtifactKey("AutoLearnDecideTask5", "0.0.1"));
+ autoLearnDecideTask5.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+ autoLearnDecideTask5.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+ autoLearnDecideTask5
+ .setTaskLogic(new AxTaskLogic(autoLearnDecideTask5.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTask autoLearnDecideTask6 = new AxTask(new AxArtifactKey("AutoLearnDecideTask6", "0.0.1"));
+ autoLearnDecideTask6.duplicateInputFields(autoLearnEstablishEvent.getParameterMap());
+ autoLearnDecideTask6.duplicateOutputFields(autoLearnDecideEvent.getParameterMap());
+ autoLearnDecideTask6
+ .setTaskLogic(new AxTaskLogic(autoLearnDecideTask6.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ logicReader.setDefaultLogic("DefaultAutoLearnTask_Logic");
+
+ final AxTask autoLearnActTask = new AxTask(new AxArtifactKey("AutoLearnActTask", "0.0.1"));
+ autoLearnActTask.duplicateInputFields(autoLearnDecideEvent.getParameterMap());
+ autoLearnActTask.duplicateOutputFields(autoLearnActEvent.getParameterMap());
+ autoLearnActTask.setTaskLogic(new AxTaskLogic(autoLearnActTask.getKey(), "TaskLogic", "MVEL", logicReader));
+
+ final AxTasks autoLearnTasks = new AxTasks(new AxArtifactKey("AutoLearnTasks", "0.0.1"));
+ autoLearnTasks.getTaskMap().put(autoLearnMatchTask.getKey(), autoLearnMatchTask);
+ autoLearnTasks.getTaskMap().put(autoLearnEstablishTask.getKey(), autoLearnEstablishTask);
+ autoLearnTasks.getTaskMap().put(autoLearnDecideTask0.getKey(), autoLearnDecideTask0);
+ autoLearnTasks.getTaskMap().put(autoLearnDecideTask1.getKey(), autoLearnDecideTask1);
+ autoLearnTasks.getTaskMap().put(autoLearnDecideTask2.getKey(), autoLearnDecideTask2);
+ autoLearnTasks.getTaskMap().put(autoLearnDecideTask3.getKey(), autoLearnDecideTask3);
+ autoLearnTasks.getTaskMap().put(autoLearnDecideTask4.getKey(), autoLearnDecideTask4);
+ autoLearnTasks.getTaskMap().put(autoLearnDecideTask5.getKey(), autoLearnDecideTask5);
+ autoLearnTasks.getTaskMap().put(autoLearnDecideTask6.getKey(), autoLearnDecideTask6);
+ autoLearnTasks.getTaskMap().put(autoLearnActTask.getKey(), autoLearnActTask);
+
+ // Policies
+ logicReader.setDefaultLogic("DefaultState_Logic");
+
+ final AxPolicy autoLearnPolicy = new AxPolicy(new AxArtifactKey("AutoLearnPolicy", "0.0.1"));
+ autoLearnPolicy.setTemplate("MEDA");
+
+ final AxState autoLearnActState = new AxState(new AxReferenceKey(autoLearnPolicy.getKey(), "Act"));
+ autoLearnActState.setTrigger(autoLearnDecideEvent.getKey());
+ final AxStateOutput alAct2Out =
+ new AxStateOutput(autoLearnActState.getKey(), AxReferenceKey.getNullKey(), autoLearnActEvent.getKey());
+ autoLearnActState.getStateOutputs().put(alAct2Out.getKey().getLocalName(), alAct2Out);
+ autoLearnActState.setTaskSelectionLogic(
+ new AxTaskSelectionLogic(autoLearnActState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+ autoLearnActState.setDefaultTask(autoLearnActTask.getKey());
+ autoLearnActState.getTaskReferences().put(autoLearnActTask.getKey(),
+ new AxStateTaskReference(autoLearnActState.getKey(), autoLearnActTask.getKey(),
+ AxStateTaskOutputType.DIRECT, alAct2Out.getKey()));
+
+ logicReader.setDefaultLogic(null);
+
+ final AxState autoLearnDecideState = new AxState(new AxReferenceKey(autoLearnPolicy.getKey(), "Decide"));
+ autoLearnDecideState.setTrigger(autoLearnEstablishEvent.getKey());
+ final AxStateOutput alDec2Act = new AxStateOutput(autoLearnDecideState.getKey(), autoLearnActState.getKey(),
+ autoLearnDecideEvent.getKey());
+ autoLearnDecideState.getStateOutputs().put(alDec2Act.getKey().getLocalName(), alDec2Act);
+ autoLearnDecideState.getContextAlbumReferences().add(autoLearnAlbum.getKey());
+ autoLearnDecideState.setTaskSelectionLogic(
+ new AxTaskSelectionLogic(autoLearnDecideState.getKey(), "TaskSelectionLogic", "JAVA", logicReader));
+ autoLearnDecideState.setDefaultTask(autoLearnDecideTask0.getKey());
+ autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask0.getKey(),
+ new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask0.getKey(),
+ AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+ autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask1.getKey(),
+ new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask1.getKey(),
+ AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+ autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask2.getKey(),
+ new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask2.getKey(),
+ AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+ autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask3.getKey(),
+ new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask3.getKey(),
+ AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+ autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask4.getKey(),
+ new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask4.getKey(),
+ AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+ autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask5.getKey(),
+ new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask5.getKey(),
+ AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+ autoLearnDecideState.getTaskReferences().put(autoLearnDecideTask6.getKey(),
+ new AxStateTaskReference(autoLearnDecideState.getKey(), autoLearnDecideTask6.getKey(),
+ AxStateTaskOutputType.DIRECT, alDec2Act.getKey()));
+
+ logicReader.setDefaultLogic("DefaultState_Logic");
+
+ final AxState autoLearnEstablishState = new AxState(new AxReferenceKey(autoLearnPolicy.getKey(), "Establish"));
+ autoLearnEstablishState.setTrigger(autoLearnMatchEvent.getKey());
+ final AxStateOutput alEst2Dec = new AxStateOutput(autoLearnEstablishState.getKey(),
+ autoLearnDecideState.getKey(), autoLearnEstablishEvent.getKey());
+ autoLearnEstablishState.getStateOutputs().put(alEst2Dec.getKey().getLocalName(), alEst2Dec);
+ autoLearnEstablishState.setTaskSelectionLogic(
+ new AxTaskSelectionLogic(autoLearnEstablishState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+ autoLearnEstablishState.setDefaultTask(autoLearnEstablishTask.getKey());
+ autoLearnEstablishState.getTaskReferences().put(autoLearnEstablishTask.getKey(),
+ new AxStateTaskReference(autoLearnEstablishState.getKey(), autoLearnEstablishTask.getKey(),
+ AxStateTaskOutputType.DIRECT, alEst2Dec.getKey()));
+
+ final AxState autoLearnMatchState = new AxState(new AxReferenceKey(autoLearnPolicy.getKey(), "Match"));
+ autoLearnMatchState.setTrigger(autoLearnTriggerEvent.getKey());
+ final AxStateOutput alMat2Est = new AxStateOutput(autoLearnMatchState.getKey(),
+ autoLearnEstablishState.getKey(), autoLearnMatchEvent.getKey());
+ autoLearnMatchState.getStateOutputs().put(alMat2Est.getKey().getLocalName(), alMat2Est);
+ autoLearnMatchState.setTaskSelectionLogic(
+ new AxTaskSelectionLogic(autoLearnMatchState.getKey(), "TaskSelectionLogic", "MVEL", logicReader));
+ autoLearnMatchState.setDefaultTask(autoLearnMatchTask.getKey());
+ autoLearnMatchState.getTaskReferences().put(autoLearnMatchTask.getKey(),
+ new AxStateTaskReference(autoLearnMatchState.getKey(), autoLearnMatchTask.getKey(),
+ AxStateTaskOutputType.DIRECT, alMat2Est.getKey()));
+
+ autoLearnPolicy.setFirstState(autoLearnMatchState.getKey().getLocalName());
+ autoLearnPolicy.getStateMap().put(autoLearnMatchState.getKey().getLocalName(), autoLearnMatchState);
+ autoLearnPolicy.getStateMap().put(autoLearnEstablishState.getKey().getLocalName(), autoLearnEstablishState);
+ autoLearnPolicy.getStateMap().put(autoLearnDecideState.getKey().getLocalName(), autoLearnDecideState);
+ autoLearnPolicy.getStateMap().put(autoLearnActState.getKey().getLocalName(), autoLearnActState);
+
+ final AxPolicies autoLearnPolicies = new AxPolicies(new AxArtifactKey("AutoLearnPolicies", "0.0.1"));
+ autoLearnPolicies.getPolicyMap().put(autoLearnPolicy.getKey(), autoLearnPolicy);
+
+ final AxKeyInformation keyInformation =
+ new AxKeyInformation(new AxArtifactKey("AutoLearnKeyInformation", "0.0.1"));
+ final AxPolicyModel autoLearnPolicyModel =
+ new AxPolicyModel(new AxArtifactKey("AutoLearnPolicyModel", "0.0.1"));
+ autoLearnPolicyModel.setPolicies(autoLearnPolicies);
+ autoLearnPolicyModel.setEvents(autoLearnEvents);
+ autoLearnPolicyModel.setTasks(autoLearnTasks);
+ autoLearnPolicyModel.setAlbums(autoLearnAlbums);
+ autoLearnPolicyModel.setSchemas(alContextSchemas);
+ autoLearnPolicyModel.setKeyInformation(keyInformation);
+ autoLearnPolicyModel.getKeyInformation().generateKeyInfo(autoLearnPolicyModel);
+
+ final AxValidationResult result = autoLearnPolicyModel.validate(new AxValidationResult());
+ if (!result.getValidationResult().equals(AxValidationResult.ValidationResult.VALID)) {
+ throw new ApexRuntimeException("model " + autoLearnPolicyModel.getID() + " is not valid" + result);
+ }
+ return autoLearnPolicyModel;
+ }
+
+}
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
new file mode 100644
index 000000000..0cc9bd3a2
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/AdaptiveDomainModelSaver.java
@@ -0,0 +1,64 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.model;
+
+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;
+
+/**
+ * This class saves sample domain models to disk in XML and JSON format.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+public final class AdaptiveDomainModelSaver {
+ /**
+ * Private default constructor to prevent subclassing.
+ */
+ private AdaptiveDomainModelSaver() {}
+
+ /**
+ * Write the AADM model to args[0].
+ *
+ * @param args Not used
+ * @throws ApexException the apex exception
+ */
+ public static void main(final String[] args) throws ApexException {
+ if (args.length != 1) {
+ System.err.println("usage: " + AdaptiveDomainModelSaver.class.getCanonicalName() + " modelDirectory");
+ return;
+ }
+
+ // Save Anomaly Detection model
+ final AxPolicyModel adPolicyModel = new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
+ final ApexModelSaver<AxPolicyModel> adModelSaver =
+ new ApexModelSaver<>(AxPolicyModel.class, adPolicyModel, args[0]);
+ adModelSaver.apexModelWriteJSON();
+ adModelSaver.apexModelWriteXML();
+
+ // Save Auto Learn model
+ final AxPolicyModel alPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
+ final ApexModelSaver<AxPolicyModel> alModelSaver =
+ new ApexModelSaver<>(AxPolicyModel.class, alPolicyModel, args[0]);
+ alModelSaver.apexModelWriteJSON();
+ alModelSaver.apexModelWriteXML();
+ }
+}
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java
new file mode 100644
index 000000000..a0b2a8f40
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AnomalyDetectionPolicy_Decide_TaskSelectionLogic.java
@@ -0,0 +1,414 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.model.java;
+
+import java.util.Arrays;
+import java.util.LinkedHashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+
+import org.apache.commons.math3.distribution.TDistribution;
+import org.apache.commons.math3.util.FastMath;
+import org.onap.policy.apex.core.engine.executor.context.TaskSelectionExecutionContext;
+import org.onap.policy.apex.examples.adaptive.concepts.AnomalyDetection;
+import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
+import org.slf4j.Logger;
+
+/**
+ * The Class AnomalyDetectionPolicy_Decide_TaskSelectionLogic.
+ */
+// CHECKSTYLE:OFF: checkstyle:className
+public class AnomalyDetectionPolicy_Decide_TaskSelectionLogic {
+ // CHECKSTYLE:ON: checkstyle:className
+
+ private Logger logger;
+ // configuration
+ private static final double ANOMALY_SENSITIVITY = 0.05;
+ private static final int FREQUENCY = 360;
+
+ /**
+ * A map to hold the Anomaly degree/levels/probabilities required for each task.<br>
+ * If there is no task defined for a calculated anomaly-degree, then the default task is used.<br>
+ * The map use (LinkedHashMap) is an insertion-ordered map, so the first interval matching a query is used.
+ */
+ // CHECKSTYLE:OFF: checkstyle:magicNumber
+ private static final Map<double[], String> TASK_INTERVALS = new LinkedHashMap<>();
+ static {
+ TASK_INTERVALS.put(new double[] { 0.0, 0.1 }, null); // null will mean default task
+ TASK_INTERVALS.put(new double[] { 0.25, 0.5 }, "AnomalyDetectionDecideTask1");
+ TASK_INTERVALS.put(new double[] { 0.5, 1.01 }, "AnomalyDetectionDecideTask2");
+ }
+ // CHECKSTYLE:ON: checkstyle:magicNumber
+
+ private volatile TaskSelectionExecutionContext executionContext;
+
+ /**
+ * Gets the task.
+ *
+ * @param executor the executor
+ * @return the task
+ */
+ public boolean getTask(final TaskSelectionExecutionContext executor) {
+ executionContext = executor;
+ logger = executionContext.logger;
+ logger.debug(executor.subject.getId());
+ logger.debug(executor.inFields.toString());
+ final double now = (Double) (executor.inFields.get("MonitoredValue"));
+ final Integer iteration = (Integer) (executor.inFields.get("Iteration"));
+ final double[] vals = this.forecastingAndAnomaly(now); // double[forecastedValue, AnomalyScore,
+ // AnomalyProbability]
+ final double anomalyness = vals[2];
+ String task = null;
+ for (final Map.Entry<double[], String> i : TASK_INTERVALS.entrySet()) {
+ if (checkInterval(anomalyness, i.getKey())) {
+ task = i.getValue();
+ break;
+ }
+ }
+ if (task == null) {
+ executionContext.subject.getDefaultTaskKey().copyTo(executionContext.selectedTask);
+ } else {
+ executionContext.subject.getTaskKey(task).copyTo(executionContext.selectedTask);
+ }
+ if (logger.isDebugEnabled()) {
+ logger.debug(
+ "TestAnomalyDetectionTSLPolicy0000DecideStateTaskSelectionLogic.getTask():\t************\t\t\t\t"
+ + "Iteration:\t" + iteration + "\tValue:\t" + now + "\tForecast:\t" + vals[0]
+ + "\tAnomalyScore:\t" + vals[1] + "\tAnomalyProbability:\t" + vals[2] + "\tInvoking Task:\t"
+ + executionContext.selectedTask);
+ }
+ return true;
+ }
+
+ /**
+ * Anomaly detection and forecast.
+ *
+ * @param value The current value
+ * @return Null if the function can not be executed correctly, otherwise double[forecastedValue, AnomalyScore,
+ * AnomalyProbability]
+ */
+ public double[] forecastingAndAnomaly(final double value) {
+ try {
+ executionContext.getContextAlbum("AnomalyDetectionAlbum").lockForWriting("AnomalyDetection");
+ } catch (final ApexException e) {
+ logger.error("Failed to acquire write lock on \"AnomalyDetection\" context", e);
+ return null;
+ }
+
+ // Get the context object
+ AnomalyDetection anomalyDetection =
+ (AnomalyDetection) executionContext.getContextAlbum("AnomalyDetectionAlbum").get("AnomalyDetection");
+ if (anomalyDetection == null) {
+ anomalyDetection = new AnomalyDetection();
+ executionContext.getContextAlbum("AnomalyDetectionAlbum").put("AnomalyDetection", anomalyDetection);
+ }
+
+ // Check the lists are initialized
+ if (!anomalyDetection.isInitialized()) {
+ anomalyDetection.init(FREQUENCY);
+ }
+
+ boolean unsetfirstround = false;
+
+ int frequency = anomalyDetection.getFrequency();
+ frequency = frequency + 1;
+
+ // reset frequency counter
+ if (frequency >= FREQUENCY) {
+ unsetfirstround = true;
+ frequency = 0;
+ }
+ anomalyDetection.setFrequency(frequency);
+
+ if (unsetfirstround && anomalyDetection.getFirstRound()) {
+ anomalyDetection.setFirstRound(false);
+ }
+
+ // --------- calculate the forecasted value - simple version
+ final Double lastForecast = anomalyDetection.getFrequencyForecasted().get(frequency);
+
+ // get forecast for current value
+ final double forecastedValue = lastForecast == null ? value : expMovingAverage(value, lastForecast);
+
+ // --------- calculate the anomalyScore
+ final double anomalyScore = lastForecast == null ? 0.0 : FastMath.abs(lastForecast - value);
+
+ anomalyDetection.getFrequencyForecasted().set(frequency, forecastedValue);
+
+ // anomaly score is ignored in the first frequency period
+ if (!anomalyDetection.getFirstRound()) {
+ ((LinkedList<Double>) anomalyDetection.getAnomalyScores()).addLast(anomalyScore);
+ }
+
+ // CHECKSTYLE:OFF: checkstyle:magicNumber
+ // max FREQUENCY*4 anomaly scores history
+ listSizeControl(anomalyDetection.getAnomalyScores(), FREQUENCY * 4);
+
+ // ---------- calculate the anomaly probability
+ double anomalyProbability = 0.0;
+ if (anomalyDetection.getAnomalyScores().size() > 30) {
+ // 0.5
+ anomalyProbability = gStatsTest(anomalyDetection.getAnomalyScores(), ANOMALY_SENSITIVITY);
+ }
+ // CHECKSTYLE:ON: checkstyle:magicNumber
+
+ try {
+ executionContext.getContextAlbum("AnomalyDetectionAlbum").unlockForWriting("AnomalyDetection");
+ } catch (final ApexException e) {
+ logger.error("Failed to release write lock on \"AnomalyDetection\" context", e);
+ return null;
+ }
+
+ return new double[] { forecastedValue, anomalyScore, anomalyProbability };
+ }
+
+ /**
+ * Is the passed value inside the interval, i.e. (value<interval[1] && value>=interval[0])
+ *
+ * @param value The value to check
+ * @param interval A 2 element double array describing an interval
+ * @return true if the value is between interval[0] (inclusive) and interval[1] (exclusive), i.e. (value<interval[1]
+ * && value>=interval[0]). Otherwise false;
+ */
+ private static boolean checkInterval(final double value, final double[] interval) {
+ if (interval == null || interval.length != 2) {
+ throw new IllegalArgumentException("something other than an interval passed to checkInterval");
+ }
+ final double min = interval[0];
+ final double max = interval[1];
+ return (value < max && value >= min);
+ }
+
+ /**
+ * calculate the anomaly probability using statistical test.
+ *
+ * @param values the values
+ * @param significanceLevel the significance level
+ * @return the double
+ */
+ private static double gStatsTest(final List<Double> values, final double significanceLevel) {
+ if (isAllEqual(values)) {
+ return 0.0;
+ }
+ // the targeted value or the last value
+ final double currentV = values.get(values.size() - 1);
+ Double[] lValuesCopy = values.toArray(new Double[values.size()]);
+ Arrays.sort(lValuesCopy); // takes ~40% of method time
+ // if(logger.isDebugEnabled()){
+ // logger.debug("values:" + Arrays.toString(lValuesCopy));
+ // }
+ // get mean
+ double mean = getMean(lValuesCopy);
+ // get the test value: v
+ double v = getV(lValuesCopy, mean, true);
+ // get the p value for the test value
+ double pValue = getPValue(lValuesCopy, v, mean); // takes approx 25% of method time
+ // if(logger.isDebugEnabled()){
+ // logger.debug("pValue:" + pValue);
+ // }
+
+ // check the critical level
+ while (pValue < significanceLevel) { // takes approx 20% of method time
+ // the score value as the anomaly probability
+ final double score = (significanceLevel - pValue) / significanceLevel;
+ if (v == currentV) {
+ return score;
+ }
+ // do the critical check again for the left values
+ lValuesCopy = removevalue(lValuesCopy, v);
+ if (isAllEqual(lValuesCopy)) {
+ return 0.0;
+ }
+ // if(logger.isDebugEnabled()){
+ // logger.debug("left values:" + Arrays.toString(lValuesCopy));
+ // }
+ mean = getMean(lValuesCopy);
+ v = getV(lValuesCopy, mean, true);
+ pValue = getPValue(lValuesCopy, v, mean);
+ }
+ return 0.0;
+ }
+
+ /**
+ * get the test value based on mean from sorted values.
+ *
+ * @param lValues the l values
+ * @param mean the mean
+ * @param maxValueOnly : only the max extreme value will be tested
+ * @return the value to be tested
+ */
+ private static double getV(final Double[] lValues, final double mean, final boolean maxValueOnly) {
+ double v = lValues[lValues.length - 1];
+ // max value as the extreme value
+ if (maxValueOnly) {
+ return v;
+ }
+ // check the extreme side
+ if ((v - mean) < (mean - lValues[0])) {
+ v = lValues[0];
+ }
+ return v;
+ }
+
+ /**
+ * calculate the P value for the t distribution.
+ *
+ * @param lValues the l values
+ * @param v the v
+ * @param mean the mean
+ * @return the p value
+ */
+ private static double getPValue(final Double[] lValues, final double v, final double mean) {
+ // calculate z value
+ final double z = FastMath.abs(v - mean) / getStdDev(lValues, mean);
+ // logger.debug("z: " + z);
+ // calculate T
+ final double n = lValues.length;
+ final double s = (z * z * n * (2.0 - n)) / (z * z * n - (n - 1.0) * (n - 1.0));
+ final double t = FastMath.sqrt(s);
+ // logger.debug("t:" + t);
+ // default p value = 0
+ double pValue = 0.0;
+ if (!Double.isNaN(t)) {
+ // t distribution with n-2 degrees of freedom
+ final TDistribution tDist = new TDistribution(n - 2);
+ pValue = n * (1.0 - tDist.cumulativeProbability(t));
+ // set max pValue = 1
+ pValue = pValue > 1.0 ? 1.0 : pValue;
+ }
+ // logger.debug("value: "+ v + " , pValue: " + pValue);
+ return pValue;
+ }
+
+ /*
+ * Some utility methods
+ */
+ // exponential = 2(n+1)
+ private static final double EMA_EXPONENT = 2.0 / (7.0 + 1.0);
+ private static final double EMA_EXPONENT_1 = (1.0 - EMA_EXPONENT);
+
+ /**
+ * exponential moving average.
+ *
+ * @param value the value
+ * @param lastForecast the last forecast
+ * @return the double
+ */
+ private static double expMovingAverage(final double value, final double lastForecast) {
+ return (value * EMA_EXPONENT) + (lastForecast * EMA_EXPONENT_1);
+ }
+
+ /**
+ * Remove the first occurence of the value v from the array.
+ *
+ * @param lValues the l values
+ * @param v the v
+ * @return the double[]
+ */
+ private static Double[] removevalue(final Double[] lValues, final double v) {
+ for (int i = 0; i < lValues.length; i++) {
+ if (lValues[i] == v) {
+ final Double[] 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 lValues;
+ }
+
+ /**
+ * get mean value of double list.
+ *
+ * @param lValues the l values
+ * @return the mean
+ */
+ private static double getMean(final Double[] lValues) {
+ double sum = 0.0;
+ for (final double d : lValues) {
+
+ sum += d;
+ }
+ return sum / lValues.length;
+ }
+
+ /**
+ * get standard deviation of double list.
+ *
+ * @param lValues the l values
+ * @param mean the mean
+ * @return stddev
+ */
+ private static double getStdDev(final Double[] lValues, final double mean) {
+ double temp = 0.0;
+ for (final double d : lValues) {
+ temp += (mean - d) * (mean - d);
+ }
+ return FastMath.sqrt(temp / lValues.length);
+ }
+
+ /**
+ * Chop head off list to make it length max .
+ *
+ * @param list the list to chop
+ * @param max the max size
+ */
+ private static void listSizeControl(final List<?> list, final int max) {
+ final int k = list.size();
+ if (k > max) {
+ // Chop the head off the list.
+ list.subList(0, k - max).clear();
+ }
+ }
+
+ /**
+ * return true if all values are equal.
+ *
+ * @param lValues the l values
+ * @return true, if checks if is all equal
+ */
+ private static boolean isAllEqual(final List<Double> lValues) {
+ final double first = lValues.get(0);
+ for (final Double d : lValues) {
+ if (d != first) {
+ return false;
+ }
+ }
+ return true;
+ }
+
+ /**
+ * return true if all values are equal.
+ *
+ * @param lValues the l values
+ * @return true, if checks if is all equal
+ */
+ private static boolean isAllEqual(final Double[] lValues) {
+ final double first = lValues[0];
+ for (final Double d : lValues) {
+ if (d != first) {
+ return false;
+ }
+ }
+ return true;
+ }
+}
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicy_Decide_TaskSelectionLogic.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicy_Decide_TaskSelectionLogic.java
new file mode 100644
index 000000000..23d4e2486
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/AutoLearnPolicy_Decide_TaskSelectionLogic.java
@@ -0,0 +1,151 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive.model.java;
+
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+
+import org.onap.policy.apex.context.ContextException;
+import org.onap.policy.apex.core.engine.executor.context.TaskSelectionExecutionContext;
+import org.onap.policy.apex.examples.adaptive.concepts.AutoLearn;
+
+/**
+ * The Class AutoLearnPolicy_Decide_TaskSelectionLogic.
+ */
+// CHECKSTYLE:OFF: checkstyle:typeName
+public class AutoLearnPolicy_Decide_TaskSelectionLogic {
+ // CHECKSTYLE:ON: checkstyle:typeName
+ private static final Random RAND = new Random(System.currentTimeMillis());
+ private static final double WANT = 50.0;
+ private int size;
+
+ /**
+ * Gets the task.
+ *
+ * @param executor the executor
+ * @return the task
+ */
+ public boolean getTask(final TaskSelectionExecutionContext executor) {
+ executor.logger.debug(executor.subject.getId());
+ executor.logger.debug(executor.inFields.toString());
+ final List<String> tasks = executor.subject.getTaskNames();
+ size = tasks.size();
+
+ try {
+ executor.getContextAlbum("AutoLearnAlbum").lockForWriting("AutoLearn");
+ } catch (final ContextException e) {
+ executor.logger.error("Failed to acquire write lock on \"autoLearn\" context", e);
+ return false;
+ }
+
+ // Get the context object
+ AutoLearn autoLearn = (AutoLearn) executor.getContextAlbum("AutoLearnAlbum").get("AutoLearn");
+ if (autoLearn == null) {
+ autoLearn = new AutoLearn();
+ }
+
+ // Check the lists are initialized
+ if (!autoLearn.isInitialized()) {
+ autoLearn.init(size);
+ }
+
+ final double now = (Double) (executor.inFields.get("MonitoredValue"));
+ final double diff = now - WANT;
+ final int option = getOption(diff, autoLearn);
+ learn(option, diff, autoLearn);
+
+ executor.getContextAlbum("AutoLearnAlbum").put("AutoLearnAlbum", autoLearn);
+
+ try {
+ executor.getContextAlbum("AutoLearnAlbum").unlockForWriting("AutoLearn");
+ } catch (final ContextException e) {
+ executor.logger.error("Failed to acquire write lock on \"autoLearn\" context", e);
+ return false;
+ }
+
+ executor.subject.getTaskKey(tasks.get(option)).copyTo(executor.selectedTask);
+ return true;
+ }
+
+ /**
+ * Gets the option.
+ *
+ * @param diff the diff
+ * @param autoLearn the auto learn
+ * @return the option
+ */
+ 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);
+ int closestupi = -1;
+ int closestdowni = -1;
+ double closestup = Double.MAX_VALUE;
+ double closestdown = Double.MIN_VALUE;
+ for (int i = 0; i < size; i++) {
+ if (Double.isNaN(avdiffs[i])) {
+ return r;
+ }
+ if (avdiffs[i] >= diff && avdiffs[i] <= closestup) {
+ closestup = avdiffs[i];
+ closestupi = i;
+ }
+ if (avdiffs[i] <= diff && avdiffs[i] >= closestdown) {
+ closestdown = avdiffs[i];
+ closestdowni = i;
+ }
+ }
+ if (closestupi == -1 || closestdowni == -1) {
+ return r;
+ }
+ if (closestupi == closestdowni) {
+ return closestupi;
+ }
+ if (Math.abs(closestdown - diff) > Math.abs(closestup - diff)) {
+ return closestupi;
+ } else {
+ return closestdowni;
+ }
+ }
+
+ /**
+ * Learn.
+ *
+ * @param option the option
+ * @param diff the diff
+ * @param autoLearn the auto learn
+ */
+ private void learn(final int option, final double diff, final AutoLearn autoLearn) {
+ final Double[] avdiffs = autoLearn.getAvDiffs().toArray(new Double[autoLearn.getAvDiffs().size()]);
+ final Long[] counts = autoLearn.getCounts().toArray(new Long[autoLearn.getCounts().size()]);
+ if (option < 0 || option >= avdiffs.length) {
+ throw new IllegalArgumentException("Error: option" + option);
+ }
+ counts[option]++;
+ if (Double.isNaN(avdiffs[option])) {
+ avdiffs[option] = diff;
+ } else {
+ avdiffs[option] = (avdiffs[option] * (counts[option] - 1) + diff) / counts[option];
+ }
+ autoLearn.setAvDiffs(Arrays.asList(avdiffs));
+ autoLearn.setCounts(Arrays.asList(counts));
+ }
+}
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/package-info.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/package-info.java
new file mode 100644
index 000000000..172be5ada
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/java/package-info.java
@@ -0,0 +1,27 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+/**
+ * Pprovides task selection logic for the adaptive domain in Java.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+
+package org.onap.policy.apex.examples.adaptive.model.java;
diff --git a/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/package-info.java b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/package-info.java
new file mode 100644
index 000000000..d38333ba8
--- /dev/null
+++ b/examples/examples-adaptive/src/main/java/org/onap/policy/apex/examples/adaptive/model/package-info.java
@@ -0,0 +1,27 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+/**
+ * Provides Java generation of the Adaptive model as well as Java task selection logic for the domain.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+
+package org.onap.policy.apex.examples.adaptive.model;
diff --git a/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AnomalyDetectionEvents.json b/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AnomalyDetectionEvents.json
new file mode 100644
index 000000000..2872f5ae4
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AnomalyDetectionEvents.json
@@ -0,0 +1,200 @@
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 0,
+ "MonitoredValue": 0.04818448765182781
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.016856740089812434,
+ "Iteration": 1
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 2,
+ "MonitoredValue": 0.07283031742656552
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 3,
+ "MonitoredValue": 0.05284674407712258
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.08685406312054446,
+ "Iteration": 4
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.09580382706345814,
+ "Iteration": 5
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 6,
+ "MonitoredValue": 0.07022220893167114
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.05027456879617627,
+ "Iteration": 7
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 8,
+ "MonitoredValue": 0.16867139038930964
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.18383434112759453,
+ "Iteration": 9
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 10,
+ "MonitoredValue": 0.11940188164467769
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 11,
+ "MonitoredValue": 0.18528189685974508
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.19934812597926355,
+ "Iteration": 12
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.2769320946213342,
+ "Iteration": 13
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 14,
+ "MonitoredValue": 0.2573073533232842
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 15,
+ "MonitoredValue": 0.2114736039819203
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.3070179558734792,
+ "Iteration": 16
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.22817748171407137,
+ "Iteration": 17
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "Iteration": 18,
+ "MonitoredValue": 0.3742338965886025
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AnomalyDetectionTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 0.33767889848226273,
+ "Iteration": 19
+}
+
diff --git a/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AutoLearnEvents.json b/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AutoLearnEvents.json
new file mode 100644
index 000000000..4a90c944d
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/examples/events/Adaptive/AutoLearnEvents.json
@@ -0,0 +1,10000 @@
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 0.0,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 90.42073086787556,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 77.69433155254976,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 71.55772312953363,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 71.55772312953363,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 82.33658469430046
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 90.42073086787556,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 79.64186930310875
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 82.33658469430046
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 78.80403683294003
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 78.8005362087235,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 82.33658469430046
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 74.25243852072533
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 77.70005535199167
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 90.42073086787556,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 74.25243852072533
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 79.64186930310875
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 71.55772312953363,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "LastMonitoredValue": 90.42073086787556,
+ "MonitoredValue": 76.94715391191704
+}
+
+{
+ "nameSpace": "org.onap.policy.apex.examples.adaptive.events",
+ "name": "AutoLearnTriggerEvent",
+ "version": "0.0.1",
+ "source": "CompaA",
+ "target": "apex",
+ "MonitoredValue": 76.94715391191704,
+ "LastMonitoredValue": 74.25243852072533
+}
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+{
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diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask0_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask0_TaskLogic.mvel
new file mode 100644
index 000000000..758062b8e
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask0_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask0: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (1.0/2.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+ ret = MAX;
+if (ret<MIN)
+ ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask0: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask1_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask1_TaskLogic.mvel
new file mode 100644
index 000000000..051766045
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask1_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask1: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (1.0/5.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+ ret = MAX;
+if (ret<MIN)
+ ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask1: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask2_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask2_TaskLogic.mvel
new file mode 100644
index 000000000..b787b2153
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask2_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask2: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (1.0/10.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+ ret = MAX;
+if (ret<MIN)
+ ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask2: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask3_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask3_TaskLogic.mvel
new file mode 100644
index 000000000..7fbc8c765
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask3_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask3: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = rand.nextGaussian()*((MAX - WANT) / 50.0);
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+ ret = MAX;
+if (ret<MIN)
+ ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask3: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask4_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask4_TaskLogic.mvel
new file mode 100644
index 000000000..a0a12d84f
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask4_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask4: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (-1.0/10.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+ ret = MAX;
+if (ret<MIN)
+ ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask4: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask5_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask5_TaskLogic.mvel
new file mode 100644
index 000000000..fc2005fdf
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask5_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask5: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (-1.0/5.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+ ret = MAX;
+if (ret < MIN)
+ ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask5: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask6_TaskLogic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask6_TaskLogic.mvel
new file mode 100644
index 000000000..5eb44839c
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/AutoLearnDecideTask6_TaskLogic.mvel
@@ -0,0 +1,40 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnDecideTask6: inFields:" + inFields);
+java.util.Random rand = new java.util.Random(System.currentTimeMillis());
+double MIN = -100.0;
+double MAX = 100.0;
+double WANT = 50.0;
+
+double inval = (Double)(inFields["MonitoredValue"]);
+double ret = inval;
+///////////////
+double diff = (-1.0/2.0) * (Math.abs(inval - WANT));
+///////////////
+ret = inval + diff;
+if (ret > MAX)
+ ret = MAX;
+if (ret<MIN)
+ ret = MIN;
+
+outFields["MonitoredValue"] = ret;
+outFields["LastMonitoredValue"] = inval;
+logger.debug("AutoLearnDecideTask6: outFields:" + outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAnomalyDetectionTask_Logic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAnomalyDetectionTask_Logic.mvel
new file mode 100644
index 000000000..8f894d297
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAnomalyDetectionTask_Logic.mvel
@@ -0,0 +1,25 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug(subject.getTaskName() + " execution logic");
+logger.debug("inFields : " + inFields);
+outFields["MonitoredValue"] = inFields["MonitoredValue"];
+outFields["Iteration"] = inFields["Iteration"];
+logger.debug("outFields : "+outFields);
+return true;
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAutoLearnTask_Logic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAutoLearnTask_Logic.mvel
new file mode 100644
index 000000000..636378396
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultAutoLearnTask_Logic.mvel
@@ -0,0 +1,26 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug("AutoLearnActTask execution logic");
+logger.debug("inFields : " + inFields);
+outFields["MonitoredValue"] = inFields["MonitoredValue"];
+outFields["LastMonitoredValue"] = inFields["LastMonitoredValue"];
+logger.debug("outFields : " + outFields);
+return true;
+
diff --git a/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultState_Logic.mvel b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultState_Logic.mvel
new file mode 100644
index 000000000..2db0a0031
--- /dev/null
+++ b/examples/examples-adaptive/src/main/resources/org/onap/policy/apex/examples/adaptive/model/mvel/DefaultState_Logic.mvel
@@ -0,0 +1,23 @@
+/*
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+logger.debug(subject.id + ":" + subject.stateName);
+subject.defaultTaskKey.copyTo(selectedTask);
+logger.debug(subject.id + ":" + subject.stateName) + ", using default task" + subject.defaultTaskKey.getID();
+return true;
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDBWrite.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDBWrite.java
new file mode 100644
index 000000000..898531c32
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionDBWrite.java
@@ -0,0 +1,60 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import java.io.File;
+import java.sql.Connection;
+import java.sql.DriverManager;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import org.onap.policy.apex.model.basicmodel.dao.DAOParameters;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+public class TestAnomalyDetectionDBWrite {
+ private Connection connection;
+ TestApexModel<AxPolicyModel> testApexModel;
+
+ @Before
+ public void setup() throws Exception {
+ Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
+ connection = DriverManager.getConnection("jdbc:derby:memory:apex_test;create=true");
+
+ testApexModel = new TestApexModel<>(AxPolicyModel.class, new TestAnomalyDetectionModelCreator());
+ }
+
+ @After
+ public void teardown() throws Exception {
+ connection.close();
+ new File("derby.log").delete();
+ }
+
+ @Test
+ public void testModelWriteReadJPA() throws Exception {
+ final DAOParameters daoParameters = new DAOParameters();
+ daoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
+ daoParameters.setPersistenceUnit("AdaptiveModelsTest");
+
+ testApexModel.testApexModelWriteReadJPA(daoParameters);
+ }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java
new file mode 100644
index 000000000..7e2fb0eb7
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModel.java
@@ -0,0 +1,81 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import static org.junit.Assert.assertTrue;
+
+import java.io.File;
+import java.sql.Connection;
+import java.sql.DriverManager;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.basicmodel.dao.DAOParameters;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+public class TestAnomalyDetectionModel {
+ private Connection connection;
+ TestApexModel<AxPolicyModel> testApexModel;
+
+ @Before
+ public void setup() throws Exception {
+ Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
+ connection = DriverManager.getConnection("jdbc:derby:memory:apex_test;create=true");
+
+ testApexModel = new TestApexModel<>(AxPolicyModel.class, new TestAnomalyDetectionModelCreator());
+ }
+
+ @After
+ public void teardown() throws Exception {
+ connection.close();
+ new File("derby.log").delete();
+ }
+
+ @Test
+ public void testModelValid() throws Exception {
+ final AxValidationResult result = testApexModel.testApexModelValid();
+ assertTrue(result.toString().equals(VALID_MODEL_STRING));
+ }
+
+ @Test
+ public void testModelWriteReadXML() throws Exception {
+ testApexModel.testApexModelWriteReadXML();
+ }
+
+ @Test
+ public void testModelWriteReadJSON() throws Exception {
+ testApexModel.testApexModelWriteReadJSON();
+ }
+
+ @Test
+ public void testModelWriteReadJPA() throws Exception {
+ final DAOParameters daoParameters = new DAOParameters();
+ daoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
+ daoParameters.setPersistenceUnit("AdaptiveModelsTest");
+
+ testApexModel.testApexModelWriteReadJPA(daoParameters);
+ }
+
+ private static final String VALID_MODEL_STRING = "***validation of model successful***";
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java
new file mode 100644
index 000000000..2b50d69ab
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionModelCreator.java
@@ -0,0 +1,56 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModelCreator;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+/**
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+public class TestAnomalyDetectionModelCreator implements TestApexModelCreator<AxPolicyModel> {
+
+ @Override
+ public AxPolicyModel getModel() {
+ return new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
+ }
+
+ @Override
+ public AxPolicyModel getMalstructuredModel() {
+ return null;
+ }
+
+ @Override
+ public AxPolicyModel getObservationModel() {
+ return null;
+ }
+
+ @Override
+ public AxPolicyModel getWarningModel() {
+ return getModel();
+ }
+
+ @Override
+ public AxPolicyModel getInvalidModel() {
+ return null;
+ }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTSLUseCase.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTSLUseCase.java
new file mode 100644
index 000000000..7d9791d46
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAnomalyDetectionTSLUseCase.java
@@ -0,0 +1,164 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertTrue;
+
+import java.io.IOException;
+import java.util.Random;
+
+import org.junit.Test;
+import org.onap.policy.apex.core.engine.EngineParameters;
+import org.onap.policy.apex.core.engine.engine.ApexEngine;
+import org.onap.policy.apex.core.engine.engine.impl.ApexEngineFactory;
+import org.onap.policy.apex.core.engine.event.EnEvent;
+import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
+import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
+import org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+import org.onap.policy.apex.plugins.executor.java.JavaExecutorParameters;
+import org.onap.policy.apex.plugins.executor.mvel.MVELExecutorParameters;
+import org.slf4j.ext.XLogger;
+import org.slf4j.ext.XLoggerFactory;
+
+/**
+ * This policy passes, and recieves a Double event context filed called "EVCDouble".<br>
+ * The policy tries to detect anomalies in the pattern of values for EVCDouble<br>
+ * See the 2 test cases below (1 short, 1 long)
+ *
+ * @author John Keeney (John.Keeney@ericsson.com)
+ */
+public class TestAnomalyDetectionTSLUseCase {
+ private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAnomalyDetectionTSLUseCase.class);
+
+ private static final int MAXITERATIONS = 3660;
+ private static final Random RAND = new Random(System.currentTimeMillis());
+
+ @Test
+ // once through the long running test below
+ public void TestAnomalyDetectionTSL() throws ApexException, InterruptedException, IOException {
+ final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
+ assertNotNull(apexPolicyModel);
+
+ final AxValidationResult validationResult = new AxValidationResult();
+ apexPolicyModel.validate(validationResult);
+ assertTrue(validationResult.isValid());
+
+ final AxArtifactKey key = new AxArtifactKey("AnomalyTSLApexEngine", "0.0.1");
+ final EngineParameters parameters = new EngineParameters();
+ parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
+ parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
+
+ final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
+
+ final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
+ apexEngine1.addEventListener("listener", listener1);
+ apexEngine1.updateModel(apexPolicyModel);
+ apexEngine1.start();
+ final EnEvent triggerEvent =
+ apexEngine1.createEvent(new AxArtifactKey("AnomalyDetectionTriggerEvent", "0.0.1"));
+ final double rval = RAND.nextGaussian();
+ triggerEvent.put("Iteration", 0);
+ triggerEvent.put("MonitoredValue", rval);
+ LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
+ apexEngine1.handleEvent(triggerEvent);
+ final EnEvent result = listener1.getResult();
+ LOGGER.info("Receiving action event {} ", result);
+ assertEquals("ExecutionIDs are different", triggerEvent.getExecutionID(), result.getExecutionID());
+ triggerEvent.clear();
+ result.clear();
+ Thread.sleep(1);
+ apexEngine1.stop();
+ }
+
+ /**
+ * This policy passes, and recieves a Double event context filed called "EVCDouble"<br>
+ * The policy tries to detect anomalies in the pattern of values for EVCDouble <br>
+ * This test case generates a SineWave-like pattern for the parameter, repeating every 360 iterations. (These Period
+ * should probably be set using TaskParameters!) Every 361st value is a random number!, so should be identified as
+ * an Anomaly. The policy has 3 Decide Tasks, and the Decide TaskSelectionLogic picks one depending on the
+ * 'Anomaliness' of the input data. <br>
+ * To plot the results grep debug results for the string "************", paste into excel and delete non-relevant
+ * columns<br>
+ *
+ * @throws ApexException the apex exception
+ * @throws InterruptedException the interrupted exception
+ * @throws IOException Signals that an I/O exception has occurred.
+ */
+ // Test is disabled by default. uncomment below, or execute using the main() method
+ // @Test
+ // EG Dos command: apex-core.engine> mvn
+ // -Dtest=org.onap.policy.apex.core.engine.ml.TestAnomalyDetectionTSLUseCase test | findstr /L /C:"Apex [main] DEBUG
+ // c.e.a.e.TaskSelectionExecutionLogging -
+ // TestAnomalyDetectionTSL_Policy0000DecideStateTaskSelectionLogic.getTask():"
+ public void TestAnomalyDetectionTSL_main() throws ApexException, InterruptedException, IOException {
+
+ final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAnomalyDetectionPolicyModel();
+ assertNotNull(apexPolicyModel);
+
+ final AxValidationResult validationResult = new AxValidationResult();
+ apexPolicyModel.validate(validationResult);
+ assertTrue(validationResult.isValid());
+
+ final AxArtifactKey key = new AxArtifactKey("AnomalyTSLApexEngine", "0.0.1");
+ final EngineParameters parameters = new EngineParameters();
+ parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
+ parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
+
+ final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
+
+ final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
+ apexEngine1.addEventListener("listener1", listener1);
+ apexEngine1.updateModel(apexPolicyModel);
+ apexEngine1.start();
+
+ final EnEvent triggerEvent =
+ apexEngine1.createEvent(new AxArtifactKey("AnomalyDetectionTriggerEvent", "0.0.1"));
+ assertNotNull(triggerEvent);
+
+ for (int iteration = 0; iteration < MAXITERATIONS; iteration++) {
+ // Trigger the policy in engine 1
+
+ double value = (Math.sin(Math.toRadians(iteration))) + (RAND.nextGaussian() / 25.0);
+ // lets make every 361st number a random value to perhaps flag as an anomaly
+ if (((iteration + 45) % 361) == 0) {
+ value = (RAND.nextGaussian() * 2.0);
+ }
+ triggerEvent.put("Iteration", iteration);
+ triggerEvent.put("MonitoredValue", value);
+ LOGGER.info("Iteration " + iteration + ":\tTriggering policy in Engine 1 with " + triggerEvent);
+ apexEngine1.handleEvent(triggerEvent);
+ final EnEvent result = listener1.getResult();
+ LOGGER.info("Iteration " + iteration + ":\tReceiving action event {} ", result);
+ triggerEvent.clear();
+ result.clear();
+ }
+ apexEngine1.stop();
+ Thread.sleep(1000);
+ }
+
+ public static void main(final String[] args) throws ApexException, InterruptedException, IOException {
+ new TestAnomalyDetectionTSLUseCase().TestAnomalyDetectionTSL_main();
+ }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestApexActionListener.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestApexActionListener.java
new file mode 100644
index 000000000..ec7c197b7
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestApexActionListener.java
@@ -0,0 +1,91 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import org.onap.policy.apex.core.engine.engine.EnEventListener;
+import org.onap.policy.apex.core.engine.event.EnEvent;
+import org.onap.policy.apex.core.infrastructure.threading.ThreadUtilities;
+
+/**
+ * The listener interface for receiving testApexAction events. The class that is interested in processing a
+ * testApexAction event implements this interface, and the object created with that class is registered with a component
+ * using the component's <code>addTestApexActionListener</code> method. When the testApexAction event occurs, that
+ * object's appropriate method is invoked.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+public class TestApexActionListener implements EnEventListener {
+ private List<EnEvent> resultEvents = new ArrayList<>();
+
+ private final String id;
+
+ /**
+ * Instantiates a new test apex action listener.
+ *
+ * @param id the id
+ */
+ public TestApexActionListener(final String id) {
+ this.id = id;
+ }
+
+ /**
+ * Gets the result.
+ *
+ * @return the result
+ */
+ public EnEvent getResult() {
+ while (resultEvents.isEmpty()) {
+ ThreadUtilities.sleep(100);
+ }
+ return resultEvents.remove(0);
+ }
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see
+ * org.onap.policy.apex.core.engine.engine.EnEventListener#onEnEvent(org.onap.policy.apex.core.engine.event.EnEvent)
+ */
+ @Override
+ public void onEnEvent(final EnEvent actionEvent) {
+ try {
+ Thread.sleep(100);
+ } catch (final InterruptedException e) {
+ e.printStackTrace();
+ }
+ if (actionEvent != null) {
+ System.out.println("Action event from engine:" + actionEvent.getName());
+ resultEvents.add(actionEvent);
+ }
+ }
+
+ /**
+ * Gets the id.
+ *
+ * @return the id
+ */
+ public String getId() {
+ return id;
+ }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDBWrite.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDBWrite.java
new file mode 100644
index 000000000..64efa6c35
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnDBWrite.java
@@ -0,0 +1,60 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import java.io.File;
+import java.sql.Connection;
+import java.sql.DriverManager;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import org.onap.policy.apex.model.basicmodel.dao.DAOParameters;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+public class TestAutoLearnDBWrite {
+ private Connection connection;
+ TestApexModel<AxPolicyModel> testApexModel;
+
+ @Before
+ public void setup() throws Exception {
+ Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
+ connection = DriverManager.getConnection("jdbc:derby:memory:apex_test;create=true");
+
+ testApexModel = new TestApexModel<>(AxPolicyModel.class, new TestAutoLearnModelCreator());
+ }
+
+ @After
+ public void teardown() throws Exception {
+ connection.close();
+ new File("derby.log").delete();
+ }
+
+ @Test
+ public void testModelWriteReadJPA() throws Exception {
+ final DAOParameters daoParameters = new DAOParameters();
+ daoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
+ daoParameters.setPersistenceUnit("AdaptiveModelsTest");
+
+ testApexModel.testApexModelWriteReadJPA(daoParameters);
+ }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java
new file mode 100644
index 000000000..aa7f621c9
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModel.java
@@ -0,0 +1,81 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import static org.junit.Assert.assertTrue;
+
+import java.io.File;
+import java.sql.Connection;
+import java.sql.DriverManager;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.basicmodel.dao.DAOParameters;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModel;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+public class TestAutoLearnModel {
+ private Connection connection;
+ TestApexModel<AxPolicyModel> testApexModel;
+
+ @Before
+ public void setup() throws Exception {
+ Class.forName("org.apache.derby.jdbc.EmbeddedDriver").newInstance();
+ connection = DriverManager.getConnection("jdbc:derby:memory:apex_test;create=true");
+
+ testApexModel = new TestApexModel<>(AxPolicyModel.class, new TestAutoLearnModelCreator());
+ }
+
+ @After
+ public void teardown() throws Exception {
+ connection.close();
+ new File("derby.log").delete();
+ }
+
+ @Test
+ public void testModelValid() throws Exception {
+ final AxValidationResult result = testApexModel.testApexModelValid();
+ assertTrue(result.toString().equals(VALID_MODEL_STRING));
+ }
+
+ @Test
+ public void testModelWriteReadXML() throws Exception {
+ testApexModel.testApexModelWriteReadXML();
+ }
+
+ @Test
+ public void testModelWriteReadJSON() throws Exception {
+ testApexModel.testApexModelWriteReadJSON();
+ }
+
+ @Test
+ public void testModelWriteReadJPA() throws Exception {
+ final DAOParameters daoParameters = new DAOParameters();
+ daoParameters.setPluginClass("org.onap.policy.apex.model.basicmodel.dao.impl.DefaultApexDao");
+ daoParameters.setPersistenceUnit("AdaptiveModelsTest");
+
+ testApexModel.testApexModelWriteReadJPA(daoParameters);
+ }
+
+ private static final String VALID_MODEL_STRING = "***validation of model successful***";
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java
new file mode 100644
index 000000000..11f1991bf
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnModelCreator.java
@@ -0,0 +1,56 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
+import org.onap.policy.apex.model.basicmodel.test.TestApexModelCreator;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+
+/**
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+public class TestAutoLearnModelCreator implements TestApexModelCreator<AxPolicyModel> {
+
+ @Override
+ public AxPolicyModel getModel() {
+ return new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
+ }
+
+ @Override
+ public AxPolicyModel getMalstructuredModel() {
+ return null;
+ }
+
+ @Override
+ public AxPolicyModel getObservationModel() {
+ return null;
+ }
+
+ @Override
+ public AxPolicyModel getWarningModel() {
+ return getModel();
+ }
+
+ @Override
+ public AxPolicyModel getInvalidModel() {
+ return null;
+ }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTSLUseCase.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTSLUseCase.java
new file mode 100644
index 000000000..88b504cef
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/TestAutoLearnTSLUseCase.java
@@ -0,0 +1,187 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+package org.onap.policy.apex.examples.adaptive;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertTrue;
+
+import java.io.IOException;
+import java.util.Random;
+
+import org.junit.Test;
+import org.onap.policy.apex.core.engine.EngineParameters;
+import org.onap.policy.apex.core.engine.engine.ApexEngine;
+import org.onap.policy.apex.core.engine.engine.impl.ApexEngineFactory;
+import org.onap.policy.apex.core.engine.event.EnEvent;
+import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
+import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
+import org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey;
+import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
+import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
+import org.onap.policy.apex.plugins.executor.java.JavaExecutorParameters;
+import org.onap.policy.apex.plugins.executor.mvel.MVELExecutorParameters;
+import org.slf4j.ext.XLogger;
+import org.slf4j.ext.XLoggerFactory;
+
+/**
+ * Test Auto learning in TSL.
+ *
+ * @author John Keeney (John.Keeney@ericsson.com)
+ */
+public class TestAutoLearnTSLUseCase {
+ private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAutoLearnTSLUseCase.class);
+
+ private static final int MAXITERATIONS = 1000;
+ private static final Random rand = new Random(System.currentTimeMillis());
+
+ @Test
+ // once through the long running test below
+ public void TestAutoLearnTSL() throws ApexException, InterruptedException, IOException {
+ final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
+ assertNotNull(apexPolicyModel);
+
+ final AxValidationResult validationResult = new AxValidationResult();
+ apexPolicyModel.validate(validationResult);
+ assertTrue(validationResult.isValid());
+
+ final AxArtifactKey key = new AxArtifactKey("AADMApexEngine", "0.0.1");
+ final EngineParameters parameters = new EngineParameters();
+ parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
+ parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
+
+ final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
+
+ final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
+ apexEngine1.addEventListener("listener", listener1);
+ apexEngine1.updateModel(apexPolicyModel);
+ apexEngine1.start();
+ final EnEvent triggerEvent = apexEngine1.createEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"));
+ final double rval = rand.nextGaussian();
+ triggerEvent.put("MonitoredValue", rval);
+ triggerEvent.put("LastMonitoredValue", 0D);
+ LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
+ apexEngine1.handleEvent(triggerEvent);
+ final EnEvent result = listener1.getResult();
+ LOGGER.info("Receiving action event {} ", result);
+ assertEquals("ExecutionIDs are different", triggerEvent.getExecutionID(), result.getExecutionID());
+ triggerEvent.clear();
+ result.clear();
+ Thread.sleep(1);
+ apexEngine1.stop();
+ }
+
+ /**
+ * This policy passes, and receives a Double event context filed called "EVCDouble"<br>
+ * The policy tries to keep the value at 50, with a Min -100, Max 100 (These should probably be set using
+ * TaskParameters!)<br>
+ * The policy has 7 Decide Tasks that manipulate the value of this field in unknown ways.<br>
+ * The Decide TSL learns the effect of each task, and then selects the appropriate task to get the value back to
+ * 50<br>
+ * After the value settles close to 50 for a while, the test Rests the value to to random number and then
+ * continues<br>
+ * To plot the results grep stdout debug results for the string "*******", paste into excel and delete non-relevant
+ * columns<br>
+ *
+ * @throws ApexException the apex exception
+ * @throws InterruptedException the interrupted exception
+ * @throws IOException Signals that an I/O exception has occurred.
+ */
+ // @Test
+ public void TestAutoLearnTSL_main() throws ApexException, InterruptedException, IOException {
+
+ final double WANT = 50.0;
+ final double toleranceTileJump = 3.0;
+
+ final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
+ assertNotNull(apexPolicyModel);
+
+ final AxValidationResult validationResult = new AxValidationResult();
+ apexPolicyModel.validate(validationResult);
+ assertTrue(validationResult.isValid());
+
+ final AxArtifactKey key = new AxArtifactKey("AADMApexEngine", "0.0.1");
+ final EngineParameters parameters = new EngineParameters();
+ parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
+ parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
+
+ final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
+
+ final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
+ apexEngine1.addEventListener("listener1", listener1);
+ apexEngine1.updateModel(apexPolicyModel);
+ apexEngine1.start();
+
+ final EnEvent triggerEvent = apexEngine1.createEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"));
+ assertNotNull(triggerEvent);
+ final double MIN = -100;
+ final double MAX = 100;
+
+ double rval = (((rand.nextGaussian() + 1) / 2) * (MAX - MIN)) + MIN;
+ triggerEvent.put("MonitoredValue", rval);
+ triggerEvent.put("LastMonitoredValue", 0);
+
+ double avval = 0;
+ double distance;
+ double avcount = 0;
+
+ for (int iteration = 0; iteration < MAXITERATIONS; iteration++) {
+ // Trigger the policy in engine 1
+ LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
+ apexEngine1.handleEvent(triggerEvent);
+ final EnEvent result = listener1.getResult();
+ LOGGER.info("Receiving action event {} ", result);
+ triggerEvent.clear();
+
+ double val = (Double) result.get("MonitoredValue");
+ final double prevval = (Double) result.get("LastMonitoredValue");
+
+ triggerEvent.put("MonitoredValue", prevval);
+ triggerEvent.put("LastMonitoredValue", val);
+
+ avcount = Math.min((avcount + 1), 20); // maintain average of only the last 20 values
+ avval = ((avval * (avcount - 1)) + val) / (avcount);
+
+ distance = Math.abs(WANT - avval);
+ if (distance < toleranceTileJump) {
+ rval = (((rand.nextGaussian() + 1) / 2) * (MAX - MIN)) + MIN;
+ val = rval;
+ triggerEvent.put("MonitoredValue", val);
+ LOGGER.info("Iteration " + iteration + ": Average " + avval + " has become closer (" + distance
+ + ") than " + toleranceTileJump + " to " + WANT + " so reseting val:\t\t\t\t\t\t\t\t" + val);
+ avval = 0;
+ avcount = 0;
+ }
+ LOGGER.info("Iteration " + iteration + ": \tpreval\t" + prevval + "\tval\t" + val + "\tavval\t" + avval);
+
+ result.clear();
+ Thread.sleep(1);
+ }
+
+ apexEngine1.stop();
+ Thread.sleep(1000);
+
+ }
+
+ public static void main(final String[] args) throws ApexException, InterruptedException, IOException {
+ new TestAutoLearnTSLUseCase().TestAutoLearnTSL_main();
+ }
+}
diff --git a/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/package-info.java b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/package-info.java
new file mode 100644
index 000000000..0e004fe8d
--- /dev/null
+++ b/examples/examples-adaptive/src/test/java/org/onap/policy/apex/examples/adaptive/package-info.java
@@ -0,0 +1,27 @@
+/*-
+ * ============LICENSE_START=======================================================
+ * Copyright (C) 2016-2018 Ericsson. 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.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ * ============LICENSE_END=========================================================
+ */
+
+/**
+ * Defines domain and policies demonstrating adaptive policies.
+ *
+ * @author Liam Fallon (liam.fallon@ericsson.com)
+ */
+
+package org.onap.policy.apex.examples.adaptive;
diff --git a/examples/examples-adaptive/src/test/resources/META-INF/persistence.xml b/examples/examples-adaptive/src/test/resources/META-INF/persistence.xml
new file mode 100644
index 000000000..7282bb562
--- /dev/null
+++ b/examples/examples-adaptive/src/test/resources/META-INF/persistence.xml
@@ -0,0 +1,69 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+ ============LICENSE_START=======================================================
+ Copyright (C) 2016-2018 Ericsson. 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.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+
+ SPDX-License-Identifier: Apache-2.0
+ ============LICENSE_END=========================================================
+-->
+
+<persistence xmlns="http://java.sun.com/xml/ns/persistence" version="2.0">
+ <persistence-unit name="AdaptiveModelsTest" transaction-type="RESOURCE_LOCAL">
+ <provider>org.eclipse.persistence.jpa.PersistenceProvider</provider>
+
+ <class>org.onap.policy.apex.model.basicmodel.dao.converters.CDATAConditioner</class>
+ <class>org.onap.policy.apex.model.basicmodel.dao.converters.UUID2String</class>
+ <class>org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey</class>
+ <class>org.onap.policy.apex.model.basicmodel.concepts.AxConcept</class>
+ <class>org.onap.policy.apex.model.basicmodel.concepts.AxKeyInfo</class>
+ <class>org.onap.policy.apex.model.basicmodel.concepts.AxKeyInformation</class>
+ <class>org.onap.policy.apex.model.basicmodel.concepts.AxModel</class>
+ <class>org.onap.policy.apex.model.basicmodel.concepts.TestEntity</class>
+ <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextSchema</class>
+ <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextSchemas</class>
+ <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextAlbum</class>
+ <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextAlbums</class>
+ <class>org.onap.policy.apex.model.contextmodel.concepts.AxContextModel</class>
+ <class>org.onap.policy.apex.model.eventmodel.concepts.AxField</class>
+ <class>org.onap.policy.apex.model.eventmodel.concepts.AxInputField</class>
+ <class>org.onap.policy.apex.model.eventmodel.concepts.AxOutputField</class>
+ <class>org.onap.policy.apex.model.eventmodel.concepts.AxEvent</class>
+ <class>org.onap.policy.apex.model.eventmodel.concepts.AxEvents</class>
+ <class>org.onap.policy.apex.model.eventmodel.concepts.AxEventModel</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxLogic</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxTaskParameter</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxTaskLogic</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxTask</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxTasks</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxTaskSelectionLogic</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxStateFinalizerLogic</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxStateOutput</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxStateTaskReference</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxState</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxPolicy</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxPolicies</class>
+ <class>org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel</class>
+
+ <properties>
+ <property name="javax.persistence.jdbc.url" value="jdbc:derby:memory:apex_test" />
+ <property name="javax.persistence.target-database" value="Derby" />
+ <property name="javax.persistence.jdbc.driver" value="org.apache.derby.jdbc.EmbeddedDriver" />
+
+ <property name="eclipselink.ddl-generation" value="drop-and-create-tables" />
+ <property name="eclipselink.ddl-generation.output-mode" value="database" />
+ <property name="eclipselink.logging.level" value="INFO" />
+ </properties>
+ </persistence-unit>
+</persistence>