1 files changed, 187 insertions, 0 deletions
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();
+    }
+}