Begin with test for learner.

feedbackloop.learner_backup/src/test/java/de/tudresden/inf/st/eraser/feedbackloop/learner_backup/LearnerTest.java

+package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
+
+import com.opencsv.CSVReader;
+import de.tudresden.inf.st.eraser.jastadd.model.*;
+import de.tudresden.inf.st.eraser.util.ParserUtils;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.io.Reader;
+import java.net.URL;
+import java.util.Collections;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.fail;
+
+/**
+ * Testing the learner.
+ *
+ * @author rschoene - Initial contribution
+ */
+public class LearnerTest {
+
+  private static final URL ACTIVITY_DATA = LearnerTest.class.getResource("/activity_data.csv");
+  private MachineLearningEncoder encoder;
+  private MachineLearningDecoder decoder;
+  private String[] itemNames = new String[]{"m_accel_x", "m_accel_y", "m_accel_z", "m_rotation_x", "m_rotation_y",
+      "m_rotation_z", "w_accel_x", "w_accel_y", "w_accel_z", "w_rotation_x", "w_rotation_y", "w_rotation_z"};
+  private String[] activityNames = new String[]{"working",
+      "walking",
+      "dancing",
+      "lying",
+      "getting up",
+      "reading"};
+  private Root root;
+
+  private Root createKnowledgeBase() {
+    Root result = Root.createEmptyRoot();
+    Group group = new Group();
+    result.getSmartHomeEntityModel().addGroup(group);
+    // init items
+    for (String itemName : itemNames) {
+      NumberItem item = new NumberItem();
+      item.setID(itemName);
+      ParserUtils.createMqttTopic(item, itemName, result);
+      group.addItem(item);
+    }
+    // init activities
+    for (int i = 0; i < activityNames.length; i++) {
+      result.getMachineLearningRoot().addActivity(new Activity(i, activityNames[i]));
+    }
+    return result;
+  }
+
+  @Before
+  public void initLearner() {
+    root = createKnowledgeBase();
+    MachineLearningHandlerFactoryImpl factory = new MachineLearningHandlerFactoryImpl();
+    factory.setKnowledgeBaseRoot(root);
+    factory.initializeFor(MachineLearningHandlerFactory.MachineLearningHandlerFactoryTarget.ACTIVITY_RECOGNITION,
+        ACTIVITY_DATA);
+    encoder = factory.createEncoder();
+    decoder = factory.createDecoder();
+    // maybe use factory.createModel() here instead
+  }
+
+  @Test
+  public void testActivities() {
+    // go through same csv as for training and test some of the values
+    try(InputStream is = ACTIVITY_DATA.openStream();
+        Reader reader = new InputStreamReader(is);
+        CSVReader csvreader = new CSVReader(reader)) {
+      int index = 0;
+      for (String[] line : csvreader) {
+        if (++index % 10 == 0) {
+          // only check every 10th line, push an update for every 12 input columns
+          for (int i = 0; i < itemNames.length; i++) {
+            int finalI = i;
+            Item item = root.getSmartHomeEntityModel().resolveItem(itemNames[i])
+                .orElseThrow(() -> new AssertionError("Item " + itemNames[finalI] + " not found"));
+            item.setStateFromString(line[i]);
+            encoder.newData(Collections.singletonList(item));
+          }
+          MachineLearningResult result = decoder.classify();
+          // check if only one item is to be updated
+          assertEquals("Not one item update", 1, result.getNumItemUpdate());
+          ItemUpdate update = result.getItemUpdate(0);
+          // check that the activity item is to be updated
+          assertEquals("activity not updated", root.getSmartHomeEntityModel().getActivityItem(), update.getItem());
+          update.apply();
+          // check if the correct activity was set
+          assertEquals("wrong activity", line[12], root.currentActivityName());
+        }
+      }
+    } catch (IOException e) {
+      e.printStackTrace();
+      fail();
+    }
+  }
+}

feedbackloop.learner_backup/src/test/resources/log4j2-test.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<Configuration>
+    <Appenders>
+        <Console name="Console">
+            <PatternLayout pattern="%highlight{%d{HH:mm:ss.SSS} %-5level} %c{1.} - %msg%n"/>
+        </Console>
+        <RollingFile name="RollingFile" fileName="logs/eraser-test.log"
+                    filePattern="logs/eraser-test-%i.log">
+            <PatternLayout pattern="%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n"/>
+            <Policies>
+                <OnStartupTriggeringPolicy/>
+            </Policies>
+            <DefaultRolloverStrategy max="20"/>
+        </RollingFile>
+    </Appenders>
+    <Loggers>
+        <Root level="debug">
+            <AppenderRef ref="Console"/>
+            <AppenderRef ref="RollingFile"/>
+        </Root>
+        <Logger name="de.tudresden.inf.st.eraser.openhab2.mqtt" level="DEBUG" additivity="false">
+            <Appender-ref ref="Console"/>
+        </Logger>
+        <!-- Stubs reduce noise-->
+        <Logger name="de.tudresden.inf.st.eraser.jastadd.model.InfluxAdapterStub" level="WARN" additivity="false">
+        </Logger>
+        <Logger name="de.tudresden.inf.st.eraser.jastadd.model.MQTTSenderStub" level="WARN" additivity="false">
+        </Logger>
+    </Loggers>
+</Configuration>

feedbackloop.learner_backup/src/test/resources/preference_data.csv

+walking,medium,120,70
+walking,bright,120,0
+walking,medium,120,70
+walking,bright,120,0
+walking,bright,120,0
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+reading,medium,180,70
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+working,medium,240,70
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+dancing,dimmer,300,40
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+dancing,medium,300,70
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+lying,medium,7,70
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+getting up,bright,60,0
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+getting up,medium,60,70
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+getting up,bright,60,0
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+getting up,dimmer,60,40
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+getting up,dark,60,100
+getting up,medium,60,70
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+getting up,medium,60,70
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+getting up,dimmer,60,40
+getting up,medium,60,70
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+getting up,dimmer,60,40
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+getting up,medium,60,70
+getting up,medium,60,70
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+getting up,dimmer,60,40
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+getting up,bright,60,0
+getting up,medium,60,70
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+getting up,medium,60,70
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