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Commit d415c8a8 authored by René Schöne's avatar René Schöne
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Fixing errors after separation of models.

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eraser-base/src/main/jastadd/MachineLearning.jrag
+ 8
− 0
View file @ d415c8a8
......@@ -85,6 +85,9 @@ aspect MachineLearning {
* Checks the ML model for all necessary children.
* @return true, if everything is alright. false otherwise
*/
public abstract boolean MachineLearningModel.check();
@Override
public boolean InternalMachineLearningModel.check() {
boolean good = true;
if (getOutputApplication() == null) {
......@@ -109,6 +112,11 @@ aspect MachineLearning {
return good;
}
@Override
public boolean ExternalMachineLearningModel.check() {
throw new UnsupportedOperationException("check not available for external ML models (yet)!");
}
//--- mlKind ---
inh String MachineLearningModel.mlKind();
eq MachineLearningRoot.getActivityRecognition().mlKind() = "ActivityRecognition";
......
eraser-base/src/main/jastadd/MachineLearning.relast
+ 2
− 2
View file @ d415c8a8
......@@ -16,8 +16,8 @@ ManualChangeEvent : ChangeEvent ;
abstract MachineLearningModel ::= ;
ExternalMachineLearningModel : MachineLearningModel ;
abstract InternalMachineLearningModel : MachineLearningModel ::= <OutputApplication:DoubleDoubleFunction> ;
rel InternalMachineLearningModel.RelevantItem* -> Item ;
rel InternalMachineLearningModel.TargetItem* -> Item ;
rel InternalMachineLearningModel.RelevantItem* <-> Item.RelevantInMachineLearningModel* ;
rel InternalMachineLearningModel.TargetItem* <-> Item.TargetInMachineLearningModel* ;
abstract ItemPreference ::= ;
rel ItemPreference.Item -> Item ;
......
eraser-base/src/main/java/de/tudresden/inf/st/eraser/openhab2/mqtt/MQTTUpdater.java
+ 9
− 18
View file @ d415c8a8
......@@ -6,19 +6,10 @@ import de.tudresden.inf.st.eraser.jastadd.model.Root;
import de.tudresden.inf.st.eraser.util.MqttReceiver;
import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;
import org.fusesource.hawtbuf.Buffer;
import org.fusesource.hawtbuf.UTF8Buffer;
import org.fusesource.mqtt.client.*;
import org.fusesource.mqtt.client.QoS;
import java.io.IOException;
import java.net.URI;
import java.util.Arrays;
import java.util.Objects;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicReference;
import java.util.concurrent.locks.Condition;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;
/**
* Update an imported model by subscribing to MQTT topics.
......@@ -37,22 +28,22 @@ public class MQTTUpdater implements AutoCloseable {
this.delegatee = new MqttReceiver();
}
public MQTTUpdater(Root model) throws IllegalArgumentException {
public MQTTUpdater(Root root) throws IllegalArgumentException {
this();
this.setModel(model);
this.setRoot(root);
}
/**
* Sets the model to update
* @param model the model to update
* Sets the model root to update
* @param root the model root to update
*/
public void setModel(Root model) {
ExternalHost host = model.getMqttRoot().getHost();
public void setRoot(Root root) {
ExternalHost host = root.getMqttRoot().getHost();
delegatee.setHost(host.getHostName(), host.getPort());
delegatee.setOnMessage((topicString, message) ->
model.getMqttRoot().resolveTopic(topicString).ifPresent(topic ->
root.getMqttRoot().resolveTopic(topicString).ifPresent(topic ->
itemUpdate(topic.getItem(), message)));
delegatee.setTopicsForSubscription(model.getMqttRoot().getIncomingPrefix() + "#");
delegatee.setTopicsForSubscription(root.getMqttRoot().getIncomingPrefix() + "#");
delegatee.setQoSForSubscription(QoS.AT_LEAST_ONCE);
}
......
eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/Application.java
+ 14
− 15
View file @ d415c8a8
......@@ -9,7 +9,6 @@ import spark.Request;
import spark.Response;
import spark.Spark;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
......@@ -26,12 +25,12 @@ import java.util.stream.Collectors;
public class Application {
private static final Logger logger = LogManager.getLogger(Application.class);
private final Root model;
private final Root root;
private final Lock lock;
private final Condition quitCondition;
private Application(Root model, Lock lock, Condition quitCondition) {
this.model = model;
private Application(Root root, Lock lock, Condition quitCondition) {
this.root = root;
this.lock = lock;
this.quitCondition = quitCondition;
}
......@@ -43,19 +42,19 @@ public class Application {
Spark.path("/activity", () -> {
Spark.get("",
(request, response) -> wrapActivityList(model.getMachineLearningRoot().getActivityList()),
(request, response) -> wrapActivityList(root.getMachineLearningRoot().getActivityList()),
mapper::writeValueAsString);
Spark.get("/current",
(request, response) -> JavaUtils.ifPresentOrElseReturn(model.currentActivity(),
(request, response) -> JavaUtils.ifPresentOrElseReturn(root.currentActivity(),
this::wrapActivity,
() -> makeError(response, 204, "No activity recognized.")),
mapper::writeValueAsString);
Spark.put("/current", (request, response) -> {
logger.info("request body: '{}', params: '{}', length={}", request.body(), request.params(), request.contentLength());
if (!model.getMachineLearningRoot().hasActivityRecognition()) {
if (!root.getMachineLearningRoot().hasActivityRecognition()) {
return makeError(response, 404, "No activity recognition model found");
}
MachineLearningModel activityRecognition = model.getMachineLearningRoot().getActivityRecognition();
MachineLearningModel activityRecognition = root.getMachineLearningRoot().getActivityRecognition();
if (activityRecognition.canSetActivity()) {
activityRecognition.setActivity(Integer.valueOf(request.body()));
return "OK";
......@@ -65,7 +64,7 @@ public class Application {
});
Spark.get("/:identifier",
(request, response) ->
JavaUtils.ifPresentOrElseReturn(model.resolveActivity(paramAsInt(request, "identifier")),
JavaUtils.ifPresentOrElseReturn(root.resolveActivity(paramAsInt(request, "identifier")),
this::wrapActivity,
() -> makeError(response, 404, "No activity for identifier " + request.params("identifier"))),
mapper::writeValueAsString);
......@@ -73,11 +72,11 @@ public class Application {
Spark.path("/events", () -> {
Spark.get("",
(request, response) -> wrapChangeEventList(model.getMachineLearningRoot().getChangeEventList()),
(request, response) -> wrapChangeEventList(root.getMachineLearningRoot().getChangeEventList()),
mapper::writeValueAsString);
Spark.get("/:identifier",
(request, response) ->
JavaUtils.ifPresentOrElseReturn(model.resolveChangeEvent(paramAsInt(request, "identifier")),
JavaUtils.ifPresentOrElseReturn(root.resolveChangeEvent(paramAsInt(request, "identifier")),
this::wrapChangeEvent,
() -> makeError(response, 404, "No event for identifier " + request.params("identifier"))),
mapper::writeValueAsString);
......@@ -86,10 +85,10 @@ public class Application {
Spark.path("/model", () -> {
Spark.get("/full", (request, response) -> {
response.type("text/plain");
return model.prettyPrint();
return root.prettyPrint();
});
Spark.get("/items",
(request, response) -> wrapItemList(model.items()),
(request, response) -> wrapItemList(root.getOpenHAB2Model().items()),
mapper::writeValueAsString);
Spark.put("/items/:identifier/state", (request, response) -> {
logger.info("request body: '{}', params: '{}', length={}", request.body(), request.params(), request.contentLength());
......@@ -124,13 +123,13 @@ public class Application {
}
private Object safeItemRoute(Request request, Response response, Function<Item, String> action) {
return JavaUtils.ifPresentOrElseReturn(model.resolveItem(request.params("identifier")), action,
return JavaUtils.ifPresentOrElseReturn(root.getOpenHAB2Model().resolveItem(request.params("identifier")), action,
() -> makeError(response, 404, "Item '" + request.body() + "' not found"));
}
private String makeHistory(Item item, Response response) {
response.type("text/plain");
InfluxAdapter influxAdapter = model.getInfluxRoot().influxAdapter();
InfluxAdapter influxAdapter = root.getInfluxRoot().influxAdapter();
influxAdapter.disableAsyncQuery();
List<? extends AbstractItemPoint> list;
if (item.asColorItem() != null) {
......
eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/DummyDataCreator.java
+ 7
− 7
View file @ d415c8a8
......@@ -10,21 +10,21 @@ import de.tudresden.inf.st.eraser.util.JavaUtils;
*/
class DummyDataCreator {
private final Root model;
private final Root root;
DummyDataCreator(Root model) {
this.model = model;
DummyDataCreator(Root root) {
this.root = root;
}
private void addDummyActivitiesToModel() {
MachineLearningRoot mlRoot = model.getMachineLearningRoot();
MachineLearningRoot mlRoot = root.getMachineLearningRoot();
mlRoot.addActivity(new Activity(1, "Sitting in armchair"));
mlRoot.addActivity(new Activity(2, "Going to sleep"));
mlRoot.addActivity(new Activity(3, "Entering house"));
}
private void addDummyChangeEventsToModel() {
MachineLearningRoot mlRoot = model.getMachineLearningRoot();
MachineLearningRoot mlRoot = root.getMachineLearningRoot();
Item iris1 = getOrCreateColorItem("iris1", "Hue Iris 1");
Item go1 = getOrCreateColorItem("go1", "Hue Go 1");
Item go2 = getOrCreateColorItem("go2", "Hue Go 2");
......@@ -40,7 +40,7 @@ class DummyDataCreator {
}
private Item getOrCreateColorItem(String name, String label) {
return model.resolveItem(name).orElseGet(() -> {
return root.getOpenHAB2Model().resolveItem(name).orElseGet(() -> {
ColorItem result = new ColorItem();
result.setID(name);
result.setLabel(label);
......@@ -53,7 +53,7 @@ class DummyDataCreator {
private RecognitionEvent newRecognitionEvent(int identifier, long timestamp, int activityIdentifier, ChangedItem... changedItems) {
RecognitionEvent result = new RecognitionEvent();
JavaUtils.ifPresentOrElse(model.resolveActivity(activityIdentifier), result::setActivity, () -> { throw new RuntimeException("No activity found for identifier " + activityIdentifier); });
JavaUtils.ifPresentOrElse(root.resolveActivity(activityIdentifier), result::setActivity, () -> { throw new RuntimeException("No activity found for identifier " + activityIdentifier); });
initChangeEvent(result, identifier, timestamp, changedItems);
return result;
}
......
eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java
+ 20
− 19
View file @ d415c8a8
......@@ -13,10 +13,7 @@ import de.tudresden.inf.st.eraser.feedbackloop.execute.ExecuteImpl;
import de.tudresden.inf.st.eraser.feedbackloop.learner.LearnerHelper;
import de.tudresden.inf.st.eraser.feedbackloop.learner.LearnerImpl;
import de.tudresden.inf.st.eraser.feedbackloop.plan.PlanImpl;
import de.tudresden.inf.st.eraser.jastadd.model.DummyMachineLearningModel;
import de.tudresden.inf.st.eraser.jastadd.model.InfluxAdapter;
import de.tudresden.inf.st.eraser.jastadd.model.NeuralNetworkRoot;
import de.tudresden.inf.st.eraser.jastadd.model.Root;
import de.tudresden.inf.st.eraser.jastadd.model.*;
import de.tudresden.inf.st.eraser.openhab2.OpenHab2Importer;
import de.tudresden.inf.st.eraser.openhab2.mqtt.MQTTUpdater;
import de.tudresden.inf.st.eraser.spark.Application;
......@@ -87,12 +84,14 @@ public class EraserStarter {
logger.info("Starting ERASER");
boolean startRest = settings.rest.use;
Root model;
Root root;
OpenHAB2Model model;
switch (settings.initModelWith) {
case openhab:
OpenHab2Importer importer = new OpenHab2Importer();
try {
model = importer.importFrom(new URL(settings.openhab.url));
root = model.getRoot();
} catch (MalformedURLException e) {
logger.error("Could not parse URL {}", settings.openhab.url);
logger.catching(e);
......@@ -104,7 +103,8 @@ public class EraserStarter {
case load:
default:
try {
model = ParserUtils.load(settings.load.realURL());
root = ParserUtils.load(settings.load.realURL());
model = root.getOpenHAB2Model();
} catch (IOException | Parser.Exception e) {
logger.error("Problems parsing the given file {}", settings.load.file);
logger.catching(e);
......@@ -114,9 +114,10 @@ public class EraserStarter {
}
// initialize activity recognition
MachineLearningRoot machineLearningRoot = root.getMachineLearningRoot();
if (settings.activity.dummy) {
logger.info("Using dummy activity recognition");
model.getMachineLearningRoot().setActivityRecognition(DummyMachineLearningModel.createDefault());
machineLearningRoot.setActivityRecognition(DummyMachineLearningModel.createDefault());
} else {
logger.error("Reading activity recognition from file is not supported yet!");
// TODO
......@@ -125,7 +126,7 @@ public class EraserStarter {
// initialize preference learning
if (settings.preference.dummy) {
logger.info("Using dummy preference learning");
model.getMachineLearningRoot().setPreferenceLearning(DummyMachineLearningModel.createDefault());
machineLearningRoot.setPreferenceLearning(DummyMachineLearningModel.createDefault());
} else {
logger.info("Reading preference learning from file {}", settings.preference.file);
Learner learner = new LearnerImpl();
......@@ -146,7 +147,7 @@ public class EraserStarter {
if (neuralNetwork == null) {
logger.error("Could not create preference model, see possible previous errors.");
} else {
model.getMachineLearningRoot().setPreferenceLearning(neuralNetwork);
machineLearningRoot.setPreferenceLearning(neuralNetwork);
neuralNetwork.setOutputApplication(zeroToThree -> 33 * zeroToThree);
JavaUtils.ifPresentOrElse(
model.resolveItem(settings.preference.affectedItem),
......@@ -156,15 +157,15 @@ public class EraserStarter {
} else {
// loading was not successful
logger.warn("Falling back to dummy preference learning");
model.getMachineLearningRoot().setPreferenceLearning(DummyMachineLearningModel.createDefault());
machineLearningRoot.setPreferenceLearning(DummyMachineLearningModel.createDefault());
}
}
model.getMachineLearningRoot().getPreferenceLearning().connectItems(settings.preference.items);
if (!model.getMachineLearningRoot().getActivityRecognition().check()) {
machineLearningRoot.getPreferenceLearning().connectItems(settings.preference.items);
if (!machineLearningRoot.getActivityRecognition().check()) {
logger.fatal("Invalid activity recognition!");
System.exit(1);
}
if (!model.getMachineLearningRoot().getPreferenceLearning().check()) {
if (!machineLearningRoot.getPreferenceLearning().check()) {
logger.fatal("Invalid preference learning!");
System.exit(1);
}
......@@ -183,9 +184,9 @@ public class EraserStarter {
analyze.setPlan(plan);
plan.setExecute(execute);
analyze.setKnowledgeBase(model);
plan.setKnowledgeBase(model);
execute.setKnowledgeBase(model);
analyze.setKnowledgeBase(root);
plan.setKnowledgeBase(root);
execute.setKnowledgeBase(root);
analyze.startAsThread(1, TimeUnit.SECONDS);
} else {
......@@ -195,7 +196,7 @@ public class EraserStarter {
if (settings.mqttUpdate) {
logger.info("Starting MQTT updater");
Thread t = new Thread(() -> {
try (MQTTUpdater updater = new MQTTUpdater(model)) {
try (MQTTUpdater updater = new MQTTUpdater(root)) {
updater.start();
updater.waitUntilReady(5, TimeUnit.SECONDS);
lock.lock();
......@@ -215,7 +216,7 @@ public class EraserStarter {
// start REST-API in new thread
logger.info("Starting REST server");
Thread t = new Thread(
() -> Application.start(settings.rest.port, model, settings.rest.createDummyMLData, lock, quitCondition),
() -> Application.start(settings.rest.port, root, settings.rest.createDummyMLData, lock, quitCondition),
"REST-API");
t.setDaemon(true);
t.start();
......@@ -249,7 +250,7 @@ public class EraserStarter {
if (analyze != null) {
analyze.stop();
}
InfluxAdapter influxAdapter = model.getInfluxRoot().influxAdapter();
InfluxAdapter influxAdapter = root.getInfluxRoot().influxAdapter();
if (influxAdapter != null) {
try {
influxAdapter.close();
......
feedbackloop.execute/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/execute/ExecuteImpl.java
+ 1
− 1
View file @ d415c8a8
......@@ -61,7 +61,7 @@ public class ExecuteImpl implements Execute {
}
private void resolveOrLogError(String itemId, Consumer<? super Item> consumer) {
Optional<Item> optionalItem = knowledgeBase.resolveItem(itemId);
Optional<Item> optionalItem = knowledgeBase.getOpenHAB2Model().resolveItem(itemId);
if (!optionalItem.isPresent()) {
logger.warn("Could not resolve '{}' as an item.", itemId);
}
......
feedbackloop.learner/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner/Main.java
+ 7
− 6
View file @ d415c8a8
......@@ -86,10 +86,10 @@ public class Main {
private static Root createModel(ArrayList<Double> normalizedInputs, int inputBiasNumber) {
//create KB Model
Root model = Root.createEmptyRoot();
Root root = Root.createEmptyRoot();
Group group = new Group();
group.setID("Group1");
model.addGroup(group);
root.getOpenHAB2Model().addGroup(group);
NumberItem monthItem = new NumberItem();
monthItem.setState(normalizedInputs.get(0));
......@@ -122,7 +122,7 @@ public class Main {
group.addItem(dayItem);
group.addItem(hourItem);
group.addItem(minuteItem);
return model;
return root;
}
/**
......@@ -130,7 +130,8 @@ public class Main {
*/
private static void createBrightnessNetwork(ArrayList<Double> all_weights, int inputBiasNumber, int hiddenlayernumber,
int hiddenlayerbias, ArrayList<Double> normalizedInputsandOutput) {
Root model = createModel(normalizedInputsandOutput, inputBiasNumber);
Root root = createModel(normalizedInputsandOutput, inputBiasNumber);
OpenHAB2Model model = root.getOpenHAB2Model();
Item monthItem = model.resolveItem("month").orElseThrow(
() -> new RuntimeException("Month not found"));
Item dayItem = model.resolveItem("day").orElseThrow(
......@@ -220,8 +221,8 @@ public class Main {
hiddenNeuron.connectTo(output3, weights.get(i + hiddenSum * 3));
}
}
model.getMachineLearningRoot().setPreferenceLearning(nn);
System.out.println(model.prettyPrint());
root.getMachineLearningRoot().setPreferenceLearning(nn);
System.out.println(root.prettyPrint());
List<String> output = new ArrayList<>();
Function<DoubleNumber, String> leafToString = classification -> Double.toString(classification.number);
......
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