diff --git a/eraser-base/src/main/jastadd/MachineLearning.jrag b/eraser-base/src/main/jastadd/MachineLearning.jrag
index 1a912dbd92455054b358ddfee00e94cb1c71ca43..d92c35ecaffb0b099b95db4618c7d0799d6a9b7a 100644
--- a/eraser-base/src/main/jastadd/MachineLearning.jrag
+++ b/eraser-base/src/main/jastadd/MachineLearning.jrag
@@ -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";
diff --git a/eraser-base/src/main/jastadd/MachineLearning.relast b/eraser-base/src/main/jastadd/MachineLearning.relast
index 0101465620191b440674f8d17933f3259c20ff42..5bfeaddda253ba37b4d730dfd3638e2c1b1b70d5 100644
--- a/eraser-base/src/main/jastadd/MachineLearning.relast
+++ b/eraser-base/src/main/jastadd/MachineLearning.relast
@@ -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 ;
diff --git a/eraser-base/src/main/java/de/tudresden/inf/st/eraser/openhab2/mqtt/MQTTUpdater.java b/eraser-base/src/main/java/de/tudresden/inf/st/eraser/openhab2/mqtt/MQTTUpdater.java
index 829726efc57bec4f931de8c1e0e046a63a9a40eb..386e6f325dc4fde274507902843a096614e09043 100644
--- a/eraser-base/src/main/java/de/tudresden/inf/st/eraser/openhab2/mqtt/MQTTUpdater.java
+++ b/eraser-base/src/main/java/de/tudresden/inf/st/eraser/openhab2/mqtt/MQTTUpdater.java
@@ -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);
}
diff --git a/eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/Application.java b/eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/Application.java
index 2ede06b3fef46cacc2dc3427ddc488f891b607e2..ee41ce60dcb3c50555095fa054af50e69e25b21b 100644
--- a/eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/Application.java
+++ b/eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/Application.java
@@ -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) {
diff --git a/eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/DummyDataCreator.java b/eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/DummyDataCreator.java
index 902f15bbd31fd7480bd413a239526b9576f68015..e548a6bc95012ba72675131395b9956f4a84466f 100644
--- a/eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/DummyDataCreator.java
+++ b/eraser.spark/src/main/java/de/tudresden/inf/st/eraser/spark/DummyDataCreator.java
@@ -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;
}
diff --git a/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java b/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java
index b207dc5fb16ff63b5601b3bab92a1b33b29c1241..1bf2b06887fb97a4479c4dad1e1a6d05603ffe81 100644
--- a/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java
+++ b/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java
@@ -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();
diff --git a/feedbackloop.execute/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/execute/ExecuteImpl.java b/feedbackloop.execute/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/execute/ExecuteImpl.java
index 1fa433ad425df55e2d9c44b66c396786cddea84e..80d2346312445914306e6c224d1f9de1b766cf6d 100644
--- a/feedbackloop.execute/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/execute/ExecuteImpl.java
+++ b/feedbackloop.execute/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/execute/ExecuteImpl.java
@@ -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);
}
diff --git a/feedbackloop.learner/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner/Main.java b/feedbackloop.learner/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner/Main.java
index 19765c1e6df76fb2e2701b3a5eebb2e302728b6e..97c90b4aca7ec11535f96d1f3c184da92a471f44 100644
--- a/feedbackloop.learner/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner/Main.java
+++ b/feedbackloop.learner/src/main/java/de/tudresden/inf/st/eraser/feedbackloop/learner/Main.java
@@ -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);