diff --git a/benchmark/src/main/java/de/tudresden/inf/st/eraser/benchmark/Benchmark.java b/benchmark/src/main/java/de/tudresden/inf/st/eraser/benchmark/Benchmark.java
index 2c1d64d5be93238589167e0c474218d3b22a952f..490c519c3dbb039aaee6e564e569088082b74b37 100644
--- a/benchmark/src/main/java/de/tudresden/inf/st/eraser/benchmark/Benchmark.java
+++ b/benchmark/src/main/java/de/tudresden/inf/st/eraser/benchmark/Benchmark.java
@@ -38,20 +38,14 @@ public class Benchmark {
private static final Logger logger = LogManager.getLogger(Benchmark.class);
private static final String ERASER_ITEM_URI = "http://localhost:4567/model/items/";
//TODO ITEM_NAME HAS TO BE DISCUSSED
- private static final String[] ACTIVITY_ITEM_NAME = {
- "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 static final String[] PREFERENCE_ITEM_NAME = {
- "w_brightness"
- };
+ private static String[] ACTIVITY_ITEM_NAME;
+ private static String[] PREFERENCE_ITEM_NAME;
private static boolean flag2 = true;
//csv_type is activity or preference
Benchmark(String a_csv_file_path, String p_csv_file_path) {
this.a_csv_file_path = a_csv_file_path;
this.p_csv_file_path = p_csv_file_path;
}
-
void start(){
String PREFERENCE_URL = "http://localhost:4567/model/items/iris1_item/state";
String ACTIVITY_URL = "http://localhost:4567/activity/current";
@@ -72,34 +66,47 @@ public class Benchmark {
p_file_reader=new FileReader(p_file);
a_csv_reader = new CSVReader(a_file_reader);
p_csv_reader = new CSVReader(p_file_reader);
+ int i = 0;
while ((((a_next_record = a_csv_reader.readNext())!= null) && flag2)){
- try{Thread.sleep(TIME_PERIOD);}catch (InterruptedException e){e.printStackTrace();}
- String[] values = Arrays.copyOf(a_next_record,12);
- setNewValue(values, ACTIVITY_ITEM_NAME,"activity");
- HttpResponse response= Request.Get(ACTIVITY_URL).execute().returnResponse();
- String status = response.getStatusLine().toString();
- if(status.contains("200")){
- flag1 = true;
- logger.info("activity should be (read direct from CSV): " + a_next_record[12]);
- logger.info(EntityUtils.toString(response.getEntity()));
- logger.info("get activity from web server: response 200 ok");
- }else{
- flag1 = false;
- flag2 = false;
- logger.info("can not get the activity from the web server");
+ if( i==0 ){
+ getCSVHeader(Arrays.copyOf(a_next_record,12),"a");
+ i++;
}
- while((((p_next_record = p_csv_reader.readNext()) != null) && flag1)) {
+ else{
try{Thread.sleep(TIME_PERIOD);}catch (InterruptedException e){e.printStackTrace();}
- String[] values1 = Arrays.copyOf(p_next_record,2);
- setNewValue(values1, PREFERENCE_ITEM_NAME,"preference");
- HttpResponse response1= Request.Get(PREFERENCE_URL).execute().returnResponse();
- String status1=response1.getStatusLine().toString();
- if(status1.contains("200")){
- flag2 = true;
- logger.info("get the iris1_item preference from web server: response 200 ok, value is: "+EntityUtils.toString(response1.getEntity()));
- }else {flag2 = false;
- logger.info("can not get the iris1_item from the web server");}
- break;
+ String[] values = Arrays.copyOf(a_next_record,12);
+ setNewValue(values, ACTIVITY_ITEM_NAME,"activity");
+ HttpResponse response= Request.Get(ACTIVITY_URL).execute().returnResponse();
+ String status = response.getStatusLine().toString();
+ if(status.contains("200")){
+ flag1 = true;
+ logger.info("activity should be (read direct from CSV): " + a_next_record[12]);
+ logger.info(EntityUtils.toString(response.getEntity()));
+ logger.info("get activity from web server: response 200 ok");
+ }else{
+ flag1 = false;
+ flag2 = false;
+ logger.info("can not get the activity from the web server");
+ }
+ int j = 0;
+ while((((p_next_record = p_csv_reader.readNext()) != null) && flag1)) {
+ if( j==0 ){
+ getCSVHeader(Arrays.copyOf(p_next_record,2),"p");
+ j++;
+ }else{
+ try{Thread.sleep(TIME_PERIOD);}catch (InterruptedException e){e.printStackTrace();}
+ String[] values1 = Arrays.copyOf(p_next_record,2);
+ setNewValue(values1, PREFERENCE_ITEM_NAME,"preference");
+ HttpResponse response1= Request.Get(PREFERENCE_URL).execute().returnResponse();
+ String status1=response1.getStatusLine().toString();
+ if(status1.contains("200")){
+ flag2 = true;
+ logger.info("get the iris1_item preference from web server: response 200 ok, value is: "+EntityUtils.toString(response1.getEntity()));
+ }else {flag2 = false;
+ logger.info("can not get the iris1_item from the web server");}
+ break;
+ }
+ }
}
}
}
@@ -107,6 +114,7 @@ public class Benchmark {
e.printStackTrace();
}
}
+
private void setNewValue(String[] values, String[] name, String file_typ){
if(file_typ.equals("activity"))
{
@@ -131,6 +139,8 @@ public class Benchmark {
}else{
String uri= ERASER_ITEM_URI + "w_brightness" +"/state";
try {
+ logger.info("debug for w_brigntess");
+ logger.info(values[1]);
HttpResponse httpResponse = Request.Put(uri)
.bodyString(values[1], ContentType.TEXT_PLAIN)
.execute().returnResponse();
@@ -143,4 +153,12 @@ public class Benchmark {
}
}
}
+
+ private void getCSVHeader(String[] a_values,String csv_typ){
+ if (csv_typ.equals("a")){
+ ACTIVITY_ITEM_NAME = a_values;
+ }else{
+ PREFERENCE_ITEM_NAME = a_values;
+ }
+ }
}
\ No newline at end of file
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 4d171f8b53d707cca9c07d12721a6586fbfaaf66..4adfd683365a749a1dbf667fe3e1b413acf06018 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
@@ -127,7 +127,7 @@ public class EraserStarter {
machineLearningModel.setEncoder(handler);
machineLearningModel.setDecoder(handler);
root.getMachineLearningRoot().setActivityRecognition(machineLearningModel);
-
+ /**
//Begin the Integration
Item item1=model.resolveItem("m_accel_x").get();
Item item2=model.resolveItem("m_accel_y").get();
@@ -172,7 +172,7 @@ public class EraserStarter {
List<ItemPreference> preference=handler.classify().getPreferences();
for(ItemPreference preference1 : preference){
preference1.apply();
- }
+ }*/
}
// initialize preference learning
@@ -189,7 +189,7 @@ public class EraserStarter {
machineLearningModel.setDecoder(handler);
root.getMachineLearningRoot().setPreferenceLearning(machineLearningModel);
//working,medium,240,70
- Item activity_item = model.resolveItem("activity").get();
+ /**Item activity_item = model.resolveItem("activity").get();
String activity=activity_item.getStateAsString();
activity_item.setStateFromString(activity);
Item brightness_item = model.resolveItem("w_brightness").get();
@@ -201,7 +201,7 @@ public class EraserStarter {
List<ItemPreference> preference=handler.classify().getPreferences();
for(ItemPreference preference1 : preference){
preference1.apply();
- }
+ }*/
}
machineLearningRoot.getPreferenceLearning().connectItems(settings.preference.items);
diff --git a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java
index 5d8021d8faf7519d9ec15305f0752cfbcd742a0c..d6c86be5a5e6bd64311066fb7225cfc66194558e 100644
--- a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java
+++ b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java
@@ -63,13 +63,14 @@ public class Learner {
String csv_url_activity;
csv_url_activity = activity_csv_url;
this.csv_file = new File(csv_url_activity);
- a_souce = new CSVDataSource(csv_file,false,CSVFormat.DECIMAL_POINT);
+ a_souce = new CSVDataSource(csv_file,true,CSVFormat.DECIMAL_POINT);
a_data = new VersatileMLDataSet(a_souce);
String[] activity_inputs={"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"
};
+
for(int i=0; i < activity_inputs.length; i++){
a_data.defineSourceColumn(activity_inputs[i], i, ColumnType.continuous);
}
@@ -87,7 +88,7 @@ public class Learner {
String csv_url_preference;
csv_url_preference = preference_csv_url;
this.csv_file = new File(csv_url_preference);
- p_source = new CSVDataSource(csv_file,false,CSVFormat.DECIMAL_POINT);
+ p_source = new CSVDataSource(csv_file,true,CSVFormat.DECIMAL_POINT);
p_data = new VersatileMLDataSet(p_source);
p_data.defineSourceColumn("activity", 0, ColumnType.nominal);
p_data.defineSourceColumn("w_brightness", 1, ColumnType.nominal);
@@ -108,7 +109,6 @@ public class Learner {
activity_train(activity_url);
preference_train(preference_url);
Encog.getInstance().shutdown();
-
}
void activity_train(String activity_csv_url){
@@ -151,7 +151,7 @@ public class Learner {
String activity_predictor(String[] new_data){
logger.info("Activity predicting ... ...");
String activity_result;
- activityDataAnalyser("../datasets/backup/activity_data.csv");
+ activityDataAnalyser("datasets/backup/activity_data.csv");
BasicNetwork activity_method = (BasicNetwork) loadObject(save_activity_model_file);
MLData input = activity_helper.allocateInputVector();
activity_helper.normalizeInputVector(new_data,input.getData(),false);
@@ -166,7 +166,7 @@ public class Learner {
logger.info("Activity predicting ... ...");
String[] preference_result;
preference_result = new String[2];
- preferenceDataAnalyser("../datasets/backup/preference_data.csv");
+ preferenceDataAnalyser("datasets/backup/preference_data.csv");
BasicNetwork preference_method = (BasicNetwork)loadObject(save_preference_model_file);
MLData input = preference_helper.allocateInputVector();
preference_helper.normalizeInputVector(new_data, input.getData(),false);
diff --git a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningImpl.java b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningImpl.java
index f2540105d7dd6dbf3f037fa19868ff1c8d2749a8..90c1837d60e5d53325f9a4a30ca59f1096e8e778 100644
--- a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningImpl.java
+++ b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningImpl.java
@@ -132,6 +132,8 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
new_data[1]=item.getStateAsString();
}
}
+ logger.info("debug for p");
+ logger.info(Arrays.toString(preference_result));
this.preference_result=learner.preference_predictor(new_data);
}
}
@@ -182,7 +184,6 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
color = Math.round(Float.valueOf(preference_result[0]));
brightness = Math.round(Float.valueOf(preference_result[1]));
}
-
ItemPreference classifiedPreference = new ItemPreferenceColor(iris1, TupleHSB.of(color, 100, brightness));
return new MachineLearningResultImpl(classifiedPreference);
default:
diff --git a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Main.java b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Main.java
index 2580847d5ace8e832bde76159dad2e85974115a2..7aa90f90c0c917d566661b648bf494b6116c6b1a 100644
--- a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Main.java
+++ b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Main.java
@@ -15,9 +15,9 @@ public class Main {
//reader.updater();
Learner learner=new Learner();
//learner.preference_train("../datasets/backup/preference_data.csv");
- //learner.train("datasets/backup/activity_data.csv","datasets/backup/preference_data.csv");
+ learner.train("datasets/backup/activity_data.csv","datasets/backup/preference_data.csv");
//0.5793968,1.2126632,-4.6244006,-0.030779,0.801127,0.590978,-3.1411927,-0.93373865,-0.31124622,-0.35992432,0.33746338,-0.79608154,dancing
- /**String[] new_data = new String[12];
+ String[] new_data = new String[12];
new_data[0]="0.5793968";
new_data[1]="1.2126632";
new_data[2]="-4.6244006";
@@ -34,9 +34,12 @@ public class Main {
String result =learner.activity_predictor(new_data);
System.out.println(result);
- List k=new ArrayList();
+ String[] new_data_1={result,"medium"};
+ learner.preference_predictor(new_data_1);
- k.add(new_data);*/
+ //List k=new ArrayList();
+
+ //k.add(new_data);*/
//learner.preference_train("datasets/backup/preference_data.csv");
//learner.train("datasets/backup/activity_data.csv","datasets/backup/preference_data.csv");
@@ -45,7 +48,7 @@ public class Main {
//activity_validation_learner();
//0.10654198,8.6574335,4.414908,0.040269,0.516884,0.853285,1.2066777,-1.1444284,9.648633,1.2207031E-4,-0.055358887,0.5834961 working
- String[] new_data = new String[12];
+ /**String[] new_data = new String[12];
new_data[0]="0.010773907";
new_data[1]="8.610746";
new_data[2]="4.4963107";