1 files changed, 67 insertions, 19 deletions

diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend
index b63451e8..45986ecf 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend
@@ -8,26 +8,35 @@ import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.predictor.LinearModel
 import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.PartialInterpretation
 import java.util.ArrayList
 import java.util.HashMap
+import java.util.List
 import java.util.Map
-import org.apache.commons.math3.stat.regression.SimpleRegression
-import java.util.stream.DoubleStream.Builder
+import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression
+import org.eclipse.xtend.lib.annotations.Accessors
 
 class PartialInterpretationMetricDistance {
         
         var KSDistance ks;
         var JSDistance js;
         var Map<Object, StateData> stateAndHistory;
-        var SimpleRegression regression;
+        var OLSMultipleLinearRegression regression;
+        List<StateData> samples;
+        
+        @Accessors(PUBLIC_GETTER)
+        var LinearModel linearModel;
         
         
         new(){
                 ks = new KSDistance(Domain.Yakinduum);
                 js = new JSDistance(Domain.Yakinduum);
-                regression = new SimpleRegression();
+                regression = new OLSMultipleLinearRegression();
+                regression.noIntercept = false;
                 stateAndHistory = new HashMap<Object, StateData>();
+                samples = new ArrayList<StateData>();
+                linearModel = new LinearModel(0.01);
         }
         
         def MetricDistanceGroup calculateMetricDistanceKS(PartialInterpretation partial){
@@ -63,37 +72,76 @@ class PartialInterpretationMetricDistance {
         }
         
         def resetRegression(Object state){
-                regression = new SimpleRegression();
+                samples.clear();
                 
                 if(stateAndHistory.containsKey(state)){
                         var data = stateAndHistory.get(state);
-                        regression.addData(data.numOfNodeFeature, data.value);
                         
-                        while(stateAndHistory.containsKey(data.lastState)){
+                        var curState = state;
+                        
+                        samples.add(data);
+                        
+                        while(stateAndHistory.containsKey(data.lastState) && data.lastState != curState){
+                                curState = data.lastState;
                                 data = stateAndHistory.get(data.lastState);
-                                regression.addData(data.numOfNodeFeature, data.value);
+                                samples.add(data);
+                        }
+                                        
+                        if(samples.size == 0){
+                                println('state: ' + state);
+                                println('last state: ' + data.lastState);
                         }
                 }
+                println("trajectory sample size:" + samples.size)
         }
         
-        def feedData(Object state, int numOfNodes, double value, Object lastState){
-                var data = new StateData(numOfNodes, value, lastState);
+        def feedData(Object state, double[] features, double value, Object lastState){
+                var data = new StateData(features, value, lastState);
                 stateAndHistory.put(state, data);
-                regression.addData(data.numOfNodeFeature, data.value);
+                samples.add(data);
         }
         
-        def getPredictionForNextDataSample(int numOfNodes, double value, int numberOfNodesToPredict){
-                var data = new StateData(numOfNodes, value, null);
-                regression.addData(data.numOfNodeFeature, data.value);
+        def getPredictionForNextDataSample(double[] features, double value, double[] featuresToPredict){        
+                if(samples.size <= 4){
+                        println('OK');
+                }       
+                var data = new StateData(features, value, null);
+                samples.add(data);
+                
+                // create training set from current data                
+                var double[][] xSamples = samples.map[it.features];
+                var double[] ySamples = samples.map[it.value];
+                
                 
-                var prediction = predict(numberOfNodesToPredict);
-                regression.removeData(data.numOfNodeFeature, data.value);
+                regression.newSampleData(ySamples, xSamples);
+                var prediction = predict(featuresToPredict);    
+                
+                //remove the last element just added
+                samples.remove(samples.size - 1);
                 return prediction;
         }
         
-        def predict(int numOfNodes){
-                var data = new StateData(numOfNodes, 0, null);
-                return regression.predict(data.numOfNodeFeature);
+        def private predict(double[] featuresToPredict){
+                var parameters = regression.estimateRegressionParameters();
+                // the regression will add an initial column for 1's, the first parameter is constant term
+                var result = parameters.get(0);
+                for(var i = 0; i < featuresToPredict.length; i++){
+                        result += parameters.get(i+1) * featuresToPredict.get(i);
+                }
+                return result;
+        }
+        
+        def double[] calculateFeature(int step, int violations){
+                var features = newDoubleArrayOfSize(5);
+                //constant term
+                features.set(0, 1);
+                
+                features.set(1, 1.0 / step);
+                features.set(2, violations);
+                features.set(3, Math.pow(violations, 2));
+                features.set(4, Math.pow(violations, 0.5));
+                
+                return features;
         }
 }