1 files changed, 91 insertions, 0 deletions

diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend
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+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend
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+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.predictor
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.StateData
+import java.util.ArrayList
+import java.util.HashMap
+import java.util.List
+import java.util.Map
+import weka.core.matrix.LinearRegression
+import weka.core.matrix.Matrix
+
+class LinearModel {
+        var double ridge;
+        var Map<Object, StateData> stateAndHistory;
+        List<StateData> samples;
+        
+        new(double ridge){
+                this.ridge = ridge;
+                stateAndHistory = new HashMap<Object, StateData>();
+                samples = new ArrayList<StateData>();
+        }
+        
+        /**
+         * reset the current train data for regression to a new trajectory
+         * @param state: the last state of the trajectory
+         */
+        def resetRegression(Object state){
+                samples.clear();
+                
+                if(stateAndHistory.containsKey(state)){
+                        var data = stateAndHistory.get(state);
+                        var curState = state;
+                        
+                        samples.add(data);
+                        
+                        //loop through data until the oldest state in the record
+                        while(stateAndHistory.containsKey(data.lastState) && data.lastState != curState){
+                                curState = data.lastState;
+                                data = stateAndHistory.get(data.lastState);
+                                samples.add(data);
+                        }
+                }
+        }
+        
+        /**
+         * Add a new data point to the current training set
+         * @param state: the state on which the new data point is calculated
+         * @param features: the set of feature value(x)
+         * @param value: the value of the state (y)
+         * @param lastState: the state which transformed to current state, used to record the trajectory
+         */
+        def feedData(Object state, double[] features, double value, Object lastState){
+                var data = new StateData(features, value, lastState);
+                stateAndHistory.put(state, data);
+                samples.add(data);
+        }
+        
+        /**
+         * get prediction for next state, without storing the data point into the training set
+         * @param features: the feature values of current state
+         * @param value: the value of the current state
+         * @param: featuresToPredict: the features of the state wanted to be predected
+         * @return the value of the state to be predicted
+         */
+        def double getPredictionForNextDataSample(double[] features, double value, double[] featuresToPredict){         
+                var data = new StateData(features, value, null);
+                samples.add(data);
+                
+                // create training set from current data                
+                val double[][] xSamples = samples.map[it.features];
+                val double[] ySamples = samples.map[it.value];
+                
+                val x = new Matrix(xSamples);
+                val y = new Matrix(ySamples, ySamples.size());  
+                
+                val regression = new LinearRegression(x, y, ridge);
+                var prediction = predict(regression.coefficients, featuresToPredict);   
+                
+                //remove the last element just added
+                samples.remove(samples.size - 1);
+                return prediction;
+        }
+        
+        def private predict(double[] parameters, double[] featuresToPredict){
+                // 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) * featuresToPredict.get(i);
+                }
+                return result;
+        }
+}
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