From 945f487a08b643392a5d5918c631640b9a0e4605 Mon Sep 17 00:00:00 2001
From: 20001LastOrder <boqi.chen@mail.mcgill.ca>
Date: Tue, 3 Nov 2020 22:52:26 -0500
Subject: add realistic solver

---
 .../metrics/calculator/predictor/LinearModel.xtend | 91 ++++++++++++++++++++++
 1 file changed, 91 insertions(+)
 create mode 100644 Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend

(limited to 'Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor')

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
new file mode 100644
index 00000000..f0ded347
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend
@@ -0,0 +1,91 @@
+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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