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diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/xtend-gen/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.java b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/xtend-gen/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.java
new file mode 100644
index 00000000..6a7f8497
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/xtend-gen/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.java
@@ -0,0 +1,90 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.predictor;
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.StateData;
+import com.google.common.base.Objects;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+@SuppressWarnings("all")
+public class LinearModel {
+  private double ridge;
+  
+  private Map<Object, StateData> stateAndHistory;
+  
+  private List<StateData> samples;
+  
+  public LinearModel(final double ridge) {
+    this.ridge = ridge;
+    HashMap<Object, StateData> _hashMap = new HashMap<Object, StateData>();
+    this.stateAndHistory = _hashMap;
+    ArrayList<StateData> _arrayList = new ArrayList<StateData>();
+    this.samples = _arrayList;
+  }
+  
+  /**
+   * reset the current train data for regression to a new trajectory
+   * @param state: the last state of the trajectory
+   */
+  public void resetRegression(final Object state) {
+    this.samples.clear();
+    boolean _containsKey = this.stateAndHistory.containsKey(state);
+    if (_containsKey) {
+      StateData data = this.stateAndHistory.get(state);
+      Object curState = state;
+      this.samples.add(data);
+      while ((this.stateAndHistory.containsKey(data.getLastState()) && (!Objects.equal(data.getLastState(), curState)))) {
+        {
+          curState = data.getLastState();
+          data = this.stateAndHistory.get(data.getLastState());
+          this.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
+   */
+  public boolean feedData(final Object state, final double[] features, final double value, final Object lastState) {
+    boolean _xblockexpression = false;
+    {
+      StateData data = new StateData(features, value, lastState);
+      this.stateAndHistory.put(state, data);
+      _xblockexpression = this.samples.add(data);
+    }
+    return _xblockexpression;
+  }
+  
+  /**
+   * 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
+   */
+  public double getPredictionForNextDataSample(final double[] features, final double value, final double[] featuresToPredict) {
+    throw new Error("Unresolved compilation problems:"
+      + "\nMatrix cannot be resolved."
+      + "\nMatrix cannot be resolved."
+      + "\nLinearRegression cannot be resolved."
+      + "\ncoefficients cannot be resolved");
+  }
+  
+  private double predict(final double[] parameters, final double[] featuresToPredict) {
+    double result = parameters[0];
+    for (int i = 0; (i < featuresToPredict.length); i++) {
+      double _result = result;
+      double _get = parameters[i];
+      double _get_1 = featuresToPredict[i];
+      double _multiply = (_get * _get_1);
+      result = (_result + _multiply);
+    }
+    return result;
+  }
+}