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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;
}
}
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