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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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