@Override
public OutputOracle forecast(InputOracle io) throws OracleException {
double AVGrmse = Double.MAX_VALUE;
Oracle best = null;
try {
SelectInstancesRP(io);
KNN.setInstances(m_Training);
m_TestSet = DataConverter.FromInputOracleToInstance(io);
logger.info("PREDICT TARGET VALUE FOR : " + m_TestSet.toStringNoWeight());
System.out.println(m_TestSet);
Neighbourshood =
KNN.kNearestNeighbours(
m_TestSet,
NumNeighbours); // >k neighbours are returned if there are more than one neighbours at
// the kth boundary.
distances = KNN.getDistances();
RMSE = RMSE(this.ConsideredOutOracle);
double actual;
for (Map.Entry<Oracle, Double[]> entry : RMSE.entrySet()) {
// actual=(entry.getValue()[0]*VarianceRMSE(entry.getValue())[0]+entry.getValue()[1]*VarianceRMSE(entry.getValue())[1])/2;
actual = (entry.getValue()[0] + entry.getValue()[1]) / 2;
if (actual <= AVGrmse) {
AVGrmse = actual;
best = entry.getKey();
}
}
logger.info("ORACLE SELECTED FOR PREDICTION : " + best.toString().split("@")[0]);
return best.forecast(io);
} catch (Exception ex) {
logger.error("forecast error " + ex);
throw new OracleException(ex);
}
}
public Knearestneighbourg() throws Exception {
// this.m_TrainingFile=trainingset;
// this.m_TestSetFile=testset;
String[] parameters = getconfiguration();
if (parameters.length < 5 || parameters.length > 5)
throw new Exception("Bad configuration of KnearestNeighbourg ,parameters number is wrong");
String[] options = new String[1];
options[0] = parameters[1];
Method method;
NumNeighbours = Integer.parseInt(parameters[2]);
ConsideredOutOracle = parameters[3];
cutoff = Double.parseDouble(parameters[4]);
// setTraining(this.m_TrainingFile);
// setTestSet(this.m_TestSetFile);
KNN = new LinearNNSearch();
Class c = Class.forName("weka.core." + parameters[0]);
Object distanceFunc = c.newInstance();
method = distanceFunc.getClass().getMethod("setOptions", String[].class);
method.invoke(distanceFunc, (Object) options);
KNN.setDistanceFunction((DistanceFunction) distanceFunc);
if (DataSets.ARFFDataSet != null) {
this.m_Training = DataSets.ARFFDataSet;
} else {
logger.error("--" + "Datasets Not instanziated");
throw new InstantiationException("Datasets Not instanziated");
}
// Instance I =getInstanceToTest(0);
// KNN.kNearestNeighbours(I,1);
}