/**
* calculates one over residual score. works pretty much the same as a stand root-mean-squared
* error metric for a single output, but weights those points that are closer to the origin (in
* the input space) higher. Assumes only one input.
*/
public double calcRSquared(PredictionTable pt) {
// the diff between the first output and prediction for
// each example
double[] errors;
// the distance from the original in the input space for
// each example
double[] distances;
int i, j;
double pred, actual;
int input = pt.getInputFeatures()[0];
int output = pt.getOutputFeatures()[0];
int numRows = pt.getNumRows();
errors = new double[numRows];
distances = new double[numRows];
// System.out.println("\n\n");
for (i = 0; i < numRows; i++) {
actual = pt.getDouble(i, output);
pred = pt.getDoublePrediction(i, 0);
errors[i] = actual - pred;
// System.out.println("bin:" + i +
// "actual:"+actual+" pred:"+pred+"error:"+errors[i]);
distances[i] = pt.getDouble(i, input);
}
// System.out.println("\n");
double weight;
double r2 = 0;
;
for (i = 0; i < numRows; i++) {
weight = calcWeight(distances[i]);
// System.out.println("dist:"+distances[i] + " weight:" + weight);
r2 += weight * errors[i] * errors[i];
// System.out.println("errors:"+errors[i] + " r2 tally:" + r2);
// System.out.println();
}
return r2;
}
