@Test
public void testQuantileNALearn() {
int ndim = 5;
QuantileExtraTrees et = getQET(100, ndim, false);
et.learnTrees(3, 3, 5);
double[] x = new double[ndim];
for (int i = 0; i < x.length; i++) {
x[i] = Double.NaN;
}
double[] val;
val = et.getQuantiles(new Matrix(x, 1, ndim), 0.5);
assertTrue(Double.isNaN(val[0]));
}
/**
* @param ndata
* @param ndim
*/
public static QuantileExtraTrees getQET(int ndata, int ndim, boolean useWeights) {
double[] output = new double[ndata];
Matrix m = new Matrix(ndata, ndim);
// generate values for all outputs
for (int row = 0; row < output.length; row++) {
m.set(row, 1, row / (double) output.length);
m.set(row, 2, 0.5);
if (row == 5 || row == 6 || row == 7) {
m.set(row, 2, Double.NaN);
}
output[row] = m.get(row, 1) > 0.55 ? 1 : 0;
}
QuantileExtraTrees et = new QuantileExtraTrees(m, output);
et.setHasNaN(true);
if (useWeights) {
double[] w = new double[ndata];
for (int i = 0; i < w.length; i++) {
w[i] = 0.5;
}
et.setWeights(w);
}
return et;
}
