Пример #1
0
  @Test
  public void testRegressionNACalc() {
    ExtraTrees et = getET(10, 5, false);
    ExtraTrees etw = getET(10, 5, true);
    double var, mean;
    mean = 4.0 / 10.0;
    var = 0.6 * Math.pow(mean, 2) + 0.4 * Math.pow(1 - mean, 2);
    assertEquals(var, et.get1NaNScore(AbstractTrees.seq(10)), 1e-6);

    mean = 3.0 / 9.0;
    var = 6 / 9.0 * Math.pow(mean, 2) + 3 / 9.0 * Math.pow(1 - mean, 2);
    assertEquals(var, et.get1NaNScore(AbstractTrees.seq(9)), 1e-6);

    // testing NaN counts:
    CutResult cr;
    cr = new CutResult();
    et.calculateCutScore(AbstractTrees.seq(9), 2, 0.5, cr);
    assertEquals(3.0, cr.nanWeigth, 1e-6);

    cr = new CutResult();
    et.calculateCutScore(AbstractTrees.seq(9), 1, 0.5, cr);
    assertEquals(0.0, cr.nanWeigth, 1e-6);

    // testing weights:
    cr = new CutResult();
    etw.calculateCutScore(AbstractTrees.seq(9), 2, 0.5, cr);
    assertEquals(1.5, cr.nanWeigth, 1e-6);
  }
Пример #2
0
 /**
  * @param ndata
  * @param ndim
  */
 public static ExtraTrees getET(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;
   }
   ExtraTrees et = new ExtraTrees(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;
 }
Пример #3
0
  @Test
  public void testRegressionNALearn() {
    int ndim = 5;
    ExtraTrees et = getET(100, ndim, false);
    ExtraTrees etw = getET(100, ndim, true);
    et.learnTrees(3, 3, 5);
    etw.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.getValues(new Matrix(x, 1, ndim));
    assertTrue(Double.isNaN(val[0]));
    val = etw.getValues(new Matrix(x, 1, ndim));
    assertTrue(Double.isNaN(val[0]));

    // checking if getRange works with NaN
    double[] col2 = ((Matrix) et.input).getCol(2);
    double[] range2 = AbstractTrees.getRange(col2);
    assertEquals(0.5, range2[0], 1e-6);
    assertEquals(0.5, range2[1], 1e-6);
  }