Ejemplo n.º 1
0
  @Test
  public void testConstant() {
    double tolerancePerc = 10.0; // 10% of correct value
    int nSamples = 500;
    int nFeatures = 3;
    int constant = 100;

    INDArray featureSet = Nd4j.zeros(nSamples, nFeatures).add(constant);
    INDArray labelSet = Nd4j.zeros(nSamples, 1);
    DataSet sampleDataSet = new DataSet(featureSet, labelSet);

    NormalizerStandardize myNormalizer = new NormalizerStandardize();
    myNormalizer.fit(sampleDataSet);
    // Checking if we gets nans
    assertFalse(Double.isNaN(myNormalizer.getStd().getDouble(0)));

    myNormalizer.transform(sampleDataSet);
    // Checking if we gets nans, because std dev is zero
    assertFalse(Double.isNaN(sampleDataSet.getFeatures().min(0, 1).getDouble(0)));
    // Checking to see if transformed values are close enough to zero
    assertEquals(
        Transforms.abs(sampleDataSet.getFeatures()).max(0, 1).getDouble(0, 0),
        0,
        constant * tolerancePerc / 100.0);

    myNormalizer.revert(sampleDataSet);
    // Checking if we gets nans, because std dev is zero
    assertFalse(Double.isNaN(sampleDataSet.getFeatures().min(0, 1).getDouble(0)));
    assertEquals(
        Transforms.abs(sampleDataSet.getFeatures().sub(featureSet)).min(0, 1).getDouble(0),
        0,
        constant * tolerancePerc / 100.0);
  }
Ejemplo n.º 2
0
  @Test
  public void testTransform() {
    /*Random dataset is generated such that
       AX + B where X is from a normal distribution with mean 0 and std 1
       The mean of above will be B and std A
       Obtained mean and std dev are compared to theoretical
       Transformed values should be the same as X with the same seed.
    */
    long randSeed = 7139183;

    int nFeatures = 2;
    int nSamples = 6400;
    int bsize = 8;
    int a = 2;
    int b = 10;
    INDArray sampleMean, sampleStd, sampleMeanDelta, sampleStdDelta, delta, deltaPerc;
    double maxDeltaPerc, sampleMeanSEM;

    genRandomDataSet normData = new genRandomDataSet(nSamples, nFeatures, a, b, randSeed);
    genRandomDataSet expectedData = new genRandomDataSet(nSamples, nFeatures, 1, 0, randSeed);
    genRandomDataSet beforeTransformData =
        new genRandomDataSet(nSamples, nFeatures, a, b, randSeed);

    NormalizerStandardize myNormalizer = new NormalizerStandardize();
    DataSetIterator normIterator = normData.getIter(bsize);
    DataSetIterator expectedIterator = expectedData.getIter(bsize);
    DataSetIterator beforeTransformIterator = beforeTransformData.getIter(bsize);

    myNormalizer.fit(normIterator);

    double tolerancePerc = 5.0; // within 5%
    sampleMean = myNormalizer.getMean();
    sampleMeanDelta = Transforms.abs(sampleMean.sub(normData.theoreticalMean));
    assertTrue(
        sampleMeanDelta.mul(100).div(normData.theoreticalMean).max(1).getDouble(0, 0)
            < tolerancePerc);
    // sanity check to see if it's within the theoretical standard error of mean
    sampleMeanSEM = sampleMeanDelta.div(normData.theoreticalSEM).max(1).getDouble(0, 0);
    assertTrue(sampleMeanSEM < 2.6); // 99% of the time it should be within this many SEMs

    tolerancePerc = 10.0; // within 10%
    sampleStd = myNormalizer.getStd();
    sampleStdDelta = Transforms.abs(sampleStd.sub(normData.theoreticalStd));
    assertTrue(
        sampleStdDelta.div(normData.theoreticalStd).max(1).mul(100).getDouble(0, 0)
            < tolerancePerc);

    normIterator.setPreProcessor(myNormalizer);
    while (normIterator.hasNext()) {
      INDArray before = beforeTransformIterator.next().getFeatures();
      INDArray after = normIterator.next().getFeatures();
      INDArray expected = expectedIterator.next().getFeatures();
      delta = Transforms.abs(after.sub(expected));
      deltaPerc = delta.div(before.sub(expected));
      deltaPerc.muli(100);
      maxDeltaPerc = deltaPerc.max(0, 1).getDouble(0, 0);
      // System.out.println("=== BEFORE ===");
      // System.out.println(before);
      // System.out.println("=== AFTER ===");
      // System.out.println(after);
      // System.out.println("=== SHOULD BE ===");
      // System.out.println(expected);
      assertTrue(maxDeltaPerc < tolerancePerc);
    }
  }