public void Array2DArrayEstimatorTest()
{
// Create the estimator
// element type is double[]
//Estimator<GaussianArray2DArray> est = null;
int length1 = ga2aArrayOfDist.GetLength(0);
int length2 = ga2aArrayOfDist.GetLength(1);
Array2DEstimator <GaussianArrayEstimator, GaussianArray2DArray, GaussianArray, double[]> est =
new Array2DEstimator <GaussianArrayEstimator, GaussianArray2DArray, GaussianArray, double[]>(
Utilities.Util.ArrayInit(length1, length2, (i, j) =>
new GaussianArrayEstimator(Utilities.Util.ArrayInit(ga2aArrayOfDist[i, j].Length, k => new GaussianEstimator()))
));
// Add some samples to the estimator
int nSamples = 5;
// Create a sample an mean variable of the right structure
double[, ][] mean = (double[, ][]) JaggedArray.ConvertToNew(
ga2aArrayOfDist, typeof(double), delegate(object elt) { return(0.0); });
double[, ][] sample = (double[, ][]) JaggedArray.ConvertToNew(
ga2aArrayOfDist, typeof(double), delegate(object elt) { return(0.0); });
// Create samples and add them to the estimator. Accumulate sum of samples at the same time
for (int nSamp = 0; nSamp < nSamples; nSamp++)
{
JaggedArray.ConvertElements2(
sample, ga2aArrayOfDist, delegate(object smp, object dst) { return(((Gaussian)dst).Sample()); });
est.Add(sample);
JaggedArray.ConvertElements2(
mean, sample, delegate(object mn, object smp) { return((double)mn + (double)smp); });
}
// Hand calculate the sample mean
JaggedArray.ConvertElements(
mean, delegate(object mn) { return(((double)mn) / ((double)nSamples)); });
// Let the estimator do the work
GaussianArray2DArray result = new GaussianArray2DArray(length1, length2, (i, j) => new GaussianArray(ga2aArrayOfDist[i, j].Length));
result = est.GetDistribution(result);
// The results should be identical to a very high precision
for (int i = 0; i < dim1; i++)
{
for (int j = 0; j < dim2; j++)
{
for (int k = 0; k < result[i, j].Count; k++)
{
Assert.True(System.Math.Abs(result[i, j][k].GetMean() - mean[i, j][k]) < 1e-9);
}
}
}
}
public void Array2DEstimatorTest()
{
int length1 = 3;
int length2 = 2;
GaussianArray2D garray = new GaussianArray2D(length1, length2, (i, j) => new Gaussian(i, j + 1));
Array2DEstimator <GaussianEstimator, GaussianArray2D, Gaussian, double> est =
new Array2DEstimator <GaussianEstimator, GaussianArray2D, Gaussian, double>(
Utilities.Util.ArrayInit(length1, length2, (i, j) => new GaussianEstimator()));
double[,] sum = new double[length1, length2];
double[,] sum2 = new double[length1, length2];
int count = 5;
for (int nSamp = 0; nSamp < count; nSamp++)
{
double[,] sample = garray.Sample();
est.Add(sample);
for (int i = 0; i < length1; i++)
{
for (int j = 0; j < length2; j++)
{
sum[i, j] += sample[i, j];
sum2[i, j] += sample[i, j] * sample[i, j];
}
}
}
GaussianArray2D expected = new GaussianArray2D(length1, length2, delegate(int i, int j)
{
double m = sum[i, j] / count;
double v = sum2[i, j] / count - m * m;
return(new Gaussian(m, v));
});
GaussianArray2D actual = new GaussianArray2D(length1, length2);
actual = est.GetDistribution(actual);
Console.WriteLine(Utilities.StringUtil.JoinColumns("result = ", actual, " should be ", expected));
Assert.True(expected.MaxDiff(actual) < 1e-9);
}