public void MixtureComponent_UnivariateMeanTransformation()
{
const float mean = 20;
const float var = 0.001f;
var gaussian = new MixtureComponent(new[] { mean }, new[] { new float[] { 2 } }, new float[] { 5 }, new[] { var }, null, null);
Assert.IsTrue(LogMath.GetLogMath().LogToLinear(gaussian.GetScore(new[] { 2 * mean + 5 })) > 10);
}
public void MixtureComponent_Clone()
{
var gaussian = new MixtureComponent(new float[] { 2 }, new[] { new float[] { 3 } }, new float[] { 4 }, new float[] { 5 }, new[] { new float[] { 6 } }, new float[] { 7 });
var clonedGaussian = gaussian.Clone();
Assert.IsTrue(!clonedGaussian.Equals(gaussian));
Assert.IsTrue(gaussian.Mean != clonedGaussian.Mean);
Assert.IsTrue(gaussian.Variance != clonedGaussian.Variance);
Assert.IsTrue(gaussian.GetScore(new float[] { 2 }) == clonedGaussian.GetScore(new float[] { 2 }));
}
public void MixtureComponent_UnivariateDensity()
{
const float minX = 10;
const float maxX = 30;
const float resolution = 0.1f;
const float mean = 20;
const float var = 3;
var gaussian = new MixtureComponent(new[] { mean }, new[] { var });
for (var curX = minX; curX <= maxX; curX += resolution)
{
var gauLogScore = gaussian.GetScore(new FloatData(new[] { curX }, 16000, 0));
var manualScore = (1 / Math.Sqrt(var * 2 * Math.PI)) * Math.Exp((-0.5 / var) * (curX - mean) * (curX - mean));
var gauScore = LogMath.GetLogMath().LogToLinear((float)gauLogScore);
Assert.AreEqual(manualScore, gauScore, 1E-5);
}
}