private void addModelToSenonePool(Pool pool, int[] array, float num, float num2)
{
if (!ModelInitializerLoader.assertionsDisabled && pool == null)
{
throw new AssertionError();
}
int gauPerState = this.mixtureWeights.getGauPerState();
if (!ModelInitializerLoader.assertionsDisabled && gauPerState <= 0)
{
throw new AssertionError();
}
int num3 = array.Length;
for (int i = 0; i < num3; i++)
{
int num4 = array[i];
MixtureComponent[] array2 = new MixtureComponent[gauPerState];
for (int j = 0; j < gauPerState; j++)
{
int id = num4 * gauPerState + j;
MixtureComponent[] array3 = array2;
int num5 = j;
array3[num5] = new MixtureComponent((float[])this.meansPool.get(id), (float[][])this.meanTransformationMatrixPool.get(0), (float[])this.meanTransformationVectorPool.get(0), (float[])this.variancePool.get(id), (float[][])this.varianceTransformationMatrixPool.get(0), (float[])this.varianceTransformationVectorPool.get(0), num, num2);
}
GaussianMixture o = new GaussianMixture(this.mixtureWeights, array2, num4);
pool.put(num4, o);
}
}
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 }));
}
private void recomputeMixtureComponents()
{
for (int i = 0; i < this.senonePool.size(); i++)
{
GaussianMixture gaussianMixture = (GaussianMixture)this.senonePool.get(i);
MixtureComponent[] mixtureComponents = gaussianMixture.getMixtureComponents();
MixtureComponent[] array = mixtureComponents;
int num = array.Length;
for (int j = 0; j < num; j++)
{
MixtureComponent mixtureComponent = array[j];
mixtureComponent.precomputeDistance();
}
}
}
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);
}
}
