/**
*
* @param bicValue
* The bicValue of the model represented by only one Gaussian.
* This parameter it's useful when this function is called
* repeatedly for different frame values and the same features
* parameter
* @param frame
* the frame which is tested for being a change point
* @param features
* the feature vectors matrix
* @return the likelihood ratio
*/
static double GetLikelihoodRatio(double bicValue, int frame, Array2DRowRealMatrix features)
{
double bicValue1, bicValue2;
int d = Segment.FeaturesSize;
double penalty = 0.5 * (d + 0.5 * d * (d + 1)) * Math.Log(features.getRowDimension()) * 2;
int nrows = features.getRowDimension(), ncols = features.getColumnDimension();
Array2DRowRealMatrix sub1, sub2;
sub1 = (Array2DRowRealMatrix)features.getSubMatrix(0, frame - 1, 0, ncols - 1);
sub2 = (Array2DRowRealMatrix)features.getSubMatrix(frame, nrows - 1, 0, ncols - 1);
bicValue1 = GetBICValue(sub1);
bicValue2 = GetBICValue(sub2);
return(bicValue - bicValue1 - bicValue2 - penalty);
}
private LinkedList getAllChangingPoints(Array2DRowRealMatrix array2DRowRealMatrix)
{
LinkedList linkedList = new LinkedList();
linkedList.add(Integer.valueOf(0));
int rowDimension = array2DRowRealMatrix.getRowDimension();
int num = 500;
int num2 = 0;
int i = num;
while (i < rowDimension)
{
int point = this.getPoint(num2, i - num2 + 1, num / 10, array2DRowRealMatrix);
if (point > 0)
{
num2 = point;
i = num2 + num;
linkedList.add(Integer.valueOf(point));
}
else
{
i += num;
}
}
linkedList.add(Integer.valueOf(rowDimension));
return(linkedList);
}
/**
*
* @param features
* Matrix with feature vectors as rows
* @return A list with all changing points detected in the file
*/
private LinkedList <Integer> GetAllChangingPoints(Array2DRowRealMatrix features)
{
LinkedList <Integer> ret = new LinkedList <Integer>();
ret.Add(0);
int framesCount = features.getRowDimension(), step = 500;
int start = 0, end = step, cp;
while (end < framesCount)
{
cp = GetPoint(start, end - start + 1, step / 10, features);
if (cp > 0)
{
start = cp;
end = start + step;
ret.Add(cp);
}
else
{
end += step;
}
}
ret.Add(framesCount);
return(ret);
}
internal virtual void printMatrix(Array2DRowRealMatrix array2DRowRealMatrix)
{
for (int i = 0; i < array2DRowRealMatrix.getRowDimension(); i++)
{
for (int j = 0; j < array2DRowRealMatrix.getColumnDimension(); j++)
{
[email protected](new StringBuilder().append(array2DRowRealMatrix.getEntry(i, j)).append(" ").toString());
}
[email protected]();
}
}
void PrintMatrix(Array2DRowRealMatrix a)
{
for (int i = 0; i < a.getRowDimension(); i++)
{
for (int j = 0; j < a.getColumnDimension(); j++)
{
Console.Write(a.getEntry(i, j) + " ");
}
Console.WriteLine();
}
}
public static double getBICValue(Array2DRowRealMatrix mat)
{
double num = (double)0f;
EigenDecomposition eigenDecomposition = new EigenDecomposition(new Covariance(mat).getCovarianceMatrix());
double[] realEigenvalues = eigenDecomposition.getRealEigenvalues();
for (int i = 0; i < realEigenvalues.Length; i++)
{
num += java.lang.Math.log(realEigenvalues[i]);
}
return(num * (double)(mat.getRowDimension() / 2));
}
/**
* @param mat
* A matrix with feature vectors as rows.
* @return Returns the BICValue of the Gaussian model that approximates the
* the feature vectors data samples
*/
public static double GetBICValue(Array2DRowRealMatrix mat)
{
double ret = 0;
EigenDecomposition ed = new EigenDecomposition(new Covariance(mat).getCovarianceMatrix());
double[] re = ed.getRealEigenvalues();
for (int i = 0; i < re.Length; i++)
{
ret += Math.Log(re[i]);
}
return(ret * (mat.getRowDimension() / 2));
}
internal virtual double getLikelihoodRatio(double num, int num2, Array2DRowRealMatrix array2DRowRealMatrix)
{
int num3 = 13;
double num4 = 0.5 * ((double)num3 + 0.5 * (double)num3 * (double)(num3 + 1)) * java.lang.Math.log((double)array2DRowRealMatrix.getRowDimension()) * 2.0;
int rowDimension = array2DRowRealMatrix.getRowDimension();
int columnDimension = array2DRowRealMatrix.getColumnDimension();
Array2DRowRealMatrix mat = (Array2DRowRealMatrix)array2DRowRealMatrix.getSubMatrix(0, num2 - 1, 0, columnDimension - 1);
Array2DRowRealMatrix mat2 = (Array2DRowRealMatrix)array2DRowRealMatrix.getSubMatrix(num2, rowDimension - 1, 0, columnDimension - 1);
double num5 = SpeakerIdentification.getBICValue(mat);
double num6 = SpeakerIdentification.getBICValue(mat2);
return(num - num5 - num6 - num4);
}
static double ComputeDistance(SpeakerCluster c1, SpeakerCluster c2)
{
int rowDim = c1.FeatureMatrix.getRowDimension() + c2.FeatureMatrix.getRowDimension();
int colDim = c1.FeatureMatrix.getColumnDimension();
Array2DRowRealMatrix combinedFeatures = new Array2DRowRealMatrix(rowDim, colDim);
combinedFeatures.setSubMatrix(c1.FeatureMatrix.getData(), 0, 0);
combinedFeatures.setSubMatrix(c2.FeatureMatrix.getData(), c1.FeatureMatrix.getRowDimension(), 0);
double bicValue = GetBICValue(combinedFeatures);
double d = Segment.FeaturesSize;
double penalty = 0.5 * (d + 0.5 * d * (d + 1)) * Math.Log(combinedFeatures.getRowDimension()) * 2;
return(bicValue - c1.GetBicValue() - c2.GetBicValue() - penalty);
}
internal virtual double computeDistance(SpeakerCluster speakerCluster, SpeakerCluster speakerCluster2)
{
int rowDimension = speakerCluster.getFeatureMatrix().getRowDimension() + speakerCluster2.getFeatureMatrix().getRowDimension();
int columnDimension = speakerCluster.getFeatureMatrix().getColumnDimension();
Array2DRowRealMatrix array2DRowRealMatrix = new Array2DRowRealMatrix(rowDimension, columnDimension);
array2DRowRealMatrix.setSubMatrix(speakerCluster.getFeatureMatrix().getData(), 0, 0);
array2DRowRealMatrix.setSubMatrix(speakerCluster2.getFeatureMatrix().getData(), speakerCluster.getFeatureMatrix().getRowDimension(), 0);
double num = SpeakerIdentification.getBICValue(array2DRowRealMatrix);
double num2 = 13.0;
double num3 = 0.5 * (num2 + 0.5 * num2 * (num2 + (double)1f)) * java.lang.Math.log((double)array2DRowRealMatrix.getRowDimension()) * 2.0;
return(num - speakerCluster.getBicValue() - speakerCluster2.getBicValue() - num3);
}