예제 #1
0
 /// <summary>
 /// Construct a new Gaussian object given its parameters.
 /// </summary>
 /// <param name="mean">Gaussian mean value.</param>
 /// <param name="covariance">Gaussian covariance matrix.</param>
 /// <param name="weight">Gaussian coefficient in the mixture.</param>
 public Gaussian(double[] mean, double[][] covariance, double weight)
 {
     Mean                  = mean;
     Covariance            = covariance;
     CovarianceDeterminant = covariance.PseudoDeterminant();
     CovarianceInverse     = covariance.PseudoInverse();
     Weight                = (!double.IsNaN(weight)) ? weight : 0;
     Multiplier            = Math.Pow(2 * Math.PI, -mean.Length / 2) / Math.Sqrt(CovarianceDeterminant);
     CanonicalVector       = CovarianceInverse.Multiply(Mean);
 }
예제 #2
0
 /// <summary>
 /// Obtain the squared Mahalanobis distance between the gaussian distribution and a point.
 /// </summary>
 /// <param name="point">Point in R^N space.</param>
 /// <returns>Squared distance between distribution and point.</returns>
 public double SquareMahalanobis(double[] point)
 {
     double[] diff = Mean.Subtract(point);
     return(diff.InnerProduct(CovarianceInverse.Multiply(diff)));
 }
예제 #3
0
 /// <summary>
 /// Evaluate the logarithm of the gaussian component, although without the weight multiplication.
 /// </summary>
 /// <param name="x">Evaluation point.</param>
 /// <returns>Distribution density.</returns>
 public double LogEvaluate(double[] x)
 {
     double[] diff = x.Subtract(Mean);
     return(Math.Log(Multiplier) - 0.5 * diff.InnerProduct(CovarianceInverse.Multiply(diff)));
 }
예제 #4
0
 /// <summary>
 /// Evaluate the gaussian component, although without the weight multiplication.
 /// </summary>
 /// <param name="x">Evaluation point.</param>
 /// <returns>Distribution density.</returns>
 public double Evaluate(double[] x)
 {
     double[] diff = x.Subtract(Mean);
     //return Multiplier * ApproximateNegExp(0.5 * diff.InnerProduct(CovarianceInverse.Multiply(diff)));
     return(Multiplier * Math.Exp(-0.5 * diff.InnerProduct(CovarianceInverse.Multiply(diff))));
 }