/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
///
public override object Clone()
{
var e = new MultivariateEmpiricalDistribution(Dimension);
e.chol = (CholeskyDecomposition)chol.Clone();
e.determinant = determinant;
e.kernel = kernel;
e.numberOfSamples = numberOfSamples;
e.repeats = (int[])repeats.Clone();
e.samples = (double[][])samples.MemberwiseClone();
e.smoothing = smoothing.MemberwiseClone();
e.sumOfWeights = sumOfWeights;
e.type = type;
if (e.weights != null)
{
e.weights = (double[])weights.Clone();
}
if (e.repeats != null)
{
e.repeats = (int[])repeats.Clone();
}
return(e);
}
/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
///
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
///
public override object Clone()
{
var clone = new MultivariateNormalDistribution(this.Dimension, false);
clone.lnconstant = lnconstant;
clone.covariance = (double[, ])covariance.Clone();
clone.mean = (double[])mean.Clone();
clone.chol = (CholeskyDecomposition)chol.Clone();
return(clone);
}
/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
public override object Clone()
{
var clone = new NormalDistribution(Dimension);
clone.constant = constant;
clone.covariance = (double[, ])covariance.Clone();
clone.mean = (double[])mean.Clone();
clone.variance = (double[])variance.Clone();
clone.chol = (CholeskyDecomposition)chol.Clone();
clone.svd = (svd != null) ? (SingularValueDecomposition)svd.Clone() : null;
return(clone);
}
/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
///
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
///
public override object Clone()
{
var clone = new MultivariateNormalDistribution(this.Dimension, false);
clone.lnconstant = lnconstant;
clone.covariance = (double[, ])covariance.Clone();
clone.mean = (double[])mean.Clone();
if (chol != null)
{
clone.chol = (CholeskyDecomposition)chol.Clone();
}
if (svd != null)
{
clone.svd = (SingularValueDecomposition)svd.Clone();
}
return(clone);
}