/// <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); }