From 370b3081266ced2f4ce2c90b29d98daf471ae09c Mon Sep 17 00:00:00 2001 From: Christoph Ruegg Date: Fri, 20 Jun 2014 10:28:53 +0200 Subject: [PATCH] Regression: clarify that the locally weighted regression routines opt out from semantic versioning --- src/Numerics/LinearRegression/WeightedRegression.cs | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/Numerics/LinearRegression/WeightedRegression.cs b/src/Numerics/LinearRegression/WeightedRegression.cs index 715d8306..9cef62c7 100644 --- a/src/Numerics/LinearRegression/WeightedRegression.cs +++ b/src/Numerics/LinearRegression/WeightedRegression.cs @@ -92,7 +92,7 @@ namespace MathNet.Numerics.LinearRegression /// /// Locally-Weighted Linear Regression using normal equations. /// - [Obsolete("Warning: This function is here to stay but its signature will likely change.")] + [Obsolete("Warning: This function is here to stay but its signature will likely change. Opting out from semantic versioning.")] public static Vector Local(Matrix x, Vector y, Vector t, double radius, Func kernel) where T : struct, IEquatable, IFormattable { // TODO: Weird kernel definition @@ -107,7 +107,7 @@ namespace MathNet.Numerics.LinearRegression /// /// Locally-Weighted Linear Regression using normal equations. /// - [Obsolete("Warning: This function is here to stay but its signature will likely change.")] + [Obsolete("Warning: This function is here to stay but its signature will likely change. Opting out from semantic versioning.")] public static Matrix Local(Matrix x, Matrix y, Vector t, double radius, Func kernel) where T : struct, IEquatable, IFormattable { // TODO: Weird kernel definition @@ -119,7 +119,7 @@ namespace MathNet.Numerics.LinearRegression return Weighted(x, y, w); } - [Obsolete("Warning: This function is here to stay but will likely be refactored and/or moved to another place.")] + [Obsolete("Warning: This function is here to stay but will likely be refactored and/or moved to another place. Opting out from semantic versioning.")] public static double GaussianKernel(double normalizedDistance) { return Math.Exp(-0.5*normalizedDistance*normalizedDistance);