Fit: add 'Linear' prefix to multidim and vector for clarity

src/Numerics/Fit.cs

@@ -143,7 +143,7 @@ namespace MathNet.Numerics
         /// Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x0) + p1*f1(x1) + ... + pk*fk(xk),
         /// returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
         /// </summary>
-        public static double[] MultiDimensional(double[][] x, double[] y, params Func<double, double>[] functions)
+        public static double[] LinearMultiDim(double[][] x, double[] y, params Func<double, double>[] functions)
         {
             return DenseMatrix
                 .OfRows(x.Length, functions.Length, x.Select(xi => functions.Select((f, k) => f(xi[k]))))
@@ -155,9 +155,9 @@ namespace MathNet.Numerics
         /// Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x0) + p1*f1(x1) + ... + pk*fk(xk),
         /// returning a function y' for the best fitting combination.
         /// </summary>
-        public static Func<double[], double> MultiDimensionalFunc(double[][] x, double[] y, params Func<double, double>[] functions)
+        public static Func<double[], double> LinearMultiDimFunc(double[][] x, double[] y, params Func<double, double>[] functions)
         {
-            var parameters = MultiDimensional(x, y, functions);
+            var parameters = LinearMultiDim(x, y, functions);
             return z => functions.Select((f, i) => parameters[i]*f(z[i])).Sum();
         }
 
@@ -165,7 +165,7 @@ namespace MathNet.Numerics
         /// Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x0) + p1*f1(x1) + ... + pk*fk(xk),
         /// returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
         /// </summary>
-        public static Vector<double> Vector(Vector<double>[] x, double[] y, Func<Vector<double>, Vector<double>> functions)
+        public static Vector<double> LinearVector(Vector<double>[] x, double[] y, Func<Vector<double>, Vector<double>> functions)
         {
             return DenseMatrix
                 .OfRowVectors(x.Select(functions).ToArray()) // PERF: Array.map instead of seq
@@ -176,9 +176,9 @@ namespace MathNet.Numerics
         /// Least-Squares fitting the points (X,y) = ((x0,x1,..,xk),y) to an arbitrary linear combination y : X -> p0*f0(x0) + p1*f1(x1) + ... + pk*fk(xk),
         /// returning a function y' for the best fitting combination.
         /// </summary>
-        public static Func<Vector<double>, double> VectorFunc(Vector<double>[] x, double[] y, Func<Vector<double>, Vector<double>> functions)
+        public static Func<Vector<double>, double> LinearVectorFunc(Vector<double>[] x, double[] y, Func<Vector<double>, Vector<double>> functions)
         {
-            var parameters = Vector(x, y, functions);
+            var parameters = LinearVector(x, y, functions);
             return z => functions(z).Select((yi, i) => parameters[i]*yi).Sum();
         }
     }