FindMinimum.OfFunction and Fit.Curve shortcuts extended to accept two more parameters #760

src/Numerics/FindMinimum.cs

@@ -79,6 +79,28 @@ namespace MathNet.Numerics
             return Tuple.Create(result.MinimizingPoint[0], result.MinimizingPoint[1], result.MinimizingPoint[2]);
         }
 
+        /// <summary>
+        /// Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
+        /// For more options and diagnostics consider to use <see cref="NelderMeadSimplex"/> directly.
+        /// </summary>
+        public static Tuple<double, double, double, double> OfFunction(Func<double, double, double, double, double> function, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double tolerance = 1e-8, int maxIterations = 1000)
+        {
+            var objective = ObjectiveFunction.Value(v => function(v[0], v[1], v[2], v[3]));
+            var result = NelderMeadSimplex.Minimum(objective, CreateVector.Dense(new[] { initialGuess0, initialGuess1, initialGuess2, initialGuess3 }), tolerance, maxIterations);
+            return Tuple.Create(result.MinimizingPoint[0], result.MinimizingPoint[1], result.MinimizingPoint[2], result.MinimizingPoint[3]);
+        }
+
+        /// <summary>
+        /// Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
+        /// For more options and diagnostics consider to use <see cref="NelderMeadSimplex"/> directly.
+        /// </summary>
+        public static Tuple<double, double, double, double, double> OfFunction(Func<double, double, double, double, double, double> function, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double initialGuess4, double tolerance = 1e-8, int maxIterations = 1000)
+        {
+            var objective = ObjectiveFunction.Value(v => function(v[0], v[1], v[2], v[3], v[4]));
+            var result = NelderMeadSimplex.Minimum(objective, CreateVector.Dense(new[] { initialGuess0, initialGuess1, initialGuess2, initialGuess3, initialGuess4 }), tolerance, maxIterations);
+            return Tuple.Create(result.MinimizingPoint[0], result.MinimizingPoint[1], result.MinimizingPoint[2], result.MinimizingPoint[3], result.MinimizingPoint[4]);
+        }
+
         /// <summary>
         /// Find vector x that minimizes the function f(x) using the Nelder-Mead Simplex algorithm.
         /// For more options and diagnostics consider to use <see cref="NelderMeadSimplex"/> directly.

src/Numerics/Fit.cs

@@ -362,6 +362,24 @@ namespace MathNet.Numerics
             return FindMinimum.OfFunction((p0, p1, p2) => Distance.Euclidean(Generate.Map(x, t => f(p0, p1, p2, t)), y), initialGuess0, initialGuess1, initialGuess2, tolerance, maxIterations);
         }
 
+        /// <summary>
+        /// Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, x),
+        /// returning its best fitting parameter p0, p1 and p2.
+        /// </summary>
+        public static Tuple<double, double, double, double> Curve(double[] x, double[] y, Func<double, double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double tolerance = 1e-8, int maxIterations = 1000)
+        {
+            return FindMinimum.OfFunction((p0, p1, p2, p3) => Distance.Euclidean(Generate.Map(x, t => f(p0, p1, p2, p3, t)), y), initialGuess0, initialGuess1, initialGuess2, initialGuess3, tolerance, maxIterations);
+        }
+
+        /// <summary>
+        /// Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, p3, x),
+        /// returning its best fitting parameter p0, p1 and p2.
+        /// </summary>
+        public static Tuple<double, double, double, double, double> Curve(double[] x, double[] y, Func<double, double, double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double initialGuess4, double tolerance = 1e-8, int maxIterations = 1000)
+        {
+            return FindMinimum.OfFunction((p0, p1, p2, p3, p4) => Distance.Euclidean(Generate.Map(x, t => f(p0, p1, p2, p3, p4, t)), y), initialGuess0, initialGuess1, initialGuess2, initialGuess3, initialGuess4, tolerance, maxIterations);
+        }
+
         /// <summary>
         /// Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p, x),
         /// returning a function y' for the best fitting curve.
@@ -391,5 +409,25 @@ namespace MathNet.Numerics
             var parameters = Curve(x, y, f, initialGuess0, initialGuess1, initialGuess2, tolerance, maxIterations);
             return z => f(parameters.Item1, parameters.Item2, parameters.Item3, z);
         }
+
+        /// <summary>
+        /// Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, x),
+        /// returning a function y' for the best fitting curve.
+        /// </summary>
+        public static Func<double, double> CurveFunc(double[] x, double[] y, Func<double, double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double tolerance = 1e-8, int maxIterations = 1000)
+        {
+            var parameters = Curve(x, y, f, initialGuess0, initialGuess1, initialGuess2, initialGuess3, tolerance, maxIterations);
+            return z => f(parameters.Item1, parameters.Item2, parameters.Item3, parameters.Item4, z);
+        }
+
+        /// <summary>
+        /// Non-linear least-squares fitting the points (x,y) to an arbitrary function y : x -> f(p0, p1, p2, x),
+        /// returning a function y' for the best fitting curve.
+        /// </summary>
+        public static Func<double, double> CurveFunc(double[] x, double[] y, Func<double, double, double, double, double, double, double> f, double initialGuess0, double initialGuess1, double initialGuess2, double initialGuess3, double initialGuess4, double tolerance = 1e-8, int maxIterations = 1000)
+        {
+            var parameters = Curve(x, y, f, initialGuess0, initialGuess1, initialGuess2, initialGuess3, initialGuess4, tolerance, maxIterations);
+            return z => f(parameters.Item1, parameters.Item2, parameters.Item3, parameters.Item4, parameters.Item5, z);
+        }
     }
 }