diff --git a/src/FSharp/FSharp.fsproj b/src/FSharp/FSharp.fsproj
index 24b296ff..8b141462 100644
--- a/src/FSharp/FSharp.fsproj
+++ b/src/FSharp/FSharp.fsproj
@@ -71,7 +71,7 @@
-
+
diff --git a/src/FSharp/LeastSquares.fs b/src/FSharp/Fit.fs
similarity index 84%
rename from src/FSharp/LeastSquares.fs
rename to src/FSharp/Fit.fs
index 28cea93d..0e9e3f8b 100644
--- a/src/FSharp/LeastSquares.fs
+++ b/src/FSharp/Fit.fs
@@ -1,4 +1,4 @@
-//
+//
// Math.NET Numerics, part of the Math.NET Project
// http://numerics.mathdotnet.com
// http://github.com/mathnet/mathnet-numerics
@@ -39,11 +39,11 @@ module Fit =
let private tofs (f:Func<_,_>) = fun a -> f.Invoke(a)
- let line x y = let p = LeastSquares.FitToLine(x,y) in (p.[0],p.[1])
- let linef x y = LeastSquares.FitToLineFunc(x,y) |> tofs
+ let line x y = let p = Fit.Line(x,y) in (p.[0],p.[1])
+ let lineF x y = Fit.LineFunc(x,y) |> tofs
- let polynomial order x y = LeastSquares.FitToPolynomial(x,y,order)
- let polynomialf order x y = LeastSquares.FitToPolynomialFunc(x,y,order) |> tofs
+ let polynomial order x y = Fit.Polynomial(x,y,order)
+ let polynomialF order x y = Fit.PolynomialFunc(x,y,order) |> tofs
let linear functions (x:float[]) (y:float[]) =
functions
@@ -51,6 +51,6 @@ module Fit =
|> DenseMatrix.ofColumnsList (Array.length x) (List.length functions)
|> fun m -> m.QR(QRMethod.Thin).Solve(DenseVector(y)).ToArray()
|> List.ofArray
- let linearf functions x y =
+ let linearF functions x y =
let parts = linear functions x y |> List.zip functions
in fun z -> parts |> List.fold (fun s (f,p) -> s+p*(f z)) 0.0
diff --git a/src/FSharpPortable/FSharpPortable.fsproj b/src/FSharpPortable/FSharpPortable.fsproj
index af6ac149..763334b2 100644
--- a/src/FSharpPortable/FSharpPortable.fsproj
+++ b/src/FSharpPortable/FSharpPortable.fsproj
@@ -77,8 +77,8 @@
BigRational.fs
-
- LeastSquares.fs
+
+ Fit.fs
RandomVariable.fs
diff --git a/src/FSharpUnitTests/CurveFittingTests.fs b/src/FSharpUnitTests/CurveFittingTests.fs
index 2fee6582..b9eae831 100644
--- a/src/FSharpUnitTests/CurveFittingTests.fs
+++ b/src/FSharpUnitTests/CurveFittingTests.fs
@@ -7,8 +7,6 @@ open FsUnit
module CurveFittingTests =
- let tofs (f:Func<_,_>) = fun a -> f.Invoke(a)
-
[]
let ``When fitting to an exact line should return exact parameters``() =
let f z = 4.0 - 1.5*z
@@ -20,7 +18,7 @@ module CurveFittingTests =
a |> should (equalWithin 1.0e-12) 4.0
b |> should (equalWithin 1.0e-12) -1.5
- let fres = LeastSquares.FitToLineFunc(x,y) |> tofs
+ let fres = Fit.lineF x y
in x |> Array.iter (fun x -> fres x |> should (equalWithin 1.0e-12) (f x))
[]
@@ -36,5 +34,5 @@ module CurveFittingTests =
(x,y) ||> Fit.linear [(fun _ -> 1.0); (Math.Sin); (Math.Cos) ]
|> should (equalWithin 1.0e-4) [ -0.287476; 4.02159; -1.46962 ]
- let fres = LeastSquares.FitToLinearCombinationFunc(x, y, (fun z -> 1.0), (fun z -> Math.Sin(z)), (fun z -> Math.Cos(z))) |> tofs
+ let fres = Fit.linearF [(fun z -> 1.0); (fun z -> Math.Sin(z)); (fun z -> Math.Cos(z))] x y
in x |> Array.iter (fun x -> fres x |> should (equalWithin 1.0e-4) (4.02159*Math.Sin(x) - 1.46962*Math.Cos(x) - 0.287476))
diff --git a/src/Numerics/LeastSquares.cs b/src/Numerics/Fit.cs
similarity index 82%
rename from src/Numerics/LeastSquares.cs
rename to src/Numerics/Fit.cs
index 5e7bdf56..86b3cf69 100644
--- a/src/Numerics/LeastSquares.cs
+++ b/src/Numerics/Fit.cs
@@ -1,4 +1,4 @@
-//
+//
// Math.NET Numerics, part of the Math.NET Project
// http://numerics.mathdotnet.com
// http://github.com/mathnet/mathnet-numerics
@@ -35,13 +35,16 @@ using MathNet.Numerics.LinearAlgebra.Generic.Factorization;
namespace MathNet.Numerics
{
- public static class LeastSquares
+ ///
+ /// Least-Squares Curve Fitting Routines
+ ///
+ public static class Fit
{
///
/// Least-Squares fitting the points (x,y) to a line y : x -> a+b*x,
/// returning its best fitting parameters as [a, b] array.
///
- public static double[] FitToLine(double[] x, double[] y)
+ public static double[] Line(double[] x, double[] y)
{
// TODO: we should use a direct algorithm instead (PERF)
return DenseMatrix
@@ -54,9 +57,9 @@ namespace MathNet.Numerics
/// Least-Squares fitting the points (x,y) to a line y : x -> a+b*x,
/// returning a function y' for the best fitting line.
///
- public static Func FitToLineFunc(double[] x, double[] y)
+ public static Func LineFunc(double[] x, double[] y)
{
- var parameters = FitToLine(x, y);
+ var parameters = Line(x, y);
double a = parameters[0], b = parameters[1];
return z => a + b*z;
}
@@ -65,7 +68,7 @@ namespace MathNet.Numerics
/// Least-Squares fitting the points (x,y) to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 + ... + pk*x^k,
/// returning its best fitting parameters as [p0, p1, p2, ..., pk] array, compatible with Evaluate.Polynomial.
///
- public static double[] FitToPolynomial(double[] x, double[] y, int order)
+ public static double[] Polynomial(double[] x, double[] y, int order)
{
// TODO: consider to use a specific algorithm instead
return DenseMatrix
@@ -78,9 +81,9 @@ namespace MathNet.Numerics
/// Least-Squares fitting the points (x,y) to a k-order polynomial y : x -> p0 + p1*x + p2*x^2 + ... + pk*x^k,
/// returning a function y' for the best fitting polynomial.
///
- public static Func FitToPolynomialFunc(double[] x, double[] y, int order)
+ public static Func PolynomialFunc(double[] x, double[] y, int order)
{
- var parameters = FitToPolynomial(x, y, order);
+ var parameters = Polynomial(x, y, order);
return z => Evaluate.Polynomial(z, parameters);
}
@@ -88,7 +91,7 @@ namespace MathNet.Numerics
/// Least-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
/// returning its best fitting parameters as [p0, p1, p2, ..., pk] array.
///
- public static double[] FitToLinearCombination(double[] x, double[] y, params Func[] functions)
+ public static double[] LinearCombination(double[] x, double[] y, params Func[] functions)
{
// TODO: consider to use a specific algorithm instead
return DenseMatrix
@@ -101,9 +104,9 @@ namespace MathNet.Numerics
/// LLeast-Squares fitting the points (x,y) to an arbitrary linear combination y : x -> p0*f0(x) + p1*f1(x) + ... + pk*fk(x),
/// returning a function y' for the best fitting combination.
///
- public static Func FitToLinearCombinationFunc(double[] x, double[] y, params Func[] functions)
+ public static Func LinearCombinationFunc(double[] x, double[] y, params Func[] functions)
{
- var parameters = FitToLinearCombination(x, y, functions);
+ var parameters = LinearCombination(x, y, functions);
return z => functions.Zip(parameters, (f, p) => p*f(z)).Sum();
}
}
diff --git a/src/Numerics/Numerics.csproj b/src/Numerics/Numerics.csproj
index d4ecee38..5c66530f 100644
--- a/src/Numerics/Numerics.csproj
+++ b/src/Numerics/Numerics.csproj
@@ -105,7 +105,7 @@
-
+
diff --git a/src/Portable/Portable.csproj b/src/Portable/Portable.csproj
index 4cf36a4f..1249d03f 100644
--- a/src/Portable/Portable.csproj
+++ b/src/Portable/Portable.csproj
@@ -198,6 +198,9 @@
Financial\AbsoluteRiskMeasures.cs
+
+ Fit.cs
+
GlobalizationHelper.cs
@@ -282,9 +285,6 @@
IPrecisionSupport.cs
-
- LeastSquares.cs
-
LinearAlgebra\Complex32\DenseMatrix.cs
diff --git a/src/UnitTests/LeastSquaresTests.cs b/src/UnitTests/FitTests.cs
similarity index 86%
rename from src/UnitTests/LeastSquaresTests.cs
rename to src/UnitTests/FitTests.cs
index 176cfd83..38e301d0 100644
--- a/src/UnitTests/LeastSquaresTests.cs
+++ b/src/UnitTests/FitTests.cs
@@ -1,4 +1,4 @@
-//
+//
// Math.NET Numerics, part of the Math.NET Project
// http://numerics.mathdotnet.com
// http://github.com/mathnet/mathnet-numerics
@@ -36,7 +36,7 @@ using NUnit.Framework;
namespace MathNet.Numerics.UnitTests
{
[TestFixture]
- public class LeastSquaresTests
+ public class FitTests
{
[Test]
public void FitsToExactLineWhenPointsAreOnLine()
@@ -44,12 +44,12 @@ namespace MathNet.Numerics.UnitTests
var x = new[] {30.0, 40.0, 50.0, 12.0, -3.4, 100.5};
var y = x.Select(z => 4.0 - 1.5*z).ToArray();
- var resp = LeastSquares.FitToLine(x, y);
+ var resp = Fit.Line(x, y);
Assert.AreEqual(2, resp.Length);
Assert.AreEqual(4.0, resp[0], 1e-12);
Assert.AreEqual(-1.5, resp[1], 1e-12);
- var resf = LeastSquares.FitToLineFunc(x, y);
+ var resf = Fit.LineFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(4.0 - 1.5*z, resf(z), 1e-12);
@@ -65,12 +65,12 @@ namespace MathNet.Numerics.UnitTests
var x = Enumerable.Range(1, 6).Select(Convert.ToDouble).ToArray();
var y = new[] {4.986, 2.347, 2.061, -2.995, -2.352, -5.782};
- var resp = LeastSquares.FitToLine(x, y);
+ var resp = Fit.Line(x, y);
Assert.AreEqual(2, resp.Length);
Assert.AreEqual(7.01013, resp[0], 1e-4);
Assert.AreEqual(-2.08551, resp[1], 1e-4);
- var resf = LeastSquares.FitToLineFunc(x, y);
+ var resf = Fit.LineFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(7.01013 - 2.08551 * z, resf(z), 1e-4);
@@ -86,7 +86,7 @@ namespace MathNet.Numerics.UnitTests
var x = Enumerable.Range(1, 6).Select(Convert.ToDouble).ToArray();
var y = new[] { 4.986, 2.347, 2.061, -2.995, -2.352, -5.782 };
- var resp = LeastSquares.FitToPolynomial(x, y, 0);
+ var resp = Fit.Polynomial(x, y, 0);
Assert.AreEqual(1, resp.Length);
Assert.AreEqual(-0.289167, resp[0], 1e-4);
Assert.AreEqual(y.Mean(), resp[0], 1e-4);
@@ -101,12 +101,12 @@ namespace MathNet.Numerics.UnitTests
var x = Enumerable.Range(1, 6).Select(Convert.ToDouble).ToArray();
var y = new[] { 4.986, 2.347, 2.061, -2.995, -2.352, -5.782 };
- var resp = LeastSquares.FitToPolynomial(x, y, 1);
+ var resp = Fit.Polynomial(x, y, 1);
Assert.AreEqual(2, resp.Length);
Assert.AreEqual(7.01013, resp[0], 1e-4);
Assert.AreEqual(-2.08551, resp[1], 1e-4);
- var resf = LeastSquares.FitToPolynomialFunc(x, y, 1);
+ var resf = Fit.PolynomialFunc(x, y, 1);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(7.01013 - 2.08551 * z, resf(z), 1e-4);
@@ -122,13 +122,13 @@ namespace MathNet.Numerics.UnitTests
var x = Enumerable.Range(1, 6).Select(Convert.ToDouble).ToArray();
var y = new[] { 4.986, 2.347, 2.061, -2.995, -2.352, -5.782 };
- var resp = LeastSquares.FitToPolynomial(x, y, 2);
+ var resp = Fit.Polynomial(x, y, 2);
Assert.AreEqual(3, resp.Length);
Assert.AreEqual(6.9703, resp[0], 1e-4);
Assert.AreEqual(-2.05564, resp[1], 1e-4);
Assert.AreEqual(-0.00426786, resp[2], 1e-6);
- var resf = LeastSquares.FitToPolynomialFunc(x, y, 2);
+ var resf = Fit.PolynomialFunc(x, y, 2);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(Evaluate.Polynomial(z, resp), resf(z), 1e-4);
@@ -144,13 +144,13 @@ namespace MathNet.Numerics.UnitTests
var x = Enumerable.Range(1, 6).Select(Convert.ToDouble).ToArray();
var y = new[] { 4.986, 2.347, 2.061, -2.995, -2.352, -5.782 };
- var resp = LeastSquares.FitToLinearCombination(x, y, z => 1.0, Math.Sin, Math.Cos);
+ var resp = Fit.LinearCombination(x, y, z => 1.0, Math.Sin, Math.Cos);
Assert.AreEqual(3, resp.Length);
Assert.AreEqual(-0.287476, resp[0], 1e-4);
Assert.AreEqual(4.02159, resp[1], 1e-4);
Assert.AreEqual(-1.46962, resp[2], 1e-4);
- var resf = LeastSquares.FitToLinearCombinationFunc(x, y, z => 1.0, Math.Sin, Math.Cos);
+ var resf = Fit.LinearCombinationFunc(x, y, z => 1.0, Math.Sin, Math.Cos);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(4.02159*Math.Sin(z) - 1.46962*Math.Cos(z) - 0.287476, resf(z), 1e-4);
diff --git a/src/UnitTests/UnitTests.csproj b/src/UnitTests/UnitTests.csproj
index d13c9564..bbe923c8 100644
--- a/src/UnitTests/UnitTests.csproj
+++ b/src/UnitTests/UnitTests.csproj
@@ -144,7 +144,7 @@
-
+