From c33e6c39f50236c6b16f4c9c4a1700f4c2c4b023 Mon Sep 17 00:00:00 2001 From: Christoph Ruegg Date: Sun, 27 Jun 2021 17:15:59 +0200 Subject: [PATCH] Math.NET Numerics: 5.0.0-alpha01 docs update --- Build.html | 10 +-- DescriptiveStatistics.html | 72 ++++++++++---------- Generate.html | 19 ++++-- IFsharpNotebook.html | 111 +++++++++++++++++++------------ LinearEquations.html | 18 +++-- MKL.html | 15 +++-- Matrix.html | 66 +++++++++++++------ Probability.html | 130 ++++++++++++++++++++++++------------- Random.html | 67 ++++++++++++------- Regression.html | 4 +- ReleaseNotes-MKL.html | 2 +- ReleaseNotes-OpenBLAS.html | 2 +- ReleaseNotes.html | 22 ++++++- index.html | 11 ++-- 14 files changed, 358 insertions(+), 191 deletions(-) diff --git a/Build.html b/Build.html index fbda6688..0d2ce427 100644 --- a/Build.html +++ b/Build.html @@ -104,14 +104,14 @@ is not required when using Visual Studio or the .NET Core SDK directly. 13: 14: -
./build.sh   # normal build and unit tests, when using bash shell on Windows or Linux.
-build.cmd    # normal build and unit tests, when using Windows CMD shell.
+
./build.sh   # normal build and unit tests, when using bash shell on Windows or Linux.
+build.cmd    # normal build and unit tests, when using Windows CMD shell.
 
 ./build.sh build              # normal build
 ./build.sh build strongname   # normal build and also build strong-named variant
 
-./build.sh test          # normal build, run unit tests
-./build.sh test quick    # normal build, run unit tests except long running ones
+./build.sh test          # normal build, run unit tests
+./build.sh test quick    # normal build, run unit tests except long running ones
 
 ./build.sh clean         # cleanup build artifacts
 ./build.sh docs          # generate documentation
@@ -273,6 +273,8 @@ this is also what the unit tests do when you run the MklTest build
 
 
 
+
type unit = Unit

Full name: Microsoft.FSharp.Core.unit

+
val using : resource:'T -> action:('T -> 'U) -> 'U (requires 'T :> System.IDisposable)

Full name: Microsoft.FSharp.Core.Operators.using

 
                 
                 

diff --git a/DescriptiveStatistics.html b/DescriptiveStatistics.html
index eaf203cb..08853bae 100644
--- a/DescriptiveStatistics.html
+++ b/DescriptiveStatistics.html
@@ -148,13 +148,13 @@ The mean is affected by outliers, so if you need a more robust estimate consider
 7: 
 8: 
 

-
let whiteNoise = Generate.Normal(1000, mean=10.0, standardDeviation=2.0)
+
let whiteNoise = Generate.Normal(1000, mean=10.0, standardDeviation=2.0)
 val samples : float [] = [|12.90021939; 9.631515037; 7.810008046; 14.13301053; ...|] 
-Statistics.Mean whiteNoise
+Statistics.Mean whiteNoise
 val it : float = 10.02162347
 
-let wave = Generate.Sinusoidal(1000, samplingRate=100., frequency=5., amplitude=0.5)
-Statistics.Mean wave
+let wave = Generate.Sinusoidal(1000, samplingRate=100., frequency=5., amplitude=0.5)
+Statistics.Mean wave
 val it : float = -4.133520783e-17
 

 
@@ -180,12 +180,12 @@ Bessel's correction with an \(N-1\) normalizer to a sa
 6: 
 7: 
 

-
Statistics.Variance whiteNoise
+
Statistics.Variance whiteNoise
 val it : float = 3.819436094
-Statistics.StandardDeviation whiteNoise
+Statistics.StandardDeviation whiteNoise
 val it : float = 1.954337764
 
-Statistics.Variance wave
+Statistics.Variance wave
 val it : float = 0.1251251251
 

 
@@ -199,7 +199,7 @@ that evaluate both in a single pass:

 
-
@@ -217,9 +217,9 @@ apply Bessel's correction to bias in case of sample data.
3: 4: 
-
@@ -257,17 +257,17 @@ and also the rest of the library.
11: 12: 
-
@@ -288,9 +288,9 @@ median-unbiased regardless of the sample distribution. If you need another conve
 3: 4: 
-
@@ -312,9 +312,9 @@ estimates the median as discussed above.
3: 4: 
-
@@ -330,9 +330,9 @@ the maximum value. All these values can be visualized in the popular box plot di
 3: 4: 
-
@@ -346,7 +346,7 @@ and is a robust indicator of spread. In box plots the IQR is the total height of
 
1: 
 2: 
 
Statistics.MeanVariance whiteNoise
+
Statistics.MeanVariance whiteNoise
 val it : float * float = (10.02162347, 3.819436094)
 

 

 
 
 
Statistics.Covariance(whiteNoise, whiteNoise)
+
Statistics.Covariance(whiteNoise, whiteNoise)
 val it : float = 3.819436094
-Statistics.Covariance(whiteNoise, wave)
+Statistics.Covariance(whiteNoise, wave)
 val it : float = 0.04397985084
 

 

 
 
 
Statistics.OrderStatistic(whiteNoise, 1)
+
Statistics.OrderStatistic(whiteNoise, 1)
 val it : float = 3.633070184
-Statistics.OrderStatistic(whiteNoise, 1000)
+Statistics.OrderStatistic(whiteNoise, 1000)
 val it : float = 16.65183566
 
-let os = Statistics.orderStatisticFunc whiteNoise
-os 250
+let os = Statistics.orderStatisticFunc whiteNoise
+os 250
 val it : float = 8.645491746
-os 500
+os 500
 val it : float = 10.11872428
-os 750
+os 750
 val it : float = 11.33170746
 

 

 
 
Statistics.Median whiteNoise
+
Statistics.Median whiteNoise
 val it : float = 10.11872428
-Statistics.Median wave
+Statistics.Median wave
 val it : float = -2.452600839e-16
 

 

 
 
 
Statistics.LowerQuartile whiteNoise
+
Statistics.LowerQuartile whiteNoise
 val it : float = 8.645491746
-Statistics.UpperQuartile whiteNoise
+Statistics.UpperQuartile whiteNoise
 val it : float = 11.33213732
 

 

 
 
Statistics.FiveNumberSummary whiteNoise
+
Statistics.FiveNumberSummary whiteNoise
 val it : float [] = [|3.633070184; 8.645937823; 10.12165054; 11.33213732; 16.65183566|] 
-Statistics.FiveNumberSummary wave
+Statistics.FiveNumberSummary wave
 val it : float [] = [|-0.5; -0.3584185509; -2.452600839e-16; 0.3584185509; 0.5|] 
 

 

-
@@ -366,9 +366,9 @@ The 0-percentile represents the minimum value, 25 the first quartile, 50 the med
 3: 4: 
-
@@ -385,7 +385,7 @@ the inverse cumulative distribution function of the sample distribution.
1: 
 2: 
 
Statistics.InterquartileRange whiteNoise
+
Statistics.InterquartileRange whiteNoise
 val it : float = 2.686199498
 

 

 
 
Statistics.Percentile(whiteNoise, 5)
+
Statistics.Percentile(whiteNoise, 5)
 val it : float = 6.693373507
-Statistics.Percentile(whiteNoise, 98)
+Statistics.Percentile(whiteNoise, 98)
 val it : float = 13.97580653
 

 

 

-
@@ -447,7 +447,7 @@ Similar to QuantileDefinition, the RankDefinition enum
 5: 6: 
-
-
@@ -495,14 +495,14 @@ function crosses \(\tau\).
12: 13: 
-
1: 
 2: 
 
Statistics.Quantile(whiteNoise, 0.98)
+
Statistics.Quantile(whiteNoise, 0.98)
 val it : float = 13.97580653
 

 

 
 
Statistics.Ranks(whiteNoise)
+
Statistics.Ranks(whiteNoise)
 val it : float [] = [|634.0; 736.0; 405.0; 395.0; 197.0; 167.0; 722.0; 44.0; ...|] 
 Statistics.Ranks([| 13.0; 14.0; 11.0; 12.0; 13.0 |], RankDefinition.Average)
 val it : float [] = [|3.5; 5.0; 1.0; 2.0; 3.5|] 
@@ -468,9 +468,9 @@ function crosses \(\tau\).

 3: 
 4: 
 
Statistics.QuantileRank(whiteNoise, 13.0)
+
Statistics.QuantileRank(whiteNoise, 13.0)
 val it : float = 0.9370045563
-Statistics.QuantileRank(whiteNoise, 6.7, RankDefinition.Average)
+Statistics.QuantileRank(whiteNoise, 6.7, RankDefinition.Average)
 val it : float = 0.04960610389
 

 

 
 
 
let ecdf = Statistics.EmpiricalCDFFunc whiteNoise
-Generate.LinearSpacedMap(20, start=3.0, stop=17.0, map=ecdf)
+
let ecdf = Statistics.EmpiricalCDFFunc whiteNoise
+Generate.LinearSpacedMap(20, start=3.0, stop=17.0, map=ecdf)
 val it : float [] =
     [|0.0; 0.001; 0.002; 0.005; 0.022; 0.05; 0.094; 0.172; 0.278; 0.423; 0.555; 
       0.705; 0.843; 0.921; 0.944; 0.983; 0.992; 0.997; 0.999; 1.0|] 
 
-let eicdf = Statistics.empiricalInvCDFFunc whiteNoise
-[ for tau in 0.0..0.05..1.0 -> eicdf tau ]
+let eicdf = Statistics.empiricalInvCDFFunc whiteNoise
+[ for tau in 0.0..0.05..1.0 -> eicdf tau ]
 val it : float [] =
     [3.633070184; 6.682142043; 7.520000817; 8.040513497; 8.347587493; 
      8.645491746; 9.02681611; 9.298987151; 9.522627142; 9.819352699; 10.11872428; 
@@ -534,6 +534,12 @@ correlation coefficient, as well as their correlation matrix for a set of vector
 double correlation = Correlation.Pearson(dataF, dataG);
 

 
+
val whiteNoise : obj

Full name: DescriptiveStatistics.whiteNoise

+
val wave : obj

Full name: DescriptiveStatistics.wave

+
val os : (int -> obj)

Full name: DescriptiveStatistics.os

+
val ecdf : obj

Full name: DescriptiveStatistics.ecdf

+
val eicdf : (float -> obj)

Full name: DescriptiveStatistics.eicdf

+
val tau : float

 
                 
                 

diff --git a/Generate.html b/Generate.html
index 059a07c6..34f9b342 100644
--- a/Generate.html
+++ b/Generate.html
@@ -77,7 +77,7 @@ single colon : and double colon :: operators in MATLAB
 

 
[ 10.0 .. 2.0 .. 15.0 ]
 val it : float list = [10.0; 12.0; 14.0] 
-[ for x in 10.0 .. 2.0 .. 15.0 -> sin x ]
+[ for x in 10.0 .. 2.0 .. 15.0 -> sin x ]
 val it : float list = [-0.5440211109; -0.536572918; 0.9906073557] 
 

 
@@ -111,7 +111,7 @@ Generate.LinearSpacedMap(15, 0.0,
 4: 
 5:
-
Generate.linearSpacedMap 15 0.0 Math.PI sin
+
Generate.linearSpacedMap 15 0.0 Math.PI sin
 val it : float [] = 
   [|0.0; 0.222520934; 0.4338837391; 0.6234898019; 0.7818314825; 0.9009688679; 
     0.9749279122; 1.0; 0.9749279122; 0.9009688679; 0.7818314825; 0.6234898019; 
@@ -245,7 +245,7 @@ Generate.Map(a, x => x 
1: 
 2: 
 

-
Array.map ((+) 1.0) a
+
Array.map ((+) 1.0) a
 val it : float [] = [|3.0; 5.0; 4.0; 7.0|] 
 

 
@@ -261,8 +261,8 @@ You can use LINQ, but that operates on sequences instead of arrays:

 2: 
 3: 
 

-
let b = [| 1.0; -1.0; 2.0; -2.0 |]
-Generate.Map2(a, b, fun x y -> x + y)
+
let b = [| 1.0; -1.0; 2.0; -2.0 |]
+Generate.Map2(a, b, fun x y -> x + y)
 val it : float [] = [|3.0; 3.0; 5.0; 4.0|] 
 

 
@@ -271,7 +271,7 @@ You can use LINQ, but that operates on sequences instead of arrays:

 
-
@@ -282,6 +282,13 @@ You can use LINQ, but that operates on sequences instead of arrays:
1: 
 2: 
 
Array.map2 (+) a b
+
Array.map2 (+) a b
 val it : float [] = [|3.0; 3.0; 5.0; 4.0|] 
 

 

 
a.Zip(b, (x, y) => x + y).ToArray();
 

 
+
val x : float

+
val sin : value:'T -> 'T (requires member Sin)

Full name: Microsoft.FSharp.Core.Operators.sin

+
module Array

from Microsoft.FSharp.Collections

+
val map : mapping:('T -> 'U) -> array:'T [] -> 'U []

Full name: Microsoft.FSharp.Collections.Array.map

+
val a : float []

Full name: Generate.a

+
val b : float []

Full name: Generate.b

+
val map2 : mapping:('T1 -> 'T2 -> 'U) -> array1:'T1 [] -> array2:'T2 [] -> 'U []

Full name: Microsoft.FSharp.Collections.Array.map2

 
                 
                 

diff --git a/IFsharpNotebook.html b/IFsharpNotebook.html
index 2ff9f805..fa2eaa33 100644
--- a/IFsharpNotebook.html
+++ b/IFsharpNotebook.html
@@ -129,54 +129,85 @@ Unfortunately loading this script requires the exact version in the path - if yo
 

 
open MathNet.Numerics.LinearAlgebra
 
-let inline (|Float|_|) (v:obj) =
-    if v :? float then Some(v :?> float) else None
-let inline (|Float32|_|) (v:obj) =
-    if v :? float32 then Some(v :?> float32) else None
-let inline (|PositiveInfinity|_|) (v: ^T) =
-    if (^T : (static member IsPositiveInfinity: 'T -> bool) (v))
-    then Some PositiveInfinity else None
-let inline (|NegativeInfinity|_|) (v: ^T) =
-    if (^T : (static member IsNegativeInfinity: 'T -> bool) (v))
-    then Some NegativeInfinity else None
-let inline (|NaN|_|) (v: ^T) =
-    if (^T : (static member IsNaN: 'T -> bool) (v))
-    then Some NaN else None
+let inline (|Float|_|) (v:obj) =
+    if v :? float then Some(v :?> float) else None
+let inline (|Float32|_|) (v:obj) =
+    if v :? float32 then Some(v :?> float32) else None
+let inline (|PositiveInfinity|_|) (v: ^T) =
+    if (^T : (static member IsPositiveInfinity: 'T -> bool) (v))
+    then Some PositiveInfinity else None
+let inline (|NegativeInfinity|_|) (v: ^T) =
+    if (^T : (static member IsNegativeInfinity: 'T -> bool) (v))
+    then Some NegativeInfinity else None
+let inline (|NaN|_|) (v: ^T) =
+    if (^T : (static member IsNaN: 'T -> bool) (v))
+    then Some NaN else None
 
-let inline formatMathValue (floatFormat:string) = function
-  | PositiveInfinity -> "\\infty"
-  | NegativeInfinity -> "-\\infty"
-  | NaN -> "\\times"
-  | Float v -> v.ToString(floatFormat)
-  | Float32 v -> v.ToString(floatFormat)
-  | v -> v.ToString()
+let inline formatMathValue (floatFormat:string) = function
+  | PositiveInfinity -> "\\infty"
+  | NegativeInfinity -> "-\\infty"
+  | NaN -> "\\times"
+  | Float v -> v.ToString(floatFormat)
+  | Float32 v -> v.ToString(floatFormat)
+  | v -> v.ToString()
 
-let inline formatMatrix (matrix: Matrix<'T>) =
-  String.concat Environment.NewLine
-    [ "\\begin{bmatrix}"
-      matrix.ToMatrixString(10,4,7,2,"\\cdots","\\vdots","\\ddots",
-        " & ", "\\\\ " + Environment.NewLine, (fun x -> formatMathValue "G4" x))
-      "\\end{bmatrix}" ]
+let inline formatMatrix (matrix: Matrix<'T>) =
+  String.concat Environment.NewLine
+    [ "\\begin{bmatrix}"
+      matrix.ToMatrixString(10,4,7,2,"\\cdots","\\vdots","\\ddots",
+        " & ", "\\\\ " + Environment.NewLine, (fun x -> formatMathValue "G4" x))
+      "\\end{bmatrix}" ]
 
-let inline formatVector (vector: Vector<'T>) =
-  String.concat Environment.NewLine
-    [ "\\begin{bmatrix}"
-      vector.ToVectorString(12, 80, "\\vdots", " & ", "\\\\ " + Environment.NewLine,
-        (fun x -> formatMathValue "G4" x))
-      "\\end{bmatrix}" ]
+let inline formatVector (vector: Vector<'T>) =
+  String.concat Environment.NewLine
+    [ "\\begin{bmatrix}"
+      vector.ToVectorString(12, 80, "\\vdots", " & ", "\\\\ " + Environment.NewLine,
+        (fun x -> formatMathValue "G4" x))
+      "\\end{bmatrix}" ]
 
-App.AddDisplayPrinter (fun (x:Matrix<float>) ->
-    { ContentType = "text/latex"; Data = formatMatrix x })
-App.AddDisplayPrinter (fun (x:Matrix<float32>) ->
-    { ContentType = "text/latex"; Data = formatMatrix x })
-App.AddDisplayPrinter (fun (x:Vector<float>) ->
-    { ContentType = "text/latex"; Data = formatVector x })
-App.AddDisplayPrinter (fun (x:Vector<float32>) ->
-    { ContentType = "text/latex"; Data = formatVector x })
+App.AddDisplayPrinter (fun (x:Matrix<float>) ->
+    { ContentType = "text/latex"; Data = formatMatrix x })
+App.AddDisplayPrinter (fun (x:Matrix<float32>) ->
+    { ContentType = "text/latex"; Data = formatMatrix x })
+App.AddDisplayPrinter (fun (x:Vector<float>) ->
+    { ContentType = "text/latex"; Data = formatVector x })
+App.AddDisplayPrinter (fun (x:Vector<float32>) ->
+    { ContentType = "text/latex"; Data = formatVector x })
 

 
 
 
+
val v : obj

+
type obj = System.Object

Full name: Microsoft.FSharp.Core.obj

+
Multiple items
val float : value:'T -> float (requires member op_Explicit)

Full name: Microsoft.FSharp.Core.Operators.float

--------------------
type float = System.Double

Full name: Microsoft.FSharp.Core.float

--------------------
type float<'Measure> = float

Full name: Microsoft.FSharp.Core.float<_>

+
union case Option.Some: Value: 'T -> Option<'T>

+
union case Option.None: Option<'T>

+
Multiple items
val float32 : value:'T -> float32 (requires member op_Explicit)

Full name: Microsoft.FSharp.Core.Operators.float32

--------------------
type float32 = System.Single

Full name: Microsoft.FSharp.Core.float32

--------------------
type float32<'Measure> = float32

Full name: Microsoft.FSharp.Core.float32<_>

+
val v : 'T (requires member IsPositiveInfinity)

+
type bool = System.Boolean

Full name: Microsoft.FSharp.Core.bool

+
val v : 'T (requires member IsNegativeInfinity)

+
val v : 'T (requires member IsNaN)

+
val formatMathValue : floatFormat:string -> _arg1:'a -> string (requires member IsNaN and member IsPositiveInfinity and member IsNegativeInfinity)

Full name: IFsharpNotebook.formatMathValue

+
val floatFormat : string

+
Multiple items
val string : value:'T -> string

Full name: Microsoft.FSharp.Core.Operators.string

--------------------
type string = System.String

Full name: Microsoft.FSharp.Core.string

+
active recognizer PositiveInfinity: 'T -> unit option

Full name: IFsharpNotebook.( |PositiveInfinity|_| )

+
active recognizer NegativeInfinity: 'T -> unit option

Full name: IFsharpNotebook.( |NegativeInfinity|_| )

+
active recognizer NaN: 'T -> unit option

Full name: IFsharpNotebook.( |NaN|_| )

+
active recognizer Float: obj -> float option

Full name: IFsharpNotebook.( |Float|_| )

+
val v : float

+
System.Double.ToString() : string
System.Double.ToString(provider: System.IFormatProvider) : string
System.Double.ToString(format: string) : string
System.Double.ToString(format: string, provider: System.IFormatProvider) : string

+
active recognizer Float32: obj -> float32 option

Full name: IFsharpNotebook.( |Float32|_| )

+
val v : float32

+
System.Single.ToString() : string
System.Single.ToString(format: string) : string
System.Single.ToString(provider: System.IFormatProvider) : string
System.Single.ToString(format: string, provider: System.IFormatProvider) : string

+
val v : 'a (requires member IsNaN and member IsPositiveInfinity and member IsNegativeInfinity)

+
System.Object.ToString() : string

+
val formatMatrix : matrix:'a -> string

Full name: IFsharpNotebook.formatMatrix

+
val matrix : 'a

+
module String

from Microsoft.FSharp.Core

+
val concat : sep:string -> strings:seq<string> -> string

Full name: Microsoft.FSharp.Core.String.concat

+
val formatVector : vector:'a -> string

Full name: IFsharpNotebook.formatVector

+
val vector : 'a

+
namespace Microsoft.FSharp.Data

 
                 
                 

diff --git a/LinearEquations.html b/LinearEquations.html
index 95c5d29e..6960e680 100644
--- a/LinearEquations.html
+++ b/LinearEquations.html
@@ -117,11 +117,11 @@ a_{m1} & a_{m2} & \cdots & a_{mn}
 4: 
 5: 
 

-
let A = matrix [[ 3.0; 2.0; -1.0 ]
+
let A = matrix [[ 3.0; 2.0; -1.0 ]
                 [ 2.0; -2.0; 4.0 ]
                 [ -1.0; 0.5; -1.0 ]]
-let b = vector [ 1.0; -2.0; 0.0 ]
-let x = A.Solve(b) // 1;-2;-2
+let b = vector [ 1.0; -2.0; 0.0 ]
+let x = A.Solve(b) // 1;-2;-2
 

 
 
@@ -158,16 +158,22 @@ become zero on the right side), by introducing a new column each. First we subtr
 5: 
 6: 
 

-
let A' = matrix [[ 3.0; 4.0; -1.0; 0.0 ]
+
let A' = matrix [[ 3.0; 4.0; -1.0; 0.0 ]
                  [ 4.0; 5.0; 0.0; -1.0 ]
                  [ 5.0; 6.0; 0.0; 0.0; ]
                  [ 6.0; 7.0; 0.0; 0.0 ]]
-let b' = vector [ 0.0; 0.0; 20.0; 0.0 ]
-let x' = A'.Solve(b') // -140; 120; 60; 40
+let b' = vector [ 0.0; 0.0; 20.0; 0.0 ]
+let x' = A'.Solve(b') // -140; 120; 60; 40
 

 
 
 
+
val A : obj

Full name: LinearEquations.A

+
val b : obj

Full name: LinearEquations.b

+
val x : obj

Full name: LinearEquations.x

+
val A' : obj

Full name: LinearEquations.A'

+
val b' : obj

Full name: LinearEquations.b'

+
val x' : obj

Full name: LinearEquations.x'

 
                 
                 

diff --git a/MKL.html b/MKL.html
index 3ecf7b73..b8af1576 100644
--- a/MKL.html
+++ b/MKL.html
@@ -174,8 +174,8 @@ directory somewhere and use them directly from there:

 
-
1: 
 2: 
 
Control.NativeProviderPath <- @"C:\MKL"
-Control.UseNativeMKL()
+
Control.NativeProviderPath <- @"C:\MKL"
+Control.UseNativeMKL()
 

 

 

@@ -189,11 +189,11 @@ MKL provider automatically.

 4: 
 5: 
 

-
open System.IO
+
open System.IO
 open MathNet.Numerics
 
-Control.NativeProviderPath <- Path.Combine(__SOURCE_DIRECTORY__,"../")
-Control.UseNativeMKL()
+Control.NativeProviderPath <- Path.Combine(__SOURCE_DIRECTORY__,"../")
+Control.UseNativeMKL()
 

 
 
@@ -320,6 +320,11 @@ Numerics MKL native provider for free for your own use. However, it does not
 redistribute it again yourself to customers of your own product. If you need to redistribute,
 buy a license from Intel. If unsure, contact the Intel sales team to clarify.

 
+
namespace Microsoft.FSharp.Control

+
namespace System

+
namespace System.IO

+
type Path =
  static val DirectorySeparatorChar : char
  static val AltDirectorySeparatorChar : char
  static val VolumeSeparatorChar : char
  static val InvalidPathChars : char[]
  static val PathSeparator : char
  static member ChangeExtension : path:string * extension:string -> string
  static member Combine : [<ParamArray>] paths:string[] -> string + 3 overloads
  static member GetDirectoryName : path:string -> string
  static member GetExtension : path:string -> string
  static member GetFileName : path:string -> string
  ...

Full name: System.IO.Path

+
Path.Combine([<System.ParamArray>] paths: string []) : string
Path.Combine(path1: string, path2: string) : string
Path.Combine(path1: string, path2: string, path3: string) : string
Path.Combine(path1: string, path2: string, path3: string, path4: string) : string

 
                 
                 

diff --git a/Matrix.html b/Matrix.html
index 013c57f6..5ad08d90 100644
--- a/Matrix.html
+++ b/Matrix.html
@@ -325,31 +325,31 @@ V.Dense(x);
 24: 
 25: 
 

-
let m1 = matrix [[ 2.0; 3.0 ]
+
let m1 = matrix [[ 2.0; 3.0 ]
                  [ 4.0; 5.0 ]]
 
-let v1 = vector [ 1.0; 2.0; 3.0 ]
+let v1 = vector [ 1.0; 2.0; 3.0 ]
 
 // dense 3x4 matrix filled with zeros.
 // (usually the type is inferred, but not for zero matrices)
-let m2 = DenseMatrix.zero<float> 3 4
+let m2 = DenseMatrix.zero<float> 3 4
 
 // dense 3x4 matrix initialized by a function
-let m3 = DenseMatrix.init 3 4 (fun i j -> float (i+j))
+let m3 = DenseMatrix.init 3 4 (fun i j -> float (i+j))
 
 // diagonal 4x4 identity matrix of single precision
-let m4 = DiagonalMatrix.identity<float32> 4
+let m4 = DiagonalMatrix.identity<float32> 4
 
 // dense 3x4 matrix created from a sequence of sequence-columns
-let x = Seq.init 4 (fun c -> Seq.init 3 (fun r -> float (100*r + c)))
-let m5 = DenseMatrix.ofColumnSeq x
+let x = Seq.init 4 (fun c -> Seq.init 3 (fun r -> float (100*r + c)))
+let m5 = DenseMatrix.ofColumnSeq x
 
 // random matrix with standard distribution:
-let m6 = DenseMatrix.randomStandard<float> 3 4
+let m6 = DenseMatrix.randomStandard<float> 3 4
 
 // random matrix with a uniform and one with a Gamma distribution:
-let m7a = DenseMatrix.random<float> 3 4 (ContinuousUniform(-2.0, 4.0))
-let m7b = DenseMatrix.random<float> 3 4 (Gamma(1.0, 2.0))
+let m7a = DenseMatrix.random<float> 3 4 (ContinuousUniform(-2.0, 4.0))
+let m7b = DenseMatrix.random<float> 3 4 (Gamma(1.0, 2.0))
 

 
 
@@ -367,14 +367,14 @@ operators .*, ./ and .% available for con
 7: 
 8: 
 

-
let m = matrix [[ 1.0; 4.0; 7.0 ]
+
let m = matrix [[ 1.0; 4.0; 7.0 ]
                 [ 2.0; 5.0; 8.0 ]
                 [ 3.0; 6.0; 9.0 ]]
 
-let v = vector [ 10.0; 20.0; 30.0 ]
+let v = vector [ 10.0; 20.0; 30.0 ]
 
-let v' = m * v
-let m' = m + 2.0*m
+let v' = m * v
+let m' = m + 2.0*m
 

 
 
@@ -571,7 +571,7 @@ m[0,2]; // -1In F#:

 
-
1: 
 
m.[2,0] // 20
+
m.[2,0] // 20
 

 

 

@@ -599,11 +599,11 @@ to overwrite those elements with the provided data.

 4: 
 5: 
 

-
let m = DenseMatrix.init 6 4 (fun i j -> float (10*i + j))
-m.[0,0..3]    // vector [0,1,2,3]
-m.[1..2,0..3] // matrix [10,11,12,13; 20,21,22,23]
+
let m = DenseMatrix.init 6 4 (fun i j -> float (10*i + j))
+m.[0,0..3]    // vector [0,1,2,3]
+m.[1..2,0..3] // matrix [10,11,12,13; 20,21,22,23]
 // overwrite a sub-matrix with the content of another matrix:
-m.[0..1,1..2] <- matrix [[ 3.0; 4.0 ]; [ 5.0; 6.0 ]]
+m.[0..1,1..2] <- matrix [[ 3.0; 4.0 ]; [ 5.0; 6.0 ]]
 

 
 
@@ -689,8 +689,8 @@ Vector<Double> v = u.Map(c =
 
-
1: 
 2: 
 
let u = DenseVector.randomStandard<Complex> 10
-let v = u |> Vector.map (fun c -> c.Real)
+
let u = DenseVector.randomStandard<Complex> 10
+let v = u |> Vector.map (fun c -> c.Real)
 

 

 

@@ -917,6 +917,30 @@ DenseMatrix 5x100-Double
 to load the MathNet.Numerics.fsx script of the F# package. Besides loading
 the assemblies it also adds proper FSI printers for both matrices and vectors.

 
+
val m1 : obj

Full name: Matrix.m1

+
val v1 : obj

Full name: Matrix.v1

+
val m2 : obj

Full name: Matrix.m2

+
Multiple items
val float : value:'T -> float (requires member op_Explicit)

Full name: Microsoft.FSharp.Core.Operators.float

--------------------
type float = System.Double

Full name: Microsoft.FSharp.Core.float

--------------------
type float<'Measure> = float

Full name: Microsoft.FSharp.Core.float<_>

+
val m3 : obj

Full name: Matrix.m3

+
val m4 : obj

Full name: Matrix.m4

+
Multiple items
val float32 : value:'T -> float32 (requires member op_Explicit)

Full name: Microsoft.FSharp.Core.Operators.float32

--------------------
type float32 = System.Single

Full name: Microsoft.FSharp.Core.float32

--------------------
type float32<'Measure> = float32

Full name: Microsoft.FSharp.Core.float32<_>

+
val x : seq<seq<float>>

Full name: Matrix.x

+
module Seq

from Microsoft.FSharp.Collections

+
val init : count:int -> initializer:(int -> 'T) -> seq<'T>

Full name: Microsoft.FSharp.Collections.Seq.init

+
val c : int

+
val r : int

+
val m5 : obj

Full name: Matrix.m5

+
val m6 : obj

Full name: Matrix.m6

+
val m7a : obj

Full name: Matrix.m7a

+
val m7b : obj

Full name: Matrix.m7b

+
val m : float

Full name: Matrix.m

+
val v : float

Full name: Matrix.v

+
val v' : float

Full name: Matrix.v'

+
val m' : float

Full name: Matrix.m'

+
val m : obj

Full name: Matrix.m

+
val u : obj

Full name: Matrix.u

+
Multiple items
type Complex =
  struct
    new : real:float * imaginary:float -> Complex
    member Equals : obj:obj -> bool + 1 overload
    member GetHashCode : unit -> int
    member Imaginary : float
    member Magnitude : float
    member Phase : float
    member Real : float
    member ToString : unit -> string + 3 overloads
    static val Zero : Complex
    static val One : Complex
    ...
  end

Full name: System.Numerics.Complex

--------------------
Complex()
Complex(real: float, imaginary: float) : unit

+
val v : obj

Full name: Matrix.v

 
                 
                 

diff --git a/Probability.html b/Probability.html
index 16f593ae..7eea1fbd 100644
--- a/Probability.html
+++ b/Probability.html
@@ -198,12 +198,12 @@ gamma2.RandomSource = 7: 
 

 
// some probability distributions
-let normal = Normal.WithMeanVariance(3.0, 1.5, a)
-let exponential = Exponential(2.4)
-let gamma = Gamma(2.0, 1.5, Random.crypto())
-let cauchy = Cauchy(0.0, 1.0, Random.mrg32k3aWith 10 false)
-let poisson = Poisson(3.0)
-let geometric = Geometric(0.8, Random.system())
+let normal = Normal.WithMeanVariance(3.0, 1.5, a)
+let exponential = Exponential(2.4)
+let gamma = Gamma(2.0, 1.5, Random.crypto())
+let cauchy = Cauchy(0.0, 1.0, Random.mrg32k3aWith 10 false)
+let poisson = Poisson(3.0)
+let geometric = Geometric(0.8, Random.system())
 

 
 
@@ -213,9 +213,9 @@ estimation from a set of samples:

 2: 
 3:
-
let estimation = LogNormal.Estimate([| 2.0; 1.5; 2.1; 1.2; 3.0; 2.4; 1.8 |])
-let mean, variance = estimation.Mean, estimation.Variance
-let moreSamples = estimation.Samples() |> Seq.take 10 |> Seq.toArray
+
let estimation = LogNormal.Estimate([| 2.0; 1.5; 2.1; 1.2; 3.0; 2.4; 1.8 |])
+let mean, variance = estimation.Mean, estimation.Variance
+let moreSamples = estimation.Samples() |> Seq.take 10 |> Seq.toArray
 

 
 
@@ -249,16 +249,16 @@ but one can easily replace this with more sophisticated random number generators
 
// sample some random numbers from these distributions
 // continuous distributions sample to floating-point numbers:
-let continuous =
-  [ yield normal.Sample()
-    yield exponential.Sample()
-    yield! gamma.Samples() |> Seq.take 10 ]
+let continuous =
+  [ yield normal.Sample()
+    yield exponential.Sample()
+    yield! gamma.Samples() |> Seq.take 10 ]
 
 // discrete distributions on the other hand sample to integers:
-let discrete =
-  [ poisson.Sample()
-    poisson.Sample()
-    geometric.Sample() ]
+let discrete =
+  [ poisson.Sample()
+    poisson.Sample()
+    geometric.Sample() ]
@@ -273,12 +273,12 @@ Note that no intermediate value caching is possible this way and parameters must 7: 
// using the default number generator (SystemRandomSource.Default)
-let w = Rayleigh.Sample(1.5)
-let x = Hypergeometric.Sample(100, 20, 5)
+let w = Rayleigh.Sample(1.5)
+let x = Hypergeometric.Sample(100, 20, 5)
 
 // or by manually providing the uniform random number generator
-let u = Normal.Sample(Random.system(), 2.0, 4.0)
-let v = Laplace.Samples(Random.mersenneTwister(), 1.0, 3.0) |> Seq.take 100 |> List.ofSeq
+let u = Normal.Sample(Random.system(), 2.0, 4.0)
+let v = Laplace.Samples(Random.mersenneTwister(), 1.0, 3.0) |> Seq.take 100 |> List.ofSeq
@@ -297,8 +297,8 @@ Laplace.Samples(samples, 1.0, 2.0) -
1: 
 2: 
 
Seq.scan (+) 0.0 (Normal.Samples(0.0, 1.0)) |> Seq.take 10 |> Seq.toArray
-Seq.scan (+) 0.0 (Cauchy.Samples(0.0, 1.0)) |> Seq.take 10 |> Seq.toArray
+
Seq.scan (+) 0.0 (Normal.Samples(0.0, 1.0)) |> Seq.take 10 |> Seq.toArray
+Seq.scan (+) 0.0 (Cauchy.Samples(0.0, 1.0)) |> Seq.take 10 |> Seq.toArray
@@ -331,26 +331,26 @@ some of them are also available with the Ln-suffix.

21: 
// distribution properties of the gamma we've configured above
-let gammaStats =
-  ( gamma.Mean,
-    gamma.Variance,
-    gamma.StdDev,
-    gamma.Entropy,
-    gamma.Skewness,
-    gamma.Mode )
+let gammaStats =
+  ( gamma.Mean,
+    gamma.Variance,
+    gamma.StdDev,
+    gamma.Entropy,
+    gamma.Skewness,
+    gamma.Mode )
 
 // probability distribution functions of the normal we've configured above.
-let nd = normal.Density(4.0)  (* PDF *)
-let ndLn = normal.DensityLn(4.0)  (* ln(PDF) *)
-let nc = normal.CumulativeDistribution(4.0)  (* CDF *)
-let nic = normal.InverseCumulativeDistribution(0.7)  (* CDF^(-1) *)
+let nd = normal.Density(4.0)  (* PDF *)
+let ndLn = normal.DensityLn(4.0)  (* ln(PDF) *)
+let nc = normal.CumulativeDistribution(4.0)  (* CDF *)
+let nic = normal.InverseCumulativeDistribution(0.7)  (* CDF^(-1) *)
 
 // Distribution functions can also be evaluated without creating an object,
 // but then you have to pass in the distribution parameters as first arguments:
-let nd2 = Normal.PDF(3.0, sqrt 1.5, 4.0)
-let ndLn2 = Normal.PDFLn(3.0, sqrt 1.5, 4.0)
-let nc2 = Normal.CDF(3.0, sqrt 1.5, 4.0)
-let nic2 = Normal.InvCDF(3.0, sqrt 1.5, 0.7)
+let nd2 = Normal.PDF(3.0, sqrt 1.5, 4.0)
+let ndLn2 = Normal.PDFLn(3.0, sqrt 1.5, 4.0)
+let nc2 = Normal.CDF(3.0, sqrt 1.5, 4.0)
+let nic2 = Normal.InvCDF(3.0, sqrt 1.5, 0.7)
@@ -376,25 +376,65 @@ This way they can be composed and transformed arbitrarily if curried:

16: 
/// Transform a sample from a distribution
-let s1 rng = tanh (Sample.normal 2.0 0.5 rng)
+let s1 rng = tanh (Sample.normal 2.0 0.5 rng)
 
 /// But we really want to transform the function, not the resulting sample:
-let s1f rng = Sample.map tanh (Sample.normal 2.0 0.5) rng
+let s1f rng = Sample.map tanh (Sample.normal 2.0 0.5) rng
 
 /// Exactly the same also works with functions generating full sequences
-let s1s rng = Sample.mapSeq tanh (Sample.normalSeq 2.0 0.5) rng
+let s1s rng = Sample.mapSeq tanh (Sample.normalSeq 2.0 0.5) rng
 
 /// Now with multiple distributions, e.g. their product:
-let s2 rng = (Sample.normal 2.0 1.5 rng) * (Sample.cauchy 2.0 0.5 rng)
-let s2f rng = Sample.map2 (*) (Sample.normal 2.0 1.5) (Sample.cauchy 2.0 0.5) rng
-let s2s rng = Sample.mapSeq2 (*) (Sample.normalSeq 2.0 1.5) (Sample.cauchySeq 2.0 0.5) rng
+let s2 rng = (Sample.normal 2.0 1.5 rng) * (Sample.cauchy 2.0 0.5 rng)
+let s2f rng = Sample.map2 (*) (Sample.normal 2.0 1.5) (Sample.cauchy 2.0 0.5) rng
+let s2s rng = Sample.mapSeq2 (*) (Sample.normalSeq 2.0 1.5) (Sample.cauchySeq 2.0 0.5) rng
 
 // Taking some samples from the composed function
-Seq.take 10 (s2s (Random.system())) |> Seq.toArray
+Seq.take 10 (s2s (Random.system())) |> Seq.toArray
+
val normal : obj

Full name: Probability.normal
+
val exponential : obj

Full name: Probability.exponential
+
val gamma : obj

Full name: Probability.gamma
+
val cauchy : obj

Full name: Probability.cauchy
+
val poisson : obj

Full name: Probability.poisson
+
val geometric : obj

Full name: Probability.geometric
+
val estimation : obj

Full name: Probability.estimation
+
val mean : obj

Full name: Probability.mean
+
val variance : obj

Full name: Probability.variance
+
val moreSamples : obj []

Full name: Probability.moreSamples
+
module Seq

from Microsoft.FSharp.Collections
+
val take : count:int -> source:seq<'T> -> seq<'T>

Full name: Microsoft.FSharp.Collections.Seq.take
+
val toArray : source:seq<'T> -> 'T []

Full name: Microsoft.FSharp.Collections.Seq.toArray
+
val continuous : obj list

Full name: Probability.continuous
+
val discrete : obj list

Full name: Probability.discrete
+
val w : obj

Full name: Probability.w
+
val x : obj

Full name: Probability.x
+
val u : obj

Full name: Probability.u
+
val v : obj list

Full name: Probability.v
+
Multiple items
module List

from Microsoft.FSharp.Collections

--------------------
type List<'T> =
  | ( [] )
  | ( :: ) of Head: 'T * Tail: 'T list
  interface IEnumerable
  interface IEnumerable<'T>
  member GetSlice : startIndex:int option * endIndex:int option -> 'T list
  member Head : 'T
  member IsEmpty : bool
  member Item : index:int -> 'T with get
  member Length : int
  member Tail : 'T list
  static member Cons : head:'T * tail:'T list -> 'T list
  static member Empty : 'T list

Full name: Microsoft.FSharp.Collections.List<_>
+
val ofSeq : source:seq<'T> -> 'T list

Full name: Microsoft.FSharp.Collections.List.ofSeq
+
val scan : folder:('State -> 'T -> 'State) -> state:'State -> source:seq<'T> -> seq<'State>

Full name: Microsoft.FSharp.Collections.Seq.scan
+
val gammaStats : obj * obj * obj * obj * obj * obj

Full name: Probability.gammaStats
+
val nd : obj

Full name: Probability.nd
+
val ndLn : obj

Full name: Probability.ndLn
+
val nc : obj

Full name: Probability.nc
+
val nic : obj

Full name: Probability.nic
+
val nd2 : obj

Full name: Probability.nd2
+
val sqrt : value:'T -> 'U (requires member Sqrt)

Full name: Microsoft.FSharp.Core.Operators.sqrt
+
val ndLn2 : obj

Full name: Probability.ndLn2
+
val nc2 : obj

Full name: Probability.nc2
+
val nic2 : obj

Full name: Probability.nic2
+
val s1 : rng:'a -> float

Full name: Probability.s1


 Transform a sample from a distribution
+
val rng : 'a
+
val tanh : value:'T -> 'T (requires member Tanh)

Full name: Microsoft.FSharp.Core.Operators.tanh
+
val s1f : rng:'a -> 'b

Full name: Probability.s1f


 But we really want to transform the function, not the resulting sample:
+
val s1s : rng:'a -> 'b

Full name: Probability.s1s


 Exactly the same also works with functions generating full sequences
+
val s2 : rng:'a -> obj

Full name: Probability.s2


 Now with multiple distributions, e.g. their product:
+
val s2f : rng:'a -> 'b

Full name: Probability.s2f
+
val s2s : rng:'a -> 'b

Full name: Probability.s2s
diff --git a/Random.html b/Random.html index 5063f527..da7d01b4 100644 --- a/Random.html +++ b/Random.html @@ -124,18 +124,18 @@ System.Random rng = SystemRandomSource.Default; 11: 12: -
let samples = Random.doubles 1000
+
let samples = Random.doubles 1000
 
 // overwrite the whole array with new random values
-Random.doubleFill samples
+Random.doubleFill samples
 
 // create an infinite sequence:
-let sampleSeq = Random.doubleSeq ()
+let sampleSeq = Random.doubleSeq ()
 
 // take a single random value
-let rng = Random.shared
-let sample = rng.NextDouble()
-let sampled = rng.NextDecimal()
+let rng = Random.shared
+let sample = rng.NextDouble()
+let sampled = rng.NextDecimal()
 

 
 
@@ -187,9 +187,9 @@ or else a combination of a random number from a shared RNG, the time and a Guid
 2: 
 3:
-
let someTimeSeed = RandomSeed.Time() // not recommended
-let someGuidSeed = RandomSeed.Guid()
-let someRobustSeed = RandomSeed.Robust() // recommended, used by default
+
let someTimeSeed = RandomSeed.Time() // not recommended
+let someGuidSeed = RandomSeed.Guid()
+let someRobustSeed = RandomSeed.Robust() // recommended, used by default
 

 
 
@@ -198,9 +198,9 @@ or else a combination of a random number from a shared RNG, the time and a Guid
 2: 
 3:
-
let samplesSeeded = Random.doublesSeed 42 1000
-Random.doubleFillSeed 42 samplesSeeded
-let samplesSeqSeeded = Random.doubleSeqSeed 42
+
let samplesSeeded = Random.doublesSeed 42 1000
+Random.doubleFillSeed 42 samplesSeeded
+let samplesSeqSeeded = Random.doubleSeqSeed 42
 

 
 
@@ -269,18 +269,18 @@ In case of the latter, all objects will be cast to their common base type 
 13: 
 
// By using the normal constructor (random1 has type MersenneTwister) 
-let random1 = MersenneTwister()
-let random1b = MersenneTwister(42) // with seed
+let random1 = MersenneTwister()
+let random1b = MersenneTwister(42) // with seed
 
 // By using the Random module (random2 has type System.Random)
-let random2 = Random.mersenneTwister ()
-let random2b = Random.mersenneTwisterSeed 42 // with seed
-let random2c = Random.mersenneTwisterWith 42 false // opt-out of thread-safety
+let random2 = Random.mersenneTwister ()
+let random2b = Random.mersenneTwisterSeed 42 // with seed
+let random2c = Random.mersenneTwisterWith 42 false // opt-out of thread-safety
 
 // Using some other algorithms:
-let random3 = Random.crypto ()
-let random4 = Random.xorshift ()
-let random5 = Random.wh2006 ()
+let random3 = Random.crypto ()
+let random4 = Random.xorshift ()
+let random5 = Random.wh2006 ()
@@ -314,11 +314,11 @@ unless explicitly disabled by a constructor argument or by setting Control 4: 5: -
let a = Random.systemShared
-let b = Random.mersenneTwisterShared
+
let a = Random.systemShared
+let b = Random.mersenneTwisterShared
 
 // or if you don't care, simply
-let c = Random.shared;
+let c = Random.shared;
 

 
 
@@ -351,6 +351,27 @@ Normal.Samples(c, 0.0, 1.0);

See Probability Distributions for details.

+
val samples : obj

Full name: Random.samples
+
val sampleSeq : obj

Full name: Random.sampleSeq
+
val rng : obj

Full name: Random.rng
+
val sample : obj

Full name: Random.sample
+
val sampled : obj

Full name: Random.sampled
+
val someTimeSeed : obj

Full name: Random.someTimeSeed
+
val someGuidSeed : obj

Full name: Random.someGuidSeed
+
val someRobustSeed : obj

Full name: Random.someRobustSeed
+
val samplesSeeded : obj

Full name: Random.samplesSeeded
+
val samplesSeqSeeded : obj

Full name: Random.samplesSeqSeeded
+
val random1 : obj

Full name: Random.random1
+
val random1b : obj

Full name: Random.random1b
+
val random2 : obj

Full name: Random.random2
+
val random2b : obj

Full name: Random.random2b
+
val random2c : obj

Full name: Random.random2c
+
val random3 : obj

Full name: Random.random3
+
val random4 : obj

Full name: Random.random4
+
val random5 : obj

Full name: Random.random5
+
val a : obj

Full name: Random.a
+
val b : obj

Full name: Random.b
+
val c : obj

Full name: Random.c
diff --git a/Regression.html b/Regression.html index f3838f65..fcccdbcb 100644 --- a/Regression.html +++ b/Regression.html @@ -89,7 +89,7 @@ Tuple<double, double> p Or in F#:

-
1: 
 
let a, b = Fit.Line ([|10.0;20.0;30.0|], [|15.0;20.0;25.0|])
+
let a, b = Fit.Line ([|10.0;20.0;30.0|], [|15.0;20.0;25.0|])
@@ -285,6 +285,8 @@ are dependent on the point of interest \(t\).

Regularization

Iterative Methods

+
val a : obj

Full name: Regression.a
+
val b : obj

Full name: Regression.b
diff --git a/ReleaseNotes-MKL.html b/ReleaseNotes-MKL.html index 179290fc..a6310cb4 100644 --- a/ReleaseNotes-MKL.html +++ b/ReleaseNotes-MKL.html @@ -58,7 +58,7 @@

MKL Provider Release Notes

-

Math.NET Numerics | MKL Provider | OpenBLAS Provider | Data Extensions

+

Math.NET Numerics | MKL Provider | OpenBLAS Provider

2.5.0 - 2021-01-01

  • r14 with Intel MKL 2020 Update 4
    • diff --git a/ReleaseNotes-OpenBLAS.html b/ReleaseNotes-OpenBLAS.html index 54cce3bb..c699c404 100644 --- a/ReleaseNotes-OpenBLAS.html +++ b/ReleaseNotes-OpenBLAS.html @@ -58,7 +58,7 @@

    OpenBLAS Provider Release Notes

    -

    Math.NET Numerics | MKL Provider | OpenBLAS Provider | Data Extensions

    +

    Math.NET Numerics | MKL Provider | OpenBLAS Provider

    0.2.0 - 2015-09-26

    • Initial version
      • diff --git a/ReleaseNotes.html b/ReleaseNotes.html index 29e062c9..a31e370b 100644 --- a/ReleaseNotes.html +++ b/ReleaseNotes.html @@ -58,7 +58,27 @@

      Math.NET Numerics Release Notes

      -

      Math.NET Numerics | MKL Provider | OpenBLAS Provider | Data Extensions

      +

      Math.NET Numerics | MKL Provider | OpenBLAS Provider

      +

      5.0.0-alpha01 - 2021-06-27

      +
        +
      • COMPATIBILITY: net5.0, net48 better supported with explicit builds
        • +
      • COMPATIBILITY: netstandard1.x, net40, net45 no longer supported
        • +
      • BREAKING: drop all which was marked as obsolete
        • +
      • BREAKING: all native provider adapters moved out to separate NuGet packages
        • +
      • BREAKING: switch many usages of tuples to value tuples (experimental)
        • +
      • Distributions: Logistic ~Bobby Ingram
        • +
      • Distributions: Perf: Cauchy avoid duplicate evaluation ~Febin
        • +
      • Precision: Perf: pre-compute negative powers ~Febin
        • +
      • Optimizations: Remove static properties in LevenbergMarquardtMinimizer ~Jong Hyun Kim
        • +
      • Fit.Curve and FindMinimum extended to accept two more parameters
        • +
      • Series: stable series summation
        • +
      • Providers: drop managed reference linear algebra provider
        • +
      • Providers: native providers no longer inherit managed providers, managed now sealed
        • +
      • Providers: MKL provider compilation switched to Intel oneAPI MKL
        • +
      • Lots of internal cleanup, leveraging newer language features
        • +
      • Data: now released always together with Numerics (no longer separate versioning)
        • +
      • Control.Describe now includes CPU architecture and family identifier if know
        • +

      4.15.0 - 2021-01-07

      • Precision: Round (with integer part rounding) ~Jon Larborn
        • diff --git a/index.html b/index.html index 6f11446b..d208bb40 100644 --- a/index.html +++ b/index.html @@ -119,9 +119,9 @@ idiomatic and includes arbitrary precision types (BigInteger, BigRational).

        4: 
        open MathNet.Numerics.LinearAlgebra
-let m = matrix [[ 1.0; 2.0 ]
+let m = matrix [[ 1.0; 2.0 ]
                 [ 3.0; 4.0 ]]
-let m' = m.Inverse()
+let m' = m.Inverse()
        @@ -145,8 +145,8 @@ For convenience our F# packages include a small script that sets everything up p SpecialFunctions.Gamma(0.5) open MathNet.Numerics.LinearAlgebra -let m : Matrix<float> = DenseMatrix.randomStandard 50 50 -(m * m.Transpose()).Determinant() +let m : Matrix<float> = DenseMatrix.randomStandard 50 50 +(m * m.Transpose()).Determinant()         @@ -281,6 +281,9 @@ DenseVector 500-Double

        See Intel MKL for details how to use native providers on Linux.

        +
        val m : obj

        Full name: index.m
        +
        val m' : obj

        Full name: index.m'
        +
        Multiple items
        val float : value:'T -> float (requires member op_Explicit)

        Full name: Microsoft.FSharp.Core.Operators.float

        --------------------
        type float = System.Double

        Full name: Microsoft.FSharp.Core.float

        --------------------
        type float<'Measure> = float

        Full name: Microsoft.FSharp.Core.float<_>