https://github.com/krdlab/lisys

LAPACK wrapper for C# + Matrix/Vector + Some well-known statistical techniques
https://github.com/krdlab/lisys

Last synced: 9 days ago
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LAPACK wrapper for C# + Matrix/Vector + Some well-known statistical techniques

• Host: GitHub
• URL: https://github.com/krdlab/lisys
• Owner: krdlab
• License: mit
• Created: 2012-01-08T15:16:53.000Z (almost 13 years ago)
• Default Branch: master
• Last Pushed: 2013-04-01T12:23:26.000Z (over 11 years ago)
• Last Synced: 2024-04-11T03:43:24.688Z (7 months ago)
• Language: C#
• Homepage:
• Size: 203 KB
• Stars: 5
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Lisys (Japanese)

## What is it?

Lisys = LAPACK wrapper for C# + Matrix/Vector + Some well-known statistical techniques

## Features

* Vector, RowVector, ColumnVector

* Matrix

* Eigenvalues/vectors

* Singular Value Decomposition

* LU Decomposition

* Solver

* Linear Discriminant Analysis

* ...etc

## Build & Run

1.  LAPACK と Mingw の lib/dll を取得

    次のサイトを参考に Windows 用の lib/dll をダウンロード

    

    (Section: "Prebuilt dynamic libraries using Mingw")

2.  "law" プロジェクトを以下のように設定

    

    Section: "Prebuilt dynamic libraries using Mingw" の Instructions に従って設定する．

3.  "law" -> "lisys" の順番でプロジェクトをビルド

4.  自身のプロジェクトにおける「参照の追加」で以下のファイルを設定

    * `lisys.dll`

    * `law.dll`

    一緒に `*.xml` がコピーされ，Visual Studio 上でパラメータヒントが出るようになります．

5.  Windows のルールに従って LAPACK 関連の DLL を配置

    * `liblapack.dll`

    * `libblas.dll`

    * `libgcc_s_dw2-1.dll`

    * `libgfortran-3.dll`

    * `libquadmath-0.dll`

## Test

NUnit を利用しています．

1. NUnit をインストール

2. test プロジェクトをビルド

3. test/data フォルダを test/bin/Debug 以下にコピー

4. NUnit から test/bin/Debug/test.dll を実行

## Sample

### sample 1

```csharp

const int C = 3;

var data = new Matrix[C];

// input

for (int i = 0; i < C; ++i)

{

    data[i] =

        File.ReadAllLines(String.Format("data/iris/data_{0}.csv", i), Encoding.UTF8)

            .Select(line =>

                line.Split(',').Select(s => Double.Parse(s)).ToRow())

            .ToMatrix();

}

Lda result = Func.Lda(data);

// Coefficients of linear discriminants

var ldc1 = new ColumnVector(result.Eigenvectors[0]);

var ldc2 = new ColumnVector(result.Eigenvectors[1]);

var coef = new Matrix(ldc1, ldc2);

for (int i = 0; i < C; ++i)

{

    var m = data[i] * coef;   // calculate scores

    // output

    File.WriteAllText(String.Format("result_{0}.csv", i), Matrix.ToCsv(m), Encoding.UTF8);

}

```

### sample 2

```csharp

var m = new Matrix(new[,] {

                    {86.0, 67.0},

                    // ...

                    {96.0, 61.0} });

// correlation matrix

var cor1 = Func.Correlate(m, Func.Target.EachColumn);

MatrixVisualizer.TestShowVisualizer(cor1);  // test method of DebuggerVisualizer

// normalization -> correlation matrix

var n = new Matrix(m);

n.Columns.ForEach((ci, cv) => {

    var avg = cv.Average;

    var std = Math.Sqrt(cv.UnbiasedVariance);

    cv.Apply((i, val) => (val - avg) / std);

});

var cor2 = Matrix.T(n) * n / (n.RowSize - 1);

MatrixVisualizer.TestShowVisualizer(cor2);

```

### other

  その他細かな利用方法は `test` や `sample` プロジェクトを参照してください．

## License

MIT License

Copyright (C) 2007- KrdLab All Rights Reserved.

## TODO

* 64bit 対応