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High Performance Computation for Tensor in .NET, keep APIs same as NumPy and SciPy.
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Plankton555 and Nucs Added basic np.random.choice functionality (Fixed #310)
* Stub files for np.random.choice

* Simplest random.choice functionality

* Simple random.choice with weighted probabilities

* Unit test stubs and updated documentation (np.random.choice)

* Added Ignore markers to tests for unimplemented features

* random.choice handles shape=null

* Improved unit tests for np.random.choice

* Added documentation to np.searchsorted
Latest commit 39eead1 Jul 4, 2019


NumPy port in C# .NET Standard

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Is it difficult to translate python machine learning code into C#? Because too many functions can’t be found in the corresponding code in the .Net SDK. NumSharp is the C# version of NumPy, which is as consistent as possible with the NumPy programming interface, including function names and parameter locations. By introducing the NumSharp tool library, you can easily convert from python code to C# code. Here is a comparison code between NumSharp and NumPy (left is python, right is C#):


If you want to read some more informations, we start a doc on

NumSharp has implemented the arange, array, max, min, reshape, normalize, unique interfaces. More and more interfaces will be added to the library gradually. If you want to use .NET to get started with machine learning, NumSharp will be your best tool library.

Implemented APIs

The NumPy class is a high-level abstraction of NDArray that allows NumSharp to be used in the same way as Python's NumPy, minimizing API differences caused by programming language features, allowing .NET developers to maximize Utilize a wide range of NumPy code resources to seamlessly translate python code into .NET code.

Check the code: src\NumSharp.Core\APIs

How to use

using NumSharp;
// create a vector
var nd = np.arange(12)

// create a matrix
nd = np.arange(12).reshape(3, 4);

// access data by index
var data = nd[1, 1];

// create a tensor
nd = np.arange(12).reshape(2, 3, 2);

// get the 2nd vector in the 1st dimension
data = n[new Shape(1)];

// get the 3rd vector in the (axis 1, axis 2) dimension
data = n[new Shape(1, 2)];

// interate ndarray
foreach (data in nd)
  // data is a ndarray or a value

Install NumSharp in NuGet

PM> Install-Package NumSharp

How to make docs

How to run benchmark

C: \> dotnet NumSharp.Benchmark.dll nparange

Reference the documents generated by DocFX.

Reference the documents host on

NumSharp is referenced by:

You might also be interested in NumSharp's sister project Numpy.NET.

NumSharp is a member project of which is the .NET based ecosystem of open-source software for mathematics, science, and engineering. Welcome to fork and pull request to add more APIs, and make reference list longer.

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