Skip to main content
Total Downloads
68.9M
68,901,354
Published
Oct 25, 2026
7d ago

Get Started

dotnet add package System.Numerics.Tensors --version 10.0.14
ProductVersions
.NET
net10.0net8.0net9.0
.NET Standard
netstandard2.0
Other
.NETFramework4.6.2

README

About

Provides methods for performing mathematical operations over tensors. This library offers both high-level tensor types and low-level primitives for working with multi-dimensional numeric data. Many operations are accelerated to use SIMD (Single instruction, multiple data) operations supported by the CPU where available.

Key Features

  • High-level tensor types: Tensor<T>, TensorSpan<T>, ReadOnlyTensorSpan<T> for working with multi-dimensional arrays
  • Low-level tensor primitives: TensorPrimitives for efficient span-based operations
  • Generic support for various numeric types (float, double, int, etc.)
  • Element-wise arithmetic: Add, Subtract, Multiply, Divide, Exp, Log, Cosh, Tanh, etc.
  • Tensor arithmetic: CosineSimilarity, Distance, Dot, Normalize, Softmax, Sigmoid, etc.
  • SIMD-accelerated operations for improved performance

How to Use

using System.Numerics.Tensors;

var movies = new[] {
    new { Title="The Lion King", Embedding= new [] { 0.10022575f, -0.23998135f } },
    new { Title="Inception", Embedding= new [] { 0.10327095f, 0.2563685f } },
    new { Title="Toy Story", Embedding= new [] { 0.095857024f, -0.201278f } },
    new { Title="Pulp Function", Embedding= new [] { 0.106827796f, 0.21676421f } },
    new { Title="Shrek", Embedding= new [] { 0.09568083f, -0.21177962f } }
};
var queryEmbedding = new[] { 0.12217915f, -0.034832448f };

// Using TensorPrimitives for low-level span operations
var top3MoviesTensorPrimitives =
    movies
        .Select(movie =>
            (
                movie.Title,
                Similarity: TensorPrimitives.CosineSimilarity(queryEmbedding, movie.Embedding)
            ))
        .OrderByDescending(movies => movies.Similarity)
        .Take(3);

foreach (var movie in top3MoviesTensorPrimitives)
{
    Console.WriteLine(movie);
}

// Using higher-level Tensor types for multi-dimensional operations
float[] data1 = [1f, 2f, 3f, 4f, 5f, 6f];
float[] data2 = [6f, 5f, 4f, 3f, 2f, 1f];
Tensor<float> tensor1 = Tensor.Create(data1, [2, 3]); // 2x3 tensor
Tensor<float> tensor2 = Tensor.Create(data2, [2, 3]); // 2x3 tensor
Tensor<float> result = tensor1 + tensor2;

Main Types

The main types provided by this library are:

  • System.Numerics.Tensors.TensorPrimitives - Low-level operations on spans of numeric data
  • System.Numerics.Tensors.Tensor<T> - Generic tensor class for multi-dimensional arrays
  • System.Numerics.Tensors.TensorSpan<T> - Span-like view over tensor data
  • System.Numerics.Tensors.ReadOnlyTensorSpan<T> - Read-only span-like view over tensor data
  • System.Numerics.Tensors.Tensor - Static class with high-level tensor operations

Additional Documentation

Feedback & Contributing

System.Numerics.Tensors is released as open source under the MIT license. Bug reports and contributions are welcome at the GitHub repository.

Downloads by Version

Depended On By

Find packages that declare this package as a dependency. NuGet does not provide a reverse-dependency endpoint, so candidates are checked on demand.

Adoption Assessment

A verified publisher signal is available. Assess fit from the package's supported frameworks, license and dependency graph rather than popularity alone.

Compatibility metadata lists 5 target frameworks; confirm the exact target used by your application.

Related Guides & Workflows

Learn how to evaluate, integrate, or audit this package in your .NET project.