TokenVector.Numerics 1.0.1

TokenVector.Numerics

🇬🇧 English | 🇻🇳 Tiếng Việt

TokenVector.Numerics.dll is a high-performance, standalone mathematical library for multidimensional tensors, Automatic Differentiation (Autograd Engine), analytical linear algebra, optimization, spline interpolation, computational physics, quantitative finance, astrodynamics, quantum computing, post-quantum cryptography, and non-Euclidean geometry built for the TokenVector ecosystem and compiler (a typed Python-like language natively compiling to .NET CIL AOT).

The library serves as the Grand Unified Runtime Math & Tensor Engine, covering multidimensional tensors, automatic differentiation (Autograd), linear algebra, matrix functions, nonlinear optimization, splines, statistical distributions & hypothesis tests, DSP & FFT signal processing, astrodynamics, quantitative finance, computational physics, quantum state simulation, lattice cryptography, structural biology, spectral graph neural networks, and hyperbolic geometry.

📋 Key Features of TokenVector.Numerics

🧪 Verification & Quality Assurance

All 75/75 automated unit tests passed with zero failures in Release mode:

dotnet test TokenVector.Numerics.sln -c Release

Passed!  - Failed: 0, Passed: 75, Skipped: 0, Total: 75, Duration: 146 ms - TokenVector.Numerics.Tests.dll (net8.0)

🚀 Quick Start & Usage

using TokenVector.Numerics.Core;
using TokenVector.Numerics.Autograd;
using TokenVector.Numerics.Autograd.NN;
using TokenVector.Numerics.Autograd.Optim;

// 1. Zero-copy Multidimensional Tensor Slicing & Broadcasting
var a = NDArray<double>.FromArray([1, 2, 3, 4, 5, 6], 2, 3);
var b = NDArray<double>.FromArray([10, 20, 30], 1, 3);
var c = a + b; // Stride-0 Broadcasting -> [2, 3]

// 2. Dynamic Compute Graph Autograd
var x = new Tensor<double>(3.0, requiresGrad: true);
var y = (x * x) + (2.0 * x) + 1.0;
y.Backward();
Console.WriteLine(x.Grad!.Buffer[0]); // dy/dx = 2*3 + 2 = 8.0

// 3. Train a Multi-Layer Perceptron (MLP)
var model = new Sequential<double>(
    new Linear<double>(inFeatures: 2, outFeatures: 8),
    new Linear<double>(inFeatures: 8, outFeatures: 1)
);
var optimizer = new AdamW<double>(model.Parameters(), lr: 0.05);

For comprehensive documentation across all 34 mathematical categories, please check the User Guide (English) or Hướng dẫn sử dụng (Tiếng Việt).