See the version list below for details.
dotnet add package MathNet.Numerics.Data.Text --version 3.0.0-alpha9
NuGet\Install-Package MathNet.Numerics.Data.Text -Version 3.0.0-alpha9
<PackageReference Include="MathNet.Numerics.Data.Text" Version="3.0.0-alpha9" />
paket add MathNet.Numerics.Data.Text --version 3.0.0-alpha9
#r "nuget: MathNet.Numerics.Data.Text, 3.0.0-alpha9"
// Install MathNet.Numerics.Data.Text as a Cake Addin #addin nuget:?package=MathNet.Numerics.Data.Text&version=3.0.0-alpha9&prerelease // Install MathNet.Numerics.Data.Text as a Cake Tool #tool nuget:?package=MathNet.Numerics.Data.Text&version=3.0.0-alpha9&prerelease
Text Data Input/Output Extensions for Math.NET Numerics, the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use.
|Product||Versions Compatible and additional computed target framework versions.|
|.NET Framework||net40 is compatible. net403 was computed. net45 was computed. net451 was computed. net452 was computed. net46 was computed. net461 was computed. net462 was computed. net463 was computed. net47 was computed. net471 was computed. net472 was computed. net48 was computed. net481 was computed.|
- MathNet.Numerics (= 3.0.0-alpha9)
NuGet packages (5)
Showing the top 5 NuGet packages that depend on MathNet.Numerics.Data.Text:
Library for nonlinear analysis of reinforced concrete membrane elements by Modified Compression Field Theory (VECCHIO; COLLINS, 1986) and Disturbed Stress Field Model (VECCHIO, 2000)..
Implementation of linear and nonlinear analysis of static problems by Finite Element Method.
Octavo.NET.Core Class Library
PhilipsTang.Mathlib .NETStandard 2.0
NeuralNet is a neural network library that is great both for a beginner looking to get started quickly, and a technical expert wishing to implement their own features.
GitHub repositories (1)
Showing the top 1 popular GitHub repositories that depend on MathNet.Numerics.Data.Text:
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.