MathNet.Numerics.Data.Text 4.0.0

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.

Install-Package MathNet.Numerics.Data.Text -Version 4.0.0
dotnet add package MathNet.Numerics.Data.Text --version 4.0.0
<PackageReference Include="MathNet.Numerics.Data.Text" Version="4.0.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add MathNet.Numerics.Data.Text --version 4.0.0
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

Release Notes

Support for Math.NET Numerics v4
In addition to .Net 4.0 and newer now also targets .Net Standard 1.3 and 2.0.

NuGet packages (2)

Showing the top 2 NuGet packages that depend on MathNet.Numerics.Data.Text:

Package Downloads
Octavo.NET.Core Class Library
PhilipsTang.Mathlib .NETStandard 2.0

GitHub repositories (1)

Showing the top 1 popular GitHub repositories that depend on MathNet.Numerics.Data.Text:

Repository Stars
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.

Version History

Version Downloads Last updated
4.0.0 13,637 2/14/2018
4.0.0-beta01 458 2/4/2018
3.2.1 2,339 4/29/2017
3.2.0 3,500 4/11/2016
3.1.1 4,979 7/13/2015
3.1.0 1,441 1/11/2015
3.0.0 1,268 7/23/2014
3.0.0-beta02 520 6/15/2014
3.0.0-beta01 550 4/23/2014
3.0.0-alpha9 511 3/29/2014
3.0.0-alpha8 516 2/26/2014
3.0.0-alpha7 518 12/30/2013
3.0.0-alpha6 531 12/3/2013
3.0.0-alpha5 581 11/15/2013
3.0.0-alpha4 512 9/23/2013
1.1.0 1,376 6/23/2013