./nugetz
/
trending/tags

#regression

123 packages tagged with “regression”

MathNet.Numerics✓

Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac.

v5.0.0↙ 58.5M
mathnumericstatisticsprobabilityintegration

MathNet.Numerics.Signed✓

Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac. This package contains strong-named assemblies for legacy use cases (not recommended).

v5.0.0↙ 37.0M
mathnumericstatisticsprobabilityintegration

MathNet.Numerics.FSharp✓

F# Modules 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. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac.

v5.0.0↙ 4.0M
fsharpF#mathnumericstatistics

Accord.Math

Contains a matrix extension library, along with a suite of numerical matrix decomposition methods, numerical optimization algorithms for constrained and unconstrained problems, special functions and other tools for scientific applications. This package is part of the Accord.NET Framework.

v3.8.0↙ 8.6M
accord.netaforge.netframeworkmathdecompositions

Accord.Statistics

Contains probability distributions, statistical models and methods such as Linear and Logistic regression, Hidden Markov Models, (Hidden) Conditional Random Fields, Principal Component Analysis, Partial Least Squares, Discriminant Analysis, Kernel methods and functions and many other related techniques. Provides methods for computing variances, standard deviations, averages, and many other statistical measures. This package is part of the Accord.NET Framework.

v3.8.0↙ 7.2M
accord.netaforge.netframeworkstatisticskernel
pkg

SharpLearning.DecisionTrees

Provides learning algorithms and models for DecisionTree regression and classification.

v0.31.8↙ 506.8K
decisiontreesmachinelearningclassificationregressionmachine
pkg

SharpLearning.RandomForest

Provides learning algorithms and models for RandomForest and ExtraTrees regression and classification.

v0.31.8↙ 373.3K
randomforestextratreesmachinelearningclassificationregression

Extreme.Numerics

Numerics.NET (formerly Extreme Optimization Numerical Libraries for .NET) are a set of libraries for numerical computing and data analysis. This is the main package that contains all the core functionality. For optimal performance, we strongly recommend also referencing one of the native packages based on Intel's Math Kernel Library (MKL). Supports .NET 5.0-8.0+, .NET Framework 4.62+, .NET Standard 2.0, and .NET Core 3.1 on Windows, Linux and Mac.

v8.1.25↙ 1.5M
mathstatisticsprobabilitymachinelearning

CenterSpace.NMath.Standard.Windows.X64

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires Visual Studio 2015-2019 C++ x64 runtime. Requires x64 hardware.

v7.4.2.16↙ 954.2K
mathstatisticsnumericsprobabilitylinear

ImageDiff

A library for producing diffs of images with highlighting.

v1.0.1↙ 177.3K
imagediffdifferencevisualregression
pkg

SharpLearning.Metrics

Provides classification, regression, impurity and ranking metrics.

v0.31.8↙ 208.7K
metricsmachinelearningclassificationregressionmachine

CsCheck

CsCheck is a C# random testing library inspired by QuickCheck. It differs in that generation and shrinking are both based on PCG, a fast random number generator. This gives the following advantages: - Automatic shrinking. Gen classes are composable with no need for Arb classes. So less boilerplate. - Random testing and shrinking are parallelized. This and PCG make it very fast. - Shrunk cases have a seed value. Simpler examples can easily be reproduced. - Shrinking can be continued later to give simpler cases for high dimensional problems. - Parallel testing and random shrinking work well together. CsCheck also makes parallel, performance and regression testing simple and fast.

v4.6.1↙ 316.1K
quickcheckrandommodel-basedmetamorphicparallel
pkg

SharpLearning.GradientBoost

Provides learning algorithms and models for GradientBoost regression and classification.

v0.31.8↙ 140.5K
gradientboostmachinelearningclassificationregressionmachine
pkg

SharpLearning.Ensemble

Provides ensemble learning for regression and classification.

v0.31.8↙ 114.0K
ensemblelearningmachinelearningclassificationregressionmachine
pkg

SharpLearning.AdaBoost

Provides learning algorithms and models for AdaBoost regression and classification.

v0.31.8↙ 104.1K
adaboostmachinelearningclassificationregressionmachine
pkg

ImageDiff-NetStandard

This is the .Net Standard port of https://github.com/richclement/ImageDiff . Sadly I did not get in contact with him so I'm publishing this.

v1.1.0↙ 67.3K
imagediffdifferencevisualregression

CenterSpace.NMath.Premium

The GPU-accelerated version of package CenterSpace.NMath. With a few minor exceptions, such as optional GPU configuration settings, the API is identical between CenterSpace.NMath.Premium and CenterSpace.NMath. If using at least .NET Framework 4.6.1 or .NET Core 2.0, we recommend using one of our NMath .NET Standard NuGet packages.

v6.2.0.58↙ 343.5K
GPUmathstatisticsnumericslinear
pkg

SharpLearning.Neural

Provides learning algorithms and models for neural net regression and classification.

v0.31.8↙ 137.4K
neuralnetworkconvolutionaldeeplearning

CenterSpace.NMath.Standard.Linux.X64

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires libgcc linux. Requires x64 hardware.

v7.4.2.16↙ 333.8K
mathstatisticsnumericsprobabilitylinear
pkg

SharpLearning.XGBoost

Provides learning algorithms and models for XGBoost regression and classification.

v0.31.8↙ 71.0K
XGBoostmachinelearningclassificationregressionmachine

CenterSpace.NMath

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. If you're using at least .NET Framework 4.6.1 or .NET Core 2.0, we recommend using one of our NMath .NET Standard NuGet packages.

v6.2.0.58↙ 172.2K
mathstatisticsnumericslinearalgebra

CenterSpace.NMath.Stats

Classes for data manipulation and statistical computation, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization.

v4.2.0.58↙ 66.3K
statisticsprobabilityregressionhypothesisANOVA

CenterSpace.NMath.Standard.Windows.X86.X64

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5 or .NET Core 2.0. Does not support .NET Framework. Requires Visual Studio 2017 C++ x86 and x64 runtimes. Requires x64 hardware.

v7.1.0.47↙ 178.5K
mathstatisticsnumericsprobabilitylinear

MathNet.Numerics.FSharp.Signed✓

F# Modules 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. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac. This package contains strong-named assemblies for legacy use cases (not recommended).

v5.0.0↙ 106.5K
fsharpF#mathnumericstatistics

Skender.Stock.Indicators✓

Transform financial market price data into technical analysis indicators such as MACD, Stochastic RSI, Average True Range, Parabolic SAR, and more.

v2.7.1↙ 5.1M
IndicatorsStockMarketTechnicalAnalysis

CenterSpace.NMath.Standard.WindowsAndLinux.X64

Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires x64 hardware. On Windows, requires Visual Studio 2017 C++ x64 runtime. On Linux, requires libgcc.

v7.1.0.47↙ 142.8K
mathstatisticsnumericsprobabilitylinear

CenterSpace.NMath.Stats.Premium

The GPU-accelerated version of package CenterSpace.NMath.Stats. With a few minor exceptions, such as optional GPU configuration settings, the API is identical between CenterSpace.NMath.Stats.Premium and CenterSpace.NMath.Stats.

v4.2.0.58↙ 43.9K
GPUstatisticsprobabilityregressionhypothesis

Ariadne

F# package for fitting Gaussian process regression models.

v0.1.4↙ 16.4K
regressionnonlineargaussianprocess
pkg

LibSVMsharp

LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines. This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018. For more information: https://github.com/ccerhan/LibSVMsharp

v1.2.5↙ 27.5K
libsvmsvmC#machinelearningclassification

ChartDirector.Net

ChartDirector is a charting library with extensive chart types, real-time charts, track cursors, hot spots, large data sets, zoom/scroll and viewports. ChartDirector supports both .NET and .NET Framework, and can be used for both web (ASP.NET) and desktop (Windows Forms/WPF) applications.

v7.1.2↙ 323.2K
ChartDirectorchartschartinggraphsgraphing