SharpLearning | Machine learning for C # .Net | Machine Learning library

by mdabros C# Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | SharpLearning Summary

SharpLearning is a C# library typically used in Artificial Intelligence, Machine Learning applications. SharpLearning has no bugs, it has no vulnerabilities and it has low support. However SharpLearning has a Non-SPDX License. You can download it from GitHub.

Machine learning for C# .Net

Support

Quality

Security

License

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Support

Quality

Security

License

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Support

SharpLearning has a low active ecosystem.

It has 298 star(s) with 70 fork(s). There are 34 watchers for this library.

It had no major release in the last 6 months.

There are 41 open issues and 44 have been closed. On average issues are closed in 117 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of SharpLearning is current.

SharpLearning Support

Best in #Machine Learning

Average in #Machine Learning

SharpLearning Support

Best in #Machine Learning

Average in #Machine Learning

Quality

SharpLearning has 0 bugs and 0 code smells.

SharpLearning Quality

Best in #Machine Learning

Average in #Machine Learning

SharpLearning Quality

Best in #Machine Learning

Average in #Machine Learning

Security

SharpLearning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

SharpLearning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

SharpLearning Security

Best in #Machine Learning

Average in #Machine Learning

SharpLearning Security

Best in #Machine Learning

Average in #Machine Learning

License

SharpLearning has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

SharpLearning License

Best in #Machine Learning

Average in #Machine Learning

SharpLearning License

Best in #Machine Learning

Average in #Machine Learning

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SharpLearning releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

SharpLearning Reuse

Best in #Machine Learning

Average in #Machine Learning

SharpLearning Reuse

Best in #Machine Learning

Average in #Machine Learning

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SharpLearning Key Features

DecisionTrees

Adaboost (trees)

GradientBoost (trees)

RandomForest

ExtraTrees

NeuralNets (layers for fully connected and convolutional nets)

Ensemble Learning

GridSearch

RandomSearch

ParticleSwarm

GlobalizedBoundedNelderMead

Hyperband

BayesianOptimization

SharpLearning Examples and Code Snippets

No Code Snippets are available at this moment for SharpLearning.

Community Discussions

Trending Discussions on SharpLearning

How to predict/score XGBoost or LightGBM in .NET Framework 4.6.1 application

Trending Discussions on SharpLearning

How to predict/score XGBoost or LightGBM in .NET Framework 4.6.1 application

QUESTION

How to predict/score XGBoost or LightGBM in .NET Framework 4.6.1 application

Asked 2019-Jul-11 at 23:29

I have a machine learning problem where I have obtained very good results on training/test data using both LightGBM and XGBoost. The next step is to obtain predictions from one of these models into an existing C# application (.NET Framework 4.6.1) Is there any library that can help me do this? What I have tried so far:

ML.NET: Should work for LigthGBM, but due to this bug it works only for .NET Core.
Windows.ML: This should be able to predict an ONNX model, and I managed to create an ONNX model from my XGBoost model. But Windows.ML seems to work only for UWP apps, at least all samples are UWP.
SharpLearning: This library has an interface to XGBoost. Unfortunately, it does not support sample weights, which I rely upon.
CNTK: Tried to load the ONNX file (similar to this example), but get: Error: ONNX (TreeEnsembleClassifier) is not supported in CNTK.

Any suggestions, or do I have to wait for ML.NET to fix the bug?

ANSWER

Answered 2019-Jul-11 at 23:29

I was able to use LightGBM in a net461 console application. The above bug only occurs if you are using packages.config to manage your NuGet packages. In order to work around the listed bug in the LightGBM nuget package, you can take one of the following approaches:

Use a new "SDK-style" .csproj, but set the TargetFramework to net461.



  
    Exe
    net461
    win-x64
  

  
    
  

  
    
      Always

Change your normal .NET Framework .csproj to use instead of packages.config. You can do this in the Package Manager Settings under Tools -> NuGet Package Manager menu. "Default package management format". You can refer to the Migrate from packages.config to PackageReference document for more info.

Source https://stackoverflow.com/questions/51579217

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install SharpLearning

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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