Sharpkit.Learn is machine learning library for .Net and mono. Currently the library is a port of the very nice scikit-learn python library and is very close to it's code base (hence the name Sharpkit.Learn). Eventually the codebase may become different. It is based on Math.Net library and different state-of-the art machine learning libraries. In particular it uses liblinear.
It supports both sparse and dense matrices. If you need extra performance, you can use various BLAS providers (like MKL and cublas) which can be plugged in into Math.Net.
- Linear regression
- Logistic regression
- Ridge regression
- Ridge classifier
- Support Vector Classifier
- Decision Trees
- Random Forests
- Nearest neighbors
- Naive Bayes
- Cross validation utilities
- PCA
- Ensemble methods
Currently the design is very close to the Scikit-learn python library. You can start by reading Scikit Learn documentation. Examples in C# and F# are available here
- Use library in your projects and report issues
- Port some scikit.learn functionality which you need The list is here: http://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm
- Implement some missing feature you need
- Perform benchmarking
- Blob about the library.
*Math.NET Numerics is covered under the terms of the MIT/X11 *Sharpkit.Learn is covered under the terms of the BSD Clause 3 license. *Liblinear is covered under the terms of the Modified BSD License.