|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Smile||5,688||121||30||24 days ago||30||December 05, 2020||9||other||Java|
|Statistical Machine Intelligence & Learning Engine|
|Machinelearning Samples||3,933||4 days ago||180||mit||PowerShell|
|Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.|
|Stellargraph||2,615||2||3||2 months ago||24||June 30, 2020||308||apache-2.0||Python|
|StellarGraph - Machine Learning on Graphs|
|Ml||1,758||3||14||15 days ago||65||June 03, 2022||41||mit||PHP|
|A high-level machine learning and deep learning library for the PHP language.|
|Mlr||1,595||65||36||2 months ago||22||October 05, 2020||9||other||R|
|Machine Learning in R|
|Pyts||1,432||2||9||2 months ago||18||October 31, 2021||39||bsd-3-clause||Python|
|A Python package for time series classification|
|R||652||2 months ago||6||mit||R|
|Collection of various algorithms implemented in R.|
|Sharplearning||310||6||14||a year ago||42||July 12, 2020||42||other||C#|
|Machine learning for C# .Net|
|Siml||299||2 years ago||9||December 21, 2020||5||mit||Jupyter Notebook|
|Machine Learning algorithms implemented from scratch|
|Fuku Ml||278||2||5 years ago||35||May 01, 2017||12||mit||Python|
|Simple machine learning library / 簡單易用的機器學習套件|
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are:
For questions and comments just contact us via mail. There you also can ask to be registerd to our mailing list.
liquidSVM is licensed under AGPL 3.0. In case you need another license, please contact me.
Installation instructions for the command line versions.
|Terminal version for Linux/OS X||liquidSVM.tar.gz|
|Terminal version for Windows (64bit)||avx2: liquidSVM.zip|
|Previous versions||v1.1 (June 2016), v1.0 (January 2016)|
On Linux and Mac on the terminal
liquidSVM can be used in the following way:
wget www.isa.uni-stuttgart.de/software/liquidSVM.tar.gz tar xzf liquidSVM.tar.gz cd liquidSVM make all scripts/mc-svm.sh banana-mc 1 2
Read the demo vignette for a tutorial on installing liquidSVM-package and how to use it and the documentation vignette for more advanced installation options and usage.
An easy usage is:
install.packages("liquidSVM") library(liquidSVM) banana <- liquidData('banana-mc') model <- mcSVM( Y~. , banana$train, display=1, threads=2) result <- test(model, banana$test) errors(result)
Read the demo notebook for a tutorial on installing liquidSVM-package and how to use it and the homepage for more advanced installation options and usage.
To install use:
pip install --user liquidSVM
and then in Python you can use it e.g. like:
from liquidSVM import * banana = LiquidData('banana-mc') model = mcSVM(banana.train, display=1, threads=2) result, err = model.test(banana.test)
The MATLAB bindings are currently getting a better interface, and this is a preview version.
It does currently not work on Windows.
For installation download the Toolbox liquidSVM.mltbx and install it in MATLAB by double clicking it. To compile and add paths issue:
Then you can use it like:
banana = liquidData('banana-mc'); model = svm_mc(banana.train, 'DISPLAY', 1, 'THREADS', 2); [result, err] = model.test(banana.test);
Most of the code also works in
if you use liquidSVM-octave.zip.
The main homepage is here.
For installation download liquidSVM-java.zip and unzip it.
The classes are all in package
de.uni_stuttgart.isa.liquidsvm and an easy example is:
LiquidData banana = new LiquidData("banana-mc"); SVM model = new MC(banana.train, new Config().display(1).threads(2)); ResultAndErrors result = model.test(banana.test);
If this is implemented in the file
Example.java this can be compiled and run using
# if you want to compile the JNI-native library: make lib # compile your Java-Code javac -classpath liquidSVM.jar Example.java # and run it java -Djava.library.path=. -cp .:liquidSVM.jar Example
This is a preview version, see Spark for more details.
Download liquidSVM-spark.zip and unzip it.
Assume you have
Spark installed in
$SPARK_HOME you can issue:
make lib export LD_LIBRARY_PATH=.:$LD_LIBRARY_PATH $SPARK_HOME/bin/spark-submit \ --class de.uni_stuttgart.isa.liquidsvm.spark.App \ liquidSVM-spark.jar banana-mc
If you have configured
Spark to be used on a cluster with
hdfs dfs -put data/covtype-full.train.csv data/covtype-full.test.csv . make lib $SPARK_HOME/bin/spark-submit --files ../libliquidsvm.so \ --conf spark.executor.extraLibraryPath=. \ --conf spark.driver.extraLibraryPath=. \ --class de.uni_stuttgart.isa.liquidsvm.spark.App \ --num-executors 14 liquidSVM-spark.jar covtype-full
covertype data set with 35.090 training and 34.910 test samples
covertype data set with 522.909 training and 58.103 test samples
Both datasets were compiled from LIBSVM's version of the covertype dataset, which in turn was taken from the UCI repository and preprocessed as in [RC02a]. Copyright for this dataset is by Jock A. Blackard and Colorado State University.
If you use liquidSVM, please cite it as:
I. Steinwart and P. Thomann. liquidSVM: A fast and versatile SVM package. ArXiv e-prints 1702.06899, February 2017.