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Shark – Machine Learning

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-Shark is a fast, modular, feature-rich open-source C++ machine learning library. -

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-It provides methods for linear and nonlinear optimization, kernel-based learning -algorithms, neural networks, and various other machine learning techniques. -It serves as a powerful toolbox for real world applications as well as for research. -Shark works on Windows, MacOS X, and Linux. It comes with extensive documentation. -Shark is licensed under the GNU Lesser General Public License. -

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News

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Shark 4.0 Released

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Juni 9, 2018: We are happy to announce the official release of Shark 4.0.0

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Shark 3.1 Released

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March 1, 2016: We are happy to announce the official release of Shark 3.1.0.

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Shark 3.0 Released

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October 27, 2015: We are happy to announce the official release of Shark 3.0.0.

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Shark moves to GitHub

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October 9, 2015: Shark moved to GitHub. Please update your repositories, see the downloads page for more details.

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Shark goes LGPL

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As of January 2014, Shark is distributed under the permissive -GNU Lesser General Public License.

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Downloads

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-The current stable version is Shark 4.0, released 9-6-2018. -

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-Shark 4 is largely incompatible with earlier versions of Shark. We have made interfaces cleaner and support now computation with floating point precision. -There is also experimental support for GPU compations which is expanded in future minor releases. -
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Source Packages

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-We have two source packages available:
-    Shark-4.0.0.zip -    Shark-4.0.0.tar.gz -

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Shark Repository

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-Get the current Shark repository snapshot: -

-git clone https://github.com/Shark-ML/Shark/ -
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OScoveragestatusservice
Linux and Mac OSlibrary and unit testsbuild results Linux and Mac OStravis-ci
Windowslibrary only, no unit testsbuild results Windowsappveyor
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Quick Start

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-Let's demonstrate basic use of Shark with very few lines of code. -This is C++, so we start with includes. -

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#include <shark/Data/Download.h> -#include <shark/Algorithms/Trainers/LDA.h> -#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h> -using namespace shark;
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-Let's load some data for learning. -

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ClassificationDataset traindata; -downloadCsvData(traindata, - "www.shark-ml.org/data/quickstart-train.csv", - LAST_COLUMN, - ' '); -
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-The next step is to create a predictive model. Here we use a simple linear classifier. -

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LinearClassifier<> classifier;
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-The core step of learning is to train the model on data using a trainer. -In Shark, the trainer is not glued to the model. Instead it is a separate object. -Here, good old Linear Discriminant Analysis (LDA) suits our needs. -

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LDA lda; -lda.train(classifier, traindata);
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-Congrats! We have a readily trained classifier. -Let's try it out by applying it to new data. -

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ClassificationDataset testdata; -downloadCsvData(testdata, - "www.shark-ml.org/data/quickstart-test.csv", - LAST_COLUMN, - ' '); -ZeroOneLoss<> loss; -double error = loss(testdata.labels(), classifier(testdata.inputs()));
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-Further reading: -

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Why Shark?

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Speed and flexibility

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-Shark provides an excellent trade-off between flexibility and -ease-of-use on the one hand, and computational efficiency on the other. -

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One for all

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-Shark offers numerous algorithms from various machine learning and computational intelligence domains in a way that they can be easily combined and extended. -

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Unique features

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-Shark comes with a lot of powerful algorithms that are to our best knowledge not implemented in any other library, for example in the domains of model selection and training of binary and multi-class SVMs, or evolutionary single- and multi-objective optimization. -

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Credits and Copyright

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Citing Shark

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-We kindly ask you to cite Shark in academic work as: -

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-Christian Igel, Verena Heidrich-Meisner, and Tobias Glasmachers. -Shark. -Journal of Machine Learning Research 9, pp. 993-996, 2008. -

-The article's bibtex entry reads: -

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-@Article{shark08,
-  author = {Christian Igel and Verena Heidrich-Meisner and Tobias Glasmachers},
-  title = {Shark},
-  journal = {Journal of Machine Learning Research},
-  year = {2008},
-  volume = {9},
-  pages = {993--996}
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License

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-The Shark library is made available under the -GNU Lesser General Public License. -

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Hosting institutions

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-The Shark machine learning library is jointly maintained by researchers from -

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- - - - - +Wait for it ... upadte ahead ... come back mid June!