{"id":37079527,"url":"https://github.com/weekend37/string-kernels","last_synced_at":"2026-01-14T09:37:27.242Z","repository":{"id":46154839,"uuid":"421577372","full_name":"weekend37/string-kernels","owner":"weekend37","description":"Implementation of the Polynomial String Kernel and linear time String Kernel implementation. 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Downloads](https://static.pepy.tech/badge/string-kernels)](https://pepy.tech/projects/string-kernels)\n\n# String Kernels\n\nThis package contains an implementation of the **Polynomial String Kernel** and a linear time **String Kernel** algorithm as described in our paper, [High Resolution Ancestry Deconvolution for Next Generation Genomic Data](https://www.biorxiv.org/content/10.1101/2021.09.19.460980v1). \u003cbr/\u003e\u003cbr/\u003e\n\n\u003cimg caption=\"String Kernel Computations\" src=\"https://raw.githubusercontent.com/weekend37/string-kernels/master/doc/fig/triangular_numbers.png\"\u003e\n\nIt offers\n\n- Linear time computation of two effective string kernels.\n\n- Compatibility with Scikit-Learn's Support Vector Machines (easy plug-in).\n\n- Multithreading.\n\n## Usage\n\nTo install the package, execute from the command line\n\n```\npip install string-kernels\n```\n\nAnd then you're all set!\n\nAssuming you have [Scikit-Learn](https://scikit-learn.org/) already installed, you can use Lodhi's string kernel via\n\n```python\nfrom sklearn import svm\nfrom stringkernels.kernels import string_kernel\nmodel = svm.SVC(kernel=string_kernel())\n```\n\nand the polynomial string kernel,\n\n```python\nfrom sklearn import svm\nfrom stringkernels.kernels import polynomial_string_kernel\nmodel = svm.SVC(kernel=polynomial_string_kernel())\n```\n\nFor morer information read the [docs](https://github.com/weekend37/string-kernels/blob/master/doc/docs.md) or take a look at the notebook [example.ipynb](https://github.com/weekend37/string-kernels/blob/master/example.ipynb) for further demonstration of usage.\n\nIf you end up using this in your research we kindly ask you to cite us! 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