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    "info": {
        "author": "Crist\u00f3bal Silva",
        "author_email": "crsilva@ing.uchile.cl",
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            "Development Status :: 3 - Alpha",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "**Warning: this is a side-project in progress so many bugs could arise. Please raise an issue if this happens.**\n\n# Kernel Adaptive Filtering for Python\n[![Build status](https://ci.appveyor.com/api/projects/status/otkhkwrimf3e4vg5?svg=true)](https://ci.appveyor.com/project/Canas/kaftools) [![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/Canas/kaftools/master/LICENSE)\n\nThis package implements several Kernel Adaptive Filtering algorithms for research purposes. It aims to be easily extendable.\n\n# Requirements\n- Python 3.4+\n- NumPy\n- SciPy\n- (Optional) Matplotlib\n\n# Features\n## Adaptive Kernel Filters\n- Kernel Least Mean Squares (KLMS) - `KlmsFilter`\n- Exogenous Kernel Least Mean Squares (KLMS-X) - `KlmsxFilter`\n- Kernel Recursive Least Squares (KRLS) - `KrlsFilter`\n\n## Sparsification Criteria\n- Novelty (KLMS)\n- Approximate Linear Dependency (KLRS)\n\n## Additional Features\n- Delayed input support (KLMS)\n- Adaptive kernel parameter learning (KLMS)\n\nFor a more visual comparison, check the [latest features sheet](https://docs.google.com/spreadsheets/d/1kvBNAqDSgNGBTcXqMDN7j_dpp949peH_-F1GYVP29y8/edit?usp=sharing).\n\n# Quickstart\nLet's do a simple example using a KLMS Filter over given input and target arrays:\n```\nfrom kaftools.filters import KlmsFilter\nfrom kaftools.kernels import GaussianKernel\n\nklms = KlmsFilter(input, target)\nklms.fit(learning_rate=0.1, kernel=GaussianKernel(sigma=0.1))\n```\n\nAnd that's it!\n\n\n",
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