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        "author": "Thijs van Ede",
        "author_email": "t.s.vanede@utwente.nl",
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        "description": "# Metrics\nPython implementation of some more uncommon metrics. Currently only longest common subsequence LCS metrics are implemented.\n\n## Dependencies\nThis package requires the following python libraries:\n 1. [numpy](http://www.numpy.org/) (automatically installed when package is installed via pip)\n\n## Installation\nThe metrics package can be installed directly from `pip`.\n```\npip3 install distance-metrics\n```\n\n## Metrics\nThe metrics library currently has support for the following modules.\n 1. Longest common subsequence metrics `distance_metrics.lcs`\n\n### Longest common subsequence metrics\nThe LCS module currently implements 2 distances:\n 1. Length of longest common subsequence (`distance_metrics.lcs.llcs(u, v)`).\n 2. [Bakkelund distance](http://hjem.ifi.uio.no/danielry/StringMetric.pdf) [1] (`metrics.lcs.bakkelund(u, v)`)\n\n#### Usage\n```python\n# Imports\nfrom distance_metrics import lcs\nimport numpy as np\n\n# Create example input arrays\nu = np.random.choice(list('ABCD'), size=20)\nv = np.random.choice(list('BCDE'), size=20)\n\n# Compute metrics\nllcs      = lcs.llcs(u, v)\nbakkelund = lcs.bakkelund(u, v)\n\n# Print values\nprint(\"LLCS     : {}\".format(llcs))\nprint(\"Bakkelund: {}\".format(bakkelund))\n```\n\n| References |                                                                                     |\n|------------|-------------------------------------------------------------------------------------|\n| 1          | [Bakkelund, D. (2009). An LCS-based string metric. Olso, Norway: University of Oslo.](http://hjem.ifi.uio.no/danielry/StringMetric.pdf) |\n\n\n",
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