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        "description": "![](resources/markovx_banner.png)\n\n### Intro ###\n[Markov Chains](https://en.wikipedia.org/wiki/Markov_chain) implementation <br>\n\n### Installation ###\nin your terminal:\n\n    > pip install markovx\n    \n### Examples ###\n\nAdding Chains\n```python\nfrom markovx.models import MarkovModel\n    \nmx = MarkovModel()\nmx.add_one('123456')\nmx.add_one('qwerty')\nmx.add_many(['admin', 'root', 'user'])\n```\n\nGenerating Chains\n```python\nmx.generate(6) # len of tokens in chain\n```\n```python\nmx.generate(6, random_init=True)\n# when True first token in chain would be assigned randomly\n# when False first token would be assigned based on observed firs tokens\n# default to False\n```\n```python\nmx.generate(6, smart_ending=True)\n# when False chain wouldn't be terminated before len(chain) == n even if model got to an end token\n# when True if model got to an end token while len(chain) < n chain would terminate\n# default to False\n```\nOrdinal Markov Chains (position dependent chains)\n```python\nfrom markovx.models import OrdinalMarkovModel\n    \nmx = OrdinalMarkovModel()\nmx.add_one('123456')\nmx.add_one('123qwe')\nmx.add_many(['qwerty', 'qwe123', 'qwe123456'])\nmx.generate(6)\n```\n\n### Contact ###\n[Tal Peretz](https://www.linkedin.com/in/tal-per/)\n\n\n\n\n\n",
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