# -*- coding: utf-8 -*- # # Copyright (C) 2009-2015 Ben Kurtovic # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from re import sub, UNICODE __all__ = ["EMPTY", "EMPTY_INTERSECTION", "MarkovChain", "MarkovChainIntersection"] class MarkovChain: """Implements a basic ngram Markov chain of words.""" START = -1 END = -2 degree = 5 # 2 for bigrams, 3 for trigrams, etc. def __init__(self, text): self.text = text self.chain = self._build() self.size = self._get_size() def _build(self): """Build and return the Markov chain from the input text.""" padding = self.degree - 1 words = sub(r"[^\w\s-]", "", self.text.lower(), flags=UNICODE).split() words = ([self.START] * padding) + words + ([self.END] * padding) chain = {} for i in range(len(words) - self.degree + 1): phrase = tuple(words[i:i+self.degree]) if phrase in chain: chain[phrase] += 1 else: chain[phrase] = 1 return chain def _get_size(self): """Return the size of the Markov chain: the total number of nodes.""" return sum(self.chain.values()) def __repr__(self): """Return the canonical string representation of the MarkovChain.""" return "MarkovChain(text={0!r})".format(self.text) def __str__(self): """Return a nice string representation of the MarkovChain.""" return "".format(self.size) class MarkovChainIntersection(MarkovChain): """Implements the intersection of two chains (i.e., their shared nodes).""" def __init__(self, mc1, mc2): self.mc1, self.mc2 = mc1, mc2 self.chain = self._build() self.size = self._get_size() def _build(self): """Build and return the Markov chain from the input chains.""" c1 = self.mc1.chain c2 = self.mc2.chain chain = {} for phrase in c1: if phrase in c2: chain[phrase] = min(c1[phrase], c2[phrase]) return chain def __repr__(self): """Return the canonical string representation of the intersection.""" res = "MarkovChainIntersection(mc1={0!r}, mc2={1!r})" return res.format(self.mc1, self.mc2) def __str__(self): """Return a nice string representation of the intersection.""" res = "" return res.format(self.size, self.mc1, self.mc2) EMPTY = MarkovChain("") EMPTY_INTERSECTION = MarkovChainIntersection(EMPTY, EMPTY)