Source code for calculus.infotheory.entropy

:author: Brigitte Bigi
:summary: Entropy estimator.

.. _This file is part of SPPAS:

     ___   __    __    __    ___
    /     |  \  |  \  |  \  /              the automatic
    \__   |__/  |__/  |___| \__             annotation and
       \  |     |     |   |    \             analysis
    ___/  |     |     |   | ___/              of speech

    Copyright (C) 2011-2021  Brigitte Bigi
    Laboratoire Parole et Langage, Aix-en-Provence, France

    Use of this software is governed by the GNU Public License, version 3.

    SPPAS is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
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    SPPAS is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
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from ..calculusexc import EmptyError, InsideIntervalError

from .utilit import log2
from .utilit import MAX_NGRAM
from .utilit import symbols_to_items

# ----------------------------------------------------------------------------

[docs]class sppasEntropy(object): """Entropy estimation. Entropy is a measure of unpredictability of information content. Entropy is one of several ways to measure diversity. If we want to look at the entropy on a large series, we could also compute the entropy for windows to measure the evenness or uncertainties. By looking at the definition, one could predict the areas that have a lot of variance would result in a higher entropy and the areas that have lower variance would result in lower entropy. """
[docs] def __init__(self, symbols, n=1): """Create a sppasEntropy instance with a list of symbols. :param symbols: (list) a vector of symbols of any type. :param n: (int) n value for n-gram estimation. n ranges 1..MAX_NGRAM """ self._symbols = list() self._ngram = 1 self.set_symbols(symbols) self.set_ngram(n)
# -----------------------------------------------------------------------
[docs] def set_symbols(self, symbols): """Set the list of symbols. :param symbols: (list) a vector of symbols of any type. """ if len(symbols) == 0: raise EmptyError self._symbols = symbols
# -----------------------------------------------------------------------
[docs] def set_ngram(self, n): """Set the n value of n-grams. :param n: (int) n value for n-gram estimation. n ranges 1..8 """ n = int(n) if 0 < n <= MAX_NGRAM: self._ngram = n else: raise InsideIntervalError(n, 1, MAX_NGRAM)
# -----------------------------------------------------------------------
[docs] def eval(self): """Estimate the Shannon entropy of a vector of symbols. Shannon's entropy measures the information contained in a message as opposed to the portion of the message that is determined (or predictable). :returns: (float) entropy value """ if len(self._symbols) == 0: raise EmptyError exr = symbols_to_items(self._symbols, self._ngram) total = len(self._symbols) - self._ngram + 1 result = 0. for symbol, occurrences in exr.items(): probability = 1.0 * occurrences / total self_information = log2(1.0 / probability) result += (probability * self_information) return result