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132 lines
4.7 KiB
Python
132 lines
4.7 KiB
Python
#!/usr/bin/env python3
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"""
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Implementation of entropy of information
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https://en.wikipedia.org/wiki/Entropy_(information_theory)
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"""
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import math
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from collections import Counter
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from string import ascii_lowercase
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from typing import Tuple
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def calculate_prob(text: str) -> None:
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"""
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This method takes path and two dict as argument
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and than calculates entropy of them.
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:param dict:
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:param dict:
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:return: Prints
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1) Entropy of information based on 1 alphabet
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2) Entropy of information based on couples of 2 alphabet
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3) print Entropy of H(X n∣Xn−1)
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Text from random books. Also, random quotes.
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>>> text = ("Behind Winston’s back the voice "
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... "from the telescreen was still "
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... "babbling and the overfulfilment")
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>>> calculate_prob(text)
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4.0
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6.0
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2.0
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>>> text = ("The Ministry of Truth—Minitrue, in Newspeak [Newspeak was the official"
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... "face in elegant lettering, the three")
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>>> calculate_prob(text)
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4.0
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5.0
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1.0
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>>> text = ("Had repulsive dashwoods suspicion sincerity but advantage now him. "
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... "Remark easily garret nor nay. Civil those mrs enjoy shy fat merry. "
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... "You greatest jointure saw horrible. He private he on be imagine "
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... "suppose. Fertile beloved evident through no service elderly is. Blind "
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... "there if every no so at. Own neglected you preferred way sincerity "
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... "delivered his attempted. To of message cottage windows do besides "
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... "against uncivil. Delightful unreserved impossible few estimating "
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... "men favourable see entreaties. She propriety immediate was improving. "
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... "He or entrance humoured likewise moderate. Much nor game son say "
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... "feel. Fat make met can must form into gate. Me we offending prevailed "
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... "discovery.")
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>>> calculate_prob(text)
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4.0
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7.0
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3.0
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"""
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single_char_strings, two_char_strings = analyze_text(text)
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my_alphas = list(" " + ascii_lowercase)
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# what is our total sum of probabilities.
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all_sum = sum(single_char_strings.values())
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# one length string
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my_fir_sum = 0
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# for each alpha we go in our dict and if it is in it we calculate entropy
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for ch in my_alphas:
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if ch in single_char_strings:
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my_str = single_char_strings[ch]
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prob = my_str / all_sum
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my_fir_sum += prob * math.log2(prob) # entropy formula.
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# print entropy
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print("{0:.1f}".format(round(-1 * my_fir_sum)))
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# two len string
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all_sum = sum(two_char_strings.values())
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my_sec_sum = 0
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# for each alpha (two in size) calculate entropy.
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for ch0 in my_alphas:
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for ch1 in my_alphas:
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sequence = ch0 + ch1
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if sequence in two_char_strings:
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my_str = two_char_strings[sequence]
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prob = int(my_str) / all_sum
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my_sec_sum += prob * math.log2(prob)
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# print second entropy
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print("{0:.1f}".format(round(-1 * my_sec_sum)))
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# print the difference between them
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print("{0:.1f}".format(round(((-1 * my_sec_sum) - (-1 * my_fir_sum)))))
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def analyze_text(text: str) -> Tuple[dict, dict]:
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"""
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Convert text input into two dicts of counts.
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The first dictionary stores the frequency of single character strings.
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The second dictionary stores the frequency of two character strings.
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"""
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single_char_strings = Counter() # type: ignore
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two_char_strings = Counter() # type: ignore
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single_char_strings[text[-1]] += 1
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# first case when we have space at start.
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two_char_strings[" " + text[0]] += 1
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for i in range(0, len(text) - 1):
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single_char_strings[text[i]] += 1
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two_char_strings[text[i : i + 2]] += 1
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return single_char_strings, two_char_strings
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def main():
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import doctest
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doctest.testmod()
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# text = (
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# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
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# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
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# "jointure saw horrible. He private he on be imagine suppose. Fertile "
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# "beloved evident through no service elderly is. Blind there if every no so "
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# "at. Own neglected you preferred way sincerity delivered his attempted. To "
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# "of message cottage windows do besides against uncivil. Delightful "
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# "unreserved impossible few estimating men favourable see entreaties. She "
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# "propriety immediate was improving. He or entrance humoured likewise "
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# "moderate. Much nor game son say feel. Fat make met can must form into "
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# "gate. Me we offending prevailed discovery. "
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# )
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# calculate_prob(text)
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if __name__ == "__main__":
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main()
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