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e95ecfaf27
* Type annotations for `strings/autocomplete_using_trie.py` * Update autocomplete_using_trie.py * Update detecting_english_programmatically.py * Update detecting_english_programmatically.py * Update frequency_finder.py * Update frequency_finder.py * Update frequency_finder.py * Update word_occurrence.py * Update frequency_finder.py * Update z_function.py * Update z_function.py * Update frequency_finder.py
63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
import os
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UPPERLETTERS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
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LETTERS_AND_SPACE = UPPERLETTERS + UPPERLETTERS.lower() + " \t\n"
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def load_dictionary() -> dict[str, None]:
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path = os.path.split(os.path.realpath(__file__))
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english_words: dict[str, None] = {}
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with open(path[0] + "/dictionary.txt") as dictionary_file:
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for word in dictionary_file.read().split("\n"):
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english_words[word] = None
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return english_words
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ENGLISH_WORDS = load_dictionary()
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def get_english_count(message: str) -> float:
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message = message.upper()
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message = remove_non_letters(message)
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possible_words = message.split()
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if possible_words == []:
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return 0.0
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matches = 0
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for word in possible_words:
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if word in ENGLISH_WORDS:
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matches += 1
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return float(matches) / len(possible_words)
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def remove_non_letters(message: str) -> str:
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letters_only = []
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for symbol in message:
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if symbol in LETTERS_AND_SPACE:
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letters_only.append(symbol)
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return "".join(letters_only)
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def is_english(
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message: str, word_percentage: int = 20, letter_percentage: int = 85
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) -> bool:
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"""
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>>> is_english('Hello World')
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True
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>>> is_english('llold HorWd')
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False
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"""
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words_match = get_english_count(message) * 100 >= word_percentage
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num_letters = len(remove_non_letters(message))
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message_letters_percentage = (float(num_letters) / len(message)) * 100
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letters_match = message_letters_percentage >= letter_percentage
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return words_match and letters_match
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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