83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
import sys
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import numpy as np
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from gensim.models import KeyedVectors
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import random
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TOLERANCE = 1e-4 # tolérance sur la similarité cosinus
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if len(sys.argv) < 3:
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print("Usage: python cemantle.py <mot> <score>")
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sys.exit(1)
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first_word = sys.argv[1].lower()
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first_score = float(sys.argv[2]) / 100
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# Détection de la langue
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lang = sys.argv[3] if len(sys.argv) > 3 else "en"
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if lang == "fr":
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TOLERANCE = 1e-2
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with open("french_words.txt") as f:
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valid_words = set(w.strip() for w in f)
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print("Mode français : dictionnaire français chargé.")
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model_path = "frWac_non_lem_no_postag_no_phrase_200_cbow_cut0.bin"
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print("Chargement du modèle français (frWac cut100)...")
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model = KeyedVectors.load_word2vec_format(model_path, binary=True)
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else:
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with open("english_words.txt") as f:
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valid_words = set(w.strip() for w in f)
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print("Mode anglais : dictionnaire anglais chargé.")
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model_path = "google_news_reduced.kv"
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print("Chargement du modèle anglais...")
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model = KeyedVectors.load(model_path)
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if first_word not in model:
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print(f"Mot inconnu du modèle: {first_word}")
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sys.exit(1)
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# Calculer la similarité cosinus entre deux vecteurs
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cos = lambda a, b: np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
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def is_valid_word(word):
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if lang == "fr":
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return word.islower() and all(c.isalpha() for c in word) and word in valid_words and 3 <= len(word) <= 15
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else:
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return word.islower() and word.isalpha() and word in valid_words and 3 <= len(word) <= 15
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# Filtrer les mots à la bonne distance du mot donné, et qui sont valides
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vec1 = model[first_word]
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words = []
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for w in model.index_to_key:
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if w == first_word:
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continue
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if not is_valid_word(w):
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continue
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sim = cos(vec1, model[w])
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if abs(sim - first_score) < TOLERANCE:
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words.append(w)
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print(f"{len(words)} mots à la distance {first_score*100:.2f} de '{first_word}' (valides)")
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while words:
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guess = random.choice(words)
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print(f"Try: {guess}")
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s = input("Score: ")
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if s == "n":
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words.remove(guess)
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print(f"Mot refusé, {len(words)} restants.")
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continue
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try:
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score = float(s) / 100
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except ValueError:
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print("Entrée invalide, recommencez.")
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continue
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vec_guess = model[guess]
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# Filtrer les mots à la bonne distance du guess
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words = [w for w in words if abs(cos(vec_guess, model[w]) - score) < TOLERANCE]
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print(f"{len(words)} mots restants à la distance {score*100:.2f} de '{guess}'")
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if not words:
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print("Plus aucun mot possible. Terminé.")
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break
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if len(words) == 1:
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print(f"Plus qu'un seul mot possible: {words[0]}")
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break
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