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main.py
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# -*-coding:utf-8-*-
import numpy as np
from utils import *
import recuperation_donnees as rd
import alignement_deterministe as ad
import alignement_phmm as ap
__author__ = "besnier"
def script1():
l_couples_ger = rd.liste_vers_paires(rd.ger)
ap.em_phmm_alphabeta(l_couples_ger)
def test_genetique():
sequence = """TCAGACCGTTCATACAGAATTGGCGATCGTTCGGCGTATCGCCGAAATCACCGCCGTAAGCCGACCAGGGGTTGCCGTTA
TCATCATATTTAATCAGCGACTGATCCACGCAGTCCCAGACGAAGCCGCCCTGTAAACGGGGATACTGACGAAACGCCTG
CCAGTATTTAGCGAAACCGCCAAGACTGTTACCCATCGCGTGGGCGTATTCGCAAAGGATCAGCGGGCGCGTCTCTCCAG
GTAGCGATAGCCAATTTTTGATGGACCATTTCGGCACAGCCGGTAAGGGCTGGTCTTCTTCCACGCGCGCGTACATCGGG
CAAATAATTTCGGTGGCCGTGGTGTCGGCTCCGCCGCCTTCATACTGCACCGGGCGGGAAGGATCGACAGATTTGATCCA
GCGATACAGCGCGTCGTGATTAGCGCCGTGGCCTGATTCATTCCCCAGCG"""
def test_linguistique1():
matrice_levenshtein = np.ones((len(alphabet_humain), len(alphabet_humain))) + -1*np.eye(len(alphabet_humain))
mots_germaniques, alph = rd.recuperer_vocabulaire(rd.langues_germaniques, rd.normaliser_ger)
distances = np.zeros((len(mots_germaniques), 3))
for i in range(len(mots_germaniques)):
mot_en, mot_de, mot_nl = mots_germaniques[i]
distances[i, 0] = ad.distance_levenshtein(mot_en, mot_de, matrice_levenshtein, 1, alphabet_humain)
distances[i, 1] = ad.distance_levenshtein(mot_en, mot_nl, matrice_levenshtein, 1, alphabet_humain)
distances[i, 2] = ad.distance_levenshtein(mot_de, mot_nl, matrice_levenshtein, 1, alphabet_humain)
print(np.mean(distances, axis=0))
# distances[i, 3] = ad.distance_levenshtein(mot_en, mot_sw, matrice_levenshtein, 1, alphabet_humain)
# distances[i, 4] = ad.distance_levenshtein(mot_sw, mot_nl, matrice_levenshtein, 1, alphabet_humain)
# distances[i, 5] = ad.distance_levenshtein(mot_de, mot_sw, matrice_levenshtein, 1, alphabet_humain)
def test_linguistique2():
matrice_remplacement = -5*np.ones((len(alphabet_humain), len(alphabet_humain))) + 6*np.eye(len(alphabet_humain))
mots_germaniques, alph = rd.recuperer_vocabulaire(rd.langues_germaniques, rd.normaliser_ger)
alignements = []
for i in range(len(mots_germaniques)):
mot_en, mot_de, mot_nl = mots_germaniques[i]
alignements.append([])
alignements[i].append(ad.aligner_needleman_wunsch(mot_en, mot_de, matrice_remplacement, 1, alphabet_humain))
alignements[i].append(ad.aligner_needleman_wunsch(mot_en, mot_nl, matrice_remplacement, 1, alphabet_humain))
alignements[i].append(ad.aligner_needleman_wunsch(mot_de, mot_nl, matrice_remplacement, 1, alphabet_humain))
print(alignements)
def test_linguistique3():
matrice_remplacement = -5*np.ones((len(alphabet_humain), len(alphabet_humain))) + 6*np.eye(len(alphabet_humain))
alignements = []
mots_romans, alph = rd.recuperer_vocabulaire(rd.langues_romanes, rd.normaliser_rom)
for i in range(len(mots_romans)):
mot_fr, mot_es, mot_it = mots_romans[i]
alignements.append([])
alignements[i].append(ad.aligner_needleman_wunsch(mot_fr, mot_es, matrice_remplacement, 1, alphabet_humain))
alignements[i].append(ad.aligner_needleman_wunsch(mot_fr, mot_it, matrice_remplacement, 1, alphabet_humain))
alignements[i].append(ad.aligner_needleman_wunsch(mot_es, mot_it, matrice_remplacement, 1, alphabet_humain))
print(alignements)
def test_linguistique4():
matrice_levenshtein = np.ones((len(alphabet_humain), len(alphabet_humain))) + -1*np.eye(len(alphabet_humain))
mots_romans, alph = rd.recuperer_vocabulaire(rd.langues_romanes, rd.normaliser_rom)
distances = np.zeros((len(mots_romans), 3))
for i in range(len(mots_romans)):
mot_fr, mot_es, mot_it = mots_romans[i]
distances[i, 0] = ad.distance_levenshtein(mot_fr, mot_es, matrice_levenshtein, 1, alphabet_humain)
distances[i, 1] = ad.distance_levenshtein(mot_fr, mot_it, matrice_levenshtein, 1, alphabet_humain)
distances[i, 2] = ad.distance_levenshtein(mot_es, mot_it, matrice_levenshtein, 1, alphabet_humain)
print(np.mean(distances, axis=0))
if __name__ == "__main__":
# script1()
test_linguistique1()
# test_linguistique2()
# test_linguistique3()
# test_linguistique4()