def cartesBienClusterisees(couche="conv1",
seuilSuppression=559,
seuilBonClustering=30,
fichier=False):
pFRJA_R_KMNI, pFRJA_V_KMNI, pFR_RV_KMNI, pCIC_R_KMNI, pCIC_V_KMNI, ind = MapsClustering(
couche, seuilSuppression, "kmeansNonInit", fichier)
pFRJA_R_KMI, pFRJA_V_KMI, pFR_RV_KMI, pCIC_R_KMI, pCIC_V_KMI, indKmeansInit = MapsClustering(
couche, seuilSuppression, "kmeansInit", fichier)
pFRJA_R_DBSCAN, pFRJA_V_DBSCAN, pFR_RV_DBSCAN, pCIC_R_DBSCAN, pCIC_V_DBSCAN, indDBSCAN = MapsClustering(
couche, seuilSuppression, "DBSCAN", fichier)
# pFRJA_R_MeanShift, pFRJA_V_MeanShift, pFR_RV_MeanShift, pCIC_R_MeanShift, pCIC_V_MeanShift, indMeanShift = MapsClustering(couche, seuilSuppression, "MeanShift", fichier)
################################################################################
#Initialisation des matrices
################################################################################
bienClusterisesKmeansNonInit = []
bienClusterisesKmeansInit = []
bienClusterisesDBSCAN = []
# bienClusterisesMeanShift = []
################################################################################
#Clustering 1
################################################################################
clus = bienClusterise(MatriceClustering=pFRJA_R_KMNI,
seuil=seuilBonClustering,
indices=ind[0])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pFRJA_R_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[0])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pFRJA_R_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[0])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[0])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Clustering 1bis
################################################################################
clus = bienClusterise(MatriceClustering=pFRJA_V_KMNI,
seuil=seuilBonClustering,
indices=ind[1])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pFRJA_V_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[1])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pFRJA_V_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[1])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[1])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Clustering 2
################################################################################
clus = bienClusterise(MatriceClustering=pFR_RV_KMNI,
seuil=seuilBonClustering,
indices=ind[2])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pFR_RV_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[2])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pFR_RV_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[2])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[2])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Clustering 3
################################################################################
clus = bienClusterise(MatriceClustering=pCIC_R_KMNI,
seuil=seuilBonClustering,
indices=ind[3])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pCIC_R_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[3])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pCIC_R_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[3])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[3])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Clustering 3bis
################################################################################
clus = bienClusterise(MatriceClustering=pCIC_V_KMNI,
seuil=seuilBonClustering,
indices=ind[4])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pCIC_V_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[4])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pCIC_V_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[4])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[4])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Enregistrement des differentes matrices
################################################################################
np.save(
'../resultats/' + couche +
'/kmeansNonInit/bienClusterisekmeansNonInit.npy',
bienClusterisesKmeansNonInit)
np.save(
'../resultats/' + couche + '/kmeansInit/bienClusterisekmeansInit.npy',
bienClusterisesKmeansInit)
np.save('../resultats/' + couche + '/DBSCAN/bienClusteriseDBSCAN.npy',
bienClusterisesDBSCAN)
def GenerationClustering(couche="conv1",
seuilSuppression=559,
seuilBonClustering=30,
fichier=True):
#############################################################################################
# Appel de MapsClustering et enregistrement des matrices des indices pour chaque algorithme de chaque couche;
# Matrice des indices : 5 colonnes representant les 5 clusterings, et dans chaque colonne les indices de cartes
# qui donnent bien deux classes.
#############################################################################################
pFRJA_R_KMNI, pFRJA_V_KMNI, pFR_RV_KMNI, pCIC_R_KMNI, pCIC_V_KMNI, ind = MapsClustering(
couche, seuilSuppression, "kmeansNonInit", fichier)
matKmeansNonInit = str(
"../resultats/" + couche +
"/kmeansNonInit/matKmeansNonInit_indices_bonnes_cartes.csv")
f1 = open(matKmeansNonInit, "wb")
writer = csv.writer(f1)
writer.writerow([
"KmeansNonInit(1)", "KmeansNonInit(1bis)", "KmeansNonInit(2)",
"KmeansNonInit(2bis)", "KmeansNonInit(3)"
])
for values in izip_longest(*ind):
writer.writerow(values)
pFRJA_R_KMI, pFRJA_V_KMI, pFR_RV_KMI, pCIC_R_KMI, pCIC_V_KMI, indKmeansInit = MapsClustering(
couche, seuilSuppression, "kmeansInit", fichier)
matKmeansInit = str("../resultats/" + couche +
"/kmeansInit/matKmeansInit_indices_bonnes_cartes.csv")
f2 = open(matKmeansInit, "wb")
writer = csv.writer(f2)
writer.writerow([
"KmeansInit(1)", "KmeansInit(1bis)", "KmeansInit(2)",
"KmeansInit(2bis)", "KmeansInit(3)"
])
for values in izip_longest(*indKmeansInit):
writer.writerow(values)
pFRJA_R_DBSCAN, pFRJA_V_DBSCAN, pFR_RV_DBSCAN, pCIC_R_DBSCAN, pCIC_V_DBSCAN, indDBSCAN = MapsClustering(
couche, seuilSuppression, "DBSCAN", fichier)
matDBSCAN = str("../resultats/" + couche +
"/DBSCAN/matDBSCAN_indices_bonnes_cartes.csv")
f3 = open(matDBSCAN, "wb")
writer = csv.writer(f3)
writer.writerow([
"DBSCAN(1)", "DBSCAN(1bis)", "DBSCAN(2)", "DBSCAN(2bis)", "DBSCAN(3)"
])
for values in izip_longest(*indDBSCAN):
writer.writerow(values)
# pFRJA_R_MeanShift, pFRJA_V_MeanShift, pFR_RV_MeanShift, pCIC_R_MeanShift, pCIC_V_MeanShift, indMeanShift = MapsClustering(couche, seuilSuppression, "MeanShift", fichier)
# matMeanShift = str("../resultats/" + couche + "/MeanShift/matMeanShift_indices_bonnes_cartes.csv")
# f4 = open(matMeanShift, "wb")
# writer = csv.writer(f4)
# writer.writerow(["MeanShift(1)", "MeanShift(1bis)", "MeanShift(2)", "MeanShift(2bis)", "MeanShift(3)"])
# for values in izip_longest(*indMeanShift):
# writer.writerow(values)
#############################################################################################
# Fichiers de cartes bon clustering
#############################################################################################
filename = str("../resultats/" + couche + "/cartes_bon_clustering22")
f = open(filename, "wb")
#############################################################################
#appel directement avec les matrices
##############################################################################
f.write("FRJA R\n")
clus = bienClusterise(MatriceClustering=pFRJA_R_KMNI,
seuil=seuilBonClustering,
indices=ind[0])
f.write("kmeansNonInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pFRJA_R_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[0])
f.write("kmeansInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pFRJA_R_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[0])
f.write("DBSCAN:" + str(clus) + "\n")
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering,indices= indMeanShift[0])
# f.write("MeanShift:" + str(clus)+"\n")
f.write("FRJA V\n")
clus = bienClusterise(MatriceClustering=pFRJA_V_KMNI,
seuil=seuilBonClustering,
indices=ind[1])
f.write("kmeansNonInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pFRJA_V_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[1])
f.write("kmeansInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pFRJA_V_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[1])
f.write("DBSCAN:" + str(clus) + "\n")
# clus = bienClusterise(MatriceClustering=pFRJA_V_MeanShift, seuil=seuilBonClustering,indices= indMeanShift[1])
# f.write("MeanShift:" + str(clus)+"\n")
f.write("FR RV\n")
clus = bienClusterise(MatriceClustering=pFR_RV_KMNI,
seuil=seuilBonClustering,
indices=ind[2])
f.write("kmeansNonInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pFR_RV_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[2])
f.write("kmeansInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pFR_RV_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[2])
f.write("DBSCAN:" + str(clus) + "\n")
# clus = bienClusterise(MatriceClustering=pFR_RV_MeanShift, seuil=seuilBonClustering, indices= indMeanShift[2])
# f.write("MeanShift:" + str(clus)+"\n")
f.write("JA correct/incorrect R\n")
clus = bienClusterise(MatriceClustering=pCIC_R_KMNI,
seuil=seuilBonClustering,
indices=ind[3])
f.write("kmeansNonInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pCIC_R_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[3])
f.write("kmeansInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pCIC_R_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[3])
f.write("DBSCAN:" + str(clus) + "\n")
# clus = bienClusterise(MatriceClustering=pCIC_R_MeanShift, seuil=seuilBonClustering, indices= indMeanShift[3])
# f.write("MeanShift:" + str(clus)+"\n")
f.write("JA correct/incorrect V\n")
clus = bienClusterise(MatriceClustering=pCIC_V_KMNI,
seuil=seuilBonClustering,
indices=ind[4])
f.write("kmeansNonInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pCIC_V_KMI,
seuil=seuilBonClustering,
indices=indKmeansInit[4])
f.write("kmeansInit:" + str(clus) + "\n")
clus = bienClusterise(MatriceClustering=pCIC_V_DBSCAN,
seuil=seuilBonClustering,
indices=indDBSCAN[4])
f.write("DBSCAN:" + str(clus) + "\n")
# clus = bienClusterise(MatriceClustering=pCIC_V_MeanShift, seuil=seuilBonClustering, indices= indMeanShift[4])
# f.write("MeanShift:" + str(clus)+"\n")
f.close()
def GenerationClustering(couche = "conv1", seuilSuppression = 559, seuilBonClustering = 30, fichier = True):
#############################################################################################
# Appel de MapsClustering et enregistrement des matrices des indices pour chaque algorithme de chaque couche;
# Matrice des indices : 5 colonnes representant les 5 clusterings, et dans chaque colonne les indices de cartes
# qui donnent bien deux classes.
#############################################################################################
pFRJA_R_KMNI, pFRJA_V_KMNI, pFR_RV_KMNI, pCIC_R_KMNI, pCIC_V_KMNI, ind = MapsClustering(couche, seuilSuppression, "kmeansNonInit", fichier)
matKmeansNonInit = str("../resultats/" + couche + "/kmeansNonInit/matKmeansNonInit_indices_bonnes_cartes.csv")
f1 = open(matKmeansNonInit, "wb")
writer = csv.writer(f1)
writer.writerow(["KmeansNonInit(1)", "KmeansNonInit(1bis)", "KmeansNonInit(2)", "KmeansNonInit(2bis)", "KmeansNonInit(3)"])
for values in izip_longest(*ind):
writer.writerow(values)
pFRJA_R_KMI, pFRJA_V_KMI, pFR_RV_KMI, pCIC_R_KMI, pCIC_V_KMI, indKmeansInit = MapsClustering(couche, seuilSuppression, "kmeansInit", fichier)
matKmeansInit = str("../resultats/" + couche + "/kmeansInit/matKmeansInit_indices_bonnes_cartes.csv")
f2 = open(matKmeansInit, "wb")
writer = csv.writer(f2)
writer.writerow(["KmeansInit(1)", "KmeansInit(1bis)", "KmeansInit(2)", "KmeansInit(2bis)", "KmeansInit(3)"])
for values in izip_longest(*indKmeansInit):
writer.writerow(values)
pFRJA_R_DBSCAN, pFRJA_V_DBSCAN, pFR_RV_DBSCAN, pCIC_R_DBSCAN, pCIC_V_DBSCAN, indDBSCAN = MapsClustering(couche, seuilSuppression, "DBSCAN", fichier)
matDBSCAN = str("../resultats/" + couche + "/DBSCAN/matDBSCAN_indices_bonnes_cartes.csv")
f3 = open(matDBSCAN, "wb")
writer = csv.writer(f3)
writer.writerow(["DBSCAN(1)", "DBSCAN(1bis)", "DBSCAN(2)", "DBSCAN(2bis)", "DBSCAN(3)"])
for values in izip_longest(*indDBSCAN):
writer.writerow(values)
# pFRJA_R_MeanShift, pFRJA_V_MeanShift, pFR_RV_MeanShift, pCIC_R_MeanShift, pCIC_V_MeanShift, indMeanShift = MapsClustering(couche, seuilSuppression, "MeanShift", fichier)
# matMeanShift = str("../resultats/" + couche + "/MeanShift/matMeanShift_indices_bonnes_cartes.csv")
# f4 = open(matMeanShift, "wb")
# writer = csv.writer(f4)
# writer.writerow(["MeanShift(1)", "MeanShift(1bis)", "MeanShift(2)", "MeanShift(2bis)", "MeanShift(3)"])
# for values in izip_longest(*indMeanShift):
# writer.writerow(values)
#############################################################################################
# Fichiers de cartes bon clustering
#############################################################################################
filename = str("../resultats/" + couche + "/cartes_bon_clustering_MeanShift")
f = open(filename, "wb")
#############################################################################
#appel directement avec les matrices
##############################################################################
f.write("FRJA R\n")
clus = bienClusterise(MatriceClustering=pFRJA_R_KMNI, seuil=seuilBonClustering, indices=ind[0])
f.write("kmeansNonInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pFRJA_R_KMI, seuil=seuilBonClustering, indices=indKmeansInit[0])
f.write("kmeansInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pFRJA_R_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[0])
f.write("DBSCAN:"+ str(clus)+"\n")
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering,indices= indMeanShift[0])
# f.write("MeanShift:" + str(clus)+"\n")
f.write("FRJA V\n")
clus = bienClusterise(MatriceClustering=pFRJA_V_KMNI, seuil=seuilBonClustering, indices=ind[1])
f.write("kmeansNonInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pFRJA_V_KMI, seuil=seuilBonClustering, indices=indKmeansInit[1])
f.write("kmeansInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pFRJA_V_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[1])
f.write("DBSCAN:" + str(clus)+"\n")
# clus = bienClusterise(MatriceClustering=pFRJA_V_MeanShift, seuil=seuilBonClustering,indices= indMeanShift[1])
# f.write("MeanShift:" + str(clus)+"\n")
f.write("FR RV\n")
#
clus = bienClusterise(MatriceClustering=pFR_RV_KMNI, seuil=seuilBonClustering, indices=ind[2])
f.write("kmeansNonInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pFR_RV_KMI, seuil=seuilBonClustering,indices=indKmeansInit[2])
f.write("kmeansInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pFR_RV_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[2])
f.write("DBSCAN:" + str(clus)+"\n")
# clus = bienClusterise(MatriceClustering=pFR_RV_MeanShift, seuil=seuilBonClustering, indices= indMeanShift[2])
# f.write("MeanShift:" + str(clus)+"\n")
f.write("JA correct/incorrect R\n")
clus = bienClusterise(MatriceClustering=pCIC_R_KMNI, seuil=seuilBonClustering, indices=ind[3])
f.write("kmeansNonInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pCIC_R_KMI, seuil=seuilBonClustering, indices=indKmeansInit[3])
f.write("kmeansInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pCIC_R_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[3])
f.write("DBSCAN:"+ str(clus)+"\n")
# clus = bienClusterise(MatriceClustering=pCIC_R_MeanShift, seuil=seuilBonClustering, indices= indMeanShift[3])
# f.write("MeanShift:" + str(clus)+"\n")
f.write("JA correct/incorrect V\n")
clus = bienClusterise(MatriceClustering=pCIC_V_KMNI, seuil=seuilBonClustering, indices=ind[4])
f.write("kmeansNonInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pCIC_V_KMI, seuil=seuilBonClustering,indices=indKmeansInit[4])
f.write("kmeansInit:" + str(clus)+"\n")
clus = bienClusterise(MatriceClustering=pCIC_V_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[4])
f.write("DBSCAN:" + str(clus)+"\n")
# clus = bienClusterise(MatriceClustering=pCIC_V_MeanShift, seuil=seuilBonClustering, indices= indMeanShift[4])
# f.write("MeanShift:" + str(clus)+"\n")
f.close()
def cartesBienClusterisantes(couche = "conv1", seuilSuppression = 559, seuilBonClustering = 30, fichier = False):
pFRJA_R_KMNI, pFRJA_V_KMNI, pFR_RV_KMNI, pCIC_R_KMNI, pCIC_V_KMNI, ind = MapsClustering(couche, seuilSuppression, "kmeansNonInit", fichier)
pFRJA_R_KMI, pFRJA_V_KMI, pFR_RV_KMI, pCIC_R_KMI, pCIC_V_KMI, indKmeansInit = MapsClustering(couche, seuilSuppression, "kmeansInit", fichier)
pFRJA_R_DBSCAN, pFRJA_V_DBSCAN, pFR_RV_DBSCAN, pCIC_R_DBSCAN, pCIC_V_DBSCAN, indDBSCAN = MapsClustering(couche, seuilSuppression, "DBSCAN", fichier)
# pFRJA_R_MeanShift, pFRJA_V_MeanShift, pFR_RV_MeanShift, pCIC_R_MeanShift, pCIC_V_MeanShift, indMeanShift = MapsClustering(couche, seuilSuppression, "MeanShift", fichier)
################################################################################
#Initialisation des matrices
################################################################################
bienClusterisesKmeansNonInit = []
bienClusterisesKmeansInit = []
bienClusterisesDBSCAN = []
# bienClusterisesMeanShift = []
################################################################################
#Clustering 1
################################################################################
clus = bienClusterise(MatriceClustering=pFRJA_R_KMNI, seuil=seuilBonClustering, indices=ind[0])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pFRJA_R_KMI, seuil=seuilBonClustering, indices=indKmeansInit[0])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pFRJA_R_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[0])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[0])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Clustering 1bis
################################################################################
clus = bienClusterise(MatriceClustering=pFRJA_V_KMNI, seuil=seuilBonClustering, indices=ind[1])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pFRJA_V_KMI, seuil=seuilBonClustering, indices=indKmeansInit[1])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pFRJA_V_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[1])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[1])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Clustering 2
################################################################################
clus = bienClusterise(MatriceClustering=pFR_RV_KMNI, seuil=seuilBonClustering, indices=ind[2])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pFR_RV_KMI, seuil=seuilBonClustering,indices=indKmeansInit[2])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pFR_RV_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[2])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[2])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Clustering 3
################################################################################
clus = bienClusterise(MatriceClustering=pCIC_R_KMNI, seuil=seuilBonClustering, indices=ind[3])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pCIC_R_KMI, seuil=seuilBonClustering, indices=indKmeansInit[3])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pCIC_R_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[3])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[3])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Clustering 3bis
################################################################################
clus = bienClusterise(MatriceClustering=pCIC_V_KMNI, seuil=seuilBonClustering, indices=ind[4])
bienClusterisesKmeansNonInit.append(clus)
clus = bienClusterise(MatriceClustering=pCIC_V_KMI, seuil=seuilBonClustering,indices=indKmeansInit[4])
bienClusterisesKmeansInit.append(clus)
clus = bienClusterise(MatriceClustering=pCIC_V_DBSCAN, seuil=seuilBonClustering, indices=indDBSCAN[4])
bienClusterisesDBSCAN.append(clus)
# clus = bienClusterise(MatriceClustering=pFRJA_R_MeanShift, seuil=seuilBonClustering, indices=indMeanShift[4])
# bienClusterisesMeanShift.append(clus)
################################################################################
#Enregistrement des differentes matrices
################################################################################
np.save('../resultats/' + couche + '/kmeansNonInit/bienClusteriseKmeansNonInit.npy', bienClusterisesKmeansNonInit)
np.save('../resultats/' + couche + '/kmeansInit/bienClusteriseKmeansInit.npy', bienClusterisesKmeansInit)
np.save('../resultats/' + couche + '/DBSCAN/bienClusteriseDBSCAN.npy', bienClusterisesDBSCAN)
# np.save('../resultats/' + couche + '/MeanShift/bienClusteriseMeanShift.npy', bienClusterisesMeanShift)
def GenerationClustering(couche = "conv1", seuilSuppression = 559, seuilBonClustering = 30, fichier = True):
#############################################################################################
# Appel de MapsClustering et enregistrement des matrices des indices pour chaque algorithme de chaque couche;
# Matrice des indices : 5 colonnes representant les 5 clusterings, et dans chaque colonne les indices de cartes
# qui donnent bien deux classes.
#############################################################################################
pFRJA_R_KMNI, pFRJA_V_KMNI, pFR_RV_KMNI, pCIC_R_KMNI, pCIC_V_KMNI, pourcentagesFR_Rlvb0, pourcentagesFR_Rlvb1,pourcentagesFR_Rlvb2, pourcentagesFR_Rlvb3, ind = Maps5Clustering(couche, seuilSuppression, fichier)
matKmeansNonInit = str("../resultats/resultats5phonemes/matKmeansNonInit_indices_bonnes_cartes.csv")
f1 = open(matKmeansNonInit, "wb")
writer = csv.writer(f1)
writer.writerow(["KmeansNonInit(1)", "KmeansNonInit(1bis)", "KmeansNonInit(2)", "KmeansNonInit(3)", "KmeansNonInit(3bis)", " 4phonemes"])
for values in izip_longest(*ind):
writer.writerow(values)
#############################################################################################
# Fichiers de cartes bon clustering
#############################################################################################
filename = str("../resultats/resultats5phonemes/cartes_bon_clustering_kmeans5")
f = open(filename, "wb")
#############################################################################
#appel directement avec les matrices
##############################################################################
f.write("FRJA R\n")
clus = bienClusterise(MatriceClustering=pFRJA_R_KMNI, seuil=seuilBonClustering, indices=ind[0])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("FRJA V\n")
clus = bienClusterise(MatriceClustering=pFRJA_V_KMNI, seuil=seuilBonClustering, indices=ind[1])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("FR RV\n")
clus = bienClusterise(MatriceClustering=pFR_RV_KMNI, seuil=seuilBonClustering, indices=ind[2])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("JA correct/incorrect R\n")
#
clus = bienClusterise(MatriceClustering=pCIC_R_KMNI, seuil=seuilBonClustering, indices=ind[3])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("JA correct/incorrect V\n")
#
clus = bienClusterise(MatriceClustering=pCIC_V_KMNI, seuil=seuilBonClustering, indices=ind[4])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("FR_Rlvb\n")
f.write("classe0")
clus = bienClusterise(MatriceClustering=pourcentagesFR_Rlvb0, seuil=seuilBonClustering, indices=ind[5])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("classe1")
clus = bienClusterise(MatriceClustering=pourcentagesFR_Rlvb1, seuil=seuilBonClustering, indices=ind[5])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("classe2")
clus = bienClusterise(MatriceClustering=pourcentagesFR_Rlvb2, seuil=seuilBonClustering, indices=ind[5])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("classe3")
clus = bienClusterise(MatriceClustering=pourcentagesFR_Rlvb3, seuil=seuilBonClustering, indices=ind[5])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.close()
def GenerationClustering(couche = "conv1", seuilSuppression = 559, seuilBonClustering = 30, fichier = True):
#############################################################################################
# Appel de MapsClustering et enregistrement des matrices des indices pour chaque algorithme de chaque couche;
# Matrice des indices : 5 colonnes representant les 5 clusterings, et dans chaque colonne les indices de cartes
# qui donnent bien deux classes.
#############################################################################################
pFRJA_R_KMNI, pFRJA_V_KMNI, pFR_RV_KMNI, pCIC_R_KMNI, pCIC_V_KMNI, pourcentagesFR_Rlvb0, pourcentagesFR_Rlvb1,pourcentagesFR_Rlvb2, pourcentagesFR_Rlvb3, ind = Maps5Clustering(couche, seuilSuppression, fichier)
matKmeansNonInit = str("../resultats/resultats5phonemes/matKmeansNonInit_indices_bonnes_cartes.csv")
f1 = open(matKmeansNonInit, "wb")
writer = csv.writer(f1)
writer.writerow(["KmeansNonInit(1)", "KmeansNonInit(1bis)", "KmeansNonInit(2)", "KmeansNonInit(3)", "KmeansNonInit(3bis)", " 4phonemes"])
for values in izip_longest(*ind):
writer.writerow(values)
#############################################################################################
# Fichiers de cartes bon clustering
#############################################################################################
filename = str("../resultats/resultats5phonemes/cartes_bon_clustering_kmeans5")
f = open(filename, "wb")
#############################################################################
#appel directement avec les matrices
##############################################################################
f.write("FRJA R\n")
clus = bienClusterise(MatriceClustering=pFRJA_R_KMNI, seuil=seuilBonClustering, indices=ind[0])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("FRJA V\n")
clus = bienClusterise(MatriceClustering=pFRJA_V_KMNI, seuil=seuilBonClustering, indices=ind[1])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("FR RV\n")
clus = bienClusterise(MatriceClustering=pFR_RV_KMNI, seuil=seuilBonClustering, indices=ind[2])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("JA correct/incorrect R\n")
#
clus = bienClusterise(MatriceClustering=pCIC_R_KMNI, seuil=seuilBonClustering, indices=ind[3])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("JA correct/incorrect V\n")
#
clus = bienClusterise(MatriceClustering=pCIC_V_KMNI, seuil=seuilBonClustering, indices=ind[4])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("FR_Rlvb\n")
f.write("classe0")
clus = bienClusterise(MatriceClustering=pourcentagesFR_Rlvb0, seuil=seuilBonClustering, indices=ind[5])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("classe1")
clus = bienClusterise(MatriceClustering=pourcentagesFR_Rlvb1, seuil=seuilBonClustering, indices=ind[5])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("classe2")
clus = bienClusterise(MatriceClustering=pourcentagesFR_Rlvb2, seuil=seuilBonClustering, indices=ind[5])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.write("classe3")
clus = bienClusterise(MatriceClustering=pourcentagesFR_Rlvb3, seuil=seuilBonClustering, indices=ind[5])
f.write("kmeansNonInit:" + str(clus)+"\n")
f.close()
