def testSVMPaseBas(nbPoint, slider, mult_donnees):
print("debut test passe bas SVM")
result = []
for r, donnees in mult_donnees.items():
print("r = " + str(r))
clf = SVC(gamma='auto', probability=True, max_iter=100, verbose=1)
m = GenericModele(clf, donnees)
m.vectorize()
m.dataToMoy(nbPoint, slider)
s = m.f1Score()
result.append([r, s])
print("reactionTime,f1score : " + str(result))
return result
def testKNNPaseBas(nbPoint, slider, mult_donnees):
print("debut test passe bas knn")
result = []
for r, donnees in mult_donnees.items():
print("r = " + str(r))
neigh = KNeighborsClassifier(n_neighbors=3)
m = GenericModele(neigh, donnees)
m.vectorize()
m.dataToMoy(nbPoint, slider)
s = m.f1Score()
result.append([r, s])
print("reactionTime,f1score : " + str(result))
return result
myFileList = ["data/subject1/Session1/1.gdf"]
reactTimeToTest = [1, 0.1, 0.04]
mesDonnees = dict()
for r in reactTimeToTest:
mesDonnees[r] = dataLoader.DataLoader(myFileList, r)
os.mkdir("resultats/TFKNN_valeurs_k")
for r, donnees in mesDonnees.items():
print(r)
result = []
for k in range(1, 20, 2):
neigh = KNeighborsClassifier(n_neighbors=k)
m = GenericModele(neigh, donnees)
m.dataToTf()
m.vectorize(tf=True)
m.dataToMoy(nbPoint, slider)
s = m.f1Score()
result.append([k, s])
plt.clf()
f = plt.figure()
plt.title("F1 score en fonction de la valeur de k")
plt.xlabel("Valeur de k")
plt.ylabel("F1 score")
plt.plot([re[0] for re in result], [re[1] for re in result])
name = "resultats/TFKNN_valeurs_k/KNN_tf_valeur_de_k_pour_time_" + str(r)
f.savefig(name + ".pdf", bbox_inches='tight')
saveResult(name + ".csv", result)
