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)