def testRiemannKNN(mult_donnees,mesDonnees_test):
print("debut test riemann KNN")
result=[]
for r,donnees in mult_donnees.items():
print("r = "+str(r))
m = GenericModele(pyriemann.classification.KNearestNeighbor(n_neighbors=3),donnees)
m.dataToCov()
m.fit()
s = m.score(mesDonnees_test[r].data,mesDonnees_test[r].labels)
result.append([r,s])
print("reactionTime,f1score : "+str(result))
return result
def testRiemannMDMPlusXdawn(mult_donnees,mesDonnees_test):
print("debut test riemann MDM")
result=[]
for r,donnees in mult_donnees.items():
print("r = "+str(r))
m = GenericModele(pyriemann.classification.MDM(),donnees)
m.dataToXdawnCov()
m.fit()
s = m.score(mesDonnees_test[r].data,mesDonnees_test[r].labels)
result.append([r,s])
print("reactionTime,f1score : "+str(result))
return result
def testSVMBrut(mult_donnees,mesDonnees_test):
print("debut test SVM brut")
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.fit()
s = m.score(mesDonnees_test[r].data,mesDonnees_test[r].labels)
result.append([r,s])
print("reactionTime,f1score : "+str(result))
return result
def testKNNBrut(mult_donnees,mesDonnees_test):
print("debut test knn brut")
result=[]
for r,donnees in mult_donnees.items():
print("r = "+str(r))
neigh = KNeighborsClassifier(n_neighbors=3)
m = GenericModele(neigh,donnees)
m.vectorize()
m.fit()
s = m.score(mesDonnees_test[r].data,mesDonnees_test[r].labels)
result.append([r,s])
print("reactionTime,f1score : "+str(result))
return result
def testKNNPaseBas(nbPoint,slider,mult_donnees,mesDonnees_test):
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)
m.fit()
s = m.score(mesDonnees_test[r].data,mesDonnees_test[r].labels)
result.append([r,s])
print("reactionTime,f1score : "+str(result))
return result
