예제 #1
0
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
예제 #2
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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
예제 #3
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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
예제 #4
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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
예제 #5
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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