def TrainNetClassifier(Trained_labels, clusterAlg, df1Norm, fitmode, nclusters, prop): x_Train, x_Test, y_Train, y_Test = train_test_split(df1Norm.values, Trained_labels, test_size=prop, shuffle=False) Features = len(x_Train[0]) clf = MLPClassifier(hidden_layer_sizes=(Features, Features, Features), max_iter=500, alpha=0.001, solver='adam', random_state=21, tol=0.00000001) predicted_cmeans = [] classesT = [i for i in range(nclusters)] if fitmode.lower() == 'full' or fitmode == "": try: # experimental if 'cmeans' in clusterAlg or 'c-means' in clusterAlg or 'fuzzy' in clusterAlg: clf = clf.fit(x_Train, y_Train) predicted_cmeans = clf.predict(x_Test) else: clf.fit(x_Train, y_Train) except: clf.fit(x_Train, y_Train) else: # experimental for xa, ya in zip(x_Train, y_Train): varw = ya.reshape(1, ) aarw = xa.reshape(1, -1) clf._partial_fit(aarw, varw, classes=classesT) return clf, predicted_cmeans, x_Test, x_Train, y_Test, y_Train