def make_classifiers():
    """

    :return:
    """

    names = ["ELM(10,tanh)", "ELM(10,tanh,LR)", "ELM(10,sinsq)", "ELM(10,tribas)", "ELM(hardlim)", "ELM(20,rbf(0.1))"]

    nh = 10

    # pass user defined transfer func
    sinsq = (lambda x: np.power(np.sin(x), 2.0))
    srhl_sinsq = MLPRandomLayer(n_hidden=nh, activation_func=sinsq)

    # use internal transfer funcs
    srhl_tanh = MLPRandomLayer(n_hidden=nh, activation_func='tanh')
    srhl_tribas = MLPRandomLayer(n_hidden=nh, activation_func='tribas')
    srhl_hardlim = MLPRandomLayer(n_hidden=nh, activation_func='hardlim')

    # use gaussian RBF
    srhl_rbf = RBFRandomLayer(n_hidden=nh*2, rbf_width=0.1, random_state=0)
    log_reg = LogisticRegression()

    classifiers = [GenELMClassifier(hidden_layer=srhl_tanh),
                   GenELMClassifier(hidden_layer=srhl_tanh, regressor=log_reg),
                   GenELMClassifier(hidden_layer=srhl_sinsq),
                   GenELMClassifier(hidden_layer=srhl_tribas),
                   GenELMClassifier(hidden_layer=srhl_hardlim),
                   GenELMClassifier(hidden_layer=srhl_rbf)]

    return names, classifiers
def make_classifiers ( ) : names = [ "ELM(10,tanh)" , "ELM(10,tanh,LR)" , "ELM(10,sinsq)" , "ELM(10,tribas)" , "ELM(hardlim)" , "ELM(20,rbf(0.1))" ] nh = 10 sinsq = ( lambda x : np . power ( np . sin ( x ) , 2.0 ) ) srhl_sinsq = MLPRandomLayer ( n_hidden = nh , activation_func = sinsq ) srhl_tanh = MLPRandomLayer ( n_hidden = nh , activation_func = 'tanh' ) srhl_tribas = MLPRandomLayer ( n_hidden = nh , activation_func = 'tribas' ) srhl_hardlim = MLPRandomLayer ( n_hidden = nh , activation_func = 'hardlim' ) srhl_rbf = RBFRandomLayer ( n_hidden = nh * 2 , rbf_width = 0.1 , random_state = 0 ) log_reg = LogisticRegression ( ) classifiers = [ GenELMClassifier ( hidden_layer = srhl_tanh ) , GenELMClassifier ( hidden_layer = srhl_tanh , regressor = log_reg ) , GenELMClassifier ( hidden_layer = srhl_sinsq ) , GenELMClassifier ( hidden_layer = srhl_tribas ) , GenELMClassifier ( hidden_layer = srhl_hardlim ) , GenELMClassifier ( hidden_layer = srhl_rbf ) ] return names , classifiers
:return: