def run_all_classifiers(dataset):
    fig,axis = dataset.plot_data()
    print "Naive Bayes"
    nb = nbc.NaiveBayes()
    params_nb = nb.train(dataset.train_X,dataset.train_y)
    print params_nb.reshape(-1)
    predict = nb.test(dataset.train_X,params_nb)
    evaluation = nb.evaluate(predict,dataset.train_y)
    predict2 = nb.test(dataset.test_X,params_nb)
    evaluation2 = nb.evaluate(predict2,dataset.test_y)
    print (evaluation,evaluation2)%"Accuracy train: %f test: %f"
    fig,axis = dataset.add_line(fig,axis,params_nb,"Naive Bayes","red")

    print "Perceptron"
    perc = percc.Perceptron()
    params_perc =  perc.train(dataset.train_X,dataset.train_y)
    print params_perc.reshape(-1)
    predict = perc.test(dataset.train_X,params_perc)
    evaluation = perc.evaluate(predict,dataset.train_y)
    predict2 = perc.test(dataset.test_X,params_perc)
    evaluation2 = perc.evaluate(predict2,dataset.test_y)
    print "Accuracy train: %f test: %f"%(evaluation,evaluation2)
    fig,axis = dataset.add_line(fig,axis,params_perc,"Perceptron","blue")

    print "MaxEnt LBFGS"
    me = mec_batch.MaxEnt_batch()
    params_me =  me.train(dataset.train_X,dataset.train_y)
    print params_me.reshape(-1)
    predict = me.test(dataset.train_X,params_me)
    evaluation = me.evaluate(predict,dataset.train_y)
    predict2 = me.test(dataset.test_X,params_me)
    evaluation2 = me.evaluate(predict2,dataset.test_y)
    print "Accuracy train: %f test: %f"%(evaluation,evaluation2)
    fig,axis = dataset.add_line(fig,axis,params_me,"ME-LBFGS","green")

    print "MaxEnt Online"
    me_online = mec_online.MaxEnt_online()
    params_me =  me_online.train(dataset.train_X,dataset.train_y)
    print params_me.reshape(-1)
    predict = me_online.test(dataset.train_X,params_me)
    evaluation = me_online.evaluate(predict,dataset.train_y)
    predict2 = me_online.test(dataset.test_X,params_me)
    evaluation2 = me.evaluate(predict2,dataset.test_y)
    print "Accuracy train: %f test: %f"%(evaluation,evaluation2)
    fig,axis = dataset.add_line(fig,axis,params_me,"ME-Online","pink")

    print "MIRA"
    mira = mirac.Mira()
    params_mira =  mira.train(dataset.train_X,dataset.train_y)
    print params_mira.reshape(-1)
    predict = mira.test(dataset.train_X,params_mira)
    evaluation = mira.evaluate(predict,dataset.train_y)
    predict2 = mira.test(dataset.test_X,params_mira)
    evaluation2 = mira.evaluate(predict2,dataset.test_y)
    print "Accuracy train: %f test: %f"%(evaluation,evaluation2)
    fig,axis = dataset.add_line(fig,axis,params_mira,"Mira","orange")

    print "SVM"
    svm = svmc.SVM()
    params_svm =  svm.train(dataset.train_X,dataset.train_y)
    print params_svm.reshape(-1)
    predict = svm.test(dataset.train_X,params_svm)
    evaluation = svm.evaluate(predict,dataset.train_y)
    predict2 = svm.test(dataset.test_X,params_svm)
    evaluation2 = svm.evaluate(predict2,dataset.test_y)
    print "Accuracy train: %f test: %f"%(evaluation,evaluation2)
    fig,axis = dataset.add_line(fig,axis,params_svm,"SVM","brown")
dataset/ETHPy150Open LxMLS/lxmls-toolkit/lxmls/run_all_classifiers.py/run_all_classifiers/swapped operands of `%`