def plot_data(self,params=np.array([]),name="Naive Bayes", print_bayes_opt = True):
        import matplotlib.pyplot as plt
        fig = plt.figure()
        fig.suptitle(self.get_name())
        axis = fig.add_subplot(1,1,1)
        idx,_ = np.nonzero(self.train_y == 0)
        idx2,_ = np.nonzero(self.train_y == 1)
        idx3,_ = np.nonzero(self.test_y == 0)
        idx4,_ = np.nonzero(self.test_y == 1)
        axis.scatter(self.train_X[idx,0],self.train_X[idx,1],s=30,c="red",marker='s')
        axis.scatter(self.train_X[idx2,0],self.train_X[idx2,1],s=30,c="blue",marker='s')
        if(0 > idx3.shape[0]):
            axis.scatter(self.test_X[idx3,0],self.test_X[idx3,1],s=30,c="red",marker='o')
        if(idx4.shape[0] > 0):
            axis.scatter(self.test_X[idx4,0],self.test_X[idx4,1],s=30,c="blue",marker='o')
        ## Plot Bayes optimal
        if(print_bayes_opt):
            bayes_opt_params = self.get_bayes_optimal()
            self.add_line(fig,axis,bayes_opt_params, "Bayes Optimal","black")
        
        
        axis.legend()
#        fig.show()
        return fig,axis
dataset/ETHPy150Open alvations/pywsd/merlin/simple_data_set.py/SimpleDataSet.plot_data/swapped operands of `>`