import matplotlib.pyplot as plt
from sklearn.datasets import make_moons

X,y = make_moons(n_samples=100,noise=0.1, random_state=10)
print(X.shape)

fig, axs = plt.subplots(1,5, figsize=(18,3))
for i,ax in zip(range(5), axs):
    X,y = make_moons(n_samples=100,noise=i*0.04, random_state=10)
    for target, color, marker in zip(range(2),['y','k'],['o','^']):
        ax.scatter(X[y==target,0], X[y==target,1],c=color, marker=marker,
                   label='class '+format(target))
        ax.set_xlabel('feature 1', fontsize=20)
        if (i==0):
            ax.set_ylabel('feature 2', fontsize=20)
        ax.set_title('Moons, noise: ' + format(i*0.04))
        ax.grid()
plt.show()
