我正在學習機器學習書中決策樹的基本知識。書中出現這個例子是為了理解一些東西（我知道這不是決策樹，但有些東西我不明白）def gini (p):    return (p)*(1-(p))-(1-p)*(1-(1-p))def entropy(p):    return -p*np.log2(p)-(1-p)*np.log2((1-p))def error(p):    return 1- np.max([p, 1-p])x=np.arange(0.0, 1.0, 0.01)ent=[entropy(p) if p != 0 else None for p in x]sc_ent = [e*0.5 if e else None for e in ent]err= [error(i) for i in x]fig = plt.figure()ax=plt.subplot(111)for i, lab, ls, c, in zip ([ent, sc_ent, gini(x), err], ['Entropy', 'Entropy(scaled)', 'Gini Impurity', 'Misclassification Error'], ['-', '-', '--', '-.'], ['black', 'lightgray','red','green', 'cyan']):    line= ax.plot(x,i,label=lab, linestyle=ls, lw=2, color =c)ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=5, fancybox=True, shadow=False)ax.axhline(y=0.5, linewidth= 1, color='k', linestyle='--')ax.axhline(y=1, linewidth= 1, color='k', linestyle='--')plt.ylim([0, 1.1])plt.xlabel('p(i=1)')plt.ylabel('Impurity index')plt.show()有人可以解釋這里發生了什么嗎？ent=[entropy(p) if p != 0 else None for p in x]sc_ent = [e*0.5 if e else None for e in ent]err= [error(i) for i in x]