损失函数绘图

label='Logistic Loss'：label 会做一个提示，

plt.legend(loc='upper right')：将每个label的提示放在右上角，

plt.savefig('1.png')：可以将生成的图片自动保存

 # 5.2 损失函数：Logistic损失(-1,1)/SVM Hinge损失/ 0/1损失
    x = np.array(np.linspace(start=-2, stop=3, num=1001, dtype=np.float))
    y_logit = np.log(1 + np.exp(-x)) / math.log(2)
    y_boost = np.exp(-x)
    y_01 = x < 0
    y_hinge = 1.0 - x
    y_hinge[y_hinge < 0] = 0
    plt.plot(x, y_logit, 'r-', label='Logistic Loss', linewidth=2)
    plt.plot(x, y_01, 'g-', label='0/1 Loss', linewidth=2)
    plt.plot(x, y_hinge, 'b-', label='Hinge Loss', linewidth=2)
    plt.plot(x, y_boost, 'm--', label='Adaboost Loss', linewidth=2)
    plt.grid()
    plt.legend(loc='upper right')
    # plt.savefig('1.png')
    plt.show()