朴素贝叶斯预测莺尾花数据

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.naive_bayes import GaussianNB
import matplotlib

#生成所有测试样本点
def make_meshgrid(x,y,h=0.02):
    x_min,x_max = x.min()-1,x.max()+1
    y_min, y_max = y.min() - 1, y.max() + 1
    xx,yy = np.meshgrid(np.arange(x_min,x_max,h),np.arange(y_min,y_max,h))
    return xx,yy

def plot_test_results(ax,clf,xx,yy,**params):
    Z = clf.predict(np.c_[xx.ravel(),yy.ravel()])
    Z = Z.reshape(xx.shape)
    ax.contourf(xx,yy,Z, **params)


if __name__ == '__main__':
    #载入iris数据集
    iris = datasets.load_iris()
    #只使用前面两个特征
    X = iris.data[:,:2]
    #样本标签值
    y = iris.target

    #创建并训练正态朴素贝叶斯分类器
    clf = GaussianNB()
    clf.fit(X,y)

    title = ('GaussianBayesClassifier')

    fig,ax = plt.subplots(figsize=(5,5))

    plt.subplots_adjust(wspace=0.4,hspace=0.4)

    X0,X1 = X[:,0],X[:,1]
    #生成所有测试样本点
    xx,yy = make_meshgrid(X0,X1)

    #显示测试样本的分类结果
    plot_test_results(ax,clf,xx,yy,cmap=plt.cm.coolwarm,alpha=0.8)

    #显示训练样本
    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
    ax.set_xlim(xx.min(), xx.max())
    ax.set_ylim(yy.min(), yy.max())
    ax.set_xlabel('x1')
    ax.set_ylabel('x2')
    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_title(title)
    plt.show()