机器学习朴素贝叶斯模型算法之鸢尾花数据实验

1、机器学习(6)-朴素贝叶斯模型算法之鸢尾花数据实验朴素贝叶斯模型是一种基于贝叶斯定理与特征条件独立假设的分类方法，是经典的机器学习算法之一，也是为数不多的基于概率论的分类算法。优点：算法逻辑简单,易于实现缺点：朴素贝叶斯模型前提是假设属性之间相互独立，但这个在现实中往往是不存在的，当属性过多或属性之间相关性大时效果不太好网上有非常多的关于朴素贝叶斯模型概率的数理方法，我不打算在这写这些数理上的东西，仅通过python代码实现并讲解朴素贝叶斯模型实现过程之所以说算法简单，在下面一大串的代码中真正和朴素贝叶斯模型有关的代码有关的就三句1、以下两句这句的目的是计算训练集中的样本各属性的平均值与标准差

2、mean=clfItems.mean(axis=O)#计算每个属性的平均值stdev=np.sqrt(np.sum(clfItems-mean)*2,axis=0)/float(len(clfItems)-1)#计算每个属性的标准差2、以下这句就是测试集中每条记录在与训练集中进行比对所应用的朴素贝叶斯算法，#测试集中每条记录的每个属性在与训练集中进行比对应用的朴素贝叶斯算法，probabilities=np.exp(-1*(testItem0:-1-trainClfDataclfItem:,0)*2/(trainClfDataclfItem:,1*2*2)/(np.sqrt(2*np.pi)*

3、trainClfDataclfItem:,1)这里应用的是GaussianNaiveBayes,高斯NB,当然还有一些别的算法：如:MultinomialNaveBayes，多项式NB、BernoulliNaveBayes，多元贝努利NB等本例使用数据集简介:以鸢尾花的特征作为数据,共有数据集包含150个数据集,分为3类setosa(i!|鸢尾)，versicolor(变色鸢尾)，virginica(维吉尼亚鸢尾)每类50个数据，每条数据包含4个属性数据和一个类别数据.通过这150个数据来演示朴素贝叶斯模型实现这还有两篇文章也是关于鸢尾花的机器学习实验，有兴趣也可以了解一下废话不说,附上全部代

4、码#-*-coding:utf-8-*-#鸢尾花的特征作为数据data=5.1,3.5,1.4,0.2,Iris-setosa4.9,3.0,1.4,0.2,Iris-setosa4.7,3.2,1.3,0.2,Iris-setosa4.6,3.1,1.5,0.2,Iris-setosa5.0,3.6,1.4,0.2,Iris-setosa5.4,3.9,1.7,0.4,Iris-setosa4.6,3.4,1.4,0.3,Iris-setosa5.0,3.4,1.5,0.2,Iris-setosa4.4,2.9,1.4,0.2,Iris-setosa4.9,3.1,1.5,0.1,Iris-

5、setosa5.4,3.7,1.5,0.2,Iris-setosa4.8,3.4,1.6,0.2,Iris-setosa4.8,3.0,1.4,0.1,Iris-setosa4.3,3.0,1.1,0.1,Iris-setosa5.8,4.0,1.2,0.2,Iris-setosa5.7,4.4,1.5,0.4,Iris-setosa5.4,3.9,1.3,0.4,Iris-setosa5.1,3.5,1.4,0.3,Iris-setosa5.7,3.8,1.7,0.3,Iris-setosa5.1,3.8,1.5,0.3,Iris-setosa5.4,3.4,1.7,0.2,Iris-set

6、osa5.1,3.7,1.5,0.4,Iris-setosa5.1,3.7,1.5,0.4,Iris-setosa4.6,3.6,1.0,0.2,Iris-setosa5.1,3.3,1.7,0.5,Iris-setosa4.8,3.4,1.9,0.2,Iris-setosa5.0,3.0,1.6,0.2,Iris-setosa5.0,3.4,1.6,0.4,Iris-setosa5.2,3.5,1.5,0.2,Iris-setosa5.2,3.4,1.4,0.2,Iris-setosa4.7,3.2,1.6,0.2,Iris-setosa4.8,3.1,1.6,0.2,Iris-setosa

7、5.4,3.4,1.5,0.4,Iris-setosa5.2,4.1,1.5,0.1,Iris-setosa5.5,4.2,1.4,0.2,Iris-setosa4.9,3.1,1.5,0.1,Iris-setosa5.0,3.2,1.2,0.2,Iris-setosa5.5,3.5,1.3,0.2,Iris-setosa4.9,3.1,1.5,0.1,Iris-setosa4.4,3.0,1.3,0.2,Iris-setosa5.1,3.4,1.5,0.2,Iris-setosa5.0,3.5,1.3,0.3,Iris-setosa4.5,2.3,1.3,0.3,Iris-setosa4.4

8、,3.2,1.3,0.2,Iris-setosa5.0,3.5,1.6,0.6,Iris-setosa5.1,3.8,1.9,0.4,Iris-setosa4.8,3.0,1.4,0.3,Iris-setosa5.1,3.8,1.6,0.2,Iris-setosa4.6,3.2,1.4,0.2,Iris-setosa5.3,3.7,1.5,0.2,Iris-setosa5.0,3.3,1.4,0.2,Iris-setosa7.0,3.2,4.7,1.4,Iris-versicolor6.4,3.2,4.5,1.5,Iris-versicolor6.9,3.1,4.9,1.5,Iris-vers

9、icolor5.5,2.3,4.0,1.3,Iris-versicolor6.5,2.8,4.6,1.5,Iris-versicolor5.7,2.8,4.5,1.3,Iris-versicolor6.3,3.3,4.7,1.6,Iris-versicolor4.9,2.4,3.3,1.0,Iris-versicolor6.6,2.9,4.6,1.3,Iris-versicolor5.2,2.7,3.9,1.4,Iris-versicolor5.0,2.0,3.5,1.0,Iris-versicolor5.9,3.0,4.2,1.5,Iris-versicolor6.0,2.2,4.0,1.0

10、,Iris-versicolor6.1,2.9,4.7,1.4,Iris-versicolor5.6,2.9,3.6,1.3,Iris-versicolor6.7,3.1,4.4,1.4,Iris-versicolor5.6,3.0,4.5,1.5,Iris-versicolor5.8,2.7,4.1,1.0,Iris-versicolor6.2,2.2,4.5,1.5,Iris-versicolor5.6,2.5,3.9,1.1,Iris-versicolor5.9,3.2,4.8,1.8,Iris-versicolor6.1,2.8,4.0,1.3,Iris-versicolor6.3,2

11、.5,4.9,1.5,Iris-versicolor6.1,2.8,4.7,1.2,Iris-versicolor6.4,2.9,4.3,1.3,Iris-versicolor6.6,3.0,4.4,1.4,Iris-versicolor6.8,2.8,4.8,1.4,Iris-versicolor6.7,3.0,5.0,1.7,Iris-versicolor6.0,2.9,4.5,1.5,Iris-versicolor5.7,2.6,3.5,1.0,Iris-versicolor5.5,2.4,3.8,1.1,Iris-versicolor5.5,2.4,3.7,1.0,Iris-versi

12、color5.8,2.7,3.9,1.2,Iris-versicolor6.0,2.7,5.1,1.6,Iris-versicolor5.4,3.0,4.5,1.5,Iris-versicolor6.0,3.4,4.5,1.6,Iris-versicolor6.7,3.1,4.7,1.5,Iris-versicolor6.7,3.1,4.7,1.5,Iris-versicolor6.3,2.3,4.4,1.3,Iris-versicolor5.6,3.0,4.1,1.3,Iris-versicolor5.5,2.5,4.0,1.3,Iris-versicolor5.5,2.6,4.4,1.2,

13、Iris-versicolor6.1,3.0,4.6,1.4,Iris-versicolor5.8,2.6,4.0,1.2,Iris-versicolor5.0,2.3,3.3,1.0,Iris-versicolor5.6,2.7,4.2,1.3,Iris-versicolor5.7,3.0,4.2,1.2,Iris-versicolor5.7,2.9,4.2,1.3,Iris-versicolor6.2,2.9,4.3,1.3,Iris-versicolor5.1,2.5,3.0,1.1,Iris-versicolor5.7,2.8,4.1,1.3,Iris-versicolor6.3,3.

14、3,6.0,2.5,Iris-virginica5.8,2.7,5.1,1.9,Iris-virginica7.1,3.0,5.9,2.1,Iris-virginica6.3,2.9,5.6,1.8,Iris-virginica6.5,3.0,5.8,2.2,Iris-virginica7.6,3.0,6.6,2.1,Iris-virginica4.9,2.5,4.5,1.7,Iris-virginica7.3,2.9,6.3,1.8,Iris-virginica6.7,2.5,5.8,1.8,Iris-virginica7.2,3.6,6.1,2.5,Iris-virginica6.5,3.

15、2,5.1,2.0,Iris-virginica6.4,2.7,5.3,1.9,Iris-virginica6.8,3.0,5.5,2.1,Iris-virginica5.7,2.5,5.0,2.0,Iris-virginica5.8,2.8,5.1,2.4,Iris-virginica6.4,3.2,5.3,2.3,Iris-virginica6.5,3.0,5.5,1.8,Iris-virginica7.7,3.8,6.7,2.2,Iris-virginica7.7,2.6,6.9,2.3,Iris-virginica6.0,2.2,5.0,1.5,Iris-virginica6.9,3.

16、2,5.7,2.3,Iris-virginica5.6,2.8,4.9,2.0,Iris-virginica7.7,2.8,6.7,2.0,Iris-virginica6.3,2.7,4.9,1.8,Iris-virginica6.7,3.3,5.7,2.1,Iris-virginica7.2,3.2,6.0,1.8,Iris-virginica6.2,2.8,4.8,1.8,Iris-virginica6.1,3.0,4.9,1.8,Iris-virginica6.4,2.8,5.6,2.1,Iris-virginica7.2,3.0,5.8,1.6,Iris-virginica7.4,2.

17、8,6.1,1.9,Iris-virginica7.9,3.8,6.4,2.0,Iris-virginica6.4,2.8,5.6,2.2,Iris-virginica6.3,2.8,5.1,1.5,Iris-virginica6.1,2.6,5.6,1.4,Iris-virginica7.7,3.0,6.1,2.3,Iris-virginica6.3,3.4,5.6,2.4,Iris-virginica6.4,3.1,5.5,1.8,Iris-virginica6.0,3.0,4.8,1.8,Iris-virginica6.9,3.1,5.4,2.1,Iris-virginica6.7,3.

18、1,5.6,2.4,Iris-virginica6.9,3.1,5.1,2.3,Iris-virginica5.8,2.7,5.1,1.9,Iris-virginica6.8,3.2,5.9,2.3,Iris-virginica6.7,3.3,5.7,2.5,Iris-virginica6.7,3.0,5.2,2.3,Iris-virginica6.3,2.5,5.0,1.9,Iris-virginica6.5,3.0,5.2,2.0,Iris-virginica6.2,3.4,5.4,2.3,Iris-virginica5.9,3.0,5.1,1.8,Iris-virginicaimportnumpyasnpimportnumpyasnp#数据处理，取得150条的数据,将类别转化为1.0,2.0,3.0数字，因为后面使用NUMPY计算比较快，在类别的类型上和属性一样使用浮点型data=data.replace(,).replace(Iris