機(jī)器學(xué)習(xí)樸素貝葉斯模型算法之鳶尾花數(shù)據(jù)實驗

1、機(jī)器學(xué)習(xí)(6)-樸素貝葉斯模型算法之鳶尾花數(shù)據(jù)實驗樸素貝葉斯模型是一種基于貝葉斯定理與特征條件獨立假設(shè)的分類方法，是經(jīng)典的機(jī)器學(xué)習(xí)算法之一，也是為數(shù)不多的基于概率論的分類算法。優(yōu)點：算法邏輯簡單,易于實現(xiàn)缺點：樸素貝葉斯模型前提是假設(shè)屬性之間相互獨立，但這個在現(xiàn)實中往往是不存在的，當(dāng)屬性過多或?qū)傩灾g相關(guān)性大時效果不太好網(wǎng)上有非常多的關(guān)于樸素貝葉斯模型概率的數(shù)理方法，我不打算在這寫這些數(shù)理上的東西，僅通過python代碼實現(xiàn)并講解樸素貝葉斯模型實現(xiàn)過程之所以說算法簡單，在下面一大串的代碼中真正和樸素貝葉斯模型有關(guān)的代碼有關(guān)的就三句1、以下兩句這句的目的是計算訓(xùn)練集中的樣本各屬性的平均值與標(biāo)準(zhǔn)差

2、mean=clfItems.mean(axis=O)#計算每個屬性的平均值stdev=np.sqrt(np.sum(clfItems-mean)*2,axis=0)/float(len(clfItems)-1)#計算每個屬性的標(biāo)準(zhǔn)差2、以下這句就是測試集中每條記錄在與訓(xùn)練集中進(jìn)行比對所應(yīng)用的樸素貝葉斯算法，#測試集中每條記錄的每個屬性在與訓(xùn)練集中進(jìn)行比對應(yīng)用的樸素貝葉斯算法，probabilities=np.exp(-1*(testItem0:-1-trainClfDataclfItem:,0)*2/(trainClfDataclfItem:,1*2*2)/(np.sqrt(2*np.pi)*

3、trainClfDataclfItem:,1)這里應(yīng)用的是GaussianNaiveBayes,高斯NB,當(dāng)然還有一些別的算法：如:MultinomialNaveBayes，多項式NB、BernoulliNaveBayes，多元貝努利NB等本例使用數(shù)據(jù)集簡介:以鳶尾花的特征作為數(shù)據(jù),共有數(shù)據(jù)集包含150個數(shù)據(jù)集,分為3類setosa(i!|鳶尾)，versicolor(變色鳶尾)，virginica(維吉尼亞鳶尾)每類50個數(shù)據(jù)，每條數(shù)據(jù)包含4個屬性數(shù)據(jù)和一個類別數(shù)據(jù).通過這150個數(shù)據(jù)來演示樸素貝葉斯模型實現(xiàn)這還有兩篇文章也是關(guān)于鳶尾花的機(jī)器學(xué)習(xí)實驗，有興趣也可以了解一下廢話不說,附上全部代

4、碼#-*-coding:utf-8-*-#鳶尾花的特征作為數(shù)據(jù)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#數(shù)據(jù)處理，取得150條的數(shù)據(jù),將類別轉(zhuǎn)化為1.0,2.0,3.0數(shù)字，因為后面使用NUMPY計算比較快，在類別的類型上和屬性一樣使用浮點型data=data.replace(,).replace(Iris