當前位置：首頁 > 编程资源 > 编程问答 >内容正文

编程问答

R Bayes

發布時間：2023/12/16 编程问答 33 豆豆

生活随笔收集整理的這篇文章主要介紹了 R Bayes 小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

安裝package：
導入e1071：
找一個數據集：
訓練并查看訓練結果：
下面看一下，這個庫如何處理標稱型特征：
補充一下，如果某個數據缺少某些特征：
參考：

安裝package：

1	> install.packages("e1071")

導入e1071：

1	> library(e1071)

找一個數據集：

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 

   > data(iris)  > iris  ????Sepal.Length Sepal.Width Petal.Length Petal.Width??? Species  1??????????? 5.1???????? 3.5????????? 1.4???????? 0.2???? setosa  2??????????? 4.9???????? 3.0????????? 1.4???????? 0.2???? setosa  3??????????? 4.7???????? 3.2????????? 1.3???????? 0.2???? setosa  4??????????? 4.6???????? 3.1????????? 1.5???????? 0.2???? setosa  5??????????? 5.0???????? 3.6????????? 1.4???????? 0.2???? setosa  6??????????? 5.4???????? 3.9????????? 1.7???????? 0.4???? setosa  7??????????? 4.6???????? 3.4????????? 1.4???????? 0.3???? setosa  8??????????? 5.0???????? 3.4????????? 1.5???????? 0.2???? setosa  9??????????? 4.4???????? 2.9????????? 1.4???????? 0.2???? setosa  10?????????? 4.9???????? 3.1????????? 1.5???????? 0.1???? setosa  11?????????? 5.4???????? 3.7????????? 1.5???????? 0.2???? setosa  12?????????? 4.8???????? 3.4????????? 1.6???????? 0.2???? setosa  13?????????? 4.8???????? 3.0????????? 1.4???????? 0.1???? setosa  14?????????? 4.3???????? 3.0????????? 1.1???????? 0.1???? setosa  15?????????? 5.8???????? 4.0????????? 1.2???????? 0.2???? setosa  16?????????? 5.7???????? 4.4????????? 1.5???????? 0.4???? setosa  17?????????? 5.4???????? 3.9????????? 1.3???????? 0.4???? setosa  18?????????? 5.1???????? 3.5????????? 1.4???????? 0.3???? setosa  19?????????? 5.7???????? 3.8????????? 1.7???????? 0.3???? setosa  20?????????? 5.1???????? 3.8????????? 1.5???????? 0.3???? setosa  21?????????? 5.4???????? 3.4????????? 1.7???????? 0.2???? setosa  22?????????? 5.1???????? 3.7????????? 1.5???????? 0.4???? setosa  23?????????? 4.6???????? 3.6????????? 1.0???????? 0.2???? setosa  24?????????? 5.1???????? 3.3????????? 1.7???????? 0.5???? setosa  25?????????? 4.8???????? 3.4????????? 1.9???????? 0.2???? setosa  26?????????? 5.0???????? 3.0????????? 1.6???????? 0.2???? setosa  27?????????? 5.0???????? 3.4????????? 1.6???????? 0.4???? setosa  28?????????? 5.2???????? 3.5????????? 1.5???????? 0.2???? setosa  29?????????? 5.2???????? 3.4????????? 1.4???????? 0.2???? setosa  30?????????? 4.7???????? 3.2????????? 1.6???????? 0.2???? setosa  31?????????? 4.8???????? 3.1????????? 1.6???????? 0.2???? setosa  32?????????? 5.4???????? 3.4????????? 1.5???????? 0.4???? setosa  33?????????? 5.2???????? 4.1????????? 1.5???????? 0.1???? setosa  34?????????? 5.5???????? 4.2????????? 1.4???????? 0.2???? setosa  35?????????? 4.9???????? 3.1????????? 1.5???????? 0.2???? setosa  36?????????? 5.0???????? 3.2????????? 1.2???????? 0.2???? setosa  37?????????? 5.5???????? 3.5????????? 1.3???????? 0.2???? setosa  38?????????? 4.9???????? 3.6????????? 1.4???????? 0.1???? setosa  39?????????? 4.4???????? 3.0????????? 1.3???????? 0.2???? setosa  40?????????? 5.1???????? 3.4????????? 1.5???????? 0.2???? setosa  41?????????? 5.0???????? 3.5????????? 1.3???????? 0.3???? setosa  42?????????? 4.5???????? 2.3????????? 1.3???????? 0.3???? setosa  43?????????? 4.4???????? 3.2????????? 1.3???????? 0.2???? setosa  44?????????? 5.0???????? 3.5????????? 1.6???????? 0.6???? setosa  45?????????? 5.1???????? 3.8????????? 1.9???????? 0.4???? setosa  46?????????? 4.8???????? 3.0????????? 1.4???????? 0.3???? setosa  47?????????? 5.1???????? 3.8????????? 1.6???????? 0.2???? setosa  48?????????? 4.6???????? 3.2????????? 1.4???????? 0.2???? setosa  49?????????? 5.3???????? 3.7????????? 1.5???????? 0.2???? setosa  50?????????? 5.0???????? 3.3????????? 1.4???????? 0.2???? setosa  51?????????? 7.0???????? 3.2????????? 4.7???????? 1.4 versicolor  52?????????? 6.4???????? 3.2????????? 4.5???????? 1.5 versicolor  53?????????? 6.9???????? 3.1????????? 4.9???????? 1.5 versicolor  54?????????? 5.5???????? 2.3????????? 4.0???????? 1.3 versicolor  55?????????? 6.5???????? 2.8????????? 4.6???????? 1.5 versicolor  56?????????? 5.7???????? 2.8????????? 4.5???????? 1.3 versicolor  57?????????? 6.3???????? 3.3????????? 4.7???????? 1.6 versicolor  58?????????? 4.9???????? 2.4????????? 3.3???????? 1.0 versicolor  59?????????? 6.6???????? 2.9????????? 4.6???????? 1.3 versicolor  60?????????? 5.2???????? 2.7????????? 3.9???????? 1.4 versicolor  61?????????? 5.0???????? 2.0????????? 3.5???????? 1.0 versicolor  62?????????? 5.9???????? 3.0????????? 4.2???????? 1.5 versicolor  63?????????? 6.0???????? 2.2????????? 4.0???????? 1.0 versicolor  64?????????? 6.1???????? 2.9????????? 4.7???????? 1.4 versicolor  65?????????? 5.6???????? 2.9????????? 3.6???????? 1.3 versicolor  66?????????? 6.7???????? 3.1????????? 4.4???????? 1.4 versicolor  67?????????? 5.6???????? 3.0????????? 4.5???????? 1.5 versicolor  68?????????? 5.8???????? 2.7????????? 4.1???????? 1.0 versicolor  69?????????? 6.2???????? 2.2????????? 4.5???????? 1.5 versicolor  70?????????? 5.6???????? 2.5????????? 3.9???????? 1.1 versicolor  71?????????? 5.9???????? 3.2????????? 4.8???????? 1.8 versicolor  72?????????? 6.1???????? 2.8????????? 4.0???????? 1.3 versicolor  73?????????? 6.3???????? 2.5????????? 4.9???????? 1.5 versicolor  74?????????? 6.1???????? 2.8????????? 4.7???????? 1.2 versicolor  75?????????? 6.4???????? 2.9????????? 4.3???????? 1.3 versicolor  76?????????? 6.6???????? 3.0????????? 4.4???????? 1.4 versicolor  77?????????? 6.8???????? 2.8????????? 4.8???????? 1.4 versicolor  78?????????? 6.7???????? 3.0????????? 5.0???????? 1.7 versicolor  79?????????? 6.0???????? 2.9????????? 4.5???????? 1.5 versicolor  80?????????? 5.7???????? 2.6????????? 3.5???????? 1.0 versicolor  81?????????? 5.5???????? 2.4????????? 3.8???????? 1.1 versicolor  82?????????? 5.5???????? 2.4????????? 3.7???????? 1.0 versicolor  83?????????? 5.8???????? 2.7????????? 3.9???????? 1.2 versicolor  84?????????? 6.0???????? 2.7????????? 5.1???????? 1.6 versicolor  85?????????? 5.4???????? 3.0????????? 4.5???????? 1.5 versicolor  86?????????? 6.0???????? 3.4????????? 4.5???????? 1.6 versicolor  87?????????? 6.7???????? 3.1????????? 4.7???????? 1.5 versicolor  88?????????? 6.3???????? 2.3????????? 4.4???????? 1.3 versicolor  89?????????? 5.6???????? 3.0????????? 4.1???????? 1.3 versicolor  90?????????? 5.5???????? 2.5????????? 4.0???????? 1.3 versicolor  91?????????? 5.5???????? 2.6????????? 4.4???????? 1.2 versicolor  92?????????? 6.1???????? 3.0????????? 4.6???????? 1.4 versicolor  93?????????? 5.8???????? 2.6????????? 4.0???????? 1.2 versicolor  94?????????? 5.0???????? 2.3????????? 3.3???????? 1.0 versicolor  95?????????? 5.6???????? 2.7????????? 4.2???????? 1.3 versicolor  96?????????? 5.7???????? 3.0????????? 4.2???????? 1.2 versicolor  97?????????? 5.7???????? 2.9????????? 4.2???????? 1.3 versicolor  98?????????? 6.2???????? 2.9????????? 4.3???????? 1.3 versicolor  99?????????? 5.1???????? 2.5????????? 3.0???????? 1.1 versicolor  100????????? 5.7???????? 2.8????????? 4.1???????? 1.3 versicolor  101????????? 6.3???????? 3.3????????? 6.0???????? 2.5? virginica  102????????? 5.8???????? 2.7????????? 5.1???????? 1.9? virginica  103????????? 7.1???????? 3.0????????? 5.9???????? 2.1? virginica  104????????? 6.3???????? 2.9????????? 5.6???????? 1.8? virginica  105????????? 6.5???????? 3.0????????? 5.8???????? 2.2? virginica  106????????? 7.6???????? 3.0????????? 6.6???????? 2.1? virginica  107????????? 4.9???????? 2.5????????? 4.5???????? 1.7? virginica  108????????? 7.3???????? 2.9????????? 6.3???????? 1.8? virginica  109????????? 6.7???????? 2.5????????? 5.8???????? 1.8? virginica  110????????? 7.2???????? 3.6????????? 6.1???????? 2.5? virginica  111????????? 6.5???????? 3.2????????? 5.1???????? 2.0? virginica  112????????? 6.4???????? 2.7????????? 5.3???????? 1.9? virginica  113????????? 6.8???????? 3.0????????? 5.5???????? 2.1? virginica  114????????? 5.7???????? 2.5????????? 5.0???????? 2.0? virginica  115????????? 5.8???????? 2.8????????? 5.1???????? 2.4? virginica  116????????? 6.4???????? 3.2????????? 5.3???????? 2.3? virginica  117????????? 6.5???????? 3.0????????? 5.5???????? 1.8? virginica  118????????? 7.7???????? 3.8????????? 6.7???????? 2.2? virginica  119????????? 7.7???????? 2.6????????? 6.9???????? 2.3? virginica  120????????? 6.0???????? 2.2????????? 5.0???????? 1.5? virginica  121????????? 6.9???????? 3.2????????? 5.7???????? 2.3? virginica  122????????? 5.6???????? 2.8????????? 4.9???????? 2.0? virginica  123????????? 7.7???????? 2.8????????? 6.7???????? 2.0? virginica  124????????? 6.3???????? 2.7????????? 4.9???????? 1.8? virginica  125????????? 6.7???????? 3.3????????? 5.7???????? 2.1? virginica  126????????? 7.2???????? 3.2????????? 6.0???????? 1.8? virginica  127????????? 6.2???????? 2.8????????? 4.8???????? 1.8? virginica  128????????? 6.1???????? 3.0????????? 4.9???????? 1.8? virginica  129????????? 6.4???????? 2.8????????? 5.6???????? 2.1? virginica  130????????? 7.2???????? 3.0????????? 5.8???????? 1.6? virginica  131????????? 7.4???????? 2.8????????? 6.1???????? 1.9? virginica  132????????? 7.9???????? 3.8????????? 6.4???????? 2.0? virginica  133????????? 6.4???????? 2.8????????? 5.6???????? 2.2? virginica  134????????? 6.3???????? 2.8????????? 5.1???????? 1.5? virginica  135????????? 6.1???????? 2.6????????? 5.6???????? 1.4? virginica  136????????? 7.7???????? 3.0????????? 6.1???????? 2.3? virginica  137????????? 6.3???????? 3.4????????? 5.6???????? 2.4? virginica  138????????? 6.4???????? 3.1????????? 5.5???????? 1.8? virginica  139????????? 6.0???????? 3.0????????? 4.8???????? 1.8? virginica  140????????? 6.9???????? 3.1????????? 5.4???????? 2.1? virginica  141????????? 6.7???????? 3.1????????? 5.6???????? 2.4? virginica  142????????? 6.9???????? 3.1????????? 5.1???????? 2.3? virginica  143????????? 5.8???????? 2.7????????? 5.1???????? 1.9? virginica  144????????? 6.8???????? 3.2????????? 5.9???????? 2.3? virginica  145????????? 6.7???????? 3.3????????? 5.7???????? 2.5? virginica  146????????? 6.7???????? 3.0????????? 5.2???????? 2.3? virginica  147????????? 6.3???????? 2.5????????? 5.0???????? 1.9? virginica  148????????? 6.5???????? 3.0????????? 5.2???????? 2.0? virginica  149????????? 6.2???????? 3.4????????? 5.4???????? 2.3? virginica  150????????? 5.9???????? 3.0????????? 5.1???????? 1.8? virginica  

Sepal意思是“花萼 ”，Petal意思是“ 花瓣”。很明顯，前四列是花萼和花瓣的特征，第五列代表相應的分類。我們可以用這個數據集進行貝葉斯訓練。?

先看一下，對這個數據集summary的結果：?
?

 1 2 3 4 5 6 7 8 

   > summary(iris)  ??Sepal.Length??? Sepal.Width???? Petal.Length??? Petal.Width????????? Species?   ?Min.?? :4.300?? Min.?? :2.000?? Min.?? :1.000?? Min.?? :0.100?? setosa??? :50?   ?1st Qu.:5.100?? 1st Qu.:2.800?? 1st Qu.:1.600?? 1st Qu.:0.300?? versicolor:50?    ?Median :5.800?? Median :3.000?? Median :4.350?? Median :1.300?? virginica :50?   ?Mean?? :5.843?? Mean?? :3.057?? Mean?? :3.758?? Mean?? :1.199?????????????????   ?3rd Qu.:6.400?? 3rd Qu.:3.300?? 3rd Qu.:5.100?? 3rd Qu.:1.800?????????????????    ?Max.?? :7.900?? Max.?? :4.400?? Max.?? :6.900?? Max.?? :2.500  

訓練并查看訓練結果：

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 

   > classifier<-naiveBayes(iris[,1:4], iris[,5])   > classifier   Naive Bayes Classifier for  Discrete Predictors   Call:  naiveBayes.default(x = iris[, 1:4], y = iris[, 5])   A-priori probabilities:  iris[, 5]  ????setosa versicolor? virginica   ?0.3333333? 0.3333333? 0.3333333    Conditional probabilities:  ????????????Sepal.Length  iris[, 5]???? [,1]????? [,2]  ??setosa???? 5.006 0.3524897  ??versicolor 5.936 0.5161711  ??virginica? 6.588 0.6358796   ????????????Sepal.Width  iris[, 5]???? [,1]????? [,2]  ??setosa???? 3.428 0.3790644  ??versicolor 2.770 0.3137983  ??virginica? 2.974 0.3224966   ????????????Petal.Length  iris[, 5]???? [,1]????? [,2]  ??setosa???? 1.462 0.1736640  ??versicolor 4.260 0.4699110  ??virginica? 5.552 0.5518947   ????????????Petal.Width  iris[, 5]???? [,1]????? [,2]  ??setosa???? 0.246 0.1053856  ??versicolor 1.326 0.1977527  ??virginica? 2.026 0.2746501   > classifier$apriori  iris[, 5]  ????setosa versicolor? virginica   ????????50???????? 50???????? 50   > classifier$tables  $Sepal.Length  ????????????Sepal.Length  iris[, 5]???? [,1]????? [,2]  ??setosa???? 5.006 0.3524897  ??versicolor 5.936 0.5161711  ??virginica? 6.588 0.6358796   $Sepal.Width  ????????????Sepal.Width  iris[, 5]???? [,1]????? [,2]  ??setosa???? 3.428 0.3790644  ??versicolor 2.770 0.3137983  ??virginica? 2.974 0.3224966   $Petal.Length  ????????????Petal.Length  iris[, 5]???? [,1]????? [,2]  ??setosa???? 1.462 0.1736640  ??versicolor 4.260 0.4699110  ??virginica? 5.552 0.5518947   $Petal.Width  ????????????Petal.Width  iris[, 5]???? [,1]????? [,2]  ??setosa???? 0.246 0.1053856  ??versicolor 1.326 0.1977527  ??virginica? 2.026 0.2746501  

classifier中：?
?

 1 2 3 4 

   A-priori probabilities:  iris[, 5]  ????setosa versicolor? virginica   ?0.3333333? 0.3333333? 0.3333333  

很好理解，就是類別的先驗概率。?
而：?
?

 1 2 3 4 5 6 

   $Petal.Width  ????????????Petal.Width  iris[, 5]???? [,1]????? [,2]  ??setosa???? 0.246 0.1053856  ??versicolor 1.326 0.1977527  ??virginica? 2.026 0.2746501  

是特征Petal.Width的條件概率，在這個貝葉斯實現中，特征是數值型數據（而且還還有小數部分），這里假設概率密度符合高斯分布。比如對于特征Petal.Width，其屬于setosa的概率符合mean為0.246，標準方差為0.1053856的高斯分布。?

預測：?
預測iris數據集中的第一個數據：?
?

 1 2 3 

   > predict(classifier, iris[1, -5])  [1] setosa  Levels: setosa versicolor virginica  

iris[1,-5]表示第一行的前4列。

看一下該分類器的效果：

 1 2 3 4 5 6 

   > table(predict(classifier, iris[,-5]), iris[,5], dnn=list('predicted','actual'))  ????????????actual  predicted??? setosa versicolor virginica  ??setosa???????? 50????????? 0???????? 0  ??versicolor????? 0???????? 47???????? 3  ??virginica?????? 0????????? 3??????? 47  

分類效果還是不錯的。

自己構造一個新的數據并預測：?
?

 1 2 3 4 

   > new_data = data.frame(Sepal.Length=7, Sepal.Width=3, Petal.Length=6, Petal.Width=2)  > predict(classifier, new_data)  [1] virginica  Levels: setosa versicolor virginica  

如果少一個特征（只有三個特征）：

 1 2 3 4 

   > new_data = data.frame(Sepal.Length=7, Sepal.Width=3, Petal.Length=6)  > predict(classifier, new_data)  [1] virginica  Levels: setosa versicolor virginica  

下面看一下，這個庫如何處理標稱型特征：

數據如下：?
?

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 

   > model = c("H", "H", "H", "H", "T", "T", "T", "T")  > place = c("B", "B", "N", "N", "B", "B", "N", "N")  > repairs = c("Y", "N", "Y", "N", "Y", "N", "Y", "N")  > dataset = data.frame(model, place, repairs)  > dataset  ??model place repairs  1???? H???? B?????? Y  2???? H???? B?????? N  3???? H???? N?????? Y  4???? H???? N?????? N  5???? T???? B?????? Y  6???? T???? B?????? N  7???? T???? N?????? Y  8???? T???? N?????? N  

貝葉斯之：?
?

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 

   > classifier<-naiveBayes(dataset[,1:2], dataset[,3])   > classifier   Naive Bayes Classifier for  Discrete Predictors   Call:  naiveBayes.default(x = dataset[, 1:2], y = dataset[, 3])   A-priori probabilities:  dataset[, 3]  ??N?? Y   0.5 0.5    Conditional probabilities:  ????????????model  dataset[, 3]?? H?? T  ???????????N 0.5 0.5  ???????????Y 0.5 0.5   ????????????place  dataset[, 3]?? B?? N  ???????????N 0.5 0.5  ???????????Y 0.5 0.5  

好了，預測一下：?
?

 1 2 3 4 

   > new_data = data.frame(model="H", place="B")  > predict(classifier, new_data)  [1] N  Levels: N Y  

perfect！?

補充一下，如果某個數據缺少某些特征：

可以用NA代替該特征：

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 

   > model = c("H", "H", "H", "H", "T", "T", "T", "T")  > place = c("B", "B", "N", "N", "B", "B", NA, NA)  > repairs = c("Y", "N", "Y", "N", "Y", "N", "Y", "N")  > dataset = data.frame(model, place, repairs)  > dataset  ??model place repairs  1???? H???? B?????? Y  2???? H???? B?????? N  3???? H???? N?????? Y  4???? H???? N?????? N  5???? T???? B?????? Y  6???? T???? B?????? N  7???? T? <NA>?????? Y  8???? T? <NA>?????? N   > classifier<-naiveBayes(dataset[,1:2], dataset[,3])   > classifier   Naive Bayes Classifier for  Discrete Predictors   Call:  naiveBayes.default(x = dataset[, 1:2], y = dataset[, 3])   A-priori probabilities:  dataset[, 3]  ??N?? Y   0.5 0.5    Conditional probabilities:  ????????????model  dataset[, 3]?? H?? T  ???????????N 0.5 0.5  ???????????Y 0.5 0.5   ????????????place  dataset[, 3]???????? B???????? N  ???????????N 0.6666667 0.3333333  ???????????Y 0.6666667 0.3333333  

參考：

http://www-users.cs.york.ac.uk/~jc/teaching/arin/R_practical/
http://pythonhosted.org//NaiveBayes/

總結

以上是生活随笔為你收集整理的R Bayes的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。

bayes

上一篇： python-patterns
下一篇： Flutter中使用connectivi