貝葉斯分類演算法以貝葉斯定理為基礎，通過先驗概率計算後驗概率，再由最大厚顏概率決定分類（同學們還是當沒看到過這句話吧）。

數學學得不咋地，就多說了，實驗報告怎麼寫也是頭疼，就直接貼程式碼了。

# 訓練例項
dataset = [['Sunny', 'Hot', 'High', 'Weak', 'no'],
['Sunny', 'Hot', 'High', 'Strong', 'no'],
['Overcast', 'Hot', 'High', 'Weak', 'yes'],
['Rainy', 'Mild', 'High', 'Weak', 'yes'],
['Rainy', 'Cool'
 
, 'Normal', 'Weak', 'yes'],
['Rainy', 'Cool', 'Normal', 'Strong', 'no'],
['Overcast', 'Cool', 'Normal', 'Strong', 'yes'],
['Sunny', 'Mild', 'High', 'Weak', 'no'],
['Sunny', 'Cool', 'Normal', 'Weak', 'yes'],
['Rainy', 'Mild', 'Normal', 'Weak', 'yes'],
['Sunny', 'Mild', 'Normal', 'Strong', 'yes'],
['Overcast'
 
, 'Mild', 'High', 'Strong', 'yes'], 
['Overcast', 'Hot', 'Normal', 'Weak', 'yes'],
['Rainy', 'Mild', 'High', 'Strong', 'no']]

# 測試例項
attributes = ('Sunny','Cool','High','Strong')

def classify(data, attrIndex):
    classes = {}
    for item in data:
        try:
            classes[item[attrIndex]].append(item)
        except
 
:
            classes[item[attrIndex]] = [item]
    return classes

def p(data, index, key):
    count = 0
    for item in data:
        if item[index] == key:
            count += 1
    return 1.0 * count / len(data)

def max(p):
    keys = list(p.keys())
    maxKey = keys[0]
    for key in keys:
        if p[key] > p[maxKey]:
            maxKey = key
    return maxKey

classes = classify(dataset, 4)
p0 = []
p1 = {}
for key in classes.keys():
    p1[key] = 1.0 * len(classes[key]) / len(dataset)
p0.append(p1)

for index in range(0, len(attributes)):
    p1 = {}
    for key in classes.keys():
        p1[key] = p(classes[key], index, attributes[index])
        print (key, attributes[index], p1[key])
    p0.append(p1)

p1 = {}
for key in classes.keys():
    P = 1.0;
    for item in p0:
        P *= item[key]
    p1[key] = P
    print (key,'\t', P)

print (max(p1))