朴素贝叶斯概念

例子：邮件分类问题：

N = (12/17)*(5/11)*(3/11)
S = (5/17)*(2/7)*(1/7)

print(N)
print(S)
# N>S 我们可以判断这是一封正常邮件

常见问题1

因为图2中 吗出现的次数是0 那么这封邮件就会被误判为正常邮件

解决如果遇到样本里面有0的情况，可以通过添加alpha进行解决。

alpha=1 统一增加1 确保不会出现无0的情况

N = (12/17)*((1/15)**4)*(3/15)
S = (5/17)*((5/11)**4)*(1/11)

print(N)
print(S)

print(S > N)

贝叶斯代码

数据切分

def split_our_data_by_class(dataset):# 按类别拆分数据 传入参数数据
    splited_data = dict() # 按字典储存
    for i in range(len(dataset)):
        vector = dataset[i]
        class_value = vector[-1]
        if (class_value not in splited_data):
            splited_data[class_value] =list()
        splited_data[class_value].append(vector)
    return splited_data

# create our dummy data

dataset = [ [0.8,2.3,0],
    [2.1,1.6,0],
    [2.0,3.6,0],
    [3.1,2.5,0],
    [3.8,4.7,0],
    [6.1,4.4,1],
    [8.6,0.3,1],
    [7.9,5.3,1],
    [9.1,2.5,1],
    [6.8,2.7,1]]

splited = split_our_data_by_class(dataset)
print(splited)

# 逐行打印
for label in splited:
    print(label)
    for row in splited[label]:
        print(row)

calculate mean and standard deviation(n-1)：计算平均值和标准差（n-1）

from math import sqrt

def calculate_the_mean(a_list_of_num): # 平均值函数
    mean = sum(a_list_of_num)/float(len(a_list_of_num)) # 求总和除以数据个数
    return mean

def calculate_the_standard_deviation(a_list_of_num): # 计算标准差
    the_mean = calculate_the_mean(a_list_of_num) # 调用平均值函数计算平均值
    the_variance = sum([(x-the_mean)**2 for x in a_list_of_num])/ float(len(a_list_of_num)-1)# {每个值减去平均值的平方除以数据的（个数-1）}结果相加的总和
    std = sqrt(the_variance) # 开方
    return std

def describe_our_data(dataset):# 调用函数
    description = [(calculate_the_mean(column),
                    calculate_the_standard_deviation(column),
                    len(column)) for column in zip(*dataset)]
    del(description[-1]) # 删除最后一列的分类标签
    return description

describe_our_data(dataset)

def describe_our_data_by_class(dataset):# 综合
    splited_data = split_our_data_by_class(dataset)
    data_description = dict()# 创建一个空字典来存储
    for class_value,rows in splited_data.items():# 通过迭代填入空字典
        data_description[class_value] = describe_our_data(rows)
    return data_description

description = describe_our_data_by_class(dataset)
for label in description:
    print(label)
    for row in description[label]:
        print(row)

description = describe_our_data_by_class(dataset)
type(description)

print(description)
print("---------")
print(description[0])
print("---------")
print(description[0][0])
print("--------")
print(description[0][0][2])