# print del_support
for index,i in enumerate(del_num): # 计算每个关联规则支持度和自信度
index_support = 0
if len(self.support) != 1:
index_support = index
support = float(self.support[index_location])/self.line_num * 100 # 支持度
s = [j for index_item,j in enumerate(self.item_name) if index_item in i]
if del_support[index]:
confidence = float(self.support[index_location])/del_support[index] * 100
if confidence > self.min_confidence:
print ','.join(s) , '->>' , self.item_name[each_location[index]] , ' min_support: ' , str(support) + '%' , ' min_confidence:' , str(confidence) + '%'
def main():
c = Apriori('basket.txt', 14, 3, 13)
d = Apriori('simple.txt', 50, 2, 6)
if __name__ == '__main__':
main()
############################################################################
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Apriori算法
Apriori(filename, min_support, item_start, item_end)
参数说明
filename:(路径)文件名
min_support:最小支持度
item_start:item起始位置
item_end:item结束位置
使用例子：
import apriori
c = apriori.Apriori('basket.txt', 11, 3, 13)
输出：
--------------------------------------------------------------------------------
The 1 loop
location [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]]
support [299, 183, 177, 303, 204, 302, 293, 287, 184, 292, 276]
num [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
