add apriori and userfc

+import math
+#!usr/bin/python
+
+
+
+def readFile(file_name):
+	contents_lines=[]
+	f = open(file_name,'r')
+	contents_lines=f.readlines()
+	f.close()
+	return contents_lines
+
+def getBooksList(file_name):
+	books_contents=readFile(file_name)
+	books_info={}
+	for book in books_contents:
+		book_info=book.split("|")
+		books_info[int(book_info[0])]=book_info[1:]
+	return books_info
+
+
+
+def getRatingInformation(ratings):
+	rates=[]
+	for line in ratings:
+		rate = line.split('\t')
+		rates.append([int(rate[0]),int(rate[1]),int(rate[2])])
+	return rates
+
+def createUserRankDic(rates):
+	user_rate_dic={}
+	item_to_user={}
+	for i in rates:
+		user_rank=(i[1],i[2])
+		if i[0] in user_rate_dic:
+			user_rate_dic[i[0]].append(user_rank)
+		else:
+			user_rate_dic[i[0]]=[user_rank]
+
+		if i[1] in item_to_user:
+			item_to_user[i[1]].append(i[0])
+		else:
+			item_to_user[i[1]]=[i[0]]
+	return user_rate_dic,item_to_user
+
+def clacSimlaryCosDist(user1,user2):
+	sum_x=0.0
+	sum_y=0.0
+	sum_xy=0.0
+	avg_x=0.0
+	avg_y=0.0
+	for key in user1:
+		avg_x+=key[1]
+	avg_x=avg_x/len(user1)
+
+	for key in user2:
+		avg_y+=key[1]
+	avg_y=avg_y/len(user2)
+
+	for key1 in user1:
+		for key2 in user2:
+			if key1[0]==key2[0]:
+				sum_xy+=(key1[1]-avg_x)*(key2[1]-avg_y)
+		sum_x+=(key1[1]-avg_x)*(key1[1]-avg_x)
+	for key2 in user2:
+		sum_y+=(key2[1]-avg_y)*(key2[1]-avg_y)
+	# print sum_x,sum_y,sum_xy
+	if sum_xy ==0.0:
+		return 0
+	sx_sy=math.sqrt(sum_x*sum_y)
+	return sum_xy/sx_sy
+
+
+
+def calcNearestNeighbor(userid,users_dic,item_dic):
+	neighbors=[]
+	for item in users_dic[userid]:
+		for neighbor in item_dic[item[0]]:
+			if neighbor != userid and neighbor not in neighbors:
+				neighbors.append(neighbor)
+	
+	neighbors_dist=[]
+	for neighbor in neighbors:
+		dist=clacSimlaryCosDist(users_dic[userid],users_dic[neighbor])
+		neighbors_dist.append([dist,neighbor])
+	neighbors_dist.sort(reverse=True)
+	return neighbors_dist
+
+
+def recommendByUserFC(file_name,userid,k=5):
+	test_contents=readFile(file_name)
+	test_rates = getRatingInformation(test_contents)
+	test_dic,test_item_to_user=createUserRankDic(test_rates)
+
+	neighbors=calcNearestNeighbor(userid,test_dic,test_item_to_user)[:k]
+
+	recommend_dic={}
+	for neighbor in neighbors:
+		neighbor_user_id=neighbor[1]
+		books=test_dic[neighbor_user_id]
+		for book in books:
+			if book[0] not in recommend_dic:
+				recommend_dic[book[0]]=neighbor[0]
+			else:
+				recommend_dic[book[0]]+=neighbor[0]
+	recommend_list=[]
+	for key in recommend_dic:
+		recommend_list.append([recommend_dic[key],key])
+
+	recommend_list.sort(reverse=True)
+	user_books = [i[0] for i in test_dic[userid]]
+
+	return [i[1] for i in recommend_list],user_books,neighbors
+
+
+if __name__ == '__main__':
+	books = readFile('u.data')
+	rates = getRatingInformation(books)
+	recommend_list,user_movie,neighbors=recommendByUserFC("u.data",222)
+
+	print recommend_list[:5]
+