# Ordering similarity
def calc_ordering_sim(ordering1, ordering2, matrix):
return similarity(ordering1, ordering2, matrix)
# Cuisine similarity
def calc_cuisine_sim(cuisine1, cuisine2, matrix):
return similarity(cuisine1, cuisine2, matrix)
if __name__ == '__main__':
# 直接執行r2R.py的時候，輸出餐廳相似矩陣的csv
from db import DBConn
conn = DBConn()
conn.open()
restaurants_num = conn.getRestaurantsNum()
order_sim＿matrix = conn.getOrderingSimMatrix()
cuisine_sim_matrix = conn.getCuisineSimMatrix()
num = len(restaurants_num)
similarities = []
for i, restaurant1 in enumerate(restaurants_num):
for j in range(i, num):
restaurant2 = restaurants_num[j]
sim = calc_r2r(restaurant1, restaurant2, conn.getRestaurantDistance(restaurant1['rid'], restaurant2['rid']),
order_sim＿matrix, cuisine_sim_matrix) if restaurant1['rid'] != restaurant2['rid'] else 1
similarities.append((restaurant1['rid'], restaurant2['rid'], sim))
conn.close()
"""
def calc_ordering_sim(u1, u2):
return similarity(u1, u2)
def calc_cuisine_sim(u1, u2):
return similarity(u1, u2)
if __name__ == '__main__':
from db import DBConn
conn = DBConn()
conn.open() # output similarity
users_info = conn.getUsersInfo()
num = len(users_info)
similarities = []
u2u_weights = {
'price': 0.25,
'ordering': 0.25,
'cuisine': 0.25,
'tag': 0.25
}
for i, user1 in enumerate(users_info):
weights = conn.getWeightWithID(user1['uid'])
for j in range(i, num):
user2 = users_info[j]
sim = calc_u2u(user1, user2, u2u_weights)
