def city_computing():
users = range(182)
axis_prefs, datas = init_data(users)
dimen = len(axis_prefs)
# print dimen
# print axis_prefs
# print datas
print datas.has_key(50)
for key in datas:
# print key
data = datas[key]
if len(data) == 1:
continue
tensor = trans(data, dimen, 2)
# print tensor
para = param(tensor)
T = pykov.Chain(para)
balance = T.steady()
# print balance
# print balance.sort(False)
# print balance.sort(True)
res = vec(balance.sort(True), dimen)
print 'user'+str(key)+": "+str(res)
# #print "item:",item
# parameter[item[0]] = item[1]
# # print parameter
preference = []
for i in range(pref_length):
# print i
if parameter.has_key(i):
preference.append(parameter[i] * pref_length / poi_length)
else:
preference.append(0.0)
return preference
if __name__ == '__main__':
axis_prefs, datas = init_poi_type_data(tuple(range(0, 182)))
print "axis_prefs: ", axis_prefs
for key in axis_prefs.keys():
print key, " ", axis_prefs[key]
print "datas: ", datas
for key in datas.keys():
data = datas[key]
# print "data_length: ", len(data)
if len(data) > 1:
tensor = trans(data, len(axis_prefs), 2)
result = param(tensor)
T = pykov.Chain(result)
res = T.steady()
print "user" + str(key) + ": ", vec(res, len(axis_prefs))
from util.tensor_old import hosvd2
from util.util import sparse
users = (1, )
train = 0.8
axis_poi, data_map, predicts, recommends = init_data(users, 0.8)
print "data_map: ", data_map
print "predicts: ", predicts
print "recommends: ", recommends
dimension = len(axis_poi)
for key in data_map:
data = data_map[key]
# print data
# tensor为列表
tensor = trans(data, dimension, 3)
# print tensor
# afunc(tensor)
# 第二步：HOSVD，重构tensor
# threshold = 0.8
# 将列表转化为高维数组
tensor = np.array(tensor)
print "tensor:"
print tensor
# sparse(tensor)
# #print "item:",item
# parameter[item[0]] = item[1]
# # print parameter
preference = []
for i in range(pref_length):
# print i
if parameter.has_key(i):
preference.append(parameter[i] * pref_length / poi_length)
else:
preference.append(0.0)
return preference
if __name__ == '__main__':
axis_prefs, datas = init_poi_type_data(tuple(range(0, 182)))
print "axis_prefs: ", axis_prefs
for key in axis_prefs.keys():
print key, " ", axis_prefs[key]
print "datas: ", datas
for key in datas.keys():
data = datas[key]
# print "data_length: ", len(data)
if len(data) > 1:
tensor = trans(data, len(axis_prefs), 2)
result = param(tensor)
T = pykov.Chain(result)
res = T.steady()
print "user"+str(key)+": ", vec(res, len(axis_prefs))
from util.tensor_old import hosvd2
from util.util import sparse
users = (1,)
train = 0.8
axis_poi, data_map, predicts, recommends = init_data(users, 0.8)
print "data_map: ", data_map
print "predicts: ", predicts
print "recommends: ", recommends
dimension = len(axis_poi)
for key in data_map:
data = data_map[key]
# print data
# tensor为列表
tensor = trans(data, dimension, 3)
# print tensor
# afunc(tensor)
# 第二步：HOSVD，重构tensor
# threshold = 0.8
# 将列表转化为高维数组
tensor = np.array(tensor)
print "tensor:"
print tensor