import numpy as np
SocialFile = u"Data/Social/Train.txt"
TestFile = u"Data/Social/Test.txt"
UserItemFile = u"Data/UserItem.csv"
Social_Data = np.loadtxt(SocialFile, delimiter=',')
Test_Data = np.loadtxt(TestFile, delimiter=',')
UserItem_Data = np.loadtxt(UserItemFile, delimiter=',', usecols=(1, 2))
print "Social_Data's is Shape :" + str(Social_Data.shape)
print "Test_Data's is Shape :" + str(Test_Data.shape)
print "UserItem_Data's is Shape :" + str(UserItem_Data.shape)
print '\n'
MatrixAdjacency_Social, MaxNode_Social = Initialize.Initialize_Social(
Social_Data)
MatrixAdjacency_Test = Initialize.Initialize_Test(Test_Data, MaxNode_Social)
MatrixAdjacency_UserItem = Initialize.Initialize_UserItem(UserItem_Data)
print 'MatrixAdjacency_Social'
print MatrixAdjacency_Social
print 'MatrixAdjacency_Test'
print MatrixAdjacency_Test
print 'MatrixAdjacency_UserItem'
print MatrixAdjacency_UserItem
T_MatrixAdjacency_UserItem = MatrixAdjacency_UserItem.T
array_Degree_User = sum(T_MatrixAdjacency_UserItem)
MatrixDegree_User = np.diag(array_Degree_User)
INV_MatrixDegree_User = np.linalg.inv(MatrixDegree_User)
array_Degree_Item = sum(MatrixAdjacency_UserItem)
# except:
# ZhiHu_DB.rollback()
# print 'error'+str(list[0][0])+str(list[0][1])
ZhiHu_DB.close()
import os
import MySQLdb
import Initialize
import numpy as np
import Evaluation_Indicators.AUC
import similarity_indicators.CommonNeighbor
SocialFile = u'Data/followees.txt'
MatrixAdjacency_Social, Maxnode = Initialize.Initialize_Social(
np.loadtxt(SocialFile, delimiter=','))
print MatrixAdjacency_Social.shape
NetFile = u'Data/Topic.txt'
MatrixAdjacency_Net = Initialize.Initialize_UserItem(
np.loadtxt(NetFile, delimiter=','))
print MatrixAdjacency_Net.shape
T_MatrixAdjacency_Net = MatrixAdjacency_Net.T
degree_user = [
sum(MatrixAdjacency_Net[i]) for i in range(len(MatrixAdjacency_Net))
]
degree_item = [
sum(T_MatrixAdjacency_Net[i]) for i in range(len(T_MatrixAdjacency_Net))
]
