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)
MatrixDegree_Item = np.diag(array_Degree_Item)
