#
# read training instances
#
# user \t item \t posIns1,posIns2,... \t negIns1,negIns2,...
#
with open(str_train_instance_path, 'r',
encoding='utf8') as file_train_instances:
for line in file_train_instances:
list_training_instance.append(line)
#
# create matA
#
if len(tensor_x_uu) != 0:
mat_a_uu = LA.linear_combination(val_user_num, val_user_num, tensor_x_uu,
vec_theta_uu)
mat_a_uu.data[:] = 2 / (1 + exp(-1 * mat_a_uu.data)) - 1
else:
mat_a_uu = csr_matrix((val_user_num, val_user_num), dtype=float)
if len(tensor_x_ui) != 0:
mat_a_ui = LA.linear_combination(val_user_num, val_item_num, tensor_x_ui,
vec_theta_ui)
mat_a_ui.data[:] = 2 / (1 + exp(-1 * mat_a_ui.data)) - 1
else:
mat_a_ui = csr_matrix((val_user_num, val_item_num), dtype=float)
if len(tensor_x_ut) != 0:
mat_a_ut = LA.linear_combination(val_user_num, val_tag_num, tensor_x_ut,
vec_theta_ut)
mat_a_ut.data[:] = 2 / (1 + exp(-1 * mat_a_ut.data)) - 1
shape=(valUserNum, valXiNum_I))
vecPI_Xi_U = csr_matrix(np.ones((valItemNum, valXiNum_U)) / valItemNum,
shape=(valItemNum, valXiNum_U))
vecPI_Xi_I = csr_matrix(np.ones((valItemNum, valXiNum_I)) / valItemNum,
shape=(valItemNum, valXiNum_I))
vecPT_Xi_U = csr_matrix(np.ones((valTagNum, valXiNum_U)) / valTagNum,
shape=(valTagNum, valXiNum_U))
vecPT_Xi_I = csr_matrix(np.ones((valTagNum, valXiNum_I)) / valTagNum,
shape=(valTagNum, valXiNum_I))
#
# create matA
#
if len(matX_UU) != 0:
matA_UU = LA.linear_combination(valUserNum, valUserNum, matX_UU,
vecTheta_UU)
matA_UU.data[:] = 1 / (1 + exp(-1 * matA_UU.data))
else:
matA_UU = csr_matrix((valUserNum, valUserNum), dtype=float)
if len(matX_UI) != 0:
matA_UI = LA.linear_combination(valUserNum, valItemNum, matX_UI,
vecTheta_UI)
matA_UI.data[:] = 1 / (1 + exp(-1 * matA_UI.data))
else:
matA_UI = csr_matrix((valUserNum, valItemNum), dtype=float)
if len(matX_UT) != 0:
matA_UT = LA.linear_combination(valUserNum, valTagNum, matX_UT,
vecTheta_UT)
matA_UT.data[:] = 1 / (1 + exp(-1 * matA_UT.data))
valTagNum = shape(tensor_x_ut[0])[1]
#
# read training instances
#
# user \t item \t posIns1,posIns2,... \t negIns1,negIns2,...
#
f_TestInstances = open(str_test_instance_path, 'r')
for line in f_TestInstances:
list_test_instance.append(line)
#
# create matA
#
if len(tensor_x_uu) != 0:
matA_UU = LA.linear_combination(valUserNum, valUserNum, tensor_x_uu,
vec_theta_uu)
matA_UU.data[:] = 1 / (1 + exp(-1 * matA_UU.data))
else:
matA_UU = csr_matrix((valUserNum, valUserNum), dtype=float)
if len(tensor_x_ui) != 0:
matA_UI = LA.linear_combination(valUserNum, valItemNum, tensor_x_ui,
vec_theta_ui)
matA_UI.data[:] = 1 / (1 + exp(-1 * matA_UI.data))
else:
matA_UI = csr_matrix((valUserNum, valItemNum), dtype=float)
if len(tensor_x_ut) != 0:
matA_UT = LA.linear_combination(valUserNum, valTagNum, tensor_x_ut,
vec_theta_ut)
matA_UT.data[:] = 1 / (1 + exp(-1 * matA_UT.data))
