transition_tensor = inreducible_tensor(mtt(data, user_num,
poi_num), user_num,
time_num, poi_num, alpha)
# equal_all_sum_one: equal
init_tensor1 = [[[
1 / (poi_num * time_num * user_num) for i in range(poi_num)
] for j in range(time_num)] for k in range(user_num)]
res1, iterator_values = tensor_three_mode_product(
transition_tensor, init_tensor1)
# print res
check_tensor = get_check_tensor(check_data, user_num, time_num,
poi_num)
residual1 = delta_tensor_norm(res1, check_tensor)
tensor_stream_res = tensor_stream(data, user_num, poi_num, 10)
print "张量流长度:" + str(len(tensor_stream_res))
# def dta(new_tensor, rank, variance_matrix_list=None, alpha=None):
# return reconstruct_tensor, new_variance_matrix_list
reconstruct_tensor = None
variance_matrix_list = None
for tensor_data in tensor_stream_res[:-2]:
# print "data:", sparsity(tensor_data)
reconstruct_tensor, variance_matrix_list = dta(
tensor.tensor(numpy.array(tensor_data)), (4, 2, 200),
variance_matrix_list)
# print sparsity(reconstruct_tensor.totensor().tondarray().tolist())
1 / (poi_num * time_num * user_num) for i in range(poi_num)
] for j in range(time_num)] for k in range(user_num)]
# user_slice_sum_one: equal
init_tensor2 = [[[1 / (poi_num * time_num) for i in range(poi_num)]
for j in range(time_num)] for k in range(user_num)]
res1, iterator_values1 = tensor_three_mode_product(
transition_tensor, init_tensor1)
res2, iterator_values2 = tensor_three_mode_product(
transition_tensor, init_tensor2)
# print res
check_tensor = get_check_tensor(check_data, user_num, time_num,
poi_num)
residual1 = delta_tensor_norm(res1, check_tensor)
residual2 = delta_tensor_norm(res2, check_tensor)
x_values.append(train_percent)
y_values1.append(residual1)
y_values2.append(residual2)
train_percent += 0.1
pylab.plot(x_values,
y_values1,
'rs',
linewidth=1,
linestyle="-",
label=u"整体")
pylab.plot(x_values,
y_values2,
time_num = settings.TIME_SLICE
poi_num = len(axis_pois)
transition_tensor = inreducible_tensor(mtt(data, user_num, poi_num), user_num, time_num, poi_num, alpha)
# equal_all_sum_one: equal
init_tensor1 = [
[[1 / (poi_num * time_num * user_num) for i in range(poi_num)] for j in range(time_num)]
for k in range(user_num)
]
res1, iterator_values = tensor_three_mode_product(transition_tensor, init_tensor1)
# print res
check_tensor = get_check_tensor(check_data, user_num, time_num, poi_num)
residual1 = delta_tensor_norm(res1, check_tensor)
tensor_stream_res = tensor_stream(data, user_num, poi_num, 10)
print "张量流长度:" + str(len(tensor_stream_res))
# def dta(new_tensor, rank, variance_matrix_list=None, alpha=None):
# return reconstruct_tensor, new_variance_matrix_list
reconstruct_tensor = None
variance_matrix_list = None
for tensor_data in tensor_stream_res[:-2]:
# print "data:", sparsity(tensor_data)
reconstruct_tensor, variance_matrix_list = dta(
tensor.tensor(numpy.array(tensor_data)), (4, 2, 200), variance_matrix_list
)
# print sparsity(reconstruct_tensor.totensor().tondarray().tolist())
poi_num = len(axis_pois)
# print data
tensor_stream_res = tensor_stream(data, user_num, poi_num, 2)
print len(tensor_stream_res)
# def dta(new_tensor, rank, variance_matrix_list=None, alpha=None):
# return reconstruct_tensor, new_variance_matrix_list
reconstruct_tensor = None
variance_matrix_list = None
for tensor_data in tensor_stream_res:
# print "data:", sparsity(tensor_data)
reconstruct_tensor, variance_matrix_list = dta(tensor.tensor(numpy.array(tensor_data)), (4, 2, 10), variance_matrix_list)
# print sparsity(reconstruct_tensor.totensor().tondarray().tolist())
res = reconstruct_tensor.totensor().tondarray().tolist()
# print res
print "最终张量:", sparsity(res)
nor_res = dta_normalize_tensor(res, user_num, time_num, poi_num)
check_tensor = get_check_tensor(check_data, user_num, time_num, poi_num)
# print check_tensor
residual = delta_tensor_norm(nor_res, check_tensor)
print residual
print nor_res
print check_data
print sparsity(check_tensor)
print validate_eigen_tensor(nor_res), validate_eigen_tensor(check_tensor)
# print data
tensor_stream_res = tensor_stream(data, user_num, poi_num, 2)
print len(tensor_stream_res)
# def dta(new_tensor, rank, variance_matrix_list=None, alpha=None):
# return reconstruct_tensor, new_variance_matrix_list
reconstruct_tensor = None
variance_matrix_list = None
for tensor_data in tensor_stream_res:
# print "data:", sparsity(tensor_data)
reconstruct_tensor, variance_matrix_list = dta(
tensor.tensor(numpy.array(tensor_data)), (4, 2, 10),
variance_matrix_list)
# print sparsity(reconstruct_tensor.totensor().tondarray().tolist())
res = reconstruct_tensor.totensor().tondarray().tolist()
# print res
print "最终张量:", sparsity(res)
nor_res = dta_normalize_tensor(res, user_num, time_num, poi_num)
check_tensor = get_check_tensor(check_data, user_num, time_num, poi_num)
# print check_tensor
residual = delta_tensor_norm(nor_res, check_tensor)
print residual
print nor_res
print check_data
print sparsity(check_tensor)
print validate_eigen_tensor(nor_res), validate_eigen_tensor(check_tensor)
user_num = len(axis_users)
poi_num = len(axis_pois)
transition_tensor = mtt(data, user_num, poi_num)
# equal_all_sum_one: equal
init_tensor1 = [[[1/(poi_num * time_num * user_num) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
# user_slice_sum_one: equal
init_tensor2 = [[[1/(poi_num * time_num) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
res1, iterator_values1 = tensor_three_mode_product(transition_tensor, init_tensor1)
res2, iterator_values2 = tensor_three_mode_product(transition_tensor, init_tensor2)
# print res
check_tensor = get_check_tensor(check_data, user_num, time_num, poi_num)
residual1 = delta_tensor_norm(res1, check_tensor)
residual2 = delta_tensor_norm(res2, check_tensor)
x_values.append(train_percent)
y_values1.append(residual1)
y_values2.append(residual2)
train_percent += 0.1
pylab.plot(x_values, y_values1, 'rs', linewidth=1, linestyle="-", label=u"整体")
pylab.plot(x_values, y_values2, 'bs', linewidth=1, linestyle="-", label=u"用户")
pylab.xlabel(u"训练集比重")
pylab.ylabel(u"准确率")
pylab.title(u"训练集比重与准确率的关系")
pylab.legend(loc='center right')
pylab.show()
