y_values1 = []
y_values2 = []
y_values3 = []
# data, axis_users, axis_pois, check_data = init_data(region, filter_count, train_percent)
data, axis_users, axis_pois, check_data = init_data_by_user(
tuple([0, 3, 4, 5, 30]), filter_count, train_percent)
user_num = len(axis_users)
poi_num = len(axis_pois)
init_tensor = [[[
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)]
transition_tensor = inreducible_tensor(mtt(data, user_num, poi_num),
user_num, time_num, poi_num, alpha)
res1, iterator_values1 = tensor_three_mode_product(transition_tensor,
init_tensor)
order = 1
while order <= settings.ITERATOR_NUMBER:
x_values.append(order)
y_values1.append(iterator_values1[order])
order += 1
label = "" + str(((settings.TIME_SLICE * user_num * poi_num)**2))
pylab.plot(x_values,
y_values1,
'rs',
linewidth=1,
linestyle="-",
label=label)
while train_percent < 1:
data, axis_users, axis_pois, check_data = init_data(
region, filter_count, train_percent)
user_num = len(axis_users)
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
train_percent = 0.95
alpha = 0.95
x_values = []
y_values1 = []
y_values2 = []
y_values3 = []
# data, axis_users, axis_pois, check_data = init_data(region, filter_count, train_percent)
data, axis_users, axis_pois, check_data = init_data_by_user(tuple([0, 3, 4, 5, 30]), filter_count, train_percent)
user_num = len(axis_users)
poi_num = len(axis_pois)
init_tensor = [[[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)]
transition_tensor = inreducible_tensor(mtt(data, user_num, poi_num), user_num, time_num, poi_num, alpha)
res1, iterator_values1 = tensor_three_mode_product(transition_tensor, init_tensor)
order = 1
while order <= settings.ITERATOR_NUMBER:
x_values.append(order)
y_values1.append(iterator_values1[order])
order += 1
label = ""+str(((settings.TIME_SLICE*user_num*poi_num)**2))
pylab.plot(x_values, y_values1, 'rs', linewidth=1, linestyle="-", label=label)
data, axis_users, axis_pois, check_data = init_data_by_user(tuple([0, 3, 4]), filter_count, train_percent)
user_num = len(axis_users)
poi_num = len(axis_pois)
temp_tensor = [[[random.choice([0, 1, 1000]) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
temp_tensor = three_tensor_hadarmard(1/three_order_tensor_first_norm(temp_tensor), temp_tensor)
print "初始张量性质:\n"
print analysis_eigen_tensor(temp_tensor, "user")
print analysis_eigen_tensor(temp_tensor, "time")
print analysis_eigen_tensor(temp_tensor, "poi")
print "\n"
temp_tensor1 = temp_tensor[:]
temp_tensor2 = temp_tensor[:]
temp_tensor3 = temp_tensor[:]
temp_tensor4 = temp_tensor[:]
res1, iterator_values1 = tensor_three_mode_product(transition_tensor, temp_tensor1)
res2, iterator_values2 = tensor_three_mode_product(transition_tensor2, temp_tensor2)
# res3, iterator_values3 = shifted_tensor_three_mode_product(transition_tensor3, temp_tensor3, alpha_shift)
res4, iterator_values4 = shifted_tensor_three_mode_product(transition_tensor3, temp_tensor4, 0, shifted=False)
print analysis_eigen_tensor(res1, "user")
print analysis_eigen_tensor(res1, "time")
print analysis_eigen_tensor(res1, "poi")
print "\n"
print analysis_eigen_tensor(res2, "user")
print analysis_eigen_tensor(res2, "time")
print analysis_eigen_tensor(res2, "poi")
print "\n"
# print analysis_eigen_tensor(res3, "user")
for j in range(time_num)] for k in range(user_num)]
temp_tensor = three_tensor_hadarmard(
1 / three_order_tensor_first_norm(temp_tensor), temp_tensor)
print "初始张量性质:\n"
print analysis_eigen_tensor(temp_tensor, "user")
print analysis_eigen_tensor(temp_tensor, "time")
print analysis_eigen_tensor(temp_tensor, "poi")
print "\n"
temp_tensor1 = temp_tensor[:]
temp_tensor2 = temp_tensor[:]
temp_tensor3 = temp_tensor[:]
temp_tensor4 = temp_tensor[:]
res1, iterator_values1 = tensor_three_mode_product(transition_tensor,
temp_tensor1)
res2, iterator_values2 = tensor_three_mode_product(transition_tensor2,
temp_tensor2)
# res3, iterator_values3 = shifted_tensor_three_mode_product(transition_tensor3, temp_tensor3, alpha_shift)
res4, iterator_values4 = shifted_tensor_three_mode_product(
transition_tensor3, temp_tensor4, 0, shifted=False)
print analysis_eigen_tensor(res1, "user")
print analysis_eigen_tensor(res1, "time")
print analysis_eigen_tensor(res1, "poi")
print "\n"
print analysis_eigen_tensor(res2, "user")
print analysis_eigen_tensor(res2, "time")
print analysis_eigen_tensor(res2, "poi")
print "\n"
y_values1 = []
y_values2 = []
y_values3 = []
y_values4 = []
order = 1
while alpha >= 0.2:
data, axis_users, axis_pois, check_data = init_data(region, filter_count, train_percent)
user_num = len(axis_users)
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_tensor = [[[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)]
res, iterator_values = tensor_three_mode_product(transition_tensor, init_tensor)
values.append(iterator_values)
alpha -= 0.2
print values
# print values.keys()
value1 = values[0]
value2 = values[1]
value3 = values[2]
value4 = values[3]
for index in range(1, settings.ITERATOR_NUMBER+1):
x_values.append(index)
y_values1.append(math.log10(value1[index]))
y_values2.append(math.log10(value2[index]))
y_values3.append(math.log10(value3[index]))
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)
]
# random_all_sum_one
temp_tensor = [[[random.random() for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
init_tensor2 = three_tensor_hadarmard(1 / three_order_tensor_first_norm(temp_tensor), temp_tensor)
# user_slice_sum_one
init_tensor3 = [
[[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__values1 = tensor_three_mode_product(transition_tensor, init_tensor1)
print res1
print sparsity(res1)
x, y, z = numpy.mgrid[0:user_num, 0:time_num, 0:poi_num]
# val = numpy.random.random(z.shape)
print x.shape, y.shape, z.shape
# Plot and show in mayavi2
# pts = mlab.points3d(x, y, z, res1, scale_factor=0.4, transparent=False)
# mlab.show()
temp_tensor = [[[random.choice([0, 1, 2, 3, 4]) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
temp_tensor_all_ramdom = three_tensor_hadarmard(1/three_order_tensor_first_norm(temp_tensor), temp_tensor)
temp_tensor_user_equal = [[[1/(poi_num * time_num) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
temp_tensor = [[[random.choice([0, 1, 2, 3, 4]) for i in range(poi_num)] for j in range(time_num)] for k in range(user_num)]
temp_tensor_user_ramdom = user_slice_sum_one(temp_tensor)
print check_six_order_transition_tensor(transition_tensor)
# print "初始张量性质:\n"
# print analysis_eigen_tensor(temp_tensor, "user")
# print analysis_eigen_tensor(temp_tensor, "time")
# print analysis_eigen_tensor(temp_tensor, "poi")
# print "\n"
res1, iterator_values1 = tensor_three_mode_product(transition_tensor, temp_tensor_all_equal)
res2, iterator_values2 = tensor_three_mode_product(transition_tensor, temp_tensor_all_ramdom)
res3, iterator_values3 = tensor_three_mode_product(transition_tensor, temp_tensor_user_equal)
res4, iterator_values4 = tensor_three_mode_product(transition_tensor, temp_tensor_user_ramdom)
print analysis_eigen_tensor(res1, "user")
print analysis_eigen_tensor(res1, "time")
print analysis_eigen_tensor(res1, "poi")
print "\n"
print analysis_eigen_tensor(res2, "user")
print analysis_eigen_tensor(res2, "time")
print analysis_eigen_tensor(res2, "poi")
print "\n"
print analysis_eigen_tensor(res3, "user")
