def mtt(data, user_num, poi_num, zero_adjustment=True):
print "共有" + str(user_num) + "个用户"
for key in data.keys():
print "用户" + str(key) + "序列为" + str(data[key])
correlation_matrix = get_correlation_matrix(data, user_num)
nor_cor_matrix = normalize(correlation_matrix)
print "归一化相关系数矩阵: ", nor_cor_matrix
transition_tensor = build_six_order_transition_tensor(data, poi_num, nor_cor_matrix, zero_adjustment)
print "转移张量非零元素占比:", sparsity(transition_tensor)
print "转移张量是否满足随机性:", check_six_order_transition_tensor(transition_tensor)
return transition_tensor
def mtt(data, user_num, poi_num, zero_adjustment=True):
print "共有" + str(user_num) + "个用户"
for key in data.keys():
print "用户" + str(key) + "序列为" + str(data[key])
correlation_matrix = get_correlation_matrix(data, user_num)
nor_cor_matrix = normalize(correlation_matrix)
print "归一化相关系数矩阵: ", nor_cor_matrix
transition_tensor = build_six_order_transition_tensor(
data, poi_num, nor_cor_matrix, zero_adjustment)
print "转移张量非零元素占比:", sparsity(transition_tensor)
print "转移张量是否满足随机性:", check_six_order_transition_tensor(transition_tensor)
return transition_tensor
def dta(new_tensor, rank, variance_matrix_list=None, alpha=None):
"""Dynamic Tensor Analysis"""
# number of order of the input tensor.
order = new_tensor.ndims()
# If the co-variacne matrices are not given,
# initialize all of them to be 0
if variance_matrix_list is None:
variance_matrix_list = []
dv = new_tensor.shape
for i in range(0, order):
variance_matrix_list.extend(
[sparse.coo_matrix(([], ([], [])), [dv[i], dv[i]])])
# If the forgetting factor is not given, it is 1.
if alpha is None:
alpha = 1
u = []
new_variance_matrix_list = []
for i in range(0, order):
if new_tensor.__class__ == tensor.tensor:
new_tensor_matricize = tenmat.tenmat(new_tensor, [i]).tondarray()
elif new_tensor.__class__ == sptensor.sptensor:
new_tensor_matricize = sptenmat.sptenmat(new_tensor,
[i]).tosparsemat()
elif new_tensor.__class__ == ttensor.ttensor:
raise TypeError("It is not supported yet.")
return
else:
raise TypeError(
"1st argument must be tensor, sptensor, or ttensor")
return
new_variance_matrix_list.extend([
numpy.array(alpha * variance_matrix_list[i] + numpy.dot(
new_tensor_matricize, new_tensor_matricize.transpose()))
])
# print "new,", new_variance_matrix_list
(eigenvalue, eigenmatrix) = eigwrapper(new_variance_matrix_list[i],
rank[i])
u.extend([numpy.array(eigenmatrix)])
# print new_tensor
core = new_tensor.ttm(u, None, 't')
reconstruct_tensor = ttensor.ttensor(core, u)
print "core:", sparsity(core.tondarray().tolist())
return reconstruct_tensor, new_variance_matrix_list
def dta(new_tensor, rank, variance_matrix_list=None, alpha=None):
"""Dynamic Tensor Analysis"""
# number of order of the input tensor.
order = new_tensor.ndims()
# If the co-variacne matrices are not given,
# initialize all of them to be 0
if variance_matrix_list is None:
variance_matrix_list = []
dv = new_tensor.shape
for i in range(0, order):
variance_matrix_list.extend([sparse.coo_matrix(([], ([], [])), [dv[i], dv[i]])])
# If the forgetting factor is not given, it is 1.
if alpha is None:
alpha = 1
u = []
new_variance_matrix_list = []
for i in range(0, order):
if new_tensor.__class__ == tensor.tensor:
new_tensor_matricize = tenmat.tenmat(new_tensor, [i]).tondarray()
elif new_tensor.__class__ == sptensor.sptensor:
new_tensor_matricize = sptenmat.sptenmat(new_tensor, [i]).tosparsemat()
elif new_tensor.__class__ == ttensor.ttensor:
raise TypeError("It is not supported yet.")
return
else:
raise TypeError("1st argument must be tensor, sptensor, or ttensor")
return
new_variance_matrix_list.extend([numpy.array(alpha*variance_matrix_list[i] + numpy.dot(new_tensor_matricize, new_tensor_matricize.transpose()))])
# print "new,", new_variance_matrix_list
(eigenvalue, eigenmatrix) = eigwrapper(new_variance_matrix_list[i], rank[i])
u.extend([numpy.array(eigenmatrix)])
# print new_tensor
core = new_tensor.ttm(u, None, 't')
reconstruct_tensor = ttensor.ttensor(core, u)
print "core:", sparsity(core.tondarray().tolist())
return reconstruct_tensor, new_variance_matrix_list
# 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())
res2 = reconstruct_tensor.totensor().tondarray().tolist()
print res2
nor_res = dta_normalize_tensor(res2, user_num, time_num, poi_num)
print "最终张量:", sparsity(res2)
print nor_res
# check_tensor = get_check_tensor(check_data, user_num, time_num, poi_num)
# print check_tensor
residual2 = delta_tensor_norm(nor_res, check_tensor)
statistic_res = get_check_tensor(data, user_num, time_num, poi_num)
residual3 = delta_tensor_norm(statistic_res, check_tensor)
x_values.append(train_percent)
y_values1.append(residual1)
y_values2.append(residual2)
y_values3.append(residual3)
train_percent += 0.2
# 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())
res2 = reconstruct_tensor.totensor().tondarray().tolist()
print res2
nor_res = dta_normalize_tensor(res2, user_num, time_num, poi_num)
print "最终张量:", sparsity(res2)
print nor_res
# check_tensor = get_check_tensor(check_data, user_num, time_num, poi_num)
# print check_tensor
residual2 = delta_tensor_norm(nor_res, check_tensor)
statistic_res = get_check_tensor(data, user_num, time_num, poi_num)
residual3 = delta_tensor_norm(statistic_res, check_tensor)
x_values.append(train_percent)
y_values1.append(residual1)
y_values2.append(residual2)
y_values3.append(residual3)
train_percent += 0.2
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()
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)
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()
