def test2():
# A = numpy.array([[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]])
# A = numpy.arange(12).reshape([4,3]) + 1
A = numpy.arange(1000).reshape([10, 10, 10])+1
(ans1, ans2) = dta(tensor.tensor(A), [2, 2, 2])
print ans1
for i in range(0, len(ans2)):
print "{0} th array\n {1}".format(i, ans2[i])
print ans1.totensor()
def test2():
# A = numpy.array([[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]])
# A = numpy.arange(12).reshape([4,3]) + 1
A = numpy.arange(1000).reshape([10, 10, 10]) + 1
(ans1, ans2) = dta(tensor.tensor(A), [2, 2, 2])
print ans1
for i in range(0, len(ans2)):
print "{0} th array\n {1}".format(i, ans2[i])
print ans1.totensor()
def sparse(data):
total = 1
# print data.shape[0]
shape = data.shape
for i in range(len(shape)):
total *= shape[i]
# print total
# print type(data), data
ts = tensor(data, data.shape)
# print 'a'
ts2 = ts.tosptensor()
print "非零元素个数: ", ts2.nnz()
print "稀疏度: ", ts2.nnz()/total
def test1():
A = ttensor.ttensor(tensor.tenrands([2, 3, 4]), [numpy.random.random([10, 2]), numpy.random.random([30, 3]), numpy.random.random([40, 4])]).totensor()
[a, b] = dta(A, [1, 2, 3])
print a
print b
Core = numpy.arange(24).reshape([2, 3, 4])
# Core = numpy.array([[1,3,5],[2,4,6]] , [[7,9,11],[8,10,12]])
u1 = numpy.array([[1, 2], [3, 4]])
u2 = numpy.array([[0, 1, 0], [1, 0, 1], [1, 1, 1]])
u3 = numpy.array([[1, 1, 1, 1], [1, 2, 3, 4], [1, 1, 1, 1]])
tt = ttensor.ttensor(tensor.tensor(Core), [u1, u2, u3])
print tt
[a, b] = dta(tt.totensor(), [1, 2, 3])
print a
print a.totensor()
print b
def sparse(data):
total = 1
# print data.shape[0]
shape = data.shape
for i in range(len(shape)):
total *= shape[i]
# print total
# print type(data), data
ts = tensor(data, data.shape)
# print 'a'
ts2 = ts.tosptensor()
print "非零元素个数: ", ts2.nnz()
print "稀疏度: ", ts2.nnz() / total
def test1():
A = ttensor.ttensor(tensor.tenrands([2, 3, 4]), [
numpy.random.random([10, 2]),
numpy.random.random([30, 3]),
numpy.random.random([40, 4])
]).totensor()
[a, b] = dta(A, [1, 2, 3])
print a
print b
Core = numpy.arange(24).reshape([2, 3, 4])
# Core = numpy.array([[1,3,5],[2,4,6]] , [[7,9,11],[8,10,12]])
u1 = numpy.array([[1, 2], [3, 4]])
u2 = numpy.array([[0, 1, 0], [1, 0, 1], [1, 1, 1]])
u3 = numpy.array([[1, 1, 1, 1], [1, 2, 3, 4], [1, 1, 1, 1]])
tt = ttensor.ttensor(tensor.tensor(Core), [u1, u2, u3])
print tt
[a, b] = dta(tt.totensor(), [1, 2, 3])
print a
print a.totensor()
print b
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())
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)
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())
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)
user_num = len(axis_users)
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)
