temp_data, time_slice, train, cluster_radius = init_data(
time_slice, train, region, cluster_radius, filter_count)
axis_pois, axis_users, train_structure_data, poi_adjacent_list, recommends, unknow_poi_set = preprocess(
temp_data, time_slice, train, cluster_radius, order)
print "train_structure_data: ", train_structure_data
print "poi_adjacent_list: ", poi_adjacent_list
print "recommends: ", recommends
print "unknow_poi_set: ", unknow_poi_set
tensor = trans(train_structure_data, poi_adjacent_list, order,
len(axis_pois), len(axis_users), time_slice)
# print "transition tensor: ", tensor
U, S, D = HOSVD(numpy.array(tensor), 0.7)
A = reconstruct(S, U)
print "reconstruct tensor: ", A
print frobenius_norm(tensor - A)
avg_precision, avg_recall, avg_f1_score, availability = recommend(
A, recommends, unknow_poi_set, time_slice, top_k, order)
print "avg_precision, avg_recall, avg_f1_score, availability: ", avg_precision, avg_recall, avg_f1_score, availability
# y_values1.append(sparsity(tensor))
# y_values2.append(sparsity(A))
y_values3.append(avg_precision)
y_values4.append(avg_recall)
y_values5.append(avg_f1_score)
x_values.append(cluster_radius)
cluster_radius += 0.05
filter_count = 30
order = 2
top_k = 1
length, height, top_left = get_length_height(region)
print "区域（长度，宽度）：", length, height
# pmpt
temp_data, time_slice, train, cluster_radius = init_data(
time_slice, train, region, cluster_radius, filter_count)
axis_pois, axis_users, train_structure_data, poi_adjacent_list, recommends, unknow_poi_set = preprocess(
temp_data, time_slice, train, cluster_radius, order)
tensor = trans(train_structure_data, poi_adjacent_list, order,
len(axis_pois), len(axis_users), time_slice)
U, S, D = HOSVD(numpy.array(tensor), 0.7)
A = reconstruct(S, U)
# factorized markov chain
temp_data2, time_slice, train2 = init_data2(region, train, time_slice,
filter_count)
axis_pois2, axis_users2, train_structure_data2, recommends2, unknow_poi_set2 = preprocess2(
temp_data2, time_slice, train2, order)
A2 = trans2(train_structure_data2, order, len(axis_pois2), time_slice, 0.7)
# tensor factorization
temp_data3, time_slice, train3 = init_data3(time_slice, train, region,
filter_count)
axis_pois3, axis_users3, train_structure_data3, recommends3, unknow_poi_set3 = preprocess3(
temp_data3, time_slice, train3, order)
tensor3 = trans3(train_structure_data3, order, len(axis_pois3),
len(axis_users3), time_slice)
# region = (39.88, 40.05, 116.05, 116.26)
region = (39.88, 40.05, 116.05, 116.26)
cluster_radius = 1
filter_count = 30
order = 2
top_k = 1
length, height, top_left = get_length_height(region)
print "区域（长度，宽度）：", length, height
# pmpt
temp_data, time_slice, train, cluster_radius = init_data(time_slice, train, region, cluster_radius, filter_count)
axis_pois, axis_users, train_structure_data, poi_adjacent_list, recommends, unknow_poi_set = preprocess(temp_data, time_slice, train, cluster_radius, order)
tensor = trans(train_structure_data, poi_adjacent_list, order, len(axis_pois), len(axis_users), time_slice)
U, S, D = HOSVD(numpy.array(tensor), 0.7)
A = reconstruct(S, U)
# factorized markov chain
temp_data2, time_slice, train2 = init_data2(region, train, time_slice, filter_count)
axis_pois2, axis_users2, train_structure_data2, recommends2, unknow_poi_set2 = preprocess2(temp_data2, time_slice, train2, order)
A2 = trans2(train_structure_data2, order, len(axis_pois2), time_slice, 0.7)
# tensor factorization
temp_data3, time_slice, train3 = init_data3(time_slice, train, region, filter_count)
axis_pois3, axis_users3, train_structure_data3, recommends3, unknow_poi_set3 = preprocess3(temp_data3, time_slice, train3, order)
tensor3 = trans3(train_structure_data3, order, len(axis_pois3), len(axis_users3), time_slice)
U3, S3, D3 = HOSVD(numpy.array(tensor3), 0.7)
A3 = reconstruct(S3, U3)
x_values = []
y_values1 = []
# print frobenius_norm(tensor-A)
# time_slice_range = (1, 2, 4, 6, 12)
# index = 0
x_values = []
y_values1 = []
y_values2 = []
y_values3 = []
y_values4 = []
while step <= 4:
res_tensor = n_step_tensor(tensor, step)
U, S, D = HOSVD(numpy.array(res_tensor), 0.7)
A = reconstruct(S, U)
avg_precision, avg_recall, avg_f1_score, availability = recommend(A, recommends[step], unknow_poi_set, time_slice, top_k, order)
print "avg_precision: ", avg_precision
print "avg_recall: ", avg_recall
print "avg_f1_score: ", avg_f1_score
print "availability: ", availability
y_values1.append(avg_precision)
y_values2.append(avg_recall)
y_values3.append(avg_f1_score)
y_values4.append(availability)
x_values.append(step)
step += 1
pylab.plot(x_values, y_values1, 'rs', linewidth=1, linestyle="-", label=u"准确率")
