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
pylab.plot(x_values,
y_values3,
'bs',
linewidth=1,
linestyle="-",
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 = []
y_values2 = []
y_values3 = []
y_values4 = []
while top_k <= 10:
avg_precision, avg_recall, avg_f1_score, availability = recommend(A, recommends, unknow_poi_set, time_slice, top_k, order)
print "avg_recall(pmpt): ", avg_recall
avg_precision2, avg_recall2, avg_f1_score2, availability2 = recommend2(A2, recommends2, unknow_poi_set2, time_slice, top_k, order)
print "avg_recall(fmc): ", avg_recall2
avg_precision3, avg_recall3, avg_f1_score3, availability3 = recommend3(A3, recommends3, unknow_poi_set3, time_slice, top_k, order)
print "avg_recall(tf): ", avg_recall3
y_values1.append(avg_recall)
y_values2.append(avg_recall2)
y_values3.append(avg_recall3)
# y_values4.append(availability)
x_values.append(top_k)
top_k += 1