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
pylab.plot(x_values,
y_values3,
tensor = trans(train_structure_data, poi_adjacent_list, order, len(axis_pois), len(axis_users), time_slice)
# print "transition tensor: ", tensor
# print "tensor: ", tensor, numpy.array(tensor).shape
# print "tensor: ", tensor[0][0]
# print "sparsity: ", sparsity_tensor(tensor[0][0])
# print "tensor.shape: ", numpy.array(tensor[0][1]).shape
#
#
# print cp(numpy.array(tensor[0][0]), n_components=1)
U, S, D = HOSVD(numpy.array(tensor), 0.7)
A = reconstruct(S, U)
print "reconstruct tensor: ", A
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 top_k <= 10:
avg_precision, avg_recall, avg_f1_score, availability = recommend(A, recommends, 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
