def group_test_relative(group_start, length, Data, Label, model, record):
global handle_type
graph = []
record.write(f'the start line is {group_start}\n')
for j in range(length):
edge_set = [-1 for i in range(length)]
for t in range(length):
if j != t:
temd = handle_data.data_extend(Data[group_start + j],
Data[group_start + t])
temi = handle_data.data_extend(Data[group_start + t],
Data[group_start + j])
temd = np.array(temd).reshape((1, -1))
temi = np.array(temi).reshape((1, -1))
if model.predict(temd) > 0:
edge_set[t] = 1
record.write(f'{j+1:2d}\t{t+1:2d}\t 1\t')
else:
record.write(f'{j+1:2d}\t{t+1:2d}\t-1\t')
if Label[group_start + j] > Label[group_start + t]:
record.write('-1\n')
else:
record.write('1\n')
if model.predict(temi) > 0:
record.write(f'{t+1:2d}\t{j+1:2d}\t 1\t')
else:
record.write(f'{t+1:2d}\t{j+1:2d}\t-1\t')
if Label[group_start + t] > Label[group_start + j]:
record.write('-1\n')
record.write('---------------------------------------\n')
else:
record.write('1\n')
record.write('---------------------------------------\n')
if model.predict(temd) == model.predict(temi):
record.write('there is a conflict!!!!!!!!!!!!!!!!!!!!\n')
graph.append(edge_set)
test_cycle(graph)
def rank_the_group(group_data, reference, model, threshold):
tem = [reference.pop()]
for each in reference:
for item in range(len(tem)):
t = handle_data.data_extend(group_data[each - 1],
group_data[tem[item] - 1])
t = np.array(t).reshape((1, -1))
if model.predict(t) > threshold:
tem.insert(item, each)
break
else:
if item == len(tem) - 1:
tem.append(each)
break
return tem
def rank_the_group(group_data, reference, model, threshold, record):
low = []
high = []
if len(reference) <= 1:
return reference
else:
pivot = random.choice(reference)
# record.write(f"the pivot is {pivot:2d}\n")
reference.remove(pivot)
for i in reference:
t = handle_data.data_extend(group_data[pivot-1], group_data[i-1])
t = np.array(t).reshape((1,-1))
if model.predict(t) > threshold:
low.append(i)
else:
high.append(i)
# record_middle_result('low', low, record)
# record_middle_result('high', high, record)
low = rank_the_group(group_data, low, model, threshold, record)
high = rank_the_group(group_data, high, model, threshold, record)
high.append(pivot)
return high + low
def rank_the_group(group_data, length, reference, model, record):
low = []
high = []
global handle_type
if len(reference) <= 1:
return reference
else:
# pivot = reference.pop()
# print('the reference is ',reference)
# print('the length is ', length)
pivot = random.choice(reference)
# if len(group_data) == length:
# record.write(f"the pivot is {pivot:2d}\n")
record.write(f"the pivot is {pivot:2d}\n")
reference.remove(pivot)
# print("pivot is ",pivot)
for i in reference:
t = handle_data.data_extend(group_data[pivot - 1],
group_data[i - 1])
t = np.array(t).reshape((1, -1))
if model.predict(t) > 0:
low.append(i)
else:
high.append(i)
record.write('low is ')
for i in low:
record.write(f'{i:2d}\t')
record.write('\n')
record.write('high is ')
for i in high:
record.write(f'{i:2d}\t')
record.write('\n')
low = rank_the_group(group_data, len(low), low, model)
high = rank_the_group(group_data, len(high), high, model)
high.append(pivot)
# print('high+low :',high+low)
return high + low