Result_of_acc_std = np.zeros([len(datasets) * 2, len(classifiers)])
for i in range(len(datasets)):
print(datasets[i])
new_path = os.path.join('.\data', datasets[i])
Data_Origi, DataLabel, n_samples, n_attr, n_class = PF.Load_Data(new_path)
#归一化处理
scaler = MinMaxScaler()
scaler.fit(Data_Origi)
Data_Origi = scaler.transform(Data_Origi)
for l in range(2):
if l == 0:
#度量学习
lmnn = LMNN(k=5, learn_rate=1e-6)
lmnn.fit(Data_Origi, DataLabel)
Data_trans = lmnn.transform(Data_Origi)
else:
Data_trans = Data_Origi
#同质化融合
Dis_Matrix = PF.Calcu_Dis(Data_trans)
CompareMatrix = PF.CompareNoiseLabel(Dis_Matrix, DataLabel)
Cluster_Checked = PF.Affinity_propagatio_Modify(CompareMatrix)
lap_ratio = PF.Count(Cluster_Checked, set_vlaue, n_samples)
Result_of_Upper[i, l] = 1 - lap_ratio
for j in range(len(classifiers)):
print(classifiers[j])
clf = classifiers[j]
scores = cross_val_score(clf, Data_trans, DataLabel, cv=cv)
Result_of_acc_ave[2 * i + l, j] = scores.mean()
Result_of_acc_std[2 * i + l, j] = scores.std()
# -*- coding: utf-8 -*- # __author: CQQ # @file: R_value.py # @time: 2020 01 12 # @email: [email protected] import Function as PF import numpy as np import os datasets = ['L1.txt', 'L2.txt', 'L3.txt'] RV = np.zeros([len(datasets), 1]) for d in range(len(datasets)): print(datasets[d]) new_path = os.path.join('.\data', datasets[d]) Data_Origi, DataLabel, n_samples, n_attr, n_class = PF.Load_Data(new_path) Dis_Matrix = PF.Calcu_Dis(Data_Origi) Label_Index_Matrix = np.argsort(Dis_Matrix) Record_Matrix = PF.RecordIndexOfClass(DataLabel) count = 0 for i in range(len(Record_Matrix)): Set_in = Record_Matrix[i] Set_out = set(range(n_samples)) - set(Set_in) for j in Set_in: if len(set(Label_Index_Matrix[j, :8]) & Set_out) - 3 > 0: count = count + 1 R_value = 1 - count * 1.0 / n_samples RV[d] = R_value
