# -*- coding: utf-8 -*-
"""
Created on Mon Sep 17 15:09:38 2018
@author: HAN_RUIZHI yb77447@umac.mo OR 501248792@qq.com
This code is the first version of BLS Python.
If you have any questions about the code or find any bugs
or errors during use, please feel free to contact me.
If you have any questions about the original paper,
please contact the authors of related paper.
"""
import numpy as np
from sklearn import preprocessing
from numpy import random
from scipy import linalg as LA
import time
#from scipy import stats
#import matplotlib.pyplot as plt
'''
#输出训练/测试准确率
'''
def show_accuracy(predictLabel,Label):
count = 0
label_1 = np.zeros(Label.shape[0])
predlabel = []
label_1 = Label.argmax(axis = 1)
predlabel = predictLabel.argmax(axis = 1)
for j in list(range(Label.shape[0])):
if label_1[j] == predlabel[j]:
count += 1
return (round(count/len(Label),5))
'''
激活函数
'''
def tansig(x):
return (2/(1+np.exp(-2*x)))-1
def sigmoid(data):
return 1.0/(1+np.exp(-data))
def linear(data):
return data
def tanh(data):
return (np.exp(data)-np.exp(-data))/(np.exp(data)+np.exp(-data))
def relu(data):
return np.maximum(data,0)
def pinv(A,reg):
return np.mat(reg*np.eye(A.shape[1])+A.T.dot(A)).I.dot(A.T)
'''
参数压缩
'''
def shrinkage(a,b):
z = np.maximum(a - b, 0) - np.maximum( -a - b, 0)
return z
'''
参数稀疏化
'''
def sparse_bls(A,b):
lam = 0.001
itrs = 50
AA = A.T.dot(A)
m = A.shape[1]
n = b.shape[1]
x1 = np.zeros([m,n])
wk = x1
ok = x1
uk = x1
L1 = np.mat(AA + np.eye(m)).I
L2 = (L1.dot(A.T)).dot(b)
for i in range(itrs):
ck = L2 + np.dot(L1,(ok - uk))
ok = shrinkage(ck + uk, lam)
uk = uk + ck - ok
wk = ok
return wk
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
def BLS(train_x,train_y,test_x,test_y,s,c,N1,N2,N3):
# u = 0
L = 0
train_x = preprocessing.scale(train_x,axis = 1)# ,with_mean = '0') #处理数据
FeatureOfInputDataWithBias = np.hstack([train_x, 0.1 * np.ones((train_x.shape[0],1))])
OutputOfFeatureMappingLayer = np.zeros([train_x.shape[0],N2*N1])
Beta1OfEachWindow = []
distOfMaxAndMin = []
minOfEachWindow = []
ymin = 0
ymax = 1
train_acc_all = np.zeros([1,L+1])
test_acc = np.zeros([1,L+1])
train_time = np.zeros([1,L+1])
test_time = np.zeros([1,L+1])
time_start=time.time()#计时开始
for i in range(N2):
random.seed(i)
weightOfEachWindow = 2 * random.randn(train_x.shape[1]+1,N1)-1; #生成每个窗口的权重系数，最后一行为偏差
# WeightOfEachWindow([],[],i) = weightOfEachWindow; #存储每个窗口的权重系数
FeatureOfEachWindow = np.dot(FeatureOfInputDataWithBias,weightOfEachWindow) #生成每个窗口的特征
#压缩每个窗口特征到[-1，1]
scaler1 = preprocessing.MinMaxScaler(feature_range=(0, 1)).fit(FeatureOfEachWindow)
FeatureOfEachWindowAfterPreprocess = scaler1.transform(FeatureOfEachWindow)
#通过稀疏化计算映射层每个窗口内的最终权重
betaOfEachWindow = sparse_bls(FeatureOfEachWindowAfterPreprocess,FeatureOfInputDataWithBias).T
#存储每个窗口的系数化权重
Beta1OfEachWindow.append(betaOfEachWindow)
#每个窗口的输出 T1
outputOfEachWindow = np.dot(FeatureOfInputDataWithBias,betaOfEachWindow)
# print('Feature nodes in window: max:',np.max(outputOfEachWindow),'min:',np.min(outputOfEachWindow))
distOfMaxAndMin.append(np.max(outputOfEachWindow,axis =0) - np.min(outputOfEachWindow,axis=0))
minOfEachWindow.append(np.min(outputOfEachWindow,axis = 0))
outputOfEachWindow = (outputOfEachWindow-minOfEachWindow[i])/distOfMaxAndMin[i]
OutputOfFeatureMappingLayer[:,N1*i:N1*(i+1)] = outputOfEachWindow
del outputOfEachWindow
del FeatureOfEachWindow
del weightOfEachWindow
#生成强化层
#以下为映射层输出加偏置（强化层输入）
InputOfEnhanceLayerWithBias = np.hstack([OutputOfFeatureMappingLayer, 0.1 * np.ones((OutputOfFeatureMappingLayer.shape[0],1))])
#生成强化层权重
if N1*N2>=N3:
random.seed(67797325)
# dim = N1*N2+1
# temp_matric = stats.ortho_group(dim)
# weightOfEnhanceLayer = temp_matric[:,0:N3]
weightOfEnhanceLayer = LA.orth(2 * random.randn(N2*N1+1,N3))-1
else:
random.seed(67797325)
weightOfEnhanceLayer = LA.orth(2 * random.randn(N2*N1+1,N3).T-1).T
tempOfOutputOfEnhanceLayer = np.dot(InputOfEnhanceLayerWithBias,weightOfEnhanceLayer)
# print('Enhance nodes: max:',np.max(tempOfOutputOfEnhanceLayer),'min:',np.min(tempOfOutputOfEnhanceLayer))
parameterOfShrink = s/np.max(tempOfOutputOfEnhanceLayer)
OutputOfEnhanceLayer = tansig(tempOfOutputOfEnhanceLayer * parameterOfShrink)
#生成最终输入
InputOfOutputLayer = np.hstack([OutputOfFeatureMappingLayer,OutputOfEnhanceLayer])
pinvOfInput = pinv(InputOfOutputLayer,c)
OutputWeight = np.dot(pinvOfInput,train_y) #全局违逆
time_end=time.time() #训练完成
trainTime = time_end - time_start
#训练输出
OutputOfTrain = np.dot(InputOfOutputLayer,OutputWeight)
trainAcc = show_accuracy(OutputOfTrain,train_y)
print('Training accurate is' ,trainAcc*100,'%')
print('Training time is ',trainTime,'s')
train_acc_all[0][0] = trainAcc
train_time[0][0] = trainTime
#测试过程
test_x = preprocessing.scale(test_x,axis = 1)#,with_mean = True,with_std = True) #处理数据 x = (x-mean(x))/std(x) x属于[-1，1]
FeatureOfInputDataWithBiasTest = np.hstack([test_x, 0.1 * np.ones((test_x.shape[0],1))])
OutputOfFeatureMappingLayerTest = np.zeros([test_x.shape[0],N2*N1])
time_start=time.time()#测试计时开始
# 映射层
for i in range(N2):
outputOfEachWindowTest = np.dot(FeatureOfInputDataWithBiasTest,Beta1OfEachWindow[i])
OutputOfFeatureMappingLayerTest[:,N1*i:N1*(i+1)] =(ymax-ymin)*(outputOfEachWindowTest-minOfEachWindow[i])/distOfMaxAndMin[i]-ymin
# 强化层
InputOfEnhanceLayerWithBiasTest = np.hstack([OutputOfFeatureMappingLayerTest, 0.1 * np.ones((OutputOfFeatureMappingLayerTest.shape[0],1))])
tempOfOutputOfEnhanceLayerTest = np.dot(InputOfEnhanceLayerWithBiasTest,weightOfEnhanceLayer)
# 强化层输出
OutputOfEnhanceLayerTest = tansig(tempOfOutputOfEnhanceLayerTest * parameterOfShrink)
# 最终层输入
InputOfOutputLayerTest = np.hstack([OutputOfFeatureMappingLayerTest,OutputOfEnhanceLayerTest])
# 最终测试输出
OutputOfTest = np.dot(InputOfOutputLayerTest,OutputWeight)
time_end=time.time() #训练完成
testTime = time_end - time_start
testAcc = show_accuracy(OutputOfTest,test_y)
print('Testing accurate is' ,testAcc * 100,'%')
print('Testing time is ',testTime,'s')
test_acc[0][0] = testAcc
test_time[0][0] = testTime
return test_acc,test_time,train_acc_all,train_time
#%%%%%%%%%%%%%%%%%%%%%%%%
'''
增加强化层节点版---BLS
参数列表：
s------收敛系数
c------正则化系数
N1-----映射层每个窗口内节点数
N2-----映射层窗口数
N3-----强化层节点数
l------步数
M------步长
'''
def BLS_AddEnhanceNodes(train_x,train_y,test_x,test_y,s,c,N1,N2,N3,L,M):
#生成映射层
'''
两个参数最重要，1）y;2)Beta1OfEachWindow
'''
u = 0
ymax = 1 #数据收缩上限
ymin = 0 #数据收缩下限
train_x = preprocessing.scale(train_x,axis = 1) #处理数据
FeatureOfInputDataWithBias = np.hstack([train_x, 0.1 * np.ones((train_x.shape[0],1))])
Ou
