errRec.append(sse)
# 判断是否收敛
iteration = iteration + 1
if sse <= eb:
print("iteration:",i)
break
# 误差信号反向传播
# DELTA和delta为局部梯度
DELTA = multiply(err,BackPropgation.dlogsig(HM,out))
wDelta = W.transpose()*DELTA
delta = multiply(wDelta,BackPropgation.dlogsig(hp,tau))
dWEX = DELTA*tauex.transpose()
dwex = delta*SampIn.transpose()
# 更新权值
if i == 0:
WEX = WEX + eta * dWEX
wex = wex + eta * dwex
else :
WEX = WEX + (1 - mc)*eta*dWEX + mc * dWEXOld
wex = wex + (1 - mc)*eta*dwex + mc * dwexOld
dWEXOld = dWEX
dwexOld = dwex
W = WEX[:,0:nHidden]
# 绘制误差曲线
X = linspace(0,1000,1000)
Untils.TrendLine(X,errRec)
# -*- coding:utf-8 -*-
# Filename : testBoltzmann01.py
import operator
import copy
import Untils
import Boltzmann
from numpy import *
import matplotlib.pyplot as plt
dataSet = Untils.loadDataSet("dataSet25.txt")
cityPosition = mat(dataSet)
m, n = shape(cityPosition)
bestx, di = Boltzmann.boltzmann(cityPosition, MAX_ITER=1000, T0=100)
# 优化前城市图,路径图
Untils.drawScatter(cityPosition, flag=False)
Untils.drawPath(list(range(m)), cityPosition)
# 显示优化后城市图,路径图
Untils.drawScatter(cityPosition, flag=False)
Untils.drawPath(bestx, cityPosition, color='b')
# 绘制误差趋势线
x0 = list(range(len(di)))
Untils.TrendLine(x0, di)
import operator
import Untils
import BackPropgation
import matplotlib.pyplot as plt
# 数据集
dataSet,classLabels = BackPropgation.loadDataSet("testSet2.txt") # 初始化时第1列为全1向量, studentTest.txt
dataSet = BackPropgation.normalize(mat(dataSet))
# 绘制数据点
# 重构dataSet数据集
dataMat = mat(ones((shape(dataSet)[0],shape(dataSet)[1])))
dataMat[:,1] = mat(dataSet)[:,0]
dataMat[:,2] = mat(dataSet)[:,1]
# 绘制数据集散点图
Untils.drawClassScatter(dataMat,transpose(classLabels),False)
# BP神经网络进行数据分类
errRec,WEX,wex = BackPropgation.bpNet(dataSet,classLabels)
# 计算和绘制分类线
x,z = BackPropgation.BPClassfier(-3.0,3.0,WEX,wex)
Untils.classfyContour(x,x,z)
# 绘制误差曲线
X = linspace(0,2000,2000)
Y = log2(errRec)+1.0e-6
Untils.TrendLine(X,Y)