class BoltzmannNet(object):def __init__(self):self.dataMat   = []        #外部导入的数据集self.MAX_ITER  = 2000      #外循环迭代次数Self.T0        = 1 000     #最高温度：这个温度变化范围应位于最大迭代范围self.Lambda    = 0.97      #温度下降参数self.iteration = 0         #达到最优时的迭代次数self.dist      = []        #存储生成的距离self.pathindx  = []        #存储生成的路径索引self.bestdist  = 0         #生成的最优路径长度self.bestpath  = []        #生成的最优路径

 def loadDataSet(self,filename):     #加载数据集self.dataMat     = []self.classLabels = []fr               = open(filename)for line in fr.readlines():lineArr = line.strip().split()self.dataMat.append([float(lineArr[0]),float(lineArr[1]),1.0])self.classLabels.append(int(float(lineArr[2])))self.dataMat = mat(self.dataMat)m,n          = shape(self.dataMat)self.nSampNum = m   #样本数量self.nSampDim = n-1 #样本维度

 def distEclud(self,vecA,vecB):#欧式距离eps = 1.0e-6return linalg.norm(vecA-vecB) + eps

 def boltzmann(self,new1,old1,T):return exp(-(new1-old1)/T)

 def pathLen(self,dist,path):N = len(path)plen = 0for i in xrange(0,N-1):   #长度为N的向量，包含1~N的整数plen += dist[path[i],path[i+1]]plen += dist[path[0],path[N-1]]return  plen

 def changePath(self,old_path):N = len(old_path)if random.rand() < 0.25:   #产生两个位置，并交换chpos = floor(random.rand(1,2)*N).tolist()[0]new_path = copy.deepcopy(old_path)new_path[int(chpos[0])] = old_path[int(chpos[1])]new_path[int(chpos[1])] = old_path[int(chpos[0])]else:d = ceil(random.rand(1,3)*N).tolist()[0]d.sort()  #随机路径排序a = int(d[0])b = int(d[1])c = int(d[2])if a != b and b != c:new_path = copy.deepcopy(old_path)new_path[a,c-1] = old_path[b-1:c-1]+old_path[a:b-1]else:new_path = self.changePath(old_path)return new_path

def drawPath(self,Seq,dataMat,color = 'b'):m,n = shape(dataMat)px  = (dataMat[Seq,0]).tolist()py  = (dataMat[Seq,1]).tolist()px.append(px[0])py.append(py[0])plt.plot(px,py,color)

def drawScatter(self,plt):px = (self.dataMat[:,0]).tolist()py = (self.dataMat[:,1]).tolist()plt.scatter(px,py,c = 'green',marker = 'o',s = 60)i = 65for x,y in zip(px,py):plt.annotate(str(chr(i)),xy = (x[0]+40,y[0]),color = 'black')i += 1

  def Trendline(self,plt,color = 'b'):plt.plot(range(len(self.dist)),self.dist,color)

    def initBMNet(self,m,n,distMat):self.pathindx = range(m)random.shuffle(self.pathindx)  #随机生成每个路径self.dist.append(self.pathLen(distMat,self.pathindx)) #每个路径对应的距离return self.T0,self.pathindx,m

#第一步：导入数据，并根据配置参数初始化网络def train(self):  #主函数[m,n] = shape(self.dataMat)distMat = self.distEclud(self.dataMat,self.dataMat.T) #转换为邻接矩阵（距离矩阵）# T为当前温度，curpath为当前路径索引，MAX_M为内循环最大迭代次数[T,curpath,MAX_M] = self.initBMNet(m,n,distMat)#进入主循环，计算最优路径step = 0 #初始化外循环迭代while step <= self.MAX_ITER:   #外循环m = 0                       #内循环计数器while m <= MAX_M:          #内循环curdist = self.pathLen(distMat,curpath) #计算当前路径距离newpath = self.changePath(curpath)      #交换产生新路径newdist = self.pathLen(distMat,newpath) #计算新路径距离#判断网络是否是一个局部稳态if (curdist > newdist):curpath = newpathself.pathindx.append(curpath)self.dist.append(newdist)self.iteration += 1       #增加迭代次数else:                         #如果网络处于局部稳定状态，则执行玻尔兹曼函数if random.rand()<self.boltzmann(newdist,curdist,T):curpath = newpathself.pathindx.append(curpath)self.dist.append(newdist)self.iteration += 1   #增加迭代次数m += 1step += 1T = T *self.Lambda   #降温，返回迭代，直至算法终止#第三步：提取最优路径self.bestdist = min(self.dist)indxes = argmin(self.dist)self.bestdist = self.pathindx[indxes]

bmNet = BoltzmannNet()
bmNet.loadDataSet("dataSet25.txt")
bmNet.train()
print u"循环迭代",bmNet.bestdist
print  u"最佳路线",bmNet.bestpath
#显示优化后的城市地图、路径图
bmNet.drawScatter(plt)
plt.show()bmNet.Trendline(plt) #绘制误差算法收敛曲线
plt.show()

#coding:utf-8from numpy import *
import copy
import matplotlib.pyplot as pltclass BoltzmannNet(object):def __init__(self):self.dataMat   = []        #外部导入的数据集self.MAX_ITER  = 2000      #外循环迭代次数self.T0        = 1000     #最高温度：这个温度变化范围应位于最大迭代范围self.Lambda    = 0.97       #温度下降参数self.iteration = 0         #达到最优时的迭代次数self.dist      = []        #存储生成的距离self.pathindx  = []        #存储生成的路径索引self.bestdist  = 0         #生成的最优路径长度self.bestpath  = []        #生成的最优路径def loadDataSet(self,filename):     #加载数据集self.dataMat     = []self.classLabels = []fr               = open(filename)for line in fr.readlines():lineArr = line.strip().split()# self.dataMat.append([float(lineArr[0]),float(lineArr[1]),1.0])self.dataMat.append([float(lineArr[0]),float(lineArr[1])])self.classLabels.append(int(float(lineArr[2])))self.dataMat = mat(self.dataMat)m,n          = shape(self.dataMat)self.nSampNum = m   #样本数量self.nSampDim = n-1 #样本维度def distEclud(self,vecA,vecB):#欧式距离distMat = zeros((vecA.shape[0],vecA.shape[0]))for i in range(len(vecA)):for j in range(len(vecA)):distMat[i][j] = linalg.norm(vecA[i]-vecB[:,j])return  distMatdef boltzmann(self,new1,old1,T):return exp(-(new1-old1)/T)def pathLen(self,dist,path):N = len(path)plen = 0for i in xrange(0,N-1):   #长度为N的向量，包含1~N的整数plen += dist[path[i],path[i+1]]plen += dist[path[0],path[N-1]]return  plendef changePath(self,old_path):N = len(old_path)if random.rand() < 0.25:   #产生两个位置，并交换chpos = floor(random.rand(1,2)*N).tolist()[0]new_path = copy.deepcopy(old_path)new_path[int(chpos[0])] = old_path[int(chpos[1])]new_path[int(chpos[1])] = old_path[int(chpos[0])]else:d = ceil(random.rand(1,3)*N).tolist()[0]d.sort()  #随机路径排序a = int(d[0])b = int(d[1])c = int(d[2])if a != b and b != c:new_path = copy.deepcopy(old_path)new_path[a:c-1] = old_path[b-1:c-1]+old_path[a:b-1]else:new_path = self.changePath(old_path)return new_pathdef drawPath(self,Seq,dataMat,color = 'b'):m,n = shape(dataMat)px  = (dataMat[Seq,0]).tolist()py  = (dataMat[Seq,1]).tolist()px.append(px[0])py.append(py[0])plt.plot(px,py,color)def drawScatter(self,plt):px = (self.dataMat[:,0]).tolist()py = (self.dataMat[:,1]).tolist()plt.scatter(px,py,c = 'green',marker = 'o',s = 60)i = 65for x,y in zip(px,py):plt.annotate(str(chr(i)),xy = (x[0]+40,y[0]),color = 'black')i += 1def Trendline(self,plt,color = 'b'):plt.plot(range(len(self.dist)),self.dist,color)def initBMNet(self,m,n,distMat):self.pathindx = range(m)random.shuffle(self.pathindx)  #随机生成每个路径self.dist.append(self.pathLen(distMat,self.pathindx)) #每个路径对应的距离return self.T0,self.pathindx,m#第一步：导入数据，并根据配置参数初始化网络def train(self):  #主函数[m,n] = shape(self.dataMat)distMat = self.distEclud(self.dataMat,self.dataMat.T) #转换为邻接矩阵（距离矩阵）# T为当前温度，curpath为当前路径索引，MAX_M为内循环最大迭代次数[T,curpath,MAX_M] = self.initBMNet(m,n,distMat)#进入主循环，计算最优路径step = 0 #初始化外循环迭代while step <= self.MAX_ITER:   #外循环m = 0                       #内循环计数器while m <= MAX_M:          #内循环curdist = self.pathLen(distMat,curpath) #计算当前路径距离newpath = self.changePath(curpath)      #交换产生新路径newdist = self.pathLen(distMat,newpath) #计算新路径距离#判断网络是否是一个局部稳态if (curdist > newdist):curpath = newpathself.pathindx.append(curpath)self.dist.append(newdist)self.iteration += 1       #增加迭代次数else:                         #如果网络处于局部稳定状态，则执行玻尔兹曼函数if random.rand()<self.boltzmann(newdist,curdist,T):curpath = newpathself.pathindx.append(curpath)self.dist.append(newdist)self.iteration += 1   #增加迭代次数m += 1step += 1T = T *self.Lambda   #降温，返回迭代，直至算法终止#第三步：提取最优路径self.bestdist = min(self.dist)indxes = argmin(self.dist)self.bestpath = self.pathindx[indxes]bmNet = BoltzmannNet()
bmNet.loadDataSet("dataSet25.txt")
bmNet.train()
print u"循环迭代",bmNet.iteration,u"次"
print u"最优解",bmNet.bestdist
print  u"最佳路线",bmNet.bestpath
#显示优化后的城市地图、路径图
bmNet.drawScatter(plt)
plt.show()bmNet.Trendline(plt) #绘制误差算法收敛曲线
plt.show()