每周一搏，提升自我。

前两篇文章主要介绍了基于math的ID3和C45算法决策树，这次将决策树生成图片。

#!/usr/bin/python#coding:utf-8'''窗体显示决策树'''import matplotlib.pyplot as plt#决策树属性设置decisionNode=dict(boxstyle="sawtooth",fc="0.8")leafNode=dict(boxstyle="round4",fc="0.8")arrow_args=dict(arrowstyle="<-")#createPlot 主函数，调用即可画出决策树，其中调用登了剩下的所有的函数，inTree的形式必须为嵌套的决策树def createPlot(inThree):	fig=plt.figure(1,facecolor='white')	fig.clf()	axprops=dict(xticks=[],yticks=[])	createPlot.ax1=plt.subplot(111,frameon=False,**axprops)  #no ticks	# createPlot.ax1=plt.subplot(111,frameon=False)  #ticks for demo puropses	plotTree.totalW=float(getNumLeafs(inThree))	plotTree.totalD=float(getTreeDepth(inThree))	plotTree.xOff=-0.5/plotTree.totalW;	plotTree.yOff=1.0	plotTree(inThree,(0.5,1.0),'')	plt.show()#决策树上节点之间的箭头设置def plotNode(nodeTxt,centerPt,parentPt,nodeType):	createPlot.ax1.annotate(nodeTxt,xy=parentPt,xycoords='axes fraction',		xytext=centerPt,textcoords='axes fraction',		va="center",ha="center",bbox=nodeType,arrowprops=arrow_args)#决策树文字的添加位置和角度def plotMidText(cntrPt,parentPt,txtString):	xMid=(parentPt[0] -cntrPt[0])/2.0 +cntrPt[0]	yMid=(parentPt[1] -cntrPt[1])/2.0 +cntrPt[1]	createPlot.ax1.text(xMid,yMid,txtString,va="center",ha="center",rotation=30)#得到叶子节点的数量def getNumLeafs(myTree):	numLeafs=0	firstStr=myTree.keys()[0]	secondDict=myTree[firstStr]	for key in secondDict.keys():		if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes			numLeafs += getNumLeafs(secondDict[key])		else: numLeafs+=1	return numLeafs#得到决策树的深度def getTreeDepth(myTree):	maxDepthh=0	firstStr=myTree.keys()[0]	secondDict=myTree[firstStr]	for key in secondDict.keys():		if type(secondDict[key]).__name__=='dict':			thisDepth=1+getTreeDepth(secondDict[key])		else: thisDepth=1		if thisDepth>maxDepthh:maxDepthh=thisDepth	return maxDepthh#父子节点之间画决策树def plotTree(myTree,parentPt,nodeTxt):	numLeafs=getNumLeafs(myTree)	depth=getTreeDepth(myTree)	firstStr=myTree.keys()[0]	cntrPt=(plotTree.xOff +(1.0+float(numLeafs))/2.0/plotTree.totalW,plotTree.yOff)	plotMidText(cntrPt,parentPt,nodeTxt)	plotNode(firstStr,cntrPt,parentPt,decisionNode)	secondDict=myTree[firstStr]	plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD	for key in secondDict.keys():		if type(secondDict[key]).__name__=='dict':			plotTree(secondDict[key],cntrPt,str(key))		else:			plotTree.xOff=plotTree.xOff+1.0/plotTree.totalW			plotNode(secondDict[key],(plotTree.xOff,plotTree.yOff),cntrPt,leafNode)			plotMidText((plotTree.xOff,plotTree.yOff),cntrPt,str(key))	plotTree.yOff=plotTree.yOff+1.0/plotTree.totalD