1、首先在官网下载graphviz
下载网址：https://www.graphviz.org/download/
; 2、安装。
打开第一步已经下载好的软件。点击下一步，在安装路径选择时可将安装路径修改为 D:\graphviz
接着一直点下一步，即可安装完成。
3、配置环境变量
右键点击”我的电脑””–>选择”属性”–>高级系统设置（滑到最下面）
–>环境变量–>系统变量中的path（双击）
–>将graphviz的安装路径下的bin文件添加进去。如果你前面安装的路径是跟我一样，直接复制这个路径即可D:\graphviz\bin
–>多次点击确定，完成环境变量配置，
; 4、测试
点击左下角搜索，输入”cmd”,或者 win+R键。
输入 dot -version （注意dot后面后一个空格）。
若出现dot不是内部或外部命令，则表示安装失败。
5、再次配置
接下来打开你安装路径下bin文件夹下面的config6（选择打开方式为记事本打开）
将里面内容删除，复制下面这段代码，保存即可。
import operator
import math
class DecisionTree:
def __init__(self):
pass
def loadData(self):
data = [
[2, 2, 1, 0, "yes"],
[2, 2, 1, 1, "no"],
[1, 2, 1, 0, "yes"],
[0, 0, 0, 0, "yes"],
[0, 0, 0, 1, "no"],
[1, 0, 0, 1, "yes"],
[2, 1, 1, 0, "no"],
[2, 0, 0, 0, "yes"],
[0, 1, 0, 0, "yes"],
[2, 1, 0, 1, "yes"],
[1, 2, 0, 0, "no"],
[0, 1, 1, 1, "no"],
]
features = ["天气", "温度", "湿度", "风速"]
return data, features
def ShannonEnt(self, data):
numData = len(data)
labelCounts = {}
for feature in data:
oneLabel = feature[-1]
labelCounts.setdefault(oneLabel, 0)
labelCounts[oneLabel] += 1
shannonEnt = 0.0
for key in labelCounts:
prob = float(labelCounts[key]) / numData
shannonEnt -= prob * math.log2(prob)
return shannonEnt
def splitData(self, data, axis, value):
retData = []
for feature in data:
if feature[axis] == value:
reducedFeature = feature[:axis]
reducedFeature.extend(feature[axis + 1 :])
retData.append(reducedFeature)
return retData
def chooseBestFeatureToSplit(self, data):
numFeature = len(data[0]) - 1
baseEntropy = self.ShannonEnt(data)
bestInfoGain = 0.0
bestFeature = -1
for i in range(numFeature):
featureList = [result[i] for result in data]
uniqueFeatureList = set(featureList)
newEntropy = 0.0
for value in uniqueFeatureList:
splitDataSet = self.splitData( data, i, value )
prob = len(splitDataSet) / float(len(data))
newEntropy += prob * self.ShannonEnt(splitDataSet)
infoGain = baseEntropy - newEntropy
if infoGain > bestInfoGain:
bestInfoGain = infoGain
bestFeature = i
return bestFeature
def majorityCnt(self, labelsList):
labelsCount = {}
for vote in labelsList:
if vote not in labelsCount.keys():
labelsCount[vote] = 0
labelsCount[vote] += 1
sortedLabelsCount = sorted(
labelsCount.iteritems(), key=operator.itemgetter(1), reverse=True
)
print(sortedLabelsCount)
return sortedLabelsCount[0][0]
def createTree(self, data, features):
features = list(features)
labelsList = [line[-1] for line in data]
if labelsList.count(labelsList[0]) == len(labelsList):
return labelsList[0]
if len(data[0]) == 1:
return self.majorityCnt(labelsList)
bestFeature = self.chooseBestFeatureToSplit(data)
bestFeatLabel = features[bestFeature]
myTree = {bestFeatLabel: {}}
del (features[bestFeature])
featureValues = [example[bestFeature] for example in data]
uniqueFeatureValues = set(featureValues)
for value in uniqueFeatureValues:
subFeatures = features[:]
myTree[bestFeatLabel][value] = self.createTree(
self.splitData(data, bestFeature, value), subFeatures
)
return myTree
def predict(self, tree, features, x):
for key1 in tree.keys():
secondDict = tree[key1]
featIndex = features.index(key1)
for key2 in secondDict.keys():
if x[featIndex] == key2:
if type(secondDict[key2]).__name__ == "dict":
classLabel = self.predict(secondDict[key2], features, x)
else:
classLabel = secondDict[key2]
return classLabel
if __name__ == "__main__":
dtree = DecisionTree()
data, features = dtree.loadData()
myTree = dtree.createTree(data, features)
print(myTree)
label = dtree.predict(myTree, features, [1, 1, 1, 0])
print("新数据[1,1,1,0]对应的是否要进行活动为:{}".format(label))
最后再次通过cmd测试是否安装成功。命令：dot -version （同样注意dot后有一个空格）
出现这段文字。显示dot版本和路径，恭喜你安装成功。
Original: https://blog.csdn.net/qq_43750528/article/details/127213064
Author: 蜡笔大新home
Title: graphviz安装教程（2022最新版）初学者适用
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