1: 'yes'
}
},
1: 'no'
}
}
}
}]
return listOfTrees[i]
myTree = retrieveTree(0)
print CA.getNumLeafs(myTree)
print CA.getTreeDepth(myTree)
myTree['no surfacing'][3] = 'maybe' #增加了映射 --- 3: 'maybe' ----
print "myTree = ", myTree
# CA.createPlot(myTree); # OK , just for next.
### ======================= start 决策树分类函数 ==================================
print "================= >>> start 决策树分类函数 =================="
dataSet, labels = createDataSet()
print CA.classify(myTree, labels, [1, 0], True)
# print CA.classify(myTree, labels, [1,1]);
### ======================= start 决策树的序列化和反序列化 ==================================
print "================= >>> start 决策树的序列化和反序列化 =================="
filename = 'classifierStorage.txt'
CA.storeTree(myTree, filename)
print CA.grabTree(filename)
