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jiangzhonglian
2017-04-07 16:51:24 +08:00
parent afd689e5d8
commit 3c4fc66fd2
19 changed files with 14 additions and 14 deletions
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14
src/python/9.RegTrees/regTrees.py
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@@ -290,8 +290,8 @@ if __name__ == "__main__":
# print mat0, '\n-----------\n', mat1
# # 回归树
# myDat = loadDataSet('input/09.RegTrees/data1.txt')
# # myDat = loadDataSet('input/09.RegTrees/data2.txt')
# myDat = loadDataSet('input/9.RegTrees/data1.txt')
# # myDat = loadDataSet('input/9.RegTrees/data2.txt')
# # print 'myDat=', myDat
# myMat = mat(myDat)
# # print 'myMat=', myMat
@@ -299,13 +299,13 @@ if __name__ == "__main__":
# print myTree
# # 1. 预剪枝就是：提起设置最大误差数和最少元素数
# myDat = loadDataSet('input/09.RegTrees/data3.txt')
# myDat = loadDataSet('input/9.RegTrees/data3.txt')
# myMat = mat(myDat)
# myTree = createTree(myMat, ops=(0, 1))
# print myTree
# # 2. 后剪枝就是：通过测试数据，对预测模型进行合并判断
# myDatTest = loadDataSet('input/09.RegTrees/data3test.txt')
# myDatTest = loadDataSet('input/9.RegTrees/data3test.txt')
# myMat2Test = mat(myDatTest)
# myFinalTree = prune(myTree, myMat2Test)
# print '\n\n\n-------------------'
@@ -313,14 +313,14 @@ if __name__ == "__main__":
# # --------
# # 模型树求解
# myDat = loadDataSet('input/09.RegTrees/data4.txt')
# myDat = loadDataSet('input/9.RegTrees/data4.txt')
# myMat = mat(myDat)
# myTree = createTree(myMat, modelLeaf, modelErr)
# print myTree
# 回归树 VS 模型树 VS 线性回归
trainMat = mat(loadDataSet('input/09.RegTrees/bikeSpeedVsIq_train.txt'))
testMat = mat(loadDataSet('input/09.RegTrees/bikeSpeedVsIq_test.txt'))
trainMat = mat(loadDataSet('input/9.RegTrees/bikeSpeedVsIq_train.txt'))
testMat = mat(loadDataSet('input/9.RegTrees/bikeSpeedVsIq_test.txt'))
# 回归树
myTree1 = createTree(trainMat, ops=(1, 20))
print myTree1