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修改 树回归的原理和后剪枝的描述

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jiangzhonglian
2017-09-21 12:49:03 +08:00
parent 627ec87a1b
commit 73fa4dbb92
2 changed files with 31 additions and 33 deletions
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60
src/python/9.RegTrees/regTrees.py
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@@ -395,12 +395,12 @@ def createForeCast(tree, testData, modelEval=regTreeEval):
if __name__ == "__main__":
# 测试数据集
testMat = mat(eye(4))
print testMat
print type(testMat)
mat0, mat1 = binSplitDataSet(testMat, 1, 0.5)
print mat0, '\n-----------\n', mat1
# # 测试数据集
# testMat = mat(eye(4))
# print testMat
# print type(testMat)
# mat0, mat1 = binSplitDataSet(testMat, 1, 0.5)
# print mat0, '\n-----------\n', mat1
# # 回归树
# myDat = loadDataSet('input/9.RegTrees/data1.txt')
@@ -431,29 +431,29 @@ if __name__ == "__main__":
# myTree = createTree(myMat, modelLeaf, modelErr)
# print myTree
# # # 回归树 VS 模型树 VS 线性回归
# 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
# yHat1 = createForeCast(myTree1, testMat[:, 0])
# print "--------------\n"
# # print yHat1
# # print "ssss==>", testMat[:, 1]
# print "回归树:", corrcoef(yHat1, testMat[:, 1],rowvar=0)[0, 1]
# # 回归树 VS 模型树 VS 线性回归
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
yHat1 = createForeCast(myTree1, testMat[:, 0])
print "--------------\n"
# print yHat1
# print "ssss==>", testMat[:, 1]
print "回归树:", corrcoef(yHat1, testMat[:, 1],rowvar=0)[0, 1]
# # 模型树
# myTree2 = createTree(trainMat, modelLeaf, modelErr, ops=(1, 20))
# yHat2 = createForeCast(myTree2, testMat[:, 0], modelTreeEval)
# print myTree2
# print "模型树:", corrcoef(yHat2, testMat[:, 1],rowvar=0)[0, 1]
# 模型树
myTree2 = createTree(trainMat, modelLeaf, modelErr, ops=(1, 20))
yHat2 = createForeCast(myTree2, testMat[:, 0], modelTreeEval)
print myTree2
print "模型树:", corrcoef(yHat2, testMat[:, 1],rowvar=0)[0, 1]
# # 线性回归
# ws, X, Y = linearSolve(trainMat)
# print ws
# m = len(testMat[:, 0])
# yHat3 = mat(zeros((m, 1)))
# for i in range(shape(testMat)[0]):
# yHat3[i] = testMat[i, 0]*ws[1, 0] + ws[0, 0]
# print "线性回归:", corrcoef(yHat3, testMat[:, 1],rowvar=0)[0, 1]
# 线性回归
ws, X, Y = linearSolve(trainMat)
print ws
m = len(testMat[:, 0])
yHat3 = mat(zeros((m, 1)))
for i in range(shape(testMat)[0]):
yHat3[i] = testMat[i, 0]*ws[1, 0] + ws[0, 0]
print "线性回归:", corrcoef(yHat3, testMat[:, 1],rowvar=0)[0, 1]