def test_createTreeEx2(self):
myDat1 = regTrees.loadDataSet('ex2.txt')
myDat1 = np.mat(myDat1)
regTree = regTrees.createTree(myDat1)
print(regTree)
myDat1 = regTrees.loadDataSet('ex2.txt')
myDat1 = np.mat(myDat1)
regTree = regTrees.createTree(myDat1, ops=(10000, 4)) # 手动调参比较敏感
print(regTree)
def test_bike(self):
trainMat = np.mat(regTrees.loadDataSet('bikeTrain.txt'))
testMat = np.mat(regTrees.loadDataSet('bikeTest.txt'))
#print(testMat, trainMat)
myTree = regTrees.createTree(trainMat, ops=(1, 20)) # 回归树
yHat = regTrees.createForeCast(myTree, testMat[:, 0])
r1 = np.corrcoef(yHat, testMat[:, 1], rowvar=0)[0, 1]
print(r1) # 回归树的相关系数
print("-" * 80)
myTree = regTrees.createTree(trainMat, regTrees.modelLeaf,
regTrees.modelErr, (1, 20)) # 模型树
yHat = regTrees.createForeCast(myTree, testMat[:, 0],
regTrees.modelTreeEval)
r2 = np.corrcoef(yHat, testMat[:, 1], rowvar=0)[0, 1]
print(r2) # 模型树的相关系数
def reDraw(tolS, tolN):
reDraw.f.clf() # clear the figure
reDraw.a = reDraw.f.add_subplot(111)
if chkBtnVar.get():
if tolN < 2: tolN = 2
myTree=regTrees.createTree(reDraw.rawDat, regTrees.modelLeaf,\
regTrees.modelErr, (tolS,tolN))
yHat = regTrees.createForeCast(myTree, reDraw.testDat, \
regTrees.modelTreeEval)
else:
myTree = regTrees.createTree(reDraw.rawDat, ops=(tolS, tolN))
yHat = regTrees.createForeCast(myTree, reDraw.testDat)
reDraw.a.scatter(reDraw.rawDat[:, 0].flatten().A[0],
reDraw.rawDat[:, 1].flatten().A[0],
s=5) #use scatter for data set
reDraw.a.plot(reDraw.testDat, yHat, linewidth=2.0) #use plot for yHat
reDraw.canvas.draw()
def test_createTreeEx3(self):
myDat1 = regTrees.loadDataSet('exp2.txt')
myDat1 = np.mat(myDat1)
regTree = regTrees.createTree(myDat1, regTrees.modelLeaf,
regTrees.modelErr, (1, 10))
print(regTree)
def test_createTree0(self):
myDat1 = regTrees.loadDataSet('ex0.txt')
myDat1 = np.mat(myDat1)
regTree = regTrees.createTree(myDat1)
print(regTree)