Merge branch 'master' of https://github.com/apachecn/MachineLearning

docs/9.树回归.md

@@ -11,3 +11,7 @@
 * 那么问题来了，如何计算连续型数值的混乱度呢？
     * `误差`：也就是计算平均差的总值(总方差=方差*样本数)
     * 二元切分方式
+* 回归树
+* 模型树(线性模型)
+* 树回归方法在预测复杂数据时 会比 简单的线性模型 更有效。
+* Tkinter 和 matplotlib的集成，构建更强大的GUI

src/python/09.RegTrees/treeExplore.py

@@ -0,0 +1,123 @@
+#!/usr/bin/python
+# coding:utf8
+
+'''
+Created on 2017-03-08
+Update on 2017-03-08
+Tree-Based Regression Methods Source Code for Machine Learning in Action Ch. 9
+@author: jiangzhonglian
+'''
+from Tkinter import *
+from numpy import *
+import regTrees
+
+import matplotlib
+matplotlib.use('TkAgg')
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+from matplotlib.figure import Figure
+
+
+def test_widget_text(root):
+    mylabel = Label(root, text="helloworld")
+    # 相当于告诉 布局管理器(Geometry Manager),如果不设定位置，默认在 0行0列的位置
+    mylabel.grid()
+
+
+# 最大为误差， 最大子叶节点的数量
+def reDraw(tolS, tolN):
+    # clear the figure
+    reDraw.f.clf()
+    reDraw.a = reDraw.f.add_subplot(111)
+
+    # 检查复选框是否选中
+    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)
+
+    # use scatter for data set
+    reDraw.a.scatter(reDraw.rawDat[:, 0], reDraw.rawDat[:, 1], s=5)
+    # use plot for yHat
+    reDraw.a.plot(reDraw.testDat, yHat, linewidth=2.0, c='red')
+    reDraw.canvas.show()
+
+
+def getInputs():
+    try:
+        tolN = int(tolNentry.get())
+    except:
+        tolN = 10
+        print "enter Integer for tolN"
+        tolNentry.delete(0, END)
+        tolNentry.insert(0, '10')
+    try:
+        tolS = float(tolSentry.get())
+    except:
+        tolS = 1.0
+        print "enter Float for tolS"
+        tolSentry.delete(0, END)
+        tolSentry.insert(0, '1.0')
+    return tolN, tolS
+
+
+# 画新的tree
+def drawNewTree():
+    # #get values from Entry boxes
+    tolN, tolS = getInputs()
+    reDraw(tolS, tolN)
+
+
+def main(root):
+    # 标题
+    Label(root, text="Plot Place Holder").grid(row=0, columnspan=3)
+    # 输入栏1, 叶子的数量
+    Label(root, text="tolN").grid(row=1, column=0)
+    global tolNentry
+    tolNentry = Entry(root)
+    tolNentry.grid(row=1, column=1)
+    tolNentry.insert(0, '10')
+    # 输入栏2, 误差量
+    Label(root, text="tolS").grid(row=2, column=0)
+    global tolSentry
+    tolSentry = Entry(root)
+    tolSentry.grid(row=2, column=1)
+    # 设置输出值
+    tolSentry.insert(0,'1.0')
+
+    # 设置提交的按钮
+    Button(root, text="确定", command=drawNewTree).grid(row=1, column=2, rowspan=3)
+
+    # 设置复选按钮
+    global chkBtnVar
+    chkBtnVar = IntVar()
+    chkBtn = Checkbutton(root, text="Model Tree", variable = chkBtnVar)
+    chkBtn.grid(row=3, column=0, columnspan=2)
+
+    # 退出按钮
+    Button(root, text="退出", fg="black", command=quit).grid(row=1, column=2)
+
+
+    # 创建一个画板 canvas
+    reDraw.f = Figure(figsize=(5, 4), dpi=100)
+    reDraw.canvas = FigureCanvasTkAgg(reDraw.f, master=root)
+    reDraw.canvas.show()
+    reDraw.canvas.get_tk_widget().grid(row=0, columnspan=3)
+
+    reDraw.rawDat = mat(regTrees.loadDataSet('testData/RT_sine.txt'))
+    reDraw.testDat = arange(min(reDraw.rawDat[:, 0]), max(reDraw.rawDat[:, 0]), 0.01)
+    reDraw(1.0, 10)
+
+
+if __name__ == "__main__":
+
+    # 创建一个事件
+    root = Tk()
+    # test_widget_text(root)
+    main(root)
+
+    # 启动事件循环
+    root.mainloop()

testData/RT_sine.txt

@@ -0,0 +1,200 @@
+0.190350	0.878049
+0.306657	-0.109413
+0.017568	0.030917
+0.122328	0.951109
+0.076274	0.774632
+0.614127	-0.250042
+0.220722	0.807741
+0.089430	0.840491
+0.278817	0.342210
+0.520287	-0.950301
+0.726976	0.852224
+0.180485	1.141859
+0.801524	1.012061
+0.474273	-1.311226
+0.345116	-0.319911
+0.981951	-0.374203
+0.127349	1.039361
+0.757120	1.040152
+0.345419	-0.429760
+0.314532	-0.075762
+0.250828	0.657169
+0.431255	-0.905443
+0.386669	-0.508875
+0.143794	0.844105
+0.470839	-0.951757
+0.093065	0.785034
+0.205377	0.715400
+0.083329	0.853025
+0.243475	0.699252
+0.062389	0.567589
+0.764116	0.834931
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