测试完：回归树 VS 模型树 VS 线性回归

src/python/regression.py

@@ -11,6 +11,7 @@ import os
 from numpy import *
 import matplotlib.pylab as plt
 
+
 def loadDataSet(fileName):      #general function to parse tab -delimited floats
     numFeat = len(open(fileName).readline().split('\t')) - 1 #get number of fields
     dataMat = []; labelMat = []
@@ -24,6 +25,7 @@ def loadDataSet(fileName):      #general function to parse tab -delimited floats
         labelMat.append(float(curLine[-1]))
     return dataMat,labelMat
 
+
 def standRegres(xArr,yArr):
     # >>> A.T  # transpose, 转置
     xMat = mat(xArr); yMat = mat(yArr).T
@@ -37,6 +39,7 @@ def standRegres(xArr,yArr):
     ws = xTx.I * (xMat.T*yMat)  # 最小二乘法求最优解
     return ws
 
+
 def plotBestFit(xArr, yArr, ws):
 
     xMat = mat(xArr)
@@ -60,6 +63,7 @@ def plotBestFit(xArr, yArr, ws):
     plt.xlabel('X'); plt.ylabel('Y')
     plt.show()
 
+
 def main1():
     # w0*x0+w1*x1+w2*x2=f(x)
     project_dir = os.path.dirname(os.path.dirname(os.getcwd()))
@@ -91,6 +95,7 @@ def lwlr(testPoint, xArr, yArr,k=1.0):
     ws = xTx.I * (xMat.T * (weights * yMat))
     return testPoint * ws
 
+
 def lwlrTest(testArr,xArr,yArr,k=1.0):  #loops over all the data points and applies lwlr to each one
     m = shape(testArr)[0]
     # m*1的矩阵
@@ -101,6 +106,7 @@ def lwlrTest(testArr,xArr,yArr,k=1.0):  #loops over all the data points and appl
         yHat[i] = lwlr(testArr[i],xArr,yArr,k)
     return yHat
 
+
 def lwlrTestPlot(xArr, yArr, yHat):
 
     xMat = mat(xArr)
@@ -123,11 +129,13 @@ def lwlrTestPlot(xArr, yArr, yHat):
     plt.xlabel('X'); plt.ylabel('Y')
     plt.show()
 
+
 def main2():
     # w0*x0+w1*x1+w2*x2=f(x)
-    project_dir = os.path.dirname(os.path.dirname(os.getcwd()))
+    # project_dir = os.path.dirname(os.path.dirname(os.getcwd()))
     # 1.收集并准备数据
-    xArr, yArr = loadDataSet("%s/resources/ex0.txt" % project_dir)
+    # xArr, yArr = loadDataSet("%s/resources/ex0.txt" % project_dir)
+    xArr, yArr = loadDataSet("testData/Regression_data.txt")
     # print xArr, '---\n', yArr
     # 2.训练模型，  f(x)=a1*x1+b2*x2+..+nn*xn中 (a1,b2, .., nn).T的矩阵值
     yHat = lwlrTest(xArr, xArr, yArr, 0.003)
@@ -136,12 +144,14 @@ def main2():
     # 数据可视化
     lwlrTestPlot(xArr, yArr, yHat)
 
-if __name__=="__main__":
+
+if __name__ == "__main__":
     # 线性回归
     # main1()
     # 局部加权线性回归
     main2()
 
+
 def rssError(yArr,yHatArr): #yArr and yHatArr both need to be arrays
     return ((yArr-yHatArr)**2).sum()