update

sherlock-coding · sherlock-coding · commit a2207b376cab · 2014-12-16T16:32:24.000+08:00
diff --git a/Homework/HousingPricePrediction.py b/Homework/HousingPricePrediction.py
@@ -1,12 +1,14 @@
 #LMS算法
 import math
+import matplotlib.pyplot as plt
 
 #求h(x,theta)
 def GetHThetaX(x,theta):
-        theta_1 = theta[1:]
-        h = theta[0] + sum([xi*thetai for xi,thetai in zip(x,theta_1)])
-        return h
+    theta_1 = theta[1:]
+    h = theta[0] + sum([xi*thetai for xi,thetai in zip(x,theta_1)])
+    return h
 
+#求J(theta)
 def GetJ(X,Y,theta):
         J = sum(pow(GetHThetaX(x,theta)-y,2) for x,y in zip(X,Y))/2
         return J
@@ -27,9 +29,14 @@ def GetGradient(X,Y,theta,j):
 
 
 #n表示n维的特征向量
-def LMS_Prediction(X,Y,n,year):
+def LMS_Prediction(X,X1,Y,n,year):
         #theta全都初始化为0
-        theta = [i for i in range(0,n)]
+        #theta = [i for i in range(0,n)]
+        theta = [-1500,1]
+        
+        plt.plot(X1,Y,'ro')
+        plt.axis([2000,2015,1,15])
+        plt.plot(X1,[theta[0]+theta[1]*x for x in X1],'y-')
 
         #learning rate设置
         alpha = 0.00000001
@@ -38,6 +45,8 @@ def LMS_Prediction(X,Y,n,year):
 
         curJ = GetJ(X,Y,theta)
         preJ = curJ
+
+
                 
         while True:
                 temp = []
@@ -53,13 +62,23 @@ def LMS_Prediction(X,Y,n,year):
                 curJ = GetJ(X,Y,theta)
                 if curJ>preJ:
                         break
+                plt.plot(X1,[theta[0]+theta[1]*x for x in X1],'b-')
+        plt.plot(X1,[theta[0]+theta[1]*x for x in X1],'r-')
+        plt.show()
         
         
 
 if __name__ == '__main__':
-        #X = [[1],[2],[5],[7], [10], [15]]
-        #Y = [2, 6, 7, 9, 14, 19]
-	X = [[2000],[2001],[2002],[2003],[2004],[2005],[2006],[2007],[2008],[2009],[2010],[2011],[2012],[2013]]
-	Y = [2.000,2.500,2.900,3.147,4.515,4.903,5.365,5.704,6.853,7.971,8.561,10.000,11.280,12.900]
-	LMS_Prediction(X,Y,2,2014)
+    X = [[2000],[2001],[2002],[2003],[2004],[2005],[2006],[2007],[2008],[2009],[2010],[2011],[2012],[2013]]
+    Y = [2.000,2.500,2.900,3.147,4.515,4.903,5.365,5.704,6.853,7.971,8.561,10.000,11.280,12.900]
+    X1 = [2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013]
+    LMS_Prediction(X,X1,Y,2,2014)
+    #X = [[1],[2],[5],[7], [10], [15]]
+    #Y = [2, 6, 7, 9, 14, 19]
+    #X = [[2000],[2001],[2002],[2003],[2004],[2005],[2006],[2007],[2008],[2009],[2010],[2011],[2012],[2013]]
+    #Y = [2.000,2.500,2.900,3.147,4.515,4.903,5.365,5.704,6.853,7.971,8.561,10.000,11.280,12.900]
+    #X1 = [2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013]
+    #pylab.scatter(X1,Y)
+    #pylab.show()
+	
 	
