更新 Pegasos python单机版

src/python/15.BigData_MapReduce/pegasos.py

@@ -10,24 +10,26 @@ from numpy import *
 
 
 def loadDataSet(fileName):
-    dataMat = []; labelMat = []
+    dataMat = []
+    labelMat = []
     fr = open(fileName)
     for line in fr.readlines():
         lineArr = line.strip().split('\t')
-        #dataMat.append([float(lineArr[0]), float(lineArr[1]), float(lineArr[2])])
+        # dataMat.append([float(lineArr[0]), float(lineArr[1]), float(lineArr[2])])
         dataMat.append([float(lineArr[0]), float(lineArr[1])])
         labelMat.append(float(lineArr[2]))
-    return dataMat,labelMat
+    return dataMat, labelMat
 
 
 def seqPegasos(dataSet, labels, lam, T):
-    m,n = shape(dataSet); w = zeros(n)
+    m, n = shape(dataSet)
+    w = zeros(n)
     for t in range(1, T+1):
         i = random.randint(m)
         eta = 1.0/(lam*t)
-        p = predict(w, dataSet[i,:])
+        p = predict(w, dataSet[i, :])
         if labels[i]*p < 1:
-            w = (1.0 - 1/t)*w + eta*labels[i]*dataSet[i,:]
+            w = (1.0 - 1/t)*w + eta*labels[i]*dataSet[i, :]
         else:
             w = (1.0 - 1/t)*w
         print w
@@ -39,25 +41,25 @@ def predict(w, x):
 
 
 def batchPegasos(dataSet, labels, lam, T, k):
-    m,n = shape(dataSet); w = zeros(n); 
+    m, n = shape(dataSet)
+    w = zeros(n)
     dataIndex = range(m)
     for t in range(1, T+1):
-        wDelta = mat(zeros(n)) # 重置 wDelta
+        wDelta = mat(zeros(n))  # 重置 wDelta
         eta = 1.0/(lam*t)
         random.shuffle(dataIndex)
-        for j in range(k):# 全部的训练集
+        for j in range(k):      # 全部的训练集
             i = dataIndex[j]
-            p = predict(w, dataSet[i,:])        # mapper 代码
+            p = predict(w, dataSet[i, :])        # mapper 代码
             if labels[i]*p < 1:                 # mapper 代码
-                wDelta += labels[i]*dataSet[i,:].A # 累积变化  
+                wDelta += labels[i]*dataSet[i, :].A # 累积变化  
         w = (1.0 - 1/t)*w + (eta/k)*wDelta       # 在每个 T上应用更改
     return w
 
 
-
-datArr,labelList = loadDataSet('testSet.txt')
+datArr, labelList = loadDataSet('input/15.BigData_MapReduce/testSet.txt')
 datMat = mat(datArr)
-#finalWs = seqPegasos(datMat, labelList, 2, 5000)
+# finalWs = seqPegasos(datMat, labelList, 2, 5000)
 finalWs = batchPegasos(datMat, labelList, 2, 50, 100)
 print finalWs
 
@@ -65,20 +67,25 @@ import matplotlib
 import matplotlib.pyplot as plt
 fig = plt.figure()
 ax = fig.add_subplot(111)
-x1=[]; y1=[]; xm1=[]; ym1=[]
+x1 = []
+y1 = []
+xm1 = []
+ym1 = []
 for i in range(len(labelList)):
     if labelList[i] == 1.0:
-        x1.append(datMat[i,0]); y1.append(datMat[i,1])
+        x1.append(datMat[i, 0])
+        y1.append(datMat[i, 1])
     else:
-        xm1.append(datMat[i,0]); ym1.append(datMat[i,1])
+        xm1.append(datMat[i, 0])
+        ym1.append(datMat[i, 1])
 ax.scatter(x1, y1, marker='s', s=90)
 ax.scatter(xm1, ym1, marker='o', s=50, c='red')
 x = arange(-6.0, 8.0, 0.1)
-y = (-finalWs[0,0]*x - 0)/finalWs[0,1]
-#y2 = (0.43799*x)/0.12316
-y2 = (0.498442*x)/0.092387 #2 iterations
-ax.plot(x,y)
-ax.plot(x,y2,'g-.')
-ax.axis([-6,8,-4,5])
-ax.legend(('50 Iterations', '2 Iterations') )
+y = (-finalWs[0, 0]*x - 0)/finalWs[0, 1]
+# y2 = (0.43799*x)/0.12316
+y2 = (0.498442*x)/0.092387  # 2 iterations
+ax.plot(x, y)
+ax.plot(x, y2, 'g-.')
+ax.axis([-6, 8, -4, 5])
+ax.legend(('50 Iterations', '2 Iterations'))
 plt.show()