添加15章代码和测试数据

src/python/15.BigData_MapReduce/pegasos.py

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+'''
+Created on 2017-04-07
+Sequential Pegasos 
+the input T is k*T in Batch Pegasos
+@author: Peter/ApacheCN-xy
+'''
+
+from numpy import *
+
+def loadDataSet(fileName):
+    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])])
+        labelMat.append(float(lineArr[2]))
+    return dataMat,labelMat
+
+def seqPegasos(dataSet, labels, lam, T):
+    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,:])
+        if labels[i]*p < 1:
+            w = (1.0 - 1/t)*w + eta*labels[i]*dataSet[i,:]
+        else:
+            w = (1.0 - 1/t)*w
+        print w
+    return w
+        
+def predict(w, x):
+    return w*x.T
+
+def batchPegasos(dataSet, labels, lam, T, k):
+    m,n = shape(dataSet); w = zeros(n); 
+    dataIndex = range(m)
+    for t in range(1, T+1):
+        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
+            i = dataIndex[j]
+            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  
+        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')
+datMat = mat(datArr)
+#finalWs = seqPegasos(datMat, labelList, 2, 5000)
+finalWs = batchPegasos(datMat, labelList, 2, 50, 100)
+print finalWs
+
+import matplotlib
+import matplotlib.pyplot as plt
+fig = plt.figure()
+ax = fig.add_subplot(111)
+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])
+    else:
+        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') )
+plt.show()