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112 lines
3.4 KiB
Python
112 lines
3.4 KiB
Python
#!/usr/bin/python
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# coding:utf8
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'''
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Created on 2017-04-07
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Sequential Pegasos
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the input T is k*T in Batch Pegasos
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@author: Peter/ApacheCN-xy
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'''
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from numpy import *
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def loadDataSet(fileName):
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dataMat = []
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labelMat = []
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fr = open(fileName)
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for line in fr.readlines():
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lineArr = line.strip().split('\t')
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# dataMat.append([float(lineArr[0]), float(lineArr[1]), float(lineArr[2])])
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dataMat.append([float(lineArr[0]), float(lineArr[1])])
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labelMat.append(float(lineArr[2]))
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return dataMat, labelMat
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def seqPegasos(dataSet, labels, lam, T):
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m, n = shape(dataSet)
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w = zeros(n)
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for t in range(1, T+1):
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i = random.randint(m)
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eta = 1.0/(lam*t)
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p = predict(w, dataSet[i, :])
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if labels[i]*p < 1:
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w = (1.0 - 1/t)*w + eta*labels[i]*dataSet[i, :]
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else:
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w = (1.0 - 1/t)*w
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print w
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return w
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def predict(w, x):
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return w*x.T # 就是预测 y 的值
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def batchPegasos(dataSet, labels, lam, T, k):
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"""batchPegasos()
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Args:
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dataMat 特征集合
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labels 分类结果集合
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lam 固定值
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T 迭代次数
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k 待处理列表大小
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Returns:
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w 回归系数
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"""
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m, n = shape(dataSet)
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w = zeros(n) # 回归系数
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dataIndex = range(m)
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for t in range(1, T+1):
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wDelta = mat(zeros(n)) # 重置 wDelta
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# 它是学习率，代表了权重调整幅度的大小。（也可以理解为随机梯度的步长，使它不断减小，便于拟合）
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# 输入T和K分别设定了迭代次数和待处理列表的大小。在T次迭代过程中，每次需要重新计算eta
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eta = 1.0/(lam*t)
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random.shuffle(dataIndex)
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for j in range(k): # 全部的训练集 内循环中执行批处理，将分类错误的值全部做累加后更新权重向量
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i = dataIndex[j]
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p = predict(w, dataSet[i, :]) # mapper 代码
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# 如果预测正确，并且预测结果的绝对值>=1，因为最大间隔为1, 认为没问题。
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# 否则算是预测错误, 通过预测错误的结果，来累计更新w.
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if labels[i]*p < 1: # mapper 代码
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wDelta += labels[i]*dataSet[i, :].A # 累积变化
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# w通过不断的随机梯度的方式来优化
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w = (1.0 - 1/t)*w + (eta/k)*wDelta # 在每个 T上应用更改
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# print '-----', w
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# print '++++++', w
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return w
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datArr, labelList = loadDataSet('input/15.BigData_MapReduce/testSet.txt')
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datMat = mat(datArr)
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# finalWs = seqPegasos(datMat, labelList, 2, 5000)
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finalWs = batchPegasos(datMat, labelList, 2, 50, 100)
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print finalWs
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import matplotlib
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import matplotlib.pyplot as plt
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fig = plt.figure()
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ax = fig.add_subplot(111)
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x1 = []
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y1 = []
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xm1 = []
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ym1 = []
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for i in range(len(labelList)):
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if labelList[i] == 1.0:
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x1.append(datMat[i, 0])
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y1.append(datMat[i, 1])
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else:
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xm1.append(datMat[i, 0])
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ym1.append(datMat[i, 1])
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ax.scatter(x1, y1, marker='s', s=90)
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ax.scatter(xm1, ym1, marker='o', s=50, c='red')
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x = arange(-6.0, 8.0, 0.1)
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y = (-finalWs[0, 0]*x - 0)/finalWs[0, 1]
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# y2 = (0.43799*x)/0.12316
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y2 = (0.498442*x)/0.092387 # 2 iterations
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ax.plot(x, y)
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ax.plot(x, y2, 'g-.')
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ax.axis([-6, 8, -4, 5])
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ax.legend(('50 Iterations', '2 Iterations'))
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plt.show()
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