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#!/usr/bin/python
# coding:utf8
'''
Created on 2017-04-07
Sequential Pegasos
the input T is k*T in Batch Pegasos
Author: Peter/ApacheCN-xy
'''
from __future__ import print_function
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 # 就是预测 y 的值
def batchPegasos(dataSet, labels, lam, T, k):
"""batchPegasos()
Args:
dataMat 特征集合
labels 分类结果集合
lam 固定值
T 迭代次数
k 待处理列表大小
Returns:
w 回归系数
"""
m, n = shape(dataSet)
w = zeros(n) # 回归系数
dataIndex = range(m)
for t in range(1, T+1):
wDelta = mat(zeros(n)) # 重置 wDelta
# 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)
# 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
eta = 1.0/(lam*t)
random.shuffle(dataIndex)
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
i = dataIndex[j]
p = predict(w, dataSet[i, :]) # mapper 代码
# 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。
# 否则算是预测错误, 通过预测错误的结果,来累计更新w.
if labels[i]*p < 1: # mapper 代码
wDelta += labels[i]*dataSet[i, :].A # 累积变化
# w通过不断的随机梯度的方式来优化
w = (1.0 - 1/t)*w + (eta/k)*wDelta # 在每个 T上应用更改
# print '-----', w
# print '++++++', w
return w
datArr, labelList = loadDataSet('db/15.BigData_MapReduce/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()