逻辑回归的分布式实现 [Logistic Regression / Machine Learning / Spark ]

1- 问题提出

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2- 逻辑回归

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3- 理论推导

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4- Python/Spark实现

# -*- coding: utf-8 -*-
from pyspark import SparkContext
from math import *

theta = [0, 0, 0]    #初始theta值
alpha = 0.001    #学习速率

def inner(x, y):
    return sum([i*j for i,j in zip(x,y)])
        
def func(lst):
    h = (1 + exp(-inner(lst, theta)))**(-1)
    return map(lambda x: (h - lst[-1]) * x, lst[:-1])


sc = SparkContext('local')

rdd = sc.textFile('/home/freyr/logisticRegression.txt')
        .map(lambda line: map(float, line.strip().split(',')))
        .map(lambda lst: [1]+lst)


for i in range(400):
    partheta = rdd.map(func)
                   .reduce(lambda x,y: [i+j for i,j in zip(x,y)])

    for j in range(3):
        theta[j] = theta[j] - alpha * partheta[j]

print 'theta = %s' % theta

 PS: logisticRegression.txt