逻辑回归进行异网（联通电信）用户年龄分类

逻辑回归算法可以高效的解决二分类问题，也可以解决多分类只是没有KNN高效，KNN天生就是解决多分类，不过KNN过于简单，适用性没有逻辑回归好。我是想通过移动用户的交际圈来判断异网用户是否25岁以下，想看看到底年轻人有多少去了联通。这里就提取了两个特征，一个是交际圈前5名的平均年龄，再一个就是平均联系亲密度。然后利用网内对网内的数据进行模型训练，准确率上去后再开始应用。 这块能用到的地方还是挺多的，比如判断用户是否会订购业务，是否即将离网，是否套餐降档等等，但是，处理实际的用户多分类问题，最佳的还是神经网络深度学习，但是运算量太大了，我的机器配置过低了。实际应用逻辑回归需要改进的地方比较多，不像理论数据，简单运算就能90以上准确率。还得继续学学别人的优化算法。

用的是python3.6，参考代码是2.7的，花了点时间看懂然后改代码，改参数，还是不熟，报错就百度，搞了一个多小时终于OK了。代码和结果如下

# -*- coding: utf-8 -*- \

Created on Wed Apr 11 19:49:13 2018

from numpy import *

import matplotlib.pyplot as plt import time

# calculate the sigmoid function def sigmoid(inX):

return 1.0 / (1 + exp(-inX))

# train a logistic regression model using some optional optimize algorithm

# input: train_x is a mat datatype, each row stands for one sample

# train_y is mat datatype too, each row is the corresponding label

# opts is optimize option include step and maximum number of iterations

def trainLogRegres(train_x, train_y, opts): # calculate training time #startTime = time.time()

numSamples, numFeatures = shape(train_x) alpha = opts['alpha']; maxIter = opts['maxIter'] weights = ones((numFeatures, 1))

# optimize through gradient descent algorilthm for k in range(maxIter):

if opts['optimizeType'] == 'gradDescent': # gradient descent algorilthm

output = sigmoid(train_x * weights)

error = train_y - output

weights = weights + alpha * train_x.transpose() * error elif opts['optimizeType'] == 'stocGradDescent': # stochastic gradient descent

for i in range(numSamples):

output = sigmoid(train_x[i, :] * weights) error = train_y[i, 0] - output

weights = weights + alpha * train_x[i, :].transpose() * error elif opts['optimizeType'] == 'smoothStocGradDescent': # smooth stochastic gradient descent

# randomly select samples to optimize for reducing cycle fluctuations

dataIndex = list(range(numSamples)) for i in range(numSamples): alpha = 4.0 / (1.0 + k + i) + 0.01

randIndex = int(random.uniform(0, len(dataIndex)))