matlib实现logistic回归算法(序一)

数据下载：http://archive.ics.uci.edu/ml/datasets/Adult

数据描述：http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names

这是针对美国某区域的一次人口普查结果，共32561条数据。具体字段如下表：

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字段名	含义	类型
age	年龄	连续变量
workclass	工作类别	分类变量，用0-7表示，Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked，
fnlwgt	序号	连续变量
education	教育程度	分类变量，0-15表示，Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
education_num	受教育时间(年)	连续变量
maritial_status	婚姻状况	分类变量，用0-6表示 Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse
occupation	职业	分类变量，0-13表示 Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
relationship	社会关系	分类变量，0-5表示 Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried
race	种族	分类变量，0-4表示 White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black
sex	性别	分类变量，0-1表示 Female, Male
capital_gain	资本收益	连续变量
capital_loss	资本消耗	连续变量
hours_per_week	每周工作小时数	连续变量
native_country	原籍(国家)	分类变量0-39表示 United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.
income	收入	分类变量0,1 表示 <=50K, >50K

首先我们根据分类预处理数据，把具体的分类字符串替换成相应的数字，以便运用logistic回归计算模型参数。对于数据中有？的字段，直接剔除掉。

处理完毕后得到adult_train.txt和verify.txt,用logstic算法训练参数，得到的参数用以验证verfiy.txt中的数据，通过比较，发现正确率仅89%,比较结果放在result.xlsx

https://pan.baidu.com/s/1qYT2bbm

clear all; close all; clc

data = load('adult_train.txt');
x = data(:,1:14);
y = data(:,15);
m = length(y); % 样本数目
x = [ones(m, 1), x]; % 输入特征增加一列，x0=1
meanx = mean(x);%求均值
sigmax = std(x);%求标准偏差
x(:,2) = (x(:,2)-meanx(2))./sigmax(2);
x(:,3) = (x(:,3)-meanx(3))./sigmax(3);
x(:,4) = (x(:,4)-meanx(4))./sigmax(4);
x(:,5) = (x(:,5)-meanx(5))./sigmax(5);
x(:,6) = (x(:,6)-meanx(6))./sigmax(6);
x(:,7) = (x(:,7)-meanx(7))./sigmax(7);
x(:,8) = (x(:,8)-meanx(8))./sigmax(8);
x(:,9) = (x(:,9)-meanx(9))./sigmax(9);
x(:,10) = (x(:,10)-meanx(10))./sigmax(10);
x(:,11) = (x(:,11)-meanx(11))./sigmax(11);
x(:,12) = (x(:,12)-meanx(12))./sigmax(12);
x(:,13) = (x(:,13)-meanx(13))./sigmax(13);
x(:,14) = (x(:,14)-meanx(14))./sigmax(14);
x(:,15) = (x(:,15)-meanx(15))./sigmax(15);
theta = zeros(size(x(1,:)))'; % 初始化theta

g = inline('1.0 ./ (1.0 + exp(-z))'); %定义logistic函数

% Newton's method
MAX_ITR = 7;
J = zeros(MAX_ITR, 1);

for i = 1:MAX_ITR
    % Calculate the hypothesis function
    z = x * theta;
    h = g(z);%转换成logistic函数

    % Calculate gradient and hessian.
    % The formulas below are equivalent to the summation formulas
    % given in the lecture videos.
    grad = (1/m).*x' * (h-y);%梯度的矢量表示法
    %diag(h),返回向量h为对角线元素的方阵
    H = (1/m).*x' * diag(h) * diag(1-h) * x;%hessian矩阵的矢量表示法

    % Calculate J (for testing convergence)
    J(i) =(1/m)*sum(-y.*log(h) - (1-y).*log(1-h));%损失函数的矢量表示法

    theta = theta - Hgrad;%H逆矩阵
end
% Display theta
theta
data1 = load('verify.txt');
x1 = data1(:,1:14);
y1 = data1(:,15);
m1 = length(y1);
x1 = [ones(m1, 1), x1];

meanx1 = mean(x1);%求均值
sigmax1 = std(x1);%求标准偏差
x1(:,2) = (x1(:,2)-meanx1(2))./sigmax1(2);
x1(:,3) = (x1(:,3)-meanx1(3))./sigmax1(3);
x1(:,4) = (x1(:,4)-meanx1(4))./sigmax1(4);
x1(:,5) = (x1(:,5)-meanx1(5))./sigmax1(5);
x1(:,6) = (x1(:,6)-meanx1(6))./sigmax1(6);
x1(:,7) = (x1(:,7)-meanx1(7))./sigmax1(7);
x1(:,8) = (x1(:,8)-meanx1(8))./sigmax1(8);
x1(:,9) = (x1(:,9)-meanx1(9))./sigmax1(9);
x1(:,10) = (x1(:,10)-meanx1(10))./sigmax1(10);
x1(:,11) = (x1(:,11)-meanx1(11))./sigmax1(11);
x1(:,12) = (x1(:,12)-meanx1(12))./sigmax1(12);
x1(:,13) = (x1(:,13)-meanx1(13))./sigmax1(13);
x1(:,14) = (x1(:,14)-meanx1(14))./sigmax1(14);
x1(:,15) = (x1(:,15)-meanx1(15))./sigmax1(15)
y2 = g(x1*theta);
y2