数据下载：
数据描述：
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：
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
clear all; close all; clcdata = 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=1meanx = 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thetag = inline('1.0 ./ (1.0 + exp(-z))'); %定义logistic函数% Newton's methodMAX_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 - H\grad;%H\逆矩阵end% Display thetathetadata1 = 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