matlab神经网络分析实战案例（附带源码及数据集） (14).zip

function Chapter_ModelDecryption %% Matlab神经网络43个案例分析 % LIBSVM参数实例详解 % by 李洋(faruto) % http://www.matlabsky.com % Email:[email protected] % http://weibo.com/faruto % http://blog.sina.com.cn/faruto % 2013.01.01 %% A Little Clean Work clear; clc; close all; format compact; %% % 首先载入数据 load heart_scale; data = heart_scale_inst; label = heart_scale_label; % 建立分类模型 model = svmtrain(label,data,'-s 0 -t 2 -c 1.2 -g 2.8'); % 利用建立的模型看其在训练集合上的分类效果 [PredictLabel,accuracy] = svmpredict(label,data,model); accuracy %% 分类模型model解密 model Parameters = model.Parameters Label = model.Label nr_class = model.nr_class totalSV = model.totalSV nSV = model.nSV %% plable = zeros(270,1); for i = 1:270 x = data(i,:); plabel(i,1) = DecisionFunction(x,model); end %% 验证自己通过决策函数预测的标签和svmpredict给出的标签相同 flag = sum(plabel == PredictLabel) %% DecisionFunction function plabel = DecisionFunction(x,model) gamma = model.Parameters(4); RBF = @(u,v)( exp(-gamma.*sum( (u-v).^2) ) ); len = length(model.sv_coef); y = 0; for i = 1:len u = model.SVs(i,:); y = y + model.sv_coef(i)*RBF(u,x); end b = -model.rho; y = y + b; if y >= 0 plabel = 1; else plabel = -1; end