lvq学习算法源码matlab

clear
clc

%获取训练样本点
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feature_num=2;
sample_num=200;
mu = [0 1];
sigma=[0.65*0.65 0; 0 0.65*0.65];
mu1 = [0 1];
mu2 = [0 -1];
r1_=mvnrnd（mu1sigmasample_num）;
r2_=mvnrnd（mu2sigmasample_num）;
% axis（[-3 3 -3 3]）;
p_train = [r1_;r2_];
p_train  = p_train‘;
t_train = ones（12*sample_num）;
t_train （sample_num+1:2*sample_num） = t_train （sample_num+1:2*sample_num） - 1;
plotpv（p_train t_train）;
t_train = ones（12*sample_num）;
t_train （sample_num+1:2*sample_num） = t_train （sample_num+1:2*sample_num） + 1;
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%lvq算法对其进行优化
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k=2;%隐中心的数目
% p_cluster（:1:k） = em（p_train（:1:sample_num） k feature_num sample_num）;
% p_cluster（:k+1:2*k） = em（p_train（:sample_num+1:2*sample_num） k feature_num sample_num）;
net_lvq = newlvq（minmax（p_train）2*k[.5 .5]0.01‘learnlv‘）;
net_lvq.trainParam.epochs=100;
tc_train = ind2vec（t_train）;
net_lvq = train（net_lvqp_traintc_train）;
p_cluster = net_lvq.IW{11}‘;
t_cluster = ones（12*k）;
t_cluster（1k+1:2*k）=t_cluster（1k+1:2*k）-1;
plotpv（p_cluster t_cluster）;
% plotpv（p_cluster t_cluster）;
t_cluster = ones（12*k）;
t_cluster（1k+1:2*k）=t_cluster（1k+1:2*k）+1;
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%建立网络
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p=p_cluster;
t=t_cluster;

% p=p_train;
% t=t_train;

net = network;
net.n