混合高斯模型的曲线拟合matlab源代码
function = fit_mix_gaussian( X,M )clear all
%
% fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm
%
% format: = fit_mix_gaussian( X,M )
%
% input: X - input samples, N*1 vector
% M - number of gaussians which are assumed to compose the distribution
%
% output: u - fitted mean for each gaussian
% sig - fitted standard deviation for each gaussian
% t - probability of each gaussian in the complete distribution
% iter- number of iterations done by the function
%
% run with default values
% if ~nargin
% % M = round(rand*5)+1;
% M=1;
% sig = rand(1,M)*3;
% u = randn(1,M)*8;
% prob= rand(1,M);
% = fit_mix_gaussian( build_mix_gaussian( u,sig,prob,1000*M ),M );
% return
% end
%%%%%%%%added by me%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
M=2;
sig = 3;
u = 8;
prob= 1;
=build_mix_gaussian( u,sig,prob,256*M );
figure;
plot(X);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% initialize and initial guesses
N = length( X );
Z = ones(N,M) * 1/M; % indicators vector
P = zeros(N,M); % probabilities vector for each sample and each model
t = ones(1,M) * 1/M; % distribution of the gaussian models in the samples
u = linspace(min(X),max(X),M); % mean vector
sig2 = ones(1,M) * var(X) / sqrt(M); % variance vector
C = 1/sqrt(2*pi); % just a constant
Ic = ones(N,1); % - enable a row replication by the * operator
Ir = ones(1,M); % - enable a column replication by the * operator
Q = zeros(N,M); % user variable to determine when we have converged to a steady solution
thresh = 1e-3;
step = N;
last_step = inf;
iter = 0;
min_iter = 10;
% main convergence loop, assume gaussians are 1D
while ((( abs((step/last_step)-1) > thresh) & (step>(N*eps)) ) | (iter<min_iter) )
% E step
% ========
Q = Z;
P = C ./ (Ic*sqrt(sig2)) .* exp( -((X*Ir - Ic*u).^2)./(2*Ic*sig2) );
%%%%%%1/N*1X1*M*exp((N*1XM-N*1X1*M)/N*1*1*M)
for m = 1:M
Z(:,m)= (P(:,m)*t(m))./(P*t(:));
end
% estimate convergence step size and update iteration number
prog_text = sprintf(repmat( '\b',1,(iter>0)*12+ceil(log10(iter+1)) ));
iter = iter + 1;
last_step = step * (1 + eps) + eps;
step = sum(sum(abs(Q-Z)));
fprintf( '%s%d iterations\n',prog_text,iter );
% M step
% ========
Zm = sum(Z); % sum each column
Zm(find(Zm==0)) = eps; % avoid devision by zero
u = (X')*Z ./ Zm;
sig2 = sum(((X*Ir - Ic*u).^2).*Z) ./ Zm;
t = Zm/N;
end
% plot the fitted distribution
% =============================
sig = sqrt( sig2 );
plot_mix_gaussian( u,sig,t ); 代码不全啊 build_mix_gaussian函数没有
请问一下这个可以用于信号分类吗？ 是这个吗？http://llwebprod2.ll.mit.edu/mission/cybersec/publications/publication-files/full_papers/0802_Reynolds_Biometrics-GMM.pdf 感觉好老的算法了。
页:
[1]