diff toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/mfademo.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/mfademo.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,81 @@
+echo on;
+
+clc;
+
+% This is a very basic demo of the mixture of factor analyzer software
+% written in Matlab by	Zoubin Ghahramani
+%			Dept of Computer Science
+%			University of Toronto
+
+pause;		% Hit any key to continue 
+
+% To demonstrate the software we generate a sample data set
+% from a mixture of two Gaussians
+
+pause;		% Hit any key to continue 
+
+X1=randn(300,5);	% zero mean 5 dim Gaussian data 
+X2=randn(200,5)+2;	% 5 dim Gaussian data with mean [1 1 1 1 1]
+X=[X1;X2];		% total 500 data points from mixture
+
+% Fitting the model is very easy. For example to fit a mixture of 2
+% factor analyzers with three factors each...
+
+pause;		% Hit any key to continue 
+
+
+[Lh,Ph,Mu,Pi,LL]=mfa(X,2,3);
+
+% Lh, Ph, Mu, and Pi are the factor loadings, observervation
+% variances, observation means for each mixture, and mixing
+% proportions. LL is the vector of log likelihoods (the learning
+% curve). For more information type: help mfa
+
+% to plot the learning curve (log likelihood at each step of EM)...
+
+pause;		% Hit any key to continue 
+
+plot(LL);
+
+% you get a more informative picture of convergence by looking at the
+% log of the first difference of the log likelihoods...
+
+pause;		% Hit any key to continue 
+
+semilogy(diff(LL)); 
+
+% you can look at some of the parameters of the fitted model... 
+
+pause;		% Hit any key to continue 
+
+Mu
+
+Pi
+
+% ...to see whether they make any sense given that me know how the
+% data was generated. 
+
+% you can also evaluate the log likelihood of another data set under
+% the model we have just fitted using the mfa_cl (for Calculate
+% Likelihood) function. For example, here we generate a test from the
+% same distribution. 
+
+
+X1=randn(300,5);
+X2=randn(200,5)+2;
+Xtest=[X1; X2];
+
+pause;		% Hit any key to continue 
+
+mfa_cl(Xtest,Lh,Ph,Mu,Pi)
+
+% we should expect the log likelihood of the test set to be lower than
+% that of the training set.
+
+% finally, we can also fit a regular factor analyzer using the ffa
+% function (Fast Factor Analysis)...
+
+pause;		% Hit any key to continue 
+
+[L,Ph,LL]=ffa(X,3);
+