refined doc for common and other functions

common/gsog.m

@@ -1,5 +1,6 @@
 function [Q, R] = gsog(X)
 % Gram-Schmidt orthogonalization
+% Written by Mo Chen ([email protected]).
 [d,n] = size(X);
 m = min(d,n);
 R = eye(m,n);

common/gson.m

@@ -1,5 +1,6 @@
 function [Q, R] = gson(X)
 % Gram-Schmidt orthonormalization which produces the same result as [Q,R]=qr(X,0)
+% Written by Mo Chen ([email protected]).
 [d,n] = size(X);
 m = min(d,n);
 R = zeros(m,n);

common/invpd.m

@@ -1,6 +1,7 @@
 function W = invpd(M)
 % Compute A\B where A is a positive definite matrix
-%   A: a positive difinie matrix
+% Input:
+%   M: a positive difinie matrix
 % Written by Michael Chen ([email protected]).
 [U,p] = chol(M);
 if p > 0

common/log1pexp.m

@@ -1,5 +1,5 @@
 function y = log1pexp(x)
-% accurately compute y = log(1+exp(x))
+% Accurately compute y = log(1+exp(x))
 % reference: Accurately Computing log(1-exp(|a|)) Martin Machler
 seed = 33.3;
 y = x;

common/logsumexp.m

@@ -1,7 +1,7 @@
 function s = logsumexp(X, dim)
 % Compute log(sum(exp(X),dim)) while avoiding numerical underflow.
 %   By default dim = 1 (columns).
-% Written by Michael Chen ([email protected]).
+% Written by Mo Chen ([email protected]).
 if nargin == 1, 
     % Determine which dimension sum will use
     dim = find(size(X)~=1,1);

common/mgsog.m

@@ -1,5 +1,6 @@
 function [Q, R] = mgsog(X)
 % Modified Gram-Schmidt orthogonalization
+% Written by Mo Chen ([email protected]).
 [d,n] = size(X);
 m = min(d,n);
 R = eye(m,n);

common/mgson.m

@@ -1,6 +1,7 @@
 function [Q, R] = mgson(X)
 % Modified Gram-Schmidt orthonormalization (numerical stable version of Gram-Schmidt algorithm) 
 % which produces the same result as [Q,R]=qr(X,0)
+% Written by Mo Chen ([email protected]).
 [d,n] = size(X);
 m = min(d,n);
 R = zeros(m,n);

common/solvpd.m

@@ -1,7 +1,7 @@
 function V = solvpd(A,B)
 % Compute A\B where A is a positive definite matrix
 %   A: a positive difinie matrix
-% Written by Michael Chen ([email protected]).
+% Written by Mo Chen ([email protected]).
 [U,p] = chol(A);
 if p > 0
     error('ERROR: the matrix is not positive definite.');

common/standardize.m

@@ -1,7 +1,7 @@
 function [Y, s] = standardize(X)
 % Unitize the vectors to be unit length
 %   By default dim = 1 (columns).
-% Written by Michael Chen ([email protected]).
+% Written by Mo Chen ([email protected]).
 if nargin == 1, 
     % Determine which dimension sum will use
     dim = find(size(X)~=1,1);

common/symeig.m

@@ -2,7 +2,7 @@
 % Compute eigenvalues and eigenvectors of symmetric matrix
 %   m == 's' smallest (default)
 %   m == 'l' largest
-% Written by Michael Chen ([email protected]).
+% Written by Mo Chen ([email protected]).
 if nargin == 2
     m = 's';
 end

common/ud.m

@@ -1,5 +1,6 @@
 function [U, D] = ud(X)
 % UD factorization U'*D*U=X'*X;
+% Written by Mo Chen ([email protected]).
 [~,R] = qr(X,0);
 d = diag(R);
 D = d.^2;

common/unitize.m

@@ -1,7 +1,7 @@
 function [Y, s] = unitize(X, dim)
 % Unitize the vectors to be unit length
 %   By default dim = 1 (columns).
-% Written by Michael Chen ([email protected]).
+% Written by Mo Chen ([email protected]).
 if nargin == 1, 
     % Determine which dimension sum will use
     dim = find(size(X)~=1,1);

other/loggmpdf.m

@@ -1,5 +1,6 @@
 function r = loggmpdf(X, model)
-
+% Compute log pdf of a Gaussian mixture model.
+% Written by Mo Chen ([email protected]).
 mu = model.mu;
 Sigma = model.Sigma;
 w = model.weight;

other/plotgm.m

@@ -1,5 +1,6 @@
 function plotgm(X, model)
-% Written by Michael Chen ([email protected]).
+% Plot 2d Gaussian mixture model.
+% Written by Mo Chen ([email protected]).
 level = 64;
 n = 256;
 

other/plotkde.m

@@ -1,6 +1,6 @@
 function plotkde(X, sigma2)
-% Written by Michael Chen ([email protected]).
-
+% Plot 2d kernel density.
+% Written by Mo Chen ([email protected]).
 if nargin < 2
     sigma2 = 1e-1;
 end