1 files changed, 0 insertions, 47 deletions
diff --git a/Wrappers/Matlab/studentst.m b/Wrappers/Matlab/studentst.m
deleted file mode 100644
index 99fed1e..0000000
--- a/Wrappers/Matlab/studentst.m
+++ /dev/null
@@ -1,47 +0,0 @@
-function [f,g,h,s,k] = studentst(r,k,s)
-% Students T penalty with 'auto-tuning'
-%
-% use:
-%   [f,g,h,{k,{s}}] = studentst(r)     - automatically fits s and k
-%   [f,g,h,{k,{s}}] = studentst(r,k)   - automatically fits s
-%   [f,g,h,{k,{s}}] = studentst(r,k,s) - use given s and k
-%
-% input:
-%   r - residual as column vector
-%   s - scale (optional)
-%   k - degrees of freedom (optional)
-% 
-% output:
-%   f - misfit (scalar)
-%   g - gradient (column vector)
-%   h - positive approximation of the Hessian (column vector, Hessian is a diagonal matrix)
-%   s,k - scale and degrees of freedom
-%
-% Tristan van Leeuwen, 2012.
-% tleeuwen@eos.ubc.ca
-
-% fit both s and k
-if nargin == 1
-    opts = optimset('maxFunEvals',1e2);
-    tmp = fminsearch(@(x)st(r,x(1),x(2)),[1;2],opts);
-    s   = tmp(1);
-    k   = tmp(2);
-end
-
-
-if nargin == 2
-    opts = optimset('maxFunEvals',1e2);
-    tmp = fminsearch(@(x)st(r,x,k),[1],opts);
-    s   = tmp(1);
-end
-
-% evaulate penalty
-[f,g,h] = st(r,s,k);
-
-
-function [f,g,h] = st(r,s,k)
-n = length(r);
-c = -n*(gammaln((k+1)/2) - gammaln(k/2) - .5*log(pi*s*k));
-f = c + .5*(k+1)*sum(log(1 + conj(r).*r/(s*k)));
-g = (k+1)*r./(s*k + conj(r).*r);
-h = (k+1)./(s*k + conj(r).*r);