From 329a104d4cb5ba50a59fb80e58de0453ba49f075 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Mon, 3 Jul 2017 22:35:23 +0100
Subject: Major reorganization, updated routines

---
 main_func/studentst.m | 94 +++++++++++++++++++++++++--------------------------
 1 file changed, 47 insertions(+), 47 deletions(-)

(limited to 'main_func/studentst.m')

diff --git a/main_func/studentst.m b/main_func/studentst.m
index 99fed1e..93e0a0a 100644
--- a/main_func/studentst.m
+++ b/main_func/studentst.m
@@ -1,47 +1,47 @@
-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);
+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);
-- 
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