function [localopt, fX, ii]=gradopt3(init,EGfn, maxit, P1, P2, P3, P4, P5, P6, P7)
% function [localopt, fX, ii]=gradopt3(init,EGfn, maxit, P1, P2, P3, P4, P5, P6, P7)
% 
% Minimizes an Energy function using Gradient function. EGfn should be a string
% containing a function that returns energy and gradient as two args. Start at
% X=init and use bold-driver-like algorithm to adjust parameters for a
% gradient-based descent. Not sure if it's the same as a method with a name. It
% seems different to quickprop, delta-bar-delta, ...
% 
% Usage is compatible with Carl's minimize (which you should go use instead):
% 	http://www.gatsby.ucl.ac.uk/~edward/code/minimize/
% or if you want a gradient only algorithm see Macopt:
% 	http://www.inference.phy.cam.ac.uk/mackay/c/macopt.html
% Also try L-BFGS-B wrapper from http://www.gatsby.ucl.ac.uk/~snelson/
% 
% Iain Murray October 2004, October 2005

maxit=abs(maxit); % compatibility with Carl's minimize

% There's a better way to do callbacks, but grab Carl's code for now:
argstr = [EGfn, '(Xprop'];
for i = 1:(nargin - 3)
  argstr = [argstr, ',P', int2str(i)];
end
argstr = [argstr, ')'];

eta=0.1;
epsilon=repmat(0.1,size(init));
X=init;Xprop=X;
[E,G]=eval(argstr);
it=0;
fX=[];
ii=[];

while (sum(epsilon)>0)&&(it<maxit)
	fprintf('Iteration/fn eval %6i;  Value %4.6e;  sum(epsilon) %4.6e;\r', it, E,sum(epsilon));
	it=it+1;
	Gscaled=epsilon.*G;
	Xprop=X-eta*Gscaled/sqrt(sum(Gscaled.^2));
	[Eprop,Gprop]=eval(argstr);
	epsilon=(G.*Gprop>0).*epsilon*1.1+(G.*Gprop<0).*epsilon*0.5;
	epsilon=epsilon/sqrt(sum(epsilon.^2));
	% Can't do this if can't evaluate function:
	if Eprop<E
		X=Xprop;
		E=Eprop;
		G=Gprop;
		eta=eta*1.1;
	else
		eta=eta/2;
	end
	fX=[fX E];
	ii=[ii it];
end

fprintf('\n');
localopt=X;