diff toolboxes/FullBNT-1.0.7/netlab3.3/graddesc.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/netlab3.3/graddesc.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,180 @@
+function [x, options, flog, pointlog] = graddesc(f, x, options, gradf, ...
+			varargin)
+%GRADDESC Gradient descent optimization.
+%
+%	Description
+%	[X, OPTIONS, FLOG, POINTLOG] = GRADDESC(F, X, OPTIONS, GRADF) uses
+%	batch gradient descent to find a local minimum of the function  F(X)
+%	whose gradient is given by GRADF(X). A log of the function values
+%	after each cycle is (optionally) returned in ERRLOG, and a log of the
+%	points visited is (optionally) returned in POINTLOG.
+%
+%	Note that X is a row vector and F returns a scalar value.  The point
+%	at which F has a local minimum is returned as X.  The function value
+%	at that point is returned in OPTIONS(8).
+%
+%	GRADDESC(F, X, OPTIONS, GRADF, P1, P2, ...) allows  additional
+%	arguments to be passed to F() and GRADF().
+%
+%	The optional parameters have the following interpretations.
+%
+%	OPTIONS(1) is set to 1 to display error values; also logs error
+%	values in the return argument ERRLOG, and the points visited in the
+%	return argument POINTSLOG. If OPTIONS(1) is set to 0, then only
+%	warning messages are displayed.  If OPTIONS(1) is -1, then nothing is
+%	displayed.
+%
+%	OPTIONS(2) is the absolute precision required for the value of X at
+%	the solution.  If the absolute difference between the values of X
+%	between two successive steps is less than OPTIONS(2), then this
+%	condition is satisfied.
+%
+%	OPTIONS(3) is a measure of the precision required of the objective
+%	function at the solution.  If the absolute difference between the
+%	objective function values between two successive steps is less than
+%	OPTIONS(3), then this condition is satisfied. Both this and the
+%	previous condition must be satisfied for termination.
+%
+%	OPTIONS(7) determines the line minimisation method used.  If it is
+%	set to 1 then a line minimiser is used (in the direction of the
+%	negative gradient).  If it is 0 (the default), then each parameter
+%	update is a fixed multiple (the learning rate) of the negative
+%	gradient added to a fixed multiple (the momentum) of the previous
+%	parameter update.
+%
+%	OPTIONS(9) should be set to 1 to check the user defined gradient
+%	function GRADF with GRADCHEK.  This is carried out at the initial
+%	parameter vector X.
+%
+%	OPTIONS(10) returns the total number of function evaluations
+%	(including those in any line searches).
+%
+%	OPTIONS(11) returns the total number of gradient evaluations.
+%
+%	OPTIONS(14) is the maximum number of iterations; default 100.
+%
+%	OPTIONS(15) is the precision in parameter space of the line search;
+%	default FOPTIONS(2).
+%
+%	OPTIONS(17) is the momentum; default 0.5.  It should be scaled by the
+%	inverse of the number of data points.
+%
+%	OPTIONS(18) is the learning rate; default 0.01.  It should be scaled
+%	by the inverse of the number of data points.
+%
+%	See also
+%	CONJGRAD, LINEMIN, OLGD, MINBRACK, QUASINEW, SCG
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+%  Set up the options.
+if length(options) < 18
+  error('Options vector too short')
+end
+
+if (options(14))
+  niters = options(14);
+else
+  niters = 100;
+end
+
+line_min_flag = 0; % Flag for line minimisation option
+if (round(options(7)) == 1)
+  % Use line minimisation
+  line_min_flag = 1;
+  % Set options for line minimiser
+  line_options = foptions;
+  if options(15) > 0
+    line_options(2) = options(15);
+  end
+else
+  % Learning rate: must be positive
+  if (options(18) > 0)
+    eta = options(18);
+  else
+    eta = 0.01;
+  end
+  % Momentum term: allow zero momentum
+  if (options(17) >= 0)
+    mu = options(17);
+  else
+    mu = 0.5;
+  end
+end
+
+% Check function string
+f = fcnchk(f, length(varargin));
+gradf = fcnchk(gradf, length(varargin));
+
+% Display information if options(1) > 0
+display = options(1) > 0;
+
+% Work out if we need to compute f at each iteration.
+% Needed if using line search or if display results or if termination
+% criterion requires it.
+fcneval = (options(7) | display | options(3));
+
+%  Check gradients
+if (options(9) > 0)
+  feval('gradchek', x, f, gradf, varargin{:});
+end
+
+dxold = zeros(1, size(x, 2));
+xold = x;
+fold = 0; % Must be initialised so that termination test can be performed
+if fcneval
+  fnew = feval(f, x, varargin{:});
+  options(10) = options(10) + 1;
+  fold = fnew;
+end
+
+%  Main optimization loop.
+for j = 1:niters
+  xold = x;
+  grad = feval(gradf, x, varargin{:});
+  options(11) = options(11) + 1;  % Increment gradient evaluation counter
+  if (line_min_flag ~= 1)
+    dx = mu*dxold - eta*grad;
+    x =  x + dx;
+    dxold = dx;
+    if fcneval
+      fold = fnew;
+      fnew = feval(f, x, varargin{:});
+      options(10) = options(10) + 1;
+    end
+  else
+    sd = - grad./norm(grad);	% New search direction.
+    fold = fnew;
+    % Do a line search: normalise search direction to have length 1
+    [lmin, line_options] = feval('linemin', f, x, sd, fold, ...
+      line_options, varargin{:});
+    options(10) = options(10) + line_options(10);
+    x = xold + lmin*sd;
+    fnew = line_options(8);
+  end
+  if nargout >= 3
+    flog(j) = fnew;
+    if nargout >= 4
+      pointlog(j, :) = x;
+    end
+  end
+  if display
+    fprintf(1, 'Cycle  %5d  Function %11.8f\n', j, fnew);
+  end
+  if (max(abs(x - xold)) < options(2) & abs(fnew - fold) < options(3))
+    % Termination criteria are met
+    options(8) = fnew;
+    return;
+  end
+end
+
+if fcneval
+  options(8) = fnew;
+else
+  options(8) = feval(f, x, varargin{:});
+  options(10) = options(10) + 1;
+end
+if (options(1) >= 0)
+  disp(maxitmess);
+end