BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 176 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/quasinew.m b/sourcecodes/bnt-master/netlab3.3/quasinew.m
new file mode 100644
index 00000000..680e7d15
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+++ b/sourcecodes/bnt-master/netlab3.3/quasinew.m
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+function [x, options, flog, pointlog] = quasinew(f, x, options, gradf, ...
+                                    varargin)
+%QUASINEW Quasi-Newton optimization.
+%
+%	Description
+%	[X, OPTIONS, FLOG, POINTLOG] = QUASINEW(F, X, OPTIONS, GRADF)  uses a
+%	quasi-Newton algorithm to find a local minimum of the function F(X)
+%	whose gradient is given by GRADF(X).  Here 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). A log of the function values after each cycle is
+%	(optionally) returned in FLOG, and a log of the points visited is
+%	(optionally) returned in POINTLOG.
+%
+%	QUASINEW(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 a measure of 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(9) should be set to 1 to check the user defined gradient
+%	function.
+%
+%	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 1E-2.
+%
+%	See also
+%	CONJGRAD, GRADDESC, LINEMIN, MINBRACK, 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
+
+% Set up options for line search
+line_options = foptions;
+% Don't need a very precise line search
+if options(15) > 0
+  line_options(2) = options(15);
+else
+  line_options(2) = 1e-2;  % Default
+end
+% Minimal fractional change in f from Newton step: otherwise do a line search
+min_frac_change = 1e-4;	
+
+display = options(1);
+
+% Next two lines allow quasinew to work with expression strings
+f = fcnchk(f, length(varargin));
+gradf = fcnchk(gradf, length(varargin));
+
+% Check gradients
+if (options(9))
+  feval('gradchek', x, f, gradf, varargin{:});
+end
+
+nparams = length(x);
+fnew = feval(f, x, varargin{:});
+options(10) = options(10) + 1;
+gradnew = feval(gradf, x, varargin{:});
+options(11) = options(11) + 1;
+p = -gradnew;		% Search direction
+hessinv = eye(nparams); % Initialise inverse Hessian to be identity matrix
+j = 1;
+if nargout >= 3
+  flog(j, :) = fnew;
+  if nargout == 4
+    pointlog(j, :) = x;
+  end
+end
+
+while (j <= niters)
+
+  xold = x;
+  fold = fnew;
+  gradold = gradnew;
+
+  x = xold + p;
+  fnew = feval(f, x, varargin{:});
+  options(10) = options(10) + 1;
+
+  % This shouldn't occur, but rest of code depends on sd being downhill
+  if (gradnew*p' >= 0)
+    p = -p;
+    if options(1) >= 0
+      warning('search direction uphill in quasinew');
+    end
+  end
+
+  % Does the Newton step reduce the function value sufficiently?
+  if (fnew >= fold + min_frac_change * (gradnew*p'))
+    % No it doesn't
+    % Minimize along current search direction: must be less than Newton step
+    [lmin, line_options] = feval('linemin', f, xold, p, fold, ...
+      line_options, varargin{:});
+    options(10) = options(10) + line_options(10);
+    options(11) = options(11) + line_options(11);
+    % Correct x and fnew to be the actual search point we have found
+    x = xold + lmin * p;
+    p = x - xold;
+    fnew = line_options(8);
+  end
+
+  % Check for termination
+  if (max(abs(x - xold)) < options(2) & max(abs(fnew - fold)) < options(3))
+    options(8) = fnew;
+    return;
+  end
+  gradnew = feval(gradf, x, varargin{:});
+  options(11) = options(11) + 1;
+  v = gradnew - gradold;
+  vdotp = v*p';
+
+  % Skip update to inverse Hessian if fac not sufficiently positive
+  if (vdotp*vdotp > eps*sum(v.^2)*sum(p.^2)) 
+    Gv = (hessinv*v')';
+    vGv = sum(v.*Gv);
+    u = p./vdotp - Gv./vGv;
+    % Use BFGS update rule
+    hessinv = hessinv + (p'*p)/vdotp - (Gv'*Gv)/vGv + vGv*(u'*u);
+  end
+
+  p = -(hessinv * gradnew')';
+
+  if (display > 0)
+    fprintf(1, 'Cycle %4d  Function %11.6f\n', j, fnew);
+  end
+
+  j = j + 1;
+  if nargout >= 3
+    flog(j, :) = fnew;
+    if nargout == 4
+      pointlog(j, :) = x;
+    end
+  end
+end
+
+% If we get here, then we haven't terminated in the given number of 
+% iterations.
+
+options(8) = fold;
+if (options(1) >= 0)
+  disp(maxitmess);
+end