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, 49 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gradchek.m b/sourcecodes/bnt-master/netlab3.3/gradchek.m
new file mode 100644
index 00000000..3f9298fb
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/gradchek.m
@@ -0,0 +1,49 @@
+function [gradient, delta] = gradchek(w, func, grad, varargin)
+%GRADCHEK Checks a user-defined gradient function using finite differences.
+%
+%	Description
+%	This function is intended as a utility for other netlab functions
+%	(particularly optimisation functions) to use.  It enables the user to
+%	check whether a gradient calculation has been correctly implmented
+%	for a given function. GRADCHEK(W, FUNC, GRAD) checks how accurate the
+%	gradient  GRAD of a function FUNC is at a parameter vector X.   A
+%	central difference formula with step size 1.0e-6 is used, and the
+%	results for both gradient function and finite difference
+%	approximation are printed. The optional return value GRADIENT is the
+%	gradient calculated using the function GRAD and the return value
+%	DELTA is the difference between the functional and finite difference
+%	methods of calculating the graident.
+%
+%	GRADCHEK(X, FUNC, GRAD, P1, P2, ...) allows additional arguments to
+%	be passed to FUNC and GRAD.
+%
+%	See also
+%	CONJGRAD, GRADDESC, HMC, OLGD, QUASINEW, SCG
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Reasonable value for step size
+epsilon = 1.0e-6;
+
+func = fcnchk(func, length(varargin));
+grad = fcnchk(grad, length(varargin));
+
+% Treat
+nparams = length(w);
+deltaf = zeros(1, nparams);
+step = zeros(1, nparams);
+for i = 1:nparams
+  % Move a small way in the ith coordinate of w
+  step(i) = 1.0;
+  fplus  = feval('linef', epsilon, func, w, step, varargin{:});
+  fminus = feval('linef', -epsilon, func, w, step, varargin{:});
+  % Use central difference formula for approximation
+  deltaf(i) = 0.5*(fplus - fminus)/epsilon;
+  step(i) = 0.0;
+end
+gradient = feval(grad, w, varargin{:});
+fprintf(1, 'Checking gradient ...\n\n');
+delta = gradient - deltaf;
+fprintf(1, '   analytic   diffs     delta\n\n');
+disp([gradient', deltaf', delta'])