From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 28 Sep 2017 15:04:40 -0500 Subject: 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 --- sourcecodes/bnt-master/nethelp3.3/gradchek.htm | 58 ++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/gradchek.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/gradchek.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/gradchek.htm b/sourcecodes/bnt-master/nethelp3.3/gradchek.htm new file mode 100644 index 00000000..0a7df6ea --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gradchek.htm @@ -0,0 +1,58 @@ + + + +Netlab Reference Manual gradchek + + + +

gradchek +

+Purpose +

+Checks a user-defined gradient function using finite differences. + +

+Synopsis +

+
+gradchek(w, func, grad)
+[gradient, delta] = gradchek(w, func, grad)
+gradchek(w, func, grad, p1, p2, ...)
+

+ + +

+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

+Pages: +Index +