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, 100 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gpgrad.m b/sourcecodes/bnt-master/netlab3.3/gpgrad.m
new file mode 100644
index 00000000..7ea531bc
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/gpgrad.m
@@ -0,0 +1,100 @@
+function g = gpgrad(net, x, t)
+%GPGRAD	Evaluate error gradient for Gaussian Process.
+%
+%	Description
+%	G = GPGRAD(NET, X, T) takes a Gaussian Process data structure NET
+%	together  with a matrix X of input vectors and a matrix T of target
+%	vectors, and evaluates the error gradient G. Each row of X
+%	corresponds to one input vector and each row of T corresponds to one
+%	target vector.
+%
+%	See also
+%	GP, GPCOVAR, GPFWD, GPERR
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+errstring = consist(net, 'gp', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+% Evaluate derivatives with respect to each hyperparameter in turn.
+ndata = size(x, 1);
+[cov, covf] = gpcovar(net, x);
+cninv = inv(cov);
+trcninv = trace(cninv);
+cninvt = cninv*t;
+
+% Function parameters
+switch net.covar_fn
+
+  case 'sqexp'		% Squared exponential
+    gfpar = trace(cninv*covf) - cninvt'*covf*cninvt;
+
+  case 'ratquad' 	% Rational quadratic
+    beta = diag(exp(net.inweights));
+    gfpar(1) = trace(cninv*covf) - cninvt'*covf*cninvt;
+    D2 = (x.*x)*beta*ones(net.nin, ndata) - 2*x*beta*x' ... 
+      + ones(ndata, net.nin)*beta*(x.*x)';
+    E = ones(size(D2));
+    L = - exp(net.fpar(2)) * covf .* log(E + D2); % d(cn)/d(nu)
+    gfpar(2) = trace(cninv*L) - cninvt'*L*cninvt;
+
+  otherwise
+    error(['Unknown covariance function ', net.covar_fn]);
+end
+
+% Bias derivative
+ndata = size(x, 1);
+fac = exp(net.bias)*ones(ndata);
+gbias = trace(cninv*fac) - cninvt'*fac*cninvt;
+
+% Noise derivative
+gnoise = exp(net.noise)*(trcninv - cninvt'*cninvt);
+
+% Input weight derivatives
+if strcmp(net.covar_fn, 'ratquad')
+  F = (exp(net.fpar(2))*E)./(E + D2);
+end
+
+nparams = length(net.inweights);
+for l = 1 : nparams
+  vect = x(:, l);
+  matx = (vect.*vect)*ones(1, ndata) ... 
+	- 2.0*vect*vect' ... 
+	+ ones(ndata, 1)*(vect.*vect)';
+  switch net.covar_fn
+    case 'sqexp'	% Squared exponential
+      dmat = -0.5*exp(net.inweights(l))*covf.*matx;
+      
+    case 'ratquad'	% Rational quadratic
+      dmat = - exp(net.inweights(l))*covf.*matx.*F;
+    otherwise
+      error(['Unknown covariance function ', net.covar_fn]);
+  end
+
+  gw1(l) = trace(cninv*dmat) - cninvt'*dmat*cninvt;
+end
+
+g1 = [gbias, gnoise, gw1, gfpar];
+g1 = 0.5*g1;
+
+% Evaluate the prior contribution to the gradient.
+if isfield(net, 'pr_mean')
+  w = gppak(net);
+  m = repmat(net.pr_mean, size(w));
+  if size(net.pr_mean) == [1 1]
+    gprior = w - m;
+    g2 = gprior/net.pr_var;
+  else
+    ngroups = size(net.pr_mean, 1);
+    gprior = net.index'.*(ones(ngroups, 1)*w - m);
+    g2 = (1./net.pr_var)'*gprior;
+  end
+else
+  gprior = 0;
+  g2 = 0;
+end
+
+g = g1 + g2;