Merge pull request #1 from ziejd2/octave_php_separate

Octave php separate

1 files changed, 62 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mlperr.m b/sourcecodes/bnt-master/netlab3.3/mlperr.m
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+++ b/sourcecodes/bnt-master/netlab3.3/mlperr.m
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+function [e, edata, eprior] = mlperr(net, x, t)
+%MLPERR	Evaluate error function for 2-layer network.
+%
+%	Description
+%	E = MLPERR(NET, X, T) takes a network data structure NET together
+%	with a matrix X of input vectors and a matrix T of target vectors,
+%	and evaluates the error function E. The choice of error function
+%	corresponds to the output unit activation function. Each row of X
+%	corresponds to one input vector and each row of T corresponds to one
+%	target vector.
+%
+%	[E, EDATA, EPRIOR] = MLPERR(NET, X, T) additionally returns the data
+%	and prior components of the error, assuming a zero mean Gaussian
+%	prior on the weights with inverse variance parameters ALPHA and BETA
+%	taken from the network data structure NET.
+%
+%	See also
+%	MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPBKP, MLPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'mlp', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+[y, z, a] = mlpfwd(net, x);
+
+switch net.outfn
+
+  case 'linear'        % Linear outputs
+    edata = 0.5*sum(sum((y - t).^2));
+
+  case 'logistic'      % Logistic outputs
+    % Ensure that log(1-y) is computable: need exp(a) > eps
+    maxcut = -log(eps);
+    % Ensure that log(y) is computable
+    mincut = -log(1/realmin - 1);
+    a = min(a, maxcut);
+    a = max(a, mincut);
+    y = 1./(1 + exp(-a));
+    edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y)));
+
+  case 'softmax'       % Softmax outputs
+    nout = size(a,2);
+    % Ensure that sum(exp(a), 2) does not overflow
+    maxcut = log(realmax) - log(nout);
+    % Ensure that exp(a) > 0
+    mincut = log(realmin);
+    a = min(a, maxcut);
+    a = max(a, mincut);
+    temp = exp(a);
+    y = temp./(sum(temp, 2)*ones(1,nout));
+    % Ensure that log(y) is computable
+    y(y<realmin) = realmin;
+    edata = - sum(sum(t.*log(y)));
+
+  otherwise
+    error(['Unknown activation function ', net.outfn]);  
+end
+[e, edata, eprior] = errbayes(net, edata);