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, 92 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mlp.m b/sourcecodes/bnt-master/netlab3.3/mlp.m
new file mode 100644
index 00000000..d1dc1cf1
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+++ b/sourcecodes/bnt-master/netlab3.3/mlp.m
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+function net = mlp(nin, nhidden, nout, outfunc, prior, beta)
+%MLP	Create a 2-layer feedforward network.
+%
+%	Description
+%	NET = MLP(NIN, NHIDDEN, NOUT, FUNC) takes the number of inputs,
+%	hidden units and output units for a 2-layer feed-forward network,
+%	together with a string FUNC which specifies the output unit
+%	activation function, and returns a data structure NET. The weights
+%	are drawn from a zero mean, unit variance isotropic Gaussian, with
+%	varianced scaled by the fan-in of the hidden or output units as
+%	appropriate. This makes use of the Matlab function RANDN and so the
+%	seed for the random weight initialization can be  set using
+%	RANDN('STATE', S) where S is the seed value.  The hidden units use
+%	the TANH activation function.
+%
+%	The fields in NET are
+%	  type = 'mlp'
+%	  nin = number of inputs
+%	  nhidden = number of hidden units
+%	  nout = number of outputs
+%	  nwts = total number of weights and biases
+%	  actfn = string describing the output unit activation function:
+%	      'linear'
+%	      'logistic
+%	      'softmax'
+%	  w1 = first-layer weight matrix
+%	  b1 = first-layer bias vector
+%	  w2 = second-layer weight matrix
+%	  b2 = second-layer bias vector
+%	 Here W1 has dimensions NIN times NHIDDEN, B1 has dimensions 1 times
+%	NHIDDEN, W2 has dimensions NHIDDEN times NOUT, and B2 has dimensions
+%	1 times NOUT.
+%
+%	NET = MLP(NIN, NHIDDEN, NOUT, FUNC, PRIOR), in which PRIOR is a
+%	scalar, allows the field NET.ALPHA in the data structure NET to be
+%	set, corresponding to a zero-mean isotropic Gaussian prior with
+%	inverse variance with value PRIOR. Alternatively, PRIOR can consist
+%	of a data structure with fields ALPHA and INDEX, allowing individual
+%	Gaussian priors to be set over groups of weights in the network. Here
+%	ALPHA is a column vector in which each element corresponds to a
+%	separate group of weights, which need not be mutually exclusive.  The
+%	membership of the groups is defined by the matrix INDX in which the
+%	columns correspond to the elements of ALPHA. Each column has one
+%	element for each weight in the matrix, in the order defined by the
+%	function MLPPAK, and each element is 1 or 0 according to whether the
+%	weight is a member of the corresponding group or not. A utility
+%	function MLPPRIOR is provided to help in setting up the PRIOR data
+%	structure.
+%
+%	NET = MLP(NIN, NHIDDEN, NOUT, FUNC, PRIOR, BETA) also sets the
+%	additional field NET.BETA in the data structure NET, where beta
+%	corresponds to the inverse noise variance.
+%
+%	See also
+%	MLPPRIOR, MLPPAK, MLPUNPAK, MLPFWD, MLPERR, MLPBKP, MLPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+net.type = 'mlp';
+net.nin = nin;
+net.nhidden = nhidden;
+net.nout = nout;
+net.nwts = (nin + 1)*nhidden + (nhidden + 1)*nout;
+
+outfns = {'linear', 'logistic', 'softmax'};
+
+if sum(strcmp(outfunc, outfns)) == 0
+  error('Undefined output function. Exiting.');
+else
+  net.outfn = outfunc;
+end
+
+if nargin > 4
+  if isstruct(prior)
+    net.alpha = prior.alpha;
+    net.index = prior.index;
+  elseif size(prior) == [1 1]
+    net.alpha = prior;
+  else
+    error('prior must be a scalar or a structure');
+  end  
+end
+
+net.w1 = randn(nin, nhidden)/sqrt(nin + 1);
+net.b1 = randn(1, nhidden)/sqrt(nin + 1);
+net.w2 = randn(nhidden, nout)/sqrt(nhidden + 1);
+net.b2 = randn(1, nout)/sqrt(nhidden + 1);
+
+if nargin == 6
+  net.beta = beta;
+end