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, 75 insertions, 0 deletions

diff --git a/sourcecodes/parameter_learning/readInputData.m b/sourcecodes/parameter_learning/readInputData.m
new file mode 100644
index 00000000..706e2751
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+++ b/sourcecodes/parameter_learning/readInputData.m
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+function  [ labels , node_sizes, cases, data] = readInputData( dfile , nnodes )
+	%  readColData  reads data from a file containing data in columns
+	%               that have text titles, and possibly other header text
+	%   
+	%  Input:
+	%     dfile  = name of the file containing the data.(required)
+	%     nnodes  = number of columns in the data file.  (required)
+    %
+    %   Function assumes the following format for the input file:
+    %       1) First line has labels for each of the nodes.  There cannot
+    %              be spaces in any node label.
+    %       2) The next line is the "node_sizes" of the nodes.  If the 
+    %           nodes are discrete, this number will be equal to the number
+    %           of states.  If the nodes are continuous, they should be 
+    %           equal to 1.  The function assumes that any nodes with
+    %           node_size = 1 is continuous.
+    %       3) The rest of the file is numeric data.  The data in the input
+    %               data has the number of columns equal to the number of 
+    %               nodes in the network and the number of rows equal to
+    %               the number of samples.
+    %
+    %
+	%  Output:
+	%     labels = cell array with node (column) labels.
+    %     node_sizes  =  vector with the size of each node
+    %     cases = cell array with the data.  The cases array is transposed
+    %       in comparison with the input data to agree with the format of
+    %       cell data used in BNT.
+
+%  open file for input, include error handling
+fin = fopen(dfile,'r');
+if fin < 0
+   error(['Could not open ',dfile,' for input']);
+end
+
+% Read in first line to get the node labels.
+labels = cell(1,nnodes);
+buffer = fgetl(fin);    %get header line as a string
+for j=1:nnodes
+    [next,buffer] = strtok(buffer);
+    labels{j} = next;
+end
+
+%  Read in the data.  Use the vetorized fscanf function to load all
+%  numerical values into one vector.  Then reshape this vector into a
+%  matrix.
+
+data = fscanf(fin,'%f');  %  Load the numerical values into one long vector
+
+
+
+
+nd = length(data);        %  total number of data points
+nr = nd/nnodes;            %  number of rows; check (next statement) to make sure
+if nr ~= round(nd/nnodes)
+   fprintf(1,'\ndata: nrow = %f\tncol = %d\n',nr,nnodes);
+   fprintf(1,'number of data points = %d does not equal nrow*ncol\n',nd);
+   error('data is not rectangular')
+end
+
+data = reshape(data,nnodes,nr)';   %  have to transpose the reshaped array
+
+
+node_sizes = zeros(1,nnodes);
+for j = 1:nnodes
+    node_sizes(j) = data(1,j);
+end
+
+nr = nr - 1;
+data(1,:) = [];
+cases = cell(nnodes,nr);
+cases(:,:) = num2cell(data');
+
+end
+%  end of readInputData.m
\ No newline at end of file