1 files changed, 63 insertions, 0 deletions
diff --git a/sourcecodes/parameter_learning/code_backup/readInput.m b/sourcecodes/parameter_learning/code_backup/readInput.m
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+++ b/sourcecodes/parameter_learning/code_backup/readInput.m
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+function [ labels, cases, bnet, node_sizes, data,labelsold] = readInput( dfile, sfile, nnodes, std_flag )
+    %readInput is to be used when reading in a network with a known structure
+    %   
+    %Input:
+	%   dfile  = name of the file containing the data (required)
+    %   sfile = name of the file containing the structure (required)
+    %   nnodes = number of nodes in the network (required)
+    %   std_flag = flag for whether or not to standardize the data.
+    %   (optional-- Default is FALSE)
+    %
+    %   See readInputData.m and readInputStructure.m for description of the
+    %       format of the dfile and sfile, respectively. 
+    %
+    %Output:
+    %   labels = cell array with the names of the nodes.
+    %   cases = cell array with the data.
+    %   bnet = BNT bayesian network with the input structure.
+
+if nargin < 4
+    std_flag = false(1);
+end
+
+    
+% read in the file with the data
+[labelsold,node_sizes,cases, data] = readInputData(dfile,nnodes);
+
+
+% read in the file with the structure
+[dag] = readInputStructure(sfile,labelsold);
+
+
+% check the ordering of the nodes and reorder if necessary
+[labels,cases,dag,node_sizes,ord_flag] = checkStructure(labelsold,cases,dag,node_sizes);
+
+dcount = 0;
+for i = 1:nnodes
+    if node_sizes(i) ~= 1
+        dcount = dcount + 1;
+    end
+end
+discrete = zeros(1,dcount);
+dcount = 0;
+for i = 1:nnodes
+    if node_sizes(i) ~= 1
+        dcount = dcount + 1;
+        discrete(dcount) = i;
+    end
+end
+
+bnet = mk_bnet(dag,node_sizes,'discrete',discrete,'names',labels);
+
+%bnet.dag
+
+checkDiscreteNodes(bnet,cases);
+
+% standardize continuous data to have a mean = 0 and std = 1
+if (std_flag)
+    [cases] = standardizeData(labels,node_sizes,cases);
+end
+        
+
+end
+%  end of readInput.m