Deleting parameter learning folder

There was an extra folder with the Matlab/Octave parameter learning files. These were out of date, so I deleted them and kept the versions in the sourcecoades directory.

1 files changed, 0 insertions, 66 deletions

diff --git a/BNW_parameter_learning/readInput.m b/BNW_parameter_learning/readInput.m
deleted file mode 100644
index f291b06d..00000000
--- a/BNW_parameter_learning/readInput.m
+++ /dev/null
@@ -1,66 +0,0 @@
-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.

-    % 

-    %  readInput is called by runBN_initial.m

-

-

-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