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, 59 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMstats/fit_partitioned_model.m b/sourcecodes/bnt-master/KPMstats/fit_partitioned_model.m
new file mode 100644
index 00000000..fb9d178d
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMstats/fit_partitioned_model.m
@@ -0,0 +1,59 @@
+function [model, partition_size] = fit_partitioned_model(...
+    inputs, outputs, selectors, sel_sizes, min_size, partition_names, fn_name, varargin)
+%function [models, partition_sizes] = fit_partitioned_model(...
+%    inputs, outputs, selectors, sel_sizes, min_size, partition_names, fn_name, varargin)
+%
+% Fit models to different subsets (columns)  of the input/output data, 
+% as chosen by the selectors matrix. If there is only output data, set input=[].
+% If there is less than min_size data in partition i, 
+% we set model{i} = []
+%
+% Example:
+% selectors = [1 2 1 1 1
+%              1 2 2 1 2]
+% sel_sizes = [2 2] so there are 4 models: (1,1), (2,1), (1,2), (2,2)
+% We fit model{1} to data from columns 1,4
+% We fit model{2} to no data
+% We fit model{3} to data from column 3,5
+% We fit model{4} to data from column 2 (assuming min_size <= 1)
+%
+% For each partition, we call the specified function with the specified arguments
+% as follows:
+%  model{i}  = fn(input(:,cols{i}), output(:,cols{i}), args)
+% (We omit input if [])
+% partition_size(i) is the amount of data in the i'th partition.
+%
+% Example use: row 1 of selectors is whether an object is present/absent
+% and row 2 is the location.
+%
+% Demo:
+% inputs = 1:5; outputs = 6:10; selectors = as above
+% fn = 'fit_partitioned_model_testfn';
+% [model, partition_size] = fit_partitioned_model(inputs, outputs, selectors, [2 2], fn)
+% should produce
+% model{1}.input = [1 4], model{1}.output = [6 9]
+% model{2} = []
+% model{3}.input = [3 5], model{3}.output = [8 10], 
+% model{4}.input = [2], model{3}.output = [7], 
+% partition_size = [2 0 2 1]
+
+
+sel_ndx = subv2ind(sel_sizes, selectors');
+Nmodels = prod(sel_sizes);
+model = cell(1, Nmodels);
+partition_size = zeros(1, Nmodels);
+for m=1:Nmodels
+  ndx = find(sel_ndx==m);
+  partition_size(m) = length(ndx);
+  if ~isempty(partition_names) % & (partition_size(m) < min_size)
+    fprintf('partition %s has size %d, min size = %d\n', ...
+	    partition_names{m}, partition_size(m), min_size);
+  end
+  if partition_size(m) >= min_size
+    if isempty(inputs)
+      model{m} = feval(fn_name, outputs(:, ndx), varargin{:});
+    else
+      model{m} = feval(fn_name, inputs(:,ndx), outputs(:, ndx), varargin{:});
+    end
+  end
+end