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authorziejd22017-09-14 16:17:47 -0500
committerGitHub2017-09-14 16:17:47 -0500
commit7cc31810d53176e805532b2789955f4eedbce6bb (patch)
tree82924642070d871f753ee41c0f3e363ff8f380da /BNW_parameter_learning/standardizeData.m
parent6882395afdadf4e982b25b5215071a0932730950 (diff)
downloadBNW-7cc31810d53176e805532b2789955f4eedbce6bb.tar.gz
Add files via upload
Adding the *.m files used in parameter learning.
Diffstat (limited to 'BNW_parameter_learning/standardizeData.m')
-rw-r--r--BNW_parameter_learning/standardizeData.m25
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diff --git a/BNW_parameter_learning/standardizeData.m b/BNW_parameter_learning/standardizeData.m
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+function [ cases ] = standardizeData( labels, node_sizes, cases )

+%standardizeData standardizes continuous nodes so they have a mean = 0

+%   and standard deviation = 1

+%   Detailed explanation goes here

+

+nnodes = size(labels,2);

+

+%fprintf(['Standardizing data for continuous nodes\n'])

+for i = 1:nnodes

+    if node_sizes(i) == 1

+        temp = cell2num(cases(i,:));

+        [temp] = standardize(temp);

+        cases(i,:) = num2cell(temp);

+    end

+end

+

+%write standardized data to file

+%fprintf(['Standardized data is written to file standardized_data.txt\n'])

+%fout = 'standardized_data.txt';

+%txt = sprintf([repmat('%s\t',1,size(labels,2))],labels{:});

+%dlmwrite(fout,txt,'');

+%dlmwrite(fout,cell2num(cases'),'-append','delimiter','\t');

+

+end

+