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| author | ziejd2 | 2017-09-14 16:17:47 -0500 |
|---|---|---|
| committer | GitHub | 2017-09-14 16:17:47 -0500 |
| commit | 7cc31810d53176e805532b2789955f4eedbce6bb (patch) | |
| tree | 82924642070d871f753ee41c0f3e363ff8f380da /BNW_parameter_learning/standardizeData.m | |
| parent | 6882395afdadf4e982b25b5215071a0932730950 (diff) | |
| download | BNW-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.m | 25 |
1 files changed, 25 insertions, 0 deletions
diff --git a/BNW_parameter_learning/standardizeData.m b/BNW_parameter_learning/standardizeData.m new file mode 100644 index 00000000..ba4ef5dd --- /dev/null +++ b/BNW_parameter_learning/standardizeData.m @@ -0,0 +1,25 @@ +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 + |
