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| author | ziejd2 | 2019-01-28 16:37:57 -0600 |
|---|---|---|
| committer | ziejd2 | 2019-01-28 16:37:57 -0600 |
| commit | 995f6673b5e725d6907ccd4f8e25033e8524f116 (patch) | |
| tree | 5df08d9503394eab1190a01a640f3c338e8c74d6 /sourcecodes/parameter_learning/code_backup/standardizeData.m | |
| parent | bb9d93322abf825368dedaafc2376ef8fdfc1e2f (diff) | |
| download | BNW-995f6673b5e725d6907ccd4f8e25033e8524f116.tar.gz | |
Deleting old versions of files
Diffstat (limited to 'sourcecodes/parameter_learning/code_backup/standardizeData.m')
| -rw-r--r-- | sourcecodes/parameter_learning/code_backup/standardizeData.m | 25 |
1 files changed, 0 insertions, 25 deletions
diff --git a/sourcecodes/parameter_learning/code_backup/standardizeData.m b/sourcecodes/parameter_learning/code_backup/standardizeData.m deleted file mode 100644 index db5e04c7..00000000 --- a/sourcecodes/parameter_learning/code_backup/standardizeData.m +++ /dev/null @@ -1,25 +0,0 @@ -function [ cases ] = standardizeData( labels, node_sizes, cases ) -%standardizeData standardizes continuous nodes so they have a mean = 0 -% and standard deviation = 1 - - -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 - |
