DaveM@16
|
1 function [linkList, featureList]= treeLinkFeatures(data, depthThresh)
|
DaveM@10
|
2 %% linkList = treeLinkFeatures(data)
|
DaveM@10
|
3 % given a dataset, a hierarchical cluster of the data is produced, and then
|
DaveM@10
|
4 % the data is traversed, such that, for each split in the data, a set of
|
DaveM@10
|
5 % features are produced, which are the ranked features that can be used to
|
DaveM@10
|
6 % separate the given dataset at that point.
|
DaveM@10
|
7
|
DaveM@9
|
8
|
DaveM@16
|
9
|
DaveM@16
|
10 if(nargin < 2)
|
DaveM@19
|
11 depthThresh = 999;
|
DaveM@16
|
12 end
|
DaveM@9
|
13 linkList = aglomCluster(data);
|
DaveM@16
|
14 linkList = depthCheck(linkList);
|
DaveM@10
|
15 listSize = size(data,1);
|
DaveM@9
|
16
|
DaveM@10
|
17 % linkList(:,4) = 0;
|
DaveM@17
|
18 featureList = cell(listSize-1,2);
|
DaveM@10
|
19 currentRow = [2*listSize-1];
|
DaveM@9
|
20
|
DaveM@12
|
21 %%
|
DaveM@15
|
22 while (~isempty(currentRow))
|
DaveM@16
|
23 if(currentRow(1) > listSize)
|
DaveM@22
|
24 row = currentRow(1) - listSize;
|
DaveM@17
|
25 % rD = linkList(row,4);
|
DaveM@16
|
26 if(linkList(row,4) < depthThresh)
|
DaveM@16
|
27 classList = traceLinkageToBinary(linkList, row);
|
DaveM@23
|
28 featureList{row,1} = rfFeatureSelection(data(classList>0,:), classList(classList>0));
|
DaveM@17
|
29 featureList{row,2} = linkList(row,4);
|
DaveM@16
|
30 end
|
DaveM@17
|
31 currentRow = [currentRow; linkList(row,1); linkList(row,2)];
|
DaveM@10
|
32 end
|
DaveM@17
|
33 currentRow = currentRow(2:end);
|
DaveM@9
|
34 end
|
DaveM@9
|
35
|
DaveM@9
|
36 end |