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, 69 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMtools/plotROCkpm.m b/sourcecodes/bnt-master/KPMtools/plotROCkpm.m
new file mode 100644
index 00000000..a77fd568
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMtools/plotROCkpm.m
@@ -0,0 +1,69 @@
+function [falseAlarmRate, detectionRate, area, th] = plotROC(confidence, testClass, col, varargin)
+% You pass the scores and the classes, and the function returns the false
+% alarm rate and the detection rate for different points across the ROC.
+%
+% [faR, dR] = plotROC(score, class)
+%
+%  faR (false alarm rate) is uniformly sampled from 0 to 1
+%  dR (detection rate) is computed using the scores.
+%
+% class = 0 => target absent
+% class = 1 => target present
+%
+% score is the output of the detector, or any other measure of detection.
+% There is no plot unless you add a third parameter that is the color of
+% the graph. For instance:
+% [faR, dR] = plotROC(score, class, 'r')
+%
+% faR, dR are size 1x1250
+
+if nargin < 3, col = []; end
+[scale01] = process_options(varargin, 'scale01', 1);
+
+S = rand('state');
+rand('state',0);
+confidence = confidence + rand(size(confidence))*10^(-10);
+rand('state',S)
+
+ndxAbs = find(testClass==0); % absent
+ndxPres = find(testClass==1); % present
+
+[th, j] = sort(confidence(ndxAbs));
+th = th(fix(linspace(1, length(th), 1250))); 
+
+cAbs = confidence(ndxAbs);
+cPres = confidence(ndxPres);
+for t=1:length(th)
+  if length(ndxPres) == 0
+    detectionRate(t) = 0;
+  else
+    detectionRate(t)  = sum(cPres>=th(t)) / length(ndxPres);
+  end
+  if length(ndxAbs) == 0
+    falseAlarmRate(t) = 0;
+  else
+    falseAlarmRate(t) = sum(cAbs>=th(t)) / length(ndxAbs);
+  end
+  
+  %detectionRate(t)  = sum(confidence(ndxPres)>=th(t)) / length(ndxPres);
+  %falseAlarmRate(t) = sum(confidence(ndxAbs)>=th(t)) / length(ndxAbs);
+  %detections(t)     = sum(confidence(ndxPres)>=th(t));
+  %falseAlarms(t)    = sum(confidence(ndxAbs)>=th(t));
+end
+
+area = sum(abs(falseAlarmRate(2:end) - falseAlarmRate(1:end-1)) .* detectionRate(2:end));
+
+if ~isempty(col)
+    h=plot(falseAlarmRate, detectionRate, [col '-']);
+    %set(h, 'linewidth', 2);
+    e = 0.05;
+    if scale01
+      axis([0-e 1+e 0-e 1+e])
+    else
+      % zoom in on the top left corner
+      axis([0-e 0.5+e 0.5-e 1+e])
+    end
+    grid on
+    ylabel('detection rate')
+    xlabel('false alarm rate')
+end