Function help/comments edited.

comik_main_with_weight_vector.m

@@ -490,7 +490,7 @@
     %
     heatmapFname = strcat(outputFolder,'/Heatmap-InstanceWeights', '_oligoLen', num2str(oligoLen), '_segmentSize', num2str(segmentSizeInBps) , '_maxDist', num2str(maxDist), '_nClusters', num2str(bestParamComb.best_nClusters), '_svmC', num2str(bestParamComb.best_C) ,'_sigma', num2str(bestParamComb.best_sigma), '.pdf');
     %
-    p = plot_heatmap(normInstanceWeightsInEachBag, heatmapFname, 0, 1);
+    p = plotHeatmap(normInstanceWeightsInEachBag, heatmapFname, 0, 1);
     logMessages(debugMsgLocation,sprintf('done!\n'), debugLevel);
     %
 end

libsvm_plotroc.m

@@ -1,10 +1,11 @@
 function [auROC auPRC] = plotroc(y,x,params)
-%plotroc draws the recevier operating characteristic(ROC) curve.
+% Reused and adapted from the Matlab code of LIBSVM.
+% plotroc draws the recevier operating characteristic(ROC) curve.
 %
-%auc = plotroc(training_label, training_instance [, libsvm_options -v cv_fold]) 
+%  auc = plotroc(training_label, training_instance [, libsvm_options -v cv_fold]) 
 %  Use cross-validation on training data to get decision values and plot ROC curve.
 %
-%auc = plotroc(testing_label, testing_instance, model) 
+%  auc = plotroc(testing_label, testing_instance, model) 
 %  Use the given model to predict testing data and obtain decision values
 %  for ROC
 %
@@ -16,6 +17,7 @@
 %	[y,x] = libsvmread('heart_scale');
 %   	model = svmtrain(y,x);
 % 	plotroc(y,x,model);
+
 	rand('state',0); % reset random seed
 	if nargin < 2
 		help plotroc
@@ -29,7 +31,6 @@
 		[predict_label,mse,deci] = libsvmpredict(y,x,model); % the procedure for predicting
 		[auROC auPRC] = roc_curve(deci*model.Label(1),y);
 	elseif exist('params') && strcmp(params, 'personal')
-		%disp('Personal auROC');
 		[auROC auPRC] = roc_curve(x, y);
 		[prc_stack_x, prc_stack_y, prc_thre, auPRC] = perfcurve(y, x, 1, 'xCrit', 'reca', 'yCrit', 'prec');
 		%% This condition takes care of scenario when

logMessages.m

@@ -1,5 +1,24 @@
 function logMessages(fid, statement, debugLevel)
 
+% LOGMESSAGES
+% Prints out log messages for CoMIK
+%
+% INPUT PARAMS
+% Param 'fid'
+%
+% 
+% Param 'statement'
+%
+%
+% Param 'debugLevel'
+%
+% ADDITIONAL NOTES
+% debugLevel 2  prints all messages
+% debugLevel 1  place holder level, may be used in the future
+% debugLevel 0  silent on most of the messages, prints very few messages for the user
+%
+% Author: snikumbh@mpi-inf.mpg.de
+
 if debugLevel == 2
     fprintf(fid, statement); %when fid is 1, this prints to command window
 end

mkl.py

@@ -1,5 +1,14 @@
 #!/usr/bin/env python
 
+# This python script performs MKL for CoMIK
+#
+# ADDITIONAL NOTES
+# We will read kernel_files from disk
+# C will be a passed argument
+#
+# Author: snikumbh@mpi-inf.mpg.de
+
+
 #system imports
 from optparse import OptionParser, OptionGroup, OptionValueError
 import numpy, sys, os
@@ -16,9 +25,6 @@
     print "Refer to instructions on http://www.shogun-toolbox.org/ for installing SHOGUN with the python_modular interface."
     raise
 
-# will read kernel_files from disk
-# C will be a passed argument
-# 
 
 parser = OptionParser()
 
@@ -54,7 +60,6 @@
 trainlabels = BinaryLabels(numpy.array(trainlabels))
 
 # assemble combined kernel
-
 with open(options.trainkernelsListFilename, 'r') as f:
     kernelFilenames = [fl.rstrip() for fl in f.readlines()]
 
@@ -67,7 +72,7 @@
     else:
         raise OptionValueError("Cannot open %s as a file. Please check if it exists." % thisFname)
 
-#trainkernel.set_normalizer(SqrtDiagKernelNormalizer(True))
+# trainkernel.set_normalizer(SqrtDiagKernelNormalizer(True))
 # train MKL
 mkl = MKLClassification(LibSVM())