Skip to main content

Matlab plot graph


To plot
=====
plot( 100*codingCov, 100*noncodingCov,'.');

Change the size of default figure
=================
figure
set(0, 'DefaultFigurePosition', [ leftPos bottomPos width height ]);

To limit the axis value
=================
xlim([0 100]); ylim([0 100]);

Mark or tick each point of axis as you wish
============================
stateName={ 'state1'; state2''; 'state3' ; 'state4';'};
set(gca,'XTickLabel',stateName)

Interactive graph with click show a message
================================
Override or select default  callBack function in mouse event . Message must be cell array

function output_txt = myCallback(obj,event_obj)
% Display the position of the data cursor
% obj          Currently not used (empty)
% event_obj    Handle to event object
% output_txt   Data cursor text string (string or cell array of strings).

fnameStat = '../gene.features/allMotifCNC.stat';
[ covCoding covNonCoding score  consensus ] = textread(fnameStat,'%f\t%f\t%f\t%s');
noMotif = 335;

pos = get(event_obj,'Position');

for i=1:noMotif
   if covCoding(i) == pos(1)  && covNonCoding(i) == pos(2)
     break;
   end
end

output_txt=  consensus(i);

if length(pos) > 2
    output_txt{end+1} = ['Z: ',num2str(pos(3),4)];
end


Comments

Popular posts from this blog

MATLAB cross validation

// use built-in function samplesize = size( matrix , 1); c = cvpartition(samplesize,  'kfold' , k); % return the indexes on each fold ///// output in matlab console K-fold cross validation partition              N: 10    NumTestSets: 4      TrainSize: 8  7  7  8       TestSize: 2  3  3  2 ////////////////////// for i=1 : k    trainIdxs = find(training(c,i) ); %training(c,i);  // 1 means in train , 0 means in test    testInxs  = find(test(c,i)       ); % test(c,i);       // 1 means in test , 0 means in train    trainMatrix = matrix (  matrix(trainIdxs ), : );    testMatrix  = matrix (  matrix(testIdxs  ), : ); end //// now calculate performance %%  calculate performance of a partiti...

MATLAB confusion matrix

%  test_class  & predicted_class must be same dimension % 'order' - describes the order of label. Here labels are 'g' as positive and 'h' as negative [C,order] = confusionmat( test_class(1: noSampleTest), predicted_class, 'order', ['g' ;'h'] ) tp = C(1,1); fn = C(1,2); fp = C(2,1); tn = C(2,2); sensitivity = tp /( tp + fn ) specificity = tn /( fp + tn ) accuracy = (tp+tn) / (tp+fn+fp+tn) tpr = sensitivity fpr = 1-specificity precision = tp /( tp + fp ) fVal = (2*tpr*precision)/(tpr+precision)

Feature subset selection Using Genetic Algorithm in MATLAB

function callGeneticAlgo global mat global trainInd global testInd [trainInd,~,testInd] = dividerand(1420,0.7,0,0.3); global counter global errList counter = 1; errList = []; fileName=  '../features/alltopPNPDMF.feature' ; mat = load(fileName); [x,fval,exitflag,output,population,score] = gaFeaSelection(1588,100,10800); % param1 = #feature excludig label % param2 =  population size % param3 = sec to test (3 hour = 10800 sec) dlmwrite('selected.GA',x,'delimiter','\n'); display('Done'); end function [x,fval,exitflag,output,population,score] = gaFeaSelection (nvars,PopulationSize_Data,TimeLimit_Data) % This is an auto generated MATLAB file from Optimization Tool. % Start with the default options options = gaoptimset; % Modify options setting options = gaoptimset(options,'PopulationType', 'bitString'); options = gaoptimset(options,'PopulationSize', PopulationSize_Data); options = gaoptimset(options,'TimeLimit', T...