OpenMP Tutorial

Some tutorial:

http://people.math.umass.edu/~johnston/PHI_WG_2014/OpenMPSlides_tamu_sc.pdf

http://www3.nd.edu/~zxu2/acms60212-40212-S12/Lec-11-03.pdf

Scheduling Tutorial

For beginners: https://people.sc.fsu.edu/~jburkardt/c_src/schedule_openmp/schedule_openmp.html

https://www.buffalo.edu/content/www/ccr/support/training-resources/tutorials/advanced-topics--e-g--mpi--gpgpu--openmp--etc--/2011-09---practical-issues-in-openmp--hpc-1-/_jcr_content/par/download/file.res/omp-II-handout-2x2.pdf

Fibonacci Number generation

https://gist.github.com/CarlEkerot/2601195

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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 partition selectedKfoldSen=[];selectedKfoldSpe=[];selectedKfoldAcc=[]; indexSen=1;indexSpe=1;indexAcc=1; if ( kfold == (P+N) )% leave one out sensitivity = sum(cvtp) /( sum(cvtp) + sum(cvfn) ) specificity = sum(cvtn) /( sum(cvfp) + sum(cvtn) ) acc

R tutorial

Install R in linux ============ In CRAN home page, the latest version is not available. So, in fedora, Open the terminal yum list R --> To check the latest available version of r yum install R --> install R version yum update R --> update current version to latest one 0 find help ============ ?exact topic name ( i.e. ?mean ) 0.0 INSTALL 3rd party package ==================== install.packages('mvtnorm' , dependencies = TRUE , lib='/home/alamt/myRlibrary/') # install new package BED file parsing (Always use read.delim it is the best) library(MASS) #library(ggplot2) dirRoot="D:/research/F5shortRNA/TestRIKEN/Rscripts/" dirData="D:/research/F5shortRNA/TestRIKEN/" setwd(dirRoot) getwd() myBed="test.bed" fnmBed=paste(dirData, myBed, sep="") # ccdsHh19.bed tmp.bed ## Read bed use read.delim - it is the best mybed=read.delim(fnmBed, header = FALSE, sep = "\t", quote = &q

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)

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