Skip to main content

C/C++ header file inlcude "" OR<> how to use std ?



Suppose your project current directory is

0.   /home/myproj/

Let assume include foder are


1. /home/include1/
2. /home/include2/

==>  # include "myhead.h"
   search for directory in order 0 , then 1 , then 2

AS IT SEARCH CURRENT DIR FIRST, USUALLY user defined headers used in this way.

==>  # include
   search for directory in order 1 , then 2 , then 0

AS IT SEARCH INCLUDE DIR FIRST,  standard headers used in this way.

Header file extension:
==================
In C : In early days it was .hxx, .hpp . Then it was .h

    #include myheader.hxx
    #include myheader.hp
    #include myheader.hpp

In C++ : No extension. They are included in namespaces.

   #include
   using namespace std; // use original namespace

you can also use it in code as std::stringfunction

Comments

Post a Comment

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...
5 comments
Read more

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)
6 comments
Read more

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...
2 comments
Read more