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class info {     char infoLine[MAXLINELEN];     char infoMark[NOSPECIES];     int count; public:     //info(){  strcpy(infoLine,""); strcpy(infoMark,""); for(int i=0;i     // for new  entry     info(char* li, int index, char mr){         strcpy(infoLine,li);         //cout << infoLine << endl;         for(int i=0;i             infoMark[i]='+' ;         infoMark[index]=mr;         count = 1;     }     // for existing entry     void insertmark(int index, char currentMark) { infoMark[index]=currentMark; count++;  }     void putEndmark(int index)     {         infoMark[index]='\0' ;     }     int getCount()  {return count;}     char *getLine() {return infoLine; }     char *getMark() {return infoMark; } }; map mymap; // it must be string . otherwise you have to handle < operator overload in info object map ::iterator it; it = mymap.find(ID); if ( it !=mymap.end() ) // found id         {            it-

void testMap() {     map mymap;     map ::iterator rit;     mymap["x1"] = 100;     mymap.insert(pair ("y2",150));     mymap["y2"] = 200;     mymap["y2"] = 400; // this value will replace previous one.     rit = mymap.find("xx");     if ( rit !=mymap.end() ) // found id     {         rit->second= 11111; // change existing content     }else // not found     {         mymap.insert( pair ("xx",150)    );     }     // show content:     for ( rit=mymap.begin() ; rit != mymap.end(); rit++ )         cout << rit->first << " => " << rit->second << endl; }

% 1 4 5 7 -- sample 1 % 2 5 6 6 -- sample 2 % in cndx = you will get  the position of each training sample in cluster no % as SOM takes each sample as a column we need to transpose feature before applying on SOM %  -------  train ----------- trainFeature = [ 1 2  ; 4 5    ; 5 6 ; 7 6 ]; % Two training data with 4 feature each . So each column is a sample net = newsom(  trainFeature'  ,[2 2] ); % 4 cluster net.trainParam.epochs = 100; [net,tr,Y,E,Pf,Af] = train(net, trainFeature'  ); distances = dist( trainFeature  ,net.IW{1}' ); [d,cndx_train ] = min(distances,[],2); % cndx gives the cluster index, d gives the distance cndx_train % the position of each training sample in cluster no bookKeep=zeros(SOMd1 * SOMd2 ,2);%one column for +ve,one column for -ve     for c=1: totTrain         index = cndx_train(c);         if(c<=totPosTrain)             bookKeep( index,1) = bookKeep( index,1) + 1; %+ve sample         else             bookKeep( index,2) = boo

NUM_EXAMPLE = 6; NOOFTRAINING = 6; NOOFCHARPERLINE = 10 ; NOOFOFFSETPERLINE = NOOFCHARPERLINE+ 2; % sometimes it maybe 2 depending upon whow data was written into file fid = fopen( 'D:\KAUST\2.Winter2010-11\SpliceSites\test.txt','r'); for i=1: NOOFTRAINING        rowno = round(rand(1)*NUM_SAMPLE) ;       offset = (rowno - 1 ) * NOOFOFFSETPERLINE;    fseek(fid,offset,'bof');    line = fgetl(fid);    frewind(fid);       disp(rowno); %    disp(offset);    disp(line);    end fclose(fid);

X = [ .5 .5]; lX = log2(X); ent = -(X * lX' )

Basic Mode :If microsoft Excel is not installed ============================= 1. Excel file must be saved in win95 format. 97/98/2000 may not work. 2. Can not read range. Read whole file.       %  or read the first sheet    ---------------------------------------     all = xlsread( 'filename' );     %  or read the specified sheet    --------------------------------- all = xlsread( 'filename','sheetname','' , 'basic'  )

2010a ================ net=newff(trainFeature,trainValue,[8 1],{'tansig' 'purelin'}); 2009a =============== net=newff(trainFeature,trainValue,[8 1]); net.layers{1}.transferFcn = 'tansig'; net.layers{2}.transferFcn = 'purelin' save net ==================  save net; %-------------------- Radial Basis Network No train -------------------   net=newrb(trainFeature,trainValue,0.0, 1, 100, 1);  save net; NO TRAIN . IT WILL AUTOMATICALLY CRATE NEW NEURON load net for testing ==================== load net;

add percent  % noise to each value ==================================== percent=.03; sample = sampleMatrix(index, :); noiseSample=[]; for i=1 :instanceSize        val = sample(i);        smallPerc = val * percent ; % 1 percent noise add        noise = smallPerc* [ rand(1) - 0.5 ]; % .*T        noisyVal = val + noise ; % small is very small 10e-9        noiseSample = [noiseSample noisyVal]; end noiseMatrix = [ noiseMatrix ; noiseSample]; Add little noise to X ====================== X  rand( 'state', 0 ) % any number for random number randn('state', 0 ) % any number for random number small = 10e-11 small* [ rand(size(X)) - .5 ] .* X + X  % small is very small 10e-9 index = ceil(a + (b-a).*rand(1) ); %

void readLine() {     FILE *fpTest;     fpTest = fopen ( "test.txt", "r" );     int countRead = 0,countLine = 0,countCodeDataLine = 0,length = 0;     if(fpTest ==NULL){         printf("error in opening test.txt file" ); // perror         exit(0);     }             while( !feof(fpTest)    )     {         fgets ( line, MAXATTRLINELEN, fpTest );         countRead++;         length = strlen(line);         printf("[%d]:%s(lineLength=%d) \n",countLine,line,length);                 if(length > 0)         {             countLine++;         }                 // count countCodeDataLine         if(length == 0) ;         else if(length ==1)         {             //printf(" %d  %d %d %d %d",line[0], '\n', '\r' , '\r\n', '\n\r');             if(line[0]==' ' || line[0] == '\n'  || line[0] == '\t' ) ;             else                 countCodeDataLine++;        

Two dim float malloc ====================     float ** PC;     PC = (float**) malloc(DIM_PC_X * sizeof(float*)); // [DIM_PC_X][DIM_PC_Y];     for(int i = 0; i < DIM_PC_X; i++)         PC[i] = (float*) malloc(DIM_PC_Y * sizeof(float)); Two dim float using new ============================ int sizeX=5,sizeY = 2;     int** ary = new int*[sizeX];     for(int i = 0; i < sizeX; ++i)             ary[i] = new int[sizeY];

As we know to run openmp in gcc , C++ project we have to compile it with g++ -fopenmp option. To configure this with eclipse you just need to add -fopenmp under GCC C++ linker command option