Пример #1
0
VImage 
VImage::composite( VImage other, VipsBlendMode mode, VOption *options )
{
	VImage v[2] = { *this, other }; 
	std::vector<VImage> ivec( v, v + VIPS_NUMBER( v ) );
	int m[1] = { static_cast<int>( mode ) }; 
	std::vector<int> mvec( m, m + VIPS_NUMBER( m ) );

	return( composite( ivec, mvec, options ) ); 
}
int main(void)
{
   const int n =6; // 4I , 2 lambda
   int info;

   // system params
   Coil coil;
   Magnet magnet;
   coil.d = .0575;
   coil.R = 0.075;

   magnet.x =  0.01;
   magnet.y =  0.0;
   magnet.Fx = 1 * pow(10,-8);
   magnet.Fy = 1.* pow(10,-8);
   magnet.gamma = 35.8 * pow(10,-6);
   double th = -19 * PI/180; // convert angle in degrees to radians

   double Bmag = 10. * pow(10,-6);
   double Bx = Bmag * cos(th);
   double By = Bmag * sin(th);

   Vector2d mvec(Bx,By);
   mvec = magnet.gamma/sqrt(Bx * Bx + By * By)*mvec;

   cout << "mvec" << endl << mvec << endl;

   MatrixXd Bmat = computeBmat(magnet.x,magnet.y,coil.R,coil.d);
   cout << "Bmat" << endl << Bmat << endl;
   //MatrixXd 

   Matrix4d A;
   cout << "A" << endl << A << endl;

   A.block(0,0,2,4) = Bmat;
   cout << "A row 1-2" << endl << A << endl;
   A.block(2,0,1,4) = mvec.transpose() * Dx(magnet.x,magnet.y,coil.R,coil.d);
   A.block(3,0,1,4) = mvec.transpose() * Dy(magnet.x,magnet.y,coil.R,coil.d);
   cout << "A " << endl << A << endl;

   cout << "A inverse is:" << endl << A.inverse() << endl;

   Vector4d BF;
   BF << Bx,By,magnet.Fx,magnet.Fy;
   cout << "BF: " << endl << BF << endl;
   Vector4d Isolve = A.inverse() * BF;

   cout << "Isolve: " << endl << Isolve << endl;
   return 0;
}
void SymmetricSolver::circulantMul(const BlockCMat& M, mvec& v, unsigned int nPhi) {
    assert(!(v.size()%nPhi));
    assert(M.nCols()*nPhi == v.size());
    
    // stuff vector into vector-of-vectors for circulant blocks
    VarVec<mvec> vv;
    for(unsigned int i=0; i<v.size()/nPhi; i++)
        vv.push_back(mvec(&v[i*nPhi], &v[i*nPhi]+nPhi));
    vv = M.lMultiply<mvec,mvec>(vv);
    
    // pull data back out
    v.getData().resize(M.nRows()*nPhi);
    for(unsigned int i=0; i<M.nRows(); i++)
        for(unsigned int j=0; j<nPhi; j++)
            v[i*nPhi+j] = vv[i][j];
}
Пример #4
0
void GenericSolver::calculateResult(ReactiveSet& R) {
#ifdef WITH_LAPACKE
    assert(my_SVD);
    mvec finalState = my_SVD->calc_pseudo_inverse(singular_epsilon) * R.incidentState;
#else
    assert(the_GF);
    gsl_vector* inc = gsl_vector_alloc(R.nDF());
    gsl_vector* fin = gsl_vector_alloc(R.nDF());
    for(unsigned int i=0; i<R.nDF(); i++)
        gsl_vector_set(inc,i,R.incidentState[i]);
    assert(!gsl_blas_dgemv(CblasNoTrans, 1., the_GF, inc, 0., fin));
    mvec finalState = mvec(R.nDF());
    for(unsigned int i=0; i<R.nDF(); i++)
        finalState[i] = gsl_vector_get(fin,i);
    gsl_vector_free(inc);
    gsl_vector_free(fin);
#endif
    
    R.setFinalState(finalState);
}
Пример #5
0
    vector<vector<int>> combinationSum(vector<int>& candidates, int target) {


        vector<vector<int>> res;
        function<bool(int, int, const vector<vector<int>>&)> ff =[&res, &candidates, &ff](int t, int start, const vector<vector<int>>& vec){
            if(t <0)
                return false;
            else if(t == 0){
                copy(vec.begin(), vec.end(), back_inserter(res));
                return true;
            }
            else if (start >= candidates.size())
                return false;
            else if (candidates[start] > t)
                return false;

            bool bret = false;
            for(int i = start; i < candidates.size() && candidates[i] <= t; ++i){
                if(candidates[i] <= t){
                    vector<vector<int>> mvec(vec.begin(), vec.end());
                    for_each(mvec.begin(), mvec.end(), [i, &candidates](vector<int>& item){item.push_back(candidates[i]);});

                    if(ff(t-candidates[i], i, mvec)) {
                        bret = true;
                    }

                }
            }
            return bret;

        };
        vector<vector<int>> vv{vector<int>{}};
        sort(candidates.begin(), candidates.end());
        if(ff(target, 0, vv)){
            return res;
        }
        else{
            return vector<vector<int>>{};
        }

    }
Пример #6
0
static void Diagonalizer_and_CrossCorrelationTable_qdiag (Diagonalizer me, CrossCorrelationTables thee, double *cweights, long maxNumberOfIterations, double delta) {
	try {
		CrossCorrelationTable c0 = (CrossCorrelationTable) thy item[1];
		double **w = my data;
		long dimension = c0 -> numberOfColumns;

		autoEigen eigen = Thing_new (Eigen);
		autoCrossCorrelationTables ccts = Data_copy (thee);
		autoNUMmatrix<double> pinv (1, dimension, 1, dimension);
		autoNUMmatrix<double> d (1, dimension, 1, dimension);
		autoNUMmatrix<double> p (1, dimension, 1, dimension);
		autoNUMmatrix<double> m1 (1, dimension, 1, dimension);
		autoNUMmatrix<double> wc (1, dimension, 1, dimension);
		autoNUMvector<double> wvec (1, dimension);
		autoNUMvector<double> wnew (1, dimension);
		autoNUMvector<double> mvec (1, dimension);

		for (long i = 1; i <= dimension; i++) // Transpose W
			for (long j = 1; j <= dimension; j++) {
				wc[i][j] = w[j][i];
			}

		// d = diag(diag(W'*C0*W));
		// W = W*d^(-1/2);

		NUMdmatrix_normalizeColumnVectors (wc.peek(), dimension, dimension, c0 -> data);

		// scale eigenvectors for sphering
		// [vb,db] = eig(C0);
		// P = db^(-1/2)*vb';

		Eigen_initFromSymmetricMatrix (eigen.peek(), c0 -> data, dimension);
		for (long i = 1; i <= dimension; i++) {
			if (eigen -> eigenvalues[i] < 0) {
				Melder_throw (U"Covariance matrix not positive definite, eigenvalue[", i, U"] is negative.");
			}
			double scalef = 1 / sqrt (eigen -> eigenvalues[i]);
			for (long j = 1; j <= dimension; j++) {
				p[dimension - i + 1][j] = scalef * eigen -> eigenvectors[i][j];
			}
		}

		// P*C[i]*P'

		for (long ic = 1; ic <= thy size; ic++) {
			CrossCorrelationTable cov1 = (CrossCorrelationTable) thy item[ic];
			CrossCorrelationTable cov2 = (CrossCorrelationTable) ccts -> item[ic];
			NUMdmatrices_multiply_VCVp (cov2 -> data, p.peek(), dimension, dimension, cov1 -> data, 1);
		}

		// W = P'\W == inv(P') * W

		NUMpseudoInverse (p.peek(), dimension, dimension, pinv.peek(), 0);

		NUMdmatrices_multiply_VpC (w, pinv.peek(), dimension, dimension, wc.peek(), dimension);

		// initialisation for order KN^3

		for (long ic = 2; ic <= thy size; ic++) {
			CrossCorrelationTable cov = (CrossCorrelationTable) ccts -> item[ic];
			// C * W
			NUMdmatrices_multiply_VC (m1.peek(), cov -> data, dimension, dimension, w, dimension);
			// D += scalef * M1*M1'
			NUMdmatrices_multiplyScaleAdd (d.peek(), m1.peek(), dimension, dimension, 2 * cweights[ic]);
		}

		long iter = 0;
		double delta_w;

		autoMelderProgress progress (U"Simultaneous diagonalization of many CrossCorrelationTables...");
		try {
			do {
				// the standard diagonality measure is rather expensive to calculate so we compare the norms of
				// differences of eigenvectors.

				delta_w = 0;
				for (long kol = 1; kol <= dimension; kol++) {
					for (long i = 1; i <= dimension; i++) {
						wvec[i] = w[i][kol];
					}

					update_one_column (ccts.peek(), d.peek(), cweights, wvec.peek(), -1, mvec.peek());

					Eigen_initFromSymmetricMatrix (eigen.peek(), d.peek(), dimension);

					// Eigenvalues already sorted; get eigenvector of smallest !

					for (long i = 1; i <= dimension; i++) {
						wnew[i] = eigen -> eigenvectors[dimension][i];
					}

					update_one_column (ccts.peek(), d.peek(), cweights, wnew.peek(), 1, mvec.peek());
					for (long i = 1; i <= dimension; i++) {
						w[i][kol] = wnew[i];
					}

					// compare norms of eigenvectors. We have to compare ||wvec +/- w_new|| because eigenvectors
					//  may change sign.

					double normp = 0, normm = 0;
					for (long j = 1; j <= dimension; j++) {
						double dm = wvec[j] - wnew[j], dp = wvec[j] + wnew[j];
						normp += dm * dm; normm += dp * dp;
					}

					normp = normp < normm ? normp : normm;
					normp = sqrt (normp);
					delta_w = normp > delta_w ? normp : delta_w;
				}
				iter++;

				Melder_progress ((double) iter / (double) (maxNumberOfIterations + 1), U"Iteration: ", iter, U", norm: ", delta_w);
			} while (delta_w > delta && iter < maxNumberOfIterations);
		} catch (MelderError) {
			Melder_clearError ();
		}

		// Revert the sphering W = P'*W;
		// Take transpose to make W*C[i]W' diagonal instead of W'*C[i]*W => (P'*W)'=W'*P

		NUMmatrix_copyElements (w, wc.peek(), 1, dimension, 1, dimension);
		NUMdmatrices_multiply_VpC (w, wc.peek(), dimension, dimension, p.peek(), dimension); // W = W'*P: final result

		// Calculate the "real" diagonality measure
	//	double dm = CrossCorrelationTables_and_Diagonalizer_getDiagonalityMeasure (thee, me, cweights, 1, thy size);

	} catch (MelderError) {
		Melder_throw (me, U" & ", thee, U": no joint diagonalization (qdiag).");
	}
}
Пример #7
0
//M+	
void mp(
	int MinCoreSize,
	int MaxCoreSize,
	int SamplingFreq,
	int NumReplicates,
	char* OutFilePath,
	std::string Kernel,
	vector<int> KernelAccessionIndex,
	vector<int> AccessionNameList,
	vector<vector<vector<int> > > ActiveAlleleByPopList,
	vector<vector<vector<int> > > TargetAlleleByPopList,
	vector<int> ActiveMaxAllelesList,
	vector<int> TargetMaxAllelesList,
	vector<std::string> FullAccessionNameList
	)	
{

	//PERFORM INITIAL MPI STUFF
	MPI_Status status; //this struct contains three fields which will contain info about the sender of a received message
						 // MPI_SOURCE, MPI_TAG, MPI_ERROR
	
	//MPI::Init ();  //Initialize MPI.
	int nproc = MPI::COMM_WORLD.Get_size ( );  //Get the number of processes.
	int procid = MPI::COMM_WORLD.Get_rank ( );  //Get the individual process ID.
	

	//set up vectors to fill with results
	//below is a stupid way to calculate the number of rows in the output file, value l (which = V1) 
	//used to monitor progress and as the maximum vector index for shared output vectors
	int l=0;
	for (int i=MinCoreSize;i<MaxCoreSize+1;i=i+SamplingFreq)
	{
		for (int j=0;j<NumReplicates;j++)
		{
			l++;
		}
	}
	
	double V1 = (double)l; //(MaxCoreSize - MinCoreSize + 1)*NumReplicates; //number of rows in output vectors
	vector<vector<double> > Results(V1, vector<double>(9)); //will contain numerical results
	vector<vector<string> > Members(V1); //will contain core set members
	
	//***MPI:  RECEIVE RESULTS AT MASTER 0
	//receive values from any slave, in any order, exiting when the number of 'receives' = the top vector size
	if ( procid == 0 ) 
	{
		//set up variables for monitoring progress
		int percent; //percent of analysis completed
		int progindex = 0;  //index to monitor progress, percent = 100*(progindex/l)

		//receive and process results from slave processors
		unsigned int i = 0;
		while (i<2*(Results.size())) //two receives per row
		{
			//probe the incoming message to determine its tag
			int nchar; //will contain the length of the char array passed with tag=1
			int vchar; //will contain the length of the vector passed with tag=0
			int tag; //tag of message from sender
			int source; //procid of sender
			
			MPI_Probe(MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
			//MPI_Get_count(&status, MPI_CHAR, &nchar); //probes the length of the message, saves it in nchar
			tag = status.MPI_TAG; //the tag defines which kind of comm it is, a vector of stats (0=resvec()) 
			                      //or a char array describing the members of the core (1=cc)
			source = status.MPI_SOURCE; //determine the source of the message so that you can define which sender to Recv from.  This will avoid an intervening message coming in after the MPI_Probe with a different length, causing a message truncated error.
			
			if (tag == 0)
			{
				//determine the length of the message tagged 0
				MPI_Get_count(&status, MPI_DOUBLE, &vchar);

				//cout <<" vchar="<<vchar<<" tag="<<tag<<" MPI_SOURCE="<<status.MPI_SOURCE<<" MPI_ERROR="<<status.MPI_ERROR<<"\n";

				//receive the vector of results, tagged 0, from:
				//MPI_Send(&resvec[0], resvec.size(), MPI_DOUBLE, 0, 0, MPI_COMM_WORLD);
				vector<double> t(10);
				MPI_Recv(&t[0], vchar, MPI_DOUBLE, source, 0, MPI_COMM_WORLD, &status);
			
				//load data from vector received onto Results, row number is last item t[9]
				for (int j=0;j<9;++j)
				{
					Results[ t[9] ][j] = t[j];
				}
				t.clear();
			}

			else if (tag == 1)
			{
				//determine the length of the message tagged 1
				MPI_Get_count(&status, MPI_CHAR, &nchar); //probes the length of the message, saves it in nchar

				//cout <<" nchar="<<nchar<<" tag="<<tag<<" MPI_SOURCE="<<status.MPI_SOURCE<<" MPI_ERROR="<<status.MPI_ERROR<<"\n";
				
				//receive the vector<string> of the core set, tagged 1, from:
				//MPI_Send(&m[0], nchar, MPI_CHAR, 0, 1, MPI_COMM_WORLD);
				//vector<string> m(nchar);
				char m[nchar];
				MPI_Recv(&m[0], nchar, MPI_CHAR, source, 1, MPI_COMM_WORLD, &status);
			
				//load core set onto Members
				//1. convert char array into a string
				string mstr(m);
			
				//2. split string on delimiter ',<!>,'
				string delim = ",<!>,";
				vector<string> mvec( countSubstring(mstr, delim) );
				unsigned int st = 0;
				std::size_t en = mstr.find(delim);
				int k = 0;
				while (en != std::string::npos)
				{
					mvec[k] = mstr.substr(st, en-st);
					st = en + delim.length();
					en = mstr.find(delim,st);
					++k;
				}
				string z = mstr.substr(st); //get row number as last item in mstr
				int zz = atoi(z.c_str()); //convert string to c-string then to int
			
				//3. load onto Members
				Members[zz] = mvec;
				
				//4. clean up
				memset(m, 0, nchar);; mstr=""; mvec.clear();
				

			}


			++i;
			
			//display progress
			progindex = progindex + 1;
			percent = 100*( progindex/(V1*2) ); //number of rows X 2 repeats needed to complete search
			printProgBar(percent); 
		}
	}//***MPI: END MASTER RECEIVE***/

	
	/***MPI:  SEND RESULTS FROM SLAVE PROCESSES***/
	else if ( procid != 0 )
	{
		unsigned int r; //r = core size, 
		//int nr, RandAcc, b, bsc, plateau; //nr = controller to repeat NumReplicates times
		int RandAcc, b, bsc, plateau; //nr = controller to repeat NumReplicates times
								//row = result vector row number, bsc = holds best sub core member, and other indexed accessions
									//plateau = index of the number of reps in optimization loop with same diversity value
		double RandomActiveDiversity;
		double AltRandomActiveDiversity;
		double StartingRandomActiveDiversity;
		double StartingAltRandomActiveDiversity;
		double RandomTargetDiversity;
		double AltRandomTargetDiversity;
		double StartingDiversity;
		double TempAltOptimizedActiveDiversity;
		double AltOptimizedActiveDiversity;
		double OptimizedTargetDiversity;
		double AltOptimizedTargetDiversity;
		double best;
		double nnew;
		vector<vector<vector<int> > > AlleleList;
		vector<vector<vector<int> > > CoreAlleles;
		vector<vector<vector<int> > > TdTempList;
		vector<vector<vector<int> > > BestSubCoreAlleles;
		std::string Standardize = "yes";  //a run that mimics the MSTRAT approach can be accomplished by setting Standardize="no", and setting up the var file so that each column in the .dat file is treated as a single locus, rather than two (or more) adjacent columns being treated as a single codominant locus.
		vector<int> AccessionsInCore;
		vector<int> AccessionsInSubCore;
		vector<int> BestSubCore;
		vector<int> BestSubCoreRevSorted;
		vector<int> TempList;
		vector<int> TempList2;
		vector<int> bestcore;
		vector<std::string> TempListStr;
	
		//seed the random number generator for each processor
		int tt;
		tt = (time(NULL));
		srand ( abs(((tt*181)*((procid-83)*359))%104729) );
			
		//do parallelization so that each rep by core size combo can be
		//handled by a distinct thread.  this involves figuring out the total
		//number of reps*coresizes taking into account the SamplingFreq
		int rsteps = 1 + floor( (MaxCoreSize - MinCoreSize) / SamplingFreq ); //number of steps from MinCoreSize to MaxCoreSize

		//***MPI: figure out where to start and stop loop for each processor
		int nreps = rsteps*NumReplicates;
		int count = nreps/(nproc-1); //p-1 assumes a master, i.e. one less processor than total
		int start = (procid-1) * count; //procid-1 makes you start at 0, assumes master is p0
		int stop;
		
		if (nreps % (nproc-1) > (procid-1))
		{
			start += procid - 1;
			stop = start + (count + 1); 
		}
		else
		{
			start += nreps % (nproc-1);
			stop = start + count;
		}
		
		//iterate thru the relevant rows
		for (int rnr=start;rnr<stop;++rnr)
		{
			r = MinCoreSize + ((rnr / NumReplicates) * SamplingFreq); //int rounds to floor
			
			//develop random starting core set
			//clear AccessionsInCore and set size
			AccessionsInCore.clear();
			AccessionsInCore.resize(r);
			
			//add kernel accessions to core, if necessary
			if (Kernel == "yes")
			{
				for (unsigned int i=0;i<KernelAccessionIndex.size();i++)
				{
					AccessionsInCore[i] = KernelAccessionIndex[i];
				}
			}

			//clear TempList and set size					
			TempList.clear();
			TempList.resize( AccessionNameList.size() );
			
			//set list of available accessions in TempList, by erasing those already in the core
			TempList = AccessionNameList;
			//expunge the kernel accessions, so they are not available for random addition below
			//KernelAccessionIndex has been reverse sorted so you don't go outside range after automatic resize by .erase
			for (unsigned int i=0;i<KernelAccessionIndex.size();i++)
			{
				b = KernelAccessionIndex[i];
				TempList.erase(TempList.begin()+b);
			}
		
			//randomly add accessions until r accessions are in the core. if there is a kernel, include those (done above)
			//plus additional, randomly selected accessions, until you get r accessions
			for (unsigned int i=KernelAccessionIndex.size();i<r;i++)
			{
				//choose an accession randomly from those available
				RandAcc = rand() % TempList.size();
				//add it to the list
				AccessionsInCore[i] = TempList[RandAcc];
			
				//remove it from the list of available accessions
				TempList.erase(TempList.begin()+RandAcc);
			}
	
			//assemble genotypes for random core and calculate diversity
			//1. put together initial list of active alleles
			CoreAlleles.clear();
			CoreAlleles.resize( AccessionsInCore.size() );
			for (unsigned int i=0;i<AccessionsInCore.size();i++)
			{
				b = AccessionsInCore[i];
				CoreAlleles[i] = ActiveAlleleByPopList[b];
			}

			//2. calculate diversity from random selection at active loci
			AlleleList.clear();
			AlleleList = CoreAlleles;
	
			MyCalculateDiversity(AlleleList, ActiveMaxAllelesList, Standardize, RandomActiveDiversity, AltRandomActiveDiversity);
			//in MyCalculateDiversity, latter two variables are updated as references
			//save them away in non-updated variables
			StartingRandomActiveDiversity = RandomActiveDiversity;
			StartingAltRandomActiveDiversity = AltRandomActiveDiversity;

			//3. calculate diversity from random selection at target loci
			AlleleList.clear();
			AlleleList.resize( AccessionsInCore.size() );
			for (unsigned int j=0;j<AccessionsInCore.size();j++)
			{
				b = AccessionsInCore[j];
				AlleleList[j] = TargetAlleleByPopList[b];
			}
			MyCalculateDiversity(AlleleList, TargetMaxAllelesList, Standardize, RandomTargetDiversity, AltRandomTargetDiversity);


			//BEGIN OPTIMIZATION
			StartingDiversity = 0; //this is the diversity recovered during the prior iteration.
			plateau = 0; //count of the number of times you have found the best value, evaluates when you are
						 //stuck on a plateau, assuming acceptance criterion allows downhill steps
			//this is the iterations step, now an indefinite loop that is broken when 
			//no improvement is made during the course of the optimization algorithm
			//If r = kernel size = MinCoreSize then do no optimization but still calculate all variables.
			if (KernelAccessionIndex.size() == r)
			{
				//assemble genotypes for core
				//1. put together initial list
				CoreAlleles.clear();
				CoreAlleles.resize(r);
				for (unsigned int i=0;i<r;i++)
				{
					b = AccessionsInCore[i];
					CoreAlleles[i] = ActiveAlleleByPopList[b];
				}
				
				AlleleList = CoreAlleles;
				
				MyCalculateDiversity(AlleleList, ActiveMaxAllelesList, Standardize, RandomActiveDiversity, AltRandomActiveDiversity);
				best = RandomActiveDiversity; //best is equivalent to OptimizedActiveDiversity
				AltOptimizedActiveDiversity = AltRandomActiveDiversity;
			}
			else
			{
				//do optimization
				while ( true )
				{
					//assemble genotypes for core
					//1. put together initial list
					CoreAlleles.clear();
					CoreAlleles.resize(r);
					for (unsigned int i=0;i<r;i++)
					{
						b = AccessionsInCore[i];
						CoreAlleles[i] = ActiveAlleleByPopList[b];
					}
		
					//2. go through all possible subsets of size r-1, one at a time, noting which is best.
					//If there is a kernel, do not swap out any of those accessions (they are retained as the
					//first KernelAccessionIndex.size() items in CoreAlleles).  Accomplished by starting for loop
					//at KernelAccessionIndex.size().
					best=0;
					for (unsigned int i=KernelAccessionIndex.size();i<CoreAlleles.size();i++)
					{
						//remove each item consecutively from the list of all populations in the core
						AlleleList.clear();
						TdTempList.clear();
				
						TdTempList = CoreAlleles; //swap to temporary vector
						TdTempList.erase( TdTempList.begin() + i);
						AlleleList = TdTempList;
			
						TempList2.clear();
						TempList2 = AccessionsInCore;
						TempList2.erase(TempList2.begin() + i);
						AccessionsInSubCore = TempList2;

						/*Data structure for SubCoreAlleles:
						SubCore 1..r
							Population 1..(r-1)
								AlleleArray 1..NumLoci		
			
						--3. fuse alleles from the same locus into a single array, for all accessions, for the current subcore
						--4. assemble a list of diversity (M) for each locus separately
						--5. standardize the M values to the maximum possible number of alleles at that locus, and add them up to get final estimate of standardized allelic diversity in the core.  then divide by the number of loci to get a number that is comparable across data sets.
						--5.5. simultaneous to the calculation, keep track of which subcore is best
						*/
			
						MyCalculateDiversity(AlleleList, ActiveMaxAllelesList, Standardize, RandomActiveDiversity, AltRandomActiveDiversity);
						nnew = RandomActiveDiversity;

						if (nnew >= best) // >= allows sideways movement during hill climbing
						{
							best = nnew;

							BestSubCore.clear();
							BestSubCore = AccessionsInSubCore;
							BestSubCoreAlleles.clear();
							BestSubCoreAlleles = AlleleList;
						}
					}  //for loop cycles thru all subcores

					//reverse sort BestSubCore to support easy assembly of pared TempList below
					BestSubCoreRevSorted = BestSubCore;
					std::sort(BestSubCoreRevSorted.begin(), BestSubCoreRevSorted.end(), std::greater<int>());
	
					/*
					6. take the subcore with greatest diversity and consecutively add each 
					possible additional accession from the base collection.  find the core of size r 
					(not r-1 subcore) that has the greatest diversity.

					suppress the IDs of those accessions found in the BestSubCore from the 
					list of all accessions to get a list of remaining accessions.*/
					TempList = AccessionNameList;
					for (unsigned int k=0;k<BestSubCoreRevSorted.size();k++)
					{
						bsc = BestSubCoreRevSorted[k];
						TempList.erase( TempList.begin() + bsc );
					}
			
					//shuffle the list of remaining accessions, so addition order is not predictable
					std::random_shuffle (TempList.begin(), TempList.end());
				
					//add each remaining accession consecutively, calculate diversity, test 
					//whether it is better than the prior one
					best = 0;
					for (unsigned int k=0;k<TempList.size();k++)
					{
						bsc = TempList[k];
			
						//define the core
						TempList2 = BestSubCore;
						TempList2.resize( TempList2.size() + 1 );
						//TempList2.push_back(i);
						TempList2[TempList2.size()-1] = bsc; //add new accession to last vector element
						AccessionsInCore = TempList2;
			
						//assemble the allelelist for the core
						TdTempList = BestSubCoreAlleles;
						TdTempList.resize( TdTempList.size() + 1 );
						//TdTempList.push_back( ActiveAlleleByPopList[i] );
						TdTempList[TdTempList.size()-1] = ActiveAlleleByPopList[bsc];
						AlleleList = TdTempList;
			
						//calculate diversity
						MyCalculateDiversity(AlleleList, ActiveMaxAllelesList, Standardize, nnew, TempAltOptimizedActiveDiversity); 
		
						//test whether current diversity is higher than the best diversity found so far
						if (nnew >= best) // >= allows sideways movement during hill climbing
						{
							best = nnew;
							bestcore = AccessionsInCore;
							//save the alternative diversity value for the best core
							AltOptimizedActiveDiversity = TempAltOptimizedActiveDiversity;
						}
					}

					AccessionsInCore = bestcore; //define starting variable for next MSTRAT iteration
		
					//if there has been no improvement from the prior iteration, you have reached
					// the plateau and should exit the repeat
					if (best == StartingDiversity) 
					{
						plateau++;
						if (plateau > 0) break;
					}
					//update starting value and repeat
					else if (best > StartingDiversity) StartingDiversity = best;
				
				} //while(true) endless loop
			}

			//7. Calculate diversity at target loci
			//assemble the target loci allelelist for the accessions in the best core
			AlleleList.clear();
			AlleleList.resize( AccessionsInCore.size() );
			for (unsigned int j=0;j<AccessionsInCore.size();j++)
			{
				b = AccessionsInCore[j];
				AlleleList[j] = TargetAlleleByPopList[b];
			}
	
			//calculate diversity at target loci based upon the optimized core selection
			MyCalculateDiversity(AlleleList, TargetMaxAllelesList, Standardize, OptimizedTargetDiversity, AltOptimizedTargetDiversity);

			//8. Assemble stats for optimized core and add to output vectors
			//create a list of accession names from the list of accession ID's in AccessionsInCore
			sort( AccessionsInCore.begin(), AccessionsInCore.end() );
			
			TempListStr.clear();
			TempListStr.resize(r);
			for (unsigned int i=0;i<AccessionsInCore.size();i++)
			{
				b = AccessionsInCore[i];
				TempListStr[i] = FullAccessionNameList[b];
			}

			/***MPI: BUILD & SEND RESULTS VECTOR***/
			//load the variables onto the results vectors
			
			//no need to calculate row number, it is the same as rnr, formula saved because it might be useful later
			//row = ((r - MinCoreSize)*NumReplicates) + nr - ( (NumReplicates*(SamplingFreq-1))*( (r-MinCoreSize)/SamplingFreq ) );
			// (r - MinCoreSize)*NumReplicates) + nr specifies row number if SamplingFreq=1
			// (NumReplicates*(SamplingFreq-1)) specifies a step value to correct when SamplingFreq>1
			// ( (r-MinCoreSize)/SamplingFreq ) specifies the replicate on core size, accounting for SamplingFreq
			// see file Calculation of row value.xlsx for development of the 'row' index
			
			//put results 0-8 into a vector, resvec, return row as last item
			vector<double> resvec(10);

			resvec[0] = double(r);
			resvec[1] = StartingRandomActiveDiversity;//RandomActiveDiversity;
			resvec[2] = best; //equivalent to OptimizedActiveDiversity
			resvec[3] = RandomTargetDiversity;
			resvec[4] = OptimizedTargetDiversity;
			resvec[5] = StartingAltRandomActiveDiversity;//AltRandomActiveDiversity;
			resvec[6] = AltOptimizedActiveDiversity;
			resvec[7] = AltRandomTargetDiversity;
			resvec[8] = AltOptimizedTargetDiversity;
			resvec[9] = double(rnr);
			
			
			//cout<<"MPI_Rank="<<MPI_Rank<<" 
			
			
			//send result vector to master 0, send row number, rnr, as last element.
			//message is tagged as 0
			//here you are pointing to the first element, then returning resvec.size() doubles-
			//worth of memory from that starting location.
			MPI_Send(&resvec[0], resvec.size(), MPI_DOUBLE, 0, 0, MPI_COMM_WORLD);
			/***MPI: END BUILD & SEND RESULTS VECTOR***/


			/***MPI: BUILD & SEND MEMBERS VECTOR***/
			//add row number as last item in TempListStr
			TempListStr.resize(TempListStr.size()+1);
			stringstream ss;
			ss << rnr;	//convert int to stringstream to string			
			TempListStr[ TempListStr.size() - 1 ] = ss.str();
		
			//convert vector<string> to a single, ',<!>,' delimited, string
			string concat;
			for (unsigned int i=0;i<TempListStr.size();++i)
			{
				concat += TempListStr[i]; //add vector element
				if (i<TempListStr.size()-1) concat += ",<!>,"; //add delimiter, except for last item
			}
			//convert the string to a char array
			char cc[concat.size()+1];
			strcpy(cc, concat.c_str());
			
			//send the char array to master0 tagged as 1
			//tagged as 1 to distinguish from result vector send
			MPI_Send(&cc, sizeof(cc), MPI_CHAR, 0, 1, MPI_COMM_WORLD);

		} //end for loop over rows
	} //***MPI:  END SEND
	

	/*MPI: MASTER 0 WRITES OUTPUT*/
	if ( procid == 0 )
	{
		//set up file stream for output file
		ofstream output; 
		output.open(OutFilePath);
		output.close(); //quick open close done to clear any existing file each time program is run
		output.open(OutFilePath, ios::out | ios::app); //open file in append mode
		output << "core size	random reference diversity	optimized reference diversity	random target diversity	optimized target diversity	alt random reference diversity	alt optimized reference diversity	alt random target diversity	alt optimized target diversity	core members" << "\n";
		
		//write out results row by row
		for (int i=0;i<V1;i++)
		{
			//write variables
			output 	<< Results[i][0] 
					<< "	" << Results[i][1] 
					<< "	" << Results[i][2] 
					<< "	" << Results[i][3] 
					<< "	" << Results[i][4] 
					<< "	" << Results[i][5] 
					<< "	" << Results[i][6] 
					<< "	" << Results[i][7] 
					<< "	" << Results[i][8] 
					<< "	" << "(";
			//write Accessions retained
			for (unsigned int j=0;j<Members[i].size();j++)
			{
				if ( j==(Members[i].size() - 1) )
				{
					//add trailing parentheses and move to next row
					output << Members[i][j] << ")\n";
				}
				else
				{
					output << Members[i][j] << ",";
				}
			}
		}
	
		//wrap up write step
		output.close();
	} /***MPI: END MASTER WRITE***/
	
	//Terminate MPI.
	//MPI::Finalize ( );

}
Пример #8
0
// Testing the I/O of the important classes of the library
// (context, keys, ciphertexts).
int main(int argc, char *argv[])
{
  ArgMapping amap;

  long r=1;
  long p=2;
  long c = 2;
  long w = 64;
  long L = 5;
  long mm=0;
  amap.arg("p", p, "plaintext base");
  amap.arg("r", r,  "lifting");
  amap.arg("c", c, "number of columns in the key-switching matrices");
  amap.arg("m", mm, "cyclotomic index","{31,127,1023}");
  amap.parse(argc, argv);

  bool useTable = (mm==0 && p==2);
  long ptxtSpace = power_long(p,r);
  long numTests = useTable? N_TESTS : 1;

  std::unique_ptr<FHEcontext> contexts[numTests];
  std::unique_ptr<FHESecKey> sKeys[numTests];
  std::unique_ptr<Ctxt> ctxts[numTests];
  std::unique_ptr<EncryptedArray> eas[numTests];
  vector<ZZX> ptxts[numTests];

  // first loop: generate stuff and write it to cout

  // open file for writing
  {fstream keyFile("iotest.txt", fstream::out|fstream::trunc);
   assert(keyFile.is_open());
  for (long i=0; i<numTests; i++) {
    long m = (mm==0)? ms[i][1] : mm;

    cout << "Testing IO: m="<<m<<", p^r="<<p<<"^"<<r<<endl;

    Vec<long> mvec(INIT_SIZE,2);
    mvec[0] = ms[i][4];  mvec[1] = ms[i][5];
    vector<long> gens(2);
    gens[0] = ms[i][6];  gens[1] = ms[i][7];
    vector<long> ords(2);
    ords[0] = ms[i][8];  ords[1] = ms[i][9];

    if (useTable && gens[0]>0)
      contexts[i].reset(new FHEcontext(m, p, r, gens, ords));
    else
      contexts[i].reset(new FHEcontext(m, p, r));
    contexts[i]->zMStar.printout();

    buildModChain(*contexts[i], L, c);  // Set the modulus chain
    if (mm==0 && m==1023) contexts[i]->makeBootstrappable(mvec);

    // Output the FHEcontext to file
    writeContextBase(keyFile, *contexts[i]);
    writeContextBase(cout, *contexts[i]);
    keyFile << *contexts[i] << endl;

    sKeys[i].reset(new FHESecKey(*contexts[i]));
    const FHEPubKey& publicKey = *sKeys[i];
    sKeys[i]->GenSecKey(w,ptxtSpace); // A Hamming-weight-w secret key
    addSome1DMatrices(*sKeys[i]);// compute key-switching matrices that we need
    eas[i].reset(new EncryptedArray(*contexts[i]));

    long nslots = eas[i]->size();

    // Output the secret key to file, twice. Below we will have two copies
    // of most things.
    keyFile << *sKeys[i] << endl;;
    keyFile << *sKeys[i] << endl;;

    vector<ZZX> b;
    long p2r = eas[i]->getContext().alMod.getPPowR();
    ZZX poly = RandPoly(0,to_ZZ(p2r)); // choose a random constant polynomial
    eas[i]->decode(ptxts[i], poly);

    ctxts[i].reset(new Ctxt(publicKey));
    eas[i]->encrypt(*ctxts[i], publicKey, ptxts[i]);
    eas[i]->decrypt(*ctxts[i], *sKeys[i], b);
    assert(ptxts[i].size() == b.size());
    for (long j = 0; j < nslots; j++) assert (ptxts[i][j] == b[j]);

    // output the plaintext
    keyFile << "[ ";
    for (long j = 0; j < nslots; j++) keyFile << ptxts[i][j] << " ";
    keyFile << "]\n";

    eas[i]->encode(poly,ptxts[i]);
    keyFile << poly << endl;

    // Output the ciphertext to file
    keyFile << *ctxts[i] << endl;
    keyFile << *ctxts[i] << endl;
    cerr << "okay " << i << endl<< endl;
  }
  keyFile.close();}
  cerr << "so far, so good\n\n";

  // second loop: read from input and repeat the computation

  // open file for read
  {fstream keyFile("iotest.txt", fstream::in);
  for (long i=0; i<numTests; i++) {

    // Read context from file
    unsigned long m1, p1, r1;
    vector<long> gens, ords;
    readContextBase(keyFile, m1, p1, r1, gens, ords);
    FHEcontext tmpContext(m1, p1, r1, gens, ords);
    keyFile >> tmpContext;
    assert (*contexts[i] == tmpContext);
    cerr << i << ": context matches input\n";

    // We define some things below wrt *contexts[i], not tmpContext.
    // This is because the various operator== methods check equality of
    // references, not equality of the referenced FHEcontext objects.
    FHEcontext& context = *contexts[i];
    FHESecKey secretKey(context);
    FHESecKey secretKey2(tmpContext);
    const FHEPubKey& publicKey = secretKey;
    const FHEPubKey& publicKey2 = secretKey2;

    keyFile >> secretKey;
    keyFile >> secretKey2;
    assert(secretKey == *sKeys[i]);
    cerr << "   secret key matches input\n";

    EncryptedArray ea(context);
    EncryptedArray ea2(tmpContext);

    long nslots = ea.size();

    // Read the plaintext from file
    vector<ZZX> a;
    a.resize(nslots);
    assert(nslots == (long)ptxts[i].size());
    seekPastChar(keyFile, '['); // defined in NumbTh.cpp
    for (long j = 0; j < nslots; j++) {
      keyFile >> a[j];
      assert(a[j] == ptxts[i][j]);
    }
    seekPastChar(keyFile, ']');
    cerr << "   ptxt matches input\n";

    // Read the encoded plaintext from file
    ZZX poly1, poly2;
    keyFile >> poly1;
    eas[i]->encode(poly2,a);
    assert(poly1 == poly2);
    cerr << "   eas[i].encode(a)==poly1 okay\n";

    ea.encode(poly2,a);
    assert(poly1 == poly2);
    cerr << "   ea.encode(a)==poly1 okay\n";

    ea2.encode(poly2,a);
    assert(poly1 == poly2);
    cerr << "   ea2.encode(a)==poly1 okay\n";

    eas[i]->decode(a,poly1);
    assert(nslots == (long)a.size());
    for (long j = 0; j < nslots; j++) assert(a[j] == ptxts[i][j]);
    cerr << "   eas[i].decode(poly1)==ptxts[i] okay\n";

    ea.decode(a,poly1);
    assert(nslots == (long)a.size());
    for (long j = 0; j < nslots; j++) assert(a[j] == ptxts[i][j]);
    cerr << "   ea.decode(poly1)==ptxts[i] okay\n";

    ea2.decode(a,poly1);
    assert(nslots == (long)a.size());
    for (long j = 0; j < nslots; j++) assert(a[j] == ptxts[i][j]);
    cerr << "   ea2.decode(poly1)==ptxts[i] okay\n";

    // Read ciperhtext from file
    Ctxt ctxt(publicKey);
    Ctxt ctxt2(publicKey2);
    keyFile >> ctxt;
    keyFile >> ctxt2;
    assert(ctxts[i]->equalsTo(ctxt,/*comparePkeys=*/false));
    cerr << "   ctxt matches input\n";

    sKeys[i]->Decrypt(poly2,*ctxts[i]);
    assert(poly1 == poly2);
    cerr << "   sKeys[i]->decrypt(*ctxts[i]) == poly1 okay\n";

    secretKey.Decrypt(poly2,*ctxts[i]);
    assert(poly1 == poly2);
    cerr << "   secretKey.decrypt(*ctxts[i]) == poly1 okay\n";

    secretKey.Decrypt(poly2,ctxt);
    assert(poly1 == poly2);
    cerr << "   secretKey.decrypt(ctxt) == poly1 okay\n";

    secretKey2.Decrypt(poly2,ctxt2);
    assert(poly1 == poly2);
    cerr << "   secretKey2.decrypt(ctxt2) == poly1 okay\n";

    eas[i]->decrypt(ctxt, *sKeys[i], a);
    assert(nslots == (long)a.size());
    for (long j = 0; j < nslots; j++) assert(a[j] == ptxts[i][j]);
    cerr << "   eas[i].decrypt(ctxt, *sKeys[i])==ptxts[i] okay\n";

    ea.decrypt(ctxt, secretKey, a);
    assert(nslots == (long)a.size());
    for (long j = 0; j < nslots; j++) assert(a[j] == ptxts[i][j]);
    cerr << "   ea.decrypt(ctxt, secretKey)==ptxts[i] okay\n";

    ea2.decrypt(ctxt2, secretKey2, a);
    assert(nslots == (long)a.size());
    for (long j = 0; j < nslots; j++) assert(a[j] == ptxts[i][j]);
    cerr << "   ea2.decrypt(ctxt2, secretKey2)==ptxts[i] okay\n";

    cerr << "test "<<i<<" okay\n\n";
  }}
  unlink("iotest.txt"); // clean up before exiting
}
Пример #9
0
void
MooseEnumTest::multiTestOne()
{
  MultiMooseEnum mme("one two three four", "two");

  CPPUNIT_ASSERT( mme.contains("one") == false );
  CPPUNIT_ASSERT( mme.contains("two") == true );
  CPPUNIT_ASSERT( mme.contains("three") == false );
  CPPUNIT_ASSERT( mme.contains("four") == false );

  mme.push_back("four");
  CPPUNIT_ASSERT( mme.contains("one") == false );
  CPPUNIT_ASSERT( mme.contains("two") == true );
  CPPUNIT_ASSERT( mme.contains("three") == false );
  CPPUNIT_ASSERT( mme.contains("four") == true );

  // isValid
  CPPUNIT_ASSERT ( mme.isValid() == true );

  mme.clear();
  CPPUNIT_ASSERT ( mme.isValid() == false );

  mme.push_back("one three");
  CPPUNIT_ASSERT( mme.contains("one") == true );
  CPPUNIT_ASSERT( mme.contains("two") == false );
  CPPUNIT_ASSERT( mme.contains("three") == true );
  CPPUNIT_ASSERT( mme.contains("four") == false );

  std::vector<std::string> mvec(2);
  mvec[0] = "one";
  mvec[1] = "two";

  std::set<std::string> mset;
  mset.insert("two");
  mset.insert("three");

  // Assign
  mme = mvec;
  CPPUNIT_ASSERT( mme.contains("one") == true );
  CPPUNIT_ASSERT( mme.contains("two") == true );
  CPPUNIT_ASSERT( mme.contains("three") == false );
  CPPUNIT_ASSERT( mme.contains("four") == false );

  mme = mset;
  CPPUNIT_ASSERT( mme.contains("one") == false );
  CPPUNIT_ASSERT( mme.contains("two") == true );
  CPPUNIT_ASSERT( mme.contains("three") == true );
  CPPUNIT_ASSERT( mme.contains("four") == false );

  // Insert
  mme.push_back(mvec);
  CPPUNIT_ASSERT( mme.contains("one") == true );
  CPPUNIT_ASSERT( mme.contains("two") == true );
  CPPUNIT_ASSERT( mme.contains("three") == true );
  CPPUNIT_ASSERT( mme.contains("four") == false );

  mme.clear();
  mme = "one four";
  CPPUNIT_ASSERT( mme.contains("one") == true );
  CPPUNIT_ASSERT( mme.contains("two") == false );
  CPPUNIT_ASSERT( mme.contains("three") == false );
  CPPUNIT_ASSERT( mme.contains("four") == true );

  mme.push_back("three four");
  CPPUNIT_ASSERT( mme.contains("one") == true );
  CPPUNIT_ASSERT( mme.contains("two") == false );
  CPPUNIT_ASSERT( mme.contains("three") == true );
  CPPUNIT_ASSERT( mme.contains("four") == true );

  // Size
  CPPUNIT_ASSERT( mme.size() == 4 );
  CPPUNIT_ASSERT( mme.unique_items_size() == 3 );

  // All but "two" should be in the Enum
  std::set<std::string> compare_set, return_set, difference;
  for (MooseEnumIterator it = mme.begin(); it != mme.end(); ++it)
    return_set.insert(*it);

  compare_set.insert("ONE");
  compare_set.insert("THREE");
  compare_set.insert("FOUR");


  std::set_symmetric_difference(return_set.begin(), return_set.end(),
                                compare_set.begin(), compare_set.end(),
                                std::inserter(difference, difference.end()));
  CPPUNIT_ASSERT( difference.size() == 0 );

  // Order and indexing
  mme.clear();
  mme = "one two four";
  CPPUNIT_ASSERT( mme.contains("three") == false );

  CPPUNIT_ASSERT( mme[0] == "one" );
  CPPUNIT_ASSERT( mme[1] == "two" );
  CPPUNIT_ASSERT( mme[2] == "four" );
}