Eigen::ArrayXXf RepSupernmotif::repSupermotifComputeMatrixDissimCosS2NM(Eigen::ArrayXXf matS_supernmotifs,Eigen::ArrayXXf matNM_supernmotifs){
//compute Cosine dissimilarity between matS_supernmotifs and matNM_supernmotifs//
int n=matS_supernmotifs.rows();
int m=matNM_supernmotifs.rows();
Eigen::MatrixXf matDistS2NM = Eigen::MatrixXf::Zero(matS_supernmotifs.rows(),matNM_supernmotifs.rows());
Eigen::VectorXf allNormMatS_supernmotifs,allNormMatNM_supernmotifs,vectTempi,vectTempj;
allNormMatS_supernmotifs= matS_supernmotifs.matrix().rowwise().norm();
allNormMatNM_supernmotifs= matNM_supernmotifs.matrix().rowwise().norm();
for (int i=0; i<n;i++)
{
for (int j=0; j<m;j++)
{
vectTempi=matS_supernmotifs.row(i);
vectTempj=matNM_supernmotifs.row(j);
matDistS2NM(i,j)=1-(vectTempi.dot(vectTempj)/(allNormMatS_supernmotifs[i]*allNormMatNM_supernmotifs[j]));
if(matDistS2NM(i,j)<0){matDistS2NM(i,j)=0;}
}
}
return matDistS2NM;
}
RepSupernmotif::RepSupernmotif(Eigen::ArrayXXf matALLMotifWeigthed,int nbSupNmotifs,int p_outputOption) {
// TODO Auto-generated constructor stub
if((p_outputOption==0)||(p_outputOption==1))//Compute the super n-motifs representation
{
int maxDim;
if(matALLMotifWeigthed.rows()>matALLMotifWeigthed.cols())
{maxDim=matALLMotifWeigthed.cols();}
else{maxDim=matALLMotifWeigthed.rows();}
if (nbSupNmotifs>maxDim)
{
cerr<<"warning: the number of super n-motifs selected (-k) is greater than the maximum number of computed super n-motifs. -k is set to the maximum number of computed super n-motifs."<<endl;
nbSupNmotifs=maxDim;
}
//compute SVD//
Eigen::JacobiSVD<Eigen::MatrixXf> svd(matALLMotifWeigthed.matrix(), Eigen::ComputeThinU);
//Eigen::BDCSVD<Eigen::MatrixXf> svd(matALLMotifWeigthed.matrix(), Eigen::ComputeThinU | Eigen::ComputeThinV);
singularVal=svd.singularValues();
//Brocken stick model
if(nbSupNmotifs==0)
{
Eigen::RowVectorXf singularVal_precent=singularVal/singularVal.sum();
Eigen::RowVectorXf brModel=singularVal;
float tempbrModel;
for(int i=0; i<brModel.size(); i++)
{
tempbrModel=0;
for(int j=i; j<brModel.size(); j++)
{
tempbrModel+=1/float(j+1);
}
brModel(i)=tempbrModel/brModel.size();
}
int nbSupNmotifs2=0;
while(singularVal_precent(nbSupNmotifs2)>brModel(nbSupNmotifs2))
{nbSupNmotifs2++;}
nbSupNmotifs=nbSupNmotifs2;
if (nbSupNmotifs<2)
{
nbSupNmotifs=2;
}
}
matS_supernmotifs=svd.matrixU().leftCols(nbSupNmotifs);
//matS_supernmotifs=randomizedSVD.matrixU().leftCols(nbSupNmotifs);//Resvd Test
for(int i=0;i<nbSupNmotifs;i++)
{matS_supernmotifs.col(i)=matS_supernmotifs.col(i)*(singularVal(i));}
//Compute the SS dissimilarities using the cosine dissimilarity
//if (p_outputOption==0)
//{matDissimS2S=repSupermotifComputeMatrixDissimCosS2S(matS_supernmotifs);}
}
else if(p_outputOption==2)
{
int maxDim;
if(matALLMotifWeigthed.rows()>matALLMotifWeigthed.cols())
{maxDim=matALLMotifWeigthed.cols();}
else{maxDim=matALLMotifWeigthed.rows();}
if (nbSupNmotifs>maxDim)
{
cerr<<"warning: the number of super n-motifs selected (-k) is greater than the maximum number of computed super n-motifs. -k is set to the maximum number of computed super n-motifs."<<endl;
nbSupNmotifs=maxDim;
}
//*******compute SVD*************************//
Eigen::JacobiSVD<Eigen::MatrixXf> svd(matALLMotifWeigthed.matrix(), Eigen::ComputeThinU | Eigen::ComputeThinV);
singularValFull=svd.singularValues();
//Brocken stick model
if(nbSupNmotifs==0)
{
Eigen::RowVectorXf singularVal_precent=singularValFull/singularValFull.sum();
Eigen::RowVectorXf brModel=singularValFull;
float tempbrModel;
for(int i=0; i<brModel.size(); i++)
{
tempbrModel=0;
for(int j=i; j<brModel.size(); j++)
{
tempbrModel+=1/float(j+1);
}
brModel(i)=tempbrModel/brModel.size();
}
int nbSupNmotifs2=0;
while(singularVal_precent(nbSupNmotifs2)>brModel(nbSupNmotifs2))
{nbSupNmotifs2++;}
nbSupNmotifs=nbSupNmotifs2;
if (nbSupNmotifs<2)
{
nbSupNmotifs=2;
}
}
singularVal=singularValFull.head(nbSupNmotifs);
matS_supernmotifs=svd.matrixU().leftCols(nbSupNmotifs);
matNM_supernmotifs=svd.matrixV().leftCols(nbSupNmotifs);
for(int i=0;i<nbSupNmotifs;i++)
{
matS_supernmotifs.col(i)=matS_supernmotifs.col(i)*(singularVal(i));
matNM_supernmotifs.col(i)=matNM_supernmotifs.col(i)*(singularVal(i));
}
//Compute the SS dissimilarities using the cosine dissimilarity
//matDissimS2S=repSupermotifComputeMatrixDissimCosS2S(matS_supernmotifs);
matDissimS2NM=repSupermotifComputeMatrixDissimCosS2NM(matS_supernmotifs,matNM_supernmotifs);
}
else if(p_outputOption==3)//Compute the secondary structure dissimilarities based on the n-motifs representation
{
cout<<"No super n-motifs computed (-p 3) ..."<<endl;
//matDissimS2S=repSupermotifComputeMatrixDissimCosS2S(matALLMotifWeigthed);
}
else if ((p_outputOption==4))
{
cout<<"No super n-motifs computed (-p 4) ..."<<endl;
}
else if (p_outputOption==5)
{
int maxDim;
if(matALLMotifWeigthed.rows()>matALLMotifWeigthed.cols())
{maxDim=matALLMotifWeigthed.cols();}
else{maxDim=matALLMotifWeigthed.rows();}
//compute SVD
if (nbSupNmotifs>maxDim)
{
cerr<<"warning: the number of super n-motifs selected (-k) is greater than the maximum number of computed super n-motifs. -k is set to the maximum number of computed super n-motifs."<<endl;
nbSupNmotifs=maxDim;
}
//compute SVD//
Eigen::JacobiSVD<Eigen::MatrixXf> svd(matALLMotifWeigthed.matrix(), Eigen::ComputeThinU | Eigen::ComputeThinV);
singularValFull=svd.singularValues();
//Brocken stick model
if(nbSupNmotifs==0)
{
Eigen::RowVectorXf singularVal_precent=singularValFull/singularValFull.sum();
Eigen::RowVectorXf brModel=singularValFull;
float tempbrModel;
for(int i=0; i<brModel.size(); i++)
{
tempbrModel=0;
for(int j=i; j<brModel.size(); j++)
{
tempbrModel+=1/float(j+1);
}
brModel(i)=tempbrModel/brModel.size();
}
int nbSupNmotifs2=0;
while(singularVal_precent(nbSupNmotifs2)>brModel(nbSupNmotifs2))
{nbSupNmotifs2++;}
nbSupNmotifs=nbSupNmotifs2;
if (nbSupNmotifs<2)
{
nbSupNmotifs=2;
}
}
matS_supernmotifsFull=svd.matrixU();
matNM_supernmotifsFull=svd.matrixV();
singularVal=singularValFull.head(nbSupNmotifs);
matS_supernmotifs=matS_supernmotifsFull.leftCols(nbSupNmotifs);
matNM_supernmotifs=matNM_supernmotifsFull.leftCols(nbSupNmotifs);;
//Compute super n-motifs representation of n-motifs
for(int i=0;i<nbSupNmotifs;i++)
{
matS_supernmotifs.col(i)=matS_supernmotifs.col(i)*(singularVal(i));
matNM_supernmotifs.col(i)=matNM_supernmotifs.col(i)*(singularVal(i));
}
//Compute the dissimilarities between SS and n-motifs in the super n-motif space using the cosine dissimilarity
matDissimS2NM=repSupermotifComputeMatrixDissimCosS2NM(matS_supernmotifs,matNM_supernmotifs);
//Compute the SS dissimilarities using the cosine dissimilarity
//matDissimS2S=repSupermotifComputeMatrixDissimCosS2S(matS_supernmotifs);
}
}
