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);
}
}
void Trainee::train(std::vector<std::pair<InputType, AnswerType>> minibatch, float learning_rate)
{
Eigen::MatrixXf dweight3 = Eigen::MatrixXf::Zero(n_outputvec, n_hid2vec);
Eigen::VectorXf dbias3 = Eigen::VectorXf::Zero(n_outputvec);
Eigen::MatrixXf dweight2 = Eigen::MatrixXf::Zero(n_hid2vec, n_hid1vec);
Eigen::VectorXf dbias2 = Eigen::VectorXf::Zero(n_hid2vec);
Eigen::MatrixXf dweight1 = Eigen::MatrixXf::Zero(n_hid1vec, n_inputvec);
Eigen::VectorXf dbias1 = Eigen::VectorXf::Zero(n_hid1vec);
/* For AdaGrad */
auto fn = [](float lhs, float rhs) -> float { return lhs != 0.0 ? lhs / rhs : 0.0; };
for(auto sample: minibatch){
Eigen::VectorXf inputvec = input2vec(sample.first);
Eigen::VectorXf z1 = feedforward(inputvec, 1);
Eigen::VectorXf z2 = feedforward(inputvec, 2); // 後付けとはいえ。この計算、あからさまに無駄だな。z1からz2を計算すべき。
// Calculate delta of output layer.
Eigen::VectorXf delta3;
delta3 = feedforward(inputvec, 3);
delta3(sample.second) -= 1.0f;
{
Eigen::ArrayXXf e = delta3 * z2.transpose();
gsq_w3 += e * e;
gsq_b3 += delta3.array() * delta3.array();
dweight3 += e.matrix();
dbias3 += delta3;
}
// Calculate delta of 2nd hidden layer.
Eigen::VectorXf delta2 = Eigen::VectorXf::Zero(n_hid2vec);
for(int j=0;j<n_hid2vec;j++){
for(int k=0;k<n_outputvec;k++) delta2(j) += delta3(k) * weight3(k, j) * (z2(j) >= 0.f ? 1.f : 0.f);
}
{
Eigen::ArrayXXf e = delta2 * z1.transpose();
gsq_w2 += e * e;
gsq_b2 += delta2.array() * delta2.array();
dweight2 += e.matrix();
dbias2 += delta2;
}
// Calculate delta of 1st hidden layer.
Eigen::VectorXf delta1 = Eigen::VectorXf::Zero(n_hid1vec);
for(int j=0;j<n_hid1vec;j++){
for(int k=0;k<n_hid2vec;k++) delta1(j) += delta2(k) * weight2(k, j) * (z1(j) >= 0.f ? 1.f : 0.f);
}
{
Eigen::ArrayXXf e = delta1 * inputvec.transpose();
gsq_w1 += e * e;
gsq_b1 += delta1.array() * delta1.array();
dweight1 += e.matrix();
dbias1 += delta1;
}
}
weight1 -= dweight1.binaryExpr(gsq_w1.sqrt().matrix(), fn) * learning_rate / minibatch.size();
bias1 -= dbias1.binaryExpr(gsq_b1.sqrt().matrix(), fn) * learning_rate / minibatch.size();
weight2 -= dweight2.binaryExpr(gsq_w2.sqrt().matrix(), fn) * learning_rate / minibatch.size();
bias2 -= dbias2.binaryExpr(gsq_b2.sqrt().matrix(), fn) * learning_rate / minibatch.size();
weight3 -= dweight3.binaryExpr(gsq_w3.sqrt().matrix(), fn) * learning_rate / minibatch.size();
bias3 -= dbias3.binaryExpr(gsq_b3.sqrt().matrix(), fn) * learning_rate / minibatch.size();
}