mfloat_t CKroneckerLMM::nLLeval(mfloat_t ldelta, const MatrixXdVec& A,const MatrixXdVec& X, const MatrixXd& Y, const VectorXd& S_C1, const VectorXd& S_R1, const VectorXd& S_C2, const VectorXd& S_R2)
{
//#define debugll
muint_t R = (muint_t)Y.rows();
muint_t C = (muint_t)Y.cols();
assert(A.size() == X.size());
assert(R == (muint_t)S_R1.rows());
assert(C == (muint_t)S_C1.rows());
assert(R == (muint_t)S_R2.rows());
assert(C == (muint_t)S_C2.rows());
muint_t nWeights = 0;
for(muint_t term = 0; term < A.size();++term)
{
assert((muint_t)(X[term].rows())==R);
assert((muint_t)(A[term].cols())==C);
nWeights+=(muint_t)(A[term].rows()) * (muint_t)(X[term].cols());
}
mfloat_t delta = exp(ldelta);
mfloat_t ldet = 0.0;//R * C * ldelta;
//build D and compute the logDet of D
MatrixXd D = MatrixXd(R,C);
for (muint_t r=0; r<R;++r)
{
if(S_R2(r)>1e-10)
{
ldet += (mfloat_t)C * log(S_R2(r));//ldet
}
else
{
std::cout << "S_R2(" << r << ")="<< S_R2(r)<<"\n";
}
}
#ifdef debugll
std::cout << ldet;
std::cout << "\n";
#endif
for (muint_t c=0; c<C;++c)
{
if(S_C2(c)>1e-10)
{
ldet += (mfloat_t)R * log(S_C2(c));//ldet
}
else
{
std::cout << "S_C2(" << c << ")="<< S_C2(c)<<"\n";
}
}
#ifdef debugll
std::cout << ldet;
std::cout << "\n";
#endif
for (muint_t r=0; r<R;++r)
{
for (muint_t c=0; c<C;++c)
{
mfloat_t SSd = S_R1.data()[r]*S_C1.data()[c] + delta;
ldet+=log(SSd);
D(r,c) = 1.0/SSd;
}
}
#ifdef debugll
std::cout << ldet;
std::cout << "\n";
#endif
MatrixXd DY = Y.array() * D.array();
VectorXd XYA = VectorXd(nWeights);
muint_t cumSumR = 0;
MatrixXd covW = MatrixXd(nWeights,nWeights);
for(muint_t termR = 0; termR < A.size();++termR){
muint_t nW_AR = A[termR].rows();
muint_t nW_XR = X[termR].cols();
muint_t rowsBlock = nW_AR * nW_XR;
MatrixXd XYAblock = X[termR].transpose() * DY * A[termR].transpose();
XYAblock.resize(rowsBlock,1);
XYA.block(cumSumR,0,rowsBlock,1) = XYAblock;
muint_t cumSumC = 0;
for(muint_t termC = 0; termC < A.size(); ++termC){
muint_t nW_AC = A[termC].rows();
muint_t nW_XC = X[termC].cols();
muint_t colsBlock = nW_AC * nW_XC;
MatrixXd block = MatrixXd::Zero(rowsBlock,colsBlock);
if (R<C)
{
for(muint_t r=0; r<R; ++r)
{
MatrixXd AD = A[termR];
AD.array().rowwise() *= D.row(r).array();
MatrixXd AA = AD * A[termC].transpose();
//sum up col matrices
MatrixXd XX = X[termR].row(r).transpose() * X[termC].row(r);
akron(block,AA,XX,true);
}
}
else
{//sum up col matrices
for(muint_t c=0; c<C; ++c)
{
MatrixXd XD = X[termR];
XD.array().colwise() *= D.col(c).array();
MatrixXd XX = XD.transpose() * X[termC];
//sum up col matrices
MatrixXd AA = A[termR].col(c) * A[termC].col(c).transpose();
akron(block,AA,XX,true);
}
}
covW.block(cumSumR, cumSumC, rowsBlock, colsBlock) = block;
cumSumC+=colsBlock;
}
cumSumR+=rowsBlock;
}
//std::cout << "covW = " << covW<<std::endl;
MatrixXd W_vec = covW.colPivHouseholderQr().solve(XYA);
//MatrixXd W_vec = covW * XYA;
//std::cout << "W = " << W_vec<<std::endl;
//std::cout << "XYA = " << XYA<<std::endl;
mfloat_t res = (Y.array()*DY.array()).sum();
mfloat_t varPred = (W_vec.array() * XYA.array()).sum();
res-= varPred;
mfloat_t sigma = res/(mfloat_t)(R*C);
mfloat_t nLL = 0.5 * ( R * C * (L2pi + log(sigma) + 1.0) + ldet);
#ifdef returnW
covW = covW.inverse();
//std::cout << "covW.inverse() = " << covW<<std::endl;
muint_t cumSum = 0;
VectorXd F_vec = W_vec.array() * W_vec.array() /covW.diagonal().array() / sigma;
for(muint_t term = 0; term < A.size();++term)
{
muint_t currSize = X[term].cols() * A[term].rows();
//W[term] = MatrixXd(X[term].cols(),A[term].rows());
W[term] = W_vec.block(cumSum,0,currSize,1);//
W[term].resize(X[term].cols(),A[term].rows());
//F_tests[term] = MatrixXd(X[term].cols(),A[term].rows());
F_tests[term] = F_vec.block(cumSum,0,currSize,1);//
F_tests[term].resize(X[term].cols(),A[term].rows());
cumSum+=currSize;
}
#endif
return nLL;
}
extendTreeR_t ExtendTree_NHC_Sort_OnlyGS_TermCond_Heading(MatrixXd tree, VectorXd vecEndNode, worldLine_t world, setting_t P,
double dKmax, double c4, double dMax, double dMaxBeta, sigma_t sigma, int iMaxIte, int iINDC)
{
int iFlag, kk, iTemp, n, idx, flagRet;
double p, r, theta, Sx, Sy;
Vector3d vec3RandomPoint;
VectorXd vecTempDiag, vecIdx, vecP1, vecP2, vecP3, vecNewNode;
MatrixXd matTemp, matTempSq, matWP, newTree, matdKappa;
VectorXd vecBeta;
extendTreeR_t funcReturn;
iFlag = 0;
while (iFlag==0) {
// select a biased random point
p=Uniform01();
if ( (iINDC==0 && p<0.1) || (iINDC==1 && p<0.05) ) {
vec3RandomPoint << vecEndNode(0), vecEndNode(1), vecEndNode(2);
} else {
r = sigma.r*Uniform01();
theta = sigma.theta0 + sigma.theta*quasi_normal_random();
Sx = sigma.sp(0) + r*cos(theta);
Sy = sigma.sp(1) + r*sin(theta);
vec3RandomPoint << Sx, Sy, vecEndNode(2);
}
std::cout << "Random Point : " << vec3RandomPoint(0) << " " << vec3RandomPoint(1) << " " << vec3RandomPoint(2) << std::endl;
// Find node that is closest to random point
matTemp.resize(tree.rows(),3);
for (iTemp=0; iTemp<tree.rows(); iTemp++) {
matTemp(iTemp,0) = tree(iTemp,0) - vec3RandomPoint(0);
matTemp(iTemp,1) = tree(iTemp,1) - vec3RandomPoint(1);
matTemp(iTemp,2) = tree(iTemp,2) - vec3RandomPoint(2);
}
matTempSq = matTemp*matTemp.transpose();
vecTempDiag.resize(matTemp.rows());
for (iTemp=0; iTemp<matTemp.rows(); iTemp++)
vecTempDiag(iTemp) = matTempSq(iTemp, iTemp);
SortVec(vecTempDiag, vecIdx); // vecTempDiag : sorted vector, vecIdx : index of vecTempDiag
// Modification 3
if ( vecIdx.rows() > iMaxIte )
n = iMaxIte;
else
n = vecIdx.rows();
/// Nonholonomic Length Decision
kk=-1;
// Modification 4
if (tree.rows() == 2) {
vecP1.resize(3); vecP2.resize(3); vecP3.resize(3);
vecP1 << tree(0,0), tree(0,1), tree(0,2);
vecP2 << tree(1,0), tree(1,1), tree(1,2);
vecP3 = vec3RandomPoint;
matWP.resize(3,3);
matWP.row(0) = vecP1.segment(0,3).transpose();
matWP.row(1) = vecP2.segment(0,3).transpose();
matWP.row(2) = vecP3.segment(0,3).transpose();
Kappa_Max_Calculation(matWP, P, matdKappa, vecBeta);
if ( (vecBeta.array().abs()<= dMaxBeta).all() )
// Method 2 : use the maximum length margin - when there is straight line, there is more margin1
P.L = 2*dMax;
else if ( (vecBeta.array().abs() > dMaxBeta).all() ) {
funcReturn.flag = 0;
funcReturn.newTree = tree;
funcReturn.INDC = iINDC;
funcReturn.sigma = sigma;
funcReturn.maxIte = iMaxIte;
return funcReturn;
}
} else if (tree.rows() > 2) {
for (iTemp=0; iTemp<n; iTemp++) {
kk = iTemp;
if ( tree(vecIdx(iTemp), tree.cols()-1) == 0 ) {
funcReturn.flag = 0;
funcReturn.newTree = tree;
funcReturn.INDC = iINDC;
funcReturn.sigma = sigma;
funcReturn.maxIte = iMaxIte;
return funcReturn;
}
vecP2.resize(tree.cols());
vecP2 = tree.row(vecIdx(iTemp)).transpose();
vecP1.resize(tree.cols());
vecP1 = tree.row( vecP2(vecP2.rows()-1)-1 ).transpose();
vecP3.resize(vec3RandomPoint.rows());
vecP3 = vec3RandomPoint;
matWP.resize(3,3);
matWP.row(0) = vecP1.segment(0,3).transpose();
matWP.row(1) = vecP2.segment(0,3).transpose();
matWP.row(2) = vecP3.segment(0,3).transpose();
Kappa_Max_Calculation(matWP, P, matdKappa, vecBeta);
if ( (vecBeta.array().abs()<= dMaxBeta).all() )
{
// Method 2 : use the maximum length margin - when there is straight line, there is more margin1
P.L = 2*dMax;
break;
} else if ( (vecBeta.array().abs() > dMaxBeta).all() && (iTemp == (n-1) ) ) {
funcReturn.flag = 0;
funcReturn.newTree = tree;
funcReturn.INDC = iINDC;
funcReturn.sigma = sigma;
funcReturn.maxIte = iMaxIte;
return funcReturn;
}
}
} //if (tree.rows() == 2)
if ( kk == -1)
idx = vecIdx(0);
else
idx = vecIdx(kk);
double cost = tree(idx,4) + P.L;
Vector3d vecNewPoint = vec3RandomPoint - tree.block(idx,0,1,3).transpose();
vecNewPoint = tree.block(idx,0,1,3).transpose()+vecNewPoint/vecNewPoint.norm()*P.L;
if ( kk == -1)
{
vecNewNode.resize(vecNewPoint.size()+5);
vecNewNode << vecNewPoint, 0, cost, P.L, 0, idx+1;
}
else
{
vecNewNode.resize(vecNewPoint.size()+vecBeta.size()+4);
vecNewNode << vecNewPoint, 0, cost, P.L, vecBeta.array().abs(), idx+1;
}
// check to see if obstacle or random point is reached
if ( Collision(vecNewNode, tree.row(idx), world, P.SZR, P.SZH, 0) == 0 )
{
newTree.resize( tree.rows()+1,tree.cols() );
newTree.block(0, 0, tree.rows(), tree.cols()) = tree;
newTree.row(tree.rows()) = vecNewNode.transpose();
iFlag = 1;
}
} // while (iFlag==0)
// check to see if new node connects directly to end node
VectorXd vecDiff(3), vecSeg1(3), vecSeg2(3);
vecSeg1 = vecNewNode.segment(0,3);
vecSeg2 = vecEndNode.segment(0,3);
vecDiff = vecSeg1 - vecSeg2;
double dTerm = vecDiff.norm();
double psi;
// 7*dMax
if ( (dTerm <= 7*dMax) && (iINDC==0) )
{
iINDC = 1;
psi = atan2( vecEndNode(1)-vecNewNode(1), vecEndNode(0)-vecNewNode(0) );
psi = psi - 2*M_PI*(psi>M_PI);
sigma.r = dTerm;
sigma.theta0 = psi;
sigma.theta = 1.0*M_PI;
sigma.sp = vecNewNode.segment(0,3);
iMaxIte = 5;
}
if ( iINDC==1 )
{
// Method 2
int end;
vecP2.resize(vecNewNode.rows());
vecP2 = vecNewNode;
end = vecP2.rows();
if ( vecP2(end-1)==0 )
{
vecP1.resize(tree.rows());
vecP1 = tree.row(0).transpose();
}
else
{
vecP1.resize(tree.rows());
vecP1 = tree.row(vecP2(end-1)-1).transpose();
}
vecP3.resize(3);
vecP3 = vecEndNode.segment(0,3);
matWP.resize(3,3);
matWP.row(0) = vecP1.segment(0,3);
matWP.row(1) = vecP2.segment(0,3);
matWP.row(2) = vecP3.segment(0,3);
double d_rm;
Kappa_Max_Calculation(matWP, P, matdKappa, vecBeta);
d_rm = ( c4*sin(vecP2(end-2)) ) / ( dKmax*cos(vecP2(end-2))*cos(vecP2(end-2)) );
if ( (2*dMax-d_rm >= 1.0*matdKappa(0,0)) &&
(Collision(vecNewNode, vecEndNode, world, P.SZR, P.SZH, 0) == 0) &&
(matdKappa(0,0) <= dTerm) )
{
flagRet = 1;
newTree(newTree.rows()-1,3) = 1;
}
else
flagRet = 0;
} else
flagRet = 0;
// if ( iINDC==1 )
funcReturn.flag = flagRet;
funcReturn.newTree = newTree;
funcReturn.INDC = iINDC;
funcReturn.sigma = sigma;
funcReturn.maxIte = iMaxIte;
return funcReturn;
}
void CTRPBPInference::infer(CGraph &graph,
map<size_t,VectorXd> &nodeBeliefs,
map<size_t,MatrixXd> &edgeBeliefs,
double &logZ)
{
//
// Algorithm workflow:
// 1. Compute the messages passed
// 2. Compute node beliefs
// 3. Compute edge beliefs
// 4. Compute logZ
//
nodeBeliefs.clear();
edgeBeliefs.clear();
const vector<CNodePtr> nodes = graph.getNodes();
const vector<CEdgePtr> edges = graph.getEdges();
multimap<size_t,CEdgePtr> edges_f = graph.getEdgesF();
size_t N_nodes = nodes.size();
size_t N_edges = edges.size();
//
// 1. Create spanning trees
//
bool allNodesAdded = false;
vector<vector<size_t > > v_trees;
vector<bool> v_addedNodes(N_nodes,false);
map<size_t,size_t> addedNodesMap;
for (size_t i = 0; i < N_nodes; i++)
addedNodesMap[ nodes[i]->getID() ] = i;
while (!allNodesAdded)
{
allNodesAdded = true;
vector<size_t> tree;
getSpanningTree( graph, tree );
// Check that the tree is not empty
if ( tree.size() )
v_trees.push_back( tree );
cout << "Tree: ";
for ( size_t i_node = 0; i_node < tree.size(); i_node++ )
{
v_addedNodes[ addedNodesMap[tree[i_node]] ] = true;
cout << tree[i_node] << " ";
}
cout << endl;
for ( size_t i_node = 0; i_node < N_nodes; i_node++ )
if ( !v_addedNodes[i_node] )
{
allNodesAdded = false;
break;
}
}
//
// 1. Compute messages passed in each tree until convergence
//
vector<vector<VectorXd> > messages;
bool maximize = false;
double totalSumOfMsgs = std::numeric_limits<double>::max();
size_t iteration;
for ( iteration = 0; iteration < m_options.maxIterations; iteration++ )
{
for ( size_t i_tree=0; i_tree < v_trees.size(); i_tree++ )
messagesLBP( graph, m_options, messages, maximize, v_trees[i_tree] );
double newTotalSumOfMsgs = 0;
for ( size_t i = 0; i < N_edges; i++ )
{
newTotalSumOfMsgs += messages[i][0].sum() + messages[i][1].sum();
}
if ( std::abs( totalSumOfMsgs - newTotalSumOfMsgs ) <
m_options.convergency )
break;
totalSumOfMsgs = newTotalSumOfMsgs;
}
//
// 2. Compute node beliefs
//
for ( size_t nodeIndex = 0; nodeIndex < N_nodes; nodeIndex++ )
{
const CNodePtr nodePtr = graph.getNode( nodeIndex );
size_t nodeID = nodePtr->getID();
VectorXd nodePotPlusIncMsg = nodePtr->getPotentials( m_options.considerNodeFixedValues );
NEIGHBORS_IT neighbors = edges_f.equal_range(nodeID);
//
// Get the messages for all the neighbors, and multiply them with the node potential
//
for ( multimap<size_t,CEdgePtr>::iterator itNeigbhor = neighbors.first;
itNeigbhor != neighbors.second;
itNeigbhor++ )
{
CEdgePtr edgePtr( (*itNeigbhor).second );
size_t edgeIndex = graph.getEdgeIndex( edgePtr->getID() );
if ( !edgePtr->getNodePosition( nodeID ) ) // nodeID is the first node in the edge
nodePotPlusIncMsg = nodePotPlusIncMsg.cwiseProduct(messages[ edgeIndex ][ 1 ]);
else // nodeID is the second node in the dege
nodePotPlusIncMsg = nodePotPlusIncMsg.cwiseProduct(messages[ edgeIndex ][ 0 ]);
}
// Normalize
nodePotPlusIncMsg = nodePotPlusIncMsg / nodePotPlusIncMsg.sum();
nodeBeliefs[ nodeID ] = nodePotPlusIncMsg;
//cout << "Beliefs of node " << nodeIndex << endl << nodePotPlusIncMsg << endl;
}
//
// 3. Compute edge beliefs
//
for ( size_t edgeIndex = 0; edgeIndex < N_edges; edgeIndex++ )
{
CEdgePtr edgePtr = edges[edgeIndex];
size_t edgeID = edgePtr->getID();
size_t ID1, ID2;
edgePtr->getNodesID( ID1, ID2 );
MatrixXd edgePotentials = edgePtr->getPotentials();
MatrixXd edgeBelief = edgePotentials;
VectorXd &message1To2 = messages[edgeIndex][0];
VectorXd &message2To1 = messages[edgeIndex][1];
//cout << "----------------------" << endl;
//cout << nodeBeliefs[ ID1 ] << endl;
//cout << "----------------------" << endl;
//cout << message2To1 << endl;
VectorXd node1Belief = nodeBeliefs[ ID1 ].cwiseQuotient( message2To1 );
VectorXd node2Belief = nodeBeliefs[ ID2 ].cwiseQuotient( message1To2 );
//cout << "----------------------" << endl;
MatrixXd node1BeliefMatrix ( edgePotentials.rows(), edgePotentials.cols() );
for ( size_t row = 0; row < edgePotentials.rows(); row++ )
for ( size_t col = 0; col < edgePotentials.cols(); col++ )
node1BeliefMatrix(row,col) = node1Belief(row);
//cout << "Node 1 belief matrix: " << endl << node1BeliefMatrix << endl;
edgeBelief = edgeBelief.cwiseProduct( node1BeliefMatrix );
MatrixXd node2BeliefMatrix ( edgePotentials.rows(), edgePotentials.cols() );
for ( size_t row = 0; row < edgePotentials.rows(); row++ )
for ( size_t col = 0; col < edgePotentials.cols(); col++ )
node2BeliefMatrix(row,col) = node2Belief(col);
//cout << "Node 2 belief matrix: " << endl << node2BeliefMatrix << endl;
edgeBelief = edgeBelief.cwiseProduct( node2BeliefMatrix );
//cout << "Edge potentials" << endl << edgePotentials << endl;
//cout << "Edge beliefs" << endl << edgeBelief << endl;
// Normalize
edgeBelief = edgeBelief / edgeBelief.sum();
edgeBeliefs[ edgeID ] = edgeBelief;
}
//
// 4. Compute logZ
//
double energyNodes = 0;
double energyEdges = 0;
double entropyNodes = 0;
double entropyEdges = 0;
// Compute energy and entropy from nodes
for ( size_t nodeIndex = 0; nodeIndex < nodes.size(); nodeIndex++ )
{
CNodePtr nodePtr = nodes[ nodeIndex ];
size_t nodeID = nodePtr->getID();
size_t N_Neighbors = graph.getNumberOfNodeNeighbors( nodeID );
// Useful computations and shorcuts
VectorXd &nodeBelief = nodeBeliefs[nodeID];
VectorXd logNodeBelief = nodeBeliefs[nodeID].array().log();
VectorXd nodePotentials = nodePtr->getPotentials( m_options.considerNodeFixedValues );
VectorXd logNodePotentials = nodePotentials.array().log();
// Entropy from the node
energyNodes += N_Neighbors*( nodeBelief.cwiseProduct( logNodeBelief ).sum() );
// Energy from the node
entropyNodes += N_Neighbors*( nodeBelief.cwiseProduct( logNodePotentials ).sum() );
}
// Compute energy and entropy from nodes
for ( size_t edgeIndex = 0; edgeIndex < N_edges; edgeIndex++ )
{
CEdgePtr edgePtr = edges[ edgeIndex ];
size_t edgeID = edgePtr->getID();
// Useful computations and shorcuts
MatrixXd &edgeBelief = edgeBeliefs[ edgeID ];
MatrixXd logEdgeBelief = edgeBelief.array().log();
MatrixXd &edgePotentials = edgePtr->getPotentials();
MatrixXd logEdgePotentials = edgePotentials.array().log();
// Entropy from the edge
energyEdges += edgeBelief.cwiseProduct( logEdgeBelief ).sum();
// Energy from the edge
entropyEdges += edgeBelief.cwiseProduct( logEdgePotentials ).sum();
}
// Final Bethe free energy
double BethefreeEnergy = ( energyNodes - energyEdges ) - ( entropyNodes - entropyEdges );
// Compute logZ
logZ = - BethefreeEnergy;
}
void CLBPInference::infer(CGraph &graph,
map<size_t,VectorXd> &nodeBeliefs,
map<size_t,MatrixXd> &edgeBeliefs,
double &logZ)
{
//
// Algorithm workflow:
// 1. Compute the messages passed
// 2. Compute node beliefs
// 3. Compute edge beliefs
// 4. Compute logZ
//
nodeBeliefs.clear();
edgeBeliefs.clear();
const vector<CNodePtr> nodes = graph.getNodes();
const vector<CEdgePtr> edges = graph.getEdges();
multimap<size_t,CEdgePtr> edges_f = graph.getEdgesF();
size_t N_nodes = nodes.size();
size_t N_edges = edges.size();
//
// 1. Compute the messages passed
//
vector<vector<VectorXd> > messages;
bool maximize = false;
messagesLBP( graph, m_options, messages, maximize );
//
// 2. Compute node beliefs
//
for ( size_t nodeIndex = 0; nodeIndex < N_nodes; nodeIndex++ )
{
const CNodePtr nodePtr = graph.getNode( nodeIndex );
size_t nodeID = nodePtr->getID();
VectorXd nodePotPlusIncMsg = nodePtr->getPotentials( m_options.considerNodeFixedValues );
NEIGHBORS_IT neighbors = edges_f.equal_range(nodeID);
//
// Get the messages for all the neighbors, and multiply them with the node potential
//
for ( multimap<size_t,CEdgePtr>::iterator itNeigbhor = neighbors.first;
itNeigbhor != neighbors.second;
itNeigbhor++ )
{
CEdgePtr edgePtr( (*itNeigbhor).second );
size_t edgeIndex = graph.getEdgeIndex( edgePtr->getID() );
if ( !edgePtr->getNodePosition( nodeID ) ) // nodeID is the first node in the edge
nodePotPlusIncMsg = nodePotPlusIncMsg.cwiseProduct(messages[ edgeIndex ][ 1 ]);
else // nodeID is the second node in the dege
nodePotPlusIncMsg = nodePotPlusIncMsg.cwiseProduct(messages[ edgeIndex ][ 0 ]);
}
// Normalize
nodePotPlusIncMsg = nodePotPlusIncMsg / nodePotPlusIncMsg.sum();
nodeBeliefs[ nodeID ] = nodePotPlusIncMsg;
//cout << "Beliefs of node " << nodeIndex << endl << nodePotPlusIncMsg << endl;
}
//
// 3. Compute edge beliefs
//
for ( size_t edgeIndex = 0; edgeIndex < N_edges; edgeIndex++ )
{
CEdgePtr edgePtr = edges[edgeIndex];
size_t edgeID = edgePtr->getID();
size_t ID1, ID2;
edgePtr->getNodesID( ID1, ID2 );
MatrixXd edgePotentials = edgePtr->getPotentials();
MatrixXd edgeBelief = edgePotentials;
VectorXd &message1To2 = messages[edgeIndex][0];
VectorXd &message2To1 = messages[edgeIndex][1];
//cout << "----------------------" << endl;
//cout << nodeBeliefs[ ID1 ] << endl;
//cout << "----------------------" << endl;
//cout << message2To1 << endl;
VectorXd node1Belief = nodeBeliefs[ ID1 ].cwiseQuotient( message2To1 );
VectorXd node2Belief = nodeBeliefs[ ID2 ].cwiseQuotient( message1To2 );
//cout << "----------------------" << endl;
MatrixXd node1BeliefMatrix ( edgePotentials.rows(), edgePotentials.cols() );
for ( size_t row = 0; row < edgePotentials.rows(); row++ )
for ( size_t col = 0; col < edgePotentials.cols(); col++ )
node1BeliefMatrix(row,col) = node1Belief(row);
//cout << "Node 1 belief matrix: " << endl << node1BeliefMatrix << endl;
edgeBelief = edgeBelief.cwiseProduct( node1BeliefMatrix );
MatrixXd node2BeliefMatrix ( edgePotentials.rows(), edgePotentials.cols() );
for ( size_t row = 0; row < edgePotentials.rows(); row++ )
for ( size_t col = 0; col < edgePotentials.cols(); col++ )
node2BeliefMatrix(row,col) = node2Belief(col);
//cout << "Node 2 belief matrix: " << endl << node2BeliefMatrix << endl;
edgeBelief = edgeBelief.cwiseProduct( node2BeliefMatrix );
//cout << "Edge potentials" << endl << edgePotentials << endl;
//cout << "Edge beliefs" << endl << edgeBelief << endl;
// Normalize
edgeBelief = edgeBelief / edgeBelief.sum();
edgeBeliefs[ edgeID ] = edgeBelief;
}
//
// 4. Compute logZ
//
double energyNodes = 0;
double energyEdges = 0;
double entropyNodes = 0;
double entropyEdges = 0;
// Compute energy and entropy from nodes
for ( size_t nodeIndex = 0; nodeIndex < nodes.size(); nodeIndex++ )
{
CNodePtr nodePtr = nodes[ nodeIndex ];
size_t nodeID = nodePtr->getID();
size_t N_Neighbors = graph.getNumberOfNodeNeighbors( nodeID );
// Useful computations and shorcuts
VectorXd &nodeBelief = nodeBeliefs[nodeID];
VectorXd logNodeBelief = nodeBeliefs[nodeID].array().log();
VectorXd nodePotentials = nodePtr->getPotentials( m_options.considerNodeFixedValues );
VectorXd logNodePotentials = nodePotentials.array().log();
// Entropy from the node
energyNodes += N_Neighbors*( nodeBelief.cwiseProduct( logNodeBelief ).sum() );
// Energy from the node
entropyNodes += N_Neighbors*( nodeBelief.cwiseProduct( logNodePotentials ).sum() );
}
// Compute energy and entropy from nodes
for ( size_t edgeIndex = 0; edgeIndex < N_edges; edgeIndex++ )
{
CEdgePtr edgePtr = edges[ edgeIndex ];
size_t edgeID = edgePtr->getID();
// Useful computations and shorcuts
MatrixXd &edgeBelief = edgeBeliefs[ edgeID ];
MatrixXd logEdgeBelief = edgeBelief.array().log();
MatrixXd &edgePotentials = edgePtr->getPotentials();
MatrixXd logEdgePotentials = edgePotentials.array().log();
// Entropy from the edge
energyEdges += edgeBelief.cwiseProduct( logEdgeBelief ).sum();
// Energy from the edge
entropyEdges += edgeBelief.cwiseProduct( logEdgePotentials ).sum();
}
// Final Bethe free energy
double BethefreeEnergy = ( energyNodes - energyEdges ) - ( entropyNodes - entropyEdges );
// Compute logZ
logZ = - BethefreeEnergy;
}
lbfgsfloatval_t sparseAECost(
void* netParam,
const lbfgsfloatval_t *ptheta,
lbfgsfloatval_t *grad,
const int n,
const lbfgsfloatval_t step)
{
instanceSP* pStruct = (instanceSP*)(netParam);
int hiddenSize = pStruct->hiddenSize;
int visibleSize = pStruct->visibleSize;
double lambda = pStruct->lambda;
double beta = pStruct->beta;
double sp = pStruct->sparsityParam;
MatrixXd& data = pStruct->data;
double cost = 0;
MatrixXd w1(hiddenSize, visibleSize);
MatrixXd w2(visibleSize, hiddenSize);
VectorXd b1(hiddenSize);
VectorXd b2(visibleSize);
for (int i=0; i<hiddenSize*visibleSize; i++)
{
*(w1.data()+i) = *ptheta;
ptheta++;
}
for (int i=0; i<visibleSize*hiddenSize; i++)
{
*(w2.data()+i) = *ptheta;
ptheta++;
}
for (int i=0; i<hiddenSize; i++)
{
*(b1.data()+i) = *ptheta;
ptheta++;
}
for (int i=0; i<visibleSize; i++)
{
*(b2.data()+i) = *ptheta;
ptheta++;
}
int ndim = data.rows();
int ndata = data.cols();
MatrixXd z2 = w1 * data + b1.replicate(1, ndata);
MatrixXd a2 = sigmoid(z2);
MatrixXd z3 = w2 * a2 + b2.replicate(1, ndata);
MatrixXd a3 = sigmoid(z3);
VectorXd rho = a2.rowwise().sum() / ndata;
VectorXd sparsityDelta = -sp / rho.array() + (1 - sp) / (1 - rho.array());
MatrixXd delta3 = (a3 - data).array() * sigmoidGrad(z3).array();
MatrixXd delta2 = (w2.transpose() * delta3 + beta * sparsityDelta.replicate(1, ndata)).array()
* sigmoidGrad(z2).array();
MatrixXd w1Grad = delta2 * data.transpose() / ndata + lambda * w1;
VectorXd b1Grad = delta2.rowwise().sum() / ndata;
MatrixXd w2Grad = delta3 * a2.transpose() / ndata + lambda * w2;
VectorXd b2Grad = delta3.rowwise().sum() / ndata;
cost = (0.5 * (a3 - data).array().pow(2)).matrix().sum() / ndata
+ 0.5 * lambda * ((w1.array().pow(2)).matrix().sum()
+ (w2.array().pow(2)).matrix().sum())
+ beta * (sp * (sp / rho.array()).log()
+ (1 - sp) * ((1 - sp) / (1 - rho.array())).log() ).matrix().sum();
double* pgrad = grad;
for (int i=0; i<hiddenSize*visibleSize; i++)
{
*pgrad = *(w1Grad.data()+i);
pgrad++;
}
for (int i=0; i<visibleSize*hiddenSize; i++)
{
*pgrad = *(w2Grad.data()+i);
pgrad++;
}
for (int i=0; i<hiddenSize; i++)
{
*pgrad = *(b1Grad.data()+i);
pgrad++;
}
for (int i=0; i<visibleSize; i++)
{
*pgrad = *(b2Grad.data()+i);
pgrad++;
}
return cost;
}