void EdgeFeatures::getFeatures(featureVector& listFeatures, DataSequence* X,
Model* m, int nodeIndex, int prevNodeIndex,
int seqLabel)
{
// These features are only used for adjacent edge in the chain
int nbNodes = -1;
if(X->getPrecomputedFeatures())
nbNodes = X->getPrecomputedFeatures()->getWidth();
else
nbNodes = (int)X->getPrecomputedFeaturesSparse()->getWidth();
if( ((prevNodeIndex == nodeIndex-1) || prevNodeIndex == nodeIndex-1 + nbNodes)
&& (prevNodeIndex != -1))
{
feature* pFeature;
int nbStateLabels = m->getNumberOfStates();
for(int s1 = 0; s1 < nbStateLabels;s1++)
{
for(int s2 = 0; s2 < nbStateLabels;s2++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + s2 + s1*nbStateLabels ;
pFeature->globalId = getIdOffset() + s2 + s1*nbStateLabels +
seqLabel*nbStateLabels*nbStateLabels ;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState = s2;
pFeature->prevNodeIndex = prevNodeIndex;
pFeature->prevNodeState = s1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = 1.0f;
}
}
}
}
void RawFeatures::getFeatures(featureVector& listFeatures, DataSequence* X, Model* m,
int nodeIndex, int prevNodeIndex, int seqLabel)
{
if(X->getPrecomputedFeatures() != NULL && prevNodeIndex == -1)
{
dMatrix * preFeatures = X->getPrecomputedFeatures();
int nbFeatures = preFeatures->getHeight();
feature* pFeature;
int idState = 0;
int nbStateLabels = m->getNumberOfStates();
for(int s = 0; s < nbStateLabels; s++)
{
for(int f = 0; f < nbFeatures; f++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + f + idState*nbFeatures;
pFeature->globalId = getIdOffset() + f + idState*nbFeatures;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState = s;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = -1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = preFeatures->getValue(f,nodeIndex);
}
idState ++;
}
}
}
void EdgeFeatures::getAllFeatures(featureVector& listFeatures, Model* m, int)
{
int nbStateLabels = m->getNumberOfStates();
int nbSeqLabels = m->getNumberOfSequenceLabels();
feature* pFeature;
if(nbSeqLabels == 0)
nbSeqLabels = 1;
for(int seqLabel = 0; seqLabel < nbSeqLabels;seqLabel++)
{
for(int s1 = 0; s1 < nbStateLabels;s1++)
{
for(int s2 = 0; s2 < nbStateLabels;s2++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset() + s2 + s1*nbStateLabels + seqLabel*nbStateLabels*nbStateLabels ;
pFeature->globalId = getIdOffset() + s2 + s1*nbStateLabels + seqLabel*nbStateLabels*nbStateLabels ;
pFeature->nodeIndex = featureTypeId;
pFeature->nodeState = s2;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = s1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = 1.0f;
}
}
}
}
void LabelEdgeFeatures::getAllFeatures(featureVector& listFeatures, Model* m, int)
{
// TODO: Should we test for statesPerLabel matrix for authorized features.
if(m->getNumberOfSequenceLabels() > 0)
{
int nbStateLabels = m->getNumberOfStates();
int nbSeqLabels = m->getNumberOfSequenceLabels();
feature* pFeature;
for(int seqLabel = 0; seqLabel < nbSeqLabels; seqLabel++)
{
for(int s = 0; s < nbStateLabels; s++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset() + s + seqLabel*nbStateLabels ;
pFeature->globalId = getIdOffset() + s + seqLabel*nbStateLabels ;
pFeature->nodeIndex = featureTypeId;
pFeature->nodeState = s;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = -1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = 1.0f;
}
}
}
}
void WindowRawFeatures::getAllFeatures(featureVector& listFeatures, Model* m,
int nbRawFeatures)
{
int idNode = 0;
int nbStateLabels = m->getNumberOfStates();
feature* pFeature;
for(int n = -WindowSize; n <= WindowSize; n++)
{
for(int s = 0; s < nbStateLabels; s++)
{
for(int f = 0; f < nbRawFeatures; f++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset() + f + s*nbRawFeatures + idNode*nbStateLabels*nbRawFeatures;
pFeature->globalId = getIdOffset() + f + s*nbRawFeatures + idNode*nbStateLabels*nbRawFeatures;
pFeature->nodeIndex = WINDOW_RAW_FEATURE_ID;
pFeature->nodeState = s;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = -1;
pFeature->sequenceLabel = -1;
pFeature->value = f;
}
}
idNode++;
}
}
void GateNodeFeatures::getPreGateFeatures(featureVector& listFeatures,
DataSequence* X, Model* m, int nodeIndex,
int prevNodeIndex, int seqLabel)
{
if( X->getPrecomputedFeatures()==NULL || prevNodeIndex!=-1 ) return;
listFeatures.clear();
int f,g,nbNodes,nbStates,nbFeaturesDense,nbSG,gXfpg;
dMatrix* preFeatures = X->getPrecomputedFeatures();
feature* pFeature;
nbNodes = preFeatures->getWidth();
nbStates = m->getNumberOfStates();
nbFeaturesDense = preFeatures->getHeight();
nbSG = nbStates*nbGates;
for( g=0; g<nbGates; g++ ) {
gXfpg = g*nbFeaturesPerGate;
for( f=0; f<nbFeaturesDense; f++ )
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + nbSG + gXfpg + f;
pFeature->globalId = getIdOffset() + nbSG + gXfpg + f;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState = -1;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = g; // this is a quick and dirty fix
pFeature->sequenceLabel = seqLabel;
pFeature->value = preFeatures->getValue(f,nodeIndex);
}
// Gate bias.
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + nbSG + gXfpg + nbFeaturesDense;
pFeature->globalId = getIdOffset() + nbSG + gXfpg + nbFeaturesDense;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState =
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = g; // this is a quick and dirty fix
pFeature->sequenceLabel = seqLabel;
pFeature->value = 1.0;
}
}
void GateNodeFeatures::getFeatures(featureVector& listFeatures,
DataSequence* X, Model* m, int nodeIndex,
int prevNodeIndex, int seqLabel)
{
if( X->getPrecomputedFeatures()==NULL || prevNodeIndex!=-1 ) return;
int idx,s,f,g,nbNodes,nbStates,nbSG,nbFeaturesDense,gXfpg,sXG;
double gateSum;
dMatrix *preFeatures = X->getPrecomputedFeatures();
feature* pFeature;
nbStates = m->getNumberOfStates();
nbSG = nbStates*nbGates;
nbFeaturesDense = preFeatures->getHeight();
nbNodes = preFeatures->getWidth();
dVector* lambda = m->getWeights();
dMatrix* gates = X->getGateMatrix();
// Precompute gate values, (row,col) = (gate,node)
if( gates->getHeight()!=nbGates || gates->getWidth()!=nbNodes )
gates->resize(nbNodes,nbGates);
for( g=0; g<nbGates; g++ ) {
gXfpg = g*(nbFeaturesDense+1); // 1: bias
// gate bias
idx = getIdOffset() + nbSG + gXfpg + nbFeaturesDense;
gateSum = (*lambda)[idx];
// gate sum
for( f=0; f<nbFeaturesDense; f++ ) {
idx = getIdOffset() + nbSG + gXfpg + f;
gateSum += (*lambda)[idx] * preFeatures->getValue(f,nodeIndex);
}
gates->setValue(g,nodeIndex,gate(gateSum));
}
// Then compute node features from gate values
for( s=0; s<nbStates; s++ ) {
sXG = s*nbGates;
for( g=0; g<nbGates; g++ ) {
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + g + sXG;
pFeature->globalId = getIdOffset() + g + sXG;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState = s;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = g; // quick-and-dirty fix
pFeature->sequenceLabel = seqLabel;
pFeature->value = gates->getValue(g,nodeIndex);
pFeature->featureTypeId = GATE_NODE_FEATURE_ID;
}
}
}
void GateNodeFeatures::getAllFeatures(featureVector& listFeatures, Model* m, int nbRawFeatures)
{
int s, f, g, nbStates, nbSG, sXG, gXfpg;
feature* pFeature;
nbStates = m->getNumberOfStates();
nbSG = nbStates*nbGates;
// Node features.
for( s=0; s<nbStates; s++ ) {
sXG = s*nbGates;
for( g=0; g<nbGates; g++ ) {
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset() + g + sXG;
pFeature->globalId = getIdOffset() + g + sXG;
pFeature->nodeIndex = GATE_NODE_FEATURE_ID;
pFeature->nodeState = s;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = -1;
pFeature->sequenceLabel = -1;
pFeature->value = g;
}
}
// Gate features.
for( g=0; g<nbGates; g++ ) {
gXfpg = g*nbFeaturesPerGate;
for( f=0; f<nbFeaturesPerGate; f++ ) {
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset() + nbSG + gXfpg+ f;
pFeature->globalId = getIdOffset() + nbSG + gXfpg+ f;
pFeature->nodeIndex = GATE_NODE_FEATURE_ID;
pFeature->nodeState = -1;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = -1;
pFeature->sequenceLabel = -1;
pFeature->value = f;
}
}
}
void LabelEdgeFeatures::getFeatures(featureVector& listFeatures, DataSequence*, Model* m, int nodeIndex, int prevNodeIndex, int seqLabel)
{
if(seqLabel != -1 && m->getNumberOfSequenceLabels() > 0 && prevNodeIndex == -1)
{
feature* pFeature;
int nbStateLabels = m->getNumberOfStates();
for(int s = 0; s < nbStateLabels; s++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + s;
pFeature->globalId = getIdOffset() + s + seqLabel*nbStateLabels ;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState = s;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = -1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = 1.0f;
}
}
}
void RawFeatures::getAllFeatures(featureVector& listFeatures, Model* m, int NbRawFeatures)
{
int nbStateLabels = m->getNumberOfStates();
feature* pFeature;
for(int s = 0; s < nbStateLabels; s++)
{
for(int f = 0; f < NbRawFeatures; f++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset() + f + s*NbRawFeatures;
pFeature->globalId = getIdOffset() + f + s*NbRawFeatures;
pFeature->nodeIndex = featureTypeId;
pFeature->nodeState = s;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = -1;
pFeature->sequenceLabel = -1;
pFeature->value = f;
}
}
}
void EdgeObservationFeatures::getAllFeatures(featureVector& listFeatures, Model* m,
int)
/* We dont need the number of raw features as the number of edge feature is
* independant from the size of the windows
*/
{
int nbStateLabels = m->getNumberOfStates();
int nbSeqLabels = m->getNumberOfSequenceLabels();
feature* pFeature;
if(nbSeqLabels == 0)
nbSeqLabels = 1;
for(int seqLabel = 0; seqLabel < nbSeqLabels;seqLabel++)
{
for(int s1 = 0; s1 < nbStateLabels;s1++)
{
for(int s2 = 0; s2 < nbStateLabels;s2++)
{
for(int f1 = 0; f1 < nbObservationFeatures;f1++)
{
for(int f2 = 0; f2 < nbObservationFeatures;f2++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset() + f2+ s2*nbObservationFeatures + f1*nbStateLabels*nbObservationFeatures + s1*nbStateLabels*nbObservationFeatures*nbObservationFeatures + seqLabel*nbStateLabels*nbStateLabels*nbObservationFeatures*nbObservationFeatures ;
pFeature->globalId = getIdOffset() + + f2+ s2*nbObservationFeatures + f1*nbStateLabels*nbObservationFeatures + s1*nbStateLabels*nbObservationFeatures*nbObservationFeatures + seqLabel*nbStateLabels*nbStateLabels*nbObservationFeatures*nbObservationFeatures ;
pFeature->nodeIndex = featureTypeId;
pFeature->nodeState = s2;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = s1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = 1.0f;
}
}
}
}
}
}
void WindowRawFeatures::getFeatures(featureVector& listFeatures,
DataSequence* X, Model* m, int nodeIndex,
int prevNodeIndex, int seqLabel)
{
int idNode = 0;
int nbStateLabels = m->getNumberOfStates();
int nbFeaturesDense = 0;
if(X->getPrecomputedFeatures() != NULL && prevNodeIndex == -1)
{
dMatrix * preFeatures = X->getPrecomputedFeatures();
nbFeaturesDense = preFeatures->getHeight();
int nbNodes = preFeatures->getWidth();
feature* pFeature;
for(int n = nodeIndex - WindowSize; n <= nodeIndex + WindowSize; n++)
{
if(n >= 0 && n < nbNodes)
{
for(int s = 0; s < nbStateLabels; s++)
{
for(int f = 0; f < nbFeaturesDense; f++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + f + s*nbFeaturesDense + idNode*nbStateLabels*nbFeaturesDense;
pFeature->globalId = getIdOffset() + f + s*nbFeaturesDense + idNode*nbStateLabels*nbFeaturesDense;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState = s;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = -1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = preFeatures->getValue(f,n); // TODO: Optimize
}
}
}
idNode++;
}
}
// Load Sparse raw features
int offsetOfPreFeatures = idNode*nbStateLabels*nbFeaturesDense;
if(X->getPrecomputedFeaturesSparse() != NULL && prevNodeIndex == -1)
{
dMatrixSparse * preFeaturesSparse = X->getPrecomputedFeaturesSparse();
int nbFeaturesSparse = (int)preFeaturesSparse->getHeight();
int nbNodes = (int)preFeaturesSparse->getWidth();
feature* pFeature;
idNode = 0;
for(int n = nodeIndex - WindowSize; n <= nodeIndex + WindowSize; n++)
{
if(n >= 0 && n < nbNodes)
{
int irIndex = (int)preFeaturesSparse->getJc()->getValue(n);
size_t numElementsInCol = preFeaturesSparse->getJc()->getValue(n+1) - irIndex;
for(int s = 0; s < nbStateLabels; s++)
{
for(unsigned int i = 0; i < numElementsInCol; i++)
{
int f = (int)preFeaturesSparse->getIr()->getValue(irIndex + i);// feature ID
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + f + s*nbFeaturesSparse + idNode*nbStateLabels*nbFeaturesSparse + offsetOfPreFeatures;
pFeature->globalId = getIdOffset() + f + s*nbFeaturesSparse + idNode*nbStateLabels*nbFeaturesSparse + offsetOfPreFeatures;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState = s;
pFeature->prevNodeIndex = -1;
pFeature->prevNodeState = -1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = preFeaturesSparse->getPr()->getValue(irIndex + i);
}
}
}
idNode++;
}
}
}
void EdgeObservationFeatures::getFeatures(featureVector& listFeatures, DataSequence* X,
Model* m, int nodeIndex, int prevNodeIndex,
int seqLabel)
{
// These features are only used for adjacent edge in the chain
int nbNodes = -1;
if(X->getPrecomputedFeatures())
{
nbNodes = X->getPrecomputedFeatures()->getWidth();
if( ((prevNodeIndex == nodeIndex-1) || prevNodeIndex == nodeIndex-1 + nbNodes)
&& (prevNodeIndex != -1))
{
dMatrix * preFeatures = X->getPrecomputedFeatures();
feature* pFeature;
int nbStateLabels = m->getNumberOfStates();
for(int f1 = 0; f1 < nbObservationFeatures;f1++)
{
for(int f2 = 0; f2 < nbObservationFeatures;f2++)
{
if(preFeatures->getValue(f1,prevNodeIndex) && preFeatures->getValue(f2,nodeIndex))
{
for(int s1 = 0; s1 < nbStateLabels;s1++)
{
for(int s2 = 0; s2 < nbStateLabels;s2++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + f2+ s2*nbObservationFeatures + f1*nbStateLabels*nbObservationFeatures + s1*nbStateLabels*nbObservationFeatures*nbObservationFeatures;
pFeature->globalId = getIdOffset() + f2+ s2*nbObservationFeatures + f1*nbStateLabels*nbObservationFeatures + s1*nbStateLabels*nbObservationFeatures*nbObservationFeatures+ seqLabel*nbStateLabels*nbStateLabels*nbObservationFeatures*nbObservationFeatures ;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState = s2;
pFeature->prevNodeIndex = prevNodeIndex;
pFeature->prevNodeState = s1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = 1.0f;
}
}
}
}
}
}
}
else
{
nbNodes = (int)X->getPrecomputedFeaturesSparse()->getWidth();
if( ((prevNodeIndex == nodeIndex-1) || prevNodeIndex == nodeIndex-1 + nbNodes)
&& (prevNodeIndex != -1))
{
dMatrixSparse * preFeaturesSparse = X->getPrecomputedFeaturesSparse();
int nbFeaturesSparse = (int)preFeaturesSparse->getHeight();
feature* pFeature;
int nbStateLabels = m->getNumberOfStates();
int irIndex1 = (int)preFeaturesSparse->getJc()->getValue(prevNodeIndex);
size_t numElementsInCol1 = preFeaturesSparse->getJc()->getValue(prevNodeIndex+1) - irIndex1;
int irIndex2 = (int)preFeaturesSparse->getJc()->getValue(nodeIndex);
size_t numElementsInCol2 = preFeaturesSparse->getJc()->getValue(nodeIndex+1) - irIndex2;
for(int i1 = 0; i1 < numElementsInCol1;i1++)
{
int f1 = (int)preFeaturesSparse->getIr()->getValue(irIndex1 + i1);
if (f1 >= nbObservationFeatures)
break;
for(int i2 = 0; i2 < numElementsInCol2;i2++)
{
int f2 = (int)preFeaturesSparse->getIr()->getValue(irIndex2 + i2);
if (f2 >= nbObservationFeatures)
break;
for(int s1 = 0; s1 < nbStateLabels;s1++)
{
for(int s2 = 0; s2 < nbStateLabels;s2++)
{
pFeature = listFeatures.addElement();
pFeature->id = getIdOffset(seqLabel) + f2+ s2*nbObservationFeatures + f1*nbStateLabels*nbObservationFeatures + s1*nbStateLabels*nbObservationFeatures*nbObservationFeatures;
pFeature->globalId = getIdOffset() + f2+ s2*nbObservationFeatures + f1*nbStateLabels*nbObservationFeatures + s1*nbStateLabels*nbObservationFeatures*nbObservationFeatures+ seqLabel*nbStateLabels*nbStateLabels*nbObservationFeatures*nbObservationFeatures ;
pFeature->nodeIndex = nodeIndex;
pFeature->nodeState = s2;
pFeature->prevNodeIndex = prevNodeIndex;
pFeature->prevNodeState = s1;
pFeature->sequenceLabel = seqLabel;
pFeature->value = 1.0f;
}
}
}
}
}
}
}
experienceNet::experienceNet(const featureVector& cVec, const experienceNetSettings& _cSet)
{
*this = experienceNet(cVec.inputSize(), _cSet);
};
