예제 #1
0
/** Flag = 0: marginloss, no label loss. The label loss will always be zero
           1: marginloss, and label loss.
*/
void CSMMMulticlassLoss::ComputeLoss(vector<unsigned int> y, vector<unsigned int> ylabel, vector<unsigned int> ybar, vector<unsigned int> ybarlabel, const CSeqMulticlassFeature::seqfeature_struct &x, const TheMatrix &w, double & marginloss, double & labelloss, int flag)
{
    unsigned int i;
    double w_dot_phi1 = 0;
    double w_dot_phi2 = 0;
    marginloss = 0;

    unsigned int start;
    if(is_first_phi1_used)
	start = 0;
    else
	start = 1;
    for(i=start; i < ybar.size(); i++)
    {
       _data->TensorPhi1(x.phi_1[ybar[i]],ybarlabel[i],0,tphi_1);
       //tphi_1->Print();
       w.Dot(*(tphi_1), w_dot_phi1);
       marginloss += w_dot_phi1;
       //printf("%d(%d):%2.4f\t",ybar[i],ybarlabel[i],marginloss);
    }	
    for(i=1;i<ybar.size();i++)
    {
       int vb = 0;
       _data->TensorPhi2(x.phi_2[ybar[i-1]][ybar[i]-ybar[i-1]-1], ybarlabel[i-1], ybarlabel[i], 0,vb,tphi_2);
       w.Dot(*(tphi_2), w_dot_phi2);
       marginloss += w_dot_phi2;
    }
    
    if(ybar.size() > 0)
    {
       
       //grad.Add(*(X[i].phi_2[ybar[ybar.size()-1]][X[i].len-1 - ybar[ybar.size()-1]-1]));////       
       _data->TensorPhi2(x.phi_2[ybar[ybar.size()-1]][x.len - ybar[ybar.size()-1]-1 ], ybarlabel[ybar.size()-1], 0, 0,0,tphi_2);
       w.Dot(*(tphi_2), w_dot_phi2);
       marginloss += w_dot_phi2;
    }
    
    //vector <unsigned int> yss = Boundry2StatSequence(y,ylabel,x.len);
    //vector <unsigned int> ybarss = Boundry2StatSequence(ybar,ybarlabel,x.len);
    //labelloss = Labelloss(yss,ybarss);
    labelloss = AllDelta(ybar,y,ybarlabel,ylabel,x.len);
}
예제 #2
0
/** find best label (without label loss): g(w) := max_y' <w,\phi(x,y')>
 *
 *  @param x [read] sequence
 *  @param w [read] weight vector
 *  @param ybar [write] found best label
 *  @param marginloss [write] margin loss <w,\Phi(x,y')> w.r.t to best y'
 */
void CSMMMulticlassLoss::find_best_label_grammer(const CSeqMulticlassFeature::seqfeature_struct &x, const TheMatrix &w, vector<unsigned int> &ybar, vector<unsigned int> &ybarlabel, double &marginloss, unsigned int personid, unsigned int classNum)
{
    using namespace std;
    
    // reset return values
    marginloss = 0;        
    ybar.clear();
    ybarlabel.clear();
    
    /** The margin value vector used in dynamic programming
     */	
    vector< vector<double> > M (x.len+1,vector<double> (classNum,0));
    
    /** The back pointers vector used in dynamic programming to retrieve the optimal path
     */
    // The positions
    vector< vector<int> > A (x.len+1,vector<int> (classNum,-1));
    // The class labels
    vector< vector<int> > C (x.len+1,vector<int> (classNum,0));
    
    
    double maxval = -SML::INFTY;
    double w_dot_phi1 = 0;
    double w_dot_phi2 = 0;
    double marginval = 0;
    unsigned int right = 0;
    unsigned int left = 0;
    unsigned int start = 0;
    unsigned int end = 0;
    unsigned int classID = 0;
    unsigned int classIDPrev = 0;
    
    double sum = 0;
    
    // compute DP statistics for positions 1 to len-1
    for(classID=0;classID<classNum;classID++)
    {
	A[1][classID] = 0;
	//C[1][classID] = 0;
    }
    
    if(is_first_phi1_used)
    {
	right =0;
	for(classID=0;classID<classNum;classID++)
	{
	    maxval = -SML::INFTY;
	    w_dot_phi1 = 0.0;
	    _data->TensorPhi1(x.phi_1[right],classID,0,tphi_1);
	    //tphi_1->Print();
	    w.Dot(*(tphi_1), w_dot_phi1);
	    marginval = w_dot_phi1;   					
	    sum = marginval;
	    if(sum > maxval)
	    {
		M[right][classID] = marginval;			
		maxval = sum;
	    }
	}
    }
    for(right=1; right < x.len+1; right++)
    {
	for(classID=0;classID<classNum;classID++)
	{		
	    // \Phi = (phi1, phi2[left,right])
	    // <w, \Phi> = <w,phi1> + <w,phi[left,right]>                
	    maxval = -SML::INFTY;
	    w_dot_phi1 = 0.0;
	    
	    if(right<x.len)
	    {
		_data->TensorPhi1(x.phi_1[right],classID,0,tphi_1);
		w.Dot(*(tphi_1), w_dot_phi1);
	    }		

	    start = max(0,int(right-maxDuration));
	    //end = right;//-minDuration+1;
	    if(lastDuration>0)
	    {			
		unsigned int lastpos = x.len-lastDuration+1 ;
		end = MIN(right,lastpos);
	    }
	    else
		end = right;
	    for(left=start; left < end; left++)
	    {
		classIDPrev = classID;
		int vb = 0;
		_data->TensorPhi2(x.phi_2[left][right-left-1], classIDPrev, classID, 0,vb,tphi_2);
		w.Dot(*(tphi_2), w_dot_phi2); 
		marginval = w_dot_phi1 + w_dot_phi2;   
		sum = M[left][classIDPrev]+marginval;
		if(sum > maxval)
		{
		    A[right][classID] = left;
		    C[right][classID] = classIDPrev;
		    M[right][classID] = M[left][classIDPrev] + marginval;						
		    maxval = sum;
		}
	    }
	    
	}
    }
        
    // get optimal path (i.e. segmentation)        
    unsigned int pos,prepos,classid,preclassid;
    int maxclassid = 0;
    maxval = -SML::INFTY;
    for(unsigned int i=0;i<classNum;i++)
    {
	sum = M[x.len][i];
	if(sum>maxval)
	{
	    maxval = sum;
	    maxclassid = i;
	}
    }
    
    pos = A[x.len][maxclassid];
    classid = C[x.len][maxclassid];
    
    if(lastDuration>0)
    {	
	pos = x.len-lastDuration;
	classid = 0;
    }
    ybar.push_back(pos);
    ybarlabel.push_back(classid);
	
    prepos = pos;
    preclassid = classid;
    while(A[pos][classid] >= 0)
    {                
	pos = A[prepos][preclassid];
	classid = C[prepos][preclassid];
	ybar.push_back(pos);//positions
	ybarlabel.push_back(classid);//class labels		
	prepos = pos;
	preclassid = classid;
    }
        
    marginloss = M[x.len][maxclassid];
    reverse(ybar.begin(), ybar.end());
    reverse(ybarlabel.begin(), ybarlabel.end());
}
예제 #3
0
/** find best label with a grammer(with label loss): g(w) := max_y' <w,\phi(x,y')> + Delta(y', y)
 *
 *  @param x [read] sequence
 *  @param y [read] actual label for x
 *  @param w [read] weight vector
 *  @param ybar [write] found best label
 *  @param marginloss [write] margin loss <w,\Phi(x,y')> w.r.t to best y'
 *  @param labelloss [write] label loss \Delta(y',y) w.r.t. to best y'
 *
 */
void CSMMMulticlassLoss::find_best_label_grammer(const vector<unsigned int> &y,const vector<unsigned int> &ylabel, const CSeqMulticlassFeature::seqfeature_struct &x, const TheMatrix &w, vector<unsigned int> &ybar,vector<unsigned int> &ybarlabel, double &marginloss, double &labelloss, unsigned int personid, unsigned int classNum)
{
    // reset return values
    marginloss = 0;
    labelloss = 0;
    ybar.clear();
    ybarlabel.clear();
    
    /** The margin value vector used in dynamic programming
     */
    
    vector< vector<double> > M (x.len+1,vector<double> (classNum,0));
    
    /** The label loss value vector used in dynamic programming
     */
    vector< vector<double> > L (x.len+1,vector<double> (classNum,0));
    
    /** The back pointers vector used in dynamic programming to retrieve the optimal path
     */
    // The positions
    vector< vector<int> > A (x.len+1,vector<int> (classNum,-1));
    // The class labels
    vector< vector<int> > C (x.len+1,vector<int> (classNum,0));
    
    
    double maxval = -SML::INFTY;
    double w_dot_phi1 = 0;
    double w_dot_phi2 = 0;
    double marginval = 0;
    double labelval = 0;
    unsigned int right = 0;
    unsigned int left = 0;
    unsigned int start = 0;
    unsigned int end = 0;
    unsigned int classID = 0;
    unsigned int classIDPrev = 0;
    
    double sum = 0;
    
    // compute DP statistics for positions 1 to len-1
//         L[0] += y.size()-2;
//         A[1] = 0;
    for(classID=0;classID<classNum;classID++)
    {
	A[1][classID] = 0;
	//C[1][classID] = 0;
    }
    
    //debug
    
    
    //printf("x.len:%d",x.len);
    if(is_first_phi1_used)
    {
	right =0;
	for(classID=0;classID<classNum;classID++)
	{
	    maxval = -SML::INFTY;
	    w_dot_phi1 = 0.0;
	    _data->TensorPhi1(x.phi_1[right],classID,0,tphi_1);
	    //tphi_1->Print();
	    w.Dot(*(tphi_1), w_dot_phi1);
	    marginval = w_dot_phi1;   					
	    sum = marginval;
	    if(sum > maxval)
	    {
		M[right][classID] = marginval;			
		maxval = sum;
	    }
	}
    }
    
    for(right=1; right < x.len+1; right++)        
    {
	
	for(classID=0;classID<classNum;classID++)
	{		
	    // \Phi = (phi1, phi2[left,right])
	    // <w, \Phi> = <w,phi1> + <w,phi[left,right]>                
	    maxval = -SML::INFTY;
	    w_dot_phi1 = 0.0;
	    
	    //w.Dot(*(x.phi_1[right]), w_dot_phi1);
	    //printf("pos:%d,classid:%d ",right,classID);fflush(stdout);
	    //x.phi_1[right]->Print();
	    if(right<x.len)
	    {
		_data->TensorPhi1(x.phi_1[right],classID,0,tphi_1);
		//tphi_1->Print();
		w.Dot(*(tphi_1), w_dot_phi1);
	    }		
	    
	    start = max(0,int(right-maxDuration));
	    //end = right;//-minDuration+1;
	    
	    if(lastDuration>0)
	    {			
		unsigned int lastpos = x.len-lastDuration+1 ;
		end = MIN(right,lastpos);
	    }
	    else
		end = right;
	    for(left=start; left < end; left++)
	    {
		classIDPrev = classID;
		labelval = PartialDelta(left,right,y,ylabel,classIDPrev,x.len);
		assert( (labelval<=x.len) && (labelval>=0) );
		int vb = 0;
		_data->TensorPhi2(x.phi_2[left][right-left-1], classIDPrev, classID, 0,vb,tphi_2);
		w.Dot(*(tphi_2), w_dot_phi2); 
		marginval = w_dot_phi1 + w_dot_phi2;   
		sum = M[left][classIDPrev]+marginval + L[left][classIDPrev]+labelval;
		if(sum > maxval)
		{
		    A[right][classID] = left;
		    C[right][classID] = classIDPrev;
		    M[right][classID] = M[left][classIDPrev] + marginval;
		    L[right][classID] = L[left][classIDPrev] + labelval;
		    maxval = sum;
		}
		
		
	    }
	    
	}
    }
    
    // get optimal path (i.e. segmentation)        
    unsigned int pos,prepos,classid,preclassid;
    
    int maxclassid = 0;
    maxval = -SML::INFTY;
    for(unsigned int i=0;i<classNum;i++)
    {
	sum = M[x.len][i] + L[x.len][i];
	if(sum>maxval)
	{
	    maxval = sum;
	    maxclassid = i;
	}
    }
    
    pos = A[x.len][maxclassid];
    classid = C[x.len][maxclassid];
    
    if(lastDuration>0)
    {	
	pos = x.len-lastDuration;
	classid = 0;
    }
    ybar.push_back(pos);
    ybarlabel.push_back(classid);

    prepos = pos;
    preclassid = classid;
    
    
    while(A[pos][classid] >= 0)
    {                
	pos = A[prepos][preclassid];
	classid = C[prepos][preclassid];
	ybar.push_back(pos);//positions
	ybarlabel.push_back(classid);//class labels		
	
	//printf("%d(%d):%2.4f ",pos,classid,L[pos][classid]);fflush(stdout);
	prepos = pos;
	preclassid = classid;
    }
    
    
    marginloss = M[x.len][maxclassid];
    labelloss = L[x.len][maxclassid];
    //printf("finished back track\n labelloss:%3.4f,marginloss:%3.4f\n",labelloss,marginloss);fflush(stdout);
    reverse(ybar.begin(), ybar.end());
    reverse(ybarlabel.begin(), ybarlabel.end());
    
    //printf("reversed\n");fflush(stdout);
    unsigned int i;
    if(verbosity>=2)
    {
	printf("y:   ");
	for(i=0;i<y.size();i++)
	{
	    printf("%d(%d) ",y[i],ylabel[i]);
	}
	fflush(stdout);
	printf("\nybar:");
	for(i=0;i<ybar.size();i++)
	{
	    printf("%d(%d) ",ybar[i],ybarlabel[i]);
	}
	fflush(stdout);
	printf("\nmargin:%f, loss:%f, totalloss:%f\n",marginloss,labelloss,marginloss+labelloss);
    }
}