C++ SparseVector::setDim方法代码示例

//.........这里部分代码省略.........
          const PrecomputedType & B = j->second;

          for (SparseVector::const_iterator i = _xstar->begin(); i != _xstar->end(); i++)
          {
            uint dim    = i->first;
            double fval = i->second;

            uint nnz = this->nnz_per_dimension[dim];
            uint nz  = this->num_examples - nnz;

            if ( nnz == 0 ) continue;
            // useful
            //if ( fval < this->f_tolerance ) continue;

            uint position = 0;

            //this->X_sorted.findFirstLargerInDimension(dim, fval, position);
            GMHIKernelRaw::sparseVectorElement fval_element;
            fval_element.value = fval;

            //std::cerr << "value to search for " << fval << endl;
            //std::cerr << "data matrix in dimension " << dim << endl;
            //for (int j = 0; j < nnz; j++)
            //    std::cerr << dataMatrix[dim][j].value << std::endl;

            GMHIKernelRaw::sparseVectorElement *it = upper_bound ( dataMatrix[dim], dataMatrix[dim] + nnz, fval_element );
            position = distance ( dataMatrix[dim], it );
            
//             /*// add zero elements
//             if ( fval_element.value > 0.0 )
//                 position += nz;*/


            bool posIsZero ( position == 0 );
            
            // special case 1:
            // new example is smaller than all known examples
            // -> resulting value = fval * sum_l=1^n alpha_l               
            if ( position == 0 )
            {
              beta += fval * B[ dim ][ nnz - 1 ];  
            }
            // special case 2:
            // new example is equal to or larger than the largest training example in this dimension
            // -> the term B[ dim ][ nnz-1 ] - B[ dim ][ indexElem ] is equal to zero and vanishes, which is logical, since all elements are smaller than the remaining prototypes!            
            else if ( position == nnz )
            {
              beta += A[ dim ][ nnz - 1 ];
            }
            // standard case: new example is larger then the smallest element, but smaller then the largest one in the corrent dimension        
            else
            {
                beta += A[ dim ][ position - 1 ] + fval * ( B[ dim ][ nnz - 1 ] - B[ dim ][ position - 1 ] );
            }
            
//             // correct upper bound to correct position, only possible if new example is not the smallest value in this dimension
//             if ( !posIsZero )
//                 position--;
// 
// 
//             double firstPart = 0.0;
//             if ( !posIsZero  )
//               firstPart = ( A[ dim ][ position ] );
// 
//             double secondPart( B[ dim ][ this->num_examples-1-nz ]);
//             if ( !posIsZero && (position >= nz) )
//                 secondPart -= B[dim][ position ];
// 
//             // but apply using the transformed one
//             beta += firstPart + secondPart* fval;
          }//for-loop over dimensions of test input

          _scores[ classno ] = beta;

        }//for-loop over 1-vs-all models

    } // if-condition wrt quantization
  _scores.setDim ( *this->knownClasses.rbegin() + 1 );


  if ( this->knownClasses.size() > 2 )
  { // multi-class classification
    _result = _scores.maxElement();
  }
  else if ( this->knownClasses.size() == 2 ) // binary setting
  {
    uint class1 = *(this->knownClasses.begin());
    uint class2 = *(this->knownClasses.rbegin());

    // since we erased the binary label vector corresponding to the smaller class number,
    // we only have scores for the larger class number
    uint class_for_which_we_have_a_score          = class2;
    uint class_for_which_we_dont_have_a_score     = class1;

    _scores[class_for_which_we_dont_have_a_score] = - _scores[class_for_which_we_have_a_score];

    _result = _scores[class_for_which_we_have_a_score] > 0.0 ? class_for_which_we_have_a_score : class_for_which_we_dont_have_a_score;
  }

}