Java DenseVector.set方法代码示例

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
protected void setupGPvalues() {

        setupQmatrix(precisionParameter.getParameterValue(0));
        int length = getCorrectFieldLength();
        DenseVector StandNorm = new DenseVector(length);
        DenseVector MultiNorm = new DenseVector(length);
        for (int i=0; i<length;i++){
            StandNorm.set(i,MathUtils.nextGaussian());
//            StandNorm.set(i,0.1);
                      }
        UpperSPDBandMatrix Qcurrent = new UpperSPDBandMatrix(weightMatrix, 1);
        BandCholesky U = new BandCholesky(length,1,true);
        U.factor(Qcurrent);
        UpperTriangBandMatrix CholeskyUpper = U.getU();

        CholeskyUpper.solve(StandNorm,MultiNorm);
        for (int i=0; i<length;i++){
            popSizeParameter.setParameterValue(i,MultiNorm.get(i));
            }
    }
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * Creates a new dense vector with the given size and paste for each entry the given value.
 *
 * @param size the size of the dense vector
 * @param value the value for each entry
 */
public DenseDoubleVector(int size, double value) {
   internalVector = new DenseVector(size);
   for (int index = 0; index < internalVector.size(); index++) {
      internalVector.set(index, value);
   }
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * @see ApacheVector#getSubVector(int, int)
 */
@Override
public AbstractVector getSubVector(int index, int n) {
  DenseVector subVector = new DenseVector(n);
  for (int i = index, j = 0; i < index + n; i++, j++) {
    double value = this.vector.get(i);
    subVector.set(j, value);
  }
  return new MtjVector(subVector);
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * Adds ranks and scores obtained from a candidate provider.
 * 
 * @param docFeats        all features
 * @param resultsAll      result entries
 */
private void addScoresAndRanks(Map<String, DenseVector>   docFeats, 
                               CandidateEntry[]           resultsAll) {
  for (CandidateEntry  e: resultsAll) {
    DenseVector oldVect = docFeats.get(e.mDocId);
    int oldSize = oldVect.size();
    DenseVector newVect = new DenseVector(oldSize + 2);
    newVect.set(0, e.mOrigRank);
    newVect.set(1, e.mOrigScore);
    for (int vi = 0; vi < oldSize; ++vi)
      newVect.set(vi + 2, oldVect.get(vi)); 
    docFeats.replace(e.mDocId, newVect);
  }    
  
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * Get TF-IDF scores for one field.
 * 
 * @param fieldIndex      an in-memory field index
 * @param similObj        an object that computes similarity.
 * @param arrDocIds       an array of document ids.
 * @param fieldName       a name of the field. 
 * @param featureId       an index/id of the feature.
 * @param query           an actual query
 * @param res             a result set to be updated.   * 
 * @throws Exception
 */
private void getFieldScores(InMemForwardIndex fieldIndex,
                        QueryDocSimilarity    similObj,
                        ArrayList<String> arrDocIds, 
                        String fieldName,
                        int featureId,
                        String query,
                        Map<String,DenseVector> res) throws Exception {    
  if (null == query) return;
  query = query.trim();
  if (query.isEmpty()) return;
  
  DocEntry queryEntry = fieldIndex.createDocEntry(query.split("\\s+"));
  
  
  if (PRINT_SCORES)
    System.out.println("InMemIndex Field: '" + fieldName + "' (getFieldScores)");
 
  for (String docId : arrDocIds) {
    DocEntry docEntry = fieldIndex.getDocEntry(docId);
    
    if (docEntry == null) {
      throw new Exception("Inconsistent data or bug: can't find document with id ='" + docId + "'");
    }
    
    float score = similObj.compute(queryEntry, docEntry);
    
    DenseVector v = res.get(docId);
    if (v == null) {
      throw new Exception(String.format("Bug, cannot retrieve a vector for docId '%s' from the result set", docId));
    }
    
    v.set(featureId, score);      
    if (PRINT_SCORES)
      System.out.println(String.format("Doc id %s %s: %g", docId, similObj.getName(), score)); 
  }      
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * Get overall match scores for one field.
 * 
 * @param fieldIndex      an in-memory field index
 * @param fieldId         a field identifier
 * @param arrDocIds       an array of document ids.
 * @param fieldName       a name of the field. 
 * @param startFeatureId  an index/id of the first feature.
 * @param query           an actual query
 * @param res             a result set to be updated.   * 
 * @throws Exception
 */
private void getFieldOverallMatchScores(InMemForwardIndex fieldIndex, int fieldId,
                        ArrayList<String> arrDocIds, 
                        String fieldName,
                        int startFeatureId,
                        String query,
                        Map<String,DenseVector> res) throws Exception {    
  if (null == query) return;
  query = query.trim();
  if (query.isEmpty()) return;
  
  DocEntry queryEntry = fieldIndex.createDocEntry(query.split("\\s+"));
  
  
  if (PRINT_SCORES)
    System.out.println("InMemIndex Field: '" + fieldName + "' (getFieldOverallMatchScores))");
 
  for (String docId : arrDocIds) {
    DocEntry docEntry = fieldIndex.getDocEntry(docId);
    
    if (docEntry == null) {
      throw new Exception("Inconsistent data or bug: can't find document with id ='" + docId + "'");
    }
    
    float score = DistanceFunctions.compOverallMatch(queryEntry, docEntry);
    
    DenseVector v = res.get(docId);
    if (v == null) {
      throw new Exception(String.format("Bug, cannot retrieve a vector for docId '%s' from the result set", docId));
    }

    float scoreQueryNorm = score / Math.max(1, queryEntry.mWordIds.length);
    
    int fid = startFeatureId;
    if (useOverallMatchFeature(fieldId)) {
      if (OVERAL_MATCH_FIELD_FEATURE_QTY != 1) {
        throw new RuntimeException("Wrong value of constant OVERAL_MATCH_FIELD_FEATURE_QTY");
      }
      v.set(fid++, score);
    }
    if (useOverallMatchFeatureQueryNorm(fieldId)) {
      if (OVERAL_MATCH_FIELD_FEATURE_QUERY_NORM_QTY!= 1) {
        throw new RuntimeException("Wrong value of constant OVERAL_MATCH_FIELD_FEATURE_QUERY_NORM_QTY");
      }
      v.set(fid++, scoreQueryNorm);
    }
    
    
    if (PRINT_SCORES) {
      if (useOverallMatchFeature(fieldId))
        System.out.println(String.format("Doc id %s %s: %g", docId, "OVERALL MATCH", score));
      if (useOverallMatchFeatureQueryNorm(fieldId))
        System.out.println(String.format("Doc id %s %s: %g", docId, "OVERALL MATCH QUERY-NORM", scoreQueryNorm));        
    }
  }      
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * Get LCS scores for one field.
 * 
 * @param fieldIndex      an in-memory field index
 * @param fieldId         a field identifier
 * @param arrDocIds       an array of document ids.
 * @param fieldName       a name of the field. 
 * @param startFeatureId  an index/id of the first feature.
 * @param query           an actual query
 * @param res             a result set to be updated.   * 
 * @throws Exception
 */
private void getFieldLCSScores(InMemForwardIndex fieldIndex, int fieldId,
                        ArrayList<String> arrDocIds, 
                        String fieldName,
                        int startFeatureId,
                        String query,
                        Map<String,DenseVector> res) throws Exception {    
  if (null == query) return;
  query = query.trim();
  if (query.isEmpty()) return;
  
  DocEntry queryEntry = fieldIndex.createDocEntry(query.split("\\s+"));
  
  boolean useLCSFeature          = useLCSFeature(fieldId);
  boolean useLCSFeatureQueryNorm = useLCSFeatureQueryNorm(fieldId);
  
  if (PRINT_SCORES)
    System.out.println("InMemIndex Field: '" + fieldName + "' (getFieldLCSScores))");
 
  for (String docId : arrDocIds) {
    DocEntry docEntry = fieldIndex.getDocEntry(docId);
    
    if (docEntry == null) {
      throw new Exception("Inconsistent data or bug: can't find document with id ='" + docId + "'");
    }
    
    float score = DistanceFunctions.compLCS(queryEntry.mWordIdSeq, docEntry.mWordIdSeq);
    
    DenseVector v = res.get(docId);
    if (v == null) {
      throw new Exception(String.format("Bug, cannot retrieve a vector for docId '%s' from the result set", docId));
    }

    float normScore = score / Math.max(1, queryEntry.mWordIdSeq.length);
    
    int fid = startFeatureId;
    
    if (useLCSFeature) {
      v.set(fid++, score);      
      if (LCS_FIELD_FEATURE_QTY != 1) {
        throw new RuntimeException("Bug: wrong value for the constant LCS_FIELD_FEATURE_QTY");
      }
    }
    if (useLCSFeatureQueryNorm) {
      v.set(fid++, normScore);
      if (LCS_FIELD_FEATURE_QUERY_NORM_QTY != 1) {
        throw new RuntimeException("Bug: wrong value for the constant LCS_FIELD_FEATURE_QUERY_NORM_QTY");
      }      
    }
    
    
    if (PRINT_SCORES) {
      if (useLCSFeatureQueryNorm) System.out.println(String.format("Doc id %s %s: %g", docId, "LCS", score));
      if (useLCSFeatureQueryNorm) System.out.println(String.format("Doc id %s %s: %g", docId, "LCS (query-norm)", normScore));
    }
  }
  
 }
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
/**
 * Reads feature weights from a file.
 * 
 * @param fileName    input file (in the RankLib format): all weights must be present
 *                    there should be no gaps!
 * @return            a sparse vector that keeps weights
 * @throws Exception
 */
public static DenseVector readFeatureWeights(String fileName) throws Exception {
  BufferedReader inFile = new BufferedReader(new FileReader(new File(fileName)));
  
  
  try {
    String line = null;
    
    while ((line = inFile.readLine()) != null) {
      line = line.trim();
      if (line.isEmpty() || line.startsWith("#")) continue;
      
      String parts0[] = line.split("\\s+");
      
      DenseVector res = new DenseVector(parts0.length);
      
      int ind = 0;
      for (String onePart: parts0) {
        try {
          String parts1[] = onePart.split(":");
          if (parts1.length != 2) {
            throw new Exception(
                String.format(
                    "The line in file '%s' has a field '%s' without exactly one ':', line: %s", fileName, onePart, line));
          }
          int partId = Integer.parseInt(parts1[0]);
          if (partId != ind + 1) {
            throw new Exception(
                String.format("Looks like there's a missing feature weight, field %d has id %d", ind + 1, partId));
          }
          res.set(ind, Double.parseDouble(parts1[1]));
          ind++;
        } catch (NumberFormatException e) {
          throw new Exception(
              String.format(
                  "The line in file '%s' has non-number '%s', line: %s", fileName, onePart, line));
        }
      }
      return res;
    }
    
  } finally {    
    inFile.close();
  }
  
  throw new Exception("No features found in '" + fileName + "'");
}
 

import no.uib.cipr.matrix.DenseVector; //导入方法依赖的package包/类
@Override
protected void updateGammaWithCovariates(DenseVector currentGamma) {

    // Handle betaParameter / designMatrix

    if (NEW_APPROACH) {

        final int N = currentGamma.size();
        double[] update = new double[N];

        if (dMatrix != null) {
            final int K = dMatrix.getColumnDimension();

            if (N != dMatrix.getRowDimension()) {
                throw new RuntimeException("Incorrect covariate dimensions (" + N + " != "
                        + dMatrix.getRowDimension() + ")");
            }

            for (int i = 0; i < N; ++i) {
                for (int j = 0; j < K; ++j) {
                    update[i] += dMatrix.getParameterValue(i, j) * betaParameter.getParameterValue(j);
                }
            }
        }

        if (covariates != null) {
            if (beta.size() != covariates.size()) {
                throw new RuntimeException("beta.size() != covariates.size()");
            }

            for (int k = 0; k < beta.size(); ++k) {

                Parameter b = beta.get(k);
                final int J = b.getDimension();
                MatrixParameter covariate = covariates.get(k);

                if ((J != covariate.getRowDimension()) ||
                        (N != covariate.getColumnDimension())) { // Note: XML current has covariates transposed
                    throw new RuntimeException("Incorrect dimensions in " + covariate.getId());
                }

                for (int i = 0; i < N; ++i) {
                    for (int j = 0; j < J; ++j) {
                        update[i] += covariate.getParameterValue(j, i) * b.getParameterValue(j);
                    }
                }
            }
        }

        for (int i = 0; i < N; ++i) {
            currentGamma.set(i, currentGamma.get(i) - update[i]);
        }

    } else {
        DenseVector currentBeta = new DenseVector(beta.size());

        for (int i = 0; i < beta.size(); i++) {
            currentBeta.set(i, beta.get(i).getParameterValue(0));
        }

        //int numMissing = fieldLength - lastObservedIndex;
        //DenseVector tempVectCov = new DenseVector(numMissing);

        //System.err.println("covariates.size(): " + covariates.size());
        //System.err.println("covariates.get(0).getColumnDimension: " + covariates.get(0).getColumnDimension());
        //System.err.println("covariates.get(0).getRowDimension: " + covariates.get(0).getRowDimension());

        for (int i = 0; i < covariates.size(); i++) {
            for (int j = 0; j < covariates.get(i).getColumnDimension(); j++) {
                // System.err.println("j: " + j);
                // System.err.println("covariates.get(i).getParameterValue(0,j): " + covariates.get(i).getParameterValue(0,j));
                currentGamma.set(j, currentGamma.get(j) - covariates.get(i).getParameterValue(0, j) * currentBeta.get(i));
            }
        }
    }
}