C++ Mat::set_size方法代码示例

void Split(const arma::Mat<T>& input,
           const arma::Row<U>& inputLabel,
           arma::Mat<T>& trainData,
           arma::Mat<T>& testData,
           arma::Row<U>& trainLabel,
           arma::Row<U>& testLabel,
           const double testRatio)
{
  const size_t testSize = static_cast<size_t>(input.n_cols * testRatio);
  const size_t trainSize = input.n_cols - testSize;
  trainData.set_size(input.n_rows, trainSize);
  testData.set_size(input.n_rows, testSize);
  trainLabel.set_size(trainSize);
  testLabel.set_size(testSize);

  const arma::Col<size_t> order =
      arma::shuffle(arma::linspace<arma::Col<size_t>>(0, input.n_cols - 1,
                                                      input.n_cols));

  for (size_t i = 0; i != trainSize; ++i)
  {
    trainData.col(i) = input.col(order[i]);
    trainLabel(i) = inputLabel(order[i]);
  }

  for (size_t i = 0; i != testSize; ++i)
  {
    testData.col(i) = input.col(order[i + trainSize]);
    testLabel(i) = inputLabel(order[i + trainSize]);
  }
}

void ConcreteGridView<DuneGridView>::getRawElementDataImpl(
        arma::Mat<CoordinateType>& vertices,
        arma::Mat<int>& elementCorners,
        arma::Mat<char>& auxData) const
{
    typedef typename DuneGridView::Grid DuneGrid;
    typedef typename DuneGridView::IndexSet DuneIndexSet;
    const int dimGrid = DuneGrid::dimension;
    const int dimWorld = DuneGrid::dimensionworld;
    const int codimVertex = dimGrid;
    const int codimElement = 0;
    typedef Dune::LeafMultipleCodimMultipleGeomTypeMapper<DuneGrid,
            Dune::MCMGElementLayout> DuneElementMapper;
    typedef typename DuneGridView::template Codim<codimVertex>::Iterator
            DuneVertexIterator;
    typedef typename DuneGridView::template Codim<codimElement>::Iterator
            DuneElementIterator;
    typedef typename DuneGridView::template Codim<codimVertex>::Geometry
            DuneVertexGeometry;
    typedef typename DuneGridView::template Codim<codimElement>::Geometry
            DuneElementGeometry;
    typedef typename DuneGrid::ctype ctype;

    const DuneIndexSet& indexSet = m_dune_gv.indexSet();

    vertices.set_size(dimWorld, indexSet.size(codimVertex));
    for (DuneVertexIterator it = m_dune_gv.template begin<codimVertex>();
         it != m_dune_gv.template end<codimVertex>(); ++it)
    {
        size_t index = indexSet.index(*it);
        const DuneVertexGeometry& geom = it->geometry();
        Dune::FieldVector<ctype, dimWorld> vertex = geom.corner(0);
        for (int i = 0; i < dimWorld; ++i)
            vertices(i, index) = vertex[i];
    }

    const int MAX_CORNER_COUNT = dimWorld == 2 ? 2 : 4;
    DuneElementMapper elementMapper(m_dune_gv.grid());
    elementCorners.set_size(MAX_CORNER_COUNT, elementMapper.size());
    for (DuneElementIterator it = m_dune_gv.template begin<codimElement>();
         it != m_dune_gv.template end<codimElement>(); ++it)
    {
        size_t index = elementMapper.map(*it);
        const Dune::GenericReferenceElement<ctype, dimGrid>& refElement =
                Dune::GenericReferenceElements<ctype, dimGrid>::general(it->type());
        const int cornerCount = refElement.size(codimVertex);
        assert(cornerCount <= MAX_CORNER_COUNT);
        for (int i = 0; i < cornerCount; ++i)
            elementCorners(i, index) = indexSet.subIndex(*it, i, codimVertex);
        for (int i = cornerCount; i < MAX_CORNER_COUNT; ++i)
            elementCorners(i, index) = -1;
    }

    auxData.set_size(0, elementCorners.n_cols);
}

    inline ParallelKinematicMachine3PUPS::ParallelKinematicMachine3PUPS() noexcept {
      setMinimalActiveJointActuations({0.39, 0.39, 0.39});
      setMaximalActiveJointActuations({0.95, 0.95, 0.95});

      setEndEffectorJointPositions({
        -0.025561381023353, 0.086293776138137, 0.12,
        0.087513292835791, -0.021010082747031, 0.12,
        -0.061951911812438, -0.065283693391106, 0.12});

      setRedundantJointStartPositions({
        -0.463708870031622, 0.417029254828353, -0.346410161513775,
        0.593012363818459, 0.193069033993384, -0.346410161513775,
        -0.129303493786837, -0.610098288821738, -0.346410161513775});

      setRedundantJointEndPositions({
        -0.247202519085512, 0.292029254828353, 0.086602540378444,
        0.376506012872349, 0.068069033993384, 0.086602540378444,
        -0.129303493786837, -0.360098288821738, 0.086602540378444});

      redundantJointStartToEndPositions_ = redundantJointEndPositions_ - redundantJointStartPositions_;
      redundantJointIndicies_ = arma::find(arma::any(redundantJointStartToEndPositions_));

      redundantJointAngles_.set_size(3, redundantJointIndicies_.n_elem);

      for (std::size_t n = 0; n < redundantJointIndicies_.n_elem; ++n) {
        const double& redundantJointXAngle = std::atan2(redundantJointStartToEndPositions_(1, n), redundantJointStartToEndPositions_(0, n));
        const double& redundantJointYAngle = std::atan2(redundantJointStartToEndPositions_(2, n), redundantJointStartToEndPositions_(1, n));
        redundantJointAngles_.col(n) = arma::Col<double>::fixed<3>({std::cos(redundantJointXAngle) * std::cos(redundantJointYAngle), std::sin(redundantJointXAngle) * std::cos(redundantJointYAngle), std::sin(redundantJointYAngle)});
      }
    }

void MainWindow::loadSeries() {
    QString seriesFile = __fileManager.openFile(0, this, "Open Series File...", "*.mat");

    if (seriesFile != "") {
        cout << "Loading start ..." << flush;
        _data.load(seriesFile.toStdString().c_str());
        cout << " done. " << endl;

        _dataT = arma::trans(_data);
        cout << _data.n_cols << "," << _data.n_rows << endl;

        _imageBuffer.set_size(_mapid.n_rows, _mapid.n_cols);
        for (int i = 0; i < _mapid.n_rows; i++) {
            for (int j = 0; j < _mapid.n_cols; j++) {
                if (_mapid(i,j) != _mapid(i,j)) {
                    _imageBuffer(i,j) = NAN;
                } else {
                    _imageBuffer(i,j) = 0;
                }
            }
        }

        ui.slider->setMaximum(_data.n_cols - 1);

        ui.minValue->setValue(_data.min());
        ui.maxValue->setValue(_data.max());

        _mapItem->setImage(_imageBuffer.memptr(), _imageBuffer.n_rows, _imageBuffer.n_cols);
        _mapItem->setRange(ui.minValue->value(), ui.maxValue->value());
        _mapItem->setInteraction(this);

        selectValue(0);
    }
}

void FastMKS<KernelType, TreeType>::Search(TreeType* queryTree,
                                           const size_t k,
                                           arma::Mat<size_t>& indices,
                                           arma::mat& kernels)
{
  // If either naive mode or single mode is specified, this must fail.
  if (naive || singleMode)
  {
    throw std::invalid_argument("can't call Search() with a query tree when "
        "single mode or naive search is enabled");
  }

  // No remapping will be necessary because we are using the cover tree.
  indices.set_size(k, queryTree->Dataset().n_cols);
  kernels.set_size(k, queryTree->Dataset().n_cols);
  kernels.fill(-DBL_MAX);

  Timer::Start("computing_products");
  typedef FastMKSRules<KernelType, TreeType> RuleType;
  RuleType rules(referenceSet, queryTree->Dataset(), indices, kernels,
      metric.Kernel());

  typename TreeType::template DualTreeTraverser<RuleType> traverser(rules);

  traverser.Traverse(*queryTree, *referenceTree);

  Log::Info << rules.BaseCases() << " base cases." << std::endl;
  Log::Info << rules.Scores() << " scores." << std::endl;

  Timer::Stop("computing_products");
}

void SpaceHelper<BasisFunctionType>::
    getNormalsAtGlobalDofInterpolationPoints_defaultImplementation(
        const Space<BasisFunctionType> &space,
        arma::Mat<CoordinateType> &normals) {
  std::vector<Point3D<CoordinateType>> vNormals;
  space.getGlobalDofNormals(vNormals);

  const size_t pointCount = vNormals.size();
  const size_t worldDim = space.worldDimension();
  normals.set_size(worldDim, pointCount);
  for (size_t p = 0; p < pointCount; ++p) {
    normals(0, p) = acc(vNormals, p).x;
    if (worldDim > 1)
      normals(1, p) = acc(vNormals, p).y;
    if (worldDim > 2)
      normals(2, p) = acc(vNormals, p).z;
  }
}

void LSHSearch<SortPolicy>::
Search(const size_t k,
       arma::Mat<size_t>& resultingNeighbors,
       arma::mat& distances,
       const size_t numTablesToSearch)
{
  // Set the size of the neighbor and distance matrices.
  resultingNeighbors.set_size(k, querySet.n_cols);
  distances.set_size(k, querySet.n_cols);
  distances.fill(SortPolicy::WorstDistance());
  resultingNeighbors.fill(referenceSet.n_cols);

  size_t avgIndicesReturned = 0;

  Timer::Start("computing_neighbors");

  // Go through every query point sequentially.
  for (size_t i = 0; i < querySet.n_cols; i++)
  {
    // Hash every query into every hash table and eventually into the
    // 'secondHashTable' to obtain the neighbor candidates.
    arma::uvec refIndices;
    ReturnIndicesFromTable(i, refIndices, numTablesToSearch);

    // An informative book-keeping for the number of neighbor candidates
    // returned on average.
    avgIndicesReturned += refIndices.n_elem;

    // Sequentially go through all the candidates and save the best 'k'
    // candidates.
    for (size_t j = 0; j < refIndices.n_elem; j++)
      BaseCase(distances, resultingNeighbors, i, (size_t) refIndices[j]);
  }

  Timer::Stop("computing_neighbors");

  distanceEvaluations += avgIndicesReturned;
  avgIndicesReturned /= querySet.n_cols;
  Log::Info << avgIndicesReturned << " distinct indices returned on average." <<
      std::endl;
}

    virtual void evaluate(const GeometricalData<CoordinateType>& geomData,
                          arma::Mat<ValueType>& result) const {
        const arma::Mat<CoordinateType>& points  = geomData.globals;
        const arma::Mat<CoordinateType>& normals = geomData.normals;

#ifndef NDEBUG
        if ((int)points.n_rows != worldDimension() ||
                (int)points.n_rows != worldDimension())
            throw std::invalid_argument(
                    "SurfaceNormalAndDomainIndexDependentFunction::evaluate(): "
                    "incompatible world dimension");
#endif

        const size_t pointCount = points.n_cols;
        result.set_size(codomainDimension(), pointCount);
        for (size_t i = 0; i < pointCount; ++i) {
            arma::Col<ValueType> activeResultColumn = result.unsafe_col(i);
            m_functor.evaluate(points.unsafe_col(i), normals.unsafe_col(i),
                               geomData.domainIndex, activeResultColumn);
        }
    }

void DefaultLocalAssemblerForOperatorsOnSurfacesUtilities<BasisFunctionType>::
    precalculateElementSizesAndCentersForSingleGrid(
        const RawGridGeometry<CoordinateType> &rawGeometry,
        std::vector<CoordinateType> &elementSizesSquared,
        arma::Mat<CoordinateType> &elementCenters,
        CoordinateType &averageElementSize) {
  const size_t elementCount = rawGeometry.elementCount();
  const int worldDim = rawGeometry.worldDimension();

  averageElementSize = 0.; // We will store here temporarily
                           // the sum of element sizes
  elementSizesSquared.resize(elementCount);
  for (int e = 0; e < elementCount; ++e) {
    elementSizesSquared[e] = elementSizeSquared(e, rawGeometry);
    averageElementSize += sqrt(elementSizesSquared[e]);
  }
  averageElementSize /= elementCount;

  elementCenters.set_size(worldDim, elementCount);
  for (int e = 0; e < elementCount; ++e)
    elementCenters.col(e) = elementCenter(e, rawGeometry);
}

void DefaultEvaluatorForIntegralOperators<BasisFunctionType, KernelType,
ResultType, GeometryFactory>::evaluate(
        Region region,
        const arma::Mat<CoordinateType>& points, arma::Mat<ResultType>& result) const
{
    const size_t pointCount = points.n_cols;
    const int outputComponentCount = m_integral->resultDimension();

    result.set_size(outputComponentCount, pointCount);
    result.fill(0.);

    const GeometricalData<CoordinateType>& trialGeomData =
            (region == EvaluatorForIntegralOperators<ResultType>::NEAR_FIELD) ?
                m_nearFieldTrialGeomData :
                m_farFieldTrialGeomData;
    const CollectionOf2dArrays<ResultType>& trialTransfValues =
            (region == EvaluatorForIntegralOperators<ResultType>::NEAR_FIELD) ?
                m_nearFieldTrialTransfValues :
                m_farFieldTrialTransfValues;
    const std::vector<CoordinateType>& weights =
            (region == EvaluatorForIntegralOperators<ResultType>::NEAR_FIELD) ?
                m_nearFieldWeights :
                m_farFieldWeights;

    // Do things in chunks of 96 points -- in order to avoid creating
    // too large arrays of kernel values
    const size_t chunkSize = 96;
    const size_t chunkCount = (pointCount + chunkSize - 1) / chunkSize;

    int maxThreadCount = 1;
    if (!m_parallelizationOptions.isOpenClEnabled()) {
        if (m_parallelizationOptions.maxThreadCount() ==
                ParallelizationOptions::AUTO)
            maxThreadCount = tbb::task_scheduler_init::automatic;
        else
            maxThreadCount = m_parallelizationOptions.maxThreadCount();
    }
    tbb::task_scheduler_init scheduler(maxThreadCount);
    typedef EvaluationLoopBody<
            BasisFunctionType, KernelType, ResultType> Body;
    {
        Fiber::SerialBlasRegion region;
        tbb::parallel_for(tbb::blocked_range<size_t>(0, chunkCount),
                          Body(chunkSize,
                               points, trialGeomData, trialTransfValues, weights,
                               *m_kernels, *m_integral, result));
    }

//    // Old serial version
//    CollectionOf4dArrays<KernelType> kernelValues;
//    GeometricalData<CoordinateType> evalPointGeomData;
//    for (size_t start = 0; start < pointCount; start += chunkSize)
//    {
//        size_t end = std::min(start + chunkSize, pointCount);
//        evalPointGeomData.globals = points.cols(start, end - 1 /* inclusive */);
//        m_kernels->evaluateOnGrid(evalPointGeomData, trialGeomData, kernelValues);
//        // View into the current chunk of the "result" array
//        _2dArray<ResultType> resultChunk(outputComponentCount, end - start,
//                                         result.colptr(start));
//        m_integral->evaluate(trialGeomData,
//                             kernelValues,
//                             weightedTrialTransfValues,
//                             resultChunk);
//    }
}

void RAModel<SortPolicy>::Search(arma::mat&& querySet,
                                 const size_t k,
                                 arma::Mat<size_t>& neighbors,
                                 arma::mat& distances)
{
  // Apply the random basis if necessary.
  if (randomBasis)
    querySet = q * querySet;

  Log::Info << "Searching for " << k << " approximate nearest neighbors with ";
  if (!Naive() && !SingleMode())
    Log::Info << "dual-tree rank-approximate " << TreeName() << " search...";
  else if (!Naive())
    Log::Info << "single-tree rank-approximate " << TreeName() << " search...";
  else
    Log::Info << "brute-force (naive) rank-approximate search...";
  Log::Info << std::endl;

  switch (treeType)
  {
    case KD_TREE:
      if (!kdTreeRA->Naive() && !kdTreeRA->SingleMode())
      {
        // Build a second tree and search.
        Timer::Start("tree_building");
        Log::Info << "Building query tree..." << std::endl;
        std::vector<size_t> oldFromNewQueries;
        typename RAType<tree::KDTree>::Tree queryTree(std::move(querySet),
            oldFromNewQueries, leafSize);
        Log::Info << "Tree built." << std::endl;
        Timer::Stop("tree_building");

        arma::Mat<size_t> neighborsOut;
        arma::mat distancesOut;
        kdTreeRA->Search(&queryTree, k, neighborsOut, distancesOut);

        // Unmap the query points.
        distances.set_size(distancesOut.n_rows, distancesOut.n_cols);
        neighbors.set_size(neighborsOut.n_rows, neighborsOut.n_cols);
        for (size_t i = 0; i < neighborsOut.n_cols; ++i)
        {
          neighbors.col(oldFromNewQueries[i]) = neighborsOut.col(i);
          distances.col(oldFromNewQueries[i]) = distancesOut.col(i);
        }
      }
      else
      {
        // Search without building a second tree.
        kdTreeRA->Search(querySet, k, neighbors, distances);
      }
      break;
    case COVER_TREE:
      // No mapping necessary.
      coverTreeRA->Search(querySet, k, neighbors, distances);
      break;
    case R_TREE:
      // No mapping necessary.
      rTreeRA->Search(querySet, k, neighbors, distances);
      break;
    case R_STAR_TREE:
      // No mapping necessary.
      rStarTreeRA->Search(querySet, k, neighbors, distances);
      break;
    case X_TREE:
      // No mapping necessary.
      xTreeRA->Search(querySet, k, neighbors, distances);
      break;
  }
}

void CF<FactorizerType>::GetRecommendations(const size_t numRecs,
                                            arma::Mat<size_t>& recommendations,
                                            arma::Col<size_t>& users)
{
  // Generate new table by multiplying approximate values.
  rating = w * h;

  // Now, we will use the decomposed w and h matrices to estimate what the user
  // would have rated items as, and then pick the best items.

  // Temporarily store feature vector of queried users.
  arma::mat query(rating.n_rows, users.n_elem);

  // Select feature vectors of queried users.
  for (size_t i = 0; i < users.n_elem; i++)
    query.col(i) = rating.col(users(i));

  // Temporary storage for neighborhood of the queried users.
  arma::Mat<size_t> neighborhood;

  // Calculate the neighborhood of the queried users.
  // This should be a templatized option.
  neighbor::AllkNN a(rating);
  arma::mat resultingDistances; // Temporary storage.
  a.Search(query, numUsersForSimilarity, neighborhood, resultingDistances);

  // Temporary storage for storing the average rating for each user in their
  // neighborhood.
  arma::mat averages = arma::zeros<arma::mat>(rating.n_rows, query.n_cols);

  // Iterate over each query user.
  for (size_t i = 0; i < neighborhood.n_cols; ++i)
  {
    // Iterate over each neighbor of the query user.
    for (size_t j = 0; j < neighborhood.n_rows; ++j)
      averages.col(i) += rating.col(neighborhood(j, i));
    // Normalize average.
    averages.col(i) /= neighborhood.n_rows;
  }

  // Generate recommendations for each query user by finding the maximum numRecs
  // elements in the averages matrix.
  recommendations.set_size(numRecs, users.n_elem);
  recommendations.fill(cleanedData.n_rows); // Invalid item number.
  arma::mat values(numRecs, users.n_elem);
  values.fill(-DBL_MAX); // The smallest possible value.
  for (size_t i = 0; i < users.n_elem; i++)
  {
    // Look through the averages column corresponding to the current user.
    for (size_t j = 0; j < averages.n_rows; ++j)
    {
      // Ensure that the user hasn't already rated the item.
      if (cleanedData(j, users(i)) != 0.0)
        continue; // The user already rated the item.

      // Is the estimated value better than the worst candidate?
      const double value = averages(j, i);
      if (value > values(values.n_rows - 1, i))
      {
        // It should be inserted.  Which position?
        size_t insertPosition = values.n_rows - 1;
        while (insertPosition > 0)
        {
          if (value <= values(insertPosition - 1, i))
            break; // The current value is the right one.
          insertPosition--;
        }

        // Now insert it into the list.
        InsertNeighbor(i, insertPosition, j, value, recommendations,
            values);
      }
    }

    // If we were not able to come up with enough recommendations, issue a
    // warning.
    if (recommendations(values.n_rows - 1, i) == cleanedData.n_rows + 1)
      Log::Warn << "Could not provide " << values.n_rows << " recommendations "
          << "for user " << users(i) << " (not enough un-rated items)!"
          << std::endl;
  }
}

//.........这里部分代码省略.........
  else if (info.Dimensionality() != dimensionality)
  {
    std::ostringstream oss;
    oss << "data::LoadARFF(): given DatasetInfo has dimensionality "
        << info.Dimensionality() << ", but data has dimensionality "
        << dimensionality;
    throw std::invalid_argument(oss.str());
  }

  for (size_t i = 0; i < types.size(); ++i)
  {
    if (types[i])
      info.Type(i) = Datatype::categorical;
    else
      info.Type(i) = Datatype::numeric;
  }

  // We need to find out how many lines of data are in the file.
  std::streampos pos = ifs.tellg();
  size_t row = 0;
  while (ifs.good())
  {
    std::getline(ifs, line, '\n');
    ++row;
  }
  // Uncount the EOF row.
  --row;

  // Since we've hit the EOF, we have to call clear() so we can seek again.
  ifs.clear();
  ifs.seekg(pos);

  // Now, set the size of the matrix.
  matrix.set_size(dimensionality, row);

  // Now we are looking at the @data section.
  row = 0;
  while (ifs.good())
  {
    std::getline(ifs, line, '\n');
    boost::trim(line);
    // Each line of the @data section must be a CSV (except sparse data, which
    // we will handle later).  So now we can tokenize the
    // CSV and parse it.  The '?' representing a missing value is not allowed,
    // so if that occurs we throw an exception.  We also throw an exception if
    // any piece of data does not match its type (categorical or numeric).

    // If the first character is {, it is sparse data, and we can just say this
    // is not handled for now...
    if (line[0] == '{')
      throw std::runtime_error("cannot yet parse sparse ARFF data");

    // Tokenize the line.
    typedef boost::tokenizer<boost::escaped_list_separator<char>> Tokenizer;
    boost::escaped_list_separator<char> sep("\\", ",", "\"");
    Tokenizer tok(line, sep);

    size_t col = 0;
    std::stringstream token;
    for (Tokenizer::iterator it = tok.begin(); it != tok.end(); ++it)
    {
      // Check that we are not too many columns in.
      if (col >= matrix.n_rows)
      {
        std::stringstream error;
        error << "Too many columns in line " << (headerLines + row) << ".";

void CF::GetRecommendations(const size_t numRecs,
                            arma::Mat<size_t>& recommendations,
                            arma::Col<size_t>& users)
{
  // We want to avoid calculating the full rating matrix, so we will do nearest
  // neighbor search only on the H matrix, using the observation that if the
  // rating matrix X = W*H, then d(X.col(i), X.col(j)) = d(W H.col(i), W
  // H.col(j)).  This can be seen as nearest neighbor search on the H matrix
  // with the Mahalanobis distance where M^{-1} = W^T W.  So, we'll decompose
  // M^{-1} = L L^T (the Cholesky decomposition), and then multiply H by L^T.
  // Then we can perform nearest neighbor search.
  arma::mat l = arma::chol(w.t() * w);
  arma::mat stretchedH = l * h; // Due to the Armadillo API, l is L^T.

  // Now, we will use the decomposed w and h matrices to estimate what the user
  // would have rated items as, and then pick the best items.

  // Temporarily store feature vector of queried users.
  arma::mat query(stretchedH.n_rows, users.n_elem);

  // Select feature vectors of queried users.
  for (size_t i = 0; i < users.n_elem; i++)
    query.col(i) = stretchedH.col(users(i));

  // Temporary storage for neighborhood of the queried users.
  arma::Mat<size_t> neighborhood;

  // Calculate the neighborhood of the queried users.
  // This should be a templatized option.
  neighbor::KNN a(stretchedH);
  arma::mat resultingDistances; // Temporary storage.
  a.Search(query, numUsersForSimilarity, neighborhood, resultingDistances);

  // Generate recommendations for each query user by finding the maximum numRecs
  // elements in the averages matrix.
  recommendations.set_size(numRecs, users.n_elem);
  recommendations.fill(cleanedData.n_rows); // Invalid item number.
  arma::mat values(numRecs, users.n_elem);
  values.fill(-DBL_MAX); // The smallest possible value.
  for (size_t i = 0; i < users.n_elem; i++)
  {
    // First, calculate average of neighborhood values.
    arma::vec averages;
    averages.zeros(cleanedData.n_rows);

    for (size_t j = 0; j < neighborhood.n_rows; ++j)
      averages += w * h.col(neighborhood(j, i));
    averages /= neighborhood.n_rows;

    // Look through the averages column corresponding to the current user.
    for (size_t j = 0; j < averages.n_rows; ++j)
    {
      // Ensure that the user hasn't already rated the item.
      if (cleanedData(j, users(i)) != 0.0)
        continue; // The user already rated the item.

      // Is the estimated value better than the worst candidate?
      const double value = averages[j];
      if (value > values(values.n_rows - 1, i))
      {
        // It should be inserted.  Which position?
        size_t insertPosition = values.n_rows - 1;
        while (insertPosition > 0)
        {
          if (value <= values(insertPosition - 1, i))
            break; // The current value is the right one.
          insertPosition--;
        }

        // Now insert it into the list.
        InsertNeighbor(i, insertPosition, j, value, recommendations,
            values);
      }
    }

    // If we were not able to come up with enough recommendations, issue a
    // warning.
    if (recommendations(values.n_rows - 1, i) == cleanedData.n_rows + 1)
      Log::Warn << "Could not provide " << values.n_rows << " recommendations "
          << "for user " << users(i) << " (not enough un-rated items)!"
          << std::endl;
  }
}

bool Load(const std::string& filename,
          arma::Mat<eT>& matrix,
          DatasetInfo& info,
          const bool fatal,
          const bool transpose)
{
  // Get the extension and load as necessary.
  Timer::Start("loading_data");

  // Get the extension.
  std::string extension = Extension(filename);

  // Catch nonexistent files by opening the stream ourselves.
  std::fstream stream;
  stream.open(filename.c_str(), std::fstream::in);

  if (!stream.is_open())
  {
    Timer::Stop("loading_data");
    if (fatal)
      Log::Fatal << "Cannot open file '" << filename << "'. " << std::endl;
    else
      Log::Warn << "Cannot open file '" << filename << "'; load failed."
          << std::endl;

    return false;
  }

  if (extension == "csv" || extension == "tsv" || extension == "txt")
  {
    // True if we're looking for commas; if false, we're looking for spaces.
    bool commas = (extension == "csv");

    std::string type;
    if (extension == "csv")
      type = "CSV data";
    else
      type = "raw ASCII-formatted data";

    Log::Info << "Loading '" << filename << "' as " << type << ".  "
        << std::flush;
    std::string separators;
    if (commas)
      separators = ",";
    else
      separators = " \t";

    // We'll load this as CSV (or CSV with spaces or tabs) according to
    // RFC4180.  So the first thing to do is determine the size of the matrix.
    std::string buffer;
    size_t cols = 0;

    std::getline(stream, buffer, '\n');
    // Count commas and whitespace in the line, ignoring anything inside
    // quotes.
    typedef boost::tokenizer<boost::escaped_list_separator<char>> Tokenizer;
    boost::escaped_list_separator<char> sep("\\", separators, "\"");
    Tokenizer tok(buffer, sep);
    for (Tokenizer::iterator i = tok.begin(); i != tok.end(); ++i)
      ++cols;

    // Now count the number of lines in the file.  We've already counted the
    // first one.
    size_t rows = 1;
    while (!stream.eof() && !stream.bad() && !stream.fail())
    {
      std::getline(stream, buffer, '\n');
      if (!stream.fail())
        ++rows;
    }

    // Now we have the size.  So resize our matrix.
    if (transpose)
    {
      matrix.set_size(cols, rows);
      info = DatasetInfo(cols);
    }
    else
    {
      matrix.set_size(rows, cols);
      info = DatasetInfo(rows);
    }

    stream.close();
    stream.open(filename, std::fstream::in);

    // Extract line by line.
    std::stringstream token;
    size_t row = 0;
    while (!stream.bad() && !stream.fail() && !stream.eof())
    {
      std::getline(stream, buffer, '\n');

      // Look at each token.  Unfortunately we have to do this character by
      // character, because things may be escaped in quotes.
      Tokenizer lineTok(buffer, sep);
      size_t col = 0;
      for (Tokenizer::iterator it = lineTok.begin(); it != lineTok.end(); ++it)
      {
        // Attempt to extract as type eT.  If that fails, we'll assume it's a
//.........这里部分代码省略.........