C++ Ref::rows方法代码示例

void FunctionApproximatorGPR::train(const Eigen::Ref<const Eigen::MatrixXd>& inputs, const Eigen::Ref<const Eigen::MatrixXd>& targets)
{
  if (isTrained())  
  {
    cerr << "WARNING: You may not call FunctionApproximatorGPR::train more than once. Doing nothing." << endl;
    cerr << "   (if you really want to retrain, call reTrain function instead)" << endl;
    return;
  }
  
  assert(inputs.rows() == targets.rows());
  assert(inputs.cols()==getExpectedInputDim());

  const MetaParametersGPR* meta_parameters_gpr = 
    dynamic_cast<const MetaParametersGPR*>(getMetaParameters());
  
  double max_covar = meta_parameters_gpr->maximum_covariance();
  VectorXd sigmas = meta_parameters_gpr->sigmas();
  
  
  // Compute the gram matrix
  // In a gram matrix, every input point is itself a center
  MatrixXd centers = inputs;
  // Replicate sigmas, because they are the same for each data point/center
  MatrixXd widths = sigmas.transpose().colwise().replicate(centers.rows()); 

  MatrixXd gram(inputs.rows(),inputs.rows());
  bool normalize_activations = false;
  bool asymmetric_kernels = false;
  BasisFunction::Gaussian::activations(centers,widths,inputs,gram,normalize_activations,asymmetric_kernels);
  
  gram *= max_covar;

  setModelParameters(new ModelParametersGPR(inputs,targets,gram,max_covar,sigmas));
  
}

// create initial points distribution
Eigen::ArrayXXd distmesh::utils::createInitialPoints(
    Functional const& distanceFunction, double const initialPointDistance,
    Functional const& elementSizeFunction, Eigen::Ref<Eigen::ArrayXXd const> const boundingBox,
    Eigen::Ref<Eigen::ArrayXXd const> const fixedPoints) {
    // extract dimension of mesh
    unsigned const dimension = boundingBox.cols();

    // initially distribute points evenly in complete bounding box
    Eigen::ArrayXi pointsPerDimension(dimension);
    for (int dim = 0; dim < dimension; ++dim) {
        pointsPerDimension(dim) = ceil((boundingBox(1, dim) - boundingBox(0, dim)) /
            (initialPointDistance * (dim == 0 ? 1.0 : sqrt(3.0) / 2.0)));
    }

    Eigen::ArrayXXd points(pointsPerDimension.prod(), dimension);
    for (int point = 0; point < points.rows(); ++point)
    for (int dim = 0; dim < dimension; ++dim) {
        int const pointIndex = (point / std::max(pointsPerDimension.topRows(dim).prod(), 1)) %
            pointsPerDimension(dim);

        points(point, dim) = boundingBox(0, dim) + (double)pointIndex * initialPointDistance *
            (dim == 0 ? 1.0 : sqrt(3.0) / 2.0);

        if (dim > 0) {
            points(point, dim - 1) += pointIndex % 2 != 0 ? initialPointDistance / 2.0 : 0.0;
        }
    }

    // reject points outside of region defined by distance function
    points = selectMaskedArrayElements<double>(points,
        distanceFunction(points) < constants::geometryEvaluationThreshold * initialPointDistance);

    // clear duplicate points
    Eigen::Array<bool, Eigen::Dynamic, 1> isUniquePoint =
        Eigen::Array<bool, Eigen::Dynamic, 1>::Constant(points.rows(), true);
    for (int i = 0; i < fixedPoints.rows(); ++i)
    for (int j = 0; j < points.rows(); ++j) {
        isUniquePoint(j) &= !(fixedPoints.row(i) == points.row(j)).all();
    }
    points = selectMaskedArrayElements<double>(points, isUniquePoint);

    // calculate probability to keep points
    Eigen::ArrayXd probability = 1.0 / elementSizeFunction(points).pow(dimension);
    probability /= probability.maxCoeff();

    // reject points with wrong probability
    points = selectMaskedArrayElements<double>(points,
        0.5 * (1.0 + Eigen::ArrayXd::Random(points.rows())) < probability);

    // combine fixed and variable points to one array
    Eigen::ArrayXXd finalPoints(points.rows() + fixedPoints.rows(), dimension);
    finalPoints << fixedPoints, points;

    return finalPoints;
}

bool mrpt::vision::pnp::CPnP::so3(const Eigen::Ref<Eigen::MatrixXd> obj_pts, const Eigen::Ref<Eigen::MatrixXd> img_pts, int n, const Eigen::Ref<Eigen::MatrixXd> cam_intrinsic, Eigen::Ref<Eigen::MatrixXd> pose_mat)
{
    try{
        // Input 2d/3d correspondences and camera intrinsic matrix
        Eigen::MatrixXd cam_in_eig,img_pts_eig, obj_pts_eig;

        // Check for consistency of input matrix dimensions
        if (img_pts.rows() != obj_pts.rows() || img_pts.cols() !=obj_pts.cols())
            throw(2);
        else if (cam_intrinsic.rows()!=3 || cam_intrinsic.cols()!=3)
            throw(3);

        if(obj_pts.rows() < obj_pts.cols())
        {
            cam_in_eig=cam_intrinsic.transpose();
            img_pts_eig=img_pts.transpose().block(0,0,n,2);
            obj_pts_eig=obj_pts.transpose();
        }
        else
        {
            cam_in_eig=cam_intrinsic;
            img_pts_eig=img_pts.block(0,0,n,2);
            obj_pts_eig=obj_pts;
        }

        // Output pose
        Eigen::Matrix3d R;
        Eigen::Vector3d t;

        // Compute pose
        mrpt::vision::pnp::p3p p(cam_in_eig);
        p.solve(R,t, obj_pts_eig, img_pts_eig);

        mrpt::vision::pnp::so3 s(obj_pts_eig, img_pts_eig, cam_in_eig, n);
        bool ret = s.compute_pose(R,t);

        Eigen::Quaterniond q(R);

        pose_mat<<t,q.vec();

        return ret;
    }
    catch(int e)
    {
        switch(e)
        {
            case  2: std::cout << "2d/3d correspondences mismatch\n Check dimension of obj_pts and img_pts" << std::endl;
            case  3: std::cout << "Camera intrinsic matrix does not have 3x3 dimensions " << std::endl;
        }
        return false;
    }
}

// check whether points lies inside or outside of polygon
Eigen::ArrayXd distmesh::utils::pointsInsidePoly(
    Eigen::Ref<Eigen::ArrayXXd const> const points,
    Eigen::Ref<Eigen::ArrayXXd const> const polygon) {
    Eigen::ArrayXd inside = Eigen::ArrayXd::Zero(points.rows());

    for (int i = 0, j = polygon.rows() - 1; i < polygon.rows(); j = i++) {
        inside = (((points.col(1) < polygon(i, 1)) != (points.col(1) < polygon(j, 1))) &&
            (points.col(0) < (polygon(j, 0) - polygon(i, 0)) * (points.col(1) - polygon(i, 1)) /
            (polygon(j, 1) - polygon(i, 1)) + polygon(i, 0))).select(1.0 - inside, inside);
    }

    return inside;
}

// determine boundary edges of given triangulation
Eigen::ArrayXi distmesh::utils::boundEdges(
    Eigen::Ref<Eigen::ArrayXXi const> const triangulation,
    Eigen::Ref<Eigen::ArrayXXi const> const _edges,
    Eigen::Ref<Eigen::ArrayXXi const> const _edgeIndices) {
    // create a new edge list, if none was given
    Eigen::ArrayXXi edges;
    if (_edges.rows() == 0) {
        edges = utils::findUniqueEdges(triangulation);
    }
    else {
        edges = _edges;
    }

    // get edge indices for each triangle in triangulation
    Eigen::ArrayXXi edgeIndices;
    if (_edgeIndices.rows() == 0) {
        edgeIndices = utils::getTriangulationEdgeIndices(triangulation, edges);
    }
    else {
        edgeIndices = _edgeIndices;
    }

    // find edges, which only appear once in triangulation
    std::set<int> uniqueEdges;
    std::vector<int> boundaryEdges;
    for (int triangle = 0; triangle < triangulation.rows(); ++triangle)
    for (int edge = 0; edge < triangulation.cols(); ++edge) {
        auto const edgeIndex = edgeIndices(triangle, edge);

        // insert edge in set to get info about multiple appearance
        if (!std::get<1>(uniqueEdges.insert(edgeIndex))) {
            // find edge in vector and delete it
            auto const it = std::find(boundaryEdges.begin(), boundaryEdges.end(), edgeIndex);
            if (it != boundaryEdges.end()) {
                boundaryEdges.erase(it);
            }
        }
        else {
            boundaryEdges.push_back(edgeIndex);
        }
    }

    // convert stl vector to eigen array
    Eigen::ArrayXi boundary(boundaryEdges.size());
    for (int edge = 0; edge < boundary.rows(); ++edge) {
        boundary(edge) = boundaryEdges[edge];
    }
    
    return boundary;
}

void FunctionApproximatorGPR::predictVariance(const Eigen::Ref<const Eigen::MatrixXd>& inputs, MatrixXd& variances)
{
  if (!isTrained())  
  {
    cerr << "WARNING: You may not call FunctionApproximatorLWPR::predict if you have not trained yet. Doing nothing." << endl;
    return;
  }

  const ModelParametersGPR* model_parameters_gpr = static_cast<const ModelParametersGPR*>(getModelParameters());
  
  
  assert(inputs.cols()==getExpectedInputDim());
  
  unsigned int n_samples = inputs.rows();
  variances.resize(n_samples,1);
  
  MatrixXd ks;
  model_parameters_gpr->kernelActivations(inputs, ks);  

  double maximum_covariance = model_parameters_gpr->maximum_covariance();
  MatrixXd gram_inv = model_parameters_gpr->gram_inv();
  
  for (unsigned int ii=0; ii<n_samples; ii++)
    variances(ii) = maximum_covariance - (ks.row(ii)*gram_inv).dot(ks.row(ii).transpose());

}

Eigen::ArrayXXi distmesh::utils::findUniqueEdges(Eigen::Ref<Eigen::ArrayXXi const> const triangulation) {
    // find all unique combinations
    auto const combinations = nOverK(triangulation.cols(), 2);

    // find unique edges for all combinations
    // guarantee direction of edges with lower node index to higher index
    std::set<std::array<int, 2>> uniqueEdges;
    std::array<int, 2> edge = {{0, 0}};
    for (int combination = 0; combination < combinations.rows(); ++combination)
    for (int triangle = 0; triangle < triangulation.rows(); ++triangle) {
        edge[0] = triangulation(triangle, combinations(combination, 0));
        edge[1] = triangulation(triangle, combinations(combination, 1));

        edge = edge[1] < edge[0] ? std::array<int, 2>{edge[1], edge[0]} : edge;

        uniqueEdges.insert(edge);
    }

    // copy set to eigen array
    Eigen::ArrayXXi edgeIndices(uniqueEdges.size(), 2);
    int index = 0;
    for (auto const& edge : uniqueEdges) {
        edgeIndices(index, 0) = edge[0];
        edgeIndices(index, 1) = edge[1];

        index++;
    }

    return edgeIndices;
}

void FunctionApproximatorGMR::predictVariance(const Eigen::Ref<const Eigen::MatrixXd>& inputs, Eigen::MatrixXd& variances)
{
  ENTERING_REAL_TIME_CRITICAL_CODE
  variances.resize(inputs.rows(),getExpectedOutputDim());
  predict(inputs,empty_prealloc_,variances);
  EXITING_REAL_TIME_CRITICAL_CODE
}

double Nullspace
(
  const Eigen::Ref<const Mat> & A,
  Eigen::Ref<Vec> nullspace
)
{
  if ( A.rows() >= A.cols() )
  {
    Eigen::JacobiSVD<Mat> svd( A, Eigen::ComputeFullV );
    nullspace = svd.matrixV().col( A.cols() - 1 );
    return svd.singularValues()( A.cols() - 1 );
  }
  // Extend A with rows of zeros to make it square. It's a hack, but it is
  // necessary until Eigen supports SVD with more columns than rows.
  Mat A_extended( A.cols(), A.cols() );
  A_extended.block( A.rows(), 0, A.cols() - A.rows(), A.cols() ).setZero();
  A_extended.block( 0, 0, A.rows(), A.cols() ) = A;
  return Nullspace( A_extended, nullspace );
}

void softmax<T>::train(const Eigen::Ref<const EigenMat> &train,
                       const std::vector<int> &labels)
{
#ifdef OCV_TEST_SOFTMAX
    gradient_check();
#endif

    auto const UniqueLabels = get_unique_labels(labels);
    auto const NumClass = UniqueLabels.size();
    weight_ = EigenMat::Random(NumClass, train.rows());
    grad_ = EigenMat::Zero(NumClass, train.rows());
    auto const TrainCols = static_cast<int>(train.cols());
    EigenMat const GroundTruth = get_ground_truth(static_cast<int>(NumClass),
                                                  TrainCols,
                                                  UniqueLabels,
                                                  labels);

    std::random_device rd;
    std::default_random_engine re(rd());
    int const Batch = (get_batch_size(TrainCols));
    int const RandomSize = TrainCols != Batch ?
                TrainCols - Batch - 1 : 0;
    std::uniform_int_distribution<int>
            uni_int(0, RandomSize);
    for(size_t i = 0; i != params_.max_iter_; ++i){
        auto const Cols = uni_int(re);
        auto const &TrainBlock =
                train.block(0, Cols, train.rows(), Batch);
        auto const &GTBlock =
                GroundTruth.block(0, Cols, NumClass, Batch);
        auto const Cost = compute_cost(TrainBlock, weight_, GTBlock);
        if(std::abs(params_.cost_ - Cost) < params_.epsillon_ ||
                Cost < 0){
            break;
        }
        params_.cost_ = Cost;
        compute_gradient(TrainBlock, weight_, GTBlock);
        weight_.array() -= grad_.array() * params_.lrate_;//*/
    }
}

//**************************************************************************************************
Eigen::MatrixRXd wholeBodyReach::pinvDampedEigen(const Eigen::Ref<Eigen::MatrixRXd> &A, double damp)
{
    // allocate memory
    int m = A.rows(), n = A.cols(), k = m<n?m:n;
    VectorXd SpinvD = VectorXd::Zero(k);
    // compute decomposition
    JacobiSVD<MatrixRXd> svd(A, ComputeThinU | ComputeThinV);    // default Eigen SVD
    VectorXd sv = svd.singularValues();
    // compute pseudoinverse of singular value matrix
    double damp2 = damp*damp;
    for (int c=0;c<k; c++)
        SpinvD(c) = sv(c) / (sv(c)*sv(c) + damp2);
    // compute damped pseudoinverse
    return svd.matrixV() * SpinvD.asDiagonal() * svd.matrixU().transpose();
}

// project points outside of domain back to boundary
void distmesh::utils::projectPointsToBoundary(
    Functional const& distanceFunction, double const initialPointDistance,
    Eigen::Ref<Eigen::ArrayXXd> points) {
    Eigen::ArrayXd distance = distanceFunction(points);

    // check for points outside of boundary
    Eigen::Array<bool, Eigen::Dynamic, 1> outside = distance > 0.0;
    if (outside.any()) {
        // calculate gradient
        Eigen::ArrayXXd gradient(points.rows(), points.cols());
        Eigen::ArrayXXd deltaX = Eigen::ArrayXXd::Zero(points.rows(), points.cols());

        for (int dim = 0; dim < points.cols(); ++dim) {
            deltaX.col(dim).fill(constants::deltaX * initialPointDistance);
            gradient.col(dim) = (distanceFunction(points + deltaX) - distance) /
                (constants::deltaX * initialPointDistance);
            deltaX.col(dim).fill(0.0);
        }

        // project points back to boundary
        points -= outside.replicate(1, points.cols()).select(
            gradient.colwise() * distance / gradient.square().rowwise().sum(), 0.0);
    }
}

//**************************************************************************************************
Eigen::MatrixRXd wholeBodyReach::nullSpaceProjector(const Eigen::Ref<MatrixRXd> A, double tol)
{
    // allocate memory
    int m = A.rows(), n = A.cols(), k = m<n?m:n;
    MatrixRXd Spinv = MatrixRXd::Zero(k,k);
    // compute decomposition
    JacobiSVD<MatrixRXd> svd(A, ComputeThinU | ComputeThinV);    // default Eigen SVD
    VectorXd sv = svd.singularValues();
    // compute pseudoinverse of singular value matrix
    for (int c=0;c<k; c++)
        if ( sv(c)> tol)
            Spinv(c,c) = 1/sv(c);
    // compute pseudoinverse
    MatrixRXd N = MatrixRXd::Identity(n,n);
    N -= svd.matrixV() * Spinv  * svd.matrixU().transpose() * A;
    return N;
}

void FunctionApproximatorGPR::predict(const Eigen::Ref<const Eigen::MatrixXd>& inputs, MatrixXd& outputs)
{
  if (!isTrained())  
  {
    cerr << "WARNING: You may not call FunctionApproximatorLWPR::predict if you have not trained yet. Doing nothing." << endl;
    return;
  }

  const ModelParametersGPR* model_parameters_gpr = static_cast<const ModelParametersGPR*>(getModelParameters());
  
  assert(inputs.cols()==getExpectedInputDim());
  unsigned int n_samples = inputs.rows();
  
  outputs.resize(n_samples,1);
  
  MatrixXd ks(n_samples,n_samples);
  model_parameters_gpr->kernelActivations(inputs, ks);
  
  
  VectorXd weights = model_parameters_gpr->weights();
  for (unsigned int ii=0; ii<n_samples; ii++)
    outputs(ii) = ks.row(ii).dot(weights);
  
}

void FunctionApproximatorGMR::train(const Eigen::Ref<const Eigen::MatrixXd>& inputs, const Eigen::Ref<const Eigen::MatrixXd>& targets)
{
  if (isTrained())  
  {
    cerr << "WARNING: You may not call FunctionApproximatorGMR::train more than once. Doing nothing." << endl;
    cerr << "   (if you really want to retrain, call reTrain function instead)" << endl;
    return;
  }
  
  assert(inputs.rows() == targets.rows()); // Must have same number of examples
  assert(inputs.cols() == getExpectedInputDim());

  const MetaParametersGMR* meta_parameters_GMR = 
    static_cast<const MetaParametersGMR*>(getMetaParameters());

  const ModelParametersGMR* model_parameters_GMR =
    static_cast<const ModelParametersGMR*>(getModelParameters());

  int n_gaussians;
  if(meta_parameters_GMR!=NULL)
      n_gaussians = meta_parameters_GMR->number_of_gaussians_;
  else if(model_parameters_GMR!=NULL)
      n_gaussians = model_parameters_GMR->priors_.size();
  else
      cerr << "FunctionApproximatorGMR::train Something wrong happened, both ModelParameters and MetaParameters are not initialized." << endl;

  int n_dims_in = inputs.cols();
  int n_dims_out = targets.cols();
  int n_dims_gmm = n_dims_in + n_dims_out;
  
  // Initialize the means, priors and covars
  std::vector<VectorXd> means(n_gaussians);
  std::vector<MatrixXd> covars(n_gaussians);
  std::vector<double> priors(n_gaussians);
  int n_observations = 0;

  for (int i = 0; i < n_gaussians; i++)
  {
    means[i] = VectorXd(n_dims_gmm);
    priors[i] = 0.0;
    covars[i] = MatrixXd(n_dims_gmm, n_dims_gmm);
  }
  
  // Put the input/output data in one big matrix
  MatrixXd data = MatrixXd(inputs.rows(), n_dims_gmm);
  data << inputs, targets;
  n_observations = data.rows();

  // Initialization
  if (inputs.cols() == 1)
    firstDimSlicingInit(data, means, priors, covars);
  else
    kMeansInit(data, means, priors, covars);
  
  // Expectation-Maximization
  expectationMaximization(data, means, priors, covars);

  // Extract the different input/output components from the means/covars which contain both
  std::vector<Eigen::VectorXd> means_x(n_gaussians);
  std::vector<Eigen::VectorXd> means_y(n_gaussians);
  std::vector<Eigen::MatrixXd> covars_x(n_gaussians);
  std::vector<Eigen::MatrixXd> covars_y(n_gaussians);
  std::vector<Eigen::MatrixXd> covars_y_x(n_gaussians);
  for (int i_gau = 0; i_gau < n_gaussians; i_gau++)
  {
    means_x[i_gau]    = means[i_gau].segment(0, n_dims_in);
    means_y[i_gau]    = means[i_gau].segment(n_dims_in, n_dims_out);

    covars_x[i_gau]   = covars[i_gau].block(0, 0, n_dims_in, n_dims_in);
    covars_y[i_gau]   = covars[i_gau].block(n_dims_in, n_dims_in, n_dims_out, n_dims_out);
    covars_y_x[i_gau] = covars[i_gau].block(n_dims_in, 0, n_dims_out, n_dims_in);
  }

  setModelParameters(new ModelParametersGMR(n_observations, priors, means_x, means_y, covars_x, covars_y, covars_y_x));

  // After training, we know the sizes of the matrices that should be cached
  preallocateMatrices(n_gaussians,n_dims_in,n_dims_out);
  
  // std::vector<VectorXd> centers;
  // std::vector<MatrixXd> slopes;
  // std::vector<VectorXd> biases;
  // std::vector<MatrixXd> inverseCovarsL;

  // // int n_dims_in = inputs.cols();
  // // int n_dims_out = targets.cols();

  // for (int i_gau = 0; i_gau < n_gaussians; i_gau++)
  // {
  //   centers.push_back(VectorXd(means[i_gau].segment(0, n_dims_in)));

  //   slopes.push_back(MatrixXd(covars[i_gau].block(n_dims_in, 0, n_dims_out, n_dims_in) * covars[i_gau].block(0, 0, n_dims_in, n_dims_in).inverse()));
    
  //   biases.push_back(VectorXd(means[i_gau].segment(n_dims_in, n_dims_out) -
  //     slopes[i_gau]*means[i_gau].segment(0, n_dims_in)));

  //   MatrixXd L = covars[i_gau].block(0, 0, n_dims_in, n_dims_in).inverse().llt().matrixL();
  //   inverseCovarsL.push_back(MatrixXd(L));
  // }

  // setModelParameters(new ModelParametersGMR(centers, priors, slopes, biases, inverseCovarsL));
//.........这里部分代码省略.........