void BSpline::updateControlPoints(const DenseMatrix &A)
{
if (A.cols() != coefficients.rows() || A.cols() != knotaverages.rows())
throw Exception("BSpline::updateControlPoints: Incompatible size of linear transformation matrix.");
coefficients = A*coefficients;
knotaverages = A*knotaverages;
}
GPUDenseMatrixOperation(const DenseMatrix& matrix)
{
mat = viennacl::matrix<ScalarType>(matrix.cols(),matrix.rows());
vec = viennacl::matrix<ScalarType>(matrix.cols(),1);
res = viennacl::matrix<ScalarType>(matrix.cols(),1);
viennacl::copy(matrix,mat);
}
GPUDenseImplicitSquareMatrixOperation(const DenseMatrix& matrix)
{
timed_context c("Storing matrices");
mat = viennacl::matrix<ScalarType>(matrix.cols(),matrix.rows());
vec = viennacl::matrix<ScalarType>(matrix.cols(),1);
res = viennacl::matrix<ScalarType>(matrix.cols(),1);
viennacl::copy(matrix,mat);
}
EmbeddingResult embed(const MatrixType& wm, IndexType target_dimension, unsigned int skip)
{
timed_context context("Randomized eigendecomposition");
DenseMatrix O(wm.rows(), target_dimension+skip);
for (IndexType i=0; i<O.rows(); ++i)
{
IndexType j=0;
for ( ; j+1 < O.cols(); j+= 2)
{
ScalarType v1 = (ScalarType)(rand()+1.f)/((float)RAND_MAX+2.f);
ScalarType v2 = (ScalarType)(rand()+1.f)/((float)RAND_MAX+2.f);
ScalarType len = sqrt(-2.f*log(v1));
O(i,j) = len*cos(2.f*M_PI*v2);
O(i,j+1) = len*sin(2.f*M_PI*v2);
}
for ( ; j < O.cols(); j++)
{
ScalarType v1 = (ScalarType)(rand()+1.f)/((float)RAND_MAX+2.f);
ScalarType v2 = (ScalarType)(rand()+1.f)/((float)RAND_MAX+2.f);
ScalarType len = sqrt(-2.f*log(v1));
O(i,j) = len*cos(2.f*M_PI*v2);
}
}
MatrixTypeOperation operation(wm);
DenseMatrix Y = operation(O);
for (IndexType i=0; i<Y.cols(); i++)
{
for (IndexType j=0; j<i; j++)
{
ScalarType r = Y.col(i).dot(Y.col(j));
Y.col(i) -= r*Y.col(j);
}
ScalarType norm = Y.col(i).norm();
if (norm < 1e-4)
{
for (int k = i; k<Y.cols(); k++)
Y.col(k).setZero();
}
Y.col(i) *= (1.f / norm);
}
DenseMatrix B1 = operation(Y);
DenseMatrix B = Y.householderQr().solve(B1);
DenseSelfAdjointEigenSolver eigenOfB(B);
if (eigenOfB.info() == Eigen::Success)
{
DenseMatrix embedding = (Y*eigenOfB.eigenvectors()).block(0, skip, wm.cols(), target_dimension);
return EmbeddingResult(embedding,eigenOfB.eigenvalues());
}
else
{
throw eigendecomposition_error("eigendecomposition failed");
}
return EmbeddingResult();
}
TapkeeOutput embedUsing(const DenseMatrix& matrix) const
{
vector<IndexType> indices(matrix.cols());
for (IndexType i=0; i<matrix.cols(); i++) indices[i] = i;
eigen_kernel_callback kcb(matrix);
eigen_distance_callback dcb(matrix);
eigen_features_callback fvcb(matrix);
return tapkee::embed(indices.begin(),indices.end(),kcb,dcb,fvcb,parameters);
}
EigendecompositionResult eigendecomposition_impl_randomized(const MatrixType& wm, IndexType target_dimension, unsigned int skip)
{
timed_context context("Randomized eigendecomposition");
DenseMatrix O(wm.rows(), target_dimension+skip);
for (IndexType i=0; i<O.rows(); ++i)
{
for (IndexType j=0; j<O.cols(); j++)
{
O(i,j) = tapkee::gaussian_random();
}
}
MatrixOperationType operation(wm);
DenseMatrix Y = operation(O);
for (IndexType i=0; i<Y.cols(); i++)
{
for (IndexType j=0; j<i; j++)
{
ScalarType r = Y.col(i).dot(Y.col(j));
Y.col(i) -= r*Y.col(j);
}
ScalarType norm = Y.col(i).norm();
if (norm < 1e-4)
{
for (int k = i; k<Y.cols(); k++)
Y.col(k).setZero();
}
Y.col(i) *= (1.f / norm);
}
DenseMatrix B1 = operation(Y);
DenseMatrix B = Y.householderQr().solve(B1);
DenseSelfAdjointEigenSolver eigenOfB(B);
if (eigenOfB.info() == Eigen::Success)
{
if (MatrixOperationType::largest)
{
assert(skip==0);
DenseMatrix selected_eigenvectors = (Y*eigenOfB.eigenvectors()).rightCols(target_dimension);
return EigendecompositionResult(selected_eigenvectors,eigenOfB.eigenvalues());
}
else
{
DenseMatrix selected_eigenvectors = (Y*eigenOfB.eigenvectors()).leftCols(target_dimension+skip).rightCols(target_dimension);
return EigendecompositionResult(selected_eigenvectors,eigenOfB.eigenvalues());
}
}
else
{
throw eigendecomposition_error("eigendecomposition failed");
}
return EigendecompositionResult();
}
DenseMatrix<FloatType> DenseMatrix<FloatType>::subtract(const DenseMatrix<FloatType> &A, const DenseMatrix<FloatType> &B)
{
MLIB_ASSERT_STR(A.rows() == B.rows() && A.cols() == B.cols(), "invalid matrix dimensions");
const UINT rows = A.m_rows;
const UINT cols = A.m_cols;
DenseMatrix<FloatType> result(A.m_rows, A.m_cols);
for(UINT row = 0; row < rows; row++)
for(UINT col = 0; col < cols; col++)
result.m_dataPtr[row * cols + col] = A.m_dataPtr[row * cols + col] - B.m_dataPtr[row * cols + col];
return result;
}
ScalarType average_neighbor_distance(const DenseMatrix& data, const Neighbors& neighbors)
{
IndexType k = neighbors[0].size();
ScalarType average_distance = 0;
for (IndexType i = 0; i < data.cols(); ++i)
{
for (IndexType j = 0; j < k; ++j)
{
average_distance += (data.col(i) - data.col(neighbors[i][j])).norm();
}
}
return average_distance / (k * data.cols());
}
/*
* Calculate coefficients of B-spline representing a multivariate polynomial
*
* The polynomial f(x), with x in R^n, has m terms on the form
* f(x) = c(0)*x(0)^E(0,0)*x(1)^E(0,1)*...*x(n-1)^E(0,n-1)
* +c(1)*x(0)^E(1,0)*x(1)^E(1,1)*...*x(n-1)^E(1,n-1)
* +...
* +c(m-1)*x(0)^E(m-1,0)*x(1)^E(m-1,1)*...*x(n-1)^E(m-1,n-1)
* where c in R^m is a vector with coefficients for each of the m terms,
* and E in N^(mxn) is a matrix with the exponents of each variable in each of the m terms,
* e.g. the first row of E defines the first term with variable exponents E(0,0) to E(0,n-1).
*
* Note: E must be a matrix of nonnegative integers
*/
DenseMatrix getBSplineBasisCoefficients(DenseVector c, DenseMatrix E, std::vector<double> lb, std::vector<double> ub)
{
unsigned int dim = E.cols();
unsigned int terms = E.rows();
assert(dim >= 1); // At least one variable
assert(terms >= 1); // At least one term (assumes that c is a column vector)
assert(terms == c.rows());
assert(dim == lb.size());
assert(dim == ub.size());
// Get highest power of each variable
DenseVector powers = E.colwise().maxCoeff();
// Store in std vector
std::vector<unsigned int> powers2;
for (unsigned int i = 0; i < powers.size(); ++i)
powers2.push_back(powers(i));
// Calculate tensor product transformation matrix T
DenseMatrix T = getTransformationMatrix(powers2, lb, ub);
// Compute power basis coefficients (lambda vector)
SparseMatrix L(T.cols(),1);
L.setZero();
for (unsigned int i = 0; i < terms; i++)
{
SparseMatrix Li(1,1);
Li.insert(0,0) = 1;
for (unsigned int j = 0; j < dim; j++)
{
int e = E(i,j);
SparseVector li(powers(j)+1);
li.reserve(1);
li.insert(e) = 1;
SparseMatrix temp = Li;
Li = kroneckerProduct(temp, li);
}
L += c(i)*Li;
}
// Compute B-spline coefficients
DenseMatrix C = T*L;
return C;
}
DenseVector ConstraintBSpline::evalJacobian(const DenseVector &x) const
{
DenseVector xa = adjustToDomainBounds(x);
DenseVector dx = DenseVector::Zero(nnzJacobian);
//return centralDifference(xa);
// Get x-values
DenseVector xx = xa.block(0,0,bspline.getNumVariables(),1);
// Evaluate B-spline Jacobian
DenseMatrix jac = bspline.evalJacobian(xx);
// Derivatives on inputs x
int k = 0;
for (int i = 0; i < jac.rows(); i++)
{
for (int j = 0; j < jac.cols(); j++)
{
dx(k++) = jac(i,j);
}
}
// Derivatives on outputs y
for (unsigned int i = 0; i < numConstraints; i++)
{
dx(k++) = -1;
}
return dx;
}
void BSpline::setControlPoints(const DenseMatrix &controlPoints)
{
if (controlPoints.cols() != numVariables + 1)
throw Exception("BSpline::setControlPoints: Incompatible size of control point matrix.");
int nc = controlPoints.rows();
knotaverages = controlPoints.block(0, 0, nc, numVariables);
coefficients = controlPoints.block(0, numVariables, nc, 1);
checkControlPoints();
}
DenseMatrix<FloatType> DenseMatrix<FloatType>::multiply(const DenseMatrix<FloatType> &A, const DenseMatrix<FloatType> &B)
{
MLIB_ASSERT_STR(A.cols() == B.rows(), "invalid dimensions");
const UINT rows = A.rows();
const UINT cols = B.cols();
const UINT innerCount = A.cols();
DenseMatrix<FloatType> result(rows, cols);
for(UINT row = 0; row < rows; row++)
for(UINT col = 0; col < cols; col++)
{
FloatType sum = 0.0;
for(UINT inner = 0; inner < innerCount; inner++)
sum += A(row, inner) * B(inner, col);
result(row, col) = sum;
}
return result;
}
SparseMatrixNeighborsPair angles_matrix_and_neighbors(const Neighbors& neighbors,
const DenseMatrix& data)
{
const IndexType k = neighbors[0].size();
const IndexType n_vectors = data.cols();
SparseTriplets sparse_triplets;
sparse_triplets.reserve(k * n_vectors);
/* I tried to find better naming, but... */
Neighbors most_collinear_neighbors_of_neighbors;
most_collinear_neighbors_of_neighbors.reserve(n_vectors);
for (IndexType i = 0; i < n_vectors; ++i)
{
const LocalNeighbors& current_neighbors = neighbors[i];
LocalNeighbors most_collinear_current_neighbors;
most_collinear_current_neighbors.reserve(k);
for (IndexType j = 0; j < k; ++j)
{
const LocalNeighbors& neighbors_of_neighbor = neighbors[current_neighbors[j]];
/* The closer the cos value to -1.0 - the closer the angle to 180.0 */
ScalarType min_cos_value = 1.0, current_cos_value;
/* This value will be updated during the seach for most collinear neighbor */
most_collinear_current_neighbors.push_back(0);
for (IndexType l = 0; l < k; ++l)
{
DenseVector neighbor_to_point = data.col(i) - data.col(current_neighbors[j]);
DenseVector neighbor_to_its_neighbor = data.col(neighbors_of_neighbor[l])
- data.col(current_neighbors[j]);
current_cos_value = neighbor_to_point.dot(neighbor_to_its_neighbor) /
(neighbor_to_point.norm() *
neighbor_to_its_neighbor.norm());
if (current_cos_value < min_cos_value)
{
most_collinear_current_neighbors[j] = neighbors_of_neighbor[l];
min_cos_value = current_cos_value;
}
}
SparseTriplet triplet(i, most_collinear_current_neighbors[j], min_cos_value);
sparse_triplets.push_back(triplet);
}
most_collinear_neighbors_of_neighbors.push_back(most_collinear_current_neighbors);
}
return SparseMatrixNeighborsPair
(sparse_matrix_from_triplets(sparse_triplets, n_vectors, n_vectors),
most_collinear_neighbors_of_neighbors);
}
std::vector<FloatType> DenseMatrix<FloatType>::multiply(const DenseMatrix<FloatType> &A, const std::vector<FloatType> &B)
{
MLIB_ASSERT_STR(A.cols() == B.size(), "invalid dimensions");
const int rows = A.m_rows;
const UINT cols = A.m_cols;
std::vector<FloatType> result(rows);
//#ifdef MLIB_OPENMP
//#pragma omp parallel for
//#endif
for(int row = 0; row < rows; row++)
{
FloatType val = 0.0;
for(UINT col = 0; col < cols; col++)
val += A.m_dataPtr[row * cols + col] * B[col];
result[row] = val;
}
return result;
}
void compute(const MatrixType& A, const Index rank)
{
if(A.cols() == 0 || A.rows() == 0)
return;
Index r = (rank < A.cols()) ? rank : A.cols();
r = (r < A.rows()) ? r : A.rows();
// Gaussian Random Matrix for A^T
DenseMatrix O(A.rows(), r);
sample_gaussian(O);
// Compute Sample Matrix of A^T
DenseMatrix Y = A.transpose() * O;
// Orthonormalize Y
gram_schmidt(Y);
// Range(B) = Range(A^T)
DenseMatrix B = A * Y;
// Gaussian Random Matrix
DenseMatrix P(B.cols(), r);
sample_gaussian(P);
// Compute Sample Matrix of B
DenseMatrix Z = B * P;
// Orthonormalize Z
gram_schmidt(Z);
// Range(C) = Range(B)
DenseMatrix C = Z.transpose() * B;
Eigen::JacobiSVD<DenseMatrix> svdOfC(C, Eigen::ComputeThinU | Eigen::ComputeThinV);
// C = USV^T
// A = Z * U * S * V^T * Y^T()
m_matrixU = Z * svdOfC.matrixU();
m_vectorS = svdOfC.singularValues();
m_matrixV = Y * svdOfC.matrixV();
}
DenseMatrix project(const DenseMatrix& projection_matrix, const DenseVector& mean_vector,
RandomAccessIterator begin, RandomAccessIterator end,
FeatureVectorCallback callback, IndexType dimension)
{
timed_context context("Data projection");
DenseVector current_vector(dimension);
DenseVector current_vector_subtracted_mean(dimension);
DenseMatrix embedding = DenseMatrix::Zero((end-begin),projection_matrix.cols());
for (RandomAccessIterator iter=begin; iter!=end; ++iter)
{
callback(*iter,current_vector);
current_vector_subtracted_mean = current_vector - mean_vector;
embedding.row(iter-begin) = projection_matrix.transpose()*current_vector_subtracted_mean;
}
return embedding;
}
void manifold_sculpting_embed(RandomAccessIterator begin, RandomAccessIterator end,
DenseMatrix& data, IndexType target_dimension,
const Neighbors& neighbors, DistanceCallback callback,
IndexType max_iteration, ScalarType squishing_rate)
{
/* Step 1: Get initial distances to each neighbor and initial
* angles between the point Pi, each neighbor Nij, and the most
* collinear neighbor of Nij.
*/
ScalarType initial_average_distance;
SparseMatrix distances_to_neighbors =
neighbors_distances_matrix(begin, end, neighbors, callback, initial_average_distance);
SparseMatrixNeighborsPair angles_and_neighbors =
angles_matrix_and_neighbors(neighbors, data);
/* Step 2: Optionally preprocess the data using PCA
* (skipped for now).
*/
ScalarType no_improvement_counter = 0, normal_counter = 0;
ScalarType current_multiplier = squishing_rate;
ScalarType learning_rate = initial_average_distance;
ScalarType best_error = DBL_MAX, current_error, point_error;
/* Step 3: Do until no improvement is made for some period
* (or until max_iteration number is reached):
*/
while (((no_improvement_counter++ < max_number_of_iterations_without_improvement)
|| (current_multiplier > multiplier_treshold))
&& (normal_counter++ < max_iteration))
{
/* Step 3a: Scale the data in non-preserved dimensions
* by a factor of squishing_rate.
*/
data.bottomRows(data.rows() - target_dimension) *= squishing_rate;
while (average_neighbor_distance(data, neighbors) < initial_average_distance)
{
data.topRows(target_dimension) /= squishing_rate;
}
current_multiplier *= squishing_rate;
/* Step 3b: Restore the previously computed relationships
* (distances to neighbors and angles to ...) by adjusting
* data points in first target_dimension dimensions.
*/
/* Start adjusting from a random point */
IndexType start_point_index = std::rand() % data.cols();
std::deque<IndexType> points_to_adjust;
points_to_adjust.push_back(start_point_index);
ScalarType steps_made = 0;
current_error = 0;
std::set<IndexType> adjusted_points;
while (!points_to_adjust.empty())
{
IndexType current_point_index = points_to_adjust.front();
points_to_adjust.pop_front();
if (adjusted_points.count(current_point_index) == 0)
{
DataForErrorFunc error_func_data = {
distances_to_neighbors,
angles_and_neighbors.first,
neighbors,
angles_and_neighbors.second,
adjusted_points,
initial_average_distance
};
adjust_point_at_index(current_point_index, data, target_dimension,
learning_rate, error_func_data, point_error);
current_error += point_error;
/* Insert all neighbors into deque */
std::copy(neighbors[current_point_index].begin(),
neighbors[current_point_index].end(),
std::back_inserter(points_to_adjust));
adjusted_points.insert(current_point_index);
}
}
if (steps_made > data.cols())
learning_rate *= learning_rate_grow_factor;
else
learning_rate *= learning_rate_shrink_factor;
if (current_error < best_error)
{
best_error = current_error;
no_improvement_counter = 0;
}
}
data.conservativeResize(target_dimension, Eigen::NoChange);
data.transposeInPlace();
}
bool ConstraintBSpline::controlPointBoundsDeduction() const
{
// Get variable bounds
auto xlb = bspline.getDomainLowerBound();
auto xub = bspline.getDomainUpperBound();
// Use these instead?
// for (unsigned int i = 0; i < bspline.getNumVariables(); i++)
// {
// xlb.at(i) = variables.at(i)->getLowerBound();
// xub.at(i) = variables.at(i)->getUpperBound();
// }
double lowerBound = variables.back()->getLowerBound(); // f(x) = y > lowerBound
double upperBound = variables.back()->getUpperBound(); // f(x) = y < upperBound
// Get knot vectors and basis degrees
auto knotVectors = bspline.getKnotVectors();
auto basisDegrees = bspline.getBasisDegrees();
// Compute n value for each variable
// Total number of control points is ns(0)*...*ns(d-1)
std::vector<unsigned int> numBasisFunctions = bspline.getNumBasisFunctions();
// Get matrix of coefficients
DenseMatrix cps = controlPoints;
DenseMatrix coeffs = cps.block(bspline.getNumVariables(), 0, 1, cps.cols());
for (unsigned int d = 0; d < bspline.getNumVariables(); d++)
{
if (assertNear(xlb.at(d), xub.at(d)))
continue;
auto n = numBasisFunctions.at(d);
auto p = basisDegrees.at(d);
std::vector<double> knots = knotVectors.at(d);
assert(knots.size() == n+p+1);
// Tighten lower bound
unsigned int i = 1;
for (; i <= n; i++)
{
// Knot interval of interest: [t_0, t_i]
// Selection matrix
DenseMatrix S = DenseMatrix::Ones(1,1);
for (unsigned int d2 = 0; d2 < bspline.getNumVariables(); d2++)
{
DenseMatrix temp(S);
DenseMatrix Sd_full = DenseMatrix::Identity(numBasisFunctions.at(d2),numBasisFunctions.at(d2));
DenseMatrix Sd(Sd_full);
if (d == d2)
Sd = Sd_full.block(0,0,n,i);
S = kroneckerProduct(temp, Sd);
}
// Control points that have support in [t_0, t_i]
DenseMatrix selc = coeffs*S;
DenseVector minCP = selc.rowwise().minCoeff();
DenseVector maxCP = selc.rowwise().maxCoeff();
double minv = minCP(0);
double maxv = maxCP(0);
// Investigate feasibility
if (minv > upperBound || maxv < lowerBound)
continue; // infeasible
else
break; // feasible
}
// New valid lower bound on x(d) is knots(i-1)
if (i > 1)
{
if (!variables.at(d)->updateLowerBound(knots.at(i-1)))
return false;
}
// Tighten upper bound
i = 1;
for (; i <= n; i++)
{
// Knot interval of interest: [t_{n+p-i}, t_{n+p}]
// Selection matrix
DenseMatrix S = DenseMatrix::Ones(1,1);
for (unsigned int d2 = 0; d2 < bspline.getNumVariables(); d2++)
{
DenseMatrix temp(S);
DenseMatrix Sd_full = DenseMatrix::Identity(numBasisFunctions.at(d2),numBasisFunctions.at(d2));
DenseMatrix Sd(Sd_full);
if (d == d2)
Sd = Sd_full.block(0,n-i,n,i);
S = kroneckerProduct(temp, Sd);
}
// Control points that have support in [t_{n+p-i}, t_{n+p}]
DenseMatrix selc = coeffs*S;
DenseVector minCP = selc.rowwise().minCoeff();
DenseVector maxCP = selc.rowwise().maxCoeff();
double minv = minCP(0);
double maxv = maxCP(0);
// Investigate feasibility
if (minv > upperBound || maxv < lowerBound)
continue; // infeasible
else
break; // feasible
}
// New valid lower bound on x(d) is knots(n+p-(i-1))
if (i > 1)
{
if (!variables.at(d)->updateUpperBound(knots.at(n+p-(i-1))))
return false;
// NOTE: the upper bound seems to not be tight! can we use knots.at(n+p-i)?
}
}
return true;
}
ConstraintQuadratic::ConstraintQuadratic(std::vector<VariablePtr> variables, DenseMatrix A, DenseMatrix b, double c, double lb, double ub)
: Constraint(variables), A(A), b(b), c(c)
{
assert(A.cols() == (int)variables.size());
assert(A.rows() == b.rows());
assert(b.cols() == 1);
numConstraints = 1;
this->lb.push_back(lb);
this->ub.push_back(ub);
jacobianCalculated = true;
hessianCalculated = true;
constraintLinear = false;
constraintConvex = false;
convexRelaxationAvailable = true;
nnzJacobian = A.rows();
nnzHessian = 0;
constraintName = "Constraint Quadratic";
// // Check for parameters for NaN
// for (int i = 0; i < A.rows(); i++)
// {
// for (int j = 0; j < A.cols(); j++)
// {
// bool nanA = false;
// if (A(i,j) != A(i,j)) nanA = true;
// assert(nanA == false);
// }
// bool nanb = false;
// if (b(i) != b(i)) nanb = true;
// assert(nanb == false);
// }
// Calculate and store Hessian
H = A.transpose() + A;
// H is symmetric so fill out lower left triangle only
for (int row = 0; row < H.rows(); row++)
{
for (int col = 0; col <= row; col++)
{
if (H(row,col) != 0)
{
nnzHessian++;
}
}
}
// Check convexity using Hessian
Eigen::EigenSolver<DenseMatrix> es(H);
DenseVector eigs = es.eigenvalues().real();
double minEigVal = 0;
for (int i = 0; i < eigs.rows(); i++)
{
if (eigs(i) < minEigVal) minEigVal = eigs(i);
}
if (minEigVal >= 0 && lb <= -INF)
{
constraintConvex = true;
}
// Note could also check that max. eigen value <= 0 and ub = INF
checkConstraintSanity();
}
