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tsgIndexManipulator.cpp
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tsgIndexManipulator.cpp
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/*
* Copyright (c) 2017, Miroslav Stoyanov
*
* This file is part of
* Toolkit for Adaptive Stochastic Modeling And Non-Intrusive ApproximatioN: TASMANIAN
*
* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions
* and the following disclaimer in the documentation and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse
* or promote products derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
* INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
* OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
* OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* UT-BATTELLE, LLC AND THE UNITED STATES GOVERNMENT MAKE NO REPRESENTATIONS AND DISCLAIM ALL WARRANTIES, BOTH EXPRESSED AND IMPLIED.
* THERE ARE NO EXPRESS OR IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY PATENT,
* COPYRIGHT, TRADEMARK, OR OTHER PROPRIETARY RIGHTS, OR THAT THE SOFTWARE WILL ACCOMPLISH THE INTENDED RESULTS OR THAT THE SOFTWARE OR ITS USE WILL NOT RESULT IN INJURY OR DAMAGE.
* THE USER ASSUMES RESPONSIBILITY FOR ALL LIABILITIES, PENALTIES, FINES, CLAIMS, CAUSES OF ACTION, AND COSTS AND EXPENSES, CAUSED BY, RESULTING FROM OR ARISING OUT OF,
* IN WHOLE OR IN PART THE USE, STORAGE OR DISPOSAL OF THE SOFTWARE.
*/
#ifndef __TSG_INDEX_MANIPULATOR_CPP
#define __TSG_INDEX_MANIPULATOR_CPP
#include "tsgIndexManipulator.hpp"
namespace TasGrid{
namespace MultiIndexManipulations{
/*!
* \internal
* \ingroup TasmanianMultiIndexManipulations
* \brief Generate a series of \b level_sets where each set has the parents/children of the previous one that satisfy the \b inside() criteria.
*
* On entry, \b level_sets must constain at least one set.
* The function takes the last set in \b level_sets and adds a new set of
* eithe the parents or children that also satisfy the \b inside() condition.
* The process is repeated until the new set is empty.
* \endinternal
*/
template<bool use_parents_direction>
void repeatAddIndexes(std::function<bool(const std::vector<int> &index)> inside, std::vector<MultiIndexSet> &level_sets){
size_t num_dimensions = level_sets.back().getNumDimensions();
bool adding = true;
while(adding){
Data2D<int> level((int) num_dimensions, 0);
int num_indexes = level_sets.back().getNumIndexes();
#pragma omp parallel
{
std::vector<int> point(num_dimensions);
Data2D<int> mlevel((int) num_dimensions, 0);
#pragma omp for
for(int i=0; i<num_indexes; i++){
std::copy_n(level_sets.back().getIndex(i), num_dimensions, point.data());
for(auto &p : point){
p += (use_parents_direction) ? -1 : 1; // parents have lower index, children have higher indexes
if (not use_parents_direction or p >= 0) {
bool is_inside = false;
#pragma omp critical(level_append)
{
is_inside = inside(point);
}
if (is_inside)
mlevel.appendStrip(point);
}
p -= (use_parents_direction) ? -1 : 1; // restore p
}
}
#pragma omp critical
{
level.append(mlevel);
}
}
adding = (level.getNumStrips() > 0);
if (adding)
level_sets.push_back(MultiIndexSet(level));
}
}
/*!
* \internal
* \ingroup TasmanianMultiIndexManipulations
* \brief Retuns the union of all \b level_sets, all sets are destroyed in the process.
*
* \endinternal
*/
inline MultiIndexSet unionSets(std::vector<MultiIndexSet> &level_sets){
long long num_levels = level_sets.size();
while(num_levels > 1){
long long stride = num_levels / 2 + (((num_levels % 2) > 0) ? 1 : 0);
#pragma omp parallel for
for(long long i=0; i<stride; i++)
if (i + stride < num_levels)
level_sets[i] += level_sets[i + stride];
num_levels = stride;
}
return std::move(level_sets[0]);
}
void completeSetToLower(MultiIndexSet &set){
size_t num_dimensions = set.getNumDimensions();
int num = set.getNumIndexes();
Data2D<int> completion((int) num_dimensions, 0);
#pragma omp parallel for
for(int i=0; i<num; i++){
std::vector<int> point(num_dimensions);
std::copy_n(set.getIndex(i), num_dimensions, point.data());
for(auto &p : point){
if (p != 0){
p--;
if (set.missing(point)){
#pragma omp critical
{
completion.appendStrip(point);
}
}
p++;
}
}
}
if (completion.getNumStrips() > 0){
std::vector<MultiIndexSet> level_sets = { MultiIndexSet(completion) };
constexpr bool use_parents = true;
repeatAddIndexes<use_parents>([&](std::vector<int> const &p) -> bool{ return set.missing(p); }, level_sets);
set += unionSets(level_sets);
}
}
/*!
* \internal
* \ingroup TasmanianMultiIndexManipulations
* \brief Generate the minimum lower complete multi-index set that includes the indexes satisfying \b criteria(), assumes \b criteria() defines a connected set.
*
* \endinternal
*/
inline MultiIndexSet generateGeneralMultiIndexSet(size_t num_dimensions, std::function<bool(const std::vector<int> &index)> criteria){
std::vector<MultiIndexSet> level_sets = { MultiIndexSet(num_dimensions, std::vector<int>(num_dimensions, 0)) };
constexpr bool use_parents = false;
repeatAddIndexes<use_parents>(criteria, level_sets);
MultiIndexSet set = unionSets(level_sets);
completeSetToLower(set);
return set;
}
/*!
* \internal
* \brief Generate the multi-index of indexes with weighs less than the \b normalized_offset.
*
* The weight of an index uses the \b weights combined with the \b rule_exactness().
* Called only when the set is guaranteed to be lower complete,
* then the one dimensional weights can be cached prior to running the selection algorithm.
*
* If \b check_limits is \b false, then \b level_limits are ignored for speedup.
* \endinternal
*/
template<bool check_limits>
MultiIndexSet selectLowerSet(ProperWeights const &weights, std::function<int(int i)> rule_exactness,
int normalized_offset, std::vector<int> const &level_limits){
size_t num_dimensions = weights.getNumDimensions();
if (weights.contour == type_level){
auto cache = generateLevelWeightsCache<int, type_level, false>(weights, rule_exactness, normalized_offset);
return generateLowerMultiIndexSet(num_dimensions,
[&](std::vector<int> const &index)->bool{
if (check_limits) for(size_t j=0; j<num_dimensions; j++) if ((level_limits[j] > -1) && (index[j] > level_limits[j])) return false;
return (getIndexWeight<int, type_level>(index.data(), cache) <= normalized_offset);
});
}else if (weights.contour == type_curved){
auto cache = generateLevelWeightsCache<double, type_curved, false>(weights, rule_exactness, normalized_offset);
double noff = (double) normalized_offset;
return generateLowerMultiIndexSet(num_dimensions,
[&](std::vector<int> const &index)->bool{
if (check_limits) for(size_t j=0; j<num_dimensions; j++) if ((level_limits[j] > -1) && (index[j] > level_limits[j])) return false;
return (std::ceil(getIndexWeight<double, type_curved>(index.data(), cache)) <= noff);
});
}else{ // type_hyperbolic
auto cache = generateLevelWeightsCache<double, type_hyperbolic, false>(weights, rule_exactness, normalized_offset);
double noff = (double) normalized_offset;
return generateLowerMultiIndexSet(num_dimensions,
[&](std::vector<int> const &index)->bool{
if (check_limits) for(size_t j=0; j<num_dimensions; j++) if ((level_limits[j] > -1) && (index[j] > level_limits[j])) return false;
return (std::ceil(getIndexWeight<double, type_hyperbolic>(index.data(), cache)) <= noff);
});
}
}
/*!
* \internal
* \ingroup TasmanianMultiIndexManipulations
* \brief Generates the minimum lower complete set that contains all indexes with weights less than \b normalized_offset.
*
* The weight of an index uses the \b weights combined with the \b rule_exactness().
* Called only for contour \b type_curved and caches values on-the-fly.
*
* If \b check_limits is \b false, then \b level_limits are ignored for speedup.
* \endinternal
*/
template<bool check_limits>
MultiIndexSet selectGeneralSet(ProperWeights const &weights, std::function<int(int i)> rule_exactness,
int normalized_offset, std::vector<int> const &level_limits){
size_t num_dimensions = weights.getNumDimensions();
std::vector<std::vector<double>> cache(num_dimensions);
for(size_t j=0; j<num_dimensions; j++) cache[j].push_back(0.0);
double noff = (double) normalized_offset;
return generateGeneralMultiIndexSet(num_dimensions,
[&](std::vector<int> const &index) -> bool{
if (check_limits) for(size_t j=0; j<num_dimensions; j++) if (index[j] > level_limits[j]) return false;
double w = 0;
for(size_t j=0; j<num_dimensions; j++){
while(index[j] >= (int) cache[j].size()){
int exactness = 1 + rule_exactness((int)(cache[j].size() - 1));
cache[j].push_back( (double) weights.linear[j] * exactness +
weights.curved[j] * std::log1p((double) exactness) );
}
w += cache[j][index[j]];
}
return (std::ceil(w) <= noff);
});
}
MultiIndexSet selectTensors(size_t num_dimensions, int offset, TypeDepth type,
std::function<int(int i)> rule_exactness, std::vector<int> const &anisotropic_weights,
std::vector<int> const &level_limits){
// special case of a full tensor selection
if ((type == type_tensor) || (type == type_iptensor) || (type == type_qptensor)){ // special case, full tensor
std::vector<int> max_exactness = (anisotropic_weights.empty()) ? std::vector<int>(num_dimensions, 1) : anisotropic_weights;
for(auto &e : max_exactness) e *= offset;
std::vector<int> num_points(num_dimensions, 0); // how many points to have in each direction
std::transform(max_exactness.begin(), max_exactness.end(), num_points.begin(),
[&](int e)-> int{
int l = 0; // level
while(rule_exactness(l) < e) l++; // get the first level that covers the weight
return l+1; // adding extra one to change interpretation from 0-index "level" to 1-index "number of points"
});
if (!level_limits.empty()){
for(size_t j=0; j<num_dimensions; j++)
if (level_limits[j] >= 0)
num_points[j] = std::min(num_points[j], level_limits[j]+1); // the +1 indicates switch from max-level to number-of-points
}
return generateFullTensorSet(num_points);
}
ProperWeights weights(num_dimensions, type, anisotropic_weights);
int normalized_offset = offset * weights.minLinear();
if (weights.provenLower()){ // if the set is guaranteed to be lower
if (level_limits.empty()){
return selectLowerSet<false>(weights, rule_exactness, normalized_offset, level_limits);
}else{
return selectLowerSet<true>(weights, rule_exactness, normalized_offset, level_limits);
}
}else{
if (level_limits.empty()){
return selectGeneralSet<false>(weights, rule_exactness, normalized_offset, level_limits);
}else{
return selectGeneralSet<true>(weights, rule_exactness, normalized_offset, level_limits);
}
}
}
std::vector<int> computeLevels(MultiIndexSet const &mset){
// cannot add as inline to the public header due to the pragma and possible "unknown pragma" message
int num_indexes = mset.getNumIndexes();
size_t num_dimensions = mset.getNumDimensions();
std::vector<int> levels((size_t) num_indexes);
#pragma omp parallel for
for(int i=0; i<num_indexes; i++){
const int* p = mset.getIndex(i);
levels[i] = std::accumulate(p, p + num_dimensions, 0);
}
return levels;
}
std::vector<int> getMaxIndexes(const MultiIndexSet &mset){
size_t num_dimensions = mset.getNumDimensions();
std::vector<int> max_index(num_dimensions, 0);
int n = mset.getNumIndexes();
#pragma omp parallel
{
std::vector<int> local_max_index(num_dimensions, 0);
#pragma omp for
for(int i=0; i<n; i++){
const int* p = mset.getIndex(i);
for(size_t j=0; j<num_dimensions; j++) if (local_max_index[j] < p[j]) local_max_index[j] = p[j];
}
#pragma omp critical
{
for(size_t j=0; j<num_dimensions; j++){
if (max_index[j] < local_max_index[j]) max_index[j] = local_max_index[j];
}
}
}
return max_index;
}
Data2D<int> computeDAGup(MultiIndexSet const &mset){
size_t num_dimensions = (size_t) mset.getNumDimensions();
int n = mset.getNumIndexes();
Data2D<int> parents(mset.getNumDimensions(), n);
#pragma omp parallel for schedule(static)
for(int i=0; i<n; i++){
std::vector<int> dad(num_dimensions);
std::copy_n(mset.getIndex(i), num_dimensions, dad.data());
int *v = parents.getStrip(i);
for(auto &d : dad){
d--;
*v = (d < 0) ? -1 : mset.getSlot(dad);
d++;
v++;
}
}
return parents;
}
MultiIndexSet selectFlaggedChildren(const MultiIndexSet &mset, const std::vector<bool> &flagged, const std::vector<int> &level_limits){
size_t num_dimensions = mset.getNumDimensions();
int n = mset.getNumIndexes();
Data2D<int> children_unsorted(num_dimensions, 0);
#ifdef _OPENMP
#pragma omp parallel
{
Data2D<int> lrefined(num_dimensions, 0);
std::vector<int> kid(num_dimensions);
if (level_limits.empty()){
#pragma omp for
for(int i=0; i<n; i++){
if (flagged[i]){
std::copy_n(mset.getIndex(i), num_dimensions, kid.data());
for(auto &k : kid){
k++;
if (mset.missing(kid)) lrefined.appendStrip(kid);
k--;
}
}
}
}else{
#pragma omp for
for(int i=0; i<n; i++){
if (flagged[i]){
std::copy_n(mset.getIndex(i), num_dimensions, kid.data());
auto ill = level_limits.begin();
for(auto &k : kid){
k++;
if (((*ill == -1) || (k <= *ill)) && mset.missing(kid))
lrefined.appendStrip(kid);
k--;
ill++;
}
}
}
}
#pragma omp critical
{
children_unsorted.append(lrefined);
}
}
#else
std::vector<int> kid(num_dimensions);
if (level_limits.empty()){
for(int i=0; i<n; i++){
if (flagged[i]){
std::copy_n(mset.getIndex(i), num_dimensions, kid.data());
for(auto &k : kid){
k++;
if (mset.missing(kid)) children_unsorted.appendStrip(kid);
k--;
}
}
}
}else{
for(int i=0; i<n; i++){
if (flagged[i]){
std::copy_n(mset.getIndex(i), num_dimensions, kid.data());
auto ill = level_limits.begin();
for(auto &k : kid){
k++;
if (((*ill == -1) || (k <= *ill)) && mset.missing(kid))
children_unsorted.appendStrip(kid);
k--;
ill++;
}
}
}
}
#endif
return MultiIndexSet(children_unsorted);
}
MultiIndexSet generateNestedPoints(const MultiIndexSet &tensors, std::function<int(int)> getNumPoints){
size_t num_dimensions = (size_t) tensors.getNumDimensions();
std::vector<MultiIndexSet> delta_sets((size_t) tensors.getNumIndexes());
#pragma omp parallel for
for(int i=0; i<tensors.getNumIndexes(); i++){
Data2D<int> raw_points(num_dimensions, 0);
std::vector<int> num_points_delta(num_dimensions);
std::vector<int> offsets(num_dimensions);
std::vector<int> index(num_dimensions);
const int *p = tensors.getIndex(i);
size_t num_total = 1;
for(size_t j=0; j<num_dimensions; j++){
num_points_delta[j] = getNumPoints(p[j]);
if (p[j] > 0){
offsets[j] = getNumPoints(p[j]-1);
num_points_delta[j] -= offsets[j];
}else{
offsets[j] = 0;
}
num_total *= (size_t) num_points_delta[j];
}
for(size_t k=0; k<num_total; k++){
size_t t = k;
for(int j = (int) num_dimensions-1; j>=0; j--){
index[j] = offsets[j] + (int) (t % num_points_delta[j]);
t /= (size_t) num_points_delta[j];
}
raw_points.appendStrip(index);
}
delta_sets[i] = MultiIndexSet(raw_points);
}
return unionSets(delta_sets);
}
MultiIndexSet generateNonNestedPoints(const MultiIndexSet &tensors, const OneDimensionalWrapper &wrapper){
size_t num_dimensions = tensors.getNumDimensions();
int num_tensors = tensors.getNumIndexes();
std::vector<MultiIndexSet> point_tensors((size_t) num_tensors);
#pragma omp parallel for
for(int t=0; t<num_tensors; t++){
std::vector<int> num_entries(num_dimensions);
const int *p = tensors.getIndex(t);
std::transform(p, p + num_dimensions, num_entries.begin(), [&](int l)->int{ return wrapper.getNumPoints(l); });
int num_total = 1;
for(auto &l : num_entries) num_total *= l;
Data2D<int> raw_points(num_dimensions, num_total);
auto iter = raw_points.rbegin();
for(int i=num_total-1; i>=0; i--){
int d = i;
auto l = num_entries.rbegin();
// in order to generate indexes in the correct order, the for loop must go backwards
for(size_t j = 0; j<num_dimensions; j++){
*iter++ = wrapper.getPointIndex(p[num_dimensions - j - 1], (d % *l));
d /= *l++;
}
}
point_tensors[t] = MultiIndexSet(raw_points);
}
return unionSets(point_tensors);
}
void resortIndexes(const MultiIndexSet &iset, std::vector<std::vector<int>> &map, std::vector<std::vector<int>> &lines1d) {
int num_dimensions = iset.getNumDimensions();
int num_tensors = iset.getNumIndexes();
int num_levels = 1 + *std::max_element(iset.begin(), iset.end());
auto match_outside_dim = [&](int d, int const*a, int const *b) -> bool {
for(int j=0; j<d; j++)
if (a[j] != b[j]) return false;
for(int j=d+1; j<num_dimensions; j++)
if (a[j] != b[j]) return false;
return true;
};
map = std::vector<std::vector<int>>(num_dimensions, std::vector<int>(num_tensors));
lines1d = std::vector<std::vector<int>>(num_dimensions);
for(auto &ji : lines1d) ji.reserve(num_levels);
#pragma omp parallel for
for(int d=0; d<num_dimensions; d++) {
// for each dimension, use the map to group indexes together
std::iota(map[d].begin(), map[d].end(), 0);
if (d != num_dimensions - 1) {
std::sort(map[d].begin(), map[d].end(), [&](int a, int b)->bool{
const int * idxa = iset.getIndex(a);
const int * idxb = iset.getIndex(b);
for(int j=0; j<num_dimensions; j++) {
if (j != d){
if (idxa[j] < idxb[j]) return true;
if (idxa[j] > idxb[j]) return false;
}
}
// lexigographical order, dimension d is the fastest moving one
if (idxa[d] < idxb[d]) return true;
if (idxa[d] > idxb[d]) return false;
return false;
});
}
if (num_dimensions == 1) {
lines1d[d].push_back(0);
lines1d[d].push_back(num_tensors);
} else {
int const *c_index = iset.getIndex(map[d][0]);
lines1d[d].push_back(0);
for(int i=1; i<num_tensors; i++) {
if (not match_outside_dim(d, c_index, iset.getIndex(map[d][i]))) {
lines1d[d].push_back(i);
c_index = iset.getIndex(map[d][i]);
}
}
lines1d[d].push_back(num_tensors);
}
}
}
std::vector<int> computeTensorWeights(MultiIndexSet const &mset){
int num_dimensions = mset.getNumDimensions();
int num_tensors = mset.getNumIndexes();
if (num_dimensions == 1) {
std::vector<int> weights(num_tensors, 0);
weights.back() = 1;
return weights;
}
std::vector<std::vector<int>> map;
std::vector<std::vector<int>> lines1d;
resortIndexes(mset, map, lines1d);
Data2D<int> dag_down(num_dimensions, num_tensors);
std::vector<int> weights(num_tensors, 0);
// the row with contiguous indexes has a trivial solution
auto const& last_jobs = lines1d[num_dimensions-1];
for(int i=0; i<static_cast<int>(last_jobs.size() - 1); i++)
weights[last_jobs[i+1] - 1] = 1;
for(int d=num_dimensions-2; d>=0; d--) {
#pragma omp parallel for
for(int job = 0; job < static_cast<int>(lines1d[d].size() - 1); job++) {
for(int i=lines1d[d][job+1]-2; i>=lines1d[d][job]; i--) {
int &val = weights[map[d][i]];
for(int j=i+1; j<lines1d[d][job+1]; j++) {
val -= weights[map[d][j]];
}
}
}
}
return weights;
}
MultiIndexSet createPolynomialSpace(const MultiIndexSet &tensors, std::function<int(int)> exactness){
size_t num_dimensions = (size_t) tensors.getNumDimensions();
int num_tensors = tensors.getNumIndexes();
std::vector<MultiIndexSet> polynomial_tensors((size_t) num_tensors);
#pragma omp parallel for
for(int i=0; i<num_tensors; i++){
std::vector<int> npoints(num_dimensions);
const int *p = tensors.getIndex(i);
for(size_t j=0; j<num_dimensions; j++)
npoints[j] = exactness(p[j]) + 1;
polynomial_tensors[i] = generateFullTensorSet(npoints);
}
return unionSets(polynomial_tensors);
}
std::vector<int> inferAnisotropicWeights(AccelerationContext const *acceleration, TypeOneDRule rule, TypeDepth depth,
MultiIndexSet const &points, std::vector<double> const &coefficients, double tol){
int num_dimensions = static_cast<int>(points.getNumDimensions());
int cols = (OneDimensionalMeta::getControurType(depth) == type_curved) ?
2 * num_dimensions + 1 : num_dimensions + 1;
int data_rows = static_cast<int>(std::count_if(coefficients.begin(), coefficients.end(), [=](double c)->bool{ return (std::abs(c) > tol); }));
Data2D<double> A(data_rows + cols, cols, 0.0);
std::vector<double> b(data_rows + cols, 0.0);
int c = 0;
for(int i=0; i<points.getNumIndexes(); i++){
if (std::abs(coefficients[i]) > tol){
int const *indx = points.getIndex(i);
for(int j=0; j<num_dimensions; j++){
A.getStrip(j)[c] = static_cast<double>(indx[j]);
}
A.getStrip(cols-1)[c] = 1.0;
b[c++] = -std::log(std::abs(coefficients[i]));
}
}
if (rule == rule_fourier){
for(int j=0; j<num_dimensions; j++){
double *cc = A.getStrip(j);
for(int i=0; i<data_rows; i++)
cc[i] = static_cast<double>((static_cast<int>(cc[i]) + 1) / 2);
}
}
if (OneDimensionalMeta::getControurType(depth) == type_hyperbolic){
for(int j=0; j<num_dimensions; j++){
double *cc = A.getStrip(j);
for(int i=0; i<data_rows; i++)
cc[i] = std::log(cc[i] + 1.0);
}
}
if (OneDimensionalMeta::getControurType(depth) == type_curved){
for(int j=0; j<num_dimensions; j++){
double *cc = A.getStrip(j);
double *vv = A.getStrip(num_dimensions + j);
for(int i=0; i<data_rows; i++)
vv[i] = std::log(cc[i] + 1.0);
}
}
// pad with a block of identity to provide regularization
// adds 0.003^2 to the diagonal of the A^T * A matrix
for(int j=0; j<cols; j++)
A.getStrip(j)[data_rows + j] = 0.003;
std::vector<double> x(cols);
TasmanianDenseSolver::solveLeastSquares(acceleration, data_rows + cols, cols, A.getStrip(0), b.data(), x.data());
std::vector<int> weights(cols - 1);
for(size_t j=0; j<weights.size(); j++)
weights[j] = static_cast<int>(x[j] * 1000.0 + 0.5);
int max_weight = *std::max_element(weights.begin(), weights.begin() + num_dimensions);
if (max_weight <= 0){ // all directions are diverging, default to isotropic total degree
std::fill_n(weights.begin(), num_dimensions, 1);
if (OneDimensionalMeta::getControurType(depth) == type_curved)
std::fill(weights.begin() + num_dimensions, weights.end(), 0);
}else{
int min_weight = max_weight;
for(int j=0; j<num_dimensions; j++)
if (weights[j] > 0 and weights[j] < min_weight) min_weight = weights[j]; // find the smallest positive weight
for(int j=0; j<num_dimensions; j++){
if (weights[j] <= 0){
weights[j] = min_weight;
if (OneDimensionalMeta::getControurType(depth) == type_curved){
if (std::abs(weights[num_dimensions + j]) > weights[j])
weights[num_dimensions + j] = (weights[j] > 0.0) ? weights[num_dimensions + j] : -weights[num_dimensions + j];
}
}
}
}
return weights;
}
} // MultiIndexManipulations
} // TasGrid
#endif