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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 __TASMANIAN_SPARSE_GRID_DYNAMIC_CONST_GLOBAL_CPP
#define __TASMANIAN_SPARSE_GRID_DYNAMIC_CONST_GLOBAL_CPP
#include "tsgDConstructGridGlobal.hpp"
namespace TasGrid{
template<bool use_ascii> void DynamicConstructorDataGlobal::write(std::ostream &os) const{
if (use_ascii == mode_ascii){ os << std::scientific; os.precision(17); }
auto tensor_refs = makeReverseReferenceVector(tensors);
IO::writeNumbers<use_ascii, IO::pad_line, int>(os, static_cast<int>(tensor_refs.size()));
for(auto d : tensor_refs){
IO::writeNumbers<use_ascii, IO::pad_rspace, double>(os, d->weight);
IO::writeVector<use_ascii, IO::pad_line>(d->tensor, os);
}
writeNodeDataList<use_ascii>(data, os);
}
template void DynamicConstructorDataGlobal::write<mode_ascii>(std::ostream &) const; // instantiate for faster build
template void DynamicConstructorDataGlobal::write<mode_binary>(std::ostream &) const;
int DynamicConstructorDataGlobal::getMaxTensor() const{
int max_tensor = 0;
for(auto const &t : tensors)
max_tensor = std::max(max_tensor, *std::max_element(t.tensor.begin(), t.tensor.end()));
return max_tensor;
}
void DynamicConstructorDataGlobal::reloadPoints(std::function<int(int)> getNumPoints){
for(auto &t : tensors){
MultiIndexSet dummy_set(num_dimensions, std::vector<int>(t.tensor));
t.points = MultiIndexManipulations::generateNestedPoints(dummy_set, getNumPoints);
t.loaded = std::vector<bool>((size_t) t.points.getNumIndexes(), false);
}
for(auto const &p : data){ // rebuild the loaded vectors
for(auto &t : tensors){
int i = t.points.getSlot(p.point);
if (i != -1) t.loaded[i] = true;
}
}
}
void DynamicConstructorDataGlobal::clearTesnors(){
for(auto t = tensors.begin(), p = tensors.before_begin(); t != tensors.end(); t++){
if (t->weight >= 0.0){
tensors.erase_after(p);
t = p;
}else{
p++;
} // at each step, p starts before t, regardless if t is erased, p ends up equal to t, then t++
}
}
MultiIndexSet DynamicConstructorDataGlobal::getInitialTensors() const{
Data2D<int> tens(num_dimensions, 0);
for(auto const &t : tensors){
if (t.weight < 0.0)
tens.appendStrip(t.tensor);
}
return MultiIndexSet(tens);
}
void DynamicConstructorDataGlobal::addTensor(const int *tensor, std::function<int(int)> getNumPoints, double weight){
tensors.emplace_front(TensorData{
weight,
std::vector<int>(tensor, tensor + num_dimensions),
MultiIndexManipulations::generateNestedPoints(MultiIndexSet(num_dimensions, std::vector<int>(tensor, tensor + num_dimensions)), getNumPoints),
std::vector<bool>(),
});
tensors.front().loaded = std::vector<bool>((size_t) tensors.front().points.getNumIndexes(), false);
for(auto const &p : data){
int slot = tensors.front().points.getSlot(p.point);
if (slot != -1) tensors.front().loaded[slot] = true;
}
if (std::all_of(tensors.front().loaded.begin(), tensors.front().loaded.end(), [](bool b)->bool{ return b; })){
tensors.front().loaded.clear();
}
}
MultiIndexSet DynamicConstructorDataGlobal::getNodesIndexes(){
std::vector<int> inodes;
auto get_weight = [](const TensorData &tensor)->double{
if (tensor.weight <= 0.0) return tensor.weight;
if (tensor.loaded.empty()) return 0.0; // should not be happening, should have ejected
int num_loaded = (int) std::count_if(tensor.loaded.begin(), tensor.loaded.end(), [](bool p)->bool{ return p; });
return tensor.weight * ((double(tensor.loaded.size()) - double(num_loaded)) / double(tensor.loaded.size()));
};
tensors.sort([&](const TensorData &a, const TensorData &b)->bool{ return (get_weight(a) < get_weight(b)); });
for(auto const &t : tensors){
if (!t.loaded.empty()){
for(int i=0; i<t.points.getNumIndexes(); i++){
if (!t.loaded[i])
inodes.insert(inodes.end(), t.points.getIndex(i), t.points.getIndex(i) + num_dimensions);
}
}
}
return MultiIndexSet(num_dimensions, std::move(inodes));
}
DynamicConstructorDataGlobal::AddPointResult
DynamicConstructorDataGlobal::addNewNode(const std::vector<int> &point, const std::vector<double> &value){
data.emplace_front(NodeData{point, value});
for(auto &t : tensors){
int slot = t.points.getSlot(point);
if (slot != -1){
t.loaded[slot] = true;
if (std::all_of(t.loaded.begin(), t.loaded.end(), [](bool a)-> bool{ return a; })){ // if all entries are true
t.loaded = std::vector<bool>();
return AddPointResult::tensor_complete;
// all points associated with this tensor have been loaded, signal to call ejectCompleteTensor()
}else{
return AddPointResult::tensor_incomplete; // point added to the tensor, but the tensor is not complete yet
}
}
}
// could not find a tensor that contains this node, must add a new tensor
return AddPointResult::tensor_missing;
}
void DynamicConstructorDataGlobal::ejectCompleteTensor(MultiIndexSet const ¤t_tensors, MultiIndexSet &new_tensors, MultiIndexSet &new_points, StorageSet &vals){
new_points = MultiIndexSet(); // reset tensors
vals = StorageSet();
Data2D<int> candidate_tensors(num_dimensions, 0); // get a list of completed tensors
for(auto &t : tensors)
if (t.loaded.empty())
candidate_tensors.appendStrip(t.tensor);
// get the completed tensors that can be added to current_tensors while still preserving lower-completion
new_tensors = MultiIndexManipulations::getLargestCompletion(current_tensors, MultiIndexSet(candidate_tensors));
if (new_tensors.empty()) return;
vals.resize((int) num_outputs, 0);
auto p = tensors.before_begin(), t = tensors.begin();
while(t != tensors.end()){
if (new_tensors.missing(t->tensor)){ // if not using this tensor, advance to the next
t++;
p++;
}else{ // if using the tensor
int num_searching = t->points.getNumIndexes();
Data2D<double> wvals(num_outputs, num_searching); // find all the values of the points
int found = 0;
auto d = data.before_begin();
auto v = data.begin();
while(found < num_searching){ // until all are found
int slot = t->points.getSlot(v->point);
if (slot == -1){ // this is not a point that we are looking for
d++;
v++;
}else{ // point found, copy the value and extract it from the list
std::copy_n(v->value.begin(), num_outputs, wvals.getStrip(slot));
data.erase_after(d);
v = d;
v++;
found++;
}
}
vals.addValues(new_points, t->points, wvals.getStrip(0));
new_points += t->points;
tensors.erase_after(p);
t = p;
t++;
}
}
}
}
#endif
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