tsgDConstructGridGlobal.hpp

/*
 * Copyright (c) 2017, Miroslav Stoyanov
 *
 * This file is part of
 * Toolkit for Adaptive Stochastic Modeling And Non-Intrusive ApproximatioN: TASMANIAN
 *
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#ifndef __TASMANIAN_SPARSE_GRID_DYNAMIC_CONST_GLOBAL_HPP
#define __TASMANIAN_SPARSE_GRID_DYNAMIC_CONST_GLOBAL_HPP
 
#include <forward_list>
 
#include "tsgIndexManipulator.hpp"


 
namespace TasGrid{

struct NodeData{
    std::vector<int> point;
    std::vector<double> value;
};

struct TensorData{
    double weight;
    std::vector<int> tensor;
    MultiIndexSet points;
    std::vector<bool> loaded;
};

template <class T>
std::vector<const T*> makeReverseReferenceVector(const std::forward_list<T> &list){
    size_t num_entries = (size_t) std::distance(list.begin(), list.end());
    std::vector<const T*> refs(num_entries);
    auto p = list.begin();
    auto r = refs.rbegin();
    while(p != list.end()) *r++ = &*p++;
    return refs;
}

template<bool use_ascii>
void writeNodeDataList(const std::forward_list<NodeData> &data, std::ostream &os){
    if (use_ascii == mode_ascii){ os << std::scientific; os.precision(17); }
 
    auto data_refs = makeReverseReferenceVector(data);
 
    IO::writeNumbers<use_ascii, IO::pad_line>(os, static_cast<int>(data_refs.size()));
    for(auto d : data_refs){
        IO::writeVector<use_ascii, IO::pad_rspace>(d->point, os);
        IO::writeVector<use_ascii, IO::pad_line>(d->value, os);
    }
}

template<typename iomode>
std::forward_list<NodeData> readNodeDataList(std::istream &is, size_t num_dimensions, size_t num_outputs){
    std::forward_list<NodeData> data;
    int num_nodes = IO::readNumber<iomode, int>(is);
 
    for(int i=0; i<num_nodes; i++){
        data.emplace_front(NodeData{
                            IO::readVector<iomode, int>(is, num_dimensions), // point
                            IO::readVector<iomode, double>(is, num_outputs)  // value
                           });
    }
 
    return data;
}
template<typename iomode>
std::forward_list<TensorData> readTensorDataList(std::istream &is, size_t num_dimensions){
    std::forward_list<TensorData> tensors;
    int num_entries = IO::readNumber<iomode, int>(is);
 
    for(int i=0; i<num_entries; i++){
        tensors.emplace_front(TensorData{
                              IO::readNumber<iomode, double>(is), // weight
                              IO::readVector<iomode, int>(is, num_dimensions), // tensor
                              MultiIndexSet(), // points, will be set later
                              std::vector<bool>() // loaded, will be set later
                              });
    }
 
    return tensors;
}

template<class RuleLike>
std::vector<double> listToNodes(std::forward_list<NodeData> const &node_list, size_t num_dimensions, RuleLike const &rule){
    std::vector<double> result(Utils::size_mult(std::distance(node_list.begin(), node_list.end()), num_dimensions));
    auto ix = result.begin();
    for(auto const &t : node_list)
        ix = MultiIndexManipulations::indexesToNodes(t.point, rule, ix);
    return result;
}
template<typename callable_method>
std::vector<double> listToLocalNodes(std::forward_list<NodeData> const &node_list, size_t num_dimensions, callable_method rule){
    std::vector<double> result(Utils::size_mult(std::distance(node_list.begin(), node_list.end()), num_dimensions));
    auto ix = result.begin();
    for(auto const &t : node_list)
        ix = std::transform(t.point.begin(), t.point.end(), ix, rule);
    return result;
}

class DynamicConstructorDataGlobal{
public:
    enum class AddPointResult {
        tensor_incomplete,
        tensor_complete,
        tensor_missing
    };

    DynamicConstructorDataGlobal(size_t cnum_dimensions, size_t cnum_outputs) : num_dimensions(cnum_dimensions), num_outputs(cnum_outputs){}
    template<typename iomode>
    DynamicConstructorDataGlobal(std::istream &is, size_t cnum_dimensions, size_t cnum_outputs, iomode) :
        num_dimensions(cnum_dimensions),
        num_outputs(cnum_outputs),
        tensors(readTensorDataList<iomode>(is, num_dimensions)),
        data(readNodeDataList<iomode>(is, num_dimensions, num_outputs))
        {}
    ~DynamicConstructorDataGlobal() = default;

    template<bool use_ascii> void write(std::ostream &os) const;

    void restrictData(int ibegin, int iend){ for(auto &d : data) d.value = std::vector<double>(d.value.begin() + ibegin, d.value.begin() + iend); }

    int getMaxTensor() const;
    double getMaxTensorWeight() const{
        double maxw = 0;
        for(auto it = tensors.begin(); it != tensors.end(); it++){
            if (it->weight > maxw) maxw = it->weight;
        }
        return maxw;
    }

    void reloadPoints(std::function<int(int)> getNumPoints);

    void clearTesnors();

    MultiIndexSet getInitialTensors() const;

    void addTensor(const int *tensor, std::function<int(int)> getNumPoints, double weight);

    MultiIndexSet getNodesIndexes();

    AddPointResult addNewNode(const std::vector<int> &point, const std::vector<double> &value); // returns whether a tensor is complete

    void ejectCompleteTensor(MultiIndexSet const &current_tensors, MultiIndexSet &new_tensors, MultiIndexSet &new_points, StorageSet &vals);
 
private:
    size_t num_dimensions, num_outputs;
    std::forward_list<TensorData> tensors;
    std::forward_list<NodeData> data;
};

struct SimpleConstructData{
    SimpleConstructData() = default;
    template<typename iomode>
    SimpleConstructData(std::istream &is, int num_dimensions, int num_outputs, iomode) :
        initial_points(MultiIndexSet(is, iomode())),
        data(readNodeDataList<iomode>(is, num_dimensions, num_outputs))
        {}
    MultiIndexSet initial_points;
    std::forward_list<NodeData> data;
    template<bool use_ascii>
    void write(std::ostream &os) const{
        initial_points.write<use_ascii>(os);
        writeNodeDataList<use_ascii>(data, os);
    }
    void restrictData(int ibegin, int iend){ for(auto &d : data) d.value = std::vector<double>(d.value.begin() + ibegin, d.value.begin() + iend); }
    std::vector<double> extractValues(MultiIndexSet const &points){
        size_t num_outputs = data.front().value.size();
        int num_points = points.getNumIndexes();
        Data2D<double> result(num_outputs, num_points);
        auto p = data.before_begin();
        auto d = data.begin();
        while(d != data.end()){
            int slot = points.getSlot(d->point);
            if (slot != -1){ // found a point
                std::copy_n(d->value.begin(), num_outputs, result.getStrip(slot));
                data.erase_after(p);
                d = p;
                d++;
            }else{
                p++;
                d++;
            }
        }
        return result.release();
    }
};
 
}
 
#endif