/*
 * Copyright © 2004 Ondra Kamenik
 * Copyright © 2019 Dynare Team
 *
 * This file is part of Dynare.
 *
 * Dynare is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Dynare is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 */

// Tensor concept.

/* Here we define a tensor class. A tensor is a mathematical object
   corresponding to an (n+1)-dimensional array. An element of such array is
   denoted [B]_α₁…αₙ^β, where β is a special index and α₁…αₙ are other indices.
   The class Tensor and its subclasses view such array as a 2D matrix, where β
   corresponds to one dimension, and α₁…αₙ unfold to the other dimension.
   Whether β corresponds to rows or columns is decided by tensor subclasses,
   however, most of our tensors will have rows indexed by β, and α₁…αₙ will
   unfold column-wise.

   There might be some symmetries in the tensor data. For instance, if α₁ is
   interchanged with α₃ and the other elements remain equal for all possible αᵢ
   and β, then there is a symmetry of α₁ and α₃.

   For any symmetry, there are basically two possible storages of the
   data. The first is unfolded storage, which stores all elements
   regardless the symmetry. The other storage type is folded, which
   stores only elements which do not repeat. We declare abstract classes
   for unfolded and folded tensor alike.

   Also, here we also define a concept of tensor index which is the n-tuple
   α₁…αₙ. It is an iterator, which iterates in dependence of symmetry and
   storage of the underlying tensor.

   Although we do not decide about possible symmetries at this point, it is
   worth noting that we implement two kinds of symmetries in subclasses. The
   first one is a full symmetry where all indices are interchangeable. The
   second one is a generalization of the first, where there are a few groups of
   indices interchangeable within a group and not across. Moreover, the groups
   are required to be consequent partitions of the index n-tuple. For example,
   we do not allow α₁ to be interchangeable with α₃ and not with α₂ at the same
   time.

   However, some intermediate results are, in fact, tensors with a symmetry not
   fitting to our concept. We develop the tensor abstraction for it, but these
   objects are not used very often. They have limited usage due to their
   specialized constructor. */

#ifndef TENSOR_H
#define TENSOR_H

#include "int_sequence.hh"
#include "twod_matrix.hh"

#include <memory>
#include <iostream>

/* Here is the Tensor class, which is nothing else than a simple subclass of
   TwoDMatrix. The unique semantically new member is ‘dim’ which is tensor
   dimension (length of α₁…αₙ). We also declare increment(), decrement() and
   getOffset() methods as pure virtual.

   We also add members for index begin and index end. This is useful, since
   begin() and end() methods do not return instance but only references, which
   prevent making additional copy of index (for example in for cycles as ‘in !=
   end()’ which would do a copy of index for each cycle). The index begin
   ‘in_beg’ is constructed as a sequence of all zeros, and ‘in_end’ is
   constructed from the sequence ‘last’ passed to the constructor, since it
   depends on subclasses. Also we have to say, along what coordinate is the
   multidimensional index. This is used only for initialization of ‘in_end’. */

class Tensor : public TwoDMatrix
{
public:
  enum class indor { along_row, along_col };

  /* The index represents n-tuple α₁…αₙ. Since its movement is dependent on the
     underlying tensor (with storage and symmetry), we maintain a reference to
     that tensor, we maintain the n-tuple (or coordinates) as IntSequence and
     also we maintain the offset number (column, or row) of the index in the
     tensor. The reference is const, since we do not need to change data
     through the index.

     Here we require the Tensor to implement increment() and decrement()
     methods, which calculate following and preceding n-tuple. Also, we need to
     calculate offset number from the given coordinates, so the tensor must
     implement method getOffset(). This method is used only in construction of
     the index from the given coordinates. As the index is created, the offset
     is automatically incremented, and decremented together with index. The
     getOffset() method can be relatively computationally complex. This must be
     kept in mind. Also we generally suppose that n-tuple of all zeros is the
     first offset (first columns or row).

     What follows is a definition of index class, the only interesting point is
     operator==() which decides only according to offset, not according to the
     coordinates. This is useful since there can be more than one of coordinate
     representations of past-the-end index. */
  class index
  {
    const Tensor &tensor;
    int offset;
    IntSequence coor;
  public:
    index(const Tensor &t, int n)
      : tensor(t), offset(0), coor(n, 0)
    {
    }
    index(const Tensor &t, IntSequence cr, int c)
      : tensor(t), offset(c), coor(std::move(cr))
    {
    }
    index(const Tensor &t, IntSequence cr)
      : tensor(t), offset(tensor.getOffset(cr)), coor(std::move(cr))
    {
    }
    index(const index &) = default;
    index(index &&) = default;
    index &operator=(const index &) = delete;
    index &operator=(index &&) = delete;
    index &
    operator++()
    {
      tensor.increment(coor);
      offset++;
      return *this;
    }
    index &
    operator--()
    {
      tensor.decrement(coor);
      offset--;
      return *this;
    }
    int
    operator*() const
    {
      return offset;
    }
    bool
    operator==(const index &n) const
    {
      return offset == n.offset;
    }
    bool
    operator!=(const index &n) const
    {
      return offset != n.offset;
    }
    const IntSequence &
    getCoor() const
    {
      return coor;
    }
    void
    print() const
    {
      std::cout << offset << ": ";
      coor.print();
    }
  };

protected:
  const index in_beg;
  const index in_end;
  int dim;
public:
  Tensor(indor io, IntSequence last, int r, int c, int d)
    : TwoDMatrix(r, c),
      in_beg(*this, d),
      in_end(*this, std::move(last), (io == indor::along_row) ? r : c),
      dim(d)
  {
  }
  Tensor(indor io, IntSequence first, IntSequence last,
         int r, int c, int d)
    : TwoDMatrix(r, c),
      in_beg(*this, std::move(first), 0),
      in_end(*this, std::move(last), (io == indor::along_row) ? r : c),
      dim(d)
  {
  }
  Tensor(int first_row, int num, Tensor &t)
    : TwoDMatrix(first_row, num, t),
      in_beg(t.in_beg),
      in_end(t.in_end),
      dim(t.dim)
  {
  }
  Tensor(const Tensor &t)
    : TwoDMatrix(t),
      in_beg(*this, t.in_beg.getCoor(), *(t.in_beg)),
      in_end(*this, t.in_end.getCoor(), *(t.in_end)),
      dim(t.dim)
  {
  }
  Tensor(Tensor &&) = default;
  ~Tensor() override = default;

  Tensor &operator=(const Tensor &) = delete;
  Tensor &operator=(Tensor &&) = delete;

  virtual void increment(IntSequence &v) const = 0;
  virtual void decrement(IntSequence &v) const = 0;
  virtual int getOffset(const IntSequence &v) const = 0;
  int
  dimen() const
  {
    return dim;
  }

  const index &
  begin() const
  {
    return in_beg;
  }
  const index &
  end() const
  {
    return in_end;
  }
};

/* Here is an abstraction for unfolded tensor. We provide a pure virtual method
   fold() which returns a new instance of folded tensor of the same symmetry.
   Also we provide static methods for incrementing and decrementing an index
   with full symmetry and general symmetry as defined above. */

class FTensor;
class UTensor : public Tensor
{
public:
  UTensor(indor io, IntSequence last, int r, int c, int d)
    : Tensor(io, std::move(last), r, c, d)
  {
  }
  UTensor(const UTensor &) = default;
  UTensor(UTensor &&) = default;
  UTensor(int first_row, int num, UTensor &t)
    : Tensor(first_row, num, t)
  {
  }
  ~UTensor() override = default;
  virtual std::unique_ptr<FTensor> fold() const = 0;

  UTensor &operator=(const UTensor &) = delete;
  UTensor &operator=(UTensor &&) = delete;

  static void increment(IntSequence &v, int nv);
  static void decrement(IntSequence &v, int nv);
  static void increment(IntSequence &v, const IntSequence &nvmx);
  static void decrement(IntSequence &v, const IntSequence &nvmx);
  static int getOffset(const IntSequence &v, int nv);
  static int getOffset(const IntSequence &v, const IntSequence &nvmx);
};

/* This is an abstraction for folded tensor. It only provides a method
   unfold(), which returns the unfolded version of the same symmetry, and
   static methods for decrementing indices.

   We also provide static methods for decrementing the IntSequence in
   folded fashion and also calculating an offset for a given
   IntSequence. However, this is relatively complex calculation, so
   this should be avoided if possible. */

class FTensor : public Tensor
{
public:
  FTensor(indor io, IntSequence last, int r, int c, int d)
    : Tensor(io, std::move(last), r, c, d)
  {
  }
  FTensor(const FTensor &) = default;
  FTensor(FTensor &&) = default;
  FTensor(int first_row, int num, FTensor &t)
    : Tensor(first_row, num, t)
  {
  }
  ~FTensor() override = default;
  virtual std::unique_ptr<UTensor> unfold() const = 0;

  FTensor &operator=(const FTensor &) = delete;
  FTensor &operator=(FTensor &&) = delete;

  static void decrement(IntSequence &v, int nv);
  static int
  getOffset(const IntSequence &v, int nv)
  {
    IntSequence vtmp(v);
    return getOffsetRecurse(vtmp, nv);
  }
private:
  static int getOffsetRecurse(IntSequence &v, int nv);
};

#endif
