/usr/include/polymake/next/IncidenceMatrix.h is in libpolymake-dev-common 3.2r2-3.
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Ewgenij Gawrilow, Michael Joswig (Technische Universitaet Berlin, Germany)
http://www.polymake.org
This program 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 2, or (at your option) any
later version: http://www.gnu.org/licenses/gpl.txt.
This program 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.
--------------------------------------------------------------------------------
*/
/** @file IncidenceMatrix.h
@brief Implementation of pm::IncidenceMatrix class
*/
#ifndef POLYMAKE_INCIDENCE_MATRIX_H
#define POLYMAKE_INCIDENCE_MATRIX_H
#include "polymake/internal/sparse2d.h"
#include "polymake/Set.h"
#include "polymake/GenericIncidenceMatrix.h"
#include "polymake/permutations.h"
namespace pm {
template <typename Set>
class incidence_proxy_base {
protected:
Set* s;
int j;
bool get() const { return s->exists(j); }
void insert() { s->insert(j); }
void erase() { s->erase(j); }
void toggle() { s->toggle(j); }
public:
typedef bool value_type;
incidence_proxy_base(Set& s_arg, int j_arg)
: s(&s_arg), j(j_arg) {}
};
template <typename TreeRef> class incidence_line;
template <bool rowwise, typename BaseRef=void> class incidence_line_factory;
template <typename symmetric> class IncidenceMatrix_base;
template <typename TreeRef>
struct incidence_line_params
: mlist_concat< typename sparse2d::line_params<TreeRef>::type,
OperationTag< BuildUnaryIt<operations::index2element> > > {};
template <typename TreeRef>
class incidence_line_base
: public modified_tree< incidence_line<TreeRef>, typename incidence_line_params<TreeRef>::type > {
protected:
typedef nothing first_arg_type;
typedef nothing second_arg_type;
~incidence_line_base();
public:
int index() const { return this->get_container().get_line_index(); }
};
template <typename Tree>
class incidence_line_base<Tree&>
: public modified_tree< incidence_line<Tree&>, typename incidence_line_params<Tree&>::type > {
protected:
typedef typename deref<Tree>::type tree_type;
typedef typename std::conditional<Tree::symmetric, Symmetric, NonSymmetric>::type symmetric;
typedef typename inherit_ref<IncidenceMatrix_base<symmetric>, Tree&>::type matrix_ref;
typedef typename attrib<matrix_ref>::plus_const const_matrix_ref;
alias<matrix_ref> matrix;
int line_index;
typedef typename alias<matrix_ref>::arg_type first_arg_type;
typedef int second_arg_type;
incidence_line_base(first_arg_type arg1, second_arg_type arg2)
: matrix(arg1), line_index(arg2) {}
public:
typename incidence_line_base::container& get_container()
{
return matrix->get_table().get_line(line_index, (tree_type*)0);
}
const typename incidence_line_base::container& get_container() const
{
return matrix->get_table().get_line(line_index, (tree_type*)0);
}
int index() const { return line_index; }
};
template <typename TreeRef>
class incidence_line
: public incidence_line_base<TreeRef>
, public GenericMutableSet<incidence_line<TreeRef>, int, operations::cmp>
{
typedef incidence_line_base<TreeRef> base_t;
friend class GenericMutableSet<incidence_line>;
template <typename> friend class IncidenceMatrix;
template <sparse2d::restriction_kind> friend class RestrictedIncidenceMatrix;
public:
incidence_line(typename base_t::first_arg_type arg1, typename base_t::second_arg_type arg2)
: base_t(arg1,arg2) {}
incidence_line& operator= (const incidence_line& other)
{
return incidence_line::generic_mutable_type::operator=(other);
}
// TODO: investigate whether the dimension check is active?
template <typename Set>
incidence_line& operator= (const GenericSet<Set, int, operations::cmp>& other)
{
return incidence_line::generic_mutable_type::operator=(other);
}
template <typename Set>
incidence_line& operator= (const Complement<Set, int, operations::cmp>& other)
{
return incidence_line::generic_mutable_type::operator=(sequence(0, this->dim()) * other);
}
incidence_line& operator= (std::initializer_list<int> l)
{
return incidence_line::generic_mutable_type::operator=(l);
}
protected:
template <typename Iterator>
void fill(Iterator src)
{
this->clear();
for (; !src.at_end(); ++src)
this->insert(*src);
}
};
template <typename TreeRef>
struct check_container_feature<incidence_line<TreeRef>, sparse_compatible> : std::true_type {};
template <typename TreeRef>
struct spec_object_traits< incidence_line<TreeRef> >
: spec_object_traits<is_container> {
static const bool is_temporary=attrib<TreeRef>::is_reference,
is_always_const=attrib<TreeRef>::is_const;
typedef typename std::conditional<is_temporary, void, typename deref<TreeRef>::type>::type masquerade_for;
static const int is_resizeable=0;
};
template <typename Iterator>
using is_sequence_of_sets=std::is_same<typename object_traits<typename iterator_traits<Iterator>::value_type>::generic_tag, is_set>;
template <typename TContainer>
using fits_for_append_to_IM
= mlist_or<isomorphic_to_container_of<TContainer, int, allow_conversion>,
isomorphic_to_container_of<TContainer, Set<int>, allow_conversion>,
isomorphic_to_container_of<TContainer, IncidenceMatrix<>, allow_conversion>,
std::is_same<typename object_traits<TContainer>::generic_tag, is_incidence_matrix> >;
template <sparse2d::restriction_kind restriction=sparse2d::only_rows>
class RestrictedIncidenceMatrix
: public matrix_methods<RestrictedIncidenceMatrix<restriction>, bool> {
protected:
typedef sparse2d::restriction_const<restriction> my_restriction;
typedef sparse2d::restriction_const<(restriction==sparse2d::only_rows ? sparse2d::only_cols : sparse2d::only_rows)> cross_restriction;
typedef sparse2d::Table<nothing, false, restriction> table_type;
table_type data;
table_type& get_table() { return data; }
const table_type& get_table() const { return data; }
template <typename Iterator, typename TLines>
static
void copy_linewise(Iterator&& src, TLines& lines, my_restriction, std::true_type)
{
copy_range(std::forward<Iterator>(src), entire(lines));
}
template <typename Iterator, typename TLines>
static
void copy_linewise(Iterator&& src, TLines& lines, my_restriction, std::false_type)
{
for (auto l_i=entire(lines); !l_i.at_end(); ++l_i, ++src)
l_i->fill(entire(*src));
}
template <typename Iterator, typename TLines, typename TSourceOrdered>
static
void copy_linewise(Iterator&& src, TLines& lines, cross_restriction, TSourceOrdered)
{
for (int i=0; !src.at_end(); ++src, ++i)
append_across(lines, *src, i);
}
template <typename TLines, typename TSet>
static
void append_across(TLines& lines, const TSet& set, int i)
{
for (auto s=entire(set); !s.at_end(); ++s)
lines[*s].push_back(i);
}
typedef incidence_proxy_base< incidence_line<typename table_type::primary_tree_type> > proxy_base;
public:
typedef bool value_type;
typedef sparse_elem_proxy<proxy_base> reference;
typedef const bool const_reference;
explicit RestrictedIncidenceMatrix(int n=0) : data(n) {}
RestrictedIncidenceMatrix(int r, int c) : data(r,c) {}
template <typename Iterator, typename THow,
typename=typename std::enable_if<is_among<THow, sparse2d::rowwise, sparse2d::columnwise>::value &&
assess_iterator_value<Iterator, can_initialize, Set<int>>::value &&
(THow::value==restriction || assess_iterator<Iterator, check_iterator_feature, end_sensitive>::value)>::type>
RestrictedIncidenceMatrix(int n, THow how, Iterator&& src)
: data(n)
{
copy_linewise(ensure_private_mutable(std::forward<Iterator>(src)), lines(*this, my_restriction()),
how, is_sequence_of_sets<Iterator>());
}
template <typename Iterator, typename THow,
typename=typename std::enable_if<is_among<THow, sparse2d::rowwise, sparse2d::columnwise>::value &&
assess_iterator_value<Iterator, can_initialize, Set<int>>::value &&
(THow::value==restriction || assess_iterator<Iterator, check_iterator_feature, end_sensitive>::value)>::type>
RestrictedIncidenceMatrix(int r, int c, THow how, Iterator&& src)
: data(r, c)
{
copy_linewise(ensure_private_mutable(std::forward<Iterator>(src)), lines(*this, my_restriction()),
how, is_sequence_of_sets<Iterator>());
}
template <typename THow, typename... TSources,
typename=typename std::enable_if<is_among<THow, sparse2d::rowwise, sparse2d::columnwise>::value &&
mlist_and_nonempty<fits_for_append_to_IM<TSources>...>::value>::type>
RestrictedIncidenceMatrix(THow how, const TSources&... src)
: data(0)
{
append_impl(how, src...);
}
RestrictedIncidenceMatrix(std::initializer_list<std::initializer_list<int>> l)
: data(l.size())
{
static_assert(restriction==sparse2d::only_rows, "a column-only restricted incidence matrix can't be constructed from an initializer list");
copy_linewise(l.begin(), pm::rows(*this), my_restriction(), std::false_type());
}
RestrictedIncidenceMatrix(RestrictedIncidenceMatrix&& M)
: data(std::move(M.data)) {}
void swap(RestrictedIncidenceMatrix& M) { data.swap(M.data); }
void clear() { data.clear(); }
protected:
proxy_base random_impl(int i, int j, std::false_type)
{
return proxy_base(this->row(i), j);
}
proxy_base random_impl(int i, int j, std::true_type)
{
return proxy_base(this->col(j), i);
}
bool random_impl(int i, int j, std::false_type) const
{
return this->row(i).exists(j);
}
bool random_impl(int i, int j, std::true_type) const
{
return this->col(j).exists(i);
}
public:
reference operator() (int i, int j)
{
return random_impl(i, j, bool_constant<restriction==sparse2d::only_cols>());
}
const_reference operator() (int i, int j) const
{
return random_impl(i, j, bool_constant<restriction==sparse2d::only_cols>());
}
bool exists(int i, int j) const
{
return random_impl(i, j, bool_constant<restriction==sparse2d::only_cols>());
}
private:
auto append_lines_start(sparse2d::rowwise, int n)
{
const int oldrows=data.rows();
data.resize_rows(oldrows+n);
return pm::rows(*this).begin()+oldrows;
}
auto append_lines_start(sparse2d::columnwise, int n)
{
const int oldcols=data.cols();
data.resize_cols(oldcols+n);
return pm::cols(*this).begin()+oldcols;
}
template <typename TContainer, typename... TMoreSources>
auto append_lines_start(my_restriction how,
typename std::enable_if<isomorphic_to_container_of<TContainer, int, allow_conversion>::value, int>::type n,
const TContainer& c, TMoreSources&&... more_src)
{
return append_lines_start(how, n+1, std::forward<TMoreSources>(more_src)...);
}
template <typename TContainer, typename... TMoreSources>
auto append_lines_start(my_restriction how,
typename std::enable_if<isomorphic_to_container_of<TContainer, Set<int>, allow_conversion>::value, int>::type n,
const TContainer& c, TMoreSources&&... more_src)
{
return append_lines_start(how, n+c.size(), std::forward<TMoreSources>(more_src)...);
}
template <typename TMatrix, typename... TMoreSources>
auto append_lines_start(my_restriction how, int n, const GenericIncidenceMatrix<TMatrix>& m, TMoreSources&&... more_src)
{
return append_lines_start(how, n+(restriction==sparse2d::only_rows ? m.rows() : m.cols()), std::forward<TMoreSources>(more_src)...);
}
template <typename TContainer, typename... TMoreSources>
auto append_lines_start(my_restriction how,
typename std::enable_if<isomorphic_to_container_of<TContainer, IncidenceMatrix<>, allow_conversion>::value, int>::type n,
const TContainer& c, TMoreSources&&... more_src)
{
for (const auto& m : c)
n += (restriction==sparse2d::only_rows ? m.rows() : m.cols());
return append_lines_start(how, n, std::forward<TMoreSources>(more_src)...);
}
template <typename... TSources>
int append_lines_start(cross_restriction, int, TSources&&...)
{
return restriction==sparse2d::only_rows ? data.cols() : data.rows();
}
template <typename Iterator, typename TSet>
void append_lines_from(my_restriction, Iterator& dst, const GenericSet<TSet, int, operations::cmp>& s)
{
*dst=s.top();
++dst;
}
template <typename Iterator, typename TContainer>
typename std::enable_if<isomorphic_to_container_of<TContainer, int, is_set>::value>::type
append_lines_from(my_restriction, Iterator& dst, const TContainer& c)
{
dst->fill(entire(c));
++dst;
}
template <typename THow, typename Iterator, typename TMatrix>
void append_lines_from(THow how, Iterator& dst, const GenericIncidenceMatrix<TMatrix>& m)
{
for (auto src=entire(sparse2d::lines(m.top(), how)); !src.at_end(); ++src)
append_lines_from(how, dst, *src);
}
template <typename Iterator, typename TContainer>
typename std::enable_if<isomorphic_to_container_of<TContainer, Set<int>, allow_conversion>::value ||
isomorphic_to_container_of<TContainer, IncidenceMatrix<>, allow_conversion>::value>::type
append_lines_from(my_restriction how, Iterator& dst, const TContainer& c)
{
for (auto src=entire(c); !src.at_end(); ++src)
append_lines_from(how, dst, *src);
}
template <typename TContainer>
typename std::enable_if<isomorphic_to_container_of<TContainer, int, allow_conversion>::value>::type
append_lines_from(cross_restriction, int& r, const TContainer& c)
{
append_across(sparse2d::lines(*this, my_restriction()), c, r);
++r;
}
template <typename THow, typename Iterator>
void append_lines(THow, Iterator&) {}
template <typename THow, typename Iterator, typename TSource, typename... TMoreSources>
void append_lines(THow how, Iterator&& dst, const TSource& src, TMoreSources&&... more_src)
{
append_lines_from(how, dst, src);
append_lines(how, dst, std::forward<TMoreSources>(more_src)...);
}
template <typename THow, typename... TSources>
void append_impl(THow how, TSources&&... src)
{
append_lines(how, append_lines_start(how, 0, std::forward<TSources>(src)...), std::forward<TSources>(src)...);
}
public:
template <typename TMatrix>
RestrictedIncidenceMatrix& operator/= (const GenericIncidenceMatrix<TMatrix>& m)
{
append_impl(sparse2d::rowwise(), m);
return *this;
}
template <typename TSet>
RestrictedIncidenceMatrix& operator/= (const GenericSet<TSet, int, operations::cmp>& s)
{
append_impl(sparse2d::rowwise(), s.top());
return *this;
}
template <typename TMatrix>
RestrictedIncidenceMatrix& operator|= (const GenericIncidenceMatrix<TMatrix>& m)
{
append_impl(sparse2d::columnwise(), m);
return *this;
}
template <typename TSet>
RestrictedIncidenceMatrix& operator|= (const GenericSet<TSet, int, operations::cmp>& s)
{
append_impl(sparse2d::columnwise(), s.top());
return *this;
}
/// append one or more rows
template <typename... TSources,
typename=typename std::enable_if<mlist_and_nonempty<fits_for_append_to_IM<TSources>...>::value>::type>
void append_rows(const TSources&... src)
{
append_impl(sparse2d::rowwise(), src...);
}
/// append one or more columns
template <typename... TSources,
typename=typename std::enable_if<mlist_and_nonempty<fits_for_append_to_IM<TSources>...>::value>::type>
void append_columns(const TSources&... src)
{
append_impl(sparse2d::columnwise(), src...);
}
void squeeze() { data.squeeze(); }
template <typename TPerm>
typename std::enable_if<isomorphic_to_container_of<TPerm, int>::value>::type
permute_rows(const TPerm& perm)
{
data.permute_rows(perm, std::false_type());
}
template <typename TPerm>
typename std::enable_if<isomorphic_to_container_of<TPerm, int>::value>::type
permute_cols(const TPerm& perm)
{
data.permute_cols(perm, std::false_type());
}
template <typename TInvPerm>
typename std::enable_if<isomorphic_to_container_of<TInvPerm, int>::value>::type
permute_inv_rows(const TInvPerm& inv_perm)
{
data.permute_rows(inv_perm, std::true_type());
}
template <typename TInvPerm>
typename std::enable_if<isomorphic_to_container_of<TInvPerm, int>::value>::type
permute_inv_cols(const TInvPerm& inv_perm)
{
data.permute_cols(inv_perm, std::true_type());
}
#if POLYMAKE_DEBUG
void check() const { data.check(); }
#endif
friend class Rows<RestrictedIncidenceMatrix>;
friend class Cols<RestrictedIncidenceMatrix>;
template <typename, typename, bool, sparse2d::restriction_kind, typename> friend class sparse2d::Rows;
template <typename, typename, bool, sparse2d::restriction_kind, typename> friend class sparse2d::Cols;
template <typename> friend class IncidenceMatrix;
};
template <sparse2d::restriction_kind restriction>
class Rows< RestrictedIncidenceMatrix<restriction> >
: public sparse2d::Rows< RestrictedIncidenceMatrix<restriction>, nothing, false, restriction,
operations::masquerade<incidence_line> > {
protected:
~Rows();
public:
typedef typename std::conditional<restriction==sparse2d::only_rows, random_access_iterator_tag, output_iterator_tag>::type
container_category;
};
template <sparse2d::restriction_kind restriction>
class Cols< RestrictedIncidenceMatrix<restriction> >
: public sparse2d::Cols< RestrictedIncidenceMatrix<restriction>, nothing, false, restriction,
operations::masquerade<incidence_line> > {
protected:
~Cols();
public:
typedef typename std::conditional<restriction==sparse2d::only_cols, random_access_iterator_tag, output_iterator_tag>::type
container_category;
};
template <sparse2d::restriction_kind restriction>
struct spec_object_traits< RestrictedIncidenceMatrix<restriction> >
: spec_object_traits<is_container> {
static const int dimension=2;
typedef typename std::conditional<restriction==sparse2d::only_rows,
Rows< RestrictedIncidenceMatrix<restriction> >,
Cols< RestrictedIncidenceMatrix<restriction> > >::type serialized;
static serialized& serialize(RestrictedIncidenceMatrix<restriction>& M)
{
return reinterpret_cast<serialized&>(M);
}
static const serialized& serialize(const RestrictedIncidenceMatrix<restriction>& M)
{
return reinterpret_cast<const serialized&>(M);
}
};
template <typename symmetric>
class IncidenceMatrix_base {
protected:
typedef sparse2d::Table<nothing, symmetric::value> table_type;
shared_object<table_type, AliasHandlerTag<shared_alias_handler>> data;
table_type& get_table() { return *data; }
const table_type& get_table() const { return *data; }
friend IncidenceMatrix_base& make_mutable_alias(IncidenceMatrix_base& alias, IncidenceMatrix_base& owner)
{
alias.data.make_mutable_alias(owner.data);
return alias;
}
IncidenceMatrix_base() = default;
IncidenceMatrix_base(int r, int c)
: data(r, c) {}
template <sparse2d::restriction_kind restriction>
explicit IncidenceMatrix_base(sparse2d::Table<nothing, symmetric::value, restriction>&& input_data)
: data(std::move(input_data)) {}
template <typename> friend class Rows;
template <typename> friend class Cols;
template <typename, typename, bool, sparse2d::restriction_kind, typename> friend class sparse2d::Rows;
template <typename, typename, bool, sparse2d::restriction_kind, typename> friend class sparse2d::Cols;
template <bool, typename> friend class incidence_line_factory;
template <typename> friend class incidence_line_base;
template <typename, int> friend class alias;
};
template <typename symmetric>
class Rows< IncidenceMatrix_base<symmetric> >
: public sparse2d::Rows< IncidenceMatrix_base<symmetric>, nothing, symmetric::value, sparse2d::full,
operations::masquerade<incidence_line> > {
protected:
~Rows();
};
template <typename symmetric>
class Cols< IncidenceMatrix_base<symmetric> >
: public sparse2d::Cols< IncidenceMatrix_base<symmetric>, nothing, symmetric::value, sparse2d::full,
operations::masquerade<incidence_line> > {
protected:
~Cols();
};
/** @class IncidenceMatrix
@brief 0/1 incidence matrix.
The only @ref persistent class from the incidence matrix family.
The implementation is based on a two-dimensional grid of <a href="AVL.html">balanced binary search (AVL) trees</a>,
the same as for @see SparseMatrix. The whole internal data structure
is attached to a smart pointer with @see {reference counting}.
A symmetric incidence matrix is a square matrix whose elements `(i,j)` and `(j,i)`
are always equal. Internally it is stored in a triangular form, avoiding redundant elements, but appears as a full square.
*/
template <typename symmetric>
class IncidenceMatrix
: public IncidenceMatrix_base<symmetric>
, public GenericIncidenceMatrix< IncidenceMatrix<symmetric> > {
protected:
typedef IncidenceMatrix_base<symmetric> base_t;
friend IncidenceMatrix& make_mutable_alias(IncidenceMatrix& alias, IncidenceMatrix& owner)
{
return static_cast<IncidenceMatrix&>(make_mutable_alias(static_cast<base_t&>(alias), static_cast<base_t&>(owner)));
}
/// initialize from a dense boolean sequence in row order
template <typename Iterator>
void init_impl(Iterator&& src, std::true_type)
{
const int n=this->cols();
for (auto r_i=entire(pm::rows(static_cast<base_t&>(*this))); !r_i.at_end(); ++r_i) {
int i=0;
if (symmetric::value) {
i=r_i.index();
std::advance(src,i);
}
for (; i<n; ++i, ++src)
if (*src) r_i->push_back(i);
}
}
/// input already ordered
template <typename Iterator>
void init_rowwise(Iterator&& src, std::true_type)
{
copy_range(std::forward<Iterator>(src), entire(pm::rows(static_cast<base_t&>(*this))));
}
/// input in uncertain order
template <typename Iterator>
void init_rowwise(Iterator&& src, std::false_type)
{
for (auto r_i=entire(pm::rows(static_cast<base_t&>(*this))); !r_i.at_end(); ++r_i, ++src)
r_i->fill(entire(*src));
}
/// initialize rowwise from a sequence of sets
template <typename Iterator>
void init_impl(Iterator&& src, std::false_type)
{
init_rowwise(std::forward<Iterator>(src), is_sequence_of_sets<Iterator>());
}
typedef incidence_proxy_base< incidence_line<typename base_t::table_type::primary_tree_type> > proxy_base;
public:
typedef typename std::conditional<symmetric::value, void, RestrictedIncidenceMatrix<> >::type unknown_columns_type;
typedef bool value_type;
typedef sparse_elem_proxy<proxy_base> reference;
typedef const bool const_reference;
/// Create an empty IncidenceMatrix.
IncidenceMatrix() {}
/// Create an empty IncidenceMatrix with @a r rows and @a c columns initialized with zeroes.
IncidenceMatrix(int r, int c)
: base_t(r,c) {}
/** @brief Create an IncidenceMatrix IncidenceMatrix with @a r rows and @a c columns and initialize it from a data sequence.
@a src should iterate either over @a r×@a c boolean values, corresponding to the
elements in the row order (the column index changes first,) or over @a r sets with
integer elements (or convertible to integer), which are assigned to the matrix rows.
In the symmetric case the redundant elements must be present in the input sequence; their values are ignored.
@param r the number of rows
@param c the number of columns
@param src an iterator
*/
template <typename Iterator>
IncidenceMatrix(int r, int c, Iterator&& src)
: base_t(r, c)
{
init_impl(ensure_private_mutable(std::forward<Iterator>(src)),
bool_constant<(object_traits<typename iterator_traits<Iterator>::value_type>::total_dimension==0)>());
}
IncidenceMatrix(const GenericIncidenceMatrix<IncidenceMatrix>& M)
: base_t(M.top()) {}
template <typename Matrix2, typename=typename std::enable_if<IncidenceMatrix::template compatible_symmetry_types<Matrix2>()>::type>
IncidenceMatrix(const GenericIncidenceMatrix<Matrix2>& M)
: base_t(M.rows(), M.cols())
{
init_impl(pm::rows(M).begin(), std::false_type());
}
template <sparse2d::restriction_kind restriction, typename=typename std::enable_if<!symmetric::value && restriction != sparse2d::full>::type>
explicit IncidenceMatrix(RestrictedIncidenceMatrix<restriction>&& M)
: base_t(std::move(M.data)) {}
/// Construct a matrix by rowwise or columnwise concatenation of given matrices and/or sets.
/// Dimensions are set automatically to encompass all input elements.
template <typename THow, typename... TSources,
typename=typename std::enable_if<!symmetric::value && is_among<THow, sparse2d::rowwise, sparse2d::columnwise>::value &&
mlist_and_nonempty<fits_for_append_to_IM<TSources>...>::value>::type>
IncidenceMatrix(THow how, const TSources&... src)
: base_t(RestrictedIncidenceMatrix<>(how, src...).data) {}
/// Construct a matrix from a given sequence of row sets.
/// Number of columns is set automatically to encompass all input elements.
template <typename Container, typename=typename std::enable_if<!symmetric::value && isomorphic_to_container_of<Container, Set<int>, allow_conversion>::value>::type>
explicit IncidenceMatrix(const Container& src)
: base_t(RestrictedIncidenceMatrix<>(src.size(), sparse2d::rowwise(), src.begin()).data) {}
/// Construct a matrix with a prescribed number of columns from a given sequence of row sets
template <typename Container,
typename=typename std::enable_if<!symmetric::value && isomorphic_to_container_of<Container, Set<int>, allow_conversion>::value>::type>
IncidenceMatrix(const Container& src, int c)
: base_t(src.size(), c)
{
init_impl(src.begin(), std::false_type());
}
IncidenceMatrix(int r, int c, std::initializer_list<bool> l)
: base_t(r, c)
{
if (POLYMAKE_DEBUG && r*c != l.size())
throw std::runtime_error("initializer_list size does not match the dimensions");
init_impl(l.begin(), std::true_type());
}
IncidenceMatrix(std::initializer_list<std::initializer_list<int>> l)
: base_t(RestrictedIncidenceMatrix<>(l).data) {}
IncidenceMatrix& operator= (const IncidenceMatrix& other) { assign(other); return *this; }
using IncidenceMatrix::generic_type::operator=;
template <sparse2d::restriction_kind restriction, typename enabled=typename std::enable_if<!symmetric::value && restriction != sparse2d::full>::type>
IncidenceMatrix& operator= (RestrictedIncidenceMatrix<restriction>&& M)
{
this->data.replace(std::move(M.data));
return *this;
}
/// Swap the contents with that of another matrix in an efficient way.
void swap(IncidenceMatrix& M) { this->data.swap(M.data); }
friend void relocate(IncidenceMatrix* from, IncidenceMatrix* to)
{
relocate(&from->data, &to->data);
}
/** @brief Extend or truncate to new dimensions (@a m rows, @a n columns).
Surviving elements keep their values, new elements are implicitly @c false.
@c IncidenceMatrix deploys an adaptive reallocation strategy similar to @c std::vector,
reserving additional stock memory by every reallocation. If you repeatedly increase the matrix dimensions by one,
the amortized reallocation costs will be proportional to the logarithm of the final dimension.
A special case, looking at the first glance like a "no operation": @c{ M.resize(M.rows(), M.cols()) },
gets rid of this extra allocated storage.
*/
void resize(int m, int n) { this->data->resize(m,n); }
/// Clear contents.
void clear() { this->data.apply(shared_clear()); }
/// Clear contents.
void clear(int r, int c) { this->data.apply(typename base_t::table_type::shared_clear(r,c)); }
/// Entry at row i column j.
reference operator() (int i, int j)
{
if (POLYMAKE_DEBUG) {
if (i<0 || i>=this->rows() || j<0 || j>=this->cols())
throw std::runtime_error("IncidenceMatrix::operator() - index out of range");
}
return proxy_base(pm::rows(static_cast<base_t&>(*this))[i],j);
}
/// Entry at row i column j (const).
const_reference operator() (int i, int j) const
{
if (POLYMAKE_DEBUG) {
if (i<0 || i>=this->rows() || j<0 || j>=this->cols())
throw std::runtime_error("IncidenceMatrix::operator() - index out of range");
}
return pm::rows(static_cast<const base_t&>(*this))[i].exists(j);
}
/// Returns the entry at position (i,j).
bool exists(int i, int j) const { return operator()(i,j); }
template <typename row_number_consumer, typename col_number_consumer>
void squeeze(const row_number_consumer& rnc, const col_number_consumer& cnc) { this->data->squeeze(rnc,cnc); }
template <typename row_number_consumer>
void squeeze(const row_number_consumer& rnc) { this->data->squeeze(rnc); }
/// Delete empty rows and columns, renumber the rest and reduce the dimensions.
void squeeze() { this->data->squeeze(); }
template <typename row_number_consumer>
void squeeze_rows(const row_number_consumer& rnc) { this->data->squeeze_rows(rnc); }
/// Delete empty rows, renumber the rest and reduce the dimensions.
void squeeze_rows() { this->data->squeeze_rows(); }
template <typename col_number_consumer>
void squeeze_cols(const col_number_consumer& cnc) { this->data->squeeze_cols(cnc); }
/// Delete empty columns, renumber the rest and reduce the dimensions.
void squeeze_cols() { this->data->squeeze_cols(); }
/// Permute the rows according to the given permutation.
template <typename TPerm>
typename std::enable_if<isomorphic_to_container_of<TPerm, int>::value>::type
permute_rows(const TPerm& perm)
{
this->data->permute_rows(perm, std::false_type());
}
/// Permute the columns according to the given permutation.
template <typename TPerm>
typename std::enable_if<isomorphic_to_container_of<TPerm, int>::value>::type
permute_cols(const TPerm& perm)
{
this->data->permute_cols(perm, std::false_type());
}
/// Permute the rows according to the inverse of the given permutation.
template <typename TInvPerm>
typename std::enable_if<isomorphic_to_container_of<TInvPerm, int>::value>::type
permute_inv_rows(const TInvPerm& inv_perm)
{
this->data->permute_rows(inv_perm, std::true_type());
}
/// Permute the columns according to the inverse of the given permutation.
template <typename TInvPerm>
typename std::enable_if<isomorphic_to_container_of<TInvPerm, int>::value>::type
permute_inv_cols(const TInvPerm& inv_perm)
{
this->data->permute_cols(inv_perm, std::true_type());
}
template <typename Perm, typename InvPerm, typename enabled=typename std::enable_if<symmetric::value, typename mproject1st<void, Perm>::type>::type>
IncidenceMatrix copy_permuted(const Perm& perm, const InvPerm& inv_perm) const
{
const int n=this->rows();
IncidenceMatrix result(n,n);
result.data.get()->copy_permuted(*this->data, perm, inv_perm);
return result;
}
#if POLYMAKE_DEBUG
void check() const { this->data->check(); }
#endif
protected:
void assign(const GenericIncidenceMatrix<IncidenceMatrix>& M) { this->data=M.top().data; }
template <typename Matrix>
void assign(const GenericIncidenceMatrix<Matrix>& M)
{
if (this->data.is_shared() || this->rows() != M.rows() || this->cols() != M.cols())
// circumvent the symmetry checks, they are already done in GenericIncidenceMatrix methods
assign(IncidenceMatrix(M.rows(), M.cols(), pm::rows(M).begin()));
else
GenericIncidenceMatrix<IncidenceMatrix>::assign(M);
}
template <typename Matrix2>
void append_rows(const Matrix2& m)
{
const int old_rows=this->rows();
this->data.apply(typename base_t::table_type::shared_add_rows(m.rows()));
copy_range(entire(pm::rows(m)), pm::rows(static_cast<base_t&>(*this)).begin()+old_rows);
}
template <typename Set2>
void append_row(const Set2& s)
{
const int old_rows=this->rows();
this->data.apply(typename base_t::table_type::shared_add_rows(1));
this->row(old_rows)=s;
}
template <typename Matrix2>
void append_cols(const Matrix2& m)
{
const int old_cols=this->cols();
this->data.apply(typename base_t::table_type::shared_add_cols(m.cols()));
copy_range(entire(pm::cols(m)), pm::cols(static_cast<base_t&>(*this)).begin()+old_cols);
}
template <typename Set2>
void append_col(const Set2& s)
{
const int old_cols=this->cols();
this->data.apply(typename base_t::table_type::shared_add_cols(1));
this->col(old_cols)=s;
}
void stretch_rows(int r)
{
this->data->resize_rows(r);
}
void stretch_cols(int c)
{
this->data->resize_cols(c);
}
template <typename> friend class GenericIncidenceMatrix;
friend class Rows<IncidenceMatrix>;
friend class Cols<IncidenceMatrix>;
template <typename, typename, bool, sparse2d::restriction_kind, typename> friend class sparse2d::Rows;
template <typename, typename, bool, sparse2d::restriction_kind, typename> friend class sparse2d::Cols;
template <typename, typename> friend class RowChain;
template <typename, typename> friend class ColChain;
};
template <typename symmetric>
struct check_container_feature< IncidenceMatrix<symmetric>, Symmetric > : symmetric {};
template <bool rowwise, typename BaseRef>
class incidence_line_factory {
public:
typedef BaseRef first_argument_type;
typedef int second_argument_type;
typedef typename std::conditional<rowwise, typename deref<BaseRef>::type::table_type::row_tree_type,
typename deref<BaseRef>::type::table_type::col_tree_type>::type
tree_type;
typedef incidence_line<typename inherit_ref<tree_type, BaseRef>::type> result_type;
result_type operator() (BaseRef matrix, int index) const
{
return result_type(matrix,index);
}
};
template <bool rowwise>
class incidence_line_factory<rowwise, void> : public operations::incomplete {};
template <bool rowwise, typename BaseRef>
struct operation_cross_const_helper< incidence_line_factory<rowwise, BaseRef> > {
typedef incidence_line_factory<rowwise, typename attrib<BaseRef>::minus_const> operation;
typedef incidence_line_factory<rowwise, typename attrib<BaseRef>::plus_const> const_operation;
};
template <bool rowwise, typename Iterator1, typename Iterator2, typename Reference1, typename Reference2>
struct binary_op_builder< incidence_line_factory<rowwise>, Iterator1, Iterator2, Reference1, Reference2>
: empty_op_builder< incidence_line_factory<rowwise,Reference1> > {};
template <typename TSymmetric>
class Rows< IncidenceMatrix<TSymmetric> >
: public modified_container_pair_impl< Rows< IncidenceMatrix<TSymmetric> >,
mlist< Container1Tag< constant_value_container< IncidenceMatrix_base<TSymmetric>& > >,
Container2Tag< sequence >,
OperationTag< pair< incidence_line_factory<true>,
BuildBinaryIt<operations::dereference2> > >,
MasqueradedTop > > {
protected:
~Rows();
public:
constant_value_container< IncidenceMatrix_base<TSymmetric>& > get_container1()
{
return this->hidden();
}
const constant_value_container< const IncidenceMatrix_base<TSymmetric>& > get_container1() const
{
return this->hidden();
}
sequence get_container2() const
{
return sequence(0, this->hidden().get_table().rows());
}
void resize(int n)
{
this->hidden().get_table().resize_rows(n);
}
};
template <typename TSymmetric>
class Cols< IncidenceMatrix<TSymmetric> >
: public modified_container_pair_impl< Cols< IncidenceMatrix<TSymmetric> >,
mlist< Container1Tag< constant_value_container< IncidenceMatrix_base<TSymmetric>& > >,
Container2Tag< sequence >,
OperationTag< pair< incidence_line_factory<false>,
BuildBinaryIt<operations::dereference2> > >,
MasqueradedTop > > {
protected:
~Cols();
public:
constant_value_container< IncidenceMatrix_base<TSymmetric>& > get_container1()
{
return this->hidden();
}
const constant_value_container< const IncidenceMatrix_base<TSymmetric>& > get_container1() const
{
return this->hidden();
}
sequence get_container2() const
{
return sequence(0, this->hidden().get_table().cols());
}
void resize(int n)
{
this->hidden().get_table().resize_cols(n);
}
};
/// Convolution of two incidence relations.
template <typename Matrix1, typename Matrix2> inline
IncidenceMatrix<>
convolute(const GenericIncidenceMatrix<Matrix1>& m1, const GenericIncidenceMatrix<Matrix2>& m2)
{
if (POLYMAKE_DEBUG || !Unwary<Matrix1>::value || !Unwary<Matrix2>::value) {
if (m1.cols() != m2.rows())
throw std::runtime_error("convolute - dimension mismatch");
}
IncidenceMatrix<> result(m1.rows(), m2.cols());
typename Rows<Matrix1>::const_iterator r1=rows(m1).begin();
for (typename Entire< Rows< IncidenceMatrix<> > >::iterator dst=entire(rows(result));
!dst.at_end(); ++dst, ++r1)
accumulate_in(entire(rows(m2.minor(*r1,All))), BuildBinary<operations::add>(), *dst);
return result;
}
template <typename TMatrix, typename Permutation> inline
typename std::enable_if<!TMatrix::is_symmetric, typename TMatrix::persistent_type>::type
permuted_rows(const GenericIncidenceMatrix<TMatrix>& m, const Permutation& perm)
{
if (POLYMAKE_DEBUG || !Unwary<TMatrix>::value) {
if (m.rows() != perm.size())
throw std::runtime_error("permuted_rows - dimension mismatch");
}
return IncidenceMatrix<>(RestrictedIncidenceMatrix<sparse2d::only_rows>(m.rows(), m.cols(), sparse2d::rowwise(), select(rows(m), perm).begin()));
}
template <typename TMatrix, typename Permutation> inline
typename std::enable_if<!TMatrix::is_symmetric, typename TMatrix::persistent_type>::type
permuted_cols(const GenericIncidenceMatrix<TMatrix>& m, const Permutation& perm)
{
if (POLYMAKE_DEBUG || !Unwary<TMatrix>::value) {
if (m.cols() != perm.size())
throw std::runtime_error("permuted_cols - dimension mismatch");
}
return IncidenceMatrix<>(RestrictedIncidenceMatrix<sparse2d::only_cols>(m.rows(), m.cols(), sparse2d::columnwise(), select(cols(m), perm).begin()));
}
template <typename TMatrix, typename Permutation> inline
typename std::enable_if<!TMatrix::is_symmetric, typename TMatrix::persistent_type>::type
permuted_inv_rows(const GenericIncidenceMatrix<TMatrix>& m, const Permutation& perm)
{
if (POLYMAKE_DEBUG || !Unwary<TMatrix>::value) {
if (m.rows() != perm.size())
throw std::runtime_error("permuted_inv_rows - dimension mismatch");
}
RestrictedIncidenceMatrix<sparse2d::only_rows> result(m.rows(), m.cols());
copy_range(entire(rows(m)), select(rows(result), perm).begin());
return IncidenceMatrix<>(std::move(result));
}
template <typename TMatrix, typename Permutation> inline
typename std::enable_if<!TMatrix::is_symmetric, typename TMatrix::persistent_type>::type
permuted_inv_cols(const GenericIncidenceMatrix<TMatrix>& m, const Permutation& perm)
{
if (POLYMAKE_DEBUG || !Unwary<TMatrix>::value) {
if (m.cols() != perm.size())
throw std::runtime_error("permuted_inv_cols - dimension mismatch");
}
RestrictedIncidenceMatrix<sparse2d::only_cols> result(m.rows(), m.cols());
copy_range(entire(cols(m)), select(cols(result), perm).begin());
return IncidenceMatrix<>(std::move(result));
}
template <typename TMatrix, typename Permutation> inline
typename std::enable_if<TMatrix::is_symmetric, typename TMatrix::persistent_type>::type
permuted_rows(const GenericIncidenceMatrix<TMatrix>& m, const Permutation& perm)
{
if (POLYMAKE_DEBUG || !Unwary<TMatrix>::value) {
if (m.rows() != perm.size())
throw std::runtime_error("permuted_rows - dimension mismatch");
}
std::vector<int> inv_perm(m.rows());
inverse_permutation(perm,inv_perm);
return m.top().copy_permuted(perm,inv_perm);
}
template <typename TMatrix, typename Permutation> inline
typename std::enable_if<TMatrix::is_symmetric && container_traits<Permutation>::is_random, typename TMatrix::persistent_type>::type
permuted_inv_rows(const GenericIncidenceMatrix<TMatrix>& m, const Permutation& inv_perm)
{
if (POLYMAKE_DEBUG || !Unwary<TMatrix>::value) {
if (m.rows() != inv_perm.size())
throw std::runtime_error("permuted_inv_rows - dimension mismatch");
}
std::vector<int> perm(m.rows());
inverse_permutation(inv_perm,perm);
return m.copy_permuted(perm,inv_perm);
}
template <typename TMatrix, typename Permutation> inline
typename std::enable_if<TMatrix::is_symmetric && !container_traits<Permutation>::is_random, typename TMatrix::persistent_type>::type
permuted_inv_rows(const GenericIncidenceMatrix<TMatrix>& m, const Permutation& inv_perm)
{
if (POLYMAKE_DEBUG || !Unwary<TMatrix>::value) {
if (m.rows() != inv_perm.size())
throw std::runtime_error("permuted_inv_rows - dimension mismatch");
}
std::vector<int> inv_perm_copy(inv_perm.size());
copy_range(entire(inv_perm), inv_perm_copy.begin());
return permuted_inv_rows(m,inv_perm_copy);
}
template <typename TMatrix, typename Permutation> inline
typename std::enable_if<TMatrix::is_symmetric, typename TMatrix::persistent_type>::type
permuted_cols(const GenericIncidenceMatrix<TMatrix>& m, const Permutation& perm)
{
return permuted_rows(m,perm);
}
template <typename TMatrix, typename Permutation> inline
typename std::enable_if<TMatrix::is_symmetric, typename TMatrix::persistent_type>::type
permuted_inv_cols(const GenericIncidenceMatrix<TMatrix>& m, const Permutation& inv_perm)
{
return permuted_inv_rows(m,inv_perm);
}
} // end namespace pm
namespace polymake {
using pm::IncidenceMatrix;
using pm::RestrictedIncidenceMatrix;
}
namespace std {
template <typename symmetric> inline
void swap(pm::IncidenceMatrix<symmetric>& M1, pm::IncidenceMatrix<symmetric>& M2) { M1.swap(M2); }
template <pm::sparse2d::restriction_kind restriction> inline
void swap(pm::RestrictedIncidenceMatrix<restriction>& M1,
pm::RestrictedIncidenceMatrix<restriction>& M2)
{
M1.swap(M2);
}
template <typename TreeRef> inline
void swap(pm::incidence_line<TreeRef>& l1, pm::incidence_line<TreeRef>& l2)
{
l1.swap(l2);
}
}
#endif // POLYMAKE_INCIDENCE_MATRIX_H
// Local Variables:
// mode:C++
// c-basic-offset:3
// indent-tabs-mode:nil
// End:
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