Say I have a vector of integers like this std::vector<int> _data;
I know that if I want to remove multiple items from _data, then I can simply call
_data.erase( std::remove_if( _data.begin(), _data.end(), [condition] ), _data.end() );
Which is much faster than eraseing multiple elements, as less movement of data is required within the vector. I'm wondering if there's something similar for insertions.
For example, if I have the following pairs
auto pair1 = { _data.begin() + 5, 5 };
auto pair2 = { _data.begin() + 12, 12 };
Can I insert both of these in one iteration using some existing std function? I know I can do something like:
_data.insert( pair2.first, pair2.second );
_data.insert( pair1.first, pair1.second );
But this is (very) slow for large vectors (talking 100,000+ elements).
EDIT: Basically, I have a custom set (and map) which use a vector as the underlying containers. I know I can just use std::set or std::map, but the number of traversals I do far outweighs the insertion/removals. Switching from a set and map to this custom set/map already cut 20% of run-time off. Currently though, insertions take approximately 10% of the remaining run time, so reducing that is important.
The order is also required, unfortunately. As much as possible, I use the unordered_ versions, but in some places the order does matter.
One way is to create another vector with capacity equal to the original size plus the number of the elements being inserted and then do an insert loop with no reallocations, O(N) complexity:
template<class T>
std::vector<T> insert_elements(std::vector<T> const& v, std::initializer_list<std::pair<std::size_t, T>> new_elements) {
std::vector<T> u;
u.reserve(v.size() + new_elements.size());
auto src = v.begin();
size_t copied = 0;
for(auto const& element : new_elements) {
auto to_copy = element.first - copied;
auto src_end = src + to_copy;
u.insert(u.end(), src, src_end);
src = src_end;
copied += to_copy;
u.push_back(element.second);
}
u.insert(u.end(), src, v.end());
return u;
}
int main() {
std::vector<int> v{1, 3, 5};
for(auto e : insert_elements(v, {{1,2}, {2,4}}))
std::cout << e << ' ';
std::cout << '\n';
}
Output:
1 2 3 4 5
Ok, we need some assumptions. Let old_end be a reverse iterator to the last element of your vector. Assume that your _data has been resized to exactly fit both its current content and what you want to insert. Assume that inp is a container of std::pair containing your data to be inserted that is ordered reversely (so first the element that is to be inserted at the hindmost position and so on). Then we can do:
std::merge(old_end, _data.rend(), inp.begin(), inp.end(), data.rend(), [int i = inp.size()-1](const &T t, const &std::pair<Iter, T> p) mutable {
if( std::distance(_data.begin(), p.first) == i ) {
--i;
return false;
}
return true;
}
But I think that is not more clear than using a good old for. The problem with the stl-algorithms is that the predicates work on values and not on iterators thats a bit annoying for this problem.
Here's my take:
template<class Key, class Value>
class LinearSet
{
public:
using Node = std::pair<Key, Value>;
template<class F>
void insert_at_multiple(F&& f)
{
std::queue<Node> queue;
std::size_t index = 0;
for (auto it = _kvps.begin(); it != _kvps.end(); ++it)
{
// The container size is left untouched here, no iterator invalidation.
if (std::optional<Node> toInsert = f(index))
{
queue.push(*it);
*it = std::move(*toInsert);
}
else
{
++index;
// Replace current node with queued one.
if (!queue.empty())
{
queue.push(std::move(*it));
*it = std::move(queue.front());
queue.pop();
}
}
}
// We now have as many displaced items in the queue as were inserted,
// add them to the end.
while (!queue.empty())
{
_kvps.emplace_back(std::move(queue.front()));
queue.pop();
}
}
private:
std::vector<Node> _kvps;
};
https://godbolt.org/z/EStKgQ
This is a linear time algorithm that doesn't need to know the number of inserted elements a priori. For each index, it asks for an element to insert there. If it gets one, it pushes the corresponding existing vector element to a queue and replaces it with the new one. Otherwise, it extracts the current item to the back of the queue and puts the item at the front of the queue into the current position (noop if no elements were inserted yet). Note that the vector size is left untouched during all this. Only at the end do we push back all items still in the queue.
Note that the indices we use for determining inserted item locations here are all pre-insertion. I find this a point of potential confusion (and it is a limitation - you can't add an element at the very end with this algorithm. Could be remedied by calling f during the second loop too, working on that...).
Here's a version that allows inserting arbitrarily many elements at the end (and everywhere else). It passes post-insertion indices to the functor!
template<class F>
void insert_at_multiple(F&& f)
{
std::queue<Node> queue;
std::size_t index = 0;
for (auto it = _kvps.begin(); it != _kvps.end(); ++it)
{
if (std::optional<Node> toInsert = f(index))
queue.push(std::move(*toInsert));
if (!queue.empty())
{
queue.push(std::move(*it));
*it = std::move(queue.front());
queue.pop();
}
++index;
}
// We now have as many displaced items in the queue as were inserted,
// add them to the end.
while (!queue.empty())
{
if (std::optional<Node> toInsert = f(index))
{
queue.push(std::move(*toInsert));
}
_kvps.emplace_back(std::move(queue.front()));
queue.pop();
++index;
}
}
https://godbolt.org/z/DMuCtJ
Again, this leaves potential for confusion over what it means to insert at indices 0 and 1 (do you end up with an original element in between the two? In the first snippet you would, in the second you wouldn't). Can you insert at the same index multiple times? With pre-insertion indices that makes sense, with post-insertion indices it doesn't. You could also write this in terms of passing the current *it (i.e. key value pair) to the functor, but that alone seems not too useful...
This is an attempt I made, which inserts in reverse order. I did get rid of the iterators/indices for this.
template<class T>
void insert( std::vector<T> &vector, const std::vector<T> &values ) {
size_t last_index = vector.size() - 1;
vector.resize( vector.size() + values.size() ); // relies on T being default constructable
size_t move_position = vector.size() - 1;
size_t last_value_index = values.size() - 1;
size_t values_size = values.size();
bool isLastIndex = false;
while ( !isLastIndex && values_size ) {
if ( values[last_value_index] > vector[last_index] ) {
vector[move_position] = std::move( values[last_value_index--] );
--values_size;
} else {
isLastIndex = last_index == 0;
vector[move_position] = std::move( vector[last_index--] );
}
--move_position;
}
if ( isLastIndex && values_size ) {
while ( values_size ) {
vector[move_position--] = std::move( values[last_value_index--] );
--values_size;
}
}
}
Tried with ICC, Clang, and GCC on Godbolt, and vector's insert was faster (for 5 numbers inserted). On my machine, MSVC, same result but less severe. I also compared with Maxim's version from his answer. I realize using Godbolt isn't a good method for comparison, but I don't have access to the 3 other compilers on my current machine.
https://godbolt.org/z/vjV2wA
Results from my machine:
My insert: 659us
Maxim insert: 712us
Vector insert: 315us
Godbolt's ICC
My insert: 470us
Maxim insert: 139us
Vector insert: 127us
Godbolt's GCC
My insert: 815us
Maxim insert: 97us
Vector insert: 97us
Godbolt's Clang:
My insert: 477us
Maxim insert: 188us
Vector insert: 96us
Related
I'm working on a D* Lite path planner in C++. The program maintains a priority queue of cells (U), each cell have two cost values, and a key can be calculated for a cell which determine it's order on the priority queue.
using Cost = float;
using HeapKey = pair<Cost, Cost>;
using KeyCompare = std::greater<std::pair<HeapKey, unsigned int>>;
vector<pair<HeapKey, unsigned int>> U;
When a cell is added it is done so by using:
U.push_back({ k, id });
push_heap(U.begin(), U.end(), KeyCompare());
As part of the path planning algorithm cells sometimes need to be removed, and here lies the current problem as far as I can see. I recently had help on this site to speed my program up quite a bit by using push_heap instead of make_heap, but now it seems that the part of the program that removes cells is the slowest part. Cells are removed from the priority queue by:
void DstarPlanner::updateVertex(unsigned int id) {
...
...
auto it = find_if(U.begin(), U.end(), [=](auto p) { return p.second == id; });
U.erase(it);
...
...
}
From my tests this seems to take roughly 80% of the time my program use for path planning. It was my hope coming here that a more time-saving method existed.
Thank you.
EDIT - Extra information.
void DstarPlanner::insertHeap(unsigned int id, HeapKey k) {
U.push_back({ k, id });
push_heap(U.begin(), U.end(), KeyCompare());
in_U[id]++;
}
void DstarPlanner::updateVertex(unsigned int id) {
Cell* u = graph.getCell(id);
if (u->id != id_goal) {
Cost mincost = infinity;
for (auto s : u->neighbors) {
mincost = min(mincost, graph.getEdgeCost(u->id, s->id) + s->g);
}
u->rhs = mincost;
}
if (in_U[id]) {
auto it = find_if(U.begin(), U.end(), [=](auto p) { return p.second == id; });
U.erase(it);
in_U[id]--;
}
if (u->g != u->rhs) {
insertHeap(id, u->calculateKey());
}
}
vector<int> DstarPlanner::ComputeShortestPath() {
vector<int> bestPath;
vector<int> emptyPath;
Cell* n = graph.getCell(id_start);
while (U.front().first < n->calculateKey() || n->rhs != n->g) {
auto uid = U.front().second;
Cell* u = graph.getCell(uid);
auto kold = U.front().first;
pop_heap(U.begin(), U.end(), KeyCompare());
U.pop_back();
in_U[u->id]--;
if (kold < u->calculateKey()) {
insertHeap(u->id, u->calculateKey());
} else if (u->g > u->rhs) {
u->g = u->rhs;
for (auto s : u->neighbors) {
if (!occupied(s->id)) {
updateVertex(s->id);
}
}
} else {
u->g = infinity;
for (auto s : u->neighbors) {
if (!occupied(s->id)) {
updateVertex(s->id);
}
}
updateVertex(u->id);
}
}
bestPath=constructPath();
return bestPath;
}
find_if does a linear search. It maybe faster to use:
std::map/std::set -> Standard binary search tree implementations
std::unordered_map/std::unordered_set -> Standard hash table implementations
These may use a lot of memory if your elements (key-value pairs) are small integers. To avoid that you can use 3rd party alternatives like boost::unordered_flat_map.
How do you re-heapify after U.erase(it)? Do you ever delete multiple nodes at once?
If deletions need to be atomic between searches, then you can
swap it with end() - 1,
erase end() - 1, and
re-heapify.
Erasing end() - 1 is O(1) while erasing it is linear in std::distance(it, end).
void DstarPlanner::updateVertex(unsigned int id) {
...
// take the id by reference since this is synchronous
auto it = find_if(U.begin(), U.end(), [&](const auto& p) { return p.second == id; });
*it = std::move(*(U.end() - 1));
U.erase((U.end() - 1));
std::make_heap(U.begin(), U.end()); // expensive!!! 3*distance(begin, end)
...
}
If you can delete multiple nodes between searches, then you can use a combination of erase + remove_if to only perform one mass re-heapify. This is important be heapify is expensive.
it = remove_if(begin, end, [](){ lambda }
erase(it, end)
re-heapify
void DstarPlanner::updateVertex(const std::vector<unsigned int>& sorted_ids) {
...
auto it = remove_if(U.begin(), U.end(), [&](const auto& p) { return std::binary_search(ids.begin(), ids.end(), p.second); });
U.erase(it, U.end());
std::make_heap(U.begin(), U.end()); // expensive!!! 3*distance(begin, end)
...
}
Doing better
You can possibly improve on this by replacing std::make_heap (which makes no assumptions about the heapiness of [begin(), end()) with a custom method that re-heapifies a former heap around "poison points" -- it only needs to initially inspect the elements around the elements that were swapped. This sounds like a pain to write and I'd only do it if the resulting program was still too slow.
Have you thought of...
Just not even removing elements from the heap? The fact you're using a heap tells me that the algorithm designers suggested a heap. If they suggested a heap, then they likely didn't envision random removals. This is speculation on my part. I'm otherwise not familiar with D* lite.
I have a heap using std::make_heap:
std::vector<int> v{1,2,3,5,9,20,3};
std::make_heap(v.begin(), v.end());
now I update the heap by changing one random element:
v[3] = 35;
Is there a way in standard library in to adjust heap again in O(log n) time where n is size of container. Basically I am looking for heapify function. I know what element has been changed.
I understand that std::make_heap is O(n log n) time. I have also gone through duplicate question but that is different in sense that it is changing max element. For that solution is already given of O(log n) complexity in that question.
I am trying to change any random element within heap.
You can just do it yourself:
void modify_heap_element(std::vector<int> &heap, size_t index, int value)
{
//while value is too large for its position, bubble up
while(index > 0 && heap[(index-1)>>1] < value)
{
size_t parent = (index-1)>>1;
heap[index]=heap[parent];
index = parent;
}
//while value is too large for its position sift down
for (;;)
{
size_t left=index*2+1;
size_t right=left+1;
if (left >= heap.size())
break;
size_t bigchild = (right >= heap.size() || heap[right] < heap[left] ?
left : right );
if (!(value < heap[bigchild]))
break;
heap[index]=heap[bigchild];
index = bigchild;
}
heap[index] = value;
}
If we look closer at your statement:
now I disturb heap by changing one random element of heap.
For heapifying in O(log n) you can only directly "disturb" the back or the front of the vector (which corresponds somehow to inserting or deleting an element). In these cases, (re)heapification can be then achieved by means of the std::push_heap and std::pop_heap algorithms, which take logarithmic running time.
That is, the back:
v.back() = 35;
std::push_heap(v.begin(), v.end()); // heapify in O(log n)
or the front:
v.front() = 35;
// places the front at the back
std::pop_heap(v.begin(), v.end()); // O(log n)
// v.back() is now 35, but it does not belong to the heap anymore
// make the back belong to the heap again
std::push_heap(v.begin(), v.end()); // O(log n)
Otherwise you need to reheapify the whole vector with std::make_heap, which takes linear running time.
Summary
It's not possible to modify an arbitrary element of the heap and achieve the heapification in logarithmic running time with the standard library (i.e., the function templates std::push_heap and std::pop_heap). However, you can always implement the heap's swim and sink operations by yourself in order to heapify in logarithmic running time.
I have been facing this problem of wanting an "updateable heap" as well. However, in the end, instead of coding a custom updateable heap or anything like that, I solved it a bit differently.
To maintain access to the best element without needing to explicitly go through the heap, you can use versioned wrappers of the elements that you want to order. Each unique, true element has a version counter, which is increased every time the element gets changed. Each wrapper inside the heap then carries a version of the element, being the version at the time the wrapper was created:
struct HeapElemWrapper
{
HeapElem * e;
size_t version;
double priority;
HeapElemWrapper(HeapElem * elem)
: e(elem), version(elem->currentVersion), priority(0.0)
{}
bool upToDate() const
{
return version == e->currentVersion;
}
// operator for ordering with heap / priority queue:
// smaller error -> higher priority
bool operator<(const HeapElemWrapper & other) const
{
return this->priority> other.priority;
}
};
When popping the topmost element from the heap, you can then simply check this wrapper element to see if it's up-to-date with the original. If not, simply dispose it and pop the next one. This method is quite efficient, and I have it seen in other applications as well. The only thing you need to take care of is that you do a pass over the heap to clean it up from outdated elements, from time to time (say, every 1000 insertions or so).
It's not possible to modify an arbitrary element of the heap in logarithmic running time without violating the heap property by just using the function templates std::pop_heap() and std::push_heap() that the standard library provides.
However, you can define your own STL-like function template, set_heap_element(), for that purpose:
template<typename RandomIt, typename T, typename Cmp>
void set_heap_element(RandomIt first, RandomIt last, RandomIt pos, T value, Cmp cmp)
{
const auto n = last - first;
*pos = std::move(value); // replace previous value
auto i = pos - first;
using std::swap;
// percolate up
while (i > 0) { // non-root node
auto parent_it = first + (i-1)/2;
if (cmp(*pos, *parent_it))
break; // parent node satisfies the heap-property
swap(*pos, *parent_it); // swap with parent
pos = parent_it;
i = pos - first;
}
// percolate down
while (2*i + 1 < n) { // non-leaf node, since it has a left child
const auto lidx = 2*i + 1, ridx = 2*i + 2;
auto lchild_it = first + lidx;
auto rchild_it = ridx < n? first + ridx: last;
auto it = pos;
if (cmp(*it, *lchild_it))
it = lchild_it;
if (rchild_it != last && cmp(*it, *rchild_it))
it = rchild_it;
if (pos == it)
break; // node satisfies the heap-property
swap(*pos, *it); // swap with child
pos = it;
i = pos - first;
}
}
Then, you can provide the following simplified overload of set_heap_element() for a max heap:
#include <functional> // std::less
template<typename RandomIt, typename T>
void set_heap_element(RandomIt first, RandomIt last, RandomIt pos, T value) {
return set_heap_element(first, last, pos, value, std::less<T>{});
}
This overload uses a std::less<T> object as the comparison function object for the original function template.
Example
In your max-heap example, set_heap_element() could be used as follows:
std::vector<int> v{1,2,3,5,9,20,3};
std::make_heap(v.begin(), v.end());
// set 4th element to 35 in O(log n)
set_heap_element(v.begin(), v.end(), v.begin() + 3, 35);
You could use std::is_heap(), which takes linear time, whenever you want to check whether the max-heap property is still satisfied by v after setting an element with the set_heap_element() function template above:
assert(std::is_heap(v.begin(), v.end()));
What about min heaps?
You can achieve the same for a min heap by passing a std::greater<int> object as the last argument of the function calls to std::make_heap(), set_heap_element() and std::is_heap():
std::vector<int> v{1,2,3,5,9,20,3};
// create a min heap
std::make_heap(v.begin(), v.end(), std::greater<int>{});
// set 4th element to 35 in O(log n)
set_heap_element(v.begin(), v.end(), v.begin() + 3, 35, std::greater<int>{});
// is the min-heap property satisfied?
assert(std::is_heap(v.begin(), v.end(), std::greater<int>{}));
I have 10,000 vector<pair<unsigned,unsigned>> and I want to merge them into a single vector such that it is lexicographically sorted and does not contain duplicates. In order to do so I wrote the following code. However, to my surprise the below code is taking a lot of time. Can someone please suggest as to how can I reduce the running time of my code?
using obj = pair<unsigned, unsigned>
vector< vector<obj> > vecOfVec; // 10,000 vector<obj>, each sorted with size()=10M
vector<obj> result;
for(auto it=vecOfVec.begin(), l=vecOfVec.end(); it!=l; ++it)
{
// append vectors
result.insert(result.end(),it->begin(),it->end());
// sort result
std::sort(result.begin(), result.end());
// remove duplicates from result
result.erase(std::unique(result.begin(), result.end()), result.end());
}
I think you should use the fact that the vector in vectOfVect are sorted.
So detecting the min value in the front on the single vectors, push_back() it in the result and remove all the values detected from the front of the vectors matching the min values (avoiding duplicates in result).
If you can delete the vecOfVec variable, something like (caution: code not tested: just to give an idea)
while ( vecOfVec.size() )
{
// detect the minimal front value
auto itc = vecOfVec.cbegin();
auto lc = vecOfVec.cend();
auto valMin = itc->front();
while ( ++itc != lc )
valMin = std::min(valMin, itc->front());
// push_back() the minimal front value in result
result.push_back(valMin);
for ( auto it = vecOfVec.begin() ; it != vecOfVec.end() ; )
{
// remove all the front values equals to valMin (this remove the
// duplicates from result)
while ( (false == it->empty()) && (valMin == it->front()) )
it->erase(it->begin());
// when a vector is empty is removed
it = ( it->empty() ? vecOfVec.erase(it) : ++it );
}
}
If you can, I suggest you to switch vecOfVec from a vector< vector<obj> > to something that permit an efficient removal from the front of single containers (stacks?) and an efficient removal of single containers (a list?).
If there are lot of duplicates, you should use set rather than vector for your result, as set is the most natural thing to store something without duplicates:
set< pair<unsigned,unsigned> > resultSet;
for (auto it=vecOfVec.begin(); it!=vecOfVec.end(); ++it)
resultSet.insert(it->begin(), it->end());
If you need to turn it into a vector, you can write
vector< pair<unsigned,unsigned> > resultVec(resultSet.begin(), resultSet.end());
Note that since your code runs over 800 billion elements, it would still take a lot of time, no matter what. At least hours, if not days.
Other ideas are:
recursively merge vectors (10000 -> 5000 -> 2500 -> ... -> 1)
to merge 10000 vectors, store 10000 iterators in a heap structure
One problem with your code is the excessive use of std::sort. Unfortunately, the quicksort algorithm (which usually is the working horse used by std::sort) is not particularly faster when encountering an already sorted array.
Moreover, you're not exploiting the fact that your initial vectors are already sorted. This can be exploited by using a heap of their next values, when you will not need to call sort again. This may be coded as follows (code tested using obj=int), but perhaps it can be made more concise.
// represents the next unused entry in one vector<obj>
template<typename obj>
struct feed
{
typename std::vector<obj>::const_iterator current, end;
feed(std::vector<obj> const&v)
: current(v.begin()), end(v.end()) {}
friend bool operator> (feed const&l, feed const&r)
{ return *(l.current) > *(r.current); }
};
// - returns the smallest element
// - set corresponding feeder to next and re-establish the heap
template<typename obj>
obj get_next(std::vector<feed<obj>>&heap)
{
auto&f = heap[0];
auto x = *(f.current++);
if(f.current == f.end) {
std::pop_heap(heap.begin(),heap.end(),std::greater<feed<obj>>{});
heap.pop_back();
} else
std::make_heap(heap.begin(),heap.end(),std::greater<feed<obj>>{});
return x;
}
template<typename obj>
std::vector<obj> merge(std::vector<std::vector<obj>>const&vecOfvec)
{
// create min heap of feed<obj> and count total number of objects
std::vector<feed<obj>> input;
input.reserve(vecOfvec.size());
size_t num_total = 0;
for(auto const&v:vecOfvec)
if(v.size()) {
num_total += v.size();
input.emplace_back(v);
}
std::make_heap(input.begin(),input.end(),std::greater<feed<obj>>{});
// append values in ascending order, avoiding duplicates
std::vector<obj> result;
result.reserve(num_total);
while(!input.empty()) {
auto x = get_next(input);
result.push_back(x);
while(!input.empty() &&
!(*(input[0].current) > x)) // remove duplicates
get_next(input);
}
return result;
}
Here is my issue, lets say I have a std::vector with ints in it.
let's say it has 50,90,40,90,80,60,80.
I know I need to remove the second, fifth and third elements. I don't necessarily always know the order of elements to remove, nor how many. The issue is by erasing an element, this changes the index of the other elements. Therefore, how could I erase these and compensate for the index change. (sorting then linearly erasing with an offset is not an option)
Thanks
I am offering several methods:
1. A fast method that does not retain the original order of the elements:
Assign the current last element of the vector to the element to erase, then erase the last element. This will avoid big moves and all indexes except the last will remain constant. If you start erasing from the back, all precomputed indexes will be correct.
void quickDelete( int idx )
{
vec[idx] = vec.back();
vec.pop_back();
}
I see this essentially is a hand-coded version of the erase-remove idiom pointed out by Klaim ...
2. A slower method that retains the original order of the elements:
Step 1: Mark all vector elements to be deleted, i.e. with a special value. This has O(|indexes to delete|).
Step 2: Erase all marked elements using v.erase( remove (v.begin(), v.end(), special_value), v.end() );. This has O(|vector v|).
The total run time is thus O(|vector v|), assuming the index list is shorter than the vector.
3. Another slower method that retains the original order of the elements:
Use a predicate and remove if as described in https://stackoverflow.com/a/3487742/280314 . To make this efficient and respecting the requirement of
not "sorting then linearly erasing with an offset", my idea is to implement the predicate using a hash table and adjust the indexes stored in the hash table as the deletion proceeds on returning true, as Klaim suggested.
Using a predicate and the algorithm remove_if you can achieve what you want : see http://www.cplusplus.com/reference/algorithm/remove_if/
Don't forget to erase the item (see remove-erase idiom).
Your predicate will simply hold the idx of each value to remove and decrease all indexes it keeps each time it returns true.
That said if you can afford just removing each object using the remove-erase idiom, just make your life simple by doing it.
Erase the items backwards. In other words erase the highest index first, then next highest etc. You won't invalidate any previous iterators or indexes so you can just use the obvious approach of multiple erase calls.
I would move the elements which you don't want to erase to a temporary vector and then replace the original vector with this.
While this answer by Peter G. in variant one (the swap-and-pop technique) is the fastest when you do not need to preserve the order, here is the unmentioned alternative which maintains the order.
With C++17 and C++20 the removal of multiple elements from a vector is possible with standard algorithms. The run time is O(N * Log(N)) due to std::stable_partition. There are no external helper arrays, no excessive copying, everything is done inplace. Code is a "one-liner":
template <class T>
inline void erase_selected(std::vector<T>& v, const std::vector<int>& selection)
{
v.resize(std::distance(
v.begin(),
std::stable_partition(v.begin(), v.end(),
[&selection, &v](const T& item) {
return !std::binary_search(
selection.begin(),
selection.end(),
static_cast<int>(static_cast<const T*>(&item) - &v[0]));
})));
}
The code above assumes that selection vector is sorted (if it is not the case, std::sort over it does the job, obviously).
To break this down, let us declare a number of temporaries:
// We need an explicit item index of an element
// to see if it should be in the output or not
int itemIndex = 0;
// The checker lambda returns `true` if the element is in `selection`
auto filter = [&itemIndex, &sorted_sel](const T& item) {
return !std::binary_search(
selection.begin(),
selection.end(),
itemIndex++);
};
This checker lambda is then fed to std::stable_partition algorithm which is guaranteed to call this lambda only once for each element in the original (unpermuted !) array v.
auto end_of_selected = std::stable_partition(
v.begin(),
v.end(),
filter);
The end_of_selected iterator points right after the last element which should remain in the output array, so we now can resize v down. To calculate the number of elements we use the std::distance to get size_t from two iterators.
v.resize(std::distance(v.begin(), end_of_selected));
This is different from the code at the top (it uses itemIndex to keep track of the array element). To get rid of the itemIndex, we capture the reference to source array v and use pointer arithmetic to calculate itemIndex internally.
Over the years (on this and other similar sites) multiple solutions have been proposed, but usually they employ multiple "raw loops" with conditions and some erase/insert/push_back calls. The idea behind stable_partition is explained beautifully in this talk by Sean Parent.
This link provides a similar solution (and it does not assume that selection is sorted - std::find_if instead of std::binary_search is used), but it also employs a helper (incremented) variable which disables the possibility to parallelize processing on larger arrays.
Starting from C++17, there is a new first argument to std::stable_partition (the ExecutionPolicy) which allows auto-parallelization of the algorithm, further reducing the run-time for big arrays. To make yourself believe this parallelization actually works, there is another talk by Hartmut Kaiser explaining the internals.
Would this work:
void DeleteAll(vector<int>& data, const vector<int>& deleteIndices)
{
vector<bool> markedElements(data.size(), false);
vector<int> tempBuffer;
tempBuffer.reserve(data.size()-deleteIndices.size());
for (vector<int>::const_iterator itDel = deleteIndices.begin(); itDel != deleteIndices.end(); itDel++)
markedElements[*itDel] = true;
for (size_t i=0; i<data.size(); i++)
{
if (!markedElements[i])
tempBuffer.push_back(data[i]);
}
data = tempBuffer;
}
It's an O(n) operation, no matter how many elements you delete. You could gain some efficiency by reordering the vector inline (but I think this way it's more readable).
This is non-trival because as you delete elements from the vector, the indexes change.
[0] hi
[1] you
[2] foo
>> delete [1]
[0] hi
[1] foo
If you keep a counter of times you delete an element and if you have a list of indexes you want to delete in sorted order then:
int counter = 0;
for (int k : IndexesToDelete) {
events.erase(events.begin()+ k + counter);
counter -= 1;
}
You can use this method, if the order of the remaining elements doesn't matter
#include <iostream>
#include <vector>
using namespace std;
int main()
{
vector< int> vec;
vec.push_back(1);
vec.push_back(-6);
vec.push_back(3);
vec.push_back(4);
vec.push_back(7);
vec.push_back(9);
vec.push_back(14);
vec.push_back(25);
cout << "The elements befor " << endl;
for(int i = 0; i < vec.size(); i++) cout << vec[i] <<endl;
vector< bool> toDeleted;
int YesOrNo = 0;
for(int i = 0; i<vec.size(); i++)
{
cout<<"You need to delete this element? "<<vec[i]<<", if yes enter 1 else enter 0"<<endl;
cin>>YesOrNo;
if(YesOrNo)
toDeleted.push_back(true);
else
toDeleted.push_back(false);
}
//Deleting, beginning from the last element to the first one
for(int i = toDeleted.size()-1; i>=0; i--)
{
if(toDeleted[i])
{
vec[i] = vec.back();
vec.pop_back();
}
}
cout << "The elements after" << endl;
for(int i = 0; i < vec.size(); i++) cout << vec[i] <<endl;
return 0;
}
Here's an elegant solution in case you want to preserve the indices, the idea is to replace the values you want to delete with a special value that is guaranteed not be used anywhere, and then at the very end, you perform the erase itself:
std::vector<int> vec = {1, 2, 3, 4, 5, 6, 7, 8, 9};
// marking 3 elements to be deleted
vec[2] = std::numeric_limits<int>::lowest();
vec[5] = std::numeric_limits<int>::lowest();
vec[3] = std::numeric_limits<int>::lowest();
// erase
vec.erase(std::remove(vec.begin(), vec.end(), std::numeric_limits<int>::lowest()), vec.end());
// print values => 1 2 5 7 8 9
for (const auto& value : vec) std::cout << ' ' << value;
std::cout << std::endl;
It's very quick if you delete a lot of elements because the deletion itself is happening only once. Items can also be deleted in any order that way.
If you use a a struct instead of an int, then you can still mark an element of that struct, for ex dead=true and then use remove_if instead of remove =>
struct MyObj
{
int x;
bool dead = false;
};
std::vector<MyObj> objs = {{1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}, {9}};
objs[2].dead = true;
objs[5].dead = true;
objs[3].dead = true;
objs.erase(std::remove_if(objs.begin(), objs.end(), [](const MyObj& obj) { return obj.dead; }), objs.end());
// print values => 1 2 5 7 8 9
for (const auto& obj : objs) std::cout << ' ' << obj.x;
std::cout << std::endl;
This one is a bit slower, around 80% the speed of the remove.
This question already has answers here:
How do I sort a std::vector by the values of a different std::vector? [duplicate]
(13 answers)
Closed 12 months ago.
I'd like to reorder the items in a vector, using another vector to specify the order:
char A[] = { 'a', 'b', 'c' };
size_t ORDER[] = { 1, 0, 2 };
vector<char> vA(A, A + sizeof(A) / sizeof(*A));
vector<size_t> vOrder(ORDER, ORDER + sizeof(ORDER) / sizeof(*ORDER));
reorder_naive(vA, vOrder);
// A is now { 'b', 'a', 'c' }
The following is an inefficient implementation that requires copying the vector:
void reorder_naive(vector<char>& vA, const vector<size_t>& vOrder)
{
assert(vA.size() == vOrder.size());
vector vCopy = vA; // Can we avoid this?
for(int i = 0; i < vOrder.size(); ++i)
vA[i] = vCopy[ vOrder[i] ];
}
Is there a more efficient way, for example, that uses swap()?
This algorithm is based on chmike's, but the vector of reorder indices is const. This function agrees with his for all 11! permutations of [0..10]. The complexity is O(N^2), taking N as the size of the input, or more precisely, the size of the largest orbit.
See below for an optimized O(N) solution which modifies the input.
template< class T >
void reorder(vector<T> &v, vector<size_t> const &order ) {
for ( int s = 1, d; s < order.size(); ++ s ) {
for ( d = order[s]; d < s; d = order[d] ) ;
if ( d == s ) while ( d = order[d], d != s ) swap( v[s], v[d] );
}
}
Here's an STL style version which I put a bit more effort into. It's about 47% faster (that is, almost twice as fast over [0..10]!) because it does all the swaps as early as possible and then returns. The reorder vector consists of a number of orbits, and each orbit is reordered upon reaching its first member. It's faster when the last few elements do not contain an orbit.
template< typename order_iterator, typename value_iterator >
void reorder( order_iterator order_begin, order_iterator order_end, value_iterator v ) {
typedef typename std::iterator_traits< value_iterator >::value_type value_t;
typedef typename std::iterator_traits< order_iterator >::value_type index_t;
typedef typename std::iterator_traits< order_iterator >::difference_type diff_t;
diff_t remaining = order_end - 1 - order_begin;
for ( index_t s = index_t(), d; remaining > 0; ++ s ) {
for ( d = order_begin[s]; d > s; d = order_begin[d] ) ;
if ( d == s ) {
-- remaining;
value_t temp = v[s];
while ( d = order_begin[d], d != s ) {
swap( temp, v[d] );
-- remaining;
}
v[s] = temp;
}
}
}
And finally, just to answer the question once and for all, a variant which does destroy the reorder vector (filling it with -1's). For permutations of [0..10], It's about 16% faster than the preceding version. Because overwriting the input enables dynamic programming, it is O(N), asymptotically faster for some cases with longer sequences.
template< typename order_iterator, typename value_iterator >
void reorder_destructive( order_iterator order_begin, order_iterator order_end, value_iterator v ) {
typedef typename std::iterator_traits< value_iterator >::value_type value_t;
typedef typename std::iterator_traits< order_iterator >::value_type index_t;
typedef typename std::iterator_traits< order_iterator >::difference_type diff_t;
diff_t remaining = order_end - 1 - order_begin;
for ( index_t s = index_t(); remaining > 0; ++ s ) {
index_t d = order_begin[s];
if ( d == (diff_t) -1 ) continue;
-- remaining;
value_t temp = v[s];
for ( index_t d2; d != s; d = d2 ) {
swap( temp, v[d] );
swap( order_begin[d], d2 = (diff_t) -1 );
-- remaining;
}
v[s] = temp;
}
}
In-place reordering of vector
Warning: there is an ambiguity about the semantic what the ordering-indices mean. Both are answered here
move elements of vector to the position of the indices
Interactive version here.
#include <iostream>
#include <vector>
#include <assert.h>
using namespace std;
void REORDER(vector<double>& vA, vector<size_t>& vOrder)
{
assert(vA.size() == vOrder.size());
// for all elements to put in place
for( int i = 0; i < vA.size() - 1; ++i )
{
// while the element i is not yet in place
while( i != vOrder[i] )
{
// swap it with the element at its final place
int alt = vOrder[i];
swap( vA[i], vA[alt] );
swap( vOrder[i], vOrder[alt] );
}
}
}
int main()
{
std::vector<double> vec {7, 5, 9, 6};
std::vector<size_t> inds {1, 3, 0, 2};
REORDER(vec, inds);
for (size_t vv = 0; vv < vec.size(); ++vv)
{
std::cout << vec[vv] << std::endl;
}
return 0;
}
output
9
7
6
5
note that you can save one test because if n-1 elements are in place the last nth element is certainly in place.
On exit vA and vOrder are properly ordered.
This algorithm performs at most n-1 swapping because each swap moves the element to its final position. And we'll have to do at most 2N tests on vOrder.
draw the elements of vector from the position of the indices
Try it interactively here.
#include <iostream>
#include <vector>
#include <assert.h>
template<typename T>
void reorder(std::vector<T>& vec, std::vector<size_t> vOrder)
{
assert(vec.size() == vOrder.size());
for( size_t vv = 0; vv < vec.size() - 1; ++vv )
{
if (vOrder[vv] == vv)
{
continue;
}
size_t oo;
for(oo = vv + 1; oo < vOrder.size(); ++oo)
{
if (vOrder[oo] == vv)
{
break;
}
}
std::swap( vec[vv], vec[vOrder[vv]] );
std::swap( vOrder[vv], vOrder[oo] );
}
}
int main()
{
std::vector<double> vec {7, 5, 9, 6};
std::vector<size_t> inds {1, 3, 0, 2};
reorder(vec, inds);
for (size_t vv = 0; vv < vec.size(); ++vv)
{
std::cout << vec[vv] << std::endl;
}
return 0;
}
Output
5
6
7
9
It appears to me that vOrder contains a set of indexes in the desired order (for example the output of sorting by index). The code example here follows the "cycles" in vOrder, where following a sub-set (could be all of vOrder) of indexes will cycle through the sub-set, ending back at the first index of the sub-set.
Wiki article on "cycles"
https://en.wikipedia.org/wiki/Cyclic_permutation
In the following example, every swap places at least one element in it's proper place. This code example effectively reorders vA according to vOrder, while "unordering" or "unpermuting" vOrder back to its original state (0 :: n-1). If vA contained the values 0 through n-1 in order, then after reorder, vA would end up where vOrder started.
template <class T>
void reorder(vector<T>& vA, vector<size_t>& vOrder)
{
assert(vA.size() == vOrder.size());
// for all elements to put in place
for( size_t i = 0; i < vA.size(); ++i )
{
// while vOrder[i] is not yet in place
// every swap places at least one element in it's proper place
while( vOrder[i] != vOrder[vOrder[i]] )
{
swap( vA[vOrder[i]], vA[vOrder[vOrder[i]]] );
swap( vOrder[i], vOrder[vOrder[i]] );
}
}
}
This can also be implemented a bit more efficiently using moves instead swaps. A temp object is needed to hold an element during the moves. Example C code, reorders A[] according to indexes in I[], also sorts I[] :
void reorder(int *A, int *I, int n)
{
int i, j, k;
int tA;
/* reorder A according to I */
/* every move puts an element into place */
/* time complexity is O(n) */
for(i = 0; i < n; i++){
if(i != I[i]){
tA = A[i];
j = i;
while(i != (k = I[j])){
A[j] = A[k];
I[j] = j;
j = k;
}
A[j] = tA;
I[j] = j;
}
}
}
If it is ok to modify the ORDER array then an implementation that sorts the ORDER vector and at each sorting operation also swaps the corresponding values vector elements could do the trick, I think.
A survey of existing answers
You ask if there is "a more efficient way". But what do you mean by efficient and what are your requirements?
Potatoswatter's answer works in O(N²) time with O(1) additional space and doesn't mutate the reordering vector.
chmike and rcgldr give answers which use O(N) time with O(1) additional space, but they achieve this by mutating the reordering vector.
Your original answer allocates new space and then copies data into it while Tim MB suggests using move semantics. However, moving still requires a place to move things to and an object like an std::string has both a length variable and a pointer. In other words, a move-based solution requires O(N) allocations for any objects and O(1) allocations for the new vector itself. I explain why this is important below.
Preserving the reordering vector
We might want that reordering vector! Sorting costs O(N log N). But, if you know you'll be sorting several vectors in the same way, such as in a Structure of Arrays (SoA) context, you can sort once and then reuse the results. This can save a lot of time.
You might also want to sort and then unsort data. Having the reordering vector allows you to do this. A use case here is for performing genomic sequencing on GPUs where maximal speed efficiency is obtained by having sequences of similar lengths processed in batches. We cannot rely on the user providing sequences in this order so we sort and then unsort.
So, what if we want the best of all worlds: O(N) processing without the costs of additional allocation but also without mutating our ordering vector (which we might, after all, want to reuse)? To find that world, we need to ask:
Why is extra space bad?
There are two reasons you might not want to allocate additional space.
The first is that you don't have much space to work with. This can occur in two situations: you're on an embedded device with limited memory. Usually this means you're working with small datasets, so the O(N²) solution is probably fine here. But it can also happen when you are working with really large datasets. In this case O(N²) is unacceptable and you have to use one of the O(N) mutating solutions.
The other reason extra space is bad is because allocation is expensive. For smaller datasets it can cost more than the actual computation. Thus, one way to achieve efficiency is to eliminate allocation.
Outline
When we mutate the ordering vector we are doing so as a way to indicate whether elements are in their permuted positions. Rather than doing this, we could use a bit-vector to indicate that same information. However, if we allocate the bit vector each time that would be expensive.
Instead, we could clear the bit vector each time by resetting it to zero. However, that incurs an additional O(N) cost per function use.
Rather, we can store a "version" value in a vector and increment this on each function use. This gives us O(1) access, O(1) clear, and an amoritzed allocation cost. This works similarly to a persistent data structure. The downside is that if we use an ordering function too often the version counter needs to be reset, though the O(N) cost of doing so is amortized.
This raises the question: what is the optimal data type for the version vector? A bit-vector maximizes cache utilization but requires a full O(N) reset after each use. A 64-bit data type probably never needs to be reset, but has poor cache utilization. Experimenting is the best way to figure this out.
Two types of permutations
We can view an ordering vector as having two senses: forward and backward. In the forward sense, the vector tell us where elements go to. In the backward sense, the vector tells us where elements are coming from. Since the ordering vector is implicitly a linked list, the backward sense requires O(N) additional space, but, again, we can amortize the allocation cost. Applying the two senses sequentially brings us back to our original ordering.
Performance
Running single-threaded on my "Intel(R) Xeon(R) E-2176M CPU # 2.70GHz", the following code takes about 0.81ms per reordering for sequences 32,767 elements long.
Code
Fully commented code for both senses with tests:
#include <algorithm>
#include <cassert>
#include <random>
#include <stack>
#include <stdexcept>
#include <vector>
///#brief Reorder a vector by moving its elements to indices indicted by another
/// vector. Takes O(N) time and O(N) space. Allocations are amoritzed.
///
///#param[in,out] values Vector to be reordered
///#param[in] ordering A permutation of the vector
///#param[in,out] visited A black-box vector to be reused between calls and
/// shared with with `backward_reorder()`
template<class ValueType, class OrderingType, class ProgressType>
void forward_reorder(
std::vector<ValueType> &values,
const std::vector<OrderingType> &ordering,
std::vector<ProgressType> &visited
){
if(ordering.size()!=values.size()){
throw std::runtime_error("ordering and values must be the same size!");
}
//Size the visited vector appropriately. Since vectors don't shrink, this will
//shortly become large enough to handle most of the inputs. The vector is 1
//larger than necessary because the first element is special.
if(visited.empty() || visited.size()-1<values.size());
visited.resize(values.size()+1);
//If the visitation indicator becomes too large, we reset everything. This is
//O(N) expensive, but unlikely to occur in most use cases if an appropriate
//data type is chosen for the visited vector. For instance, an unsigned 32-bit
//integer provides ~4B uses before it needs to be reset. We subtract one below
//to avoid having to think too much about off-by-one errors. Note that
//choosing the biggest data type possible is not necessarily a good idea!
//Smaller data types will have better cache utilization.
if(visited.at(0)==std::numeric_limits<ProgressType>::max()-1)
std::fill(visited.begin(), visited.end(), 0);
//We increment the stored visited indicator and make a note of the result. Any
//value in the visited vector less than `visited_indicator` has not been
//visited.
const auto visited_indicator = ++visited.at(0);
//For doing an early exit if we get everything in place
auto remaining = values.size();
//For all elements that need to be placed
for(size_t s=0;s<ordering.size() && remaining>0;s++){
assert(visited[s+1]<=visited_indicator);
//Ignore already-visited elements
if(visited[s+1]==visited_indicator)
continue;
//Don't rearrange if we don't have to
if(s==visited[s])
continue;
//Follow this cycle, putting elements in their places until we get back
//around. Use move semantics for speed.
auto temp = std::move(values[s]);
auto i = s;
for(;s!=(size_t)ordering[i];i=ordering[i],--remaining){
std::swap(temp, values[ordering[i]]);
visited[i+1] = visited_indicator;
}
std::swap(temp, values[s]);
visited[i+1] = visited_indicator;
}
}
///#brief Reorder a vector by moving its elements to indices indicted by another
/// vector. Takes O(2N) time and O(2N) space. Allocations are amoritzed.
///
///#param[in,out] values Vector to be reordered
///#param[in] ordering A permutation of the vector
///#param[in,out] visited A black-box vector to be reused between calls and
/// shared with with `forward_reorder()`
template<class ValueType, class OrderingType, class ProgressType>
void backward_reorder(
std::vector<ValueType> &values,
const std::vector<OrderingType> &ordering,
std::vector<ProgressType> &visited
){
//The orderings form a linked list. We need O(N) memory to reverse a linked
//list. We use `thread_local` so that the function is reentrant.
thread_local std::stack<OrderingType> stack;
if(ordering.size()!=values.size()){
throw std::runtime_error("ordering and values must be the same size!");
}
//Size the visited vector appropriately. Since vectors don't shrink, this will
//shortly become large enough to handle most of the inputs. The vector is 1
//larger than necessary because the first element is special.
if(visited.empty() || visited.size()-1<values.size());
visited.resize(values.size()+1);
//If the visitation indicator becomes too large, we reset everything. This is
//O(N) expensive, but unlikely to occur in most use cases if an appropriate
//data type is chosen for the visited vector. For instance, an unsigned 32-bit
//integer provides ~4B uses before it needs to be reset. We subtract one below
//to avoid having to think too much about off-by-one errors. Note that
//choosing the biggest data type possible is not necessarily a good idea!
//Smaller data types will have better cache utilization.
if(visited.at(0)==std::numeric_limits<ProgressType>::max()-1)
std::fill(visited.begin(), visited.end(), 0);
//We increment the stored visited indicator and make a note of the result. Any
//value in the visited vector less than `visited_indicator` has not been
//visited.
const auto visited_indicator = ++visited.at(0);
//For doing an early exit if we get everything in place
auto remaining = values.size();
//For all elements that need to be placed
for(size_t s=0;s<ordering.size() && remaining>0;s++){
assert(visited[s+1]<=visited_indicator);
//Ignore already-visited elements
if(visited[s+1]==visited_indicator)
continue;
//Don't rearrange if we don't have to
if(s==visited[s])
continue;
//The orderings form a linked list. We need to follow that list to its end
//in order to reverse it.
stack.emplace(s);
for(auto i=s;s!=(size_t)ordering[i];i=ordering[i]){
stack.emplace(ordering[i]);
}
//Now we follow the linked list in reverse to its beginning, putting
//elements in their places. Use move semantics for speed.
auto temp = std::move(values[s]);
while(!stack.empty()){
std::swap(temp, values[stack.top()]);
visited[stack.top()+1] = visited_indicator;
stack.pop();
--remaining;
}
visited[s+1] = visited_indicator;
}
}
int main(){
std::mt19937 gen;
std::uniform_int_distribution<short> value_dist(0,std::numeric_limits<short>::max());
std::uniform_int_distribution<short> len_dist (0,std::numeric_limits<short>::max());
std::vector<short> data;
std::vector<short> ordering;
std::vector<short> original;
std::vector<size_t> progress;
for(int i=0;i<1000;i++){
const int len = len_dist(gen);
data.clear();
ordering.clear();
for(int i=0;i<len;i++){
data.push_back(value_dist(gen));
ordering.push_back(i);
}
original = data;
std::shuffle(ordering.begin(), ordering.end(), gen);
forward_reorder(data, ordering, progress);
assert(original!=data);
backward_reorder(data, ordering, progress);
assert(original==data);
}
}
Never prematurely optimize. Meassure and then determine where you need to optimize and what. You can end with complex code that is hard to maintain and bug-prone in many places where performance is not an issue.
With that being said, do not early pessimize. Without changing the code you can remove half of your copies:
template <typename T>
void reorder( std::vector<T> & data, std::vector<std::size_t> const & order )
{
std::vector<T> tmp; // create an empty vector
tmp.reserve( data.size() ); // ensure memory and avoid moves in the vector
for ( std::size_t i = 0; i < order.size(); ++i ) {
tmp.push_back( data[order[i]] );
}
data.swap( tmp ); // swap vector contents
}
This code creates and empty (big enough) vector in which a single copy is performed in-order. At the end, the ordered and original vectors are swapped. This will reduce the copies, but still requires extra memory.
If you want to perform the moves in-place, a simple algorithm could be:
template <typename T>
void reorder( std::vector<T> & data, std::vector<std::size_t> const & order )
{
for ( std::size_t i = 0; i < order.size(); ++i ) {
std::size_t original = order[i];
while ( i < original ) {
original = order[original];
}
std::swap( data[i], data[original] );
}
}
This code should be checked and debugged. In plain words the algorithm in each step positions the element at the i-th position. First we determine where the original element for that position is now placed in the data vector. If the original position has already been touched by the algorithm (it is before the i-th position) then the original element was swapped to order[original] position. Then again, that element can already have been moved...
This algorithm is roughly O(N^2) in the number of integer operations and thus is theoretically worse in performance time as compare to the initial O(N) algorithm. But it can compensate if the N^2 swap operations (worst case) cost less than the N copy operations or if you are really constrained by memory footprint.
It's an interesting intellectual exercise to do the reorder with O(1) space requirement but in 99.9% of the cases the simpler answer will perform to your needs:
void permute(vector<T>& values, const vector<size_t>& indices)
{
vector<T> out;
out.reserve(indices.size());
for(size_t index: indices)
{
assert(0 <= index && index < values.size());
out.push_back(std::move(values[index]));
}
values = std::move(out);
}
Beyond memory requirements, the only way I can think of this being slower would be due to the memory of out being in a different cache page than that of values and indices.
You could do it recursively, I guess - something like this (unchecked, but it gives the idea):
// Recursive function
template<typename T>
void REORDER(int oldPosition, vector<T>& vA,
const vector<int>& vecNewOrder, vector<bool>& vecVisited)
{
// Keep a record of the value currently in that position,
// as well as the position we're moving it to.
// But don't move it yet, or we'll overwrite whatever's at the next
// position. Instead, we first move what's at the next position.
// To guard against loops, we look at vecVisited, and set it to true
// once we've visited a position.
T oldVal = vA[oldPosition];
int newPos = vecNewOrder[oldPosition];
if (vecVisited[oldPosition])
{
// We've hit a loop. Set it and return.
vA[newPosition] = oldVal;
return;
}
// Guard against loops:
vecVisited[oldPosition] = true;
// Recursively re-order the next item in the sequence.
REORDER(newPos, vA, vecNewOrder, vecVisited);
// And, after we've set this new value,
vA[newPosition] = oldVal;
}
// The "main" function
template<typename T>
void REORDER(vector<T>& vA, const vector<int>& newOrder)
{
// Initialise vecVisited with false values
vector<bool> vecVisited(vA.size(), false);
for (int x = 0; x < vA.size(); x++)
{
REORDER(x, vA, newOrder, vecVisited);
}
}
Of course, you do have the overhead of vecVisited. Thoughts on this approach, anyone?
To iterate through the vector is O(n) operation. Its sorta hard to beat that.
Your code is broken. You cannot assign to vA and you need to use template parameters.
vector<char> REORDER(const vector<char>& vA, const vector<size_t>& vOrder)
{
assert(vA.size() == vOrder.size());
vector<char> vCopy(vA.size());
for(int i = 0; i < vOrder.size(); ++i)
vCopy[i] = vA[ vOrder[i] ];
return vA;
}
The above is slightly more efficient.
It is not clear by the title and the question if the vector should be ordered with the same steps it takes to order vOrder or if vOrder already contains the indexes of the desired order.
The first interpretation has already a satisfying answer (see chmike and Potatoswatter), I add some thoughts about the latter.
If the creation and/or copy cost of object T is relevant
template <typename T>
void reorder( std::vector<T> & data, std::vector<std::size_t> & order )
{
std::size_t i,j,k;
for(i = 0; i < order.size() - 1; ++i) {
j = order[i];
if(j != i) {
for(k = i + 1; order[k] != i; ++k);
std::swap(order[i],order[k]);
std::swap(data[i],data[j]);
}
}
}
If the creation cost of your object is small and memory is not a concern (see dribeas):
template <typename T>
void reorder( std::vector<T> & data, std::vector<std::size_t> const & order )
{
std::vector<T> tmp; // create an empty vector
tmp.reserve( data.size() ); // ensure memory and avoid moves in the vector
for ( std::size_t i = 0; i < order.size(); ++i ) {
tmp.push_back( data[order[i]] );
}
data.swap( tmp ); // swap vector contents
}
Note that the two pieces of code in dribeas answer do different things.
I was trying to use #Potatoswatter's solution to sort multiple vectors by a third one and got really confused by output from using the above functions on a vector of indices output from Armadillo's sort_index. To switch from a vector output from sort_index (the arma_inds vector below) to one that can be used with #Potatoswatter's solution (new_inds below), you can do the following:
vector<int> new_inds(arma_inds.size());
for (int i = 0; i < new_inds.size(); i++) new_inds[arma_inds[i]] = i;
I came up with this solution which has the space complexity of O(max_val - min_val + 1), but it can be integrated with std::sort and benefits from std::sort's O(n log n) decent time complexity.
std::vector<int32_t> dense_vec = {1, 2, 3};
std::vector<int32_t> order = {1, 0, 2};
int32_t max_val = *std::max_element(dense_vec.begin(), dense_vec.end());
std::vector<int32_t> sparse_vec(max_val + 1);
int32_t i = 0;
for(int32_t j: dense_vec)
{
sparse_vec[j] = order[i];
i++;
}
std::sort(dense_vec.begin(), dense_vec.end(),
[&sparse_vec](int32_t i1, int32_t i2) {return sparse_vec[i1] < sparse_vec[i2];});
The following assumptions made while writing this code:
Vector values start from zero.
Vector does not contain repeated values.
We have enough memory to sacrifice in order to use std::sort
This should avoid copying the vector:
void REORDER(vector<char>& vA, const vector<size_t>& vOrder)
{
assert(vA.size() == vOrder.size());
for(int i = 0; i < vOrder.size(); ++i)
if (i < vOrder[i])
swap(vA[i], vA[vOrder[i]]);
}