How to use CGAL Arrangement_2 zone? - cgal

Although there is some documentation related to the zone free function of Arrangement_2 module, it is not mentioned in any example files and the usage is not obvious.
Assuming that I have an arrangement of points and line segments based on CGAL::Arr_linear_traits_2, I want to print out all faces visited when walking along a given Segment_2. How can I do that?

You need to use the "assign" function:
void segment_intersect(Arrangement_2 &arr, Segment_2 &c)
{
std::vector<CGAL::Object> zone_elems;
Arrangement_2::Face_handle face;
CGAL::zone(arr, c, std::back_inserter(zone_elems));
for ( int i = 0; i < (int)zone_elems.size(); ++i )
{
if ( assign(face, zone_elems[i]) )
//print the face index...
}
}

The usage actually is quite obvious. To get all elements intersected, this code is enough:
void segment_intersect(Arrangement_2 &arr, Segment_2 &c)
{
std::vector<CGAL::Object> zone_elems;
CGAL::zone(arr, c, std::back_inserter(zone_elems));
}
I have yet to find out how to extract faces out of the vector.

Related

Parallel Dynamic Programming with CUDA

It is my first attempt to implement recursion with CUDA. The goal is to extract all the combinations from a set of chars "12345" using the power of CUDA to parallelize dynamically the task. Here is my kernel:
__device__ char route[31] = { "_________________________"};
__device__ char init[6] = { "12345" };
__global__ void Recursive(int depth) {
// up to depth 6
if (depth == 5) return;
// newroute = route - idx
int x = depth * 6;
printf("%s\n", route);
int o = 0;
int newlen = 0;
for (int i = 0; i<6; ++i)
{
if (i != threadIdx.x)
{
route[i+x-o] = init[i];
newlen++;
}
else
{
o = 1;
}
}
Recursive<<<1,newlen>>>(depth + 1);
}
__global__ void RecursiveCount() {
Recursive <<<1,5>>>(0);
}
The idea is to exclude 1 item (the item corresponding to the threadIdx) in each different thread. In each recursive call, using the variable depth, it works over a different base (variable x) on the route device variable.
I expect the kernel prompts something like:
2345_____________________
1345_____________________
1245_____________________
1234_____________________
2345_345_________________
2345_245_________________
2345_234_________________
2345_345__45_____________
2345_345__35_____________
2345_345__34_____________
..
2345_245__45_____________
..
But it prompts ...
·_____________
·_____________
·_____________
·_____________
·_____________
·2345
·2345
·2345
·2345
...
What I´m doing wrong?
What I´m doing wrong?
I may not articulate every problem with your code, but these items should get you a lot closer.
I recommend providing a complete example. In my view it is basically required by Stack Overflow, see item 1 here, note use of the word "must". Your example is missing any host code, including the original kernel call. It's only a few extra lines of code, why not include it? Sure, in this case, I can deduce what the call must have been, but why not just include it? Anyway, based on the output you indicated, it seems fairly evident the launch configuration of the host launch would have to be <<<1,1>>>.
This doesn't seem to be logical to me:
I expect the kernel prompts something like:
2345_____________________
The very first thing your kernel does is print out the route variable, before making any changes to it, so I would expect _____________________. However we can "fix" this by moving the printout to the end of the kernel.
You may be confused about what a __device__ variable is. It is a global variable, and there is only one copy of it. Therefore, when you modify it in your kernel code, every thread, in every kernel, is attempting to modify the same global variable, at the same time. That cannot possibly have orderly results, in any thread-parallel environment. I chose to "fix" this by making a local copy for each thread to work on.
You have an off-by-1 error, as well as an extent error in this loop:
for (int i = 0; i<6; ++i)
The off-by-1 error is due to the fact that you are iterating over 6 possible items (that is, i can reach a value of 5) but there are only 5 items in your init variable (the 6th item being a null terminator. The correct indexing starts out over 0-4 (with one of those being skipped). On subsequent iteration depths, its necessary to reduce this indexing extent by 1. Note that I've chosen to fix the first error here by increasing the length of init. There are other ways to fix, of course. My method inserts an extra _ between depths in the result.
You assume that at each iteration depth, the correct choice of items is the same, and in the same order, i.e. init. However this is not the case. At each depth, the choices of items must be selected not from the unchanging init variable, but from the choices passed from previous depth. Therefore we need a local, per-thread copy of init also.
A few other comments about CUDA Dynamic Parallelism (CDP). When passing pointers to data from one kernel scope to a child scope, local space pointers cannot be used. Therefore I allocate for the local copy of route from the heap, so it can be passed to child kernels. init can be deduced from route, so we can use an ordinary local variable for myinit.
You're going to quickly hit some dynamic parallelism (and perhaps memory) limits here if you continue this. I believe the total number of kernel launches for this is 5^5, which is 3125 (I'm doing this quickly, I may be mistaken). CDP has a pending launch limit of 2000 kernels by default. We're not hitting this here according to what I see, but you'll run into that sooner or later if you increase the depth or width of this operation. Furthermore, in-kernel allocations from the device heap are by default limited to 8KB. I don't seem to be hitting that limit, but probably I am, so my design should probably be modified to fix that.
Finally, in-kernel printf output is limited to the size of a particular buffer. If this technique is not already hitting that limit, it will soon if you increase the width or depth.
Here is a worked example, attempting to address the various items above. I'm not claiming it is defect free, but I think the output is closer to your expectations. Note that due to character limits on SO answers, I've truncated/excerpted some of the output.
$ cat t1639.cu
#include <stdio.h>
__device__ char route[31] = { "_________________________"};
__device__ char init[7] = { "12345_" };
__global__ void Recursive(int depth, const char *oroute) {
char *nroute = (char *)malloc(31);
char myinit[7];
if (depth == 0) memcpy(myinit, init, 6);
else memcpy(myinit, oroute+(depth-1)*6, 6);
myinit[6] = 0;
if (nroute == NULL) {printf("oops\n"); return;}
memcpy(nroute, oroute, 30);
nroute[30] = 0;
// up to depth 6
if (depth == 5) return;
// newroute = route - idx
int x = depth * 6;
//printf("%s\n", nroute);
int o = 0;
int newlen = 0;
for (int i = 0; i<(6-depth); ++i)
{
if (i != threadIdx.x)
{
nroute[i+x-o] = myinit[i];
newlen++;
}
else
{
o = 1;
}
}
printf("%s\n", nroute);
Recursive<<<1,newlen>>>(depth + 1, nroute);
}
__global__ void RecursiveCount() {
Recursive <<<1,5>>>(0, route);
}
int main(){
RecursiveCount<<<1,1>>>();
cudaDeviceSynchronize();
}
$ nvcc -o t1639 t1639.cu -rdc=true -lcudadevrt -arch=sm_70
$ cuda-memcheck ./t1639
========= CUDA-MEMCHECK
2345_____________________
1345_____________________
1245_____________________
1235_____________________
1234_____________________
2345__345________________
2345__245________________
2345__235________________
2345__234________________
2345__2345_______________
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2345__345___35____3______
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2345__245___45___________
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2345__235___35___________
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2345__235___23____3______
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2345__235___23____3______
2345__235___23____3_____3
2345__235___23____2______
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2345__235___23____23____3
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2345__235___235___35_____
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2345__235___235___25____2
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2345__234___34___________
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2345__234___34____4______
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2345__234___23____2______
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2345__2345__345__________
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2345__2345__345___45____5
2345__2345__345___45____4
2345__2345__345___35____5
2345__2345__345___35____3
2345__2345__345___34____4
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2345__2345__245___45_____
2345__2345__245___25_____
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2345__2345__245___45____5
2345__2345__245___45____4
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2345__2345__245___25____2
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2345__2345__235___35_____
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1345__345________________
1345__145________________
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1345__1345_______________
1345__345___45___________
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...
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========= ERROR SUMMARY: 0 errors
$
The answer given by Robert Crovella is correct at the 5th point, the mistake was in the using of init in every recursive call, but I want to clarify something that can be useful for other beginners with CUDA.
I used this variable because when I tried to launch a child kernel passing a local variable I always got the exception: Error: a pointer to local memory cannot be passed to a launch as an argument.
As I´m C# expert developer I´m not used to using pointers (Ref does the low-level-work for that) so I thought there was no way to do it in CUDA/c programming.
As Robert shows in its code it is possible copying the pointer with memalloc for using it as a referable argument.
Here is a kernel simplified as an example of deep recursion.
__device__ char init[6] = { "12345" };
__global__ void Recursive(int depth, const char* route) {
// up to depth 6
if (depth == 5) return;
//declaration for a referable argument (point 6)
char* newroute = (char*)malloc(6);
memcpy(newroute, route, 5);
int o = 0;
int newlen = 0;
for (int i = 0; i < (6 - depth); ++i)
{
if (i != threadIdx.x)
{
newroute[i - o] = route[i];
newlen++;
}
else
{
o = 1;
}
}
printf("%s\n", newroute);
Recursive <<<1, newlen>>>(depth + 1, newroute);
}
__global__ void RecursiveCount() {
Recursive <<<1, 5>>>(0, init);
}
I don't add the main call because I´m using ManagedCUDA for C# but as Robert says it can be figured-out how the call RecursiveCount is.
About ending arrays of char with /0 ... sorry but I don't know exactly what is the benefit; this code works fine without them.

Sage: Iterate over increasing sequences

I have a problem that I am unwilling to believe hasn't been solved before in Sage.
Given a pair of integers (d,n) as input, I'd like to receive a list (or set, or whatever) of all nondecreasing sequences of length d all of whose entries are no greater than n.
Similarly, I'd like another function which returns all strictly increasing sequences of length d whose entries are no greater than n.
For example, for d = 2 n=3, I'd receive the output:
[[1,2], [1,3], [2,3]]
or
[[1,1], [1,2], [1,3], [2,2], [2,3], [3,3]]
depending on whether I'm using increasing or nondecreasing.
Does anyone know of such a function?
Edit Of course, if there is such a method for nonincreasing or decreasing sequences, I can modify that to fit my purposes. Just something to iterate over sequences
I needed this algorithm too and I finally managed to write one today. I will share the code here, but I only started to learn coding last week, so it is not pretty.
Idea Input=(r,d). Step 1) Create a class "ListAndPosition" that has a list L of arrays Integer[r+1]'s, and an integer q between 0 and r. Step 2) Create a method that receives a ListAndPosition (L,q) and screens sequentially the arrays in L checking if the integer at position q is less than the one at position q+1, if so, it adds a new array at the bottom of the list with that entry ++. When done, the Method calls itself again with the new list and q-1 as input.
The code for Step 1)
import java.util.ArrayList;
public class ListAndPosition {
public static Integer r=5;
public final ArrayList<Integer[]> L;
public int q;
public ListAndPosition(ArrayList<Integer[]> L, int q) {
this.L = L;
this.q = q;
}
public ArrayList<Integer[]> getList(){
return L;
}
public int getPosition() {
return q;
}
public void decreasePosition() {
q--;
}
public void showList() {
for(int i=0;i<L.size();i++){
for(int j=0; j<r+1 ; j++){
System.out.print(""+L.get(i)[j]);
}
System.out.println("");
}
}
}
The code for Step 2)
import java.util.ArrayList;
public class NonDecreasingSeqs {
public static Integer r=5;
public static Integer d=3;
public static void main(String[] args) {
//Creating the first array
Integer[] firstArray;
firstArray = new Integer[r+1];
for(int i=0;i<r;i++){
firstArray[i] = 0;
}
firstArray[r] = d;
//Creating the starting listAndDim
ArrayList<Integer[]> L = new ArrayList<Integer[]>();
L.add(firstArray);
ListAndPosition Lq = new ListAndPosition(L,r-1);
System.out.println(""+nonDecSeqs(Lq).size());
}
public static ArrayList<Integer[]> nonDecSeqs(ListAndPosition Lq){
int iterations = r-1-Lq.getPosition();
System.out.println("How many arrays in the list after "+iterations+" iterations? "+Lq.getList().size());
System.out.print("Should we stop the iteration?");
if(0<Lq.getPosition()){
System.out.println(" No, position = "+Lq.getPosition());
for(int i=0;i<Lq.getList().size();i++){
//Showing particular array
System.out.println("Array of L #"+i+":");
for(int j=0;j<r+1;j++){
System.out.print(""+Lq.getList().get(i)[j]);
}
System.out.print("\nCan it be modified at position "+Lq.getPosition()+"?");
if(Lq.getList().get(i)[Lq.getPosition()]<Lq.getList().get(i)[Lq.getPosition()+1]){
System.out.println(" Yes, "+Lq.getList().get(i)[Lq.getPosition()]+"<"+Lq.getList().get(i)[Lq.getPosition()+1]);
{
Integer[] tempArray = new Integer[r+1];
for(int j=0;j<r+1;j++){
if(j==Lq.getPosition()){
tempArray[j] = new Integer(Lq.getList().get(i)[j])+1;
}
else{
tempArray[j] = new Integer(Lq.getList().get(i)[j]);
}
}
Lq.getList().add(tempArray);
}
System.out.println("New list");Lq.showList();
}
else{
System.out.println(" No, "+Lq.getList().get(i)[Lq.getPosition()]+"="+Lq.getList().get(i)[Lq.getPosition()+1]);
}
}
System.out.print("Old position = "+Lq.getPosition());
Lq.decreasePosition();
System.out.println(", new position = "+Lq.getPosition());
nonDecSeqs(Lq);
}
else{
System.out.println(" Yes, position = "+Lq.getPosition());
}
return Lq.getList();
}
}
Remark: I needed my sequences to start at 0 and end at d.
This is probably not a very good answer to your question. But you could, in principle, use Partitions and the max_slope=-1 argument. Messing around with filtering lists of IntegerVectors sounds equally inefficient and depressing for other reasons.
If this has a canonical name, it might be in the list of sage-combinat functionality, and there is even a base class you could perhaps use for integer lists, which is basically what you are asking about. Maybe you could actually get what you want using IntegerListsLex? Hope this proves helpful.
This question can be solved by using the class "UnorderedTuples" described here:
http://doc.sagemath.org/html/en/reference/combinat/sage/combinat/tuple.html
To return all all nondecreasing sequences with entries between 0 and n-1 of length d, you may type:
UnorderedTuples(range(n),d)
This returns the nondecreasing sequence as a list. I needed an immutable object (because the sequences would become keys of a dictionary). So I used the "tuple" method to turn the lists into tuples:
immutables = []
for s in UnorderedTuples(range(n),d):
immutables.append(tuple(s))
return immutables
And I also wrote a method which picks out only the increasing sequences:
def isIncreasing(list):
for i in range(len(list) - 1):
if list[i] >= list[i+1]:
return false
return true
The method that returns only strictly increasing sequences would look like
immutables = []
for s in UnorderedTuples(range(n),d):
if isIncreasing(s):
immutables.append(tuple(s))
return immutables

Optimized recalculating all pairs shortest path when removing vertexes dynamically from an undirected graph

I use following dijkstra implementation to calculate all pairs shortest paths in an undirected graph. After calling calculateAllPaths(), dist[i][j] contains shortest path length between i and j (or Integer.MAX_VALUE if no such path available).
The problem is that some vertexes of my graph are removing dynamically and I should recalculate all paths from scratch to update dist matrix. I'm seeking for a solution to optimize update speed by avoiding unnecessary calculations when a vertex removes from my graph. I already search for solution and I now there is some algorithms such as LPA* to do this, but they seem very complicated and I guess a simpler solution may solve my problem.
public static void calculateAllPaths()
{
for(int j=graph.length/2+graph.length%2;j>=0;j--)
{
calculateAllPathsFromSource(j);
}
}
public static void calculateAllPathsFromSource(int s)
{
final boolean visited[] = new boolean[graph.length];
for (int i=0; i<dist.length; i++)
{
if(i == s)
{
continue;
}
//visit next node
int next = -1;
int minDist = Integer.MAX_VALUE;
for (int j=0; j<dist[s].length; j++)
{
if (!visited[j] && dist[s][j] < minDist)
{
next = j;
minDist = dist[s][j];
}
}
if(next == -1)
{
continue;
}
visited[next] = true;
for(int v=0;v<graph.length;v++)
{
if(v == next || graph[next][v] == -1)
{
continue;
}
int md = dist[s][next] + graph[next][v];
if(md < dist[s][v])
{
dist[s][v] = dist[v][s] = md;
}
}
}
}
If you know that vertices are only being removed dynamically, then instead of just storing the best path matrix dist[i][j], you could also store the permutation of each such path. Say, instead of dist[i][j] you make a custom class myBestPathInfo, and the array of an instance of this, say myBestPathInfo[i][j], contain members best distance as well as permutation of the best path. Preferably, the best path permutation is described as an ordered set of some vertex objects, where the latter are of reference type and unique for each vertex (however used in several myBestPathInfo instances). Such objects could include a boolean property isActive (true/false).
Whenever a vertex is removed, you traverse through the best path permutations for each vertex-vertex pair, to make sure no vertex has been deactivated. Finally, only for broken paths (deactivated vertices) do you re-run Dijkstra's algorithm.
Another solution would be to solve the shortest path for all pairs using linear programming (LP) techniques. A removed vertex can be easily implemented as an additional constraint in your program (e.g. flow in <=0 and and flow out of vertex <= 0*), after which the re-solving of the shortest path LP:s can use the previous optimal solution as a feasible basic feasible solution (BFS) in the dual LPs. This property holds since adding a constraint in the primal LP is equivalent to an additional variable in the dual; hence, previously optimal primal BFS will be feasible in dual after additional constraints. (on-the-fly starting on simplex solver for LPs).

Is there a less ugly way to do input in D than scanf()?

Currently the only way I know how to do input in D is with the scanf() function. But god damn it's ugly. You would think that since it's an upgrade from C that they would have fixed that.
I'm looking for a way to do it with a single argument. Currently you have to do:
int foo = 0;
scanf("%i", &foo);
writeln("%i", foo);
But it would look a lot cleaner with a single argument. Something like:
int foo = 0;
scanf(foo);
writeln(foo);
Thanks.
readf("%d", &foo); allows working with std.stdio.File rather than C FILE*
foo = readln().strip().to!int();
For reading entire files with lines formatted in the same way:
int[] numbers = slurp!int("filename", "%d");
There's a really cool user-input module here:
https://github.com/Abscissa/scriptlike/blob/master/src/scriptlike/interact.d
Example code:
if (userInput!bool("Do you want to continue?"))
{
auto outputFolder = pathLocation("Where you do want to place the output?");
auto color = menu!string("What color would you like to use?", ["Blue", "Green"]);
}
auto num = require!(int, "a > 0 && a <= 10")("Enter a number from 1 to 10");
The above answers are great. I just want to add my 2 cents.
I often have the following simple function lying around:
T read(T)()
{
T obj;
readf(" %s", &obj);
return obj;
}
It's generic and pretty handy - it swallows any white space and reads any type you ask. You can use it like this:
auto number = read!int;
auto floating_number = read!float;
// etc.

Output the nodes in a cycle existing in a directed graph

While I understand that we can detect cycles with the DFS algorithm by detecting back-edges http://cs.wellesley.edu/~cs231/fall01/dfs.pdf. I am not being able to figure out how to output the nodes in the cycle in an efficient and "clean" manner while following the above said method.
Would be gratfeull for some help
Thanks
This is how i did it in my own implementation. It deviates a little bit in the naming conventions from the one used in your PDF but it should be obvious what it does.
All m_* variables are vectors, except m_topoOrder and m_cycle which are stacks.
The nodes of the cycle will be in m_cycle.
The m_onStack keeps track of nodes which are on the recursive call stack.
For a complete description i suggest the book Algorithms by Robert Sedgewick.
void QxDigraph::dfs(int v)
{
m_marked[v] = true;
m_onStack[v] = true;
foreach(int w, m_adj[v]) {
if(hasCycle()) return;
else if(!m_marked[w])
{
m_edgeTo[w] = v;
dfs(w);
}
else if(m_onStack[w])
{
m_cycle.clear();
for(int x=v; x!=w; x = m_edgeTo[x])
m_cycle.push(x);
m_cycle.push(w);
m_cycle.push(v);
}
}
m_onStack[v] = false;
m_topoOrder.push(v);
}