I have a 2D matrix represented as a vector of values, an index representing the first cell and a pair of coordinate representing the second cell.
vector<double> matrix;
auto index = 10;
auto x1 = index % width;
auto y1 = index / width;
auto x2 = ...
auto y2 = ...
I need to find the distance between these two cells, where the distance is equals to 1 for the first "ring" of the 8 neighbor cells, 2 for the second ring, and so on.
Is there a way faster than the euclidean distance?
What you need is something like a modified Manhattan Distance. I think there may be a specific name for your use case, but I don't know it. Anyway, this is how I'd do it.
Suppose the two points are x rows away and y columns away. Then x+y is the Manhattan Distance. But in your case, diagonal movements are also allowed. So, if you moved diagonally towards the point initially, you'd cover the smaller of x and y, with some amount remaining in the other. You can then move horizontally/vertically to cover the remaining distance. Hence, the distance by your metric would be max(x,y).
Given points (x1,y1) and (x2,y2), the answer would be max(|x1-x2|,|y1-y2|)
Related
Given an integer 3D coordinate system, a center point P, a vector in some direction V, and a max sphere radius R:
I want to iterate over only integer points in a fashion that starts at P and goes along direction V until reaching the max radius R.
Then, for some small angle T iterate all points within the cone (or spherical sector) around V.
Incrementally expand T until T is pi/2 radians and every point within the sphere has been iterated.
I need to do this with O(1) space complexity. So the order of the points can't be precomputed/sorted but must result naturally from some math.
Example:
// Vector3 represents coordinates x, y, z
// where (typically) x is left/right, y is up/down, z is depth
Vector3 center = Vector3(0, 0, 0); // could be anything
Vector3 direction = Vector3(0, 100, 0); // could be anything
int radius = 4;
double piHalf = acos(0.0); // half of pi
std::queue<Vector3> list;
for (double angle = 0; angle < piHalf; angle+= .1)
{
int x = // confusion begins here
int y = // ..
int z = // ..
list.push(Vector3(x, y, z));
}
See picture for this example
The first coordinates that should be caught are:
A(0,0,0), C(0,1,0), D(0,2,0), E(0,3,0), B(0,4,0)
Then, expanding the angle somewhat (orange cone):
K(-1,0,3), X(1,0,3), (0,1,3), (0,-1,3)
Expanding the angle a bit more (green cone):
F(1,1,3), (-1,-1,3), (1,-1,3) (-1,1,3)
My guess for what would be next is:
L(1,0,2), (-1,0,2), (0,1,2), (0,-1,2)
M(2,0,3) would be hit somewhat after
Extra notes and observations:
A cone will hit a max of four points at its base, if the vector is perpendicular to an axis and originates at an integer point. It may also hit points along the cone wall depending on the angle
I am trying to do this in c++
I am aware of how to check whether a point X is within any given cone or spherical vector by comparing the angle between V and PX with T and am currently using this knowledge for a lesser solution.
This is not a homework question, I am working on a 3D video game~
iterate all integer positions Q in your sphere
simple 3x nested for loops through x,y,z in range <P-R,P+R> will do. Just check inside sphere so
u=(x,y,z)-P;
dot(u,u) <= R*R
test if point Q is exactly on V
simply by checking angle between PQ and V by dot product:
u = Q-P
u = u/|u|
v = V/|V|
if (dot(u,v)==1) point Q is on V
test if points is exactly on surface of "cone"
simply by checking angle between PQ and V by dot product:
u = Q-P
u = u/|u|
v = V/|V|
if (dot(u,v)==cos(T/2)) point Q is on "cone"
where I assume T is full "cone" angle not the half one.
Beware you need to use floats/double for this and make the comparison with some margin for error like:
if (fabs(dot(u,v)-1.0 )<1e-6) point Q is on V
if (fabs(dot(u,v)-cos(T/2))<1e-6) point Q is on "cone"
I have an NxN grid with 2 points, the source and destination. I need to move step by step from the source to the destination (which is also moving). How do I determine what the next point is to move to?
One way is to assess all 8 points and see which yields the lowest distance using an Euclidian distance. However, I was hoping there is a cool (mathematical) trick which will yield more elegant results.
Your question statement allows moving diagonally, which is faster (since it's moving both horizontally and vertically in a single step): this solution will always do that unless it has the same x or y coordinate as the target.
using Position = pair<int,int>;
Position move(Position const ¤t, Position const &target) {
// horizontal and vertical distances
const int dx = target.first - current.first;
const int dy = target.second - current.second;
// horizontal and vertical steps [-1,+1]
const int sx = dx ? dx/abs(dx) : 0;
const int sy = dy ? dy/abs(dy) : 0;
return { current.first + sx, current.second + sy };
}
I'm not sure if this counts as a cool mathematical trick though, it just depends on knowing that:
dx = target.x-current.x is positive if you should move in the positive x-direction, negative if you should go in the negative direction, and zero if you should go straight up/down
dx/abs(dx) keeps the sign and removes the magnitude, so it's always one of -1,0,+1 (avoiding however division by zero)
I suppose that answer to your question is Bresenham's line algorithm. It allows to build sequence of integer points between start and end points in your grid. Anyway you can adapt ideas from it to your problem
For more information see https://www.cs.helsinki.fi/group/goa/mallinnus/lines/bresenh.html
I would simply use some vector math, take dest minus source as a vector, and then calculate the angle between that vector and some reference vector, e.g. <1, 0>, with standard methods.
Then you can simply divide the circle in 8 (or 4 if your prefer) sections and determine in which section your vector lies from the angle you obtained.
See euclidean space for how to calculate the angle between two vectors.
I am trying to write an efficient algorithm that counts the number of points inside a Sphere of Radius R and Dimension D. The sphere is always at the origin. Suppose we have a sphere of dimension 2 (circle) with radius 5.
My strategy is to generate all possible points within the first quadrant, so for the above example we know that (1,2) is in the circle, so must all + / - combinations of that point which is simply dimension squared. So for each point found in a single quadrant of an n-dimensional sphere we add 2 ^ dimension to the total count.
I'm not sure if there is a much more efficient solution to this problem but this is what I have so far in terms of implementation.
int count_lattice_points(const double radius, const int dimension) {
int R = static_cast<int>(radius);
int count = 0;
std::vector<int> points;
std::vector<int> point;
for(int i = 0; i <= R; i++)
points.push_back(i);
do {
for(int i = 0; i < dimension - 1; i++)
point.push_back(points.at(i));
if(isPointWithinSphere(point, radius)) count += std::pow(2,dimension);
point.clear();
}while(std::next_permutation(points.begin(), points.end()));
return count + 3;
}
What can I fix or improve in this situation ?
For 2D case this is Gauss's circle problem. One possible formula:
N(r) = 1 + 4 * r + 4 * Sum[i=1..r]{Floor(Sqrt(r^2-i^2))}
(central point + four quadrants, 4*r for points at the axis, others for in-quadrant region).
Note that there is no known simple closed math expression for 2D case.
In general your idea with quadrants, octants etc is right, but checking all the points is too expensive.
One might find the number of ways to compose all squares from 0 to r^2 from 1..D
integer squares (extension of (4) formula).
Note that combinatorics would help to make calculation faster. For example, it is enough to find the number of ways to
make X^2 from D natural squares, and multiply by 2^D (different sign combinations); find the number of ways to make X^2 from D-1 natural squares, and multiply by D*2^(D-1) (different sign combinations + D places for zero addend) etc
Example for D=2, R=3
addends: 0,1,4,9
possible sum compositions number of variants
0 0+0 1
1 0+1,1+0 2*2=4
2 1+1 4
4 0+4,4+0 2*2=4
5 1+4,4+1 2*4=8
8 4+4 4
9 0+9,9+0 2*2=4
-------------------------------------
29
I presented my algorithm for 2D here (with some source code and an ugly but handy illustration):
https://stackoverflow.com/a/42373448/5298879
It's around 3.4x faster than MBo's counting points between the origin and the edge of the circle in one of the quarters.
You just imagine an inscribed square and count only one-eighth of what's outside that square inside that circle.
public static int gaussCircleProblem(int radius) {
int allPoints=0; //holds the sum of points
double y=0; //will hold the precise y coordinate of a point on the circle edge for a given x coordinate.
long inscribedSquare=(long) Math.sqrt(radius*radius/2); //the length of the side of an inscribed square in the upper right quarter of the circle
int x=(int)inscribedSquare; //will hold x coordinate - starts on the edge of the inscribed square
while(x<=radius){
allPoints+=(long) y; //returns floor of y, which is initially 0
x++; //because we need to start behind the inscribed square and move outwards from there
y=Math.sqrt(radius*radius-x*x); // Pythagorean equation - returns how many points there are vertically between the X axis and the edge of the circle for given x
}
allPoints*=8; //because we were counting points in the right half of the upper right corner of that circle, so we had just one-eightth
allPoints+=(4*inscribedSquare*inscribedSquare); //how many points there are in the inscribed square
allPoints+=(4*radius+1); //the loop and the inscribed square calculations did not touch the points on the axis and in the center
return allPoints;
}
An approach similar to that described by MBo, including source code, can be found at
https://monsiterdex.wordpress.com/2013/04/05/integer-lattice-in-n-dimensional-sphere-count-of-points-with-integer-coordinates-using-parallel-programming-part-i/.
The approach consists in finding partitions of the radius, and then for each partition in the sphere compute the number of ways it can be represented in the sphere by both permuting coordinates and flipping the signs of nonzero coordinates.
I have object A, with a speed. Speed is specified as 3D vector a = (x, y, z). Position is 3D point A [X, Y, Z]. I need to find out, if the current speed leads this object to another object B on position B [X, Y, Z].
I've sucessfully implemented this in 2 dimensions, ignoring the third one:
/*A is projectile, B is static object*/
//entity is object A
// - .v[3] is the speed vector
//position[3] is array of coordinates of object B
double vector[3]; //This is the vector c = A-B
this->entityVector(-1, entity.id, vector); //Fills the correct data
double distance = vector_size(vector); //This is distance |AB|
double speed = vector_size(entity.v); //This is size of speed vector a
float dist_angle = (float)atan2(vector[2],vector[0])*(180.0/M_PI); //Get angle of vector c as seen from Y axis - using X, Z
float speed_angle = (float)atan2((double)entity.v[2],entity.v[0])*(180.0/M_PI); //Get angle of vector a seen from Y axis - using X, Z
dist_angle = deg180to360(dist_angle); //Converts value to 0-360
speed_angle = deg180to360(speed_angle); //Converts value to 0-360
int diff = abs((int)compare_degrees(dist_angle, speed_angle)); //Returns the difference of vectors direction
I need to create the very same comparison to make it work in 3D - right now, the Y positions and Y vector coordinates are ignored.
What calculation should I do to get the second angle?
Edit based on answer:
I am using spherical coordinates and comparing their angles to check if two vectors are pointing in the same direction. With one vector being the A-B and another A's speed, I'me checking id A is heading to B.
I'm assuming the "second angle" you're looking for is φ. That is to say, you're using spherical coordinates:
(x,y,z) => (r,θ,φ)
r = sqrt(x^2 + y^2 + z^2)
θ = cos^-1(z/r)
φ = tan^-1(y/x)
However, if all you want to do is find if A is moving with velocity a towards B, you can use a dot product for a basic answer.
1st vector: B - A (vector pointing from A to B)
2nd vector: a (velocity)
dot product: a * (B-A)
If the dot product is 0, it means that you're not getting any closer - you're moving around a sphere of constant radius ||B-A|| with B at the center. If the dot product > 0, you're moving towards the point, and if the dot product < 0, you're moving away from it.
I have an array that represents a grid
For the sake of this example we will start the array at 1 rather that 0 because I realized after doing the picture, and can't be bothered to edit it
In this example blue would have an index of 5, green an index of 23 and red 38
Each color represents an object and the array index represents where the object is. I have implemented very simple gravity, whereby if the grid underneath is empty x + (WIDTH * (y + 1)) then the grid below is occupied by this object, and the grid that the object was in becomes empty.
This all works well in its current form, but what I want to do is make it so that red is the gravity point, so that in this example, blue will move to array index 16 and then 27.
This is not too bad, but how would the object be able to work out dynamically where to move, as in the example of the green grid? How can I get it to move to the correct index?
Also, what would be the best way to iterate through the array to 'find' the location of red? I should also note that red won't always be at 38
Any questions please ask, also thank you for your help.
This sounds very similar to line rasterization. Just imagine the grid to be a grid of pixels. Now when you draw a line from the green point to the red point, the pixels/cells that the line will pass are the cells that the green point should travel along, which should indeed be the shortest path from the green point to the red point along the discrete grid cells. You then just stop once you encounter a non-empty grid cell.
Look for Bresenham's algorithm as THE school book algorithm for line rasterization.
And for searching the red point, just iterate over the array linearly until you have it and then keep track of its grid position, like William already suggested in his answer.
x = x position
y = y position
cols = number of columns across in your grid
(y * cols) + x = index in array absolute value for any x, y
you could generalize this in a function:
int get_index(int x, int y, int gridcols)
{
return (gridcols * y) + x;
}
It should be noted that this works for ZERO BASED INDICES.
This is assuming I am understanding what you're talking about at all...
As for the second question, for any colored element you have, you should keep a value in memory (possibly stored in a structure) that keeps track of its position so you don't have to search for it at all.
struct _THING {
int xpos;
int ypos;
};
Using the get_index() function, you could find the index of the grid cell below it by calling like this:
index_below = get_index(thing.x, thing.y + 1, gridcols);
thing.y++; // increment the thing's y now since it has moved down
simple...
IF YOU WANT TO DO IT IN REVERSE, as in finding the x,y position by the array index, you can use the modulus operator and division.
ypos = array_index / total_cols; // division without remainder
xpos = array_index % total_cols; // gives the remainder
You could generalize this in a function like this:
// x and y parameters are references, and return values using these references
void get_positions_from_index(int array_index, int total_columns, int& x, int& y)
{
y = array_index / total_columns;
x = array_index % total_columns;
}
Whenever you're referring to an array index, it must be zero-based. However, when you are referring to the number of columns, that value will be 1-based for the calculations. x and y positions will also be zero based.
Probably easiest would be to work entirely in a system of (x,y) coordinates to calculate gravity and switch to the array coordinates when you finally need to lookup and store objects.
In your example, consider (2, 4) (red) to be the center of gravity; (5, 1) (blue) needs to move in the direction (2-5, 4-1) == (-3, 3) by the distance _n_. You get decide how simple you want n to be -- it could be that you move your objects to an adjoining element, including diagonals, so move (blue) to (5-1, 1+1) == (4, 2). Or perhaps you could move objects by some scalar multiple of the unit vector that describes the direction you need to move. (Say, heavier objects move further because the attraction of gravity is stronger. Or, lighter objects move further because they have less inertia to overcome. Or objects move further the closer they are to the gravity well, because gravity is an inverse square law).
Once you've sorted out the virtual coordinates of your universe, then convert your numbers (4, 2) via some simple linear formulas: 4*columns + 2 -- or just use multidimensional arrays and truncate your floating-point results to get your array indexes.