Supercover DDA algorithm - algorithm

I'm trying to figure out how to make a supercover DDA algorithm. Or in other words, a DDA algorithm that will cover ALL grid points crossed by a line. See the image below.
The image was drawn by me and might not be 100% accurate but it shows the general idea. I also want to note the examples on the lower half of the image do not have integer start and end coordinates, this is necessary.
If you need to know, I intend to use this for line of sight ray casting.
I'm capable of implementing a typical DDA algorithm, but my problem is, how can I modify it to cover all points?
Thanks!
My current implementation of the DDA algorithm in Lua
function dline(x0,y0, x1,y1) -- floating point input
local dx = x1-x0
local dy = y1-y0
local s = math.max(math.abs(dx),math.abs(dy))
dx = dx/s
dy = dy/s
local x = x0
local y = y0
local i = 0
return function() -- iterator intended for a for loop
if i <= s then
local rx,ry = x,y
x = x+dx
y = y+dy
i = i+1
return rx,ry
end
end
end

Sorry, I don't ask questions too often, mainly because I'm not that good. But I'll tell you what I am good at! Solving my own problem! :D
As a note, the image in my question shows the lines crossing diagonals if the line passes through a point precisely, this algorithm does not, but after some thought, crossing diagonals is not desirable for me.
Thanks to this article I found.
Here's the new implementation
function line(x0,y0, x1,y1)
local vx,vy = x1-x0, y1-y0 -- get the differences
local dx = math.sqrt(1 + (vy/vx)^2) -- length of vector <1, slope>
local dy = math.sqrt(1 + (vx/vy)^2) -- length of vector <1/slope, 1>
local ix,iy = math.floor(x0), math.floor(y0) -- initialize starting positions
local sx,ex -- sx is the increment direction
-- ex is the distance from x0 to ix
if vx < 0 then
sx = -1
ex = (x0-ix) * dx
else
sx = 1
ex = (ix + 1-x0) * dx -- subtract from 1 instead of 0
-- to make up for flooring ix
end
local sy,ey
if vy < 0 then
sy = -1
ey = (y0-iy) * dy
else
sy = 1
ey = (iy + 1-y0) * dy
end
local done = false
local len = math.sqrt(vx^2 + vy^2)
return function()
if math.min(ex,ey) <= len then
local rx,ry = ix,iy
if ex < ey then
ex = ex + dx
ix = ix + sx
else
ey = ey + dy
iy = iy + sy
end
return rx,ry
elseif not done then -- return the final two coordinates
done = true
return ix,iy
end
end
end

You can do it in the same time complexity as a normal dda algorithm by simply adding a few checks on adjacent squares.

Related

Speed up matlab code with backward multiplication using vectorization

I need to decrease the runtime of the following piece of code written in Matlab :
dt = 0.001; dt05 = dt^0.5; length_t = 1.0e6;
%a: array containing length_t elements
y0 = [1.5 2.0 1.0];y = zeros(length_t,3);y(1,:) = y0;
for i = 1:length_t-1
dy = f(y(i,:); %call to some function
y(i+1,1) = y(i,1) + dt*dy(1) ;
y(i+1,2) = y(1,2) + a(1:i)*(y(i:-1:1,2)-y(1,2)) + dt05*dy(2) ;
y(i+1,3) = y(1,3) + a(1:i)*(y(i:-1:1,3)-y(1,3)) + dt05*dy(3) ;
end
The slowest steps are the calculations of y(i+1,2) and y(i+1,3) (because they require all the previous y(:,2:3) values). How can I speed up this code by vectorization and/or using a GPU?
EDIT: a is given by
a(1) = 0.5; a (2:length_t) = cumprod( (1-((1+a(1))./(2:length_t))) )*a(1);
and f is some function like:
function dy = f(y)
k12 = 1.0; k02 = 2.0;
dy(1) = - k12*y(1)*y(2);
dy(2) = k12*y(1) - k02*y(2);
dy(3) = (k12+k02)*(y(1)+y(2)+y(3));
dy = [dy(1) dy(2) dy(3)];
end
Note that I do NOT have DSP knowledge. I hope someone can write a better answer or correct mine.
If you can tolerate some approximations:
You can see that ratio a(i+1)/a(i) tends towards 1. This means that you can calculate a*y exactly for the first N elements (N depending on your desired accuracy), then add N+1-th element to variable AY and decrease variable AY by a magic factor depending on i. That way you can save yourself a lot of multiplications at the cost of this AY being somewhat inaccurate estimate of the actual product.
Your y(i,2) would then be somewhat like (csa = cumsum(a);):
y(i,2) = a(1:N) * y(i:-1:i-N) + AY + dt05_thingy + (1-csa(i))*y(1,2);
y(i,3) = ...
AY = AY*MF(i,N) + a(N)*y(i-N);
Magic factor would depend on N and perhaps also i. Precalculate R=a(2:end)./a(1:end-1); and use MF(N, i>N) = R(N+(i-N)/2) - so take the middle ratio for the elements you are approximating.

Can anyone explain how different is this hybrid PSOGA from normal GA?

Does this code have mutation, selection, and crossover, just like the original genetic algorithm.
Since this, a hybrid algorithm (i.e PSO with GA) does it use all steps of original GA or skips some
of them.Please do tell me.
I am just new to this and still trying to understand. Thank you.
%%% Hybrid GA and PSO code
function [gbest, gBestScore, all_scores] = QAP_PSO_GA(CreatePopFcn, FitnessFcn, UpdatePosition, ...
nCity, nPlant, nPopSize, nIters)
% Set algorithm parameters
constant = 0.95;
c1 = 1.5; %1.4944; %2;
c2 = 1.5; %1.4944; %2;
w = 0.792 * constant;
% Allocate memory and initialize
gBestScore = inf;
all_scores = inf * ones(nPopSize, nIters);
x = CreatePopFcn(nPopSize, nCity);
v = zeros(nPopSize, nCity);
pbest = x;
% update lbest
cost_p = inf * ones(1, nPopSize); %feval(FUN, pbest');
for i=1:nPopSize
cost_p(i) = FitnessFcn(pbest(i, 1:nPlant));
end
lbest = update_lbest(cost_p, pbest, nPopSize);
for iter = 1 : nIters
if mod(iter,1000) == 0
parents = randperm(nPopSize);
for i = 1:nPopSize
x(i,:) = (pbest(i,:) + pbest(parents(i),:))/2;
% v(i,:) = pbest(parents(i),:) - x(i,:);
% v(i,:) = (v(i,:) + v(parents(i),:))/2;
end
else
% Update velocity
v = w*v + c1*rand(nPopSize,nCity).*(pbest-x) + c2*rand(nPopSize,nCity).*(lbest-x);
% Update position
x = x + v;
x = UpdatePosition(x);
end
% Update pbest
cost_x = inf * ones(1, nPopSize);
for i=1:nPopSize
cost_x(i) = FitnessFcn(x(i, 1:nPlant));
end
s = cost_x<cost_p;
cost_p = (1-s).*cost_p + s.*cost_x;
s = repmat(s',1,nCity);
pbest = (1-s).*pbest + s.*x;
% update lbest
lbest = update_lbest(cost_p, pbest, nPopSize);
% update global best
all_scores(:, iter) = cost_x;
[cost,index] = min(cost_p);
if (cost < gBestScore)
gbest = pbest(index, :);
gBestScore = cost;
end
% draw current fitness
figure(1);
plot(iter,min(cost_x),'cp','MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',8)
hold on
str=strcat('Best fitness: ', num2str(min(cost_x)));
disp(str);
end
end
% Function to update lbest
function lbest = update_lbest(cost_p, x, nPopSize)
sm(1, 1)= cost_p(1, nPopSize);
sm(1, 2:3)= cost_p(1, 1:2);
[cost, index] = min(sm);
if index==1
lbest(1, :) = x(nPopSize, :);
else
lbest(1, :) = x(index-1, :);
end
for i = 2:nPopSize-1
sm(1, 1:3)= cost_p(1, i-1:i+1);
[cost, index] = min(sm);
lbest(i, :) = x(i+index-2, :);
end
sm(1, 1:2)= cost_p(1, nPopSize-1:nPopSize);
sm(1, 3)= cost_p(1, 1);
[cost, index] = min(sm);
if index==3
lbest(nPopSize, :) = x(1, :);
else
lbest(nPopSize, :) = x(nPopSize-2+index, :);
end
end
If you are new to Optimization, I recommend you first to study each algorithm separately, then you may study how GA and PSO maybe combined, Although you must have basic mathematical skills in order to understand the operators of the two algorithms and in order to test the efficiency of these algorithm (this is what really matter).
This code chunk is responsible for parent selection and crossover:
parents = randperm(nPopSize);
for i = 1:nPopSize
x(i,:) = (pbest(i,:) + pbest(parents(i),:))/2;
% v(i,:) = pbest(parents(i),:) - x(i,:);
% v(i,:) = (v(i,:) + v(parents(i),:))/2;
end
Is not really obvious how selection randperm is done (I have no experience about Matlab).
And this is the code that is responsible for updating the velocity and position of each particle:
% Update velocity
v = w*v + c1*rand(nPopSize,nCity).*(pbest-x) + c2*rand(nPopSize,nCity).*(lbest-x);
% Update position
x = x + v;
x = UpdatePosition(x);
This version of velocity updating strategy is utilizing what is called Interia-Weight W, which basically mean we are preserving the velocity history of each particle (not completely recomputing it).
It worth mentioning that velocity updating is done more often than crossover (each 1000 iteration).

Diamond-Square algorithm: How do I determine what tiles I need to run a diamond function and what tiles I need to run a square function on?

I'm working on a diamond-square heightmap generator and I've been stuck on a certain part for a while now.
I'm having trouble determining which tiles I need to run a square() function on and which tiles I need to run a diamond() function on.
I took a look at this guide: http://www.playfuljs.com/realistic-terrain-in-130-lines/ and I took their for loop they're using as an example, but it didn't seem to work at all.
The preferred language for the answer is Lua (or just kindly point me in the right direction). I just need someone to tell me what I need to do to get a for loop that works for both diamond and square functions.
-- height constraints
local min_height = 10
local max_height = 100
-- the grid
local K = 4
local M = 2^K -- the field is cyclic integer grid 0 <= x,y < M (x=M is the same point as x=0)
local heights = {} -- min_height <= heights[x][y] <= max_height
for x = 0, M-1 do
heights[x] = {}
end
-- set corners height (all 4 corners are the same point because of cyclic field)
heights[0][0] = (min_height + max_height) / 2
local delta_height = (max_height - min_height) * 0.264
local side = M
local sqrt2 = 2^0.5
repeat
local dbl_side = side
side = side/2
-- squares
for x = side, M, dbl_side do
for y = side, M, dbl_side do
local sum =
heights[(x-side)%M][(y-side)%M]
+ heights[(x-side)%M][(y+side)%M]
+ heights[(x+side)%M][(y-side)%M]
+ heights[(x+side)%M][(y+side)%M]
heights[x][y] = sum/4 + (2*math.random()-1) * delta_height
end
end
delta_height = delta_height / sqrt2
-- diamonds
for x = 0, M-1, side do
for y = (x+side) % dbl_side, M-1, dbl_side do
local sum =
heights[(x-side)%M][y]
+ heights[x][(y-side)%M]
+ heights[x][(y+side)%M]
+ heights[(x+side)%M][y]
heights[x][y] = sum/4 + (2*math.random()-1) * delta_height
end
end
delta_height = delta_height / sqrt2
until side == 1
-- draw field
for x = 0, M-1 do
local s = ''
for y = 0, M-1 do
s = s..' '..tostring(math.floor(heights[x][y]))
end
print(s)
end

Kaczmarz animation

i am asking for help.. I want to animate the Kaczmarz method on Matlab. It's method allows to find solution of system of equations by the serial projecting solution vector on hyperplanes, which which is given by the eqations of system.
And i want make animation of this vector moving (like the point is going on the projected vectors).
%% System of equations
% 2x + 3y = 4;
% x - y = 2;
% 6x + y = 15;
%%
A = [2 3;1 -1; 6 1];
f = [4; 2; 15];
resh = pinv(A)*f
x = -10:0.1:10;
e1 = (1 - 2*x)/3;
e2 = (x - 2);
e3 = 15 - 6*x;
plot(x,e1)
grid on
%
axis([0 4 -2 2])
hold on
plot(x,e2)
hold on
plot(x,e3)
hold on
precision = 0.001; % точность
iteration = 100; % количество итераций
lambda = 0.75; % лямбда
[m,n] = size(A);
x = zeros(n,1);
%count of norms
for i = 1:m
nrm(i) = norm(A(i,:));
end
for i = 1:1:iteration
j = mod(i-1,m) + 1;
if (nrm(j) <= 0), continue, end;
predx = x;
x = x + ((f(j) - A(j,:)*x)*A(j,:)')/(nrm(j))^2;
p = plot(x);
set(p)
%pause 0.04;
hold on;
if(norm(predx - x) <= precision), break, end
end
I wrote the code for this method, by don't imagine how make the animation, how I can use the set function.
In your code there are a lot of redundant and random pieces. Do not call hold on more than once, it does nothing. Also set(p) does nothing, you want to set some ps properties to something, then you use set.
Also, you are plotting the result, but not the "change". The change is a line between the previous and current, and that is the only reason you'd want to have a variable such as predx, to plot. SO USE IT!
Anyway, this following code plots your algorithm. I added a repeated line to plot in green and then delete, so you can see what the last step does. I also changed the plots in the begging to just plot in red so its more clear what is each of the things.
Change your loop for:
for i = 1:1:iteration
j = mod(i-1,m) + 1;
if (nrm(j) <= 0), continue, end;
predx = x;
x = x + ((f(j) - A(j,:)*x)*A(j,:)')/(nrm(j))^2;
plot([predx(1) x(1)],[predx(2) x(2)],'b'); %plot line
c=plot([predx(1) x(1)],[predx(2) x(2)],'g'); %plot it in green
pause(0.1)
children = get(gca, 'children'); %delete the green line
delete(children(1));
drawnow
% hold on;
if(norm(predx - x) <= precision), break, end
end
This will show:

Find area of two overlapping circles using monte carlo method

Actually i have two intersecting circles as specified in the figure
i want to find the area of each part separately using Monte carlo method in Matlab .
The code doesn't draw the rectangle or the circles correctly so
i guess what is wrong is my calculation for the x and y and i am not much aware about the geometry equations for solving it so i need help about the equations.
this is my code so far :
n=1000;
%supposing that a rectangle will contain both circles so :
% the mid point of the distance between 2 circles will be (0,6)
% then by adding the radius of the left and right circles the total distance
% will be 27 , 11 from the left and 16 from the right
% width of rectangle = 24
x=27.*rand(n-1)-11;
y=24.*rand(n-1)+2;
count=0;
for i=1:n
if((x(i))^2+(y(i))^2<=25 && (x(i))^2+(y(i)-12)^2<=100)
count=count+1;
figure(2);
plot(x(i),y(i),'b+')
hold on
elseif(~(x(i))^2+(y(i))^2<=25 &&(x(i))^2+(y(i)-12)^2<=100)
figure(2);
plot(x(i),y(i),'y+')
hold on
else
figure(2);
plot(x(i),y(i),'r+')
end
end
Here are the errors I found:
x = 27*rand(n,1)-5
y = 24*rand(n,1)-12
The rectangle extents were incorrect, and if you use rand(n-1) will give you a (n-1) by (n-1) matrix.
and
first If:
(x(i))^2+(y(i))^2<=25 && (x(i)-12)^2+(y(i))^2<=100
the center of the large circle is at x=12 not y=12
Second If:
~(x(i))^2+(y(i))^2<=25 &&(x(i)-12)^2+(y(i))^2<=100
This code can be improved by using logical indexing.
For example, using R, you could do (Matlab code is left as an excercise):
n = 10000
x = 27*runif(n)-5
y = 24*runif(n)-12
plot(x,y)
r = (x^2 + y^2)<=25 & ((x-12)^2 + y^2)<=100
g = (x^2 + y^2)<=25
b = ((x-12)^2 + y^2)<=100
points(x[g],y[g],col="green")
points(x[b],y[b],col="blue")
points(x[r],y[r],col="red")
which gives:
Here is my generic solution for any two circles (without any hardcoded value):
function [ P ] = circles_intersection_area( k1, k2, N )
%CIRCLES_INTERSECTION_AREA Summary...
% Adnan A.
x1 = k1(1);
y1 = k1(2);
r1 = k1(3);
x2 = k2(1);
y2 = k2(2);
r2 = k2(3);
if sqrt((x1-x2)*(x1-x2) + (y1-y2)*(y1-y2)) >= (r1 + r2)
% no intersection
P = 0;
return
end
% Wrapper rectangle config
a_min = x1 - r1 - 2*r2;
a_max = x1 + r1 + 2*r2;
b_min = y1 - r1 - 2*r2;
b_max = y1 + r1 + 2*r2;
% Monte Carlo algorithm
n = 0;
for i = 1:N
rand_x = unifrnd(a_min, a_max);
rand_y = unifrnd(b_min, b_max);
if sqrt((rand_x - x1)^2 + (rand_y - y1)^2) < r1 && sqrt((rand_x - x2)^2 + (rand_y - y2)^2) < r2
% is a point in the both of circles
n = n + 1;
plot(rand_x,rand_y, 'go-');
hold on;
else
plot(rand_x,rand_y, 'ko-');
hold on;
end
end
P = (a_max - a_min) * (b_max - b_min) * n / N;
end
Call it like: circles_intersection_area([-0.4,0,1], [0.4,0,1], 10000) where the first param is the first circle (x,y,r) and the second param is the second circle.
Without using For loop.
n = 100000;
data = rand(2,n);
data = data*2*30 - 30;
x = data(1,:);
y = data(2,:);
plot(x,y,'ro');
inside5 = find(x.^2 + y.^2 <=25);
hold on
plot (x(inside5),y(inside5),'bo');
hold on
inside12 = find(x.^2 + (y-12).^2<=144);
plot (x(inside12),y(inside12),'g');
hold on
insidefinal1 = find(x.^2 + y.^2 <=25 & x.^2 + (y-12).^2>=144);
insidefinal2 = find(x.^2 + y.^2 >=25 & x.^2 + (y-12).^2<=144);
% plot(x(insidefinal1),y(insidefinal1),'bo');
hold on
% plot(x(insidefinal2),y(insidefinal2),'ro');
insidefinal3 = find(x.^2 + y.^2 <=25 & x.^2 + (y-12).^2<=144);
% plot(x(insidefinal3),y(insidefinal3),'ro');
area1=(60^2)*(length(insidefinal1)/n);
area3=(60^2)*(length(insidefinal2)/n);
area2= (60^2)*(length(insidefinal3)/n);

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