how to Improve the speed matlab - matlab

This is my matlab code. It runs too slow and I had no clue how to improve it.
Could you help me to improve the speed?
What I would like to do is to create some random points and then remove the random points to make them similar to my target points.
syms Dx Dy p q;
a = 0;
num = 10;
x = rand(1,num);
y = rand(1,num);
figure(1)
scatter(x,y,'.','g')
%num_x = xlsread('F:\bin\test_2');% num 1024
%figure(2)
%scatter(num_x(:,1),num_x(:,2),'.','r');
q = 0;
num_q = 10;
x_q = randn(1,num_q);
y_q = randn(1,num_q);
%figure(2)
hold on;
scatter(x_q,y_q,'.','r')
for i = 1:num_q;
for j = 1:num_q;
qx(i,j) = x_q(i) - x_q(j);
qy(i,j) = y_q(i) - y_q(j);
%qx(i,j) = num_x(i,1) - num_x(j,1);
%qy(i,j) = num_x(i,2) - num_x(j,2);
%d~(s(i),s(j))
if ((qx(i,j))^2+(qy(i,j)^2))> 0.01 % find neighbours
qx(i,j) = 0;
qy(i,j) = 0;
end
end
end
for i = 1:num_q;
for j = 1:num_q;
if qx(i,j)>0&&qy(i,j)>0
q = q + exp(-(((Dx - qx(i,j))^2)+((Dy - qy(i,j))^2))/4);%exp(-(((Dx - qx(i,j))^2)+((Dy - qy(i,j))^2))/4);
end
end
end
%I = ones(num,num); % I(s) should from a grayscale image
%r = 1./sqrt(I);
for s = 1:100;
for i = 1:num;
for j = 1:num;
dx(i,j) = x(i) - x(j);
dy(i,j) = y(i) - y(j);
%d~(s(i),s(j))
if ((dx(i,j))^2+(dy(i,j)^2))> 0.05 % delta p, find neighbours
dx(i,j) = 0;
dy(i,j) = 0;
end
end
end
p = 0;
for i = 1:num;
for j = 1:num;
if dx(i,j)>0&&dy(i,j)>0
p = p + exp(-(((Dx - dx(i,j))^2)+((Dy - dy(i,j))^2))/4);
end
end
end
p = p - q;
sum = 0;
for i = 1:num;
for j = 1:num;
if dx(i,j)>0&&dy(i,j)>0;
kx(i,j) = (1/2)*(Dx-dx(i,j))*exp((-(Dx-dx(i,j))^2+(Dy-dy(i,j))^2)/4);
ky(i,j) = (1/2)*(Dy-dy(i,j))*exp((-(Dx-dx(i,j))^2+(Dy-dy(i,j))^2)/4);
end
end
end
sum_x = ones(1,num);% 1行N列0矩阵
sum_y = ones(1,num);
%fx = zeros(1,num);
for i = 1:num;
for j = 1:num;
if dx(i,j)>0&&dy(i,j)>0;
fx(i) = p*kx(i,j);% j is neighbour to i
fy(i) = p*ky(i,j);
%fx(i) = matlabFunction(fx(i));
%fy(i) = matlabFunction(fy(i));
%P =quad2d(#(Dx,Dy) fx,0,0.01,0,0.01);
%fx =quad(#(Dx) fx,0,0.01);
%fx(i) =quad(#(Dy) fx(i),0,0.01);
%Q =quad2d(#(Dx,Dy) fy,0,0.01,0,0.01);
fx(i) = double(int(int(fx(i),Dx,0,0.01),Dy,0,0.01));
fy(i) = double(int(int(fy(i),Dx,0,0.01),Dy,0,0.01));
%fx(i) = vpa(p*kx(i,j));
%fy(i) = vpa(p*ky(i,j));
%fx(i) = dblquad(#(Dx,Dy)fx(i),0,0.01,0,0.01);
%fy(i) = dblquad(#(Dx,Dy)fy(i),0,0.01,0,0.01);
sum_x(i) = sum_x(i) + fx(i);
sum_y(i) = sum_y(i) + fy(i);
end
end
end
for i = 1:num;
sum_x = 4.*sum_x./num;
sum_y = 4.*sum_y./num;
x(i) = x(i) - 0.05*sum_x(i);
y(i) = y(i) - 0.05*sum_y(i);
end
a = a+1
end
hold on;
scatter(x,y,'.','b')

The fast version of your loop should be something like:
qx = bsxfun(#minus, x_q.', x_q);
qy = bsxfun(#minus, y_q.', y_q);
il = (qx.^2 + qy.^2 >= 0.01);
qx(il) = 0;
qy(il) = 0;
il = qx>0 && qy>0;
q = sum(exp(-((Dx-qx(il)).^2 + (Dy-qy(il)).^2)/4));
%// etc. for vectorization of the inner loops

Related

MATLAB 'parfor' Loops Very Slow When Compared With 'for' loop

I have a script that I'm running, and at one point I have a loop over n objects, where I want n to be fairly large.
I have access to a server, so I put in a parfor loop. However, this is incredibly slow compared with a standard for loops.
For example, running a certain configuration ( the one below ) with the parfor loop on 35 workers took 68 seconds, whereas the for loop took 2.3 seconds.
I know there's stuff to do with array-broadcasting that can cause issues, but I don't know a lot about this.
n = 20;
r = 1/30;
tic
X = rand([2,n-1]);
X = [X,[0.5;0.5]];
D = sq_distance(X,X);
A = sparse((D < r) - eye(n));
% Infected set I
I = n;
[S,C] = graphconncomp(A);
compnum = C(I);
I_new = find(C == compnum);
I = I_new;
figure%('visible','off')
gplot(A,X')
hold on
plot(X(1,I),X(2,I),'r.')
hold off
title('time = 0')
axis([0,1,0,1])
time = 0;
t_max = 10; t_int = 1/100;
TIME = 1; T_plot = t_int^(-1) /100;
loops = t_max / T_plot;
F(loops) = struct('cdata',[],'colormap',[]);
F(1) = getframe;
% Probability of healing in interval of length t_int
heal_rate = 1/3; % (higher number is faster heal)
p_heal = t_int * heal_rate;
numhealed = 0;
while time < t_max
time = time+t_int;
steps = poissrnd(t_int,[n,1]);
parfor k = 1:n
for s = 1:steps(k)
unit_vec = unif_unitvector;
X_new = X(:,k) + unit_vec*t_int;
if ( X_new < 1 == ones(2,1) ) ...
& ( X_new > 0 == ones(2,1) )
X(:,k) = X_new;
end
end
end
D = sq_distance(X,X);
A = sparse((D < r) - eye(n));
[S,C] = graphconncomp(A);
particles_healed = binornd(ones(length(I),1),p_heal);
still_infected = find(particles_healed == 0);
I = I(still_infected);
numhealed = numhealed + sum(particles_healed);
I_new = I;
% compnum = zeros(length(I),1);
for i = 1:length(I)
compnum = C(I(i));
I_new = union(I_new,find(C == compnum));
end
I = I_new;
if time >= T_plot*TIME
gplot(A,X')
hold on
plot(X(1,I),X(2,I),'r.')
hold off
title(sprintf('time = %1g',time))
axis([0,1,0,1])
% fprintf('number healed = %1g\n',numhealed)
numhealed = 0;
F(TIME) = getframe;
TIME = TIME + 1;
end
end
toc

Simulate a custom function in Matlab

I'd like to simulate the following function:
l(t) = mu + Σ (1 + (t-t_i)/alpha)^(beta)
I wrote the function as follow:
function[l] = custom(m,t,H,par)
k = length(H);
n = length(t);
l = par.mu(m)*ones(n,1);
for i = 1:n
for j = 1:k
h = H{j};
h = h(h < t(i));
if ~isempty(h)
d = t(i) - h;
l(i) = l(i) + sum((1+ (d/par.alpha(m,j)))^ par.beta(m,j));
end
end
end
Now, how can I simulate this function for t=1000, par.mu= 0.5, par.alpha = 0.1, par.beta = 0.3?
I have the following code for simulation of this function but it's not efficient...How can I make it better?
function[h] = simcustom(t,par)
h = -log(rand)/par.mu;
if h < t
do5 = 1;
i = 0;
j = 0;
k = 0;
n = 1;
while true
if do5;
k = k + 1;
Lstar(k) = custom(1,h(n),{h},par); %#ok
end
j = j + 1;
U(j) = rand; %#ok
i = i + 1;
u(i) = -log(U(j))/Lstar(k); %#ok
if i == 1
Tstar(i) = u(i); %#ok
else
Tstar(i) = Tstar(i-1) + u(i);
end
if Tstar(i) > t
h = {h};
break
end
j = j + 1;
U(j) = rand;
if U(j) <= custom(1,Tstar(i),{h},par)/Lstar(k)
n = n + 1;
h = [h Tstar(i)];
do5 = true;
else
k = k + 1;
Lstar(k) = custom(1,Tstar(i),{h},par);
do5 = false;
end
end
else
h = {[]};
end
And here is the application:
par2.mu = 0.5;
par2.alpha = 0.1;
par2.beta = 0.3;
t = 1000;
h2 = simcustom(t,par2);

Vectorizing 4 nested for loops

I'm trying to vectorize the 2 inner nested for loops, but I can't come up with a way to do this. The FS1 and FS2 functions have been written to accept argument for N_theta and N_e, which is what the loops are iterating over
%% generate regions
for raw_r=1:visual_field_width
for raw_c=1:visual_field_width
r = raw_r - center_r;
c = raw_c - center_c;
% convert (r,c) to polar: (eccentricity, angle)
e = sqrt(r^2+c^2)*deg_per_pixel;
a = mod(atan2(r,c),2*pi);
for nt=1:N_theta
for ne=1:N_e
regions(raw_r, raw_c, nt, ne) = ...
FS_1(nt-1,a,N_theta) * ...
FS_2(ne-1,e,N_e,e0_in_deg, e_max);
end
end
end
end
Ideally, I could replace the two inner nested for loops by:
regions(raw_r,raw_c,:,:) = FS_1(:,a,N_theta) * FS_2(:,N_e,e0_in_deg,e_max);
But this isn't possible. Maybe I'm missing an easy fix or vectorization technique? e0_in_deg and e_max are parameters.
The FS_1 function is
function h = FS_1(n,theta,N,t)
if nargin==2
N = 9;
t=1/2;
elseif nargin==3
t=1/2;
end
w = (2*pi)/N;
theta = theta + w/4;
if n==0 && theta>(3/2)*pi
theta = theta - 2*pi;
end
h = FS_f((theta - (w*n + 0.5*w*(1-t)))/w);
the FS_2 function is
function g = FS_gne(n,e,N,e0, e_max)
if nargin==2
N = 10;
e0 = .5;
elseif nargin==3
e0 = .5;
end
w = (log(e_max) - log(e0))/N;
g = FS_f((log(e)-log(e0)-w*(n+1))/w);
and the FS_f function is
function f = FS_f(x, t)
if nargin<2
t = 0.5;
end
f = zeros(size(x));
% case 1
idx = x>-(1+t)/2 & x<=(t-1)/2;
f(idx) = (cos(0.5*pi*((x(idx)-(t-1)/2)/t))).^2;
% case 2
idx = x>(t-1)/2 & x<=(1-t)/2;
f(idx) = 1;
% case 3
idx = x>(1-t)/2 & x<=(1+t)/2;
f(idx) = -(cos(0.5*pi*((x(idx)-(1+t)/2)/t))).^2+1;
I had to assume values for the constants, and then used ndgrid to find the possible configurations and sub2ind to get the indices. Doing this I removed all loops. Let me know if this produced the correct values.
function RunningFunction
%% generate regions
visual_field_width = 10;
center_r = 2;
center_c = 3;
deg_per_pixel = 17;
N_theta = 2;
N_e = 5;
e0_in_deg = 35;
e_max = 17;
[raw_r, raw_c, nt, ne] = ndgrid(1:visual_field_width, 1:visual_field_width, 1:N_theta, 1:N_e);
ind = sub2ind(size(raw_r), raw_r, raw_c, nt, ne);
r = raw_r - center_r;
c = raw_c - center_c;
% convert (r,c) to polar: (eccentricity, angle)
e = sqrt(r.^2+c.^2)*deg_per_pixel;
a = mod(atan2(r,c),2*pi);
regions(ind) = ...
FS_1(nt-1,a,N_theta) .* ...
FS_2(ne-1,e,N_e,e0_in_deg, e_max);
regions = reshape(regions, size(raw_r));
end
function h = FS_1(n,theta,N,t)
if nargin==2
N = 9;
t=1/2;
elseif nargin==3
t=1/2;
end
w = (2*pi)./N;
theta = theta + w/4;
theta(n==0 & theta>(3/2)*pi) = theta(n==0 & theta>(3/2)*pi) - 2*pi;
h = FS_f((theta - (w*n + 0.5*w*(1-t)))/w);
end
function g = FS_2(n,e,N,e0, e_max)
if nargin==2
N = 10;
e0 = .5;
elseif nargin==3
e0 = .5;
end
w = (log(e_max) - log(e0))/N;
g = FS_f((log(e)-log(e0)-w*(n+1))/w);
end
function f = FS_f(x, t)
if nargin<2
t = 0.5;
end
f = zeros(size(x));
% case 1
idx = x>-(1+t)/2 & x<=(t-1)/2;
f(idx) = (cos(0.5*pi*((x(idx)-(t-1)/2)/t))).^2;
% case 2
idx = x>(t-1)/2 & x<=(1-t)/2;
f(idx) = 1;
% case 3
idx = x>(1-t)/2 & x<=(1+t)/2;
f(idx) = -(cos(0.5*pi*((x(idx)-(1+t)/2)/t))).^2+1;
end

How to Run a Script for 30 - 40 Values with Increasing Magnitude

I would like to run the following for 30 to 40 values of the current (I0) with increasing magnitude. Can someone please tell me how to make this happen?
GNa = 400;
GK = 200;
GL = 2;
ENa = 99;
EK = -85;
VL = -65;
C = 2;
dt = .01;
t = 0:dt:200;
I0 = 200;
It= I0 + 0*t;
It(1:40/dt)=0;
m = zeros(1,length(t));
n = zeros(1,length(t));
h = zeros(1,length(t));
V = zeros(1,length(t));
V(1) = -65;
am = zeros(1,length(t));
bm = zeros(1,length(t));
an = zeros(1,length(t));
bn = zeros(1,length(t));
ah = zeros(1,length(t));
bh = zeros(1,length(t));
for i=1:length(t)-1
I=It(i);
am(i) = (0.1*(V(i) + 40))/(1 - exp(-0.1*(V(i)+40)));
bm(i) = 4*exp(-0.0556*(V(i)+65));
ah(i) = 0.07*exp(-0.05*(V(i)+65));
bh(i) = 1./(1+exp(-0.1*(V(i)+35)));
an(i) = (0.01*(V(i)+55))/(1 - exp(-0.1*(V(i)+55)));
bn(i) = 0.125*exp(-0.0125*(V(i)+65));
m(i+1) = m(i) + dt*(am(i)*(1 - m(i)) - bm(i)*m(i));
h(i+1) = h(i) + dt*(ah(i)*(1 - h(i)) - bh(i)*h(i));
n(i+1) = n(i) + dt*(an(i)*(1 - n(i)) - bn(i)*n(i));
V(i+1) = V(i) + dt*(((-GL*(V(i) - VL) - GNa*(m(i).^3)*h(i)*(V(i) - ENa) - GK* (n(i).^4)*(V(i) - EK)+ I))/C);
end
You can always wrap this in another for loop, no?
I0list = 200:2.5:300 % 41 values including endpoints
for j = 1:length(IOlist)
I0 = IOlist(j);
% Rest of your code, minus setting I0, goes here
end
If you need to store all of the values, make m etc. be m = zeros(length(IOlist),length(t)); instead, and then use both indices when assigning, e.g.
am(j,i) = ...

Perform step-by-step integral

I have this piece of code:
time = 614.4;
Uhub = 11;
HubHt = 90;
TI = 'A';
N1 = 4096;
N2 = 32;
N3 = 32;
L1 = Uhub*time;
L2 = 150;
L3 = 220;
V = L1*L2*L3;
gamma = 3.9;
c = 1.476;
b = 5.6;
if HubHt < 60
lambda1 = 0.7*HubHt;
else
lambda1 = 42;
end
L = 0.8*lambda1;
if isequal(TI,'A')
Iref = 0.16;
sigma1 = Iref*(0.75*Uhub + b);
elseif isequal(TI,'B')
Iref = 0.14;
sigma1 = Iref*(0.75*Uhub + b);
elseif isequal(TI,'C')
Iref = 0.12;
sigma1 = Iref*(0.75*Uhub + b);
else
sigma1 = str2num(TI)*Uhub/100;
end
sigma_iso = 0.55*sigma1;
%% Wave number vectors
ik1 = cat(2,(-N1/2:-1/2),(1/2:N1/2));
ik2 = -N2/2:N2/2-1;
ik3 = -N3/2:N3/2-1;
[x y z] = ndgrid(ik1,ik2,ik3);
k1 = reshape((2*pi*L/L1)*x,N1*N2*N3,1);
k2 = reshape((2*pi*L/L2)*y,N1*N2*N3,1);
k3 = reshape((2*pi*L/L3)*z,N1*N2*N3,1);
k = sqrt(k1.^2 + k2.^2 + k3.^2);
Now I should calculate
where
The procedure to calculate the integral is
At the moment I'm using this loop
E = #(k) (1.453*k.^4)./((1 + k.^2).^(17/6));
E_int = zeros(1,N1*N2*N3);
E_int(1) = 1.5;
for i = 2:(N1*N2*N3)
E_int(i) = E_int(i) + quad(E,i-1,i);
end
neglecting for the k>400 approximation. I believe that my loop is not right.
How would you suggest to calculate the integral?
I thank you in advance.
WKR,
Francesco
This is a list of correction from the more obvious to the possibly more subtle. (Indeed I start from what you wrote in the final part going upwards).
From what you write:
E = #(k) (1.453*k.^4)./((1 + k.^2).^(17/6));
E_int = zeros(1,N1*N2*N3);
E_int(1) = 1.5;
for i = 2:(N1*N2*N3)
%//No point in doing this:
%//E_int(i) = E_int(i) + quad(E,i-1,i);
%//According to what you write, it should be:
E_int(i) = E_int(i-1) + quad(E,i-1,i);
end
You could speed the whole thing up by doing
%//Independent integration on segments
Local_int = arrayfun(#(i)quad(E,i-1,i), 2:(N1*N2*N3));
Local_int = [1.5 Local_int];
%//integral additivity
E_int = cumsum(Local_int);
Moreover, if the known condition (point 2.) really is "... ( = 1.5 if k' = 0)", then the whole implementation should really be more like
%//Independent integration on segments
Local_int = arrayfun(#(i)quad(E,i-1,i), 2:(N1*N2*N3));
%//integral additivity + cumulative removal of queues
E_int = 1.5 - [0 fliplr(cumsum(fliplr(Local_int)))]; %//To remove queues