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How to solve unable to resolve function.

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Hi, I try to run a code but when I try run in another pc, it works well but when I try to run in my own pc, the error :
Unable to resolve the name 'pf.k'.
Error in pfmdtesting (line 5)
k = pf.k;
keep coming out. Any ideas to solve this error? Really appreciate your help.
%% 初期設定
T = 100;
%%
k = pf.k;
if k == 1
error('error: k must be an integer greater or equal than 2');
end
%% Initialize variables
Ns = 50; % number of particles
nx = 50;
%nx = size(xk1);
xk = zeros(nx,1,Ns);
wkm1 = pf.w(:, k-1); % weights of last iteration
if k == 2
for i = 1:Ns % simulate initial particles
pf.particles(:,i,1) = pf.gen_x0(); % at time k=1
end
wkm1 = repmat(1/Ns, Ns, 1); % all particles have the same weight
end
%% Separate memory
xkm1 = pf.particles(:,:,k-1); % extract particles from last iteration;
xk = zeros(size(xkm1)); % = zeros(nx,Ns);
wk = zeros(size(wkm1)); % = zeros(Ns,1);
%% Observation equation y[k] = obs(k, x[k], v[k]);
ny = 1; % number of observations
obs = @(k, xk,vk) xk(1) + vk; % (returns column vector)
%% PDF of process noise and noise generator function
nu = 50; % size of the vector of process noise
sigma_u = sqrt(10);
p_sys_noise = @(u) normpdf(u, 0, sigma_u);
gen_sys_noise = @(u) normrnd(0, sigma_u); % sample from p_sys_noise (returns column vector)
%% PDF of observation noise and noise generator function
nv = 1; % size of the vector of observation noise
sigma_v = sqrt(10);
p_obs_noise = @(v) normpdf(v, 0, sigma_v);
gen_obs_noise = @(v) normrnd(0, sigma_v); % sample from p_obs_noise (returns column vector)
%% Initial PDF
% p_x0 = @(x) normpdf(x, 0,sqrt(10)); % initial pdf
gen_x0 = @(x) [89.4;93.75;90;94.38;98.75;94.38;96.25;94.38;96.25;100;95;96.25;95;95;98.75;98.125;97.5;98.75;100;100;91.875;93.75;93.125;96.25;98.125;98.125;99.375;100;100;100;93.75;92.5;96.25;95.625;99.375;97.5;100;99.375;99.375;100;95.625;95.625;97.5;97.5;98.75;99.375;99.375;91.25;98.125;100]+ones(50,1)*normrnd(0, sqrt(10)); % sample from p_x0 (returns column vector)
%% Transition prior PDF p(x[k] | x[k-1])
% (under the suposition of additive process noise)
% p_xk_given_xkm1 = @(k, xk, xkm1) p_sys_noise(xk - sys(k, xkm1, 0));
%% Observation likelihood PDF p(y[k] | x[k])
% (under the suposition of additive process noise)
p_yk_given_xk = @(k, yk, xk) p_obs_noise(yk - obs(k, xk, 0));
%% Separate memory space
x = zeros(nx,T); y = zeros(ny,T);
u = zeros(nu,T); v = zeros(nv,T);
%% Simulate system
xh0 = [89.4;93.75;90;94.38;98.75;94.38;96.25;94.38;96.25;100;95;96.25;95;95;98.75;98.125;97.5;98.75;100;100;91.875;93.75;93.125;96.25;98.125;98.125;99.375;100;100;100;93.75;92.5;96.25;95.625;99.375;97.5;100;99.375;99.375;100;95.625;95.625;97.5;97.5;98.75;99.375;99.375;91.25;98.125;100]; % initial state
u(:,1) = 0; % initial process noise
v(:,1) = gen_obs_noise(sigma_v); % initial observation noise
x(:,1) = xh0;
y(:,1) = obs(1, xh0, v(:,1));
for k = 2:T
% here we are basically sampling from p_xk_given_xkm1 and from p_yk_given_xk
u(:,k) = gen_sys_noise(); % simulate process noise
v(:,k) = gen_obs_noise(); % simulate observation noise
x(:,k) = sys(k, x(:,k-1), u(:,k)); % simulate state
y(:,k) = obs(k, x(:,k), v(:,k)); % simulate observation
end
%% Separate memory
xh = zeros(nx, T); xh(:,1) = xh0;
yh = zeros(ny, T); yh(:,1) = obs(1, xh0, 0);
pf.k = 1; % initial iteration number
pf.Ns = 50; % number of particles
pf.w = zeros(pf.Ns, T); % weights
pf.particles = zeros(nx, pf.Ns, T); % particles
pf.gen_x0 = gen_x0; % function for sampling from initial pdf p_x0
pf.p_yk_given_xk = p_yk_given_xk; % function of the observation likelihood PDF p(y[k] | x[k])
pf.gen_sys_noise = gen_sys_noise; % function for generating system noise
%pf.p_x0 = p_x0; % initial prior PDF p(x[0])
%pf.p_xk_given_ xkm1 = p_xk_given_xkm1; % transition prior PDF p(x[k] | x[k-1])
%% Estimate state
for k = 2:T
fprintf('Iteration = %d/%d\n',k,T);
% state estimation
pf.k = k;
%[xh(:,k), pf] = particle_filter(sys, y(:,k), pf, 'multinomial_resampling');
[xh(:,k), pf] = particle_filter(sys, y(:,k), pf, 'systematic_resampling');
% filtered observation
yh(:,k) = obs(k, xh(:,k), 0);
end
%% Make plots of the evolution of the density
figure
hold on;
xi = 1:T;
yi = -25:0.25:25;
[xx,yy] = meshgrid(xi,yi);
den = zeros(size(xx));
xhmode = zeros(size(xh));
for i = xi
% for each time step perform a kernel density estimation
den(:,i) = ksdensity(pf.particles(1,:,i), yi,'kernel','epanechnikov');
[~, idx] = max(den(:,i));
% estimate the mode of the density
xhmode(i) = yi(idx);
plot3(repmat(xi(i),length(yi),1), yi', den(:,i));
end
view(3);
box on;
title('Evolution of the state density','FontSize',14)
figure
mesh(xx,yy,den);
title('Evolution of the state density','FontSize',14)
%% plot of the state vs estimated state by the particle filter vs particle paths
figure
hold on;
%h1 = plot(1:T,squeeze(pf.particles),'y');
h1 = plot(1:T,squeeze(pf.particles(2,:,:)),'y');
h2 = plot(1:T,x(1,:),'b','LineWidth',1);
h3 = plot(1:T,xh(1,:),'r','LineWidth',1);
h4 = plot(1:T,y(1,:),'g.','LineWidth',1);
legend([h2 h3 h1(1)],'state','mean of estimated state','particle paths');
title('State vs estimated state by the particle filter vs particle paths','FontSize',14);
%% plot of the observation vs filtered observation by the particle filter
figure
plot(1:T,y,'b', 1:T,yh,'r');
legend('observation','filtered observation');
title('Observation vs filtered observation by the particle filter','FontSize',14);
return;

Answers (1)

Askic V
Askic V on 6 Dec 2022
It is because pf is a structure or an object and your script didn't define it anywhere.
Please run
whos
in the command windows on the another PC (where your code execute properly) before running the script and you should see that there exists pf (particle filter) structure/object defined.

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