Subscript assignment dimension mismatch when calling function

2018 6 19
1 个回答
回答已采纳
更新时间：2018 6 19
2 次查看（30 天）

0 个投票

I created a function that stochastically simulates cellular birth and death populations. When I copy and paste the function into a workspace, it runs fine. When I try to call the function in a livescript, I get the error "Subscript assignment dimension mismatch: line 191 pop_new(n,:) = population" This error only appears when the function is called, not when it is run separately. I have also checked to make sure the two vectors are the same size. They are both 1x7 doubles.
Here is the livescript code
mp_v = [1E-9, 1E-8, 1E-7];
mc_v = [1E-7, 1E-6, 1E-5];
pop_initial_v = [1E11, 0, 0, 0, 0, 0;
1E10, 0, 0, 0, 0, 0;
1E9, 0, 0, 0, 0, 0;
1E8, 0, 0, 0, 0, 0];
mpc7_v = [1E-9, 1E-8, 1E-7];
f = zeros(108,10);
n = 1;
for i=1:length(mp_v)
for j = 1:length(mc_v)
for k = 1:length(pop_initial_v(1,:))
for m = 1:length(mpc7_v)
mp = mp_v(i);
mc = mc_v(j);
pop_initial = pop_initial_v(k,:);
mpc7 = mpc7_v(m);
[ end_pop ] = t790m(mp, mc, mpc7, pop_initial);
f(n,:) = [n, mp, mc, pop_initial(1), mpc7, population(1), population(2), population(3), population(4), population(5)];
n = n+1;
end
end
end
end
and here is the function code
function [end_pop] = t790m(mp, mc, mpc7, pop_initial)
alpha = [.21, .21, .21, .21, .21, .21, .21]; %birth rate, for now assumed to be the same for all cells
beta = [.19, .19, .19, .19, .19, .19, .19]; %death rate before treatment
beta2 = [.23, .19, .19, .19, .19, .19, .19]; %death rate during gefitinib treatment
beta3 = [.23, .23, .21, .20, .20, .19, .19]; %death rate during PF00299804 treatment
tau_initial = NaN;
epsilon = .01; %error tolerance
treatment_end = 1000;
initial_tauleap = true;
initial_hybrid = true;
initial_thresh = 1E5;
treatment1_start = 20;
treatment2_start = 300;
treatment2_end = 600;
%tau leaping method
t = 0; %time vector
cells = pop_initial;
n = 1; %counter
tau = tau_initial;
singlessa = NaN;
while (sum(t)<=treatment2_end && sum(cells(end,:)>0))
currtime = sum(t);
population = cells(end,:);
if currtime>=treatment1_start && currtime<treatment2_start
gamma = beta2; %death rate during treatment 1
elseif currtime>=treatment2_start && currtime<=treatment_end
gamma = beta3; %death rate during treatment 2
else
gamma = beta;
end
events_s = [alpha(1)*(1-mp)*(1-mpc7)*population(1); %birth of s cells
gamma(1)*population(1); %death of s cell
mp*alpha(1)*population(1); %s cell mutates into r cell
mpc7*alpha(1)*population(1)]; %s mutates into c797s
state_change_s = [1, 0, 0, 0, 0, 0, 0;
-1, 0, 0, 0, 0, 0, 0;
0, 1, 0, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 1];%change in cellular populations due to each event
events = zeros(25,1);
events(1:4) = events_s;
state_change = zeros(25,7);
state_change(1:4,:) = state_change_s;
if (initial_hybrid && n>2)
if (population(1) < initial_thresh)
rb = alpha; rb(1) = 0;
rd = gamma; rd(1) = 0;
events(1:2) = 0;
end
if (population(2) > 0) %r cells have appeared
events(5:8) = [alpha(2)*(1-mc)*(1-mpc7)*population(2); %r replicates
gamma(2)*population(2); %r dies
mc*alpha(2)*population(2); %r mutates into rc1 cell
mpc7*alpha(2)*population(2)]; %r mutates into c797s
state_change(5:8,:) = [0, 1, 0, 0, 0, 0, 0;
0, -1, 0, 0, 0, 0, 0;
0, 0, 1, 0, 0, 0, 0;
0, 0, 0, 0, 0, 0, 1];
if (population(2) > initial_thresh)
rb = alpha; rb(2) = 0;
rd = gamma; rd(2) = 0;
events(5:6) = 0;
end
end
if (population(3) > 0) %rc1 cells have appeared
events(9:12) = [alpha(3)*(1-mc)*(1-mpc7)*population(3); %rc1 replicates
gamma(3)*population(3); %rc1 dies
mc*alpha(3)*population(3); %rc1 mutates into rc2 cell
mpc7*alpha(3)*population(3)]; %rc1 mutates into c797s
state_change(9:12,:) = [0, 0, 1, 0, 0, 0, 0;
0, 0, -1, 0, 0, 0, 0;
0, 0, 0, 1, 0, 0, 0;
0, 0, 0, 0, 0, 0, 1];
if (population(3) > initial_thresh)
rb = alpha; rb(3) = 0;
rd = gamma; rd(3) = 0;
events(9:10) = 0;
end
end
if (population(4) > 0) %rc2 cells have appeared
events(13:16) = [alpha(4)*(1-mc)*(1-mpc7)*population(4); %rc2 cell replicates
gamma(4)*population(4); %rc2 cell dies
mc*alpha(4)*population(4); %rc2 cell mutates into rc3 cell
mpc7*alpha(4)*population(4)]; %rc2 cell mutates into c797s
state_change(13:16,:) = [0, 0, 0, 1, 0, 0, 0;
0, 0, 0, -1, 0, 0, 0;
0, 0, 0, 0, 1, 0, 0;
0, 0, 0, 0, 0, 0, 1];
if (population(4) > initial_thresh)
rb = alpha; rb(4) = 0;
rd = gamma; rd(4) = 0;
events(13:14) = 0;
end
end
if (population(5) > 0) %rc3 cells have appeared
events(17:20) = [alpha(5)*population(5); %rc3 cells replicates
gamma(5)*population(5); %rc3 cell dies
mc*alpha(5)*population(5); %rc3 cell mutates into rc4 cell
mpc7*alpha(5)*population(5)]; %rc3 cell mutates into c797s
state_change(17:20,:) = [0, 0, 0, 0, 1, 0, 0;
0, 0, 0, 0, -1, 0, 0;
0, 0, 0, 0, 0, 1, 0;
0, 0, 0, 0, 0, 0, 1];
if (population(5) > initial_thresh)
rb = alpha; rb(5) = 0;
rd = gamma; rd(5) = 0;
events(17:18) = 0;
end
end
if (population(6) > 0) %rc4 cells have appeared
events(21:23) = [alpha(6)*(1-mpc7)*population(6); %rc4 cell replicates
gamma(6)*population(6); %rc4 cell dies
mpc7*alpha(6)*population(6)]; %rc4 cell mutates into c797s cell
state_change(21:23,:) = [0, 0, 0, 0, 0, 1, 0;
0, 0, 0, 0, 0, -1, 0;
0, 0, 0, 0, 0, 0, 1];
if (population(6) > initial_thresh)
rb = alpha; rb(6) = 0;
rd = gamma; rd(6) = 0;
events(21:22) = 0;
end
end
if (population(7) > 0) %c797s cells have appeared
events(24:25) = [alpha(7)*population(7); %c797s cell replicates
gamma(7)*population(7)]; %c797s cell divides
state_change(24:25,:) = [0, 0, 0, 0, 0, 0, 1;
0, 0, 0, 0, 0, 0, -1];
if (population(7) > initial_thresh)
rb = alpha; rb(7) = 0;
rd = gamma; rd(7) = 0;
events(24:25) = 0;
end
end
end
if (initial_tauleap && isnan(singlessa))
if (isnan(tau))
%partial derivatives from jacobian
partial_derivative = [alpha(1)*(1-mp)*(1-mpc7), 0, 0, 0, 0, 0, 0; %S cells
gamma(1), 0, 0, 0, 0, 0, 0;
mp*alpha(1), 0, 0, 0, 0, 0, 0;
mpc7*alpha(1), 0, 0, 0, 0, 0, 0;
0, alpha(2)*(1-mc)*(1-mpc7), 0, 0, 0, 0, 0; %r cells
0, gamma(2), 0, 0, 0, 0, 0;
0, mc*alpha(2), 0, 0, 0, 0, 0;
0, mpc7*alpha(2), 0, 0, 0, 0, 0;
0, 0, alpha(3)*(1-mc)*(1-mpc7), 0, 0, 0, 0; %rc1 cells
0, 0, gamma(3), 0, 0, 0, 0;
0, 0, mc*alpha(3), 0, 0, 0, 0;
0, 0, mpc7*alpha(3), 0, 0, 0, 0;
0, 0, 0, alpha(4)*(1-mc)*(1-mpc7), 0, 0, 0; %rc2 cells
0, 0, 0, gamma(4), 0, 0, 0;
0, 0, 0, alpha(4)*mc, 0, 0, 0;
0, 0, 0, alpha(4)*mpc7, 0, 0, 0;
0, 0, 0, 0, alpha(5)*(1-mc)*(1-mpc7), 0, 0; %rc3 cells
0, 0, 0, 0, gamma(5), 0, 0;
0, 0, 0, 0, alpha(5)*mc, 0, 0;
0, 0, 0, 0, alpha(5)*mpc7, 0, 0;
0, 0, 0, 0, 0, alpha(6)*(1-mpc7), 0; %rc4 cells
0, 0, 0, 0, 0, gamma(6), 0;
0, 0, 0, 0, 0, alpha(6)*mpc7, 0;
0, 0, 0, 0, 0, 0, alpha(7); %c797s cells
0, 0, 0, 0, 0, 0, gamma(7)];
total_rate = sum(events);
fjj = partial_derivative * state_change';
mu = fjj*events; %mean
std = (fjj.^2)*events; %standard deviation
tau = min(min(epsilon*total_rate./abs(mu), (epsilon^2)*total_rate^2./std));
end
if (tau < 1/total_rate)
singlessa = 10;
tau = tau_initial;
continue
else
pop_new = zeros(800,7);
pop_new(:,:) = 1E14;
pop_new(n,:) = population;
pop_new(n,:) = pop_new(n,:) + (state_change'*poissrnd(events*tau))'; %state_change'*poissrnd(events*tau)new population sizes
pop_new(n,:) = round(pop_new(n,:));
if sum(pop_new(n)<0)>0
tau = tau/2;
continue
else
time_elapsed = tau;
t(n) = time_elapsed;
cells(n,:) = pop_new(n,:);
tau = tau_initial;
n = n+1;
end
end
else
r1 = rand;
time_elapsed = -log(r1)./total_rate_s;
if (total_rate > 0)
t(n) = time_elapsed;
idx = randsample(length(events_s),1,true,events_s);
cells(n,:) = round(population + state_change_s(idx,:));
n = n+1;
end
if (initial_tauleap)
singlessa = singlessa - 1;
if (singlessa < 1)
singlessa = NaN;
end
end
end
if (currtime + time_elapsed > treatment2_end && time_elapsed > .1*treatment2_end)
tspancalc = treatment2_end - sum(t(1:end-1));
net = alpha(1) - gamma(1);
cells(end,1) = cells(end-1,1)*exp(net*tspancalc);
net = alpha(2) - gamma(2);
cells(end,2) = cells(end-1,2)*exp(net*tspancalc);
net = alpha(3)-gamma(3);
cells(end,3) = cells(end-1,3)*exp(net*tspancalc);
net = alpha(4)-gamma(4);
cells(end,4) = cells(end-1,4)*exp(net*tspancalc);
net = alpha(5)-gamma(5);
cells(end,5) = cells(end-1,5)*exp(net*tspancalc);
net = alpha(6)-gamma(6);
cells(end,6) = cells(end-1,6)*exp(net*tspancalc);
net = alpha(7) - gamma(7);
cells(end,7) = cells(end-1,7)*exp(net*tspancalc);
t(end) = tspancalc;
end
if (initial_hybrid && n>2)
time_elapsed = t(end);
if (population(1) > initial_thresh)
net = alpha(1)-gamma(1);
cells(end,1) = cells(end,1)+cells(end-1,1)*(exp(net*time_elapsed)-1);
end
if (population(2) > initial_thresh)
net = alpha(2)-gamma(2);
cells(end,2) = cells(end,2)+cells(end-1,2)*(exp(net*time_elapsed)-1);
end
if (population(3) > initial_thresh)
net = alpha(3)-gamma(3);
cells(end,3) = cells(end,3)+cells(end-1,3)*(exp(net*time_elapsed)-1);
end
if (population(4) > initial_thresh)
net = alpha(4)-gamma(4);
cells(end,4) = cells(end,4)+cells(end-1,4)*(exp(net*time_elapsed)-1);
end
if (population(5) > initial_thresh)
net = alpha(5)-gamma(5);
cells(end,5) = cells(end,5)+cells(end-1,5)*(exp(net*time_elapsed)-1);
end
if (population(6) > initial_thresh)
net = alpha(6)-gamma(6);
cells(end,6) = cells(end,6)+cells(end-1,6)*(exp(net*time_elapsed)-1);
end
if (population(6) > initial_thresh)
net = alpha(7)-gamma(7);
cells(end,7) = cells(end,7)+cells(end-1,7)*(exp(net*time_elapsed)-1);
end
end
end
t = cumsum(t'); t = [0; t]; cells = [pop_initial; cells]; end_pop = population;
%s_cells = population(1); %r_cells = population(2); %rc1_cells = population(3); %rc2_cells = population(4); %c797s = population(7) end

请先登录，再回答此问题。

 采纳的回答

James Tursa
James Tursa 2018-6-19

1 个投票

Type the following at the MATLAB prompt
dbstop if error
Then run your code. When the error occurs, the code will pause at the offending line with all current variables still intact. Examine them to see what the dimensions really are, and then you can backtrack to figure out why the dimensions are not what you expected.

更多回答（0 个）

类别

在 帮助中心 和 File Exchange 中查找有关 Student's t Distribution 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by