Month: September 2024
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Sikkim Invite Code
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Sikkim Invite Code
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Sikkim Invitation Code 667811500744
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Sikkim Invite code is 667811500744, use this code at the time of sign up to get a free bonus of Rs.52 on. Sikkim colour prediction app lets you have fun guessing the next winning colour for a chance to earn rewards.The Sikkim is quite a fresh website, which is why, in the starting, you can get many bonuses on the Sikkim app. So, don’t hold up for long and register on the Sikkim app.Sikkim Invite Code Sikkim Invitation Code667811500744Signup BonusRs. 1500Sikkim Invite Code667811500744Sikkim Invitation Code 667811500744667811500744 is a Sikkim app invite code. You will get a sign up bonus upto Rs.52 on using the code at the time of registration. You can also earn by sharing your invite code with your friends.Sikkim offers you Daily Bonus, Weekly Bonus, Monthly Bonus, Rebate, Referral Bonus and many more advantages. Here you can easily do the Agent work also and make daily huge profits just by giving predictions to there team and make them play the games. Sikkim Colour Prediction Game On Sikkim Games you can play the Sikkim Colour Prediction Games in which you can either predict the Colours or the numbers as you can easily make your money 2X by predicting colours and 9X by predicting the numbers in less than 30 sec. Here, you can play Wingo, Trx Wingo, K3, 5D. On Sikkim App you can play many other games also like Aviator, Casino and many more games through which you can make daily huge money by investing your few seconds only. About SikkimThe Sikkim Colour Prediction app is an entertaining platform where users can predict the next winning colour or numbers. It is easy to play- simply choose a color you think will be selected, and if you are right you can easily double your money or even 10X in just 30sec. This app offers a simple way to pass the time while testing your luck. It is a popular way for those who enjoy casual games with a chance to win. Read More
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Join us for the Microsoft Nonprofit Virtual Partner Summit on September 30th, 7:30am – 9:30am PST where you’ll have the opportunity to hear directly from Microsoft nonprofit industry leaders about the latest updates in the nonprofit sector.
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Simulink crashes when running MEX functions created with Visual Studio 2022 17.10.4
We have been running MATLAB R2023B Update 7 with Simulink models using S-Functions built with VS 2022 17.9.0. When we updated to VS 2022 17.10.4 we starting having MATLAB crashes as soon as Simulink starts executing models with these MEX functions. Is this a known issue, and do we know if moving to Update 8 or 9 will fix it?We have been running MATLAB R2023B Update 7 with Simulink models using S-Functions built with VS 2022 17.9.0. When we updated to VS 2022 17.10.4 we starting having MATLAB crashes as soon as Simulink starts executing models with these MEX functions. Is this a known issue, and do we know if moving to Update 8 or 9 will fix it? We have been running MATLAB R2023B Update 7 with Simulink models using S-Functions built with VS 2022 17.9.0. When we updated to VS 2022 17.10.4 we starting having MATLAB crashes as soon as Simulink starts executing models with these MEX functions. Is this a known issue, and do we know if moving to Update 8 or 9 will fix it? mex, simulink MATLAB Answers — New Questions
“timeout” setting not work in webread function
Hi:
I use parallel loop to grab data using "webread" , I set up the timeout option to be 10 seconds,
options = weboptions;
options.Timeout = 10;
then use command like below:
timeCost=[];
parfor i=1:1:1e4
tic
webReadTmp=webread(command,options);
timeCost(i)=toc;
end
I use "timeCost" variable to record the time needed for each webread operation. however I notice there are some webread cost more than 30 second, which means the "timeout" setting is not working here.
is there any mistake with my command?
Thanks!
YuHi:
I use parallel loop to grab data using "webread" , I set up the timeout option to be 10 seconds,
options = weboptions;
options.Timeout = 10;
then use command like below:
timeCost=[];
parfor i=1:1:1e4
tic
webReadTmp=webread(command,options);
timeCost(i)=toc;
end
I use "timeCost" variable to record the time needed for each webread operation. however I notice there are some webread cost more than 30 second, which means the "timeout" setting is not working here.
is there any mistake with my command?
Thanks!
Yu Hi:
I use parallel loop to grab data using "webread" , I set up the timeout option to be 10 seconds,
options = weboptions;
options.Timeout = 10;
then use command like below:
timeCost=[];
parfor i=1:1:1e4
tic
webReadTmp=webread(command,options);
timeCost(i)=toc;
end
I use "timeCost" variable to record the time needed for each webread operation. however I notice there are some webread cost more than 30 second, which means the "timeout" setting is not working here.
is there any mistake with my command?
Thanks!
Yu "timeout" setting not work in webread function MATLAB Answers — New Questions
Integral2 seems to substitute non-scalar values of variable into integrand. Why?
Dear community,
I am struggling with an error that my code produces. I am trying to do a maximum-likelihood estimation (MLE). I am importing CSV data; each row is an observation and an observation is four-dimensional with column names "bad_bought", "good_bought", "bad_notbought", "good_notbought".
Each observation is assumed to have latent variables and that are drawn from a joint-normal distribution with mean and variance and and correlation . An observation’s numeric values for the four columns are given by
"bad_bought":
"good_bought":
"bad_notbought":
"good_notbought":
where the error terms are iid, normal, and have variance .
I understand that I can calculate the joint distribution of the four answers (for given ) directly. However, I want to use numerical integration, as in the code below, because the model will eventually become more complicated necessitating it.
I receive two type of error messages:
First, and this is copy-pasted below, MATLAB tells me my mvnpdf arguments do not have the right dimensions. I do not understand that. [t1, t2], mu_vec are both of dimensions 1×2, i.e., they have the same number of columns. This error disappears if I replace the first argument of mvnpdf with [20, 20] (or some other numbers). I do not understand why; should integral2 not substitute scalar values for t1 and t2?
Second, and this appears if I replace [t1,t2] with [20,20] in the argument of mvnpdf, I receive the error message telling me I cannot use "*" to multiply the normpdf values together. This suggests to me that again t1 and t2 are not scalars.
Could you help? Thank you!
% Load necessary data
data = readtable(‘df_restricted.csv’);
% Select relevant columns and convert to matrix
data_matrix = table2array(data(:, {‘bad_bought’, ‘good_bought’, ‘bad_notbought’, ‘good_notbought’}));
% Define the negative log-likelihood function
function neg_log_lik = neg_log_lik_real(params, data_matrix)
% Extract parameters
mu1 = params(1);
mu2 = params(2);
v1 = params(3);
v2 = params(4);
rho = params(5);
v = params(6);
% Mean vector
mu_vec = [mu1, mu2];
disp(size(mu_vec))
% Covariance matrix
cov_matrix = [v1, rho * sqrt(v1 * v2); rho * sqrt(v1 * v2), v2];
disp(size(cov_matrix))
log_sum = 0;
% Loop through each data point
for i = 1:size(data_matrix, 1)
% Define the integrand for 2-dimensional integration
f = @(t1, t2) ( …
mvnpdf([t1, t2], mu_vec, cov_matrix) …
* normpdf(data_matrix(i, 1) – (t1 – t2), 0, sqrt(v)) …
* normpdf(data_matrix(i, 2) – (t1 + t2), 0, sqrt(v)) …
* normpdf(data_matrix(i, 3) – (t1 + t2), 0, sqrt(v)) …
* normpdf(data_matrix(i, 4) – (t1 – t2), 0, sqrt(v)) …
);
% Before integral2 call
% Perform 2-dimensional numerical integration using integral2
pdf_value = integral2(f, -Inf, Inf, -Inf, Inf, ‘AbsTol’, 1e-6, ‘RelTol’, 1e-6);
% Take log of the result and accumulate
log_value = log(pdf_value);
log_sum = log_sum + log_value;
end
% Return negative log-likelihood
neg_log_lik = -log_sum;
end
% Set initial parameters
initial_params = [56, 11, 119, 131, -0.36, 419];
% Define the function handle for optimization
neg_log_lik_handle = @(params) neg_log_lik_real(params, data_matrix);
% Use fminunc to minimize the negative log-likelihood
options = optimoptions(‘fminunc’, ‘Algorithm’, ‘quasi-newton’, ‘MaxIterations’, 500, ‘TolFun’, 1e-8);
[estimated_params, fval, exitflag, output] = fminunc(neg_log_lik_handle, initial_params, options);
% Display the results
disp(‘Estimated parameters:’);
disp(estimated_params);
disp(‘Final log-likelihood value:’);
disp(-fval);
First error:
Error using mvnpdf (line 67)
X and MU must have the same number of columns.
Error in
Structural_Estimation>@(t1,t2)(mvnpdf([t1,t2],mu_vec,cov_matrix)*normpdf(data_matrix(i,1)-(t1-t2),0,sqrt(v))*normpdf(data_matrix(i,2)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,3)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,4)-(t1-t2),0,sqrt(v)))
(line 34)
mvnpdf([t1, t2], mu_vec, cov_matrix) …
First error:
Error in integral2Calc>@(y)fun(xi*ones(size(y)),y) (line 18)
@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions), …
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
Error in
integral2Calc>@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions)
(line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
Error in
integral2Calc>@(x)arrayfun(@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions),x,ymin(x),ymax(x))
(line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
Error in integral2Calc>integral2i (line 20)
[q,errbnd] = integralCalc(innerintegral,xmin,xmax,opstruct.integralOptions);
Error in integral2Calc (line 7)
[q,errbnd] = integral2i(fun,xmin,xmax,ymin,ymax,optionstruct);
Error in integral2 (line 105)
Q = integral2Calc(fun,xmin,xmax,yminfun,ymaxfun,opstruct);
Error in Structural_Estimation>neg_log_lik_real (line 43)
pdf_value = integral2(f, -Inf, Inf, -Inf, Inf, ‘AbsTol’, 1e-6, ‘RelTol’, 1e-6);
Error in Structural_Estimation>@(params)neg_log_lik_real(params,data_matrix) (line 58)
neg_log_lik_handle = @(params) neg_log_lik_real(params, data_matrix);
Error in fminunc (line 233)
f = feval(funfcn{3},x,varargin{:});
Error in Structural_Estimation (line 62)
[estimated_params, fval, exitflag, output] = fminunc(neg_log_lik_handle, initial_params, options);
Caused by:
Failure in initial objective function evaluation. FMINUNC cannot continue.
—-
Second error:
Error using *
Incorrect dimensions for matrix multiplication. Check that the number of columns in the first matrix
matches the number of rows in the second matrix. To operate on each element of the matrix
individually, use TIMES (.*) for elementwise multiplication.
Error in Structural_Estimation>@(t1,t2)(mvnpdf([20,20],mu_vec,cov_matrix)*normpdf(data_matrix(i,1)-(t1-t2),0,sqrt(v))*normpdf(data_matrix(i,2)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,3)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,4)-(t1-t2),0,sqrt(v))) (line 36)
* normpdf(data_matrix(i, 2) – (t1 + t2), 0, sqrt(v)) …
^
Error in integral2Calc>@(y)fun(xi*ones(size(y)),y) (line 18)
@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions), …
^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
^^^^^^
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2Calc>@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions) (line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
^^^^^^^^^^^^^^^^^
Error in integral2Calc>@(x)arrayfun(@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions),x,ymin(x),ymax(x)) (line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
^^^^^^
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2Calc>integral2i (line 20)
[q,errbnd] = integralCalc(innerintegral,xmin,xmax,opstruct.integralOptions);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2Calc (line 7)
[q,errbnd] = integral2i(fun,xmin,xmax,ymin,ymax,optionstruct);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2 (line 105)
Q = integral2Calc(fun,xmin,xmax,yminfun,ymaxfun,opstruct);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in Structural_Estimation>neg_log_lik_real (line 43)
pdf_value = integral2(f, -Inf, Inf, -Inf, Inf, ‘AbsTol’, 1e-6, ‘RelTol’, 1e-6);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in Structural_Estimation>@(params)neg_log_lik_real(params,data_matrix) (line 58)
neg_log_lik_handle = @(params) neg_log_lik_real(params, data_matrix);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in fminunc (line 233)
f = feval(funfcn{3},x,varargin{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in Structural_Estimation (line 62)
[estimated_params, fval, exitflag, output] = fminunc(neg_log_lik_handle, initial_params, options);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Caused by:
Failure in initial objective function evaluation. FMINUNC cannot continue.
Related documentationDear community,
I am struggling with an error that my code produces. I am trying to do a maximum-likelihood estimation (MLE). I am importing CSV data; each row is an observation and an observation is four-dimensional with column names "bad_bought", "good_bought", "bad_notbought", "good_notbought".
Each observation is assumed to have latent variables and that are drawn from a joint-normal distribution with mean and variance and and correlation . An observation’s numeric values for the four columns are given by
"bad_bought":
"good_bought":
"bad_notbought":
"good_notbought":
where the error terms are iid, normal, and have variance .
I understand that I can calculate the joint distribution of the four answers (for given ) directly. However, I want to use numerical integration, as in the code below, because the model will eventually become more complicated necessitating it.
I receive two type of error messages:
First, and this is copy-pasted below, MATLAB tells me my mvnpdf arguments do not have the right dimensions. I do not understand that. [t1, t2], mu_vec are both of dimensions 1×2, i.e., they have the same number of columns. This error disappears if I replace the first argument of mvnpdf with [20, 20] (or some other numbers). I do not understand why; should integral2 not substitute scalar values for t1 and t2?
Second, and this appears if I replace [t1,t2] with [20,20] in the argument of mvnpdf, I receive the error message telling me I cannot use "*" to multiply the normpdf values together. This suggests to me that again t1 and t2 are not scalars.
Could you help? Thank you!
% Load necessary data
data = readtable(‘df_restricted.csv’);
% Select relevant columns and convert to matrix
data_matrix = table2array(data(:, {‘bad_bought’, ‘good_bought’, ‘bad_notbought’, ‘good_notbought’}));
% Define the negative log-likelihood function
function neg_log_lik = neg_log_lik_real(params, data_matrix)
% Extract parameters
mu1 = params(1);
mu2 = params(2);
v1 = params(3);
v2 = params(4);
rho = params(5);
v = params(6);
% Mean vector
mu_vec = [mu1, mu2];
disp(size(mu_vec))
% Covariance matrix
cov_matrix = [v1, rho * sqrt(v1 * v2); rho * sqrt(v1 * v2), v2];
disp(size(cov_matrix))
log_sum = 0;
% Loop through each data point
for i = 1:size(data_matrix, 1)
% Define the integrand for 2-dimensional integration
f = @(t1, t2) ( …
mvnpdf([t1, t2], mu_vec, cov_matrix) …
* normpdf(data_matrix(i, 1) – (t1 – t2), 0, sqrt(v)) …
* normpdf(data_matrix(i, 2) – (t1 + t2), 0, sqrt(v)) …
* normpdf(data_matrix(i, 3) – (t1 + t2), 0, sqrt(v)) …
* normpdf(data_matrix(i, 4) – (t1 – t2), 0, sqrt(v)) …
);
% Before integral2 call
% Perform 2-dimensional numerical integration using integral2
pdf_value = integral2(f, -Inf, Inf, -Inf, Inf, ‘AbsTol’, 1e-6, ‘RelTol’, 1e-6);
% Take log of the result and accumulate
log_value = log(pdf_value);
log_sum = log_sum + log_value;
end
% Return negative log-likelihood
neg_log_lik = -log_sum;
end
% Set initial parameters
initial_params = [56, 11, 119, 131, -0.36, 419];
% Define the function handle for optimization
neg_log_lik_handle = @(params) neg_log_lik_real(params, data_matrix);
% Use fminunc to minimize the negative log-likelihood
options = optimoptions(‘fminunc’, ‘Algorithm’, ‘quasi-newton’, ‘MaxIterations’, 500, ‘TolFun’, 1e-8);
[estimated_params, fval, exitflag, output] = fminunc(neg_log_lik_handle, initial_params, options);
% Display the results
disp(‘Estimated parameters:’);
disp(estimated_params);
disp(‘Final log-likelihood value:’);
disp(-fval);
First error:
Error using mvnpdf (line 67)
X and MU must have the same number of columns.
Error in
Structural_Estimation>@(t1,t2)(mvnpdf([t1,t2],mu_vec,cov_matrix)*normpdf(data_matrix(i,1)-(t1-t2),0,sqrt(v))*normpdf(data_matrix(i,2)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,3)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,4)-(t1-t2),0,sqrt(v)))
(line 34)
mvnpdf([t1, t2], mu_vec, cov_matrix) …
First error:
Error in integral2Calc>@(y)fun(xi*ones(size(y)),y) (line 18)
@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions), …
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
Error in
integral2Calc>@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions)
(line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
Error in
integral2Calc>@(x)arrayfun(@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions),x,ymin(x),ymax(x))
(line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
Error in integral2Calc>integral2i (line 20)
[q,errbnd] = integralCalc(innerintegral,xmin,xmax,opstruct.integralOptions);
Error in integral2Calc (line 7)
[q,errbnd] = integral2i(fun,xmin,xmax,ymin,ymax,optionstruct);
Error in integral2 (line 105)
Q = integral2Calc(fun,xmin,xmax,yminfun,ymaxfun,opstruct);
Error in Structural_Estimation>neg_log_lik_real (line 43)
pdf_value = integral2(f, -Inf, Inf, -Inf, Inf, ‘AbsTol’, 1e-6, ‘RelTol’, 1e-6);
Error in Structural_Estimation>@(params)neg_log_lik_real(params,data_matrix) (line 58)
neg_log_lik_handle = @(params) neg_log_lik_real(params, data_matrix);
Error in fminunc (line 233)
f = feval(funfcn{3},x,varargin{:});
Error in Structural_Estimation (line 62)
[estimated_params, fval, exitflag, output] = fminunc(neg_log_lik_handle, initial_params, options);
Caused by:
Failure in initial objective function evaluation. FMINUNC cannot continue.
—-
Second error:
Error using *
Incorrect dimensions for matrix multiplication. Check that the number of columns in the first matrix
matches the number of rows in the second matrix. To operate on each element of the matrix
individually, use TIMES (.*) for elementwise multiplication.
Error in Structural_Estimation>@(t1,t2)(mvnpdf([20,20],mu_vec,cov_matrix)*normpdf(data_matrix(i,1)-(t1-t2),0,sqrt(v))*normpdf(data_matrix(i,2)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,3)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,4)-(t1-t2),0,sqrt(v))) (line 36)
* normpdf(data_matrix(i, 2) – (t1 + t2), 0, sqrt(v)) …
^
Error in integral2Calc>@(y)fun(xi*ones(size(y)),y) (line 18)
@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions), …
^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
^^^^^^
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2Calc>@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions) (line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
^^^^^^^^^^^^^^^^^
Error in integral2Calc>@(x)arrayfun(@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions),x,ymin(x),ymax(x)) (line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
^^^^^^
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2Calc>integral2i (line 20)
[q,errbnd] = integralCalc(innerintegral,xmin,xmax,opstruct.integralOptions);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2Calc (line 7)
[q,errbnd] = integral2i(fun,xmin,xmax,ymin,ymax,optionstruct);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2 (line 105)
Q = integral2Calc(fun,xmin,xmax,yminfun,ymaxfun,opstruct);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in Structural_Estimation>neg_log_lik_real (line 43)
pdf_value = integral2(f, -Inf, Inf, -Inf, Inf, ‘AbsTol’, 1e-6, ‘RelTol’, 1e-6);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in Structural_Estimation>@(params)neg_log_lik_real(params,data_matrix) (line 58)
neg_log_lik_handle = @(params) neg_log_lik_real(params, data_matrix);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in fminunc (line 233)
f = feval(funfcn{3},x,varargin{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in Structural_Estimation (line 62)
[estimated_params, fval, exitflag, output] = fminunc(neg_log_lik_handle, initial_params, options);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Caused by:
Failure in initial objective function evaluation. FMINUNC cannot continue.
Related documentation Dear community,
I am struggling with an error that my code produces. I am trying to do a maximum-likelihood estimation (MLE). I am importing CSV data; each row is an observation and an observation is four-dimensional with column names "bad_bought", "good_bought", "bad_notbought", "good_notbought".
Each observation is assumed to have latent variables and that are drawn from a joint-normal distribution with mean and variance and and correlation . An observation’s numeric values for the four columns are given by
"bad_bought":
"good_bought":
"bad_notbought":
"good_notbought":
where the error terms are iid, normal, and have variance .
I understand that I can calculate the joint distribution of the four answers (for given ) directly. However, I want to use numerical integration, as in the code below, because the model will eventually become more complicated necessitating it.
I receive two type of error messages:
First, and this is copy-pasted below, MATLAB tells me my mvnpdf arguments do not have the right dimensions. I do not understand that. [t1, t2], mu_vec are both of dimensions 1×2, i.e., they have the same number of columns. This error disappears if I replace the first argument of mvnpdf with [20, 20] (or some other numbers). I do not understand why; should integral2 not substitute scalar values for t1 and t2?
Second, and this appears if I replace [t1,t2] with [20,20] in the argument of mvnpdf, I receive the error message telling me I cannot use "*" to multiply the normpdf values together. This suggests to me that again t1 and t2 are not scalars.
Could you help? Thank you!
% Load necessary data
data = readtable(‘df_restricted.csv’);
% Select relevant columns and convert to matrix
data_matrix = table2array(data(:, {‘bad_bought’, ‘good_bought’, ‘bad_notbought’, ‘good_notbought’}));
% Define the negative log-likelihood function
function neg_log_lik = neg_log_lik_real(params, data_matrix)
% Extract parameters
mu1 = params(1);
mu2 = params(2);
v1 = params(3);
v2 = params(4);
rho = params(5);
v = params(6);
% Mean vector
mu_vec = [mu1, mu2];
disp(size(mu_vec))
% Covariance matrix
cov_matrix = [v1, rho * sqrt(v1 * v2); rho * sqrt(v1 * v2), v2];
disp(size(cov_matrix))
log_sum = 0;
% Loop through each data point
for i = 1:size(data_matrix, 1)
% Define the integrand for 2-dimensional integration
f = @(t1, t2) ( …
mvnpdf([t1, t2], mu_vec, cov_matrix) …
* normpdf(data_matrix(i, 1) – (t1 – t2), 0, sqrt(v)) …
* normpdf(data_matrix(i, 2) – (t1 + t2), 0, sqrt(v)) …
* normpdf(data_matrix(i, 3) – (t1 + t2), 0, sqrt(v)) …
* normpdf(data_matrix(i, 4) – (t1 – t2), 0, sqrt(v)) …
);
% Before integral2 call
% Perform 2-dimensional numerical integration using integral2
pdf_value = integral2(f, -Inf, Inf, -Inf, Inf, ‘AbsTol’, 1e-6, ‘RelTol’, 1e-6);
% Take log of the result and accumulate
log_value = log(pdf_value);
log_sum = log_sum + log_value;
end
% Return negative log-likelihood
neg_log_lik = -log_sum;
end
% Set initial parameters
initial_params = [56, 11, 119, 131, -0.36, 419];
% Define the function handle for optimization
neg_log_lik_handle = @(params) neg_log_lik_real(params, data_matrix);
% Use fminunc to minimize the negative log-likelihood
options = optimoptions(‘fminunc’, ‘Algorithm’, ‘quasi-newton’, ‘MaxIterations’, 500, ‘TolFun’, 1e-8);
[estimated_params, fval, exitflag, output] = fminunc(neg_log_lik_handle, initial_params, options);
% Display the results
disp(‘Estimated parameters:’);
disp(estimated_params);
disp(‘Final log-likelihood value:’);
disp(-fval);
First error:
Error using mvnpdf (line 67)
X and MU must have the same number of columns.
Error in
Structural_Estimation>@(t1,t2)(mvnpdf([t1,t2],mu_vec,cov_matrix)*normpdf(data_matrix(i,1)-(t1-t2),0,sqrt(v))*normpdf(data_matrix(i,2)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,3)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,4)-(t1-t2),0,sqrt(v)))
(line 34)
mvnpdf([t1, t2], mu_vec, cov_matrix) …
First error:
Error in integral2Calc>@(y)fun(xi*ones(size(y)),y) (line 18)
@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions), …
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
Error in
integral2Calc>@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions)
(line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
Error in
integral2Calc>@(x)arrayfun(@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions),x,ymin(x),ymax(x))
(line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
Error in integral2Calc>integral2i (line 20)
[q,errbnd] = integralCalc(innerintegral,xmin,xmax,opstruct.integralOptions);
Error in integral2Calc (line 7)
[q,errbnd] = integral2i(fun,xmin,xmax,ymin,ymax,optionstruct);
Error in integral2 (line 105)
Q = integral2Calc(fun,xmin,xmax,yminfun,ymaxfun,opstruct);
Error in Structural_Estimation>neg_log_lik_real (line 43)
pdf_value = integral2(f, -Inf, Inf, -Inf, Inf, ‘AbsTol’, 1e-6, ‘RelTol’, 1e-6);
Error in Structural_Estimation>@(params)neg_log_lik_real(params,data_matrix) (line 58)
neg_log_lik_handle = @(params) neg_log_lik_real(params, data_matrix);
Error in fminunc (line 233)
f = feval(funfcn{3},x,varargin{:});
Error in Structural_Estimation (line 62)
[estimated_params, fval, exitflag, output] = fminunc(neg_log_lik_handle, initial_params, options);
Caused by:
Failure in initial objective function evaluation. FMINUNC cannot continue.
—-
Second error:
Error using *
Incorrect dimensions for matrix multiplication. Check that the number of columns in the first matrix
matches the number of rows in the second matrix. To operate on each element of the matrix
individually, use TIMES (.*) for elementwise multiplication.
Error in Structural_Estimation>@(t1,t2)(mvnpdf([20,20],mu_vec,cov_matrix)*normpdf(data_matrix(i,1)-(t1-t2),0,sqrt(v))*normpdf(data_matrix(i,2)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,3)-(t1+t2),0,sqrt(v))*normpdf(data_matrix(i,4)-(t1-t2),0,sqrt(v))) (line 36)
* normpdf(data_matrix(i, 2) – (t1 + t2), 0, sqrt(v)) …
^
Error in integral2Calc>@(y)fun(xi*ones(size(y)),y) (line 18)
@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions), …
^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
^^^^^^
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2Calc>@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions) (line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
^^^^^^^^^^^^^^^^^
Error in integral2Calc>@(x)arrayfun(@(xi,y1i,y2i)integralCalc(@(y)fun(xi*ones(size(y)),y),y1i,y2i,opstruct.integralOptions),x,ymin(x),ymax(x)) (line 17)
innerintegral = @(x)arrayfun(@(xi,y1i,y2i)integralCalc( …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc>iterateScalarValued (line 334)
fx = FUN(t);
^^^^^^
Error in integralCalc>vadapt (line 148)
[q,errbnd] = iterateScalarValued(u,tinterval,pathlen, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integralCalc (line 113)
[q,errbnd] = vadapt(@minusInfToInfInvTransform,interval, …
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2Calc>integral2i (line 20)
[q,errbnd] = integralCalc(innerintegral,xmin,xmax,opstruct.integralOptions);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2Calc (line 7)
[q,errbnd] = integral2i(fun,xmin,xmax,ymin,ymax,optionstruct);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in integral2 (line 105)
Q = integral2Calc(fun,xmin,xmax,yminfun,ymaxfun,opstruct);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in Structural_Estimation>neg_log_lik_real (line 43)
pdf_value = integral2(f, -Inf, Inf, -Inf, Inf, ‘AbsTol’, 1e-6, ‘RelTol’, 1e-6);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in Structural_Estimation>@(params)neg_log_lik_real(params,data_matrix) (line 58)
neg_log_lik_handle = @(params) neg_log_lik_real(params, data_matrix);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in fminunc (line 233)
f = feval(funfcn{3},x,varargin{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Error in Structural_Estimation (line 62)
[estimated_params, fval, exitflag, output] = fminunc(neg_log_lik_handle, initial_params, options);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Caused by:
Failure in initial objective function evaluation. FMINUNC cannot continue.
Related documentation optimization, integral2, integral, mle MATLAB Answers — New Questions
How can I save a figure (in jpeg and pdf) from this code?
Display a geographic globe in a figure created using the uifigure function.
uif = uifigure;
g = geoglobe(uif);Display a geographic globe in a figure created using the uifigure function.
uif = uifigure;
g = geoglobe(uif); Display a geographic globe in a figure created using the uifigure function.
uif = uifigure;
g = geoglobe(uif); figure, save, pdf, jpeg, saving, globe, earth, help MATLAB Answers — New Questions
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Filter design differences between DSP System and Signal Processing toolboxes (fdesign/design)
The following two lines have different behavior depending on installed toolboxes:
d = fdesign.highpass(‘N,F3db’,3,1,1000);
Hd = design(d);
Normally, I have BOTH DSP System and Signal Processing toolboxes installed. When I run these lines with both toolboxes installed I get the following error:
Error using fdesign.abstracttype/superdesign (line 90)
For symmetric FIR filters, only even orders are allowed.
Now, if I uninstall DSP System toolbox this pair of lines runs as I expect, and makes a 1Hz highpass filter through which I can run my data in lines following the fdesign/design pair of statements. I presume this means that fdesign/design are available in both toolboxes but for some reason only the one in the Signal Processing toolbox works as I expect, meanwhile if I have DSP System installed that version takes precedence and gives me errors.
Is this behavior correct or is it a bug to be reported to Mathworks? I inherited the code from which these lines are excerpted. I also inherited a Matlab license with several toolboxes I know little about (such as DSP System). Though I’ve been working with Matlab on and off for decades I’ve never encountered a case of a function with a "split personality" such as I’m describing with fdesign/design. So from my point of view (limited DSP knowledge) this looks like a bug, but it is possible in my lack of understanding these functions are working correctly with both toolboxes, and therefore I need to learn what I have done (or not done) to cause my errors.The following two lines have different behavior depending on installed toolboxes:
d = fdesign.highpass(‘N,F3db’,3,1,1000);
Hd = design(d);
Normally, I have BOTH DSP System and Signal Processing toolboxes installed. When I run these lines with both toolboxes installed I get the following error:
Error using fdesign.abstracttype/superdesign (line 90)
For symmetric FIR filters, only even orders are allowed.
Now, if I uninstall DSP System toolbox this pair of lines runs as I expect, and makes a 1Hz highpass filter through which I can run my data in lines following the fdesign/design pair of statements. I presume this means that fdesign/design are available in both toolboxes but for some reason only the one in the Signal Processing toolbox works as I expect, meanwhile if I have DSP System installed that version takes precedence and gives me errors.
Is this behavior correct or is it a bug to be reported to Mathworks? I inherited the code from which these lines are excerpted. I also inherited a Matlab license with several toolboxes I know little about (such as DSP System). Though I’ve been working with Matlab on and off for decades I’ve never encountered a case of a function with a "split personality" such as I’m describing with fdesign/design. So from my point of view (limited DSP knowledge) this looks like a bug, but it is possible in my lack of understanding these functions are working correctly with both toolboxes, and therefore I need to learn what I have done (or not done) to cause my errors. The following two lines have different behavior depending on installed toolboxes:
d = fdesign.highpass(‘N,F3db’,3,1,1000);
Hd = design(d);
Normally, I have BOTH DSP System and Signal Processing toolboxes installed. When I run these lines with both toolboxes installed I get the following error:
Error using fdesign.abstracttype/superdesign (line 90)
For symmetric FIR filters, only even orders are allowed.
Now, if I uninstall DSP System toolbox this pair of lines runs as I expect, and makes a 1Hz highpass filter through which I can run my data in lines following the fdesign/design pair of statements. I presume this means that fdesign/design are available in both toolboxes but for some reason only the one in the Signal Processing toolbox works as I expect, meanwhile if I have DSP System installed that version takes precedence and gives me errors.
Is this behavior correct or is it a bug to be reported to Mathworks? I inherited the code from which these lines are excerpted. I also inherited a Matlab license with several toolboxes I know little about (such as DSP System). Though I’ve been working with Matlab on and off for decades I’ve never encountered a case of a function with a "split personality" such as I’m describing with fdesign/design. So from my point of view (limited DSP knowledge) this looks like a bug, but it is possible in my lack of understanding these functions are working correctly with both toolboxes, and therefore I need to learn what I have done (or not done) to cause my errors. fdesign, dsp system toolbox MATLAB Answers — New Questions
stream3 returns error: Sample points must be unique
Hello, I searched the community forum and tried my best to resolve the issue myself, but I could not do it.
Here is my code:
clc; close; clear all;
format compact
%% Cylindrical Coordinates
% Generate cylindrical coordinates
phi = [0 pi/2 pi 3*pi/2];
rho = [0.25 2];
z = [1 2];
% Create a meshgrid for theta, r, and z
[Phi, Rho, Z] = meshgrid(phi, rho, z);
% Convert cylindrical coordinates to Cartesian coordinates
[X, Y, Z] = pol2cart(Phi, Rho, Z);
% Combine X, Y, Z into a single matrix
points = [X(:), Y(:), Z(:)];
% Find unique rows and their indices
[unique_points, ~, ~] = unique(points, ‘rows’);
% Check for duplicates (optional)
num_duplicates = size(points, 1) – size(unique_points, 1);
if num_duplicates > 0
fprintf(‘There are %d duplicate points.n’, num_duplicates);
else
fprintf(‘All points are unique.n’);
end
% Plot the points in 3D space (optional)
figure;
% plot3(X, Y, Z, ‘o’,’DisplayName’,’Sample Points’);
xlabel(‘X’);
ylabel(‘Y’);
zlabel(‘Z’);
title(‘3D Cylindrical Coordinates’);
grid on; axis equal; hold on;
%% Circular magnetic field
% Current in negative z-direction (negative values mean the current
% direction is in positive z-direction)
I = 1;
% Absolute Value of the cylindrical H-field at corresponding positions of
% the radius in phi-direction (all other components are zero)
Hc_phi = 1/(2*pi*Rho);
% Calculate the x-, y- and z-components of the cylindrical field
Hc_x = Hc_phi .* sin(Phi);
Hc_y = -Hc_phi .* cos(Phi);
Hc_z = zeros(size(Z));
% Draw the vector-arrows of the cylindrical field
quiver3(X,Y,Z,Hc_x,Hc_y,Hc_z,’AutoScale’,’off’);
%% Longitudinal magnetic field
Hl = 0.4; % Absolute field strength (+ = neg. z-direction)
Hl_x = zeros(size(X));
Hl_y = zeros(size(Y));
Hl_z = -Hl .* ones(size(Z));
% Draw the vector-arrows of the longitudinal field
quiver3(X,Y,Z,Hl_x,Hl_y,Hl_z,’AutoScale’,’off’);
%% Helical magnetic field (sum of cylindrical and longitudinal field)
H_x = Hc_x + Hl_x;
H_y = Hc_y + Hl_y;
H_z = Hc_z + Hl_z;
% Draw the vector arrows of the helical field
quiver3(X,Y,Z,H_x,H_y,H_z,’AutoScale’,’off’,’LineWidth’,1.5)
% Draw the fieldlines of the helical field
[startX,startY,startZ] = meshgrid([-1 1],[-1 1],1);
% startpoints = [startX(:), startY(:), startZ(:)]; % unique, manually checked
% samplepoints = [X(:), Y(:), Z(:)]; % unique, manually checked
verts = stream3(X,Y,Z,H_x,H_y,H_z,startX,startY,startZ);
lineobj = streamline(verts);
view(3)
Sorry, it might be a bit overwhelming. Ultimately, I just want to get streamlines of H_x, H_y and H_z on the X, Y, Z grid. But I get the following output of the script:
All points are unique.
Error using matlab.internal.math.interp1
Sample points must be unique.
Error in interp1 (line 188)
VqLite = matlab.internal.math.interp1(X,V,method,method,Xqcol);
Error in stream3 (line 66)
syi=interp1(yy(:),1:szu(1),sy(k));
Error in test (line 62)
verts = stream3(X,Y,Z,H_x,H_y,H_z,startX,startY,startZ);
What is the Problem here? Is my check for unique points wrong?
Thank you very much in advance!Hello, I searched the community forum and tried my best to resolve the issue myself, but I could not do it.
Here is my code:
clc; close; clear all;
format compact
%% Cylindrical Coordinates
% Generate cylindrical coordinates
phi = [0 pi/2 pi 3*pi/2];
rho = [0.25 2];
z = [1 2];
% Create a meshgrid for theta, r, and z
[Phi, Rho, Z] = meshgrid(phi, rho, z);
% Convert cylindrical coordinates to Cartesian coordinates
[X, Y, Z] = pol2cart(Phi, Rho, Z);
% Combine X, Y, Z into a single matrix
points = [X(:), Y(:), Z(:)];
% Find unique rows and their indices
[unique_points, ~, ~] = unique(points, ‘rows’);
% Check for duplicates (optional)
num_duplicates = size(points, 1) – size(unique_points, 1);
if num_duplicates > 0
fprintf(‘There are %d duplicate points.n’, num_duplicates);
else
fprintf(‘All points are unique.n’);
end
% Plot the points in 3D space (optional)
figure;
% plot3(X, Y, Z, ‘o’,’DisplayName’,’Sample Points’);
xlabel(‘X’);
ylabel(‘Y’);
zlabel(‘Z’);
title(‘3D Cylindrical Coordinates’);
grid on; axis equal; hold on;
%% Circular magnetic field
% Current in negative z-direction (negative values mean the current
% direction is in positive z-direction)
I = 1;
% Absolute Value of the cylindrical H-field at corresponding positions of
% the radius in phi-direction (all other components are zero)
Hc_phi = 1/(2*pi*Rho);
% Calculate the x-, y- and z-components of the cylindrical field
Hc_x = Hc_phi .* sin(Phi);
Hc_y = -Hc_phi .* cos(Phi);
Hc_z = zeros(size(Z));
% Draw the vector-arrows of the cylindrical field
quiver3(X,Y,Z,Hc_x,Hc_y,Hc_z,’AutoScale’,’off’);
%% Longitudinal magnetic field
Hl = 0.4; % Absolute field strength (+ = neg. z-direction)
Hl_x = zeros(size(X));
Hl_y = zeros(size(Y));
Hl_z = -Hl .* ones(size(Z));
% Draw the vector-arrows of the longitudinal field
quiver3(X,Y,Z,Hl_x,Hl_y,Hl_z,’AutoScale’,’off’);
%% Helical magnetic field (sum of cylindrical and longitudinal field)
H_x = Hc_x + Hl_x;
H_y = Hc_y + Hl_y;
H_z = Hc_z + Hl_z;
% Draw the vector arrows of the helical field
quiver3(X,Y,Z,H_x,H_y,H_z,’AutoScale’,’off’,’LineWidth’,1.5)
% Draw the fieldlines of the helical field
[startX,startY,startZ] = meshgrid([-1 1],[-1 1],1);
% startpoints = [startX(:), startY(:), startZ(:)]; % unique, manually checked
% samplepoints = [X(:), Y(:), Z(:)]; % unique, manually checked
verts = stream3(X,Y,Z,H_x,H_y,H_z,startX,startY,startZ);
lineobj = streamline(verts);
view(3)
Sorry, it might be a bit overwhelming. Ultimately, I just want to get streamlines of H_x, H_y and H_z on the X, Y, Z grid. But I get the following output of the script:
All points are unique.
Error using matlab.internal.math.interp1
Sample points must be unique.
Error in interp1 (line 188)
VqLite = matlab.internal.math.interp1(X,V,method,method,Xqcol);
Error in stream3 (line 66)
syi=interp1(yy(:),1:szu(1),sy(k));
Error in test (line 62)
verts = stream3(X,Y,Z,H_x,H_y,H_z,startX,startY,startZ);
What is the Problem here? Is my check for unique points wrong?
Thank you very much in advance! Hello, I searched the community forum and tried my best to resolve the issue myself, but I could not do it.
Here is my code:
clc; close; clear all;
format compact
%% Cylindrical Coordinates
% Generate cylindrical coordinates
phi = [0 pi/2 pi 3*pi/2];
rho = [0.25 2];
z = [1 2];
% Create a meshgrid for theta, r, and z
[Phi, Rho, Z] = meshgrid(phi, rho, z);
% Convert cylindrical coordinates to Cartesian coordinates
[X, Y, Z] = pol2cart(Phi, Rho, Z);
% Combine X, Y, Z into a single matrix
points = [X(:), Y(:), Z(:)];
% Find unique rows and their indices
[unique_points, ~, ~] = unique(points, ‘rows’);
% Check for duplicates (optional)
num_duplicates = size(points, 1) – size(unique_points, 1);
if num_duplicates > 0
fprintf(‘There are %d duplicate points.n’, num_duplicates);
else
fprintf(‘All points are unique.n’);
end
% Plot the points in 3D space (optional)
figure;
% plot3(X, Y, Z, ‘o’,’DisplayName’,’Sample Points’);
xlabel(‘X’);
ylabel(‘Y’);
zlabel(‘Z’);
title(‘3D Cylindrical Coordinates’);
grid on; axis equal; hold on;
%% Circular magnetic field
% Current in negative z-direction (negative values mean the current
% direction is in positive z-direction)
I = 1;
% Absolute Value of the cylindrical H-field at corresponding positions of
% the radius in phi-direction (all other components are zero)
Hc_phi = 1/(2*pi*Rho);
% Calculate the x-, y- and z-components of the cylindrical field
Hc_x = Hc_phi .* sin(Phi);
Hc_y = -Hc_phi .* cos(Phi);
Hc_z = zeros(size(Z));
% Draw the vector-arrows of the cylindrical field
quiver3(X,Y,Z,Hc_x,Hc_y,Hc_z,’AutoScale’,’off’);
%% Longitudinal magnetic field
Hl = 0.4; % Absolute field strength (+ = neg. z-direction)
Hl_x = zeros(size(X));
Hl_y = zeros(size(Y));
Hl_z = -Hl .* ones(size(Z));
% Draw the vector-arrows of the longitudinal field
quiver3(X,Y,Z,Hl_x,Hl_y,Hl_z,’AutoScale’,’off’);
%% Helical magnetic field (sum of cylindrical and longitudinal field)
H_x = Hc_x + Hl_x;
H_y = Hc_y + Hl_y;
H_z = Hc_z + Hl_z;
% Draw the vector arrows of the helical field
quiver3(X,Y,Z,H_x,H_y,H_z,’AutoScale’,’off’,’LineWidth’,1.5)
% Draw the fieldlines of the helical field
[startX,startY,startZ] = meshgrid([-1 1],[-1 1],1);
% startpoints = [startX(:), startY(:), startZ(:)]; % unique, manually checked
% samplepoints = [X(:), Y(:), Z(:)]; % unique, manually checked
verts = stream3(X,Y,Z,H_x,H_y,H_z,startX,startY,startZ);
lineobj = streamline(verts);
view(3)
Sorry, it might be a bit overwhelming. Ultimately, I just want to get streamlines of H_x, H_y and H_z on the X, Y, Z grid. But I get the following output of the script:
All points are unique.
Error using matlab.internal.math.interp1
Sample points must be unique.
Error in interp1 (line 188)
VqLite = matlab.internal.math.interp1(X,V,method,method,Xqcol);
Error in stream3 (line 66)
syi=interp1(yy(:),1:szu(1),sy(k));
Error in test (line 62)
verts = stream3(X,Y,Z,H_x,H_y,H_z,startX,startY,startZ);
What is the Problem here? Is my check for unique points wrong?
Thank you very much in advance! stream3 MATLAB Answers — New Questions
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Hello,
I have a series of points X,Y,Z and I need to plot the isosurface describing these points.
The number of uniques X values is not equal to the unique numbers of the Y values.
Not sure how to produce the X abd Y vexros an the coirresponding Z vector.
Thak youHello,
I have a series of points X,Y,Z and I need to plot the isosurface describing these points.
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Not sure how to produce the X abd Y vexros an the coirresponding Z vector.
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n July, we announced the general availability of the Microsoft Entra Suite and Microsoft’s Security Service Edge (SSE) solution which includes Microsoft Entra Internet Access and Microsoft Entra Private Access.
Microsoft’s vision for SSE
Microsoft’s SSE solution aims to revolutionize the way organizations secure access to any cloud or on-premises applications. It unifies identity and network access through Conditional Access, the Zero Trust policy engine, helping to eliminate security loopholes and bolster your organization’s security stance against threats. Delivered from one of the largest global private networks, the solution ensures a fast and consistent hybrid work experience. With flexible deployment options across other SSE and networking solutions, you can choose to route specific traffic profiles through Microsoft’s SSE solution.
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