Unable to extract field 'build_problem' from 'mxArray'.

coder.extrinsic("DeePC");
persistent deepc
if isempty(deepc)
    deepc= DeePC(col_data,T_ini,horizon);
    deepc = build_problem(deepc,...
        LAMBDA_G_REGULARIZER, LAMBDA_Y_REGULARIZER, LAMBDA_U_REGULARIZER);
end

function result = build_problem(obj, ...
    LAMBDA_G_REGULARIZER, LAMBDA_Y_REGULARIZER, LAMBDA_U_REGULARIZER)
result = obj.build_problem(LAMBDA_G_REGULARIZER, LAMBDA_Y_REGULARIZER, LAMBDA_U_REGULARIZER);
end
function obj = build_problem(obj,...
    lambda_g,lambda_y,lambda_u,lambda_proj)
if nargin < 2, lambda_g = 0 ; end
if nargin < 3, lambda_y = 0 ; end
if nargin < 4, lambda_u = 0 ; end
if nargin < 5, lambda_proj = 0 ;end
% Assertions
if exist('build_loss', 'file') == 2
    % nothing to do 
else 
    disp('Loss function callback cannot be none');
end
assert(lambda_g >= 0 && lambda_y >= 0,'Regularizers must be non-negative');
assert(lambda_u >= 0,'Regularizer of u_ini must be non-negative');
assert(lambda_proj >= 0,'The projection regularizer must be non-negative');
obj.optimization_problem = []; % optimization problem is not build yet
%Build variables
%'full' indicates that the type of the variable is a full asymmetric matrix
u_ini = sdpvar(obj.M*obj.T_ini,1,'full'); % u_ini is the initial input
y_ini = sdpvar(obj.P*obj.T_ini,1,'full'); % y_ini is the initial output
u = sdpvar(obj.M*obj.horizon,1,'full');
y = sdpvar(obj.P*obj.horizon,1,'full');
g = sdpvar(obj.T-obj.T_ini-obj.horizon+1,1,'full');
slack_u = sdpvar(obj.T_ini*obj.P,1,'full');
slack_y = sdpvar(obj.T_ini*obj.M,1,'full');
U_p = obj.Up;
U_f = obj.Uf;
Y_p = obj.Yp;
Y_f = obj.Yf;

if  lambda_proj > DeePC.SMALL_NUMBER
    % Compute projection matrix (for the least square solution)
    Zp = [U_p;Y_p;U_f];
    ZpInv = pinv(Zp);
    I = eye(obj.T-obj.T_ini-obj.horizon+1);
    I_min_P = I-(ZpInv*Zp);
end

A = [U_p;Y_p;U_f;Y_f];
b = [u_ini+slack_u;y_ini+slack_y;u;y];
% Build constraints
constraints = [];
constraints = [constraints,A*g == b];
if isclose(lambda_y,0)
    constraints = [constraints, norm(slack_y,2) <= DeePC.SMALL_NUMBER];
end
if isclose(lambda_u,0)
    constraints = [constraints, norm(slack_u,2) <= DeePC.SMALL_NUMBER];
end
%Reshape u and y
%in order to calculate loss in all step k(1 to horizon)
u = reshape(u,[obj.horizon, obj.M]);
y = reshape(y,[obj.horizon, obj.P]);
if exist('build_constraints','file')==2
    Constraints = build_constraints(u,y);
else
    Constraints = [];
end
constraints = [constraints,Constraints];
% Build Loss
loss  = build_loss(u,y);
if lambda_g > DeePC.SMALL_NUMBER
    regulariers = lambda_g * norm(g,1); % add L1 norm of g
else
    regulariers = 0;
end
if lambda_y > DeePC.SMALL_NUMBER
    regulariers = regulariers + lambda_y*norm(slack_y,1);
end
if lambda_proj > DeePC.SMALL_NUMBER
    % Add projection regularizer
    regulariers = regulariers + lambda_proj*norm(I_min_P*g);
end
if lambda_u > DeePC.SMALL_NUMBER
    % Add L1 norm of slack_u
    regulariers = regulariers + lambda_u*norm(slack_u,1);
end
% Define the total loss
problem_loss = loss + regulariers;
% Define the optimization problem
obj.optimization_problem = OptimizationProblem(u_ini,y_ini,u,y,g,...
    slack_u,slack_y,constraints,problem_loss);
end

Answer 1

[Walter Roberson]

You are declaring the function build_problem twice in the same scope.

You cannot declare class-dependent methods inside a MATLAB Function Block.

    deepc= DeePC(col_data,T_ini,horizon);
    deepc = build_problem(deepc,...
        LAMBDA_G_REGULARIZER, LAMBDA_Y_REGULARIZER, LAMBDA_U_REGULARIZER);

You appear to be changing the type of deepc, which is not something that can be done in a MATLAB Function Block.

[Zhenting]

but deepc is a class instance, i have to use a constuctor and add a property "problem" by using build_problem function.

[Walter Roberson]

You still cannot alter the datatype of deepc . You need something like

    deepc1 = DeePC(col_data,T_ini,horizon);
    deepc = build_problem(deepc1,...
        LAMBDA_G_REGULARIZER, LAMBDA_Y_REGULARIZER, LAMBDA_U_REGULARIZER);

[Zhenting]

yes i have a try, but comes the same error

[Walter Roberson]

You still have the problem that you are declaring the function build_problem twice.