Fix header doc.

matlab/estimation/likelihood_quadratic_approximation.m

@@ -14,7 +14,7 @@ function [f, df, d2f, R2] = likelihood_quadratic_approximation(particles, likeli
 % - R2                   [double]   scalar, goodness of fit measure.
 %
 % REMARKS
-% [1] Function f takes a n×m matrix as input argument (the function is evaluated in m points) and returns a m×1 vector.
+% [1] Function f takes a n×m matrix as input argument (the approximated likelihood is evaluated in m points) and returns a m×1 vector.
 % [2] Funtion df takes a n×1 vector as input argument (the point where the gradient is computed) and returns a n×1 vector.
 
 % Copyright © 2024 Dynare Team
@@ -121,7 +121,6 @@ function [f, df, d2f, R2] = likelihood_quadratic_approximation(particles, likeli
 
     function xx = dcrossproducts(x)
         xx = zeros(n, n*(n+1)/2);
-        size(xx)
         for i = 1:n
             base = (i-1)*n-sum(0:i-2);
             incol = 1;
@@ -138,4 +137,55 @@ function [f, df, d2f, R2] = likelihood_quadratic_approximation(particles, likeli
         end
     end
 
+    return % --*-- Unit tests --*--
+
+    %@test:1
+    % Create data
+    X = randn(10,1000);
+    Y = 1 + rand(1,10)*X.^2+0.01*randn(1,1000);
+
+    % Perform approximation
+    try
+        [f,df, d2f,R2] = likelihood_quadratic_approximation(X,Y);
+        t(1) = true;
+    catch
+        t(1) = false;
+    end
+
+    % Test returned arguments
+    if t(1)
+        try
+            y = f(randn(10,100));
+            t(2) = true;
+            if ~(rows(y)==100 && columns(y)==1)
+                t(2) = false;
+            end
+        catch
+            t(2) = false;
+        end
+        try
+            dy = df(zeros(10,1));
+            t(3) = true;
+            if ~(rows(dy)==10 && columns(dy)==1)
+                t(3) = false;
+            end
+        catch
+            t(3) = false;
+        end
+        t(4) = true;
+        if ~(rows(d2f)==10 && columns(d2f)==10)
+            t(4) = false
+        end
+        if ~(rows(d2f)==10 && columns(d2f)==10)
+            t(4) = false
+        end
+        t(4) = issymmetric(d2f);
+        t(4) = ispd(d2f);
+        t(5) = isscalar(R2);
+        t(5) = (R2>0) & (R2<1); % Note that in a nonlinear model nothing ensures that these inequalities are satisfied.
+    end
+
+    T = all(t);
+    %@eof:1
+
 end

matlab/utilities/general/ispd.m

@@ -1,30 +1,15 @@
 function [test, penalty] = ispd(A)
 
-%@info:
-%! @deftypefn {Function File} {[@var{test}, @var{penalty}  =} ispd (@var{A})
-%! @anchor{ispd}
-%! @sp 1
-%! Tests if the square matrix @var{A} is positive definite.
-%! @sp 2
-%! @strong{Inputs}
-%! @sp 1
-%! @table @ @var
-%! @item A
-%! A square matrix.
-%! @end table
-%! @sp 2
-%! @strong{Outputs}
-%! @sp 1
-%! @table @ @var
-%! @item test
-%! Integer scalar equal to 1 if @var{A} is a positive definite sqquare matrix, 0 otherwise.
-%! @item penalty
-%! Absolute value of the uum of the negative eigenvalues of A. This output argument is optional.
-%! @end table
-%! @end deftypefn
-%@eod:
+% Test if the square matrix A is positive definite.
+%
+% INPUTS
+% - A       [double]   n×n matrix.
+%
+% OUTPUTS
+% - test    [logical]  scalar, true if and only if matrix A is positive definite (and symmetric...)
+% - penalty [double]   scalar, absolute value of the uum of the negative eigenvalues of A. This output argument is optional.
 
-% Copyright © 2007-2022 Dynare Team
+% Copyright © 2007-2024 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -54,6 +39,7 @@ if nargout>1
         if isoctave && any(any(~isfinite(A))) % workaround for https://savannah.gnu.org/bugs/index.php?63082
             penalty = 1;
         else
+            % TODO: the penalty is only concerned with negative eigenvalues, we should also consider the case of non symmetric matrix A.
             a = diag(eig(A));
             k = find(a<0);
             if k > 0
@@ -61,4 +47,4 @@ if nargout>1
             end
         end
     end
-end
+end