From 79959aa58754ac2d3e28e7da9828c91cee65176c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?S=C3=A9bastien=20Villemot?= <sebastien@dynare.org>
Date: Fri, 15 Dec 2023 15:29:25 +0100
Subject: [PATCH] Remove duplicate branches in if/then/else statements
Detected by MATLAB Code Analyzer app.
---
matlab/kalman/likelihood/kalman_filter.m | 11 ++++-------
matlab/kalman/likelihood/kalman_filter_d.m | 10 +++-------
matlab/kalman/likelihood/kalman_filter_fast.m | 10 +++-------
.../likelihood/missing_observations_kalman_filter.m | 10 +++-------
.../missing_observations_kalman_filter_d.m | 10 +++-------
matlab/parallel/masterParallel.m | 12 ++----------
6 files changed, 18 insertions(+), 45 deletions(-)
diff --git a/matlab/kalman/likelihood/kalman_filter.m b/matlab/kalman/likelihood/kalman_filter.m
index 73dc490cbc..b85d3edada 100644
--- a/matlab/kalman/likelihood/kalman_filter.m
+++ b/matlab/kalman/likelihood/kalman_filter.m
@@ -194,13 +194,10 @@ while notsteady && t<=last
end
end
if badly_conditioned_F
- if ~all(abs(F(:))<kalman_tol)
- % Use univariate filter (will remove observations with zero variance prediction error)
- return
- else
- % Pathological case, discard draw.
- return
- end
+ % if ~all(abs(F(:))<kalman_tol), then use univariate filter (will remove
+ % observations with zero variance prediction error), otherwise this is a
+ % pathological case and the draw is discarded
+ return
else
F_singular = false;
if rescale_prediction_error_covariance
diff --git a/matlab/kalman/likelihood/kalman_filter_d.m b/matlab/kalman/likelihood/kalman_filter_d.m
index c00efe6628..8c0b850aca 100644
--- a/matlab/kalman/likelihood/kalman_filter_d.m
+++ b/matlab/kalman/likelihood/kalman_filter_d.m
@@ -87,13 +87,9 @@ while rank(Z*Pinf*Z',diffuse_kalman_tol) && (t<=last)
else %rank of F_{\infty,t} is 0
Fstar = Z*Pstar*Z' + H; % (5.7) in DK (2012)
if rcond(Fstar) < kalman_tol %F_{*} is singular
- if ~all(abs(Fstar(:))<kalman_tol)
- % The univariate diffuse kalman filter should be used.
- return
- else %rank 0
- %pathological case, discard draw
- return
- end
+ % if ~all(abs(Fstar(:))<kalman_tol), then use univariate diffuse filter,
+ % otherwise this is a pathological case and the draw is discarded
+ return
else
iFstar = inv(Fstar);
dFstar = det(Fstar);
diff --git a/matlab/kalman/likelihood/kalman_filter_fast.m b/matlab/kalman/likelihood/kalman_filter_fast.m
index 2e7472b747..e7815f52ee 100644
--- a/matlab/kalman/likelihood/kalman_filter_fast.m
+++ b/matlab/kalman/likelihood/kalman_filter_fast.m
@@ -151,13 +151,9 @@ while notsteady && t<=last
v = Y(:,t)-a(Z);
end
if rcond(F) < kalman_tol
- if ~all(abs(F(:))<kalman_tol)
- % The univariate diffuse kalman filter should be used.
- return
- else
- %pathological case, discard draw
- return
- end
+ % if ~all(abs(F(:))<kalman_tol), then use univariate diffuse filter, otherwise
+ % this is a pathological case and the draw is discarded
+ return
else
F_singular = 0;
dF = det(F);
diff --git a/matlab/kalman/likelihood/missing_observations_kalman_filter.m b/matlab/kalman/likelihood/missing_observations_kalman_filter.m
index 4ad63011b8..bf4407e617 100644
--- a/matlab/kalman/likelihood/missing_observations_kalman_filter.m
+++ b/matlab/kalman/likelihood/missing_observations_kalman_filter.m
@@ -191,13 +191,9 @@ while notsteady && t<=last
end
end
if badly_conditioned_F && (~occbin_.status || (occbin_.status && t<first_period_occbin_update))
- if ~all(abs(F(:))<kalman_tol)
- % Use univariate filter.
- return
- else
- % Pathological case, discard draw
- return
- end
+ % if ~all(abs(F(:))<kalman_tol), then use univariate filter, otherwise this is a
+ % pathological case and the draw is discarded
+ return
else
F_singular = false;
end
diff --git a/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m b/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
index bdc64985a2..8754eaf63e 100644
--- a/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
+++ b/matlab/kalman/likelihood/missing_observations_kalman_filter_d.m
@@ -105,13 +105,9 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
else %rank of F_{\infty,t} is 0
Fstar = ZZ*Pstar*ZZ' + H(d_index,d_index); % (5.7) in DK (2012)
if rcond(Fstar) < kalman_tol %F_{*} is singular
- if ~all(abs(Fstar(:))<kalman_tol)
- % The univariate diffuse kalman filter should be used.
- return
- else %rank 0
- %pathological case, discard draw
- return
- end
+ % if ~all(abs(Fstar(:))<kalman_tol), then use univariate diffuse filter,
+ % otherwise this is a pathological case and the draw is discarded
+ return
else
iFstar = inv(Fstar);
dFstar = det(Fstar);
diff --git a/matlab/parallel/masterParallel.m b/matlab/parallel/masterParallel.m
index e9a7f8070f..3110a4a61e 100644
--- a/matlab/parallel/masterParallel.m
+++ b/matlab/parallel/masterParallel.m
@@ -710,11 +710,7 @@ if parallel_recover ==0
pcerdone(j) = prtfrc;
idCPU(j) = njob;
if isoctave || options_.console_mode
- if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem))
- statusString = [statusString, int2str(j), ' %3.f%% done! '];
- else
- statusString = [statusString, int2str(j), ' %3.f%% done! '];
- end
+ statusString = [statusString, int2str(j), ' %3.f%% done! '];
else
status_String{j} = waitbarString;
status_Title{j} = waitbarTitle;
@@ -722,11 +718,7 @@ if parallel_recover ==0
catch % ME
% To define!
if isoctave || options_.console_mode
- if (~ispc || strcmpi('unix',Parallel(indPC).OperatingSystem))
- statusString = [statusString, int2str(j), ' %3.f%% done! '];
- else
- statusString = [statusString, int2str(j), ' %3.f%% done! '];
- end
+ statusString = [statusString, int2str(j), ' %3.f%% done! '];
end
end
end
--
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