From 3f2cfd2037d68db62dfea9dffa382cb62cbccc51 Mon Sep 17 00:00:00 2001 From: "daniel.weindl" Date: Fri, 24 Feb 2017 13:29:45 +0100 Subject: [PATCH 01/16] Objective function wrapper for MEIGO must not be in /private --- private/meigoDummy.m => meigoDummy.m | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename private/meigoDummy.m => meigoDummy.m (100%) diff --git a/private/meigoDummy.m b/meigoDummy.m similarity index 100% rename from private/meigoDummy.m rename to meigoDummy.m From 312ac9aee5068c747f6fb62b843b426f91575bbd Mon Sep 17 00:00:00 2001 From: Paul Stapor Date: Fri, 24 Feb 2017 14:54:28 +0100 Subject: [PATCH 02/16] Fixed a little bug: plotting failure if first point non-evaluable --- plotMultiStarts.m | 1 + 1 file changed, 1 insertion(+) diff --git a/plotMultiStarts.m b/plotMultiStarts.m index 8d485eb..df8ef6b 100644 --- a/plotMultiStarts.m +++ b/plotMultiStarts.m @@ -100,6 +100,7 @@ %% PLOT OBJECTIVES subplot(2,2,1); +n_finished_starts = 0; for j = 1 : n_starts if ~isnan(parameters.MS.logPost(j)) n_finished_starts = j; From 8af64a3dda6d0672bb6ae18e56d37921cb45fba3 Mon Sep 17 00:00:00 2001 From: "daniel.weindl" Date: Fri, 24 Feb 2017 15:06:04 +0100 Subject: [PATCH 03/16] Added a first test case for getMultiStarts --- runPestoTests.m | 6 +++ tests/ConversionReactionTest.m | 79 ++++++++++++++++++++++++++++++++++ 2 files changed, 85 insertions(+) create mode 100644 runPestoTests.m create mode 100644 tests/ConversionReactionTest.m diff --git a/runPestoTests.m b/runPestoTests.m new file mode 100644 index 0000000..ede0627 --- /dev/null +++ b/runPestoTests.m @@ -0,0 +1,6 @@ +function runPestoTests() + % runPestoTests Run a set of PESTO unit tests + import matlab.unittest.TestSuite + run(TestSuite.fromFolder('tests')) +end + diff --git a/tests/ConversionReactionTest.m b/tests/ConversionReactionTest.m new file mode 100644 index 0000000..c0edbeb --- /dev/null +++ b/tests/ConversionReactionTest.m @@ -0,0 +1,79 @@ +%% Test Class Definition +classdef ConversionReactionTest < matlab.unittest.TestCase + properties + oldPath + parameters + options + theta_true = [-2.5;-2]; + llh_true = 32.275546152684690; + t = (0:10)'; % time points + sigma2 = 0.015^2; % measurement noise + y = [0.0244; 0.0842; 0.1208; 0.1724; 0.2315; 0.2634; ... + 0.2831; 0.3084; 0.3079; 0.3097; 0.3324]; % Measurement data + objectiveFunction + optionsMultistart + end + + methods(TestMethodSetup) + function setPath(testCase) + testCase.oldPath = path(); + addpath(fullfile(fileparts(mfilename('fullpath')), '..','examples', 'conversion_reaction')); + end + + function setupParameters(testCase) + testCase.parameters.name = {'log_{10}(k_1)','log_{10}(k_2)'}; + testCase.parameters.min = [-7,-7]; + testCase.parameters.max = [ 3, 3]; + testCase.parameters.number = length(testCase.parameters.name); + end + + function setupObjectiveFunction(testCase) + testCase.objectiveFunction = @(theta) logLikelihoodCR(theta, testCase.t, testCase.y, testCase.sigma2, 'log'); + end + + function setupMultiStartOptions(testCase) + testCase.optionsMultistart = PestoOptions(); + testCase.optionsMultistart.obj_type = 'log-posterior'; + testCase.optionsMultistart.n_starts = 4; + testCase.optionsMultistart.comp_type = 'sequential'; + testCase.optionsMultistart.mode = 'silent'; + end + + end + + methods(TestMethodTeardown) + function restorePath(testCase) + path(testCase.oldPath); + end + end + + %% Test Method Block + methods (Test) + function testObjectiveFunction(testCase) + actLLH = testCase.objectiveFunction(testCase.theta_true); + testCase.fatalAssertEqual(actLLH, testCase.llh_true, 'AbsTol', eps) + end + + function testGetMultiStarts(testCase) + numStarts = testCase.optionsMultistart.n_starts; + numParams = testCase.parameters.number; + multiStartParams = getMultiStarts(testCase.parameters, testCase.objectiveFunction, testCase.optionsMultistart); + + testCase.assertEqual(multiStartParams.MS.n_starts, testCase.optionsMultistart.n_starts); + + testCase.verifySize(multiStartParams.MS.par0, [numParams, numStarts]); + testCase.verifySize(multiStartParams.MS.par, [numParams, numStarts]); + testCase.verifySize(multiStartParams.MS.logPost0, [numStarts, 1]); + testCase.verifySize(multiStartParams.MS.logPost, [numStarts, 1]); + testCase.verifySize(multiStartParams.MS.gradient, [numParams, numStarts]); + testCase.verifySize(multiStartParams.MS.hessian, [numParams, numParams, numStarts]); + testCase.verifySize(multiStartParams.MS.n_objfun, [numStarts, 1]); + testCase.verifySize(multiStartParams.MS.n_iter, [numStarts, 1]); + testCase.verifySize(multiStartParams.MS.t_cpu, [numStarts, 1]); + testCase.verifySize(multiStartParams.MS.exitflag, [numStarts, 1]); + + testCase.assertEqual(multiStartParams.MS.logPost, ones(numStarts, 1) * testCase.llh_true, 'RelTol', 1e-2) + testCase.assertEqual(multiStartParams.MS.par, repmat(testCase.theta_true, 1, numStarts), 'RelTol', 1e-1) + end + end +end \ No newline at end of file From 50f024110c2db7bbee2bef3146108825da1af3e0 Mon Sep 17 00:00:00 2001 From: "daniel.weindl" Date: Fri, 24 Feb 2017 16:40:06 +0100 Subject: [PATCH 04/16] Changed nonsensical test in getParameterSamples.m --- getParameterSamples.m | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/getParameterSamples.m b/getParameterSamples.m index e130f46..2254e4f 100644 --- a/getParameterSamples.m +++ b/getParameterSamples.m @@ -139,11 +139,11 @@ else % if no multi-start local optimization was done, we need an input for % Sigma_0 and theta_0 to do sampling - if (~isfield(parameters, 'user') || isempty(parameters.user)) - if (~isfield(parameters.user, 'Sigma_0') || ~isfield(parameters.user, 'theta_0') || isempty(parameters.user.Sigma_0) || isempty(parameters.user.theta_0)) + if (~isfield(parameters, 'user') || isempty(parameters.user) ... + || ~isfield(parameters.user, 'Sigma_0') || ~isfield(parameters.user, 'theta_0') ... + || isempty(parameters.user.Sigma_0) || isempty(parameters.user.theta_0)) error(['You have to specify an initial parameters vector theta_0 and covariance matrix Sigma_0' ... ' or perform a optimization first.']); - end else Sigma_0 = parameters.user.Sigma_0; theta_0 = parameters.user.theta_0; From c912a8ab6f10d4de816d9059b53d85f377f2fa51 Mon Sep 17 00:00:00 2001 From: "daniel.weindl" Date: Sun, 26 Feb 2017 16:06:09 +0100 Subject: [PATCH 05/16] Set fmincon options only if fmincon is used as local solver --- getMultiStarts.m | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/getMultiStarts.m b/getMultiStarts.m index 69caeb9..6bbacfb 100644 --- a/getMultiStarts.m +++ b/getMultiStarts.m @@ -121,7 +121,9 @@ fprintf(' \nOptimization:\n=============\n'); case 'silent' % no output % Force fmincon to be silent. - options.localOptimizerOptions.Display = 'off'; + if strcmp(options.localOptimizer, 'fmincon') + options.localOptimizerOptions.Display = 'off'; + end end %% Initialization of random number generator From 2040143d6981425ace0df38dfaafcf2f3f34d336 Mon Sep 17 00:00:00 2001 From: "daniel.weindl" Date: Sun, 26 Feb 2017 16:06:51 +0100 Subject: [PATCH 06/16] Only plot in 'visual'-mode --- getParameterConfidenceIntervals.m | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/getParameterConfidenceIntervals.m b/getParameterConfidenceIntervals.m index 2937837..b475e69 100644 --- a/getParameterConfidenceIntervals.m +++ b/getParameterConfidenceIntervals.m @@ -115,6 +115,9 @@ end end -plotConfidenceIntervals(parameters, alpha, [], options); +switch options.mode + case 'visual' + plotConfidenceIntervals(parameters, alpha, [], options); +end end From 8cdb9f7ec827d830f4d2c273cdf48d512d5e007d Mon Sep 17 00:00:00 2001 From: "daniel.weindl" Date: Sun, 26 Feb 2017 16:07:38 +0100 Subject: [PATCH 07/16] Added more tests based on the conversion reaction example --- tests/ConversionReactionTest.m | 134 ++++++++++++++++++++++++++------- 1 file changed, 105 insertions(+), 29 deletions(-) diff --git a/tests/ConversionReactionTest.m b/tests/ConversionReactionTest.m index c0edbeb..d141dba 100644 --- a/tests/ConversionReactionTest.m +++ b/tests/ConversionReactionTest.m @@ -11,7 +11,6 @@ y = [0.0244; 0.0842; 0.1208; 0.1724; 0.2315; 0.2634; ... 0.2831; 0.3084; 0.3079; 0.3097; 0.3324]; % Measurement data objectiveFunction - optionsMultistart end methods(TestMethodSetup) @@ -30,15 +29,7 @@ function setupParameters(testCase) function setupObjectiveFunction(testCase) testCase.objectiveFunction = @(theta) logLikelihoodCR(theta, testCase.t, testCase.y, testCase.sigma2, 'log'); end - - function setupMultiStartOptions(testCase) - testCase.optionsMultistart = PestoOptions(); - testCase.optionsMultistart.obj_type = 'log-posterior'; - testCase.optionsMultistart.n_starts = 4; - testCase.optionsMultistart.comp_type = 'sequential'; - testCase.optionsMultistart.mode = 'silent'; - end - + end methods(TestMethodTeardown) @@ -51,29 +42,114 @@ function restorePath(testCase) methods (Test) function testObjectiveFunction(testCase) actLLH = testCase.objectiveFunction(testCase.theta_true); - testCase.fatalAssertEqual(actLLH, testCase.llh_true, 'AbsTol', eps) + testCase.fatalAssertEqual(actLLH, testCase.llh_true, 'AbsTol', eps, 'Likelihood function result is wrong.') end - function testGetMultiStarts(testCase) - numStarts = testCase.optionsMultistart.n_starts; - numParams = testCase.parameters.number; - multiStartParams = getMultiStarts(testCase.parameters, testCase.objectiveFunction, testCase.optionsMultistart); + function testGetMultiStartsFminconSingleStart(testCase) + optionsMS = PestoOptions(); + optionsMS.obj_type = 'log-posterior'; + optionsMS.n_starts = 1; + optionsMS.comp_type = 'sequential'; + optionsMS.mode = 'silent'; + + multiStartParams = getMultiStarts(testCase.parameters, testCase.objectiveFunction, optionsMS); + testCase.verifyMultiStartResults(multiStartParams, optionsMS); + end + + function testGetMultiStartsFmincon(testCase) + optionsMultistart = PestoOptions(); + optionsMultistart.obj_type = 'log-posterior'; + optionsMultistart.n_starts = 4; + optionsMultistart.comp_type = 'sequential'; + optionsMultistart.mode = 'silent'; + + multiStartParams = getMultiStarts(testCase.parameters, testCase.objectiveFunction, optionsMultistart); + testCase.verifyMultiStartResults(multiStartParams, optionsMultistart); + end + + function testGetMultiStartsMeigo(testCase) + testCase.assumeTrue(exist('MEIGO', 'file') == 2,'MEIGO not in path.'); + + MeigoOptions = struct(... + 'maxeval', 1e3, ... + 'iterprint', 0, ... + 'local', struct('solver', 'fmincon', ... + 'finish', 'fmincon', ... + 'iterprint', 0) ... + ); + + optionsMultistartMeigo = PestoOptions(); + optionsMultistartMeigo.obj_type = 'log-posterior'; + optionsMultistartMeigo.comp_type = 'sequential'; + optionsMultistartMeigo.mode = 'silent'; + optionsMultistartMeigo.localOptimizer = 'meigo-ess'; + optionsMultistartMeigo.localOptimizerOptions = MeigoOptions; + optionsMultistartMeigo.n_starts = 2; + + parametersMeigo = getMultiStarts(testCase.parameters, testCase.objectiveFunction, optionsMultistartMeigo); + testCase.verifyMultiStartResults(parametersMeigo, optionsMultistartMeigo); + end + + function testSamplingSingleChainAM(testCase) + optionsMultistart = PestoOptions(); + optionsMultistart.obj_type = 'log-posterior'; + optionsMultistart.n_starts = 2; + optionsMultistart.comp_type = 'sequential'; + optionsMultistart.mode = 'silent'; - testCase.assertEqual(multiStartParams.MS.n_starts, testCase.optionsMultistart.n_starts); + optionsMultistart.MCMC.sampling_scheme = 'single-chain'; + optionsMultistart.SC.proposal_scheme = 'AM'; + optionsMultistart.MCMC.nsimu_warmup = 2e2; + optionsMultistart.MCMC.thinning = 10; + optionsMultistart.MCMC.nsimu_run = 2e3; - testCase.verifySize(multiStartParams.MS.par0, [numParams, numStarts]); - testCase.verifySize(multiStartParams.MS.par, [numParams, numStarts]); - testCase.verifySize(multiStartParams.MS.logPost0, [numStarts, 1]); - testCase.verifySize(multiStartParams.MS.logPost, [numStarts, 1]); - testCase.verifySize(multiStartParams.MS.gradient, [numParams, numStarts]); - testCase.verifySize(multiStartParams.MS.hessian, [numParams, numParams, numStarts]); - testCase.verifySize(multiStartParams.MS.n_objfun, [numStarts, 1]); - testCase.verifySize(multiStartParams.MS.n_iter, [numStarts, 1]); - testCase.verifySize(multiStartParams.MS.t_cpu, [numStarts, 1]); - testCase.verifySize(multiStartParams.MS.exitflag, [numStarts, 1]); + testCase.parameters.user.theta_0 = testCase.theta_true; + testCase.parameters.user.Sigma_0 = diag([1,1]); + + parameters = getParameterSamples(testCase.parameters, testCase.objectiveFunction, optionsMultistart); + end + + function testConfidenceIntervals(testCase) + + options = PestoOptions(); + options.obj_type = 'log-posterior'; + options.n_starts = 1; + options.comp_type = 'sequential'; + options.mode = 'silent'; + + multiStartParams = getMultiStarts(testCase.parameters, testCase.objectiveFunction, options); + + alpha = [0.9, 0.95, 0.99]; + multiStartParams = getParameterConfidenceIntervals(multiStartParams, alpha, options); + end + end + + methods + function verifyMultiStartResults(testCase, multiStartParams, optionsMultistart) + numStarts = optionsMultistart.n_starts; + numParams = testCase.parameters.number; + + testCase.assertEqual(multiStartParams.MS.n_starts, optionsMultistart.n_starts, 'Incorrect number of starts'); + + testCase.verifySize(multiStartParams.MS.par0, [numParams, numStarts], 'Error in par0 dimensions.'); + testCase.verifySize(multiStartParams.MS.par, [numParams, numStarts], 'Error in par dimensions.'); + testCase.verifySize(multiStartParams.MS.logPost0, [numStarts, 1], 'Error in logPost0 dimensions.'); + testCase.verifySize(multiStartParams.MS.logPost, [numStarts, 1], 'Error in logPost dimensions.'); + testCase.verifySize(multiStartParams.MS.gradient, [numParams, numStarts], 'Error in gradient dimensions.'); + testCase.verifySize(multiStartParams.MS.n_objfun, [numStarts, 1], 'Error in n_objfun dimensions.'); + testCase.verifySize(multiStartParams.MS.n_iter, [numStarts, 1], 'Error in n_iter dimensions.'); + testCase.verifySize(multiStartParams.MS.t_cpu, [numStarts, 1], 'Error in t_cpu dimensions.'); + testCase.verifySize(multiStartParams.MS.exitflag, [numStarts, 1], 'Error in exitflag dimensions.'); + + if numStarts == 1 + testCase.verifySize(multiStartParams.MS.hessian, [numParams, numParams], 'Error in hessian dimensions.'); + else + testCase.verifySize(multiStartParams.MS.hessian, [numParams, numParams, numStarts], 'Error in hessian dimensions.'); + end - testCase.assertEqual(multiStartParams.MS.logPost, ones(numStarts, 1) * testCase.llh_true, 'RelTol', 1e-2) - testCase.assertEqual(multiStartParams.MS.par, repmat(testCase.theta_true, 1, numStarts), 'RelTol', 1e-1) - end + testCase.assertEqual(multiStartParams.MS.logPost, ones(numStarts, 1) * testCase.llh_true, 'RelTol', 1e-2, 'Incorrect optimal log likelihood') + testCase.assertEqual(multiStartParams.MS.par, repmat(testCase.theta_true, 1, numStarts), 'RelTol', 1e-1, 'Incorrect optimal parameters.') + + end end end \ No newline at end of file From 30f3b30835ef1dbdce5b5bf4c516b0a464ac9905 Mon Sep 17 00:00:00 2001 From: Paul Stapor Date: Sun, 26 Feb 2017 19:53:56 +0100 Subject: [PATCH 08/16] Corrected some comments and the printed output --- examples/conversion_reaction/mainConversionReaction.m | 4 +++- getParameterConfidenceIntervals.m | 5 +++++ getParameterProfiles.m | 2 +- getPropertyConfidenceIntervals.m | 10 +++++++++- getPropertyProfiles.m | 2 +- getPropertySamples.m | 2 +- 6 files changed, 20 insertions(+), 5 deletions(-) diff --git a/examples/conversion_reaction/mainConversionReaction.m b/examples/conversion_reaction/mainConversionReaction.m index 1d15a4f..fa20547 100644 --- a/examples/conversion_reaction/mainConversionReaction.m +++ b/examples/conversion_reaction/mainConversionReaction.m @@ -4,8 +4,10 @@ % * getMultiStarts() % * getParameterProfiles() % * getParameterSamples() -% * plotParameterUncertainty() +% * getParameterConfidenceIntervals() +% * getPropertyMultiStarts() % * getPropertyProfiles() +% * getPropertySamples() % * getPropertyConfidenceIntervals() % % This example provides a model for the interconversion of two species diff --git a/getParameterConfidenceIntervals.m b/getParameterConfidenceIntervals.m index b475e69..c188f07 100644 --- a/getParameterConfidenceIntervals.m +++ b/getParameterConfidenceIntervals.m @@ -115,9 +115,14 @@ end end +%% Output switch options.mode case 'visual' plotConfidenceIntervals(parameters, alpha, [], options); + disp('-> Calculation of confidence intervals for parameters FINISHED.'); + case 'text' + disp('-> Calculation of confidence intervals for parameters FINISHED.'); + case 'silent' % no output end end diff --git a/getParameterProfiles.m b/getParameterProfiles.m index 5892a91..123e944 100644 --- a/getParameterProfiles.m +++ b/getParameterProfiles.m @@ -180,7 +180,7 @@ %% Output switch options.mode - case {'visual','text'}, disp('-> Profile calculation FINISHED.'); + case {'visual','text'}, disp('-> Profile calculation for parameters FINISHED.'); case 'silent' % no output end diff --git a/getPropertyConfidenceIntervals.m b/getPropertyConfidenceIntervals.m index 16f6c39..78b7d17 100644 --- a/getPropertyConfidenceIntervals.m +++ b/getPropertyConfidenceIntervals.m @@ -99,6 +99,14 @@ end end -plotConfidenceIntervals(properties, alpha, [], options); +%% Output +switch options.mode + case 'visual' + plotConfidenceIntervals(properties, alpha, [], options); + disp('-> Calculation of confidence intervals for properties FINISHED.'); + case 'text' + disp('-> Calculation of confidence intervals for properties FINISHED.'); + case 'silent' % no output +end end diff --git a/getPropertyProfiles.m b/getPropertyProfiles.m index 396d3a9..903909e 100644 --- a/getPropertyProfiles.m +++ b/getPropertyProfiles.m @@ -405,7 +405,7 @@ %% Output switch options.mode - case {'visual','text'}, disp('-> Profile calculation FINISHED.'); + case {'visual','text'}, disp('-> Profile calculation for properties FINISHED.'); case 'silent' % no output end diff --git a/getPropertySamples.m b/getPropertySamples.m index c38803c..70a1d03 100644 --- a/getPropertySamples.m +++ b/getPropertySamples.m @@ -172,7 +172,7 @@ %% Output switch options.mode - case {'visual','text'}, disp('-> Property evaluation for sample FINISHED.'); + case {'visual','text'}, disp('-> Property evaluation for samples FINISHED.'); case 'silent' % no output end From 287581a218b23582c29b3db3d79b86c97f8a87c1 Mon Sep 17 00:00:00 2001 From: Paul Stapor Date: Mon, 27 Feb 2017 17:22:33 +0100 Subject: [PATCH 09/16] Enzymatic catalysis example should now work well and illustrate Meigo and sampling --- @PestoOptions/PestoOptions.m | 6 +-- @PestoPlottingOptions/PestoPlottingOptions.m | 2 +- .../enzymatic_catalysis/HessianApproxEC.m | 24 ----------- .../mainEnzymaticCatalysis.m | 40 ++++++------------- 4 files changed, 17 insertions(+), 55 deletions(-) delete mode 100644 examples/enzymatic_catalysis/HessianApproxEC.m diff --git a/@PestoOptions/PestoOptions.m b/@PestoOptions/PestoOptions.m index 3353041..d624a1d 100644 --- a/@PestoOptions/PestoOptions.m +++ b/@PestoOptions/PestoOptions.m @@ -216,12 +216,12 @@ 'linSolver', 'Dense', ... 'gamma', 0, ... 'eps', 1e-8, ... - 'minCond', 1e-12, ... + 'minCond', 1e-10, ... 'hessian', 'user-supplied', ... 'gradient', true, ... 'MaxStep', 0.1, ... - 'MinStep', 0.5e-4, ... - 'MaxNumSteps', 1e7, ... + 'MinStep', 1e-4, ... + 'MaxNumSteps', 1e5, ... 'RelTol', 1e-5, ... 'AbsTol', 1e-8 ... ); diff --git a/@PestoPlottingOptions/PestoPlottingOptions.m b/@PestoPlottingOptions/PestoPlottingOptions.m index c088aba..96b9cd0 100644 --- a/@PestoPlottingOptions/PestoPlottingOptions.m +++ b/@PestoPlottingOptions/PestoPlottingOptions.m @@ -106,7 +106,7 @@ % * .sp_ms: marker size for scatter plot (default = 5) % * .name: name of legend entry (default = 'S') S = struct('plot_type', 0, ... - 'bins', 'conservative', ... + 'bins', 'optimal', ... 'scaling', [], ... 'hist_col', [0.7,0.7,0.7], ... 'sp_col', [0.7,0.7,0.7], ... diff --git a/examples/enzymatic_catalysis/HessianApproxEC.m b/examples/enzymatic_catalysis/HessianApproxEC.m deleted file mode 100644 index ad3410c..0000000 --- a/examples/enzymatic_catalysis/HessianApproxEC.m +++ /dev/null @@ -1,24 +0,0 @@ -function Hessian = HessianApproxEC(theta, objectiveFunction, type, lambda) -% Objective function for examples/enzymatic_catalysis -% -% HessianApproxEC.m provides an approximation of the Hessian matrix for -% Hessian based optimization in the enzymatic catalysis example. -% -% Parameters: -% theta: Model parameters [theta_1, theta_2, theta_3, theta_4]' -% objectiveFunction: Objective function of the problem -% type: Fisher information matrix or Finite Differences - -% Return values: -% Hessian: An approximation of the Hessian matrix - -if (strcmp(type, 'FIM')) - [Hessian, ~, ~] = objectiveWrap(theta, objectiveFunction, 'log-posterior', 3); -elseif (strcmp(type, 'FD')) - % Initialization - [Hessian, ~, ~] = objectiveWrap(theta, objectiveFunction, 'log-posterior', 1); -else - error('Not a known type of Hessian approximation in this example'); -end - -end \ No newline at end of file diff --git a/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m b/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m index e2c8cc8..b609ea7 100644 --- a/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m +++ b/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m @@ -9,7 +9,7 @@ % Demonstrates furthermore: % * how to do sampling without multi-start local optimization beforehand % * the value of multi-start local optimization before sampling -% * how to use Hessian information for optimization +% * how to use the MEIGO toolbox for optimization % % This example provides a model for the reaction of a species X_1 to a % species X_4, which is catalyzed by an enzyme X_2. @@ -88,9 +88,10 @@ %% Parameter Sampling % An adapted Metropolis-Hastings-Algorithm is used to explore the parameter -% space. Without Multi-start local optimization, this is not extremly -% effective, but for small problems, this is feasible and PESTO also allows -% sampling without previous parameter optimization. +% space. Without Multi-start local optimization, this is not recommended +% (since it is in genereal ineffective), but for small problems, this is +% feasible and PESTO also allows sampling without previous parameter +% optimization. % Length of the chain % optionsPesto.MCMC.nsimu_warmup = 2e4; @@ -124,17 +125,16 @@ % uncomment: MeigoOptions = struct(... - 'maxeval', 500, ... + 'maxeval', 1000, ... 'local', struct('solver', 'fmincon', ... 'finish', 'fmincon', ... 'iterprint', 0) ... ); -optionsMeigo = optionsPesto.copy(); -optionsMeigo.localOptimizer = 'meigo-ess'; -optionsMeigo.localOptimizerOptions = MeigoOptions; -optionsMeigo.n_starts = 1; -parameters = getMultiStarts(parameters, objectiveFunction, optionsMeigo); +optionsPesto.localOptimizer = 'meigo-ess'; +optionsPesto.localOptimizerOptions = MeigoOptions; +optionsPesto.n_starts = 1; +parameters = getMultiStarts(parameters, objectiveFunction, optionsPesto); % Options for an alternative multi-start local optimization % @@ -154,23 +154,9 @@ % The result of the sampling is compared with profile likelihoods. optionsPesto.profile_method = 'integration'; -optionsPesto.solver = struct('gamma', 10, ... - 'type', 'ode113', ... - 'algorithm', 'Adams', ... - 'nonlinSolver', 'Newton', ... - 'linSolver', 'Dense', ... - 'eps', 1e-8, ... - 'minCond', 1e-10, ... - 'gradient', true, ... - 'hessian', 'user-supplied', ... - 'MaxStep', 0.1, ... - 'MinStep', 1e-5, ... - 'MaxNumSteps', 1e4, ... - 'RelTol', 1e-5, ... - 'AbsTol', 1e-8, ... - 'hessianStep', 1e-7, ... - 'gradientStep', 1e-7 ... - ); +optionsPesto.solver.gamma = 10; +optionsPesto.solver.type = 'ode113'; +optionsPesto.solver.hessian = 'user-supplied'; parameters = getParameterProfiles(parameters, objectiveFunction, optionsPesto); From dd43282200e4466c3f6d05faaa2d6a3b0037d2f6 Mon Sep 17 00:00:00 2001 From: Paul Stapor Date: Tue, 28 Feb 2017 00:07:50 +0100 Subject: [PATCH 10/16] The three toy examples are commented and do what they should now --- .../mainConversionReaction.m | 4 +-- .../mainEnzymaticCatalysis.m | 8 ++--- examples/mRNA_transfection/mainTransfection.m | 36 ++++++++++--------- 3 files changed, 26 insertions(+), 22 deletions(-) diff --git a/examples/conversion_reaction/mainConversionReaction.m b/examples/conversion_reaction/mainConversionReaction.m index fa20547..0dfb992 100644 --- a/examples/conversion_reaction/mainConversionReaction.m +++ b/examples/conversion_reaction/mainConversionReaction.m @@ -26,8 +26,8 @@ % % Profile likelihood calculation is done using getParameterProfiles(). % -% Multi-chain Monte-Carlo sampling is performed by getParameterSamples() -% and plotted using plotParameterUncertainty(). +% Sngle-chain Monte-Carlo sampling is performed by getParameterSamples() +% and plotted. diff --git a/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m b/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m index b609ea7..c084303 100644 --- a/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m +++ b/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m @@ -10,6 +10,7 @@ % * how to do sampling without multi-start local optimization beforehand % * the value of multi-start local optimization before sampling % * how to use the MEIGO toolbox for optimization +% * how to compute profile likelihoods via ODE integration % % This example provides a model for the reaction of a species X_1 to a % species X_4, which is catalyzed by an enzyme X_2. @@ -26,10 +27,9 @@ % optimization based on these measurements, demonstrating the use of % getMultiStarts(). % -% The Profile likelihoods are calculated in two different ways: First, the -% calculation is done by repeated reoptimization using -% getParameterProfiles(), then it is done by integrating the an ODE along -% the profile path using integrateParameterProfiles(). +% The Profile likelihoods are calculated by integrating the an ODE along +% the profile path using getParameterProfiles with the option +% optionsPesto.profile_method = 'integration'. diff --git a/examples/mRNA_transfection/mainTransfection.m b/examples/mRNA_transfection/mainTransfection.m index 5b4701c..d9fd55c 100644 --- a/examples/mRNA_transfection/mainTransfection.m +++ b/examples/mRNA_transfection/mainTransfection.m @@ -10,6 +10,11 @@ % * getPropertySamples() % * getPropertyConfidenceIntervals() % +% Demonstrates furthermore: +% * how to carry out uncertainty analysis for local (non-global) optimum +% * how to use the PSwarm toolbox for optimization (commented) and what +% problems may occur when no gradient based approach is used +% % This example is a model for mRNA transfection, taken from the paper % "Single-cell mRNA transfection studies: Delivery, kinetics and statistics % by numbers", by Leonhardt C et al., Nanomedicine: NBM, 2014, vol.10 @@ -159,27 +164,25 @@ % This section uses PSwarm, a particle swarm optimizer % (Install from http://www.norg.uminho.pt/aivaz/pswarm/ and uncomment) +% +% optionsMultistartPSwarm = optionsMultistart.copy(); +% optionsMultistartPSwarm.localOptimizer = 'pswarm'; +% optionsMultistartPSwarm.localOptimizerOptions.MaxObj = 25000; +% optionsMultistartPSwarm.localOptimizerOptions.MaxIter = 1000; +% optionsMultistartPSwarm.localOptimizerOptions.Size = 100; +% optionsMultistartPSwarm.localOptimizerOptions.Social = 0.5; +% optionsMultistartPSwarm.localOptimizerOptions.Cognitial = 0.9; +% optionsMultistartPSwarm.localOptimizerOptions.IPrint = -1; +% +% parameters = getMultiStarts(parameters, objectiveFunction, optionsMultistartPSwarm); -optionsMultistartPSwarm = optionsMultistart.copy(); -optionsMultistartPSwarm.localOptimizer = 'pswarm'; -optionsMultistartPSwarm.localOptimizerOptions.MaxObj = 25000; -optionsMultistartPSwarm.localOptimizerOptions.MaxIter = 1000; -optionsMultistartPSwarm.localOptimizerOptions.Size = 100; -optionsMultistartPSwarm.localOptimizerOptions.Social = 0.5; -optionsMultistartPSwarm.localOptimizerOptions.Cognitial = 0.9; -optionsMultistartPSwarm.localOptimizerOptions.IPrint = -1; -optionsMultistartPSwarm.n_starts = 20; - -parameters = getMultiStarts(parameters, objectiveFunction, optionsMultistartPSwarm); -% This is an alternativ section which uses Multi-start local optimization -% -% % Optimization -% parameters = getMultiStarts(parameters, objectiveFunction, optionsMultistart); +% This section uses multi-start local optimization +parameters = getMultiStarts(parameters, objectiveFunction, optionsMultistart); %% Collection of results, check for bimodality -% Check if a second mode was found +% Check if a second optimum was found, which is good enough for iMode = 2 : optionsMultistart.n_starts if (parameters.MS.logPost(iMode) > 39.5) if (abs(parameters.MS.par(3,iMode) - parameters.MS.par(3,1)) > 0.1) @@ -222,6 +225,7 @@ % Computation for the second mode optionsMultistart.MAP_index = MAP_index2; optionsMultistart.parameter_index = [3, 4]; +optionsMultistart.fh = figure(); parametersAlt = getParameterProfiles(parametersAlt, objectiveFunction, optionsMultistart); %% Single-chain Markov chain Monte-Carlo sampling -- Parameters From 6ed322f8c960a315ba2851b0cf01acd9ec160deb Mon Sep 17 00:00:00 2001 From: Paul Stapor Date: Tue, 28 Feb 2017 19:53:38 +0100 Subject: [PATCH 11/16] Added a quick test file for method testing, debugged profile computation for profile_method = mixed --- @PestoOptions/PestoOptions.m | 19 ++-- .../jakstat_signaling/mainJakstatSignaling.m | 11 ++- getParameterProfiles.m | 89 ++++++++++++++----- private/getParProfilesByIntegration.m | 16 ++-- private/getParProfilesByOptimization.m | 4 +- tests/RosenbrockTest.m | 57 ++++++++++++ 6 files changed, 144 insertions(+), 52 deletions(-) create mode 100644 tests/RosenbrockTest.m diff --git a/@PestoOptions/PestoOptions.m b/@PestoOptions/PestoOptions.m index d624a1d..5f382ac 100644 --- a/@PestoOptions/PestoOptions.m +++ b/@PestoOptions/PestoOptions.m @@ -153,19 +153,18 @@ %% get(Parameter|Property)Profiles options % The following options are for getParameterProfiles only: - % Indices of the parameters for which the profile - % is calculated (default = 1:parameters.number). + + % Indices of the parameters for which the profile is calculated. + % Default: profile_optim_index will be set to 1:parameters.number + % if both indices are left empty parameter_index = []; + profile_optim_index = []; + profile_integ_index = []; - % Specifies the method for profile calculation - % 'optimization' (default), 'integration', 'mixed' + % How should profiles be computed? ('optimization', 'integration', + % 'mixed') Default: 'optimization' profile_method = 'optimization'; - % Indices which specify which profile whould be optimized and - % which should be integrated, if profile_method is set to 'mixed' - % (0: optimization, 1: integration) - parameter_method_index = []; - % Indices of the properties for which the profile % is to be calculated (default = 1:properties.number). property_index = []; @@ -210,7 +209,7 @@ 'update', 1.25); % options for Profile integration - solver = struct('type', 'CVODE', ... + solver = struct('type', 'ode113', ... 'algorithm', 'Adams', ... 'nonlinSolver', 'Newton', ... 'linSolver', 'Dense', ... diff --git a/examples/jakstat_signaling/mainJakstatSignaling.m b/examples/jakstat_signaling/mainJakstatSignaling.m index b029ed0..6fad273 100644 --- a/examples/jakstat_signaling/mainJakstatSignaling.m +++ b/examples/jakstat_signaling/mainJakstatSignaling.m @@ -3,6 +3,9 @@ % Demonstrates the use of: % * getMultiStarts() % +% Demostrates furhtermore +% * how to implement a user-supplied guess for intial parameters +% % This example provides a model for the JakStat signaling pathway with an % time resolved input of the drug EPO. The model has been taken from the % papers "Identification of nucleocytoplasmic cycling as a remote sensor in @@ -84,18 +87,14 @@ optionsMultistart = PestoOptions(); optionsMultistart.n_starts = 10; optionsMultistart.trace = true; +optionsMultistart.obj_type = 'log-posterior'; optionsMultistart.mode = 'visual'; optionsMultistart.fmincon = optimset(... 'Algorithm','interior-point',... 'GradObj', 'on',... - 'Display', 'iter-detailed', ... + 'Display', 'iter', ... 'MaxIter', 800,... - 'TolCon', 0,... 'TolFun', 1e-10,... - 'TolX', 1e-12,... - 'MaxProjCGIter', 100,... - 'TolPCG', 0.001,... - 'PrecondBandWidth', Inf,... 'MaxFunEvals', 1000*parameters.number); %% Perform Multistart optimization diff --git a/getParameterProfiles.m b/getParameterProfiles.m index 123e944..50477c0 100644 --- a/getParameterProfiles.m +++ b/getParameterProfiles.m @@ -87,18 +87,59 @@ % Check and assign options options.P.min = parameters.min; options.P.max = parameters.max; -if isempty(options.parameter_index) - options.parameter_index = 1:parameters.number; -end +options.profileReoptimizationOptions.MaxFunEvals = 200 * parameters.number; if (isempty(options.MAP_index)) options.MAP_index = 1; end -options.profileReoptimizationOptions.algorithm = 'interior-point'; -options.profileReoptimizationOptions.MaxIter = 500; -options.profileReoptimizationOptions.GradConstr = 'on'; -options.profileReoptimizationOptions.TolCon = 1e-6; -options.profileReoptimizationOptions.MaxFunEvals = 200 * parameters.number; +% Process, which profiles should be computed in which manner +if isempty(options.parameter_index) + switch options.profile_method + case 'optimization' + options.profile_optim_index = 1:parameters.number; + if ~isempty(options.profile_integ_index) + options.profile_optim_index(options.profile_integ_index) = []; + end + + case 'integration' + options.profile_integ_index = 1:parameters.number; + if ~isempty(options.profile_optim_index) + options.profile_integ_index(options.profile_optim_index) = []; + end + + case 'mixed' + if (isempty(options.profile_optim_index) && isempty(options.profile_integ_index)) + warning('You specified profile computation method to be "mixed", but did not specify the precise method. Doing optimization for all profiles now!'); + options.profile_method = 'optimization'; + options.profile_optim_index = 1:parameters.number; + end + if length(unique([options.profile_optim_index options.profile_integ_index])) < length([options.profile_optim_index options.profile_integ_index]) + error('Some profiles seem to be computed twice. Please redefine consistent options!'); + end + end + options.parameter_index = sort(unique([options.profile_optim_index options.profile_integ_index])); +else + if (length(unique([options.profile_optim_index, options.profile_integ_index])) < length(unique([options.parameter_index, options.profile_optim_index, options.profile_integ_index]))) + error('Inconsistent settings for indices in profile calculation.'); + end + + switch options.profile_method + case 'optimization' + options.profile_optim_index = options.parameter_index; + if ~isempty(options.profile_integ_index) + options.profile_optim_index(options.profile_integ_index) = []; + end + + case 'integration' + options.profile_integ_index = options.parameter_index; + if ~isempty(options.profile_optim_index) + options.profile_integ_index(options.profile_optim_index) = []; + end + + case 'mixed' + options.parameter_index = sort(unique([options.profile_optim_index options.profile_integ_index])); + end +end %% Initialization and figure generation fh = []; @@ -113,7 +154,7 @@ fprintf(' \nProfile likelihood caculation:\n===============================\n'); case 'silent' % no output % Force fmincon to be silent. - options.profileReoptimizationOptions = optimset(options.profileReoptimizationOptions,'display','off'); + options.profileReoptimizationOptions.display = 'off'; end %% Initialization of parameter struct @@ -139,31 +180,31 @@ [parameters, fh] = getParProfilesByIntegration(parameters, objective_function, options, fh); case 'mixed' - % Checking if the method index was set - if length(options.parameter_method_index) ~= length(options.parameter_index) - warning('The vector of indices for the profile calculation method was not properly set. Doing optimization for all profiles.'); - options.options.parameter_method_index = ones(size(parameter_index)); - end - if strcmp(options.comp_type,'sequential') for j = options.parameter_index currentOptions = options.copy(); - currentOptions.parameter_index = j; - if (currentOptions.parameter_method_index(j) == 0) - [parameters, fh] = getParProfilesByOptimization(parameters, objective_function, currentOptions, fh); - elseif (currentOptions.parameter_method_index(j) == 1) + if sum(j == options.profile_integ_index) == 1 + currentOptions.profile_integ_index = j; [parameters, fh] = getParProfilesByIntegration(parameters, objective_function, currentOptions, fh); + elseif sum(j == options.profile_integ_index) == 0 + currentOptions.profile_optim_index = j; + [parameters, fh] = getParProfilesByOptimization(parameters, objective_function, currentOptions, fh); + else + error('Some really strange error for the profile calculation indices occured'); end end elseif strcmp(options.comp_type,'parallel') parfor j = options.parameter_index currentOptions = options.copy(); - currentOptions.parameter_index = j; - if (currentOptions.parameter_method_index(j) == 0) - getParProfilesByOptimization(parameters, objective_function, currentOptions); - elseif (currentOptions.parameter_method_index(j) == 1) - getParProfilesByIntegration(parameters, objective_function, currentOptions); + if sum(j == options.profile_integ_index) == 1 + currentOptions.profile_integ_index = j; + getParProfilesByIntegration(parameters, objective_function, currentOptions, fh); + elseif sum(j == options.profile_integ_index) == 0 + currentOptions.profile_optim_index = j; + getParProfilesByOptimization(parameters, objective_function, currentOptions, fh); + else + error('Some really strange error for the profile calculation indices occured'); end end diff --git a/private/getParProfilesByIntegration.m b/private/getParProfilesByIntegration.m index 8633ad2..323ffea 100644 --- a/private/getParProfilesByIntegration.m +++ b/private/getParProfilesByIntegration.m @@ -91,15 +91,13 @@ % Profile calculation if strcmp(options.comp_type, 'sequential') - for j = options.parameter_index - tic; + for j = options.profile_integ_index parameters = integrateProfileForParameterI(parameters, objectiveFunction, j, options, fh); - disp(toc); end elseif strcmp(options.comp_type, 'parallel') - parfor j = options.parameter_index + parfor j = options.profile_integ_index integrateProfileForParameterI(parameters, objectiveFunction, j, options, fh); end @@ -358,12 +356,10 @@ % Output if ~strcmp(options.comp_type,'parallel') - if strcmp(options.profile_method, 'integration') - switch options.mode - case 'visual', fh = plotParameterProfiles(parameters, '1D', fh, options.parameter_index, options.plot_options); - case 'text' % no output - case 'silent' % no output - end + switch options.mode + case 'visual', fh = plotParameterProfiles(parameters, '1D', fh, options.parameter_index, options.plot_options); + case 'text' % no output + case 'silent' % no output end end end diff --git a/private/getParProfilesByOptimization.m b/private/getParProfilesByOptimization.m index a4accb3..67ff6a1 100644 --- a/private/getParProfilesByOptimization.m +++ b/private/getParProfilesByOptimization.m @@ -82,12 +82,12 @@ %% Profile calculation if strcmp(options.comp_type,'sequential') - for i = options.parameter_index + for i = options.profile_optim_index parameters = optimizeProfileForParameterI(parameters, objective_function, i, options, fh); end elseif strcmp(options.comp_type,'parallel') - parfor i = options.parameter_index + parfor i = options.profile_optim_index optimizeProfileForParameterI(parameters, objective_function, i, options, fh); end diff --git a/tests/RosenbrockTest.m b/tests/RosenbrockTest.m new file mode 100644 index 0000000..4c228b0 --- /dev/null +++ b/tests/RosenbrockTest.m @@ -0,0 +1,57 @@ +function RosenbrockTest() + % This is a test with a Rosenbrock function for Pesto + + % Cleaning up + clear all; + close all; + clc; + + % Define objective function and true parameter value + objectiveFunction = @(x) rosenbrock(x, 1, 100); + theta_true = [1; 1]; + + % General Options + options = PestoOptions(); + options.obj_type = 'negative log-posterior'; + options.comp_type = 'sequential'; + + % OPtimization options + options.localOptimizer = 'fmincon'; + options.n_starts = 20; + + % Profile options + options.profile_method = 'mixed'; + options.profile_optim_index = 1; + options.profile_integ_index = 2; + + % Sampling Options + options.MCMC.sampling_scheme = 'single-chain'; + options.SC.proposal_scheme = 'AM'; + options.MCMC.nsimu_warmup = 1e3; + options.MCMC.nsimu_run = 1e5; + + % Plotting Options + options.mode = 'visual'; + options.plot_options.add_points.par = theta_true; + options.plot_options.add_points.logPost = objectiveFunction(theta_true); + + % Parameter setting + parameters.min = [-10, -10]; + parameters.max = [10, 10]; + parameters.number = 2; + parameters.name = {'x', 'y'}; + + % Call the routies + parameters = getMultiStarts(parameters, objectiveFunction, options); + parameters = getParameterProfiles(parameters, objectiveFunction, options); + parameters = getParameterSamples(parameters, objectiveFunction, options); + getParameterConfidenceIntervals(parameters, [0.8, 0.9,0.95,0.99]); +end + +function [y, dy, ddy] = rosenbrock(x, a, b) + y = (a - x(1))^2 + b * (x(2) - x(1)^2)^2; + dy = [2 * (x(1) - a) - 4 * b * x(1) * (x(2) - x(1)^2); ... + 2 * b * (x(2) - x(1)^2)]; + ddy = [2*x(1) - 4*b*x(2) + 12*b*x(1)^2, -4*b*x(1); ... + -4*b*x(1), 2*b]; +end \ No newline at end of file From 8d217d290bc6cf356d0c7bb8d66dfb9fa0da1aab Mon Sep 17 00:00:00 2001 From: Paul Stapor Date: Wed, 1 Mar 2017 10:18:49 +0100 Subject: [PATCH 12/16] JAK/STAT example works now agian --- examples/jakstat_signaling/mainJakstatSignaling.m | 14 ++++++++------ getMultiStarts.m | 12 ++++++++++-- 2 files changed, 18 insertions(+), 8 deletions(-) diff --git a/examples/jakstat_signaling/mainJakstatSignaling.m b/examples/jakstat_signaling/mainJakstatSignaling.m index 6fad273..02e27ab 100644 --- a/examples/jakstat_signaling/mainJakstatSignaling.m +++ b/examples/jakstat_signaling/mainJakstatSignaling.m @@ -84,12 +84,14 @@ objectiveFunction = @(theta) logLikelihoodJakstat(theta, amiData); % PestoOptions -optionsMultistart = PestoOptions(); -optionsMultistart.n_starts = 10; -optionsMultistart.trace = true; -optionsMultistart.obj_type = 'log-posterior'; -optionsMultistart.mode = 'visual'; -optionsMultistart.fmincon = optimset(... +optionsMultistart = PestoOptions(); +optionsMultistart.n_starts = 10; +optionsMultistart.trace = true; +optionsMultistart.proposal = 'user-supplied'; +optionsMultistart.obj_type = 'log-posterior'; +optionsMultistart.mode = 'visual'; +optionsMultistart.localOptimizer = 'fmincon'; +optionsMultistart.localOptimizerOptions = optimset(... 'Algorithm','interior-point',... 'GradObj', 'on',... 'Display', 'iter', ... diff --git a/getMultiStarts.m b/getMultiStarts.m index 6bbacfb..6418e09 100644 --- a/getMultiStarts.m +++ b/getMultiStarts.m @@ -145,8 +145,16 @@ rand(parameters.number,options.n_starts - size(parameters.guess,2))))]; case 'user-supplied' % Sampling from user-supplied function - par0 = [parameters.guess,... - parameters.init_fun(parameters.guess,parameters.min,parameters.max,options.n_starts - size(parameters.guess,2))]; + if (~isfield(parameters, 'init_fun') || isempty(parameters.init_fun)) + if size(parameters.guess,2) < options.n_starts + error('You did not define an initial function and do not provide enough starting points in parameters.guess. Aborting.'); + else + par0 = [parameters.guess(:,1:options.n_starts)]; + end + else + par0 = [parameters.guess,... + parameters.init_fun(parameters.guess,parameters.min,parameters.max,options.n_starts - size(parameters.guess,2))]; + end end parameters.MS.n_starts = options.n_starts; parameters.MS.par0 = par0(:,options.start_index); From c0e27871c6c6639caa2ccbc10190da5b494923f9 Mon Sep 17 00:00:00 2001 From: Paul Stapor Date: Thu, 2 Mar 2017 00:23:18 +0100 Subject: [PATCH 13/16] debugging of examples --- examples/Pom1p_gradient_formation/mainPom1.m | 2 +- examples/erbb_signaling/mainErbBSignaling.m | 3 +-- getMultiStarts.m | 21 ++++++++++++-------- private/getParProfilesByIntegration.m | 10 +++++----- tests/RosenbrockTest.m | 17 ++++++++++------ 5 files changed, 31 insertions(+), 22 deletions(-) diff --git a/examples/Pom1p_gradient_formation/mainPom1.m b/examples/Pom1p_gradient_formation/mainPom1.m index 9247c58..07a8a41 100644 --- a/examples/Pom1p_gradient_formation/mainPom1.m +++ b/examples/Pom1p_gradient_formation/mainPom1.m @@ -79,7 +79,7 @@ options.optionsMultistart = PestoOptions(); options.optionsMultistart.obj_type = 'log-posterior'; options.optionsMultistart.comp_type = 'sequential'; -options.optionsMultistart.fmincon = optimset(options.optionsMultistart.fmincon,... +options.optionsMultistart.localOptimizerOptions = optimset(options.optionsMultistart.fmincon,... 'Algorithm','interior-point',... 'Display','off',... 'GradObj','on',... diff --git a/examples/erbb_signaling/mainErbBSignaling.m b/examples/erbb_signaling/mainErbBSignaling.m index fb62bd2..e9ce19b 100644 --- a/examples/erbb_signaling/mainErbBSignaling.m +++ b/examples/erbb_signaling/mainErbBSignaling.m @@ -68,8 +68,7 @@ optionsMultistart.comp_type = 'sequential'; optionsMultistart.mode = 'text'; optionsMultistart.rng = 0; -optionsMultistart.fmincon = optimset(optionsMultistart.fmincon,... - 'Algorithm','interior-point',... +optionsMultistart.localOptimizerOptions = optimset('Algorithm','interior-point',... 'GradObj', 'on',... 'Display', 'iter', ... 'MaxIter', 1000,... diff --git a/getMultiStarts.m b/getMultiStarts.m index 6418e09..6a8ea32 100644 --- a/getMultiStarts.m +++ b/getMultiStarts.m @@ -225,20 +225,25 @@ clear(fun.function(s_start(1):s_end(2))); end + % Define the negative log-posterior funtion + % (fmincon needs the neagtive log posterior for optimization) + negLogPost = @(theta) @(theta) objectiveWrap(theta,objective_function,options.obj_type,options.objOutNumber); + negLogPostWErrorCount = @(theta) objectiveWrapWErrorCount(theta,objective_function,options.obj_type,options.objOutNumber); + % Reset error count error_count = 0; % Test evaluation of objective function at starting point if (strcmp(options.localOptimizer, 'fmincon')) if (strcmp(options.localOptimizerOptions.Hessian, 'on')) - [J_0,~,~] = objectiveWrapWErrorCount(parameters.MS.par0(:,i),objective_function,options.obj_type,options.objOutNumber); + [J_0,~,~] = negLogPostWErrorCount(parameters.MS.par0(:,i)); % objectiveWrapWErrorCount(parameters.MS.par0(:,i),objective_function,options.obj_type,options.objOutNumber); elseif (strcmp(options.localOptimizerOptions.GradObj, 'on')) - [J_0,~] = objectiveWrapWErrorCount(parameters.MS.par0(:,i),objective_function,options.obj_type,options.objOutNumber); + [J_0,~] = negLogPostWErrorCount(parameters.MS.par0(:,i)); % objectiveWrapWErrorCount(parameters.MS.par0(:,i),objective_function,options.obj_type,options.objOutNumber); else - J_0 = objectiveWrapWErrorCount(parameters.MS.par0(:,i),objective_function,options.obj_type,options.objOutNumber); + J_0 = negLogPostWErrorCount(parameters.MS.par0(:,i)); % objectiveWrapWErrorCount(parameters.MS.par0(:,i),objective_function,options.obj_type,options.objOutNumber); end else - J_0 = objectiveWrapWErrorCount(parameters.MS.par0(:,i),objective_function,options.obj_type,options.objOutNumber); + J_0 = negLogPostWErrorCount(parameters.MS.par0(:,i)); % objectiveWrapWErrorCount(parameters.MS.par0(:,i),objective_function,options.obj_type,options.objOutNumber); end parameters.MS.logPost0(i) = -J_0; @@ -250,7 +255,7 @@ %% fmincon as local optimizer % Optimization using fmincon [theta,J_opt,parameters.MS.exitflag(i),results_fmincon,~,gradient_opt,hessian_opt] = ... - fmincon(@(theta) objectiveWrapWErrorCount(theta,objective_function,options.obj_type,options.objOutNumber),... % negative log-likelihood function + fmincon(negLogPostWErrorCount,... % negative log-likelihood function parameters.MS.par0(:,i),... % initial parameter parameters.constraints.A ,parameters.constraints.b ,... % linear inequality constraints parameters.constraints.Aeq,parameters.constraints.beq,... % linear equality constraints @@ -258,18 +263,18 @@ parameters.max,... % upper bound [],options.localOptimizerOptions); % options - % Assignment of reseults + % Assignment of results parameters.MS.J(1, i) = -J_0; parameters.MS.logPost(i) = -J_opt; parameters.MS.par(:,i) = theta; parameters.MS.gradient(:,i) = gradient_opt; if isempty(hessian_opt) if strcmp(options.localOptimizerOptions.Hessian,'on') - [~,~,hessian_opt] = objectiveWrap(theta,objective_function,options.obj_type,options.objOutNumber); + [~,~,hessian_opt] = negLogPost(theta); % objectiveWrap(theta,objective_function,options.obj_type,options.objOutNumber); end elseif max(hessian_opt(:)) == 0 if strcmp(options.localOptimizerOptions.Hessian,'on') - [~,~,hessian_opt] = objectiveWrap(theta,objective_function,options.obj_type,options.objOutNumber); + [~,~,hessian_opt] = negLogPost(theta); % objectiveWrap(theta,objective_function,options.obj_type,options.objOutNumber); end end parameters.MS.n_objfun(i) = results_fmincon.funcCount; diff --git a/private/getParProfilesByIntegration.m b/private/getParProfilesByIntegration.m index 323ffea..e00ab87 100644 --- a/private/getParProfilesByIntegration.m +++ b/private/getParProfilesByIntegration.m @@ -675,8 +675,8 @@ try % Reduce linear system by implicit funtion theorem A1 = [HL(1 : ind-1, :); HL(ind+1 : npar, :); s*GG']; - b1 = [-GL(1 : ind-1) * s * options.solver.gamma; ... - -GL(ind+1 : npar) * s * options.solver.gamma; ... + b1 = [-GL(1 : ind-1) * options.solver.gamma; ... + -GL(ind+1 : npar) * options.solver.gamma; ... 1]; A2 = [A1(1:end-1, 1:ind-1), A1(1:end-1, ind+1:end)]; b2 = b1(1:end-1) - s * A1(1:end-1, ind); @@ -719,8 +719,8 @@ try % Reduce linear system by implicit funtion theorem A1 = [HL(1 : ind-1, :); HL(ind+1 : npar, :); s*GG']; - b1 = [-GL(1 : ind-1) * s * options.solver.gamma; ... - -GL(ind+1 : npar) * s * options.solver.gamma; ... + b1 = [-GL(1 : ind-1) * options.solver.gamma; ... + -GL(ind+1 : npar) * options.solver.gamma; ... 1]; A2 = [A1(1:end-1, 1:ind-1), A1(1:end-1, ind+1:end)]; b2 = b1(1:end-1) - s * A1(1:end-1, ind); @@ -759,7 +759,7 @@ elseif strcmp(flag, 'reinit') % Replace old by new values for next call lastTheta = theta; - [~,ind] = max(hess); + [~,ind] = max(max(hess)); ind = ind(1); lastHess = [hess(1:ind-1,1:ind-1), lastHess(1:ind-1,ind), hess(1:ind-1,ind+1:end); ... lastHess(ind,:); hess(ind+1:end,1:ind-1), lastHess(ind+1:end,ind), hess(ind+1:end,ind+1:end)]; diff --git a/tests/RosenbrockTest.m b/tests/RosenbrockTest.m index 4c228b0..4cff72e 100644 --- a/tests/RosenbrockTest.m +++ b/tests/RosenbrockTest.m @@ -17,12 +17,17 @@ function RosenbrockTest() % OPtimization options options.localOptimizer = 'fmincon'; - options.n_starts = 20; + options.n_starts = 1; % Profile options - options.profile_method = 'mixed'; - options.profile_optim_index = 1; - options.profile_integ_index = 2; + options.profile_method = 'integration'; +% options.profile_optim_index = 1; +% options.profile_integ_index = 2; + options.solver.hessian = 'user-supplied'; + options.solver.gamma = 100; + options.solver.type = 'ode113'; + options.solver.RelTol = 1e-8; + options.solver.AbsTol = 1e-10; % Sampling Options options.MCMC.sampling_scheme = 'single-chain'; @@ -44,8 +49,8 @@ function RosenbrockTest() % Call the routies parameters = getMultiStarts(parameters, objectiveFunction, options); parameters = getParameterProfiles(parameters, objectiveFunction, options); - parameters = getParameterSamples(parameters, objectiveFunction, options); - getParameterConfidenceIntervals(parameters, [0.8, 0.9,0.95,0.99]); +% parameters = getParameterSamples(parameters, objectiveFunction, options); +% getParameterConfidenceIntervals(parameters, [0.8, 0.9,0.95,0.99]); end function [y, dy, ddy] = rosenbrock(x, a, b) From fff6750ebbaeb99286c84832dfa10ac7c5094901 Mon Sep 17 00:00:00 2001 From: "daniel.weindl" Date: Fri, 3 Mar 2017 16:17:10 +0100 Subject: [PATCH 14/16] Only include relevant parts in the documentation - who wants to read 165 pages? Added PDF documentation for website. --- doc/MatlabDocMaker.m | 2 ++ doc/PESTO-doc.pdf | Bin 0 -> 329282 bytes doc/config/Doxyfile.template | 9 ++++++--- 3 files changed, 8 insertions(+), 3 deletions(-) create mode 100644 doc/PESTO-doc.pdf diff --git a/doc/MatlabDocMaker.m b/doc/MatlabDocMaker.m index 8bb5eaf..566d9d8 100644 --- a/doc/MatlabDocMaker.m +++ b/doc/MatlabDocMaker.m @@ -466,6 +466,8 @@ function create(varargin) if s ~= 0 warn = [warn sprintf('LaTeX compiler output:\n') latexmsg]; latexerr = true; + else + copyfile(fullfile(latexdir,'refman.pdf'), fullfile(fileparts(mfilename('fullpath')),'PESTO-doc.pdf')) end else fprintf('cannot compile LaTeX output: no refman.tex found...'); diff --git a/doc/PESTO-doc.pdf b/doc/PESTO-doc.pdf new file mode 100644 index 0000000000000000000000000000000000000000..13c9140a0cfdeac364ae50d7356f092de5f1ee8a GIT binary patch literal 329282 zcmb5W1CTCFw+{MpVVF z+!?X6EYIo8(G2d@W3#rcsiI8GRPZQDZALhFvt-y zF*3q1h*>&0yAX0PbHFgjnA(}USP(L?u>2m>S!gn8NV! 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NO # If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will # be included in the documentation. @@ -473,7 +473,7 @@ EXTRACT_PACKAGE = NO # included in the documentation. # The default value is: NO. -EXTRACT_STATIC = YES +EXTRACT_STATIC = NO # If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined # locally in source files will be included in the documentation. If set to NO, @@ -489,7 +489,7 @@ EXTRACT_LOCAL_CLASSES = YES # included. # The default value is: NO. -EXTRACT_LOCAL_METHODS = YES +EXTRACT_LOCAL_METHODS = NO # If this flag is set to YES, the members of anonymous namespaces will be # extracted and appear in the documentation as a namespace called @@ -862,6 +862,9 @@ RECURSIVE = YES EXCLUDE = doc \ DRAM \ + private \ + examples \ + tests \ README.md # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or From f05004174ae6c357e2ada5501377f59844812db0 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 3 Mar 2017 16:32:14 +0100 Subject: [PATCH 15/16] Reference documentation in Readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 7c7d1df..256a079 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ PESTO features include: * Visualization routines * and more -These function are demonstrated in several systems biology examples included in the `examples/` directory. +These function are demonstrated in several systems biology examples included in the [`examples/`](examples/) directory. ## Installation @@ -26,4 +26,4 @@ See [ ```LICENSE```](LICENSE) file in the PESTO source directory. ## Documentation -Further PESTO documentation is available in the [ ```/doc```](doc) folder. \ No newline at end of file +PESTO usage is demonstrated in various [examples](examples/). Further documentation is available in [```doc/PESTO-doc.pdf```](doc/PESTO-doc.pdf). From 30b00532441bb7511902356547e785974fb4f9ae Mon Sep 17 00:00:00 2001 From: Paul Stapor Date: Fri, 3 Mar 2017 16:44:34 +0100 Subject: [PATCH 16/16] Fixing typos --- .../mainConversionReaction.m | 2 +- .../mainEnzymaticCatalysis.m | 3 +- examples/mRNA_transfection/mainTransfection.m | 7 ++- getMultiStarts.m | 11 ++-- private/getParProfilesByIntegration.m | 55 +++++++++++++------ 5 files changed, 50 insertions(+), 28 deletions(-) diff --git a/examples/conversion_reaction/mainConversionReaction.m b/examples/conversion_reaction/mainConversionReaction.m index 0dfb992..faaeed3 100644 --- a/examples/conversion_reaction/mainConversionReaction.m +++ b/examples/conversion_reaction/mainConversionReaction.m @@ -26,7 +26,7 @@ % % Profile likelihood calculation is done using getParameterProfiles(). % -% Sngle-chain Monte-Carlo sampling is performed by getParameterSamples() +% Single-chain Monte-Carlo sampling is performed by getParameterSamples() % and plotted. diff --git a/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m b/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m index c084303..4b991fa 100644 --- a/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m +++ b/examples/enzymatic_catalysis/mainEnzymaticCatalysis.m @@ -27,7 +27,7 @@ % optimization based on these measurements, demonstrating the use of % getMultiStarts(). % -% The Profile likelihoods are calculated by integrating the an ODE along +% The Profile likelihoods are calculated by integrating an ODE following % the profile path using getParameterProfiles with the option % optionsPesto.profile_method = 'integration'. @@ -155,7 +155,6 @@ optionsPesto.profile_method = 'integration'; optionsPesto.solver.gamma = 10; -optionsPesto.solver.type = 'ode113'; optionsPesto.solver.hessian = 'user-supplied'; parameters = getParameterProfiles(parameters, objectiveFunction, optionsPesto); diff --git a/examples/mRNA_transfection/mainTransfection.m b/examples/mRNA_transfection/mainTransfection.m index d9fd55c..bc7a79b 100644 --- a/examples/mRNA_transfection/mainTransfection.m +++ b/examples/mRNA_transfection/mainTransfection.m @@ -27,7 +27,10 @@ % these measurements, demonstrating the use of getMultiStarts(). The model % fit is then visualized. % -% Profile likelihood calculation is done using getParameterProfiles(). +% Profile likelihood calculation is done by optimization and integration +% using getParameterProfiles() with the option +% optionsMultistart.profile_method = 'mixed'to have comparison of both +% methods. % % Multi-chain Monte-Carlo sampling is performed by getParameterSamples() % and plotted using plotParameterUncertainty(). @@ -220,11 +223,11 @@ % by using repeated reoptimization. The information about the profiles is % then written to the parameters struct. +% Profiles are computed using optimization by getParameterProfiles() parameters = getParameterProfiles(parameters, objectiveFunction, optionsMultistart); % Computation for the second mode optionsMultistart.MAP_index = MAP_index2; -optionsMultistart.parameter_index = [3, 4]; optionsMultistart.fh = figure(); parametersAlt = getParameterProfiles(parametersAlt, objectiveFunction, optionsMultistart); diff --git a/getMultiStarts.m b/getMultiStarts.m index 6a8ea32..f816c04 100644 --- a/getMultiStarts.m +++ b/getMultiStarts.m @@ -191,6 +191,12 @@ parameters.MS.time_trace = nan(maxOptimSteps+1,length(options.start_index)); end +% Define the negative log-posterior funtion +% (fmincon needs the neagtive log posterior for optimization) +negLogPost = @(theta) @(theta) objectiveWrap(theta,objective_function,options.obj_type,options.objOutNumber); +negLogPostWErrorCount = @(theta) objectiveWrapWErrorCount(theta,objective_function,options.obj_type,options.objOutNumber); + + waitbarFields1 = {'logPost', 'logPost0', 'n_objfun', 'n_iter', 't_cpu', 'exitflag'}; waitbarFields2 = {'par', 'par0', 'gradient', 'fval_trace', 'time_trace'}; waitbarFields3 = {'hessian', 'par_trace'}; @@ -225,11 +231,6 @@ clear(fun.function(s_start(1):s_end(2))); end - % Define the negative log-posterior funtion - % (fmincon needs the neagtive log posterior for optimization) - negLogPost = @(theta) @(theta) objectiveWrap(theta,objective_function,options.obj_type,options.objOutNumber); - negLogPostWErrorCount = @(theta) objectiveWrapWErrorCount(theta,objective_function,options.obj_type,options.objOutNumber); - % Reset error count error_count = 0; diff --git a/private/getParProfilesByIntegration.m b/private/getParProfilesByIntegration.m index e00ab87..80fd94d 100644 --- a/private/getParProfilesByIntegration.m +++ b/private/getParProfilesByIntegration.m @@ -78,8 +78,6 @@ fh = []; end - % !!! IMPORTANT: Implement a check for the negative log-posterior here... - %% Preperation of folder if options.save [~,~,~] = mkdir(options.foldername); @@ -88,6 +86,10 @@ %% Profile calculation -- SEQUENTIAL + % Assign the wrapped objective function + negLogPost = @(theta) @(theta) objectiveWrap(theta,objectiveFunction,options.obj_type,options.objOutNumber); + logPost = @(theta) posLogPost(theta, negLogPost); + % Profile calculation if strcmp(options.comp_type, 'sequential') @@ -114,7 +116,7 @@ -function parameters = integrateProfileForParameterI(parameters, objectiveFunction, j, options, fh) +function parameters = integrateProfileForParameterI(parameters, objective_function, j, options, fh) % Define global variables for communication across ODE solver @@ -178,7 +180,7 @@ llhHistory = parameters.MS.logPost(options.MAP_index); reachedEnd = 0; OutputFunction = @(t, y, flag) checkOptimality(t, y, flag, s, j, ... - parameters.MS.logPost(options.MAP_index), objectiveFunction, borders, options); + parameters.MS.logPost(options.MAP_index), objective_function, borders, options); if ~strcmp(options.solver.hessian, 'user-supplied') approximateHessian(parameters.MS.par(:,options.MAP_index), -parameters.MS.gradient(:,options.MAP_index), parameters.MS.hessian(:,:,options.MAP_index), [], 'init'); @@ -205,20 +207,20 @@ switch options.solver.type case {'ode45', 'ode15s', 'ode113'} odeMatlabOptions.OutputFcn = OutputFunction; - odeMatlabOptions.Events = @(t,y) getEndProfile(t, s, y, j, borders, objectiveFunction, options, parameters.MS.logPost(1)); + odeMatlabOptions.Events = @(t,y) getEndProfile(t, s, y, j, borders, objective_function, options, parameters.MS.logPost(1)); if (strcmp(options.solver.type, 'ode15s')) - [t,y] = ode15s(@(t,y) getRhsRed(t, s, y, j, borders, objectiveFunction, parameterFunction, options),[s*theta(j), s*T], theta, odeMatlabOptions); + [t,y] = ode15s(@(t,y) getRhsRed(t, s, y, j, borders, objective_function, parameterFunction, options),[s*theta(j), s*T], theta, odeMatlabOptions); elseif (strcmp(options.solver.type, 'ode45')) - [t,y] = ode45(@(t,y) getRhsRed(t, s, y, j, borders, objectiveFunction, parameterFunction, options),[s*theta(j), s*T], theta, odeMatlabOptions); + [t,y] = ode45(@(t,y) getRhsRed(t, s, y, j, borders, objective_function, parameterFunction, options),[s*theta(j), s*T], theta, odeMatlabOptions); else - [t,y] = ode113(@(t,y) getRhsRed(t, s, y, j, borders, objectiveFunction, parameterFunction, options),[s*theta(j), s*T], theta, odeMatlabOptions); + [t,y] = ode113(@(t,y) getRhsRed(t, s, y, j, borders, objective_function, parameterFunction, options),[s*theta(j), s*T], theta, odeMatlabOptions); end % If yCorrection is set to inf, then the ODE is too stiff, % some steps of optimization based calculation have to be done if (yCorrection == inf) - addY = doOptimizationSteps(parameters, y, objectiveFunction, borders, j, s, options); + addY = doOptimizationSteps(parameters, y, objective_function, borders, j, s, options); y = [y; addY]; else % If reoptimization has to be done, correct the values in y by the optimized ones @@ -238,8 +240,8 @@ end case 'CVODE' - cvodeOptions.RootsFn = @(t,y) getEndProfile(t, s, y, j, borders, objectiveFunction, options, parameters.MS.logPost(1)); - CVodeInit(@(t,y,data) getRhsRed(t, s, y, j, borders, objectiveFunction, parameterFunction, options), options.solver.algorithm, options.solver.nonlinSolver, s*t0, theta, cvodeOptions); + cvodeOptions.RootsFn = @(t,y) getEndProfile(t, s, y, j, borders, objective_function, options, parameters.MS.logPost(1)); + CVodeInit(@(t,y,data) getRhsRed(t, s, y, j, borders, objective_function, parameterFunction, options), options.solver.algorithm, options.solver.nonlinSolver, s*t0, theta, cvodeOptions); killCounter = 0; reachedEndCVODE = 0; @@ -258,7 +260,7 @@ 'LinearSolver', options.solver.linSolver, ...'StabilityLimDet', true, ... 'MaxNumSteps', options.solver.MaxNumSteps ...'MaxOrder', 12 ); - CVodeInit(@(t,y,data) getRhsRed(t, s, y, j, borders, objectiveFunction, parameterFunction, options), 'BDF', 'Newton', s*t_00, y_00, cvodeKillOptions); + CVodeInit(@(t,y,data) getRhsRed(t, s, y, j, borders, objective_function, parameterFunction, options), 'BDF', 'Newton', s*t_00, y_00, cvodeKillOptions); killCounter = 0; end [~, t_tmp, y_tmp] = CVode(s*T, 'OneStep'); @@ -274,7 +276,7 @@ 'LinearSolver', options.solver.linSolver, ...'StabilityLimDet', true, ... 'MaxNumSteps', options.solver.MaxNumSteps ...'MaxOrder', 12 ); - CVodeInit(@(t,y,data) getRhsRed(t, s, y, j, borders, objectiveFunction, parameterFunction, options), options.solver.algorithm, options.solver.nonlinSolver, s*t_00, y_00, cvodeOptions); + CVodeInit(@(t,y,data) getRhsRed(t, s, y, j, borders, objective_function, parameterFunction, options), options.solver.algorithm, options.solver.nonlinSolver, s*t_00, y_00, cvodeOptions); [~, t_tmp, y_tmp] = CVode(s*(t_00 + 0.025), 'OneStep'); end killCounter = 1; @@ -302,10 +304,10 @@ CVodeFree; case 'ode15sDAE' - daeMatlabOptions.Mass = @(t, y) getMassmatrixDAE(t, s, y, j, objectiveFunction, parameterFunction, options); - daeMatlabOptions.Events = @(t,y) getEndProfile(t, s, y, j, borders, objectiveFunction, options, parameters.MS.logPost(1)); - daeMatlabOptions.Mass = @(t,y) getMassmatrixDAE(t, s, y, j, objectiveFunction, parameterFunction, options); - [~, y] = ode15s(@(t,y) getRhsDAE(t, s, y, 1, j, objectiveFunction, options), [s*t0, s*T], theta, daeMatlabOptions); + daeMatlabOptions.Mass = @(t, y) getMassmatrixDAE(t, s, y, j, objective_function, parameterFunction, options); + daeMatlabOptions.Events = @(t,y) getEndProfile(t, s, y, j, borders, objective_function, options, parameters.MS.logPost(1)); + daeMatlabOptions.Mass = @(t,y) getMassmatrixDAE(t, s, y, j, objective_function, parameterFunction, options); + [~, y] = ode15s(@(t,y) getRhsDAE(t, s, y, 1, j, objective_function, options), [s*t0, s*T], theta, daeMatlabOptions); if (s == 1) theta = y'; @@ -667,7 +669,7 @@ new_Data = []; return; end - + % calculate hessian of parameter function [~, GG, ~] = parameterFunction(y, ind); @@ -905,3 +907,20 @@ end end +function varargout = posLogPost(theta, negLogPost) + switch nargout + case 1 + J = negLogPost(theta); + varargout{1} = -J; + case 2 + [J,G] = negLogPost(theta); + varargout{1} = -J; + varargout{2} = -G; + + case 3 + [J,G,H] = negLogPost(theta); + varargout{1} = -J; + varargout{2} = -G; + varargout{3} = -H; + end +end