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20170224 Add Multi-Objective Global Optimization (MOGO) as new class
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Lazloo
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Feb 24, 2017
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,115 @@ | ||
| classdef MOGO<handle | ||
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| %% Public Members | ||
| properties(GetAccess='public',SetAccess='public') | ||
| KrigingAnalyzeObj = []; | ||
| CovModelChoice = 6; | ||
| SaveInputDataOverIterations = cell(0); | ||
| SaveOutputDataOverIterations = cell(0); | ||
| SaveCovarParametersOverIterations = cell(0); | ||
| InputDataRange = []; | ||
| nInputVar = 0; | ||
| % Number of realizations drawn during conditional simulation | ||
| nRealizations = 5e2; | ||
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| InputData = []; | ||
| OutputData = []; | ||
| end | ||
| properties(GetAccess='public',SetAccess='protected') | ||
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| % Saving optimization results in each iteration | ||
| GlobalUncertainity = []; | ||
| GlobalUncertainityNorm = []; | ||
| HVVec = []; | ||
| end | ||
| %% Private Members | ||
| properties(GetAccess='private',SetAccess='private') | ||
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| end | ||
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| %% Protected Members | ||
| properties(GetAccess='protected',SetAccess='protected') | ||
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| end | ||
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| methods(Abstract) | ||
| % These function have to be user defined in each inherinted class | ||
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| % ---------------------------------------------------------------- | ||
| % Used in "generateData" | ||
| [output] = objFct(obj,varargin) | ||
| end | ||
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| methods | ||
| %% Constructor | ||
| function obj = MOGO() | ||
| obj.KrigingAnalyzeObj = BayesianOptimizationClass(); | ||
| obj.KrigingAnalyzeObj.addKrigingObject(1,'Test Name') | ||
| obj.KrigingAnalyzeObj.KrigingObjects{1}.setNormInput(true) | ||
| obj.KrigingAnalyzeObj.KrigingObjects{1}.setNormOutput(true) | ||
| obj.KrigingAnalyzeObj.KrigingObjects{1}.setShowDetails(true) | ||
| end | ||
| %% Copy Operator for a shallow copy | ||
| % ---------------------------------------------------------------- | ||
| function copy = copyObj(obj) | ||
| % Create a shallow copy of the calling object. | ||
| copy = eval(class(obj)); | ||
| meta = eval(['?',class(obj)]); | ||
| for p = 1: size(meta.Properties,1) | ||
| pname = meta.Properties{p}.Name; | ||
| try | ||
| eval(['copy.',pname,' = obj.',pname,';']); | ||
| catch | ||
| error(['\nCould not copy ',pname,'.\n']); | ||
| % fprintf(['\nCould not copy ',pname,'.\n']); | ||
| end | ||
| end | ||
| end | ||
| %% General Methods | ||
| [] = calcKriging(obj,varargin) | ||
| % ---------------------------------------------------------------- | ||
| function []=saveObject(obj,varargin) | ||
| index = varargin{1}; | ||
| save(strcat('tmpSave_',num2str(index)),'obj') | ||
| end | ||
| % ---------------------------------------------------------------- | ||
| [] = saveTmpResults(obj,iIter) | ||
| % ---------------------------------------------------------------- | ||
| [] = estimateParetoCurve(obj,iKrigingEstimation) | ||
| % ---------------------------------------------------------------- | ||
| [] = setVariableNames(obj,inputNames,OutputNames) | ||
| % ---------------------------------------------------------------- | ||
| [] = calcAndSaveNewSamples(obj,iterationNumber) | ||
| % ---------------------------------------------------------------- | ||
| [inputData,outputData]=collectAllDataUntilGivenIteration(obj,finalIteration) | ||
| % ---------------------------------------------------------------- | ||
| function []=SaveCurrentCovarParameter(obj,iterationNumber) | ||
| obj.SaveCovarParametersOverIterations{iterationNumber} = obj.KrigingAnalyzeObj.KrigingObjects{1}.getCovariogramModelParameters; | ||
| end | ||
| % ---------------------------------------------------------------- | ||
| []=generateNewData(obj,varargin); | ||
| %% Set function | ||
| function []=setInputDataRange(obj,InputDataRange) | ||
| % Check | ||
| if size(InputDataRange,1)== 2 && size(InputDataRange,2)== obj.nInputVar && obj.nInputVar~=2 | ||
| InputDataRange = InputDataRange'; | ||
| end | ||
| obj.InputDataRange = InputDataRange; | ||
| end | ||
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| function []=setInputData(obj,InputData) | ||
| obj.resetData | ||
| obj.InputData = InputData; | ||
| obj.KrigingAnalyzeObj.KrigingObjects{1}.setInputData(obj.InputData) | ||
| obj.SaveInputDataOverIterations{1} = obj.InputData; | ||
| obj.nInputVar = size(InputData,2); | ||
| end | ||
| % ---------------------------------------------------------------- | ||
| function []=setOutputData(obj,OutputData) | ||
| obj.OutputData = OutputData; | ||
| obj.KrigingAnalyzeObj.KrigingObjects{1}.setOutputData(obj.OutputData) | ||
| obj.SaveOutputDataOverIterations{1} = obj.OutputData; | ||
| end | ||
| end | ||
| end | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,23 @@ | ||
| function [newSamplePoint] = calcAndSaveNewSamples(obj,varargin) | ||
| % [] = calcAndSaveNewSamplesMultiObj(obj,iterationNumber,OptimizationAlgorithm,UseOnlyMaximumValue) | ||
| iterationNumber = varargin{1}; | ||
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| if obj.KrigingAnalyzeObj.getnKrigingObjects>1 | ||
| obj.KrigingAnalyzeObj.determineParetoSet(1:obj.KrigingAnalyzeObj.getnKrigingObjects) | ||
| end | ||
| % obj.nParetoSamples = obj.KrigingAnalyzeObj.getnParetoSetExperiments; | ||
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| % Save the covariogram parameters | ||
| obj.SaveCovarParametersOverIterations{iterationNumber-1} = obj.KrigingAnalyzeObj.KrigingObjects{1}.getCovariogramModelParameters; | ||
| newSamplePoint = obj.KrigingAnalyzeObj.calcNewSamplesViaMCMC(1:obj.KrigingAnalyzeObj.getnKrigingObjects,'DRAM'); | ||
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| % Generate New data points (For ome cases input variables are aslo part of the ouput of the obj function) | ||
| obj.SaveInputDataOverIterations{iterationNumber} = newSamplePoint; | ||
| obj.generateNewData(iterationNumber); | ||
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| % Save data points | ||
| obj.InputData = [obj.InputData;obj.SaveInputDataOverIterations{iterationNumber}]; | ||
| obj.OutputData = [obj.OutputData;obj.SaveOutputDataOverIterations{iterationNumber}]; | ||
| % obj.KrigingAnalyzeObj.getNewSamples | ||
| end |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,28 @@ | ||
| function [] = calcKriging(obj,varargin) | ||
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| % Create kriging model for each object | ||
| for iKrigingObject = 1:obj.KrigingAnalyzeObj.getnKrigingObjects | ||
| obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.setUseMatlabRegressionGP(false); | ||
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| obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.setInputData(obj.InputData); | ||
| obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.setOutputData(obj.OutputData(:,iKrigingObject)); | ||
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| obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.setCovariogramModelChoice(obj.CovModelChoice) | ||
| maxValue = 1e2; | ||
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| if obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.getCovariogramModelChoice==3 | ||
| obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.setLBCovariogramModelParameters([ones(1,obj.KrigingAnalyzeObj.KrigingObjects{1}.getnCovariogramParameters)]*1e-10) | ||
| obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.setUBCovariogramModelParameters([maxValue,maxValue,2,2,maxValue,maxValue]) | ||
| else | ||
| obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.setLBCovariogramModelParameters([ones(1,obj.KrigingAnalyzeObj.KrigingObjects{1}.getnCovariogramParameters)]*1e-10) | ||
| obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.setUBCovariogramModelParameters([ones(1,obj.KrigingAnalyzeObj.KrigingObjects{1}.getnCovariogramParameters)]*maxValue) | ||
| end | ||
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| obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.generateRegressionGPModel() | ||
| % obj.KrigingAnalyzeObj.KrigingObjects{iKrigingObject}.calcCovariogramMatrix | ||
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| end | ||
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| obj.setInputDataRange(obj.InputDataRange(1:obj.nInputVar,:)); | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,18 @@ | ||
| function [inputData,outputData]=collectAllDataUntilGivenIteration(obj,finalIteration) | ||
| % First allocation | ||
| nData=0; | ||
| for iIter=1:finalIteration | ||
| nData = nData + size(obj.SaveInputDataOverIterations{iIter},1); | ||
| end | ||
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| % Now collecting | ||
| inputData = zeros(nData,obj.nInputVar); | ||
| outputData = zeros(nData,1); | ||
| iRow = 0; | ||
| for iIter=1:finalIteration | ||
| nDataIter = size(obj.SaveInputDataOverIterations{iIter},1); | ||
| inputData(iRow+1:iRow+nDataIter,:) = obj.SaveInputDataOverIterations{iIter}; | ||
| outputData(iRow+1:iRow+nDataIter,1) = obj.SaveOutputDataOverIterations{iIter}; | ||
| iRow = iRow + nDataIter; | ||
| end | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,18 @@ | ||
| function [] = estimateParetoCurve(obj,iKrigingEstimation) | ||
| nObj = obj.KrigingAnalyzeObj.getnKrigingObjects; | ||
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| obj.KrigingAnalyzeObj.predictParetoCurve(1:nObj,obj.nRealizations,(10^3),30); | ||
| globalUncertainity=obj.KrigingAnalyzeObj.getGlobalParetoUncertainity; | ||
| globalUncertainityNorm=obj.KrigingAnalyzeObj.getGlobalParetoUncertainityNorm; | ||
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| refPoint = (1-obj.KrigingAnalyzeObj.getMinMax(1:nObj))/2; | ||
| transformedOutput = -bsxfun(@times,obj.KrigingAnalyzeObj.getMinMax(1:nObj),obj.OutputData); | ||
| HV=Hypervolume_MEX(transformedOutput,refPoint); | ||
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| obj.GlobalUncertainity(iKrigingEstimation) = globalUncertainity; | ||
| obj.GlobalUncertainityNorm(iKrigingEstimation) = globalUncertainityNorm; | ||
| obj.HVVec(iKrigingEstimation) = HV; | ||
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| end | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,4 @@ | ||
| function [obj2] = externalFunction(obj,varargin) | ||
| obj2 = obj; | ||
| end | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,11 @@ | ||
| function [] = generateNewData(obj,varargin) | ||
| iterationNumber = varargin{1}; | ||
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| outputProto = obj.objFct(obj.SaveInputDataOverIterations{iterationNumber}); | ||
| obj.SaveOutputDataOverIterations{iterationNumber} = outputProto(:,1:end-obj.nInputVar); | ||
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| % Save Input Data | ||
| obj.SaveInputDataOverIterations{iterationNumber} = outputProto(:,end-obj.nInputVar+1:end); | ||
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| end | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,14 @@ | ||
| function [varargout] = sampleFromDistribution(obj,varargin) | ||
| muVec = varargin{1}; | ||
| stdVec = varargin{2}; | ||
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| distA = randn(1e5,1); | ||
| distB = randn(1e5,1); | ||
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| distA = distA*stdVec(1) + muVec(1); | ||
| distB = distB*stdVec(2) + muVec(2); | ||
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| varargout{1} = distA; | ||
| varargout{2} = distB; | ||
| end | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,22 @@ | ||
| function [] = saveTmpResults(obj,iIter) | ||
| %% Backup | ||
| fileNameUnc = strcat('globalUncertainityVec_iIter',num2str(iIter)); | ||
| fileNameIn = strcat('input_iIter',num2str(iIter)); | ||
| fileNameOut = strcat('output_iIter',num2str(iIter)); | ||
| fileNameNormUnc = strcat('globalUncertainityNormVec_iIter',num2str(iIter)); | ||
| fileNameHV = strcat('HVMatrixVec_iIter',num2str(iIter)); | ||
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| % Save | ||
| input = obj.InputData; | ||
| output = obj.OutputData; | ||
| GU = obj.GlobalUncertainity; | ||
| GUN = obj.GlobalUncertainityNorm; | ||
| HVVec = obj.HVVec; | ||
| save(fileNameIn,'input') | ||
| save(fileNameOut,'output') | ||
| save(fileNameUnc,'GU') | ||
| save(fileNameNormUnc,'GUN') | ||
| save(fileNameHV,'HVVec') | ||
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| end | ||
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