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ChangeLog
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= New Features =
== 1.22 release (in-progress) == #release-1.22
=== Library ===
==== Major changes ====
==== New classes ====
==== API changes ====
* Removed deprecated (Non)LinearLeastSquaresCalibration::getCandidate method
* Removed deprecated (Non)GaussianLinearCalibration::getCandidate method
* Removed deprecated DomainIntersection|Union|DisjunctiveUnion(Domain, Domain) ctors
* Removed deprecated EllipticalDistribution::getInverseCorrelation method
* Removed deprecated Basis::getDimension method
* Removed deprecated HSICStat ctors relying on weight matrix
=== Documentation ===
=== Python module ===
=== Miscellaneous ===
== 1.21 release (2023-06-20) == #release-1.21
=== Library ===
==== Major changes ====
* New GEV estimation services: MLE, profiled likelihood, r-maxima, time-varying, return level
==== New classes ====
* SmolyakExperiment (openturns.experimental)
* Physical|StandardSpaceCrossEntropyImportanceSampling, CrossEntropyResult (openturns.experimental)
* LeastSquaresExpansion, IntegrationExpansion (openturns.experimental)
* UniformOverMesh (openturns.experimental)
* GeneralizedExtremeValueValidation (openturns.experimental)
* Coles (openturns.usecases.coles)
* Linthurst (openturns.usescases.linthurst)
==== API changes ====
* Deprecated LinearLeastSquaresCalibration::getCandidate, use getStartingPoint
* Deprecated NonLinearLeastSquaresCalibration::getCandidate, use getStartingPoint
* Deprecated GaussianLinearCalibration::getCandidate, use getParameterMean
* Deprecated NonGaussianLinearCalibration::getCandidate, use getParameterMean
* Removed SequentialStrategy
* Removed TensorApproximationAlgorithm, TensorApproximationResult, CanonicalTensorEvaluation, CanonicalTensorGradient
* Removed FunctionalChaosSobolIndices.getSobolGrouped(Total)Index(int)
* Removed FunctionalChaosSobolIndices::summary
* Removed CorrelationAnalysis::PearsonCorrelation|SpearmanCorrelation|PCC|PRCC
* Removed Distribution.getStandardMoment
* Removed deprecated LinearModelAlgorithm | LinearModelStepwiseAlgorithm ctors
* Removed FunctionalChaosAlgorithm(Function) ctors
* Removed MetaModelResult(Function, Function, ...) ctor
* Removed FunctionalChaosResult.getComposedModel, MetaModelResult.getModel and associated attributes for 1.21
* Removed FunctionalChaosResult Function ctor
* Removed (Process)Sample::computeCenteredMoment
* Removed Distribution::getCenteredMoment
* Removed IterativeMoments::getCenteredMoments
* Removed GeneralLinearModelAlgorithm | KrigingAlgorithm ctors
* Removed Drawable.draw|clean & Graph.draw|clean|getRCommand|makeR*|GetExtensionMap methods
* Removed Sample.storeToTemporaryFile|streamToRFormat methods
* Removed GaussianProcess.GIBBS
* Deprecated DomainIntersection|Union|DisjunctiveUnion(Domain, Domain) ctors
* KrigingResult.getTrendCoefficients now returns a single Point
* GeneralLinearModelResult.getTrendCoefficients now returns a single Point
* KrigingResult.getBasisCollection was removed in favor of KrigingResult.getBasis which returns a single Basis
* GeneralLinearModelResult.getBasisCollection was removed in favor of GeneralLinearModelResult.getBasis which returns a single Basis
* Deprecated EllipticalDistribution::getInverseCorrelation
* HSICStat arguments updated (covariance matrices instead of data + covariance models)
* Deprecated HSICStat relying on weight matrix
* Deprecated Basis::getDimension, use getInputDimension instead
* Removed thinning from Gibbs and all MetropolisHastings classes
* Removed burn-in from Gibbs and all MetropolisHastings classes except RandomWalkMetropolisHastings
* RandomWalkMetropolisHastings::getRealization|Sample no longer remove states reached during burn-in
* RandomWalkMetropolisHastings default adaptation parameters were changed to enable adaptation
* Split BoxCoxFactory::build into buildWithGraph, buildWithLM, buildWithGLM
=== Documentation ===
* Add API documentation to common use cases pages
* Added new use case: Linthurst
=== Python module ===
=== Miscellaneous ===
* Add HypothesisTest::LikelihoodRatioTest for nested model selection
* Add VisualTest::DrawPPplot
* Add VisualTest.Draw(Upper|Lower)(Tail|Extremal)DependenceFunction methods to plot dependence functions
* Add Distribution.compute(Upper|Lower)(Tail|Extremal)DependenceMatrix methods to compute dependence coefficients
* Enable Pagmo.moead_gen with pagmo>=2.19
* Enable Bonmin.Ecp/iFP algorithms with bonmin>=1.8.9
* BoxCoxFactory handles linear model
=== Bug fixes ===
* #2045 ([Debian] NLopt issues during tests on arm64, ppc64el, s390x)
* #2046 ([Debian] DistFunc_binomial issues in tests on arm64 and ppc64el)
* #2047 ([Debian] pythoninstallcheck_DistributionFactory_std issues on arm64, armel, armhf, mips64el)
* #2153 (HSIC computation cost)
* #2185 (Error: no member named '__1' in namespace 'std' ARM64 Android Termux)
* #2193 (Tiny spelling issues)
* #2194 (SubsetSampling: bug in 1.15)
* #2200 (out of bound probas)
* #2204 (Build fails with primesieve-11.0)
* #2205 (Edges of a PolygonArray)
* #2206 (Only Contour legends are shown in a graph mixing Contour and other Drawable (Curve, Cloud,...))
* #2209 (The Felhberg algorithm can fail, sometimes)
* #2210 (Python Domain.getImplementation does not work)
* #2213 (Some script fails)
* #2229 (Unary minus for distribution)
* #2240 (The Viewer sometimes fail)
* #2250 (HSIC Target sensitivity bad filtering)
* #2252 (The examples of calibration are difficult to understand)
* #2255 (The description of a Distribution is wrong)
* #2268 (MeixnerDistribution: slow pdfgradient)
* #2274 (Basis accepts functions with different input (output) dimensions)
* #2281 (The marginal of FireSatelliteModel can fail)
* #2285 (The input and output descriptions does not go down to the metamodel)
* #2287 (DesignProxy.computeDesign() can produce a segmentation fault)
* #2296 (DiracCovarianceModel.discretize() is buggy)
* #2297 (Legend location with grid layout)
* #2299 (Allow openturns as subproject)
* #2306 (Dirichlet::computeConditionalPDF can produce NaNs and Infs)
* #2323 (Minor wording issues in the cross entropy importance sampling example)
* #2327 (A sign mistake in FGM document)
* #2328 (Sample.add weird behaviour)
* #2338 (HSIC with large amount of data makes crash)
== 1.20 release (2022-11-08) == #release-1.20
=== Library ===
==== New classes ====
* IndependentCopulaFactory
* FieldToPointFunctionalChaosAlgorithm (openturns.experimental)
* FieldFunctionalChaosResult (openturns.experimental)
* FieldFunctionalChaosSobolIndices (openturns.experimental)
* CorrelationAnalysis
* UserDefinedMetropolisHastings (openturns.experimental)
* QuantileMatchingFactory
* UniformMuSigma
==== API changes ====
* Removed coupling_tools.execute get_stderr,get_stdout arguments
* Removed deprecated Nlopt|Ceres|Bonmin|CMinpack|Ipopt|TBB|HMatrixFactory::IsAvailable methods
* Deprecated SequentialStrategy
* Deprecated TensorApproximationAlgorithm, TensorApproximationResult, CanonicalTensorEvaluation, CanonicalTensorGradient
* Deprecated FunctionalChaosSobolIndices.getSobolGrouped(Total)Index(int)
* Deprecated static CorrelationAnalysis::PearsonCorrelation|SpearmanCorrelation|PCC|PRCC, use the methods of the new CorrelationAnalysis class instead.
* Removed static CorrelationAnalysis::SRC|SRRC due to a bug: #1753.
* Deprecated Distribution::getStandardMoment
* Inverted ctors arguments order in LinearModelAlgorithm & LinearModelStepwiseAlgorithm : first sample, then basis
* Deprecated oldest LinearModelAlgorithm(X, basis, Y) and LinearModelStepwiseAlgorithm(X, basis, Y,...) ctors
* Deprecated FunctionalChaos(Function, ...) ctors
* Deprecated MetaModelResult(Function, Function, ...) ctor
* Deprecated FunctionalChaosResult.getComposedModel, MetaModelResult.getModel
* Deprecated FunctionalChaosResult(Function, ...) ctor
* Deprecated (Process)Sample::computeCenteredMoment in favor of computeCentralMoment
* Deprecated Distribution::getCenteredMoment in favor of getCentralMoment
* Deprecated IterativeMoments::getCenteredMoments in favor of getCentralMoments
* Deprecated GeneralLinearModelAlgorithm | KrigingAlgorithm ctors using collection of basis
* Deprecated Drawable.draw|clean & Graph.draw|clean|getRCommand methods for legacy R graphs
* Deprecated SampleImplementation.storeToTemporaryFile|streamToRFormat
* Deprecated GaussianProcess.GIBBS in favor of GaussianProcess.GALLIGAOGIBBS
* Deprecated FunctionalChaosSobolIndices::summary in favor of __str__
* Deprecated BoxCoxFactory::build method using collection of basis
=== Documentation ===
* Added example galleries at the end of API doc pages
* Added new use case: WingWeightModel and example of use
* Added new use case: FireSatelliteModel and example of use
=== Python module ===
* New openturns.experimental submodule introducing newest classes until stabilization
=== Miscellaneous ===
* Chaos for mixed variables
=== Bug fixes ===
* #1214 (There is no example with IntegrationStrategy on a database)
* #1333 (Polynomial chaos with mixed variables (improvement))
* #1473 (FunctionalChaosResult should provide the used input/output samples )
* #1568 (There is no example to bootstrap the polynomial chaos)
* #2030 (There is no example which shows how to calibrate a model without observed inputs)
* #2043 (Some attributes of PythonDistribution are changed during the lifecycle of the object)
* #2058 (TruncatedNormal failure)
* #2059 (Pb with computeConditionalPDF in KernelMixture)
* #2064 (drop getGroupedSobolIndex(int) ?)
* #2073 (getCenteredMoment(0))
* #2076 (Problem in TruncatedDistribution of discrete distributions)
* #2083 (Problem in the QQplot of a discrete distribution)
* #2088 (Why is this library overriding the SIGINT handler?)
* #2089 (Cannot load Python objects with large attributes in a Study)
* #2097 (LinearModelStepwiseAlgorithm ctor order)
* #2091 (Compressed H5 files?)
* #2094 (getSampleAtVertex() is not robust)
* #2095 (Triangular::computeCharacteristicfunction() produces NaNs)
* #2098 (LinearModelStepwiseAlgorithm null basis)
* #2101 (drop FunctionalChaosSobolIndices::summary)
* #2103 (FunctionalChaosRandomVector notes)
* #2110 (HSIC draw indices method does not handle input sample description names)
* #2115 (Add mini-galleries of examples on the API pages)
* #2121 (GaussianProcess Gibbs sampling method issues)
* #2123 (MixtureClassifier 'grade' method does not work with a Sample input)
* #2125 (SciPyDistribution __init__ failure with scipy 1.9.0)
* #2129 (get samples from Wishart distribution)
* #2139 (KarhunenLoeveSVDAlgorithm seems to truncate the expansion from v1.19)
* #2140 (GeneralizedParetoFactory.buildMethodOfMoments estimating wrong parameter)
* #2145 (Calibration wo obs input API)
* #2152 (P1LagrangeInterpolation can make Python fail)
* #2154 (SpaceFillingMinDist: LaTeX typo)
* #2157 (Slow creation of mixtures when the atom distributions are costly to copy)
* #2161 (Drop Normal SPD check)
* #2176 (Missing documentation on Kriging Result methods)
== 1.19 release (2022-05-10) == #release-1.19
=== Library ===
==== Major changes ====
* HSIC sensitivity indices
* RandomWalkMetropolisHastings now updates all components at a time, previously updated componentwise
* Iterative statistics
==== New classes ====
* RandomVectorMetropolisHastings
* IndependentMetropolisHastings
* Gibbs
* HSICEstimatorConditionalSensitivity
* HSICEstimatorGlobalSensitivity
* HSICEstimatorTargetSensitivity
* HSICUStat
* HSICVStat
* IterativeExtrema
* IterativeMoments
* IterativeThresholdExceedance
* TensorProductExperiment
* NAIS
* Pagmo
* GalambosCopula
==== API changes ====
* Removed deprecated Hanning alias
* Removed deprecated AdaptiveDirectionalSampling alias
* Removed deprecated VisualTest::DrawCobWeb method
* Removed deprecated shims module
* Removed deprecated TBB.SetNumberOfThreads/GetNumberOfThreads
* Removed deprecated MultiFORM.setMaximumNumberOfDesignPoints/getMaximumNumberOfDesignPoints
* Removed deprecated SubsetSampling.getNumberOfSteps
* coupling_tools.execute: deprecated get_stderr,get_stdout for capture_output, returns CompletedProcess
* Deprecated Nlopt|Ceres|Bonmin|CMinpack|Ipopt|TBB|HMatrixFactory::IsAvailable in favor of PlatformInfo::HasFeature, see also GetFeatures
* RandomWalkMetropolisHastings API breaks to split the likelihood definition
* Deprecated CalibrationStrategy class
=== Documentation ===
=== Python module ===
* Hide C++ getImplementation methods and add Python-specific getImplementation methods with automatic dynamic typecasting
* Add Sample.asDataFrame/BuildFromDataFrame pandas conversion methods
=== Miscellaneous ===
* Add Collection::select(Indices) method
* Parallelized SymbolicFunction evaluation (enabled from a sample size>100 with exprtk)
* Sample csv export: allow one to set precision and format
* Sample csv import: handle nan/inf values
* Add EnumerateFunction::getBasisSizeFromTotalDegree(maximumDegree)
=== Bug fixes ===
* #1260 (RandomWalkMetropolisHastings does not handle non-symmetric proposals properly)
* #1263 (Static functions are implemented in C++ classes instead of namespaces)
* #1334 (Multi-objective optimization)
* #1444 (The calibration examples are unclear)
* #1830 (Incompatible documentation for CauchyModel)
* #1914 (Cobyla & NLopt should accept LeastSquaresProblem)
* #1921 (Adaptive Directional Stratification: the coefficient of variation of the probability is inconsistent with repeated probability results)
* #1922 (Adaptive Directional Stratification: the coefficient of variation cannot be used as a stopping criterion)
* #1926 (Examples have broken graphics)
* #1931 (Interface class getImplementation methods are useless in Python)
* #1939 (Contour curves outside graph window)
* #1940 (getMaximumDegreeStrataIndex has a bug with hyperbolic rule)
* #1943 (TriangularMatrix * Point --> ScalarCollection)
* #1946 (Doc search issues)
* #1947 (The constructor of FunctionalChaosResult is undocumented)
* #1949 (The comparison operator of ComposedDistribution is wrong)
* #1958 (Performance of OrthogonalUniVariatePolynomial (precision, speed))
* #1959 (Cobyla run causes segfault when empty ctor is used)
* #1993 (IsotropicCovarianceModel has no default ctor)
* #1994 (default GaussProductExperiment is invalid)
* #1997 (KLSVD spectrum cutoff error)
* #2012 (Cannot sample a ConditionedGaussianProcess on a mesh containing conditioning points)
* #2014 (Precision issue with the Python KrigingAlgorithm test)
* #2019 (Parametric stationary covariance models doc section needs refresh)
* #2020 (Problem with the UserDefined distribution)
* #2025 (The lack of Bonmin generates an error during example compilation)
* #2031 (Multi-objective optimization example question)
* #2032 (Documentation of NAIS)
== 1.18 release (2021-11-10) == #release-1.18
=== Library ===
==== Major changes ====
==== New classes ====
* DistanceToDomainFunction
==== API changes ====
* Removed deprecated MultiStart::setStartingPoints/getStartingPoints
* Removed deprecated KarhunenLoeveResult::getEigenValues
* Removed deprecated coupling_tools.execute is_shell/workdir/shell_exe/check_exit_code arguments
* RandomVector::get/setParameter and getParameterDescription available to PythonRandomVector
* Implemented CovarianceModel::computeCrossCovariance
* Deprecated Hanning in favor of Hann
* Deprecated AdaptiveDirectionalSampling in favor of AdaptiveDirectionalStratification
* Deprecated VisualTest::DrawCobWeb in favor of DrawParallelCoordinates
* IndicatorFunction constructor takes a Domain as input
* Intervals and DomainUnions get new method computeDistanceToDomain(Point or Sample)
* Renamed some ResourceMap keys: cache-max-size>Cache-MaxSize, parallel-threads>TBB-ThreadsNumber
* Deprecated TBB.SetNumberOfThreads/GetNumberOfThreads
* Deprecated MultiFORM.setMaximumNumberOfDesignPoints/getMaximumNumberOfDesignPoints
* Deprecated SubsetSampling.getNumberOfSteps
* Normal and Student distributions: no need to specify the R parameter if it is the identity matrix
* KrigingResult::getConditionalMean and getConditionalMarginalVariance yield Samples instead of Points when applied to Samples
* Deprecated shims module
=== Documentation ===
=== Python module ===
=== Miscellaneous ===
=== Bug fixes ===
* #1840 (HMatrixFactory leaks)
* #1842 (libOT.so.0.0.0 doesn't have a SONAME)
* #1843 (Ali-Mikhail-Haq copula parameter value)
* #1844 (MaximumDistribution::computePDF is wrong when all the marginals are equal and independent)
* #1845 (MemoizeFunction does not propagate to finite difference gradient&hessian)
* #1847 (ParametricFunction require unnecessary function evaluations)
* #1854 (Sample indexing does not work on np.int64 type)
* #1856 (Speed up Normal computeSurvivalFunction)
* #1857 (ProductCovarianceModel fails when constructed with DiracCovarianceModel)
* #1858 (Normal::computeCDF(sample) crash for large dimensions)
* #1861 (Still some instabilities in Kriging with ot.StationaryFunctionalCovarianceModel)
* #1864 (FORM - IMPORTANCE FACTOR)
* #1868 (Not compatible with hmat-oss-1.7.1: no member named 'compressionMethod' in 'hmat_settings_t')
* #1870 (TruncatedDistribution fails to compute quantiles)
* #1874 (ProcessSample.getSampleAtVertex() may be useful as a public method)
* #1877 (How to model singular multivariate distributions?)
* #1878 (Rename Hanning filtering window)
* #1879 (Adaptive Directional Sampling Algorithm: the drawProbabilityConvergence graph may be wrong)
* #1880 (Adaptive Directional Sampling: some enhancements proposed)
* #1882 (Is Distribution::getLinearCorrelation useful ?)
* #1883 (Strange behavior of FORM ?)
* #1884 (setDesign can lead to wrong Sobol' indices)
* #1891 (Correlation of Halton sequence at high dimensions)
* #1911 (DirectionalSampling freezes python if not correctly initialized)
* #1912 (splitter: missing doc)
* #1915 (computePDFGradient(const Sample &) should rely on computePDFGradient(const Point &) in DistributionImplementation)
* #1918 (PythonDistribution does not allow one to overload the isDiscrete() method)
== 1.17 release (2021-05-12) == #release-1.17
=== Library ===
==== Major changes ====
==== New classes ====
* VertexValuePointToFieldFunction
* KarhunenLoeveReduction
* KarhunenLoeveValidation
* IsotropicCovarianceModel
* KroneckerCovarianceModel
* VonMisesFactory
* KFoldSplitter, LeaveOneOutSplitter
==== API changes ====
* Removed deprecated Pairs alias
* Removed deprecated SORMResult::getEventProbabilityHohenBichler/getGeneralisedReliabilityIndexHohenBichler
* Removed deprecated OptimizationResult::getLagrangeMultipliers, OptimizationAlgorithm::computeLagrangeMultipliers
* Removed deprecated FittingTest::Kolmogorov, FittingTest::BestModelKolmogorov (DistributionFactory argument only)
* Deprecated Sample::computeStandardDeviationPerComponent, use computeStandardDeviation
* Deprecated KarhunenLoeveResult::getEigenValues, use getEigenvalues
* Removed StationaryCovarianceModel
* CovarianceModel.computeAsScalar allows scalars
* Swapped SimulatedAnnealingLHS constructor SpaceFilling/TemperatureProfile arguments
* Added EfficientGlobalOptimization::getKrigingResult, gets the updated version of the KrigingResult passed to the constructor
* Deprecated MultiStart::setStartingPoints/getStartingPoints, use MultiStart::setStartingSample/getStartingSample instead
* MultiStart::setStartingPoint/getStartingPoint throws (previously did nothing)
=== Documentation ===
* Fixed example and plot of Kolmogorov statistics.
* Added a new example showing how to combine RandomWalkMetropolisHastings and PythonDistribution
=== Python module ===
* Serialize Python wrapper objects using dill (PythonDistribution, PythonFunction ...)
=== Miscellaneous ===
=== Bug fixes ===
* #1010 (The doc for ExpectationSimulationAlgorithm is confusing)
* #1052 (The Dirichlet and Normal distributions only have 1D graphics in the doc)
* #1224 (The examples in the doc of the DomainEvent class are unclear)
* #1229 (Better encapsulation of optional 3rd-party headers)
* #1240 (There is no theory documentation for ExpectationSimulationAlgorithm)
* #1257 (Cannot create a SimulatedAnnealingLHS without specifying the temperature profile)
* #1287 (The constructors of KernelSmoothing have no doc)
* #1418 (The Contour example does not present the second constructor)
* #1425 (There is no method to create a train / test pair)
* #1431 (The figure for LHSExperiment is not accurate)
* #1459 (There is no example which shows how to set a column of a Sample)
* #1497 (There is no example to create a multivariate Normal distribution)
* #1506 (The Brent class has no example)
* #1570 (Wrong formula for MauntzKucherenkoSensitivityAlgorithm)
* #1650 (There is no example of a parametric StationaryFunctionalCovarianceModel)
* #1656 (The help page of ExpectationSimulationAlgorithm is unclear)
* #1661 (XMLH5StorageManager does not store IndicesCollection into HDF5 files)
* #1662 (Operator() of a CovarianceModel with multidimensional output should yield object of type SquareMatrix)
* #1680 (QuadraticBasisFactory multiplies cross products by 2)
* #1710 (Misleading y labels in VisualTest.DrawPairsMarginals())
* #1713 (The doc of the ResourceMap does not match the content of the ResourceMap)
* #1714 (The log-PDF of the Pareto is wrong)
* #1721 (The `draw` method of `SobolSimulationAlgorithm` does not reuse the descriptions)
* #1723 (There is no interesting example of the ExprTk feature of SymbolicFunction)
* #1725 (The BetaFactory help page has wrong equations)
* #1729 (LinearModelAnalysis sometimes fail)
* #1731 (The Extreme value example may be improved)
* #1737 (Low rank tensor doc issues)
* #1742 (The example which shows how to set the figure size is wrong.)
* #1751 (DrawCorrelationCoefficients has a wrong Text height)
* #1752 (Error while estimating the reduced log-likelihood when using a StationaryFunctionalCovarianceModel)
* #1758 (The LinearModelStepwiseAlgorithm has no example)
* #1759 (Kriging model with StationaryFunctionalCovarianceModel might provide bad results)
* #1768 (Bug in ExponentiallyDampedCosineModel & SphericalModel)
* #1771 (GridLayout hides the titles in the graph)
* #1772 (MinimumVolumeClassifier cannot draw 1D samples)
* #1774 (ExponentialModel::partialGradient is wrong)
* #1775 (ExponentialModel does not account correlation with Covariance ctor)
* #1776 (Typo in the Branin use case implementation)
* #1781 (The link_to_an_external_code example has small bugs)
* #1784 (The drawPDF method of Histogram sometimes fail)
* #1787 (CovarianceMatrix from SymmetricMatrix raises InvalidArgumentException)
* #1790 (LinearModelResult.getFormula() method is not updated by Stepwise regression)
* #1794 (Some doc examples seem to behave with new infrastructure)
* #1796 (VonMisesFactory is missing)
* #1803 (The API example of Experiment has a format issue)
* #1805 (Brent's implementation has a stability issue)
* #1807 (Beta::computeCharacteristicFunction is wrong if the lower bound is not zero)
* #1815 (1.16 fails with dlib-cpp-19.22)
* #1818 (Problem with wilksNumber)
* #1820 (Incoherent results in LinearModelAnalysis)
* #1835 (RegularizedIncompleteBeta returns nan)
* #1836 (Hypergeometric results differ without boost)
* #1837 (Poor performance when using pickle on OT objects)
== 1.16 release (2020-11-16) == #release-1.16
=== Library ===
==== Major changes ====
* Drop normalization in KrigingAlgorithm
* Drop normalization & transformation handling in GeneralLinearModelAlgorithm
* XML/H5 storage (hdf5 library)
* C++11 requirement
==== New classes ====
* BlockIndependentDistribution
* FejerAlgorithm
* GridLayout
* MinimumVolumeClassifier
* StationaryFunctionalCovarianceModel
* XMLH5StorageManager
==== API changes ====
* Removed deprecated Weibull, WeibullFactory, WeibullMuSigma classes
* Removed deprecated GumbelAB class
* Removed deprecated Event class
* Removed deprecated EnumerateFunction constructors
* Removed various deprecated distribution accessors
* Deprecated Pairs class, see VisualTest.DrawPairs
* Deprecated SORMResult::getEventProbabilityHohenBichler, use SORMResult::getEventProbabilityHohenbichler instead
* Deprecated SORMResult::getGeneralisedReliabilityIndexHohenBichler, use SORMResult::getGeneralisedReliabilityIndexHohenbichler instead
* Renamed SobolSequence::MaximumNumberOfDimension as SobolSequence::MaximumDimension
* Added VisualTest::DrawLinearModel(linearModelResult), useful if the test is performed on the training samples
* Added VisualTest::DrawLinearModelResidual(linearModelResult), useful if the test is performed on the training samples
* Deprecated OptimizationResult::getLagrangeMultipliers
* Moved OptimizationAlgorithm::computeLagrangeMultipliers to OptimizationResult
* Added AIC & BestModelAIC static methods in FittingTest
* Added AICC & BestModelAICC static methods in FittingTest
* Moved BuildDistribution from FunctionalChaosAlgorithm to MetaModelAlgorithm
* Added Drawable::BuildRainbowPalette(size)
* Added Drawable::BuildTableauPalette(size), which is now the default palette.
* Added Drawable::ConvertFromRGBIntoHSV
* Added FittingTest::Lilliefors, BestModelLilliefors
* Deprecated FittingTest::BestModelKolmogorov(Sample, DistributionFactoryCollection, TestResult), use BestModelLilliefors
* Deprecated FittingTest::Kolmogorov(Sample, DistributionFactory, TestResult, level), use Lilliefors
* MetamodelValidation: now computePredictivityFactor returns Point, drawValidation return GridLayout
* Deprecated coupling_tools.execute is_shell/workdir/shell_exe/check_exit_code arguments
=== Documentation ===
* Sphinx-gallery used to render examples
==== API documentation ====
* Clarified SobolIndicesExperiment page, notations now consistent with SobolIndicesAlgorithm page
* Clarified SobolIndicesAlgorithm and (Saltellli|Martinez|MauntzKucherenko|Jansen)SensitivityAlgorithm pages, corrected formulas
* Documented how to turn warnings off or write them on a file
=== Python module ===
* Renamed Viewer *_kwargs arguments to *_kw (matplotlib convention)
* Add ProcessSample Field accessors
=== Miscellaneous ===
* Do not compute Lagrange multipliers by default during an optimization
* Add ResourceMap::FindKeys
* Allow computeLogPDF methods to output values lower than SpecFunc::LogMinScalar
=== Bug fixes ===
* #1001 (Add method SobolSimulationResult::draw)
* #1259 (The diagonal of a scatter plot matrix should have the histograms)
* #1267 (Some CSV files cannot be imported)
* #1377 (The `setKnownParameter` method is not compatible with `buildEstimator`)
* #1407 (GeneralLinearModelAlgorithm mishandles user-specified scale parameter when normalize is True)
* #1415 (The BuildDistribution static method should not use the KS-test)
* #1421 (UserDefinedStationaryCovarianceModel doc suggests input dimension can be >1)
* #1436 (The style of the curves is unpleasing to my eyes)
* #1447 (Highly inaccurate result in reliability model when using subset of RandomVector)
* #1465 (The Sample constructor based on a list and an integer should not exist)
* #1470 (setNbModes is sometimes ignored)
* #1474 (optimization defaults)
* #1507 (Leak in Collection typemaps)
* #1510 (ot.Ceres('LEVENBERG_MARQUARDT') and ot.Ceres('DOGLEG') do not handle bound constraints)
* #1515 (KernelSmoothing build failure)
* #1520 (The NLopt test is dubious)
* #1521 (Basis of MonomialFunction)
* #1529 (The error of the NonLinearLeastSquaresCalibration and GaussianNonLinearCalibration are different)
* #1540 (SubsetSampling: incorrect event sample)
* #1547 (Mesh does not check the simplices indices)
* #1549 (Doc of evaluation operator of KrigingResult)
* #1553 (Optimization algorithms ignore MaxEvaluationNumber parameter in SORM)
* #1556 (WeibullMin::computePDFGradient yields the partial derivatives in the wrong order)
* #1558 (Example estimate_multivariate_normal: FittingTest::BestModelBIC fails to compute the BIC)
* #1564 (Set a Point makes OT crash)
* #1567 (The API doc of SobolIndicesExperiment has a format issue)
* #1573 (LinearModelAnalysis::drawQQPlot line is not the first bisector)
* #1578 (Option to suppressing and/or save warnings?)
* #1581 (LinearModelAlgorithm run() fails to parse Sample description)
* #1586 (Documentation: description error in the API for the FittingTest_BestModelKolmogorov and FittingTest_BestModelChiSquaredclasses)
* #1590 (The equation of the Fejer quadrature rule is triplicated)
* #1592 (SubsetSampling returns an error if Pf=1)
* #1594 (LinearLeastSquaresCalibration and CalibrationResult)
* #1599 (FieldToPointConnection-BlockSize is missing)
* #1603 (FieldToPointConnection generates an invalid exception)
* #1605 (MaximumLikelihoodFactory cannot be used with FittingTest.Kolmogorov)
* #1624 (The graphs_loglikelihood_contour example has a bug)
* #1642 (Big white space at the beginning of examples)
* #1643 (Problem in MaximumDistribution PDF)
* #1647 (MCMC::computeLogLikelihood does not compute the log-likelihood)
* #1651 (Cobyla freezes in 0T1.16rc1)
* #1658 (TimeSeries accessor)
* #1660 (Cannot extract continuous modes from KLResult when dimension>1)
* #1668 (LevelSetMesher does not take into account the comparison operator)
== 1.15 release (2020-05-25) == #release-1.15
=== Library ===
==== Major changes ====
* New EV solver for KarhunenLoeveP1Algorithm (Spectra), with sparse matrix and HMatrix support
* Enable HMat AcaRandom compression method
==== New classes ====
* Ipopt optimization solver
==== Documentation ====
==== API changes ====
* Removed deprecated OptimizationAlgorithm::GetLeastSquaresAlgorithmNames
* Removed deprecated GaussianNonLinearCalibration,NonLinearLeastSquaresCalibration::set,getAlgorithm
* Removed deprecated MethodOfMomentsFactory::set,getOptimizationProblem
* ResourceMap::Set* methods no longer add new keys, the new Add* methods must be used instead
* Removed OPTpp
* Renamed HistogramFactory::computeSilvermanBandwidth into HistogramFactory::computeBandwidth.
=== Python module ===
* ProcessSample __getitem__ returns Sample instead of Field
* Implement list indexing
=== Miscellaneous ===
* Add Sample::getMarginal(Description)
* Fixed TBB performance when used together with OpenBLAS
=== Bug fixes ===
* #1124 (DistributionFactory::buildAsXXX methods not documented)
* #1213 (The legend of the graphics in MetaModelValidation is wrong)
* #1222 (There is no kriging example based on HMAT)
* #1331 (FittingTest_BestModelBIC sometimes fail)
* #1335 (The return of the unsafe ResourceMap)
* #1337 (The rDiscrete function has no help page)
* #1349 (Problem in the graph of Histogram)
* #1351 (Text has a zero size)
* #1354 (The doc of GeneralizedParetoFactory does not reflect the implementation)
* #1371 (Memory leak in ot.TruncatedDistribution)
* #1372 (wrap MultiStart::OptimizationResultCollection)
* #1374 (The setKnownParameter of the factories are not documented enough)
* #1376 (The View class has no example)
* #1378 (Kriging-related covariance model weirdness)
* #1383 (The ExprTk engine for SymbolicFunction does not document the "var" keyword)
* #1384 (The ExprTk engine is not case-sensitive)
* #1388 (Kolmogorov fails on a PythonDistribution)
* #1390 (The doc for the `computeQuantile` method does not describe the optional `tail` argument)
* #1393 (SobolSimulationAlgorithm should be simpler)
* #1395 (Indexing Sample improvement)
* #1403 (Python import otagrum throws an error)
* #1404 (Bug in KrigingAlgorithm+hmat-oss)
* #1405 (Most of the simulation algorithms for rare event fail on a coronavirus example)
* #1416 (MethodOfMomentsFactory has no setOptimizationBounds method)
* #1419 (BoundingVolumeHierarchy segaults/hangs)
* #1423 (The computeSilvermanBandwidth of the HistogramFactory has no help)
* #1432 (Expected improvement-based EfficientGlobalOptimization stopping criterion could be improved)
* #1437 (OrderStatisticsMarginalChecker bound message)
* #1438 (Normal distribution: computeComplementaryCDF)
* #1443 (Not all distributions have a getRoughness() method)
* #1448 (Memory consumption leads to crash)
* #1449 (P1LagrangeInterpolation sometimes fails)
* #1455 (GLM::setCovarianceModel could lead to unexpected behavior of parameter optimization in KrigingAlgorithm)
* #1456 (truncation of distribution)
* #1461 (ComparisonOperator().getImplementation().getClassName() segfault)
* #1471 (The graphics of KarhunenLoeveQuadratureAlgorithm has no axes)
* #1485 (The Normal().getRoughness() method is wrong)
* #1495 (Wrong formula for Expected Improvement evaluation)
== 1.14 release (2019-11-13) == #release-1.14
=== Library ===
==== IMPORTANT: Distributions parametrization changes ====
* New argument ordering in Frechet ctor: scale(beta), shape(alpha), location(gamma) (swapped alpha and beta)
* New parametrization in Gumbel: scale(beta), position(gamma) (beta=1/alpha for first argument)
* New parametrization in Beta: shape(alpha), shape(beta), location(a), location(b) (beta=t-r for second argument)
* New parametrization in InverseGamma: rate(lambda), shape(k) (swapped arguments)
* New parametrization in InverseNormal: location(mu), rate(lambda) (swapped arguments)
* New parametrization in Laplace: mean(mu), rate(lambda) (swapped arguments)
=> One can use "import openturns.shims as ot" to maintain compatibility with older scripts
==== Major changes ====
* New optimization solvers (Dlib, Bonmin for mixed integer optimization problems)
* New distributions (SquaredNormal, WeibullMax, Pareto, DiscreteCompoundDistribution, MixedHistogramUserDefined)
* New estimators (ParetoFactory, GeneralizedExtremeValueFactory, LeastSquaresDistributionFactory, PlackettCopulaFactory)
* New copulas (JoeCopula, MarshallOlkinCopula, PlackettCopula)
* Linear model learner (LinearModelStepwiseAlgorithm)
* System events (IntersectionEvent, UnionEvent, SystemFORM, MultiFORM)
==== New classes ====
* Dlib
* SquaredNormal
* NullHessian
* GumbelLambdaGamma
* LogNormalMuErrorFactor
* WeibullMax
* WeibullMaxFactory
* WeibullMaxMuSigma
* GeneralizedExtremeValueFactory
* Pareto
* LeastSquaresDistributionFactory
* ParetoFactory
* DiscreteCompoundDistribution
* Bonmin
* IntersectionEvent
* UnionEvent
* SystemFORM
* MultiFORM
* JoeCopula
* MarginalEvaluation/Gradient/Hessian
* MarshallOlkinCopula
* LinearModelStepwiseAlgorithm
* MixedHistogramUserDefined
* PlackettCopula
* PlackettCopulaFactory
==== API changes ====
* FittingTest methods to return fitted distributions with factory as argument
* Removed deprecated specific RandomVector constructors
* Removed deprecated HypothesisTest::Smirnov
* Removed deprecated FittingTest::TwoSamplesKolmogorov
* Removed deprecated LinearModel, LinearModelFactory
* Removed deprecated HypothesisTest::(Partial|Full)Regression
* Removed deprecated OptimizationProblem(levelFunction, levelValue) ctor
* Removed deprecated (Linear|Quadratic)(LeastSquares|Taylor)::getResponseSurface
* Removed deprecated VisualTest::DrawEmpiricalCDF,DrawHistogram,DrawClouds
* Moved dot to Point::dot
* Deprecated Weibull in favor of WeibullMin
* Deprecated WeibullMuSigma in favor of WeibullMinMuSigma
* Deprecated WeibullFactory in favor of WeibullMinFactory, buildAsWeibull
* Deprecated GumbelAB
* Deprecated GaussianNonLinearCalibration,NonLinearLeastSquaresCalibration::set,getAlgorithm
* Added getConditionalMarginalCovariance method to KrigingResult
* Added getConditionalMarginalVariance method to KrigingResult
* Deprecated OptimizationAlgorithm::GetLeastSquaresAlgorithmNames
* Added linearity features to Function
* Added 'removeKey' method to ResourceMap
* Removed Copula class
* Deprecated Event class, use ThresholdEvent/ProcessEvent/DomainEvent classes
* Deprecated EnumerateFunction constructors
* Added a minimum probability accessor to SubsetSampling
* Added a UniVariateFunction interface to solvers and integration algorithms
=== Python module ===
* Add Domain.__contains__ operator
=== Miscellaneous ===
* Add GeneralizedPareto location parameter
* Add getSobolGroupedTotalIndex method for FunctionalChaosSobolIndices for the indice of a group of variables
=== Bug fixes ===
* #997 (Adding minimum volume set examples)
* #1004 (The doc for SobolSimulationAlgorithm has issues)
* #1006 (Text drawable does not handle size)
* #1130 (Inconsistency in FittingTest)
* #1160 (2-d GaussianProcess realization graph regression)
* #1169 (Missing key in ResourceMap)
* #1173 (There is no dot product example)
* #1185 (Bug with Normal.computeMinimumLevelSet method)
* #1190 (computeProbability clamped by Domain-SmallVolume)
* #1197 (Doc error: TrendTransform)
* #1198 (Doc error: ValueFunction)
* #1202 (Sample::sort & Sample::sortAccordingToAComponent only return new Samples)
* #1204 (sortAccordingToAComponent does not check its inputs arguments)
* #1209 (LinearModelStepwiseAlgorithm from otlm does not exist in OT)
* #1216 (The CalibrationResult doc does not match the code)
* #1247 (KrigingAlgorithm could not compute amplitude analytically with ProductCovarianceModel )
* #1264 (ProductCovarianceModel ignore active parameters of its 1d marginals)
* #1282 (The dlib example fails)
* #1283 (LogNormalFactory::buildMethodOfLeastSquares has no doc)
* #1289 (The help page of PythonFunction has formatting issues)
* #1303 (Rosenblatt transformation segfault)
== 1.13 release (2019-06-06) == #release-1.13
=== Library ===
==== Major changes ====
* Added OPT++ solvers
* Improved a lot the performance of all the Rosenblatt related computations
* Added elementary calibration capabilities
* Added CMinpack, Ceres Solver least-squares solvers
==== New classes ====
* ParametricPointToFieldFunction
* LinearModelResult, LinearModelAlgorithm, LinearModelAnalysis
* OPTpp
* NearestPointProblem, LeastSquaresProblem
* CMinpack
* Ceres
* DiscreteMarkovChain
* CalibrationAlgorithm, CalibrationResult, GaussianLinearCalibration, LinearLeastSquaresCalibration
* NonLinearLeastSquaresCalibration, GaussianNonLinearCalibration
* Hypergeometric
==== API changes ====
* Removed deprecated FunctionalChaosRandomVector::getSobol* methods
* Removed deprecated UserDefinedCovarianceModel constructor based on a Collection<CovarianceMatrix>
* Removed deprecated FieldFunction,FieldToPointFunction::getSpatialDimension
* Removed deprecated LinearModelRSquared, LinearModelAdjustedRSquared
* Deprecated LinearModel, LinearModelFactory
* Moved HypothesisTest::(Partial|Full)Regression to LinearModelTest
* Deprecated OptimizationProblem(levelFunction, levelValue)
* Deprecated (Linear|Quadratic)(LeastSquares|Taylor)::getResponseSurface
* FittingTest::BestModelBIC returns the Distribution and the BIC value
* Deprecated VisualTest::DrawEmpiricalCDF,DrawHistogram,DrawClouds
* Add statistic attribute and accessor to the TestResult class
* Deprecated Point::getDescription,setDescription
=== Python module ===
=== Miscellaneous ===
* Changed bugtracker to GitHub issues (https://github.com/openturns/openturns/issues)
* Dropped rot dependency
=== Bug fixes ===
* #289 (Move the LinearModelFactory class from the Base namespace to the Uncertainty namespace)
* #579 (There is no example of a typical "Central Tendency" study)
* #839 (more optim solvers details)
* #931 (The PostAnalyticalImportanceSampling and PostAnalyticalControlledImportanceSampling are too briefly documented)
* #979 (Viewer does not handle BarPlot's fillStyle)
* #977 (HypothesisTest::ChiSquared is bogus)
* #980 (CorrelationAnalysis_SRC scales the coefficients so that they sum to 1)
* #981 (HypothesisTest_{Full, Partial} regression should be moved to LinearModelTest)
* #982 (LinearModelTest::LinearModelDurbinWatson with several factors)
* #983 (Bogus Normal parameter distribution)
* #989 (Fixed the documentation of BIC according to the code)
* #995 (Pip does not recognize conda install of openturns)
* #1000 (SaltelliSensitivityAlgorithm with LowDiscrepancyExperiment produces wrong results)
* #1005 (The stopping criteria of SobolSimulationAlgorithm is weird)
* #1024 (The unary operator is undefined for Normal distribution)
* #1025 (Empty throw in PythonWrappingFunctions)
* #1028 (Build fails with clang-60: token is not a valid binary operator in a preprocessor subexpression)
* #1031 (The doc for LogNormal and LogNormalMuSigma is confusing )
* #1035 (ProbabilitySimulationResult does not provide the distribution of the probability)
* #1036 (Doc error for KrigingAlgorithm method getReducedLogLikelihoodFunction())
* #1043 (The DrawSobolIndices doc is wrong)
* #1045 (LowDiscrepancyExperiment/ComposedCopula correlation across blocks ?)
* #1051 (The default value of computeSecondOrder in SobolIndicesExperiment should be False)
* #1054 (Optimization algorithms ignore MaxEvaluationNumber parameter)
* #1057 (FittingTest.BestModelBIC to return bic value)
* #1058 (GEV problem)
* #1064 (The summary of a FunctionalChaosSobolIndices fails with more than 14 dimensions)
* #1071 (Event cannot be created from a RandomVector)
* #1078 (KrigingAlgorithm documentation still references "inputTransformation")
* #1080 (Brent, Secant, Bissection documentation)
* #1085 (GPD documentation)
* #1090 (PythonFunction can make OT crash)
* #1092 (The parameterGradient of a ParametricFunction can loss accuracy)
* #1099 (PythonDistribution does not work with ConditionalDistribution)
* #1111 (Function::draw() does not take the scale parameter properly into account)
* #1112 (Several bugs in Multinomial)
* #1119 (The parameter description is not taken into account in CalibrationResult)
* #1129 (Segmentation fault with RandomMixture)
* #1139 (CopulaImplementation should derive from DistributionImplementation)
* #1143 (The ResourceMap is not correctly formatted in help pages)
* #1148 (Fails to find cminpack)
* #1155 (The description of Points can be set, but get is empty)
== 1.12 release (2018-11-08) == #release-1.12
=== Library ===
==== Major changes ====
* FieldFunction and co knows its input/output meshes before evaluation
* SobolSequence has been extended from maximum dimension 40 to 1111
* Parametrized statistical tests by first kind risk instead of 1-risk
* FittingTest now compute a correct p-value in Kolmogorov tests even if the parameters are estimated from the tested sample
* Completed documentation migration with process content
==== New classes ====
* ProbabilitySimulationResult
* ExpectationSimulationAlgorithm, ExpectationSimulationResult
* ExtremeValueCopula
* ChaospyDistribution
* SobolSimulationAlgorithm, SobolSimulationResult
* FractionalBrownianMotionModel
==== API changes ====
* Removed deprecated MonteCarlo, ImportanceSampling, QuasiMonteCarlo, RandomizedQuasiMonteCarlo, RandomizedLHS classes
* Removed deprecated Field::getDimension, getSpatialDimension, getSpatialMean, getTemporalMean methods
* Remove deprecated Process::getDimension, getSpatialDimension methods
* Removed deprecated CovarianceModel::getDimension, getSpatialDimension, getSpatialCorrelation, setSpatialCorrelation methods
* Removed deprecated SpectralModel::getDimension, getSpatialDimension, getSpatialCorrelation methods
* Removed deprecated TruncatedDistribution single bound accessors
* Removed deprecated Function constructors
* Removed deprecated Sample,SampleImplementation::operator*(SquareMatrix) and operator/(SquareMatrix) (and in-place operators)
* Removed deprecated SampleImplementation::scale(SquareMatrix)
* Removed deprecated Domain(a, b) constructor
* Removed deprecated Domain,DomainImplementation::numericallyContains, isEmpty, isNumericallyEmpty, getVolume, getNumericalVolume, computeVolume
* Removed deprecated Domain,DomainImplementation::getLowerBound, getUpperBound methods
* Removed deprecated Interval,Mesh::computeVolume
* Removed deprecated SobolIndicesAlgorithm::[sg]etBootstrapConfidenceLevel
* Removed deprecated Mesh::getVerticesToSimplicesMap,computeSimplexVolume
* Removed deprecated Evaluation,EvaluationImplementation,EvaluationProxy,Function,FunctionImplementation,ParametricEvaluation::operator(inP,parameter)
* Removed deprecated Gradient,GradientImplementation,ParametricGradient::gradient(inP,parameter)
* Removed deprecated Hessian,HessianImplementation,ParametricHessian::operator(inP,parameter)
* Removed ExponentialCauchy, SecondOrderModel, SecondOrderImplementation
* LowDiscrepancyExperiment to work with dependent distributions
* Deprecated UserDefinedCovarianceModel constructor based on a Collection<CovarianceMatrix>
* Deprecated FieldFunction,FieldToPointFunction::getSpatialDimension
* Removed VertexFunction class
* Deprecated LinearModelRSquared, LinearModelAdjustedRSquared
* Added ResourceMap::GetType to access the type of a given key.
* Extended OrthogonalBasis::build() to multi-indices (see ticket #967)
* Deprecated specific RandomVector constructors
=== Python module ===
=== Miscellaneous ===
* CMake >=2.8.8 is required to build OpenTURNS
* The format of openturns.conf has changed to make a distinction between types of keys
=== Bug fixes ===
* #660 (Strange behavior of KernelSmoothing)
* #684 (Strange behavior of Student distribution in multidimensional case)
* #778 (DirectionalSampling is unstable)
* #915 (Distribution.computeQuantile properties)
* #949 (LowDiscrepancyExperiment for distributions having dependent copula)
* #952 (Limitation on distribution type for polynomial chaos?)
* #953 (RandomMixture can fail with truncated distribution)
* #954 (CompositeProcess::getFuture() broken)
* #955 (StudentFactory does not estimate the standard deviation)
* #957 (RandomMixture::getDispersionIndicator() takes a long time)
* #958 (The Sobol' indices plot is wrong for chaos)
* #959 (The PolygonArray and Polygon classes poorly manage the colors)
* #960 (Polygon sometimes make OT crash)
* #961 (The PolygonArray class seem to ignore the legends)
* #962 (The doc of MetaModelValidation.computePredictivityFactor is wrong)
* #963 (VisualTest_DrawHistogram sometimes has overlapping X labels)
* #964 (Study does not load LogNormalMuSigma variables from XML)
* #965 (A RandomVector from a RandomVector can make OT crash)
* #968 (Empty legend make OT crash)
* #970 (Composing gaussian copulas can crash the chaos)
* #972 (FittingTest::ChiSquared slow and buggy)
* #974 (Default constructor of the TruncatedDistribution)
* #975 (EmpiricalBernsteinCopula is a copula as sample is truncated)
== 1.11 release (2018-05-11) == #release-1.11
=== Library ===
==== Major changes ====
==== New classes ====
* FrechetFactory
* Fehlberg
* TranslationFunction
* DomainComplement, DomainIntersection, DomainUnion, DomainDisjunctiveUnion, DomainDifference
* SmoothedUniform
* NearestNeighbourAlgorithm, RegularGridNearestNeighbour, NaiveNearestNeighbour, NearestNeighbour1D
* EnclosingSimplex, EnclosingSimplexImplementation, NaiveEnclosingSimplex, RegularGridEnclosingSimplex, EnclosingSimplexMonotonic1D, BoundingVolumeHierarchy
* IndicesCollection
* SymbolicParserExprTk
* EvaluationProxy
* MemoizeFunction, MemoizeEvaluation
* Evaluation, Gradient, Hessian
* MeshDomain
* NormInfEnumerateFunction
* FunctionalChaosSobolIndices
* P1LagrangeInterpolation
* FilonQuadrature
==== API changes ====
* Removed deprecated NumericalMathFunction class
* Removed deprecated QuadraticNumericalMathFunction class
* Removed deprecated LinearNumericalMathFunction class
* Removed deprecated NumericalSample class
* Removed deprecated NumericalPoint[WithDescription] class
* Removed deprecated NumericalScalarCollection class
* Removed deprecated NumericalComplexCollection class
* Removed deprecated PosteriorRandomVector class
* Removed deprecated ConditionedNormalProcess class
* Removed deprecated ResourceMap::[SG]AsNumericalScalar methods
* Removed deprecated SpecFunc::*NumericalScalar* constants
* Removed deprecated PlatformInfo::GetConfigureCommandLine method
* Removed deprecated Field::getSample method
* Removed deprecated SobolIndicesAlgorithm::Generate method
* Removed deprecated NumericalScalar, NumericalComplex types
* Removed deprecated Function constructors
* CorrelationAnalysis::(Pearson|Spearman)Correlation accepts a multivariate sample and returns a Point
* Deprecated Field::getDimension, getSpatialDimension, getSpatialMean, getTemporalMean
* Deprecated Process::getDimension, getSpatialDimension
* Deprecated CovarianceModel::getDimension, getSpatialDimension, getSpatialCorrelation, setSpatialCorrelation
* Deprecated SecondOrderModel::getDimension, getSpatialDimension
* Deprecated SpectralModel::getDimension, getSpatialDimension, getSpatialCorrelation
* Deprecated TruncatedDistribution single bound accessors in favor of setBounds/getBounds
* Deprecated remaining analytical & database Function ctors
* Mesh constructor no longer builds a KDTree, new method Mesh::setNearestNeighbourAlgorithm must be called explicitly
* Deprecated Domain::numericallyContains, isEmpty, isNumericallyEmpty, getVolume, getNumericalVolume, computeVolume, getLowerBound, getUpperBound
* Add an argument to KarhunenLoeveQuadratureAlgorithm to pass domain bounds
* Add an argument to Mesh::streamToVTKFile() and Mesh::exportToVTKFile() to pass custom simplices
* All KDTree methods are modified
* IndicesCollection is no more an alias to Collection<Indices>, it has its own class and has a fixed size
* Removed BipartiteGraph::add method
* Deprecated usage of _e and _pi constants when defining analytical functions, e_ and pi_ must be used instead.
* Removed Function::enableHistory,disableHistory,isHistoryEnabled,clearHistory,getHistoryInput,getHistoryOutput,getInputPointHistory,getInputParameterHistory
* Removed Function::enableCache,disableCache,isCacheEnabled,getCacheHits,addCacheContent,getCacheInput,getCacheOutput,clearCache
* Added Drawable::getPaletteAsNormalizedRGBA() to get the palette into a native matplotlib format.
* Changed DistributionImplementation::getCopula,getMarginal,getStandardRepresentative,getStandardDistribution to return a Distribution instead of Pointer<DistributionImplementation>
* Changed DistributionFactoryImplementation::build to return a Distribution instead of Pointer<DistributionImplementation>
* Changed ProcessImplementation::getMarginal to return a Process instead of Pointer<ProcessImplementation>
* Changed RandomVector::getAntecedent,getMarginal to return a RandomVector instead of Pointer<RandomVectorImplementation>
* Changed all {get,set}{Evaluation,Gradient,Hessian} methods to work on Evaluation/Gradient/Hessian instead of Pointer
* Deprecated SobolIndicesAlgorithm::setBootstrapConfidenceLevel, getBootstrapConfidenceLevel
* TNC, Cobyla, EGO are parametrized by setMaximumEvaluationNumber instead of setMaximumIterationNumber
* Removed all KDTree services from Mesh,RegularGrid,Field::getNearestVertex, getNearestVertexIndex, etc, client code must use NearestNeighbourAlgorithm if needed
* Changed Mesh to not derive from DomainImplementation
* Deprecated Mesh::computeSimplexVolume, use Mesh::computeSimplicesVolume instead
* Deprecated Mesh,Interval::computeVolume
* Deprecated FunctionalChaosRandomVector::getSobol* methods in favor of FunctionalChaosSensitivity ones
* DeprecatedEvaluation,EvaluationImplementation,EvaluationProxy,Function,FunctionImplementation,ParametricEvaluation::operator()(inP,parameter)
* Deprecated Gradient,GradientImplementation,ParametricGradient::gradient(inP,parameter)
* Deprecated Hessian,HessianImplementation,ParametricHessian::hessian(inP,parameter)
=== Python module ===
* Depend on swig>=2.0.9
=== Miscellaneous ===
* Multivariate TruncatedDistribution
* Asymptotic Sobol' variance estimators
=== Bug fixes ===
* #870 (Problem with TensorizedCovarianceModel with spatial dim > 1)
* #926 (Covariance model active parameter set behavior)
* #928 (Covariance model/ Field/Process properties naming)
* #932 (Optim algo iteration/evaluation number)
* #937 (Small bugs in FunctionalChaosResult::get{Residuals, RelativeErrors})
* #938 (DistributionFactory::buildEstimator to handle Exception)
* #939 (The help of the getMaximumDegreeStrataIndex method is wrong.)
* #941 (Duplicate with SobolIndicesExperiment)
* #944 (Inline plots crash in notebooks if too many points)