Repository: heal-research/operon Branch: main Commit: 0c0f116ec5ac Files: 301 Total size: 19.6 MB Directory structure: gitextract_jvogax4q/ ├── .clang-format ├── .clang-tidy ├── .codespellrc ├── .envrc ├── .github/ │ └── workflows/ │ ├── build-linux.yml │ ├── build-macos.yml │ ├── build-windows.yml │ ├── ci.yml │ └── test-linux.yml ├── .gitignore ├── .readthedocs.yml ├── BUILDING.md ├── CMakeLists.txt ├── CMakePresets.json ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── HACKING.md ├── LICENSE ├── README.md ├── cli/ │ ├── CMakeLists.txt │ └── source/ │ ├── operator_factory.cpp │ ├── operator_factory.hpp │ ├── operon_gp.cpp │ ├── operon_nsgp.cpp │ ├── operon_parse_model.cpp │ ├── pareto_front.cpp │ ├── pareto_front.hpp │ ├── reporter.hpp │ ├── util.cpp │ └── util.hpp ├── cmake/ │ ├── coverage.cmake │ ├── dev-mode.cmake │ ├── docs.cmake │ ├── get-git-revision.cmake │ ├── get-git-revision.cmake.in │ ├── install-config.cmake │ ├── install-rules.cmake │ ├── lint-targets.cmake │ ├── lint.cmake │ ├── open-cpp-coverage.cmake.example │ ├── prelude.cmake │ ├── project-is-top-level.cmake │ ├── spell-targets.cmake │ ├── spell.cmake │ ├── variables.cmake │ └── windows-set-path.cmake ├── data/ │ ├── AirfoilSelfNoise.csv │ ├── AirfoilSelfNoise.json │ ├── Breiman-I.csv │ ├── Breiman-I.json │ ├── Chemical-I.csv │ ├── Chemical-I.json │ ├── Chemical-II.csv │ ├── Chemical-II.json │ ├── Concrete.csv │ ├── Concrete.json │ ├── Flow_stress.csv │ ├── Flow_stress.json │ ├── FrictionDyn-OneHot.csv │ ├── FrictionDyn-OneHot.json │ ├── FrictionStat-OneHot.csv │ ├── FrictionStat-OneHot.json │ ├── Friedman-I.csv │ ├── Friedman-I.json │ ├── Friedman-II.csv │ ├── Friedman-II.json │ ├── GP-Challenge.csv │ ├── GP-Challenge.json │ ├── NasaBattery-1_10min.csv │ ├── NasaBattery-1_10min.json │ ├── NasaBattery-2_20min.csv │ ├── NasaBattery-2_20min.json │ ├── Nikuradse_1.csv │ ├── Nikuradse_1.json │ ├── Nikuradse_2.csv │ ├── Nikuradse_2.json │ ├── Pagie-1.csv │ ├── Pagie-1.json │ ├── Poly-10.csv │ ├── Poly-10.json │ ├── Sextic.csv │ ├── Sextic.json │ ├── Vladislavleva-1.csv │ ├── Vladislavleva-1.json │ ├── Vladislavleva-2.csv │ ├── Vladislavleva-2.json │ ├── Vladislavleva-3.csv │ ├── Vladislavleva-3.json │ ├── Vladislavleva-4.csv │ ├── Vladislavleva-4.json │ ├── Vladislavleva-5.csv │ ├── Vladislavleva-5.json │ ├── Vladislavleva-6.csv │ ├── Vladislavleva-6.json │ ├── Vladislavleva-7.csv │ ├── Vladislavleva-7.json │ ├── Vladislavleva-8.csv │ └── Vladislavleva-8.json ├── docs/ │ ├── Doxyfile.in │ ├── conf.py.in │ └── pages/ │ └── about.dox ├── example/ │ ├── CMakeLists.txt │ ├── custom_primitives.cpp │ └── empty_example.cpp ├── flake.nix ├── include/ │ └── operon/ │ ├── algorithms/ │ │ ├── config.hpp │ │ ├── ga_base.hpp │ │ ├── gp.hpp │ │ ├── nsga2.hpp │ │ └── solution_archive.hpp │ ├── analyzers/ │ │ ├── analyzer_base.hpp │ │ └── diversity.hpp │ ├── ceres/ │ │ ├── integer_sequence_algorithm.h │ │ ├── jet.h │ │ ├── jet_fwd.h │ │ ├── jet_traits.h │ │ ├── port.h │ │ └── tiny_solver.h │ ├── collections/ │ │ ├── bitset.hpp │ │ └── projection.hpp │ ├── core/ │ │ ├── aligned_allocator.hpp │ │ ├── buildinfo.hpp.in │ │ ├── comparison.hpp │ │ ├── concepts.hpp │ │ ├── constants.hpp │ │ ├── contracts.hpp │ │ ├── dataset.hpp │ │ ├── dispatch.hpp │ │ ├── distance.hpp │ │ ├── individual.hpp │ │ ├── node.hpp │ │ ├── operator.hpp │ │ ├── problem.hpp │ │ ├── pset.hpp │ │ ├── range.hpp │ │ ├── subtree.hpp │ │ ├── symbol_library.hpp │ │ ├── tree.hpp │ │ ├── types.hpp │ │ ├── variable.hpp │ │ └── version.hpp │ ├── error_metrics/ │ │ ├── correlation_coefficient.hpp │ │ ├── error_metrics.hpp │ │ ├── mean_absolute_error.hpp │ │ ├── mean_squared_error.hpp │ │ ├── normalized_mean_squared_error.hpp │ │ ├── r2_score.hpp │ │ ├── root_mean_squared_error.hpp │ │ └── sum_of_squared_errors.hpp │ ├── formatter/ │ │ └── formatter.hpp │ ├── hash/ │ │ ├── hash.hpp │ │ ├── metrohash64.hpp │ │ └── zobrist.hpp │ ├── interpreter/ │ │ ├── backend/ │ │ │ ├── arma/ │ │ │ │ ├── derivatives.hpp │ │ │ │ └── functions.hpp │ │ │ ├── arma.hpp │ │ │ ├── blaze/ │ │ │ │ ├── derivatives.hpp │ │ │ │ └── functions.hpp │ │ │ ├── blaze.hpp │ │ │ ├── eigen/ │ │ │ │ ├── derivatives.hpp │ │ │ │ └── functions.hpp │ │ │ ├── eigen.hpp │ │ │ ├── eve/ │ │ │ │ ├── derivatives.hpp │ │ │ │ └── functions.hpp │ │ │ ├── eve.hpp │ │ │ ├── fast_approx/ │ │ │ │ ├── derivatives.hpp │ │ │ │ ├── functions.hpp │ │ │ │ └── impl/ │ │ │ │ ├── aq.hpp │ │ │ │ ├── exp.hpp │ │ │ │ ├── inv.hpp │ │ │ │ ├── log.hpp │ │ │ │ ├── pow.hpp │ │ │ │ ├── sqrt.hpp │ │ │ │ ├── tanh.hpp │ │ │ │ └── trig.hpp │ │ │ ├── fast_v1.hpp │ │ │ ├── fast_v2.hpp │ │ │ ├── fast_v3.hpp │ │ │ ├── fastor/ │ │ │ │ ├── derivatives.hpp │ │ │ │ └── functions.hpp │ │ │ ├── fastor.hpp │ │ │ ├── plain/ │ │ │ │ ├── derivatives.hpp │ │ │ │ └── functions.hpp │ │ │ ├── plain.hpp │ │ │ ├── vdt/ │ │ │ │ ├── derivatives.hpp │ │ │ │ └── functions.hpp │ │ │ └── vdt.hpp │ │ ├── derivatives.hpp │ │ ├── dual.hpp │ │ ├── functions.hpp │ │ └── interpreter.hpp │ ├── mdspan/ │ │ └── mdspan.hpp │ ├── operators/ │ │ ├── creator.hpp │ │ ├── crossover.hpp │ │ ├── evaluator.hpp │ │ ├── generator.hpp │ │ ├── initializer.hpp │ │ ├── local_search.hpp │ │ ├── mutation.hpp │ │ ├── non_dominated_sorter.hpp │ │ ├── reinserter.hpp │ │ └── selector.hpp │ ├── optimizer/ │ │ ├── dynamic_cost_function.hpp │ │ ├── likelihood/ │ │ │ ├── gaussian_likelihood.hpp │ │ │ ├── likelihood_base.hpp │ │ │ └── poisson_likelihood.hpp │ │ ├── lm_cost_function.hpp │ │ ├── optimizer.hpp │ │ ├── solvers/ │ │ │ └── sgd.hpp │ │ └── tiny_cost_function.hpp │ ├── parser/ │ │ └── infix.hpp │ └── random/ │ └── random.hpp ├── operon.nix ├── ports/ │ ├── aria-csv/ │ │ ├── portfile.cmake │ │ └── vcpkg.json │ ├── fluky/ │ │ ├── portfile.cmake │ │ └── vcpkg.json │ ├── infix-parser/ │ │ ├── portfile.cmake │ │ └── vcpkg.json │ ├── lbfgs/ │ │ ├── portfile.cmake │ │ └── vcpkg.json │ └── vstat/ │ ├── portfile.cmake │ └── vcpkg.json ├── rtd/ │ ├── build.rst │ ├── conf.py │ ├── example.rst │ ├── features.rst │ ├── index.rst │ └── requirements.txt ├── source/ │ ├── algorithms/ │ │ ├── gp.cpp │ │ ├── nsga2.cpp │ │ └── solution_archive.cpp │ ├── core/ │ │ ├── dataset.cpp │ │ ├── distance.cpp │ │ ├── node.cpp │ │ ├── pset.cpp │ │ ├── tree.cpp │ │ └── version.cpp │ ├── formatter/ │ │ ├── dot.cpp │ │ ├── infix.cpp │ │ ├── postfix.cpp │ │ └── tree.cpp │ ├── hash/ │ │ ├── hash.cpp │ │ ├── metrohash64.cpp │ │ └── zobrist.cpp │ ├── interpreter/ │ │ └── interpreter.cpp │ ├── operators/ │ │ ├── creator/ │ │ │ ├── balanced.cpp │ │ │ ├── creator.cpp │ │ │ ├── koza.cpp │ │ │ └── ptc2.cpp │ │ ├── crossover.cpp │ │ ├── evaluator.cpp │ │ ├── evaluator_error_metrics.cpp │ │ ├── generator/ │ │ │ ├── basic.cpp │ │ │ ├── brood.cpp │ │ │ ├── os.cpp │ │ │ └── poly.cpp │ │ ├── local_search.cpp │ │ ├── mutation.cpp │ │ ├── non_dominated_sorter/ │ │ │ ├── best_order_sort.cpp │ │ │ ├── deductive_sort.cpp │ │ │ ├── dominance_degree_sort.cpp │ │ │ ├── efficient_sort.cpp │ │ │ ├── hierarchical_sort.cpp │ │ │ ├── merge_sort.cpp │ │ │ ├── rank_intersect.cpp │ │ │ └── rank_ordinal.cpp │ │ └── selector/ │ │ ├── proportional.cpp │ │ └── tournament.cpp │ └── parser/ │ └── infix.cpp ├── test/ │ ├── CMakeLists.txt │ └── source/ │ ├── implementation/ │ │ ├── autodiff.cpp │ │ ├── crossover.cpp │ │ ├── details.cpp │ │ ├── dispatch_table.cpp │ │ ├── error_metrics.cpp │ │ ├── evaluation.cpp │ │ ├── evaluator.cpp │ │ ├── hashing.cpp │ │ ├── infix_parser.cpp │ │ ├── initialization.cpp │ │ ├── mutation.cpp │ │ ├── nondominatedsort.cpp │ │ ├── optimizer.cpp │ │ ├── random.cpp │ │ ├── selection.cpp │ │ └── zobrist.cpp │ ├── operon_test.cpp │ ├── operon_test.hpp │ ├── performance/ │ │ ├── autodiff.cpp │ │ ├── creator.cpp │ │ ├── distance.cpp │ │ ├── evaluation.cpp │ │ ├── infix_parser.cpp │ │ └── nondominatedsort.cpp │ └── thirdparty/ │ ├── elki_stats.hpp │ └── nanobench.h ├── tools/ │ ├── bench_ndsort.py │ ├── benchmark_cache.py │ └── compare_operon.py └── vcpkg.json ================================================ FILE CONTENTS ================================================ ================================================ FILE: .clang-format ================================================ --- Language: Cpp # BasedOnStyle: WebKit AccessModifierOffset: -4 AlignAfterOpenBracket: DontAlign AlignConsecutiveAssignments: false AlignConsecutiveDeclarations: false AlignEscapedNewlines: Right AlignOperands: false AlignTrailingComments: false AllowAllParametersOfDeclarationOnNextLine: true AllowShortBlocksOnASingleLine: false AllowShortCaseLabelsOnASingleLine: false AllowShortFunctionsOnASingleLine: All AllowShortIfStatementsOnASingleLine: false AllowShortLoopsOnASingleLine: false AlwaysBreakAfterDefinitionReturnType: None AlwaysBreakAfterReturnType: None AlwaysBreakBeforeMultilineStrings: false AlwaysBreakTemplateDeclarations: MultiLine BinPackArguments: true BinPackParameters: true BraceWrapping: AfterClass: false AfterControlStatement: false AfterEnum: false AfterFunction: true AfterNamespace: false AfterObjCDeclaration: false AfterStruct: false AfterUnion: false AfterExternBlock: false BeforeCatch: false BeforeElse: false IndentBraces: false SplitEmptyFunction: true SplitEmptyRecord: true SplitEmptyNamespace: true BreakBeforeBinaryOperators: All BreakBeforeBraces: WebKit BreakBeforeInheritanceComma: false BreakInheritanceList: BeforeColon BreakBeforeTernaryOperators: true BreakConstructorInitializersBeforeComma: false BreakConstructorInitializers: BeforeComma BreakAfterJavaFieldAnnotations: false BreakStringLiterals: true ColumnLimit: 0 CommentPragmas: '^ IWYU pragma:' CompactNamespaces: false ConstructorInitializerAllOnOneLineOrOnePerLine: false ConstructorInitializerIndentWidth: 4 ContinuationIndentWidth: 4 Cpp11BracedListStyle: false DerivePointerAlignment: false DisableFormat: false ExperimentalAutoDetectBinPacking: false FixNamespaceComments: false ForEachMacros: - foreach - Q_FOREACH - BOOST_FOREACH IncludeBlocks: Preserve IncludeCategories: - Regex: '^"(llvm|llvm-c|clang|clang-c)/' Priority: 2 - Regex: '^(<|"(gtest|gmock|isl|json)/)' Priority: 3 - Regex: '.*' Priority: 1 IncludeIsMainRegex: '(Test)?$' IndentCaseLabels: false IndentPPDirectives: None IndentWidth: 4 IndentWrappedFunctionNames: false JavaScriptQuotes: Leave JavaScriptWrapImports: true KeepEmptyLinesAtTheStartOfBlocks: true MacroBlockBegin: '' MacroBlockEnd: '' MaxEmptyLinesToKeep: 1 NamespaceIndentation: Inner ObjCBinPackProtocolList: Auto ObjCBlockIndentWidth: 4 ObjCSpaceAfterProperty: true ObjCSpaceBeforeProtocolList: true PenaltyBreakAssignment: 2 PenaltyBreakBeforeFirstCallParameter: 19 PenaltyBreakComment: 300 PenaltyBreakFirstLessLess: 120 PenaltyBreakString: 1000 PenaltyBreakTemplateDeclaration: 10 PenaltyExcessCharacter: 1000000 PenaltyReturnTypeOnItsOwnLine: 60 PointerAlignment: Left ReflowComments: true SortIncludes: true SortUsingDeclarations: true SpaceAfterCStyleCast: false SpaceAfterTemplateKeyword: true SpaceBeforeAssignmentOperators: true SpaceBeforeCpp11BracedList: true SpaceBeforeCtorInitializerColon: true SpaceBeforeInheritanceColon: true SpaceBeforeParens: ControlStatements SpaceBeforeRangeBasedForLoopColon: true SpaceInEmptyParentheses: false SpacesBeforeTrailingComments: 1 SpacesInAngles: false SpacesInContainerLiterals: true SpacesInCStyleCastParentheses: false SpacesInParentheses: false SpacesInSquareBrackets: false Standard: Cpp11 StatementMacros: - Q_UNUSED - QT_REQUIRE_VERSION TabWidth: 8 UseTab: Never ... ================================================ FILE: .clang-tidy ================================================ --- # Enable ALL the things! Except not really # misc-non-private-member-variables-in-classes: the options don't do anything Checks: "*,\ -google-readability-todo,\ -readability-identifier-length,\ -altera-unroll-loops,\ -fuchsia-*,\ fuchsia-multiple-inheritance,\ -llvm-header-guard,\ -llvm-include-order,\ -llvmlibc-*,\ -cppcoreguidelines-pro-bounds-pointer-arithmetic,\ -cppcoreguidelines-macro-usage,\ -cppcoreguidelines-owning-memory,\ -altera-struct-pack-align,\ -altera-id-dependent-backward-branch,\ -bugprone-easily-swappable-parameters,\ -boost-use-ranges,\ -modernize-use-ranges,\ -misc-non-private-member-variables-in-classes" WarningsAsErrors: '' CheckOptions: - key: 'bugprone-argument-comment.StrictMode' value: 'true' # Prefer using enum classes with 2 values for parameters instead of bools - key: 'bugprone-argument-comment.CommentBoolLiterals' value: 'true' - key: 'bugprone-misplaced-widening-cast.CheckImplicitCasts' value: 'true' - key: 'bugprone-sizeof-expression.WarnOnSizeOfIntegerExpression' value: 'true' - key: 'bugprone-suspicious-string-compare.WarnOnLogicalNotComparison' value: 'true' - key: 'readability-simplify-boolean-expr.ChainedConditionalReturn' value: 'true' - key: 'readability-simplify-boolean-expr.ChainedConditionalAssignment' value: 'true' - key: 'readability-uniqueptr-delete-release.PreferResetCall' value: 'true' - key: 'cppcoreguidelines-init-variables.MathHeader' value: '' - key: 'cppcoreguidelines-narrowing-conversions.PedanticMode' value: 'true' - key: 'readability-else-after-return.WarnOnUnfixable' value: 'true' - key: 'readability-else-after-return.WarnOnConditionVariables' value: 'true' - key: 'readability-inconsistent-declaration-parameter-name.Strict' value: 'true' - key: 'readability-qualified-auto.AddConstToQualified' value: 'true' - key: 'readability-redundant-access-specifiers.CheckFirstDeclaration' value: 'true' # These seem to be the most common identifier styles - key: 'readability-identifier-naming.AbstractClassCase' value: 'CamelCase' - key: 'readability-identifier-naming.ClassCase' value: 'CamelCase' - key: 'readability-identifier-naming.ClassConstantCase' value: 'CamelCase' - key: 'readability-identifier-naming.ClassMemberCase' value: 'camelBack' - key: 'readability-identifier-naming.ClassMethodCase' value: 'CamelCase' - key: 'readability-identifier-naming.ConstantCase' value: 'camelBack' - key: 'readability-identifier-naming.ConstantMemberCase' value: 'camelBack' - key: 'readability-identifier-naming.ConstantParameterCase' value: 'camelBack' - key: 'readability-identifier-naming.ConstantPointerParameterCase' value: 'camelBack' - key: 'readability-identifier-naming.ConstexprFunctionCase' value: 'CamelCase' - key: 'readability-identifier-naming.ConstexprMethodCase' value: 'CamelCase' - key: 'readability-identifier-naming.ConstexprVariableCase' value: 'aNy_CasE' - key: 'readability-identifier-naming.EnumCase' value: 'CamelCase' - key: 'readability-identifier-naming.EnumConstantCase' value: 'CamelCase' - key: 'readability-identifier-naming.FunctionCase' value: 'CamelCase' - key: 'readability-identifier-naming.GlobalConstantCase' value: 'CamelCase' - key: 'readability-identifier-naming.GlobalConstantPointerCase' value: 'CamelCase' - key: 'readability-identifier-naming.GlobalFunctionCase' value: 'CamelCase' - key: 'readability-identifier-naming.GlobalPointerCase' value: 'CamelCase' - key: 'readability-identifier-naming.GlobalVariableCase' value: 'camelBack' - key: 'readability-identifier-naming.InlineNamespaceCase' value: 'CamelCase' - key: 'readability-identifier-naming.LocalConstantCase' value: 'camelBack' - key: 'readability-identifier-naming.LocalConstantPointerCase' value: 'camelBack' - key: 'readability-identifier-naming.LocalPointerCase' value: 'camelBack' - key: 'readability-identifier-naming.LocalVariableCase' value: 'camelBack' - key: 'readability-identifier-naming.MacroDefinitionCase' value: 'UPPER_CASE' - key: 'readability-identifier-naming.MemberCase' value: 'camelBack' - key: 'readability-identifier-naming.MethodCase' value: 'CamelCase' - key: 'readability-identifier-naming.NamespaceCase' value: 'aNy_CasE' - key: 'readability-identifier-naming.ParameterCase' value: 'camelBack' - key: 'readability-identifier-naming.ParameterPackCase' value: 'camelBack' - key: 'readability-identifier-naming.PointerParameterCase' value: 'camelBack' - key: 'readability-identifier-naming.PrivateMemberCase' value: 'camelBack' - key: 'readability-identifier-naming.PrivateMemberSuffix' value: '_' - key: 'readability-identifier-naming.PrivateMethodCase' value: 'CamelCase' - key: 'readability-identifier-naming.ProtectedMemberCase' value: 'camelBack' - key: 'readability-identifier-naming.ProtectedMemberSuffix' value: '_' - key: 'readability-identifier-naming.ProtectedMethodCase' value: 'CamelCase' - key: 'readability-identifier-naming.PublicMemberCase' value: 'CamelCase' - key: 'readability-identifier-naming.PublicMethodCase' value: 'CamelCase' - key: 'readability-identifier-naming.ScopedEnumConstantCase' value: 'CamelCase' - key: 'readability-identifier-naming.StaticConstantCase' value: 'CamelCase' - key: 'readability-identifier-naming.StaticVariableCase' value: 'camelBack' - key: 'readability-identifier-naming.StructCase' value: 'CamelCase' - key: 'readability-identifier-naming.TemplateParameterCase' value: 'camelBack' - key: 'readability-identifier-naming.TemplateTemplateParameterCase' value: 'camelBack' - key: 'readability-identifier-naming.TypeAliasCase' value: 'CamelCase' - key: 'readability-identifier-naming.TypedefCase' value: 'CamelCase' - key: 'readability-identifier-naming.TypeTemplateParameterCase' value: 'CamelCase' - key: 'readability-identifier-naming.UnionCase' value: 'CamelCase' - key: 'readability-identifier-naming.ValueTemplateParameterCase' value: 'CamelCase' - key: 'readability-identifier-naming.VariableCase' value: 'camelBack' - key: 'readability-identifier-naming.VirtualMethodCase' value: 'CamelCase' ... ================================================ FILE: .codespellrc ================================================ [codespell] builtin = clear,rare,en-GB_to_en-US,names,informal,code check-filenames = check-hidden = skip = */.git,*/build quiet-level = 2 ================================================ FILE: .envrc ================================================ use flake . --builders '' ================================================ FILE: .github/workflows/build-linux.yml ================================================ name: build-linux on: push: branches: [ main ] pull_request: branches: [ main ] env: # Customize the CMake build type here (Release, Debug, RelWithDebInfo, etc.) BUILD_TYPE: Release jobs: build: # The CMake configure and build commands are platform agnostic and should work equally well on Windows or Mac. # You can convert this to a matrix build if you need cross-platform coverage. # See: https://docs.github.com/en/free-pro-team@latest/actions/learn-github-actions/managing-complex-workflows#using-a-build-matrix runs-on: ubuntu-latest if: github.ref_name == 'main' steps: - uses: actions/checkout@v4 - uses: cachix/install-nix-action@v27 - name: Build working-directory: ${{github.workspace}} # Configure CMake in a 'build' subdirectory. `CMAKE_BUILD_TYPE` is only required if you are using a single-configuration generator such as make. # See https://cmake.org/cmake/help/latest/variable/CMAKE_BUILD_TYPE.html?highlight=cmake_build_type run: nix build ================================================ FILE: .github/workflows/build-macos.yml ================================================ name: build-macos on: push: branches: [ main ] pull_request: branches: [ main ] env: # Customize the CMake build type here (Release, Debug, RelWithDebInfo, etc.) BUILD_TYPE: Release jobs: build: # The CMake configure and build commands are platform agnostic and should work equally well on Windows or Mac. # You can convert this to a matrix build if you need cross-platform coverage. # See: https://docs.github.com/en/free-pro-team@latest/actions/learn-github-actions/managing-complex-workflows#using-a-build-matrix runs-on: macos-14 if: github.ref_name == 'main' steps: - uses: actions/checkout@v4 - uses: cachix/install-nix-action@v27 - name: Build working-directory: ${{github.workspace}} # Configure CMake in a 'build' subdirectory. `CMAKE_BUILD_TYPE` is only required if you are using a single-configuration generator such as make. # See https://cmake.org/cmake/help/latest/variable/CMAKE_BUILD_TYPE.html?highlight=cmake_build_type run: nix build ================================================ FILE: .github/workflows/build-windows.yml ================================================ name: build-windows on: push: branches: [ main ] pull_request: branches: [ main ] env: # Customize the CMake build type here (Release, Debug, RelWithDebInfo, etc.) BUILD_TYPE: Release jobs: build: # The CMake configure and build commands are platform agnostic and should work equally well on Windows or Mac. # You can convert this to a matrix build if you need cross-platform coverage. # See: https://docs.github.com/en/free-pro-team@latest/actions/learn-github-actions/managing-complex-workflows#using-a-build-matrix runs-on: windows-latest steps: - uses: actions/checkout@v2 - uses: lukka/run-vcpkg@v10 with: vcpkgGitCommitId: '43e35945783d078d1216927b85447b2eba34a7cc' - name: Configure CMake # Configure CMake in a 'build' subdirectory. `CMAKE_BUILD_TYPE` is only required if you are using a single-configuration generator such as make. # See https://cmake.org/cmake/help/latest/variable/CMAKE_BUILD_TYPE.html?highlight=cmake_build_type run: cmake --preset build-windows-vcpkg -B ${{github.workspace}}/build -DCMAKE_BUILD_TYPE=${{env.BUILD_TYPE}} -DBUILD_CLI_PROGRAMS=ON -DBUILD_TESTING=OFF - name: Build # Build your program with the given configuration run: cmake --build ${{github.workspace}}/build --config ${{env.BUILD_TYPE}} -t operon_operon ================================================ FILE: .github/workflows/ci.yml ================================================ name: Continuous Integration on: push: branches: - main pull_request: branches: - main jobs: lint: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 with: { python-version: "3.8" } - name: Install codespell run: pip3 install codespell - name: Lint run: cmake -D FORMAT_COMMAND=clang-format-11 -P cmake/lint.cmake - name: Spell check if: always() run: cmake -P cmake/spell.cmake coverage: needs: [lint] runs-on: ubuntu-latest # To enable coverage, go to https://codecov.io/, acquire a token, put it # into your secrets (Settings > Secrets > New repository secret), delete # the last line from the conditional below and edit the "" # placeholder to your GitHub name. # If you do not wish to use codecov, then simply delete this job from the # workflow. if: github.repository_owner == '' && false steps: - uses: actions/checkout@v2 - name: Install LCov run: sudo apt-get update -q && sudo apt-get install lcov -q -y - name: Configure run: cmake --preset=ci-coverage - name: Build run: cmake --build build/coverage -j 2 - name: Test working-directory: build/coverage run: ctest --output-on-failure -j 2 - name: Process coverage info run: cmake --build build/coverage -t coverage - name: Submit to codecov.io uses: codecov/codecov-action@v1 with: file: build/coverage/coverage.info sanitize: needs: [lint] runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Configure env: { CXX: clang++-11 } run: cmake --preset=ci-sanitize - name: Build run: cmake --build build/sanitize -j 2 - name: Test working-directory: build/sanitize env: ASAN_OPTIONS: "strict_string_checks=1:\ detect_stack_use_after_return=1:\ check_initialization_order=1:\ strict_init_order=1:\ detect_leaks=1" UBSAN_OPTIONS: print_stacktrace=1 run: ctest --output-on-failure -j 2 test: needs: [lint] strategy: matrix: os: [macos, ubuntu, windows] type: [shared, static] include: - { type: shared, shared: YES } - { type: static, shared: NO } runs-on: ${{ matrix.os }}-latest steps: - uses: actions/checkout@v2 - name: Install static analyzers if: matrix.os == 'ubuntu' run: sudo apt-get install clang-tidy cppcheck -y -q - name: Configure run: cmake --preset=ci-${{ matrix.os }} -D BUILD_SHARED_LIBS=${{ matrix.shared }} - name: Build run: cmake --build build --config Release -j 2 - name: Install run: cmake --install build --config Release --prefix prefix - name: Test working-directory: build run: ctest --output-on-failure -C Release -j 2 docs: # Deploy docs only when builds succeed needs: [sanitize, test] runs-on: ubuntu-latest # To enable, first you have to create an orphaned gh-pages branch: # # git switch --orphan gh-pages # git commit --allow-empty -m "Initial commit" # git push -u origin gh-pages # # Edit the placeholder below to your GitHub name, so this action # runs only in your repository and no one else's fork. After these, delete # this comment and the last line in the conditional below. # If you do not wish to use GitHub Pages for deploying documentation, then # simply delete this job similarly to the coverage one. if: github.ref == 'refs/heads/cpp20' && github.event_name == 'push' && github.repository_owner == '' && false steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 with: { python-version: "3.8" } - name: Install m.css dependencies run: pip3 install jinja2 Pygments - name: Install Doxygen run: sudo apt-get update -q && sudo apt-get install doxygen -q -y - name: Build docs run: cmake -B build -D "CMAKE_PROJECT_INCLUDE=$PWD/cmake/docs.cmake" && cmake --build build --target docs - name: Deploy docs uses: peaceiris/actions-gh-pages@v3 with: github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: build/docs/html ================================================ FILE: .github/workflows/test-linux.yml ================================================ name: tests on: push: branches: [main] pull_request: branches: [main] jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: cachix/install-nix-action@v27 - name: Configure run: nix develop -c cmake --preset build-linux -B build -DUSE_SINGLE_PRECISION=ON -Doperon_DEVELOPER_MODE=ON - name: Build test binary run: nix develop -c cmake --build build --target operon_test -j4 - name: Run tests run: nix develop -c ctest --test-dir build --output-on-failure -j4 ================================================ FILE: .gitignore ================================================ .DS_Store .idea/ .vs/ .vscode/ .cache/ build*/ cmake/open-cpp-coverage.cmake cmake-build-*/ prefix/ CMakeLists.txt.user CMakeUserPresets.json project-include-after.cmake .direnv ================================================ FILE: .readthedocs.yml ================================================ # .readthedocs.yml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the version of Python and other tools you might need build: os: ubuntu-22.04 tools: python: "3.11" # Build documentation in the docs/ directory with Sphinx sphinx: configuration: rtd/conf.py # Build documentation with MkDocs #mkdocs: # configuration: mkdocs.yml # Optionally build your docs in additional formats such as PDF and ePub formats: all # Optionally set the version of Python and requirements required to build your docs python: install: - requirements: rtd/requirements.txt ================================================ FILE: BUILDING.md ================================================ # Building with CMake ## Build This project doesn't require any special command-line flags to build to keep things simple. ### Using nix The recommended way to build operon is to use nix package manager. ```sh nix develop --extra-experimental-features nix-command --extra-experimental-features flakes ``` It downloads the required packages in the correct version (as specified in flake.nix) and starts a 'nix develop' shell. Here are the steps for building in release mode with a single-configuration generator, like the Unix Makefiles one: ```sh cmake -S . -B build -D CMAKE_BUILD_TYPE=Release cmake --build build ``` Here are the steps for building in release mode with a multi-configuration generator, like the Visual Studio ones: ```sh cmake -S . -B build cmake --build build --config Release ``` ### Using vcpkg Alternatively you can use vcpkg. You require cmake version >= 3.20 because we are using vcpkg ports. You can install a recent cmake version via vckpg: ```sh ./vcpkg install vcpkg-cmake ``` After installation of this cmake version you can use it with the following options: ```sh ./downloads/tools/cmake-3.21.1-linux/cmake-3.21.1-linux-x86_64/bin/cmake \ --preset build-ubuntu-vcpkg -S . -B build -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_TOOLCHAIN_FILE=../vcpkg/scripts/buildsystems/vcpkg.cmake ``` Additional build flags such as ```-DBUILD_CLI_PROGRAMS=ON -DUSE_SINGLE_PRECISION=ON -DUSE_OPENLIBM=ON``` may be added to the command. ## Install This project doesn't require any special command-line flags to install to keep things simple. As a prerequisite, the project has to be built with the above commands already. The below commands require at least CMake 3.15 to run, because that is the version in which [Install a Project][1] was added. Here is the command for installing the release mode artifacts with a single-configuration generator, like the Unix Makefiles one: ```sh cmake --install build ``` Here is the command for installing the release mode artifacts with a multi-configuration generator, like the Visual Studio ones: ```sh cmake --install build --config Release ``` [1]: https://cmake.org/cmake/help/latest/manual/cmake.1.html#install-a-project ================================================ FILE: CMakeLists.txt ================================================ # SPDX-License-Identifier: MIT # SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research cmake_minimum_required(VERSION 3.20) include(cmake/prelude.cmake) project( operon VERSION 0.3.1 DESCRIPTION "Fast and scalable genetic programming library for symbolic regression." HOMEPAGE_URL "https://operongp.readthedocs.io/en/latest/" LANGUAGES CXX ) set(CMAKE_CXX_STANDARD 20) set(CMAKE_CXX_STANDARD_REQUIRED TRUE) docs_early_return() include(cmake/project-is-top-level.cmake) include(cmake/variables.cmake) # ---- Attempt to get revision information from git ---- find_package(Git) # retrieve revision number for version info if(GIT_FOUND AND EXISTS "${CMAKE_SOURCE_DIR}/.git") include(cmake/get-git-revision.cmake) EXECUTE_PROCESS(COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD OUTPUT_VARIABLE SHORT_SHA OUTPUT_STRIP_TRAILING_WHITESPACE) SET(REVISION ${SHORT_SHA} CACHE STRING "git short sha" FORCE) # only use the plugin to tie the configure state to the sha to force rebuilds # of files that depend on version.h get_git_head_revision(REFSPEC COMMITHASH.cmake) else() message(WARNING "Git not found, cannot set version info") SET(REVISION "unknown") endif() # ---- Declare library ---- add_library( operon_operon source/algorithms/gp.cpp source/algorithms/nsga2.cpp source/algorithms/solution_archive.cpp source/core/dataset.cpp source/core/distance.cpp source/core/node.cpp source/core/pset.cpp source/core/tree.cpp source/core/version.cpp source/formatter/dot.cpp source/formatter/infix.cpp source/formatter/postfix.cpp source/formatter/tree.cpp source/hash/hash.cpp source/hash/metrohash64.cpp source/hash/zobrist.cpp source/interpreter/interpreter.cpp source/operators/creator/creator.cpp source/operators/creator/balanced.cpp source/operators/creator/koza.cpp source/operators/creator/ptc2.cpp source/operators/crossover.cpp source/operators/evaluator_error_metrics.cpp source/operators/evaluator.cpp source/operators/generator/basic.cpp source/operators/generator/brood.cpp source/operators/generator/os.cpp source/operators/generator/poly.cpp source/operators/local_search.cpp source/operators/mutation.cpp source/operators/non_dominated_sorter/best_order_sort.cpp source/operators/non_dominated_sorter/deductive_sort.cpp source/operators/non_dominated_sorter/dominance_degree_sort.cpp source/operators/non_dominated_sorter/efficient_sort.cpp source/operators/non_dominated_sorter/hierarchical_sort.cpp source/operators/non_dominated_sorter/merge_sort.cpp source/operators/non_dominated_sorter/rank_intersect.cpp source/operators/non_dominated_sorter/rank_ordinal.cpp source/operators/selector/proportional.cpp source/operators/selector/tournament.cpp source/parser/infix.cpp ) add_library(operon::operon ALIAS operon_operon) # ---- Required dependencies ---- find_package(AriaCsvParser REQUIRED) find_package(cpp-sort REQUIRED) find_package(Eigen3 REQUIRED) find_package(eve REQUIRED) find_package(FastFloat REQUIRED) find_package(fluky REQUIRED) find_package(fmt REQUIRED) find_package(gtl REQUIRED) find_package(lbfgs REQUIRED) find_package(libassert REQUIRED) find_package(mdspan REQUIRED) find_package(Microsoft.GSL CONFIG REQUIRED) find_package(infix-parser REQUIRED) find_package(Taskflow REQUIRED) find_package(Threads REQUIRED) find_package(tl-expected REQUIRED) find_package(unordered_dense REQUIRED) find_package(vstat REQUIRED) find_package(xxHash) if(xxHash_FOUND) target_link_libraries(operon_operon PRIVATE xxHash::xxhash) else() find_package(PkgConfig REQUIRED) pkg_check_modules(xxhash IMPORTED_TARGET xxhash) if (NOT xxhash_FOUND) pkg_check_modules(xxhash IMPORTED_TARGET libxxhash) endif() if(xxhash_FOUND) target_link_libraries(operon_operon PRIVATE PkgConfig::xxhash) else() message(FATAL_ERROR "xxHash dependency could not be found.") endif() endif() # ---- Optional dependencies set(HAVE_CERES FALSE) if (USE_CERES) find_package(Ceres) # use the Ceres optimizer for coefficients tuning if (Ceres_FOUND) set(HAVE_CERES TRUE) endif() else() set(Ceres_VERSION "n/a") endif() if (USE_JEMALLOC) find_package(PkgConfig) if(PkgConfig_FOUND) pkg_check_modules(jemalloc IMPORTED_TARGET jemalloc) if (jemalloc_FOUND) target_link_libraries(operon_operon PUBLIC PkgConfig::jemalloc) endif() endif() endif() if(NOT MATH_BACKEND) set(MATH_BACKEND "Eigen") endif() message(STATUS "MATH: ${MATH_BACKEND}") if (MATH_BACKEND STREQUAL "Arma") find_package(BLAS REQUIRED) find_package(LAPACK REQUIRED) find_package(Armadillo REQUIRED) target_link_libraries(operon_operon PUBLIC BLAS::BLAS LAPACK::LAPACK ${ADMADILLO_LIBRARIES}) target_compile_definitions(operon_operon PUBLIC OPERON_MATH_ARMA) elseif (MATH_BACKEND STREQUAL "Blaze") find_package(blaze REQUIRED) find_package(xsimd REQUIRED) target_link_libraries(operon_operon INTERFACE xsimd) target_compile_definitions(operon_operon PUBLIC OPERON_MATH_BLAZE BLAZE_USE_SHARED_MEMORY_PARALLELIZATION=0 BLAZE_USE_XSIMD=1 EIGEN_DONT_PARALLELIZE) elseif (MATH_BACKEND STREQUAL "Fastor") find_package(Fastor REQUIRED) find_package(sleef REQUIRED) target_link_libraries(operon_operon PUBLIC Fastor::Fastor sleef::sleef) target_compile_definitions(operon_operon PUBLIC OPERON_MATH_FASTOR FASTOR_USE_SLEEF_U35) elseif (MATH_BACKEND STREQUAL "Eve") target_compile_definitions(operon_operon PUBLIC OPERON_MATH_EVE) elseif (MATH_BACKEND STREQUAL "Vdt") find_package(vdt REQUIRED) target_link_libraries(operon_operon PUBLIC vdt::vdt) target_compile_definitions(operon_operon PUBLIC OPERON_MATH_VDT) elseif (MATH_BACKEND STREQUAL "Eigen") message(STATUS "Using Eigen backend") target_compile_definitions(operon_operon PUBLIC OPERON_MATH_EIGEN EIGEN_DONT_PARALLELIZE) elseif(MATH_BACKEND STREQUAL "Stl") target_compile_definitions(operon_operon PUBLIC OPERON_MATH_STL) elseif (MATH_BACKEND STREQUAL "Fast_v1") target_compile_definitions(operon_operon PUBLIC OPERON_MATH_FAST_V1) elseif (MATH_BACKEND STREQUAL "Fast_v2") target_compile_definitions(operon_operon PUBLIC OPERON_MATH_FAST_V2) elseif (MATH_BACKEND STREQUAL "Fast_v3") target_compile_definitions(operon_operon PUBLIC OPERON_MATH_FAST_V3) endif() # print summary of enabled/disabled features feature_summary(WHAT ENABLED_FEATURES DESCRIPTION "Enabled features:" QUIET_ON_EMPTY) feature_summary(WHAT DISABLED_FEATURES DESCRIPTION "Disabled features:" QUIET_ON_EMPTY) include(GenerateExportHeader) generate_export_header( operon_operon BASE_NAME operon EXPORT_FILE_NAME export/operon/operon_export.hpp CUSTOM_CONTENT_FROM_VARIABLE pragma_suppress_c4251 ) # ---- Timestamp the current build ---- string(TIMESTAMP OPERON_BUILD_TIMESTAMP "%Y-%m-%dT%H:%M:%SZ") # ---- Add build information ---- configure_file(${CMAKE_CURRENT_SOURCE_DIR}/include/operon/core/buildinfo.hpp.in ${CMAKE_BINARY_DIR}/buildinfo.hpp) if(NOT BUILD_SHARED_LIBS) target_compile_definitions(operon_operon PUBLIC OPERON_STATIC_DEFINE) endif() set_target_properties( operon_operon PROPERTIES CXX_VISIBILITY_PRESET hidden VISIBILITY_INLINES_HIDDEN YES VERSION "${PROJECT_VERSION}" SOVERSION "${PROJECT_VERSION_MAJOR}" EXPORT_NAME operon OUTPUT_NAME operon ) target_include_directories( operon_operon ${operon_warning_guard} PUBLIC "$" ) target_include_directories( operon_operon SYSTEM PUBLIC "$" ) target_include_directories( operon_operon PRIVATE "${PROJECT_BINARY_DIR}" ) if(Ceres_FOUND AND USE_CERES) endif() target_link_libraries(operon_operon INTERFACE eve::eve # required by vstat ) target_link_libraries(operon_operon PUBLIC Eigen3::Eigen Threads::Threads fluky::fluky fmt::fmt lbfgs::lbfgs libassert::assert infix-parser::infix-parser # required by infix parser std::mdspan vstat::vstat ) target_link_libraries(operon_operon PRIVATE AriaCsvParser::AriaCsvParser Taskflow::Taskflow cpp-sort::cpp-sort unordered_dense::unordered_dense gtl::gtl tl::expected ) if (USE_CERES AND Ceres_FOUND) target_link_libraries(operon_operon PUBLIC Ceres::ceres) endif() target_compile_features(operon_operon PUBLIC cxx_std_20) if(MSVC) target_compile_options(operon_operon PRIVATE "/std:c++latest") else() if (UNIX AND NOT APPLE) target_link_options(operon_operon PRIVATE "-Wl,--no-undefined") endif() target_compile_options(operon_operon PRIVATE "-fno-math-errno") endif() target_compile_definitions(operon_operon PUBLIC "$<$:USE_SINGLE_PRECISION>" "$<$:HAVE_CERES>" ) # ---- Install rules ---- if(NOT CMAKE_SKIP_INSTALL_RULES) include(cmake/install-rules.cmake) endif() # ---- Command-line programs ---- if(PROJECT_IS_TOP_LEVEL) option(BUILD_CLI_PROGRAMS "Build command-line programs." TRUE) if (BUILD_CLI_PROGRAMS) add_subdirectory(cli) endif() endif() # ---- Examples ---- if(PROJECT_IS_TOP_LEVEL) option(BUILD_EXAMPLES "Build examples tree." "${operon_DEVELOPER_MODE}") if(BUILD_EXAMPLES) add_subdirectory(example) endif() endif() # ---- Developer mode ---- if(NOT operon_DEVELOPER_MODE) return() elseif(NOT PROJECT_IS_TOP_LEVEL) message( AUTHOR_WARNING "Developer mode is intended for developers of operon" ) endif() include(cmake/dev-mode.cmake) ================================================ FILE: CMakePresets.json ================================================ { "version": 6, "cmakeMinimumRequired": { "major": 3, "minor": 23, "patch": 0 }, "configurePresets": [ { "name": "cmake-pedantic", "hidden": true, "warnings": { "dev": true, "deprecated": true, "uninitialized": true, "unusedCli": true, "systemVars": false }, "errors": { "dev": true, "deprecated": true } }, { "name": "dev-mode", "hidden": true, "inherits": "cmake-pedantic", "cacheVariables": { "operon_DEVELOPER_MODE": "ON" } }, { "name": "cppcheck", "hidden": true, "cacheVariables": { "CMAKE_CXX_CPPCHECK": "cppcheck;--inline-suppr" } }, { "name": "clang-tidy", "hidden": true, "cacheVariables": { "CMAKE_CXX_CLANG_TIDY": "clang-tidy;--header-filter=${sourceDir}/* -test/source/thirdparty/*" } }, { "name": "cpp-std", "description": "This preset makes sure the project actually builds with at least the specified standard", "hidden": true, "cacheVariables": { "CMAKE_CXX_EXTENSIONS": "OFF", "CMAKE_CXX_STANDARD": "20", "CMAKE_CXX_STANDARD_REQUIRED": "ON" } }, { "name": "cpp-build", "hidden": true, "cacheVariables": { "CMAKE_CXX_FLAGS": "$env{CXX_WARNINGS} $env{CXX_OPT}", "CMAKE_BUILD_TYPE": "Release" } }, { "name": "build-windows", "hidden": false, "cacheVariables": { "CMAKE_CXX_FLAGS": "/W4 /permissive- /utf-8 /volatile:iso /EHsc /Zc:__cplusplus /Zc:throwingNew" } }, { "name": "build-linux", "generator": "Unix Makefiles", "hidden": false, "inherits": ["cpp-std", "cpp-build"], "environment": { "CXX_WARNINGS": "-Wall -Wextra -Werror -pedantic", "CXX_OPT": "-fsized-deallocation -fno-math-errno -march=x86-64-v3" } }, { "name": "build-osx", "generator": "Unix Makefiles", "hidden": false, "inherits": ["cpp-std", "cpp-build"], "environment": { "CXX_WARNINGS": "-Wall -Wextra -pedantic", "CXX_OPT": "-fsized-deallocation -fno-math-errno" } } ] } ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Code of Conduct * You will be judged by your contributions first, and your sense of humor second. * Nobody owes you anything. ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing ## Code of Conduct Please see the [`CODE_OF_CONDUCT.md`](CODE_OF_CONDUCT.md) document. ## Getting started Helpful notes for developers can be found in the [`HACKING.md`](HACKING.md) document. In addition to he above, if you use the presets file as instructed, then you should NOT check it into source control, just as the CMake documentation suggests. ================================================ FILE: HACKING.md ================================================ # Hacking Here is some wisdom to help you build and test this project as a developer and potential contributor. If you plan to contribute, please read the [CONTRIBUTING](CONTRIBUTING.md) guide. ## Developer mode Build system targets that are only useful for developers of this project are hidden if the `operon_DEVELOPER_MODE` option is disabled. Enabling this option makes tests and other developer targets and options available. Not enabling this option means that you are a consumer of this project and thus you have no need for these targets and options. Developer mode is always set to on in CI workflows. ### Presets This project makes use of [presets][1] to simplify the process of configuring the project. As a developer, you are recommended to always have the [latest CMake version][2] installed to make use of the latest Quality-of-Life additions. You have a few options to pass `operon_DEVELOPER_MODE` to the configure command, but this project prefers to use presets. As a developer, you should create a `CMakeUserPresets.json` file at the root of the project: ```json { "version": 1, "cmakeMinimumRequired": { "major": 3, "minor": 14, "patch": 0 }, "configurePresets": [ { "name": "dev", "binaryDir": "${sourceDir}/build/dev", "inherits": ["dev-mode", "ci-"] } ] } ``` You should replace `` in your newly created presets file with the name of the operating system you have, which may be `win64` or `unix`. You can see what these correspond to in the [`CMakePresets.json`](CMakePresets.json) file. `CMakeUserPresets.json` is also the perfect place in which you can put all sorts of things that you would otherwise want to pass to the configure command in the terminal. ### Configure, build and test If you followed the above instructions, then you can configure, build and test the project respectively with the following commands from the project root on Windows: ```sh cmake --preset=dev cmake --build build/dev --config Release cd build/dev && ctest -C Release ``` And here is the same on a Unix based system (Linux, macOS): ```sh cmake --preset=dev cmake --build build/dev cd build/dev && ctest ``` [1]: https://cmake.org/cmake/help/latest/manual/cmake-presets.7.html [2]: https://cmake.org/download/ ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2019-2022 Heal Research Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================
# Modern C++ framework for Symbolic Regression [![License](https://img.shields.io/github/license/heal-research/operon?style=flat)](https://github.com/heal-research/operon/blob/master/LICENSE) [![build-linux](https://github.com/heal-research/operon/actions/workflows/build-linux.yml/badge.svg)](https://github.com/heal-research/operon/actions/workflows/build-linux.yml) [![tests](https://github.com/heal-research/operon/actions/workflows/test-linux.yml/badge.svg)](https://github.com/heal-research/operon/actions/workflows/test-linux.yml) [![build-macos](https://github.com/heal-research/operon/actions/workflows/build-macos.yml/badge.svg)](https://github.com/heal-research/operon/actions/workflows/build-macos.yml) [![build-windows](https://github.com/heal-research/operon/actions/workflows/build-windows.yml/badge.svg)](https://github.com/heal-research/operon/actions/workflows/build-windows.yml) [![Documentation Status](https://readthedocs.org/projects/operongp/badge/?version=latest)](https://operongp.readthedocs.io/en/latest/?badge=latest) [![Matrix Channel](https://img.shields.io/matrix/operon%3Amatrix.org)](https://matrix.to/#/#operon:matrix.org) *Operon* is a modern C++ framework for [symbolic regression](https://en.wikipedia.org/wiki/Symbolic_regression) that uses [genetic programming](https://en.wikipedia.org/wiki/Genetic_programming) to explore a hypothesis space of possible mathematical expressions in order to find the best-fitting model for a given [regression target](https://en.wikipedia.org/wiki/Regression_analysis). Its main purpose is to help develop accurate and interpretable white-box models in the area of [system identification](https://en.wikipedia.org/wiki/System_identification). More in-depth documentation available at https://operongp.readthedocs.io/. ## How does it work? Broadly speaking, genetic programming (GP) is said to evolve a population of "computer programs" ― [AST](https://en.wikipedia.org/wiki/Abstract_syntax_tree)-like structures encoding behavior for a given problem domain ― following the principles of [natural selection](https://en.wikipedia.org/wiki/Natural_selection). It repeatedly combines random program parts keeping only the best results ― the "fittest". Here, the biological concept of [fitness](https://en.wikipedia.org/wiki/Survival_of_the_fittest) is defined as a measure of a program's ability to solve a certain task. In symbolic regression, the programs represent mathematical expressions typically encoded as [expression trees](https://en.wikipedia.org/wiki/Binary_expression_tree). Fitness is usually defined as [goodness of fit](https://en.wikipedia.org/wiki/Goodness_of_fit) between the dependent variable and the prediction of a tree-encoded model. Iterative selection of best-scoring models followed by random recombination leads naturally to a self-improving process that is able to uncover patterns in the data:

## Build instructions The project requires CMake and a compiler supporting C++20. The recommended way to build Operon is via either [nix](https://github.com/NixOS/nix) or [vcpkg](https://github.com/microsoft/vcpkg). Check out [https://github.com/heal-research/operon/blob/master/BUILDING.md](BUILDING.md) for detailed build instructions and how to enable/disable certain features. ### Nix First, you have to [install nix](https://nixos.org/download.html) and [enable flakes](https://nixos.wiki/wiki/Flakes). For a portable install, see [nix-portable](https://github.com/DavHau/nix-portable). To create a development shell: ``` nix develop github:heal-research/operon --no-write-lock-file ``` To build Operon (a symlink to the nix store called `result` will be created). ``` nix build github:heal-research/operon --no-write-lock-file ``` ### Vcpkg Select the build generator appropriate for your system and point CMake to the `vcpkg.cmake` toolchain file ``` cmake -S . -B build -G "Visual Studio 16 2019" -A x64 \ -DCMAKE_TOOLCHAIN_FILE=..\vcpkg\scripts\buildsystems\vcpkg.cmake \ -DVCPKG_OVERLAY_PORTS=.\ports ``` The file `CMakePresets.json` contains some presets that you may find useful. For using `clang-cl` instead of `cl`, pass `-TClangCL` to the above ([official documentation](https://docs.microsoft.com/en-us/cpp/build/clang-support-cmake?view=msvc-170)). ## Python wrapper Python bindings for the Operon library are available as a separate project: [PyOperon](https://github.com/heal-research/pyoperon), which also includes a [scikit-learn](https://scikit-learn.org/stable/index.html) compatible regressor. ## Bibtex info If you find _Operon_ useful you can cite our work as: ``` @inproceedings{10.1145/3377929.3398099, author = {Burlacu, Bogdan and Kronberger, Gabriel and Kommenda, Michael}, title = {Operon C++: An Efficient Genetic Programming Framework for Symbolic Regression}, year = {2020}, isbn = {9781450371278}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3377929.3398099}, doi = {10.1145/3377929.3398099}, booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion}, pages = {1562–1570}, numpages = {9}, keywords = {symbolic regression, genetic programming, C++}, location = {Canc\'{u}n, Mexico}, series = {GECCO '20} } ``` _Operon_ was also featured in a recent survey of symbolic regression methods, where it showed good results: ``` @article{DBLP:journals/corr/abs-2107-14351, author = {William G. La Cava and Patryk Orzechowski and Bogdan Burlacu and Fabr{\'{\i}}cio Olivetti de Fran{\c{c}}a and Marco Virgolin and Ying Jin and Michael Kommenda and Jason H. Moore}, title = {Contemporary Symbolic Regression Methods and their Relative Performance}, journal = {CoRR}, volume = {abs/2107.14351}, year = {2021}, url = {https://arxiv.org/abs/2107.14351}, eprinttype = {arXiv}, eprint = {2107.14351}, timestamp = {Tue, 03 Aug 2021 14:53:34 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2107-14351.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ================================================ FILE: cli/CMakeLists.txt ================================================ cmake_minimum_required(VERSION 3.20) project(operonCli LANGUAGES CXX) include(../cmake/project-is-top-level.cmake) include(../cmake/windows-set-path.cmake) if (PROJECT_IS_TOP_LEVEL) find_package(operon REQUIRED) endif() find_package(cxxopts REQUIRED) find_package(scn REQUIRED) function(add_operon_cli NAME) add_executable(${NAME} source/${NAME}.cpp source/operator_factory.cpp source/pareto_front.cpp source/util.cpp ) target_link_libraries(${NAME} PRIVATE operon::operon cxxopts::cxxopts scn::scn) target_compile_features(${NAME} PRIVATE cxx_std_20) set_target_properties(${NAME} PROPERTIES CXX_VISIBILITY_PRESET hidden VISIBILITY_INLINES_HIDDEN YES ) if(MSVC) target_compile_options(${NAME} PRIVATE "/std:c++latest") else() if (UNIX AND NOT APPLE) target_link_options(${NAME} PRIVATE "-Wl,--no-undefined") endif() endif() install(TARGETS ${NAME} RUNTIME COMPONENT operonCli_Runtime DESTINATION "${CMAKE_INSTALL_BINDIR}" ) endfunction() add_operon_cli(operon_gp) add_operon_cli(operon_nsgp) add_operon_cli(operon_parse_model) ================================================ FILE: cli/source/operator_factory.cpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #include "operator_factory.hpp" #include // for runtime_error #include // for format #include #include "operon/operators/creator.hpp" // for CreatorBase, BalancedTreeC... #include "operon/operators/evaluator.hpp" // for Evaluator, EvaluatorBase #include "operon/operators/generator.hpp" // for OffspringGeneratorBase #include "operon/operators/reinserter.hpp" // for OffspringGeneratorBase #include "operon/operators/selector.hpp" #include "operon/operators/local_search.hpp" #include "operon/optimizer/optimizer.hpp" #include namespace Operon { class PrimitiveSet; } namespace Operon { class Problem; } namespace Operon { struct CrossoverBase; } namespace Operon { struct MutatorBase; } namespace Operon { struct Variable; } namespace Operon { namespace detail { auto GetErrorString(std::string const& name, std::string const& arg) { return fmt::format("unable to parse {} argument '{}'", name, arg); } } // namespace detail auto ParseReinserter(std::string const& str, ComparisonCallback&& comp) -> std::unique_ptr { std::unique_ptr reinserter; if (str == "keep-best") { reinserter = std::make_unique(std::move(comp)); } else if (str == "replace-worst") { reinserter = std::make_unique(std::move(comp)); } else { throw std::invalid_argument(detail::GetErrorString("reinserter", str)); } return reinserter; } auto ParseSelector(std::string const& str, ComparisonCallback&& comp) -> std::unique_ptr { auto tok = Split(str, ':'); auto name = tok[0]; std::unique_ptr selector; constexpr size_t defaultTournamentSize{5}; if (name == "tournament") { selector = std::make_unique(std::move(comp)); size_t tournamentSize{defaultTournamentSize}; if (tok.size() > 1) { auto result = scn::scan(tok[1], "{}"); ENSURE(result); tournamentSize = result->value(); } dynamic_cast(selector.get())->SetTournamentSize(tournamentSize); } else if (name == "proportional") { selector = std::make_unique(std::move(comp)); dynamic_cast(selector.get())->SetObjIndex(0); } else if (name == "rank") { selector = std::make_unique(std::move(comp)); size_t tournamentSize{defaultTournamentSize}; if (tok.size() > 1) { auto result = scn::scan(tok[1], "{}"); ENSURE(result); tournamentSize = result->value(); } dynamic_cast(selector.get())->SetTournamentSize(tournamentSize); } else if (name == "random") { selector = std::make_unique(); } else { throw std::invalid_argument(detail::GetErrorString("selector", str)); } return selector; } auto ParseCreator(std::string const& str, PrimitiveSet const& pset, std::vector const& inputs, size_t maxLength) -> std::unique_ptr { std::unique_ptr creator; auto tok = Split(str, ':'); auto name = tok[0]; double bias{0}; // irregularity bias (used by btc and ptc20) if(tok.size() > 1) { auto res = scn::scan(tok[1], "{}"); ENSURE(res); bias = res->value(); } if (str == "btc") { creator = std::make_unique(&pset, inputs, bias, maxLength); } else if (str == "ptc2") { creator = std::make_unique(&pset, inputs, bias, maxLength); } else if (str == "grow") { creator = std::make_unique(&pset, inputs, maxLength); } else { throw std::invalid_argument(detail::GetErrorString("creator", str)); } return creator; } auto ParseEvaluator(std::string const& str, Problem& problem, ScalarDispatch& dtable, bool scale) -> std::unique_ptr { using T = ScalarDispatch; std::unique_ptr evaluator; if (str == "r2") { evaluator = std::make_unique>(&problem, &dtable, Operon::R2{}, scale); } else if (str == "c2") { evaluator = std::make_unique>(&problem, &dtable, Operon::C2{}, scale); } else if (str == "nmse") { evaluator = std::make_unique>(&problem, &dtable, Operon::NMSE{}, scale); } else if (str == "mse") { evaluator = std::make_unique>(&problem, &dtable, Operon::MSE{}, scale); } else if (str == "rmse") { evaluator = std::make_unique>(&problem, &dtable, Operon::RMSE{}, scale); } else if (str == "mae") { evaluator = std::make_unique>(&problem, &dtable, Operon::MAE{}, scale); } else if (str == "mdl_gauss") { evaluator = std::make_unique>>(&problem, &dtable); } else if (str == "mdl_poisson") { evaluator = std::make_unique>>(&problem, &dtable); } else if (str == "fbf_gauss") { evaluator = std::make_unique>>(&problem, &dtable); } else if (str == "fbf_poisson") { throw std::runtime_error("fbf_poisson is not supported: the fractional Bayes factor is derived under Gaussian assumptions"); } else if (str == "gauss") { evaluator = std::make_unique>(&problem, &dtable); } else { throw std::runtime_error(fmt::format("unable to parse evaluator metric '{}'\n", str)); } return evaluator; } auto ParseGenerator(std::string const& str, EvaluatorBase& eval, CrossoverBase& cx, MutatorBase& mut, SelectorBase& femSel, SelectorBase& maleSel, CoefficientOptimizer const* coeffOptimizer = nullptr) -> std::unique_ptr { std::unique_ptr generator; auto tok = Split(str, ':'); auto name = tok[0]; if (name == "basic") { generator = std::make_unique(&eval, &cx, &mut, &femSel, &maleSel, coeffOptimizer); } else if (name == "os") { size_t maxSelectionPressure{100}; double comparisonFactor{0}; if (tok.size() > 1) { maxSelectionPressure = scn::scan(tok[1], "{}")->value(); } if (tok.size() > 2) { comparisonFactor = scn::scan(tok[2], "{}")->value(); } generator = std::make_unique(&eval, &cx, &mut, &femSel, &maleSel, coeffOptimizer); dynamic_cast(generator.get())->MaxSelectionPressure(maxSelectionPressure); dynamic_cast(generator.get())->ComparisonFactor(comparisonFactor); } else if (name == "brood") { generator = std::make_unique(&eval, &cx, &mut, &femSel, &maleSel, coeffOptimizer); size_t broodSize{BroodOffspringGenerator::DefaultBroodSize}; if (tok.size() > 1) { broodSize = scn::scan(tok[1], "{}")->value(); } dynamic_cast(generator.get())->BroodSize(broodSize); } else if (name == "poly") { generator = std::make_unique(&eval, &cx, &mut, &femSel, &maleSel, coeffOptimizer); size_t polygenicSize{PolygenicOffspringGenerator::DefaultBroodSize}; if (tok.size() > 1) { polygenicSize = scn::scan(tok[1], "{}")->value(); } dynamic_cast(generator.get())->PolygenicSize(polygenicSize); } else { throw std::invalid_argument(detail::GetErrorString("generator", str)); } return generator; } auto ParseOptimizer(std::string const& /*str*/, Problem const& /*problem*/, ScalarDispatch const& /*dtable*/) -> std::unique_ptr { throw std::runtime_error("not implemented"); } } // namespace Operon ================================================ FILE: cli/source/operator_factory.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_CLI_OPERATOR_FACTORY_HPP #define OPERON_CLI_OPERATOR_FACTORY_HPP #include // for size_t #include // for unique_ptr, make_unique #include // for operator==, string #include // for addressof #include // for vector #include "operon/core/dispatch.hpp" // for DispatchTable #include "operon/core/types.hpp" // for Span #include "operon/core/individual.hpp" // for Comparison #include "operon/interpreter/interpreter.hpp" // for Interpreter #include "operon/optimizer/optimizer.hpp" #include "util.hpp" // for Split namespace Operon { struct EvaluatorBase; } namespace Operon { class KeepBestReinserter; } namespace Operon { class OffspringGeneratorBase; } namespace Operon { class PrimitiveSet; } namespace Operon { class Problem; } namespace Operon { class ReinserterBase; } namespace Operon { class ReplaceWorstReinserter; } namespace Operon { class SelectorBase; } namespace Operon { struct CreatorBase; } namespace Operon { struct CrossoverBase; } namespace Operon { struct ErrorMetric; } namespace Operon { class CoefficientOptimizer; } namespace Operon { struct MutatorBase; } namespace Operon { struct Variable; } namespace Operon { auto ParseReinserter(std::string const& str, ComparisonCallback&& comp) -> std::unique_ptr; auto ParseSelector(std::string const& str, ComparisonCallback&& comp) -> std::unique_ptr; auto ParseCreator(std::string const& str, PrimitiveSet const& pset, std::vector const& inputs, size_t maxLength) -> std::unique_ptr; auto ParseEvaluator(std::string const& str, Problem& problem, ScalarDispatch& dtable, bool scale = true) -> std::unique_ptr; auto ParseErrorMetric(std::string const& str) -> std::tuple, bool>; auto ParseGenerator(std::string const& str, EvaluatorBase& eval, CrossoverBase& cx, MutatorBase& mut, SelectorBase& femSel, SelectorBase& maleSel, CoefficientOptimizer const* cOpt) -> std::unique_ptr; auto ParseOptimizer(std::string const& str, Problem const& problem, ScalarDispatch const& dtable) -> std::unique_ptr; } // namespace Operon #endif ================================================ FILE: cli/source/operon_gp.cpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #include "reporter.hpp" #include #include #include #include #include #include #include #include #include "operon/algorithms/gp.hpp" #include "operon/hash/zobrist.hpp" #include "operon/core/problem.hpp" #include "operon/core/version.hpp" #include "operon/formatter/formatter.hpp" #include "operon/interpreter/interpreter.hpp" #include "operon/operators/creator.hpp" #include "operon/operators/crossover.hpp" #include "operon/operators/evaluator.hpp" #include "operon/operators/generator.hpp" #include "operon/operators/initializer.hpp" #include "operon/operators/mutation.hpp" #include "operon/operators/reinserter.hpp" #include "operon/operators/selector.hpp" #include "operon/optimizer/optimizer.hpp" #include "operator_factory.hpp" #include "util.hpp" auto main(int argc, char** argv) -> int { auto opts = Operon::InitOptions("operon_gp", "Genetic programming symbolic regression"); auto result = Operon::ParseOptions(std::move(opts), argc, argv); // parse and set default values Operon::GeneticAlgorithmConfig config {}; config.Generations = result["generations"].as(); config.PopulationSize = result["population-size"].as(); config.PoolSize = result["pool-size"].as(); config.Evaluations = result["evaluations"].as(); config.Iterations = result["iterations"].as(); config.CrossoverProbability = result["crossover-probability"].as(); config.MutationProbability = result["mutation-probability"].as(); config.TimeLimit = result["timelimit"].as(); config.Seed = std::random_device {}(); // parse remaining configuration Operon::Range trainingRange; Operon::Range testRange; std::unique_ptr dataset; std::string targetName; bool showPrimitiveSet = false; auto threads = std::thread::hardware_concurrency(); auto primitiveSetConfig = Operon::PrimitiveSet::Arithmetic; auto maxLength = result["maxlength"].as(); auto maxDepth = result["maxdepth"].as(); auto crossoverInternalProbability = result["crossover-internal-probability"].as(); auto symbolic = result["symbolic"].as(); try { for (const auto& kv : result.arguments()) { const auto& key = kv.key(); const auto& value = kv.value(); if (key == "dataset") { dataset = std::make_unique(value, true); ENSURE(!dataset->IsView()); } if (key == "seed") { config.Seed = kv.as(); } if (key == "train") { trainingRange = Operon::ParseRange(value); } if (key == "test") { testRange = Operon::ParseRange(value); } if (key == "target") { targetName = value; } if (key == "maxlength") { maxLength = kv.as(); } if (key == "maxdepth") { maxDepth = kv.as(); } if (key == "enable-symbols") { auto mask = Operon::ParsePrimitiveSetConfig(value); primitiveSetConfig |= mask; } if (key == "disable-symbols") { auto mask = ~Operon::ParsePrimitiveSetConfig(value); primitiveSetConfig &= mask; } if (key == "threads") { threads = static_cast(kv.as()); } if (key == "show-primitives") { showPrimitiveSet = true; } } if (showPrimitiveSet) { Operon::PrintPrimitives(primitiveSetConfig); return EXIT_SUCCESS; } // set the target Operon::Variable target; auto res = dataset->GetVariable(targetName); if (!res) { fmt::print(stderr, "error: target variable {} does not exist in the dataset.", targetName); return EXIT_FAILURE; } target = *res; auto const rows { dataset->Rows() }; if (result.count("train") == 0) { trainingRange = Operon::Range { 0, 2 * rows / 3 }; // by default use 66% of the data as training } if (result.count("test") == 0) { // if no test range is specified, we try to infer a reasonable range based on the trainingRange if (trainingRange.Start() > 0) { testRange = Operon::Range { 0, trainingRange.Start() }; } else if (trainingRange.End() < rows) { testRange = Operon::Range { trainingRange.End(), rows }; } else { testRange = Operon::Range { 0, 1 }; } } // validate training range if (trainingRange.Start() >= rows || trainingRange.End() > rows) { fmt::print(stderr, "error: the training range {}:{} exceeds the available data range ({} rows)\n", trainingRange.Start(), trainingRange.End(), dataset->Rows()); return EXIT_FAILURE; } if (trainingRange.Start() > trainingRange.End()) { fmt::print(stderr, "error: invalid training range {}:{}\n", trainingRange.Start(), trainingRange.End()); return EXIT_FAILURE; } std::vector inputs; if (result.count("inputs") == 0) { inputs = dataset->VariableHashes(); std::erase(inputs, target.Hash); } else { auto str = result["inputs"].as(); auto tokens = Operon::Split(str, ','); for (auto const& tok : tokens) { if (auto res = dataset->GetVariable(tok); res.has_value()) { inputs.push_back(res->Hash); } else { fmt::print(stderr, "error: variable {} does not exist in the dataset.", tok); return EXIT_FAILURE; } } } Operon::Problem problem(std::move(dataset)); problem.SetTrainingRange(trainingRange); problem.SetTestRange(testRange); problem.SetTarget(target.Hash); problem.SetInputs(inputs); problem.ConfigurePrimitiveSet(primitiveSetConfig); std::unique_ptr creator; creator = ParseCreator(result["creator"].as(), problem.GetPrimitiveSet(), problem.GetInputs(), maxLength); auto [amin, amax] = problem.GetPrimitiveSet().FunctionArityLimits(); Operon::UniformTreeInitializer treeInitializer(creator.get()); auto const initialMinDepth = result["creator-mindepth"].as(); auto const initialMaxDepth = result["creator-maxdepth"].as(); treeInitializer.ParameterizeDistribution(amin + 1, maxLength); treeInitializer.SetMinDepth(initialMinDepth); treeInitializer.SetMaxDepth(initialMaxDepth); // NOLINT // std::unique_ptr coeffInitializer; std::unique_ptr onePoint; if (symbolic) { using Dist = std::uniform_int_distribution; coeffInitializer = std::make_unique>(); int constexpr range { 5 }; dynamic_cast*>(coeffInitializer.get())->ParameterizeDistribution(-range, +range); onePoint = std::make_unique>(); dynamic_cast*>(onePoint.get())->ParameterizeDistribution(-range, +range); } else { using Dist = std::normal_distribution; coeffInitializer = std::make_unique>(); dynamic_cast(coeffInitializer.get())->ParameterizeDistribution(Operon::Scalar { 0 }, Operon::Scalar { 1 }); onePoint = std::make_unique>(); dynamic_cast*>(onePoint.get())->ParameterizeDistribution(Operon::Scalar { 0 }, Operon::Scalar { 1 }); } Operon::SubtreeCrossover crossover { crossoverInternalProbability, maxDepth, maxLength }; Operon::MultiMutation mutator {}; Operon::ChangeVariableMutation changeVar { problem.GetInputs() }; Operon::ChangeFunctionMutation changeFunc { problem.GetPrimitiveSet() }; Operon::ReplaceSubtreeMutation replaceSubtree { creator.get(), coeffInitializer.get(), maxDepth, maxLength }; Operon::InsertSubtreeMutation insertSubtree { creator.get(), coeffInitializer.get(), maxDepth, maxLength }; Operon::RemoveSubtreeMutation removeSubtree { problem.GetPrimitiveSet() }; Operon::DiscretePointMutation discretePoint; for (auto v : Operon::Math::Constants) { discretePoint.Add(static_cast(v), 1); } mutator.Add(onePoint.get(), 1.0); mutator.Add(&changeVar, 1.0); mutator.Add(&changeFunc, 1.0); mutator.Add(&replaceSubtree, 1.0); mutator.Add(&insertSubtree, 1.0); mutator.Add(&removeSubtree, 1.0); mutator.Add(&discretePoint, 1.0); Operon::ScalarDispatch dtable; auto scale = result["linear-scaling"].as(); auto evaluator = Operon::ParseEvaluator(result["objective"].as(), problem, dtable, scale); evaluator->SetBudget(config.Evaluations); auto optimizer = std::make_unique>(&dtable, &problem); optimizer->SetIterations(config.Iterations); Operon::CoefficientOptimizer cOpt { optimizer.get() }; EXPECT(problem.TrainingRange().Size() > 0); auto comp = [](auto const& lhs, auto const& rhs) { return lhs[0] < rhs[0]; }; auto femaleSelector = Operon::ParseSelector(result["female-selector"].as(), comp); auto maleSelector = Operon::ParseSelector(result["male-selector"].as(), comp); auto generator = Operon::ParseGenerator(result["offspring-generator"].as(), *evaluator, crossover, mutator, *femaleSelector, *maleSelector, &cOpt); auto reinserter = Operon::ParseReinserter(result["reinserter"].as(), comp); std::unique_ptr cache; if (result["transposition-cache"].as()) { Operon::RandomGenerator cacheRng(config.Seed); cache = std::make_unique(cacheRng, static_cast(maxLength)); config.Cache = cache.get(); } Operon::RandomGenerator random(config.Seed); if (result["shuffle"].as()) { problem.GetDataset()->Shuffle(random); } if (result["standardize"].as()) { problem.StandardizeData(problem.TrainingRange()); } tf::Executor executor(threads); Operon::GeneticProgrammingAlgorithm gp { config, &problem, &treeInitializer, coeffInitializer.get(), generator.get(), reinserter.get() }; auto const* ptr = dynamic_cast const*>(evaluator.get()); Operon::Reporter> reporter(ptr); gp.Run(executor, random, [&]() { reporter(executor, gp); }); auto best = reporter.GetBest(); fmt::print("{}\n", Operon::InfixFormatter::Format(best.Genotype, *problem.GetDataset(), 6)); } catch (std::exception& e) { fmt::print(stderr, "error: {}\n", e.what()); return EXIT_FAILURE; } return 0; } ================================================ FILE: cli/source/operon_nsgp.cpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #include #include #include #include #include #include #include #include #include #include "operon/algorithms/nsga2.hpp" #include "operon/hash/zobrist.hpp" #include "operon/core/problem.hpp" #include "operon/core/version.hpp" #include "operon/formatter/formatter.hpp" #include "operon/hash/hash.hpp" #include "operon/interpreter/interpreter.hpp" #include "operon/operators/creator.hpp" #include "operon/operators/crossover.hpp" #include "operon/operators/evaluator.hpp" #include "operon/operators/generator.hpp" #include "operon/operators/initializer.hpp" #include "operon/operators/mutation.hpp" #include "operon/operators/non_dominated_sorter.hpp" #include "operon/operators/reinserter.hpp" #include "operon/operators/selector.hpp" #include "operon/optimizer/likelihood/gaussian_likelihood.hpp" #include "operon/optimizer/optimizer.hpp" #include "operon/optimizer/solvers/sgd.hpp" #include "operator_factory.hpp" #include "pareto_front.hpp" #include "reporter.hpp" #include "util.hpp" auto main(int argc, char** argv) -> int { auto opts = Operon::InitOptions("operon_gp", "Genetic programming symbolic regression"); auto result = Operon::ParseOptions(std::move(opts), argc, argv); // parse and set default values Operon::GeneticAlgorithmConfig config {}; config.Generations = result["generations"].as(); config.PopulationSize = result["population-size"].as(); config.PoolSize = result["pool-size"].as(); config.Epsilon = result["epsilon"].as(); config.Evaluations = result["evaluations"].as(); config.Iterations = result["iterations"].as(); config.CrossoverProbability = result["crossover-probability"].as(); config.MutationProbability = result["mutation-probability"].as(); config.LocalSearchProbability = result["local-search-probability"].as(); config.LamarckianProbability = result["lamarckian-probability"].as(); config.TimeLimit = result["timelimit"].as(); config.Seed = std::random_device {}(); // parse remaining config options Operon::Range trainingRange; Operon::Range testRange; std::unique_ptr dataset; std::string targetName; bool showPrimitiveSet = false; auto threads = std::thread::hardware_concurrency(); auto primitiveSetConfig = Operon::PrimitiveSet::Arithmetic; auto maxLength = result["maxlength"].as(); auto maxDepth = result["maxdepth"].as(); auto crossoverInternalProbability = result["crossover-internal-probability"].as(); auto symbolic = result["symbolic"].as(); try { for (const auto& kv : result.arguments()) { const auto& key = kv.key(); const auto& value = kv.value(); if (key == "dataset") { dataset = std::make_unique(value, true); ENSURE(!dataset->IsView()); } if (key == "seed") { config.Seed = kv.as(); } if (key == "train") { trainingRange = Operon::ParseRange(value); } if (key == "test") { testRange = Operon::ParseRange(value); } if (key == "target") { targetName = value; } if (key == "maxlength") { maxLength = kv.as(); } if (key == "maxdepth") { maxDepth = kv.as(); } if (key == "enable-symbols") { auto mask = Operon::ParsePrimitiveSetConfig(value); primitiveSetConfig |= mask; } if (key == "disable-symbols") { auto mask = ~Operon::ParsePrimitiveSetConfig(value); primitiveSetConfig &= mask; } if (key == "threads") { threads = static_cast(kv.as()); } if (key == "show-primitives") { showPrimitiveSet = true; } } if (showPrimitiveSet) { Operon::PrintPrimitives(primitiveSetConfig); return EXIT_SUCCESS; } // set the target Operon::Variable target; auto res = dataset->GetVariable(targetName); if (!res) { fmt::print(stderr, "error: target variable {} does not exist in the dataset.", targetName); return EXIT_FAILURE; } target = *res; auto const rows { dataset->Rows() }; if (result.count("train") == 0) { trainingRange = Operon::Range { 0, 2 * rows / 3 }; // by default use 66% of the data as training } if (result.count("test") == 0) { // if no test range is specified, we try to infer a reasonable range based on the trainingRange if (trainingRange.Start() > 0) { testRange = Operon::Range { 0, trainingRange.Start() }; } else if (trainingRange.End() < rows) { testRange = Operon::Range { trainingRange.End(), dataset->Rows() }; } else { testRange = Operon::Range { 0, 1 }; } } // validate training range if (trainingRange.Start() >= rows || trainingRange.End() > rows) { fmt::print(stderr, "error: the training range {}:{} exceeds the available data range ({} rows)\n", trainingRange.Start(), trainingRange.End(), dataset->Rows()); return EXIT_FAILURE; } if (trainingRange.Start() > trainingRange.End()) { fmt::print(stderr, "error: invalid training range {}:{}\n", trainingRange.Start(), trainingRange.End()); return EXIT_FAILURE; } std::vector inputs; if (result.count("inputs") == 0) { inputs = dataset->VariableHashes(); std::erase(inputs, target.Hash); } else { auto str = result["inputs"].as(); auto tokens = Operon::Split(str, ','); for (auto const& tok : tokens) { if (auto res = dataset->GetVariable(tok); res.has_value()) { inputs.push_back(res->Hash); } else { fmt::print(stderr, "error: variable {} does not exist in the dataset.", tok); return EXIT_FAILURE; } } } Operon::Problem problem(std::move(dataset)); problem.SetTrainingRange(trainingRange); problem.SetTestRange(testRange); problem.SetTarget(target.Hash); problem.SetInputs(inputs); problem.ConfigurePrimitiveSet(primitiveSetConfig); std::unique_ptr creator; creator = ParseCreator(result["creator"].as(), problem.GetPrimitiveSet(), problem.GetInputs(), maxLength); auto [amin, amax] = problem.GetPrimitiveSet().FunctionArityLimits(); Operon::UniformTreeInitializer treeInitializer(creator.get()); auto const initialMinDepth = result["creator-mindepth"].as(); auto const initialMaxDepth = result["creator-maxdepth"].as(); auto const initialMaxLength = result["creator-maxlength"].as(); treeInitializer.ParameterizeDistribution(amin + 1, initialMaxLength); treeInitializer.SetMinDepth(initialMinDepth); treeInitializer.SetMaxDepth(initialMaxDepth); // NOLINT std::unique_ptr coeffInitializer; std::unique_ptr onePoint; if (symbolic) { using Dist = std::uniform_int_distribution; coeffInitializer = std::make_unique>(); int constexpr range { 5 }; dynamic_cast*>(coeffInitializer.get())->ParameterizeDistribution(-range, +range); onePoint = std::make_unique>(); dynamic_cast*>(onePoint.get())->ParameterizeDistribution(-range, +range); } else { using Dist = std::normal_distribution; coeffInitializer = std::make_unique>(); dynamic_cast(coeffInitializer.get())->ParameterizeDistribution(Operon::Scalar { 0 }, Operon::Scalar { 1 }); onePoint = std::make_unique>(); dynamic_cast*>(onePoint.get())->ParameterizeDistribution(Operon::Scalar { 0 }, Operon::Scalar { 1 }); } Operon::SubtreeCrossover crossover { crossoverInternalProbability, maxDepth, maxLength }; Operon::MultiMutation mutator {}; Operon::ChangeVariableMutation changeVar { problem.GetInputs() }; Operon::ChangeFunctionMutation changeFunc { problem.GetPrimitiveSet() }; Operon::ReplaceSubtreeMutation replaceSubtree { creator.get(), coeffInitializer.get(), maxDepth, maxLength }; Operon::InsertSubtreeMutation insertSubtree { creator.get(), coeffInitializer.get(), maxDepth, maxLength }; Operon::RemoveSubtreeMutation removeSubtree { problem.GetPrimitiveSet() }; Operon::DiscretePointMutation discretePoint; for (auto v : Operon::Math::Constants) { discretePoint.Add(static_cast(v), 1); } mutator.Add(onePoint.get(), 1.0); mutator.Add(&changeVar, 1.0); mutator.Add(&changeFunc, 1.0); mutator.Add(&replaceSubtree, 1.0); mutator.Add(&insertSubtree, 1.0); mutator.Add(&removeSubtree, 1.0); mutator.Add(&discretePoint, 1.0); Operon::ScalarDispatch dtable; // DynamicPrimitives::Saxpy> f{}; // dtable.RegisterCallable(12345UL, f, f); auto scale = result["linear-scaling"].as(); auto errorEvaluator = Operon::ParseEvaluator(result["objective"].as(), problem, dtable, scale); errorEvaluator->SetBudget(config.Evaluations); auto optimizer = std::make_unique>(&dtable, &problem); optimizer->SetIterations(config.Iterations); Operon::LengthEvaluator lengthEvaluator(&problem, maxLength); // Operon::EntropyEvaluator entropyEvaluator(&problem); Operon::MultiEvaluator evaluator(&problem); evaluator.SetBudget(config.Evaluations); evaluator.Add(errorEvaluator.get()); evaluator.Add(&lengthEvaluator); // evaluator.Add(&entropyEvaluator); EXPECT(problem.TrainingRange().Size() > 0); Operon::CrowdedComparison comp; auto femaleSelector = Operon::ParseSelector(result["female-selector"].as(), comp); auto maleSelector = Operon::ParseSelector(result["male-selector"].as(), comp); Operon::CoefficientOptimizer cOpt { optimizer.get() }; auto generator = Operon::ParseGenerator(result["offspring-generator"].as(), evaluator, crossover, mutator, *femaleSelector, *maleSelector, &cOpt); auto reinserter = Operon::ParseReinserter(result["reinserter"].as(), comp); std::unique_ptr cache; if (result["transposition-cache"].as()) { Operon::RandomGenerator cacheRng(config.Seed); cache = std::make_unique(cacheRng, static_cast(maxLength)); config.Cache = cache.get(); } Operon::RandomGenerator random(config.Seed); if (result["shuffle"].as()) { problem.GetDataset()->Shuffle(random); } if (result["standardize"].as()) { problem.StandardizeData(problem.TrainingRange()); } tf::Executor executor(threads); Operon::RankIntersectSorter sorter; // Operon::RankOrdinalSorter sorter; // Operon::MergeSorter sorter; // Operon::BestOrderSorter sorter; // Operon::EfficientBinarySorter sorter; Operon::NSGA2 gp { config, &problem, &treeInitializer, coeffInitializer.get(), generator.get(), reinserter.get(), &sorter }; auto const* ptr = dynamic_cast const*>(errorEvaluator.get()); Operon::Reporter> reporter(ptr); gp.Run(executor, random, [&]() { reporter(executor, gp); }); auto best = reporter.GetBest(); fmt::print("{}\n", Operon::InfixFormatter::Format(best.Genotype, *problem.GetDataset(), std::numeric_limits::digits)); if (result.contains("pareto-front")) { Operon::WriteParetoFront(result["pareto-front"].as(), gp.Individuals(), dtable, problem, scale); } } catch (std::exception& e) { fmt::print(stderr, "error: {}\n", e.what()); return EXIT_FAILURE; } return 0; } ================================================ FILE: cli/source/operon_parse_model.cpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #include #include #include #include "operon/core/dataset.hpp" #include "operon/core/types.hpp" #include "operon/core/dispatch.hpp" #include "operon/core/problem.hpp" #include "operon/formatter/formatter.hpp" #include "operon/optimizer/likelihood/gaussian_likelihood.hpp" #include "operon/optimizer/optimizer.hpp" #include "operon/parser/infix.hpp" #include "operon/interpreter/interpreter.hpp" #include "operon/operators/evaluator.hpp" #include "util.hpp" #include "reporter.hpp" #include #include #include #include namespace { enum class ParseError : std::uint8_t { Success = 0, MissingDataset = 1, MissingInfix = 2, NoOptions = 3, UnknownError = 4 }; auto ParseOptions(int argc, char** argv) noexcept -> tl::expected { cxxopts::Options opts("operon_parse_model", "Parse and evaluate a model in infix form"); opts.add_options() ("dataset", "Dataset file name (csv) (required)", cxxopts::value()) ("target", "Name of the target variable (if none provided, model output will be printed)", cxxopts::value()) ("range", "Data range [A:B)", cxxopts::value()) ("scale", "Linear scaling slope:intercept", cxxopts::value()) ("optimizer", "Optimizer for model coefficients (lm, lbfgs, sgd)", cxxopts::value()->default_value("lm")) ("likelihood", "Optimizer loss function (gaussian, poisson)", cxxopts::value()->default_value("gaussian")) ("iterations", "Optimizer iterations", cxxopts::value()->default_value("50")) ("debug", "Show some debugging information", cxxopts::value()->default_value("false")) ("format", "Format string (see https://fmt.dev/latest/syntax.html)", cxxopts::value()->default_value(":>#8.4g")) ("help", "Print help"); opts.allow_unrecognised_options(); cxxopts::ParseResult result; try { result = opts.parse(argc, argv); } catch (cxxopts::exceptions::parsing const& ex) { fmt::print(stderr, "error: {}. rerun with --help to see available options.\n", ex.what()); return tl::make_unexpected(ParseError::UnknownError); }; if (result.arguments().empty() || result.count("help") > 0) { fmt::print("{}\n", opts.help()); return tl::make_unexpected(ParseError::NoOptions); } if (result.count("dataset") == 0) { fmt::print(stderr, "error: no dataset was specified.\n"); return tl::make_unexpected(ParseError::MissingDataset); } if (result.unmatched().empty()) { fmt::print(stderr, "error: no infix string was provided.\n"); return tl::make_unexpected(ParseError::MissingInfix); } return result; } auto ParseOptimizer(Operon::ScalarDispatch const* dtable, Operon::Problem const* problem, std::string const& optimizer, std::string const& likelihood) { std::unique_ptr opt; if (optimizer == "lm") { opt = std::make_unique>(dtable, problem); } else if (optimizer == "lbfgs") { if (likelihood == "gaussian") { opt = std::make_unique>>(dtable, problem); } else if (likelihood == "poisson") { opt = std::make_unique>>(dtable, problem); } } else if (optimizer == "sgd") { if (likelihood == "gaussian") { opt = std::make_unique>>(dtable, problem); } else if (likelihood == "poisson") { opt = std::make_unique>>(dtable, problem); } } return opt; } } // namespace auto main(int argc, char** argv) -> int { auto out = ParseOptions(argc, argv); if (!out.has_value()) { return EXIT_FAILURE; } auto const& result = out.value(); Operon::Dataset ds(result["dataset"].as(), /*hasHeader=*/true); auto infix = result.unmatched().front(); auto model = Operon::InfixParser::Parse(infix, ds); Operon::ScalarDispatch dtable; Operon::Range range{0, ds.Rows()}; if (result["range"].count() > 0) { auto res = scn::scan(result["range"].as(), "{}:{}"); ENSURE(res); auto [a, b] = res->values(); range = Operon::Range{a, b}; } int constexpr defaultPrecision{6}; if (result["debug"].as()) { fmt::print("\nInput string:\n{}\n", infix); fmt::print("Parsed tree:\n{}\n", Operon::InfixFormatter::Format(model, ds, defaultPrecision)); fmt::print("Data range: {}:{}\n", range.Start(), range.End()); fmt::print("Scale: {}\n", result["scale"].count() > 0 ? result["scale"].as() : std::string("auto")); } using Interpreter = Operon::Interpreter; auto est = Interpreter::Evaluate(model, ds, range); std::string format = result["format"].as(); if (result["target"].count() > 0) { auto tgt = ds.GetValues(result["target"].as()).subspan(range.Start(), range.Size()); Operon::Scalar a{0}; Operon::Scalar b{0}; if (result["scale"].count() > 0) { auto res = scn::scan(result["scale"].as(), "{}:{}"); ENSURE(res); a = std::get<0>(res->values()); b = std::get<1>(res->values()); } else { auto [a_, b_] = Operon::FitLeastSquares(est, tgt); a = static_cast(a_); b = static_cast(b_); } std::ranges::transform(est, est.begin(), [&](auto v) { return (v * a) + b; }); auto r2 = -Operon::R2{}(Operon::Span{est}, tgt); auto rs = -Operon::C2{}(Operon::Span{est}, tgt); auto mae = Operon::MAE{}(Operon::Span{est}, tgt); auto mse = Operon::MSE{}(Operon::Span{est}, tgt); auto rmse = Operon::RMSE{}(Operon::Span{est}, tgt); auto nmse = Operon::NMSE{}(Operon::Span{est}, tgt); Operon::Problem problem{&ds}; problem.SetTrainingRange(range); problem.SetTestRange(range); Operon::RandomGenerator rng{0}; Operon::Individual ind; ind.Genotype = model; Operon::Interpreter interpreter{&dtable, &ds, &ind.Genotype}; Operon::MinimumDescriptionLengthEvaluator> mdlEval{&problem, &dtable}; auto mdl = mdlEval(rng, ind).front(); auto opt = ParseOptimizer(&dtable, &problem, result["optimizer"].as(), result["likelihood"].as()); opt->SetIterations(result["iterations"].as()); auto summary = opt->Optimize(rng, model); std::vector> stats{ {"slope", a, format}, {"intercept", b, format}, {"r2", r2, format}, {"rs", rs, format}, {"mae", mae, format}, {"mse", mse, format}, {"rmse", rmse, format}, {"nmse", nmse, format}, {"mdl", mdl, format} }; Operon::Reporter::PrintStats(stats, /*printHeader=*/true); if (opt->Iterations() > 0) { fmt::print("optimization summary:\n"); fmt::print("status: {}\n", summary.Success); fmt::print("initial cost: {}\n", summary.InitialCost); fmt::print("final cost: {}\n", summary.FinalCost); } } else { std::string out{}; for (auto v : est) { fmt::format_to(std::back_inserter(out), fmt::runtime(fmt::format("{{{}}}\n", format)), v); } fmt::print("{}", out); } return EXIT_SUCCESS; } ================================================ FILE: cli/source/pareto_front.cpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #include "pareto_front.hpp" #include #include #include #include #include #include #include "operon/core/node.hpp" #include "operon/core/types.hpp" #include "operon/formatter/formatter.hpp" #include "operon/interpreter/interpreter.hpp" #include "operon/operators/evaluator.hpp" #include "operon/optimizer/likelihood/gaussian_likelihood.hpp" namespace Operon { auto WriteParetoFront(std::string const& path, Operon::Span population, ScalarDispatch const& dtable, Problem const& problem, bool linearScaling) -> void { auto const* ds = problem.GetDataset(); auto const trainRange = problem.TrainingRange(); auto const testRange = problem.TestRange(); auto const targetTrain = problem.TargetValues(trainRange); auto const targetTest = problem.TargetValues(testRange); // collect rank-0 individuals sorted by first objective std::vector front; for (auto const& ind : population) { if (ind.Rank == 0) { front.push_back(&ind); } } std::ranges::sort(front, [](auto const* a, auto const* b) { return (*a)[0] < (*b)[0]; }); auto jsonNum = [](double v) -> std::string { if (!std::isfinite(v)) { return "null"; } return fmt::format("{:.17g}", v); }; auto out = fmt::output_file(path); out.print("[\n"); for (auto i = 0UL; i < front.size(); ++i) { auto const* ind = front[i]; Interpreter interp{&dtable, ds, &ind->Genotype}; auto estimTrain = interp.Evaluate(ind->Genotype.GetCoefficients(), trainRange); auto estimTest = interp.Evaluate(ind->Genotype.GetCoefficients(), testRange); if (linearScaling) { auto [a, b] = FitLeastSquares( Span{estimTrain}, Span{targetTrain}); auto const as = static_cast(a); auto const bs = static_cast(b); for (auto& v : estimTrain) { v = (v * as) + bs; } for (auto& v : estimTest) { v = (v * as) + bs; } } auto const r2Train = -R2{}(estimTrain, targetTrain); auto const r2Test = -R2{}(estimTest, targetTest); auto const mseTrain = MSE{}(estimTrain, targetTrain); auto const mseTest = MSE{}(estimTest, targetTest); auto const nmseTrain = NMSE{}(estimTrain, targetTrain); auto const nmseTest = NMSE{}(estimTest, targetTest); auto const maeTrain = MAE{}(estimTrain, targetTrain); auto const maeTest = MAE{}(estimTest, targetTest); // weighted node count and structural complexity static auto const MulHash = Node{NodeType::Mul}.HashValue; static auto const ParamHash = Node{NodeType::Constant}.HashValue; Set uniqueSymbols; auto k = 0.0; for (auto const& node : ind->Genotype.Nodes()) { auto const isWeighted = node.IsVariable() && node.Value != Scalar{1}; k += isWeighted ? 3.0 : 1.0; uniqueSymbols.insert(node.HashValue); if (isWeighted) { uniqueSymbols.insert(MulHash); uniqueSymbols.insert(ParamHash); } } auto const q = static_cast(uniqueSymbols.size()); auto const fCompl = q > 0.0 ? k * std::log(q) : 0.0; // MLE sigma estimate from training residuals auto const n = static_cast(trainRange.Size()); auto const sigmaHat = static_cast(std::sqrt(mseTrain)); Scalar sigmaArr[] = {sigmaHat}; Span sigmaSpan{sigmaArr, 1}; // FBF (no Jacobian required) auto const p = static_cast(ind->Genotype.GetCoefficients().size()); auto const b = 1.0 / std::sqrt(n); auto const fbfParams = (p / 2.0) * (0.5 * std::log(n) + std::log(Math::Tau) + 1.0 - std::log(3.0)); auto const nll = static_cast(GaussianLikelihood::ComputeLikelihood( {estimTrain.data(), estimTrain.size()}, targetTrain, sigmaSpan)); auto const fbfLikelihood = (1.0 - b) * nll; auto fbf = fCompl + fbfParams + fbfLikelihood; if (!std::isfinite(fbf)) { fbf = std::numeric_limits::quiet_NaN(); } // MDL (requires Jacobian for Fisher diagonal) // Fisher = J^T J / σ² (Gaussian) auto const coeffs = ind->Genotype.GetCoefficients(); auto const jac = interp.JacRev(coeffs, trainRange); auto const nrows = static_cast(trainRange.Size()); auto const ncols = static_cast(coeffs.size()); Eigen::Map const> jacMap(jac.data(), nrows, ncols); auto const sigma2 = static_cast(mseTrain); auto const fisherDiag = (jacMap.colwise().squaredNorm().transpose().array() / sigma2); auto cComplexity = fCompl; auto cParameters = 0.0; constexpr auto eps = std::numeric_limits::epsilon(); auto pi = 0; for (auto const& node : ind->Genotype.Nodes()) { if (node.Optimize) { auto const fi = static_cast(fisherDiag(pi)); auto const di = std::sqrt(12.0 / fi); auto const ci = std::abs(static_cast(coeffs[pi])); if (std::isfinite(ci) && std::isfinite(di) && ci / di >= 1.0) { cParameters += 0.5 * std::log(fi) + std::log(ci); } ++pi; } else { if (std::abs(node.Value) >= static_cast(eps)) { cComplexity += std::log(std::abs(node.Value)); } } } cParameters -= (p / 2.0) * std::log(3.0); auto const cLikelihood = nll; auto mdl = cComplexity + cParameters + cLikelihood; if (!std::isfinite(mdl)) { mdl = std::numeric_limits::quiet_NaN(); } auto expr = InfixFormatter::Format(ind->Genotype, *ds, std::numeric_limits::digits10); std::string escaped; escaped.reserve(expr.size()); for (char c : expr) { if (c == '"') { escaped += "\\\""; } else if (c == '\\') { escaped += "\\\\"; } else { escaped += c; } } std::string objArr = "["; for (auto j = 0UL; j < ind->Fitness.size(); ++j) { if (j > 0) { objArr += ", "; } objArr += jsonNum(ind->Fitness[j]); } objArr += "]"; if (i > 0) { out.print(",\n"); } out.print( " {{\"id\": {}, \"expression\": \"{}\", \"length\": {}, \"complexity\": {}, \"objectives\": {},\n" " \"r2_train\": {}, \"r2_test\": {},\n" " \"mse_train\": {}, \"mse_test\": {},\n" " \"nmse_train\": {}, \"nmse_test\": {},\n" " \"mae_train\": {}, \"mae_test\": {},\n" " \"mdl\": {}, \"fbf\": {}}}", i, escaped, ind->Genotype.AdjustedLength(), static_cast(k), objArr, jsonNum(r2Train), jsonNum(r2Test), jsonNum(mseTrain), jsonNum(mseTest), jsonNum(nmseTrain), jsonNum(nmseTest), jsonNum(maeTrain), jsonNum(maeTest), jsonNum(mdl), jsonNum(fbf)); } out.print("\n]\n"); } } // namespace Operon ================================================ FILE: cli/source/pareto_front.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_CLI_PARETO_FRONT_HPP #define OPERON_CLI_PARETO_FRONT_HPP #include "operon/core/dispatch.hpp" #include "operon/core/individual.hpp" #include "operon/core/problem.hpp" #include "operon/core/types.hpp" #include namespace Operon { // Write the rank-0 individuals from `population` to a JSON file at `path`. // Each entry contains the infix expression, tree length, weighted complexity (k), // raw objective values, R²/MSE/NMSE/MAE on train+test, MDL, and FBF. // If `linearScaling` is true, a least-squares (a, b) fit is applied to each // individual's train predictions before computing all metrics. auto WriteParetoFront(std::string const& path, Operon::Span population, ScalarDispatch const& dtable, Problem const& problem, bool linearScaling) -> void; } // namespace Operon #endif ================================================ FILE: cli/source/reporter.hpp ================================================ #ifndef OPERON_CLI_REPORTER_HPP #define OPERON_CLI_REPORTER_HPP #include #include #include #include namespace Operon { template class Reporter { gsl::not_null evaluator_; mutable Operon::Individual best_; public: explicit Reporter(gsl::not_null evaluator) : evaluator_(evaluator) {} static auto PrintStats(std::vector> const& stats, bool printHeader) -> void { std::vector widths; auto out = fmt::memory_buffer(); for (auto const& [name, value, format] : stats) { fmt::format_to(std::back_inserter(out), fmt::runtime(fmt::format("{{{}}}", format)), value); auto width = std::max(name.size(), fmt::to_string(out).size()); widths.push_back(width); out.clear(); } if (printHeader) { for (auto i = 0UL; i < stats.size(); ++i) { fmt::print("{} ", fmt::format("{:>{}}", std::get<0>(stats[i]), widths[i])); } fmt::print("\n"); } for (auto i = 0UL; i < stats.size(); ++i) { fmt::format_to(std::back_inserter(out), fmt::runtime(fmt::format("{{{}}}", std::get<2>(stats[i]))), std::get<1>(stats[i])); fmt::print("{} ", fmt::format("{:>{}}", fmt::to_string(out), widths[i])); out.clear(); } fmt::print("\n"); } auto GetBest() const -> Operon::Individual const& { return best_; } auto operator()(tf::Executor& executor, Operon::GeneticAlgorithmBase const& gp) const -> void { auto const config = gp.GetConfig(); auto const pop = gp.Parents(); auto const off = gp.Offspring(); constexpr auto idx{0}; auto getBest = [&](Operon::Span pop) -> Operon::Individual { const auto minElem = std::min_element(pop.begin(), pop.end(), [&](auto const& lhs, auto const& rhs) { return lhs[idx] < rhs[idx]; }); return *minElem; }; best_ = getBest(pop); ENSURE(best_.Size() > 0); tf::Taskflow tf; tf.name("report results"); auto const* problem = gp.GetProblem(); auto trainingRange = problem->TrainingRange(); auto testRange = problem->TestRange(); auto targetTrain = problem->TargetValues(trainingRange); auto targetTest = problem->TargetValues(testRange); Operon::Vector estimatedTrain; Operon::Vector estimatedTest; auto const* dataset = problem->GetDataset(); auto dtable = evaluator_->GetDispatchTable(); using Interpreter = typename Evaluator::TInterpreter; auto evaluate = tf.emplace([&](tf::Subflow& sf) { sf.emplace([&]() { Interpreter interpreter{dtable, dataset, &best_.Genotype}; estimatedTrain = interpreter.Evaluate(best_.Genotype.GetCoefficients(), trainingRange); ENSURE(trainingRange.Size() > 0 && estimatedTrain.size() == trainingRange.Size()); }).name("eval train"); sf.emplace([&]() { Interpreter interpreter{dtable, dataset, &best_.Genotype}; estimatedTest = interpreter.Evaluate(best_.Genotype.GetCoefficients(), testRange); ENSURE(testRange.Size() > 0 && estimatedTest.size() == testRange.Size()); }).name("eval test"); }); // scale values Operon::Scalar a{1.0}; Operon::Scalar b{0.0}; auto linearScaling = tf.emplace([&]() { auto [a_, b_] = Operon::FitLeastSquares(estimatedTrain, targetTrain); a = static_cast(a_); b = static_cast(b_); // add scaling terms to the tree auto& nodes = best_.Genotype.Nodes(); auto const sz = nodes.size(); if (std::abs(a - Operon::Scalar{1}) > std::numeric_limits::epsilon()) { nodes.emplace_back(Operon::Node::Constant(a)); nodes.emplace_back(Operon::NodeType::Mul); } if (std::abs(b) > std::numeric_limits::epsilon()) { nodes.emplace_back(Operon::Node::Constant(b)); nodes.emplace_back(Operon::NodeType::Add); } if (nodes.size() > sz) { best_.Genotype.UpdateNodes(); } }).name("linear scaling"); double r2Train{}; double r2Test{}; double nmseTrain{}; double nmseTest{}; double maeTrain{}; double maeTest{}; auto scale = tf.emplace([&](tf::Subflow& sf) { sf.emplace([&]() { ENSURE(estimatedTrain.size() == trainingRange.Size()); Eigen::Map> estimated(estimatedTrain.data(), std::ssize(estimatedTrain)); estimated = estimated * a + b; }).name("scale train"); sf.emplace([&]() { ENSURE(estimatedTest.size() == testRange.Size()); Eigen::Map> estimated(estimatedTest.data(), std::ssize(estimatedTest)); estimated = estimated * a + b; }).name("scale test"); }); auto calcStats = tf.emplace([&]() { ENSURE(!best_.Genotype.Empty()); // negate the R2 because this is an internal fitness measure (minimization) which we here repurpose r2Train = -Operon::R2{}(estimatedTrain, targetTrain); r2Test = -Operon::R2{}(estimatedTest, targetTest); nmseTrain = Operon::NMSE{}(estimatedTrain, targetTrain); nmseTest = Operon::NMSE{}(estimatedTest, targetTest); maeTrain = Operon::MAE{}(estimatedTrain, targetTrain); maeTest = Operon::MAE{}(estimatedTest, targetTest); }).name("calc stats"); double avgLength = 0; double avgQuality = 0; double totalMemory = 0; auto getSize = [](Operon::Individual const& ind) { return sizeof(ind) + sizeof(ind.Genotype) + sizeof(Operon::Node) * ind.Genotype.Nodes().capacity(); }; auto calculateLength = tf.transform_reduce(pop.begin(), pop.end(), avgLength, std::plus{}, [](auto const& ind) { return ind.Genotype.Length(); }).name("calc length"); auto calculateQuality = tf.transform_reduce(pop.begin(), pop.end(), avgQuality, std::plus{}, [idx=idx](auto const& ind) { return ind[idx]; }).name("calc quality"); auto calculatePopMemory = tf.transform_reduce(pop.begin(), pop.end(), totalMemory, std::plus{}, [&](auto const& ind) { return getSize(ind); }).name("calc parent mem"); auto calculateOffMemory = tf.transform_reduce(off.begin(), off.end(), totalMemory, std::plus{}, [&](auto const& ind) { return getSize(ind); }).name("calc child mem"); // define task graph evaluate.precede(linearScaling); linearScaling.precede(scale); calcStats.succeed(scale); calcStats.precede(calculateLength, calculateQuality, calculatePopMemory, calculateOffMemory); // taskflow.dump(std::cout); executor.corun(tf); // executor.wait_for_all(); avgLength /= static_cast(pop.size()); avgQuality /= static_cast(pop.size()); using T = std::tuple; auto const* format = ":>#8.3g"; // see https://fmt.dev/latest/syntax.html auto [resEval, jacEval, callCount, cfTime ] = evaluator_->Stats(); std::array stats { T{ "iteration", gp.Generation(), ":>" }, T{ "r2_tr", r2Train, format }, T{ "r2_te", r2Test, format }, T{ "mae_tr", maeTrain, format }, T{ "mae_te", maeTest, format }, T{ "nmse_tr", nmseTrain, format }, T{ "nmse_te", nmseTest, format }, T{ "best_fit", best_[idx], format }, T{ "avg_fit", avgQuality, format }, T{ "best_len", best_.Genotype.Length(), format }, T{ "avg_len", avgLength, format }, T{ "eval_cnt", callCount, ":>" }, T{ "res_eval", resEval, ":>" }, T{ "jac_eval", jacEval, ":>" }, T{ "opt_time", cfTime, ":>" }, T{ "seed", config.Seed, ":>10" }, T{ "elapsed", gp.Elapsed(), ":>"}, }; PrintStats({ stats.begin(), stats.end() }, gp.Generation() == 0); } }; } // namespace Operon #endif ================================================ FILE: cli/source/util.cpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #include #include #include #include #include #include #include #include #include #include #include #include #include #include "util.hpp" #include "operon/core/node.hpp" #include "operon/core/pset.hpp" #include "operon/core/version.hpp" #include "operon/core/types.hpp" using Operon::NodeType; namespace Operon { static const Operon::Map Primitives { { "add", NodeType::Add }, { "mul", NodeType::Mul }, { "sub", NodeType::Sub }, { "div", NodeType::Div }, { "fmin", NodeType::Fmin }, { "fmax", NodeType::Fmax }, { "aq", NodeType::Aq }, { "pow", NodeType::Pow }, { "powabs", NodeType::Powabs }, { "abs", NodeType::Abs }, { "acos", NodeType::Acos }, { "asin", NodeType::Asin }, { "atan", NodeType::Atan }, { "cbrt", NodeType::Cbrt }, { "ceil", NodeType::Ceil }, { "cos", NodeType::Cos }, { "cosh", NodeType::Cosh }, { "exp", NodeType::Exp }, { "floor", NodeType::Floor }, { "log", NodeType::Log }, { "logabs", NodeType::Logabs }, { "log1p", NodeType::Log1p }, { "sin", NodeType::Sin }, { "sinh", NodeType::Sinh }, { "sqrt", NodeType::Sqrt }, { "sqrtabs", NodeType::Sqrtabs }, { "tan", NodeType::Tan }, { "tanh", NodeType::Tanh }, { "square", NodeType::Square }, { "dyn", NodeType::Dynamic }, { "constant", NodeType::Constant }, { "variable", NodeType::Variable } }; auto Split(const std::string& s, char delimiter) -> std::vector { std::vector tokens; std::string token; std::istringstream tokenStream(s); while (std::getline(tokenStream, token, delimiter)) { tokens.push_back(token); } return tokens; } // splits a string into substrings separated by delimiter // formats a duration as dd:hh:mm:ss.ms auto FormatDuration(std::chrono::duration d) -> std::string { auto h = std::chrono::duration_cast(d); auto m = std::chrono::duration_cast(d - h); auto s = std::chrono::duration_cast(d - h - m); auto l = std::chrono::duration_cast(d - h - m - s); return fmt::format("{:#02d}:{:#02d}:{:#02d}.{:#03d}", h.count(), m.count(), s.count(), l.count()); } auto FormatBytes(size_t bytes) -> std::string { constexpr std::array sizes{" KMGT"}; constexpr size_t base{1024}; auto p = static_cast(std::floor(std::log2(bytes) / std::log2(base))); return fmt::format("{:.2f} {}b", static_cast(bytes) / std::pow(base, p), sizes.at(p)); } auto ParseRange(std::string const& str) -> std::pair { auto result = scn::scan(str, "{}:{}"); if (!result) { throw std::runtime_error(fmt::format("could not parse range '{}'", str)); } auto [a, b] = result->values(); return std::make_pair(a, b); } auto ParsePrimitiveSetConfig(const std::string& options) -> PrimitiveSetConfig { PrimitiveSetConfig config{}; for (auto& s : Split(options, ',')) { if (auto it = Primitives.find(s); it != Primitives.end()) { config |= it->second; } else { throw std::runtime_error(fmt::format("Unrecognized symbol {}\n", s)); } } return config; } auto PrintPrimitives(PrimitiveSetConfig config) -> void { PrimitiveSet tmpSet; tmpSet.SetConfig(config); fmt::print("Built-in primitives:\n"); fmt::print("{:<8}\t{:<50}\t{:>7}\t\t{:>9}\n", "Symbol", "Description", "Enabled", "Frequency"); for (size_t i = 0; i < Operon::NodeTypes::Count; ++i) { auto type = static_cast(i); auto hash = Node(type).HashValue; auto enabled = tmpSet.Contains(hash) && tmpSet.IsEnabled(hash); auto freq = enabled ? tmpSet.Frequency(hash) : 0U; Node node(type); fmt::print("{:<8}\t{:<50}\t{:>7}\t\t{:>9}\n", node.Name(), node.Desc(), enabled, freq != 0U ? std::to_string(freq) : "-"); } } auto InitOptions(std::string const& name, std::string const& desc, int width) -> cxxopts::Options { cxxopts::Options opts(name, desc); opts.set_width(width); std::string const symbols = "add, sub, mul, div, exp, log, square, sqrt, cbrt, sin, cos, tan, asin, acos, atan, sinh, cosh, tanh, abs, aq, ceil, floor, fmin, fmax, log1p, logabs, sqrtabs, pow, powabs"; opts.add_options() ("dataset", "Dataset file name (csv) (required)", cxxopts::value()) ("shuffle", "Shuffle the input data", cxxopts::value()->default_value("false")) ("standardize", "Standardize the training partition (zero mean, unit variance)", cxxopts::value()->default_value("false")) ("train", "Training range specified as start:end (required)", cxxopts::value()) ("test", "Test range specified as start:end", cxxopts::value()) ("target", "Name of the target variable (required)", cxxopts::value()) ("inputs", "Comma-separated list of input variables", cxxopts::value()) ("epsilon", "Tolerance for fitness comparison (needed e.g. for eps-dominance)", cxxopts::value()->default_value("1e-6")) ("objective", "The error metric used for calculating fitness", cxxopts::value()->default_value("r2")) ("linear-scaling", "Apply linear scaling on model predictions", cxxopts::value()->default_value("true")) ("population-size", "Population size", cxxopts::value()->default_value("1000")) ("pool-size", "Recombination pool size (how many generated offspring per generation)", cxxopts::value()->default_value("1000")) ("seed", "Random number seed", cxxopts::value()->default_value("0")) ("generations", "Number of generations", cxxopts::value()->default_value("1000")) ("evaluations", "Evaluation budget", cxxopts::value()->default_value("1000000")) ("iterations", "Local optimization iterations", cxxopts::value()->default_value("0")) ("selection-pressure", "Selection pressure", cxxopts::value()->default_value("100")) ("maxlength", "Maximum length", cxxopts::value()->default_value("50")) ("maxdepth", "Maximum depth", cxxopts::value()->default_value("10")) ("crossover-probability", "The probability to apply crossover", cxxopts::value()->default_value("1.0")) ("crossover-internal-probability", "Crossover bias towards swapping function nodes", cxxopts::value()->default_value("0.9")) ("mutation-probability", "The probability to apply mutation", cxxopts::value()->default_value("0.25")) ("creator", "Tree creator operator to initialize the population with.", cxxopts::value()->default_value("btc")) ("creator-mindepth", "Minimum tree depth (applies to the grow tree creator)", cxxopts::value()->default_value("1")) ("creator-maxdepth", "Minimum tree depth (applies to all tree creators)", cxxopts::value()->default_value("100")) ("creator-maxlength", "Maximum tree length (applies to all tree creators)", cxxopts::value()->default_value("50")) ("female-selector", "Female selection operator, with optional parameters separated by : (eg, --selector tournament:5)", cxxopts::value()->default_value("tournament")) ("male-selector", "Male selection operator, with optional parameters separated by : (eg, --selector tournament:5)", cxxopts::value()->default_value("tournament")) ("offspring-generator", "OffspringGenerator operator, with optional parameters separated by : (eg --offspring-generator brood:10:10)", cxxopts::value()->default_value("basic")) ("reinserter", "Reinsertion operator merging offspring in the recombination pool back into the population", cxxopts::value()->default_value("keep-best")) ("enable-symbols", "Comma-separated list of enabled symbols ("+symbols+")", cxxopts::value()) ("local-search-probability", "Probability for local search", cxxopts::value()->default_value("1.0")) ("lamarckian-probability", "Probability that the local search improvements are saved back into the chromosome", cxxopts::value()->default_value("1.0")) ("disable-symbols", "Comma-separated list of disabled symbols ("+symbols+")", cxxopts::value()) ("symbolic", "Operate in symbolic mode - no coefficient tuning or coefficient mutation", cxxopts::value()->default_value("false")) ("show-primitives", "Display the primitive set used by the algorithm") ("threads", "Number of threads to use for parallelism", cxxopts::value()->default_value("0")) ("timelimit", "Time limit after which the algorithm will terminate", cxxopts::value()->default_value(std::to_string(std::numeric_limits::max()))) ("transposition-cache", "Cache fitness values keyed by Zobrist hash of tree structure; most effective with coefficient optimization enabled", cxxopts::value()->default_value("false")) ("pareto-front", "Write rank-0 Pareto front to this JSON file after the run (only effective with Pareto-based algorithms, e.g. operon_nsgp)", cxxopts::value()) ("debug", "Debug mode (more information displayed)") ("help", "Print help") ("version", "Print version and program information"); return opts; } auto ParseOptions(cxxopts::Options&& opts, int argc, char** argv) -> cxxopts::ParseResult { cxxopts::ParseResult result; try { result = opts.parse(argc, argv); } catch (cxxopts::exceptions::parsing const& ex) { fmt::print(stderr, "error: {}. rerun with --help to see available options.\n", ex.what()); std::exit(EXIT_FAILURE); } if (result.arguments().empty() || result.contains("help")) { fmt::print("{}\n", opts.help()); std::exit(EXIT_SUCCESS); } if (result.contains("version")) { fmt::print("{}\n", Operon::Version()); std::exit(EXIT_SUCCESS); } if (!result.contains("target")) { fmt::print(stderr, "error: no target variable was specified.\n"); std::exit(EXIT_FAILURE); } if (!result.contains("dataset")) { fmt::print(stderr, "error: no dataset was specified.\n"); std::exit(EXIT_FAILURE); } return result; } } // namespace Operon ================================================ FILE: cli/source/util.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_CLI_UTIL_HPP #define OPERON_CLI_UTIL_HPP #include "operon/core/node.hpp" #include #include #include #include #include #include #include namespace Operon { constexpr int optionsWidth = 200; auto ParseRange(std::string const& str) -> std::pair; auto Split(const std::string& s, char delimiter) -> std::vector; auto FormatBytes(size_t bytes) -> std::string; auto FormatDuration(std::chrono::duration d) -> std::string; auto ParsePrimitiveSetConfig(const std::string& options) -> PrimitiveSetConfig; auto PrintPrimitives(PrimitiveSetConfig config) -> void; auto InitOptions(std::string const& name, std::string const& desc, int width = optionsWidth) -> cxxopts::Options; auto ParseOptions(cxxopts::Options&& opts, int argc, char** argv) -> cxxopts::ParseResult; } // namespace Operon #endif ================================================ FILE: cmake/coverage.cmake ================================================ # ---- Variables ---- # We use variables separate from what CTest uses, because those have # customization issues set( COVERAGE_TRACE_COMMAND lcov -c -q -o "${PROJECT_BINARY_DIR}/coverage.info" -d "${PROJECT_BINARY_DIR}" --include "${PROJECT_SOURCE_DIR}/*" CACHE STRING "; separated command to generate a trace for the 'coverage' target" ) set( COVERAGE_HTML_COMMAND genhtml --legend -f -q "${PROJECT_BINARY_DIR}/coverage.info" -p "${PROJECT_SOURCE_DIR}" -o "${PROJECT_BINARY_DIR}/coverage_html" CACHE STRING "; separated command to generate an HTML report for the 'coverage' target" ) # ---- Coverage target ---- add_custom_target( coverage COMMAND ${COVERAGE_TRACE_COMMAND} COMMAND ${COVERAGE_HTML_COMMAND} COMMENT "Generating coverage report" VERBATIM ) ================================================ FILE: cmake/dev-mode.cmake ================================================ include(CTest) if(BUILD_TESTING) add_subdirectory(test) endif() option(BUILD_MCSS_DOCS "Build documentation using Doxygen and m.css" OFF) if(BUILD_MCSS_DOCS) include(cmake/docs.cmake) endif() option(ENABLE_COVERAGE "Enable coverage support separate from CTest's" OFF) if(ENABLE_COVERAGE) include(cmake/coverage.cmake) endif() if(CMAKE_HOST_SYSTEM_NAME STREQUAL "Windows") include(cmake/open-cpp-coverage.cmake OPTIONAL) endif() set(CMAKE_EXPORT_COMPILE_COMMANDS ON CACHE INTERNAL "") if(CMAKE_EXPORT_COMPILE_COMMANDS) set(CMAKE_CXX_STANDARD_INCLUDE_DIRECTORIES ${CMAKE_CXX_IMPLICIT_INCLUDE_DIRECTORIES}) endif() include(cmake/lint-targets.cmake) include(cmake/spell-targets.cmake) ================================================ FILE: cmake/docs.cmake ================================================ # ---- Redefine docs_early_return ---- # This function must be a macro, so the return() takes effect in the calling # scope. This prevents other targets from being available and potentially # requiring dependencies. This cuts down on the time it takes to generate # documentation in CI. macro(docs_early_return) return() endmacro() # ---- Dependencies ---- include(FetchContent) FetchContent_Declare( mcss URL https://github.com/friendlyanon/m.css/releases/download/release-1/mcss.zip URL_MD5 00cd2757ebafb9bcba7f5d399b3bec7f SOURCE_DIR "${PROJECT_BINARY_DIR}/mcss" UPDATE_DISCONNECTED YES ) FetchContent_MakeAvailable(mcss) find_package(Python3 3.9 REQUIRED) # ---- Declare documentation target ---- set( DOXYGEN_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/docs" CACHE PATH "Path for the generated Doxygen documentation" ) set(working_dir "${PROJECT_BINARY_DIR}/docs") foreach(file IN ITEMS Doxyfile conf.py) configure_file("docs/${file}.in" "${working_dir}/${file}" @ONLY) endforeach() set(mcss_script "${mcss_SOURCE_DIR}/documentation/doxygen.py") set(config "${working_dir}/conf.py") add_custom_target( docs COMMAND "${CMAKE_COMMAND}" -E remove_directory "${DOXYGEN_OUTPUT_DIRECTORY}/html" "${DOXYGEN_OUTPUT_DIRECTORY}/xml" COMMAND "${Python3_EXECUTABLE}" "${mcss_script}" "${config}" COMMENT "Building documentation using Doxygen and m.css" WORKING_DIRECTORY "${working_dir}" VERBATIM ) ================================================ FILE: cmake/get-git-revision.cmake ================================================ # - Returns a version string from Git # # These functions force a re-configure on each git commit so that you can # trust the values of the variables in your build system. # # get_git_head_revision( [ALLOW_LOOKING_ABOVE_CMAKE_SOURCE_DIR]) # # Returns the refspec and sha hash of the current head revision # # git_describe( [ ...]) # # Returns the results of git describe on the source tree, and adjusting # the output so that it tests false if an error occurs. # # git_describe_working_tree( [ ...]) # # Returns the results of git describe on the working tree (--dirty option), # and adjusting the output so that it tests false if an error occurs. # # git_get_exact_tag( [ ...]) # # Returns the results of git describe --exact-match on the source tree, # and adjusting the output so that it tests false if there was no exact # matching tag. # # git_local_changes() # # Returns either "CLEAN" or "DIRTY" with respect to uncommitted changes. # Uses the return code of "git diff-index --quiet HEAD --". # Does not regard untracked files. # # Requires CMake 2.6 or newer (uses the 'function' command) # # Original Author: # 2009-2020 Ryan Pavlik # http://academic.cleardefinition.com # # Copyright 2009-2013, Iowa State University. # Copyright 2013-2020, Ryan Pavlik # Copyright 2013-2020, Contributors # SPDX-License-Identifier: BSL-1.0 # Distributed under the Boost Software License, Version 1.0. # (See accompanying file LICENSE_1_0.txt or copy at # http://www.boost.org/LICENSE_1_0.txt) if(__get_git_revision_description) return() endif() set(__get_git_revision_description YES) # We must run the following at "include" time, not at function call time, # to find the path to this module rather than the path to a calling list file get_filename_component(_gitdescmoddir ${CMAKE_CURRENT_LIST_FILE} PATH) # Function _git_find_closest_git_dir finds the next closest .git directory # that is part of any directory in the path defined by _start_dir. # The result is returned in the parent scope variable whose name is passed # as variable _git_dir_var. If no .git directory can be found, the # function returns an empty string via _git_dir_var. # # Example: Given a path C:/bla/foo/bar and assuming C:/bla/.git exists and # neither foo nor bar contain a file/directory .git. This wil return # C:/bla/.git # function(_git_find_closest_git_dir _start_dir _git_dir_var) set(cur_dir "${_start_dir}") set(git_dir "${_start_dir}/.git") while(NOT EXISTS "${git_dir}") # .git dir not found, search parent directories set(git_previous_parent "${cur_dir}") get_filename_component(cur_dir ${cur_dir} DIRECTORY) if(cur_dir STREQUAL git_previous_parent) # We have reached the root directory, we are not in git set(${_git_dir_var} "" PARENT_SCOPE) return() endif() set(git_dir "${cur_dir}/.git") endwhile() set(${_git_dir_var} "${git_dir}" PARENT_SCOPE) endfunction() function(get_git_head_revision _refspecvar _hashvar) _git_find_closest_git_dir("${CMAKE_CURRENT_SOURCE_DIR}" GIT_DIR) if("${ARGN}" STREQUAL "ALLOW_LOOKING_ABOVE_CMAKE_SOURCE_DIR") set(ALLOW_LOOKING_ABOVE_CMAKE_SOURCE_DIR TRUE) else() set(ALLOW_LOOKING_ABOVE_CMAKE_SOURCE_DIR FALSE) endif() if(NOT "${GIT_DIR}" STREQUAL "") file(RELATIVE_PATH _relative_to_source_dir "${CMAKE_SOURCE_DIR}" "${GIT_DIR}") if("${_relative_to_source_dir}" MATCHES "[.][.]" AND NOT ALLOW_LOOKING_ABOVE_CMAKE_SOURCE_DIR) # We've gone above the CMake root dir. set(GIT_DIR "") endif() endif() if("${GIT_DIR}" STREQUAL "") set(${_refspecvar} "GITDIR-NOTFOUND" PARENT_SCOPE) set(${_hashvar} "GITDIR-NOTFOUND" PARENT_SCOPE) return() endif() # Check if the current source dir is a git submodule or a worktree. # In both cases .git is a file instead of a directory. # if(NOT IS_DIRECTORY ${GIT_DIR}) # The following git command will return a non empty string that # points to the super project working tree if the current # source dir is inside a git submodule. # Otherwise the command will return an empty string. # execute_process( COMMAND "${GIT_EXECUTABLE}" rev-parse --show-superproject-working-tree WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}" OUTPUT_VARIABLE out ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) if(NOT "${out}" STREQUAL "") # If out is empty, GIT_DIR/CMAKE_CURRENT_SOURCE_DIR is in a submodule file(READ ${GIT_DIR} submodule) string(REGEX REPLACE "gitdir: (.*)$" "\\1" GIT_DIR_RELATIVE ${submodule}) string(STRIP ${GIT_DIR_RELATIVE} GIT_DIR_RELATIVE) get_filename_component(SUBMODULE_DIR ${GIT_DIR} PATH) get_filename_component(GIT_DIR ${SUBMODULE_DIR}/${GIT_DIR_RELATIVE} ABSOLUTE) set(HEAD_SOURCE_FILE "${GIT_DIR}/HEAD") else() # GIT_DIR/CMAKE_CURRENT_SOURCE_DIR is in a worktree file(READ ${GIT_DIR} worktree_ref) # The .git directory contains a path to the worktree information directory # inside the parent git repo of the worktree. # string(REGEX REPLACE "gitdir: (.*)$" "\\1" git_worktree_dir ${worktree_ref}) string(STRIP ${git_worktree_dir} git_worktree_dir) _git_find_closest_git_dir("${git_worktree_dir}" GIT_DIR) set(HEAD_SOURCE_FILE "${git_worktree_dir}/HEAD") endif() else() set(HEAD_SOURCE_FILE "${GIT_DIR}/HEAD") endif() set(GIT_DATA "${CMAKE_CURRENT_BINARY_DIR}/CMakeFiles/git-data") if(NOT EXISTS "${GIT_DATA}") file(MAKE_DIRECTORY "${GIT_DATA}") endif() if(NOT EXISTS "${HEAD_SOURCE_FILE}") return() endif() set(HEAD_FILE "${GIT_DATA}/HEAD") configure_file("${HEAD_SOURCE_FILE}" "${HEAD_FILE}" COPYONLY) configure_file("${_gitdescmoddir}/get-git-revision.cmake.in" "${GIT_DATA}/grabRef.cmake" @ONLY) include("${GIT_DATA}/grabRef.cmake") set(${_refspecvar} "${HEAD_REF}" PARENT_SCOPE) set(${_hashvar} "${HEAD_HASH}" PARENT_SCOPE) endfunction() function(git_describe _var) if(NOT GIT_FOUND) find_package(Git QUIET) endif() get_git_head_revision(refspec hash) if(NOT GIT_FOUND) set(${_var} "GIT-NOTFOUND" PARENT_SCOPE) return() endif() if(NOT hash) set(${_var} "HEAD-HASH-NOTFOUND" PARENT_SCOPE) return() endif() # TODO sanitize #if((${ARGN}" MATCHES "&&") OR # (ARGN MATCHES "||") OR # (ARGN MATCHES "\\;")) # message("Please report the following error to the project!") # message(FATAL_ERROR "Looks like someone's doing something nefarious with git_describe! Passed arguments ${ARGN}") #endif() #message(STATUS "Arguments to execute_process: ${ARGN}") execute_process( COMMAND "${GIT_EXECUTABLE}" describe --tags --always ${hash} ${ARGN} WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}" RESULT_VARIABLE res OUTPUT_VARIABLE out ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) if(NOT res EQUAL 0) set(out "${out}-${res}-NOTFOUND") endif() set(${_var} "${out}" PARENT_SCOPE) endfunction() function(git_describe_working_tree _var) if(NOT GIT_FOUND) find_package(Git QUIET) endif() if(NOT GIT_FOUND) set(${_var} "GIT-NOTFOUND" PARENT_SCOPE) return() endif() execute_process( COMMAND "${GIT_EXECUTABLE}" describe --dirty ${ARGN} WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}" RESULT_VARIABLE res OUTPUT_VARIABLE out ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) if(NOT res EQUAL 0) set(out "${out}-${res}-NOTFOUND") endif() set(${_var} "${out}" PARENT_SCOPE) endfunction() function(git_get_exact_tag _var) git_describe(out --exact-match ${ARGN}) set(${_var} "${out}" PARENT_SCOPE) endfunction() function(git_local_changes _var) if(NOT GIT_FOUND) find_package(Git QUIET) endif() get_git_head_revision(refspec hash) if(NOT GIT_FOUND) set(${_var} "GIT-NOTFOUND" PARENT_SCOPE) return() endif() if(NOT hash) set(${_var} "HEAD-HASH-NOTFOUND" PARENT_SCOPE) return() endif() execute_process( COMMAND "${GIT_EXECUTABLE}" diff-index --quiet HEAD -- WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}" RESULT_VARIABLE res OUTPUT_VARIABLE out ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) if(res EQUAL 0) set(${_var} "CLEAN" PARENT_SCOPE) else() set(${_var} "DIRTY" PARENT_SCOPE) endif() endfunction() ================================================ FILE: cmake/get-git-revision.cmake.in ================================================ # # Internal file for GetGitRevisionDescription.cmake # # Requires CMake 2.6 or newer (uses the 'function' command) # # Original Author: # 2009-2010 Ryan Pavlik # http://academic.cleardefinition.com # Iowa State University HCI Graduate Program/VRAC # # Copyright 2009-2012, Iowa State University # Copyright 2011-2015, Contributors # Distributed under the Boost Software License, Version 1.0. # (See accompanying file LICENSE_1_0.txt or copy at # http://www.boost.org/LICENSE_1_0.txt) # SPDX-License-Identifier: BSL-1.0 set(HEAD_HASH) file(READ "@HEAD_FILE@" HEAD_CONTENTS LIMIT 1024) string(STRIP "${HEAD_CONTENTS}" HEAD_CONTENTS) if(HEAD_CONTENTS MATCHES "ref") # named branch string(REPLACE "ref: " "" HEAD_REF "${HEAD_CONTENTS}") if(EXISTS "@GIT_DIR@/${HEAD_REF}") configure_file("@GIT_DIR@/${HEAD_REF}" "@GIT_DATA@/head-ref" COPYONLY) else() configure_file("@GIT_DIR@/packed-refs" "@GIT_DATA@/packed-refs" COPYONLY) file(READ "@GIT_DATA@/packed-refs" PACKED_REFS) if(${PACKED_REFS} MATCHES "([0-9a-z]*) ${HEAD_REF}") set(HEAD_HASH "${CMAKE_MATCH_1}") endif() endif() else() # detached HEAD configure_file("@GIT_DIR@/HEAD" "@GIT_DATA@/head-ref" COPYONLY) endif() if(NOT HEAD_HASH) file(READ "@GIT_DATA@/head-ref" HEAD_HASH LIMIT 1024) string(STRIP "${HEAD_HASH}" HEAD_HASH) endif() ================================================ FILE: cmake/install-config.cmake ================================================ include(CMakeFindDependencyMacro) # PUBLIC dependencies find_dependency(Eigen3 CONFIG REQUIRED) find_dependency(Threads REQUIRED) find_dependency(fluky CONFIG REQUIRED) find_dependency(fmt CONFIG REQUIRED) find_dependency(lbfgs CONFIG REQUIRED) find_dependency(libassert CONFIG REQUIRED) find_dependency(infix-parser CONFIG REQUIRED) find_dependency(mdspan CONFIG REQUIRED) find_dependency(vstat CONFIG REQUIRED) # INTERFACE dependencies find_dependency(eve CONFIG REQUIRED) # PRIVATE dependencies (needed for static library) find_dependency(AriaCsvParser CONFIG REQUIRED) find_dependency(Taskflow CONFIG REQUIRED) find_dependency(cpp-sort CONFIG REQUIRED) find_dependency(unordered_dense CONFIG REQUIRED) find_dependency(tl-expected CONFIG REQUIRED) include("${CMAKE_CURRENT_LIST_DIR}/operonTargets.cmake") check_required_components(operon) ================================================ FILE: cmake/install-rules.cmake ================================================ if(PROJECT_IS_TOP_LEVEL) set(CMAKE_INSTALL_INCLUDEDIR include/operon CACHE PATH "") endif() include(CMakePackageConfigHelpers) include(GNUInstallDirs) # find_package() call for consumers to find this project set(package operon) install( DIRECTORY include/ "${PROJECT_BINARY_DIR}/export/" DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}" COMPONENT operon_Development ) install( TARGETS operon_operon EXPORT operonTargets RUNTIME # COMPONENT operon_Runtime LIBRARY # COMPONENT operon_Runtime NAMELINK_COMPONENT operon_Development ARCHIVE # COMPONENT operon_Development INCLUDES # DESTINATION "${CMAKE_INSTALL_INCLUDEDIR}" ) write_basic_package_version_file( "${package}ConfigVersion.cmake" COMPATIBILITY SameMajorVersion ) # Allow package maintainers to freely override the path for the configs set( operon_INSTALL_CMAKEDIR "${CMAKE_INSTALL_DATADIR}/${package}" CACHE PATH "CMake package config location relative to the install prefix" ) mark_as_advanced(operon_INSTALL_CMAKEDIR) install( FILES cmake/install-config.cmake DESTINATION "${operon_INSTALL_CMAKEDIR}" RENAME "${package}Config.cmake" COMPONENT operon_Development ) install( FILES "${PROJECT_BINARY_DIR}/${package}ConfigVersion.cmake" DESTINATION "${operon_INSTALL_CMAKEDIR}" COMPONENT operon_Development ) install( EXPORT operonTargets NAMESPACE operon:: DESTINATION "${operon_INSTALL_CMAKEDIR}" COMPONENT operon_Development ) if(PROJECT_IS_TOP_LEVEL) include(CPack) endif() ================================================ FILE: cmake/lint-targets.cmake ================================================ set( FORMAT_PATTERNS source/*.cpp source/*.hpp include/*.hpp test/*.cpp test/*.hpp example/*.cpp example/*.hpp CACHE STRING "; separated patterns relative to the project source dir to format" ) set(FORMAT_COMMAND clang-format CACHE STRING "Formatter to use") add_custom_target( format-check COMMAND "${CMAKE_COMMAND}" -D "FORMAT_COMMAND=${FORMAT_COMMAND}" -D "PATTERNS=${FORMAT_PATTERNS}" -P "${PROJECT_SOURCE_DIR}/cmake/lint.cmake" WORKING_DIRECTORY "${PROJECT_SOURCE_DIR}" COMMENT "Linting the code" VERBATIM ) add_custom_target( format-fix COMMAND "${CMAKE_COMMAND}" -D "FORMAT_COMMAND=${FORMAT_COMMAND}" -D "PATTERNS=${FORMAT_PATTERNS}" -D FIX=YES -P "${PROJECT_SOURCE_DIR}/cmake/lint.cmake" WORKING_DIRECTORY "${PROJECT_SOURCE_DIR}" COMMENT "Fixing the code" VERBATIM ) ================================================ FILE: cmake/lint.cmake ================================================ cmake_minimum_required(VERSION 3.14) macro(default name) if(NOT DEFINED "${name}") set("${name}" "${ARGN}") endif() endmacro() default(FORMAT_COMMAND clang-format) default( PATTERNS source/*.cpp source/*.hpp include/*.hpp test/*.cpp test/*.hpp example/*.cpp example/*.hpp ) default(FIX NO) set(flag --output-replacements-xml) set(args OUTPUT_VARIABLE output) if(FIX) set(flag -i) set(args "") endif() file(GLOB_RECURSE files ${PATTERNS}) set(badly_formatted "") set(output "") string(LENGTH "${CMAKE_SOURCE_DIR}/" path_prefix_length) foreach(file IN LISTS files) execute_process( COMMAND "${FORMAT_COMMAND}" --style=file "${flag}" "${file}" WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}" RESULT_VARIABLE result ${args} ) if(NOT result EQUAL "0") message(FATAL_ERROR "'${file}': formatter returned with ${result}") endif() if(NOT FIX AND output MATCHES "\n ...) function(windows_set_path TEST) if(NOT CMAKE_HOST_SYSTEM_NAME STREQUAL "Windows") return() endif() set(path "") set(glue "") foreach(target IN LISTS ARGN) get_target_property(type "${target}" TYPE) if(type STREQUAL "SHARED_LIBRARY") set(path "${path}${glue}$") set(glue "\;") # backslash is important endif() endforeach() if(NOT path STREQUAL "") set_property(TEST "${TEST}" PROPERTY ENVIRONMENT "PATH=${path}") endif() endfunction() ================================================ FILE: 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8000,2,0.2286,71.3,0.00293031,115.996 10000,2,0.2286,71.3,0.00293031,113.086 400,2,0.2286,55.5,0.00313525,122.292 500,2,0.2286,55.5,0.00313525,124.692 630,2,0.2286,55.5,0.00313525,126.842 800,2,0.2286,55.5,0.00313525,128.492 1000,2,0.2286,55.5,0.00313525,129.002 1250,2,0.2286,55.5,0.00313525,128.762 1600,2,0.2286,55.5,0.00313525,126.752 2000,2,0.2286,55.5,0.00313525,124.612 2500,2,0.2286,55.5,0.00313525,123.862 3150,2,0.2286,55.5,0.00313525,123.742 4000,2,0.2286,55.5,0.00313525,122.232 5000,2,0.2286,55.5,0.00313525,120.472 6300,2,0.2286,55.5,0.00313525,118.712 315,2,0.2286,39.6,0.00346574,120.137 400,2,0.2286,39.6,0.00346574,122.147 500,2,0.2286,39.6,0.00346574,125.157 630,2,0.2286,39.6,0.00346574,127.417 800,2,0.2286,39.6,0.00346574,129.037 1000,2,0.2286,39.6,0.00346574,129.147 1250,2,0.2286,39.6,0.00346574,128.257 1600,2,0.2286,39.6,0.00346574,125.837 2000,2,0.2286,39.6,0.00346574,122.797 2500,2,0.2286,39.6,0.00346574,121.397 3150,2,0.2286,39.6,0.00346574,121.627 4000,2,0.2286,39.6,0.00346574,120.227 5000,2,0.2286,39.6,0.00346574,118.827 6300,2,0.2286,39.6,0.00346574,116.417 315,2,0.2286,31.7,0.00372371,120.147 400,2,0.2286,31.7,0.00372371,123.417 500,2,0.2286,31.7,0.00372371,126.677 630,2,0.2286,31.7,0.00372371,129.057 800,2,0.2286,31.7,0.00372371,130.307 1000,2,0.2286,31.7,0.00372371,130.307 1250,2,0.2286,31.7,0.00372371,128.677 1600,2,0.2286,31.7,0.00372371,125.797 2000,2,0.2286,31.7,0.00372371,123.037 2500,2,0.2286,31.7,0.00372371,121.407 3150,2,0.2286,31.7,0.00372371,121.527 4000,2,0.2286,31.7,0.00372371,120.527 5000,2,0.2286,31.7,0.00372371,118.267 6300,2,0.2286,31.7,0.00372371,115.137 500,4,0.2286,71.3,0.00400603,126.758 630,4,0.2286,71.3,0.00400603,129.038 800,4,0.2286,71.3,0.00400603,130.688 1000,4,0.2286,71.3,0.00400603,131.708 1250,4,0.2286,71.3,0.00400603,131.718 1600,4,0.2286,71.3,0.00400603,129.468 2000,4,0.2286,71.3,0.00400603,126.218 2500,4,0.2286,71.3,0.00400603,124.338 3150,4,0.2286,71.3,0.00400603,124.108 4000,4,0.2286,71.3,0.00400603,121.728 5000,4,0.2286,71.3,0.00400603,121.118 6300,4,0.2286,71.3,0.00400603,118.618 8000,4,0.2286,71.3,0.00400603,112.848 10000,4,0.2286,71.3,0.00400603,113.108 12500,4,0.2286,71.3,0.00400603,114.258 16000,4,0.2286,71.3,0.00400603,112.768 20000,4,0.2286,71.3,0.00400603,109.638 400,4,0.2286,55.5,0.0042862,123.274 500,4,0.2286,55.5,0.0042862,127.314 630,4,0.2286,55.5,0.0042862,129.964 800,4,0.2286,55.5,0.0042862,131.864 1000,4,0.2286,55.5,0.0042862,132.134 1250,4,0.2286,55.5,0.0042862,131.264 1600,4,0.2286,55.5,0.0042862,128.264 2000,4,0.2286,55.5,0.0042862,124.254 2500,4,0.2286,55.5,0.0042862,122.384 3150,4,0.2286,55.5,0.0042862,122.394 4000,4,0.2286,55.5,0.0042862,120.654 5000,4,0.2286,55.5,0.0042862,120.034 6300,4,0.2286,55.5,0.0042862,117.154 8000,4,0.2286,55.5,0.0042862,112.524 315,4,0.2286,39.6,0.00473801,122.229 400,4,0.2286,39.6,0.00473801,123.879 500,4,0.2286,39.6,0.00473801,127.039 630,4,0.2286,39.6,0.00473801,129.579 800,4,0.2286,39.6,0.00473801,130.469 1000,4,0.2286,39.6,0.00473801,129.969 1250,4,0.2286,39.6,0.00473801,128.339 1600,4,0.2286,39.6,0.00473801,125.319 2000,4,0.2286,39.6,0.00473801,121.659 2500,4,0.2286,39.6,0.00473801,119.649 3150,4,0.2286,39.6,0.00473801,120.419 4000,4,0.2286,39.6,0.00473801,119.159 5000,4,0.2286,39.6,0.00473801,117.649 6300,4,0.2286,39.6,0.00473801,114.249 8000,4,0.2286,39.6,0.00473801,113.129 250,4,0.2286,31.7,0.00509068,120.189 315,4,0.2286,31.7,0.00509068,123.609 400,4,0.2286,31.7,0.00509068,126.149 500,4,0.2286,31.7,0.00509068,128.939 630,4,0.2286,31.7,0.00509068,130.349 800,4,0.2286,31.7,0.00509068,130.869 1000,4,0.2286,31.7,0.00509068,129.869 1250,4,0.2286,31.7,0.00509068,128.119 1600,4,0.2286,31.7,0.00509068,125.229 2000,4,0.2286,31.7,0.00509068,122.089 2500,4,0.2286,31.7,0.00509068,120.209 3150,4,0.2286,31.7,0.00509068,120.229 4000,4,0.2286,31.7,0.00509068,118.859 5000,4,0.2286,31.7,0.00509068,115.969 6300,4,0.2286,31.7,0.00509068,112.699 400,5.3,0.2286,71.3,0.0051942,127.700 500,5.3,0.2286,71.3,0.0051942,129.880 630,5.3,0.2286,71.3,0.0051942,131.800 800,5.3,0.2286,71.3,0.0051942,133.480 1000,5.3,0.2286,71.3,0.0051942,134.000 1250,5.3,0.2286,71.3,0.0051942,133.380 1600,5.3,0.2286,71.3,0.0051942,130.460 2000,5.3,0.2286,71.3,0.0051942,125.890 2500,5.3,0.2286,71.3,0.0051942,123.740 3150,5.3,0.2286,71.3,0.0051942,123.120 4000,5.3,0.2286,71.3,0.0051942,120.330 5000,5.3,0.2286,71.3,0.0051942,118.050 6300,5.3,0.2286,71.3,0.0051942,116.920 8000,5.3,0.2286,71.3,0.0051942,114.900 10000,5.3,0.2286,71.3,0.0051942,111.350 250,5.3,0.2286,39.6,0.00614329,127.011 315,5.3,0.2286,39.6,0.00614329,129.691 400,5.3,0.2286,39.6,0.00614329,131.221 500,5.3,0.2286,39.6,0.00614329,132.251 630,5.3,0.2286,39.6,0.00614329,132.011 800,5.3,0.2286,39.6,0.00614329,129.491 1000,5.3,0.2286,39.6,0.00614329,125.581 1250,5.3,0.2286,39.6,0.00614329,125.721 1600,5.3,0.2286,39.6,0.00614329,123.081 2000,5.3,0.2286,39.6,0.00614329,117.911 2500,5.3,0.2286,39.6,0.00614329,116.151 3150,5.3,0.2286,39.6,0.00614329,118.441 4000,5.3,0.2286,39.6,0.00614329,115.801 5000,5.3,0.2286,39.6,0.00614329,115.311 6300,5.3,0.2286,39.6,0.00614329,112.541 200,7.3,0.2286,71.3,0.0104404,138.758 250,7.3,0.2286,71.3,0.0104404,139.918 315,7.3,0.2286,71.3,0.0104404,139.808 400,7.3,0.2286,71.3,0.0104404,139.438 500,7.3,0.2286,71.3,0.0104404,136.798 630,7.3,0.2286,71.3,0.0104404,133.768 800,7.3,0.2286,71.3,0.0104404,130.748 1000,7.3,0.2286,71.3,0.0104404,126.838 1250,7.3,0.2286,71.3,0.0104404,127.358 1600,7.3,0.2286,71.3,0.0104404,125.728 2000,7.3,0.2286,71.3,0.0104404,122.708 2500,7.3,0.2286,71.3,0.0104404,122.088 3150,7.3,0.2286,71.3,0.0104404,120.458 4000,7.3,0.2286,71.3,0.0104404,119.208 5000,7.3,0.2286,71.3,0.0104404,115.298 6300,7.3,0.2286,71.3,0.0104404,115.818 200,7.3,0.2286,55.5,0.0111706,135.234 250,7.3,0.2286,55.5,0.0111706,136.384 315,7.3,0.2286,55.5,0.0111706,136.284 400,7.3,0.2286,55.5,0.0111706,135.924 500,7.3,0.2286,55.5,0.0111706,133.174 630,7.3,0.2286,55.5,0.0111706,130.934 800,7.3,0.2286,55.5,0.0111706,128.444 1000,7.3,0.2286,55.5,0.0111706,125.194 1250,7.3,0.2286,55.5,0.0111706,125.724 1600,7.3,0.2286,55.5,0.0111706,123.354 2000,7.3,0.2286,55.5,0.0111706,120.354 2500,7.3,0.2286,55.5,0.0111706,118.994 3150,7.3,0.2286,55.5,0.0111706,117.134 4000,7.3,0.2286,55.5,0.0111706,117.284 5000,7.3,0.2286,55.5,0.0111706,113.144 6300,7.3,0.2286,55.5,0.0111706,111.534 200,7.3,0.2286,39.6,0.0123481,130.989 250,7.3,0.2286,39.6,0.0123481,131.889 315,7.3,0.2286,39.6,0.0123481,132.149 400,7.3,0.2286,39.6,0.0123481,132.039 500,7.3,0.2286,39.6,0.0123481,130.299 630,7.3,0.2286,39.6,0.0123481,128.929 800,7.3,0.2286,39.6,0.0123481,126.299 1000,7.3,0.2286,39.6,0.0123481,122.539 1250,7.3,0.2286,39.6,0.0123481,123.189 1600,7.3,0.2286,39.6,0.0123481,121.059 2000,7.3,0.2286,39.6,0.0123481,117.809 2500,7.3,0.2286,39.6,0.0123481,116.559 3150,7.3,0.2286,39.6,0.0123481,114.309 4000,7.3,0.2286,39.6,0.0123481,114.079 5000,7.3,0.2286,39.6,0.0123481,111.959 6300,7.3,0.2286,39.6,0.0123481,110.839 200,7.3,0.2286,31.7,0.0132672,128.679 250,7.3,0.2286,31.7,0.0132672,130.089 315,7.3,0.2286,31.7,0.0132672,130.239 400,7.3,0.2286,31.7,0.0132672,130.269 500,7.3,0.2286,31.7,0.0132672,128.169 630,7.3,0.2286,31.7,0.0132672,126.189 800,7.3,0.2286,31.7,0.0132672,123.209 1000,7.3,0.2286,31.7,0.0132672,119.099 1250,7.3,0.2286,31.7,0.0132672,120.509 1600,7.3,0.2286,31.7,0.0132672,119.039 2000,7.3,0.2286,31.7,0.0132672,115.309 2500,7.3,0.2286,31.7,0.0132672,114.709 3150,7.3,0.2286,31.7,0.0132672,113.229 4000,7.3,0.2286,31.7,0.0132672,112.639 5000,7.3,0.2286,31.7,0.0132672,111.029 6300,7.3,0.2286,31.7,0.0132672,110.689 800,0,0.1524,71.3,0.0015988,125.817 1000,0,0.1524,71.3,0.0015988,127.307 1250,0,0.1524,71.3,0.0015988,128.927 1600,0,0.1524,71.3,0.0015988,129.667 2000,0,0.1524,71.3,0.0015988,128.647 2500,0,0.1524,71.3,0.0015988,128.127 3150,0,0.1524,71.3,0.0015988,129.377 4000,0,0.1524,71.3,0.0015988,128.857 5000,0,0.1524,71.3,0.0015988,126.457 6300,0,0.1524,71.3,0.0015988,125.427 8000,0,0.1524,71.3,0.0015988,122.527 10000,0,0.1524,71.3,0.0015988,120.247 12500,0,0.1524,71.3,0.0015988,117.087 16000,0,0.1524,71.3,0.0015988,113.297 500,0,0.1524,55.5,0.00172668,120.573 630,0,0.1524,55.5,0.00172668,123.583 800,0,0.1524,55.5,0.00172668,126.713 1000,0,0.1524,55.5,0.00172668,128.583 1250,0,0.1524,55.5,0.00172668,129.953 1600,0,0.1524,55.5,0.00172668,130.183 2000,0,0.1524,55.5,0.00172668,129.673 2500,0,0.1524,55.5,0.00172668,127.763 3150,0,0.1524,55.5,0.00172668,127.753 4000,0,0.1524,55.5,0.00172668,127.233 5000,0,0.1524,55.5,0.00172668,125.203 6300,0,0.1524,55.5,0.00172668,123.303 8000,0,0.1524,55.5,0.00172668,121.903 10000,0,0.1524,55.5,0.00172668,119.253 12500,0,0.1524,55.5,0.00172668,117.093 16000,0,0.1524,55.5,0.00172668,112.803 500,0,0.1524,39.6,0.00193287,119.513 630,0,0.1524,39.6,0.00193287,124.403 800,0,0.1524,39.6,0.00193287,127.903 1000,0,0.1524,39.6,0.00193287,130.033 1250,0,0.1524,39.6,0.00193287,131.023 1600,0,0.1524,39.6,0.00193287,131.013 2000,0,0.1524,39.6,0.00193287,129.633 2500,0,0.1524,39.6,0.00193287,126.863 3150,0,0.1524,39.6,0.00193287,125.603 4000,0,0.1524,39.6,0.00193287,125.343 5000,0,0.1524,39.6,0.00193287,123.453 6300,0,0.1524,39.6,0.00193287,121.313 8000,0,0.1524,39.6,0.00193287,120.553 10000,0,0.1524,39.6,0.00193287,115.413 500,0,0.1524,31.7,0.00209405,121.617 630,0,0.1524,31.7,0.00209405,125.997 800,0,0.1524,31.7,0.00209405,129.117 1000,0,0.1524,31.7,0.00209405,130.987 1250,0,0.1524,31.7,0.00209405,131.467 1600,0,0.1524,31.7,0.00209405,130.817 2000,0,0.1524,31.7,0.00209405,128.907 2500,0,0.1524,31.7,0.00209405,125.867 3150,0,0.1524,31.7,0.00209405,124.207 4000,0,0.1524,31.7,0.00209405,123.807 5000,0,0.1524,31.7,0.00209405,122.397 6300,0,0.1524,31.7,0.00209405,119.737 8000,0,0.1524,31.7,0.00209405,117.957 630,2.7,0.1524,71.3,0.00243851,127.404 800,2.7,0.1524,71.3,0.00243851,127.394 1000,2.7,0.1524,71.3,0.00243851,128.774 1250,2.7,0.1524,71.3,0.00243851,130.144 1600,2.7,0.1524,71.3,0.00243851,130.644 2000,2.7,0.1524,71.3,0.00243851,130.114 2500,2.7,0.1524,71.3,0.00243851,128.334 3150,2.7,0.1524,71.3,0.00243851,127.054 4000,2.7,0.1524,71.3,0.00243851,126.534 5000,2.7,0.1524,71.3,0.00243851,124.364 6300,2.7,0.1524,71.3,0.00243851,121.944 8000,2.7,0.1524,71.3,0.00243851,120.534 10000,2.7,0.1524,71.3,0.00243851,116.724 12500,2.7,0.1524,71.3,0.00243851,113.034 16000,2.7,0.1524,71.3,0.00243851,110.364 500,2.7,0.1524,39.6,0.00294804,121.009 630,2.7,0.1524,39.6,0.00294804,125.809 800,2.7,0.1524,39.6,0.00294804,128.829 1000,2.7,0.1524,39.6,0.00294804,130.589 1250,2.7,0.1524,39.6,0.00294804,130.829 1600,2.7,0.1524,39.6,0.00294804,130.049 2000,2.7,0.1524,39.6,0.00294804,128.139 2500,2.7,0.1524,39.6,0.00294804,125.589 3150,2.7,0.1524,39.6,0.00294804,122.919 4000,2.7,0.1524,39.6,0.00294804,121.889 5000,2.7,0.1524,39.6,0.00294804,121.499 6300,2.7,0.1524,39.6,0.00294804,119.209 8000,2.7,0.1524,39.6,0.00294804,116.659 10000,2.7,0.1524,39.6,0.00294804,112.589 12500,2.7,0.1524,39.6,0.00294804,108.649 400,5.4,0.1524,71.3,0.00401199,124.121 500,5.4,0.1524,71.3,0.00401199,126.291 630,5.4,0.1524,71.3,0.00401199,128.971 800,5.4,0.1524,71.3,0.00401199,131.281 1000,5.4,0.1524,71.3,0.00401199,133.201 1250,5.4,0.1524,71.3,0.00401199,134.111 1600,5.4,0.1524,71.3,0.00401199,133.241 2000,5.4,0.1524,71.3,0.00401199,131.111 2500,5.4,0.1524,71.3,0.00401199,127.591 3150,5.4,0.1524,71.3,0.00401199,123.311 4000,5.4,0.1524,71.3,0.00401199,121.431 5000,5.4,0.1524,71.3,0.00401199,120.061 6300,5.4,0.1524,71.3,0.00401199,116.411 400,5.4,0.1524,55.5,0.00433288,126.807 500,5.4,0.1524,55.5,0.00433288,129.367 630,5.4,0.1524,55.5,0.00433288,131.807 800,5.4,0.1524,55.5,0.00433288,133.097 1000,5.4,0.1524,55.5,0.00433288,132.127 1250,5.4,0.1524,55.5,0.00433288,130.777 1600,5.4,0.1524,55.5,0.00433288,130.567 2000,5.4,0.1524,55.5,0.00433288,128.707 2500,5.4,0.1524,55.5,0.00433288,124.077 3150,5.4,0.1524,55.5,0.00433288,121.587 4000,5.4,0.1524,55.5,0.00433288,119.737 5000,5.4,0.1524,55.5,0.00433288,118.757 6300,5.4,0.1524,55.5,0.00433288,117.287 8000,5.4,0.1524,55.5,0.00433288,114.927 315,5.4,0.1524,39.6,0.00485029,125.347 400,5.4,0.1524,39.6,0.00485029,127.637 500,5.4,0.1524,39.6,0.00485029,129.937 630,5.4,0.1524,39.6,0.00485029,132.357 800,5.4,0.1524,39.6,0.00485029,132.757 1000,5.4,0.1524,39.6,0.00485029,130.507 1250,5.4,0.1524,39.6,0.00485029,127.117 1600,5.4,0.1524,39.6,0.00485029,126.267 2000,5.4,0.1524,39.6,0.00485029,124.647 2500,5.4,0.1524,39.6,0.00485029,120.497 3150,5.4,0.1524,39.6,0.00485029,119.137 4000,5.4,0.1524,39.6,0.00485029,117.137 5000,5.4,0.1524,39.6,0.00485029,117.037 6300,5.4,0.1524,39.6,0.00485029,116.677 315,5.4,0.1524,31.7,0.00525474,125.741 400,5.4,0.1524,31.7,0.00525474,127.781 500,5.4,0.1524,31.7,0.00525474,129.681 630,5.4,0.1524,31.7,0.00525474,131.471 800,5.4,0.1524,31.7,0.00525474,131.491 1000,5.4,0.1524,31.7,0.00525474,128.241 1250,5.4,0.1524,31.7,0.00525474,123.991 1600,5.4,0.1524,31.7,0.00525474,123.761 2000,5.4,0.1524,31.7,0.00525474,122.771 2500,5.4,0.1524,31.7,0.00525474,119.151 3150,5.4,0.1524,31.7,0.00525474,118.291 4000,5.4,0.1524,31.7,0.00525474,116.181 5000,5.4,0.1524,31.7,0.00525474,115.691 6300,5.4,0.1524,31.7,0.00525474,115.591 315,7.2,0.1524,71.3,0.00752039,128.713 400,7.2,0.1524,71.3,0.00752039,130.123 500,7.2,0.1524,71.3,0.00752039,132.043 630,7.2,0.1524,71.3,0.00752039,134.853 800,7.2,0.1524,71.3,0.00752039,136.023 1000,7.2,0.1524,71.3,0.00752039,134.273 1250,7.2,0.1524,71.3,0.00752039,132.513 1600,7.2,0.1524,71.3,0.00752039,130.893 2000,7.2,0.1524,71.3,0.00752039,128.643 2500,7.2,0.1524,71.3,0.00752039,124.353 3150,7.2,0.1524,71.3,0.00752039,116.783 4000,7.2,0.1524,71.3,0.00752039,119.343 5000,7.2,0.1524,71.3,0.00752039,118.343 6300,7.2,0.1524,71.3,0.00752039,116.603 8000,7.2,0.1524,71.3,0.00752039,113.333 10000,7.2,0.1524,71.3,0.00752039,110.313 250,7.2,0.1524,39.6,0.00909175,127.488 315,7.2,0.1524,39.6,0.00909175,130.558 400,7.2,0.1524,39.6,0.00909175,132.118 500,7.2,0.1524,39.6,0.00909175,132.658 630,7.2,0.1524,39.6,0.00909175,133.198 800,7.2,0.1524,39.6,0.00909175,132.358 1000,7.2,0.1524,39.6,0.00909175,128.338 1250,7.2,0.1524,39.6,0.00909175,122.428 1600,7.2,0.1524,39.6,0.00909175,120.058 2000,7.2,0.1524,39.6,0.00909175,120.228 2500,7.2,0.1524,39.6,0.00909175,117.478 3150,7.2,0.1524,39.6,0.00909175,111.818 4000,7.2,0.1524,39.6,0.00909175,114.258 5000,7.2,0.1524,39.6,0.00909175,113.288 6300,7.2,0.1524,39.6,0.00909175,112.688 8000,7.2,0.1524,39.6,0.00909175,111.588 10000,7.2,0.1524,39.6,0.00909175,110.868 200,9.9,0.1524,71.3,0.0193001,134.319 250,9.9,0.1524,71.3,0.0193001,135.329 315,9.9,0.1524,71.3,0.0193001,135.459 400,9.9,0.1524,71.3,0.0193001,135.079 500,9.9,0.1524,71.3,0.0193001,131.279 630,9.9,0.1524,71.3,0.0193001,129.889 800,9.9,0.1524,71.3,0.0193001,128.879 1000,9.9,0.1524,71.3,0.0193001,126.349 1250,9.9,0.1524,71.3,0.0193001,122.679 1600,9.9,0.1524,71.3,0.0193001,121.789 2000,9.9,0.1524,71.3,0.0193001,120.779 2500,9.9,0.1524,71.3,0.0193001,119.639 3150,9.9,0.1524,71.3,0.0193001,116.849 4000,9.9,0.1524,71.3,0.0193001,115.079 5000,9.9,0.1524,71.3,0.0193001,114.569 6300,9.9,0.1524,71.3,0.0193001,112.039 200,9.9,0.1524,55.5,0.0208438,131.955 250,9.9,0.1524,55.5,0.0208438,133.235 315,9.9,0.1524,55.5,0.0208438,132.355 400,9.9,0.1524,55.5,0.0208438,131.605 500,9.9,0.1524,55.5,0.0208438,127.815 630,9.9,0.1524,55.5,0.0208438,127.315 800,9.9,0.1524,55.5,0.0208438,126.565 1000,9.9,0.1524,55.5,0.0208438,124.665 1250,9.9,0.1524,55.5,0.0208438,121.635 1600,9.9,0.1524,55.5,0.0208438,119.875 2000,9.9,0.1524,55.5,0.0208438,119.505 2500,9.9,0.1524,55.5,0.0208438,118.365 3150,9.9,0.1524,55.5,0.0208438,115.085 4000,9.9,0.1524,55.5,0.0208438,112.945 5000,9.9,0.1524,55.5,0.0208438,112.065 6300,9.9,0.1524,55.5,0.0208438,110.555 200,9.9,0.1524,39.6,0.0233328,127.315 250,9.9,0.1524,39.6,0.0233328,128.335 315,9.9,0.1524,39.6,0.0233328,128.595 400,9.9,0.1524,39.6,0.0233328,128.345 500,9.9,0.1524,39.6,0.0233328,126.835 630,9.9,0.1524,39.6,0.0233328,126.465 800,9.9,0.1524,39.6,0.0233328,126.345 1000,9.9,0.1524,39.6,0.0233328,123.835 1250,9.9,0.1524,39.6,0.0233328,120.555 1600,9.9,0.1524,39.6,0.0233328,118.545 2000,9.9,0.1524,39.6,0.0233328,117.925 2500,9.9,0.1524,39.6,0.0233328,116.295 3150,9.9,0.1524,39.6,0.0233328,113.525 4000,9.9,0.1524,39.6,0.0233328,112.265 5000,9.9,0.1524,39.6,0.0233328,111.135 6300,9.9,0.1524,39.6,0.0233328,109.885 200,9.9,0.1524,31.7,0.0252785,127.299 250,9.9,0.1524,31.7,0.0252785,128.559 315,9.9,0.1524,31.7,0.0252785,128.809 400,9.9,0.1524,31.7,0.0252785,128.939 500,9.9,0.1524,31.7,0.0252785,127.179 630,9.9,0.1524,31.7,0.0252785,126.049 800,9.9,0.1524,31.7,0.0252785,125.539 1000,9.9,0.1524,31.7,0.0252785,122.149 1250,9.9,0.1524,31.7,0.0252785,118.619 1600,9.9,0.1524,31.7,0.0252785,117.119 2000,9.9,0.1524,31.7,0.0252785,116.859 2500,9.9,0.1524,31.7,0.0252785,114.729 3150,9.9,0.1524,31.7,0.0252785,112.209 4000,9.9,0.1524,31.7,0.0252785,111.459 5000,9.9,0.1524,31.7,0.0252785,109.949 6300,9.9,0.1524,31.7,0.0252785,108.689 200,12.6,0.1524,71.3,0.0483159,128.354 250,12.6,0.1524,71.3,0.0483159,129.744 315,12.6,0.1524,71.3,0.0483159,128.484 400,12.6,0.1524,71.3,0.0483159,127.094 500,12.6,0.1524,71.3,0.0483159,121.664 630,12.6,0.1524,71.3,0.0483159,123.304 800,12.6,0.1524,71.3,0.0483159,123.054 1000,12.6,0.1524,71.3,0.0483159,122.044 1250,12.6,0.1524,71.3,0.0483159,120.154 1600,12.6,0.1524,71.3,0.0483159,120.534 2000,12.6,0.1524,71.3,0.0483159,117.504 2500,12.6,0.1524,71.3,0.0483159,115.234 3150,12.6,0.1524,71.3,0.0483159,113.334 4000,12.6,0.1524,71.3,0.0483159,108.034 5000,12.6,0.1524,71.3,0.0483159,108.034 6300,12.6,0.1524,71.3,0.0483159,107.284 200,12.6,0.1524,39.6,0.0584113,114.750 250,12.6,0.1524,39.6,0.0584113,115.890 315,12.6,0.1524,39.6,0.0584113,116.020 400,12.6,0.1524,39.6,0.0584113,115.910 500,12.6,0.1524,39.6,0.0584113,114.900 630,12.6,0.1524,39.6,0.0584113,116.550 800,12.6,0.1524,39.6,0.0584113,116.560 1000,12.6,0.1524,39.6,0.0584113,114.670 1250,12.6,0.1524,39.6,0.0584113,112.160 1600,12.6,0.1524,39.6,0.0584113,110.780 2000,12.6,0.1524,39.6,0.0584113,109.520 2500,12.6,0.1524,39.6,0.0584113,106.880 3150,12.6,0.1524,39.6,0.0584113,106.260 4000,12.6,0.1524,39.6,0.0584113,104.500 5000,12.6,0.1524,39.6,0.0584113,104.130 6300,12.6,0.1524,39.6,0.0584113,103.380 800,0,0.0508,71.3,0.000740478,130.960 1000,0,0.0508,71.3,0.000740478,129.450 1250,0,0.0508,71.3,0.000740478,128.560 1600,0,0.0508,71.3,0.000740478,129.680 2000,0,0.0508,71.3,0.000740478,131.060 2500,0,0.0508,71.3,0.000740478,131.310 3150,0,0.0508,71.3,0.000740478,135.070 4000,0,0.0508,71.3,0.000740478,134.430 5000,0,0.0508,71.3,0.000740478,134.430 6300,0,0.0508,71.3,0.000740478,133.040 8000,0,0.0508,71.3,0.000740478,130.890 10000,0,0.0508,71.3,0.000740478,128.740 12500,0,0.0508,71.3,0.000740478,125.220 800,0,0.0508,55.5,0.00076193,124.336 1000,0,0.0508,55.5,0.00076193,125.586 1250,0,0.0508,55.5,0.00076193,127.076 1600,0,0.0508,55.5,0.00076193,128.576 2000,0,0.0508,55.5,0.00076193,131.456 2500,0,0.0508,55.5,0.00076193,133.956 3150,0,0.0508,55.5,0.00076193,134.826 4000,0,0.0508,55.5,0.00076193,134.946 5000,0,0.0508,55.5,0.00076193,134.556 6300,0,0.0508,55.5,0.00076193,132.796 8000,0,0.0508,55.5,0.00076193,130.156 10000,0,0.0508,55.5,0.00076193,127.636 12500,0,0.0508,55.5,0.00076193,125.376 800,0,0.0508,39.6,0.000791822,126.508 1000,0,0.0508,39.6,0.000791822,127.638 1250,0,0.0508,39.6,0.000791822,129.148 1600,0,0.0508,39.6,0.000791822,130.908 2000,0,0.0508,39.6,0.000791822,132.918 2500,0,0.0508,39.6,0.000791822,134.938 3150,0,0.0508,39.6,0.000791822,135.938 4000,0,0.0508,39.6,0.000791822,135.308 5000,0,0.0508,39.6,0.000791822,134.308 6300,0,0.0508,39.6,0.000791822,131.918 8000,0,0.0508,39.6,0.000791822,128.518 10000,0,0.0508,39.6,0.000791822,125.998 12500,0,0.0508,39.6,0.000791822,123.988 800,0,0.0508,31.7,0.000812164,122.790 1000,0,0.0508,31.7,0.000812164,126.780 1250,0,0.0508,31.7,0.000812164,129.270 1600,0,0.0508,31.7,0.000812164,131.010 2000,0,0.0508,31.7,0.000812164,133.010 2500,0,0.0508,31.7,0.000812164,134.870 3150,0,0.0508,31.7,0.000812164,135.490 4000,0,0.0508,31.7,0.000812164,134.110 5000,0,0.0508,31.7,0.000812164,133.230 6300,0,0.0508,31.7,0.000812164,130.340 8000,0,0.0508,31.7,0.000812164,126.590 10000,0,0.0508,31.7,0.000812164,122.450 12500,0,0.0508,31.7,0.000812164,119.070 1600,4.2,0.0508,71.3,0.00142788,124.318 2000,4.2,0.0508,71.3,0.00142788,129.848 2500,4.2,0.0508,71.3,0.00142788,131.978 3150,4.2,0.0508,71.3,0.00142788,133.728 4000,4.2,0.0508,71.3,0.00142788,133.598 5000,4.2,0.0508,71.3,0.00142788,132.828 6300,4.2,0.0508,71.3,0.00142788,129.308 8000,4.2,0.0508,71.3,0.00142788,125.268 10000,4.2,0.0508,71.3,0.00142788,121.238 12500,4.2,0.0508,71.3,0.00142788,117.328 1000,4.2,0.0508,39.6,0.00152689,125.647 1250,4.2,0.0508,39.6,0.00152689,128.427 1600,4.2,0.0508,39.6,0.00152689,130.197 2000,4.2,0.0508,39.6,0.00152689,132.587 2500,4.2,0.0508,39.6,0.00152689,133.847 3150,4.2,0.0508,39.6,0.00152689,133.587 4000,4.2,0.0508,39.6,0.00152689,131.807 5000,4.2,0.0508,39.6,0.00152689,129.777 6300,4.2,0.0508,39.6,0.00152689,125.717 8000,4.2,0.0508,39.6,0.00152689,120.397 10000,4.2,0.0508,39.6,0.00152689,116.967 800,8.4,0.0508,71.3,0.00529514,127.556 1000,8.4,0.0508,71.3,0.00529514,129.946 1250,8.4,0.0508,71.3,0.00529514,132.086 1600,8.4,0.0508,71.3,0.00529514,133.846 2000,8.4,0.0508,71.3,0.00529514,134.476 2500,8.4,0.0508,71.3,0.00529514,134.226 3150,8.4,0.0508,71.3,0.00529514,131.966 4000,8.4,0.0508,71.3,0.00529514,126.926 5000,8.4,0.0508,71.3,0.00529514,121.146 400,8.4,0.0508,55.5,0.00544854,121.582 500,8.4,0.0508,55.5,0.00544854,123.742 630,8.4,0.0508,55.5,0.00544854,126.152 800,8.4,0.0508,55.5,0.00544854,128.562 1000,8.4,0.0508,55.5,0.00544854,130.722 1250,8.4,0.0508,55.5,0.00544854,132.252 1600,8.4,0.0508,55.5,0.00544854,133.032 2000,8.4,0.0508,55.5,0.00544854,133.042 2500,8.4,0.0508,55.5,0.00544854,131.542 3150,8.4,0.0508,55.5,0.00544854,128.402 4000,8.4,0.0508,55.5,0.00544854,122.612 5000,8.4,0.0508,55.5,0.00544854,115.812 400,8.4,0.0508,39.6,0.00566229,120.015 500,8.4,0.0508,39.6,0.00566229,122.905 630,8.4,0.0508,39.6,0.00566229,126.045 800,8.4,0.0508,39.6,0.00566229,128.435 1000,8.4,0.0508,39.6,0.00566229,130.195 1250,8.4,0.0508,39.6,0.00566229,131.205 1600,8.4,0.0508,39.6,0.00566229,130.965 2000,8.4,0.0508,39.6,0.00566229,129.965 2500,8.4,0.0508,39.6,0.00566229,127.465 3150,8.4,0.0508,39.6,0.00566229,123.965 4000,8.4,0.0508,39.6,0.00566229,118.955 400,8.4,0.0508,31.7,0.00580776,120.076 500,8.4,0.0508,31.7,0.00580776,122.966 630,8.4,0.0508,31.7,0.00580776,125.856 800,8.4,0.0508,31.7,0.00580776,128.246 1000,8.4,0.0508,31.7,0.00580776,129.516 1250,8.4,0.0508,31.7,0.00580776,130.156 1600,8.4,0.0508,31.7,0.00580776,129.296 2000,8.4,0.0508,31.7,0.00580776,127.686 2500,8.4,0.0508,31.7,0.00580776,125.576 3150,8.4,0.0508,31.7,0.00580776,122.086 4000,8.4,0.0508,31.7,0.00580776,118.106 200,11.2,0.0508,71.3,0.014072,125.941 250,11.2,0.0508,71.3,0.014072,127.101 315,11.2,0.0508,71.3,0.014072,128.381 400,11.2,0.0508,71.3,0.014072,129.281 500,11.2,0.0508,71.3,0.014072,130.311 630,11.2,0.0508,71.3,0.014072,133.611 800,11.2,0.0508,71.3,0.014072,136.031 1000,11.2,0.0508,71.3,0.014072,136.941 1250,11.2,0.0508,71.3,0.014072,136.191 1600,11.2,0.0508,71.3,0.014072,135.191 2000,11.2,0.0508,71.3,0.014072,133.311 2500,11.2,0.0508,71.3,0.014072,130.541 3150,11.2,0.0508,71.3,0.014072,127.141 4000,11.2,0.0508,71.3,0.014072,122.471 200,11.2,0.0508,39.6,0.0150478,125.010 250,11.2,0.0508,39.6,0.0150478,126.430 315,11.2,0.0508,39.6,0.0150478,128.990 400,11.2,0.0508,39.6,0.0150478,130.670 500,11.2,0.0508,39.6,0.0150478,131.960 630,11.2,0.0508,39.6,0.0150478,133.130 800,11.2,0.0508,39.6,0.0150478,133.790 1000,11.2,0.0508,39.6,0.0150478,132.430 1250,11.2,0.0508,39.6,0.0150478,130.050 1600,11.2,0.0508,39.6,0.0150478,126.540 2000,11.2,0.0508,39.6,0.0150478,124.420 2500,11.2,0.0508,39.6,0.0150478,122.170 3150,11.2,0.0508,39.6,0.0150478,119.670 4000,11.2,0.0508,39.6,0.0150478,115.520 200,15.4,0.0508,71.3,0.0264269,123.595 250,15.4,0.0508,71.3,0.0264269,124.835 315,15.4,0.0508,71.3,0.0264269,126.195 400,15.4,0.0508,71.3,0.0264269,126.805 500,15.4,0.0508,71.3,0.0264269,127.285 630,15.4,0.0508,71.3,0.0264269,129.645 800,15.4,0.0508,71.3,0.0264269,131.515 1000,15.4,0.0508,71.3,0.0264269,131.865 1250,15.4,0.0508,71.3,0.0264269,130.845 1600,15.4,0.0508,71.3,0.0264269,130.065 2000,15.4,0.0508,71.3,0.0264269,129.285 2500,15.4,0.0508,71.3,0.0264269,127.625 3150,15.4,0.0508,71.3,0.0264269,125.715 4000,15.4,0.0508,71.3,0.0264269,122.675 5000,15.4,0.0508,71.3,0.0264269,119.135 6300,15.4,0.0508,71.3,0.0264269,115.215 8000,15.4,0.0508,71.3,0.0264269,112.675 200,15.4,0.0508,55.5,0.0271925,122.940 250,15.4,0.0508,55.5,0.0271925,124.170 315,15.4,0.0508,55.5,0.0271925,125.390 400,15.4,0.0508,55.5,0.0271925,126.500 500,15.4,0.0508,55.5,0.0271925,127.220 630,15.4,0.0508,55.5,0.0271925,129.330 800,15.4,0.0508,55.5,0.0271925,130.430 1000,15.4,0.0508,55.5,0.0271925,130.400 1250,15.4,0.0508,55.5,0.0271925,130.000 1600,15.4,0.0508,55.5,0.0271925,128.200 2000,15.4,0.0508,55.5,0.0271925,127.040 2500,15.4,0.0508,55.5,0.0271925,125.630 3150,15.4,0.0508,55.5,0.0271925,123.460 4000,15.4,0.0508,55.5,0.0271925,120.920 5000,15.4,0.0508,55.5,0.0271925,117.110 6300,15.4,0.0508,55.5,0.0271925,112.930 200,15.4,0.0508,39.6,0.0282593,121.783 250,15.4,0.0508,39.6,0.0282593,122.893 315,15.4,0.0508,39.6,0.0282593,124.493 400,15.4,0.0508,39.6,0.0282593,125.353 500,15.4,0.0508,39.6,0.0282593,125.963 630,15.4,0.0508,39.6,0.0282593,127.443 800,15.4,0.0508,39.6,0.0282593,128.423 1000,15.4,0.0508,39.6,0.0282593,127.893 1250,15.4,0.0508,39.6,0.0282593,126.743 1600,15.4,0.0508,39.6,0.0282593,124.843 2000,15.4,0.0508,39.6,0.0282593,123.443 2500,15.4,0.0508,39.6,0.0282593,122.413 3150,15.4,0.0508,39.6,0.0282593,120.513 4000,15.4,0.0508,39.6,0.0282593,118.113 5000,15.4,0.0508,39.6,0.0282593,114.453 6300,15.4,0.0508,39.6,0.0282593,109.663 200,15.4,0.0508,31.7,0.0289853,119.975 250,15.4,0.0508,31.7,0.0289853,121.225 315,15.4,0.0508,31.7,0.0289853,122.845 400,15.4,0.0508,31.7,0.0289853,123.705 500,15.4,0.0508,31.7,0.0289853,123.695 630,15.4,0.0508,31.7,0.0289853,124.685 800,15.4,0.0508,31.7,0.0289853,125.555 1000,15.4,0.0508,31.7,0.0289853,124.525 1250,15.4,0.0508,31.7,0.0289853,123.255 1600,15.4,0.0508,31.7,0.0289853,121.485 2000,15.4,0.0508,31.7,0.0289853,120.835 2500,15.4,0.0508,31.7,0.0289853,119.945 3150,15.4,0.0508,31.7,0.0289853,118.045 4000,15.4,0.0508,31.7,0.0289853,115.635 5000,15.4,0.0508,31.7,0.0289853,112.355 6300,15.4,0.0508,31.7,0.0289853,108.185 200,19.7,0.0508,71.3,0.0341183,118.005 250,19.7,0.0508,71.3,0.0341183,119.115 315,19.7,0.0508,71.3,0.0341183,121.235 400,19.7,0.0508,71.3,0.0341183,123.865 500,19.7,0.0508,71.3,0.0341183,126.995 630,19.7,0.0508,71.3,0.0341183,128.365 800,19.7,0.0508,71.3,0.0341183,124.555 1000,19.7,0.0508,71.3,0.0341183,121.885 1250,19.7,0.0508,71.3,0.0341183,121.485 1600,19.7,0.0508,71.3,0.0341183,120.575 2000,19.7,0.0508,71.3,0.0341183,120.055 2500,19.7,0.0508,71.3,0.0341183,118.385 3150,19.7,0.0508,71.3,0.0341183,116.225 4000,19.7,0.0508,71.3,0.0341183,113.045 200,19.7,0.0508,39.6,0.036484,125.974 250,19.7,0.0508,39.6,0.036484,127.224 315,19.7,0.0508,39.6,0.036484,129.864 400,19.7,0.0508,39.6,0.036484,130.614 500,19.7,0.0508,39.6,0.036484,128.444 630,19.7,0.0508,39.6,0.036484,120.324 800,19.7,0.0508,39.6,0.036484,119.174 1000,19.7,0.0508,39.6,0.036484,118.904 1250,19.7,0.0508,39.6,0.036484,118.634 1600,19.7,0.0508,39.6,0.036484,117.604 2000,19.7,0.0508,39.6,0.036484,117.724 2500,19.7,0.0508,39.6,0.036484,116.184 3150,19.7,0.0508,39.6,0.036484,113.004 4000,19.7,0.0508,39.6,0.036484,108.684 2500,0,0.0254,71.3,0.000400682,133.707 3150,0,0.0254,71.3,0.000400682,137.007 4000,0,0.0254,71.3,0.000400682,138.557 5000,0,0.0254,71.3,0.000400682,136.837 6300,0,0.0254,71.3,0.000400682,134.987 8000,0,0.0254,71.3,0.000400682,129.867 10000,0,0.0254,71.3,0.000400682,130.787 12500,0,0.0254,71.3,0.000400682,133.207 16000,0,0.0254,71.3,0.000400682,130.477 20000,0,0.0254,71.3,0.000400682,123.217 2000,0,0.0254,55.5,0.00041229,127.623 2500,0,0.0254,55.5,0.00041229,130.073 3150,0,0.0254,55.5,0.00041229,130.503 4000,0,0.0254,55.5,0.00041229,133.223 5000,0,0.0254,55.5,0.00041229,135.803 6300,0,0.0254,55.5,0.00041229,136.103 8000,0,0.0254,55.5,0.00041229,136.163 10000,0,0.0254,55.5,0.00041229,134.563 12500,0,0.0254,55.5,0.00041229,131.453 16000,0,0.0254,55.5,0.00041229,125.683 20000,0,0.0254,55.5,0.00041229,121.933 1600,0,0.0254,39.6,0.000428464,124.156 2000,0,0.0254,39.6,0.000428464,130.026 2500,0,0.0254,39.6,0.000428464,131.836 3150,0,0.0254,39.6,0.000428464,133.276 4000,0,0.0254,39.6,0.000428464,135.346 5000,0,0.0254,39.6,0.000428464,136.536 6300,0,0.0254,39.6,0.000428464,136.826 8000,0,0.0254,39.6,0.000428464,135.866 10000,0,0.0254,39.6,0.000428464,133.376 12500,0,0.0254,39.6,0.000428464,129.116 16000,0,0.0254,39.6,0.000428464,124.986 1000,0,0.0254,31.7,0.000439472,125.127 1250,0,0.0254,31.7,0.000439472,127.947 1600,0,0.0254,31.7,0.000439472,129.267 2000,0,0.0254,31.7,0.000439472,130.697 2500,0,0.0254,31.7,0.000439472,132.897 3150,0,0.0254,31.7,0.000439472,135.227 4000,0,0.0254,31.7,0.000439472,137.047 5000,0,0.0254,31.7,0.000439472,138.607 6300,0,0.0254,31.7,0.000439472,138.537 8000,0,0.0254,31.7,0.000439472,137.207 10000,0,0.0254,31.7,0.000439472,134.227 12500,0,0.0254,31.7,0.000439472,128.977 16000,0,0.0254,31.7,0.000439472,125.627 2000,4.8,0.0254,71.3,0.000848633,128.398 2500,4.8,0.0254,71.3,0.000848633,130.828 3150,4.8,0.0254,71.3,0.000848633,133.378 4000,4.8,0.0254,71.3,0.000848633,134.928 5000,4.8,0.0254,71.3,0.000848633,135.468 6300,4.8,0.0254,71.3,0.000848633,134.498 8000,4.8,0.0254,71.3,0.000848633,131.518 10000,4.8,0.0254,71.3,0.000848633,127.398 12500,4.8,0.0254,71.3,0.000848633,127.688 16000,4.8,0.0254,71.3,0.000848633,124.208 20000,4.8,0.0254,71.3,0.000848633,119.708 1600,4.8,0.0254,55.5,0.000873218,121.474 2000,4.8,0.0254,55.5,0.000873218,125.054 2500,4.8,0.0254,55.5,0.000873218,129.144 3150,4.8,0.0254,55.5,0.000873218,132.354 4000,4.8,0.0254,55.5,0.000873218,133.924 5000,4.8,0.0254,55.5,0.000873218,135.484 6300,4.8,0.0254,55.5,0.000873218,135.164 8000,4.8,0.0254,55.5,0.000873218,132.184 10000,4.8,0.0254,55.5,0.000873218,126.944 12500,4.8,0.0254,55.5,0.000873218,125.094 16000,4.8,0.0254,55.5,0.000873218,124.394 20000,4.8,0.0254,55.5,0.000873218,121.284 500,4.8,0.0254,39.6,0.000907475,116.366 630,4.8,0.0254,39.6,0.000907475,118.696 800,4.8,0.0254,39.6,0.000907475,120.766 1000,4.8,0.0254,39.6,0.000907475,122.956 1250,4.8,0.0254,39.6,0.000907475,125.026 1600,4.8,0.0254,39.6,0.000907475,125.966 2000,4.8,0.0254,39.6,0.000907475,128.916 2500,4.8,0.0254,39.6,0.000907475,131.236 3150,4.8,0.0254,39.6,0.000907475,133.436 4000,4.8,0.0254,39.6,0.000907475,134.996 5000,4.8,0.0254,39.6,0.000907475,135.426 6300,4.8,0.0254,39.6,0.000907475,134.336 8000,4.8,0.0254,39.6,0.000907475,131.346 10000,4.8,0.0254,39.6,0.000907475,126.066 500,4.8,0.0254,31.7,0.000930789,116.128 630,4.8,0.0254,31.7,0.000930789,120.078 800,4.8,0.0254,31.7,0.000930789,122.648 1000,4.8,0.0254,31.7,0.000930789,125.348 1250,4.8,0.0254,31.7,0.000930789,127.408 1600,4.8,0.0254,31.7,0.000930789,128.718 2000,4.8,0.0254,31.7,0.000930789,130.148 2500,4.8,0.0254,31.7,0.000930789,132.588 3150,4.8,0.0254,31.7,0.000930789,134.268 4000,4.8,0.0254,31.7,0.000930789,135.328 5000,4.8,0.0254,31.7,0.000930789,135.248 6300,4.8,0.0254,31.7,0.000930789,132.898 8000,4.8,0.0254,31.7,0.000930789,127.008 630,9.5,0.0254,71.3,0.00420654,125.726 800,9.5,0.0254,71.3,0.00420654,127.206 1000,9.5,0.0254,71.3,0.00420654,129.556 1250,9.5,0.0254,71.3,0.00420654,131.656 1600,9.5,0.0254,71.3,0.00420654,133.756 2000,9.5,0.0254,71.3,0.00420654,134.976 2500,9.5,0.0254,71.3,0.00420654,135.956 3150,9.5,0.0254,71.3,0.00420654,136.166 4000,9.5,0.0254,71.3,0.00420654,134.236 5000,9.5,0.0254,71.3,0.00420654,131.186 6300,9.5,0.0254,71.3,0.00420654,127.246 400,9.5,0.0254,55.5,0.0043284,120.952 500,9.5,0.0254,55.5,0.0043284,123.082 630,9.5,0.0254,55.5,0.0043284,125.452 800,9.5,0.0254,55.5,0.0043284,128.082 1000,9.5,0.0254,55.5,0.0043284,130.332 1250,9.5,0.0254,55.5,0.0043284,132.202 1600,9.5,0.0254,55.5,0.0043284,133.062 2000,9.5,0.0254,55.5,0.0043284,134.052 2500,9.5,0.0254,55.5,0.0043284,134.152 3150,9.5,0.0254,55.5,0.0043284,133.252 4000,9.5,0.0254,55.5,0.0043284,131.582 5000,9.5,0.0254,55.5,0.0043284,128.412 6300,9.5,0.0254,55.5,0.0043284,124.222 200,9.5,0.0254,39.6,0.00449821,116.074 250,9.5,0.0254,39.6,0.00449821,116.924 315,9.5,0.0254,39.6,0.00449821,119.294 400,9.5,0.0254,39.6,0.00449821,121.154 500,9.5,0.0254,39.6,0.00449821,123.894 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475,0,0,228,0,932,594,365,41.93 198.6,132.4,0,192,0,978.4,825.5,3,9.13 304,76,0,228,0,932,670,180,50.95 332.5,142.5,0,228,0,932,594,28,33.02 304,76,0,228,0,932,670,270,54.38 266,114,0,228,0,932,670,270,51.73 310,0,0,192,0,971,850.6,3,9.87 190,190,0,228,0,932,670,270,50.66 266,114,0,228,0,932,670,180,48.7 342,38,0,228,0,932,670,270,55.06 139.6,209.4,0,192,0,1047,806.9,360,44.7 332.5,142.5,0,228,0,932,594,7,30.28 190,190,0,228,0,932,670,28,40.86 485,0,0,146,0,1120,800,28,71.99 374,189.2,0,170.1,10.1,926.1,756.7,3,34.4 313.3,262.2,0,175.5,8.6,1046.9,611.8,3,28.8 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4 425,106.3,0,151.4,18.6,936,803.7,3,36.3 375,93.8,0,126.6,23.4,852.1,992.6,3,29 475,118.8,0,181.1,8.9,852.1,781.5,3,37.8 469,117.2,0,137.8,32.2,852.1,840.5,3,40.2 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4 388.6,97.1,0,157.9,12.1,852.1,925.7,3,28.1 531.3,0,0,141.8,28.2,852.1,893.7,3,41.3 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4 318.8,212.5,0,155.7,14.3,852.1,880.4,3,25.2 401.8,94.7,0,147.4,11.4,946.8,852.1,3,41.1 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 323.7,282.8,0,183.8,10.3,942.7,659.9,3,28.3 379.5,151.2,0,153.9,15.9,1134.3,605,3,28.6 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 286.3,200.9,0,144.7,11.2,1004.6,803.7,3,24.4 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 439,177,0,186,11.1,884.9,707.9,3,39.3 389.9,189,0,145.9,22,944.7,755.8,3,40.6 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 337.9,189,0,174.9,9.5,944.7,755.8,3,24.1 374,189.2,0,170.1,10.1,926.1,756.7,7,46.2 313.3,262.2,0,175.5,8.6,1046.9,611.8,7,42.8 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2 425,106.3,0,151.4,18.6,936,803.7,7,46.8 375,93.8,0,126.6,23.4,852.1,992.6,7,45.7 475,118.8,0,181.1,8.9,852.1,781.5,7,55.6 469,117.2,0,137.8,32.2,852.1,840.5,7,54.9 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2 388.6,97.1,0,157.9,12.1,852.1,925.7,7,34.9 531.3,0,0,141.8,28.2,852.1,893.7,7,46.9 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2 318.8,212.5,0,155.7,14.3,852.1,880.4,7,33.4 401.8,94.7,0,147.4,11.4,946.8,852.1,7,54.1 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9 323.7,282.8,0,183.8,10.3,942.7,659.9,7,49.8 379.5,151.2,0,153.9,15.9,1134.3,605,7,47.1 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9 286.3,200.9,0,144.7,11.2,1004.6,803.7,7,38 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9 439,177,0,186,11.1,884.9,707.9,7,56.1 389.9,189,0,145.9,22,944.7,755.8,7,59.09 362.6,189,0,164.9,11.6,944.7,755.8,7,22.9 337.9,189,0,174.9,9.5,944.7,755.8,7,35.1 374,189.2,0,170.1,10.1,926.1,756.7,28,61.09 313.3,262.2,0,175.5,8.6,1046.9,611.8,28,59.8 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29 425,106.3,0,151.4,18.6,936,803.7,28,61.8 375,93.8,0,126.6,23.4,852.1,992.6,28,56.7 475,118.8,0,181.1,8.9,852.1,781.5,28,68.3 469,117.2,0,137.8,32.2,852.1,840.5,28,66.9 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29 388.6,97.1,0,157.9,12.1,852.1,925.7,28,50.7 531.3,0,0,141.8,28.2,852.1,893.7,28,56.4 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29 318.8,212.5,0,155.7,14.3,852.1,880.4,28,55.5 401.8,94.7,0,147.4,11.4,946.8,852.1,28,68.5 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 323.7,282.8,0,183.8,10.3,942.7,659.9,28,74.7 379.5,151.2,0,153.9,15.9,1134.3,605,28,52.2 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 286.3,200.9,0,144.7,11.2,1004.6,803.7,28,67.7 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 439,177,0,186,11.1,884.9,707.9,28,66 389.9,189,0,145.9,22,944.7,755.8,28,74.5 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 337.9,189,0,174.9,9.5,944.7,755.8,28,49.9 374,189.2,0,170.1,10.1,926.1,756.7,56,63.4 313.3,262.2,0,175.5,8.6,1046.9,611.8,56,64.9 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3 425,106.3,0,151.4,18.6,936,803.7,56,64.9 375,93.8,0,126.6,23.4,852.1,992.6,56,60.2 475,118.8,0,181.1,8.9,852.1,781.5,56,72.3 469,117.2,0,137.8,32.2,852.1,840.5,56,69.3 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3 388.6,97.1,0,157.9,12.1,852.1,925.7,56,55.2 531.3,0,0,141.8,28.2,852.1,893.7,56,58.8 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3 318.8,212.5,0,155.7,14.3,852.1,880.4,56,66.1 401.8,94.7,0,147.4,11.4,946.8,852.1,56,73.7 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 323.7,282.8,0,183.8,10.3,942.7,659.9,56,80.2 379.5,151.2,0,153.9,15.9,1134.3,605,56,54.9 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 286.3,200.9,0,144.7,11.2,1004.6,803.7,56,72.99 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 439,177,0,186,11.1,884.9,707.9,56,71.7 389.9,189,0,145.9,22,944.7,755.8,56,79.4 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 337.9,189,0,174.9,9.5,944.7,755.8,56,59.89 374,189.2,0,170.1,10.1,926.1,756.7,91,64.9 313.3,262.2,0,175.5,8.6,1046.9,611.8,91,66.6 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2 425,106.3,0,151.4,18.6,936,803.7,91,66.7 375,93.8,0,126.6,23.4,852.1,992.6,91,62.5 475,118.8,0,181.1,8.9,852.1,781.5,91,74.19 469,117.2,0,137.8,32.2,852.1,840.5,91,70.7 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2 388.6,97.1,0,157.9,12.1,852.1,925.7,91,57.6 531.3,0,0,141.8,28.2,852.1,893.7,91,59.2 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2 318.8,212.5,0,155.7,14.3,852.1,880.4,91,68.1 401.8,94.7,0,147.4,11.4,946.8,852.1,91,75.5 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 379.5,151.2,0,153.9,15.9,1134.3,605,91,56.5 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 286.3,200.9,0,144.7,11.2,1004.6,803.7,91,76.8 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 439,177,0,186,11.1,884.9,707.9,91,73.3 389.9,189,0,145.9,22,944.7,755.8,91,82.6 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 337.9,189,0,174.9,9.5,944.7,755.8,91,67.8 222.4,0,96.7,189.3,4.5,967.1,870.3,3,11.58 222.4,0,96.7,189.3,4.5,967.1,870.3,14,24.45 222.4,0,96.7,189.3,4.5,967.1,870.3,28,24.89 222.4,0,96.7,189.3,4.5,967.1,870.3,56,29.45 222.4,0,96.7,189.3,4.5,967.1,870.3,100,40.71 233.8,0,94.6,197.9,4.6,947,852.2,3,10.38 233.8,0,94.6,197.9,4.6,947,852.2,14,22.14 233.8,0,94.6,197.9,4.6,947,852.2,28,22.84 233.8,0,94.6,197.9,4.6,947,852.2,56,27.66 233.8,0,94.6,197.9,4.6,947,852.2,100,34.56 194.7,0,100.5,165.6,7.5,1006.4,905.9,3,12.45 194.7,0,100.5,165.6,7.5,1006.4,905.9,14,24.99 194.7,0,100.5,165.6,7.5,1006.4,905.9,28,25.72 194.7,0,100.5,165.6,7.5,1006.4,905.9,56,33.96 194.7,0,100.5,165.6,7.5,1006.4,905.9,100,37.34 190.7,0,125.4,162.1,7.8,1090,804,3,15.04 190.7,0,125.4,162.1,7.8,1090,804,14,21.06 190.7,0,125.4,162.1,7.8,1090,804,28,26.4 190.7,0,125.4,162.1,7.8,1090,804,56,35.34 190.7,0,125.4,162.1,7.8,1090,804,100,40.57 212.1,0,121.6,180.3,5.7,1057.6,779.3,3,12.47 212.1,0,121.6,180.3,5.7,1057.6,779.3,14,20.92 212.1,0,121.6,180.3,5.7,1057.6,779.3,28,24.9 212.1,0,121.6,180.3,5.7,1057.6,779.3,56,34.2 212.1,0,121.6,180.3,5.7,1057.6,779.3,100,39.61 230,0,118.3,195.5,4.6,1029.4,758.6,3,10.03 230,0,118.3,195.5,4.6,1029.4,758.6,14,20.08 230,0,118.3,195.5,4.6,1029.4,758.6,28,24.48 230,0,118.3,195.5,4.6,1029.4,758.6,56,31.54 230,0,118.3,195.5,4.6,1029.4,758.6,100,35.34 190.3,0,125.2,161.9,9.9,1088.1,802.6,3,9.45 190.3,0,125.2,161.9,9.9,1088.1,802.6,14,22.72 190.3,0,125.2,161.9,9.9,1088.1,802.6,28,28.47 190.3,0,125.2,161.9,9.9,1088.1,802.6,56,38.56 190.3,0,125.2,161.9,9.9,1088.1,802.6,100,40.39 166.1,0,163.3,176.5,4.5,1058.6,780.1,3,10.76 166.1,0,163.3,176.5,4.5,1058.6,780.1,14,25.48 166.1,0,163.3,176.5,4.5,1058.6,780.1,28,21.54 166.1,0,163.3,176.5,4.5,1058.6,780.1,56,28.63 166.1,0,163.3,176.5,4.5,1058.6,780.1,100,33.54 168,42.1,163.8,121.8,5.7,1058.7,780.1,3,7.75 168,42.1,163.8,121.8,5.7,1058.7,780.1,14,17.82 168,42.1,163.8,121.8,5.7,1058.7,780.1,28,24.24 168,42.1,163.8,121.8,5.7,1058.7,780.1,56,32.85 168,42.1,163.8,121.8,5.7,1058.7,780.1,100,39.23 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,3,18 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,14,30.39 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,28,45.71 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,56,50.77 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,100,53.9 213.8,98.1,24.5,181.7,6.7,1066,785.5,3,13.18 213.8,98.1,24.5,181.7,6.7,1066,785.5,14,17.84 213.8,98.1,24.5,181.7,6.7,1066,785.5,28,40.23 213.8,98.1,24.5,181.7,6.7,1066,785.5,56,47.13 213.8,98.1,24.5,181.7,6.7,1066,785.5,100,49.97 229.7,0,118.2,195.2,6.1,1028.1,757.6,3,13.36 229.7,0,118.2,195.2,6.1,1028.1,757.6,14,22.32 229.7,0,118.2,195.2,6.1,1028.1,757.6,28,24.54 229.7,0,118.2,195.2,6.1,1028.1,757.6,56,31.35 229.7,0,118.2,195.2,6.1,1028.1,757.6,100,40.86 238.1,0,94.1,186.7,7,949.9,847,3,19.93 238.1,0,94.1,186.7,7,949.9,847,14,25.69 238.1,0,94.1,186.7,7,949.9,847,28,30.23 238.1,0,94.1,186.7,7,949.9,847,56,39.59 238.1,0,94.1,186.7,7,949.9,847,100,44.3 250,0,95.7,187.4,5.5,956.9,861.2,3,13.82 250,0,95.7,187.4,5.5,956.9,861.2,14,24.92 250,0,95.7,187.4,5.5,956.9,861.2,28,29.22 250,0,95.7,187.4,5.5,956.9,861.2,56,38.33 250,0,95.7,187.4,5.5,956.9,861.2,100,42.35 212.5,0,100.4,159.3,8.7,1007.8,903.6,3,13.54 212.5,0,100.4,159.3,8.7,1007.8,903.6,14,26.31 212.5,0,100.4,159.3,8.7,1007.8,903.6,28,31.64 212.5,0,100.4,159.3,8.7,1007.8,903.6,56,42.55 212.5,0,100.4,159.3,8.7,1007.8,903.6,100,42.92 212.6,0,100.4,159.4,10.4,1003.8,903.8,3,13.33 212.6,0,100.4,159.4,10.4,1003.8,903.8,14,25.37 212.6,0,100.4,159.4,10.4,1003.8,903.8,28,37.4 212.6,0,100.4,159.4,10.4,1003.8,903.8,56,44.4 212.6,0,100.4,159.4,10.4,1003.8,903.8,100,47.74 212,0,124.8,159,7.8,1085.4,799.5,3,19.52 212,0,124.8,159,7.8,1085.4,799.5,14,31.35 212,0,124.8,159,7.8,1085.4,799.5,28,38.5 212,0,124.8,159,7.8,1085.4,799.5,56,45.08 212,0,124.8,159,7.8,1085.4,799.5,100,47.82 231.8,0,121.6,174,6.7,1056.4,778.5,3,15.44 231.8,0,121.6,174,6.7,1056.4,778.5,14,26.77 231.8,0,121.6,174,6.7,1056.4,778.5,28,33.73 231.8,0,121.6,174,6.7,1056.4,778.5,56,42.7 231.8,0,121.6,174,6.7,1056.4,778.5,100,45.84 251.4,0,118.3,188.5,5.8,1028.4,757.7,3,17.22 251.4,0,118.3,188.5,5.8,1028.4,757.7,14,29.93 251.4,0,118.3,188.5,5.8,1028.4,757.7,28,29.65 251.4,0,118.3,188.5,5.8,1028.4,757.7,56,36.97 251.4,0,118.3,188.5,5.8,1028.4,757.7,100,43.58 251.4,0,118.3,188.5,6.4,1028.4,757.7,3,13.12 251.4,0,118.3,188.5,6.4,1028.4,757.7,14,24.43 251.4,0,118.3,188.5,6.4,1028.4,757.7,28,32.66 251.4,0,118.3,188.5,6.4,1028.4,757.7,56,36.64 251.4,0,118.3,188.5,6.4,1028.4,757.7,100,44.21 181.4,0,167,169.6,7.6,1055.6,777.8,3,13.62 181.4,0,167,169.6,7.6,1055.6,777.8,14,21.6 181.4,0,167,169.6,7.6,1055.6,777.8,28,27.77 181.4,0,167,169.6,7.6,1055.6,777.8,56,35.57 181.4,0,167,169.6,7.6,1055.6,777.8,100,45.37 182,45.2,122,170.2,8.2,1059.4,780.7,3,7.32 182,45.2,122,170.2,8.2,1059.4,780.7,14,21.5 182,45.2,122,170.2,8.2,1059.4,780.7,28,31.27 182,45.2,122,170.2,8.2,1059.4,780.7,56,43.5 182,45.2,122,170.2,8.2,1059.4,780.7,100,48.67 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,3,7.4 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,14,23.51 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,28,31.12 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,56,39.15 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,100,48.15 290.4,0,96.2,168.1,9.4,961.2,865,3,22.5 290.4,0,96.2,168.1,9.4,961.2,865,14,34.67 290.4,0,96.2,168.1,9.4,961.2,865,28,34.74 290.4,0,96.2,168.1,9.4,961.2,865,56,45.08 290.4,0,96.2,168.1,9.4,961.2,865,100,48.97 277.1,0,97.4,160.6,11.8,973.9,875.6,3,23.14 277.1,0,97.4,160.6,11.8,973.9,875.6,14,41.89 277.1,0,97.4,160.6,11.8,973.9,875.6,28,48.28 277.1,0,97.4,160.6,11.8,973.9,875.6,56,51.04 277.1,0,97.4,160.6,11.8,973.9,875.6,100,55.64 295.7,0,95.6,171.5,8.9,955.1,859.2,3,22.95 295.7,0,95.6,171.5,8.9,955.1,859.2,14,35.23 295.7,0,95.6,171.5,8.9,955.1,859.2,28,39.94 295.7,0,95.6,171.5,8.9,955.1,859.2,56,48.72 295.7,0,95.6,171.5,8.9,955.1,859.2,100,52.04 251.8,0,99.9,146.1,12.4,1006,899.8,3,21.02 251.8,0,99.9,146.1,12.4,1006,899.8,14,33.36 251.8,0,99.9,146.1,12.4,1006,899.8,28,33.94 251.8,0,99.9,146.1,12.4,1006,899.8,56,44.14 251.8,0,99.9,146.1,12.4,1006,899.8,100,45.37 249.1,0,98.8,158.1,12.8,987.8,889,3,15.36 249.1,0,98.8,158.1,12.8,987.8,889,14,28.68 249.1,0,98.8,158.1,12.8,987.8,889,28,30.85 249.1,0,98.8,158.1,12.8,987.8,889,56,42.03 249.1,0,98.8,158.1,12.8,987.8,889,100,51.06 252.3,0,98.8,146.3,14.2,987.8,889,3,21.78 252.3,0,98.8,146.3,14.2,987.8,889,14,42.29 252.3,0,98.8,146.3,14.2,987.8,889,28,50.6 252.3,0,98.8,146.3,14.2,987.8,889,56,55.83 252.3,0,98.8,146.3,14.2,987.8,889,100,60.95 246.8,0,125.1,143.3,12,1086.8,800.9,3,23.52 246.8,0,125.1,143.3,12,1086.8,800.9,14,42.22 246.8,0,125.1,143.3,12,1086.8,800.9,28,52.5 246.8,0,125.1,143.3,12,1086.8,800.9,56,60.32 246.8,0,125.1,143.3,12,1086.8,800.9,100,66.42 275.1,0,121.4,159.5,9.9,1053.6,777.5,3,23.8 275.1,0,121.4,159.5,9.9,1053.6,777.5,14,38.77 275.1,0,121.4,159.5,9.9,1053.6,777.5,28,51.33 275.1,0,121.4,159.5,9.9,1053.6,777.5,56,56.85 275.1,0,121.4,159.5,9.9,1053.6,777.5,100,58.61 297.2,0,117.5,174.8,9.5,1022.8,753.5,3,21.91 297.2,0,117.5,174.8,9.5,1022.8,753.5,14,36.99 297.2,0,117.5,174.8,9.5,1022.8,753.5,28,47.4 297.2,0,117.5,174.8,9.5,1022.8,753.5,56,51.96 297.2,0,117.5,174.8,9.5,1022.8,753.5,100,56.74 213.7,0,174.7,154.8,10.2,1053.5,776.4,3,17.57 213.7,0,174.7,154.8,10.2,1053.5,776.4,14,33.73 213.7,0,174.7,154.8,10.2,1053.5,776.4,28,40.15 213.7,0,174.7,154.8,10.2,1053.5,776.4,56,46.64 213.7,0,174.7,154.8,10.2,1053.5,776.4,100,50.08 213.5,0,174.2,154.6,11.7,1052.3,775.5,3,17.37 213.5,0,174.2,154.6,11.7,1052.3,775.5,14,33.7 213.5,0,174.2,154.6,11.7,1052.3,775.5,28,45.94 213.5,0,174.2,154.6,11.7,1052.3,775.5,56,51.43 213.5,0,174.2,154.6,11.7,1052.3,775.5,100,59.3 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,3,30.45 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,14,47.71 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,28,63.14 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,56,66.82 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,100,66.95 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,3,27.42 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,14,35.96 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,28,55.51 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,56,61.99 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,100,63.53 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,3,18.02 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,14,38.6 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,28,52.2 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,56,53.96 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,100,56.63 218.9,0,124.1,158.5,11.3,1078.7,794.9,3,15.34 218.9,0,124.1,158.5,11.3,1078.7,794.9,14,26.05 218.9,0,124.1,158.5,11.3,1078.7,794.9,28,30.22 218.9,0,124.1,158.5,11.3,1078.7,794.9,56,37.27 218.9,0,124.1,158.5,11.3,1078.7,794.9,100,46.23 376,0,0,214.6,0,1003.5,762.4,3,16.28 376,0,0,214.6,0,1003.5,762.4,14,25.62 376,0,0,214.6,0,1003.5,762.4,28,31.97 376,0,0,214.6,0,1003.5,762.4,56,36.3 376,0,0,214.6,0,1003.5,762.4,100,43.06 500,0,0,140,4,966,853,28,67.57 475,0,59,142,1.9,1098,641,28,57.23 315,137,0,145,5.9,1130,745,28,81.75 505,0,60,195,0,1030,630,28,64.02 451,0,0,165,11.3,1030,745,28,78.8 516,0,0,162,8.2,801,802,28,41.37 520,0,0,170,5.2,855,855,28,60.28 528,0,0,185,6.9,920,720,28,56.83 520,0,0,175,5.2,870,805,28,51.02 385,0,136,158,20,903,768,28,55.55 500.1,0,0,200,3,1124.4,613.2,28,44.13 450.1,50,0,200,3,1124.4,613.2,28,39.38 397,17.2,158,167,20.8,967,633,28,55.65 333,17.5,163,167,17.9,996,652,28,47.28 334,17.6,158,189,15.3,967,633,28,44.33 405,0,0,175,0,1120,695,28,52.3 200,200,0,190,0,1145,660,28,49.25 516,0,0,162,8.3,801,802,28,41.37 145,116,119,184,5.7,833,880,28,29.16 160,128,122,182,6.4,824,879,28,39.4 234,156,0,189,5.9,981,760,28,39.3 250,180,95,159,9.5,860,800,28,67.87 475,0,0,162,9.5,1044,662,28,58.52 285,190,0,163,7.6,1031,685,28,53.58 356,119,0,160,9,1061,657,28,59 275,180,120,162,10.4,830,765,28,76.24 500,0,0,151,9,1033,655,28,69.84 165,0,143.6,163.8,0,1005.6,900.9,3,14.4 165,128.5,132.1,175.1,8.1,1005.8,746.6,3,19.42 178,129.8,118.6,179.9,3.6,1007.3,746.8,3,20.73 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,3,14.94 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,3,21.29 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,3,23.08 167,75.4,167,164,7.9,1007.3,770.1,3,15.52 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,3,15.82 190.3,0,125.2,166.6,9.9,1079,798.9,3,12.55 250,0,95.7,191.8,5.3,948.9,857.2,3,8.49 213.5,0,174.2,159.2,11.7,1043.6,771.9,3,15.61 194.7,0,100.5,170.2,7.5,998,901.8,3,12.18 251.4,0,118.3,192.9,5.8,1043.6,754.3,3,11.98 165,0,143.6,163.8,0,1005.6,900.9,14,16.88 165,128.5,132.1,175.1,8.1,1005.8,746.6,14,33.09 178,129.8,118.6,179.9,3.6,1007.3,746.8,14,34.24 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,14,31.81 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,14,29.75 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,14,33.01 167,75.4,167,164,7.9,1007.3,770.1,14,32.9 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,14,29.55 190.3,0,125.2,166.6,9.9,1079,798.9,14,19.42 250,0,95.7,191.8,5.3,948.9,857.2,14,24.66 213.5,0,174.2,159.2,11.7,1043.6,771.9,14,29.59 194.7,0,100.5,170.2,7.5,998,901.8,14,24.28 251.4,0,118.3,192.9,5.8,1043.6,754.3,14,20.73 165,0,143.6,163.8,0,1005.6,900.9,28,26.2 165,128.5,132.1,175.1,8.1,1005.8,746.6,28,46.39 178,129.8,118.6,179.9,3.6,1007.3,746.8,28,39.16 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,28,41.2 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,28,33.69 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,28,38.2 167,75.4,167,164,7.9,1007.3,770.1,28,41.41 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,28,37.81 190.3,0,125.2,166.6,9.9,1079,798.9,28,24.85 250,0,95.7,191.8,5.3,948.9,857.2,28,27.22 213.5,0,174.2,159.2,11.7,1043.6,771.9,28,44.64 194.7,0,100.5,170.2,7.5,998,901.8,28,37.27 251.4,0,118.3,192.9,5.8,1043.6,754.3,28,33.27 165,0,143.6,163.8,0,1005.6,900.9,56,36.56 165,128.5,132.1,175.1,8.1,1005.8,746.6,56,53.72 178,129.8,118.6,179.9,3.6,1007.3,746.8,56,48.59 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,56,51.72 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,56,35.85 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,56,53.77 167,75.4,167,164,7.9,1007.3,770.1,56,53.46 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,56,48.99 190.3,0,125.2,166.6,9.9,1079,798.9,56,31.72 250,0,95.7,191.8,5.3,948.9,857.2,56,39.64 213.5,0,174.2,159.2,11.7,1043.6,771.9,56,51.26 194.7,0,100.5,170.2,7.5,998,901.8,56,43.39 251.4,0,118.3,192.9,5.8,1043.6,754.3,56,39.27 165,0,143.6,163.8,0,1005.6,900.9,100,37.96 165,128.5,132.1,175.1,8.1,1005.8,746.6,100,55.02 178,129.8,118.6,179.9,3.6,1007.3,746.8,100,49.99 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,100,53.66 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,100,37.68 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,100,56.06 167,75.4,167,164,7.9,1007.3,770.1,100,56.81 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,100,50.94 190.3,0,125.2,166.6,9.9,1079,798.9,100,33.56 250,0,95.7,191.8,5.3,948.9,857.2,100,41.16 213.5,0,174.2,159.2,11.7,1043.6,771.9,100,52.96 194.7,0,100.5,170.2,7.5,998,901.8,100,44.28 251.4,0,118.3,192.9,5.8,1043.6,754.3,100,40.15 446,24,79,162,11.6,967,712,28,57.03 446,24,79,162,11.6,967,712,28,44.42 446,24,79,162,11.6,967,712,28,51.02 446,24,79,162,10.3,967,712,28,53.39 446,24,79,162,11.6,967,712,3,35.36 446,24,79,162,11.6,967,712,3,25.02 446,24,79,162,11.6,967,712,3,23.35 446,24,79,162,11.6,967,712,7,52.01 446,24,79,162,11.6,967,712,7,38.02 446,24,79,162,11.6,967,712,7,39.3 446,24,79,162,11.6,967,712,56,61.07 446,24,79,162,11.6,967,712,56,56.14 446,24,79,162,11.6,967,712,56,55.25 446,24,79,162,10.3,967,712,56,54.77 387,20,94,157,14.3,938,845,28,50.24 387,20,94,157,13.9,938,845,28,46.68 387,20,94,157,11.6,938,845,28,46.68 387,20,94,157,14.3,938,845,3,22.75 387,20,94,157,13.9,938,845,3,25.51 387,20,94,157,11.6,938,845,3,34.77 387,20,94,157,14.3,938,845,7,36.84 387,20,94,157,13.9,938,845,7,45.9 387,20,94,157,11.6,938,845,7,41.67 387,20,94,157,14.3,938,845,56,56.34 387,20,94,157,13.9,938,845,56,47.97 387,20,94,157,11.6,938,845,56,61.46 355,19,97,145,13.1,967,871,28,44.03 355,19,97,145,12.3,967,871,28,55.45 491,26,123,210,3.9,882,699,28,55.55 491,26,123,201,3.9,822,699,28,57.92 491,26,123,210,3.9,882,699,3,25.61 491,26,123,210,3.9,882,699,7,33.49 491,26,123,210,3.9,882,699,56,59.59 491,26,123,201,3.9,822,699,3,29.55 491,26,123,201,3.9,822,699,7,37.92 491,26,123,201,3.9,822,699,56,61.86 424,22,132,178,8.5,822,750,28,62.05 424,22,132,178,8.5,882,750,3,32.01 424,22,132,168,8.9,822,750,28,72.1 424,22,132,178,8.5,822,750,7,39 424,22,132,178,8.5,822,750,56,65.7 424,22,132,168,8.9,822,750,3,32.11 424,22,132,168,8.9,822,750,7,40.29 424,22,132,168,8.9,822,750,56,74.36 202,11,141,206,1.7,942,801,28,21.97 202,11,141,206,1.7,942,801,3,9.85 202,11,141,206,1.7,942,801,7,15.07 202,11,141,206,1.7,942,801,56,23.25 284,15,141,179,5.5,842,801,28,43.73 284,15,141,179,5.5,842,801,3,13.4 284,15,141,179,5.5,842,801,7,24.13 284,15,141,179,5.5,842,801,56,44.52 359,19,141,154,10.9,942,801,28,62.94 359,19,141,154,10.9,942,801,28,59.49 359,19,141,154,10.9,942,801,3,25.12 359,19,141,154,10.9,942,801,3,23.64 359,19,141,154,10.9,942,801,7,35.75 359,19,141,154,10.9,942,801,7,38.61 359,19,141,154,10.9,942,801,56,68.75 359,19,141,154,10.9,942,801,56,66.78 436,0,0,218,0,838.4,719.7,28,23.85 289,0,0,192,0,913.2,895.3,90,32.07 289,0,0,192,0,913.2,895.3,3,11.65 393,0,0,192,0,940.6,785.6,3,19.2 393,0,0,192,0,940.6,785.6,90,48.85 393,0,0,192,0,940.6,785.6,28,39.6 480,0,0,192,0,936.2,712.2,28,43.94 480,0,0,192,0,936.2,712.2,7,34.57 480,0,0,192,0,936.2,712.2,90,54.32 480,0,0,192,0,936.2,712.2,3,24.4 333,0,0,192,0,931.2,842.6,3,15.62 255,0,0,192,0,889.8,945,90,21.86 255,0,0,192,0,889.8,945,7,10.22 289,0,0,192,0,913.2,895.3,7,14.6 255,0,0,192,0,889.8,945,28,18.75 333,0,0,192,0,931.2,842.6,28,31.97 333,0,0,192,0,931.2,842.6,7,23.4 289,0,0,192,0,913.2,895.3,28,25.57 333,0,0,192,0,931.2,842.6,90,41.68 393,0,0,192,0,940.6,785.6,7,27.74 255,0,0,192,0,889.8,945,3,8.2 158.8,238.2,0,185.7,0,1040.6,734.3,7,9.62 239.6,359.4,0,185.7,0,941.6,664.3,7,25.42 238.2,158.8,0,185.7,0,1040.6,734.3,7,15.69 181.9,272.8,0,185.7,0,1012.4,714.3,28,27.94 193.5,290.2,0,185.7,0,998.2,704.3,28,32.63 255.5,170.3,0,185.7,0,1026.6,724.3,7,17.24 272.8,181.9,0,185.7,0,1012.4,714.3,7,19.77 239.6,359.4,0,185.7,0,941.6,664.3,28,39.44 220.8,147.2,0,185.7,0,1055,744.3,28,25.75 397,0,0,185.7,0,1040.6,734.3,28,33.08 382.5,0,0,185.7,0,1047.8,739.3,7,24.07 210.7,316.1,0,185.7,0,977,689.3,7,21.82 158.8,238.2,0,185.7,0,1040.6,734.3,28,21.07 295.8,0,0,185.7,0,1091.4,769.3,7,14.84 255.5,170.3,0,185.7,0,1026.6,724.3,28,32.05 203.5,135.7,0,185.7,0,1076.2,759.3,7,11.96 397,0,0,185.7,0,1040.6,734.3,7,25.45 381.4,0,0,185.7,0,1104.6,784.3,28,22.49 295.8,0,0,185.7,0,1091.4,769.3,28,25.22 228,342.1,0,185.7,0,955.8,674.3,28,39.7 220.8,147.2,0,185.7,0,1055,744.3,7,13.09 316.1,210.7,0,185.7,0,977,689.3,28,38.7 135.7,203.5,0,185.7,0,1076.2,759.3,7,7.51 238.1,0,0,185.7,0,1118.8,789.3,28,17.58 339.2,0,0,185.7,0,1069.2,754.3,7,21.18 135.7,203.5,0,185.7,0,1076.2,759.3,28,18.2 193.5,290.2,0,185.7,0,998.2,704.3,7,17.2 203.5,135.7,0,185.7,0,1076.2,759.3,28,22.63 290.2,193.5,0,185.7,0,998.2,704.3,7,21.86 181.9,272.8,0,185.7,0,1012.4,714.3,7,12.37 170.3,155.5,0,185.7,0,1026.6,724.3,28,25.73 210.7,316.1,0,185.7,0,977,689.3,28,37.81 228,342.1,0,185.7,0,955.8,674.3,7,21.92 290.2,193.5,0,185.7,0,998.2,704.3,28,33.04 381.4,0,0,185.7,0,1104.6,784.3,7,14.54 238.2,158.8,0,185.7,0,1040.6,734.3,28,26.91 186.2,124.1,0,185.7,0,1083.4,764.3,7,8 339.2,0,0,185.7,0,1069.2,754.3,28,31.9 238.1,0,0,185.7,0,1118.8,789.3,7,10.34 252.5,0,0,185.7,0,1111.6,784.3,28,19.77 382.5,0,0,185.7,0,1047.8,739.3,28,37.44 252.5,0,0,185.7,0,1111.6,784.3,7,11.48 316.1,210.7,0,185.7,0,977,689.3,7,24.44 186.2,124.1,0,185.7,0,1083.4,764.3,28,17.6 170.3,155.5,0,185.7,0,1026.6,724.3,7,10.73 272.8,181.9,0,185.7,0,1012.4,714.3,28,31.38 339,0,0,197,0,968,781,3,13.22 339,0,0,197,0,968,781,7,20.97 339,0,0,197,0,968,781,14,27.04 339,0,0,197,0,968,781,28,32.04 339,0,0,197,0,968,781,90,35.17 339,0,0,197,0,968,781,180,36.45 339,0,0,197,0,968,781,365,38.89 236,0,0,194,0,968,885,3,6.47 236,0,0,194,0,968,885,14,12.84 236,0,0,194,0,968,885,28,18.42 236,0,0,194,0,968,885,90,21.95 236,0,0,193,0,968,885,180,24.1 236,0,0,193,0,968,885,365,25.08 277,0,0,191,0,968,856,14,21.26 277,0,0,191,0,968,856,28,25.97 277,0,0,191,0,968,856,3,11.36 277,0,0,191,0,968,856,90,31.25 277,0,0,191,0,968,856,180,32.33 277,0,0,191,0,968,856,360,33.7 254,0,0,198,0,968,863,3,9.31 254,0,0,198,0,968,863,90,26.94 254,0,0,198,0,968,863,180,27.63 254,0,0,198,0,968,863,365,29.79 307,0,0,193,0,968,812,180,34.49 307,0,0,193,0,968,812,365,36.15 307,0,0,193,0,968,812,3,12.54 307,0,0,193,0,968,812,28,27.53 307,0,0,193,0,968,812,90,32.92 236,0,0,193,0,968,885,7,9.99 200,0,0,180,0,1125,845,7,7.84 200,0,0,180,0,1125,845,28,12.25 225,0,0,181,0,1113,833,7,11.17 225,0,0,181,0,1113,833,28,17.34 325,0,0,184,0,1063,783,7,17.54 325,0,0,184,0,1063,783,28,30.57 275,0,0,183,0,1088,808,7,14.2 275,0,0,183,0,1088,808,28,24.5 300,0,0,184,0,1075,795,7,15.58 300,0,0,184,0,1075,795,28,26.85 375,0,0,186,0,1038,758,7,26.06 375,0,0,186,0,1038,758,28,38.21 400,0,0,187,0,1025,745,28,43.7 400,0,0,187,0,1025,745,7,30.14 250,0,0,182,0,1100,820,7,12.73 250,0,0,182,0,1100,820,28,20.87 350,0,0,186,0,1050,770,7,20.28 350,0,0,186,0,1050,770,28,34.29 203.5,305.3,0,203.5,0,963.4,630,7,19.54 250.2,166.8,0,203.5,0,977.6,694.1,90,47.71 157,236,0,192,0,935.4,781.2,90,43.38 141.3,212,0,203.5,0,971.8,748.5,28,29.89 166.8,250.2,0,203.5,0,975.6,692.6,3,6.9 122.6,183.9,0,203.5,0,958.2,800.1,90,33.19 183.9,122.6,0,203.5,0,959.2,800,3,4.9 102,153,0,192,0,887,942,3,4.57 102,153,0,192,0,887,942,90,25.46 122.6,183.9,0,203.5,0,958.2,800.1,28,24.29 166.8,250.2,0,203.5,0,975.6,692.6,28,33.95 200,133,0,192,0,965.4,806.2,3,11.41 108.3,162.4,0,203.5,0,938.2,849,28,20.59 305.3,203.5,0,203.5,0,965.4,631,7,25.89 108.3,162.4,0,203.5,0,938.2,849,90,29.23 116,173,0,192,0,909.8,891.9,90,31.02 141.3,212,0,203.5,0,971.8,748.5,7,10.39 157,236,0,192,0,935.4,781.2,28,33.66 133,200,0,192,0,927.4,839.2,28,27.87 250.2,166.8,0,203.5,0,977.6,694.1,7,19.35 173,116,0,192,0,946.8,856.8,7,11.39 192,288,0,192,0,929.8,716.1,3,12.79 192,288,0,192,0,929.8,716.1,28,39.32 153,102,0,192,0,888,943.1,3,4.78 288,192,0,192,0,932,717.8,3,16.11 305.3,203.5,0,203.5,0,965.4,631,28,43.38 236,157,0,192,0,972.6,749.1,7,20.42 173,116,0,192,0,946.8,856.8,3,6.94 212,141.3,0,203.5,0,973.4,750,7,15.03 236,157,0,192,0,972.6,749.1,3,13.57 183.9,122.6,0,203.5,0,959.2,800,90,32.53 166.8,250.2,0,203.5,0,975.6,692.6,7,15.75 102,153,0,192,0,887,942,7,7.68 288,192,0,192,0,932,717.8,28,38.8 212,141.3,0,203.5,0,973.4,750,28,33 102,153,0,192,0,887,942,28,17.28 173,116,0,192,0,946.8,856.8,28,24.28 183.9,122.6,0,203.5,0,959.2,800,28,24.05 133,200,0,192,0,927.4,839.2,90,36.59 192,288,0,192,0,929.8,716.1,90,50.73 133,200,0,192,0,927.4,839.2,7,13.66 305.3,203.5,0,203.5,0,965.4,631,3,14.14 236,157,0,192,0,972.6,749.1,90,47.78 108.3,162.4,0,203.5,0,938.2,849,3,2.33 157,236,0,192,0,935.4,781.2,7,16.89 288,192,0,192,0,932,717.8,7,23.52 212,141.3,0,203.5,0,973.4,750,3,6.81 212,141.3,0,203.5,0,973.4,750,90,39.7 153,102,0,192,0,888,943.1,28,17.96 236,157,0,192,0,972.6,749.1,28,32.88 116,173,0,192,0,909.8,891.9,28,22.35 183.9,122.6,0,203.5,0,959.2,800,7,10.79 108.3,162.4,0,203.5,0,938.2,849,7,7.72 203.5,305.3,0,203.5,0,963.4,630,28,41.68 203.5,305.3,0,203.5,0,963.4,630,3,9.56 133,200,0,192,0,927.4,839.2,3,6.88 288,192,0,192,0,932,717.8,90,50.53 200,133,0,192,0,965.4,806.2,7,17.17 200,133,0,192,0,965.4,806.2,28,30.44 250.2,166.8,0,203.5,0,977.6,694.1,3,9.73 122.6,183.9,0,203.5,0,958.2,800.1,3,3.32 153,102,0,192,0,888,943.1,90,26.32 200,133,0,192,0,965.4,806.2,90,43.25 116,173,0,192,0,909.8,891.9,3,6.28 173,116,0,192,0,946.8,856.8,90,32.1 250.2,166.8,0,203.5,0,977.6,694.1,28,36.96 305.3,203.5,0,203.5,0,965.4,631,90,54.6 192,288,0,192,0,929.8,716.1,7,21.48 157,236,0,192,0,935.4,781.2,3,9.69 153,102,0,192,0,888,943.1,7,8.37 141.3,212,0,203.5,0,971.8,748.5,90,39.66 116,173,0,192,0,909.8,891.9,7,10.09 141.3,212,0,203.5,0,971.8,748.5,3,4.83 122.6,183.9,0,203.5,0,958.2,800.1,7,10.35 166.8,250.2,0,203.5,0,975.6,692.6,90,43.57 203.5,305.3,0,203.5,0,963.4,630,90,51.86 310,0,0,192,0,1012,830,3,11.85 310,0,0,192,0,1012,830,7,17.24 310,0,0,192,0,1012,830,28,27.83 310,0,0,192,0,1012,830,90,35.76 310,0,0,192,0,1012,830,120,38.7 331,0,0,192,0,1025,821,3,14.31 331,0,0,192,0,1025,821,7,17.44 331,0,0,192,0,1025,821,28,31.74 331,0,0,192,0,1025,821,90,37.91 331,0,0,192,0,1025,821,120,39.38 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1.4248,1.250,102.5,0.08033,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.4243,1.250,106.2,0.07993,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.4246,1.250,107.5,0.08080,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7088,0.262,30.8,0.10367,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7088,0.262,35.0,0.10149,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7088,0.262,35.5,0.10030,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7086,0.262,36.2,0.10032,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7088,0.262,36.6,0.09965,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7086,0.262,35.9,0.10001,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7082,1.251,91.6,0.08490,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7083,1.251,109.0,0.08192,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7080,1.250,118.5,0.08110,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7084,1.250,122.1,0.08072,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7083,1.250,124.5,0.08041,0,1,0,0,0,0,0,0,0,0,0,0,0,0 1.7081,1.250,130.2,0.08036,0,1,0,0,0,0,0,0,0,0,0,0,0,0 0.2863,0.709,19.8,0.10785,0,0,1,0,0,0,0,0,0,0,0,0,0,0 0.2864,0.709,22.5,0.10705,0,0,1,0,0,0,0,0,0,0,0,0,0,0 0.2863,0.709,23.4,0.10665,0,0,1,0,0,0,0,0,0,0,0,0,0,0 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1.4292,1.250,87.5,0.08370,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.4296,1.250,93.7,0.08288,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.4294,1.249,96.1,0.08262,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7135,0.262,25.8,0.10155,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7139,0.262,29.8,0.10016,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7135,0.262,30.6,0.09926,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7136,0.262,31.3,0.09882,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7136,0.262,31.0,0.09867,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7137,0.262,31.3,0.09866,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7120,1.250,75.4,0.08562,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7117,1.250,92.6,0.08261,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7124,1.250,101.8,0.08153,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7117,1.250,109.1,0.08096,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7122,1.250,110.9,0.08251,0,0,1,0,0,0,0,0,0,0,0,0,0,0 1.7120,1.250,111.1,0.08041,0,0,1,0,0,0,0,0,0,0,0,0,0,0 0.2854,0.709,19.8,0.11952,0,0,0,1,0,0,0,0,0,0,0,0,0,0 0.2862,0.709,20.9,0.11844,0,0,0,1,0,0,0,0,0,0,0,0,0,0 0.2862,0.709,22.2,0.11826,0,0,0,1,0,0,0,0,0,0,0,0,0,0 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================================================ { "metadata": { "name": "Vladislavleva-7", "filename": "Vladislavleva-7.csv", "formula": "F7(X1, X2) = (X1 - 3)(X2 - 3) + 2 * sin((X1 - 4)(X2 - 4))", "kind": "synthetic", "target": "Y", "test_rows": { "end": 1300, "start": 300 }, "training_rows": { "end": 300, "start": 0 } }, "timestamp": "Mon, 16 Sep 2019 18:48:21 +0000" } ================================================ FILE: data/Vladislavleva-8.csv ================================================ X1,X2,Y 4.42333200694372,6.01606719632774,0.10559134469785 1.27750320640102,2.68184236191023,0.88967188760059 0.731360815810357,2.13661607701485,2.67076250893977 2.11403749856726,0.706792236644959,-0.71503412633775 0.832712296764998,3.25991608342387,1.74289727244048 5.51530765236013,4.11630249695533,1.34078655588532 2.4528663021519,4.90892599747957,0.0629488677543561 4.36419206417962,5.95692387006872,0.103312174476219 4.24655080982404,4.98090141679414,0.0922543592592547 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6.35,4.55,2.45050190664875 6.35,4.75,1.92813171327248 6.35,4.95,1.53276574116552 6.35,5.15,1.23306108968953 6.35,5.35,1.00461861253036 6.35,5.55,0.829127998364855 6.35,5.75,0.693086276205697 6.35,5.95,0.586600579170236 6.35,6.15,0.502423656121456 6.35,6.35,0.435226439448447 ================================================ FILE: data/Vladislavleva-8.json ================================================ { "metadata": { "name": "Vladislavleva-8", "filename": "Vladislavleva-8.csv", "formula": "F8(X1, X2) = ((X1 - 3)^4 + (X2 - 3)³ - (X2 -3)) / ((X2 - 2)^4 + 10)", "kind": "synthetic", "target": "Y", "test_rows": { "end": 1206, "start": 50 }, "training_rows": { "end": 50, "start": 0 } }, "timestamp": "Mon, 16 Sep 2019 18:48:21 +0000" } ================================================ FILE: docs/Doxyfile.in ================================================ # Configuration for Doxygen for use with CMake # Only options that deviate from the default are included # To create a new Doxyfile containing all available options, call `doxygen -g` # Get Project name and version from CMake PROJECT_NAME = "@PROJECT_NAME@" PROJECT_NUMBER = "@PROJECT_VERSION@" # Add sources INPUT = "@PROJECT_SOURCE_DIR@/README.md" "@PROJECT_SOURCE_DIR@/include" "@PROJECT_SOURCE_DIR@/docs/pages" EXTRACT_ALL = YES RECURSIVE = YES OUTPUT_DIRECTORY = "@DOXYGEN_OUTPUT_DIRECTORY@" # Use the README as a main page USE_MDFILE_AS_MAINPAGE = "@PROJECT_SOURCE_DIR@/README.md" # set relative include paths FULL_PATH_NAMES = YES STRIP_FROM_PATH = "@PROJECT_SOURCE_DIR@/include" "@PROJECT_SOURCE_DIR@" STRIP_FROM_INC_PATH = # We use m.css to generate the html documentation, so we only need XML output GENERATE_XML = YES GENERATE_HTML = NO GENERATE_LATEX = NO XML_PROGRAMLISTING = NO CREATE_SUBDIRS = NO # Include all directories, files and namespaces in the documentation # Disable to include only explicitly documented objects M_SHOW_UNDOCUMENTED = YES ================================================ FILE: docs/conf.py.in ================================================ DOXYFILE = 'Doxyfile' LINKS_NAVBAR1 = [ (None, 'pages', [(None, 'about')]), (None, 'namespaces', []), ] ================================================ FILE: docs/pages/about.dox ================================================ /** * @page about About * @section about-doxygen Doxygen documentation * This page is auto generated using * Doxygen, making use of some useful * special commands. */ ================================================ FILE: example/CMakeLists.txt ================================================ cmake_minimum_required(VERSION 3.14) project(operonExamples CXX) include(../cmake/project-is-top-level.cmake) include(../cmake/project-is-top-level.cmake) if(PROJECT_IS_TOP_LEVEL) find_package(operon REQUIRED) endif() add_custom_target(run-examples) function(add_example NAME) add_executable("${NAME}" "${NAME}.cpp") target_link_libraries("${NAME}" PRIVATE operon::operon) target_compile_features("${NAME}" PRIVATE cxx_std_17) add_custom_target("run_${NAME}" COMMAND "${NAME}" VERBATIM) add_dependencies("run_${NAME}" "${NAME}") add_dependencies(run-examples "run_${NAME}") endfunction() add_example(empty_example) add_example(custom_primitives) ================================================ FILE: example/custom_primitives.cpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research // // Demonstration: symbolic regression using runtime-registered primitives. // // All mathematical functions beyond the basic arithmetic set are registered // at runtime via RegisterUnaryFunction / RegisterBinaryFunction. The example // shows that user-defined callables participate in tree generation, evaluation, // and coefficient optimisation on equal footing with built-in operators. // // Custom primitives added in this example: // sincos(x) = sin(x) + cos(x) (unary, explicit derivative) // gaussian(x) = exp(-x * x) (unary, Jet auto-diff fallback) // hypot(a, b) = sqrt(a^2 + b^2) (binary, Jet auto-diff fallback) // // Usage: // custom_primitives // // The Poly-10 dataset ships with the repository under data/Poly-10.csv. #include #include #include #include #include #include #include "operon/algorithms/config.hpp" #include "operon/algorithms/gp.hpp" #include "operon/core/dataset.hpp" #include "operon/core/problem.hpp" #include "operon/core/symbol_library.hpp" #include "operon/formatter/formatter.hpp" #include "operon/hash/hash.hpp" #include "operon/operators/creator.hpp" #include "operon/operators/crossover.hpp" #include "operon/operators/evaluator.hpp" #include "operon/operators/generator.hpp" #include "operon/operators/initializer.hpp" #include "operon/operators/mutation.hpp" #include "operon/operators/local_search.hpp" #include "operon/operators/reinserter.hpp" #include "operon/operators/selector.hpp" #include "operon/optimizer/optimizer.hpp" auto main(int argc, char** argv) -> int { if (argc < 2) { fmt::print(stderr, "usage: {} \n", argv[0]); fmt::print(stderr, " (dataset ships with the repo under data/Poly-10.csv)\n"); return 1; } Operon::Dataset dataset(argv[1], /*hasHeader=*/true); Operon::Problem problem(std::make_unique(dataset)); problem.SetTrainingRange({ 0, 250 }); problem.SetTestRange({ 250, 500 }); problem.SetTarget("Y"); auto inputs = dataset.VariableHashes(); std::erase(inputs, dataset.GetVariable("Y")->Hash); problem.SetInputs(inputs); // Arithmetic built-ins form the structural backbone of expressions. // No transcendental functions from NodeType — those come from our // runtime-registered callables below. problem.ConfigurePrimitiveSet( Operon::NodeType::Constant | Operon::NodeType::Variable | Operon::NodeType::Add | Operon::NodeType::Sub | Operon::NodeType::Mul | Operon::NodeType::Div); // register custom primitives using DT = Operon::ScalarDispatch; DT dtable; auto& pset = problem.GetPrimitiveSet(); // sincos(x) = sin(x) + cos(x) — explicit derivative provided Operon::RegisterUnaryFunction(dtable, pset, { .Hash = Operon::Hasher{}("sincos"), .Name = "sincos", .Desc = "sin(x) + cos(x)", .Arity = 1, .Frequency = 2 }, [](auto v) { using std::sin, std::cos; return sin(v) + cos(v); }, [](auto v) { using std::sin, std::cos; return cos(v) - sin(v); }); // gaussian(x) = exp(-x²) — derivative via Jet auto-diff Operon::RegisterUnaryFunction(dtable, pset, { .Hash = Operon::Hasher{}("gaussian"), .Name = "gaussian", .Desc = "exp(-x * x)", .Arity = 1, .Frequency = 2 }, [](auto v) { using std::exp; return exp(-v * v); }); // hypot(a, b) = sqrt(a² + b²) — derivative via Jet auto-diff Operon::RegisterBinaryFunction(dtable, pset, { .Hash = Operon::Hasher{}("hypot"), .Name = "hypot", .Desc = "sqrt(a^2 + b^2)", .Arity = 2, .Frequency = 1 }, [](auto a, auto b) { using std::sqrt; return sqrt((a * a) + (b * b)); }); fmt::print("Primitives in use:\n"); for (auto const& node : pset.EnabledPrimitives()) { fmt::print(" {:<12} arity={}\n", node.Name(), node.Arity); } fmt::print("\n"); // set up GP operators constexpr size_t MaxLength = 50; constexpr size_t MaxDepth = 10; auto [arityMin, arityMax] = pset.FunctionArityLimits(); Operon::BalancedTreeCreator creator { &pset, problem.GetInputs(), /* bias= */ 0.0, MaxLength }; Operon::NormalCoefficientInitializer coeffInit; coeffInit.ParameterizeDistribution(Operon::Scalar{0}, Operon::Scalar{1}); Operon::UniformTreeInitializer treeInit { &creator }; treeInit.ParameterizeDistribution(arityMin + 1, MaxLength); treeInit.SetMinDepth(1); treeInit.SetMaxDepth(MaxDepth); Operon::SubtreeCrossover crossover { 0.9, MaxDepth, MaxLength }; Operon::OnePointMutation> onePoint; Operon::ChangeFunctionMutation changeFunc { pset }; Operon::ChangeVariableMutation changeVar { problem.GetInputs() }; Operon::RemoveSubtreeMutation removeSub { pset }; Operon::InsertSubtreeMutation insertSub { &creator, &coeffInit, MaxDepth, MaxLength }; Operon::MultiMutation mutator; mutator.Add(&onePoint, 1.0); mutator.Add(&changeFunc, 1.0); mutator.Add(&changeVar, 1.0); mutator.Add(&removeSub, 1.0); mutator.Add(&insertSub, 1.0); // evaluator, coefficient optimizer, selectors, offspring generator Operon::Evaluator
evaluator { &problem, &dtable, Operon::MSE{}, /*linearScaling=*/true }; evaluator.SetBudget(std::numeric_limits::max()); // generations is the only stop criterion Operon::LevenbergMarquardtOptimizer lmOptimizer { &dtable, &problem }; Operon::CoefficientOptimizer coeffOpt { &lmOptimizer }; auto comp = [](auto const& a, auto const& b) { return a[0] < b[0]; }; Operon::TournamentSelector femaleSelector { comp }; Operon::TournamentSelector maleSelector { comp }; Operon::BasicOffspringGenerator generator { &evaluator, &crossover, &mutator, &femaleSelector, &maleSelector, &coeffOpt }; Operon::ReplaceWorstReinserter reinserter { comp }; Operon::GeneticAlgorithmConfig config { .Generations = 100, .Evaluations = std::numeric_limits::max(), .Iterations = 2, .PopulationSize = 200, .PoolSize = 200, .Seed = 42, }; Operon::RandomGenerator rng { config.Seed }; Operon::GeneticProgrammingAlgorithm gp { config, &problem, &treeInit, &coeffInit, &generator, &reinserter }; tf::Executor executor(std::thread::hardware_concurrency()); size_t gen = 0; auto report = [&]() { ++gen; if (gen % 10 != 0) { return; } auto const* first = gp.Individuals().data(); auto const* last = first + config.PopulationSize; auto const* best = std::min_element(first, last, [](auto const& a, auto const& b) { return a[0] < b[0]; }); fmt::print(" gen {:4d} MSE(train)={:.6f} len={}\n", gen, best->Fitness[0], best->Genotype.Length()); }; fmt::print("Running GP ({} generations, population {})...\n\n", config.Generations, config.PopulationSize); gp.Run(executor, rng, report); auto const* first = gp.Individuals().data(); auto const* last = first + config.PopulationSize; auto const* best = std::min_element(first, last, [](auto const& a, auto const& b) { return a[0] < b[0]; }); fmt::print("\nBest model (MSE={:.6f}, length={}):\n {}\n", best->Fitness[0], best->Genotype.Length(), Operon::InfixFormatter::Format(best->Genotype, dataset, 6)); return 0; } ================================================ FILE: example/empty_example.cpp ================================================ auto main() -> int { return 0; } ================================================ FILE: flake.nix ================================================ { description = "Operon development environment"; inputs = { flake-parts.url = "github:hercules-ci/flake-parts"; nixpkgs.url = "github:nixos/nixpkgs/master"; foolnotion.url = "github:foolnotion/nur-pkg"; fluky.url = "github:foolnotion/fluky"; lbfgs.url = "github:foolnotion/lbfgs"; infix-parser.url = "github:foolnotion/infix-parser"; vstat.url = "github:heal-research/vstat"; vdt.url = "github:foolnotion/vdt/master"; # make everything follow nixpkgs foolnotion.inputs.nixpkgs.follows = "nixpkgs"; lbfgs.inputs.nixpkgs.follows = "nixpkgs"; infix-parser.inputs.nixpkgs.follows = "nixpkgs"; vstat.inputs.nixpkgs.follows = "nixpkgs"; vdt.inputs.nixpkgs.follows = "nixpkgs"; fluky.inputs.nixpkgs.follows = "nixpkgs"; }; outputs = inputs@{ self, flake-parts, nixpkgs, foolnotion, fluky, infix-parser, vdt, vstat, lbfgs, }: flake-parts.lib.mkFlake { inherit inputs; } { systems = [ "x86_64-linux" "x86_64-darwin" "aarch64-linux" "aarch64-darwin" ]; perSystem = { pkgs, system, ... }: let pkgs = import self.inputs.nixpkgs { inherit system; overlays = [ foolnotion.overlay (final: prev: { fluky = fluky.packages.${system}.default; lbfgs = lbfgs.packages.${system}.default; infix-parser = infix-parser.packages.${system}.default; vdt = vdt.packages.${system}.default; vstat = vstat.packages.${system}.default; }) ]; }; enableShared = !pkgs.stdenv.hostPlatform.isStatic; enableTesting = true; inherit (pkgs.llvmPackages_21) stdenv; operon = import ./operon.nix { inherit stdenv pkgs system; inherit enableShared enableTesting; }; in rec { packages = { default = operon.overrideAttrs (old: { cmakeFlags = old.cmakeFlags ++ [ "-DBUILD_CLI_PROGRAMS=ON" "-DCPM_USE_LOCAL_PACKAGES=ON" ]; }); cli = operon.overrideAttrs (old: { cmakeFlags = old.cmakeFlags ++ [ "-DBUILD_CLI_PROGRAMS=ON" "-DCPM_USE_LOCAL_PACKAGES=ON" ]; }); cli-static = operon.overrideAttrs (old: { cmakeFlags = old.cmakeFlags ++ [ "-DBUILD_CLI_PROGRAMS=ON" "-DCPM_USE_LOCAL_PACKAGES=ON" ]; }); library = operon.overrideAttrs (old: { enableShared = true; }); library-static = operon.overrideAttrs (old: { enableShared = false; }); }; devShells.compare = pkgs.mkShell { name = "operon-compare"; packages = [ (pkgs.python3.withPackages ( ps: with ps; [ scipy tabulate ] )) ]; }; devShells.default = stdenv.mkDerivation { name = "operon"; nativeBuildInputs = operon.nativeBuildInputs ++ (with pkgs; [ clang-tools cppcheck include-what-you-use cmake-language-server ]); buildInputs = operon.buildInputs ++ pkgs.lib.optionals (pkgs.stdenv.hostPlatform.system == "x86_64-linux") ( with pkgs; [ gdb graphviz hyperfine ] ); }; apps.operon-gp.program = "${packages.default}/bin/operon_gp"; apps.operon-nsgp.program = "${packages.default}/bin/operon_nsgp"; apps.operon-parse-model.program = "${packages.default}/bin/operon_parse_model"; }; }; } ================================================ FILE: include/operon/algorithms/config.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_ALGORITHM_CONFIG_HPP #define OPERON_ALGORITHM_CONFIG_HPP #include namespace Operon { class Zobrist; // forward declaration — include operon/hash/zobrist.hpp to use struct GeneticAlgorithmConfig { size_t Generations; // generation limit size_t Evaluations; // evaluation budget size_t Iterations; // local search iterations size_t PopulationSize; size_t PoolSize; size_t Seed; // random seed size_t TimeLimit{~std::size_t{0}}; // time limit double CrossoverProbability{1.0}; double MutationProbability{0.25}; double LocalSearchProbability{1.0}; double LamarckianProbability{1.0}; double Epsilon{0}; // used when comparing fitness values Zobrist* Cache{nullptr}; // optional transposition cache; null = disabled }; } // namespace Operon #endif ================================================ FILE: include/operon/algorithms/ga_base.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef GA_BASE_HPP #define GA_BASE_HPP #include #include #include "operon/operators/generator.hpp" #include "config.hpp" namespace Operon { class Problem; class ReinserterBase; struct CoefficientInitializerBase; struct TreeInitializerBase; class GeneticAlgorithmBase { public: virtual ~GeneticAlgorithmBase() = default; GeneticAlgorithmBase(const GeneticAlgorithmBase&) = default; GeneticAlgorithmBase(GeneticAlgorithmBase&&) = delete; auto operator=(const GeneticAlgorithmBase&) -> GeneticAlgorithmBase& = default; auto operator=(GeneticAlgorithmBase&&) -> GeneticAlgorithmBase& = delete; GeneticAlgorithmBase(GeneticAlgorithmConfig config, gsl::not_null problem, gsl::not_null treeInit, gsl::not_null coeffInit, gsl::not_null generator, gsl::not_null reinserter) : config_(config) , problem_(problem) , treeInit_(treeInit) , coeffInit_(coeffInit) , generator_(generator) , reinserter_(reinserter) , individuals_(config.PopulationSize + config.PoolSize) , parents_(individuals_.data(), config.PopulationSize) , offspring_(individuals_.data() + config.PopulationSize, config.PoolSize) { generator_->SetCache(config.Cache); } [[nodiscard]] auto Parents() const -> Operon::Span { return { parents_.data(), parents_.size() }; } auto Parents() -> Operon::Span { return parents_; } [[nodiscard]] auto Offspring() const -> Operon::Span { return { offspring_.data(), offspring_.size() }; } auto Offspring() -> Operon::Span { return offspring_; } [[nodiscard]] auto Individuals() -> Operon::Vector& { return individuals_; } [[nodiscard]] auto Individuals() const -> Operon::Vector const& { return individuals_; } [[nodiscard]] auto GetProblem() const -> const Problem* { return problem_.get(); } [[nodiscard]] auto GetConfig() const -> GeneticAlgorithmConfig { return config_; } [[nodiscard]] auto GetTreeInitializer() const -> TreeInitializerBase const* { return treeInit_.get(); } [[nodiscard]] auto GetCoefficientInitializer() const -> CoefficientInitializerBase const* { return coeffInit_.get(); } [[nodiscard]] auto GetGenerator() const -> OffspringGeneratorBase const* { return generator_.get(); } [[nodiscard]] auto GetReinserter() const -> ReinserterBase const* { return reinserter_.get(); } [[nodiscard]] auto Generation() const -> size_t { return generation_; } auto Generation() -> size_t& { return generation_; } [[nodiscard]] auto Elapsed() const -> double { return elapsed_; } auto Elapsed() -> double& { return elapsed_; } [[nodiscard]] auto IsFitted() const -> bool { return isFitted_; } auto IsFitted() -> bool& { return isFitted_; } auto Reset() -> void { generation_ = 0; elapsed_ = 0; GetGenerator()->Evaluator()->Reset(); } auto RestoreIndividuals(std::vector inds) -> void { EXPECT(inds.size() == config_.PoolSize + config_.PopulationSize, "Mismatched number of individuals (must match pool/population sizes)"); individuals_ = std::move(inds); parents_ = Operon::Span(individuals_.data(), config_.PoolSize); offspring_ = Operon::Span(individuals_.data() + config_.PoolSize, config_.PopulationSize); } private: GeneticAlgorithmConfig config_; gsl::not_null problem_; gsl::not_null treeInit_; gsl::not_null coeffInit_; gsl::not_null generator_; gsl::not_null reinserter_; Operon::Vector individuals_; Operon::Span parents_; Operon::Span offspring_; size_t generation_{0}; double elapsed_{0}; // elapsed time in microseconds bool isFitted_{false}; }; } // namespace Operon #endif ================================================ FILE: include/operon/algorithms/gp.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef GP_HPP #define GP_HPP #include // for size_t #include // for reference_wrapper, function #include // for OPERON_EXPORT #include "operon/algorithms/config.hpp" // for GeneticAlgorithmConfig #include "operon/algorithms/ga_base.hpp" #include "operon/core/types.hpp" // for Span, Vector, RandomGenerator #include "operon/operators/generator.hpp" // for OffspringGeneratorBase // forward declaration namespace tf { class Executor; } // namespace tf namespace Operon { class Problem; class ReinserterBase; struct CoefficientInitializerBase; struct TreeInitializerBase; class OPERON_EXPORT GeneticProgrammingAlgorithm : public GeneticAlgorithmBase { public: GeneticProgrammingAlgorithm(GeneticAlgorithmConfig config, gsl::not_null problem, gsl::not_null treeInit, gsl::not_null coeffInit, gsl::not_null generator, gsl::not_null reinserter) : GeneticAlgorithmBase(config, problem, treeInit, coeffInit, generator, reinserter) { } auto Run(tf::Executor& /*executor*/, Operon::RandomGenerator& /*rng*/, std::function /*report*/ = nullptr, /*warmStart*/ bool = false) -> void; auto Run(Operon::RandomGenerator& /*rng*/, std::function /*report*/ = nullptr, size_t /*threads*/ = 0, /*warmStart*/ bool = false) -> void; }; } // namespace Operon #endif ================================================ FILE: include/operon/algorithms/nsga2.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_NSGA2_HPP #define OPERON_NSGA2_HPP #include // for reference_wrapper, function #include // for OPERON_EXPORT #include // for thread #include // for move #include // for vector #include "operon/algorithms/config.hpp" // for GeneticAlgorithmConfig #include "operon/algorithms/ga_base.hpp" #include "operon/core/individual.hpp" // for Individual #include "operon/core/types.hpp" // for Span, Vector, RandomGenerator #include "operon/operators/evaluator.hpp" // for EvaluatorBase #include "operon/operators/generator.hpp" // for OffspringGeneratorBase // forward declaration namespace tf { class Executor; } namespace Operon { class NondominatedSorterBase; class Problem; class ReinserterBase; struct CoefficientInitializerBase; struct TreeInitializerBase; class OPERON_EXPORT NSGA2 : public GeneticAlgorithmBase { gsl::not_null sorter_; Operon::Vector> fronts_; Operon::Vector best_; // best Pareto front auto UpdateDistance(Operon::Span pop) -> void; auto Sort(Operon::Span pop) -> void; public: NSGA2(GeneticAlgorithmConfig config, gsl::not_null problem, gsl::not_null treeInit, gsl::not_null coeffInit, gsl::not_null generator, gsl::not_null reinserter, gsl::not_null sorter) : GeneticAlgorithmBase(config, problem, treeInit, coeffInit, generator, reinserter), sorter_(sorter) { auto const n { GetGenerator()->Evaluator()->ObjectiveCount() }; for (auto& ind : Individuals()) { ind.Fitness.resize(n, EvaluatorBase::ErrMax); } } [[nodiscard]] auto Best() const -> Operon::Span { return { best_.data(), best_.size() }; } auto Run(tf::Executor& /*executor*/, Operon::RandomGenerator&/*rng*/, std::function /*report*/ = nullptr, /*warmStart*/ bool = false) -> void; auto Run(Operon::RandomGenerator& /*rng*/, std::function /*report*/ = nullptr, size_t /*threads*/= 0, /*warmStart*/ bool = false) -> void; }; } // namespace Operon #endif ================================================ FILE: include/operon/algorithms/solution_archive.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_SOLUTION_ARCHIVE_HPP #define OPERON_SOLUTION_ARCHIVE_HPP #include "operon/core/individual.hpp" #include "operon/operators/non_dominated_sorter.hpp" namespace Operon { class SolutionArchive { public: auto Insert(Operon::Individual const& individual) -> bool; auto Insert(Operon::Span individuals) -> int64_t; [[nodiscard]] auto Solutions() const { return Operon::Span { archive_ }; } auto Clear() { archive_.clear(); } private: Operon::Scalar eps_{}; std::vector archive_; }; } // namespace Operon #endif ================================================ FILE: include/operon/analyzers/analyzer_base.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_ANALYZER_BASE_HPP #define OPERON_ANALYZER_BASE_HPP #include "operon/core/operator.hpp" namespace Operon { template class PopulationAnalyzerBase : public OperatorBase { public: virtual void Prepare(Operon::Span pop) = 0; }; } // namespace Operon #endif ================================================ FILE: include/operon/analyzers/diversity.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef DIVERSITY_HPP #define DIVERSITY_HPP #include #include #include #include "operon/analyzers/analyzer_base.hpp" #include "operon/core/operator.hpp" #include "operon/core/distance.hpp" #include "operon/core/tree.hpp" namespace Operon { namespace { inline auto MakeHashes(Tree& tree, Operon::HashMode m) -> Operon::Vector { Operon::Vector hashes(tree.Length()); [[maybe_unused]] auto h = tree.Hash(m); std::transform(tree.Nodes().begin(), tree.Nodes().end(), hashes.begin(), [](const auto& node) { return node.CalculatedHashValue; }); std::stable_sort(hashes.begin(), hashes.end()); return hashes; } } // namespace template class PopulationDiversityAnalyzer final : PopulationAnalyzerBase { public: auto operator()(Operon::RandomGenerator& /*unused*/) const -> double { return diversity_; } void Prepare(Operon::Span pop) { std::vector indices(pop.size()); std::iota(indices.begin(), indices.end(), 0); std::vector> hashes; hashes.reserve(pop.size()); std::transform(indices.begin(), indices.end(), std::back_inserter(hashes), [&](auto i) { return MakeHashes(pop[i], M); }); vstat::univariate_accumulator acc; for (auto i = 0UL; i < pop.size() - 1; ++i) { for (auto j = i+1; j < pop.size(); ++j) { acc(Operon::Distance::Jaccard(hashes[i], hashes[j])); } } diversity_ = vstat::univariate_statistics(acc).mean; } private: double diversity_{}; }; } // namespace Operon #endif ================================================ FILE: include/operon/ceres/integer_sequence_algorithm.h ================================================ // Ceres Solver - A fast non-linear least squares minimizer // Copyright 2022 Google Inc. All rights reserved. // http://ceres-solver.org/ // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * Neither the name of Google Inc. nor the names of its contributors may be // used to endorse or promote products derived from this software without // specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: jodebo_beck@gmx.de (Johannes Beck) // sergiu.deitsch@gmail.com (Sergiu Deitsch) // // Algorithms to be used together with integer_sequence, like computing the sum // or the exclusive scan (sometimes called exclusive prefix sum) at compile // time. #ifndef CERES_PUBLIC_INTERNAL_INTEGER_SEQUENCE_ALGORITHM_H_ #define CERES_PUBLIC_INTERNAL_INTEGER_SEQUENCE_ALGORITHM_H_ #include #include "jet_fwd.h" namespace ceres { namespace internal { // Implementation of calculating the sum of an integer sequence. // Recursively instantiate SumImpl and calculate the sum of the N first // numbers. This reduces the number of instantiations and speeds up // compilation. // // Examples: // 1) integer_sequence: // Value = 5 // // 2) integer_sequence: // Value = 4 + 2 + SumImpl>::Value // Value = 4 + 2 + 0 // // 3) integer_sequence: // Value = 2 + 1 + SumImpl>::Value // Value = 2 + 1 + 4 template struct SumImpl; // Strip of and sum the first number. template struct SumImpl> { static constexpr T Value = N + SumImpl>::Value; }; // Strip of and sum the first two numbers. template struct SumImpl> { static constexpr T Value = N1 + N2 + SumImpl>::Value; }; // Strip of and sum the first four numbers. template struct SumImpl> { static constexpr T Value = N1 + N2 + N3 + N4 + SumImpl>::Value; }; // Only one number is left. 'Value' is just that number ('recursion' ends). template struct SumImpl> { static constexpr T Value = N; }; // No number is left. 'Value' is the identity element (for sum this is zero). template struct SumImpl> { static constexpr T Value = T(0); }; // Calculate the sum of an integer sequence. The resulting sum will be stored in // 'Value'. template class Sum { using T = typename Seq::value_type; public: static constexpr T Value = SumImpl::Value; }; // Implementation of calculating an exclusive scan (exclusive prefix sum) of an // integer sequence. Exclusive means that the i-th input element is not included // in the i-th sum. Calculating the exclusive scan for an input array I results // in the following output R: // // R[0] = 0 // R[1] = I[0]; // R[2] = I[0] + I[1]; // R[3] = I[0] + I[1] + I[2]; // ... // // In C++17 std::exclusive_scan does the same operation at runtime (but // cannot be used to calculate the prefix sum at compile time). See // https://en.cppreference.com/w/cpp/algorithm/exclusive_scan for a more // detailed description. // // Example for integer_sequence (seq := integer_sequence): // T , Sum, Ns... , Rs... // ExclusiveScanImpl, seq> // ExclusiveScanImpl, seq> // ExclusiveScanImpl, seq> // ExclusiveScanImpl, seq> // ^^^^^^^^^^^^^^^^^ // resulting sequence template struct ExclusiveScanImpl; template struct ExclusiveScanImpl, std::integer_sequence> { using Type = typename ExclusiveScanImpl, std::integer_sequence>::Type; }; // End of 'recursion'. The resulting type is SeqOut. template struct ExclusiveScanImpl, SeqOut> { using Type = SeqOut; }; // Calculates the exclusive scan of the specified integer sequence. The last // element (the total) is not included in the resulting sequence so they have // same length. This means the exclusive scan of integer_sequence // will be integer_sequence. template class ExclusiveScanT { using T = typename Seq::value_type; public: using Type = typename ExclusiveScanImpl>::Type; }; // Helper to use exclusive scan without typename. template using ExclusiveScan = typename ExclusiveScanT::Type; // Removes all elements from a integer sequence corresponding to specified // ValueToRemove. // // This type should not be used directly but instead RemoveValue. template struct RemoveValueImpl; // Final filtered sequence template struct RemoveValueImpl, std::integer_sequence> { using type = std::integer_sequence; }; // Found a matching value template struct RemoveValueImpl, std::integer_sequence> : RemoveValueImpl, std::integer_sequence> {}; // Move one element from the tail to the head template struct RemoveValueImpl, std::integer_sequence> : RemoveValueImpl, std::integer_sequence> {}; // Start recursion by splitting the integer sequence into two separate ones template struct RemoveValueImpl> : RemoveValueImpl, std::integer_sequence> {}; // RemoveValue takes an integer Sequence of arbitrary type and removes all // elements matching ValueToRemove. // // In contrast to RemoveValueImpl, this implementation deduces the value type // eliminating the need to specify it explicitly. // // As an example, RemoveValue, 4>::type will // not transform the type of the original sequence. However, // RemoveValue, 2>::type will generate a new // sequence of type std::integer_sequence by removing the value 2. template struct RemoveValue : RemoveValueImpl { }; // Convenience template alias for RemoveValue. template using RemoveValue_t = typename RemoveValue::type; // Determines whether the values of an integer sequence are all the same. // // The integer sequence must contain at least one value. The predicate is // undefined for empty sequences. The evaluation result of the predicate for a // sequence containing only one value is defined to be true. template struct AreAllEqual; // The predicate result for a sequence containing one element is defined to be // true. template struct AreAllEqual> : std::true_type {}; // Recursion end. template struct AreAllEqual> : std::integral_constant {}; // Recursion for sequences containing at least two elements. template // clang-format off struct AreAllEqual > : std::integral_constant < bool, AreAllEqual >::value && AreAllEqual >::value > // clang-format on {}; // Convenience variable template for AreAllEqual. template constexpr bool AreAllEqual_v = AreAllEqual::value; // Predicate determining whether an integer sequence is either empty or all // values are equal. template struct IsEmptyOrAreAllEqual; // Empty case. template struct IsEmptyOrAreAllEqual> : std::true_type {}; // General case for sequences containing at least one value. template struct IsEmptyOrAreAllEqual> : AreAllEqual> {}; // Convenience variable template for IsEmptyOrAreAllEqual. template constexpr bool IsEmptyOrAreAllEqual_v = IsEmptyOrAreAllEqual::value; } // namespace internal } // namespace ceres #endif // CERES_PUBLIC_INTERNAL_INTEGER_SEQUENCE_ALGORITHM_H_ ================================================ FILE: include/operon/ceres/jet.h ================================================ // Ceres Solver - A fast non-linear least squares minimizer // Copyright 2022 Google Inc. All rights reserved. // http://ceres-solver.org/ // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * Neither the name of Google Inc. nor the names of its contributors may be // used to endorse or promote products derived from this software without // specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: keir@google.com (Keir Mierle) // // A simple implementation of N-dimensional dual numbers, for automatically // computing exact derivatives of functions. // // While a complete treatment of the mechanics of automatic differentiation is // beyond the scope of this header (see // http://en.wikipedia.org/wiki/Automatic_differentiation for details), the // basic idea is to extend normal arithmetic with an extra element, "e," often // denoted with the greek symbol epsilon, such that e != 0 but e^2 = 0. Dual // numbers are extensions of the real numbers analogous to complex numbers: // whereas complex numbers augment the reals by introducing an imaginary unit i // such that i^2 = -1, dual numbers introduce an "infinitesimal" unit e such // that e^2 = 0. Dual numbers have two components: the "real" component and the // "infinitesimal" component, generally written as x + y*e. Surprisingly, this // leads to a convenient method for computing exact derivatives without needing // to manipulate complicated symbolic expressions. // // For example, consider the function // // f(x) = x^2 , // // evaluated at 10. Using normal arithmetic, f(10) = 100, and df/dx(10) = 20. // Next, argument 10 with an infinitesimal to get: // // f(10 + e) = (10 + e)^2 // = 100 + 2 * 10 * e + e^2 // = 100 + 20 * e -+- // -- | // | +--- This is zero, since e^2 = 0 // | // +----------------- This is df/dx! // // Note that the derivative of f with respect to x is simply the infinitesimal // component of the value of f(x + e). So, in order to take the derivative of // any function, it is only necessary to replace the numeric "object" used in // the function with one extended with infinitesimals. The class Jet, defined in // this header, is one such example of this, where substitution is done with // templates. // // To handle derivatives of functions taking multiple arguments, different // infinitesimals are used, one for each variable to take the derivative of. For // example, consider a scalar function of two scalar parameters x and y: // // f(x, y) = x^2 + x * y // // Following the technique above, to compute the derivatives df/dx and df/dy for // f(1, 3) involves doing two evaluations of f, the first time replacing x with // x + e, the second time replacing y with y + e. // // For df/dx: // // f(1 + e, y) = (1 + e)^2 + (1 + e) * 3 // = 1 + 2 * e + 3 + 3 * e // = 4 + 5 * e // // --> df/dx = 5 // // For df/dy: // // f(1, 3 + e) = 1^2 + 1 * (3 + e) // = 1 + 3 + e // = 4 + e // // --> df/dy = 1 // // To take the gradient of f with the implementation of dual numbers ("jets") in // this file, it is necessary to create a single jet type which has components // for the derivative in x and y, and passing them to a templated version of f: // // template // T f(const T &x, const T &y) { // return x * x + x * y; // } // // // The "2" means there should be 2 dual number components. // // It computes the partial derivative at x=10, y=20. // Jet x(10, 0); // Pick the 0th dual number for x. // Jet y(20, 1); // Pick the 1st dual number for y. // Jet z = f(x, y); // // LOG(INFO) << "df/dx = " << z.v[0] // << "df/dy = " << z.v[1]; // // Most users should not use Jet objects directly; a wrapper around Jet objects, // which makes computing the derivative, gradient, or jacobian of templated // functors simple, is in autodiff.h. Even autodiff.h should not be used // directly; instead autodiff_cost_function.h is typically the file of interest. // // For the more mathematically inclined, this file implements first-order // "jets". A 1st order jet is an element of the ring // // T[N] = T[t_1, ..., t_N] / (t_1, ..., t_N)^2 // // which essentially means that each jet consists of a "scalar" value 'a' from T // and a 1st order perturbation vector 'v' of length N: // // x = a + \sum_i v[i] t_i // // A shorthand is to write an element as x = a + u, where u is the perturbation. // Then, the main point about the arithmetic of jets is that the product of // perturbations is zero: // // (a + u) * (b + v) = ab + av + bu + uv // = ab + (av + bu) + 0 // // which is what operator* implements below. Addition is simpler: // // (a + u) + (b + v) = (a + b) + (u + v). // // The only remaining question is how to evaluate the function of a jet, for // which we use the chain rule: // // f(a + u) = f(a) + f'(a) u // // where f'(a) is the (scalar) derivative of f at a. // // By pushing these things through sufficiently and suitably templated // functions, we can do automatic differentiation. Just be sure to turn on // function inlining and common-subexpression elimination, or it will be very // slow! // // WARNING: Most Ceres users should not directly include this file or know the // details of how jets work. Instead the suggested method for automatic // derivatives is to use autodiff_cost_function.h, which is a wrapper around // both jets.h and autodiff.h to make taking derivatives of cost functions for // use in Ceres easier. #ifndef CERES_PUBLIC_JET_H_ #define CERES_PUBLIC_JET_H_ #include #include #include #include // NOLINT #include #include #include #include #include "Eigen/Core" #include "jet_traits.h" #include "port.h" #include "jet_fwd.h" // Here we provide partial specializations of std::common_type for the Jet class // to allow determining a Jet type with a common underlying arithmetic type. // Such an arithmetic type can be either a scalar or an another Jet. An example // for a common type, say, between a float and a Jet is a Jet (i.e., std::common_type_t> and // ceres::Jet refer to the same type.) // // The partial specialization are also used for determining compatible types by // means of SFINAE and thus allow such types to be expressed as operands of // logical comparison operators. Missing (partial) specialization of // std::common_type for a particular (custom) type will therefore disable the // use of comparison operators defined by Ceres. // // Since these partial specializations are used as SFINAE constraints, they // enable standard promotion rules between various scalar types and consequently // their use in comparison against a Jet without providing implicit // conversions from a scalar, such as an int, to a Jet (see the implementation // of logical comparison operators below). template struct std::common_type> { using type = ceres::Jet, N>; }; template struct std::common_type, U> { using type = ceres::Jet, N>; }; template struct std::common_type, ceres::Jet> { using type = ceres::Jet, N>; }; namespace ceres { template struct Jet { enum { DIMENSION = N }; using Scalar = T; // Default-construct "a" because otherwise this can lead to false errors about // uninitialized uses when other classes relying on default constructed T // (where T is a Jet). This usually only happens in opt mode. Note that // the C++ standard mandates that e.g. default constructed doubles are // initialized to 0.0; see sections 8.5 of the C++03 standard. Jet() : a() { v.setConstant(Scalar()); } // Constructor from scalar: a + 0. explicit Jet(const T& value) { a = value; v.setConstant(Scalar()); } // Constructor from scalar plus variable: a + t_i. Jet(const T& value, int k) { a = value; v.setConstant(Scalar()); v[k] = T(1.0); } // Constructor from scalar and vector part // The use of Eigen::DenseBase allows Eigen expressions // to be passed in without being fully evaluated until // they are assigned to v template EIGEN_STRONG_INLINE Jet(const T& a, const Eigen::DenseBase& v) : a(a), v(v) {} // Compound operators Jet& operator+=(const Jet& y) { *this = *this + y; return *this; } Jet& operator-=(const Jet& y) { *this = *this - y; return *this; } Jet& operator*=(const Jet& y) { *this = *this * y; return *this; } Jet& operator/=(const Jet& y) { *this = *this / y; return *this; } // Compound with scalar operators. Jet& operator+=(const T& s) { *this = *this + s; return *this; } Jet& operator-=(const T& s) { *this = *this - s; return *this; } Jet& operator*=(const T& s) { *this = *this * s; return *this; } Jet& operator/=(const T& s) { *this = *this / s; return *this; } // The scalar part. T a; // The infinitesimal part. Eigen::Matrix v; // This struct needs to have an Eigen aligned operator new as it contains // fixed-size Eigen types. EIGEN_MAKE_ALIGNED_OPERATOR_NEW }; // Unary + template inline Jet const& operator+(const Jet& f) { return f; } // TODO(keir): Try adding __attribute__((always_inline)) to these functions to // see if it causes a performance increase. // Unary - template inline Jet operator-(const Jet& f) { return Jet(-f.a, -f.v); } // Binary + template inline Jet operator+(const Jet& f, const Jet& g) { return Jet(f.a + g.a, f.v + g.v); } // Binary + with a scalar: x + s template inline Jet operator+(const Jet& f, T s) { return Jet(f.a + s, f.v); } // Binary + with a scalar: s + x template inline Jet operator+(T s, const Jet& f) { return Jet(f.a + s, f.v); } // Binary - template inline Jet operator-(const Jet& f, const Jet& g) { return Jet(f.a - g.a, f.v - g.v); } // Binary - with a scalar: x - s template inline Jet operator-(const Jet& f, T s) { return Jet(f.a - s, f.v); } // Binary - with a scalar: s - x template inline Jet operator-(T s, const Jet& f) { return Jet(s - f.a, -f.v); } // Binary * template inline Jet operator*(const Jet& f, const Jet& g) { return Jet(f.a * g.a, f.a * g.v + f.v * g.a); } // Binary * with a scalar: x * s template inline Jet operator*(const Jet& f, T s) { return Jet(f.a * s, f.v * s); } // Binary * with a scalar: s * x template inline Jet operator*(T s, const Jet& f) { return Jet(f.a * s, f.v * s); } // Binary / template inline Jet operator/(const Jet& f, const Jet& g) { // This uses: // // a + u (a + u)(b - v) (a + u)(b - v) // ----- = -------------- = -------------- // b + v (b + v)(b - v) b^2 // // which holds because v*v = 0. const T g_a_inverse = T(1.0) / g.a; const T f_a_by_g_a = f.a * g_a_inverse; return Jet(f_a_by_g_a, (f.v - f_a_by_g_a * g.v) * g_a_inverse); } // Binary / with a scalar: s / x template inline Jet operator/(T s, const Jet& g) { const T minus_s_g_a_inverse2 = -s / (g.a * g.a); return Jet(s / g.a, g.v * minus_s_g_a_inverse2); } // Binary / with a scalar: x / s template inline Jet operator/(const Jet& f, T s) { const T s_inverse = T(1.0) / s; return Jet(f.a * s_inverse, f.v * s_inverse); } // Binary comparison operators for both scalars and jets. At least one of the // operands must be a Jet. Promotable scalars (e.g., int, float, double etc.) // can appear on either side of the operator. std::common_type_t is used as an // SFINAE constraint to selectively enable compatible operand types. This allows // comparison, for instance, against int literals without implicit conversion. // In case the Jet arithmetic type is a Jet itself, a recursive expansion of Jet // value is performed. #define CERES_DEFINE_JET_COMPARISON_OPERATOR(op) \ template >* = nullptr> \ constexpr bool operator op(const Lhs& f, const Rhs& g) noexcept( \ noexcept(internal::AsScalar(f) op internal::AsScalar(g))) { \ using internal::AsScalar; \ return AsScalar(f) op AsScalar(g); \ } CERES_DEFINE_JET_COMPARISON_OPERATOR(<) // NOLINT CERES_DEFINE_JET_COMPARISON_OPERATOR(<=) // NOLINT CERES_DEFINE_JET_COMPARISON_OPERATOR(>) // NOLINT CERES_DEFINE_JET_COMPARISON_OPERATOR(>=) // NOLINT CERES_DEFINE_JET_COMPARISON_OPERATOR(==) // NOLINT CERES_DEFINE_JET_COMPARISON_OPERATOR(!=) // NOLINT #undef CERES_DEFINE_JET_COMPARISON_OPERATOR // Pull some functions from namespace std. // // This is necessary because we want to use the same name (e.g. 'sqrt') for // double-valued and Jet-valued functions, but we are not allowed to put // Jet-valued functions inside namespace std. using std::abs; using std::acos; using std::asin; using std::atan; using std::atan2; using std::cbrt; using std::ceil; using std::copysign; using std::cos; using std::cosh; using std::erf; using std::erfc; using std::exp; using std::exp2; using std::expm1; using std::fdim; using std::floor; using std::fma; using std::fmax; using std::fmin; using std::fpclassify; using std::hypot; using std::isfinite; using std::isinf; using std::isnan; using std::isnormal; using std::log; using std::log10; using std::log1p; using std::log2; using std::norm; using std::pow; using std::signbit; using std::sin; using std::sinh; using std::sqrt; using std::tan; using std::tanh; // MSVC (up to 1930) defines quiet comparison functions as template functions // which causes compilation errors due to ambiguity in the template parameter // type resolution for using declarations in the ceres namespace. Workaround the // issue by defining specific overload and bypass MSVC standard library // definitions. #if defined(_MSC_VER) inline bool isgreater(double lhs, double rhs) noexcept(noexcept(std::isgreater(lhs, rhs))) { return std::isgreater(lhs, rhs); } inline bool isless(double lhs, double rhs) noexcept(noexcept(std::isless(lhs, rhs))) { return std::isless(lhs, rhs); } inline bool islessequal(double lhs, double rhs) noexcept(noexcept(std::islessequal(lhs, rhs))) { return std::islessequal(lhs, rhs); } inline bool isgreaterequal(double lhs, double rhs) noexcept( noexcept(std::isgreaterequal(lhs, rhs))) { return std::isgreaterequal(lhs, rhs); } inline bool islessgreater(double lhs, double rhs) noexcept( noexcept(std::islessgreater(lhs, rhs))) { return std::islessgreater(lhs, rhs); } inline bool isunordered(double lhs, double rhs) noexcept(noexcept(std::isunordered(lhs, rhs))) { return std::isunordered(lhs, rhs); } #else using std::isgreater; using std::isgreaterequal; using std::isless; using std::islessequal; using std::islessgreater; using std::isunordered; #endif #ifdef CERES_HAS_CPP20 using std::lerp; using std::midpoint; #endif // defined(CERES_HAS_CPP20) // Legacy names from pre-C++11 days. // clang-format off CERES_DEPRECATED_WITH_MSG("ceres::IsFinite will be removed in a future Ceres Solver release. Please use ceres::isfinite.") inline bool IsFinite(double x) { return std::isfinite(x); } CERES_DEPRECATED_WITH_MSG("ceres::IsInfinite will be removed in a future Ceres Solver release. Please use ceres::isinf.") inline bool IsInfinite(double x) { return std::isinf(x); } CERES_DEPRECATED_WITH_MSG("ceres::IsNaN will be removed in a future Ceres Solver release. Please use ceres::isnan.") inline bool IsNaN(double x) { return std::isnan(x); } CERES_DEPRECATED_WITH_MSG("ceres::IsNormal will be removed in a future Ceres Solver release. Please use ceres::isnormal.") inline bool IsNormal(double x) { return std::isnormal(x); } // clang-format on // In general, f(a + h) ~= f(a) + f'(a) h, via the chain rule. // abs(x + h) ~= abs(x) + sgn(x)h template inline Jet abs(const Jet& f) { return Jet(abs(f.a), copysign(T(1), f.a) * f.v); } // copysign(a, b) composes a float with the magnitude of a and the sign of b. // Therefore, the function can be formally defined as // // copysign(a, b) = sgn(b)|a| // // where // // d/dx |x| = sgn(x) // d/dx sgn(x) = 2δ(x) // // sgn(x) being the signum function. Differentiating copysign(a, b) with respect // to a and b gives: // // d/da sgn(b)|a| = sgn(a) sgn(b) // d/db sgn(b)|a| = 2|a|δ(b) // // with the dual representation given by // // copysign(a + da, b + db) ~= sgn(b)|a| + (sgn(a)sgn(b) da + 2|a|δ(b) db) // // where δ(b) is the Dirac delta function. template inline Jet copysign(const Jet& f, const Jet g) { // The Dirac delta function δ(b) is undefined at b=0 (here it's // infinite) and 0 everywhere else. T d = fpclassify(g) == FP_ZERO ? std::numeric_limits::infinity() : T(0); T sa = copysign(T(1), f.a); // sgn(a) T sb = copysign(T(1), g.a); // sgn(b) // The second part of the infinitesimal is 2|a|δ(b) which is either infinity // or 0 unless a or any of the values of the b infinitesimal are 0. In the // latter case, the corresponding values become NaNs (multiplying 0 by // infinity gives NaN). We drop the constant factor 2 since it does not change // the result (its values will still be either 0, infinity or NaN). return Jet(copysign(f.a, g.a), sa * sb * f.v + abs(f.a) * d * g.v); } // log(a + h) ~= log(a) + h / a template inline Jet log(const Jet& f) { const T a_inverse = T(1.0) / f.a; return Jet(log(f.a), f.v * a_inverse); } // log10(a + h) ~= log10(a) + h / (a log(10)) template inline Jet log10(const Jet& f) { // Most compilers will expand log(10) to a constant. const T a_inverse = T(1.0) / (f.a * log(T(10.0))); return Jet(log10(f.a), f.v * a_inverse); } // log1p(a + h) ~= log1p(a) + h / (1 + a) template inline Jet log1p(const Jet& f) { const T a_inverse = T(1.0) / (T(1.0) + f.a); return Jet(log1p(f.a), f.v * a_inverse); } // exp(a + h) ~= exp(a) + exp(a) h template inline Jet exp(const Jet& f) { const T tmp = exp(f.a); return Jet(tmp, tmp * f.v); } // expm1(a + h) ~= expm1(a) + exp(a) h template inline Jet expm1(const Jet& f) { const T tmp = expm1(f.a); const T expa = tmp + T(1.0); // exp(a) = expm1(a) + 1 return Jet(tmp, expa * f.v); } // sqrt(a + h) ~= sqrt(a) + h / (2 sqrt(a)) template inline Jet sqrt(const Jet& f) { const T tmp = sqrt(f.a); const T two_a_inverse = T(1.0) / (T(2.0) * tmp); return Jet(tmp, f.v * two_a_inverse); } // cos(a + h) ~= cos(a) - sin(a) h template inline Jet cos(const Jet& f) { return Jet(cos(f.a), -sin(f.a) * f.v); } // acos(a + h) ~= acos(a) - 1 / sqrt(1 - a^2) h template inline Jet acos(const Jet& f) { const T tmp = -T(1.0) / sqrt(T(1.0) - f.a * f.a); return Jet(acos(f.a), tmp * f.v); } // sin(a + h) ~= sin(a) + cos(a) h template inline Jet sin(const Jet& f) { return Jet(sin(f.a), cos(f.a) * f.v); } // asin(a + h) ~= asin(a) + 1 / sqrt(1 - a^2) h template inline Jet asin(const Jet& f) { const T tmp = T(1.0) / sqrt(T(1.0) - f.a * f.a); return Jet(asin(f.a), tmp * f.v); } // tan(a + h) ~= tan(a) + (1 + tan(a)^2) h template inline Jet tan(const Jet& f) { const T tan_a = tan(f.a); const T tmp = T(1.0) + tan_a * tan_a; return Jet(tan_a, tmp * f.v); } // atan(a + h) ~= atan(a) + 1 / (1 + a^2) h template inline Jet atan(const Jet& f) { const T tmp = T(1.0) / (T(1.0) + f.a * f.a); return Jet(atan(f.a), tmp * f.v); } // sinh(a + h) ~= sinh(a) + cosh(a) h template inline Jet sinh(const Jet& f) { return Jet(sinh(f.a), cosh(f.a) * f.v); } // cosh(a + h) ~= cosh(a) + sinh(a) h template inline Jet cosh(const Jet& f) { return Jet(cosh(f.a), sinh(f.a) * f.v); } // tanh(a + h) ~= tanh(a) + (1 - tanh(a)^2) h template inline Jet tanh(const Jet& f) { const T tanh_a = tanh(f.a); const T tmp = T(1.0) - tanh_a * tanh_a; return Jet(tanh_a, tmp * f.v); } // The floor function should be used with extreme care as this operation will // result in a zero derivative which provides no information to the solver. // // floor(a + h) ~= floor(a) + 0 template inline Jet floor(const Jet& f) { return Jet(floor(f.a)); } // The ceil function should be used with extreme care as this operation will // result in a zero derivative which provides no information to the solver. // // ceil(a + h) ~= ceil(a) + 0 template inline Jet ceil(const Jet& f) { return Jet(ceil(f.a)); } // Some new additions to C++11: // cbrt(a + h) ~= cbrt(a) + h / (3 a ^ (2/3)) template inline Jet cbrt(const Jet& f) { const T derivative = T(1.0) / (T(3.0) * cbrt(f.a * f.a)); return Jet(cbrt(f.a), f.v * derivative); } // exp2(x + h) = 2^(x+h) ~= 2^x + h*2^x*log(2) template inline Jet exp2(const Jet& f) { const T tmp = exp2(f.a); const T derivative = tmp * log(T(2)); return Jet(tmp, f.v * derivative); } // log2(x + h) ~= log2(x) + h / (x * log(2)) template inline Jet log2(const Jet& f) { const T derivative = T(1.0) / (f.a * log(T(2))); return Jet(log2(f.a), f.v * derivative); } // Like sqrt(x^2 + y^2), // but acts to prevent underflow/overflow for small/large x/y. // Note that the function is non-smooth at x=y=0, // so the derivative is undefined there. template inline Jet hypot(const Jet& x, const Jet& y) { // d/da sqrt(a) = 0.5 / sqrt(a) // d/dx x^2 + y^2 = 2x // So by the chain rule: // d/dx sqrt(x^2 + y^2) = 0.5 / sqrt(x^2 + y^2) * 2x = x / sqrt(x^2 + y^2) // d/dy sqrt(x^2 + y^2) = y / sqrt(x^2 + y^2) const T tmp = hypot(x.a, y.a); return Jet(tmp, x.a / tmp * x.v + y.a / tmp * y.v); } // Like sqrt(x^2 + y^2 + z^2), // but acts to prevent underflow/overflow for small/large x/y/z. // Note that the function is non-smooth at x=y=z=0, // so the derivative is undefined there. template inline Jet hypot(const Jet& x, const Jet& y, const Jet& z) { // d/da sqrt(a) = 0.5 / sqrt(a) // d/dx x^2 + y^2 + z^2 = 2x // So by the chain rule: // d/dx sqrt(x^2 + y^2 + z^2) // = 0.5 / sqrt(x^2 + y^2 + z^2) * 2x // = x / sqrt(x^2 + y^2 + z^2) // d/dy sqrt(x^2 + y^2 + z^2) = y / sqrt(x^2 + y^2 + z^2) // d/dz sqrt(x^2 + y^2 + z^2) = z / sqrt(x^2 + y^2 + z^2) const T tmp = hypot(x.a, y.a, z.a); return Jet(tmp, x.a / tmp * x.v + y.a / tmp * y.v + z.a / tmp * z.v); } // Like x * y + z but rounded only once. template inline Jet fma(const Jet& x, const Jet& y, const Jet& z) { // d/dx fma(x, y, z) = y // d/dy fma(x, y, z) = x // d/dz fma(x, y, z) = 1 return Jet(fma(x.a, y.a, z.a), y.a * x.v + x.a * y.v + z.v); } // Return value of fmax() and fmin() on equality // --------------------------------------------- // // There is arguably no good answer to what fmax() & fmin() should return on // equality, which for Jets by definition ONLY compares the scalar parts. We // choose what we think is the least worst option (averaging as Jets) which // minimises undesirable/unexpected behaviour as used, and also supports client // code written against Ceres versions prior to type promotion being supported // in Jet comparisons (< v2.1). // // The std::max() convention of returning the first argument on equality is // problematic, as it means that the derivative component may or may not be // preserved (when comparing a Jet with a scalar) depending upon the ordering. // // Always returning the Jet in {Jet, scalar} cases on equality is problematic // as it is inconsistent with the behaviour that would be obtained if the scalar // was first cast to Jet and the {Jet, Jet} case was used. Prior to type // promotion (Ceres v2.1) client code would typically cast constants to Jets // e.g: fmax(x, T(2.0)) which means the {Jet, Jet} case predominates, and we // still want the result to be order independent. // // Our intuition is that preserving a non-zero derivative is best, even if // its value does not match either of the inputs. Averaging achieves this // whilst ensuring argument ordering independence. This is also the approach // used by the Jax library, and TensorFlow's reduce_max(). // Returns the larger of the two arguments, with Jet averaging on equality. // NaNs are treated as missing data. // // NOTE: This function is NOT subject to any of the error conditions specified // in `math_errhandling`. template >* = nullptr> inline decltype(auto) fmax(const Lhs& x, const Rhs& y) { using J = std::common_type_t; // As x == y may set FP exceptions in the presence of NaNs when used with // non-default compiler options so we avoid its use here. if (isnan(x) || isnan(y) || islessgreater(x, y)) { return isnan(x) || isless(x, y) ? J{y} : J{x}; } // x == y (scalar parts) return the average of their Jet representations. #if defined(CERES_HAS_CPP20) return midpoint(J{x}, J{y}); #else return (J{x} + J{y}) * typename J::Scalar(0.5); #endif // defined(CERES_HAS_CPP20) } // Returns the smaller of the two arguments, with Jet averaging on equality. // NaNs are treated as missing data. // // NOTE: This function is NOT subject to any of the error conditions specified // in `math_errhandling`. template >* = nullptr> inline decltype(auto) fmin(const Lhs& x, const Rhs& y) { using J = std::common_type_t; // As x == y may set FP exceptions in the presence of NaNs when used with // non-default compiler options so we avoid its use here. if (isnan(x) || isnan(y) || islessgreater(x, y)) { return isnan(x) || isgreater(x, y) ? J{y} : J{x}; } // x == y (scalar parts) return the average of their Jet representations. #if defined(CERES_HAS_CPP20) return midpoint(J{x}, J{y}); #else return (J{x} + J{y}) * typename J::Scalar(0.5); #endif // defined(CERES_HAS_CPP20) } // Returns the positive difference (f - g) of two arguments and zero if f <= g. // If at least one argument is NaN, a NaN is return. // // NOTE At least one of the argument types must be a Jet, the other one can be a // scalar. In case both arguments are Jets, their dimensionality must match. template >* = nullptr> inline decltype(auto) fdim(const Lhs& f, const Rhs& g) { using J = std::common_type_t; if (isnan(f) || isnan(g)) { return std::numeric_limits::quiet_NaN(); } return isgreater(f, g) ? J{f - g} : J{}; } // erf is defined as an integral that cannot be expressed analytically // however, the derivative is trivial to compute // erf(x + h) = erf(x) + h * 2*exp(-x^2)/sqrt(pi) template inline Jet erf(const Jet& x) { // We evaluate the constant as follows: // 2 / sqrt(pi) = 1 / sqrt(atan(1.)) // On POSIX systems it is defined as M_2_SQRTPI, but this is not // portable and the type may not be T. The above expression // evaluates to full precision with IEEE arithmetic and, since it's // constant, the compiler can generate exactly the same code. gcc // does so even at -O0. return Jet(erf(x.a), x.v * exp(-x.a * x.a) * (T(1) / sqrt(atan(T(1))))); } // erfc(x) = 1-erf(x) // erfc(x + h) = erfc(x) + h * (-2*exp(-x^2)/sqrt(pi)) template inline Jet erfc(const Jet& x) { // See in erf() above for the evaluation of the constant in the derivative. return Jet(erfc(x.a), -x.v * exp(-x.a * x.a) * (T(1) / sqrt(atan(T(1))))); } // Bessel functions of the first kind with integer order equal to 0, 1, n. // // Microsoft has deprecated the j[0,1,n]() POSIX Bessel functions in favour of // _j[0,1,n](). Where available on MSVC, use _j[0,1,n]() to avoid deprecated // function errors in client code (the specific warning is suppressed when // Ceres itself is built). inline double BesselJ0(double x) { #if defined(CERES_MSVC_USE_UNDERSCORE_PREFIXED_BESSEL_FUNCTIONS) return _j0(x); #else return j0(x); #endif } inline double BesselJ1(double x) { #if defined(CERES_MSVC_USE_UNDERSCORE_PREFIXED_BESSEL_FUNCTIONS) return _j1(x); #else return j1(x); #endif } inline double BesselJn(int n, double x) { #if defined(CERES_MSVC_USE_UNDERSCORE_PREFIXED_BESSEL_FUNCTIONS) return _jn(n, x); #else return jn(n, x); #endif } // For the formulae of the derivatives of the Bessel functions see the book: // Olver, Lozier, Boisvert, Clark, NIST Handbook of Mathematical Functions, // Cambridge University Press 2010. // // Formulae are also available at http://dlmf.nist.gov // See formula http://dlmf.nist.gov/10.6#E3 // j0(a + h) ~= j0(a) - j1(a) h template inline Jet BesselJ0(const Jet& f) { return Jet(BesselJ0(f.a), -BesselJ1(f.a) * f.v); } // See formula http://dlmf.nist.gov/10.6#E1 // j1(a + h) ~= j1(a) + 0.5 ( j0(a) - j2(a) ) h template inline Jet BesselJ1(const Jet& f) { return Jet(BesselJ1(f.a), T(0.5) * (BesselJ0(f.a) - BesselJn(2, f.a)) * f.v); } // See formula http://dlmf.nist.gov/10.6#E1 // j_n(a + h) ~= j_n(a) + 0.5 ( j_{n-1}(a) - j_{n+1}(a) ) h template inline Jet BesselJn(int n, const Jet& f) { return Jet( BesselJn(n, f.a), T(0.5) * (BesselJn(n - 1, f.a) - BesselJn(n + 1, f.a)) * f.v); } // Classification and comparison functionality referencing only the scalar part // of a Jet. To classify the derivatives (e.g., for sanity checks), the dual // part should be referenced explicitly. For instance, to check whether the // derivatives of a Jet 'f' are reasonable, one can use // // isfinite(f.v.array()).all() // !isnan(f.v.array()).any() // // etc., depending on the desired semantics. // // NOTE: Floating-point classification and comparison functions and operators // should be used with care as no derivatives can be propagated by such // functions directly but only by expressions resulting from corresponding // conditional statements. At the same time, conditional statements can possibly // introduce a discontinuity in the cost function making it impossible to // evaluate its derivative and thus the optimization problem intractable. // Determines whether the scalar part of the Jet is finite. template inline bool isfinite(const Jet& f) { return isfinite(f.a); } // Determines whether the scalar part of the Jet is infinite. template inline bool isinf(const Jet& f) { return isinf(f.a); } // Determines whether the scalar part of the Jet is NaN. template inline bool isnan(const Jet& f) { return isnan(f.a); } // Determines whether the scalar part of the Jet is neither zero, subnormal, // infinite, nor NaN. template inline bool isnormal(const Jet& f) { return isnormal(f.a); } // Determines whether the scalar part of the Jet f is less than the scalar // part of g. // // NOTE: This function does NOT set any floating-point exceptions. template >* = nullptr> inline bool isless(const Lhs& f, const Rhs& g) { using internal::AsScalar; return isless(AsScalar(f), AsScalar(g)); } // Determines whether the scalar part of the Jet f is greater than the scalar // part of g. // // NOTE: This function does NOT set any floating-point exceptions. template >* = nullptr> inline bool isgreater(const Lhs& f, const Rhs& g) { using internal::AsScalar; return isgreater(AsScalar(f), AsScalar(g)); } // Determines whether the scalar part of the Jet f is less than or equal to the // scalar part of g. // // NOTE: This function does NOT set any floating-point exceptions. template >* = nullptr> inline bool islessequal(const Lhs& f, const Rhs& g) { using internal::AsScalar; return islessequal(AsScalar(f), AsScalar(g)); } // Determines whether the scalar part of the Jet f is less than or greater than // (f < g || f > g) the scalar part of g. // // NOTE: This function does NOT set any floating-point exceptions. template >* = nullptr> inline bool islessgreater(const Lhs& f, const Rhs& g) { using internal::AsScalar; return islessgreater(AsScalar(f), AsScalar(g)); } // Determines whether the scalar part of the Jet f is greater than or equal to // the scalar part of g. // // NOTE: This function does NOT set any floating-point exceptions. template >* = nullptr> inline bool isgreaterequal(const Lhs& f, const Rhs& g) { using internal::AsScalar; return isgreaterequal(AsScalar(f), AsScalar(g)); } // Determines if either of the scalar parts of the arguments are NaN and // thus cannot be ordered with respect to each other. template >* = nullptr> inline bool isunordered(const Lhs& f, const Rhs& g) { using internal::AsScalar; return isunordered(AsScalar(f), AsScalar(g)); } // Categorize scalar part as zero, subnormal, normal, infinite, NaN, or // implementation-defined. template inline int fpclassify(const Jet& f) { return fpclassify(f.a); } // Determines whether the scalar part of the argument is negative. template inline bool signbit(const Jet& f) { return signbit(f.a); } // Legacy functions from the pre-C++11 days. template CERES_DEPRECATED_WITH_MSG( "ceres::IsFinite will be removed in a future Ceres Solver release. Please " "use ceres::isfinite.") inline bool IsFinite(const Jet& f) { return isfinite(f); } template CERES_DEPRECATED_WITH_MSG( "ceres::IsNaN will be removed in a future Ceres Solver release. Please use " "ceres::isnan.") inline bool IsNaN(const Jet& f) { return isnan(f); } template CERES_DEPRECATED_WITH_MSG( "ceres::IsNormal will be removed in a future Ceres Solver release. Please " "use ceres::isnormal.") inline bool IsNormal(const Jet& f) { return isnormal(f); } // The jet is infinite if any part of the jet is infinite. template CERES_DEPRECATED_WITH_MSG( "ceres::IsInfinite will be removed in a future Ceres Solver release. " "Please use ceres::isinf.") inline bool IsInfinite(const Jet& f) { return isinf(f); } #ifdef CERES_HAS_CPP20 // Computes the linear interpolation a + t(b - a) between a and b at the value // t. For arguments outside of the range 0 <= t <= 1, the values are // extrapolated. // // Differentiating lerp(a, b, t) with respect to a, b, and t gives: // // d/da lerp(a, b, t) = 1 - t // d/db lerp(a, b, t) = t // d/dt lerp(a, b, t) = b - a // // with the dual representation given by // // lerp(a + da, b + db, t + dt) // ~= lerp(a, b, t) + (1 - t) da + t db + (b - a) dt . template inline Jet lerp(const Jet& a, const Jet& b, const Jet& t) { return Jet{lerp(a.a, b.a, t.a), (T(1) - t.a) * a.v + t.a * b.v + (b.a - a.a) * t.v}; } // Computes the midpoint a + (b - a) / 2. // // Differentiating midpoint(a, b) with respect to a and b gives: // // d/da midpoint(a, b) = 1/2 // d/db midpoint(a, b) = 1/2 // // with the dual representation given by // // midpoint(a + da, b + db) ~= midpoint(a, b) + (da + db) / 2 . template inline Jet midpoint(const Jet& a, const Jet& b) { Jet result{midpoint(a.a, b.a)}; // To avoid overflow in the differential, compute // (da + db) / 2 using midpoint. for (int i = 0; i < N; ++i) { result.v[i] = midpoint(a.v[i], b.v[i]); } return result; } #endif // defined(CERES_HAS_CPP20) // atan2(b + db, a + da) ~= atan2(b, a) + (- b da + a db) / (a^2 + b^2) // // In words: the rate of change of theta is 1/r times the rate of // change of (x, y) in the positive angular direction. template inline Jet atan2(const Jet& g, const Jet& f) { // Note order of arguments: // // f = a + da // g = b + db T const tmp = T(1.0) / (f.a * f.a + g.a * g.a); return Jet(atan2(g.a, f.a), tmp * (-g.a * f.v + f.a * g.v)); } // Computes the square x^2 of a real number x (not the Euclidean L^2 norm as // the name might suggest). // // NOTE: While std::norm is primarily intended for computing the squared // magnitude of a std::complex<> number, the current Jet implementation does not // support mixing a scalar T in its real part and std::complex and in the // infinitesimal. Mixed Jet support is necessary for the type decay from // std::complex to T (the squared magnitude of a complex number is always // real) performed by std::norm. // // norm(x + h) ~= norm(x) + 2x h template inline Jet norm(const Jet& f) { return Jet(norm(f.a), T(2) * f.a * f.v); } // pow -- base is a differentiable function, exponent is a constant. // (a+da)^p ~= a^p + p*a^(p-1) da template inline Jet pow(const Jet& f, double g) { T const tmp = g * pow(f.a, g - T(1.0)); return Jet(pow(f.a, g), tmp * f.v); } // pow -- base is a constant, exponent is a differentiable function. // We have various special cases, see the comment for pow(Jet, Jet) for // analysis: // // 1. For f > 0 we have: (f)^(g + dg) ~= f^g + f^g log(f) dg // // 2. For f == 0 and g > 0 we have: (f)^(g + dg) ~= f^g // // 3. For f < 0 and integer g we have: (f)^(g + dg) ~= f^g but if dg // != 0, the derivatives are not defined and we return NaN. template inline Jet pow(T f, const Jet& g) { Jet result; if (fpclassify(f) == FP_ZERO && g > 0) { // Handle case 2. result = Jet(T(0.0)); } else { if (f < 0 && g == floor(g.a)) { // Handle case 3. result = Jet(pow(f, g.a)); for (int i = 0; i < N; i++) { if (fpclassify(g.v[i]) != FP_ZERO) { // Return a NaN when g.v != 0. result.v[i] = std::numeric_limits::quiet_NaN(); } } } else { // Handle case 1. T const tmp = pow(f, g.a); result = Jet(tmp, log(f) * tmp * g.v); } } return result; } // pow -- both base and exponent are differentiable functions. This has a // variety of special cases that require careful handling. // // 1. For f > 0: // (f + df)^(g + dg) ~= f^g + f^(g - 1) * (g * df + f * log(f) * dg) // The numerical evaluation of f * log(f) for f > 0 is well behaved, even for // extremely small values (e.g. 1e-99). // // 2. For f == 0 and g > 1: (f + df)^(g + dg) ~= 0 // This cases is needed because log(0) can not be evaluated in the f > 0 // expression. However the function f*log(f) is well behaved around f == 0 // and its limit as f-->0 is zero. // // 3. For f == 0 and g == 1: (f + df)^(g + dg) ~= 0 + df // // 4. For f == 0 and 0 < g < 1: The value is finite but the derivatives are not. // // 5. For f == 0 and g < 0: The value and derivatives of f^g are not finite. // // 6. For f == 0 and g == 0: The C standard incorrectly defines 0^0 to be 1 // "because there are applications that can exploit this definition". We // (arbitrarily) decree that derivatives here will be nonfinite, since that // is consistent with the behavior for f == 0, g < 0 and 0 < g < 1. // Practically any definition could have been justified because mathematical // consistency has been lost at this point. // // 7. For f < 0, g integer, dg == 0: (f + df)^(g + dg) ~= f^g + g * f^(g - 1) df // This is equivalent to the case where f is a differentiable function and g // is a constant (to first order). // // 8. For f < 0, g integer, dg != 0: The value is finite but the derivatives are // not, because any change in the value of g moves us away from the point // with a real-valued answer into the region with complex-valued answers. // // 9. For f < 0, g noninteger: The value and derivatives of f^g are not finite. template inline Jet pow(const Jet& f, const Jet& g) { Jet result; if (fpclassify(f) == FP_ZERO && g >= 1) { // Handle cases 2 and 3. if (g > 1) { result = Jet(T(0.0)); } else { result = f; } } else { if (f < 0 && g == floor(g.a)) { // Handle cases 7 and 8. T const tmp = g.a * pow(f.a, g.a - T(1.0)); result = Jet(pow(f.a, g.a), tmp * f.v); for (int i = 0; i < N; i++) { if (fpclassify(g.v[i]) != FP_ZERO) { // Return a NaN when g.v != 0. result.v[i] = T(std::numeric_limits::quiet_NaN()); } } } else { // Handle the remaining cases. For cases 4,5,6,9 we allow the log() // function to generate -HUGE_VAL or NaN, since those cases result in a // nonfinite derivative. T const tmp1 = pow(f.a, g.a); T const tmp2 = g.a * pow(f.a, g.a - T(1.0)); T const tmp3 = tmp1 * log(f.a); result = Jet(tmp1, tmp2 * f.v + tmp3 * g.v); } } return result; } // Note: This has to be in the ceres namespace for argument dependent lookup to // function correctly. Otherwise statements like CHECK_LE(x, 2.0) fail with // strange compile errors. template inline std::ostream& operator<<(std::ostream& s, const Jet& z) { s << "[" << z.a << " ; "; for (int i = 0; i < N; ++i) { s << z.v[i]; if (i != N - 1) { s << ", "; } } s << "]"; return s; } } // namespace ceres namespace std { template struct numeric_limits> { static constexpr bool is_specialized = true; static constexpr bool is_signed = std::numeric_limits::is_signed; static constexpr bool is_integer = std::numeric_limits::is_integer; static constexpr bool is_exact = std::numeric_limits::is_exact; static constexpr bool has_infinity = std::numeric_limits::has_infinity; static constexpr bool has_quiet_NaN = std::numeric_limits::has_quiet_NaN; static constexpr bool has_signaling_NaN = std::numeric_limits::has_signaling_NaN; static constexpr bool is_iec559 = std::numeric_limits::is_iec559; static constexpr bool is_bounded = std::numeric_limits::is_bounded; static constexpr bool is_modulo = std::numeric_limits::is_modulo; static constexpr std::float_denorm_style has_denorm = std::numeric_limits::has_denorm; static constexpr std::float_round_style round_style = std::numeric_limits::round_style; static constexpr int digits = std::numeric_limits::digits; static constexpr int digits10 = std::numeric_limits::digits10; static constexpr int max_digits10 = std::numeric_limits::max_digits10; static constexpr int radix = std::numeric_limits::radix; static constexpr int min_exponent = std::numeric_limits::min_exponent; static constexpr int min_exponent10 = std::numeric_limits::max_exponent10; static constexpr int max_exponent = std::numeric_limits::max_exponent; static constexpr int max_exponent10 = std::numeric_limits::max_exponent10; static constexpr bool traps = std::numeric_limits::traps; static constexpr bool tinyness_before = std::numeric_limits::tinyness_before; static constexpr ceres::Jet min CERES_PREVENT_MACRO_SUBSTITUTION() noexcept { return ceres::Jet((std::numeric_limits::min)()); } static constexpr ceres::Jet lowest() noexcept { return ceres::Jet(std::numeric_limits::lowest()); } static constexpr ceres::Jet epsilon() noexcept { return ceres::Jet(std::numeric_limits::epsilon()); } static constexpr ceres::Jet round_error() noexcept { return ceres::Jet(std::numeric_limits::round_error()); } static constexpr ceres::Jet infinity() noexcept { return ceres::Jet(std::numeric_limits::infinity()); } static constexpr ceres::Jet quiet_NaN() noexcept { return ceres::Jet(std::numeric_limits::quiet_NaN()); } static constexpr ceres::Jet signaling_NaN() noexcept { return ceres::Jet(std::numeric_limits::signaling_NaN()); } static constexpr ceres::Jet denorm_min() noexcept { return ceres::Jet(std::numeric_limits::denorm_min()); } static constexpr ceres::Jet max CERES_PREVENT_MACRO_SUBSTITUTION() noexcept { return ceres::Jet((std::numeric_limits::max)()); } }; } // namespace std namespace Eigen { // Creating a specialization of NumTraits enables placing Jet objects inside // Eigen arrays, getting all the goodness of Eigen combined with autodiff. template struct NumTraits> { using Real = ceres::Jet; using NonInteger = ceres::Jet; using Nested = ceres::Jet; using Literal = ceres::Jet; static typename ceres::Jet dummy_precision() { return ceres::Jet(1e-12); } static inline Real epsilon() { return Real(std::numeric_limits::epsilon()); } static inline int digits10() { return NumTraits::digits10(); } enum { IsComplex = 0, IsInteger = 0, IsSigned, ReadCost = 1, AddCost = 1, // For Jet types, multiplication is more expensive than addition. MulCost = 3, HasFloatingPoint = 1, RequireInitialization = 1 }; template struct Div { enum { #if defined(EIGEN_VECTORIZE_AVX) AVX = true, #else AVX = false, #endif // Assuming that for Jets, division is as expensive as // multiplication. Cost = 3 }; }; static inline Real highest() { return Real((std::numeric_limits::max)()); } static inline Real lowest() { return Real(-(std::numeric_limits::max)()); } }; // Specifying the return type of binary operations between Jets and scalar types // allows you to perform matrix/array operations with Eigen matrices and arrays // such as addition, subtraction, multiplication, and division where one Eigen // matrix/array is of type Jet and the other is a scalar type. This improves // performance by using the optimized scalar-to-Jet binary operations but // is only available on Eigen versions >= 3.3 template struct ScalarBinaryOpTraits, T, BinaryOp> { using ReturnType = ceres::Jet; }; template struct ScalarBinaryOpTraits, BinaryOp> { using ReturnType = ceres::Jet; }; } // namespace Eigen #endif // CERES_PUBLIC_JET_H_ ================================================ FILE: include/operon/ceres/jet_fwd.h ================================================ // Ceres Solver - A fast non-linear least squares minimizer // Copyright 2022 Google Inc. All rights reserved. // http://ceres-solver.org/ // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * Neither the name of Google Inc. nor the names of its contributors may be // used to endorse or promote products derived from this software without // specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: sergiu.deitsch@gmail.com (Sergiu Deitsch) // #ifndef CERES_PUBLIC_JET_FWD_H_ #define CERES_PUBLIC_JET_FWD_H_ namespace ceres { // Jet forward declaration necessary for the following partial specialization of // std::common_type and type traits. template struct Jet; } // namespace ceres #endif // CERES_PUBLIC_JET_FWD_H_ ================================================ FILE: include/operon/ceres/jet_traits.h ================================================ // Ceres Solver - A fast non-linear least squares minimizer // Copyright 2022 Google Inc. All rights reserved. // http://ceres-solver.org/ // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * Neither the name of Google Inc. nor the names of its contributors may be // used to endorse or promote products derived from this software without // specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: sergiu.deitsch@gmail.com (Sergiu Deitsch) // #ifndef CERES_PUBLIC_INTERNAL_JET_TRAITS_H_ #define CERES_PUBLIC_INTERNAL_JET_TRAITS_H_ #include #include #include #include "integer_sequence_algorithm.h" #include "jet_fwd.h" namespace ceres { namespace internal { // Predicate that determines whether any of the Types is a Jet. template struct AreAnyJet : std::false_type {}; template struct AreAnyJet : AreAnyJet {}; template struct AreAnyJet, Types...> : std::true_type {}; // Convenience variable template for AreAnyJet. template inline constexpr bool AreAnyJet_v = AreAnyJet::value; // Extracts the underlying floating-point from a type T. template struct UnderlyingScalar { using type = T; }; template struct UnderlyingScalar> : UnderlyingScalar {}; // Convenience template alias for UnderlyingScalar type trait. template using UnderlyingScalar_t = typename UnderlyingScalar::type; // Predicate determining whether all Types in the pack are the same. // // Specifically, the predicate applies std::is_same recursively to pairs of // Types in the pack. template inline constexpr bool AreAllSame_v = (std::is_same::value && ...); // Determines the rank of a type. This allows to ensure that types passed as // arguments are compatible to each other. The rank of Jet is determined by the // dimensions of the dual part. The rank of a scalar is always 0. // Non-specialized types default to a rank of -1. template struct Rank : std::integral_constant {}; // The rank of a scalar is 0. template struct Rank::value>> : std::integral_constant {}; // The rank of a Jet is given by its dimensionality. template struct Rank> : std::integral_constant {}; // Convenience variable template for Rank. template inline constexpr int Rank_v = Rank::value; // Constructs an integer sequence of ranks for each of the Types in the pack. template using Ranks_t = std::integer_sequence...>; // Returns the scalar part of a type. This overload acts as an identity. template constexpr decltype(auto) AsScalar(T&& value) noexcept { return std::forward(value); } // Recursively unwraps the scalar part of a Jet until a non-Jet scalar type is // encountered. template constexpr decltype(auto) AsScalar(const Jet& value) noexcept( noexcept(AsScalar(value.a))) { return AsScalar(value.a); } } // namespace internal // Type trait ensuring at least one of the types is a Jet, // the underlying scalar types are the same and Jet dimensions match. // // The type trait can be further specialized if necessary. // // This trait is a candidate for a concept definition once C++20 features can // be used. template // clang-format off struct CompatibleJetOperands : std::integral_constant < bool, // At least one of the types is a Jet internal::AreAnyJet_v && // The underlying floating-point types are exactly the same internal::AreAllSame_v...> && // Non-zero ranks of types are equal internal::IsEmptyOrAreAllEqual_v, 0>> > // clang-format on {}; // Single Jet operand is always compatible. template struct CompatibleJetOperands> : std::true_type {}; // Single non-Jet operand is always incompatible. template struct CompatibleJetOperands : std::false_type {}; // Empty operands are always incompatible. template <> struct CompatibleJetOperands<> : std::false_type {}; // Convenience variable template ensuring at least one of the types is a Jet, // the underlying scalar types are the same and Jet dimensions match. // // This trait is a candidate for a concept definition once C++20 features can // be used. template inline constexpr bool CompatibleJetOperands_v = CompatibleJetOperands::value; // Type trait ensuring at least one of the types is a Jet, // the underlying scalar types are compatible among each other and Jet // dimensions match. // // The type trait can be further specialized if necessary. // // This trait is a candidate for a concept definition once C++20 features can // be used. template // clang-format off struct PromotableJetOperands : std::integral_constant < bool, // Types can be compatible among each other internal::AreAnyJet_v && // Non-zero ranks of types are equal internal::IsEmptyOrAreAllEqual_v, 0>> > // clang-format on {}; // Convenience variable template ensuring at least one of the types is a Jet, // the underlying scalar types are compatible among each other and Jet // dimensions match. // // This trait is a candidate for a concept definition once C++20 features can // be used. template inline constexpr bool PromotableJetOperands_v = PromotableJetOperands::value; } // namespace ceres #endif // CERES_PUBLIC_INTERNAL_JET_TRAITS_H_ ================================================ FILE: include/operon/ceres/port.h ================================================ // Ceres Solver - A fast non-linear least squares minimizer // Copyright 2022 Google Inc. All rights reserved. // http://ceres-solver.org/ // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * Neither the name of Google Inc. nor the names of its contributors may be // used to endorse or promote products derived from this software without // specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: keir@google.com (Keir Mierle) #ifndef CERES_PUBLIC_INTERNAL_PORT_H_ #define CERES_PUBLIC_INTERNAL_PORT_H_ // A macro to mark a function/variable/class as deprecated. // We use compiler specific attributes rather than the c++ // attribute because they do not mix well with each other. #if defined(_MSC_VER) #define CERES_DEPRECATED_WITH_MSG(message) __declspec(deprecated(message)) #elif defined(__GNUC__) #define CERES_DEPRECATED_WITH_MSG(message) __attribute__((deprecated(message))) #else // In the worst case fall back to c++ attribute. #define CERES_DEPRECATED_WITH_MSG(message) [[deprecated(message)]] #endif #ifndef CERES_GET_FLAG #define CERES_GET_FLAG(X) X #endif // Indicates whether C++20 is currently active #ifndef CERES_HAS_CPP20 #if __cplusplus >= 202002L || (defined(_MSVC_LANG) && _MSVC_LANG >= 202002L) #define CERES_HAS_CPP20 #endif // __cplusplus >= 202002L || (defined(_MSVC_LANG) && _MSVC_LANG >= // 202002L) #endif // !defined(CERES_HAS_CPP20) // Prevents symbols from being substituted by the corresponding macro definition // under the same name. For instance, min and max are defined as macros on // Windows (unless NOMINMAX is defined) which causes compilation errors when // defining or referencing symbols under the same name. // // To be robust in all cases particularly when NOMINMAX cannot be used, use this // macro to annotate min/max declarations/definitions. Examples: // // int max CERES_PREVENT_MACRO_SUBSTITUTION(); // min CERES_PREVENT_MACRO_SUBSTITUTION(a, b); // max CERES_PREVENT_MACRO_SUBSTITUTION(a, b); // // NOTE: In case the symbols for which the substitution must be prevented are // used within another macro, the substitution must be inhibited using parens as // // (std::numerical_limits::max)() // // since the helper macro will not work here. Do not use this technique in // general case, because it will prevent argument-dependent lookup (ADL). // #define CERES_PREVENT_MACRO_SUBSTITUTION // Yes, it's empty #endif // CERES_PUBLIC_INTERNAL_PORT_H_ ================================================ FILE: include/operon/ceres/tiny_solver.h ================================================ // Ceres Solver - A fast non-linear least squares minimizer // Copyright 2021 Google Inc. All rights reserved. // http://ceres-solver.org/ // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * Neither the name of Google Inc. nor the names of its contributors may be // used to endorse or promote products derived from this software without // specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: mierle@gmail.com (Keir Mierle) // // WARNING WARNING WARNING // WARNING WARNING WARNING Tiny solver is experimental and will change. // WARNING WARNING WARNING // // A tiny least squares solver using Levenberg-Marquardt, intended for solving // small dense problems with low latency and low overhead. The implementation // takes care to do all allocation up front, so that no memory is allocated // during solving. This is especially useful when solving many similar problems; // for example, inverse pixel distortion for every pixel on a grid. // // Note: This code has no dependencies beyond Eigen, including on other parts of // Ceres, so it is possible to take this file alone and put it in another // project without the rest of Ceres. // // Algorithm based off of: // // [1] K. Madsen, H. Nielsen, O. Tingleoff. // Methods for Non-linear Least Squares Problems. // http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf #ifndef CERES_PUBLIC_TINY_SOLVER_H_ #define CERES_PUBLIC_TINY_SOLVER_H_ #include #include #include "Eigen/Dense" namespace ceres { // To use tiny solver, create a class or struct that allows computing the cost // function (described below). This is similar to a ceres::CostFunction, but is // different to enable statically allocating all memory for the solver // (specifically, enum sizes). Key parts are the Scalar typedef, the enums to // describe problem sizes (needed to remove all heap allocations), and the // operator() overload to evaluate the cost and (optionally) jacobians. // // struct TinySolverCostFunctionTraits { // typedef double Scalar; // enum { // NUM_RESIDUALS = OR Eigen::Dynamic, // NUM_PARAMETERS = OR Eigen::Dynamic, // }; // bool operator()(const double* parameters, // double* residuals, // double* jacobian) const; // // int NumResiduals() const; -- Needed if NUM_RESIDUALS == Eigen::Dynamic. // int NumParameters() const; -- Needed if NUM_PARAMETERS == Eigen::Dynamic. // }; // // For operator(), the size of the objects is: // // double* parameters -- NUM_PARAMETERS or NumParameters() // double* residuals -- NUM_RESIDUALS or NumResiduals() // double* jacobian -- NUM_RESIDUALS * NUM_PARAMETERS in column-major format // (Eigen's default); or nullptr if no jacobian // requested. // // An example (fully statically sized): // // struct MyCostFunctionExample { // typedef double Scalar; // enum { // NUM_RESIDUALS = 2, // NUM_PARAMETERS = 3, // }; // bool operator()(const double* parameters, // double* residuals, // double* jacobian) const { // residuals[0] = x + 2*y + 4*z; // residuals[1] = y * z; // if (jacobian) { // jacobian[0 * 2 + 0] = 1; // First column (x). // jacobian[0 * 2 + 1] = 0; // // jacobian[1 * 2 + 0] = 2; // Second column (y). // jacobian[1 * 2 + 1] = z; // // jacobian[2 * 2 + 0] = 4; // Third column (z). // jacobian[2 * 2 + 1] = y; // } // return true; // } // }; // // The solver supports either statically or dynamically sized cost // functions. If the number of residuals is dynamic then the Function // must define: // // int NumResiduals() const; // // If the number of parameters is dynamic then the Function must // define: // // int NumParameters() const; // template >> class TinySolver { public: // This class needs to have an Eigen aligned operator new as it contains // fixed-size Eigen types. EIGEN_MAKE_ALIGNED_OPERATOR_NEW enum { NUM_RESIDUALS = Function::NUM_RESIDUALS, NUM_PARAMETERS = Function::NUM_PARAMETERS }; using Scalar = typename Function::Scalar; using Parameters = typename Eigen::Matrix; enum Status { // max_norm |J'(x) * f(x)| < gradient_tolerance GRADIENT_TOO_SMALL, // ||dx|| <= parameter_tolerance * (||x|| + parameter_tolerance) RELATIVE_STEP_SIZE_TOO_SMALL, // cost_threshold > ||f(x)||^2 / 2 COST_TOO_SMALL, // num_iterations >= max_num_iterations HIT_MAX_ITERATIONS, // (new_cost - old_cost) < function_tolerance * old_cost COST_CHANGE_TOO_SMALL, // TODO(sameeragarwal): Deal with numerical failures. }; struct Options { int max_num_iterations = 50; // max_norm |J'(x) * f(x)| < gradient_tolerance Scalar gradient_tolerance = 1e-10; // ||dx|| <= parameter_tolerance * (||x|| + parameter_tolerance) Scalar parameter_tolerance = 1e-8; // (new_cost - old_cost) < function_tolerance * old_cost Scalar function_tolerance = 1e-6; // cost_threshold > ||f(x)||^2 / 2 Scalar cost_threshold = std::numeric_limits::epsilon(); Scalar initial_trust_region_radius = 1e4; }; struct Summary { // 1/2 ||f(x_0)||^2 Scalar initial_cost = -1; // 1/2 ||f(x)||^2 Scalar final_cost = -1; // max_norm(J'f(x)) Scalar gradient_max_norm = -1; int iterations = -1; Status status = HIT_MAX_ITERATIONS; }; bool Update(const Function& function, const Parameters& x) { if (!function(x.data(), residuals_.data(), jacobian_.data())) { return false; } residuals_ = -residuals_; // On the first iteration, compute a diagonal (Jacobi) scaling // matrix, which we store as a vector. if (summary.iterations == 0) { // jacobi_scaling = 1 / (1 + diagonal(J'J)) // // 1 is added to the denominator to regularize small diagonal // entries. jacobi_scaling_ = 1.0 / (1.0 + jacobian_.colwise().norm().array()); } // This explicitly computes the normal equations, which is numerically // unstable. Nevertheless, it is often good enough and is fast. // // TODO(sameeragarwal): Refactor this to allow for DenseQR // factorization. jacobian_ = jacobian_ * jacobi_scaling_.asDiagonal(); jtj_ = jacobian_.transpose() * jacobian_; g_ = jacobian_.transpose() * residuals_; summary.gradient_max_norm = g_.array().abs().maxCoeff(); cost_ = residuals_.squaredNorm() / 2; return true; } const Summary& Solve(const Function& function, Parameters* x_and_min) { Initialize(function); assert(x_and_min); Parameters& x = *x_and_min; summary = Summary(); summary.iterations = 0; // TODO(sameeragarwal): Deal with failure here. Update(function, x); summary.initial_cost = cost_; summary.final_cost = cost_; if (summary.gradient_max_norm < options.gradient_tolerance) { summary.status = GRADIENT_TOO_SMALL; return summary; } if (cost_ < options.cost_threshold) { summary.status = COST_TOO_SMALL; return summary; } Scalar u = 1.0 / options.initial_trust_region_radius; Scalar v = 2; for (summary.iterations = 1; summary.iterations < options.max_num_iterations; summary.iterations++) { jtj_regularized_ = jtj_; const Scalar min_diagonal = 1e-6; const Scalar max_diagonal = 1e32; for (int i = 0; i < lm_diagonal_.rows(); ++i) { lm_diagonal_[i] = std::sqrt( u * (std::min)((std::max)(jtj_(i, i), min_diagonal), max_diagonal)); jtj_regularized_(i, i) += lm_diagonal_[i] * lm_diagonal_[i]; } // TODO(sameeragarwal): Check for failure and deal with it. linear_solver_.compute(jtj_regularized_); lm_step_ = linear_solver_.solve(g_); dx_ = jacobi_scaling_.asDiagonal() * lm_step_; // Adding parameter_tolerance to x.norm() ensures that this // works if x is near zero. const Scalar parameter_tolerance = options.parameter_tolerance * (x.norm() + options.parameter_tolerance); if (dx_.norm() < parameter_tolerance) { summary.status = RELATIVE_STEP_SIZE_TOO_SMALL; break; } x_new_ = x + dx_; // TODO(keir): Add proper handling of errors from user eval of cost // functions. function(&x_new_[0], &f_x_new_[0], nullptr); const Scalar cost_change = (2 * cost_ - f_x_new_.squaredNorm()); // TODO(sameeragarwal): Better more numerically stable evaluation. const Scalar model_cost_change = lm_step_.dot(2 * g_ - jtj_ * lm_step_); // rho is the ratio of the actual reduction in error to the reduction // in error that would be obtained if the problem was linear. See [1] // for details. Scalar rho(cost_change / model_cost_change); if (rho > 0) { // Accept the Levenberg-Marquardt step because the linear // model fits well. x = x_new_; if (std::abs(cost_change) < options.function_tolerance) { cost_ = f_x_new_.squaredNorm() / 2; summary.status = COST_CHANGE_TOO_SMALL; break; } // TODO(sameeragarwal): Deal with failure. Update(function, x); if (summary.gradient_max_norm < options.gradient_tolerance) { summary.status = GRADIENT_TOO_SMALL; break; } if (cost_ < options.cost_threshold) { summary.status = COST_TOO_SMALL; break; } Scalar tmp = Scalar(2 * rho - 1); u = u * (std::max)(Scalar(1 / 3.), Scalar(1) - tmp * tmp * tmp); v = 2; } else { // Reject the update because either the normal equations failed to solve // or the local linear model was not good (rho < 0). // Additionally if the cost change is too small, then terminate. if (std::abs(cost_change) < options.function_tolerance) { // Terminate summary.status = COST_CHANGE_TOO_SMALL; break; } // Reduce the size of the trust region. u *= v; v *= 2; } } summary.final_cost = cost_; return summary; } Options options; Summary summary; private: // Preallocate everything, including temporary storage needed for solving the // linear system. This allows reusing the intermediate storage across solves. LinearSolver linear_solver_; Scalar cost_; Parameters dx_, x_new_, g_, jacobi_scaling_, lm_diagonal_, lm_step_; Eigen::Matrix residuals_, f_x_new_; Eigen::Matrix jacobian_; Eigen::Matrix jtj_, jtj_regularized_; // The following definitions are needed for template metaprogramming. template struct enable_if; template struct enable_if { using type = T; }; // The number of parameters and residuals are dynamically sized. template typename enable_if<(R == Eigen::Dynamic && P == Eigen::Dynamic), void>::type Initialize(const Function& function) { Initialize(function.NumResiduals(), function.NumParameters()); } // The number of parameters is dynamically sized and the number of // residuals is statically sized. template typename enable_if<(R == Eigen::Dynamic && P != Eigen::Dynamic), void>::type Initialize(const Function& function) { Initialize(function.NumResiduals(), P); } // The number of parameters is statically sized and the number of // residuals is dynamically sized. template typename enable_if<(R != Eigen::Dynamic && P == Eigen::Dynamic), void>::type Initialize(const Function& function) { Initialize(R, function.NumParameters()); } // The number of parameters and residuals are statically sized. template typename enable_if<(R != Eigen::Dynamic && P != Eigen::Dynamic), void>::type Initialize(const Function& /* function */) {} void Initialize(int num_residuals, int num_parameters) { dx_.resize(num_parameters); x_new_.resize(num_parameters); g_.resize(num_parameters); jacobi_scaling_.resize(num_parameters); lm_diagonal_.resize(num_parameters); lm_step_.resize(num_parameters); residuals_.resize(num_residuals); f_x_new_.resize(num_residuals); jacobian_.resize(num_residuals, num_parameters); jtj_.resize(num_parameters, num_parameters); jtj_regularized_.resize(num_parameters, num_parameters); } }; } // namespace ceres #endif // CERES_PUBLIC_TINY_SOLVER_H_ ================================================ FILE: include/operon/collections/bitset.hpp ================================================ #ifndef OPERON_BITSET_HPP #define OPERON_BITSET_HPP #include #include #include #include namespace Operon { template class Bitset { static constexpr std::size_t bits_per_word = 64; static constexpr std::size_t num_words = (N + bits_per_word - 1) / bits_per_word; uint64_t words_[num_words]{}; [[nodiscard]] constexpr auto WordOf(std::size_t i) const -> std::size_t { return i / bits_per_word; } [[nodiscard]] constexpr auto BitOf(std::size_t i) const -> std::size_t { return i % bits_per_word; } public: constexpr Bitset() = default; // Set / clear / test constexpr void Set(std::size_t i) { words_[WordOf(i)] |= (uint64_t{1} << BitOf(i)); } constexpr void Clear(std::size_t i) { words_[WordOf(i)] &= ~(uint64_t{1} << BitOf(i)); } [[nodiscard]] constexpr auto Test(std::size_t i) const -> bool { return (words_[WordOf(i)] >> BitOf(i)) & 1; } // Bitwise operations constexpr auto operator|(Bitset other) const -> Bitset { Bitset r; for (std::size_t i = 0; i < num_words; ++i) r.words_[i] = words_[i] | other.words_[i]; return r; } constexpr auto operator|=(Bitset other) -> Bitset& { for (std::size_t i = 0; i < num_words; ++i) { words_[i] |= other.words_[i]; } return *this; } constexpr auto operator&(Bitset other) const -> Bitset { Bitset r; for (std::size_t i = 0; i < num_words; ++i) r.words_[i] = words_[i] & other.words_[i]; return r; } constexpr auto operator&=(Bitset other) -> Bitset& { for (std::size_t i = 0; i < num_words; ++i) { words_[i] &= other.words_[i]; } return *this; } constexpr auto operator~() const -> Bitset { Bitset r; for (std::size_t i = 0; i < num_words; ++i) { r.words_[i] = ~words_[i]; } // Mask off unused bits in the last word so Count()/ForEach() stay correct. if constexpr (N % bits_per_word != 0) { r.words_[num_words - 1] &= (uint64_t{1} << (N % bits_per_word)) - 1; } return r; } [[nodiscard]] constexpr auto Any() const -> bool { for (std::size_t i = 0; i < num_words; ++i) { if (words_[i]) { return true; } } return false; } [[nodiscard]] constexpr auto None() const -> bool { return !Any(); } [[nodiscard]] constexpr auto Count() const -> std::size_t { std::size_t c = 0; for (std::size_t i = 0; i < num_words; ++i) { c += static_cast(std::popcount(words_[i])); } return c; } // Iterate over set bits efficiently template constexpr void ForEach(F&& f) const { for (std::size_t w = 0; w < num_words; ++w) { auto bits = words_[w]; while (bits) { auto idx = static_cast(std::countr_zero(bits)); std::forward(f)((w * bits_per_word) + idx); bits &= bits - 1; // clear lowest set bit } } } constexpr auto operator==(Bitset const&) const -> bool = default; }; } // namespace Operon #endif ================================================ FILE: include/operon/collections/projection.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_PROJECTION_HPP #define OPERON_PROJECTION_HPP #include #include #include namespace Operon { template struct ProjectionIterator { using T = typename std::iterator_traits::value_type; using R = std::invoke_result_t; // projection iterator traits using value_type = std::remove_reference_t; // NOLINT using pointer = void; //NOLINT using reference = value_type&; // NOLINT using difference_type = typename std::iterator_traits::difference_type; // NOLINT using iterator_category = typename std::iterator_traits::iterator_category; // NOLINT explicit ProjectionIterator(InputIt it, Func const& f) : it_(it), pr_(f) { } explicit ProjectionIterator(InputIt it, Func&& f) : it_(it), pr_(std::move(f)) { } inline auto operator*() const noexcept -> value_type { return std::invoke(pr_, std::forward::reference>(*it_)); } inline auto operator==(ProjectionIterator const& rhs) const noexcept -> bool { return it_ == rhs.it_; } inline auto operator!=(ProjectionIterator const& rhs) const noexcept -> bool { return !(*this == rhs); } inline auto operator<(ProjectionIterator const& rhs) const noexcept -> bool { return it_ < rhs.it_; } inline auto operator>(ProjectionIterator const& rhs) const noexcept -> bool { return rhs < *this; } inline auto operator<=(ProjectionIterator const& rhs) const noexcept -> bool { return !(*this > rhs); } inline auto operator>=(ProjectionIterator const& rhs) const noexcept -> bool { return !(rhs < *this); } inline auto operator+(ProjectionIterator const& rhs) const noexcept -> difference_type { return it_ + rhs.it_; } inline auto operator-(ProjectionIterator const& rhs) const noexcept -> difference_type { return it_ - rhs.it_; } inline auto operator++() noexcept -> ProjectionIterator& { ++it_; return *this; } inline auto operator++(int) noexcept -> ProjectionIterator { auto ret = *this; ++*this; return ret; } inline auto operator--() noexcept -> ProjectionIterator& { --it_; return *this; } inline auto operator--(int) noexcept -> ProjectionIterator { auto ret = *this; --*this; return ret; } inline auto operator+=(int n) noexcept -> ProjectionIterator& { it_ += n; return *this; } inline auto operator+(int n) const noexcept -> ProjectionIterator { auto ret = *this; ret += n; return ret; } inline auto operator-=(int n) noexcept -> ProjectionIterator& { it_ -= n; return *this; } inline auto operator-(int n) const noexcept -> ProjectionIterator { auto ret = *this; ret -= n; return ret; } private: InputIt it_; Func pr_; }; template struct Projection { using InputIt = ProjectionIterator; explicit Projection(Container const& c, Func const& f) : beg_(c.begin(), f), end_(c.end(), f) { } auto begin() const -> InputIt { return beg_; } // NOLINT auto end() const -> InputIt { return end_; } // NOLINT bool empty() const noexcept { return beg_ == end_; } // NOLINT private: InputIt beg_; InputIt end_; }; } // namespace Operon #endif ================================================ FILE: include/operon/core/aligned_allocator.hpp ================================================ #ifndef OPERON_ALIGNED_ALLOCATOR_HPP #define OPERON_ALIGNED_ALLOCATOR_HPP #include #include #include #include #include template requires (Alignment >= alignof(T)) class AlignedAllocator { public: // NOLINTBEGIN(readability-identifier-naming) using value_type = T; using size_type = std::size_t; using difference_type = std::ptrdiff_t; using propagate_on_container_move_assignment = std::true_type; template struct rebind { using other = AlignedAllocator; }; constexpr AlignedAllocator() noexcept = default; constexpr AlignedAllocator(AlignedAllocator const& other) noexcept = default; template explicit constexpr AlignedAllocator(AlignedAllocator const&) noexcept { } auto operator=(AlignedAllocator const&) noexcept -> AlignedAllocator& = default; auto operator=(AlignedAllocator&&) noexcept -> AlignedAllocator& = default; constexpr ~AlignedAllocator() = default; template auto operator==(AlignedAllocator const&) -> bool { return true; } template auto operator!=(AlignedAllocator const&) -> bool { return false; } [[nodiscard]] auto allocate(std::size_t n) -> T* { if (n > std::numeric_limits::max() / sizeof(T)) { throw std::bad_array_new_length(); } return static_cast(::operator new[](n * sizeof(T), std::align_val_t{Alignment})); } #if defined(__cpp_lib_allocate_at_least) [[nodiscard]] auto allocate_at_least(std::size_t num_elements) -> std::allocation_result { auto* p = allocate(num_elements); return std::allocation_result(p, num_elements); } #endif auto deallocate(T* p, [[maybe_unused]] std::size_t n) -> void { ::operator delete[](p, std::align_val_t{Alignment}); } // NOLINTEND(readability-identifier-naming) }; #endif ================================================ FILE: include/operon/core/buildinfo.hpp.in ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BUILDINFO_HPP #define OPERON_BUILDINFO_HPP #define OPERON_PLATFORM "${CMAKE_SYSTEM}" #define OPERON_ARCH "${CMAKE_SYSTEM_PROCESSOR}" #define OPERON_BUILD "${CMAKE_BUILD_TYPE}" #define OPERON_COMPILER_ID "${CMAKE_CXX_COMPILER_ID}" #define OPERON_COMPILER_VERSION "${CMAKE_CXX_COMPILER_VERSION}" #define OPERON_COMPILER_FLAGS "${CMAKE_CXX_FLAGS_DEBUG} ${CMAKE_CXX_FLAGS_RELEASE} ${CMAKE_CXX_FLAGS}" // Add more of your stuff here #define OPERON_REVISION "${REVISION}" #define OPERON_PLATFORM_CMAKE_VERSION "${CMAKE_VERSION}" #define OPERON_TINY_SOLVER USE_TINY_SOLVER #define OPERON_CERES_VERSION "${Ceres_VERSION}" #define OPERON_EIGEN_VERSION "${Eigen3_VERSION}" #define OPERON_TF_VERSION "${Taskflow_VERSION}" #define OPERON_BUILD_TIMESTAMP "${OPERON_BUILD_TIMESTAMP}" #endif // OPERON_BUILDINFO_HPP ================================================ FILE: include/operon/core/comparison.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_COMPARISON_HPP #define OPERON_COMPARISON_HPP #include #include #include #include "contracts.hpp" #include "types.hpp" namespace Operon { enum class Dominance : int { Equal = 0, Left = 1, Right = 2, None = 3 }; struct Equal { template inline auto operator()(T a, T b, T eps = 0.0) const noexcept -> bool { return std::abs(a - b) <= eps; } template inline auto operator()(Input1 first1, Input1 last1, Input2 first2, Input2 last2, typename std::iterator_traits::value_type eps = 0.0) const noexcept -> bool { return std::equal(first1, last1, first2, last2, [&](auto a, auto b) { return (*this)(a, b, eps); }); } template inline auto operator()(R1&& r1, R2&& r2, Operon::Scalar eps = 0.0) const noexcept -> bool { return (*this)(std::begin(r1), std::end(r1), std::begin(r2), std::end(r2), eps); } }; template struct Less { template inline auto operator()(T a, T b, T eps = 0.0) const noexcept -> bool { if constexpr (CheckNan) { if (std::isnan(a)) { return false; } if (std::isnan(b)) { return true; } } return a < b && b - a > eps; } template inline auto operator()(Input1 first1, Input1 last1, Input2 first2, Input2 last2, typename std::iterator_traits::value_type eps = 0.0) const noexcept -> bool { return std::lexicographical_compare(first1, last1, first2, last2, [&](auto a, auto b) { return (*this)(a, b, eps); }); } template inline auto operator()(R1&& r1, R2&& r2, Operon::Scalar eps = 0.0) const noexcept -> bool { return (*this)(std::begin(r1), std::end(r1), std::begin(r2), std::end(r2), eps); } }; template struct LessEqual { template auto operator()(T a, T b, T eps = 0.0) const noexcept -> bool { return Less{}(a, b, eps) || Equal{}(a, b, eps); } }; template struct ParetoDominance { template inline auto operator()(Input1 first1, Input1 last1, Input2 first2, Input2 last2) const noexcept -> Dominance { uint8_t r{0}; uint8_t v{0}; for (; first1 != last1 && first2 != last2; ++first1, ++first2) { auto const a = *first1; auto const b = *first2; r |= (a < b); v |= (a > b); } return static_cast(r | static_cast(v << 1U)); } template inline auto operator()(Input1 first1, Input1 last1, Input2 first2, Input2 last2, typename std::iterator_traits::value_type eps) const noexcept -> Dominance { Operon::Less cmp; uint8_t r{0}; uint8_t v{0}; for (; first1 != last1 && first2 != last2; ++first1, ++first2) { auto const a = *first1; auto const b = *first2; r |= cmp(a, b, eps); v |= cmp(b, a, eps); } return static_cast(r | static_cast(v << 1U)); } template inline auto operator()(R1&& r1, R2&& r2) const noexcept -> Dominance { return (*this)(std::begin(r1), std::end(r1), std::begin(r2), std::end(r2)); } template inline auto operator()(R1&& r1, R2&& r2, Operon::Scalar eps) const noexcept -> Dominance { return (*this)(std::begin(r1), std::end(r1), std::begin(r2), std::end(r2), eps); } }; } // namespace Operon #endif ================================================ FILE: include/operon/core/concepts.hpp ================================================ #ifndef OPERON_CONCEPTS_HPP #define OPERON_CONCEPTS_HPP #include #include namespace Operon::Concepts { // T is an arithmetic number template concept Arithmetic = std::is_arithmetic_v; } // namespace Operon::Concepts #endif ================================================ FILE: include/operon/core/constants.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_CONSTANTS_HPP #define OPERON_CONSTANTS_HPP #include #include namespace Operon { // hashing mode for tree nodes: // - Strict: hash both node label and coefficient (for leaf nodes) // - Relaxed: hash only the node label enum HashMode { Strict = 0x1, Relaxed = 0x2 }; enum HashFunction { XXHash, MetroHash, FNV1Hash, }; // M_PI, M_PI_2, M_PI_4, M_1_PI, M_2_PI, M_2_SQRTPI, M_SQRT2, M_SQRT1_2, M_E, M_LOG2E, M_LOG10E, M_LN2, M_LN10 struct Math { static double constexpr Pi = 3.141592653589793115997963468544185161590576171875; static double constexpr Tau = 2 * Pi; static double constexpr InvPi = 0.318309886183790691216444201927515678107738494873046875; static double constexpr SqrtPi = 1.772453850905515881919427556567825376987457275390625; static double constexpr E = 2.718281828459045090795598298427648842334747314453125; static double constexpr Log2E = 1.442695040888963387004650940070860087871551513671875; static double constexpr Log10E = 0.43429448190325181666793241674895398318767547607421875; static double constexpr LogE2 = 0.69314718055994528622676398299518041312694549560546875; static double constexpr LogE10 = 2.30258509299404590109361379290930926799774169921875; static double constexpr Sqrt2 = 1.4142135623730951454746218587388284504413604736328125; static double constexpr InvSqrt2 = 0.707106781186547461715008466853760182857513427734375; static std::array constexpr Constants { Pi, Tau, InvPi, SqrtPi, E, Log2E, Log10E, LogE2, LogE10, Sqrt2, InvSqrt2 }; }; static constexpr auto Align64 = 64U; } // namespace Operon #endif ================================================ FILE: include/operon/core/contracts.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_CONTRACTS_HPP #define OPERON_CONTRACTS_HPP #include // actually fall back to the libassert macros which are more powerful and featureful #define ENSURE ASSERT #define EXPECT ASSERT #endif ================================================ FILE: include/operon/core/dataset.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef DATASET_H #define DATASET_H #include #include #include #include "operon/operon_export.hpp" #include "contracts.hpp" #include "range.hpp" #include "types.hpp" #include "variable.hpp" namespace Operon { // a dataset variable described by: name, hash value (for hashing), data column index class OPERON_EXPORT Dataset { public: // some useful aliases using Variables = Operon::Map; using Matrix = Eigen::Array; using Map = Eigen::Map; private: Variables variables_; Matrix values_; Map map_; Dataset(); // read data from a csv file and return a map (view of the data) auto ReadCsv(std::string const& path, bool hasHeader) -> Matrix; // this method ensures the same ordering of variables in the variables vector // based on index, name, hash value void InitializeVariables(std::vector const&); public: explicit Dataset(const std::string& path, bool hasHeader = false); Dataset(Dataset const& rhs) : variables_(rhs.variables_) , values_(rhs.values_) , map_(values_.data(), values_.rows(), values_.cols()) { } Dataset(Dataset&& rhs) noexcept : variables_(std::move(rhs.variables_)) , values_(std::move(rhs.values_)) , map_(rhs.map_) { } Dataset(std::vector const& vars, std::vector> const& vals); explicit Dataset(std::vector> const& vals); //explicit Dataset(Eigen::Ref ref); Dataset(gsl::not_null data, Eigen::Index rows, Eigen::Index cols); explicit Dataset(Matrix vals); ~Dataset() = default; auto operator=(Dataset rhs) -> Dataset& { Swap(rhs); return *this; } auto operator=(Dataset&& rhs) noexcept -> Dataset& { if (this != &rhs) { variables_ = std::move(rhs.variables_); values_ = std::move(rhs.values_); new (&map_) Map(rhs.map_.data(), rhs.map_.rows(), rhs.map_.cols()); // we use placement new (no allocation) } return *this; } void Swap(Dataset& rhs) noexcept { variables_.swap(rhs.variables_); values_.swap(rhs.values_); new (&map_) Map(values_.data(), values_.rows(), values_.cols()); // we use placement new (no allocation) } auto operator==(Dataset const& rhs) const noexcept -> bool { return Rows() == rhs.Rows() && Cols() == rhs.Cols() && variables_.size() == rhs.variables_.size() && std::equal(variables_.begin(), variables_.end(), rhs.variables_.begin()) && values_.isApprox(rhs.values_); } // check if we own the data or if we are a view over someone else's data [[nodiscard]] auto IsView() const noexcept -> bool { return values_.data() != map_.data(); } template [[nodiscard]] auto Rows() const -> T { return static_cast(map_.rows()); } template [[nodiscard]] auto Cols() const -> T { return static_cast(map_.cols()); } template [[nodiscard]] auto Dimensions() const -> std::pair { return { Rows(), Cols() }; } [[nodiscard]] auto Values() const -> Eigen::Ref { return map_; } [[nodiscard]] auto VariableNames() const -> std::vector; void SetVariableNames(std::vector const& names); [[nodiscard]] auto VariableHashes() const -> std::vector; [[nodiscard]] auto VariableIndices() const -> std::vector; [[nodiscard]] auto GetValues(std::string const& name) const noexcept -> Operon::Span; [[nodiscard]] auto GetValues(Operon::Hash hash) const noexcept -> Operon::Span; [[nodiscard]] auto GetValues(int64_t index) const noexcept -> Operon::Span; [[nodiscard]] auto GetValues(Variable const& variable) const noexcept -> Operon::Span { return GetValues(variable.Hash); } [[nodiscard]] auto GetVariable(const std::string& name) const noexcept -> std::optional; [[nodiscard]] auto GetVariable(Operon::Hash hash) const noexcept -> std::optional; [[nodiscard]] auto GetVariables() const noexcept -> std::vector { std::vector variables; variables.reserve(variables_.size()); auto const& values = variables_.values(); std::transform(values.begin(), values.end(), std::back_inserter(variables), [](auto const& p) { return p.second; }); return variables; } void Shuffle(Operon::RandomGenerator& random); void Normalize(size_t i, Range range); void PermuteRows(std::vector const& indices); // standardize column i using mean and stddev calculated over the specified range void Standardize(size_t i, Range range); }; } // namespace Operon #endif ================================================ FILE: include/operon/core/dispatch.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_EVAL_DETAIL #define OPERON_EVAL_DETAIL #include #include #include #include #include "concepts.hpp" #include "node.hpp" #include "range.hpp" #include "types.hpp" #include "aligned_allocator.hpp" namespace Operon { namespace Backend { template static auto constexpr BatchSize = 512UL / sizeof(T); static auto constexpr DefaultAlignment = 32UL; template using Accessor = aligned_accessor; template using Container = std::vector>; template> using Buffer = Operon::MDArray, std::layout_left, Container>; template> using View = Operon::MDSpan, std::layout_left>; template> using ColumnView = Operon::MDSpan, std::layout_left>; template auto Ptr(View view, std::integral auto col) -> Backend::View::element_type* { return view.data_handle() + (col * S); } // utility template auto Fill(Backend::View view, int idx, T value) { auto* p = view.data_handle() + (idx * S); std::fill_n(p, S, value); }; } // namespace Backend // detect missing specializations for functions template> struct Func { auto operator()(std::vector const& /*nodes*/, Backend::View /*primal*/, std::integral auto /*node index*/, std::integral auto... /*child indices*/) { throw std::runtime_error(fmt::format("backend error: missing specialization for function: {}\n", Operon::Node{N}.Name())); } }; // detect missing specializations for function derivatives template> struct Diff { auto operator()(std::vector const& /*nodes*/, Backend::View /*primal*/, Backend::View /*trace*/, std::integral auto /*node index*/, std::integral auto /*partial index*/) { throw std::runtime_error(fmt::format("backend error: missing specialization for derivative: {}\n", Operon::Node{N}.Name())); } }; // data types used by the dispatch table and the interpreter struct Dispatch { template //requires std::is_arithmetic_v static auto constexpr DefaultBatchSize{ 512UL / sizeof(T) }; template using Callable = std::function const&, Backend::View, size_t, Operon::Range)>; template using CallableDiff = std::function const&, Backend::View, Backend::View, int, int)>; // dispatching mechanism // compared to the simple/naive way of evaluating n-ary symbols, this method has the following advantages: // 1) improved performance: the naive method accumulates into the result for each argument, leading to unnecessary assignments // 2) minimizing the number of intermediate steps which might improve floating point accuracy of some operations // if arity > 4, one accumulation is performed every 4 args template requires Node::IsNary static void NaryOp(Operon::Vector const& nodes, Backend::View data, size_t parentIndex, Operon::Range /*unused*/) { static_assert(Type < NodeType::Aq); const auto nextArg = [&](size_t i) { return i - (nodes[i].Length + 1); }; auto arg1 = parentIndex - 1; bool continued = false; auto const call = [&](bool continued, int result, auto... args) { if (continued) { Func{}(nodes, data, result, args...); } else { Func{}(nodes, data, result, args...); } }; int arity = nodes[parentIndex].Arity; while (arity > 0) { switch (arity) { case 1: { call(continued, parentIndex, arg1); arity = 0; break; } case 2: { auto arg2 = nextArg(arg1); call(continued, parentIndex, arg1, arg2); arity = 0; break; } case 3: { auto arg2 = nextArg(arg1); auto arg3 = nextArg(arg2); call(continued, parentIndex, arg1, arg2, arg3); arity = 0; break; } default: { auto arg2 = nextArg(arg1); auto arg3 = nextArg(arg2); auto arg4 = nextArg(arg3); call(continued, parentIndex, arg1, arg2, arg3, arg4); arity -= 4; arg1 = nextArg(arg4); break; } } continued = true; } } template requires Node::IsBinary static void BinaryOp(Operon::Vector const& nodes, Backend::View m, size_t i, Operon::Range /*unused*/) { auto j = i - 1; auto k = j - nodes[j].Length - 1; Func{}(nodes, m, i, j, k); } template requires Node::IsUnary static void UnaryOp(Operon::Vector const& nodes, Backend::View m, size_t i, Operon::Range /*unused*/) { Func{}(nodes, m, i, i-1); } struct Noop { template void operator()(Args&&... /*unused*/) {} }; template static void DiffOp(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, int i, int j) { Diff{}(nodes, primal, trace, i, j); } template static constexpr auto MakeFunctionCall() -> Dispatch::Callable { if constexpr (Node::IsNary) { return Callable{NaryOp}; } else if constexpr (Node::IsBinary) { return Callable{BinaryOp}; } else if constexpr (Node::IsUnary) { return Callable{UnaryOp}; } } template static constexpr auto MakeDiffCall() -> Dispatch::CallableDiff { // this constexpr if here returns NOOP in case of non-arithmetic types (duals) if constexpr (std::is_arithmetic_v) { return CallableDiff{DiffOp}; } else { return Dispatch::Noop{}; } } }; // struct Dispatch namespace detail { // return the index of type T in Tuple template static auto constexpr TypeIndexImpl() { std::size_t i{0}; for (bool x : { std::is_same_v... }) { if (x) { break; } ++i; } return i; } template concept ExtentsLike = requires { T::size() and std::is_array_v; }; template struct DefaultIndex { using Type = std::size_t; }; template struct IntegerIndex { using Type = T::value_type; }; } // namespace detail template struct DispatchTable { private: static constexpr auto S = sizeof...(Ts); using TypeHolder = std::tuple; using LastType = std::tuple_element_t; using IndexType = std::conditional_t, detail::DefaultIndex, detail::IntegerIndex>::Type; static constexpr auto N = detail::ExtentsLike ? S-1 : S; static std::array constexpr Sizes = [](std::integer_sequence){ constexpr auto f = [&]() { if constexpr (detail::ExtentsLike) { constexpr auto t = [](std::integer_sequence){ return std::tuple{I...}; }(LastType{}); if constexpr (TypeIdx < LastType::size()) { return std::get(t); } else { return Dispatch::DefaultBatchSize>; } } else { return Dispatch::DefaultBatchSize>; } }; return std::array{f.template operator()()...}; }(std::make_integer_sequence{}); template requires (detail::TypeIndexImpl() < N) static auto constexpr TypeIndex = detail::TypeIndexImpl(); public: using SupportedTypes = TypeHolder; template requires Operon::Concepts::Arithmetic static constexpr std::size_t BatchSize = Sizes[TypeIndex]; template requires Operon::Concepts::Arithmetic using Backend = Backend::View>; template requires Operon::Concepts::Arithmetic using Callable = Dispatch::Callable>; template requires Operon::Concepts::Arithmetic using CallableDiff = Dispatch::CallableDiff>; private: template requires Operon::Concepts::Arithmetic static constexpr auto MakeFunction() { return Dispatch::MakeFunctionCall>(); } template requires Operon::Concepts::Arithmetic static constexpr auto MakeDerivative() { return Dispatch::MakeDiffCall>(); } template static constexpr auto MakeTuple() { return [](std::index_sequence){ return std::make_tuple( std::make_tuple(MakeFunction>()...), std::make_tuple(MakeDerivative>()...) ); }(std::make_index_sequence{}); } using TFun = decltype([](std::index_sequence){ return std::make_tuple(Callable>{}...); }(std::make_index_sequence{})); using TDer = decltype([](std::index_sequence){ return std::make_tuple(CallableDiff>{}...); }(std::make_index_sequence{})); using Tuple = std::tuple; using Map = Operon::Map; Map map_; public: DispatchTable() { auto constexpr f = [](auto i) { return static_cast(i); }; [&](std::index_sequence){ (map_.insert({ Node(f(I)).HashValue, MakeTuple() }), ...); }(std::make_index_sequence{}); } ~DispatchTable() = default; auto operator=(DispatchTable const& other) -> DispatchTable& { if (this != &other) { map_ = other.map_; } return *this; } auto operator=(DispatchTable&& other) noexcept -> DispatchTable& { map_ = std::move(other.map_); return *this; } template static constexpr auto SupportsType = TypeIndex < N; explicit DispatchTable(Map const& map) : map_(map) { } explicit DispatchTable(Map&& map) : map_(std::move(map)) { } explicit DispatchTable(std::unordered_map const& map) : map_(map.begin(), map.end()) { } DispatchTable(DispatchTable const& other) : map_(other.map_) { } DispatchTable(DispatchTable &&other) noexcept : map_(std::move(other.map_)) { } auto GetMap() -> Map& { return map_; } auto GetMap() const -> Map const& { return map_; } template auto GetFunction(Operon::Hash const h) -> Callable& { return const_cast&>(const_cast const*>(*this)->GetFunction(h)); // NOLINT } template auto GetDerivative(Operon::Hash const h) -> CallableDiff& { return const_cast&>(const_cast const*>(*this)->GetDerivative(h)); // NOLINT } template [[nodiscard]] auto GetFunction(Operon::Hash const h) const -> Callable const& { if (auto it = map_.find(h); it != map_.end()) { return std::get(TypeIndex)>(std::get<0>(it->second)); } throw std::runtime_error(fmt::format("Hash value {} is not in the map\n", h)); } template [[nodiscard]] auto GetDerivative(Operon::Hash const h) const -> CallableDiff const& { if (auto it = map_.find(h); it != map_.end()) { return std::get(TypeIndex)>(std::get<1>(it->second)); } throw std::runtime_error(fmt::format("Hash value {} is not in the map\n", h)); } template [[nodiscard]] auto Get(Operon::Hash const h) const -> std::tuple, CallableDiff> { if (auto it = map_.find(h); it != map_.end()) { return std::get(TypeIndex)>(it->second); } throw std::runtime_error(fmt::format("Hash value {} is not in the map\n", h)); } // Insert (or overwrite) the callable and derivative for one scalar type T. // Creates a default-constructed map entry if the hash is absent, then sets // only the type-slot for T, leaving other types in the entry untouched. template requires Operon::Concepts::Arithmetic void RegisterFunction(Operon::Hash hash, Callable f, CallableDiff df = {}) { auto& entry = map_[hash]; std::get>(std::get<0>(entry)) = std::move(f); std::get>(std::get<1>(entry)) = std::move(df); } template [[nodiscard]] auto TryGetFunction(Operon::Hash const h) const noexcept -> std::optional> { if (auto it = map_.find(h); it != map_.end()) { return std::optional{ std::get>(std::get<0>(it->second)) }; } return {}; } template [[nodiscard]] auto TryGetDerivative(Operon::Hash const h) const noexcept -> std::optional> { if (auto it = map_.find(h); it != map_.end()) { return std::optional{ std::get>(std::get<1>(it->second)) }; } return {}; } [[nodiscard]] auto Contains(Operon::Hash hash) const noexcept -> bool { return map_.contains(hash); } }; // struct DispatchTable using ScalarDispatch = DispatchTable; } // namespace Operon #endif ================================================ FILE: include/operon/core/distance.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_DISTANCE_HPP #define OPERON_DISTANCE_HPP #include "types.hpp" #include "operon/operon_export.hpp" namespace Operon::Distance { auto OPERON_EXPORT Jaccard(Operon::Vector const& lhs, Operon::Vector const& rhs) noexcept -> double; auto OPERON_EXPORT SorensenDice(Operon::Vector const& lhs, Operon::Vector const& rhs) noexcept -> double; } // namespace Operon::Distance #endif ================================================ FILE: include/operon/core/individual.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_INDIVIDUAL_HPP #define OPERON_INDIVIDUAL_HPP #include "comparison.hpp" #include "tree.hpp" #include "types.hpp" #include #include namespace Operon { struct LexicographicalComparison; // fwd def struct Individual { Tree Genotype; Operon::Vector Fitness; size_t Rank{}; // domination rank; used by NSGA2 Operon::Scalar Distance{}; // crowding distance; used by NSGA2 inline auto operator[](size_t const i) noexcept -> Operon::Scalar& { return Fitness[i]; } inline auto operator[](size_t const i) const noexcept -> Operon::Scalar { return Fitness[i]; } [[nodiscard]] inline auto Size() const noexcept -> size_t { return Fitness.size(); } Individual() : Individual(1) { } explicit Individual(size_t nObj) : Fitness(nObj, std::numeric_limits::max()) { } }; struct SingleObjectiveComparison { explicit SingleObjectiveComparison(size_t idx) : obj_(idx) { } SingleObjectiveComparison() : SingleObjectiveComparison(0) { } auto operator()(Individual const& lhs, Individual const& rhs, Operon::Scalar eps = 0) const -> bool { return Operon::Less{}(lhs[obj_], rhs[obj_], eps); } [[nodiscard]] auto GetObjectiveIndex() const -> size_t { return obj_; } void SetObjectiveIndex(size_t obj) { obj_ = obj; } private: size_t obj_; // objective index }; struct LexicographicalComparison { auto operator()(Individual const& lhs, Individual const& rhs, Operon::Scalar eps = 0) const -> bool { EXPECT(std::size(lhs.Fitness) == std::size(rhs.Fitness)); auto const& fit1 = lhs.Fitness; auto const& fit2 = rhs.Fitness; return Less{}(fit1.begin(), fit1.end(), fit2.begin(), fit2.end(), eps); } }; // TODO: use a collection of SingleObjectiveComparison functors // returns true if lhs dominates rhs struct ParetoComparison { // assumes minimization in every dimension auto operator()(Individual const& lhs, Individual const& rhs, Operon::Scalar eps = 0) const -> bool { EXPECT(std::size(lhs.Fitness) == std::size(rhs.Fitness)); auto const& fit1 = lhs.Fitness; auto const& fit2 = rhs.Fitness; return ParetoDominance{}(fit1.begin(), fit1.end(), fit2.begin(), fit2.end(), eps) == Dominance::Left; } }; struct CrowdedComparison { auto operator()(Individual const& lhs, Individual const& rhs, Operon::Scalar eps = 0) const -> bool { EXPECT(std::size(lhs.Fitness) == std::size(rhs.Fitness)); if (lhs.Rank != rhs.Rank) { return lhs.Rank < rhs.Rank; } return Operon::Less{}(rhs.Distance, lhs.Distance, eps); } }; using ComparisonCallback = std::function; } // namespace Operon #endif ================================================ FILE: include/operon/core/node.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_CORE_NODE_HPP #define OPERON_CORE_NODE_HPP #include #include #include "operon/collections/bitset.hpp" #include "operon/operon_export.hpp" #include "types.hpp" namespace Operon { enum class NodeType : uint8_t { // n-ary symbols Add = 0, Mul, Sub, Div, Fmin, Fmax, // binary symbols Aq, Pow, Powabs, // unary symbols Abs, Acos, Asin, Atan, Cbrt, Ceil, Cos, Cosh, Exp, Floor, Log, Logabs, Log1p, Sin, Sinh, Sqrt, Sqrtabs, Tan, Tanh, Square, // nullary symbols (dynamic can be anything) Dynamic, Constant, Variable }; using UnderlyingNodeType = std::underlying_type_t; struct NodeTypes { // total number of distinct node types static auto constexpr Count = static_cast(NodeType::Variable) + 1UL; // returns the index of the given type in the NodeType enum static constexpr auto GetIndex(NodeType type) -> size_t { return static_cast(type); } static auto constexpr NoType{static_cast(0xFFU)}; }; // A bitset over all node types, used to represent primitive set configurations. using PrimitiveSetConfig = Bitset; // Build a PrimitiveSetConfig from two individual NodeType values. inline constexpr auto operator|(NodeType lhs, NodeType rhs) -> PrimitiveSetConfig { PrimitiveSetConfig c{}; c.Set(NodeTypes::GetIndex(lhs)); c.Set(NodeTypes::GetIndex(rhs)); return c; } // Extend a PrimitiveSetConfig with one more NodeType. inline constexpr auto operator|(PrimitiveSetConfig lhs, NodeType rhs) -> PrimitiveSetConfig { lhs.Set(NodeTypes::GetIndex(rhs)); return lhs; } inline constexpr auto operator|=(PrimitiveSetConfig& lhs, NodeType rhs) -> PrimitiveSetConfig& { lhs.Set(NodeTypes::GetIndex(rhs)); return lhs; } struct Node { Operon::Hash HashValue; // needs to be unique for each node type Operon::Hash mutable CalculatedHashValue; // for arithmetic terminal nodes whose hash value depends on their children Operon::Scalar Value; // value for constants or weighting factor for variables uint16_t Arity; // 0-65535 uint16_t Length; // 0-65535 uint16_t Depth; // 0-65535 uint16_t Level; // length of the path to the root node uint16_t Parent; // index of parent node NodeType Type; bool IsEnabled; bool Optimize; Node() = default; explicit Node(NodeType type) noexcept : Node(type, static_cast(type)) { } explicit Node(NodeType type, Operon::Hash hashValue) noexcept : HashValue(hashValue) , CalculatedHashValue(hashValue) , Arity(0UL) , Length(0UL) , Depth(1UL) , Level(0UL) , Parent(0UL) , Type(type) { if (Type < NodeType::Abs) // Add, Mul, Sub, Div, Fmin, Fmax, Aq, Pow, Powabs { Arity = 2; } else if (Type < NodeType::Dynamic) // Abs through Square (unary) { Arity = 1; } Length = Arity; IsEnabled = true; Optimize = IsLeaf(); // we only optimize leaf nodes Value = 1.; } static auto Constant(double value) { Node node(NodeType::Constant); node.Value = static_cast(value); return node; } [[nodiscard]] OPERON_EXPORT auto Name() const noexcept -> std::string const&; [[nodiscard]] OPERON_EXPORT auto Desc() const noexcept -> std::string const&; // Register a display name (and optional description) for a Dynamic node hash. // After registration, Name() and Desc() return the provided strings instead of "dyn". static OPERON_EXPORT void RegisterName(Operon::Hash hash, std::string name, std::string desc = {}); // comparison operators inline auto operator==(const Node& rhs) const noexcept -> bool { return CalculatedHashValue == rhs.CalculatedHashValue; } inline auto operator!=(const Node& rhs) const noexcept -> bool { return !((*this) == rhs); } inline auto operator<(const Node& rhs) const noexcept -> bool { return std::tie(HashValue, CalculatedHashValue) < std::tie(rhs.HashValue, rhs.CalculatedHashValue); } inline auto operator<=(const Node& rhs) const noexcept -> bool { return ((*this) == rhs || (*this) < rhs); } inline auto operator>(const Node& rhs) const noexcept -> bool { return !((*this) <= rhs); } inline auto operator>=(const Node& rhs) const noexcept -> bool { return !((*this) < rhs); } [[nodiscard]] inline auto IsLeaf() const noexcept -> bool { return Arity == 0; } [[nodiscard]] inline auto IsCommutative() const noexcept -> bool { return Is(); } template [[nodiscard]] inline auto Is() const -> bool { return ((Type == T) || ...); } [[nodiscard]] inline auto IsConstant() const -> bool { return Is(); } [[nodiscard]] inline auto IsVariable() const -> bool { return Is(); } [[nodiscard]] inline auto IsAddition() const -> bool { return Is(); } [[nodiscard]] inline auto IsSubtraction() const -> bool { return Is(); } [[nodiscard]] inline auto IsMultiplication() const -> bool { return Is(); } [[nodiscard]] inline auto IsDivision() const -> bool { return Is(); } [[nodiscard]] inline auto IsAq() const -> bool { return Is(); } [[nodiscard]] inline auto IsPow() const -> bool { return Is(); } [[nodiscard]] inline auto IsPowabs() const -> bool { return Is(); } [[nodiscard]] inline auto IsExp() const -> bool { return Is(); } [[nodiscard]] inline auto IsLog() const -> bool { return Is(); } [[nodiscard]] inline auto IsSin() const -> bool { return Is(); } [[nodiscard]] inline auto IsCos() const -> bool { return Is(); } [[nodiscard]] inline auto IsTan() const -> bool { return Is(); } [[nodiscard]] inline auto IsTanh() const -> bool { return Is(); } [[nodiscard]] inline auto IsSquareRoot() const -> bool { return Is(); } [[nodiscard]] inline auto IsCubeRoot() const -> bool { return Is(); } [[nodiscard]] inline auto IsSquare() const -> bool { return Is(); } [[nodiscard]] inline auto IsDynamic() const -> bool { return Is(); } template static auto constexpr IsNary = Type < NodeType::Aq; template static auto constexpr IsBinary = Type > NodeType::Fmax && Type < NodeType::Abs; template static auto constexpr IsUnary = Type > NodeType::Powabs && Type < NodeType::Dynamic; template static auto constexpr IsNullary = Type > NodeType::Square; }; } // namespace Operon #endif ================================================ FILE: include/operon/core/operator.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_OPERATOR_HPP #define OPERON_OPERATOR_HPP #include "types.hpp" namespace Operon { template struct OperatorBase { using ReturnType = Ret; using ArgumentType = std::tuple; // all operators take a random device (source of randomness) as the first parameter virtual auto operator()(Operon::RandomGenerator& /*random*/, Args... /*args*/) const -> Ret = 0; virtual ~OperatorBase() = default; OperatorBase() = default; OperatorBase(OperatorBase const& other) = default; OperatorBase(OperatorBase&& other) noexcept = default; auto operator=(OperatorBase const& other) -> OperatorBase& = default; auto operator=(OperatorBase&& other) noexcept -> OperatorBase& = default; }; } // namespace Operon #endif ================================================ FILE: include/operon/core/problem.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef PROBLEM_HPP #define PROBLEM_HPP #include #include #include #include #include "dataset.hpp" #include "pset.hpp" #include "range.hpp" namespace Operon { class Problem { [[nodiscard]] auto GetVariable(auto t) const -> Operon::Variable { if (auto v = dataset_->GetVariable(t); v.has_value()) { return *v; } PANIC("cannot map argument to any known variable"); } auto ValidateInputs(auto const& inputs) const { using T = typename std::remove_cvref_t::value_type; static_assert(std::is_same_v || std::is_same_v, "the inputs must be strings or hashes"); for (auto const& x : inputs) { (void) GetVariable(x); } } std::unique_ptr dataset_; Range training_; Range test_; Range validation_; PrimitiveSet pset_; Operon::Variable target_; Operon::Set inputs_; bool ownership_{true}; public: Problem(const Problem&) = delete; Problem(Problem&&) = delete; auto operator=(const Problem&) -> Problem& = delete; auto operator=(Problem&&) -> Problem& = delete; explicit Problem(std::unique_ptr dataset) : dataset_ { std::move(dataset) } { SetDefaultInputs(); } explicit Problem(gsl::not_null dataset) : Problem(std::unique_ptr{dataset}) { ownership_ = false; } ~Problem() { if (!ownership_) { std::ignore = dataset_.release(); } } template auto SetTarget(T t) { target_ = GetVariable>(t); } auto SetTrainingRange(Operon::Range range) { training_ = range; } auto SetTrainingRange(int begin, int end) { training_ = Operon::Range(begin, end); } auto SetTestRange(Operon::Range range) { test_ = range; } auto SetTestRange(int begin, int end) { test_ = Operon::Range(begin, end); } auto SetValidationRange(Operon::Range range) { validation_ = range; } auto SetValidationRange(int begin, int end) { validation_ = Operon::Range(begin, end); } auto SetInputs(auto const& inputs) { ValidateInputs(inputs); inputs_.clear(); for (auto const& x : inputs) { inputs_.insert(GetVariable(x).Hash); } } [[nodiscard]] auto GetInputs() const -> std::vector const& { return inputs_.values(); } // set all variables except the target as inputs auto SetDefaultInputs() -> void { inputs_.clear(); for (auto const& v : dataset_->GetVariables()) { if (v.Hash != target_.Hash) { inputs_.insert(v.Hash); } } } [[nodiscard]] auto TrainingRange() const -> Range { return training_; } [[nodiscard]] auto TestRange() const -> Range { return test_; } [[nodiscard]] auto ValidationRange() const -> Range { return validation_; } [[nodiscard]] auto TargetVariable() const -> Variable const& { return target_; } [[nodiscard]] auto InputVariables() const -> std::vector { std::vector variables; variables.reserve(inputs_.size()); std::transform(inputs_.values().begin(), inputs_.values().end(), std::back_inserter(variables), [&](auto h) { return GetVariable(h); }); return variables; } [[nodiscard]] auto GetPrimitiveSet() const -> PrimitiveSet const& { return pset_; } auto GetPrimitiveSet() -> PrimitiveSet& { return pset_; } auto ConfigurePrimitiveSet(Operon::PrimitiveSetConfig config) { pset_.SetConfig(config); } [[nodiscard]] auto GetDataset() const -> Dataset const* { return dataset_.get(); } auto GetDataset() -> Dataset* { return dataset_.get(); } [[nodiscard]] auto TargetValues() const -> Operon::Span { return dataset_->GetValues(target_.Index); } [[nodiscard]] auto TargetValues(Operon::Range range) const -> Operon::Span { return dataset_->GetValues(target_.Index).subspan(range.Start(), range.Size()); } void StandardizeData(Range range) { for (auto const& v : inputs_) { dataset_->Standardize(GetVariable(v).Index, range); } } void NormalizeData(Range range) { for (auto const& v : inputs_) { dataset_->Normalize(GetVariable(v).Index, range); } } }; } // namespace Operon #endif ================================================ FILE: include/operon/core/pset.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_PSET_HPP #define OPERON_PSET_HPP #include "contracts.hpp" #include "node.hpp" namespace Operon { class PrimitiveSet { using Primitive = std::tuple< Node, size_t, // 1: frequency size_t, // 2: min arity size_t // 3: max arity >; enum { NODE = 0, FREQUENCY = 1, MINARITY = 2, MAXARITY = 3}; // for accessing tuple elements more easily Operon::Map pset_; [[nodiscard]] auto OPERON_EXPORT GetPrimitive(Operon::Hash hash) const -> Primitive const&; auto GetPrimitive(Operon::Hash hash) -> Primitive& { return const_cast(const_cast(this)->GetPrimitive(hash)); // NOLINT } public: static constexpr PrimitiveSetConfig Arithmetic = NodeType::Constant | NodeType::Variable | NodeType::Add | NodeType::Sub | NodeType::Mul | NodeType::Div; static constexpr PrimitiveSetConfig TypeCoherent = Arithmetic | NodeType::Pow | NodeType::Exp | NodeType::Log | NodeType::Sin | NodeType::Cos | NodeType::Square; static constexpr PrimitiveSetConfig Full = TypeCoherent | NodeType::Aq | NodeType::Tan | NodeType::Tanh | NodeType::Sqrt | NodeType::Cbrt; PrimitiveSet() = default; explicit PrimitiveSet(PrimitiveSetConfig config) { SetConfig(config); } [[nodiscard]] auto Primitives() const -> decltype(pset_) const& { return pset_; } auto AddPrimitive(Operon::Node node, size_t frequency, size_t minArity, size_t maxArity) -> bool { auto [_, ok] = pset_.insert({ node.HashValue, Primitive { node, frequency, minArity, maxArity } }); return ok; } // Register a user-defined function symbol for use in tree generation. // arity must match what the registered callable in the DispatchTable expects. auto AddFunction(Operon::Hash hash, uint16_t arity, size_t frequency = 1) -> bool { Node node(NodeType::Dynamic, hash); node.Arity = arity; node.Length = arity; node.Optimize = false; // function nodes are never coefficient-optimized return AddPrimitive(node, frequency, arity, arity); } void RemovePrimitive(Operon::Node node) { pset_.erase(node.HashValue); } void RemovePrimitive(Operon::Hash hash) { pset_.erase(hash); } void SetConfig(PrimitiveSetConfig config) { pset_.clear(); for (size_t i = 0; i < Operon::NodeTypes::Count; ++i) { auto t = static_cast(i); Operon::Node n(t); if (config.Test(i)) { pset_[n.HashValue] = { n, 1, n.Arity, n.Arity }; } } } [[nodiscard]] auto EnabledPrimitives() const -> std::vector { std::vector nodes; for (auto const& [k, v] : pset_) { auto [node, freq, min_arity, max_arity] = v; if (node.IsEnabled && freq > 0) { nodes.push_back(node); } } return nodes; } [[nodiscard]] auto Config() const -> PrimitiveSetConfig { PrimitiveSetConfig conf{}; for (auto [k, v] : pset_) { auto const& [node, freq, min_arity, max_arity] = v; if (node.IsEnabled && freq > 0) { conf.Set(static_cast(node.Type)); } } return conf; } [[nodiscard]] auto Frequency(Operon::Hash hash) const -> size_t { auto const& p = GetPrimitive(hash); return std::get(p); } void SetFrequency(Operon::Hash hash, size_t frequency) { auto& p = GetPrimitive(hash); std::get(p) = frequency; } [[nodiscard]] auto Contains(Operon::Hash hash) const -> bool { return pset_.contains(hash); } [[nodiscard]] auto IsEnabled(Operon::Hash hash) const -> bool { auto const& p = GetPrimitive(hash); return std::get(p).IsEnabled; } void SetEnabled(Operon::Hash hash, bool enabled) { auto& p = GetPrimitive(hash); std::get(p).IsEnabled = enabled; } void Enable(Operon::Hash hash) { SetEnabled(hash, /*enabled=*/true); } void Disable(Operon::Hash hash) { SetEnabled(hash, /*enabled=*/false); } // Returns the largest tree length <= targetLen that is achievable with the // current primitive set. A length n is achievable if n == 1 (a single leaf, // assuming the pset has at least one enabled terminal) or (n-1) can be // expressed as a sum of available function arities. // Precondition: the pset must have at least one enabled terminal symbol. // This is used by tree creators to snap an impossible requested length down // to the nearest valid one, so they never return a tree larger than requested. // Cost: O(|pset| + targetLen * |distinct arities|) per call — negligible for // typical GP parameters (maxLength <= 200, |pset| <= 30). [[nodiscard]] OPERON_EXPORT auto AchievableLength(size_t targetLen) const -> size_t; [[nodiscard]] auto FunctionArityLimits() const -> std::pair { auto minArity = std::numeric_limits::max(); auto maxArity = std::numeric_limits::min(); for (auto const& [key, val] : pset_) { if (std::get(val).IsLeaf()) { continue; } minArity = std::min(minArity, std::get(val)); maxArity = std::max(maxArity, std::get(val)); } return { minArity, maxArity }; } OPERON_EXPORT auto SampleRandomSymbol(Operon::RandomGenerator& random, size_t minArity, size_t maxArity) const -> Operon::Node; void SetMinimumArity(Operon::Hash hash, size_t minArity) { EXPECT(minArity <= MaximumArity(hash)); auto& p = GetPrimitive(hash); std::get(p) = minArity; } [[nodiscard]] auto MinimumArity(Operon::Hash hash) const -> size_t { auto const& p = GetPrimitive(hash); return std::get(p); } void SetMaximumArity(Operon::Hash hash, size_t maxArity) { EXPECT(maxArity >= MinimumArity(hash)); auto& p = GetPrimitive(hash); std::get(p) = maxArity; } [[nodiscard]] auto MaximumArity(Operon::Hash hash) const -> size_t { auto const& p = GetPrimitive(hash); return std::get(p); } [[nodiscard]] auto MinMaxArity(Operon::Hash hash) const -> std::tuple { auto const& p = GetPrimitive(hash); return { std::get(p), std::get(p) }; } void SetMinMaxArity(Operon::Hash hash, size_t minArity, size_t maxArity) { EXPECT(maxArity >= minArity); auto& p = GetPrimitive(hash); std::get(p) = minArity; std::get(p) = maxArity; } // convenience overloads void SetFrequency(Operon::Node node, size_t frequency) { SetFrequency(node.HashValue, frequency); } [[nodiscard]] auto Frequency(Operon::Node node) const -> size_t { return Frequency(node.HashValue); } [[nodiscard]] auto Contains(Operon::Node node) const -> bool { return Contains(node.HashValue); } [[nodiscard]] auto IsEnabled(Operon::Node node) const -> bool { return IsEnabled(node.HashValue); } void SetEnabled(Operon::Node node, bool enabled) { SetEnabled(node.HashValue, enabled); } void Enable(Operon::Node node) { SetEnabled(node, /*enabled=*/true); } void Disable(Operon::Node node) { SetEnabled(node, /*enabled=*/false); } void SetMinimumArity(Operon::Node node, size_t minArity) { SetMinimumArity(node.HashValue, minArity); } [[nodiscard]] auto MinimumArity(Operon::Node node) const -> size_t { return MinimumArity(node.HashValue); } void SetMaximumArity(Operon::Node node, size_t maxArity) { SetMaximumArity(node.HashValue, maxArity); } [[nodiscard]] auto MaximumArity(Operon::Node node) const -> size_t { return MaximumArity(node.HashValue); } [[nodiscard]] auto MinMaxArity(Operon::Node node) const -> std::tuple { return MinMaxArity(node.HashValue); } void SetMinMaxArity(Operon::Node node, size_t minArity, size_t maxArity) { SetMinMaxArity(node.HashValue, minArity, maxArity); } }; } // namespace Operon #endif ================================================ FILE: include/operon/core/range.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_RANGE_HPP #define OPERON_RANGE_HPP #include #include "contracts.hpp" namespace Operon { class Range { public: [[nodiscard]] inline auto Start() const noexcept -> std::size_t { return range_.first; } [[nodiscard]] inline auto End() const noexcept -> std::size_t { return range_.second; } [[nodiscard]] inline auto Size() const noexcept -> std::size_t { return range_.second - range_.first; } [[nodiscard]] auto Bounds() const noexcept -> std::pair { return range_; } Range() = default; Range(std::size_t start, std::size_t end) : range_(CheckRange(start, end)) { } explicit Range(std::pair range) : range_(CheckRange(range.first, range.second)) { } auto operator=(std::pair p) -> Range& { auto [start, end] = p; range_ = CheckRange(start, end); return *this; } private: static auto CheckRange(std::size_t start, std::size_t end) -> std::pair { EXPECT(start <= end); return { start, end }; } std::pair range_; }; } // namespace Operon #endif ================================================ FILE: include/operon/core/subtree.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_SUBTREE_HPP #define OPERON_SUBTREE_HPP #include "node.hpp" namespace Operon { // a non-owning view over a subtree (part of a tree) with the ability // to iterate over child nodes or child node indices template requires std::is_same_v || std::is_same_v struct Subtree { Subtree(Operon::Span nodes, std::size_t parent) : nodes_(nodes), parent_(parent) {} struct Sentinel {}; template struct Enumerator { using value_type = std::tuple; // NOLINT using reference = std::tuple; // NOLINT using difference_type = std::ptrdiff_t; // NOLINT using iterator_tag = std::forward_iterator_tag; // NOLINT Enumerator(Operon::Span nodes, std::size_t parent) : Iter{nodes, parent} {} auto operator++() -> Enumerator& { ++Iter; ++Index; return *this; } auto operator++(int) -> Enumerator { auto tmp{*this}; ++(*this); return tmp; } auto begin() const { return *this; } // NOLINT auto end() const { return Sentinel{}; } // NOLINT inline auto operator==(Enumerator const& other) { return Iter == other.Iter && Index == other.Index; } inline auto operator!=(Enumerator const& other) { return !(*this == other); } inline auto operator<(Enumerator const& other) { return std::tie(Index, Iter) < std::tie(other.Index, other.Iter); } inline auto operator==(Sentinel /*unused*/) const -> bool { return Iter.Done(); } inline auto operator*() const -> reference { return { Index, *Iter }; } inline auto operator*() -> reference { return { Index, *Iter }; } Iterator Iter; std::size_t Index{}; }; template struct SubtreeIterator { using value_type = std::conditional_t; // NOLINT using reference = std::conditional_t; // NOLINT using difference_type = std::ptrdiff_t; // NOLINT using iterator_tag = std::forward_iterator_tag; // NOLINT SubtreeIterator(Operon::Span nodes, std::size_t parent) : Nodes(nodes), Parent(parent), Child(parent-1) { EXPECT(parent > 0); } inline auto begin() const { return *this; } // NOLINT inline auto end() const { return Sentinel{}; } // NOLINT inline auto operator==(SubtreeIterator const& other) const { return Parent == other.Parent && Child == other.Child && Index == other.Index && Nodes.data() == other.Nodes.data(); } inline auto operator<(SubtreeIterator const& other) const { return Index < other.Index; } inline auto operator++() -> SubtreeIterator& { Child -= Nodes[Child].Length + 1; Index += 1; return *this; } inline auto operator++(int) -> SubtreeIterator { auto tmp{*this}; ++(*this); return tmp; } inline auto operator==(Sentinel /*unused*/) const -> bool { return Done(); } inline auto operator*() const -> reference { if constexpr (ReturnIndices) { return Child; } else { return Nodes[Child]; } } inline auto operator*() -> reference { if constexpr (ReturnIndices) { return Child; } else { return Nodes[Child]; } } [[nodiscard]] inline auto Done() const -> bool { return Index >= Nodes[Parent].Arity; } Operon::Span Nodes; std::size_t Parent; std::size_t Child; std::size_t Index{}; }; using IndexIterator = SubtreeIterator; using NodeIterator = SubtreeIterator; [[nodiscard]] inline auto Indices() const { return IndexIterator{nodes_, parent_}; } [[nodiscard]] inline auto EnumerateIndices() const { return Enumerator{nodes_, parent_}; } [[nodiscard]] inline auto Nodes() const { return NodeIterator{nodes_, parent_}; } [[nodiscard]] inline auto EnumerateNodes() const { return Enumerator{nodes_, parent_}; } private: Operon::Span nodes_; std::size_t parent_; }; } // namespace Operon #endif ================================================ FILE: include/operon/core/symbol_library.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_SYMBOL_LIBRARY_HPP #define OPERON_SYMBOL_LIBRARY_HPP #include "dispatch.hpp" #include "node.hpp" #include "pset.hpp" #include "operon/interpreter/dual.hpp" // symbol_library.hpp: scalar-lambda adapters for registering user-defined // symbols into a DispatchTable. // // The adapters follow the same conventions as built-in Func<>/Diff<> // specialisations: // - Primal callables read nodes[i].Value as the node weight and multiply. // - Derivative callables write ∂f/∂child into trace[child_index], evaluated // at the child's primal value, without the weight (the interpreter handles // weight accumulation during the reverse/forward trace). // // If no derivative is provided, one is generated automatically via // ceres::Jet (forward-mode AD with a single tangent component). // The lambda must accept a generic (auto) argument for this to work — // if it is typed to T specifically, provide an explicit derivative instead. namespace Operon { // Wrap a scalar unary lambda f(x) -> y into a batched Callable. // Reads the argument from primal column i-1 and writes weight * f(arg) to // column i, where weight = nodes[i].Value. template auto MakeUnaryCallable(F primal) -> typename DTable::template Callable { constexpr auto S = DTable::template BatchSize; return [fn = std::move(primal)]( Operon::Vector const& nodes, Backend::View data, size_t i, Operon::Range /*rg*/) { auto const w = static_cast(nodes[i].Value); auto* dst = Backend::Ptr(data, i); auto const* src = Backend::Ptr(data, i - 1); for (auto k = 0UL; k < S; ++k) { dst[k] = w * fn(src[k]); } }; } // Wrap a scalar binary lambda f(a, b) -> y into a batched Callable. // First child is at index j = i-1, second at k = j - nodes[j].Length - 1. // Writes weight * f(a, b) to column i, where weight = nodes[i].Value. template auto MakeBinaryCallable(F primal) -> typename DTable::template Callable { constexpr auto S = DTable::template BatchSize; return [fn = std::move(primal)]( Operon::Vector const& nodes, Backend::View data, size_t i, Operon::Range /*rg*/) { auto const j = i - 1; auto const k = j - nodes[j].Length - 1; auto const w = static_cast(nodes[i].Value); auto* dst = Backend::Ptr(data, i); auto const* lhs = Backend::Ptr(data, j); auto const* rhs = Backend::Ptr(data, k); for (auto s = 0UL; s < S; ++s) { dst[s] = w * fn(lhs[s], rhs[s]); } }; } // Wrap a scalar unary derivative lambda df(x) -> ∂f/∂x into a batched // CallableDiff. Reads the child's primal value from primal[j] and writes // the partial derivative into trace[j]. template auto MakeUnaryDiff(DF deriv) -> typename DTable::template CallableDiff { constexpr auto S = DTable::template BatchSize; return [fn = std::move(deriv)]( Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, int /*i*/, int j) { auto* res = Backend::Ptr(trace, j); auto const* pj = Backend::Ptr(primal, j); for (auto s = 0UL; s < S; ++s) { res[s] = fn(pj[s]); } }; } // Wrap two scalar partial-derivative lambdas (dfA, dfB) for a binary // function into a batched CallableDiff. // dfA(a, b) = ∂f/∂a (called when j is the first child, i-1) // dfB(a, b) = ∂f/∂b (called when j is the second child) // Reads both children's primal values and writes the appropriate partial // derivative into trace[j]. template auto MakeBinaryDiff(DFa derivA, DFb derivB) -> typename DTable::template CallableDiff { constexpr auto S = DTable::template BatchSize; return [fna = std::move(derivA), fnb = std::move(derivB)]( Operon::Vector const& nodes, Backend::View primal, Backend::View trace, int i, int j) { auto const first = i - 1; auto const second = first - static_cast(nodes[first].Length) - 1; auto* res = Backend::Ptr(trace, j); auto const* pa = Backend::Ptr(primal, first); auto const* pb = Backend::Ptr(primal, second); if (j == first) { for (auto s = 0UL; s < S; ++s) { res[s] = fna(pa[s], pb[s]); } } else { for (auto s = 0UL; s < S; ++s) { res[s] = fnb(pa[s], pb[s]); } } }; } // Generate a batched CallableDiff for a unary function using forward-mode // AD (ceres::Jet). The primal lambda must accept a generic argument // (i.e., use auto or be templated) so it can be instantiated with Jet. template auto MakeUnaryAutoDiff(F primal) -> typename DTable::template CallableDiff { constexpr auto S = DTable::template BatchSize; return [fn = std::move(primal)]( Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, int /*i*/, int j) { using Jet = ceres::Jet; auto* res = Backend::Ptr(trace, j); auto const* pj = Backend::Ptr(primal, j); for (auto s = 0UL; s < S; ++s) { res[s] = fn(Jet{pj[s], 0}).v[0]; // seed x with unit tangent } }; } // Generate a batched CallableDiff for a binary function using forward-mode // AD (ceres::Jet). Seeds one argument at a time; called once per child. template auto MakeBinaryAutoDiff(F primal) -> typename DTable::template CallableDiff { constexpr auto S = DTable::template BatchSize; return [fn = std::move(primal)]( Operon::Vector const& nodes, Backend::View primal, Backend::View trace, int i, int j) { using Jet = ceres::Jet; auto const first = i - 1; auto const second = first - static_cast(nodes[first].Length) - 1; auto* res = Backend::Ptr(trace, j); auto const* pa = Backend::Ptr(primal, first); auto const* pb = Backend::Ptr(primal, second); if (j == first) { for (auto s = 0UL; s < S; ++s) { res[s] = fn(Jet{pa[s], 0}, Jet{pb[s]}).v[0]; // ∂f/∂a } } else { for (auto s = 0UL; s < S; ++s) { res[s] = fn(Jet{pa[s]}, Jet{pb[s], 0}).v[0]; // ∂f/∂b } } }; } // Register a unary function from scalar lambdas. // primal: f(x) -> y (required; must use auto/generic argument for // auto-diff to work) // deriv: df(x) -> ∂f/∂x (optional; if omitted, Jet auto-diff is used) template void RegisterUnary(DTable& dt, Operon::Hash hash, F primal, DF deriv = {}) { if constexpr (std::is_same_v, Dispatch::Noop>) { // auto-diff: primal is copied into both the callable and the diff adapter dt.template RegisterFunction(hash, MakeUnaryCallable(primal), MakeUnaryAutoDiff(std::move(primal))); } else { dt.template RegisterFunction(hash, MakeUnaryCallable(std::move(primal)), MakeUnaryDiff(std::move(deriv))); } } // Register a binary function from scalar lambdas. // primal: f(a, b) -> y (required; must use auto/generic arguments) // derivA: df_da(a,b) -> ∂f/∂a (optional; if omitted, Jet is used) // derivB: df_db(a,b) -> ∂f/∂b (optional; if omitted, Jet is used) template void RegisterBinary(DTable& dt, Operon::Hash hash, F primal, DFa derivA = {}, DFb derivB = {}) { constexpr auto aIsNoop = std::is_same_v, Dispatch::Noop>; constexpr auto bIsNoop = std::is_same_v, Dispatch::Noop>; static_assert(aIsNoop == bIsNoop, "RegisterBinary: provide both partial derivatives or neither; " "supplying only one is not supported."); if constexpr (aIsNoop) { dt.template RegisterFunction(hash, MakeBinaryCallable(primal), MakeBinaryAutoDiff(std::move(primal))); } else { dt.template RegisterFunction(hash, MakeBinaryCallable(std::move(primal)), MakeBinaryDiff(std::move(derivA), std::move(derivB))); } } // Bundles the metadata needed to register a user-defined function symbol. struct FunctionInfo { Operon::Hash Hash; std::string Name; std::string Desc; uint16_t Arity; size_t Frequency{1}; }; // Register a unary function in the dispatch table, PrimitiveSet, and name // registry in a single call. An optional explicit derivative may be supplied; // if omitted, Jet auto-diff is used. template void RegisterUnaryFunction(DTable& dt, PrimitiveSet& pset, FunctionInfo const& info, F primal, DF deriv = {}) { RegisterUnary(dt, info.Hash, std::move(primal), std::move(deriv)); pset.AddFunction(info.Hash, info.Arity, info.Frequency); if (!info.Name.empty()) { Node::RegisterName(info.Hash, info.Name, info.Desc); } } // Register a binary function in the dispatch table, PrimitiveSet, and name // registry in a single call. Optional explicit partial derivatives may be // supplied; if omitted, Jet auto-diff is used. template void RegisterBinaryFunction(DTable& dt, PrimitiveSet& pset, FunctionInfo const& info, F primal, DFa derivA = {}, DFb derivB = {}) { RegisterBinary(dt, info.Hash, std::move(primal), std::move(derivA), std::move(derivB)); pset.AddFunction(info.Hash, info.Arity, info.Frequency); if (!info.Name.empty()) { Node::RegisterName(info.Hash, info.Name, info.Desc); } } } // namespace Operon #endif ================================================ FILE: include/operon/core/tree.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef TREE_HPP #define TREE_HPP #include #include #include #include #include "contracts.hpp" #include "subtree.hpp" #include "operon/operon_export.hpp" namespace Operon { class OPERON_EXPORT Tree { // NOLINT public: Tree() = default; Tree(std::initializer_list list) : nodes_(list) { } explicit Tree(Operon::Vector vec) : nodes_(std::move(vec)) { } Tree(Tree const& rhs) // NOLINT : nodes_(rhs.nodes_) { } Tree(Tree&& rhs) noexcept : nodes_(std::move(rhs.nodes_)) { } ~Tree() = default; auto operator=(Tree rhs) -> Tree& { Swap(*this, rhs); return *this; } // no need for move assignment operator because we use the copy-swap idiom friend void Swap(Tree& lhs, Tree& rhs) noexcept { std::swap(lhs.nodes_, rhs.nodes_); } auto UpdateNodes() -> Tree&; auto Sort() -> Tree&; auto Reduce() -> Tree&; auto Simplify() -> Tree&; // performs hashing in a manner similar to Merkle trees // aggregating hash values from the leafs towards the root node [[nodiscard]] auto Hash(Operon::HashMode mode) const -> Tree const&; // splice a subtree rooted at node with index i as a new tree [[nodiscard]] auto Splice(size_t i) const -> Tree { EXPECT(i < Length()); auto const& n = nodes_[i]; auto it = nodes_.begin() + static_cast(i); return Tree({it - n.Length, it + 1}).UpdateNodes(); } void SetEnabled(size_t i, bool enabled) { for (auto j = i - nodes_[i].Length; j <= i; ++j) { nodes_[j].IsEnabled = enabled; } } auto Nodes() & -> Operon::Vector& { return nodes_; } auto Nodes() && -> Operon::Vector&& { return std::move(nodes_); } [[nodiscard]] auto Nodes() const& -> Operon::Vector const& { return nodes_; } [[nodiscard]] auto CoefficientsCount() const { return std::count_if(nodes_.cbegin(), nodes_.cend(), [](auto const& s) { return s.Optimize; }); } void SetCoefficients(Operon::Span coefficients); [[nodiscard]] auto GetCoefficients() const -> std::vector; [[nodiscard]] auto ApplyCoefficients(Operon::Span coefficients) const { auto tree{ *this }; tree.SetCoefficients(coefficients); return tree; } auto operator[](size_t i) noexcept -> Node& { return nodes_[i]; } auto operator[](size_t i) const noexcept -> Node const& { return nodes_[i]; } [[nodiscard]] auto Length() const noexcept -> size_t { return nodes_.size(); } [[nodiscard]] auto AdjustedLength() const noexcept -> size_t { auto length = [](auto const& n) { if (n.IsConstant()) { return 1; } return n.Value == Operon::Scalar{1} ? 1 : 3; }; return std::transform_reduce(nodes_.begin(), nodes_.end(), 0UL, std::plus{}, length); } [[nodiscard]] auto VisitationLength() const noexcept -> size_t; [[nodiscard]] auto Depth() const noexcept -> size_t; [[nodiscard]] auto Empty() const noexcept -> bool { return nodes_.empty(); } [[nodiscard]] auto HashValue() const -> Operon::Hash { return nodes_.empty() ? 0 : nodes_.back().CalculatedHashValue; } [[nodiscard]] auto Children(size_t i) { return Subtree{nodes_, i}.Nodes(); } [[nodiscard]] auto Children(size_t i) const { return Subtree{nodes_, i}.Nodes(); } [[nodiscard]] auto Indices(size_t i) const { return Subtree{nodes_, i}.Indices(); } // convenience methods static auto Indices(auto const& nodes, auto i) { return Subtree{ nodes, static_cast(i) }.Indices(); } static auto EnumerateIndices(auto const& nodes, auto i) { return Subtree{ nodes, static_cast(i) }.EnumerateIndices(); } static auto Nodes(auto const& nodes, auto i) { return Subtree{ nodes, static_cast(i) }.Nodes(); } static auto Nodes(auto& nodes, auto i) { return Subtree { nodes, static_cast(i) }.Nodes(); } static auto EnumerateNodes(auto const& nodes, auto i) { return Subtree{ nodes, static_cast(i) }.EnumerateNodes(); } static auto EnumerateNodes(auto& nodes, auto i) { return Subtree{ nodes, static_cast(i) }.EnumerateNodes(); } private: Operon::Vector nodes_; }; } // namespace Operon #endif // TREE_H ================================================ FILE: include/operon/core/types.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_TYPES_HPP #define OPERON_TYPES_HPP #include #include #include #include #include #include "constants.hpp" #include "operon/mdspan/mdspan.hpp" #include "aligned_allocator.hpp" namespace Operon { using Hash = uint64_t; constexpr HashFunction HashFunc = HashFunction::XXHash; using RandomGenerator = fluky::xoshiro256ss; template > using Vector = std::vector; template using Span = std::span; template> using MDSpan = std::mdspan; template> using MDArray = std::experimental::mdarray; template , class KeyEqual = std::equal_to, class AllocatorOrContainer = std::allocator>, class Bucket = ankerl::unordered_dense::bucket_type::standard, class BucketContainer = ankerl::unordered_dense::detail::default_container_t> using Map = ankerl::unordered_dense::detail::table; template , class KeyEqual = std::equal_to, class AllocatorOrContainer = std::allocator>, class Bucket = ankerl::unordered_dense::bucket_type::standard, class BucketContainer = ankerl::unordered_dense::detail::default_container_t> using SegmentedMap = ankerl::unordered_dense::detail::table; template , class KeyEqual = std::equal_to, class AllocatorOrContainer = std::allocator, class Bucket = ankerl::unordered_dense::bucket_type::standard, class BucketContainer = ankerl::unordered_dense::detail::default_container_t> using Set = ankerl::unordered_dense::detail::table; template , class KeyEqual = std::equal_to, class AllocatorOrContainer = std::allocator, class Bucket = ankerl::unordered_dense::bucket_type::standard, class BucketContainer = ankerl::unordered_dense::detail::default_container_t> using SegmentedSet = ankerl::unordered_dense::detail::table; template using Seq = std::integer_sequence; #if defined(USE_SINGLE_PRECISION) using Scalar = float; #else using Scalar = double; #endif } // namespace Operon #endif ================================================ FILE: include/operon/core/variable.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_VARIABLE_HPP #define OPERON_VARIABLE_HPP #include "types.hpp" namespace Operon { struct Variable { std::string Name; Operon::Hash Hash{0}; int64_t Index{0}; constexpr auto operator==(Variable const& rhs) const noexcept -> bool { return std::tie(Name, Hash, Index) == std::tie(rhs.Name, rhs.Hash, rhs.Index); } constexpr auto operator!=(Variable const& rhs) const noexcept -> bool { return !(*this == rhs); } }; } // namespace Operon #endif ================================================ FILE: include/operon/core/version.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_VERSION_HPP #define OPERON_VERSION_HPP #include #include #include #include "operon/operon_export.hpp" namespace Operon { // NOLINTBEGIN auto OPERON_EXPORT Version() -> std::string; } // namespace Operon #endif ================================================ FILE: include/operon/error_metrics/correlation_coefficient.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_METRICS_CORRELATION_COEFFICIENT_HPP #define OPERON_METRICS_CORRELATION_COEFFICIENT_HPP #include #include #include #include "operon/core/types.hpp" namespace Operon { template inline auto CorrelationCoefficient(InputIt1 begin1, InputIt1 end1, InputIt2 begin2) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); return vstat::bivariate::accumulate(begin1, end1, begin2).correlation; } template inline auto CorrelationCoefficient(InputIt1 begin1, InputIt1 end1, InputIt2 begin2, InputIt3 begin3) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); return vstat::bivariate::accumulate(begin1, end1, begin2, begin3).correlation; } template inline auto CorrelationCoefficient(Operon::Span x, Operon::Span y) -> double { static_assert(std::is_arithmetic_v, "T must be an arithmetic type."); EXPECT(x.size() == y.size()); EXPECT(!x.empty()); return vstat::bivariate::accumulate(x.begin(), x.end(), y.begin()).correlation; } template inline auto CorrelationCoefficient(Operon::Span x, Operon::Span y, Operon::Span w) -> double { static_assert(std::is_arithmetic_v, "T must be an arithmetic type."); EXPECT(x.size() == y.size()); EXPECT(!x.empty()); return vstat::bivariate::accumulate(x.begin(), x.end(), y.begin(), w.begin()).correlation; } template inline auto SquaredCorrelation(InputIt1 begin1, InputIt1 end1, InputIt2 begin2) noexcept -> double { auto r = CorrelationCoefficient(begin1, end1, begin2); return r * r; } template inline auto SquaredCorrelation(InputIt1 begin1, InputIt1 end1, InputIt2 begin2, InputIt3 begin3) noexcept -> double { auto r = CorrelationCoefficient(begin1, end1, begin2, begin3); return r * r; } template inline auto SquaredCorrelation(Operon::Span x, Operon::Span y) -> double { auto r = CorrelationCoefficient(x, y); return r * r; } template inline auto SquaredCorrelation(Operon::Span x, Operon::Span y, Operon::Span w) -> double { auto r = CorrelationCoefficient(x, y, w); return r * r; } } // namespace Operon #endif ================================================ FILE: include/operon/error_metrics/error_metrics.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_ERROR_METRICS_HPP #define OPERON_ERROR_METRICS_HPP #include "operon/error_metrics/correlation_coefficient.hpp" #include "operon/error_metrics/mean_absolute_error.hpp" #include "operon/error_metrics/mean_squared_error.hpp" #include "operon/error_metrics/normalized_mean_squared_error.hpp" #include "operon/error_metrics/r2_score.hpp" #include "operon/error_metrics/root_mean_squared_error.hpp" #include "operon/error_metrics/sum_of_squared_errors.hpp" #endif ================================================ FILE: include/operon/error_metrics/mean_absolute_error.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_METRICS_MEAN_ABSOLUTE_ERROR_HPP #define OPERON_METRICS_MEAN_ABSOLUTE_ERROR_HPP #include #include #include #include "operon/core/types.hpp" namespace Operon { template inline auto MeanAbsoluteError(InputIt1 begin1, InputIt1 end1, InputIt2 begin2) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); return vstat::univariate::accumulate(begin1, end1, begin2, [](auto a, auto b) { return std::abs(a-b); }).mean; } template inline auto MeanAbsoluteError(InputIt1 begin1, InputIt1 end1, InputIt2 begin2, InputIt3 begin3) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); return vstat::univariate::accumulate(begin1, end1, begin2, begin3, [](auto a, auto b) { return std::abs(a-b); }).mean; } template inline auto MeanAbsoluteError(Operon::Span x, Operon::Span y) -> double { static_assert(std::is_arithmetic_v, "T must be an arithmetic type."); EXPECT(x.size() == y.size()); EXPECT(!x.empty()); return vstat::univariate::accumulate(x.begin(), x.end(), y.begin(), [](auto a, auto b) { return std::abs(a-b); }).mean; } template inline auto MeanAbsoluteError(Operon::Span x, Operon::Span y, Operon::Span w) -> double { static_assert(std::is_arithmetic_v, "T must be an arithmetic type."); EXPECT(x.size() == y.size()); EXPECT(!x.empty()); return vstat::univariate::accumulate(x.begin(), x.end(), y.begin(), w.begin(), [](auto a, auto b) { return std::abs(a-b); }).mean; } } // namespace Operon #endif ================================================ FILE: include/operon/error_metrics/mean_squared_error.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_METRICS_MEAN_SQUARED_ERROR_HPP #define OPERON_METRICS_MEAN_SQUARED_ERROR_HPP #include #include #include #include "operon/core/types.hpp" namespace Operon { template inline auto MeanSquaredError(InputIt1 begin1, InputIt1 end1, InputIt2 begin2) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); auto sqres = [](auto a, auto b){ auto e = a-b; return e*e; }; return vstat::univariate::accumulate(begin1, end1, begin2, sqres).mean; } template inline auto MeanSquaredError(InputIt1 begin1, InputIt1 end1, InputIt2 begin2, InputIt3 begin3) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); auto sqres = [](auto a, auto b){ auto e = a-b; return e*e; }; return vstat::univariate::accumulate(begin1, end1, begin2, begin3, sqres).mean; } template inline auto MeanSquaredError(Operon::Span x, Operon::Span y) noexcept -> double { static_assert(std::is_arithmetic_v, "T must be an arithmetic type."); EXPECT(x.size() == y.size()); EXPECT(!x.empty()); return MeanSquaredError(x.begin(), x.end(), y.begin()); } template inline auto MeanSquaredError(Operon::Span x, Operon::Span y, Operon::Span w) noexcept -> double { static_assert(std::is_arithmetic_v, "T must be an arithmetic type."); EXPECT(x.size() == y.size()); EXPECT(!x.empty()); return MeanSquaredError(x.begin(), x.end(), y.begin(), w.begin()); } } // namespace Operon #endif ================================================ FILE: include/operon/error_metrics/normalized_mean_squared_error.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_METRICS_NORMALIZED_MEAN_SQUARED_ERROR_HPP #define OPERON_METRICS_NORMALIZED_MEAN_SQUARED_ERROR_HPP #include #include #include #include "operon/core/types.hpp" #include "mean_squared_error.hpp" namespace Operon { template inline auto NormalizedMeanSquaredError(InputIt1 begin1, InputIt1 end1, InputIt2 begin2) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); auto varY = vstat::univariate::accumulate(begin2, begin2 + std::distance(begin1, end1)).variance; if (varY > 0) { return MeanSquaredError(begin1, end1, begin2) / varY; } return 0.0; } template inline auto NormalizedMeanSquaredError(InputIt1 begin1, InputIt1 end1, InputIt2 begin2, InputIt3 begin3) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); auto varY = vstat::univariate::accumulate(begin2, begin2 + std::distance(begin1, end1), begin3).variance; if (varY > 0) { return MeanSquaredError(begin1, end1, begin2, begin3) / varY; } return 0.0; } template inline auto NormalizedMeanSquaredError(Operon::Span x, Operon::Span y) noexcept -> double { return NormalizedMeanSquaredError(x.begin(), x.end(), y.begin()); } template inline auto NormalizedMeanSquaredError(Operon::Span x, Operon::Span y, Operon::Span w) noexcept -> double { return NormalizedMeanSquaredError(x.begin(), x.end(), y.begin(), w.begin()); } } // namespace Operon #endif ================================================ FILE: include/operon/error_metrics/r2_score.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_METRICS_R2_SCORE_HPP #define OPERON_METRICS_R2_SCORE_HPP #include #include #include #include "operon/core/types.hpp" namespace Operon { template inline auto R2Score(InputIt1 begin1, InputIt1 end1, InputIt2 begin2) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); using vstat::univariate::accumulate; auto sqres = [](auto a, auto b) { auto e=a-b; return e*e; }; auto const ssr = accumulate(begin1, end1, begin2, sqres).sum; auto const sst = accumulate(begin2, begin2 + std::distance(begin1, end1)).ssr; if (sst < std::numeric_limits::epsilon()) { return std::numeric_limits::lowest(); } return 1.0 - ssr / sst; } template inline auto R2Score(InputIt1 begin1, InputIt1 end1, InputIt2 begin2, InputIt3 begin3) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); using vstat::univariate::accumulate; auto sqres = [](auto a, auto b) { auto e=a-b; return e*e; }; auto const ssr = accumulate(begin1, end1, begin2, begin3, sqres).sum; auto end2 = begin2 + std::distance(begin1, end1); auto const m = accumulate(begin2, end2).mean; auto const sst = accumulate(begin2, end2, begin3, [&](auto v) { return sqres(v, m); }).sum; if (sst < std::numeric_limits::epsilon()) { return std::numeric_limits::lowest(); } return 1.0 - ssr / sst; } template inline auto R2Score(Operon::Span x, Operon::Span y) noexcept -> double { EXPECT(y.size() == x.size()); return R2Score(x.begin(), x.end(), y.begin()); } template inline auto R2Score(Operon::Span x, Operon::Span y, Operon::Span w) noexcept -> double { EXPECT(y.size() == x.size()); return R2Score(x.begin(), x.end(), y.begin(), w.begin()); } } // namespace Operon #endif ================================================ FILE: include/operon/error_metrics/root_mean_squared_error.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_METRICS_ROOT_MEAN_SQUARED_ERROR_HPP #define OPERON_METRICS_ROOT_MEAN_SQUARED_ERROR_HPP #include #include #include #include "operon/core/types.hpp" #include "mean_squared_error.hpp" namespace Operon { template inline auto RootMeanSquaredError(InputIt1 begin1, InputIt1 end1, InputIt2 begin2) noexcept -> double { return std::sqrt(MeanSquaredError(begin1, end1, begin2)); } template inline auto RootMeanSquaredError(InputIt1 begin1, InputIt1 end1, InputIt2 begin2, InputIt3 begin3) noexcept -> double { return std::sqrt(MeanSquaredError(begin1, end1, begin2, begin3)); } template inline auto RootMeanSquaredError(Operon::Span x, Operon::Span y) noexcept -> double { return std::sqrt(MeanSquaredError(x, y)); } template inline auto RootMeanSquaredError(Operon::Span x, Operon::Span y, Operon::Span w) noexcept -> double { return std::sqrt(MeanSquaredError(x, y, w)); } } // namespace Operon #endif ================================================ FILE: include/operon/error_metrics/sum_of_squared_errors.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_METRICS_SUM_OF_SQUARED_RESIDUALS_HPP #define OPERON_METRICS_SUM_OF_SQUARED_RESIDUALS_HPP #include #include #include #include "operon/core/types.hpp" namespace Operon { template inline auto SumOfSquaredErrors(InputIt1 begin1, InputIt1 end1, InputIt2 begin2) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); auto sqres = [](auto a, auto b){ auto e = a-b; return e*e; }; return vstat::univariate::accumulate(begin1, end1, begin2, sqres).sum; } template inline auto SumOfSquaredErrors(InputIt1 begin1, InputIt1 end1, InputIt2 begin2, InputIt3 begin3) noexcept -> double { using V1 = typename std::iterator_traits::value_type; using V2 = typename std::iterator_traits::value_type; static_assert(std::is_arithmetic_v, "InputIt1: value_type must be arithmetic."); static_assert(std::is_arithmetic_v, "InputIt2: value_type must be arithmetic."); static_assert(std::is_same_v, "The types must be the same"); auto sqres = [](auto a, auto b){ auto e = a-b; return e*e; }; return vstat::univariate::accumulate(begin1, end1, begin2, begin3, sqres).sum; } template inline auto SumOfSquaredErrors(Operon::Span x, Operon::Span y) noexcept -> double { static_assert(std::is_arithmetic_v, "T must be an arithmetic type."); EXPECT(x.size() == y.size()); EXPECT(!x.empty()); return SumOfSquaredErrors(x.begin(), x.end(), y.begin()); } template inline auto SumOfSquaredErrors(Operon::Span x, Operon::Span y, Operon::Span w) noexcept -> double { static_assert(std::is_arithmetic_v, "T must be an arithmetic type."); EXPECT(x.size() == y.size()); EXPECT(!x.empty()); return SumOfSquaredErrors(x.begin(), x.end(), y.begin(), w.begin()); } } // namespace Operon #endif ================================================ FILE: include/operon/formatter/formatter.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_FORMAT_HPP #define OPERON_FORMAT_HPP #include #include "operon/core/tree.hpp" namespace Operon { class Dataset; class OPERON_EXPORT TreeFormatter { static auto FormatNode(Tree const& tree, Operon::Map variableNames, size_t i, std::string& current, std::string indent, bool isLast, bool initialMarker, int decimalPrecision) -> void; public: static auto Format(Tree const& tree, Dataset const& dataset, int decimalPrecision = 2) -> std::string; static auto Format(Tree const& tree, Operon::Map const& variableNames, int decimalPrecision = 2) -> std::string; }; class OPERON_EXPORT InfixFormatter { static auto FormatNode(Tree const& tree, Operon::Map const& variableNames, size_t i, std::string& current, int decimalPrecision) -> void; public: static auto Format(Tree const& tree, Dataset const& dataset, int decimalPrecision = 2) -> std::string; static auto Format(Tree const& tree, Operon::Map const& variableNames, int decimalPrecision = 2) -> std::string; }; class OPERON_EXPORT PostfixFormatter { static auto FormatNode(Tree const& tree, Operon::Map const& variableNames, size_t i, std::string& current, int decimalPrecision) -> void; public: static auto Format(Tree const& tree, Dataset const& dataset, int decimalPrecision = 2) -> std::string; static auto Format(Tree const& tree, Operon::Map const& variableNames, int decimalPrecision = 2) -> std::string; }; struct OPERON_EXPORT DotFormatter { static auto Format(Tree const& tree, Dataset const& dataset, int decimalPrecision = 2) -> std::string; static auto Format(Tree const& tree, Operon::Map const& variableNames, int decimalPrecision = 2) -> std::string; }; } // namespace Operon #endif ================================================ FILE: include/operon/hash/hash.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_HASH_HPP #define OPERON_HASH_HPP #include #include #include #include "operon/core/constants.hpp" #include "operon/operon_export.hpp" namespace Operon { struct OPERON_EXPORT Hasher { using is_transparent = void; // enable transparent lookup NOLINT auto operator()(uint8_t const* key, size_t len) const noexcept -> uint64_t; auto operator()(std::string_view key) const noexcept -> uint64_t; auto operator()(std::string const& key) const noexcept -> uint64_t; auto operator()(char const* key) const noexcept -> uint64_t; }; } // namespace Operon #endif ================================================ FILE: include/operon/hash/metrohash64.hpp ================================================ // SPDX-License-Identifier: Apache-2.0 // SPDX-FileCopyrightText: Copyright 2015-2018 J. Andrew Rogers #ifndef METROHASH_METROHASH_64_H #define METROHASH_METROHASH_64_H #include namespace Operon::HashUtil { // NOLINTBEGIN(*) class MetroHash64 { public: static const uint32_t bits = 64; // NOLINT // Constructor initializes the same as Initialize() explicit MetroHash64(uint64_t seed=0); // Initializes internal state for new hash with optional seed void Initialize(uint64_t seed=0); // Update the hash state with a string of bytes. If the length // is sufficiently long, the implementation switches to a bulk // hashing algorithm directly on the argument buffer for speed. void Update(const uint8_t* buffer, uint64_t length); // Constructs the final hash and writes it to the argument buffer. // After a hash is finalized, this instance must be Initialized()-ed // again or the behavior of Update() and Finalize() is undefined. void Finalize(uint8_t* hash); // A non-incremental function implementation. This can be significantly // faster than the incremental implementation for some usage patterns. static void Hash(const uint8_t * buffer, uint64_t length, uint8_t* hash, uint64_t seed=0); private: static const uint64_t k0 = 0xD6D018F5;// NOLINT static const uint64_t k1 = 0xA2AA033B;// NOLINT static const uint64_t k2 = 0x62992FC1;// NOLINT static const uint64_t k3 = 0x30BC5B29;// NOLINT // NOLINT struct { uint64_t v[4]; } state;// NOLINT struct { uint8_t b[32]; } input;// NOLINT uint64_t bytes;// NOLINT uint64_t vseed;// NOLINT }; // NOLINTEND(*) } // namespace Operon::HashUtil #endif // #ifndef METROHASH_METROHASH_64_H ================================================ FILE: include/operon/hash/zobrist.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_HASH_ZOBRIST_HPP #define OPERON_HASH_ZOBRIST_HPP #include #include #include "operon/core/node.hpp" #include "operon/core/tree.hpp" #include "operon/core/types.hpp" #include "operon/operon_export.hpp" namespace Operon { // Position-aware Zobrist hash for GP trees, plus a transposition table that // caches fitness vectors keyed by structural hash. The hash captures tree // topology and variable identity but not coefficient values — by design, so // that a cached fitness (computed after local search) can be reused for any // structurally identical tree regardless of its current coefficients. // // Ownership: the caller constructs one Zobrist per experiment (or per run) and // passes a raw pointer into GeneticAlgorithmConfig::Cache. The algorithm // borrows the pointer; the caller is responsible for lifetime. class OPERON_EXPORT Zobrist { using Value = Operon::Vector; using Extents = std::extents(NodeTypes::Count), std::dynamic_extent>; using Table = Operon::MDArray; Table table_; struct TranspositionTable; std::unique_ptr tt_; mutable std::atomic hits_{0}; public: Zobrist(Operon::RandomGenerator& rng, int maxLength); ~Zobrist(); Zobrist(Zobrist const&) = delete; Zobrist(Zobrist&&) = delete; auto operator=(Zobrist const&) -> Zobrist& = delete; auto operator=(Zobrist&&) -> Zobrist& = delete; [[nodiscard]] auto Rows() const { return table_.extent(0); } [[nodiscard]] auto Cols() const { return table_.extent(1); } [[nodiscard]] auto ComputeHash(Operon::Node const& n, int pos) const -> Operon::Hash { auto const i = NodeTypes::GetIndex(n.Type); auto h = table_(i, pos); if (n.IsVariable()) { h ^= n.HashValue; } return h; } [[nodiscard]] auto ComputeHash(Operon::Tree const& tree) const -> Operon::Hash { EXPECT(std::ssize(tree.Nodes()) <= Cols()); Operon::Hash h{}; auto const& nodes = tree.Nodes(); for (auto i = 0; i < std::ssize(nodes); ++i) { h ^= ComputeHash(nodes[i], i); } return h; } // Returns true and fills `val` if the hash is found; thread-safe. [[nodiscard]] auto TryGet(Operon::Hash hash, Value& val) const -> bool; // Inserts or increments the visit counter; thread-safe. auto Insert(Operon::Hash hash, Value const& val) -> void; // Clears the transposition table and resets the hit counter. // NOT safe to call concurrently with TryGet or Insert — call only after // the algorithm has fully stopped (e.g. after GeneticAlgorithm::Run returns). auto Clear() -> void; [[nodiscard]] auto Hits() const -> std::size_t { return hits_.load(); } [[nodiscard]] auto Size() const -> std::size_t; }; } // namespace Operon #endif ================================================ FILE: include/operon/interpreter/backend/arma/derivatives.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_ARMA_DERIVATIVES_HPP #define OPERON_BACKEND_ARMA_DERIVATIVES_HPP #include "operon/core/node.hpp" #include "functions.hpp" namespace Operon::Backend { namespace detail { template inline auto IsNaN(T value) { return std::isnan(value); } template struct FComp { auto operator()(auto x, auto y) const { using T = std::common_type_t; if ((IsNaN(x) && IsNaN(y)) || (x == y)) { return std::numeric_limits::quiet_NaN(); } if (IsNaN(x)) { return T{0}; } if (IsNaN(y)) { return T{1}; } return static_cast(Compare{}(T{x}, T{y})); } }; } // namespace detail template auto Add(std::vector const& /*nodes*/, Backend::View /*primal*/, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j).fill(T{1}); } template auto Mul(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = Col(primal, i) / Col(primal, j); } template auto Sub(std::vector const& nodes, Backend::View /*primal*/, Backend::View trace, std::integral auto i, std::integral auto j) { auto v = (nodes[i].Arity == 1 || j < i-1) ? T{-1} : T{+1}; Col(trace, j).fill(v); } template auto Div(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto const& n = nodes[i]; if (n.Arity == 1) { Col(trace, j) = -T{1} / (arma::square(Col(primal, j))); } else { Col(trace, j) = (j == i-1 ? T{1} : T{-1}) * Col(primal, i) / Col(primal, j); } } template auto Aq(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { Col(trace, j) = Col(primal, i) / Col(primal, j); } else { Col(trace, j) = -Col(primal, j) % arma::pow(Col(primal, i), T{3}) / arma::square(Col(primal, i-1)); } } template auto Pow(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { auto const k = j - (nodes[j].Length + 1); Col(trace, j) = Col(primal, i) % Col(primal, k) / Col(primal, j); } else { auto const k = i-1; Col(trace, j) = Col(primal, i) % arma::log(Col(primal, k)); } } template auto Powabs(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { auto const k = j - (nodes[j].Length + 1); Col(trace, j) = Col(primal, i) % Col(primal, k) % arma::sign(Col(primal, j)) / arma::abs(Col(primal, j)); } else { auto const k = i-1; Col(trace, j) = Col(primal, i) % arma::log(arma::abs(Col(primal, k))); } } template auto Min(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto const* jp = Ptr(primal, j); auto const* kp = Ptr(primal, k); std::transform(jp, jp+S, kp, Ptr(trace, j), detail::FComp>{}); } template auto Max(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto const* jp = Ptr(primal, j); auto const* kp = Ptr(primal, k); std::transform(jp, jp+S, kp, Ptr(trace, j), detail::FComp>{}); } template auto Square(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{2} * Col(primal, j); } template auto Abs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = arma::sign(Col(primal, j)); } template auto Ceil(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = arma::ceil(Col(primal, j)); } template auto Floor(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = arma::floor(Col(primal, j)); } template auto Exp(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = Col(primal, i); } template auto Log(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (Col(primal, j)); } template auto Log1p(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (T{1} + Col(primal, j)); } template auto Logabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = arma::sign(Col(primal, j)) / arma::abs(Col(primal, j)); } template auto Sin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = arma::cos(Col(primal, j)); } template auto Cos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = -arma::sin(Col(primal, j)); } template auto Tan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} + arma::square(arma::tan(Col(primal, j))); } template auto Sinh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = arma::cosh(Col(primal, j)); } template auto Cosh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = arma::sinh(Col(primal, j)); } template auto Tanh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} - arma::square(arma::tanh(Col(primal, j))); } template auto Asin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (arma::sqrt(T{1} - arma::square(Col(primal, j)))); } template auto Acos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = -T{1} / (arma::sqrt(((T{1} - arma::square(Col(primal, j)))))); } template auto Atan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (T{1} + arma::square(Col(primal, j))); } template auto Sqrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = T{1} / (T{2} * Col(primal, i)); } template auto Sqrtabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = arma::sign(Col(primal, j)) / (T{2} * Col(primal, i)); } template auto Cbrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = T{1} / (T{3} * arma::square(Col(primal, i))); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/arma/functions.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_ARMA_FUNCTIONS_HPP #define OPERON_BACKEND_ARMA_FUNCTIONS_HPP #define ARMA_DONT_USE_WRAPPER #include #include "operon/core/dispatch.hpp" namespace Operon::Backend { template auto Map(T* ptr) { return arma::Mat(ptr, S, 1, /*copy_aux_mem =*/false, /*strict=*/true); } template auto Map(T const* ptr) { using U = std::remove_const_t; return arma::Mat(const_cast(ptr), S, 1, /*copy_aux_mem =*/false, /*strict=*/true); } template auto Col(Backend::View view, std::integral auto col) { return Map(view.data_handle() + col * S); } template auto Col(Backend::View view, std::integral auto col) { return Map(view.data_handle() + col * S); } // utility template auto Fill(T* res, T weight, T value) { Map(res).fill(value); } // n-ary functions template auto Add(T* res, T weight, auto const*... args) { Map(res) = weight * (Map(args) + ...); } template auto Mul(T* res, T weight, auto const*... args) { Map(res) = weight * (Map(args) % ...); } template auto Sub(T* res, T weight, auto* first, auto const*... rest) { static_assert(sizeof...(rest) > 0); Map(res) = weight * Map(first) - (Map(rest) + ...); } template auto Div(T* res, T weight, auto const* first, auto const*... rest) { static_assert(sizeof...(rest) > 0); Map(res) = weight * Map(first) / (Map(rest) * ...); } template auto Min(T* res, T weight, auto const* first, auto const*... args) { static_assert(sizeof...(args) > 0); for (auto i = 0UL; i < S; ++i) { res[i] = weight * std::min({first[i], args[i]...}); } } template auto Max(T* res, T weight, auto* first, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = weight * std::max({first[i], args[i]...}); } } // binary functions template auto Aq(T* res, T weight, T const* a, T const* b) { Map(res) = weight * Map(a) / arma::sqrt((T{1} + arma::square(Map(b)))); } template auto Pow(T* res, T weight, T const* a, T const* b) { Map(res) = weight * arma::pow(Map(a), Map(b)); } template auto Powabs(T* res, T weight, T const* a, T const* b) { Map(res) = weight * arma::pow(arma::abs(Map(a)), Map(b)); } // unary functions template auto Cpy(T* res, T weight, T const* arg) { Map(res) = weight * Map(arg); } template auto Neg(T* res, T weight, T const* arg) { Map(res) = weight * -Map(arg); } template auto Inv(T* res, T weight, T const* arg) { Map(res) = weight / Map(arg); } template auto Abs(T* res, T weight, T const* arg) { Map(res) = weight * arma::abs(Map(arg)); } template auto Ceil(T* res, T weight, T const* arg) { Map(res) = weight * arma::ceil(Map(arg)); } template auto Floor(T* res, T weight, T const* arg) { Map(res) = weight * arma::floor(Map(arg)); } template auto Square(T* res, T weight, T const* arg) { Map(res) = weight * arma::square(Map(arg)); } template auto Exp(T* res, T weight, T const* arg) { Map(res) = weight * arma::exp(Map(arg)); } template auto Log(T* res, T weight, T const* arg) { Map(res) = weight * arma::log(Map(arg)); } template auto Log1p(T* res, T weight, T const* arg) { Map(res) = weight * arma::log1p(Map(arg)); } template auto Logabs(T* res, T weight, T const* arg) { Map(res) = weight * arma::log(arma::abs(Map(arg))); } template auto Sin(T* res, T weight, T const* arg) { Map(res) = weight * arma::sin(Map(arg)); } template auto Cos(T* res, T weight, T const* arg) { Map(res) = weight * arma::cos(Map(arg)); } template auto Tan(T* res, T weight, T const* arg) { Map(res) = weight * arma::tan(Map(arg)); } template auto Asin(T* res, T weight, T const* arg) { Map(res) = weight * arma::asin(Map(arg)); } template auto Acos(T* res, T weight, T const* arg) { Map(res) = weight * arma::acos(Map(arg)); } template auto Atan(T* res, T weight, T const* arg) { Map(res) = weight * arma::atan(Map(arg)); } template auto Sinh(T* res, T weight, T const* arg) { Map(res) = weight * arma::sinh(Map(arg)); } template auto Cosh(T* res, T weight, T const* arg) { Map(res) = weight * arma::cosh(Map(arg)); } template auto Tanh(T* res, T weight, T const* arg) { Map(res) = weight * arma::tanh(Map(arg)); } template auto Sqrt(T* res, T weight, T const* arg) { Map(res) = weight * arma::sqrt(Map(arg)); } template auto Sqrtabs(T* res, T weight, T const* arg) { Map(res) = weight * arma::sqrt(arma::abs(Map(arg))); } template auto Cbrt(T* res, T weight, T const* arg) { Map(res) = weight * arma::cbrt(Map(arg)); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/arma.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_ARMA_HPP #define OPERON_BACKEND_ARMA_HPP #include "arma/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/backend/blaze/derivatives.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_BLAZE_DERIVATIVES_HPP #define OPERON_BACKEND_BLAZE_DERIVATIVES_HPP #include "operon/core/node.hpp" #include "functions.hpp" namespace Operon::Backend { namespace detail { template inline auto IsNaN(T value) { return std::isnan(value); } template struct FComp { auto operator()(auto x, auto y) const { using T = std::common_type_t; if ((IsNaN(x) && IsNaN(y)) || (x == y)) { return std::numeric_limits::quiet_NaN(); } if (IsNaN(x)) { return T{0}; } if (IsNaN(y)) { return T{1}; } return static_cast(Compare{}(T{x}, T{y})); } }; } // namespace detail template auto Add(std::vector const& /*nodes*/, Backend::View /*primal*/, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1}; } template auto Mul(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = Col(primal, i) / Col(primal, j); } template auto Sub(std::vector const& nodes, Backend::View /*primal*/, Backend::View trace, std::integral auto i, std::integral auto j) { auto v = (nodes[i].Arity == 1 || j < i-1) ? T{-1} : T{+1}; Col(trace, j) = v; } template auto Div(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto const& n = nodes[i]; if (n.Arity == 1) { Col(trace, j) = -T{1} / (Col(primal, j) * Col(primal, j)); } else { Col(trace, j) = (j == i-1 ? T{1} : T{-1}) * Col(primal, i) / Col(primal, j); } } template auto Aq(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { Col(trace, j) = Col(primal, i) / Col(primal, j); } else { Col(trace, j) = -Col(primal, j) * blaze::pow(Col(primal, i), T{3}) / (Col(primal, i-1) * Col(primal, i-1)); } } template auto Pow(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { auto const k = j - (nodes[j].Length + 1); Col(trace, j) = Col(primal, i) * Col(primal, k) / Col(primal, j); } else { auto const k = i-1; Col(trace, j) = Col(primal, i) * blaze::log(Col(primal, k)); } } template auto Powabs(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { auto const k = j - (nodes[j].Length + 1); Col(trace, j) = Col(primal, i) * Col(primal, k) * blaze::sign(Col(primal, j)) / blaze::abs(Col(primal, j)); } else { auto const k = i-1; Col(trace, j) = Col(primal, i) * blaze::log(blaze::abs(Col(primal, k))); } } template auto Min(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto const* a = Ptr(primal, j); std::transform(a, a + S, Ptr(primal, k), Ptr(trace, j), detail::FComp>{}); } template auto Max(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto const* a = Ptr(primal, j); std::transform(a, a + S, Ptr(primal, k), Ptr(trace, j), detail::FComp>{}); } template auto Square(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{2} * Col(primal, j); } template auto Abs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = blaze::sign(Col(primal, j)); } template auto Ceil(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = blaze::ceil(Col(primal, j)); } template auto Floor(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = blaze::floor(Col(primal, j)); } template auto Exp(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { // fmt::print("dexp 1: {} => {}\n", std::span{Ptr(trace, j), S}, std::span{Ptr(primal, i), S}); // Col(trace, j) = Col(primal, i); std::copy_n(Ptr(primal, i), S, Ptr(trace, j)); // fmt::print("dexp 2: {} => {}\n", std::span{Ptr(trace, j), S}, std::span{Ptr(primal, i), S}); } template auto Log(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (Col(primal, j)); } template auto Log1p(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (T{1} + Col(primal, j)); } template auto Logabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = blaze::sign(Col(primal, j)) / blaze::abs(Col(primal, j)); } template auto Sin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = blaze::cos(Col(primal, j)); } template auto Cos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = -blaze::sin(Col(primal, j)); } template auto Tan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { blaze::DynamicVector t = blaze::tan(Col(primal, j)); Col(trace, j) = T{1} + t*t; } template auto Sinh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = blaze::cosh(Col(primal, j)); } template auto Cosh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = blaze::sinh(Col(primal, j)); } template auto Tanh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} - blaze::pow(blaze::tanh(Col(primal, j)), T{2}); } template auto Asin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (blaze::sqrt(T{1} - Col(primal, j) * Col(primal, j))); } template auto Acos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = -T{1} / (blaze::sqrt(T{1} - Col(primal, j) * Col(primal, j))); } template auto Atan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (T{1} + Col(primal, j) * Col(primal, j)); } template auto Sqrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = T{1} / (T{2} * Col(primal, i)); } template auto Sqrtabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = blaze::sign(Col(primal, j)) / (T{2} * Col(primal, i)); } template auto Cbrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = T{1} / (T{3} * Col(primal, i) * Col(primal, i)); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/blaze/functions.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_BLAZE_FUNCTIONS_HPP #define OPERON_BACKEND_BLAZE_FUNCTIONS_HPP #include #include "operon/core/dispatch.hpp" namespace Operon::Backend { namespace detail { template using CVector = blaze::CustomVector; } // namespace detail template inline auto Map(T* res) -> detail::CVector { return detail::CVector(res, S); } template inline auto Map(T const* res) -> detail::CVector> { using U = std::remove_const_t; return detail::CVector(const_cast(res), S); } template auto Col(Backend::View view, std::integral auto col) { return Map(view.data_handle() + col * S); } template auto Col(Backend::View view, std::integral auto col) { return Map(view.data_handle() + col * S); } // utility template auto Fill(T* res, T value) { std::fill_n(res, S, value); } template auto Fill(T* res, int n, T value) { std::fill_n(res, n, value); } // n-ary functions template auto Add(T* res, T weight, auto const*... args) { Map(res) = weight * (Map(args) + ...); } template auto Mul(T* res, T weight, auto const*... args) { Map(res) = weight * (Map(args) * ...); } template auto Sub(T* res, T weight, auto* first, auto const*... rest) { static_assert(sizeof...(rest) > 0); Map(res) = weight * (Map(first) - (Map(rest) + ...)); } template auto Div(T* res, T weight, auto const* first, auto const*... rest) { static_assert(sizeof...(rest) > 0); Map(res) = weight * Map(first) / (Map(rest) * ...); } template auto Min(T* res, T weight, auto const* first, auto const*... args) { static_assert(sizeof...(args) > 0); for (auto i = 0U; i < S; ++i) { res[i] = weight * std::min({first[i], args[i]...}); } } template auto Max(T* res, T weight, auto* first, auto const*... args) { static_assert(sizeof...(args) > 0); for (auto i = 0U; i < S; ++i) { res[i] = weight * std::max({first[i], args[i]...}); } } // binary functions template auto Aq(T* res, T weight, T const* a, T const* b) { blaze::DynamicVector x = Map(b); Map(res) = weight * Map(a) / blaze::sqrt(T{1} + x * x); } template auto Pow(T* res, T weight, T const* a, T const* b) { Map(res) = weight * blaze::pow(Map(a), Map(b)); } template auto Powabs(T* res, T weight, T const* a, T const* b) { Map(res) = weight * blaze::pow(blaze::abs(Map(a)), Map(b)); } // unary functions template auto Cpy(T* res, T weight, T const* arg) { Map(res) = weight * Map(arg); } template auto Neg(T* res, T weight, T const* arg) { Map(res) = weight * -Map(arg); } template auto Inv(T* res, T weight, T const* arg) { Map(res) = weight / Map(arg); } template auto Abs(T* res, T weight, T const* arg) { Map(res) = weight * blaze::abs(Map(arg)); } template auto Ceil(T* res, T weight, T const* arg) { Map(res) = weight * blaze::ceil(Map(arg)); } template auto Floor(T* res, T weight, T const* arg) { Map(res) = weight * blaze::floor(Map(arg)); } template auto Square(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * x * x; }); } template auto Exp(T* res, T weight, T const* arg) { Map(res) = weight * blaze::exp(Map(arg)); } template auto Log(T* res, T weight, T const* arg) { Map(res) = weight * blaze::log(Map(arg)); } template auto Log1p(T* res, T weight, T const* arg) { Map(res) = weight * blaze::log1p(Map(arg)); } template auto Logabs(T* res, T weight, T const* arg) { Map(res) = weight * blaze::log(blaze::abs(Map(arg))); } template auto Sin(T* res, T weight, T const* arg) { Map(res) = weight * blaze::sin(Map(arg)); } template auto Cos(T* res, T weight, T const* arg) { Map(res) = weight * blaze::cos(Map(arg)); } template auto Tan(T* res, T weight, T const* arg) { Map(res) = weight * blaze::tan(Map(arg)); } template auto Asin(T* res, T weight, T const* arg) { Map(res) = weight * blaze::asin(Map(arg)); } template auto Acos(T* res, T weight, T const* arg) { Map(res) = weight * blaze::acos(Map(arg)); } template auto Atan(T* res, T weight, T const* arg) { Map(res) = weight * blaze::atan(Map(arg)); } template auto Sinh(T* res, T weight, T const* arg) { Map(res) = weight * blaze::sinh(Map(arg)); } template auto Cosh(T* res, T weight, T const* arg) { Map(res) = weight * blaze::cosh(Map(arg)); } template auto Tanh(T* res, T weight, T const* arg) { Map(res) = weight * blaze::tanh(Map(arg)); } template auto Sqrt(T* res, T weight, T const* arg) { Map(res) = weight * blaze::sqrt(Map(arg)); } template auto Sqrtabs(T* res, T weight, T const* arg) { Map(res) = weight * blaze::sqrt(blaze::abs(Map(arg))); } template auto Cbrt(T* res, T weight, T const* arg) { Map(res) = weight * blaze::cbrt(Map(arg)); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/blaze.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_BLAZE_HPP #define OPERON_BACKEND_BLAZE_HPP #include "blaze/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/backend/eigen/derivatives.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_EIGEN_DERIVATIVES_HPP #define OPERON_BACKEND_EIGEN_DERIVATIVES_HPP #include "operon/core/node.hpp" #include "functions.hpp" namespace Operon::Backend { namespace detail { template inline auto IsNaN(T value) { return std::isnan(value); } template struct FComp { auto operator()(auto x, auto y) const { using T = std::common_type_t; if ((IsNaN(x) && IsNaN(y)) || (x == y)) { return std::numeric_limits::quiet_NaN(); } if (IsNaN(x)) { return T{0}; } if (IsNaN(y)) { return T{1}; } return static_cast(Compare{}(T{x}, T{y})); } }; } // namespace detail // in order to efficiently compute the derivatives, in many cases we can use the value of the primal too (column index i) // we store the value of the derivative in the trace at column index j template auto Add(Operon::Vector const& /*nodes*/, Backend::View /*primal*/, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j).setConstant(T{1}); } template auto Mul(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = Col(primal, i) / Col(primal, j); } template auto Sub(Operon::Vector const& nodes, Backend::View /*primal*/, Backend::View trace, std::integral auto i, std::integral auto j) { auto v = (nodes[i].Arity == 1 || j < i-1) ? T{-1} : T{+1}; Col(trace, j).setConstant(v); } template auto Div(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto const& n = nodes[i]; if (n.Arity == 1) { Col(trace, j) = -Col(primal, j).square().inverse(); } else { Col(trace, j) = (j == i-1 ? T{1} : T{-1}) * Col(primal, i) / Col(primal, j); } } template auto Aq(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { Col(trace, j) = Col(primal, i) / Col(primal, j); } else { Col(trace, j) = -Col(primal, j) * Col(primal, i).pow(T{3}) / Col(primal, i-1).square(); } } template auto Pow(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { auto const k = j - (nodes[j].Length + 1); Col(trace, j) = Col(primal, i) * Col(primal, k) / Col(primal, j); } else { auto const k = i-1; Col(trace, j) = Col(primal, i) * Col(primal, k).log(); } } template auto Powabs(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { auto const k = j - (nodes[j].Length + 1); Col(trace, j) = Col(primal, i) * Col(primal, k) * Col(primal, j).sign() / Col(primal, j).abs(); } else { auto const k = i-1; Col(trace, j) = Col(primal, i) * Col(primal, k).abs().log(); } } template auto Min(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; Col(trace, j) = Col(primal, j).binaryExpr(Col(primal, k), detail::FComp>{}); } template auto Max(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; Col(trace, j) = Col(primal, j).binaryExpr(Col(primal, k), detail::FComp>{}); } template auto Square(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{2} * Col(primal, j); } template auto Abs(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Col(primal, j).sign(); } template auto Ceil(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Col(primal, j).ceil(); } template auto Floor(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Col(primal, j).floor(); } template auto Exp(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = Col(primal, i); } template auto Log(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Col(primal, j).inverse(); } template auto Log1p(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = (T{1} + Col(primal, j)).inverse(); } template auto Logabs(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Col(primal, j).sign() / Col(primal, j).abs(); } template auto Sin(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Col(primal, j).cos(); } template auto Cos(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = -Col(primal, j).sin(); } template auto Tan(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} + Col(primal, j).tan().square(); } template auto Sinh(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Col(primal, j).cosh(); } template auto Cosh(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Col(primal, j).sinh(); } template auto Tanh(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} - Col(primal, j).tanh().square(); } template auto Asin(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = (T{1} - Col(primal, j).square()).sqrt().inverse(); } template auto Acos(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = -((T{1} - Col(primal, j).square()).sqrt().inverse()); } template auto Atan(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = (T{1} + Col(primal, j).square()).inverse(); } template auto Sqrt(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = (T{2} * Col(primal, i)).inverse(); } template auto Sqrtabs(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = Col(primal, j).sign() / (T{2} * Col(primal, i)); } template auto Cbrt(Operon::Vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = (T{3} * Col(primal, i).square()).inverse(); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/eigen/functions.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_EIGEN_FUNCTIONS_HPP #define OPERON_BACKEND_EIGEN_FUNCTIONS_HPP #include #include #include "operon/core/dispatch.hpp" namespace Operon::Backend { template using Map = Eigen::Map, Eigen::Array, S, 1> const, Eigen::Array>>; template auto Col(Backend::View view, std::integral auto col) { return Map(view.data_handle() + col * S); } template auto Col(Backend::View view, std::integral auto col) { return Map(view.data_handle() + col * S); } // utility template auto Fill(T* res, T value) { Map(res, S, 1).setConstant(value); } template auto Fill(T* res, int n, T value) { Eigen::Map>(res, n, 1).setConstant(value); } // n-ary functions template auto Add(T* res, T weight, auto const*... args) { Map(res, S, 1) = weight * (Map(args, S, 1) + ...); } template auto Mul(T* res, T weight, auto const*... args) { Map(res, S, 1) = weight * (Map(args, S, 1) * ...); } template auto Sub(T* res, T weight, auto* first, auto const*... rest) { static_assert(sizeof...(rest) > 0); Map(res, S, 1) = weight * Map(first, S, 1) - (Map(rest, S, 1) + ...); } template auto Div(T* res, T weight, auto const* first, auto const*... rest) { static_assert(sizeof...(rest) > 0); Map(res, S, 1) = weight * Map(first, S, 1) / (Map(rest, S, 1) * ...); } template auto Min(T* res, T weight, auto const* first, auto const*... args) { static_assert(sizeof...(args) > 0); Map(res, S, 1) = weight * weight * (Map(first, S, 1).min(Map(args, S, 1)), ...); } template auto Max(T* res, T weight, auto* first, auto const*... args) { static_assert(sizeof...(args) > 0); Map(res, S, 1) = weight * (Map(first, S, 1).max(Map(args, S, 1)), ...); } // binary functions template auto Aq(T* res, T weight, T const* a, T const* b) { Map(res, S, 1) = weight * Map(a, S, 1) / (T{1} + Map(b, S, 1).square()).sqrt(); } template auto Pow(T* res, T weight, T const* a, T const* b) { Map(res, S, 1) = weight * Map(a, S, 1).pow(Map(b, S, 1)); } template auto Powabs(T* res, T weight, T const* a, T const* b) { Map(res, S, 1) = weight * Map(a, S, 1).abs().pow(Map(b, S, 1)); } // unary functions template auto Cpy(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1); } template auto Neg(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * -Map(arg, S, 1); } template auto Inv(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).inverse(); } template auto Abs(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).abs(); } template auto Ceil(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).ceil(); } template auto Floor(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).floor(); } template auto Square(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).square(); } template auto Exp(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).exp(); } template auto Log(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).log(); } template auto Log1p(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).log1p(); } template auto Logabs(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).abs().log(); } template auto Sin(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).sin(); } template auto Cos(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).cos(); } template auto Tan(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).tan(); } template auto Asin(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).asin(); } template auto Acos(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).acos(); } template auto Atan(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).atan(); } template auto Sinh(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).sinh(); } template auto Cosh(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).cosh(); } template auto Tanh(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).tanh(); } template auto Sqrt(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).sqrt(); } template auto Sqrtabs(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).abs().sqrt(); } template auto Cbrt(T* res, T weight, T const* arg) { Map(res, S, 1) = weight * Map(arg, S, 1).unaryExpr([](auto x) { return std::cbrt(x); }); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/eigen.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_EIGEN_HPP #define OPERON_BACKEND_EIGEN_HPP #include "eigen/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/backend/eve/derivatives.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_EVE_DERIVATIVES_HPP #define OPERON_BACKEND_EVE_DERIVATIVES_HPP #include "functions.hpp" namespace Operon::Backend { namespace detail { template inline auto IsNaN(T value) { return eve::all(eve::is_nan(value)); } template struct FComp { auto operator()(auto x, auto y) const { using T = std::common_type_t; if ((IsNaN(x) && IsNaN(y)) || eve::all(x == y)) { return std::numeric_limits::quiet_NaN(); } if (IsNaN(x)) { return T{0}; } if (IsNaN(y)) { return T{1}; } return static_cast(Compare{}(T{x}, T{y})); } }; } // namespace detail template> auto Add(std::vector const& /*nodes*/, Backend::View /*primal*/, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { std::ranges::fill_n(Ptr(trace, j), S, T{1}); } template auto Mul(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { using W = eve::wide; auto constexpr L = W::size(); auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); for(auto s = 0UL; s < S; s += L) { eve::store(W{pi+s} / W{pj+s}, res+s); } } template auto Sub(std::vector const& nodes, Backend::View /*primal*/, Backend::View trace, std::integral auto i, std::integral auto j) { auto v = (nodes[i].Arity == 1 || j < i-1) ? T{-1} : T{+1}; std::fill_n(Ptr(trace, j), S, v); } template auto Div(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { using W = eve::wide; auto constexpr L = W::size(); auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (nodes[i].Arity == 1) { for (auto s = 0UL; s < S; s += L) { eve::store(-eve::rec(eve::sqr(W{pj+s})), res+s); } } else { auto v = j == i-1 ? T{1} : T{-1}; for (auto s = 0UL; s < S; s += L) { eve::store(v * W{pi+s} / W{pj+s}, res+s); } } } template auto Aq(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const k = i-1; auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); auto const* pk = Ptr(primal, k); if (j == i-1) { for (auto s = 0UL; s < S; s += L) { eve::store(W{pi+s} / W{pj+s}, res+s); } } else { for (auto s = 0UL; s < S; s += L) { W a{pi+s}; W b{pj+s}; W c{pk+s}; W r = -b * a * a * a / (c * c); eve::store(r, res+s); } } } template auto Pow(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { auto const k = j - (nodes[j].Length + 1); auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; s += L) { eve::store(W{pi+s} * W{pk+s} / W{pj+s}, res+s); } } else { auto const k = i-1; auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; s += L) { eve::store(W{pi+s} * eve::log(W{pk+s}), res+s); } } } template auto Powabs(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { auto const k = j - (nodes[j].Length + 1); auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; s += L) { eve::store(W{pi+s} * W{pk+s} * eve::sign(W{pj+s}) / eve::abs(W{pj+s}), res+s); } } else { auto const k = i-1; auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; s += L) { eve::store(W{pi+s} * eve::log(eve::abs(W{pk+s})), res+s); } } } template auto Min(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); auto const* pk = Ptr(primal, k); std::transform(pj, pj+S, pk, res, detail::FComp>{}); } template auto Max(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); auto const* pk = Ptr(primal, k); std::transform(pj, pj+S, pk, res, detail::FComp>{}); } template auto Square(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(T{2} * W{pj+s}, res+s); } } template auto Abs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::sign(W{pj+s}), res+s); } } template auto Ceil(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::ceil(W{pj+s}), res+s); } } template auto Floor(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::floor(W{pj+s}), res+s); } } template auto Exp(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { std::ranges::copy_n(Ptr(primal, i), S, Ptr(trace, j)); } template auto Log(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::rec(W{pj+s}), res+s); } } template auto Log1p(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::rec(T{1} + W{pj+s}), res+s); } } template auto Logabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::sign(W{pj+s}) / eve::abs(W{pj+s}), res+s); } } template auto Sin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::cos(W{pj+s}), res+s); } } template auto Cos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(-eve::sin(W{pj+s}), res+s); } } template auto Tan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(T{1} + eve::sqr(eve::tan(W{pj+s})), res+s); } } template auto Sinh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::cosh(W{pj+s}), res+s); } } template auto Cosh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::sinh(W{pj+s}), res+s); } } template auto Tanh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(T{1} - eve::sqr(eve::tanh(W{pj+s})), res+s); } } template auto Asin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::rec(eve::sqrt(T{1} - eve::sqr(W{pj+s}))), res+s); } } template auto Acos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(-eve::rec(eve::sqrt(T{1} - eve::sqr(W{pj+s}))), res+s); } } template auto Atan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::rec(T{1} + eve::sqr(W{pj+s})), res+s); } } template auto Sqrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); for (auto s = 0UL; s < S; s += L) { eve::store(eve::rec(T{2} * W{pi+s}), res+s); } } template auto Sqrtabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); for (auto s = 0UL; s < S; s += L) { eve::store(eve::sign(W{pj+s}) / (T{2} * W{pi+s}), res+s); } } template auto Cbrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { using W = eve::wide; static constexpr auto L = W::size(); auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); for (auto s = 0UL; s < S; s += L) { eve::store(eve::rec(T{3} * eve::sqr(W{pi+s})), res+s); } } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/eve/functions.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_EVE_FUNCTIONS_HPP #define OPERON_BACKEND_EVE_FUNCTIONS_HPP #include #include #include #include "operon/core/dispatch.hpp" namespace Operon::Backend { // utility template auto Fill(T* res, T value) { std::ranges::fill_n(res, S, value); } template auto Fill(T* res, int n, T value) { std::ranges::fill_n(res, n, value); } // n-ary functions template requires (S % eve::wide::size() == 0) auto Add(T* res, T weight, auto const*... args) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * (W{args+i} + ...), res+i); } } template requires (S % eve::wide::size() == 0) auto Mul(T* res, T weight, auto const*... args) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * (W{args+i} * ...), res+i); } } template requires (S % eve::wide::size() == 0) auto Sub(T* res, T weight, auto const*... args) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::sub(W{args+i}...), res+i); } } template requires (S % eve::wide::size() == 0) auto Div(T* res, T weight, auto const*... args) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::div(W{args+i}...), res+i); } } template requires (S % eve::wide::size() == 0) auto Min(T* res, T weight, auto const*... args) { return eve::algo::transform_to(eve::views::zip(std::span{args, S}...), res, eve::min); } template requires (S % eve::wide::size() == 0) auto Max(T* res, T weight, auto const*... args) { return eve::algo::transform_to(eve::views::zip(std::span{args, S}...), res, eve::max); } // binary functions template requires (S % eve::wide::size() == 0) auto Aq(T* res, T weight, T const* a, T const* b) { eve::algo::transform_to(eve::views::zip(std::span{a, S}, b), res, [weight](auto t) { return weight * eve::get<0>(t) / eve::sqrt(T{1} + eve::sqr(eve::get<1>(t))); }); } template requires (S % eve::wide::size() == 0) auto Pow(T* res, T weight, T const* a, T const* b) { eve::algo::transform_to(eve::views::zip(std::span{a, S}, b), res, [weight](auto t) { return weight * eve::pow(eve::get<0>(t), eve::get<1>(t)); }); } template requires (S % eve::wide::size() == 0) auto Powabs(T* res, T weight, T const* a, T const* b) { eve::algo::transform_to(eve::views::zip(std::span{a, S}, b), res, [weight](auto t) { return weight * eve::pow(eve::abs(eve::get<0>(t)), eve::get<1>(t)); }); } // unary functions template requires (S % eve::wide::size() == 0) auto Cpy(T* res, T w, T const* arg) { //eve::algo::copy(std::span{arg, S}, res); std::transform(arg, arg+S, res, [w](auto x) { return w * x; }); } template requires (S % eve::wide::size() == 0) auto Neg(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::minus(W{arg+i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Inv(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::rec(W{arg+i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Abs(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::abs(W{arg+i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Ceil(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::ceil(W{arg+i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Floor(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::floor(W{arg+i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Square(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::sqr(W{arg+i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Exp(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::exp(W{arg+i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Log(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::log(W{arg+i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Log1p(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::log1p(W{arg+i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Logabs(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::log(eve::abs(W{arg+i})), res+i); } } template requires (S % eve::wide::size() == 0) auto Sin(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::sin(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Cos(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::cos(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Tan(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::tan(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Asin(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::asin(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Acos(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::acos(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Atan(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::atan(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Sinh(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::sinh(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Cosh(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::cosh(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Tanh(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::tanh(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Sqrt(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::sqrt(W{arg + i}), res+i); } } template requires (S % eve::wide::size() == 0) auto Sqrtabs(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::sqrt(eve::abs(W{arg + i})), res+i); } } template requires (S % eve::wide::size() == 0) auto Cbrt(T* res, T weight, T const* arg) { using W = eve::wide; constexpr auto L = W::size(); for (auto i = 0UL; i < S; i += L) { eve::store(weight * eve::cbrt(W{arg + i}), res+i); } } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/eve.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_EVE_HPP #define OPERON_BACKEND_EVE_HPP #include "eve/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/derivatives.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_FAST_APPROX_DERIVATIVES_HPP #define OPERON_BACKEND_FAST_APPROX_DERIVATIVES_HPP #include "functions.hpp" namespace Operon::Backend { namespace detail { template inline auto IsNaN(T value) { return std::isnan(value); } template struct FComp { auto operator()(auto x, auto y) const { using T = std::common_type_t; if ((IsNaN(x) && IsNaN(y)) || (x == y)) { return std::numeric_limits::quiet_NaN(); } if (IsNaN(x)) { return T{0}; } if (IsNaN(y)) { return T{1}; } return static_cast(Compare{}(T{x}, T{y})); } }; template inline auto Sgn(T x) { return (T{0} < x) - (x < T{0}); } } // namespace detail template auto Add(std::vector const& /*nodes*/, Backend::View /*primal*/, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { std::ranges::fill_n(Ptr(trace, j), S, T{1}); } template auto Mul(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto *res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); std::transform(pi, pi+S, pj, res, [](auto x, auto y) { return x * detail::fast_approx::Inv(y); }); } template auto Sub(std::vector const& nodes, Backend::View /*primal*/, Backend::View trace, std::integral auto i, std::integral auto j) { auto v = (nodes[i].Arity == 1 || j < i-1) ? T{-1} : T{+1}; std::ranges::fill_n(Ptr(trace, j), S, v); } template auto Div(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto const& n = nodes[i]; auto *res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (n.Arity == 1) { std::transform(pj, pj+S, res, [](auto x) { return -detail::fast_approx::Inv(x * x); }); } else { auto v = j == i-1 ? T{1} : T{-1}; std::transform(pi, pi+S, pj, res, [v](auto x, auto y) { return detail::fast_approx::Div(v*x, y); }); } } template auto Aq(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto *res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { std::transform(pi, pi+S, pj, res, detail::fast_approx::Div); } else { auto const* pk = Ptr(primal, i-1); constexpr auto p = -3.F / 2.F; for (auto s = 0UL; s < S; ++s) { auto const b = pj[s]; auto const c = pk[s]; res[s] = -b * c * detail::fast_approx::Pow(1 + b * b, p); } } } template auto Pow(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { auto const k = j - (nodes[j].Length + 1); auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; ++s) { res[s] = detail::fast_approx::Div(pi[s] * pk[s], pj[s]); } } else { auto const k = i-1; auto const* pk = Ptr(primal, k); std::transform(pi, pi+S, pk, res, [](auto x, auto y) { return x * detail::fast_approx::Log(y); }); } } template auto Powabs(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { auto const k = j - (nodes[j].Length + 1); auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; ++s) { res[s] = detail::fast_approx::Div(pi[s] * pk[s] * detail::Sgn(pj[s]), std::abs(pj[s])); } } else { auto const k = i-1; auto const* pk = Ptr(primal, k); std::transform(pi, pi+S, pk, res, [](auto x, auto y) { return x * detail::fast_approx::Log(std::abs(y)); }); } } template auto Min(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); auto const* pk = Ptr(primal, k); std::transform(pj, pj+S, pk, res, detail::FComp>{}); } template auto Max(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); auto const* pk = Ptr(primal, k); std::transform(pj, pj+S, pk, res, detail::FComp>{}); } template auto Square(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return T{2} * x; }); } template auto Abs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, detail::Sgn); } template auto Ceil(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return std::ceil(x); }); } template auto Floor(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return std::floor(x); }); } template auto Exp(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { std::ranges::copy_n(Ptr(primal, i), S, Ptr(trace, j)); } template auto Log(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return detail::fast_approx::Inv(x); }); } template auto Log1p(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return detail::fast_approx::Inv(T{1} + x); }); } template auto Logabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return detail::fast_approx::Div(detail::Sgn(x), std::abs(x)); }); } template auto Sin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return detail::fast_approx::Cos(x); }); } template auto Cos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return -detail::fast_approx::Sin(x); }); } template auto Tan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x) { return T{1} + x * x; }); } template auto Sinh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return std::cosh(x); }); } template auto Cosh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return std::sinh(x); }); } template auto Tanh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x) { return T{1} - x * x; }); } template auto Asin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return detail::fast_approx::ISqrt(T{1} - x * x); }); } template auto Acos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return -detail::fast_approx::ISqrt(T{1} - x * x); }); } template auto Atan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return detail::fast_approx::Inv(T{1} + x * x); }); } template auto Sqrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x){ return detail::fast_approx::Inv(T{2} * x); }); } template auto Sqrtabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); std::transform(pi, pi+S, pj, res, [](auto x, auto y){ return detail::fast_approx::Div(detail::Sgn(y), T{2} * x); }); } template auto Cbrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x){ return detail::fast_approx::Inv(T{3} * x*x); }); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/functions.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_fast_approx_FUNCTIONS_HPP #define OPERON_BACKEND_fast_approx_FUNCTIONS_HPP #include "operon/interpreter/backend/backend.hpp" #include "operon/core/node.hpp" #include "impl/aq.hpp" #include "impl/exp.hpp" #include "impl/inv.hpp" #include "impl/log.hpp" #include "impl/pow.hpp" #include "impl/sqrt.hpp" #include "impl/trig.hpp" #include "impl/tanh.hpp" namespace Operon::Backend { namespace detail::fast_approx { static constexpr auto Precision = OPERON_MATH_FAST_APPROX_PRECISION; // unary details inline auto constexpr Inv(Operon::Scalar x) -> Operon::Scalar { return fast_approx::InvImpl(x); } inline auto constexpr Log(Operon::Scalar x) -> Operon::Scalar { return fast_approx::LogImpl(x); } inline auto constexpr Log1p(Operon::Scalar x) -> Operon::Scalar { return fast_approx::Log1pImpl(x); } inline auto constexpr Logabs(Operon::Scalar x) -> Operon::Scalar { return fast_approx::LogabsImpl(x); } inline auto constexpr Exp(Operon::Scalar x) -> Operon::Scalar { return fast_approx::ExpImpl(x); } inline auto constexpr Sin(Operon::Scalar x) -> Operon::Scalar { return fast_approx::SinImpl(x); } inline auto constexpr Cos(Operon::Scalar x) -> Operon::Scalar { return fast_approx::CosImpl(x); } inline auto constexpr Tan(Operon::Scalar x) -> Operon::Scalar { return fast_approx::TanImpl(x); } inline auto constexpr Sinh(Operon::Scalar x) -> Operon::Scalar { auto const e = Exp(x); return (e*e - 1.F) * Inv(e+e); } inline auto constexpr Cosh(Operon::Scalar x) -> Operon::Scalar { auto const e = Exp(x); return (e*e + 1.F) * Inv(e+e); } inline auto constexpr ISqrt(Operon::Scalar x) -> Operon::Scalar { return fast_approx::ISqrtImpl(x); } inline auto constexpr Sqrt(Operon::Scalar x) -> Operon::Scalar { return fast_approx::SqrtImpl(x); } inline auto constexpr Sqrtabs(Operon::Scalar x) -> Operon::Scalar { return fast_approx::SqrtabsImpl(x); } inline auto constexpr Div(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { return fast_approx::DivImpl(x, y); } inline auto constexpr Pow(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { return fast_approx::PowImpl(x, y); } inline auto constexpr Powabs(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { return fast_approx::PowabsImpl(x, y); } inline auto constexpr Tanh(Operon::Scalar x) -> Operon::Scalar { return fast_approx::TanhImpl(x); } inline auto constexpr Aq(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { return fast_approx::AqImpl(x, y); } } // namespace detail::fast_approx // utility template auto Fill(T* res, T value) { std::ranges::fill_n(res, S, value); } template auto Fill(T* res, int n, T value) { std::ranges::fill_n(res, n, value); } // unary functions template auto Add(T* res, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = (args[i] + ...); } } template auto Mul(T* res, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = (args[i] * ...); } } template auto Sub(T* res, auto const* first, auto const*... rest) { for (auto i = 0UL; i < S; ++i) { if constexpr (sizeof...(rest) == 0) { res[i] = -first[i]; } else { res[i] = first[i] - (rest[i] + ...); } } } template auto Div(T* res, auto const* first, auto const*... rest) { for (auto i = 0UL; i < S; ++i) { if constexpr (sizeof...(rest) == 0) { res[i] = detail::fast_approx::Inv(first[i]); } else { res[i] = detail::fast_approx::Div(first[i], (rest[i] * ...)); } } } template auto Min(T* res, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = std::min({args[i]...}); } } template auto Max(T* res, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = std::max({args[i]...}); } } // binary functions template auto Aq(T* res, T const* a, T const* b) { std::transform(a, a+S, b, res, detail::fast_approx::Aq); } template auto Pow(T* res, T const* a, T const* b) { std::transform(a, a+S, b, res, detail::fast_approx::Pow); } template auto Powabs(T* res, T const* a, T const* b) { std::transform(a, a+S, b, res, detail::fast_approx::Powabs); } // unary functions template auto Cpy(T* res, T const* arg) { std::ranges::copy_n(arg, S, res); } template auto Neg(T* res, T const* arg) { std::transform(arg, arg+S, res, std::negate{}); } template auto Inv(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Inv); } template auto Abs(T* res, T const* arg) { std::transform(arg, arg+S, res, [](auto x) { return std::abs(x); }); } template auto Ceil(T* res, T const* arg) { std::transform(arg, arg+S, res, [](auto x) { return std::ceil(x); }); } template auto Floor(T* res, T const* arg) { std::transform(arg, arg+S, res, [](auto x) { return std::floor(x); }); } template auto Exp(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Exp); } template auto Log(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Log); } template auto Log1p(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Log1p); } template auto Logabs(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Logabs); } template auto Sin(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Sin); } template auto Cos(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Cos); } template auto Tan(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Tan); } template auto Asin(T* res, T const* arg) { std::transform(arg, arg+S, res, [](auto x) { return std::asin(x); }); } template auto Acos(T* res, T const* arg) { std::transform(arg, arg+S, res, [](auto x) { return std::acos(x); }); } template auto Atan(T* res, T const* arg) { std::transform(arg, arg+S, res, [](auto x) { return std::atan(x); }); } template auto Sinh(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Sinh); } template auto Cosh(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Sinh); } template auto Tanh(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Tanh); } template auto Sqrt(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Sqrt); } template auto Sqrtabs(T* res, T const* arg) { std::transform(arg, arg+S, res, detail::fast_approx::Sqrtabs); } template auto Square(T* res, T const* arg) { std::transform(arg, arg+S, res, [](auto x) { return x * x; }); } template auto Cbrt(T* res, T const* arg) { std::transform(arg, arg+S, res, [](auto x) { return std::cbrt(x); }); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/impl/aq.hpp ================================================ #ifndef OPERON_BACKEND_FAST_APPROX_AQ_HPP #define OPERON_BACKEND_FAST_APPROX_AQ_HPP #include "operon/core/types.hpp" #include "inv.hpp" #include "sqrt.hpp" namespace Operon::Backend::detail::fast_approx { template inline auto constexpr AqImpl(Operon::Scalar x1, Operon::Scalar x2) { auto constexpr p{9999999980506447872.F}; return std::abs(x2) > p ? DivImpl

(x1, std::abs(x2)) : x1 * ISqrtImpl

(1 + x2*x2); } } // namespace Operon::Backend::detail::fast_approx #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/impl/exp.hpp ================================================ #ifndef OPERON_BACKEND_FAST_APPROX_EXP_HPP #define OPERON_BACKEND_FAST_APPROX_EXP_HPP #include "operon/core/types.hpp" namespace Operon::Backend::detail::fast_approx { inline auto constexpr ExpV1(Operon::Scalar x) -> Operon::Scalar { if (std::isnan(x)) { return std::numeric_limits::quiet_NaN(); } if (x == 0) { return 1.F; } if (x < -85.F) { return 0; } if (x > +85.F) { return std::numeric_limits::infinity(); } auto const f = x * 12102203.161561485 + 1065054451; auto const i = static_cast(f); return std::bit_cast(i); } // http://stackoverflow.com/questions/10552280/fast-exp-calculation-possible-to-improve-accuracy-without-losing-too-much-perfo/10792321#10792321 inline auto constexpr ExpV2(Operon::Scalar x) -> Operon::Scalar { if (std::isnan(x)) { return std::numeric_limits::quiet_NaN(); } if (x == 0) { return 1.F; } if (x < -85.F) { return 0; } if (x > +85.F) { return std::numeric_limits::infinity(); } float t = x * 1.442695041F; float fi = std::floor(t); float f = t - fi; int i = static_cast(fi); auto xf = (0.3371894346f * f + 0.657636276f) * f + 1.00172476f; /* compute 2^f */ auto xi = (std::bit_cast(xf) + (i << 23)); /* scale by 2^i */ return std::bit_cast(xi); } inline auto constexpr ExpV3(Operon::Scalar x) -> Operon::Scalar { if (std::isnan(x)) { return std::numeric_limits::quiet_NaN(); } if (x == 0) { return 1.F; } if (x < -85.F) { return 0; } if (x > +85.F) { return std::numeric_limits::infinity(); } constexpr float a = (1U << 23U) / 0.69314718f; constexpr float b = (1U << 23U) * (127 - 0.043677448f); x = a * x + b; constexpr float c = (1U << 23U); constexpr float d = (1U << 23U) * 255; if (x < c || x > d) x = (x < c) ? 0.0f : d; return std::bit_cast(static_cast(x)); } template inline auto constexpr ExpImpl(Operon::Scalar x) -> Operon::Scalar { if constexpr (P == 0) { return ExpV1(x); } else { return ExpV2(x); } } } // namespace Operon::Backend::detail::fast_approx #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/impl/inv.hpp ================================================ #ifndef OPERON_BACKEND_FAST_APPROX_INV_HPP #define OPERON_BACKEND_FAST_APPROX_INV_HPP #include "operon/core/types.hpp" namespace Operon::Backend::detail::fast_approx { template inline auto constexpr InvImpl(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_same_v, "this function only works in single-precision mode."); constexpr auto inf{ std::numeric_limits::infinity() }; constexpr auto nan = std::numeric_limits::quiet_NaN(); if (x == -0) { return -inf; } if (x == +0) { return inf; } if (std::isinf(x)) { return 0; } if (std::isnan(x)) { return nan; } auto sx = (x < 0) ? -1.F : 1.F; x = sx * x; auto constexpr m{0x7EF127EA}; auto xi = static_cast(m - std::bit_cast(x)); auto xf = std::bit_cast(xi); auto w = x * xf; // Efficient Iterative Approximation Improvement in horner polynomial form. if constexpr (P == 1) { xf = xf * (2 - w); // perform one iteration, err = -3.36e-3 * 2^(-flr(log2(x))) } else if constexpr (P == 2) { xf = xf * (4 + w * (-6 + w * (4 - w))); // perform two iterations, err = -1.13e-5 * 2^(-flr(log2(x))) } else if constexpr (P >= 3) { xf = xf * (8 + w * (-28 + w * (56 + w * (-70 + w *(56 + w * (-28 + w * (8 - w))))))); // perform three iterations, err = +-6.8e-8 * 2^(-flr(log2(x))) } return xf * sx; } template inline auto constexpr DivImpl(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { static_assert(std::is_same_v, "this function only works in single-precision mode."); constexpr auto inf{ std::numeric_limits::infinity() }; constexpr auto nan = std::numeric_limits::quiet_NaN(); if (x == 0) { return y == 0 ? nan : 0.F; } if (std::isnan(x)) { return nan; } if (y == -0) { return -inf; } if (y == +0) { return +inf; } return x * InvImpl

(y); } } // namespace Operon::Backend::detail::fast_approx #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/impl/log.hpp ================================================ #ifndef OPERON_BACKEND_FAST_APPROX_LOG_HPP #define OPERON_BACKEND_FAST_APPROX_LOG_HPP #include "operon/core/types.hpp" namespace Operon::Backend::detail::fast_approx { template inline auto constexpr LogImpl(Operon::Scalar x) -> Operon::Scalar { constexpr auto inf { std::numeric_limits::infinity() }; constexpr auto nan { std::numeric_limits::quiet_NaN() }; if (std::isnan(x)) { return nan; } if (x < 0) { return nan; } if (x == 0) { return -inf; } if (x == 1) { return 0.F; } if (x == inf) { return inf; } if constexpr (P == 0) { return (std::bit_cast(x) - 1065353217) * 8.262958405176314e-8F; } else { auto bx = std::bit_cast(x); auto ex = bx >> 23U; auto t = static_cast(ex) - static_cast(127); bx = 1065353216 | (bx & 8388607); x = std::bit_cast(bx); if constexpr (P == 1) { return -1.49278+(2.11263+(-0.729104+0.10969*x)*x)*x+0.6931471806*t; } else { return -1.7417939+(2.8212026+(-1.4699568+(0.44717955-0.056570851*x)*x)*x)*x+0.6931471806*t; } } } template inline auto constexpr Log1pImpl(Operon::Scalar x) -> Operon::Scalar { return LogImpl

(1 + x); } template inline auto constexpr LogabsImpl(Operon::Scalar x) -> Operon::Scalar { return LogImpl

(std::abs(x)); } } // namespace Operon::Backend::detail::fast_approx #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/impl/pow.hpp ================================================ #ifndef OPERON_BACKEND_FAST_APPROX_POW_HPP #define OPERON_BACKEND_FAST_APPROX_POW_HPP #include "inv.hpp" #include "exp.hpp" #include "log.hpp" namespace Operon::Backend::detail::fast_approx { inline auto constexpr PowV1(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { constexpr auto inf { std::numeric_limits::infinity() }; constexpr auto nan { std::numeric_limits::quiet_NaN() }; if (std::isnan(x)) { return nan; } if (std::isnan(y)) { return nan; } if (x == 0) { return y < 0 ? inf : x; } if (x < 0) { return nan; } if (y == 0) { return 1.F; } if (y < -85.F) { return 0; } if (y > 85.F) { return inf; } auto xi = std::bit_cast(x); auto a = static_cast(y * (xi - 1064866805) + static_cast(1064866805)); return std::bit_cast(a); } inline auto PowV2(Operon::Scalar x, Operon::Scalar y) { auto log2 = [](Operon::Scalar x) { auto i = std::bit_cast(x); auto f = std::bit_cast((i & 0x007FFFFF) | 0x3f000000); auto y = i * 1.1920928955078125e-7f; return y - 124.22551499f - 1.498030302f * f - 1.72587999f / (0.3520887068f + f); }; auto pow2 = [](Operon::Scalar p) { float offset = (p < 0) ? 1.0f : 0.0f; float clipp = (p < -126) ? -126.0f : p; float z = clipp - static_cast(clipp) + offset; auto i = std::uint32_t((1 << 23U) * (clipp + 121.2740575f + 27.7280233f / (4.84252568f - z) - 1.49012907f * z)); return std::bit_cast(i); }; return pow2(y * log2(x)); } template inline auto PowImpl(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { if constexpr (P == 0) { return PowV1(x, y); } // else { return PowV2(x, y); } else { return ExpImpl

(y * LogImpl

(x)); } } template inline auto constexpr PowabsImpl(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { return PowImpl

(std::abs(x), y); } } // namespace Operon::Backend::detail::fast_approx #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/impl/sqrt.hpp ================================================ #ifndef OPERON_BACKEND_FAST_APPROX_SQRT_HPP #define OPERON_BACKEND_FAST_APPROX_SQRT_HPP #include "operon/core/types.hpp" namespace Operon::Backend::detail::fast_approx { template inline auto constexpr ISqrtImpl(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_same_v, "this function only works in single-precision mode."); constexpr auto nan{ std::numeric_limits::quiet_NaN() }; constexpr auto inf{ std::numeric_limits::infinity() }; constexpr auto fast_sqrt_constant{static_cast(0x5F3759DF)}; if (std::isnan(x)) { return nan; } if (x < 0) { return nan; } if (x == -0) { return -inf; } if (x == +0) { return +inf; } auto xt = x * 0.5F; auto xi = std::bit_cast(x); xi = fast_sqrt_constant - (xi >> 1U); auto xf = std::bit_cast(xi); for (auto i = 0UL; i < P; ++i) { xf = xf * (float{1.5} - (xt * (xf * xf))); } return xf; } template inline auto constexpr SqrtImpl(Operon::Scalar x) -> Operon::Scalar { constexpr auto nan = std::numeric_limits::quiet_NaN(); if (std::isnan(x)) { return nan; } if (x < 0) { return nan; } if (x == 0) { return 0; } if constexpr (P == 0) { auto xi = std::bit_cast(x); xi -= 0x3F800000; xi = ((((xi >> 31U) & 1) << 31U) | ((xi >> 1U) & 0x7FFFFFFF)); xi += 0x3F800000; xi &= 0xFFFFFFFF; return std::bit_cast(xi); } else { return x * ISqrtImpl

(x); } } template inline auto constexpr SqrtabsImpl(Operon::Scalar x) { return SqrtImpl

(std::abs(x)); } } // namespace Operon::Backend::detail::fast_approx #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/impl/tanh.hpp ================================================ #ifndef OPERON_BACKEND_FAST_APPROX_TANH_HPP #define OPERON_BACKEND_FAST_APPROX_TANH_HPP #include "operon/core/types.hpp" #include "inv.hpp" namespace Operon::Backend::detail::fast_approx { template inline auto constexpr TanhImpl(Operon::Scalar x) -> Operon::Scalar { constexpr auto nan { std::numeric_limits::quiet_NaN() }; if (std::isnan(x)) { return nan; } if (x == 0) { return 0; } if constexpr (P == 0) { if (x <= -3) { return -1.F; } if (x >= +3) { return +1.F; } auto const r = InvImpl

(x * x + 3.F); auto constexpr a = 8.F / 3.F; auto constexpr b = 1.F / 9.F; return x * (a * r + b); } else { if (x < -85) { return -1.F; } if (x > +85) { return +1.F; } auto expZeroShift = [](auto x) { constexpr auto shift{23U}; constexpr auto ff{127U}; auto a = DivImpl

((1U << shift), std::numbers::ln2_v); auto b = (ff * (1U << shift)); auto f = a * x + b; auto i = static_cast(f); return std::bit_cast(i); }; auto a = expZeroShift(x); auto b = expZeroShift(-x); return DivImpl

(a-b, a+b); } } } // namespace Operon::Backend::detail::fast_approx #endif ================================================ FILE: include/operon/interpreter/backend/fast_approx/impl/trig.hpp ================================================ #ifndef OPERON_BACKEND_FAST_APPROX_TRIG_HPP #define OPERON_BACKEND_FAST_APPROX_TRIG_HPP #include "operon/core/types.hpp" #include "inv.hpp" #include "exp.hpp" namespace Operon::Backend::detail::fast_approx { template inline auto constexpr CosImpl(Operon::Scalar x) -> Operon::Scalar { if (!std::isfinite(x)) { return std::numeric_limits::quiet_NaN(); } if (x == 0) { return 1.F; } if constexpr (P == 0) { auto constexpr invPi = std::numbers::inv_pi_v; x = std::abs(x) * invPi + 1.5F; x = x - 2 * static_cast(static_cast(x/2)) - 1.F; return 4 * (x < 0 ? (x*x + x) : (-x*x + x)); } else { constexpr float tp = std::numbers::inv_pi_v/2; constexpr float a{.25F}; constexpr float b{16.F}; constexpr float c{.50F}; constexpr float d{.225F}; x *= tp; x -= a + std::floor(x + a); x *= b * (std::abs(x) - c); if constexpr (P >= 1) { x += d * x * (std::abs(x) - 1.F); // another step for extra precision } return x; } } template inline auto constexpr SinImpl(Operon::Scalar x) -> Operon::Scalar { if (!std::isfinite(x)) { return std::numeric_limits::quiet_NaN(); } if (x == 0) { return x; } if constexpr (P == 0) { auto constexpr invPi = std::numbers::inv_pi_v; auto const offset = x < 0 ? 2.F : 1.F; x = std::abs(x) * invPi + offset; x = x - 2 * static_cast(static_cast(x/2)) - 1.F; return 4 * (x < 0 ? (x*x + x) : (-x*x + x)); } else { constexpr float tp = std::numbers::pi_v/2; return CosImpl

(x-tp); } } template inline auto constexpr TanImpl(Operon::Scalar x) -> Operon::Scalar { if (x == 0) { return x; } return DivImpl

(SinImpl

(x), CosImpl

(x)); } } // namespace Operon::Backend::detail::fast_approx #endif ================================================ FILE: include/operon/interpreter/backend/fast_v1.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_FAST_V1_HPP #define OPERON_BACKEND_FAST_V1_HPP #define OPERON_MATH_FAST_APPROX_PRECISION 0 #include "fast_approx/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/backend/fast_v2.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_FAST_V2_HPP #define OPERON_BACKEND_FAST_V2_HPP #define OPERON_MATH_FAST_APPROX_PRECISION 1 #include "fast_approx/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/backend/fast_v3.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_FAST_V3_HPP #define OPERON_BACKEND_FAST_V3_HPP #define OPERON_MATH_FAST_APPROX_PRECISION 2 #include "fast_approx/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/backend/fastor/derivatives.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_FASTOR_DERIVATIVES_HPP #define OPERON_BACKEND_FASTOR_DERIVATIVES_HPP #include "operon/interpreter/dual.hpp" #include "functions.hpp" namespace Operon::Backend { namespace detail { template inline auto IsNaN(T value) { return std::isnan(value); } template<> inline auto IsNaN(Operon::Dual value) { return ceres::isnan(value); } // NOLINT template struct FComp { auto operator()(auto x, auto y) const { using T = std::common_type_t; if ((IsNaN(x) && IsNaN(y)) || (x == y)) { return std::numeric_limits::quiet_NaN(); } if (IsNaN(x)) { return T{0}; } if (IsNaN(y)) { return T{1}; } return static_cast(Compare{}(T{x}, T{y})); } }; template inline auto Sgn(T x) { return (T{0} < x) - (x < T{0}); } } // namespace detail template auto Add(std::vector const& /*nodes*/, Backend::View /*primal*/, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j).fill(T{1.0}); } template auto Mul(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = Col(primal, i) / Col(primal, j); } template auto Sub(std::vector const& nodes, Backend::View /*primal*/, Backend::View trace, std::integral auto i, std::integral auto j) { auto v = (nodes[i].Arity == 1 || j < i-1) ? T{-1} : T{+1}; Col(trace, j).fill(v); } template auto Div(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto const& n = nodes[i]; if (n.Arity == 1) { Col(trace, j) = -T{1} / (Col(primal, j) * Col(primal, j)); } else { Col(trace, j) = (j == i-1 ? T{1} : T{-1}) * Col(primal, i) / Col(primal, j); } } template auto Aq(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { Col(trace, j) = Col(primal, i) / Col(primal, j); } else { Col(trace, j) = -Col(primal, j) * Fastor::pow(Col(primal, i), 3) / Fastor::pow(Col(primal, i-1), 2); } } template auto Pow(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { auto const k = j - (nodes[j].Length + 1); Col(trace, j) = Col(primal, i) * Col(primal, k) / Col(primal, j); } else { auto const k = i-1; Col(trace, j) = Col(primal, i) * Fastor::log(Col(primal, k)); } } template auto Powabs(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { if (j == i-1) { auto const k = j - (nodes[j].Length + 1); auto const* a = Ptr(primal, j); std::transform(a, a + S, Ptr(trace, j), [](auto x) { return detail::Sgn(x) / std::abs(x); }); Col(trace, j) = Col(primal, i) * Col(primal, k) * Col(trace, j); } else { auto const k = i-1; Col(trace, j) = Col(primal, i) * Fastor::log(Fastor::abs(Col(primal, k))); } } template auto Min(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto const* a = Ptr(primal, j); std::transform(a, a + S, Ptr(primal, k), Ptr(trace, j), detail::FComp>{}); } template auto Max(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto const* a = Ptr(primal, j); std::transform(a, a + S, Ptr(primal, k), Ptr(trace, j), detail::FComp>{}); } template auto Square(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{2} * Col(primal, j); } template auto Abs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto const* a = Ptr(primal, j); std::transform(a, a + S, Ptr(trace, j), detail::Sgn); } template auto Ceil(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Fastor::ceil(Col(primal, j)); } template auto Floor(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Fastor::floor(Col(primal, j)); } template auto Exp(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { std::ranges::copy_n(Ptr(primal, i), S, Ptr(trace, j)); } template auto Log(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / Col(primal, j); } template auto Log1p(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (T{1} + Col(primal, j)); } template auto Logabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto const* a = Ptr(primal, j); std::transform(a, a + S, Ptr(trace, j), [](auto x) { return detail::Sgn(x) / std::abs(x); }); } template auto Sin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Fastor::cos(Col(primal, j)); } template auto Cos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = -Fastor::sin(Col(primal, j)); } template auto Tan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} + Fastor::pow(Fastor::tan(Col(primal, j)), 2); } template auto Sinh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Fastor::cosh(Col(primal, j)); } template auto Cosh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = Fastor::sinh(Col(primal, j)); } template auto Tanh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} - Fastor::pow(Fastor::tanh(Col(primal, j)), 2); } template auto Asin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / Fastor::sqrt(T{1} - Fastor::pow(Col(primal, j), 2)); } template auto Acos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = -T{1} / Fastor::sqrt(T{1} - Fastor::pow(Col(primal, j), 2)); } template auto Atan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { Col(trace, j) = T{1} / (T{1} + Fastor::pow(Col(primal, j), 2)); } template auto Sqrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = T{1} / (T{2} * Col(primal, i)); } template auto Sqrtabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); std::transform(pj, pj + S, pi, Ptr(trace, j), [](auto x, auto y) { return detail::Sgn(x) / (T{2} * y); }); } template auto Cbrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Col(trace, j) = T{1} / (T{3} * Fastor::pow(Col(primal, i), 2)); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/fastor/functions.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_FASTOR_FUNCTIONS_HPP #define OPERON_BACKEND_FASTOR_FUNCTIONS_HPP // #define FASTOR_DONT_ALIGN // avoid segfaults due to wrong assumptions about alignment #include #include "operon/core/dispatch.hpp" #include "operon/core/node.hpp" namespace Operon::Backend { template auto Map(T* ptr) { return Fastor::TensorMap(ptr); } template auto Map(T const* ptr) { return Fastor::TensorMap(const_cast(ptr)); } template auto Col(Backend::View view, std::integral auto col) { using U = std::remove_const_t; return Map(const_cast(view.data_handle() + col * S)); } template auto Fill(T* res, T value) { std::ranges::fill_n(res, S, value); } template auto Fill(T* res, int n, T value) { std::ranges::fill_n(res, n, value); } // n-ary functions template auto Add(T* res, T weight, auto const*... args) { Map(res) = weight * (Map(args) + ...); } template auto Mul(T* res, T weight, auto const*... args) { Map(res) = weight * (Map(args) * ...); } template auto Sub(T* res, T weight, auto const* first, auto const*... rest) { static_assert(sizeof...(rest) > 0); Map(res) = weight * Map(first) - (Map(rest) + ...); } template auto Div(T* res, T weight, auto const* first, auto const*... rest) { static_assert(sizeof...(rest) > 0); Map(res) = weight * Map(first) / (Map(rest) * ...); } template auto Min(T* res, T weight, auto const* first, auto const*... args) { static_assert(sizeof...(args) > 0); Map(res) = weight * (Fastor::min(Map(first), Map(args)), ...); } template auto Max(T* res, T weight, auto const* first, auto const*... args) { static_assert(sizeof...(args) > 0); Map(res) = weight * (Fastor::max(Map(first), Map(args)), ...); } // binary functions template auto Aq(T* res, T weight, T* a, T* b) { auto m = Map(b); Map(res) = weight * Map(a) / Fastor::sqrt(T{1} + m * m); } template auto Pow(T* res, T weight, T* a, T* b) { Map(res) = weight * Fastor::pow(Map(a), Map(b)); } template auto Powabs(T* res, T weight, T* a, T* b) { Map(res) = weight * Fastor::pow(Fastor::abs(Map(a)), Map(b)); } // unary functions template auto Cpy(T* res, T weight, T* arg) { Map(res) = weight * Map(arg); } template auto Neg(T* res, T weight, T* arg) { Map(res) = weight * -Map(arg); } template auto Inv(T* res, T weight, T* arg) { Map(res) = weight / Map(arg); } template auto Abs(T* res, T weight, T* arg) { Map(res) = weight * Fastor::abs(Map(arg)); } template auto Square(T* res, T weight, T* arg) { auto a = Map(arg); Map(res) = a * a; } template auto Ceil(T* res, T weight, T* arg) { Map(res) = weight * Fastor::ceil(Map(arg)); } template auto Floor(T* res, T weight, T* arg) { Map(res) = weight * Fastor::floor(Map(arg)); } template auto Exp(T* res, T weight, T* arg) { Map(res) = weight * Fastor::exp(Map(arg)); } template auto Log(T* res, T weight, T* arg) { Map(res) = weight * Fastor::log(Map(arg)); } template auto Log1p(T* res, T weight, T* arg) { Map(res) = weight * Fastor::log1p(Map(arg)); } template auto Logabs(T* res, T weight, T* arg) { Map(res) = weight * Fastor::log(Fastor::abs(Map(arg))); } template auto Sin(T* res, T weight, T* arg) { Map(res) = weight * Fastor::sin(Map(arg)); } template auto Cos(T* res, T weight, T* arg) { Map(res) = weight * Fastor::cos(Map(arg)); } template auto Tan(T* res, T weight, T* arg) { Map(res) = weight * Fastor::tan(Map(arg)); } template auto Asin(T* res, T weight, T* arg) { Map(res) = weight * Fastor::asin(Map(arg)); } template auto Acos(T* res, T weight, T* arg) { Map(res) = weight * Fastor::acos(Map(arg)); } template auto Atan(T* res, T weight, T* arg) { Map(res) = weight * Fastor::atan(Map(arg)); } template auto Sinh(T* res, T weight, T* arg) { Map(res) = weight * Fastor::sinh(Map(arg)); } template auto Cosh(T* res, T weight, T* arg) { Map(res) = weight * Fastor::cosh(Map(arg)); } template auto Tanh(T* res, T weight, T* arg) { Map(res) = weight * Fastor::tanh(Map(arg)); } template auto Sqrt(T* res, T weight, T* arg) { Map(res) = weight * Fastor::sqrt(Map(arg)); } template auto Sqrtabs(T* res, T weight, T* arg) { Map(res) = weight * Fastor::sqrt(Fastor::abs(Map(arg))); } template auto Cbrt(T* res, T weight, T* arg) { Map(res) = weight * Fastor::cbrt(Map(arg)); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/fastor.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_FASTOR_HPP #define OPERON_BACKEND_FASTOR_HPP #include "fastor/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/backend/plain/derivatives.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_PLAIN_DERIVATIVES_HPP #define OPERON_BACKEND_PLAIN_DERIVATIVES_HPP #include "operon/core/node.hpp" #include "functions.hpp" namespace Operon::Backend { namespace detail { template inline auto IsNaN(T value) { return std::isnan(value); } template struct FComp { auto operator()(auto x, auto y) const { using T = std::common_type_t; if ((IsNaN(x) && IsNaN(y)) || (x == y)) { return std::numeric_limits::quiet_NaN(); } if (IsNaN(x)) { return T{0}; } if (IsNaN(y)) { return T{1}; } return static_cast(Compare{}(T{x}, T{y})); } }; template inline auto Sgn(T x) { return (T{0} < x) - (x < T{0}); } } // namespace detail template auto Add(std::vector const& /*nodes*/, Backend::View /*primal*/, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { std::ranges::fill_n(Ptr(trace, j), S, T{1}); } template auto Mul(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto *res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); std::transform(pi, pi+S, pj, res, std::divides{}); } template auto Sub(std::vector const& nodes, Backend::View /*primal*/, Backend::View trace, std::integral auto i, std::integral auto j) { auto v = (nodes[i].Arity == 1 || j < i-1) ? T{-1} : T{+1}; std::ranges::fill_n(Ptr(trace, j), S, v); } template auto Div(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto const& n = nodes[i]; auto *res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (n.Arity == 1) { std::transform(pj, pj+S, res, [](auto x) { return -T{1} / (x * x); }); } else { auto v = j == i-1 ? T{1} : T{-1}; std::transform(pi, pi+S, pj, res, [v](auto x, auto y) { return v * x / y; }); } } template auto Aq(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto *res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { std::transform(pi, pi+S, pj, res, std::divides{}); } else { auto const* pk = Ptr(primal, i-1); for (auto s = 0UL; s < S; ++s) { auto const a = pi[s]; auto const b = pj[s]; auto const c = pk[s]; res[s] = -b * a * a * a / (c * c); } } } template auto Pow(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { auto const k = j - (nodes[j].Length + 1); auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; ++s) { res[s] = pi[s] * pk[s] / pj[s]; } } else { auto const k = i-1; auto const* pk = Ptr(primal, k); std::transform(pi, pi+S, pk, res, [](auto x, auto y) { return x * std::log(y); }); } } template auto Powabs(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { auto const k = j - (nodes[j].Length + 1); auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; ++s) { res[s] = pi[s] * pk[s] * detail::Sgn(pj[s]) / std::abs(pj[s]); } } else { auto const k = i-1; auto const* pk = Ptr(primal, k); std::transform(pi, pi+S, pk, res, [](auto x, auto y) { return x * std::log(std::abs(y)); }); } } template auto Min(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); auto const* pk = Ptr(primal, k); std::transform(pj, pj+S, pk, res, detail::FComp>{}); } template auto Max(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); auto const* pk = Ptr(primal, k); std::transform(pj, pj+S, pk, res, detail::FComp>{}); } template auto Square(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return T{2} * x; }); } template auto Abs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, detail::Sgn); } template auto Ceil(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return std::ceil(x); }); } template auto Floor(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return std::floor(x); }); } template auto Exp(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { std::ranges::copy_n(Ptr(primal, i), S, Ptr(trace, j)); } template auto Log(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return T{1} / x; }); } template auto Log1p(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return T{1} / (T{1} + x); }); } template auto Logabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return detail::Sgn(x) / std::abs(x); }); } template auto Sin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return std::cos(x); }); } template auto Cos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return -std::sin(x); }); } template auto Tan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x) { return T{1} + x * x; }); } template auto Sinh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return std::cosh(x); }); } template auto Cosh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return std::sinh(x); }); } template auto Tanh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x) { return T{1} - x * x; }); } template auto Asin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return T{1} / std::sqrt(T{1} - x * x); }); } template auto Acos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return -T{1} / std::sqrt(T{1} - x * x); }); } template auto Atan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return T{1} / (T{1} + x * x); }); } template auto Sqrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x){ return T{1} / (T{2} * x); }); } template auto Sqrtabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); std::transform(pi, pi+S, pj, res, [](auto x, auto y){ return detail::Sgn(y) / (T{2} * x); }); } template auto Cbrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { // Col(trace, j) = (T{3} * Col(primal, i).square()).inverse(); auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x){ return T{1} / (T{3} * x*x); }); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/plain/functions.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_PLAIN_FUNCTIONS_HPP #define OPERON_BACKEND_PLAIN_FUNCTIONS_HPP #include "operon/core/dispatch.hpp" #include "operon/core/node.hpp" namespace Operon::Backend { // utility template auto Fill(T* res, T value) { std::ranges::fill_n(res, S, value); } template auto Fill(T* res, int n, T value) { std::ranges::fill_n(res, n, value); } // unary functions template auto Add(T* res, T weight, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = weight * (args[i] + ...); } } template auto Mul(T* res, T weight, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = weight * (args[i] * ...); } } template auto Sub(T* res, T weight, auto const* first, auto const*... rest) { for (auto i = 0UL; i < S; ++i) { if constexpr (sizeof...(rest) == 0) { res[i] = weight * -first[i]; } else { res[i] = weight * (first[i] - (rest[i] + ...)); } } } template auto Div(T* res, T weight, auto const* first, auto const*... rest) { for (auto i = 0UL; i < S; ++i) { if constexpr (sizeof...(rest) == 0) { res[i] = weight / first[i]; } else { res[i] = weight * first[i] / (rest[i] * ...); } } } template auto Min(T* res, T weight, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = weight * std::min({args[i]...}); } } template auto Max(T* res, T weight, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = weight * std::max({args[i]...}); } } // binary functions template auto Aq(T* res, T weight, T const* a, T const* b) { std::transform(a, a+S, b, res, [&](auto x, auto y) { return weight * x / std::sqrt(T{1} + y*y); }); } template auto Pow(T* res, T weight, T const* a, T const* b) { std::transform(a, a+S, b, res, [&](auto x, auto y) { return weight * std::pow(x, y); }); } template auto Powabs(T* res, T weight, T const* a, T const* b) { std::transform(a, a+S, b, res, [&](auto x, auto y) { return weight * std::pow(std::abs(x), y); }); } // unary functions template auto Cpy(T* res, T w, T const* arg) { //std::copy_n(arg, S, res); std::transform(arg, arg+S, res, [w](auto x) { return w * x; }); } template auto Neg(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * -x; }); } template auto Inv(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight / x; }); } template auto Abs(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::abs(x); }); } template auto Square(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * x * x; }); } template auto Ceil(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::ceil(x); }); } template auto Floor(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::floor(x); }); } template auto Exp(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::exp(x); }); } template auto Log(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::log(x); }); } template auto Log1p(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::log1p(x); }); } template auto Logabs(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::log(std::abs(x)); }); } template auto Sin(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::sin(x); }); } template auto Cos(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::cos(x); }); } template auto Tan(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::tan(x); }); } template auto Asin(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::asin(x); }); } template auto Acos(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::acos(x); }); } template auto Atan(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::atan(x); }); } template auto Sinh(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::sinh(x); }); } template auto Cosh(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::cosh(x); }); } template auto Tanh(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::tanh(x); }); } template auto Sqrt(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::sqrt(x); }); } template auto Sqrtabs(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto v){ return weight * std::sqrt(std::abs(v)); }); } template auto Cbrt(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::cbrt(x); }); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/plain.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_PLAIN_HPP #define OPERON_BACKEND_PLAIN_HPP #include "plain/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/backend/vdt/derivatives.hpp ================================================ #ifndef OPERON_BACKEND_VDT_DERIVATIVES_HPP #define OPERON_BACKEND_VDT_DERIVATIVES_HPP #include "functions.hpp" namespace Operon::Backend { namespace detail { template inline auto IsNaN(T value) { return std::isnan(value); } template struct FComp { auto operator()(auto x, auto y) const { using T = std::common_type_t; if ((IsNaN(x) && IsNaN(y)) || (x == y)) { return std::numeric_limits::quiet_NaN(); } if (IsNaN(x)) { return T{0}; } if (IsNaN(y)) { return T{1}; } return static_cast(Compare{}(T{x}, T{y})); } }; template inline auto Sgn(T x) { return (T{0} < x) - (x < T{0}); } } // namespace detail template auto Add(std::vector const& /*nodes*/, Backend::View /*primal*/, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { std::ranges::fill_n(Ptr(trace, j), S, T{1}); } template auto Mul(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto *res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); std::transform(pi, pi+S, pj, res, [](auto x, auto y) { return x * detail::vdt::Inv(y); }); } template auto Sub(std::vector const& nodes, Backend::View /*primal*/, Backend::View trace, std::integral auto i, std::integral auto j) { auto v = (nodes[i].Arity == 1 || j < i-1) ? T{-1} : T{+1}; std::ranges::fill_n(Ptr(trace, j), S, v); } template auto Div(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto const& n = nodes[i]; auto *res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (n.Arity == 1) { std::transform(pj, pj+S, res, [](auto x) { return -detail::vdt::Inv(x * x); }); } else { auto v = j == i-1 ? T{1} : T{-1}; std::transform(pi, pi+S, pj, res, [v](auto x, auto y) { return detail::vdt::Div(v*x, y); }); } } template auto Aq(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto *res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { std::transform(pi, pi+S, pj, res, detail::vdt::Div); } else { auto const* pk = Ptr(primal, i-1); for (auto s = 0UL; s < S; ++s) { auto const a = pi[s]; auto const b = pj[s]; auto const c = pk[s]; res[s] = -detail::vdt::Div(b * a * a * a, c * c); } } } template auto Pow(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { auto const k = j - (nodes[j].Length + 1); auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; ++s) { res[s] = detail::vdt::Div(pi[s] * pk[s], pj[s]); } } else { auto const k = i-1; auto const* pk = Ptr(primal, k); std::transform(pi, pi+S, pk, res, [](auto x, auto y) { return x * detail::vdt::Log(y); }); } } template auto Powabs(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); if (j == i-1) { auto const k = j - (nodes[j].Length + 1); auto const* pk = Ptr(primal, k); for (auto s = 0UL; s < S; ++s) { res[s] = detail::vdt::Div(pi[s] * pk[s] * detail::Sgn(pj[s]), std::abs(pj[s])); } } else { auto const k = i-1; auto const* pk = Ptr(primal, k); std::transform(pi, pi+S, pk, res, [](auto x, auto y) { return x * detail::vdt::Log(std::abs(y)); }); } } template auto Min(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); auto const* pk = Ptr(primal, k); std::transform(pj, pj+S, pk, res, detail::FComp>{}); } template auto Max(std::vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto k = j == i - 1 ? (j - nodes[j].Length - 1) : i - 1; auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); auto const* pk = Ptr(primal, k); std::transform(pj, pj+S, pk, res, detail::FComp>{}); } template auto Square(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return T{2} * x; }); } template auto Abs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, detail::Sgn); } template auto Ceil(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return std::ceil(x); }); } template auto Floor(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return std::floor(x); }); } template auto Exp(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { std::ranges::copy_n(Ptr(primal, i), S, Ptr(trace, j)); } template auto Log(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return detail::vdt::Inv(x); }); } template auto Log1p(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return detail::vdt::Inv(T{1} + x); }); } template auto Logabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return detail::vdt::Div(detail::Sgn(x), std::abs(x)); }); } template auto Sin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return detail::vdt::Cos(x); }); } template auto Cos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x){ return -detail::vdt::Sin(x); }); } template auto Tan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x) { return T{1} + x * x; }); } template auto Sinh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return std::cosh(x); }); } template auto Cosh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return std::sinh(x); }); } template auto Tanh(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x) { return T{1} - x * x; }); } template auto Asin(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return detail::vdt::ISqrt(T{1} - x * x); }); } template auto Acos(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return -detail::vdt::ISqrt(T{1} - x * x); }); } template auto Atan(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto /*i*/, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pj = Ptr(primal, j); std::transform(pj, pj+S, res, [](auto x) { return detail::vdt::Inv(T{1} + x * x); }); } template auto Sqrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x){ return detail::vdt::Inv(T{2} * x); }); } template auto Sqrtabs(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); auto const* pj = Ptr(primal, j); std::transform(pi, pi+S, pj, res, [](auto x, auto y){ return detail::vdt::Div(detail::Sgn(y), T{2} * x); }); } template auto Cbrt(std::vector const& /*nodes*/, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { // Col(trace, j) = (T{3} * Col(primal, i).square()).inverse(); auto* res = Ptr(trace, j); auto const* pi = Ptr(primal, i); std::transform(pi, pi+S, res, [](auto x){ return detail::vdt::Inv(T{3} * x*x); }); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/vdt/functions.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_BACKEND_VDT_FUNCTIONS_HPP #define OPERON_BACKEND_VDT_FUNCTIONS_HPP #include #include "operon/core/dispatch.hpp" #include "operon/core/node.hpp" namespace Operon::Backend { namespace detail::vdt { // we need wrappers due to VDT calling conventions inline auto Exp(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); auto constexpr nan{ std::numeric_limits::quiet_NaN() }; auto constexpr inf{ std::numeric_limits::infinity() }; if (std::isnan(x)) { return nan; } if (x == 0) { return 1.F; } if (x == -inf) { return 0; } if (x == +inf) { return inf; } if constexpr (std::is_same_v) { return ::vdt::fast_expf(x); } else { return ::vdt::fast_exp(x); } } inline auto Log(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); auto constexpr nan{ std::numeric_limits::quiet_NaN() }; auto constexpr inf{ std::numeric_limits::infinity() }; if (std::isnan(x)) { return nan; } if (x < 0) { return nan; } if (x == 0) { return -inf; } if (x == 1) { return 0.F; } if (x == inf) { return inf; } if constexpr (std::is_same_v) { return ::vdt::fast_logf(x); } else { return ::vdt::fast_log(x); } } inline auto Logabs(Operon::Scalar x) -> Operon::Scalar { return Log(std::abs(x)); } inline auto Log1p(Operon::Scalar x) -> Operon::Scalar { return Log(1 + x); } inline auto Inv(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); if constexpr (std::is_same_v) { return ::vdt::fast_invf(x); } else { return ::vdt::fast_inv(x); } } inline auto ISqrt(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); auto constexpr nan{ std::numeric_limits::quiet_NaN() }; auto constexpr inf{ std::numeric_limits::infinity() }; if (std::isnan(x)) { return nan; } if (x < 0) { return nan; } if (x == -0) { return -inf; } if (x == +0) { return +inf; } if constexpr (std::is_same_v) { return ::vdt::fast_isqrtf(x); } else { return ::vdt::fast_isqrt(x); } } inline auto Sqrt(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); constexpr auto nan = std::numeric_limits::quiet_NaN(); if (std::isnan(x)) { return nan; } if (x < 0) { return nan; } if (x == 0) { return 0; } if constexpr (std::is_same_v) { return x * ::vdt::fast_isqrtf(x); } else { return x * ::vdt::fast_isqrt(x); } } inline auto Sqrtabs(Operon::Scalar x) -> Operon::Scalar { return Sqrt(std::abs(x)); } inline auto Cbrt(Operon::Scalar x) -> Operon::Scalar { return std::cbrt(x); } inline auto Floor(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); return static_cast(::vdt::details::fpfloor(x)); } inline auto Div(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { static_assert(std::is_arithmetic_v); return x * Inv(y); } inline auto Aq(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { static_assert(std::is_arithmetic_v); return x * ISqrt(Operon::Scalar{1} + y * y); } inline auto Pow(Operon::Scalar x, Operon::Scalar y) -> Operon::Scalar { static_assert(std::is_arithmetic_v); auto constexpr nan{ std::numeric_limits::quiet_NaN() }; auto constexpr inf{ std::numeric_limits::infinity() }; if (std::isnan(x)) { return nan; } if (std::isnan(y)) { return nan; } if (x == 0) { return y < 0 ? inf : x; } if (x < 0) { return nan; } if (y == 0) { return 1.F; } if (y == -inf) { return 0; } if (y == +inf) { return inf; } return Exp(y * Log(x)); } inline auto Acos(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); if constexpr (std::is_same_v) { return ::vdt::fast_acosf(x); } else { return ::vdt::fast_acos(x); } } inline auto Asin(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); if constexpr (std::is_same_v) { return ::vdt::fast_asinf(x); } else { return ::vdt::fast_asin(x); } } inline auto Atan(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); if constexpr (std::is_same_v) { return ::vdt::fast_atanf(x); } else { return ::vdt::fast_atan(x); } } inline auto Cos(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); if (!std::isfinite(x)) { return std::numeric_limits::quiet_NaN(); } if (x == 0) { return 1.F; } if constexpr (std::is_same_v) { return ::vdt::fast_cosf(x); } else { return ::vdt::fast_cos(x); } } inline auto Sin(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); if (!std::isfinite(x)) { return std::numeric_limits::quiet_NaN(); } if (x == 0) { return x; } if constexpr (std::is_same_v) { return ::vdt::fast_sinf(x); } else { return ::vdt::fast_sin(x); } } inline auto Tan(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); if constexpr (std::is_same_v) { return ::vdt::fast_tanf(x); } else { return ::vdt::fast_tan(x); } } inline auto Sinh(Operon::Scalar x) -> Operon::Scalar { auto const e = Exp(x); return Div(e * e - Operon::Scalar{1}, e + e); } inline auto Cosh(Operon::Scalar x) -> Operon::Scalar { auto const e = Exp(x); return Div(e * e + Operon::Scalar{1}, e + e); } inline auto Tanh(Operon::Scalar x) -> Operon::Scalar { static_assert(std::is_arithmetic_v); constexpr auto nan { std::numeric_limits::quiet_NaN() }; if (std::isnan(x)) { return nan; } if (x == 0) { return 0; } if constexpr (std::is_same_v) { return ::vdt::fast_tanhf(x); } else { return ::vdt::fast_tanh(x); } } } // namespace detail::vdt // utility template auto Fill(T* res, T value) { std::ranges::fill_n(res, S, value); } template auto Fill(T* res, int n, T value) { std::ranges::fill_n(res, n, value); } // unary functions template auto Add(T* res, T weight, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = weight * (args[i] + ...); } } template auto Mul(T* res, T weight, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = weight * (args[i] * ...); } } template auto Sub(T* res, T weight, auto const* first, auto const*... rest) { for (auto i = 0UL; i < S; ++i) { if constexpr (sizeof...(rest) == 0) { res[i] = weight * -first[i]; } else { res[i] = weight * (first[i] - (rest[i] + ...)); } } } template auto Div(T* res, T weight, auto const* first, auto const*... rest) { for (auto i = 0UL; i < S; ++i) { if constexpr (sizeof...(rest) == 0) { res[i] = weight * detail::vdt::Inv(first[i]); } else { res[i] = weight * detail::vdt::Div(first[i], (rest[i] * ...)); } } } template auto Min(T* res, T weight, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = weight * std::min({args[i]...}); } } template auto Max(T* res, T weight, auto const*... args) { for (auto i = 0UL; i < S; ++i) { res[i] = weight * std::max({args[i]...}); } } // binary functions template auto Aq(T* res, T weight, T const* a, T const* b) { std::transform(a, a+S, b, res, [&](auto x, auto y) { return weight * detail::vdt::Aq(x, y); }); } template auto Pow(T* res, T weight, T const* a, T const* b) { std::transform(a, a+S, b, res, [&](auto x, auto y) { return weight * detail::vdt::Pow(x, y); }); } template auto Powabs(T* res, T weight, T const* a, T const* b) { std::transform(a, a+S, b, res, [&](auto x, auto y) { return weight * detail::vdt::Pow(std::abs(x), y); }); } // unary functions template auto Cpy(T* res, T weight, T const* arg) { std::ranges::transform(arg, arg+S, res, [&](auto x) { return weight * x; }); } template auto Neg(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * -x; }); } template auto Inv(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Inv(x); }); } template auto Abs(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::abs(x); }); } template auto Square(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * x * x; }); } template auto Ceil(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * std::ceil(x); }); } template auto Floor(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Floor(x); }); } template auto Exp(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Exp(x); }); } template auto Log(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Log(x); }); } template auto Log1p(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Log1p(x); }); } template auto Logabs(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Logabs(x); }); } template auto Sin(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Sin(x); }); } template auto Cos(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Cos(x); }); } template auto Tan(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Tan(x); }); } template auto Asin(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Asin(x); }); } template auto Acos(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Acos(x); }); } template auto Atan(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Atan(x); }); } template auto Sinh(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Sinh(x); }); } template auto Cosh(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Cosh(x); }); } template auto Tanh(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Tanh(x); }); } template auto Sqrt(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Sqrt(x); }); } template auto Sqrtabs(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Sqrtabs(x); }); } template auto Cbrt(T* res, T weight, T const* arg) { std::transform(arg, arg+S, res, [&](auto x) { return weight * detail::vdt::Cbrt(x); }); } } // namespace Operon::Backend #endif ================================================ FILE: include/operon/interpreter/backend/vdt.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_BACKEND_VDT_HPP #define OPERON_BACKEND_VDT_HPP #include "vdt/derivatives.hpp" #endif ================================================ FILE: include/operon/interpreter/derivatives.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_INTERPRETER_DERIVATIVES_HPP #define OPERON_INTERPRETER_DERIVATIVES_HPP #include "functions.hpp" #include namespace Operon { // n-ary functions template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Add(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Sub(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Mul(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Div(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Min(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Max(nodes, primal, trace, i, j); } }; // binary functions template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Aq(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Pow(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Powabs(nodes, primal, trace, i, j); } }; // unary functions template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Abs(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Acos(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Asin(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Atan(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Cbrt(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Ceil(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Cos(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Cosh(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Exp(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Floor(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Log(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Logabs(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Log1p(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Sin(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Sinh(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Sqrt(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Sqrtabs(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Square(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Tan(nodes, primal, trace, i, j); } }; template struct Diff { auto operator()(Operon::Vector const& nodes, Backend::View primal, Backend::View trace, std::integral auto i, std::integral auto j) { Backend::Tanh(nodes, primal, trace, i, j); } }; } // namespace Operon #endif ================================================ FILE: include/operon/interpreter/dual.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_AUTODIFF_FORWARD_DUAL_HPP #define OPERON_AUTODIFF_FORWARD_DUAL_HPP #include "operon/core/types.hpp" #if defined(HAVE_CERES) #include #else #include "operon/ceres/jet.h" #endif namespace Operon { using Dual = ceres::Jet; } // namespace Operon #endif ================================================ FILE: include/operon/interpreter/functions.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_INTERPRETER_FUNCTIONS_HPP #define OPERON_INTERPRETER_FUNCTIONS_HPP #include "operon/core/node.hpp" #include "operon/core/dispatch.hpp" #if defined(OPERON_MATH_EIGEN) #include "operon/interpreter/backend/eigen.hpp" #elif defined(OPERON_MATH_EVE) #include "operon/interpreter/backend/eve.hpp" #elif defined(OPERON_MATH_ARMA) #include "operon/interpreter/backend/arma.hpp" #elif defined(OPERON_MATH_BLAZE) #include "operon/interpreter/backend/blaze.hpp" #elif defined(OPERON_MATH_FASTOR) #include "operon/interpreter/backend/fastor.hpp" #elif defined(OPERON_MATH_STL) #include "operon/interpreter/backend/plain.hpp" #elif defined(OPERON_MATH_VDT) #include "operon/interpreter/backend/vdt.hpp" #elif defined(OPERON_MATH_XTENSOR) #include "operon/interpreter/backend/xtensor.hpp" #elif defined(OPERON_MATH_FAST_V1) #include "operon/interpreter/backend/fast_v1.hpp" #elif defined(OPERON_MATH_FAST_V2) #include "operon/interpreter/backend/fast_v2.hpp" #elif defined(OPERON_MATH_FAST_V3) #include "operon/interpreter/backend/fast_v3.hpp" #endif namespace Operon { template auto Ptr(Backend::View view, std::integral auto i) { return view.data_handle() + (i * S); } // n-ary operations template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto... args) { auto const w = nodes[result].Value; if constexpr (C) { Backend::Add(Ptr(view, result), w, Ptr(view, result), Ptr(view, args)...); } else { Backend::Add(Ptr(view, result), w, (Ptr(view, args))...); } } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto... args) { auto const w = nodes[result].Value; if constexpr (C) { Backend::Mul(Ptr(view, result), w, Ptr(view, result), (Ptr(view, args))...); } else { Backend::Mul(Ptr(view, result), w, (Ptr(view, args))...); } } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto first, std::integral auto... args) { auto const w = nodes[result].Value; if constexpr (C) { if constexpr (sizeof...(args) == 0) { Backend::Sub(Ptr(view, result), w, Ptr(view, result), Ptr(view, first)); } else { Backend::Sub(Ptr(view, result), w, Ptr(view, result), Ptr(view, first), (Ptr(view, args))...); } } else { if constexpr (sizeof...(args) == 0) { Backend::Neg(Ptr(view, result), w, Ptr(view, first)); } else { Backend::Sub(Ptr(view, result), w, Ptr(view, first), (Ptr(view, args))...); } } } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto first, std::integral auto... args) { auto const w = nodes[result].Value; if constexpr (C) { if constexpr (sizeof...(args) == 0) { Backend::Div(Ptr(view, result), w, Ptr(view, result), Ptr(view, first)); } else { Backend::Div(Ptr(view, result), w, Ptr(view, result), Ptr(view, first), (Ptr(view, args))...); } } else { if constexpr (sizeof...(args) == 0) { Backend::Inv(Ptr(view, result), w, Ptr(view, first)); } else { Backend::Div(Ptr(view, result), w, Ptr(view, first), (Ptr(view, args))...); } } } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto first, std::integral auto... args) { auto const w = nodes[result].Value; if constexpr (C) { if constexpr (sizeof...(args) == 0) { Backend::Min(Ptr(view, result), w, Ptr(view, result), Ptr(view, first)); } else { Backend::Min(Ptr(view, result), w, Ptr(view, result), Ptr(view, first), (Ptr(view, args))...); } } else { if constexpr (sizeof...(args) == 0) { Backend::Cpy(Ptr(view, result), w, Ptr(view, first)); } else { Backend::Min(Ptr(view, result), w, Ptr(view, first), (Ptr(view, args))...); } } } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto first, std::integral auto... args) { auto const w = nodes[result].Value; if constexpr (C) { if constexpr (sizeof...(args) == 0) { Backend::Max(Ptr(view, result), w, Ptr(view, result), Ptr(view, first)); } else { Backend::Max(Ptr(view, result), w, Ptr(view, result), Ptr(view, first), (Ptr(view, args))...); } } else { if constexpr (sizeof...(args) == 0) { Backend::Cpy(Ptr(view, result), w, Ptr(view, first)); } else { Backend::Max(Ptr(view, result), w, Ptr(view, first), (Ptr(view, args))...); } } } }; // binary operations template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i, std::integral auto j) { auto const w = nodes[result].Value; Backend::Aq(Ptr(view, result), w, Ptr(view, i), Ptr(view, j)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i, std::integral auto j) { auto const w = nodes[result].Value; Backend::Pow(Ptr(view, result), w, Ptr(view, i), Ptr(view, j)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i, std::integral auto j) { auto const w = nodes[result].Value; Backend::Powabs(Ptr(view, result), w, Ptr(view, i), Ptr(view, j)); } }; // unary operations template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Abs(Ptr(view, result), w, Ptr(view, i)); } }; // unary operations template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Square(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Exp(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Log(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Logabs(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Log1p(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Sqrt(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Sqrtabs(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Cbrt(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Ceil(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Floor(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Sin(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Cos(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Tan(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Asin(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Acos(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Atan(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Sinh(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Cosh(Ptr(view, result), w, Ptr(view, i)); } }; template struct Func { auto operator()(Operon::Vector const& nodes, Backend::View view, std::integral auto result, std::integral auto i) { auto const w = nodes[result].Value; Backend::Tanh(Ptr(view, result), w, Ptr(view, i)); } }; } // namespace Operon #endif ================================================ FILE: include/operon/interpreter/interpreter.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_INTERPRETER_HPP #define OPERON_INTERPRETER_HPP #include #include #include #include #include "operon/core/dataset.hpp" #include "operon/core/tree.hpp" #include "operon/core/types.hpp" #include "operon/core/dispatch.hpp" #include "operon/formatter/formatter.hpp" #include "derivatives.hpp" // #include "tape.hpp" namespace Operon { enum class LikelihoodType : uint8_t { Gaussian, Poisson }; template struct InterpreterBase { InterpreterBase() = default; InterpreterBase(const InterpreterBase&) = default; InterpreterBase(InterpreterBase&&) = default; auto operator=(const InterpreterBase&) -> InterpreterBase& = default; auto operator=(InterpreterBase&&) -> InterpreterBase& = default; virtual ~InterpreterBase() = default; // evaluate model output virtual auto Evaluate(Operon::Span coeff, Operon::Range range, Operon::Span result) const -> void = 0; virtual auto Evaluate(Operon::Span coeff, Operon::Range range) const -> Operon::Vector = 0; // evaluate model jacobian in reverse mode virtual auto JacRev(Operon::Span coeff, Operon::Range range, Operon::Span jacobian) const -> void = 0; virtual auto JacRev(Operon::Span coeff, Operon::Range range) const -> Eigen::Array = 0; // evaluate model jacobian in forward mode virtual auto JacFwd(Operon::Span coeff, Operon::Range range, Operon::Span jacobian) const -> void = 0; virtual auto JacFwd(Operon::Span coeff, Operon::Range range) const -> Eigen::Array = 0; // getters [[nodiscard]] virtual auto GetTree() const -> Operon::Tree const* = 0; [[nodiscard]] virtual auto GetDataset() const -> Operon::Dataset const* = 0; }; template requires DTable::template SupportsType struct Interpreter : public InterpreterBase { using DispatchTable = DTable; static constexpr auto BatchSize = DTable::template BatchSize; Interpreter(gsl::not_null dtable, gsl::not_null dataset, gsl::not_null tree) : dtable_(dtable) , dataset_(dataset) , tree_(tree) { } auto Primal() const { return primal_; } auto Trace() const { return trace_; } auto Evaluate(Operon::Span coeff, Operon::Range range, Operon::Span result) const -> void final { InitContext(coeff, range); auto const len{ static_cast(range.Size()) }; constexpr int64_t S{ BatchSize }; auto* ptr = primal_.data() + ((primal_.extent(1) - 1) * S); for (auto row = 0L; row < len; row += S) { ForwardPass(range, row, /*trace=*/false); if (std::ssize(result) == len) { auto rem = std::min(S, len - row); std::ranges::copy(std::span(ptr, rem), result.data() + row); } } } auto Evaluate(Operon::Span coeff, Operon::Range range) const -> Operon::Vector final { Operon::Vector res(range.Size()); this->Evaluate(coeff, range, {res.data(), res.size()}); ENSURE(res.size() == range.Size()); return res; } auto JacRev(Operon::Span coeff, Operon::Range range, Operon::Span jacobian) const -> void final { InitContext(coeff, range); auto const len{ static_cast(range.Size()) }; auto const& nodes = tree_->Nodes(); auto const nn { std::ssize(nodes) }; constexpr int64_t S{ BatchSize }; trace_ = Backend::Buffer(S, nn); Backend::Fill(trace_, nn-1, T{1}); Eigen::Map> jac(jacobian.data(), len, coeff.size()); for (auto row = 0L; row < len; row += S) { ForwardPass(range, row, /*trace=*/true); ReverseTrace(range, row, jac); } } auto JacRev(Operon::Span coeff, Operon::Range range) const -> Eigen::Array final { auto const nr{ static_cast(range.Size()) }; Eigen::Array jacobian(nr, coeff.size()); JacRev(coeff, range, { jacobian.data(), static_cast(jacobian.size()) }); return jacobian; } auto JacFwd(Operon::Span coeff, Operon::Range range, Operon::Span jacobian) const -> void final { InitContext(coeff, range); auto const& nodes = tree_->Nodes(); auto const nNodes = std::ssize(nodes); auto const nRows = static_cast(range.Size()); trace_ = Backend::Buffer(BatchSize, nNodes); Backend::Fill(trace_, nNodes-1, T{1}); Eigen::Map> jac(jacobian.data(), nRows, coeff.size()); for (int row = 0; row < nRows; row += BatchSize) { ForwardPass(range, row, /*trace=*/true); ForwardTrace(range, row, jac); } } auto JacFwd(Operon::Span coeff, Operon::Range range) const -> Eigen::Array final { auto const nRows = static_cast(range.Size()); Eigen::Array jacobian(nRows, coeff.size()); JacFwd(coeff, range, { jacobian.data(), static_cast(jacobian.size()) }); return jacobian; } [[nodiscard]] auto GetTree() const -> Operon::Tree const* { return tree_.get(); } [[nodiscard]] auto GetDataset() const -> Operon::Dataset const* { return dataset_.get(); } auto GetDispatchTable() const { return dtable_.get(); } static auto Evaluate(Operon::Tree const& tree, Operon::Dataset const& dataset, Operon::Range const range) -> Operon::Vector { auto coeff = tree.GetCoefficients(); DTable dt; return Interpreter{&dt, &dataset, &tree}.Evaluate(coeff, range); } static auto Evaluate(Operon::Tree const& tree, Operon::Dataset const& dataset, Operon::Range const range, Operon::Span coeff) -> Operon::Vector { DTable dt; return Interpreter{&dt, &dataset, &tree}.Evaluate(coeff, range); } private: // private members using Data = std::tuple, std::optional const>, std::optional const> >; gsl::not_null dtable_; gsl::not_null dataset_; gsl::not_null tree_; // mutable internal state (used by all the forward/reverse passes) mutable Operon::Vector context_; mutable Backend::Buffer primal_; mutable Backend::Buffer trace_; // private methods auto ForwardPass(Operon::Range range, int row, bool trace = false) const -> void { auto const& nodes = tree_->Nodes(); auto const nNodes = std::ssize(nodes); auto const rangeStart = static_cast(range.Start()); auto const rangeSize = static_cast(range.Size()); constexpr int64_t S = BatchSize; auto rem = std::min(S, rangeSize - row); Operon::Range rg(rangeStart + row, rangeStart + row + rem); // forward pass - compute primal and trace for (auto i = 0L; i < nNodes; ++i) { if (nodes[i].IsConstant()) { continue; } auto const& [ p, v, f, df ] = context_[i]; auto* ptr = primal_.data() + (i * S); if (nodes[i].IsVariable()) { std::ranges::transform(v.subspan(row, rem), ptr, [p](auto x) { return x * p; }); } else { std::invoke(*f, nodes, primal_, i, rg); // first compute the partials if (trace && df) { for (auto j : Tree::Indices(nodes, i)) { std::invoke(*df, nodes, primal_, trace_, i, j); } } // apply weight after partials are computed //if (p != T{1}) { // std::ranges::transform(std::span(ptr, rem), ptr, [p](auto x) { return x * p; }); //} } } } auto ForwardTrace(Operon::Range range, int row, Eigen::Ref> jac) const -> void { auto const rangeSize = static_cast(range.Size()); auto const& nodes = tree_->Nodes(); auto const nNodes = std::ssize(nodes); constexpr int64_t S = BatchSize; auto const remainingRows = std::min(S, rangeSize - row); Eigen::Array dot(S, nNodes); Operon::Vector cidx(jac.cols()); Eigen::Map> primal(primal_.data(), S, nNodes); Eigen::Map> trace(trace_.data(), S, nNodes); for (auto i = 0L, j = 0L; i < nNodes; ++i) { if (nodes[i].Optimize) { cidx[j++] = i; } } auto k{0}; for (auto c : cidx) { dot.topRows(remainingRows).setConstant(T{0}); dot.col(c).head(remainingRows).setConstant(T{1}); for (auto i = 0; i < nNodes; ++i) { if (nodes[i].IsLeaf()) { continue; } for (auto x : Tree::Indices(nodes, i)) { auto j{ static_cast(x) }; if (nodes[j].IsLeaf() && j != c) { continue; } dot.col(i).head(remainingRows) += dot.col(j).head(remainingRows) * trace.col(j).head(remainingRows) * std::get<0>(context_[i]); } } jac.col(k++).segment(row, remainingRows) = dot.col(nNodes-1).head(remainingRows) * primal.col(c).head(remainingRows) / std::get<0>(context_[c]); } } auto ReverseTrace(Operon::Range range, int row, Eigen::Ref> jac) const -> void { auto const rangeSize = static_cast(range.Size()); auto const& nodes = tree_->Nodes(); auto const nNodes = std::ssize(nodes); constexpr int64_t S = BatchSize; auto const remainingRows = std::min(S, rangeSize - row); auto k{jac.cols()}; Eigen::Map> primal(primal_.data(), S, nNodes); Eigen::Map> trace(trace_.data(), S, nNodes); for (auto i = nNodes-1; i >= 0L; --i) { auto w = std::get<0>(context_[i]); if (nodes[i].Optimize) { jac.col(--k).segment(row, remainingRows) = trace.col(i).head(remainingRows) * primal.col(i).head(remainingRows) / w; } if (nodes[i].IsLeaf()) { continue; } for (auto j : Tree::Indices(nodes, i)) { auto const x { static_cast(j) }; trace.col(x).head(remainingRows) *= trace.col(i).head(remainingRows) * w; } } } // init tree info into context_ and initializes primal_ columns auto InitContext(Operon::Span coeff, Operon::Range range) const { auto const& nodes = tree_->Nodes(); auto const nRows = static_cast(range.Size()); auto const nNodes = std::ssize(nodes); constexpr int64_t S{ BatchSize }; primal_ = Backend::Buffer(S, nNodes); std::ranges::fill_n(primal_.data(), S * nNodes, T{0}); context_.clear(); context_.reserve(nNodes); auto const& dt = dtable_.get(); // aggregate necessary info about the tree into a context object for (int64_t i = 0, j = 0; i < nNodes; ++i) { auto const& n = nodes[i]; auto const* ptr = n.IsVariable() ? dataset_->GetValues(n.HashValue).subspan(range.Start(), range.Size()).data() : nullptr; auto variableValues = std::tuple_element_t<1, Data>(ptr, nRows); auto nodeCoefficient = (!coeff.empty() && n.Optimize) ? T{coeff[j++]} : T{n.Value}; auto nodeFunction = dt->template TryGetFunction(n.HashValue); auto nodeDerivative = dt->template TryGetDerivative(n.HashValue); if (!n.IsLeaf() && !nodeFunction) { throw std::runtime_error(fmt::format("Missing primitive for node {}\n", n.Name())); } context_.emplace_back(nodeCoefficient, variableValues, nodeFunction, nodeDerivative); if (n.IsConstant()) { Backend::Fill(primal_, i, T{nodeCoefficient}); } } } }; // convenience method to interpret many trees in parallel (mostly useful from the python wrapper) auto OPERON_EXPORT EvaluateTrees(Operon::Vector const& trees, Operon::Dataset const* dataset, Operon::Range range, size_t nthread = 0) -> Operon::Vector>; auto OPERON_EXPORT EvaluateTrees(Operon::Vector const& trees, Operon::Dataset const* dataset, Operon::Range range, std::span result, size_t nthread = 0) -> void; } // namespace Operon #endif ================================================ FILE: include/operon/mdspan/mdspan.hpp ================================================ #ifndef OPERON_MDSPAN_HPP #define OPERON_MDSPAN_HPP #define MDSPAN_IMPL_STANDARD_NAMESPACE std #define MDSPAN_IMPL_PROPOSED_NAMESPACE experimental #include #include #include template constexpr auto extents_size = [](std::index_sequence) { return (Extents::static_extent(Idx) * ...); }(std::make_index_sequence{}); #if defined(MDSPAN_IMPL_COMPILER_MSVC) || defined(__INTEL_COMPILER) # define _MDSPAN_RESTRICT_KEYWORD __restrict #elif defined(__GNUC__) || defined(__clang__) # define _MDSPAN_RESTRICT_KEYWORD __restrict__ #else # define _MDSPAN_RESTRICT_KEYWORD #endif #define _MDSPAN_RESTRICT_POINTER( ELEMENT_TYPE ) ELEMENT_TYPE * _MDSPAN_RESTRICT_KEYWORD // https://en.cppreference.com/w/c/language/restrict gives examples // of the kinds of optimizations that may apply to restrict. For instance, // "[r]estricted pointers can be assigned to unrestricted pointers freely, // the optimization opportunities remain in place // as long as the compiler is able to analyze the code:" // // void f(int n, float * restrict r, float * restrict s) { // float * p = r, * q = s; // OK // while(n-- > 0) *p++ = *q++; // almost certainly optimized just like *r++ = *s++ // } // // This is relevant because restrict_accessor::reference is _not_ restrict. // (It's not formally correct to apply C restrict wording to C++ references. // However, GCC defines this extension: // // https://gcc.gnu.org/onlinedocs/gcc/Restricted-Pointers.html // // In what follows, I'll assume that this has a reasonable definition.) // The idea is that even though p[i] has type ElementType& and not ElementType& restrict, // the compiler can figure out that the reference comes from a pointer based on p, // which is marked restrict. // // Note that any performance improvements can only be determined by experiment. // Compilers are not required to do anything with restrict. // Any use of this keyword is not Standard C++, // so you'll have to refer to the compiler's documentation, // look at the assembler output, and do performance experiments. // // NOLINTBEGIN(*) template struct restrict_accessor { using offset_policy = std::default_accessor; using element_type = ElementType; using reference = ElementType&; using data_handle_type = _MDSPAN_RESTRICT_POINTER( ElementType ); constexpr restrict_accessor() noexcept = default; MDSPAN_TEMPLATE_REQUIRES( class OtherElementType, /* requires */ (std::is_convertible::value) ) constexpr explicit restrict_accessor(restrict_accessor/*noname*/) noexcept {} constexpr auto access(data_handle_type p, size_t i) const noexcept -> reference { return p[i]; } constexpr auto offset(data_handle_type p, size_t i) const noexcept -> typename offset_policy::data_handle_type { return p + i; } }; // NOTE (mfh 2022/08/08) BYTE_ALIGNMENT must be unsigned and a power of 2. #if defined(__cpp_lib_assume_aligned) # define _MDSPAN_ASSUME_ALIGNED( ELEMENT_TYPE, POINTER, BYTE_ALIGNMENT ) (std::assume_aligned< BYTE_ALIGNMENT >( POINTER )) constexpr char assume_aligned_method[] = "std::assume_aligned"; #elif defined(__ICL) # define _MDSPAN_ASSUME_ALIGNED( ELEMENT_TYPE, POINTER, BYTE_ALIGNMENT ) POINTER constexpr char assume_aligned_method[] = "(none)"; #elif defined(__ICC) # define _MDSPAN_ASSUME_ALIGNED( ELEMENT_TYPE, POINTER, BYTE_ALIGNMENT ) POINTER constexpr char assume_aligned_method[] = "(none)"; #elif defined(__clang__) # define _MDSPAN_ASSUME_ALIGNED( ELEMENT_TYPE, POINTER, BYTE_ALIGNMENT ) POINTER constexpr char assume_aligned_method[] = "(none)"; #elif defined(__GNUC__) // __builtin_assume_aligned returns void* # define _MDSPAN_ASSUME_ALIGNED( ELEMENT_TYPE, POINTER, BYTE_ALIGNMENT ) reinterpret_cast< ELEMENT_TYPE* >(__builtin_assume_aligned( POINTER, BYTE_ALIGNMENT )) constexpr char assume_aligned_method[] = "__builtin_assume_aligned"; #else # define _MDSPAN_ASSUME_ALIGNED( ELEMENT_TYPE, POINTER, BYTE_ALIGNMENT ) POINTER constexpr char assume_aligned_method[] = "(none)"; #endif // Some compilers other than Clang or GCC like to define __clang__ or __GNUC__. // Thus, we order the tests from most to least specific. #if defined(__ICL) # define _MDSPAN_ALIGN_VALUE_ATTRIBUTE( BYTE_ALIGNMENT ) __declspec(align_value( BYTE_ALIGNMENT )) constexpr char align_attribute_method[] = "__declspec(align_value(BYTE_ALIGNMENT))"; #elif defined(__ICC) # define _MDSPAN_ALIGN_VALUE_ATTRIBUTE( BYTE_ALIGNMENT ) __attribute__((align_value( BYTE_ALIGNMENT ))) constexpr char align_attribute_method[] = "__attribute__((align_value(BYTE_ALIGNMENT)))"; #elif defined(__clang__) # define _MDSPAN_ALIGN_VALUE_ATTRIBUTE( BYTE_ALIGNMENT ) __attribute__((align_value( BYTE_ALIGNMENT ))) constexpr char align_attribute_method[] = "__attribute__((align_value(BYTE_ALIGNMENT)))"; #else # define _MDSPAN_ALIGN_VALUE_ATTRIBUTE( BYTE_ALIGNMENT ) constexpr char align_attribute_method[] = "(none)"; #endif constexpr auto is_nonzero_power_of_two(const std::size_t x) -> bool { // Just checking __cpp_lib_int_pow2 isn't enough for some GCC versions. // The header exists, but std::has_single_bit does not. #if defined(__cpp_lib_int_pow2) && __cplusplus >= 202002L return std::has_single_bit(x); #else return x != 0 && (x & (x - 1)) == 0; #endif } template constexpr auto valid_byte_alignment(const std::size_t byte_alignment) -> bool { return is_nonzero_power_of_two(byte_alignment) && byte_alignment >= alignof(ElementType); } // We define aligned_pointer_t through a struct // so we can check whether the byte alignment is valid. // This makes it impossible to use the alias // with an invalid byte alignment. template struct aligned_pointer { static_assert(valid_byte_alignment(byte_alignment), "byte_alignment must be a power of two no less than " "the minimum required alignment of ElementType."); #if defined(__ICC) // x86-64 ICC 2021.5.0 emits warning #3186 ("expected typedef declaration") here. // No other compiler (including Clang, which has a similar type attribute) has this issue. # pragma warning push # pragma warning disable 3186 #endif using type = ElementType* _MDSPAN_ALIGN_VALUE_ATTRIBUTE( byte_alignment ); #if defined(__ICC) # pragma warning pop #endif }; template using aligned_pointer_t = typename aligned_pointer::type; template auto bless(ElementType* ptr, std::integral_constant /* ba */ ) -> aligned_pointer_t { return _MDSPAN_ASSUME_ALIGNED( ElementType, ptr, byte_alignment ); } template struct aligned_accessor { using offset_policy = std::default_accessor; using element_type = ElementType; using reference = ElementType&; using data_handle_type = aligned_pointer_t; constexpr aligned_accessor() noexcept = default; MDSPAN_TEMPLATE_REQUIRES( class OtherElementType, std::size_t other_byte_alignment, /* requires */ (std::is_convertible::value && other_byte_alignment == ByteAlignment) ) constexpr explicit aligned_accessor(aligned_accessor) noexcept {} constexpr auto access(data_handle_type p, size_t i) const noexcept -> reference { // This may declare alignment twice, depending on // if we have an attribute for marking pointer types. return _MDSPAN_ASSUME_ALIGNED( ElementType, p, ByteAlignment )[i]; } constexpr typename offset_policy::data_handle_type offset(data_handle_type p, size_t i) const noexcept { return p + i; } }; // NOLINTEND(*) #endif ================================================ FILE: include/operon/operators/creator.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_CREATOR_HPP #define OPERON_CREATOR_HPP #include #include #include #include "operon/core/operator.hpp" #include "operon/operon_export.hpp" namespace Operon { class Tree; class PrimitiveSet; // the creator builds a new tree using the existing pset and allowed inputs struct OPERON_EXPORT CreatorBase : public OperatorBase { // maxLength: upper bound for the precomputed achievability table. // Pass the same value as the GP run's maximum tree length to avoid // per-call DP allocation. Pass 0 to disable precomputation and fall // back to a stateless per-call DP in PrimitiveSet::AchievableLength. CreatorBase(gsl::not_null pset, std::vector variables, size_t maxLength); [[nodiscard]] auto GetPrimitiveSet() const -> PrimitiveSet const* { return pset_.get(); } void SetPrimitiveSet(gsl::not_null pset); [[nodiscard]] auto GetVariables() const -> Operon::Span { return variables_; } auto SetVariables(Operon::Span variables) { variables_ = std::vector(variables.begin(), variables.end()); } protected: // Returns the largest tree length <= targetLen achievable with the current // pset. Uses the precomputed snap-down table when targetLen <= maxLength_ // (O(1) lookup, no allocation). Falls back to pset->AchievableLength otherwise. [[nodiscard]] auto AchievableLength(size_t targetLen) const -> size_t; private: auto BuildAchievable() -> void; gsl::not_null pset_; std::vector variables_; std::vector snap_; // snap_[i] = largest achievable length <= i+1 size_t maxLength_; // size of snap_ table (0 = disabled) }; // this tree creator expands bread-wise using a "horizon" of open expansion slots // at the end the breadth sequence of nodes is converted to a postfix sequence // if the depth is not limiting, the target length is guaranteed to be reached class OPERON_EXPORT BalancedTreeCreator final : public CreatorBase { public: BalancedTreeCreator(gsl::not_null pset, std::vector variables, double bias, size_t maxLength) : CreatorBase(pset, std::move(variables), maxLength) , irregularityBias_(bias) { } auto operator()(Operon::RandomGenerator& random, size_t targetLen, size_t minDepth, size_t maxDepth) const -> Tree override; void SetBias(double bias) { irregularityBias_ = bias; } [[nodiscard]] auto GetBias() const -> double { return irregularityBias_; } private: double irregularityBias_; }; class OPERON_EXPORT GrowTreeCreator final : public CreatorBase { public: GrowTreeCreator(gsl::not_null pset, std::vector variables, size_t maxLength) : CreatorBase(pset, std::move(variables), maxLength) { } auto operator()(Operon::RandomGenerator& random, size_t targetLen, size_t minDepth, size_t maxDepth) const -> Tree override; }; class OPERON_EXPORT ProbabilisticTreeCreator final : public CreatorBase { public: ProbabilisticTreeCreator(gsl::not_null pset, std::vector variables, double bias, size_t maxLength) : CreatorBase(pset, std::move(variables), maxLength) , irregularityBias_(bias) { } auto operator()(Operon::RandomGenerator& random, size_t targetLen, size_t minDepth, size_t maxDepth) const -> Tree override; void SetBias(double bias) { irregularityBias_ = bias; } [[nodiscard]] auto GetBias() const -> double { return irregularityBias_; } private: double irregularityBias_; }; } // namespace Operon #endif ================================================ FILE: include/operon/operators/crossover.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_CROSSOVER_HPP #define OPERON_CROSSOVER_HPP #include #include #include #include #include #include #include "operon/operon_export.hpp" #include "operon/core/operator.hpp" #include "operon/core/tree.hpp" #include "operon/core/node.hpp" #include "operon/core/types.hpp" namespace Operon { // crossover takes two parent trees and returns a child struct CrossoverBase : public OperatorBase { using Limits = std::pair; static auto FindCompatibleSwapLocations(Operon::RandomGenerator& random, Tree const& lhs, Tree const& rhs, size_t maxDepth, size_t maxLength, double internalProbability = 1.0) -> std::pair; static auto SelectRandomBranch(Operon::RandomGenerator& random, Tree const& tree, double internalProb, Limits length, Limits level, Limits depth) -> size_t; static auto Cross(const Tree& lhs, const Tree& rhs, /* index of subtree 1 */ size_t i, /* index of subtree 2 */ size_t j) -> Tree; }; class OPERON_EXPORT SubtreeCrossover : public CrossoverBase { public: SubtreeCrossover(double internalProbability, size_t maxDepth, size_t maxLength) : internalProbability_(internalProbability) , maxDepth_(maxDepth) , maxLength_(maxLength) { } auto operator()(Operon::RandomGenerator& random, const Tree& lhs, const Tree& rhs) const -> Tree override; [[nodiscard]] auto InternalProbability() const -> double { return internalProbability_; } [[nodiscard]] auto MaxDepth() const -> size_t { return maxDepth_; } [[nodiscard]] auto MaxLength() const -> size_t { return maxLength_; } private: double internalProbability_; size_t maxDepth_; size_t maxLength_; }; } // namespace Operon #endif ================================================ FILE: include/operon/operators/evaluator.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_EVALUATOR_HPP #define OPERON_EVALUATOR_HPP #include #include #include #include "operon/collections/projection.hpp" #include "operon/core/individual.hpp" #include "operon/core/operator.hpp" #include "operon/core/problem.hpp" #include "operon/core/types.hpp" #include "operon/interpreter/interpreter.hpp" #include "operon/operon_export.hpp" #include "operon/optimizer/likelihood/gaussian_likelihood.hpp" #include "operon/optimizer/likelihood/likelihood_base.hpp" #include "operon/optimizer/likelihood/poisson_likelihood.hpp" namespace Operon { enum class ErrorType : int { SSE, MSE, NMSE, RMSE, MAE, R2, C2 }; struct OPERON_EXPORT ErrorMetric { using Iterator = Operon::Span::iterator; using ProjIterator = ProjectionIterator; explicit ErrorMetric(ErrorType type) : type_(type) { } auto operator()(Operon::Span x, Operon::Span y) const -> double; auto operator()(Operon::Span x, Operon::Span y, Operon::Span w) const -> double; auto operator()(Iterator beg1, Iterator end1, Iterator beg2) const -> double; auto operator()(Iterator beg1, Iterator end1, Iterator beg2, Iterator beg3) const -> double; private: ErrorType type_; }; struct OPERON_EXPORT SSE : public ErrorMetric { SSE() : ErrorMetric(ErrorType::SSE) {} }; struct OPERON_EXPORT MSE : public ErrorMetric { MSE() : ErrorMetric(ErrorType::MSE) {} }; struct OPERON_EXPORT NMSE : public ErrorMetric { NMSE() : ErrorMetric(ErrorType::NMSE) {} }; struct OPERON_EXPORT RMSE : public ErrorMetric { RMSE() : ErrorMetric(ErrorType::RMSE) {} }; struct OPERON_EXPORT MAE : public ErrorMetric { MAE() : ErrorMetric(ErrorType::MAE) {} }; struct OPERON_EXPORT R2 : public ErrorMetric { R2() : ErrorMetric(ErrorType::R2) {} }; struct OPERON_EXPORT C2 : public ErrorMetric { C2() : ErrorMetric(ErrorType::C2) {} }; auto OPERON_EXPORT FitLeastSquares(Operon::Span estimated, Operon::Span target) noexcept -> std::pair; auto OPERON_EXPORT FitLeastSquares(Operon::Span estimated, Operon::Span target) noexcept -> std::pair; using E1 = OperatorBase, Individual const&>; using E2 = OperatorBase, Individual const&, Operon::Span>; struct EvaluatorBase : public E1, E2 { using ReturnType = E1::ReturnType; mutable std::atomic_ulong ResidualEvaluations { 0 }; // NOLINT mutable std::atomic_ulong JacobianEvaluations { 0 }; // NOLINT mutable std::atomic_ulong CallCount { 0 }; // NOLINT mutable std::atomic_ulong CostFunctionTime { 0 }; // NOLINT static constexpr size_t DefaultEvaluationBudget = 100'000; static auto constexpr ErrMax { std::numeric_limits::max() }; auto operator()(Operon::RandomGenerator& /*unused*/, Operon::Individual const& /*unused*/, Operon::Span /*unused*/) const -> ReturnType override = 0; auto operator()(Operon::RandomGenerator& rng, Operon::Individual const& ind) const -> ReturnType override = 0; explicit EvaluatorBase(gsl::not_null problem) : problem_(problem) { } template static auto Evaluate(Derived const* self, Operon::RandomGenerator& rng, Operon::Individual const& ind, std::span buf) { ENSURE(buf.size() >= self->GetProblem()->TrainingRange().Size()); return std::invoke(*self, rng, ind, buf); } template static auto Evaluate(Derived const* self, Operon::RandomGenerator& rng, Operon::Individual const& ind) { std::vector buf(self->GetProblem()->TrainingRange().Size()); return std::invoke(*self, rng, ind, buf); } virtual void Prepare(Operon::Span /*pop*/) const { } virtual auto ObjectiveCount() const -> std::size_t { return 1UL; } auto TotalEvaluations() const -> size_t { return ResidualEvaluations + JacobianEvaluations; } void SetBudget(size_t value) { budget_ = value; } auto Budget() const -> size_t { return budget_; } // virtual because more complex evaluators (e.g. MultiEvaluator) might need to calculate it differently virtual auto BudgetExhausted() const -> bool { return TotalEvaluations() >= Budget(); } virtual auto Stats() const -> std::tuple { return std::tuple{ ResidualEvaluations.load(), JacobianEvaluations.load(), CallCount.load(), CostFunctionTime.load() }; } auto Population() const -> Operon::Span { return population_; } auto SetPopulation(Operon::Span pop) const { population_ = pop; } auto GetProblem() const -> Problem const* { return problem_; } auto SetProblem(gsl::not_null problem) { problem_ = problem; } void Reset() const { ResidualEvaluations = 0; JacobianEvaluations = 0; CallCount = 0; CostFunctionTime = 0; } private: mutable Operon::Span population_; gsl::not_null problem_; size_t budget_ = DefaultEvaluationBudget; }; class OPERON_EXPORT UserDefinedEvaluator : public EvaluatorBase { public: UserDefinedEvaluator(gsl::not_null problem, std::function func) : EvaluatorBase(problem) , fref_(std::move(func)) { } // the func signature taking a pointer to the rng is a workaround for pybind11, since the random generator is non-copyable we have to pass a pointer UserDefinedEvaluator(gsl::not_null problem, std::function func) : EvaluatorBase(problem) , fptr_(std::move(func)) { } auto operator()(Operon::RandomGenerator& rng, Individual const& ind, Operon::Span /*args*/) const -> typename EvaluatorBase::ReturnType override { ++this->CallCount; return fptr_ ? fptr_(&rng, ind) : fref_(rng, ind); } auto operator()(Operon::RandomGenerator& rng, Individual const& ind) const -> typename EvaluatorBase::ReturnType override { return EvaluatorBase::Evaluate(this, rng, ind); } private: std::function fref_; std::function fptr_; // workaround for pybind11 }; template class OPERON_EXPORT Evaluator : public EvaluatorBase { public: using TDispatch = DTable; using TInterpreter = Operon::Interpreter; explicit Evaluator(gsl::not_null problem, gsl::not_null dtable, ErrorMetric error = MSE{}, bool linearScaling = true) : EvaluatorBase(problem) , dtable_(dtable) , error_(error) , scaling_(linearScaling) { } auto GetDispatchTable() const -> DTable const* { return dtable_.get(); } auto operator()(Operon::RandomGenerator& rng, Individual const& ind, Operon::Span buf) const -> typename EvaluatorBase::ReturnType override; auto operator()(Operon::RandomGenerator& rng, Individual const& ind) const -> typename EvaluatorBase::ReturnType override; private: gsl::not_null dtable_; ErrorMetric error_; bool scaling_{false}; }; class OPERON_EXPORT MultiEvaluator : public EvaluatorBase { public: explicit MultiEvaluator(Problem const* problem) : EvaluatorBase(problem) { } auto Add(EvaluatorBase const* evaluator) { evaluators_.emplace_back(evaluator); } auto Prepare(Operon::Span pop) const -> void override { for (auto const& e : evaluators_) { e->Prepare(pop); } } auto ObjectiveCount() const -> std::size_t override { return std::transform_reduce(evaluators_.begin(), evaluators_.end(), 0UL, std::plus {}, [](auto const eval) { return eval->ObjectiveCount(); }); } auto operator()(Operon::RandomGenerator& rng, Individual const& ind) const -> typename EvaluatorBase::ReturnType override { return EvaluatorBase::Evaluate(this, rng, ind); } auto operator()(Operon::RandomGenerator& rng, Individual const& ind, Operon::Span buf) const -> typename EvaluatorBase::ReturnType override { EvaluatorBase::ReturnType fit; fit.reserve(ind.Size()); for (auto const& ev: evaluators_) { auto f = (*ev)(rng, ind, buf); std::copy(f.begin(), f.end(), std::back_inserter(fit)); } return fit; } auto Stats() const -> std::tuple final { auto resEval{0UL}; auto jacEval{0UL}; auto callCnt{0UL}; auto cfTime{0UL}; for (auto const& ev: evaluators_) { auto [re, je, cc, ct] = ev->Stats(); resEval += re; jacEval += je; callCnt += cc; cfTime += ct; } return std::tuple{resEval + ResidualEvaluations.load(), jacEval + JacobianEvaluations.load(), callCnt + CallCount.load(), cfTime + CostFunctionTime.load()}; } auto BudgetExhausted() const -> bool final { auto [re, je, cc, ct] = Stats(); return re + je >= Budget(); } auto Evaluators() const { return evaluators_; } private: std::vector> evaluators_; }; class OPERON_EXPORT AggregateEvaluator final : public EvaluatorBase { public: enum class AggregateType : int { Min, Max, Median, Mean, HarmonicMean, Sum }; explicit AggregateEvaluator(gsl::not_null evaluator) : EvaluatorBase(evaluator->GetProblem()) , evaluator_(evaluator) { } auto SetAggregateType(AggregateType type) { aggtype_ = type; } auto GetAggregateType() const { return aggtype_; } auto operator()(Operon::RandomGenerator& rng, Individual const& ind) const -> typename EvaluatorBase::ReturnType override; auto operator()(Operon::RandomGenerator& rng, Individual const& ind, Operon::Span buf) const -> typename EvaluatorBase::ReturnType override; private: gsl::not_null evaluator_; AggregateType aggtype_ { AggregateType::Mean }; }; // a couple of useful user-defined evaluators (mostly to avoid calling lambdas from python) // TODO: think about a better design class OPERON_EXPORT LengthEvaluator : public UserDefinedEvaluator { public: explicit LengthEvaluator(gsl::not_null problem, size_t maxlength = 1) : UserDefinedEvaluator(problem, [maxlength](Operon::RandomGenerator& /*unused*/, Operon::Individual const& ind) { return EvaluatorBase::ReturnType { static_cast(ind.Genotype.Length()) / static_cast(maxlength) }; }) { } }; class OPERON_EXPORT ShapeEvaluator : public UserDefinedEvaluator { public: explicit ShapeEvaluator(Operon::Problem const* problem) : UserDefinedEvaluator(problem, [](Operon::RandomGenerator& /*unused*/, Operon::Individual const& ind) { return EvaluatorBase::ReturnType { static_cast(ind.Genotype.VisitationLength()) }; }) { } }; class OPERON_EXPORT DiversityEvaluator : public EvaluatorBase { public: explicit DiversityEvaluator(Operon::Problem const* problem, Operon::HashMode hashmode = Operon::HashMode::Strict, std::size_t sampleSize = 100) : EvaluatorBase(problem) , hashmode_(hashmode) , sampleSize_(sampleSize) { } auto operator()(Operon::RandomGenerator& /*random*/, Individual const& ind) const -> typename EvaluatorBase::ReturnType override; auto operator()(Operon::RandomGenerator& /*random*/, Individual const& ind, Operon::Span buf) const -> typename EvaluatorBase::ReturnType override; auto Prepare(Operon::Span pop) const -> void override; private: mutable Operon::Map> divmap_; Operon::HashMode hashmode_ { Operon::HashMode::Strict }; std::size_t sampleSize_ {}; }; template class OPERON_EXPORT MinimumDescriptionLengthEvaluator final : public Evaluator { using Base = Evaluator; public: explicit MinimumDescriptionLengthEvaluator(Operon::Problem const* problem, DTable const* dtable) : Base(problem, dtable, SSE{}) { } auto Sigma() const { return std::span{sigma_}; } auto SetSigma(std::vector sigma) const -> void { sigma_ = std::move(sigma); } auto operator()(Operon::RandomGenerator& rng, Operon::Individual const& ind) const -> typename EvaluatorBase::ReturnType override { return EvaluatorBase::Evaluate(this, rng, ind); } auto operator()(Operon::RandomGenerator& /*random*/, Individual const& ind, Operon::Span buf) const -> typename EvaluatorBase::ReturnType override { ++Base::CallCount; auto const* dtable = Base::GetDispatchTable(); auto const* problem = Base::GetProblem(); auto const* dataset = problem->GetDataset(); auto const& nodes = ind.Genotype.Nodes(); // this call will optimize the tree coefficients and compute the SSE auto const& tree = ind.Genotype; Operon::Interpreter interpreter{dtable, dataset, &ind.Genotype}; auto parameters = tree.GetCoefficients(); auto const p { static_cast(parameters.size()) }; auto const trainingRange = problem->TrainingRange(); ENSURE(buf.size() >= trainingRange.Size()); ++Base::ResidualEvaluations; interpreter.Evaluate(parameters, trainingRange, buf); auto estimatedValues = buf; auto targetValues = problem->TargetValues(trainingRange); Operon::Scalar profiledSigma{}; if (sigma_.empty() && Lik::UsesSigma) { // profile MLE σ̂ = sqrt(SSR/n) from residuals auto const nObs = static_cast(trainingRange.Size()); auto ssr = 0.0; for (auto i = 0; i < static_cast(trainingRange.Size()); ++i) { auto const e = static_cast(estimatedValues[i]) - static_cast(targetValues[i]); ssr += e * e; } profiledSigma = std::max(static_cast(std::sqrt(ssr / nObs)), std::numeric_limits::epsilon()); } auto const effectiveSigma = (sigma_.empty() && Lik::UsesSigma) ? std::span{&profiledSigma, 1} // profiled : std::span{sigma_}; // fixed scalar, per-sample, or empty (Poisson unweighted) // codelength of the complexity // count number of unique functions // - count weight * variable as three nodes // - compute complexity c of the remaining numerical values // (that are not part of the coefficients that are optimized) static auto const MulHash = Node{NodeType::Mul}.HashValue; static auto const ParamHash = Node{NodeType::Constant}.HashValue; Operon::Set uniqueFunctions; // to count the number of unique symbol types auto k{0.0}; // number of nodes auto cComplexity { 0.0 }; // codelength of the parameters ++Base::JacobianEvaluations; Eigen::Matrix jac = interpreter.JacRev(parameters, trainingRange); // jacobian auto fisherMatrix = Lik::ComputeFisherMatrix(estimatedValues, {jac.data(), static_cast(jac.size())}, effectiveSigma); auto fisherDiag = fisherMatrix.diagonal().array(); ENSURE(fisherDiag.size() == p); auto cParameters { 0.0 }; auto constexpr eps = std::numeric_limits::epsilon(); // machine epsilon for zero comparison for (auto i = 0, pi = 0; i < std::ssize(nodes); ++i) { auto const& n = nodes[i]; // weighted variables count as 3 symbol types: the variable itself, plus implicit MUL and PARAM // unit-weight variables count as 1 (interpreter skips the multiplication, expression is equivalent) auto const isWeighted = n.IsVariable() && n.Value != Operon::Scalar{1}; k += isWeighted ? 3 : 1; uniqueFunctions.insert(n.HashValue); if (isWeighted) { uniqueFunctions.insert(MulHash); uniqueFunctions.insert(ParamHash); } if (n.Optimize) { // DL of the parameters to be optimized auto const di = std::sqrt(12 / fisherDiag(pi)); auto const ci = std::abs(parameters[pi]); if (std::isfinite(ci) && std::isfinite(di) && ci / di >= 1) { cParameters += (0.5 * std::log(fisherDiag(pi))) + std::log(ci); } ++pi; } else { // DL of the remaining tree structure if (std::abs(n.Value) < eps) { continue; } cComplexity += std::log(std::abs(n.Value)); } } auto q { static_cast(uniqueFunctions.size()) }; if (q > 0) { cComplexity += k * std::log(q); } cParameters -= p/2 * std::log(3); auto cLikelihood = Lik::ComputeLikelihood(estimatedValues, targetValues, effectiveSigma); auto mdl = cComplexity + cParameters + cLikelihood; if (!std::isfinite(mdl)) { mdl = EvaluatorBase::ErrMax; } return typename EvaluatorBase::ReturnType { static_cast(mdl) }; } private: mutable std::vector sigma_; }; template class OPERON_EXPORT FractionalBayesFactorEvaluator final : public Evaluator { using Base = Evaluator; public: explicit FractionalBayesFactorEvaluator(Operon::Problem const* problem, DTable const* dtable) : Base(problem, dtable, SSE{}) { } auto Sigma() const { return std::span{sigma_}; } auto SetSigma(std::vector sigma) const -> void { sigma_ = std::move(sigma); } auto operator()(Operon::RandomGenerator& rng, Operon::Individual const& ind) const -> typename EvaluatorBase::ReturnType override { return EvaluatorBase::Evaluate(this, rng, ind); } auto operator()(Operon::RandomGenerator& /*random*/, Individual const& ind, Operon::Span buf) const -> typename EvaluatorBase::ReturnType override { ++Base::CallCount; auto const* dtable = Base::GetDispatchTable(); auto const* problem = Base::GetProblem(); auto const* dataset = problem->GetDataset(); auto const& nodes = ind.Genotype.Nodes(); auto const& tree = ind.Genotype; Operon::Interpreter interpreter{dtable, dataset, &tree}; auto parameters = tree.GetCoefficients(); auto const p { static_cast(parameters.size()) }; auto const trainingRange = problem->TrainingRange(); auto const n { static_cast(trainingRange.Size()) }; ENSURE(buf.size() >= trainingRange.Size()); ++Base::ResidualEvaluations; interpreter.Evaluate(parameters, trainingRange, buf); auto estimatedValues = buf; auto targetValues = problem->TargetValues(trainingRange); double mlNLL{}; Operon::Scalar profiledSigma{}; if (sigma_.empty() && Lik::UsesSigma) { // profile MLE σ̂ = sqrt(SSR/n); NLL = 0.5·n·(log(2π·σ̂²)+1), clamped to avoid log(0) auto ssr = 0.0; for (auto i = 0; i < static_cast(trainingRange.Size()); ++i) { auto const e = static_cast(estimatedValues[i]) - static_cast(targetValues[i]); ssr += e * e; } profiledSigma = std::max(static_cast(std::sqrt(ssr / n)), std::numeric_limits::epsilon()); auto const s = static_cast(profiledSigma); mlNLL = 0.5 * n * (std::log(Operon::Math::Tau * s * s) + 1.0); } auto const effectiveSigma = (sigma_.empty() && Lik::UsesSigma) ? std::span{&profiledSigma, 1} // profiled : std::span{sigma_}; // fixed scalar, per-sample, or empty (Poisson unweighted) // structural complexity: k * log(q), matching Julia func_compl static auto const MulHash = Node{NodeType::Mul}.HashValue; static auto const ParamHash = Node{NodeType::Constant}.HashValue; Operon::Set uniqueSymbols; auto k { 0.0 }; for (auto const& node : nodes) { // weighted variables count as 3 symbol types: the variable itself, plus implicit MUL and PARAM // unit-weight variables count as 1 (interpreter skips the multiplication, expression is equivalent) auto const isWeighted = node.IsVariable() && node.Value != Operon::Scalar{1}; k += isWeighted ? 3 : 1; uniqueSymbols.insert(node.HashValue); if (isWeighted) { uniqueSymbols.insert(MulHash); uniqueSymbols.insert(ParamHash); } } auto const q { static_cast(uniqueSymbols.size()) }; auto const fComplexity = q > 0 ? k * std::log(q) : 0.0; // parameter term: (p/2) * (-log(b) + log(2π·nup)) // b = 1/sqrt(n) => -log(b) = 0.5·log(n) // nup = exp(1 - log(3)) => log(nup) = 1 - log(3) // combined: (p/2) * (0.5·log(n) + log(2π) + 1 - log(3)) auto const b { 1.0 / std::sqrt(n) }; auto const cParameters = (p / 2.0) * (0.5 * std::log(n) + std::log(Operon::Math::Tau) + 1.0 - std::log(3.0)); // fractional likelihood: (1 - b) * NLL // Profiled case: use pre-computed NLL (avoids second O(n) pass inside ComputeLikelihood). // Fixed scalar or per-sample: delegate to ComputeLikelihood. auto const nll = (sigma_.empty() && Lik::UsesSigma) ? mlNLL : static_cast(Lik::ComputeLikelihood(estimatedValues, targetValues, effectiveSigma)); auto const cLikelihood = (1.0 - b) * nll; auto fbf = fComplexity + cParameters + cLikelihood; if (!std::isfinite(fbf)) { fbf = EvaluatorBase::ErrMax; } return typename EvaluatorBase::ReturnType { static_cast(fbf) }; } private: mutable std::vector sigma_; }; template class OPERON_EXPORT BayesianInformationCriterionEvaluator final : public Evaluator { using Base = Evaluator; public: explicit BayesianInformationCriterionEvaluator(Operon::Problem const* problem, DTable const* dtable) : Base(problem, dtable, MSE{}) { } auto operator()(Operon::RandomGenerator& /*random*/, Individual const& ind, Operon::Span buf) const -> typename EvaluatorBase::ReturnType override; }; template class OPERON_EXPORT AkaikeInformationCriterionEvaluator final : public Evaluator { using Base = Evaluator; public: explicit AkaikeInformationCriterionEvaluator(Operon::Problem const* problem, DTable const* dtable) : Base(problem, dtable, MSE{}) { } auto operator()(Operon::RandomGenerator& /*random*/, Individual const& ind, Operon::Span buf) const -> typename EvaluatorBase::ReturnType override; }; template> requires (DTable::template SupportsType) class OPERON_EXPORT LikelihoodEvaluator final : public Evaluator { using Base = Evaluator; public: explicit LikelihoodEvaluator(Operon::Problem const* problem, DTable const* dtable) : Base(problem, dtable), sigma_(1, 0.001) { } auto operator()(Operon::RandomGenerator& /*rng*/, Individual const& ind, Operon::Span buf) const -> typename EvaluatorBase::ReturnType override { ++Base::CallCount; auto const* dtable = Base::Evaluator::GetDispatchTable(); auto const* problem = Base::Evaluator::GetProblem(); auto const* dataset = problem->GetDataset(); auto const* tree = &ind.Genotype; // this call will optimize the tree coefficients and compute the SSE Operon::Interpreter interpreter{dtable, dataset, tree}; auto parameters = tree->GetCoefficients(); auto const trainingRange = problem->TrainingRange(); ENSURE(buf.size() >= trainingRange.Size()); ++Base::ResidualEvaluations; interpreter.Evaluate(parameters, trainingRange, buf); auto estimatedValues = buf; auto targetValues = problem->TargetValues(trainingRange); auto lik = Likelihood::ComputeLikelihood(estimatedValues, targetValues, sigma_); return typename EvaluatorBase::ReturnType { static_cast(lik) }; } auto Sigma() const { return std::span{sigma_}; } auto SetSigma(std::vector sigma) const -> void { sigma_ = std::move(sigma); } private: mutable std::vector sigma_; }; template using GaussianLikelihoodEvaluator = LikelihoodEvaluator>; template using PoissonLikelihoodEvaluator = LikelihoodEvaluator>; } // namespace Operon #endif ================================================ FILE: include/operon/operators/generator.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_GENERATOR_HPP #define OPERON_GENERATOR_HPP #include "operon/core/operator.hpp" #include "operon/hash/zobrist.hpp" #include "operon/operators/crossover.hpp" #include "operon/operators/evaluator.hpp" #include "operon/operators/mutation.hpp" #include "operon/operators/selector.hpp" #include "operon/operators/local_search.hpp" #include "operon/optimizer/optimizer.hpp" namespace Operon { struct RecombinationResult { std::optional Child; std::optional Parent1; std::optional Parent2; explicit operator bool() const { return Child.has_value(); } }; class OffspringGeneratorBase : public OperatorBase, /* crossover prob. */ double, /* mutation prob. */ double, /* local search prob. */ double, /* lamarckian prob */ double, /* memory buffer */ Operon::Span> { public: OffspringGeneratorBase(EvaluatorBase const* eval, CrossoverBase const* cx, MutatorBase const* mut, SelectorBase const* femSel, SelectorBase const* maleSel, CoefficientOptimizer const* coeffOptimizer = nullptr) : evaluator_(eval) , crossover_(cx) , mutator_(mut) , femaleSelector_(femSel) , maleSelector_(maleSel) , coeffOptimizer_{coeffOptimizer} { } [[nodiscard]] auto FemaleSelector() const -> SelectorBase const* { return femaleSelector_.get(); } [[nodiscard]] auto MaleSelector() const -> SelectorBase const* { return maleSelector_.get(); } [[nodiscard]] auto Crossover() const -> CrossoverBase const* { return crossover_.get(); } [[nodiscard]] auto Mutator() const -> MutatorBase const* { return mutator_.get(); } [[nodiscard]] auto Evaluator() const -> EvaluatorBase const* { return evaluator_.get(); } [[nodiscard]] auto Optimizer() const -> CoefficientOptimizer const* { return coeffOptimizer_; } virtual auto Prepare(Operon::Span pop) const -> void { FemaleSelector()->Prepare(pop); MaleSelector()->Prepare(pop); Evaluator()->Prepare(pop); } [[nodiscard]] virtual auto Terminate() const -> bool { return evaluator_->BudgetExhausted(); } auto SetCache(Zobrist* cache) const { cache_ = cache; } [[nodiscard]] auto Cache() const -> Zobrist* { return cache_; } auto Generate(Operon::RandomGenerator& random, double pCrossover, double pMutation, double pLocal, double pLamarck, Operon::Span buf, RecombinationResult& res) const -> void { auto pop = FemaleSelector()->Population(); if (!res.Parent1) { res.Parent1 = pop[ (*FemaleSelector())(random) ]; } if (!res.Parent2) { res.Parent2 = pop[ (*MaleSelector())(random) ]; } res.Child = Individual{Evaluator()->ObjectiveCount()}; using BernoulliTrial = std::bernoulli_distribution; res.Child->Genotype = BernoulliTrial{pCrossover}(random) ? (*Crossover())(random, res.Parent1->Genotype, res.Parent2->Genotype) : res.Parent1->Genotype; if (BernoulliTrial{pMutation}(random)) { res.Child->Genotype = (*Mutator())(random, std::move(res.Child->Genotype)); } auto evaluate = [&]() { if (BernoulliTrial{pLocal}(random)) { auto c = res.Child->Genotype.GetCoefficients(); // save original coefficients auto [optimizedTree, summary] = (*Optimizer())(random, std::move(res.Child->Genotype)); Evaluator()->ResidualEvaluations += summary.FunctionEvaluations; Evaluator()->JacobianEvaluations += summary.JacobianEvaluations; res.Child->Genotype = std::move(optimizedTree); res.Child->Fitness = (*Evaluator())(random, *res.Child, buf); if(!BernoulliTrial{pLamarck}(random)) { res.Child->Genotype.SetCoefficients(c); // restore original coefficients } } else { res.Child->Fitness = (*Evaluator())(random, *res.Child, buf); } for (auto& v : res.Child->Fitness) { if (!std::isfinite(v)) { v = std::numeric_limits::max(); } } }; if (cache_ != nullptr) { auto const hash = cache_->ComputeHash(res.Child->Genotype); Operon::Vector cached(Evaluator()->ObjectiveCount()); if (cache_->TryGet(hash, cached)) { res.Child->Fitness = cached; } else { evaluate(); cache_->Insert(hash, res.Child->Fitness); } } else { evaluate(); } } auto Generate(Operon::RandomGenerator& random, double pCrossover, double pMutation, double pLocal, double pLamarck, Operon::Span buf) const -> RecombinationResult { RecombinationResult res; Generate(random, pCrossover, pMutation, pLocal, pLamarck, buf, res); return res; } private: gsl::not_null evaluator_; gsl::not_null crossover_; gsl::not_null mutator_; gsl::not_null femaleSelector_; gsl::not_null maleSelector_; CoefficientOptimizer const* coeffOptimizer_; mutable Zobrist* cache_{nullptr}; }; class OPERON_EXPORT BasicOffspringGenerator final : public OffspringGeneratorBase { public: explicit BasicOffspringGenerator(EvaluatorBase const* eval, CrossoverBase const* cx, MutatorBase const* mut, SelectorBase const* femSel, SelectorBase const* maleSel, CoefficientOptimizer const* coeffOptimizer = nullptr) : OffspringGeneratorBase(eval, cx, mut, femSel, maleSel, coeffOptimizer) { } auto operator()(Operon::RandomGenerator& random, double pCrossover, double pMutation, double pLocal, double pLamarck, Operon::Span buf) const -> std::optional final; }; class OPERON_EXPORT BroodOffspringGenerator : public OffspringGeneratorBase { public: explicit BroodOffspringGenerator(EvaluatorBase const* eval, CrossoverBase const* cx, MutatorBase const* mut, SelectorBase const* femSel, SelectorBase const* maleSel, CoefficientOptimizer const* coeffOptimizer = nullptr) : OffspringGeneratorBase(eval, cx, mut, femSel, maleSel, coeffOptimizer) , broodSize_(DefaultBroodSize) { } auto operator()(Operon::RandomGenerator& random, double pCrossover, double pMutation, double pLocal, double pLamarck, Operon::Span buf) const -> std::optional final; void BroodSize(size_t value) { broodSize_ = value; } [[nodiscard]] auto BroodSize() const -> size_t { return broodSize_; } static constexpr size_t DefaultBroodSize { 10 }; private: size_t broodSize_; }; class OPERON_EXPORT PolygenicOffspringGenerator : public OffspringGeneratorBase { public: explicit PolygenicOffspringGenerator(EvaluatorBase const* eval, CrossoverBase const* cx, MutatorBase const* mut, SelectorBase const* femSel, SelectorBase const* maleSel, CoefficientOptimizer const* coeffOptimizer = nullptr) : OffspringGeneratorBase(eval, cx, mut, femSel, maleSel, coeffOptimizer) , broodSize_(DefaultBroodSize) { } auto operator()(Operon::RandomGenerator& random, double pCrossover, double pMutation, double pLocal, double pLamarck, Operon::Span buf) const -> std::optional final; void PolygenicSize(size_t value) { broodSize_ = value; } [[nodiscard]] auto PolygenicSize() const -> size_t { return broodSize_; } static constexpr size_t DefaultBroodSize = 10; private: size_t broodSize_; }; class OPERON_EXPORT OffspringSelectionGenerator : public OffspringGeneratorBase { public: explicit OffspringSelectionGenerator(EvaluatorBase const* eval, CrossoverBase const* cx, MutatorBase const* mut, SelectorBase const* femSel, SelectorBase const* maleSel, CoefficientOptimizer const* coeffOptimizer = nullptr) : OffspringGeneratorBase(eval, cx, mut, femSel, maleSel, coeffOptimizer) { } auto operator()(Operon::RandomGenerator& random, double pCrossover, double pMutation, double pLocal, double pLamarck, Operon::Span buf) const -> std::optional final; void MaxSelectionPressure(size_t value) { maxSelectionPressure_ = value; } auto MaxSelectionPressure() const -> size_t { return maxSelectionPressure_; } void ComparisonFactor(double value) { comparisonFactor_ = value; } auto ComparisonFactor() const -> double { return comparisonFactor_; } void Prepare(const Operon::Span pop) const override { OffspringGeneratorBase::Prepare(pop); lastEvaluations_ = this->Evaluator()->TotalEvaluations(); } auto SelectionPressure() const -> double { auto n = this->FemaleSelector()->Population().size(); if (n == 0U) { return 0; } auto e = this->Evaluator()->TotalEvaluations() - lastEvaluations_; return static_cast(e) / static_cast(n); } auto Terminate() const -> bool override { return OffspringGeneratorBase::Terminate() || SelectionPressure() > static_cast(maxSelectionPressure_); }; static constexpr size_t DefaultMaxSelectionPressure { 100 }; static constexpr double DefaultComparisonFactor { 1.0 }; private: mutable size_t lastEvaluations_{0}; size_t maxSelectionPressure_{DefaultMaxSelectionPressure}; double comparisonFactor_{0}; }; } // namespace Operon #endif ================================================ FILE: include/operon/operators/initializer.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_INITIALIZER_HPP #define OPERON_INITIALIZER_HPP #include #include #include "operon/core/tree.hpp" #include "operon/operators/creator.hpp" namespace Operon { struct CoefficientInitializerBase : public OperatorBase { }; struct TreeInitializerBase : public OperatorBase { }; template struct OPERON_EXPORT CoefficientInitializer : public CoefficientInitializerBase { using NodeCheckCallback = std::function; explicit CoefficientInitializer(NodeCheckCallback callback) : callback_(std::move(callback)) { } CoefficientInitializer() : CoefficientInitializer([](auto const& node) { return node.IsLeaf(); }) { } auto operator()(Operon::RandomGenerator& random, Operon::Tree& tree) const -> void override { for (auto& node : tree.Nodes()) { if (callback_(node)) { node.Value = Dist(params_)(random); } } } template auto ParameterizeDistribution(Args... args) const -> void { params_ = typename Dist::param_type { std::forward(args)... }; } private: mutable typename Dist::param_type params_; NodeCheckCallback callback_; }; template struct OPERON_EXPORT TreeInitializer : public TreeInitializerBase { explicit TreeInitializer(Operon::CreatorBase const* creator) : creator_(creator) { } auto operator()(Operon::RandomGenerator& random) const -> Operon::Tree override { auto targetLen = std::max(size_t { 1 }, static_cast(std::round(Dist(params_)(random)))); return creator_->operator()(random, targetLen, minDepth_, maxDepth_); // initialize tree } template auto ParameterizeDistribution(Args... args) const -> void { params_ = typename Dist::param_type { std::forward(args)... }; } void SetMinDepth(size_t minDepth) { minDepth_ = minDepth; } auto MinDepth() const -> size_t { return minDepth_; } void SetMaxDepth(size_t maxDepth) { maxDepth_ = maxDepth; } auto MaxDepth() const -> size_t { return maxDepth_; } void SetCreator(gsl::not_null creator) { creator_ = creator; } [[nodiscard]] auto Creator() const -> CreatorBase const* { return creator_.get(); } static constexpr size_t DefaultMaxDepth { 1000 }; // we don't want a depth restriction to limit the achievable shapes/lengths private: mutable typename Dist::param_type params_; gsl::not_null creator_; size_t minDepth_ { 1 }; size_t maxDepth_ { DefaultMaxDepth }; }; // wraps a creator and generates trees from a given size distribution using UniformCoefficientInitializer = CoefficientInitializer>; using NormalCoefficientInitializer = CoefficientInitializer>; using UniformTreeInitializer = TreeInitializer>; using NormalTreeInitializer = TreeInitializer>; } // namespace Operon #endif ================================================ FILE: include/operon/operators/local_search.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2024 Heal Research #ifndef OPERON_LOCAL_SEARCH_HPP #define OPERON_LOCAL_SEARCH_HPP #include #include "operon/core/operator.hpp" #include "operon/operon_export.hpp" namespace Operon { // forward declarations class Tree; class OptimizerBase; struct OptimizerSummary; class OPERON_EXPORT CoefficientOptimizer : public OperatorBase, Operon::Tree> { public: explicit CoefficientOptimizer(gsl::not_null optimizer) : optimizer_(optimizer) { } // convenience auto operator()(Operon::RandomGenerator& rng, Operon::Tree tree) const -> std::tuple override; private: gsl::not_null optimizer_; }; } // namespace Operon #endif ================================================ FILE: include/operon/operators/mutation.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_MUTATION_HPP #define OPERON_MUTATION_HPP #include #include #include #include #include #include "operon/operon_export.hpp" #include "operon/core/operator.hpp" #include "operon/core/pset.hpp" #include "operon/core/tree.hpp" #include "operon/core/contracts.hpp" #include "operon/core/node.hpp" #include "operon/core/types.hpp" #include "operon/random/random.hpp" namespace Operon { struct CoefficientInitializerBase; struct CreatorBase; // the mutator can work in place or return a copy (child) struct MutatorBase : public OperatorBase { }; template struct OPERON_EXPORT OnePointMutation : public MutatorBase { auto operator()(Operon::RandomGenerator& random, Tree tree) const -> Tree override { auto& nodes = tree.Nodes(); // sample a random leaf auto it = Operon::Random::Sample(random, nodes.begin(), nodes.end(), [](auto const& n) { return n.IsLeaf(); }); EXPECT(it < nodes.end()); it->Value += Dist(params_)(random); return tree; } template auto ParameterizeDistribution(Args... args) const -> void { params_ = typename Dist::param_type { std::forward(args)... }; } private: mutable typename Dist::param_type params_; }; template struct OPERON_EXPORT MultiPointMutation : public MutatorBase { auto operator()(Operon::RandomGenerator& random, Tree tree) const -> Tree override { for (auto& node : tree.Nodes()) { if (node.IsLeaf()) { node.Value += Dist(params_)(random); } } return tree; } template auto ParameterizeDistribution(Args... args) const -> void { params_ = typename Dist::param_type { std::forward(args)... }; } private: mutable typename Dist::param_type params_; }; struct OPERON_EXPORT DiscretePointMutation : public MutatorBase { auto operator()(Operon::RandomGenerator& random, Tree tree) const -> Tree override; auto Add(Operon::Scalar value, Operon::Scalar weight = 1.0) -> void { values_.push_back(value); weights_.push_back(weight); } private: std::vector weights_; std::vector values_; }; struct OPERON_EXPORT MultiMutation : public MutatorBase { auto operator()(Operon::RandomGenerator& /*random*/, Tree /*args*/) const -> Tree override; void Add(gsl::not_null op, double prob) { operators_.emplace_back(op); probabilities_.push_back(prob); } [[nodiscard]] auto Count() const -> size_t { return operators_.size(); } private: std::vector> operators_; std::vector probabilities_; }; struct OPERON_EXPORT ChangeVariableMutation : public MutatorBase { explicit ChangeVariableMutation(Operon::Span variables) : variables_(variables.begin(), variables.end()) { } auto operator()(Operon::RandomGenerator& /*random*/, Tree /*args*/) const -> Tree override; private: std::vector variables_; }; struct OPERON_EXPORT ChangeFunctionMutation : public MutatorBase { explicit ChangeFunctionMutation(PrimitiveSet ps) : pset_(std::move(ps)) { } auto operator()(Operon::RandomGenerator& /*random*/, Tree /*args*/) const -> Tree override; private: PrimitiveSet pset_; }; struct OPERON_EXPORT RemoveSubtreeMutation final : public MutatorBase { explicit RemoveSubtreeMutation(PrimitiveSet ps) : pset_(std::move(ps)) { } auto operator()(Operon::RandomGenerator& /*random*/, Tree /*args*/) const -> Tree override; private: PrimitiveSet pset_; }; struct OPERON_EXPORT InsertSubtreeMutation final : public MutatorBase { InsertSubtreeMutation(gsl::not_null creator, gsl::not_null coeffInit, size_t maxDepth, size_t maxLength) : creator_(creator) , coefficientInitializer_(coeffInit) , maxDepth_(maxDepth) , maxLength_(maxLength) { } auto operator()(Operon::RandomGenerator& /*random*/, Tree /*args*/) const -> Tree override; private: gsl::not_null creator_; gsl::not_null coefficientInitializer_; size_t maxDepth_; size_t maxLength_; }; struct OPERON_EXPORT ReplaceSubtreeMutation : public MutatorBase { ReplaceSubtreeMutation(gsl::not_null creator, gsl::not_null coeffInit, size_t maxDepth, size_t maxLength) : creator_(creator) , coefficientInitializer_(coeffInit) , maxDepth_(maxDepth) , maxLength_(maxLength) { } auto operator()(Operon::RandomGenerator& /*random*/, Tree /*args*/) const -> Tree override; private: gsl::not_null creator_; gsl::not_null coefficientInitializer_; size_t maxDepth_; size_t maxLength_; }; struct OPERON_EXPORT ShuffleSubtreesMutation : public MutatorBase { auto operator()(Operon::RandomGenerator& /*random*/, Tree /*args*/) const -> Tree override; }; } // namespace Operon #endif ================================================ FILE: include/operon/operators/non_dominated_sorter.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_OPERATORS_NONDOMINATED_SORTER_HPP #define OPERON_OPERATORS_NONDOMINATED_SORTER_HPP #include "operon/core/individual.hpp" #include "operon/core/types.hpp" #include "operon/operon_export.hpp" // aggregate performancae statistics such as sort duration #include namespace Operon { enum EfficientSortStrategy : int { Binary, Sequential }; class NondominatedSorterBase { public: using Result = Operon::Vector>; mutable struct { size_t LexicographicalComparisons{0}; // both lexicographical and single-objective size_t SingleValueComparisons{0}; size_t DominanceComparisons{0}; size_t RankComparisons{0}; size_t InnerOps{0}; double MeanRank{0}; double MeanND{0}; std::chrono::duration Duration{0}; } Stats; void Reset() { Stats = { 0, 0, 0, 0, 0, 0., 0. }; } // this method needs to be implemented by all deriving classes virtual auto Sort(Operon::Span, Operon::Scalar) const -> Result = 0; auto operator()(Operon::Span pop, Operon::Scalar eps = 0) const -> Result { return Sort(pop, eps); } }; struct OPERON_EXPORT DeductiveSorter : public NondominatedSorterBase { auto Sort(Operon::Span pop, Operon::Scalar eps) const -> NondominatedSorterBase::Result override; }; struct OPERON_EXPORT DominanceDegreeSorter : public NondominatedSorterBase { auto Sort(Operon::Span pop, Operon::Scalar eps) const -> NondominatedSorterBase::Result override; }; struct OPERON_EXPORT HierarchicalSorter : public NondominatedSorterBase { auto Sort(Operon::Span pop, Operon::Scalar eps) const -> NondominatedSorterBase::Result override; }; struct OPERON_EXPORT EfficientBinarySorter : public NondominatedSorterBase { auto Sort(Operon::Span pop, Operon::Scalar eps) const -> NondominatedSorterBase::Result override; }; struct OPERON_EXPORT EfficientSequentialSorter : public NondominatedSorterBase { auto Sort(Operon::Span pop, Operon::Scalar eps) const -> NondominatedSorterBase::Result override; }; struct OPERON_EXPORT MergeSorter : public NondominatedSorterBase { auto Sort(Operon::Span pop, Operon::Scalar eps) const -> NondominatedSorterBase::Result override; }; struct OPERON_EXPORT RankOrdinalSorter : public NondominatedSorterBase { auto Sort(Operon::Span pop, Operon::Scalar eps) const -> NondominatedSorterBase::Result override; }; struct OPERON_EXPORT RankIntersectSorter : public NondominatedSorterBase { auto Sort(Operon::Span pop, Operon::Scalar eps) const -> NondominatedSorterBase::Result override; }; struct OPERON_EXPORT BestOrderSorter : public NondominatedSorterBase { auto Sort(Operon::Span pop, Operon::Scalar eps) const -> NondominatedSorterBase::Result override; }; } // namespace Operon #endif ================================================ FILE: include/operon/operators/reinserter.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_REINSERTER_HPP #define OPERON_REINSERTER_HPP #include #include "operon/core/operator.hpp" #include "operon/core/individual.hpp" namespace Operon { class ReinserterBase : public OperatorBase, Operon::Span> { public: explicit ReinserterBase(ComparisonCallback cb) : comp_(std::move(cb)) { } inline void Sort(Operon::Span inds) const { std::stable_sort(inds.begin(), inds.end(), comp_); } [[nodiscard]] inline auto Compare(Individual const& lhs, Individual const& rhs) const -> bool { return comp_(lhs, rhs); } private: ComparisonCallback comp_; }; class OPERON_EXPORT KeepBestReinserter : public ReinserterBase { public: explicit KeepBestReinserter(ComparisonCallback const& cb) : ReinserterBase(cb) { } // keep the best |pop| individuals from pop+pool void operator()(Operon::RandomGenerator& /*random*/, Operon::Span pop, Operon::Span pool) const override { // sort the population and the recombination pool Sort(pop); Sort(pool); // merge the best individuals from pop+pool into pop size_t i = 0; size_t j = 0; while (i < pool.size() && j < pop.size()) { if (Compare(pool[i], pop[j])) { std::swap(pool[i], pop[j]); ++i; } ++j; } } }; class OPERON_EXPORT ReplaceWorstReinserter : public ReinserterBase { public: explicit ReplaceWorstReinserter(ComparisonCallback const& cb) : ReinserterBase(cb) { } // replace the worst individuals in pop with the best individuals from pool void operator()(Operon::RandomGenerator& /*random*/, Operon::Span pop, Operon::Span pool) const override { // typically the pool and the population are the same size if (pop.size() > pool.size()) { Sort(pop); } else if (pop.size() < pool.size()) { Sort(pool); } auto offset = static_cast(std::min(pop.size(), pool.size())); std::swap_ranges(pool.begin(), pool.begin() + offset, pop.end() - offset); } }; } // namespace Operon #endif ================================================ FILE: include/operon/operators/selector.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_SELECTOR_HPP #define OPERON_SELECTOR_HPP #include "operon/core/individual.hpp" #include "operon/core/operator.hpp" namespace Operon { // the selector a vector of individuals and returns the index of a selected individual per each call of operator() // this operator is meant to be a lightweight object that is initialized with a population and some other parameters on-the-fly class SelectorBase : public OperatorBase { public: using SelectableType = Individual; SelectorBase() : comp_(nullptr) { } explicit SelectorBase(ComparisonCallback&& cb) : comp_(std::move(cb)) { } explicit SelectorBase(ComparisonCallback cb) : comp_(std::move(cb)) { } virtual void Prepare(Operon::Span pop) const { this->population_ = Operon::Span(pop); }; auto Population() const -> Operon::Span { return population_; } [[nodiscard]] inline auto Compare(Individual const& lhs, Individual const& rhs) const -> bool { return comp_(lhs, rhs); } private: mutable Operon::Span population_; ComparisonCallback comp_; }; class OPERON_EXPORT TournamentSelector : public SelectorBase { public: explicit TournamentSelector(ComparisonCallback&& cb) : SelectorBase(cb) , tournamentSize_(DefaultTournamentSize) { } explicit TournamentSelector(ComparisonCallback const& cb) : SelectorBase(cb) , tournamentSize_(DefaultTournamentSize) { } auto operator()(Operon::RandomGenerator& random) const -> size_t override; void SetTournamentSize(size_t size) { tournamentSize_ = size; } auto GetTournamentSize() const -> size_t { return tournamentSize_; } static constexpr size_t DefaultTournamentSize = 5; private: size_t tournamentSize_; }; class OPERON_EXPORT RankTournamentSelector : public SelectorBase { public: explicit RankTournamentSelector(ComparisonCallback&& cb) : SelectorBase(cb){ } explicit RankTournamentSelector(ComparisonCallback const& cb) : SelectorBase(cb){ } auto operator()(Operon::RandomGenerator& random) const -> size_t override; void Prepare(Operon::Span pop) const override; void SetTournamentSize(size_t size) { tournamentSize_ = size; } auto GetTournamentSize() const -> size_t { return tournamentSize_; } static constexpr size_t DefaultTournamentSize = 5; private: size_t tournamentSize_{}; mutable std::vector indices_; }; class OPERON_EXPORT ProportionalSelector : public SelectorBase { public: explicit ProportionalSelector(ComparisonCallback&& cb) : SelectorBase(cb) { } explicit ProportionalSelector(ComparisonCallback const& cb) : SelectorBase(cb) { } auto operator()(Operon::RandomGenerator& random) const -> size_t override; void Prepare(Operon::Span pop) const override; void SetObjIndex(size_t objIndex) { idx_ = objIndex; } auto GetObjIndex() const -> size_t { return idx_; } private: void Prepare() const; // discrete CDF of the population fitness values mutable std::vector> fitness_; size_t idx_ = 0; }; class OPERON_EXPORT RandomSelector : public SelectorBase { public: auto operator()(Operon::RandomGenerator& random) const -> size_t override { return std::uniform_int_distribution(0, Population().size() - 1)(random); } }; } //namespace Operon #endif ================================================ FILE: include/operon/optimizer/dynamic_cost_function.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_OPTIMIZER_COST_FUNCTION_HPP #define OPERON_OPTIMIZER_COST_FUNCTION_HPP #ifdef HAVE_CERES #include #include #ifndef CERES_EXPORT #define CERES_EXPORT OPERON_EXPORT #endif #include #include "operon/core/contracts.hpp" namespace Operon { template struct DynamicCostFunction final : public ceres::DynamicCostFunction { using Scalar = typename CostFunctor::Scalar; explicit DynamicCostFunction(CostFunctor const& cf) : cf_(cf) { static_assert(CostFunctor::Storage == Eigen::RowMajor, "Operon::DynamicCostFunction requires row-major storage."); this->mutable_parameter_block_sizes()->push_back(cf_.NumParameters()); set_num_residuals(cf_.NumResiduals()); ENSURE(cf_.NumParameters() > 0); ENSURE(cf_.NumResiduals() > 0); } // required by ceres auto Evaluate(double const* const* parameters, double* residuals, double** jacobians) const -> bool override { EXPECT(parameters != nullptr && parameters[0] != nullptr); if constexpr (std::is_same_v) { if (jacobians != nullptr) { EXPECT(jacobians[0] != nullptr); } return cf_(parameters[0], residuals, jacobians == nullptr ? nullptr : jacobians[0]); } else { // we need to make a copy int const nr = this->num_residuals(); int const np = this->parameter_block_sizes().front(); ENSURE(nr > 0); ENSURE(np > 0); Eigen::Map> pMap(parameters[0], np); Eigen::Map> rMap(residuals, nr); Eigen::Matrix param = pMap.cast(); Eigen::Matrix resid(nr); if (jacobians == nullptr) { auto success = cf_(param.data(), resid.data(), nullptr); if (!success) { return false; } } else { Eigen::Matrix jacob(nr, np); auto success = cf_(param.data(), resid.data(), jacob.data()); if (!success) { return false; } Eigen::Map> jMap(jacobians[0], nr, np); jMap = jacob.template cast(); } rMap = resid.template cast(); return true; } } void AddParameterBlock(int /*size*/) override { throw std::runtime_error("This method should not be used."); } void SetNumResiduals(int /*num_residuals*/) override { throw std::runtime_error("This method should not be used."); } auto Functor() -> CostFunctor& { return cf_; } [[nodiscard]] auto Functor() const -> CostFunctor const& { return cf_; } private: CostFunctor cf_; }; } // namespace Operon #endif #endif ================================================ FILE: include/operon/optimizer/likelihood/gaussian_likelihood.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_GAUSSIAN_LIKELIHOOD_HPP #define OPERON_GAUSSIAN_LIKELIHOOD_HPP #include #include #include #include "operon/core/concepts.hpp" #include "operon/core/types.hpp" #include "operon/interpreter/interpreter.hpp" #include "likelihood_base.hpp" namespace Operon { namespace detail { struct SquaredResidual { template auto operator()(T const x, T const y) const -> T { auto const e = x - y; return e * e; } template auto operator()(T const x, T const y, T const w) const -> T { auto const e = w * (x - y); return e * e; } }; } // namespace detail // Pure static struct satisfying Concepts::Likelihood. // Use this type anywhere only the statistical computation is needed // (e.g. MinimumDescriptionLengthEvaluator, LevenbergMarquardtOptimizer). template struct GaussianLikelihood { using Scalar = T; using Matrix = Eigen::Matrix; using Vector = Eigen::Matrix; static constexpr bool UsesSigma = true; // sigma is required; empty span is invalid static auto ComputeLikelihood(Span x, Span y, Span s) noexcept -> Scalar { EXPECT(!s.empty()); static_assert(std::is_arithmetic_v); auto const n{ std::ssize(x) }; constexpr Scalar z{0.5}; if (s.size() == 1) { auto s2 = s[0] * s[0]; auto ssr = vstat::univariate::accumulate(x.begin(), x.end(), y.begin(), detail::SquaredResidual{}).sum; return z * (n * std::log(Operon::Math::Tau * s2) + ssr / s2) ; } if (s.size() == x.size()) { auto const t = std::sqrt(Operon::Math::Tau); auto sum{0.0}; for (auto i = 0; i < n; ++i) { auto const si{ s[i] }; auto const ei{ x[i] - y[i] }; auto const pi{ ei / si }; sum += std::log(si * t) + z * pi * pi; } return sum; } return std::numeric_limits::quiet_NaN(); } static auto ComputeFisherMatrix(Span pred, Span jac, Span sigma) -> Matrix { EXPECT(!sigma.empty()); auto const rows = pred.size(); auto const cols = jac.size() / pred.size(); Eigen::Map m(jac.data(), rows, cols); if (sigma.size() == 1) { auto const s2 = sigma[0] * sigma[0]; Matrix f = m.transpose() * m; f.array() /= s2; return f; } EXPECT(sigma.size() == rows); Eigen::Map s{sigma.data(), std::ssize(pred)}; // F = J^T diag(1/σᵢ²) J = (diag(1/σᵢ) J)^T (diag(1/σᵢ) J) Matrix scaledJ = s.array().inverse().matrix().asDiagonal() * m; return scaledJ.transpose() * scaledJ; } }; // Callable loss object for gradient-based optimizers (L-BFGS, SGD). // Inherits LikelihoodBase for the virtual operator() interface; // static methods delegate to GaussianLikelihood so this type also // satisfies Concepts::Likelihood and can be passed to // OptimizerBase::ComputeLikelihood / ComputeFisherMatrix. template struct GaussianLoss : public LikelihoodBase { static constexpr bool UsesSigma = true; GaussianLoss(gsl::not_null rng, gsl::not_null const*> interpreter, Operon::Span target, Operon::Range const range, std::size_t const batchSize = 0) : LikelihoodBase(interpreter) , rng_(rng) , target_{target.data(), std::ssize(target)} , range_{range} , bs_{batchSize == 0 ? range.Size() : batchSize} , np_{static_cast(interpreter->GetTree()->CoefficientsCount())} , nr_{range_.Size()} , jac_{bs_, np_} { } using Scalar = typename LikelihoodBase::Scalar; using Vector = typename LikelihoodBase::Vector; using Ref = typename LikelihoodBase::Ref; using Cref = typename LikelihoodBase::Cref; using Matrix = typename LikelihoodBase::Matrix; // Callable by L-BFGS / SGD optimizers: returns loss and fills gradient. auto operator()(Cref x, Ref grad) const noexcept -> Operon::Scalar final { ++feval_; auto const& interpreter = this->GetInterpreter(); Operon::Span c{x.data(), static_cast(x.size())}; auto range = SelectRandomRange(); auto primal = interpreter->Evaluate(c, range); auto target = target_.segment(range.Start(), range.Size()); Eigen::Map const> primalMap{primal.data(), std::ssize(primal)}; auto e = primalMap - target; if (grad.size() != 0) { assert(grad.size() == x.size()); ++jeval_; interpreter->JacRev(c, range, {jac_.data(), np_ * bs_}); grad = (e.matrix().asDiagonal() * jac_.matrix()).colwise().sum(); } return static_cast(e.square().sum()) * Operon::Scalar{0.5}; } // Static delegation — GaussianLoss also satisfies Concepts::Likelihood. static auto ComputeLikelihood(Span x, Span y, Span s) noexcept -> Scalar { return GaussianLikelihood::ComputeLikelihood(x, y, s); } static auto ComputeFisherMatrix(Span pred, Span jac, Span sigma) -> Matrix { return GaussianLikelihood::ComputeFisherMatrix(pred, jac, sigma); } auto NumParameters() const -> std::size_t { return np_; } auto NumObservations() const -> std::size_t { return nr_; } auto FunctionEvaluations() const -> std::size_t { return feval_; } auto JacobianEvaluations() const -> std::size_t { return jeval_; } private: auto SelectRandomRange() const -> Operon::Range { if (bs_ >= range_.Size()) { return range_; } auto s = std::uniform_int_distribution{0UL, range_.Size()-bs_}(*rng_); return Operon::Range{range_.Start() + s, range_.Start() + s + bs_}; } gsl::not_null rng_; Eigen::Map const> target_; Operon::Range const range_; // range of the training data NOLINT std::size_t bs_; // batch size std::size_t np_; // number of parameters to optimize std::size_t nr_; // number of data points (rows) mutable Eigen::Array jac_; mutable std::size_t feval_{}; mutable std::size_t jeval_{}; }; } // namespace Operon #endif ================================================ FILE: include/operon/optimizer/likelihood/likelihood_base.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_LIKELIHOOD_BASE_HPP #define OPERON_LIKELIHOOD_BASE_HPP #include #include #include #include #include "operon/core/types.hpp" #include "operon/interpreter/interpreter.hpp" namespace Operon { namespace Concepts { template concept Likelihood = requires( Operon::Span x, Operon::Span y, Operon::Span z ) { { T::ComputeLikelihood(x, y, z) } -> std::same_as; { T::ComputeFisherMatrix(z, y, z) } -> std::convertible_to>; }; } // namespace Concepts template struct LikelihoodBase { using Scalar = T; using Matrix = Eigen::Matrix; using Vector = Eigen::Matrix; using Ref = Eigen::Ref; using Cref = Eigen::Ref const&; using scalar_t = T; // for lbfgs solver NOLINT explicit LikelihoodBase(gsl::not_null const*> interpreter) : interpreter_(interpreter) { } [[nodiscard]] auto GetInterpreter() const -> InterpreterBase const* { return interpreter_.get(); } // compute function and gradient when called by the optimizer [[nodiscard]] virtual auto operator()(Cref, Ref) const noexcept -> Scalar = 0; // compute the likelihood value when called standalone [[nodiscard]] virtual auto FunctionEvaluations() const -> std::size_t = 0; [[nodiscard]] virtual auto JacobianEvaluations() const -> std::size_t = 0; [[nodiscard]] virtual auto NumParameters() const -> std::size_t = 0; [[nodiscard]] virtual auto NumObservations() const -> std::size_t = 0; private: gsl::not_null const*> interpreter_; }; namespace Concepts { // Tighter concept for gradient-based optimizer loss functions. // Requires Likelihood (static methods) plus derivation from LikelihoodBase // (operator(), FunctionEvaluations, JacobianEvaluations, etc.). // Prevents confusing template errors when a static-only *Likelihood struct // is mistakenly passed to LBFGSOptimizer or SGDOptimizer. template concept OptimizerLoss = Likelihood && std::derived_from>; } // namespace Concepts } // namespace Operon #endif ================================================ FILE: include/operon/optimizer/likelihood/poisson_likelihood.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_POISSON_LIKELIHOOD_HPP #define OPERON_POISSON_LIKELIHOOD_HPP #include "operon/core/concepts.hpp" #include "operon/core/types.hpp" #include "operon/interpreter/interpreter.hpp" #include "likelihood_base.hpp" #include #include #include #include namespace Operon { namespace detail { struct Poisson { template auto operator()(T const x, T const y) const -> T { return x - y * std::log(x) + std::lgamma(y+1); } template auto operator()(T const x, T const y, T const w) const -> T { return (*this)(w * x, y); } }; struct PoissonLog { template auto operator()(T const x, T const y) const -> T { return std::exp(x) - x * y + std::lgamma(y+1); } template auto operator()(T const x, T const y, T const w) const -> T { return (*this)(x * w, y); } }; } // namespace detail // Pure static struct satisfying Concepts::Likelihood. // Use this type anywhere only the statistical computation is needed // (e.g. MinimumDescriptionLengthEvaluator). template struct PoissonLikelihood { using Scalar = T; using Matrix = Eigen::Matrix; using Vector = Eigen::Matrix; static constexpr bool UsesSigma = false; // w is an optional weight, not sigma; empty = unweighted static auto ComputeLikelihood(Span x, Span y, Span w) -> Scalar { using F = std::conditional_t; vstat::univariate_accumulator acc; if (w.empty()) { for (auto i = 0UL; i < x.size(); ++i) { acc(F{}(x[i], y[i])); } } else if (w.size() == 1) { for (auto i = 0UL; i < x.size(); ++i) { acc(F{}(x[i], y[i], w[0])); } } else if (w.size() == x.size()) { for (auto i = 0UL; i < x.size(); ++i) { acc(F{}(x[i], y[i], w[i])); } } else { throw std::runtime_error("incompatible weights"); } return vstat::univariate_statistics(acc).sum; } static auto ComputeFisherMatrix(Span pred, Span jac, Span /*not used*/) -> Matrix { auto const rows = pred.size(); auto const cols = jac.size() / pred.size(); Eigen::Map m(jac.data(), rows, cols); Eigen::Map s{pred.data(), std::ssize(pred)}; if constexpr (LogInput) { return (s.array().exp().matrix().asDiagonal() * m).transpose() * m; } else { return (s.array().inverse().matrix().asDiagonal() * m).transpose() * m; } } }; // Callable loss object for gradient-based optimizers (L-BFGS, SGD). // Inherits LikelihoodBase for the virtual operator() interface; // static methods delegate to PoissonLikelihood so this type // also satisfies Concepts::Likelihood. template struct PoissonLoss : public LikelihoodBase { static constexpr bool UsesSigma = false; PoissonLoss(gsl::not_null rng, gsl::not_null const*> interpreter, Operon::Span target, Operon::Range const range, std::size_t const batchSize = 0) : LikelihoodBase(interpreter) , rng_{rng} , target_(target) , range_(range) , batchSize_(batchSize == 0 ? range.Size() : batchSize) , numParameters_{static_cast(interpreter->GetTree()->CoefficientsCount())} , numResiduals_{range_.Size()} , jac_{batchSize_, numParameters_} { } using Scalar = typename LikelihoodBase::Scalar; using scalar_t = Scalar; // needed by lbfgs library NOLINT using Vector = typename LikelihoodBase::Vector; using Ref = typename LikelihoodBase::Ref; using Cref = typename LikelihoodBase::Cref; using Matrix = typename LikelihoodBase::Matrix; // Callable by L-BFGS / SGD optimizers: returns loss and fills gradient. auto operator()(Cref x, Ref g) const noexcept -> Operon::Scalar final { ++feval_; auto const* interpreter = this->GetInterpreter(); Operon::Span c{x.data(), static_cast(x.size())}; auto r = SelectRandomRange(); auto p = interpreter->Evaluate(c, r); auto t = target_.subspan(r.Start(), r.Size()); auto pmap = Eigen::Map const>(p.data(), std::ssize(p)); auto tmap = Eigen::Map const>(t.data(), std::ssize(t)); if (g.size() != 0) { ++jeval_; interpreter->JacRev(c, r, { jac_.data(), numParameters_ * batchSize_ }); if constexpr (LogInput) { g = ((pmap.exp() - tmap).matrix().asDiagonal() * jac_.matrix()).colwise().sum(); } else { g = ((1 - tmap * pmap.inverse()).matrix().asDiagonal() * jac_.matrix()).colwise().sum(); } } if constexpr (LogInput) { return (pmap.exp() - tmap * pmap).sum(); } else { return (pmap - tmap * pmap.log()).sum(); } } // Static delegation — PoissonLoss also satisfies Concepts::Likelihood. static auto ComputeLikelihood(Span x, Span y, Span w) -> Scalar { return PoissonLikelihood::ComputeLikelihood(x, y, w); } static auto ComputeFisherMatrix(Span pred, Span jac, Span sigma) -> Matrix { return PoissonLikelihood::ComputeFisherMatrix(pred, jac, sigma); } auto NumParameters() const -> std::size_t { return numParameters_; } auto NumObservations() const -> std::size_t { return numResiduals_; } auto FunctionEvaluations() const -> std::size_t { return feval_; } auto JacobianEvaluations() const -> std::size_t { return jeval_; } private: auto SelectRandomRange() const -> Operon::Range { if (batchSize_ >= range_.Size()) { return range_; } auto s = std::uniform_int_distribution{0UL, range_.Size()-batchSize_}(*rng_); return Operon::Range{range_.Start() + s, range_.Start() + s + batchSize_}; } gsl::not_null rng_; Operon::Span target_; Operon::Range const range_; // NOLINT std::size_t batchSize_; // batch size std::size_t numParameters_; // number of parameters to optimize std::size_t numResiduals_; // number of data points (rows) mutable Eigen::Array jac_; mutable std::size_t feval_{}; mutable std::size_t jeval_{}; }; } // namespace Operon #endif ================================================ FILE: include/operon/optimizer/lm_cost_function.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_LM_COST_FUNCTION_HPP #define OPERON_LM_COST_FUNCTION_HPP #include #include #include "operon/interpreter/interpreter.hpp" namespace Operon { // this cost function is adapted to work with both solvers from Ceres: the normal one and the tiny solver // for this, a number of template parameters are necessary: // - the AutodiffCalculator will compute and return the Jacobian matrix // - the StorageOrder specifies the format of the jacobian (row-major for the big Ceres solver, column-major for the tiny solver) template struct LMCostFunction { static auto constexpr Storage{ StorageOrder }; using Scalar = Operon::Scalar; enum { NUM_RESIDUALS = Eigen::Dynamic, // NOLINT NUM_PARAMETERS = Eigen::Dynamic, // NOLINT }; explicit LMCostFunction(gsl::not_null const*> interpreter, Operon::Span target, Operon::Range const range) : interpreter_(interpreter) , target_{target} , range_{range} , numResiduals_{range.Size()} , numParameters_{static_cast(interpreter->GetTree()->CoefficientsCount())} { } inline auto Evaluate(Scalar const* parameters, Scalar* residuals, Scalar* jacobian) const -> bool // NOLINT { EXPECT(target_.size() == numResiduals_); EXPECT(parameters != nullptr); Operon::Span params{ parameters, numParameters_ }; if (jacobian != nullptr) { ++jacobianCallCount_; Operon::Span jac{jacobian, static_cast(numResiduals_ * numParameters_)}; interpreter_->JacRev(params, range_, jac); } if (residuals != nullptr) { ++residualCallCount_; Operon::Span res{ residuals, static_cast(numResiduals_) }; interpreter_->Evaluate(params, range_, res); Eigen::Map> x(residuals, numResiduals_); Eigen::Map const> y(target_.data(), numResiduals_); x -= y; } return true; } // ceres solver - jacobian must be in row-major format // tiny solver - jacobian must be in col-major format auto operator()(Scalar const* parameters, Scalar* residuals, Scalar* jacobian) const -> bool { return Evaluate(parameters, residuals, jacobian); } [[nodiscard]] auto NumResiduals() const -> int { return numResiduals_; } [[nodiscard]] auto NumParameters() const -> int { return numParameters_; } // required by Eigen::LevenbergMarquardt using JacobianType = Eigen::Matrix; using QRSolver = Eigen::ColPivHouseholderQR; // there is no real documentation but looking at Eigen unit tests, these functions should return zero // see: https://gitlab.com/libeigen/eigen/-/blob/master/unsupported/test/NonLinearOptimization.cpp auto operator()(Eigen::Matrix const& input, Eigen::Matrix& residual) const -> int { Evaluate(input.data(), residual.data(), nullptr); return 0; } auto df(Eigen::Matrix const& input, Eigen::Matrix& jacobian) const -> int // NOLINT { static_assert(StorageOrder == Eigen::ColMajor, "Eigen::LevenbergMarquardt requires the Jacobian to be stored in column-major format."); Evaluate(input.data(), nullptr, jacobian.data()); return 0; } [[nodiscard]] auto values() const -> int { return NumResiduals(); } // NOLINT [[nodiscard]] auto inputs() const -> int { return NumParameters(); } // NOLINT [[nodiscard]] auto ResidualCalls() const -> std::size_t { return residualCallCount_.load(); } [[nodiscard]] auto JacobianCalls() const -> std::size_t { return jacobianCallCount_.load(); } private: gsl::not_null const*> interpreter_; Operon::Span target_; Operon::Range const range_; // NOLINT std::size_t numResiduals_; std::size_t numParameters_; mutable std::atomic_size_t jacobianCallCount_{0}; mutable std::atomic_size_t residualCallCount_{0}; }; } // namespace Operon #endif ================================================ FILE: include/operon/optimizer/optimizer.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_OPTIMIZER_HPP #define OPERON_OPTIMIZER_HPP #include #include #include #include "operon/error_metrics/sum_of_squared_errors.hpp" #if defined(HAVE_CERES) #include #else #include "operon/ceres/tiny_solver.h" #endif #include #include "dynamic_cost_function.hpp" #include "likelihood/gaussian_likelihood.hpp" #include "likelihood/poisson_likelihood.hpp" // GaussianLoss / PoissonLoss are defined in the same headers above. #include "lm_cost_function.hpp" #include "operon/core/comparison.hpp" #include "operon/core/dispatch.hpp" #include "operon/core/problem.hpp" #include "solvers/sgd.hpp" namespace Operon { enum class OptimizerType : int { Tiny, Eigen, Ceres }; struct OptimizerSummary { std::vector InitialParameters; std::vector FinalParameters; Operon::Scalar InitialCost{}; Operon::Scalar FinalCost{}; int Iterations{}; int FunctionEvaluations{}; int JacobianEvaluations{}; bool Success{}; }; class OptimizerBase { gsl::not_null problem_; // batch size for loss functions (default = 0 -> use entire data range) mutable std::size_t batchSize_{0}; mutable std::size_t iterations_{100}; // NOLINT public: explicit OptimizerBase(gsl::not_null problem) : problem_ { problem } { } OptimizerBase(const OptimizerBase&) = default; OptimizerBase(OptimizerBase&&) = delete; auto operator=(const OptimizerBase&) -> OptimizerBase& = default; auto operator=(OptimizerBase&&) -> OptimizerBase& = delete; virtual ~OptimizerBase() = default; [[nodiscard]] auto GetProblem() const -> Problem const* { return problem_.get(); } [[nodiscard]] auto BatchSize() const -> std::size_t { return batchSize_; } [[nodiscard]] auto Iterations() const -> std::size_t { return iterations_; } auto SetBatchSize(std::size_t batchSize) const { batchSize_ = batchSize; } auto SetIterations(std::size_t iterations) const { iterations_ = iterations; } [[nodiscard]] virtual auto Optimize(Operon::RandomGenerator& rng, Tree const& tree) const -> OptimizerSummary = 0; [[nodiscard]] virtual auto ComputeLikelihood(Operon::Span x, Operon::Span y, Operon::Span w) const -> Operon::Scalar = 0; [[nodiscard]] virtual auto ComputeFisherMatrix(Operon::Span pred, Operon::Span jac, Operon::Span sigma) const -> Eigen::Matrix = 0; }; namespace detail { inline auto CheckSuccess(double initialCost, double finalCost) { constexpr auto CHECK_NAN{true}; return Operon::Less{}(finalCost, initialCost); } } // namespace detail template struct LevenbergMarquardtOptimizer : public OptimizerBase { explicit LevenbergMarquardtOptimizer(gsl::not_null dtable, gsl::not_null problem) : OptimizerBase{problem}, dtable_{dtable} { } [[nodiscard]] auto Optimize(Operon::RandomGenerator& /*unused*/, Operon::Tree const& tree) const -> OptimizerSummary final { auto const* dtable = this->GetDispatchTable(); auto const* problem = this->GetProblem(); auto const* dataset = problem->GetDataset(); auto range = problem->TrainingRange(); auto target = problem->TargetValues(range); auto iterations = this->Iterations(); Operon::Interpreter interpreter{dtable, dataset, &tree}; Operon::LMCostFunction cf{gsl::not_null const*>{&interpreter}, target, range}; ceres::TinySolver solver; solver.options.max_num_iterations = static_cast(iterations+1); auto x0 = tree.GetCoefficients(); OptimizerSummary summary; summary.InitialParameters = x0; auto m0 = Eigen::Map>(x0.data(), x0.size()); if (!x0.empty()) { typename decltype(solver)::Parameters p = m0.cast(); solver.Solve(cf, &p); m0 = p.template cast(); } summary.FinalParameters = x0; summary.InitialCost = solver.summary.initial_cost; summary.FinalCost = solver.summary.final_cost; summary.Iterations = solver.summary.iterations; summary.FunctionEvaluations = cf.ResidualCalls(); summary.JacobianEvaluations = cf.JacobianCalls(); summary.Success = detail::CheckSuccess(summary.InitialCost, summary.FinalCost); return summary; } auto GetDispatchTable() const -> DTable const* { return dtable_.get(); } [[nodiscard]] auto ComputeLikelihood(Operon::Span x, Operon::Span y, Operon::Span w) const -> Operon::Scalar final { return GaussianLikelihood::ComputeLikelihood(x, y, w); } [[nodiscard]] auto ComputeFisherMatrix(Operon::Span pred, Operon::Span jac, Operon::Span sigma) const -> Eigen::Matrix final { return GaussianLikelihood::ComputeFisherMatrix(pred, jac, sigma); } private: gsl::not_null dtable_; }; template struct LevenbergMarquardtOptimizer final : public OptimizerBase { explicit LevenbergMarquardtOptimizer(gsl::not_null dtable, gsl::not_null problem) : OptimizerBase{problem}, dtable_{dtable} { } [[nodiscard]] auto Optimize(Operon::RandomGenerator& /*unused*/, Operon::Tree const& tree) const -> OptimizerSummary final { auto const* dtable = this->GetDispatchTable(); auto const* problem = this->GetProblem(); auto const* dataset = problem->GetDataset(); auto range = problem->TrainingRange(); auto target = problem->TargetValues(range); auto iterations = this->Iterations(); Operon::Interpreter interpreter{dtable, dataset, &tree}; Operon::LMCostFunction cf{&interpreter, target, range}; Eigen::LevenbergMarquardt lm(cf); lm.setMaxfev(static_cast(iterations+2)); auto x0 = tree.GetCoefficients(); OptimizerSummary summary; summary.InitialParameters = x0; if (!x0.empty()) { Eigen::Map> m0(x0.data(), std::ssize(x0)); Eigen::Matrix m = m0; // do the minimization loop manually because we want to extract the initial cost Eigen::LevenbergMarquardtSpace::Status status = lm.minimizeInit(m); summary.InitialCost = summary.FinalCost = lm.fnorm() * lm.fnorm() * 0.5; // get the initial cost after calling minimizeInit() if (status != Eigen::LevenbergMarquardtSpace::ImproperInputParameters) { do { status = lm.minimizeOneStep(m); } while (status == Eigen::LevenbergMarquardtSpace::Running); } m0 = m; } summary.FinalParameters = x0; summary.FinalCost = lm.fnorm() * lm.fnorm(); summary.Iterations = static_cast(lm.iterations()); summary.FunctionEvaluations = static_cast(cf.ResidualCalls()); summary.JacobianEvaluations = static_cast(cf.JacobianCalls()); summary.Success = detail::CheckSuccess(summary.InitialCost, summary.FinalCost); return summary; } auto GetDispatchTable() const -> DTable const* { return dtable_.get(); } [[nodiscard]] auto ComputeLikelihood(Operon::Span x, Operon::Span y, Operon::Span w) const -> Operon::Scalar final { return GaussianLikelihood::ComputeLikelihood(x, y, w); } [[nodiscard]] auto ComputeFisherMatrix(Operon::Span pred, Operon::Span jac, Operon::Span sigma) const -> Eigen::Matrix final { return GaussianLikelihood::ComputeFisherMatrix(pred, jac, sigma); } private: gsl::not_null dtable_; }; #if defined(HAVE_CERES) template struct LevenbergMarquardtOptimizer final : public OptimizerBase { explicit LevenbergMarquardtOptimizer(gsl::not_null dtable, gsl::not_null problem) : OptimizerBase{problem}, dtable_{dtable} { } auto Optimize(Operon::RandomGenerator& /*unused*/, Operon::Tree const& tree) -> std::vector final { auto const* tree = this->GetTree(); auto const* ds = this->GetDataset(); auto const* dt = this->GetDispatchTable(); auto x0 = tree->GetCoefficients(); Operon::LMCostFunction cf(tree, ds, target, range, dt); auto costFunction = new Operon::DynamicCostFunction(cf); // NOLINT ceres::Solver::Summary s; if (!initialParameters.empty()) { auto sz = std::ssize(finalParameters); Eigen::Map> m0(finalParameters.data(), sz); Eigen::VectorXd params = m0.template cast(); ceres::Problem problem; problem.AddResidualBlock(dynamicCostFunction, nullptr, params.data()); ceres::Solver::Options options; options.linear_solver_type = ceres::DENSE_QR; options.logging_type = ceres::LoggingType::SILENT; options.max_num_iterations = static_cast(iterations); options.minimizer_progress_to_stdout = false; options.num_threads = 1; options.trust_region_strategy_type = ceres::LEVENBERG_MARQUARDT; options.use_inner_iterations = false; Solve(options, &problem, &s); m0 = params.cast(); } return Operon::OptimizerSummary { .InitialParameters = initialParameters, .FinalParameters = finalParameters, .InitialCost = static_cast(s.initial_cost), .FinalCost = static_cast(s.final_cost), .Iterations = static_cast(s.iterations.size()), .FunctionEvaluations = s.num_residual_evaluations, .JacobianEvaluations = s.num_jacobian_evaluations, .Success = detail::CheckSuccess(s.initial_cost, s.final_cost) }; } auto GetDispatchTable() const -> DTable const& { return dtable_.get(); } [[nodiscard]] auto ComputeLikelihood(Operon::Span x, Operon::Span y, Operon::Span w) const -> Operon::Scalar final { return GaussianLikelihood::ComputeLikelihood(x, y, w); } [[nodiscard]] auto ComputeFisherMatrix(Operon::Span pred, Operon::Span jac, Operon::Span sigma) const -> Eigen::Matrix final { return GaussianLikelihood::ComputeFisherMatrix(pred, jac, sigma); } private: gsl::not_null dtable_; }; #endif template> struct LBFGSOptimizer final : public OptimizerBase { LBFGSOptimizer(gsl::not_null dtable, gsl::not_null problem) : OptimizerBase{problem}, dtable_{dtable} { } [[nodiscard]] auto Optimize(Operon::RandomGenerator& rng, Operon::Tree const& tree) const -> OptimizerSummary final { auto const* dtable = this->GetDispatchTable(); auto const* problem = this->GetProblem(); auto const* dataset = problem->GetDataset(); auto range = problem->TrainingRange(); auto target = problem->TargetValues(range); auto iterations = this->Iterations(); auto batchSize = this->BatchSize(); if (batchSize == 0) { batchSize = range.Size(); } Operon::Interpreter interpreter{dtable, dataset, &tree}; LossFunction loss{&rng, &interpreter, target, range, batchSize}; auto cost = [&](auto const& coeff) { auto pred = interpreter.Evaluate(coeff, range); return 0.5 * Operon::SumOfSquaredErrors(pred.begin(), pred.end(), target.begin()); }; auto coeff = tree.GetCoefficients(); Eigen::Map const> x0(coeff.data(), std::ssize(coeff)); lbfgs::solver solver{loss}; solver.max_iterations = iterations; solver.max_line_search_iterations = iterations; auto const f0 = cost(coeff); OptimizerSummary summary; summary.InitialParameters = coeff; summary.InitialCost = f0; if (auto res = solver.optimize(x0)) { auto xf = res.value(); std::copy(xf.begin(), xf.end(), coeff.begin()); } summary.FinalParameters = coeff; auto const f1 = cost(coeff); summary.FinalCost = f1; summary.Success = detail::CheckSuccess(f0, f1); auto const funEvals = loss.FunctionEvaluations(); auto const jacEvals = loss.JacobianEvaluations(); auto const rangeSize = range.Size(); summary.FunctionEvaluations = static_cast(static_cast(funEvals + jacEvals) * batchSize / rangeSize); summary.JacobianEvaluations = summary.FunctionEvaluations; return summary; } auto GetDispatchTable() const -> DTable const* { return dtable_.get(); } [[nodiscard]] auto ComputeLikelihood(Operon::Span x, Operon::Span y, Operon::Span w) const -> Operon::Scalar override { return LossFunction::ComputeLikelihood(x, y, w); } [[nodiscard]] auto ComputeFisherMatrix(Operon::Span pred, Operon::Span jac, Operon::Span sigma) const -> Eigen::Matrix final { return LossFunction::ComputeFisherMatrix(pred, jac, sigma); } private: gsl::not_null dtable_; }; template> struct SGDOptimizer final : public OptimizerBase { SGDOptimizer(gsl::not_null dtable, gsl::not_null problem) : OptimizerBase{problem} , dtable_{dtable} , update_{std::make_unique>(Operon::Scalar{0.01})} { } SGDOptimizer(gsl::not_null dtable, gsl::not_null problem, UpdateRule::LearningRateUpdateRule const& update) : OptimizerBase{problem} , dtable_{dtable} , update_{update.Clone(0)} { } auto GetDispatchTable() const -> DTable const* { return dtable_.get(); } [[nodiscard]] auto Optimize(Operon::RandomGenerator& rng, Operon::Tree const& tree) const -> OptimizerSummary final { auto const* dtable = this->GetDispatchTable(); auto const* problem = this->GetProblem(); auto const* dataset = problem->GetDataset(); auto range = problem->TrainingRange(); auto target = problem->TargetValues(range); auto iterations = this->Iterations(); auto batchSize = this->BatchSize(); if (batchSize == 0) { batchSize = range.Size(); } Operon::Interpreter interpreter{dtable, dataset, &tree}; LossFunction loss{&rng, &interpreter, target, range, batchSize}; auto cost = [&](auto const& coeff) { auto pred = interpreter.Evaluate(coeff, range); return 0.5 * Operon::SumOfSquaredErrors(pred.begin(), pred.end(), target.begin()); }; auto coeff = tree.GetCoefficients(); auto const f0 = cost(coeff); OptimizerSummary summary; summary.InitialParameters = coeff; summary.InitialCost = f0; auto rule = update_->Clone(coeff.size()); SGDSolver solver(&loss, rule.get()); Eigen::Map const> x0(coeff.data(), std::ssize(coeff)); auto x = solver.Optimize(x0, iterations); std::copy(x.begin(), x.end(), coeff.begin()); auto const f1 = cost(coeff); summary.FinalParameters = coeff; summary.FinalCost = f1; summary.Success = detail::CheckSuccess(f0, f1); summary.Iterations = solver.Epochs(); auto const funEvals = loss.FunctionEvaluations(); auto const jacEvals = loss.JacobianEvaluations(); auto const rangeSize = range.Size(); summary.FunctionEvaluations = static_cast(static_cast(funEvals + jacEvals) * batchSize / rangeSize); summary.JacobianEvaluations = summary.FunctionEvaluations; return summary; } [[nodiscard]] auto ComputeLikelihood(Operon::Span x, Operon::Span y, Operon::Span w) const -> Operon::Scalar override { return LossFunction::ComputeLikelihood(x, y, w); } [[nodiscard]] auto ComputeFisherMatrix(Operon::Span pred, Operon::Span jac, Operon::Span sigma) const -> Eigen::Matrix final { return LossFunction::ComputeFisherMatrix(pred, jac, sigma); } auto SetUpdateRule(std::unique_ptr update) { update_ = std::move(update); } auto UpdateRule() const { return update_.get(); } private: gsl::not_null dtable_; std::unique_ptr update_{nullptr}; }; } // namespace Operon #endif ================================================ FILE: include/operon/optimizer/solvers/sgd.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_SOLVER_SGD_HPP #define OPERON_SOLVER_SGD_HPP #include "operon/core/types.hpp" #include #include #include #include #include #include #include #include #include namespace Operon { namespace UpdateRule { struct LearningRateUpdateRule { using T = Operon::Scalar; using U = Eigen::Array; // apply the learning rate to the gradient [[nodiscard]] virtual auto Update(Eigen::Ref const& gradient) const -> U = 0; virtual auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void = 0; virtual auto Print(std::ostream&) const -> std::ostream& = 0; virtual auto SetDimension(int dim) const -> void = 0; [[nodiscard]] virtual auto Clone(int dim) const -> std::unique_ptr = 0; friend auto operator<<(std::ostream& os, LearningRateUpdateRule const& rule) -> std::ostream& { os << rule.Name() << "\n"; return rule.Print(os); } LearningRateUpdateRule(const LearningRateUpdateRule&) = delete; LearningRateUpdateRule(LearningRateUpdateRule&&) = delete; auto operator=(const LearningRateUpdateRule&) -> LearningRateUpdateRule& = delete; auto operator=(LearningRateUpdateRule&&) -> LearningRateUpdateRule& = delete; explicit LearningRateUpdateRule(std::string name) : name_(std::move(name)) { } [[nodiscard]] auto Name() const -> std::string const& { return name_; } virtual ~LearningRateUpdateRule() = default; private: std::string name_; }; template > class Constant : public LearningRateUpdateRule { public: using Scalar = T; using Vector = U; private: using Base = LearningRateUpdateRule; T r_{0.1}; public: explicit Constant(Eigen::Index /*dim*/= 0, T r = 0.1) : Base("constant") , r_(r) { } explicit Constant(T r = 0.1) : Constant(0, r) { } [[nodiscard]] auto Update(Eigen::Ref const& gradient) const -> U final { U result(gradient.size()); Update(gradient, result); return result; } auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void final { EXPECT(result.size() == gradient.size()); result = r_ * gradient; } auto Print(std::ostream& os) const -> std::ostream& final { os << "constant learning rate update rule\n"; os << "learning rate: " << r_ << "\n"; return os; } auto SetDimension(int /*unused*/) const -> void final { } [[nodiscard]] auto Clone(int dim) const -> std::unique_ptr final { return std::make_unique>(dim, r_); } }; template > class Momentum : public LearningRateUpdateRule { using Base = LearningRateUpdateRule; T r_{0.01}; // learning rate T b_{0.9}; // beta mutable U m_; // first moment public: using Scalar = T; using Vector = U; explicit Momentum(Eigen::Index dim, T r = 0.01, T b = 0.9) : Base("momentum") , r_ { r } , b_ { b } , m_ { U::Zero(dim) } { } auto Update(Eigen::Ref const& gradient) const -> U final { U result(gradient.size()); Update(gradient, result); return result; } auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void final { EXPECT(result.size() == gradient.size()); m_ = m_ * b_ + gradient; result = r_ * m_; } auto Print(std::ostream& os) const -> std::ostream& final { os << "learning rate: " << r_ << "\n" << "beta : " << b_ << "\n" << "first moment : " << m_.transpose() << "\n"; return os; } auto SetDimension(int dim) const -> void final { if (m_.size() != dim) { m_ = U::Zero(dim); } } auto Clone(int dim) const -> std::unique_ptr final { if (dim == 0) { dim = m_.size(); } return std::make_unique>(dim, r_, b_); } }; template > class RmsProp : public LearningRateUpdateRule { using Base = LearningRateUpdateRule; T r_{0.01}; // learning rate T b_{0.9}; // beta T e_{1e-6}; // epsilon mutable U m_; // second moment public: using Scalar = T; using Vector = U; explicit RmsProp(Eigen::Index dim, T r = 0.01, T b = 0.9, T e = 1e-6) : Base("rmsprop") , r_ { r } , b_ { b } , e_ { e } , m_ { U::Zero(dim) } { } auto Update(Eigen::Ref const& gradient) const -> U final { U result(gradient.size()); Update(gradient, result); return result; } auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void final { EXPECT(result.size() == gradient.size()); m_ = b_ * m_ + (T { 1 } - b_) * gradient.square(); result = r_ / (m_.sqrt() + e_) * gradient; } auto Print(std::ostream& os) const -> std::ostream& final { os << "learning rate: " << r_ << "\n" << "beta : " << b_ << "\n" << "epsilon : " << e_ << "\n" << "moment : " << m_.transpose() << "\n"; return os; } auto SetDimension(int dim) const -> void final { if (m_.size() != dim) { m_ = U::Zero(dim); } } auto Clone(int dim) const -> std::unique_ptr final { if (dim == 0) { dim = m_.size(); } return std::make_unique>(dim, r_, b_); } }; template > class AdaDelta : public LearningRateUpdateRule { using Base = LearningRateUpdateRule; T b_{0.9}; // beta T e_{1e-6}; // epsilon mutable U m_; // moment gradient mutable U s_; // moment delta mutable U d_; // previous delta public: using Scalar = T; using Vector = U; explicit AdaDelta(Eigen::Index dim, T b = 0.9, T e = 1e-6) : Base("adadelta") , b_ { b } , e_ { e } , m_ { U::Zero(dim) } , s_ { U::Zero(dim) } , d_ { U::Zero(dim) } { } auto Update(Eigen::Ref const& gradient) const -> U final { U result(gradient.size()); Update(gradient, result); return result; } auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void final { EXPECT(result.size() == gradient.size()); m_ = b_ * m_ + (T { 1 } - b_) * gradient.square(); s_ = b_ * s_ + (T { 1 } - b_) * d_.square(); d_ = ((s_ + e_) / (m_ + e_)).sqrt() * gradient; result = d_; } auto Print(std::ostream& os) const -> std::ostream& final { os << "beta : " << b_ << "\n" << "epsilon : " << e_ << "\n" << "moment : " << m_.transpose() << "\n" << "moment delta : " << s_.transpose() << "\n" << "prev delta : " << d_.transpose() << "\n"; return os; } auto SetDimension(int dim) const -> void final { if (m_.size() != dim) { m_ = U::Zero(dim); s_ = U::Zero(dim); d_ = U::Zero(dim); } } auto Clone(int dim) const -> std::unique_ptr final { if (dim == 0) { dim = m_.size(); } return std::make_unique>(dim, b_, e_); } }; template > class AdaMax : public LearningRateUpdateRule { using Base = LearningRateUpdateRule; T r_{0.01}; // base learning rate T b1_{0.9}; // exponential decay rate first moment T b2_{0.999}; // exponential decay rate second moment mutable U m_; // first moment mutable U v_; // second moment public: using Scalar = T; using Vector = U; explicit AdaMax(Eigen::Index dim, T r = 0.01, T b1 = 0.9, T b2 = 0.999) : Base("adamax") , r_ { r } , b1_ { b1 } , b2_ { b2 } , m_ { U::Zero(dim) } , v_ { U::Zero(dim) } { } auto Update(Eigen::Ref const& gradient) const -> U final { U result(gradient.size()); Update(gradient, result); return result; } auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void final { EXPECT(result.size() == gradient.size()); m_ -= (T { 1 } - b1_) * (m_ - gradient); v_ = (b2_ * v_).cwiseMax(gradient.abs()); result = r_ * m_ / v_; } auto Print(std::ostream& os) const -> std::ostream& final { os << "learning rate: " << r_ << "\n" << "b1 : " << b1_ << "\n" << "b2 : " << b2_ << "\n" << "m1 : " << m_.transpose() << "\n" << "m2 : " << v_.transpose() << "\n"; return os; } auto SetDimension(int dim) const -> void final { if (m_.size() != dim) { m_ = U::Zero(dim); v_ = U::Zero(dim); } } auto Clone(int dim) const -> std::unique_ptr final { if (dim == 0) { dim = m_.size(); } return std::make_unique>(dim, r_, b1_, b2_); } }; template > class Adam : public LearningRateUpdateRule { using Base = LearningRateUpdateRule; T r_{0.01}; // base learning rate T e_{1e-8}; // epsilon T b1_{0.9}; // exponential decay rate first moment T b2_{0.999}; // exponential decay rate second moment mutable U m_; // first moment mutable U v_; // second moment bool debias_ { false }; mutable uint64_t t_ { 0 }; // time step public: using Scalar = T; using Vector = U; explicit Adam(Eigen::Index dim, T r = 0.01, T e = 1e-8, T b1 = 0.9, T b2 = 0.999, bool debias = false) : Base("adam") , r_ { r } , e_ { e } , b1_ { b1 } , b2_ { b2 } , m_ { U::Zero(dim) } , v_ { U::Zero(dim) } , debias_ { debias } { } auto Update(Eigen::Ref const& gradient) const -> U final { U result(gradient.size()); Update(gradient, result); return result; } auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void final { EXPECT(result.size() == gradient.size()); ++t_; // update moments m_ -= (T { 1 } - b1_) * (m_ - gradient); v_ -= (T { 1 } - b2_) * (v_ - gradient.square()); if (debias_) { m_ /= (T { 1 } - std::pow(b1_, t_)); v_ /= (T { 1 } - std::pow(b2_, t_)); } result = r_ * m_ / (v_.sqrt() + e_); } auto Print(std::ostream& os) const -> std::ostream& final { os << "adam\n"; os << "lrate: " << r_ << "\n"; os << "eps: " << e_ << "\n"; os << "m1: " << m_.transpose() << "\n"; os << "m2: " << v_.transpose() << "\n"; os << "b1: " << b1_ << "\n"; os << "b2: " << b2_ << "\n"; return os; } auto SetDimension(int dim) const -> void final { if (m_.size() != dim) { m_ = U::Zero(dim); v_ = U::Zero(dim); } } auto Clone(int dim) const -> std::unique_ptr final { if (dim == 0) { dim = m_.size(); } return std::make_unique>(dim, r_, e_, b1_, b2_, debias_); } }; template > class YamAdam : public LearningRateUpdateRule { using Base = LearningRateUpdateRule; T e_ { 1e-6 }; // epsilon mutable U m_ { 0.0 }; // first moment mutable U v_ { 0.0 }; // second moment mutable U s_ { 0.0 }; // second moment of delta mutable U d_ { 0.0 }; // current delta mutable U b_ { 0.0 }; // exponential moving average coefficient mutable U dp_ { 0.0 }; // previous delta public: explicit YamAdam(Eigen::Index dim, T e = 1e-6) : Base("yamadam") , e_ { e } , m_ { U::Zero(dim) } , v_ { U::Zero(dim) } , s_ { U::Zero(dim) } , d_ { U::Zero(dim) } , b_ { U::Zero(dim) } , dp_ { U::Zero(dim) } { } auto Update(Eigen::Ref const& gradient) const -> U final { U result(gradient.size()); Update(gradient, result); return result; } auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void final { EXPECT(result.size() == gradient.size()); dp_ = d_; m_ = b_ * m_ + (T { 1 } - b_) * gradient; v_ = b_ * v_ + (T { 1 } - b_) * (gradient - m_).square(); s_ = b_ * s_ + (T { 1 } - b_) * d_.square(); d_ = ((s_ + e_) / (v_ + e_)).sqrt() * m_; b_ = (T { 1 } + (d_.abs() + e_) / (dp_.abs() + e_)).inverse().exp() - e_; result = d_; } auto Print(std::ostream& os) const -> std::ostream& final { os << "eps: " << e_ << "\n"; os << "m1: " << m_.transpose() << "\n"; os << "m2: " << v_.transpose() << "\n"; os << "md: " << s_.transpose() << "\n"; os << "d: " << d_.transpose() << "\n"; os << "b: " << b_.transpose() << "\n"; os << "dp: " << dp_.transpose() << "\n"; return os; } auto SetDimension(int dim) const -> void final { if (m_.size() != dim) { m_ = U::Zero(dim); v_ = U::Zero(dim); s_ = U::Zero(dim); d_ = U::Zero(dim); b_ = U::Zero(dim); dp_ = U::Zero(dim); } } auto Clone(int dim) const -> std::unique_ptr final { if (dim == 0) { dim = m_.size(); } return std::make_unique>(dim, e_); } }; template > class AmsGrad : public LearningRateUpdateRule { using Base = LearningRateUpdateRule; T r_; // learning rate T e_; // epsilon T b1_; // exponential decay rate first moment T b2_; // exponential decay rate second moment mutable U m_; // first moment mutable U v_; // second moment public: explicit AmsGrad(Eigen::Index dim, T r = 0.01, T e = 1e-6, T b1 = 0.9, T b2 = 0.999) : Base("amsgrad") , r_ { r } , e_ { e } , b1_ { b1 } , b2_ { b2 } , m_ { U::Zero(dim) } , v_ { U::Zero(dim) } { } auto Update(Eigen::Ref const& gradient) const -> U final { U result(gradient.size()); Update(gradient, result); return result; } auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void final { EXPECT(result.size() == gradient.size()); m_ = b1_ * m_ + (T{1} - b1_) * gradient; v_ = (b2_ * v_ + (T{1} - b2_)).cwiseMax(v_) * gradient.square(); result = r_ * m_ / (v_.sqrt() + e_); } auto Print(std::ostream& os) const -> std::ostream& final { os << "lrate: " << r_ << "\n" << "eps : " << e_ << "\n" << "m1 : " << m_.transpose() << "\n" << "m2 : " << v_.transpose() << "\n" << "b1 : " << b1_ << "\n" << "b2 : " << b2_ << "\n"; return os; } auto SetDimension(int dim) const -> void final { if (m_.size() != dim) { m_ = U::Zero(dim); v_ = U::Zero(dim); } } auto Clone(int dim) const -> std::unique_ptr final { if (dim == 0) { dim = m_.size(); } return std::make_unique>(dim, r_, e_, b1_, b2_); } }; template > class Yogi : public LearningRateUpdateRule { using Base = LearningRateUpdateRule; T r_{0.01}; // learning rate T e_{1e-8}; // epsilon T b1_{0.9}; // exponential decay rate first moment T b2_{0.999}; // exponential decay rate second moment mutable U m_; // first moment mutable U v_; // second moment bool debias_ { false }; mutable uint64_t t_ { 0 }; // time step public: explicit Yogi(Eigen::Index dim, T r = 0.01, T e = 1e-8, T b1 = 0.9, T b2 = 0.999, bool debias = false) : Base("yogi") , r_ { r } , e_ { e } , b1_ { b1 } , b2_ { b2 } , m_ { U::Zero(dim) } , v_ { U::Zero(dim) } , debias_ { debias } { } auto Update(Eigen::Ref const& gradient) const -> U final { U result(gradient.size()); Update(gradient, result); return result; } auto Update(Eigen::Ref const& gradient, Eigen::Ref result) const -> void final { EXPECT(result.size() == gradient.size()); ++t_; m_ -= (T { 1 } - b1_) * (m_ - gradient); v_ -= (T { 1 } - b2_) * (v_ - gradient.square()).sign() * gradient.square(); if (debias_) { m_ /= (T { 1 } - std::pow(b1_, t_)); v_ /= (T { 1 } - std::pow(b2_, t_)); } result = r_ * m_ / (v_.sqrt() + e_); } auto Print(std::ostream& os) const -> std::ostream& final { os << "lrate: " << r_ << "\n" << "eps : " << e_ << "\n" << "m1 : " << m_.transpose() << "\n" << "m2 : " << v_.transpose() << "\n" << "b1 : " << b1_ << "\n" << "b2 : " << b2_ << "\n"; return os; } auto SetDimension(int dim) const -> void final { if (m_.size() != dim) { m_ = U::Zero(dim); v_ = U::Zero(dim); } } auto Clone(int dim) const -> std::unique_ptr final { if (dim == 0) { dim = m_.size(); } return std::make_unique>(dim, r_, e_, b1_, b2_, debias_); } }; } // namespace UpdateRule template struct SGDSolver { using Scalar = typename Functor::Scalar; using Vector = Eigen::Array; explicit SGDSolver(gsl::not_nullfunctor, gsl::not_null update) : functor_(functor) , rule_(update) { } auto Optimize(Eigen::Ref const x0, int epochs = 1000) const noexcept -> Vector { auto const* fun = functor_.get(); EXPECT(x0.size() == static_cast(fun->NumParameters())); Vector grad(x0.size()); Vector x = x0; Vector beta(x0.size()); static constexpr auto tol { 1e-8 }; for (epochs_ = 0; epochs_ < epochs; ++epochs_) { std::invoke(*fun, x, grad); // apply learning rate to grad and write result in beta rule_->Update(grad, beta); if ((beta.abs() < tol).all()) { converged_ = true; break; } x -= beta; } return x; } auto Epochs() const { return epochs_; } private: gsl::not_null functor_; gsl::not_null rule_; mutable int epochs_ {1000}; mutable bool converged_ { false }; }; } // namespace Operon #endif ================================================ FILE: include/operon/optimizer/tiny_cost_function.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_OPTIMIZER_TINY_HPP #define OPERON_OPTIMIZER_TINY_HPP #include #include #include "operon/interpreter/interpreter.hpp" namespace Operon { // this cost function is adapted to work with both solvers from Ceres: the normal one and the tiny solver // for this, a number of template parameters are necessary: // - the AutodiffCalculator will compute and return the Jacobian matrix // - the StorageOrder specifies the format of the jacobian (row-major for the big Ceres solver, column-major for the tiny solver) template struct CostFunction { static auto constexpr Storage{ StorageOrder }; using Scalar = Operon::Scalar; enum { NUM_RESIDUALS = Eigen::Dynamic, // NOLINT NUM_PARAMETERS = Eigen::Dynamic, // NOLINT }; explicit CostFunction(gsl::not_null const*> interpreter, Operon::Span target, Operon::Range const range) : interpreter_{interpreter} , target_{target} , range_{range} , numResiduals_{range.Size()} , numParameters_{ParameterCount(interpreter_->GetTree())} { } inline auto Evaluate(Scalar const* parameters, Scalar* residuals, Scalar* jacobian) const -> bool // NOLINT { EXPECT(target_.size() == numResiduals_); EXPECT(parameters != nullptr); Operon::Span params{ parameters, numParameters_ }; if (jacobian != nullptr) { Operon::Span jac{jacobian, static_cast(numResiduals_ * numParameters_)}; interpreter_->JacRev(params, range_, jac); } if (residuals != nullptr) { Operon::Span res{ residuals, static_cast(numResiduals_) }; interpreter_->Evaluate(params, range_, res); Eigen::Map> x(residuals, numResiduals_); Eigen::Map const> y(target_.data(), numResiduals_); x -= y; } return true; } // ceres solver - jacobian must be in row-major format // tiny solver - jacobian must be in col-major format auto operator()(Scalar const* parameters, Scalar* residuals, Scalar* jacobian) const -> bool { return Evaluate(parameters, residuals, jacobian); } [[nodiscard]] auto NumResiduals() const -> int { return numResiduals_; } [[nodiscard]] auto NumParameters() const -> int { return numParameters_; } // required by Eigen::LevenbergMarquardt using JacobianType = Eigen::Matrix; using QRSolver = Eigen::ColPivHouseholderQR; // there is no real documentation but looking at Eigen unit tests, these functions should return zero // see: https://gitlab.com/libeigen/eigen/-/blob/master/unsupported/test/NonLinearOptimization.cpp auto operator()(Eigen::Matrix const& input, Eigen::Matrix& residual) const -> int { Evaluate(input.data(), residual.data(), nullptr); return 0; } auto df(Eigen::Matrix const& input, Eigen::Matrix& jacobian) const -> int // NOLINT { static_assert(StorageOrder == Eigen::ColMajor, "Eigen::LevenbergMarquardt requires the Jacobian to be stored in column-major format."); Evaluate(input.data(), nullptr, jacobian.data()); //std::cout << "jacobian:\n" << jacobian << "\n"; return 0; } [[nodiscard]] auto values() const -> int { return NumResiduals(); } // NOLINT [[nodiscard]] auto inputs() const -> int { return NumParameters(); } // NOLINT private: gsl::not_null const*> interpreter_; Operon::Span target_; Operon::Range const range_; // NOLINT std::size_t numResiduals_; std::size_t numParameters_; inline auto ParameterCount(auto const& tree) const -> std::size_t { auto const& nodes = tree.Nodes(); return std::count_if(nodes.cbegin(), nodes.cend(), [](auto const& n) { return n.Optimize; }); } }; } // namespace Operon #endif ================================================ FILE: include/operon/parser/infix.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research #ifndef OPERON_PARSER_HPP #define OPERON_PARSER_HPP #include #include #include "operon/core/dataset.hpp" #include "operon/core/tree.hpp" #include "operon/core/types.hpp" namespace Operon { struct OPERON_EXPORT InfixParser { static auto Parse(std::string_view infix, bool reduce = false) -> Tree; static auto Parse(std::string_view infix, Dataset const& dataset, bool reduce = false) -> Tree; }; } // namespace Operon #endif ================================================ FILE: include/operon/random/random.hpp ================================================ // SPDX-License-Identifier: MIT // SPDX-FileCopyrightText: Copyright 2019-2023 Heal Research // #ifndef OPERON_RANDOM_HPP #define OPERON_RANDOM_HPP #include #include #include #include "operon/core/contracts.hpp" namespace Operon::Random { template auto Uniform(R& random, T a, T b) -> T { static_assert(std::is_arithmetic_v, "T must be an arithmetic type."); using Dist = std::conditional_t, std::uniform_int_distribution, std::uniform_real_distribution>; return Dist(a,b)(random); } template auto Sample(R& random, InputIterator start, InputIterator end) -> InputIterator { auto dist = std::distance(start, end); if (dist <= 1) { return start; } std::advance(start, Uniform(random, decltype(dist){0}, dist-1)); return start; } template auto Sample(R& random, InputIterator start, InputIterator end, std::add_pointer_t condition) -> InputIterator { auto n = std::count_if(start, end, condition); if (n == 0) { return end; // no element satisfies the condition } auto m = Uniform(random, decltype(n){0}, n-1); InputIterator it; for (it = start; it < end; ++it) { if (condition(*it) && 0 == m--) { break; } } ENSURE(start <= it && it <= end); return it; } // sample n elements and write them to the output iterator template auto Sample(R& random, InputIterator start, InputIterator end, OutputIterator out, size_t n) -> OutputIterator { EXPECT(start < end); return std::sample(start, end, out, n, random); } } // namespace Operon::Random #endif ================================================ FILE: operon.nix ================================================ { stdenv, pkgs, system, enableShared ? true, enableTesting ? true, }: stdenv.mkDerivation rec { name = "operon"; src = ./.; cmakePreset = { "x86_64-linux" = "build-linux"; "x86_64-darwin" = "build-linux"; "aarch64-darwin" = "build-osx"; } ."${system}"; cmakeFlags = [ "--preset ${cmakePreset}" "-DUSE_SINGLE_PRECISION=ON" "-DBUILD_SHARED_LIBS=${if enableShared then "YES" else "NO"}" ]; cmakeBuildType = "Release"; nativeBuildInputs = with pkgs; [ cmake git ]; buildInputs = (with pkgs; [ aria-csv ceres-solver cpp-sort cpptrace cxxopts eigen eve fast-float fluky fmt_11 gtl icu lbfgs libassert libdwarf mdspan microsoft-gsl pkg-config infix-parser (scnlib.overrideAttrs { inherit enableShared; }) simdutf # required by scnlib taskflow tl-expected unordered_dense vdt vstat xxHash zstd ]) ++ ( with pkgs; pkgs.lib.optionals enableTesting [ catch2_3 nanobench ] ); } ================================================ FILE: ports/aria-csv/portfile.cmake ================================================ vcpkg_from_github( OUT_SOURCE_PATH SOURCE_PATH REPO AriaFallah/csv-parser REF 544c764d0585c61d4c3bd3a023a825f3d7de1f31 SHA512 2a16cb0d112f467337ebcd7a9911b50112632c7ff477b562bf9ecacff7a6879d70406de1cca7c097ce7379b61313f1d3062643164e057506a359f7591f2ed778 HEAD_REF main ) set(VCPKG_BUILD_TYPE release) # we only need the hpp file file(MAKE_DIRECTORY "${CURRENT_PACKAGES_DIR}/include/aria-csv") file(COPY "${SOURCE_PATH}/parser.hpp" DESTINATION "${CURRENT_PACKAGES_DIR}/include/aria-csv") file( INSTALL "${SOURCE_PATH}/LICENSE" DESTINATION "${CURRENT_PACKAGES_DIR}/share/${PORT}" RENAME copyright) ================================================ FILE: ports/aria-csv/vcpkg.json ================================================ { "name": "aria-csv", "version": "1.0.0", "description": "Fast, header-only, C++11 CSV parser.", "homepage": "https://github.com/AriaFallah/csv-parser" } ================================================ FILE: ports/fluky/portfile.cmake ================================================ vcpkg_from_github( OUT_SOURCE_PATH SOURCE_PATH REPO foolnotion/fluky REF 19320e7499cf0958268dc11fec28a6e41ac332e4 SHA512 466f40f5aaa9b4bce66a6daeb6141c17f68972b529ac71db96545d87b50701c8a2fa7112b2f4250b98e7bf516e738a5562cfa1a18dc69a803ca21a8494755ae5 HEAD_REF main ) include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake/vcpkg_cmake_build.cmake") include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake/vcpkg_cmake_install.cmake") include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake-config/vcpkg_cmake_config_fixup.cmake") set(VCPKG_BUILD_TYPE release) vcpkg_configure_cmake( SOURCE_PATH "${SOURCE_PATH}" PREFER_NINJA OPTIONS -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF ) vcpkg_cmake_install() vcpkg_cmake_config_fixup(PACKAGE_NAME fluky CONFIG_PATH lib/cmake/fluky DO_NOT_DELETE_PARENT_CONFIG_PATH) file(REMOVE_RECURSE "${CURRENT_PACKAGES_DIR}/debug/include" "${CURRENT_PACKAGES_DIR}/debug/share" "${CURRENT_PACKAGES_DIR}/lib") file( INSTALL "${SOURCE_PATH}/LICENSE" DESTINATION "${CURRENT_PACKAGES_DIR}/share/${PORT}" RENAME copyright) ================================================ FILE: ports/fluky/vcpkg.json ================================================ { "name": "fluky", "version-string": "0.1.0", "description": "C++20 random number generation utilities." } ================================================ FILE: ports/infix-parser/portfile.cmake ================================================ vcpkg_from_github( OUT_SOURCE_PATH SOURCE_PATH REPO foolnotion/infix-parser REF 18ea752693bc489000b53b7b8387322f91193c48 SHA512 b6d8bd8bba5080c6d99be76b9b9270dc6bcb21b060cf48821737001fbbe8bef1cd5ba7175996a1daf453927e1e264c9dce5cd66b9e6a59954bd9ce65a663bb34 HEAD_REF main ) include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake/vcpkg_cmake_build.cmake") include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake/vcpkg_cmake_install.cmake") include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake-config/vcpkg_cmake_config_fixup.cmake") set(VCPKG_BUILD_TYPE release) vcpkg_configure_cmake( SOURCE_PATH "${SOURCE_PATH}" PREFER_NINJA OPTIONS -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF ) vcpkg_cmake_install() vcpkg_cmake_config_fixup(PACKAGE_NAME infix-parser CONFIG_PATH lib/cmake/infix-parser DO_NOT_DELETE_PARENT_CONFIG_PATH) file(REMOVE_RECURSE "${CURRENT_PACKAGES_DIR}/debug/include" "${CURRENT_PACKAGES_DIR}/debug/share" "${CURRENT_PACKAGES_DIR}/lib") file( INSTALL "${SOURCE_PATH}/LICENSE" DESTINATION "${CURRENT_PACKAGES_DIR}/share/${PORT}" RENAME copyright) ================================================ FILE: ports/infix-parser/vcpkg.json ================================================ { "name": "infix-parser", "version-string": "0.1.0", "description": "A fast C++20 infix expression parser built on lexy." } ================================================ FILE: ports/lbfgs/portfile.cmake ================================================ vcpkg_from_github( OUT_SOURCE_PATH SOURCE_PATH REPO foolnotion/lbfgs REF b2e339b7d169ec3905f0f724958ed060acf592c9 SHA512 a26355d918631f94935a4ee0968f975341b05842f0222e85963f1e3ac5ea75fa8c87975a33c8779d1408600c62b287ada60424814aaede995c26f1daa6752ab6 HEAD_REF main ) include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake/vcpkg_cmake_build.cmake") include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake/vcpkg_cmake_install.cmake") include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake-config/vcpkg_cmake_config_fixup.cmake") set(VCPKG_BUILD_TYPE release) vcpkg_configure_cmake( SOURCE_PATH "${SOURCE_PATH}" PREFER_NINJA OPTIONS -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF ) vcpkg_cmake_install() vcpkg_cmake_config_fixup(PACKAGE_NAME lbfgs CONFIG_PATH lib/cmake/lbfgs DO_NOT_DELETE_PARENT_CONFIG_PATH) file(REMOVE_RECURSE "${CURRENT_PACKAGES_DIR}/debug/include" "${CURRENT_PACKAGES_DIR}/debug/share" "${CURRENT_PACKAGES_DIR}/lib") file( INSTALL "${SOURCE_PATH}/LICENSE" DESTINATION "${CURRENT_PACKAGES_DIR}/share/${PORT}" RENAME copyright) ================================================ FILE: ports/lbfgs/vcpkg.json ================================================ { "name": "lbfgs", "version-string": "0.1.0", "description": "C++ header-only L-BFGS optimization library.", "dependencies": [ { "name": "tl-expected" } ] } ================================================ FILE: ports/vstat/portfile.cmake ================================================ vcpkg_from_github( OUT_SOURCE_PATH SOURCE_PATH REPO heal-research/vstat REF cd753d0467ccc96389fb969551eccceca97f38d1 SHA512 e7fc8bbd37dbc78892ed13be75245d0bdb7b7dc6ba86c13e0ab3ddec9a909a665eb719a2f069d08c1f60448c55572fa6a88e8fd62229f01fa508362157326889 HEAD_REF main ) include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake/vcpkg_cmake_build.cmake") include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake/vcpkg_cmake_install.cmake") include("${VCPKG_ROOT_DIR}/ports/vcpkg-cmake-config/vcpkg_cmake_config_fixup.cmake") set(VCPKG_BUILD_TYPE release) vcpkg_configure_cmake( SOURCE_PATH "${SOURCE_PATH}" PREFER_NINJA OPTIONS -DBUILD_TESTING=OFF -DBUILD_EXAMPLES=OFF -DCPM_USE_LOCAL_PACKAGES=ON ) vcpkg_cmake_install() vcpkg_cmake_config_fixup(PACKAGE_NAME vstat CONFIG_PATH lib/cmake/vstat DO_NOT_DELETE_PARENT_CONFIG_PATH) file(REMOVE_RECURSE "${CURRENT_PACKAGES_DIR}/debug/include" "${CURRENT_PACKAGES_DIR}/debug/share" "${CURRENT_PACKAGES_DIR}/lib") file( INSTALL "${SOURCE_PATH}/LICENSE" DESTINATION "${CURRENT_PACKAGES_DIR}/share/${PORT}" RENAME copyright) ================================================ FILE: ports/vstat/vcpkg.json ================================================ { "name": "vstat", "version-string": "1.0.0-beta", "description": "C++17 library of SIMD-enabled sample statistics (mean, variance, covariance, correlation).", "dependencies": [ { "name": "eve" } ] } ================================================ FILE: rtd/build.rst ================================================ Build instructions ================== The project requires ``CMake`` and a ``C++17`` compliant compiler. Configuration options ^^^^^^^^^^^^^^^^^^^^^ .. note:: These options are specified to ``CMake`` in the form ``-D

)DELIM"; } char const* pyperf() noexcept { return R"DELIM({ "benchmarks": [ { "runs": [ { "values": [ {{#measurement}} {{elapsed}}{{^-last}}, {{/last}}{{/measurement}} ] } ] } ], "metadata": { "loops": {{sum(iterations)}}, "inner_loops": {{batch}}, "name": "{{title}}", "unit": "second" }, "version": "1.0" })DELIM"; } char const* json() noexcept { return R"DELIM({ "results": [ {{#result}} { "title": "{{title}}", "name": "{{name}}", "unit": "{{unit}}", "batch": {{batch}}, "complexityN": {{complexityN}}, "epochs": {{epochs}}, "clockResolution": {{clockResolution}}, "clockResolutionMultiple": {{clockResolutionMultiple}}, "maxEpochTime": {{maxEpochTime}}, "minEpochTime": {{minEpochTime}}, "minEpochIterations": {{minEpochIterations}}, "epochIterations": {{epochIterations}}, "warmup": {{warmup}}, "relative": {{relative}}, "median(elapsed)": {{median(elapsed)}}, "medianAbsolutePercentError(elapsed)": {{medianAbsolutePercentError(elapsed)}}, "median(instructions)": {{median(instructions)}}, "medianAbsolutePercentError(instructions)": {{medianAbsolutePercentError(instructions)}}, "median(cpucycles)": {{median(cpucycles)}}, "median(contextswitches)": {{median(contextswitches)}}, "median(pagefaults)": {{median(pagefaults)}}, "median(branchinstructions)": {{median(branchinstructions)}}, "median(branchmisses)": {{median(branchmisses)}}, "totalTime": {{sumProduct(iterations, elapsed)}}, "measurements": [ {{#measurement}} { "iterations": {{iterations}}, "elapsed": {{elapsed}}, "pagefaults": {{pagefaults}}, "cpucycles": {{cpucycles}}, "contextswitches": {{contextswitches}}, "instructions": {{instructions}}, "branchinstructions": {{branchinstructions}}, "branchmisses": {{branchmisses}} }{{^-last}},{{/-last}} {{/measurement}} ] }{{^-last}},{{/-last}} {{/result}} ] })DELIM"; } ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) struct Node { enum class Type { tag, content, section, inverted_section }; char const* begin; char const* end; std::vector children; Type type; template // NOLINTNEXTLINE(hicpp-avoid-c-arrays,modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays) bool operator==(char const (&str)[N]) const noexcept { // NOLINTNEXTLINE(cppcoreguidelines-pro-bounds-array-to-pointer-decay) return static_cast(std::distance(begin, end) + 1) == N && 0 == strncmp(str, begin, N - 1); } }; ANKERL_NANOBENCH(IGNORE_PADDED_POP) // NOLINTNEXTLINE(misc-no-recursion) static std::vector parseMustacheTemplate(char const** tpl) { std::vector nodes; while (true) { auto const* begin = std::strstr(*tpl, "{{"); auto const* end = begin; if (begin != nullptr) { // NOLINTNEXTLINE(cppcoreguidelines-pro-bounds-pointer-arithmetic) begin += 2; end = std::strstr(begin, "}}"); } if (begin == nullptr || end == nullptr) { // nothing found, finish node // NOLINTNEXTLINE(cppcoreguidelines-pro-bounds-pointer-arithmetic) nodes.emplace_back(Node{*tpl, *tpl + std::strlen(*tpl), std::vector{}, Node::Type::content}); return nodes; } // NOLINTNEXTLINE(cppcoreguidelines-pro-bounds-pointer-arithmetic) nodes.emplace_back(Node{*tpl, begin - 2, std::vector{}, Node::Type::content}); // we found a tag // NOLINTNEXTLINE(cppcoreguidelines-pro-bounds-pointer-arithmetic) *tpl = end + 2; switch (*begin) { case '/': // finished! bail out return nodes; case '#': // NOLINTNEXTLINE(cppcoreguidelines-pro-bounds-pointer-arithmetic) nodes.emplace_back(Node{begin + 1, end, parseMustacheTemplate(tpl), Node::Type::section}); break; case '^': // NOLINTNEXTLINE(cppcoreguidelines-pro-bounds-pointer-arithmetic) nodes.emplace_back(Node{begin + 1, end, parseMustacheTemplate(tpl), Node::Type::inverted_section}); break; default: nodes.emplace_back(Node{begin, end, std::vector{}, Node::Type::tag}); break; } } } static bool generateFirstLast(Node const& n, size_t idx, size_t size, std::ostream& out) { ANKERL_NANOBENCH_LOG("n.type=" << static_cast(n.type)); bool const matchFirst = n == "-first"; bool const matchLast = n == "-last"; if (!matchFirst && !matchLast) { return false; } bool doWrite = false; if (n.type == Node::Type::section) { doWrite = (matchFirst && idx == 0) || (matchLast && idx == size - 1); } else if (n.type == Node::Type::inverted_section) { doWrite = (matchFirst && idx != 0) || (matchLast && idx != size - 1); } if (doWrite) { for (auto const& child : n.children) { if (child.type == Node::Type::content) { out.write(child.begin, std::distance(child.begin, child.end)); } } } return true; } static bool matchCmdArgs(std::string const& str, std::vector& matchResult) { matchResult.clear(); auto idxOpen = str.find('('); auto idxClose = str.find(')', idxOpen); if (idxClose == std::string::npos) { return false; } matchResult.emplace_back(str.substr(0, idxOpen)); // split by comma matchResult.emplace_back(); for (size_t i = idxOpen + 1; i != idxClose; ++i) { if (str[i] == ' ' || str[i] == '\t') { // skip whitespace continue; } if (str[i] == ',') { // got a comma => new string matchResult.emplace_back(); continue; } // no whitespace no comma, append matchResult.back() += str[i]; } return true; } static bool generateConfigTag(Node const& n, Config const& config, std::ostream& out) { using detail::d; if (n == "title") { out << config.mBenchmarkTitle; return true; } if (n == "name") { out << config.mBenchmarkName; return true; } if (n == "unit") { out << config.mUnit; return true; } if (n == "batch") { out << config.mBatch; return true; } if (n == "complexityN") { out << config.mComplexityN; return true; } if (n == "epochs") { out << config.mNumEpochs; return true; } if (n == "clockResolution") { out << d(detail::clockResolution()); return true; } if (n == "clockResolutionMultiple") { out << config.mClockResolutionMultiple; return true; } if (n == "maxEpochTime") { out << d(config.mMaxEpochTime); return true; } if (n == "minEpochTime") { out << d(config.mMinEpochTime); return true; } if (n == "minEpochIterations") { out << config.mMinEpochIterations; return true; } if (n == "epochIterations") { out << config.mEpochIterations; return true; } if (n == "warmup") { out << config.mWarmup; return true; } if (n == "relative") { out << config.mIsRelative; return true; } return false; } // NOLINTNEXTLINE(readability-function-cognitive-complexity) static std::ostream& generateResultTag(Node const& n, Result const& r, std::ostream& out) { if (generateConfigTag(n, r.config(), out)) { return out; } // match e.g. "median(elapsed)" // g++ 4.8 doesn't implement std::regex :( // static std::regex const regOpArg1("^([a-zA-Z]+)\\(([a-zA-Z]*)\\)$"); // std::cmatch matchResult; // if (std::regex_match(n.begin, n.end, matchResult, regOpArg1)) { std::vector matchResult; if (matchCmdArgs(std::string(n.begin, n.end), matchResult)) { if (matchResult.size() == 2) { if (matchResult[0] == "context") { return out << r.context(matchResult[1]); } auto m = Result::fromString(matchResult[1]); if (m == Result::Measure::_size) { return out << 0.0; } if (matchResult[0] == "median") { return out << r.median(m); } if (matchResult[0] == "average") { return out << r.average(m); } if (matchResult[0] == "medianAbsolutePercentError") { return out << r.medianAbsolutePercentError(m); } if (matchResult[0] == "sum") { return out << r.sum(m); } if (matchResult[0] == "minimum") { return out << r.minimum(m); } if (matchResult[0] == "maximum") { return out << r.maximum(m); } } else if (matchResult.size() == 3) { auto m1 = Result::fromString(matchResult[1]); auto m2 = Result::fromString(matchResult[2]); if (m1 == Result::Measure::_size || m2 == Result::Measure::_size) { return out << 0.0; } if (matchResult[0] == "sumProduct") { return out << r.sumProduct(m1, m2); } } } // match e.g. "sumProduct(elapsed, iterations)" // static std::regex const regOpArg2("^([a-zA-Z]+)\\(([a-zA-Z]*)\\s*,\\s+([a-zA-Z]*)\\)$"); // nothing matches :( throw std::runtime_error("command '" + std::string(n.begin, n.end) + "' not understood"); } static void generateResultMeasurement(std::vector const& nodes, size_t idx, Result const& r, std::ostream& out) { for (auto const& n : nodes) { if (!generateFirstLast(n, idx, r.size(), out)) { ANKERL_NANOBENCH_LOG("n.type=" << static_cast(n.type)); switch (n.type) { case Node::Type::content: out.write(n.begin, std::distance(n.begin, n.end)); break; case Node::Type::inverted_section: throw std::runtime_error("got a inverted section inside measurement"); case Node::Type::section: throw std::runtime_error("got a section inside measurement"); case Node::Type::tag: { auto m = Result::fromString(std::string(n.begin, n.end)); if (m == Result::Measure::_size || !r.has(m)) { out << 0.0; } else { out << r.get(idx, m); } break; } } } } } static void generateResult(std::vector const& nodes, size_t idx, std::vector const& results, std::ostream& out) { auto const& r = results[idx]; for (auto const& n : nodes) { if (!generateFirstLast(n, idx, results.size(), out)) { ANKERL_NANOBENCH_LOG("n.type=" << static_cast(n.type)); switch (n.type) { case Node::Type::content: out.write(n.begin, std::distance(n.begin, n.end)); break; case Node::Type::inverted_section: throw std::runtime_error("got a inverted section inside result"); case Node::Type::section: if (n == "measurement") { for (size_t i = 0; i < r.size(); ++i) { generateResultMeasurement(n.children, i, r, out); } } else { throw std::runtime_error("got a section inside result"); } break; case Node::Type::tag: generateResultTag(n, r, out); break; } } } } } // namespace templates // helper stuff that only intended to be used internally namespace detail { char const* getEnv(char const* name); bool isEndlessRunning(std::string const& name); bool isWarningsEnabled(); template T parseFile(std::string const& filename, bool* fail); void gatherStabilityInformation(std::vector& warnings, std::vector& recommendations); void printStabilityInformationOnce(std::ostream* outStream); // remembers the last table settings used. When it changes, a new table header is automatically written for the new entry. uint64_t& singletonHeaderHash() noexcept; // determines resolution of the given clock. This is done by measuring multiple times and returning the minimum time difference. Clock::duration calcClockResolution(size_t numEvaluations) noexcept; // formatting utilities namespace fmt { // adds thousands separator to numbers ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) class NumSep : public std::numpunct { public: explicit NumSep(char sep); char do_thousands_sep() const override; std::string do_grouping() const override; private: char mSep; }; ANKERL_NANOBENCH(IGNORE_PADDED_POP) // RAII to save & restore a stream's state ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) class StreamStateRestorer { public: explicit StreamStateRestorer(std::ostream& s); ~StreamStateRestorer(); // sets back all stream info that we remembered at construction void restore(); // don't allow copying / moving StreamStateRestorer(StreamStateRestorer const&) = delete; StreamStateRestorer& operator=(StreamStateRestorer const&) = delete; StreamStateRestorer(StreamStateRestorer&&) = delete; StreamStateRestorer& operator=(StreamStateRestorer&&) = delete; private: std::ostream& mStream; std::locale mLocale; std::streamsize const mPrecision; std::streamsize const mWidth; std::ostream::char_type const mFill; std::ostream::fmtflags const mFmtFlags; }; ANKERL_NANOBENCH(IGNORE_PADDED_POP) // Number formatter class Number { public: Number(int width, int precision, double value); Number(int width, int precision, int64_t value); ANKERL_NANOBENCH(NODISCARD) std::string to_s() const; private: friend std::ostream& operator<<(std::ostream& os, Number const& n); std::ostream& write(std::ostream& os) const; int mWidth; int mPrecision; double mValue; }; // helper replacement for std::to_string of signed/unsigned numbers so we are locale independent std::string to_s(uint64_t n); std::ostream& operator<<(std::ostream& os, Number const& n); class MarkDownColumn { public: MarkDownColumn(int w, int prec, std::string tit, std::string suff, double val) noexcept; ANKERL_NANOBENCH(NODISCARD) std::string title() const; ANKERL_NANOBENCH(NODISCARD) std::string separator() const; ANKERL_NANOBENCH(NODISCARD) std::string invalid() const; ANKERL_NANOBENCH(NODISCARD) std::string value() const; private: int mWidth; int mPrecision; std::string mTitle; std::string mSuffix; double mValue; }; // Formats any text as markdown code, escaping backticks. class MarkDownCode { public: explicit MarkDownCode(std::string const& what); private: friend std::ostream& operator<<(std::ostream& os, MarkDownCode const& mdCode); std::ostream& write(std::ostream& os) const; std::string mWhat{}; }; std::ostream& operator<<(std::ostream& os, MarkDownCode const& mdCode); } // namespace fmt } // namespace detail } // namespace nanobench } // namespace ankerl // implementation ///////////////////////////////////////////////////////////////////////////////// namespace ankerl { namespace nanobench { // NOLINTNEXTLINE(readability-function-cognitive-complexity) void render(char const* mustacheTemplate, std::vector const& results, std::ostream& out) { detail::fmt::StreamStateRestorer const restorer(out); out.precision(std::numeric_limits::digits10); auto nodes = templates::parseMustacheTemplate(&mustacheTemplate); for (auto const& n : nodes) { ANKERL_NANOBENCH_LOG("n.type=" << static_cast(n.type)); switch (n.type) { case templates::Node::Type::content: out.write(n.begin, std::distance(n.begin, n.end)); break; case templates::Node::Type::inverted_section: throw std::runtime_error("unknown list '" + std::string(n.begin, n.end) + "'"); case templates::Node::Type::section: if (n == "result") { const size_t nbResults = results.size(); for (size_t i = 0; i < nbResults; ++i) { generateResult(n.children, i, results, out); } } else if (n == "measurement") { if (results.size() != 1) { throw std::runtime_error( "render: can only use section 'measurement' here if there is a single result, but there are " + detail::fmt::to_s(results.size())); } // when we only have a single result, we can immediately go into its measurement. auto const& r = results.front(); for (size_t i = 0; i < r.size(); ++i) { generateResultMeasurement(n.children, i, r, out); } } else { throw std::runtime_error("render: unknown section '" + std::string(n.begin, n.end) + "'"); } break; case templates::Node::Type::tag: if (results.size() == 1) { // result & config are both supported there generateResultTag(n, results.front(), out); } else { // This just uses the last result's config. if (!generateConfigTag(n, results.back().config(), out)) { throw std::runtime_error("unknown tag '" + std::string(n.begin, n.end) + "'"); } } break; } } } void render(std::string const& mustacheTemplate, std::vector const& results, std::ostream& out) { render(mustacheTemplate.c_str(), results, out); } void render(char const* mustacheTemplate, const Bench& bench, std::ostream& out) { render(mustacheTemplate, bench.results(), out); } void render(std::string const& mustacheTemplate, const Bench& bench, std::ostream& out) { render(mustacheTemplate.c_str(), bench.results(), out); } namespace detail { PerformanceCounters& performanceCounters() { # if defined(__clang__) # pragma clang diagnostic push # pragma clang diagnostic ignored "-Wexit-time-destructors" # endif static PerformanceCounters pc; # if defined(__clang__) # pragma clang diagnostic pop # endif return pc; } // Windows version of doNotOptimizeAway // see https://github.com/google/benchmark/blob/v1.7.1/include/benchmark/benchmark.h#L514 // see https://github.com/facebook/folly/blob/v2023.01.30.00/folly/lang/Hint-inl.h#L54-L58 // see https://learn.microsoft.com/en-us/cpp/preprocessor/optimize # if defined(_MSC_VER) # pragma optimize("", off) void doNotOptimizeAwaySink(void const*) {} # pragma optimize("", on) # endif template T parseFile(std::string const& filename, bool* fail) { std::ifstream fin(filename); // NOLINT(misc-const-correctness) T num{}; fin >> num; if (fail != nullptr) { *fail = fin.fail(); } return num; } char const* getEnv(char const* name) { # if defined(_MSC_VER) # pragma warning(push) # pragma warning(disable : 4996) // getenv': This function or variable may be unsafe. # endif return std::getenv(name); // NOLINT(concurrency-mt-unsafe) # if defined(_MSC_VER) # pragma warning(pop) # endif } bool isEndlessRunning(std::string const& name) { auto const* const endless = getEnv("NANOBENCH_ENDLESS"); return nullptr != endless && endless == name; } // True when environment variable NANOBENCH_SUPPRESS_WARNINGS is either not set at all, or set to "0" bool isWarningsEnabled() { auto const* const suppression = getEnv("NANOBENCH_SUPPRESS_WARNINGS"); return nullptr == suppression || suppression == std::string("0"); } void gatherStabilityInformation(std::vector& warnings, std::vector& recommendations) { warnings.clear(); recommendations.clear(); # if defined(DEBUG) warnings.emplace_back("DEBUG defined"); bool const recommendCheckFlags = true; # else bool const recommendCheckFlags = false; # endif bool recommendPyPerf = false; # if defined(__linux__) auto nprocs = sysconf(_SC_NPROCESSORS_CONF); if (nprocs <= 0) { warnings.emplace_back("couldn't figure out number of processors - no governor, turbo check possible"); } else { // check frequency scaling for (long id = 0; id < nprocs; ++id) { auto idStr = detail::fmt::to_s(static_cast(id)); auto sysCpu = "/sys/devices/system/cpu/cpu" + idStr; auto minFreq = parseFile(sysCpu + "/cpufreq/scaling_min_freq", nullptr); auto maxFreq = parseFile(sysCpu + "/cpufreq/scaling_max_freq", nullptr); if (minFreq != maxFreq) { auto minMHz = d(minFreq) / 1000.0; auto maxMHz = d(maxFreq) / 1000.0; warnings.emplace_back("CPU frequency scaling enabled: CPU " + idStr + " between " + detail::fmt::Number(1, 1, minMHz).to_s() + " and " + detail::fmt::Number(1, 1, maxMHz).to_s() + " MHz"); recommendPyPerf = true; break; } } auto fail = false; auto currentGovernor = parseFile("/sys/devices/system/cpu/cpu0/cpufreq/scaling_governor", &fail); if (!fail && "performance" != currentGovernor) { warnings.emplace_back("CPU governor is '" + currentGovernor + "' but should be 'performance'"); recommendPyPerf = true; } auto noTurbo = parseFile("/sys/devices/system/cpu/intel_pstate/no_turbo", &fail); if (!fail && noTurbo == 0) { warnings.emplace_back("Turbo is enabled, CPU frequency will fluctuate"); recommendPyPerf = true; } } # endif if (recommendCheckFlags) { recommendations.emplace_back("Make sure you compile for Release"); } if (recommendPyPerf) { recommendations.emplace_back("Use 'pyperf system tune' before benchmarking. See https://github.com/psf/pyperf"); } } void printStabilityInformationOnce(std::ostream* outStream) { static bool shouldPrint = true; if (shouldPrint && (nullptr != outStream) && isWarningsEnabled()) { auto& os = *outStream; shouldPrint = false; std::vector warnings; std::vector recommendations; gatherStabilityInformation(warnings, recommendations); if (warnings.empty()) { return; } os << "Warning, results might be unstable:" << std::endl; for (auto const& w : warnings) { os << "* " << w << std::endl; } os << std::endl << "Recommendations" << std::endl; for (auto const& r : recommendations) { os << "* " << r << std::endl; } } } // remembers the last table settings used. When it changes, a new table header is automatically written for the new entry. uint64_t& singletonHeaderHash() noexcept { static uint64_t sHeaderHash{}; return sHeaderHash; } ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined") inline uint64_t hash_combine(uint64_t seed, uint64_t val) { return seed ^ (val + UINT64_C(0x9e3779b9) + (seed << 6U) + (seed >> 2U)); } // determines resolution of the given clock. This is done by measuring multiple times and returning the minimum time difference. Clock::duration calcClockResolution(size_t numEvaluations) noexcept { auto bestDuration = Clock::duration::max(); Clock::time_point tBegin; Clock::time_point tEnd; for (size_t i = 0; i < numEvaluations; ++i) { tBegin = Clock::now(); do { tEnd = Clock::now(); } while (tBegin == tEnd); bestDuration = (std::min)(bestDuration, tEnd - tBegin); } return bestDuration; } // Calculates clock resolution once, and remembers the result Clock::duration clockResolution() noexcept { static Clock::duration const sResolution = calcClockResolution(20); return sResolution; } ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) struct IterationLogic::Impl { enum class State { warmup, upscaling_runtime, measuring, endless }; explicit Impl(Bench const& bench) : mBench(bench) , mResult(bench.config()) { printStabilityInformationOnce(mBench.output()); // determine target runtime per epoch mTargetRuntimePerEpoch = detail::clockResolution() * mBench.clockResolutionMultiple(); if (mTargetRuntimePerEpoch > mBench.maxEpochTime()) { mTargetRuntimePerEpoch = mBench.maxEpochTime(); } if (mTargetRuntimePerEpoch < mBench.minEpochTime()) { mTargetRuntimePerEpoch = mBench.minEpochTime(); } if (isEndlessRunning(mBench.name())) { std::cerr << "NANOBENCH_ENDLESS set: running '" << mBench.name() << "' endlessly" << std::endl; mNumIters = (std::numeric_limits::max)(); mState = State::endless; } else if (0 != mBench.warmup()) { mNumIters = mBench.warmup(); mState = State::warmup; } else if (0 != mBench.epochIterations()) { // exact number of iterations mNumIters = mBench.epochIterations(); mState = State::measuring; } else { mNumIters = mBench.minEpochIterations(); mState = State::upscaling_runtime; } } // directly calculates new iters based on elapsed&iters, and adds a 10% noise. Makes sure we don't underflow. ANKERL_NANOBENCH(NODISCARD) uint64_t calcBestNumIters(std::chrono::nanoseconds elapsed, uint64_t iters) noexcept { auto doubleElapsed = d(elapsed); auto doubleTargetRuntimePerEpoch = d(mTargetRuntimePerEpoch); auto doubleNewIters = doubleTargetRuntimePerEpoch / doubleElapsed * d(iters); auto doubleMinEpochIters = d(mBench.minEpochIterations()); if (doubleNewIters < doubleMinEpochIters) { doubleNewIters = doubleMinEpochIters; } doubleNewIters *= 1.0 + 0.2 * mRng.uniform01(); // +0.5 for correct rounding when casting // NOLINTNEXTLINE(bugprone-incorrect-roundings) return static_cast(doubleNewIters + 0.5); } ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined") void upscale(std::chrono::nanoseconds elapsed) { if (elapsed * 10 < mTargetRuntimePerEpoch) { // we are far below the target runtime. Multiply iterations by 10 (with overflow check) if (mNumIters * 10 < mNumIters) { // overflow :-( showResult("iterations overflow. Maybe your code got optimized away?"); mNumIters = 0; return; } mNumIters *= 10; } else { mNumIters = calcBestNumIters(elapsed, mNumIters); } } void add(std::chrono::nanoseconds elapsed, PerformanceCounters const& pc) noexcept { # if defined(ANKERL_NANOBENCH_LOG_ENABLED) auto oldIters = mNumIters; # endif switch (mState) { case State::warmup: if (isCloseEnoughForMeasurements(elapsed)) { // if elapsed is close enough, we can skip upscaling and go right to measurements // still, we don't add the result to the measurements. mState = State::measuring; mNumIters = calcBestNumIters(elapsed, mNumIters); } else { // not close enough: switch to upscaling mState = State::upscaling_runtime; upscale(elapsed); } break; case State::upscaling_runtime: if (isCloseEnoughForMeasurements(elapsed)) { // if we are close enough, add measurement and switch to always measuring mState = State::measuring; mTotalElapsed += elapsed; mTotalNumIters += mNumIters; mResult.add(elapsed, mNumIters, pc); mNumIters = calcBestNumIters(mTotalElapsed, mTotalNumIters); } else { upscale(elapsed); } break; case State::measuring: // just add measurements - no questions asked. Even when runtime is low. But we can't ignore // that fluctuation, or else we would bias the result mTotalElapsed += elapsed; mTotalNumIters += mNumIters; mResult.add(elapsed, mNumIters, pc); if (0 != mBench.epochIterations()) { mNumIters = mBench.epochIterations(); } else { mNumIters = calcBestNumIters(mTotalElapsed, mTotalNumIters); } break; case State::endless: mNumIters = (std::numeric_limits::max)(); break; } if (static_cast(mResult.size()) == mBench.epochs()) { // we got all the results that we need, finish it showResult(""); mNumIters = 0; } ANKERL_NANOBENCH_LOG(mBench.name() << ": " << detail::fmt::Number(20, 3, d(elapsed.count())) << " elapsed, " << detail::fmt::Number(20, 3, d(mTargetRuntimePerEpoch.count())) << " target. oldIters=" << oldIters << ", mNumIters=" << mNumIters << ", mState=" << static_cast(mState)); } // NOLINTNEXTLINE(readability-function-cognitive-complexity) void showResult(std::string const& errorMessage) const { ANKERL_NANOBENCH_LOG(errorMessage); if (mBench.output() != nullptr) { // prepare column data /////// std::vector columns; auto rMedian = mResult.median(Result::Measure::elapsed); if (mBench.relative()) { double d = 100.0; if (!mBench.results().empty()) { d = rMedian <= 0.0 ? 0.0 : mBench.results().front().median(Result::Measure::elapsed) / rMedian * 100.0; } columns.emplace_back(11, 1, "relative", "%", d); } if (mBench.complexityN() > 0) { columns.emplace_back(14, 0, "complexityN", "", mBench.complexityN()); } columns.emplace_back(22, 2, mBench.timeUnitName() + "/" + mBench.unit(), "", rMedian / (mBench.timeUnit().count() * mBench.batch())); columns.emplace_back(22, 2, mBench.unit() + "/s", "", rMedian <= 0.0 ? 0.0 : mBench.batch() / rMedian); double const rErrorMedian = mResult.medianAbsolutePercentError(Result::Measure::elapsed); columns.emplace_back(10, 1, "err%", "%", rErrorMedian * 100.0); double rInsMedian = -1.0; if (mBench.performanceCounters() && mResult.has(Result::Measure::instructions)) { rInsMedian = mResult.median(Result::Measure::instructions); columns.emplace_back(18, 2, "ins/" + mBench.unit(), "", rInsMedian / mBench.batch()); } double rCycMedian = -1.0; if (mBench.performanceCounters() && mResult.has(Result::Measure::cpucycles)) { rCycMedian = mResult.median(Result::Measure::cpucycles); columns.emplace_back(18, 2, "cyc/" + mBench.unit(), "", rCycMedian / mBench.batch()); } if (rInsMedian > 0.0 && rCycMedian > 0.0) { columns.emplace_back(9, 3, "IPC", "", rCycMedian <= 0.0 ? 0.0 : rInsMedian / rCycMedian); } if (mBench.performanceCounters() && mResult.has(Result::Measure::branchinstructions)) { double const rBraMedian = mResult.median(Result::Measure::branchinstructions); columns.emplace_back(17, 2, "bra/" + mBench.unit(), "", rBraMedian / mBench.batch()); if (mResult.has(Result::Measure::branchmisses)) { double p = 0.0; if (rBraMedian >= 1e-9) { p = 100.0 * mResult.median(Result::Measure::branchmisses) / rBraMedian; } columns.emplace_back(10, 1, "miss%", "%", p); } } columns.emplace_back(12, 2, "total", "", mResult.sumProduct(Result::Measure::iterations, Result::Measure::elapsed)); // write everything auto& os = *mBench.output(); // combine all elements that are relevant for printing the header uint64_t hash = 0; hash = hash_combine(std::hash{}(mBench.unit()), hash); hash = hash_combine(std::hash{}(mBench.title()), hash); hash = hash_combine(std::hash{}(mBench.timeUnitName()), hash); hash = hash_combine(std::hash{}(mBench.timeUnit().count()), hash); hash = hash_combine(std::hash{}(mBench.relative()), hash); hash = hash_combine(std::hash{}(mBench.performanceCounters()), hash); if (hash != singletonHeaderHash()) { singletonHeaderHash() = hash; // no result yet, print header os << std::endl; for (auto const& col : columns) { os << col.title(); } os << "| " << mBench.title() << std::endl; for (auto const& col : columns) { os << col.separator(); } os << "|:" << std::string(mBench.title().size() + 1U, '-') << std::endl; } if (!errorMessage.empty()) { for (auto const& col : columns) { os << col.invalid(); } os << "| :boom: " << fmt::MarkDownCode(mBench.name()) << " (" << errorMessage << ')' << std::endl; } else { for (auto const& col : columns) { os << col.value(); } os << "| "; auto showUnstable = isWarningsEnabled() && rErrorMedian >= 0.05; if (showUnstable) { os << ":wavy_dash: "; } os << fmt::MarkDownCode(mBench.name()); if (showUnstable) { auto avgIters = d(mTotalNumIters) / d(mBench.epochs()); // NOLINTNEXTLINE(bugprone-incorrect-roundings) auto suggestedIters = static_cast(avgIters * 10 + 0.5); os << " (Unstable with ~" << detail::fmt::Number(1, 1, avgIters) << " iters. Increase `minEpochIterations` to e.g. " << suggestedIters << ")"; } os << std::endl; } } } ANKERL_NANOBENCH(NODISCARD) bool isCloseEnoughForMeasurements(std::chrono::nanoseconds elapsed) const noexcept { return elapsed * 3 >= mTargetRuntimePerEpoch * 2; } uint64_t mNumIters = 1; // NOLINT(misc-non-private-member-variables-in-classes) Bench const& mBench; // NOLINT(misc-non-private-member-variables-in-classes) std::chrono::nanoseconds mTargetRuntimePerEpoch{}; // NOLINT(misc-non-private-member-variables-in-classes) Result mResult; // NOLINT(misc-non-private-member-variables-in-classes) Rng mRng{123}; // NOLINT(misc-non-private-member-variables-in-classes) std::chrono::nanoseconds mTotalElapsed{}; // NOLINT(misc-non-private-member-variables-in-classes) uint64_t mTotalNumIters = 0; // NOLINT(misc-non-private-member-variables-in-classes) State mState = State::upscaling_runtime; // NOLINT(misc-non-private-member-variables-in-classes) }; ANKERL_NANOBENCH(IGNORE_PADDED_POP) IterationLogic::IterationLogic(Bench const& bench) : mPimpl(new Impl(bench)) {} IterationLogic::~IterationLogic() { delete mPimpl; } uint64_t IterationLogic::numIters() const noexcept { ANKERL_NANOBENCH_LOG(mPimpl->mBench.name() << ": mNumIters=" << mPimpl->mNumIters); return mPimpl->mNumIters; } void IterationLogic::add(std::chrono::nanoseconds elapsed, PerformanceCounters const& pc) noexcept { mPimpl->add(elapsed, pc); } void IterationLogic::moveResultTo(std::vector& results) noexcept { results.emplace_back(std::move(mPimpl->mResult)); } # if ANKERL_NANOBENCH(PERF_COUNTERS) ANKERL_NANOBENCH(IGNORE_PADDED_PUSH) class LinuxPerformanceCounters { public: struct Target { Target(uint64_t* targetValue_, bool correctMeasuringOverhead_, bool correctLoopOverhead_) : targetValue(targetValue_) , correctMeasuringOverhead(correctMeasuringOverhead_) , correctLoopOverhead(correctLoopOverhead_) {} uint64_t* targetValue{}; // NOLINT(misc-non-private-member-variables-in-classes) bool correctMeasuringOverhead{}; // NOLINT(misc-non-private-member-variables-in-classes) bool correctLoopOverhead{}; // NOLINT(misc-non-private-member-variables-in-classes) }; LinuxPerformanceCounters() = default; LinuxPerformanceCounters(LinuxPerformanceCounters const&) = delete; LinuxPerformanceCounters(LinuxPerformanceCounters&&) = delete; LinuxPerformanceCounters& operator=(LinuxPerformanceCounters const&) = delete; LinuxPerformanceCounters& operator=(LinuxPerformanceCounters&&) = delete; ~LinuxPerformanceCounters(); // quick operation inline void start() {} inline void stop() {} bool monitor(perf_sw_ids swId, Target target); bool monitor(perf_hw_id hwId, Target target); ANKERL_NANOBENCH(NODISCARD) bool hasError() const noexcept { return mHasError; } // Just reading data is faster than enable & disabling. // we subtract data ourselves. inline void beginMeasure() { if (mHasError) { return; } // NOLINTNEXTLINE(hicpp-signed-bitwise,cppcoreguidelines-pro-type-vararg) mHasError = -1 == ioctl(mFd, PERF_EVENT_IOC_RESET, PERF_IOC_FLAG_GROUP); if (mHasError) { return; } // NOLINTNEXTLINE(hicpp-signed-bitwise,cppcoreguidelines-pro-type-vararg) mHasError = -1 == ioctl(mFd, PERF_EVENT_IOC_ENABLE, PERF_IOC_FLAG_GROUP); } inline void endMeasure() { if (mHasError) { return; } // NOLINTNEXTLINE(hicpp-signed-bitwise,cppcoreguidelines-pro-type-vararg) mHasError = (-1 == ioctl(mFd, PERF_EVENT_IOC_DISABLE, PERF_IOC_FLAG_GROUP)); if (mHasError) { return; } auto const numBytes = sizeof(uint64_t) * mCounters.size(); auto ret = read(mFd, mCounters.data(), numBytes); mHasError = ret != static_cast(numBytes); } void updateResults(uint64_t numIters); // rounded integer division template static inline T divRounded(T a, T divisor) { return (a + divisor / 2) / divisor; } ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined") static inline uint32_t mix(uint32_t x) noexcept { x ^= x << 13U; x ^= x >> 17U; x ^= x << 5U; return x; } template ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined") void calibrate(Op&& op) { // clear current calibration data, for (auto& v : mCalibratedOverhead) { v = UINT64_C(0); } // create new calibration data auto newCalibration = mCalibratedOverhead; for (auto& v : newCalibration) { v = (std::numeric_limits::max)(); } for (size_t iter = 0; iter < 100; ++iter) { beginMeasure(); op(); endMeasure(); if (mHasError) { return; } for (size_t i = 0; i < newCalibration.size(); ++i) { auto diff = mCounters[i]; if (newCalibration[i] > diff) { newCalibration[i] = diff; } } } mCalibratedOverhead = std::move(newCalibration); { // calibrate loop overhead. For branches & instructions this makes sense, not so much for everything else like cycles. // marsaglia's xorshift: mov, sal/shr, xor. Times 3. // This has the nice property that the compiler doesn't seem to be able to optimize multiple calls any further. // see https://godbolt.org/z/49RVQ5 uint64_t const numIters = 100000U + (std::random_device{}() & 3U); uint64_t n = numIters; uint32_t x = 1234567; beginMeasure(); while (n-- > 0) { x = mix(x); } endMeasure(); detail::doNotOptimizeAway(x); auto measure1 = mCounters; n = numIters; beginMeasure(); while (n-- > 0) { // we now run *twice* so we can easily calculate the overhead x = mix(x); x = mix(x); } endMeasure(); detail::doNotOptimizeAway(x); auto measure2 = mCounters; for (size_t i = 0; i < mCounters.size(); ++i) { // factor 2 because we have two instructions per loop auto m1 = measure1[i] > mCalibratedOverhead[i] ? measure1[i] - mCalibratedOverhead[i] : 0; auto m2 = measure2[i] > mCalibratedOverhead[i] ? measure2[i] - mCalibratedOverhead[i] : 0; auto overhead = m1 * 2 > m2 ? m1 * 2 - m2 : 0; mLoopOverhead[i] = divRounded(overhead, numIters); } } } private: bool monitor(uint32_t type, uint64_t eventid, Target target); std::map mIdToTarget{}; // start with minimum size of 3 for read_format std::vector mCounters{3}; std::vector mCalibratedOverhead{3}; std::vector mLoopOverhead{3}; uint64_t mTimeEnabledNanos = 0; uint64_t mTimeRunningNanos = 0; int mFd = -1; bool mHasError = false; }; ANKERL_NANOBENCH(IGNORE_PADDED_POP) LinuxPerformanceCounters::~LinuxPerformanceCounters() { if (-1 != mFd) { close(mFd); } } bool LinuxPerformanceCounters::monitor(perf_sw_ids swId, LinuxPerformanceCounters::Target target) { return monitor(PERF_TYPE_SOFTWARE, swId, target); } bool LinuxPerformanceCounters::monitor(perf_hw_id hwId, LinuxPerformanceCounters::Target target) { return monitor(PERF_TYPE_HARDWARE, hwId, target); } // overflow is ok, it's checked ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined") void LinuxPerformanceCounters::updateResults(uint64_t numIters) { // clear old data for (auto& id_value : mIdToTarget) { *id_value.second.targetValue = UINT64_C(0); } if (mHasError) { return; } mTimeEnabledNanos = mCounters[1] - mCalibratedOverhead[1]; mTimeRunningNanos = mCounters[2] - mCalibratedOverhead[2]; for (uint64_t i = 0; i < mCounters[0]; ++i) { auto idx = static_cast(3 + i * 2 + 0); auto id = mCounters[idx + 1U]; auto it = mIdToTarget.find(id); if (it != mIdToTarget.end()) { auto& tgt = it->second; *tgt.targetValue = mCounters[idx]; if (tgt.correctMeasuringOverhead) { if (*tgt.targetValue >= mCalibratedOverhead[idx]) { *tgt.targetValue -= mCalibratedOverhead[idx]; } else { *tgt.targetValue = 0U; } } if (tgt.correctLoopOverhead) { auto correctionVal = mLoopOverhead[idx] * numIters; if (*tgt.targetValue >= correctionVal) { *tgt.targetValue -= correctionVal; } else { *tgt.targetValue = 0U; } } } } } bool LinuxPerformanceCounters::monitor(uint32_t type, uint64_t eventid, Target target) { *target.targetValue = (std::numeric_limits::max)(); if (mHasError) { return false; } auto pea = perf_event_attr(); std::memset(&pea, 0, sizeof(perf_event_attr)); pea.type = type; pea.size = sizeof(perf_event_attr); pea.config = eventid; pea.disabled = 1; // start counter as disabled pea.exclude_kernel = 1; pea.exclude_hv = 1; // NOLINTNEXTLINE(hicpp-signed-bitwise) pea.read_format = PERF_FORMAT_GROUP | PERF_FORMAT_ID | PERF_FORMAT_TOTAL_TIME_ENABLED | PERF_FORMAT_TOTAL_TIME_RUNNING; const int pid = 0; // the current process const int cpu = -1; // all CPUs # if defined(PERF_FLAG_FD_CLOEXEC) // since Linux 3.14 const unsigned long flags = PERF_FLAG_FD_CLOEXEC; # else const unsigned long flags = 0; # endif // NOLINTNEXTLINE(cppcoreguidelines-pro-type-vararg) auto fd = static_cast(syscall(__NR_perf_event_open, &pea, pid, cpu, mFd, flags)); if (-1 == fd) { return false; } if (-1 == mFd) { // first call: set to fd, and use this from now on mFd = fd; } uint64_t id = 0; // NOLINTNEXTLINE(hicpp-signed-bitwise,cppcoreguidelines-pro-type-vararg) if (-1 == ioctl(fd, PERF_EVENT_IOC_ID, &id)) { // couldn't get id return false; } // insert into map, rely on the fact that map's references are constant. mIdToTarget.emplace(id, target); // prepare readformat with the correct size (after the insert) auto size = 3 + 2 * mIdToTarget.size(); mCounters.resize(size); mCalibratedOverhead.resize(size); mLoopOverhead.resize(size); return true; } PerformanceCounters::PerformanceCounters() : mPc(new LinuxPerformanceCounters()) , mVal() , mHas() { // HW events mHas.cpuCycles = mPc->monitor(PERF_COUNT_HW_REF_CPU_CYCLES, LinuxPerformanceCounters::Target(&mVal.cpuCycles, true, false)); if (!mHas.cpuCycles) { // Fallback to cycles counter, reference cycles not available in many systems. mHas.cpuCycles = mPc->monitor(PERF_COUNT_HW_CPU_CYCLES, LinuxPerformanceCounters::Target(&mVal.cpuCycles, true, false)); } mHas.instructions = mPc->monitor(PERF_COUNT_HW_INSTRUCTIONS, LinuxPerformanceCounters::Target(&mVal.instructions, true, true)); mHas.branchInstructions = mPc->monitor(PERF_COUNT_HW_BRANCH_INSTRUCTIONS, LinuxPerformanceCounters::Target(&mVal.branchInstructions, true, false)); mHas.branchMisses = mPc->monitor(PERF_COUNT_HW_BRANCH_MISSES, LinuxPerformanceCounters::Target(&mVal.branchMisses, true, false)); // mHas.branchMisses = false; // SW events mHas.pageFaults = mPc->monitor(PERF_COUNT_SW_PAGE_FAULTS, LinuxPerformanceCounters::Target(&mVal.pageFaults, true, false)); mHas.contextSwitches = mPc->monitor(PERF_COUNT_SW_CONTEXT_SWITCHES, LinuxPerformanceCounters::Target(&mVal.contextSwitches, true, false)); mPc->start(); mPc->calibrate([] { auto before = ankerl::nanobench::Clock::now(); auto after = ankerl::nanobench::Clock::now(); (void)before; (void)after; }); if (mPc->hasError()) { // something failed, don't monitor anything. mHas = PerfCountSet{}; } } PerformanceCounters::~PerformanceCounters() { // no need to check for nullptr, delete nullptr has no effect delete mPc; } void PerformanceCounters::beginMeasure() { mPc->beginMeasure(); } void PerformanceCounters::endMeasure() { mPc->endMeasure(); } void PerformanceCounters::updateResults(uint64_t numIters) { mPc->updateResults(numIters); } # else PerformanceCounters::PerformanceCounters() = default; PerformanceCounters::~PerformanceCounters() = default; void PerformanceCounters::beginMeasure() {} void PerformanceCounters::endMeasure() {} void PerformanceCounters::updateResults(uint64_t) {} # endif ANKERL_NANOBENCH(NODISCARD) PerfCountSet const& PerformanceCounters::val() const noexcept { return mVal; } ANKERL_NANOBENCH(NODISCARD) PerfCountSet const& PerformanceCounters::has() const noexcept { return mHas; } // formatting utilities namespace fmt { // adds thousands separator to numbers NumSep::NumSep(char sep) : mSep(sep) {} char NumSep::do_thousands_sep() const { return mSep; } std::string NumSep::do_grouping() const { return "\003"; } // RAII to save & restore a stream's state StreamStateRestorer::StreamStateRestorer(std::ostream& s) : mStream(s) , mLocale(s.getloc()) , mPrecision(s.precision()) , mWidth(s.width()) , mFill(s.fill()) , mFmtFlags(s.flags()) {} StreamStateRestorer::~StreamStateRestorer() { restore(); } // sets back all stream info that we remembered at construction void StreamStateRestorer::restore() { mStream.imbue(mLocale); mStream.precision(mPrecision); mStream.width(mWidth); mStream.fill(mFill); mStream.flags(mFmtFlags); } Number::Number(int width, int precision, int64_t value) : mWidth(width) , mPrecision(precision) , mValue(d(value)) {} Number::Number(int width, int precision, double value) : mWidth(width) , mPrecision(precision) , mValue(value) {} std::ostream& Number::write(std::ostream& os) const { StreamStateRestorer const restorer(os); os.imbue(std::locale(os.getloc(), new NumSep(','))); os << std::setw(mWidth) << std::setprecision(mPrecision) << std::fixed << mValue; return os; } std::string Number::to_s() const { std::stringstream ss; write(ss); return ss.str(); } std::string to_s(uint64_t n) { std::string str; do { str += static_cast('0' + static_cast(n % 10)); n /= 10; } while (n != 0); std::reverse(str.begin(), str.end()); return str; } std::ostream& operator<<(std::ostream& os, Number const& n) { return n.write(os); } MarkDownColumn::MarkDownColumn(int w, int prec, std::string tit, std::string suff, double val) noexcept : mWidth(w) , mPrecision(prec) , mTitle(std::move(tit)) , mSuffix(std::move(suff)) , mValue(val) {} std::string MarkDownColumn::title() const { std::stringstream ss; ss << '|' << std::setw(mWidth - 2) << std::right << mTitle << ' '; return ss.str(); } std::string MarkDownColumn::separator() const { std::string sep(static_cast(mWidth), '-'); sep.front() = '|'; sep.back() = ':'; return sep; } std::string MarkDownColumn::invalid() const { std::string sep(static_cast(mWidth), ' '); sep.front() = '|'; sep[sep.size() - 2] = '-'; return sep; } std::string MarkDownColumn::value() const { std::stringstream ss; auto width = mWidth - 2 - static_cast(mSuffix.size()); ss << '|' << Number(width, mPrecision, mValue) << mSuffix << ' '; return ss.str(); } // Formats any text as markdown code, escaping backticks. MarkDownCode::MarkDownCode(std::string const& what) { mWhat.reserve(what.size() + 2); mWhat.push_back('`'); for (char const c : what) { mWhat.push_back(c); if ('`' == c) { mWhat.push_back('`'); } } mWhat.push_back('`'); } std::ostream& MarkDownCode::write(std::ostream& os) const { return os << mWhat; } std::ostream& operator<<(std::ostream& os, MarkDownCode const& mdCode) { return mdCode.write(os); } } // namespace fmt } // namespace detail // provide implementation here so it's only generated once Config::Config() = default; Config::~Config() = default; Config& Config::operator=(Config const&) = default; Config& Config::operator=(Config&&) noexcept(ANKERL_NANOBENCH(NOEXCEPT_STRING_MOVE)) = default; Config::Config(Config const&) = default; Config::Config(Config&&) noexcept = default; // provide implementation here so it's only generated once Result::~Result() = default; Result& Result::operator=(Result const&) = default; Result& Result::operator=(Result&&) noexcept(ANKERL_NANOBENCH(NOEXCEPT_STRING_MOVE)) = default; Result::Result(Result const&) = default; Result::Result(Result&&) noexcept = default; namespace detail { template inline constexpr typename std::underlying_type::type u(T val) noexcept { return static_cast::type>(val); } } // namespace detail // Result returned after a benchmark has finished. Can be used as a baseline for relative(). Result::Result(Config benchmarkConfig) : mConfig(std::move(benchmarkConfig)) , mNameToMeasurements{detail::u(Result::Measure::_size)} {} void Result::add(Clock::duration totalElapsed, uint64_t iters, detail::PerformanceCounters const& pc) { using detail::d; using detail::u; double const dIters = d(iters); mNameToMeasurements[u(Result::Measure::iterations)].push_back(dIters); mNameToMeasurements[u(Result::Measure::elapsed)].push_back(d(totalElapsed) / dIters); if (pc.has().pageFaults) { mNameToMeasurements[u(Result::Measure::pagefaults)].push_back(d(pc.val().pageFaults) / dIters); } if (pc.has().cpuCycles) { mNameToMeasurements[u(Result::Measure::cpucycles)].push_back(d(pc.val().cpuCycles) / dIters); } if (pc.has().contextSwitches) { mNameToMeasurements[u(Result::Measure::contextswitches)].push_back(d(pc.val().contextSwitches) / dIters); } if (pc.has().instructions) { mNameToMeasurements[u(Result::Measure::instructions)].push_back(d(pc.val().instructions) / dIters); } if (pc.has().branchInstructions) { double branchInstructions = 0.0; // correcting branches: remove branch introduced by the while (...) loop for each iteration. if (pc.val().branchInstructions > iters + 1U) { branchInstructions = d(pc.val().branchInstructions - (iters + 1U)); } mNameToMeasurements[u(Result::Measure::branchinstructions)].push_back(branchInstructions / dIters); if (pc.has().branchMisses) { // correcting branch misses double branchMisses = d(pc.val().branchMisses); if (branchMisses > branchInstructions) { // can't have branch misses when there were branches... branchMisses = branchInstructions; } // assuming at least one missed branch for the loop branchMisses -= 1.0; if (branchMisses < 1.0) { branchMisses = 1.0; } mNameToMeasurements[u(Result::Measure::branchmisses)].push_back(branchMisses / dIters); } } } Config const& Result::config() const noexcept { return mConfig; } inline double calcMedian(std::vector& data) { if (data.empty()) { return 0.0; } std::sort(data.begin(), data.end()); auto midIdx = data.size() / 2U; if (1U == (data.size() & 1U)) { return data[midIdx]; } return (data[midIdx - 1U] + data[midIdx]) / 2U; } double Result::median(Measure m) const { // create a copy so we can sort auto data = mNameToMeasurements[detail::u(m)]; return calcMedian(data); } double Result::average(Measure m) const { using detail::d; auto const& data = mNameToMeasurements[detail::u(m)]; if (data.empty()) { return 0.0; } // create a copy so we can sort return sum(m) / d(data.size()); } double Result::medianAbsolutePercentError(Measure m) const { // create copy auto data = mNameToMeasurements[detail::u(m)]; // calculates MdAPE which is the median of percentage error // see https://support.numxl.com/hc/en-us/articles/115001223503-MdAPE-Median-Absolute-Percentage-Error auto med = calcMedian(data); // transform the data to absolute error for (auto& x : data) { x = (x - med) / x; if (x < 0) { x = -x; } } return calcMedian(data); } double Result::sum(Measure m) const noexcept { auto const& data = mNameToMeasurements[detail::u(m)]; return std::accumulate(data.begin(), data.end(), 0.0); } double Result::sumProduct(Measure m1, Measure m2) const noexcept { auto const& data1 = mNameToMeasurements[detail::u(m1)]; auto const& data2 = mNameToMeasurements[detail::u(m2)]; if (data1.size() != data2.size()) { return 0.0; } double result = 0.0; for (size_t i = 0, s = data1.size(); i != s; ++i) { result += data1[i] * data2[i]; } return result; } bool Result::has(Measure m) const noexcept { return !mNameToMeasurements[detail::u(m)].empty(); } double Result::get(size_t idx, Measure m) const { auto const& data = mNameToMeasurements[detail::u(m)]; return data.at(idx); } bool Result::empty() const noexcept { return 0U == size(); } size_t Result::size() const noexcept { auto const& data = mNameToMeasurements[detail::u(Measure::elapsed)]; return data.size(); } double Result::minimum(Measure m) const noexcept { auto const& data = mNameToMeasurements[detail::u(m)]; if (data.empty()) { return 0.0; } // here its save to assume that at least one element is there return *std::min_element(data.begin(), data.end()); } double Result::maximum(Measure m) const noexcept { auto const& data = mNameToMeasurements[detail::u(m)]; if (data.empty()) { return 0.0; } // here its save to assume that at least one element is there return *std::max_element(data.begin(), data.end()); } std::string const& Result::context(char const* variableName) const { return mConfig.mContext.at(variableName); } std::string const& Result::context(std::string const& variableName) const { return mConfig.mContext.at(variableName); } Result::Measure Result::fromString(std::string const& str) { if (str == "elapsed") { return Measure::elapsed; } if (str == "iterations") { return Measure::iterations; } if (str == "pagefaults") { return Measure::pagefaults; } if (str == "cpucycles") { return Measure::cpucycles; } if (str == "contextswitches") { return Measure::contextswitches; } if (str == "instructions") { return Measure::instructions; } if (str == "branchinstructions") { return Measure::branchinstructions; } if (str == "branchmisses") { return Measure::branchmisses; } // not found, return _size return Measure::_size; } // Configuration of a microbenchmark. Bench::Bench() { mConfig.mOut = &std::cout; } Bench::Bench(Bench&&) noexcept = default; Bench& Bench::operator=(Bench&&) noexcept(ANKERL_NANOBENCH(NOEXCEPT_STRING_MOVE)) = default; Bench::Bench(Bench const&) = default; Bench& Bench::operator=(Bench const&) = default; Bench::~Bench() noexcept = default; double Bench::batch() const noexcept { return mConfig.mBatch; } double Bench::complexityN() const noexcept { return mConfig.mComplexityN; } // Set a baseline to compare it to. 100% it is exactly as fast as the baseline, >100% means it is faster than the baseline, <100% // means it is slower than the baseline. Bench& Bench::relative(bool isRelativeEnabled) noexcept { mConfig.mIsRelative = isRelativeEnabled; return *this; } bool Bench::relative() const noexcept { return mConfig.mIsRelative; } Bench& Bench::performanceCounters(bool showPerformanceCounters) noexcept { mConfig.mShowPerformanceCounters = showPerformanceCounters; return *this; } bool Bench::performanceCounters() const noexcept { return mConfig.mShowPerformanceCounters; } // Operation unit. Defaults to "op", could be e.g. "byte" for string processing. // If u differs from currently set unit, the stored results will be cleared. // Use singular (byte, not bytes). Bench& Bench::unit(char const* u) { if (u != mConfig.mUnit) { mResults.clear(); } mConfig.mUnit = u; return *this; } Bench& Bench::unit(std::string const& u) { return unit(u.c_str()); } std::string const& Bench::unit() const noexcept { return mConfig.mUnit; } Bench& Bench::timeUnit(std::chrono::duration const& tu, std::string const& tuName) { mConfig.mTimeUnit = tu; mConfig.mTimeUnitName = tuName; return *this; } std::string const& Bench::timeUnitName() const noexcept { return mConfig.mTimeUnitName; } std::chrono::duration const& Bench::timeUnit() const noexcept { return mConfig.mTimeUnit; } // If benchmarkTitle differs from currently set title, the stored results will be cleared. Bench& Bench::title(const char* benchmarkTitle) { if (benchmarkTitle != mConfig.mBenchmarkTitle) { mResults.clear(); } mConfig.mBenchmarkTitle = benchmarkTitle; return *this; } Bench& Bench::title(std::string const& benchmarkTitle) { if (benchmarkTitle != mConfig.mBenchmarkTitle) { mResults.clear(); } mConfig.mBenchmarkTitle = benchmarkTitle; return *this; } std::string const& Bench::title() const noexcept { return mConfig.mBenchmarkTitle; } Bench& Bench::name(const char* benchmarkName) { mConfig.mBenchmarkName = benchmarkName; return *this; } Bench& Bench::name(std::string const& benchmarkName) { mConfig.mBenchmarkName = benchmarkName; return *this; } std::string const& Bench::name() const noexcept { return mConfig.mBenchmarkName; } Bench& Bench::context(char const* variableName, char const* variableValue) { mConfig.mContext[variableName] = variableValue; return *this; } Bench& Bench::context(std::string const& variableName, std::string const& variableValue) { mConfig.mContext[variableName] = variableValue; return *this; } Bench& Bench::clearContext() { mConfig.mContext.clear(); return *this; } // Number of epochs to evaluate. The reported result will be the median of evaluation of each epoch. Bench& Bench::epochs(size_t numEpochs) noexcept { mConfig.mNumEpochs = numEpochs; return *this; } size_t Bench::epochs() const noexcept { return mConfig.mNumEpochs; } // Desired evaluation time is a multiple of clock resolution. Default is to be 1000 times above this measurement precision. Bench& Bench::clockResolutionMultiple(size_t multiple) noexcept { mConfig.mClockResolutionMultiple = multiple; return *this; } size_t Bench::clockResolutionMultiple() const noexcept { return mConfig.mClockResolutionMultiple; } // Sets the maximum time each epoch should take. Default is 100ms. Bench& Bench::maxEpochTime(std::chrono::nanoseconds t) noexcept { mConfig.mMaxEpochTime = t; return *this; } std::chrono::nanoseconds Bench::maxEpochTime() const noexcept { return mConfig.mMaxEpochTime; } // Sets the maximum time each epoch should take. Default is 100ms. Bench& Bench::minEpochTime(std::chrono::nanoseconds t) noexcept { mConfig.mMinEpochTime = t; return *this; } std::chrono::nanoseconds Bench::minEpochTime() const noexcept { return mConfig.mMinEpochTime; } Bench& Bench::minEpochIterations(uint64_t numIters) noexcept { mConfig.mMinEpochIterations = (numIters == 0) ? 1 : numIters; return *this; } uint64_t Bench::minEpochIterations() const noexcept { return mConfig.mMinEpochIterations; } Bench& Bench::epochIterations(uint64_t numIters) noexcept { mConfig.mEpochIterations = numIters; return *this; } uint64_t Bench::epochIterations() const noexcept { return mConfig.mEpochIterations; } Bench& Bench::warmup(uint64_t numWarmupIters) noexcept { mConfig.mWarmup = numWarmupIters; return *this; } uint64_t Bench::warmup() const noexcept { return mConfig.mWarmup; } Bench& Bench::config(Config const& benchmarkConfig) { mConfig = benchmarkConfig; return *this; } Config const& Bench::config() const noexcept { return mConfig; } Bench& Bench::output(std::ostream* outstream) noexcept { mConfig.mOut = outstream; return *this; } ANKERL_NANOBENCH(NODISCARD) std::ostream* Bench::output() const noexcept { return mConfig.mOut; } std::vector const& Bench::results() const noexcept { return mResults; } Bench& Bench::render(char const* templateContent, std::ostream& os) { ::ankerl::nanobench::render(templateContent, *this, os); return *this; } Bench& Bench::render(std::string const& templateContent, std::ostream& os) { ::ankerl::nanobench::render(templateContent, *this, os); return *this; } std::vector Bench::complexityBigO() const { std::vector bigOs; auto rangeMeasure = BigO::collectRangeMeasure(mResults); bigOs.emplace_back("O(1)", rangeMeasure, [](double) { return 1.0; }); bigOs.emplace_back("O(n)", rangeMeasure, [](double n) { return n; }); bigOs.emplace_back("O(log n)", rangeMeasure, [](double n) { return std::log2(n); }); bigOs.emplace_back("O(n log n)", rangeMeasure, [](double n) { return n * std::log2(n); }); bigOs.emplace_back("O(n^2)", rangeMeasure, [](double n) { return n * n; }); bigOs.emplace_back("O(n^3)", rangeMeasure, [](double n) { return n * n * n; }); std::sort(bigOs.begin(), bigOs.end()); return bigOs; } Rng::Rng() : mX(0) , mY(0) { std::random_device rd; std::uniform_int_distribution dist; do { mX = dist(rd); mY = dist(rd); } while (mX == 0 && mY == 0); } ANKERL_NANOBENCH_NO_SANITIZE("integer", "undefined") uint64_t splitMix64(uint64_t& state) noexcept { uint64_t z = (state += UINT64_C(0x9e3779b97f4a7c15)); z = (z ^ (z >> 30U)) * UINT64_C(0xbf58476d1ce4e5b9); z = (z ^ (z >> 27U)) * UINT64_C(0x94d049bb133111eb); return z ^ (z >> 31U); } // Seeded as described in romu paper (update april 2020) Rng::Rng(uint64_t seed) noexcept : mX(splitMix64(seed)) , mY(splitMix64(seed)) { for (size_t i = 0; i < 10; ++i) { operator()(); } } // only internally used to copy the RNG. Rng::Rng(uint64_t x, uint64_t y) noexcept : mX(x) , mY(y) {} Rng Rng::copy() const noexcept { return Rng{mX, mY}; } Rng::Rng(std::vector const& data) : mX(0) , mY(0) { if (data.size() != 2) { throw std::runtime_error("ankerl::nanobench::Rng::Rng: needed exactly 2 entries in data, but got " + detail::fmt::to_s(data.size())); } mX = data[0]; mY = data[1]; } std::vector Rng::state() const { std::vector data(2); data[0] = mX; data[1] = mY; return data; } BigO::RangeMeasure BigO::collectRangeMeasure(std::vector const& results) { BigO::RangeMeasure rangeMeasure; for (auto const& result : results) { if (result.config().mComplexityN > 0.0) { rangeMeasure.emplace_back(result.config().mComplexityN, result.median(Result::Measure::elapsed)); } } return rangeMeasure; } BigO::BigO(std::string bigOName, RangeMeasure const& rangeMeasure) : mName(std::move(bigOName)) { // estimate the constant factor double sumRangeMeasure = 0.0; double sumRangeRange = 0.0; for (const auto& rm : rangeMeasure) { sumRangeMeasure += rm.first * rm.second; sumRangeRange += rm.first * rm.first; } mConstant = sumRangeMeasure / sumRangeRange; // calculate root mean square double err = 0.0; double sumMeasure = 0.0; for (const auto& rm : rangeMeasure) { auto diff = mConstant * rm.first - rm.second; err += diff * diff; sumMeasure += rm.second; } auto n = detail::d(rangeMeasure.size()); auto mean = sumMeasure / n; mNormalizedRootMeanSquare = std::sqrt(err / n) / mean; } BigO::BigO(const char* bigOName, RangeMeasure const& rangeMeasure) : BigO(std::string(bigOName), rangeMeasure) {} std::string const& BigO::name() const noexcept { return mName; } double BigO::constant() const noexcept { return mConstant; } double BigO::normalizedRootMeanSquare() const noexcept { return mNormalizedRootMeanSquare; } bool BigO::operator<(BigO const& other) const noexcept { return std::tie(mNormalizedRootMeanSquare, mName) < std::tie(other.mNormalizedRootMeanSquare, other.mName); } std::ostream& operator<<(std::ostream& os, BigO const& bigO) { return os << bigO.constant() << " * " << bigO.name() << ", rms=" << bigO.normalizedRootMeanSquare(); } std::ostream& operator<<(std::ostream& os, std::vector const& bigOs) { detail::fmt::StreamStateRestorer const restorer(os); os << std::endl << "| coefficient | err% | complexity" << std::endl << "|--------------:|-------:|------------" << std::endl; for (auto const& bigO : bigOs) { os << "|" << std::setw(14) << std::setprecision(7) << std::scientific << bigO.constant() << " "; os << "|" << detail::fmt::Number(6, 1, bigO.normalizedRootMeanSquare() * 100.0) << "% "; os << "| " << bigO.name(); os << std::endl; } return os; } } // namespace nanobench } // namespace ankerl #endif // ANKERL_NANOBENCH_IMPLEMENT #endif // ANKERL_NANOBENCH_H_INCLUDED ================================================ FILE: tools/bench_ndsort.py ================================================ #!/usr/bin/env python3 """ Non-dominated sort benchmark runner and reporter. Runs operon_test "[performance][ndsort]" multiple times, collects the nanobench CSV output from each run, and aggregates across runs using median ± IQR. Usage: python tools/bench_ndsort.py [options] Examples: # Quick run (3 repetitions, all sections) python tools/bench_ndsort.py # More stable estimate, 2-objective section only python tools/bench_ndsort.py --runs 10 --section "2 objectives" # Extended benchmark (includes RO, BOS) python tools/bench_ndsort.py --tag "[.][ndsort-extended]" --runs 5 # Plot results python tools/bench_ndsort.py --plot Output columns: sorter – algorithm short name (RS, MS, ENS-SS, ENS-BS) n – population size med_ns – median elapsed time per iteration across runs, in nanoseconds iqr_ns – interquartile range (Q3 − Q1) across runs, in nanoseconds cv% – coefficient of variation (IQR / median × 100) as run-stability indicator """ import argparse import csv import io import re import statistics import subprocess import sys from collections import defaultdict from pathlib import Path try: from tabulate import tabulate HAS_TABULATE = True except ImportError: HAS_TABULATE = False try: import matplotlib.pyplot as plt HAS_MPL = True except ImportError: HAS_MPL = False # ── CSV parsing ─────────────────────────────────────────────────────────────── # nanobench CSV columns (semicolon-delimited, no header line): # title ; name ; unit ; batch ; median(elapsed_s) ; MdAPE% ; ... _COL_TITLE = 0 _COL_NAME = 1 _COL_ELAPSED = 4 # seconds per iteration def _parse_csv_lines(text: str) -> list[dict]: """Extract nanobench CSV rows from mixed stdout (Catch2 + nanobench).""" rows = [] for line in text.splitlines(): line = line.strip() if not line.startswith('"'): continue reader = csv.reader(io.StringIO(line), delimiter=';') try: fields = next(reader) except StopIteration: continue if len(fields) < 5: continue try: elapsed_s = float(fields[_COL_ELAPSED]) except ValueError: continue raw_name = fields[_COL_NAME].strip() # Name format: "SORTER/N", e.g. "RS/1000" m = re.fullmatch(r'([^/]+)/(\d+)', raw_name) if not m: continue rows.append({ 'section': fields[_COL_TITLE].strip(), 'sorter': m.group(1), 'n': int(m.group(2)), 'ns': elapsed_s * 1e9, }) return rows # ── Benchmark runner ────────────────────────────────────────────────────────── def run_once(binary: Path, tag: str, section: str | None, timeout: int) -> str: cmd = [str(binary), tag] if section: cmd += ['--section', section] try: proc = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout) except subprocess.TimeoutExpired: print(f" [WARN] run timed out after {timeout}s", file=sys.stderr) return "" except Exception as exc: print(f" [WARN] run failed: {exc}", file=sys.stderr) return "" return proc.stdout # ── Statistics ──────────────────────────────────────────────────────────────── def _quantile(data: list[float], q: float) -> float: """Linear interpolation quantile (same as numpy default).""" data = sorted(data) n = len(data) if n == 1: return data[0] idx = q * (n - 1) lo, hi = int(idx), min(int(idx) + 1, n - 1) return data[lo] + (idx - lo) * (data[hi] - data[lo]) def _iqr(data: list[float]) -> float: return _quantile(data, 0.75) - _quantile(data, 0.25) def aggregate(all_rows: list[dict]) -> dict: """ Group by (section, sorter, n) and compute median ± IQR across runs. Returns {(section, sorter, n): {'med': float, 'iqr': float, 'n_runs': int}}. """ groups: dict[tuple, list[float]] = defaultdict(list) for row in all_rows: key = (row['section'], row['sorter'], row['n']) groups[key].append(row['ns']) result = {} for key, values in groups.items(): med = statistics.median(values) iq = _iqr(values) result[key] = {'med': med, 'iqr': iq, 'n_runs': len(values)} return result # ── Formatting ──────────────────────────────────────────────────────────────── def _fmt_ns(ns: float) -> str: if ns >= 1e9: return f"{ns/1e9:.2f} s" if ns >= 1e6: return f"{ns/1e6:.2f} ms" if ns >= 1e3: return f"{ns/1e3:.2f} µs" return f"{ns:.1f} ns" def print_tables(agg: dict, runs: int) -> None: sections = sorted({s for s, _, _ in agg}) for section in sections: # Collect (sorter, n) pairs for this section entries = {(so, n): v for (se, so, n), v in agg.items() if se == section} if not entries: continue sorters = sorted({so for so, _ in entries}) ns = sorted({n for _, n in entries}) print(f"\n── {section} ({runs} run{'s' if runs != 1 else ''}) ──") table = [] for n in ns: row = [n] for so in sorters: v = entries.get((so, n)) if v is None: row.append("—") else: med, iqr = v['med'], v['iqr'] cv = 100 * iqr / med if med else 0 row.append(f"{_fmt_ns(med)} ±{_fmt_ns(iqr)} ({cv:.0f}%)") table.append(row) headers = ["n"] + sorters if HAS_TABULATE: print(tabulate(table, headers=headers, tablefmt="simple", colalign=["right"] + ["right"] * len(sorters))) else: col_w = max(len(h) for h in headers[1:] + [str(r[1]) for r in table if len(r) > 1]) col_w = max(col_w, 24) header_line = f"{'n':>8}" + "".join(f" {h:>{col_w}}" for h in sorters) print(header_line) print("-" * len(header_line)) for row in table: print(f"{row[0]:>8}" + "".join(f" {str(v):>{col_w}}" for v in row[1:])) # ── Plotting ────────────────────────────────────────────────────────────────── def plot_results(agg: dict) -> None: if not HAS_MPL: print("matplotlib not available — skipping plot", file=sys.stderr) return sections = sorted({s for s, _, _ in agg}) ncols = len(sections) fig, axes = plt.subplots(1, ncols, figsize=(6 * ncols, 5), squeeze=False) for ax, section in zip(axes[0], sections): entries = {(so, n): v for (se, so, n), v in agg.items() if se == section} sorters = sorted({so for so, _ in entries}) ns = sorted({n for _, n in entries}) for so in sorters: ys = [entries[(so, n)]['med'] / 1e6 for n in ns if (so, n) in entries] errs = [entries[(so, n)]['iqr'] / 1e6 for n in ns if (so, n) in entries] xs = [n for n in ns if (so, n) in entries] ax.errorbar(xs, ys, yerr=errs, label=so, marker='o', capsize=3) ax.set_title(section) ax.set_xlabel("n") ax.set_ylabel("median elapsed (ms)") ax.legend() ax.grid(True, alpha=0.3) fig.tight_layout() plt.show() # ── Entry point ─────────────────────────────────────────────────────────────── def main() -> None: p = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, ) p.add_argument("--binary", type=Path, default=None, help="Path to operon_test binary " "(default: /build/test/operon_test)") p.add_argument("--runs", type=int, default=3, help="Number of benchmark repetitions for IQR estimation (default: 3)") p.add_argument("--section", default=None, help="Run only this Catch2 section, e.g. '2 objectives'") p.add_argument("--tag", default="[performance][ndsort]", help="Catch2 tag filter (default: [performance][ndsort])") p.add_argument("--timeout", type=int, default=600, help="Per-run timeout in seconds (default: 600)") p.add_argument("--plot", action="store_true", help="Show a matplotlib plot of median ± IQR vs n") args = p.parse_args() repo_root = Path(__file__).resolve().parent.parent binary = args.binary or (repo_root / "build" / "test" / "operon_test") if not binary.exists(): sys.exit(f"Binary not found: {binary}\nBuild first or pass --binary ") print(f"Binary : {binary}") print(f"Tag : {args.tag}") print(f"Section: {args.section or '(all)'}") print(f"Runs : {args.runs}") all_rows: list[dict] = [] for i in range(1, args.runs + 1): print(f" run {i}/{args.runs}...", end=" ", flush=True) stdout = run_once(binary, args.tag, args.section, args.timeout) rows = _parse_csv_lines(stdout) all_rows.extend(rows) print(f"{len(rows)} entries") if not all_rows: sys.exit("No CSV rows parsed — check that the binary outputs nanobench CSV to stdout") agg = aggregate(all_rows) print_tables(agg, args.runs) if args.plot: plot_results(agg) if not HAS_TABULATE: print("\n(install tabulate for nicer tables: pip install tabulate)", file=sys.stderr) if __name__ == "__main__": main() ================================================ FILE: tools/benchmark_cache.py ================================================ #!/usr/bin/env python3 """Benchmark Operon GP/NSGP with and without the transposition cache. Sweeps over: - dataset : one or more problems (loaded from JSON metadata) - algorithm : gp, nsgp - iterations : 1, 10, 50, 100 (local optimisation iterations) - cache : on, off - seed : --seed-start … --seed-start + --seeds - 1 For each combination the corresponding CLI binary is invoked and the final-generation row is captured and written to a CSV file. Dataset JSON files next to each CSV are used to resolve --target and --train/--test splits automatically. Extra arguments after -- are forwarded verbatim to every CLI call (e.g. --threads, --generations). --label tags every row with a machine/run identifier, useful when distributing experiments across machines and merging results later. --estimate runs one seed per unique (problem, algorithm, iterations, cache) combo, prints a per-combo timing table, and reports how many seeds fit in a given time budget — useful for planning overnight runs. Usage: python tools/benchmark_cache.py [benchmark options] -- Examples: # Estimate timing before committing (8-hour overnight budget) python tools/benchmark_cache.py --estimate --budget 8h -- \\ --threads 4 --generations 150 # All datasets in data/, default settings python tools/benchmark_cache.py -- --threads 16 --generations 150 # Specific datasets only python tools/benchmark_cache.py \\ --problems Chemical-I Chemical-II Nikuradse_1 -- \\ --threads 16 --generations 150 # Distribute across two machines (same seeds, tagged for comparison): # machine A (zen3): --label zen3 --seeds 100 # machine B (zen2): --label zen2 --seeds 100 # Or split seeds to halve wall time: # machine A: --label zen3 --seed-start 0 --seeds 50 # machine B: --label zen2 --seed-start 50 --seeds 50 # Merge afterwards: # { head -1 zen3.csv; tail -n +2 zen3.csv; tail -n +2 zen2.csv; } > combined.csv """ import argparse import csv import json import subprocess import sys import time from concurrent.futures import ProcessPoolExecutor, as_completed from itertools import product from pathlib import Path REPO_ROOT = Path(__file__).resolve().parent.parent BUILD_DIR = REPO_ROOT / "build" / "cli" DATA_DIR = REPO_ROOT / "data" # Columns emitted by the CLI (whitespace-separated, last data line before expression) CLI_COLUMNS = [ "iteration", "r2_tr", "r2_te", "mae_tr", "mae_te", "nmse_tr", "nmse_te", "best_fit", "avg_fit", "best_len", "avg_len", "eval_cnt", "res_eval", "jac_eval", "opt_time", "seed", "elapsed", ] META_COLUMNS = ["label", "problem", "algorithm", "ls_iterations", "cache"] ALL_COLUMNS = META_COLUMNS + CLI_COLUMNS def fmt_duration(seconds: float) -> str: seconds = int(seconds) if seconds < 60: return f"{seconds}s" if seconds < 3600: return f"{seconds // 60}m{seconds % 60:02d}s" h, rem = divmod(seconds, 3600) return f"{h}h{rem // 60:02d}m" def parse_budget(s: str) -> float: """Parse a duration string like '8h', '90m', '3600s' into seconds.""" s = s.strip() if s.endswith("h"): return float(s[:-1]) * 3600 if s.endswith("m"): return float(s[:-1]) * 60 if s.endswith("s"): return float(s[:-1]) raise argparse.ArgumentTypeError(f"Invalid duration '{s}': use e.g. 8h, 90m, 3600s") def repair_csv(path: Path) -> bool: """Truncate any incomplete final row (no trailing newline). Returns True if repaired.""" if not path.exists() or path.stat().st_size == 0: return False with path.open("rb") as f: content = f.read() if content.endswith(b"\n"): return False last_newline = content.rfind(b"\n") if last_newline < 0: return False with path.open("r+b") as f: f.truncate(last_newline + 1) return True def load_problem(name: str) -> dict: """Return {name, csv_path, target, train, test} from the JSON sidecar.""" json_path = DATA_DIR / f"{name}.json" if not json_path.exists(): raise FileNotFoundError(f"No metadata file: {json_path}") meta = json.loads(json_path.read_text())["metadata"] tr = meta["training_rows"] te = meta["test_rows"] return { "name": name, "csv": str(DATA_DIR / meta["filename"]), "target": meta["target"], "train": f"{tr['start']}:{tr['end']}", "test": f"{te['start']}:{te['end']}", } def discover_problems() -> list[str]: """Return names of all problems that have both a CSV and a JSON file.""" return sorted( p.stem for p in DATA_DIR.glob("*.json") if (DATA_DIR / p.stem).with_suffix(".csv").exists() ) def run_one( problem: dict, algorithm: str, iterations: int, cache: bool, seed: int, extra_args: list[str], label: str, ) -> tuple[dict[str, str] | None, float]: """Run one experiment; returns (row_or_None, wall_seconds).""" binary = BUILD_DIR / f"operon_{algorithm}" cmd = [ str(binary), "--dataset", problem["csv"], "--target", problem["target"], "--train", problem["train"], "--test", problem["test"], "--iterations", str(iterations), "--seed", str(seed), *extra_args, ] if cache: cmd.append("--transposition-cache") t0 = time.monotonic() result = subprocess.run(cmd, capture_output=True, text=True) wall = time.monotonic() - t0 if result.returncode != 0: lbl = f"{problem['name']} {algorithm} iter={iterations} cache={'on' if cache else 'off'} seed={seed}" print(f"\n ERROR [{lbl}]: {result.stderr.strip()}", file=sys.stderr) return None, wall lines = [ln for ln in result.stdout.splitlines() if ln.strip()] if len(lines) < 2: print(f"\n UNEXPECTED OUTPUT (seed={seed})", file=sys.stderr) return None, wall tokens = lines[-2].split() if len(tokens) != len(CLI_COLUMNS): print( f"\n COLUMN MISMATCH (seed={seed}): expected {len(CLI_COLUMNS)}, got {len(tokens)}", file=sys.stderr, ) return None, wall row: dict[str, str] = dict(zip(CLI_COLUMNS, tokens)) row["label"] = label row["problem"] = problem["name"] row["algorithm"] = algorithm row["ls_iterations"] = str(iterations) row["cache"] = "yes" if cache else "no" return row, wall def run_estimate( problems: list[dict], algorithms: list[str], ls_iterations: list[int], extra_args: list[str], jobs: int, budget_seconds: float | None, ) -> None: """Run one seed per combo, print a timing table, and report overnight feasibility.""" combos = list(product(problems, algorithms, ls_iterations, [False, True])) print(f"Running {len(combos)} timing probes (1 seed each, {jobs} parallel) …\n") timings: dict[tuple, float] = {} futures_map = {} with ProcessPoolExecutor(max_workers=jobs) as executor: for prob, alg, iters, cache in combos: fut = executor.submit(run_one, prob, alg, iters, cache, 0, extra_args, "") futures_map[fut] = (prob["name"], alg, iters, cache) done = 0 for future in as_completed(futures_map): key = futures_map[future] _, wall = future.result() timings[key] = wall done += 1 print(f" [{done}/{len(combos)}] {key[0]} {key[1]} iter={key[2]} cache={'on' if key[3] else 'off'} {wall:.1f}s", end="\r", flush=True) print() # Print table col_w = max(len(p["name"]) for p in problems) header = f" {'problem':<{col_w}} {'alg':<5} {'iter':>4} {'cache':>5} {'sec/seed':>8}" print(f"\n{header}") print(" " + "-" * (len(header) - 2)) total_per_seed = 0.0 for prob, alg, iters, cache in sorted(combos, key=lambda c: (c[0]["name"], c[1], c[2], c[3])): t = timings.get((prob["name"], alg, iters, cache), float("nan")) total_per_seed += t print(f" {prob['name']:<{col_w}} {alg:<5} {iters:>4} {'yes' if cache else 'no':>5} {t:>8.1f}") # Account for parallelism: wall time per seed ≈ total_per_seed / jobs wall_per_seed = total_per_seed / jobs print(f"\n Total serial time per seed : {fmt_duration(total_per_seed)}") print(f" Wall time per seed ({jobs} jobs): {fmt_duration(wall_per_seed)}") if budget_seconds is not None: feasible = int(budget_seconds / wall_per_seed) print(f"\n Budget: {fmt_duration(budget_seconds)}") print(f" → ~{feasible} seeds feasible " f"(total wall time ≈ {fmt_duration(feasible * wall_per_seed)})") print(f" → for 100 seeds you need ≈ {fmt_duration(100 * wall_per_seed)}") else: print(f"\n For 100 seeds: ≈ {fmt_duration(100 * wall_per_seed)} " f"(pass --budget 8h to check overnight feasibility)") def main() -> None: parser = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, ) parser.add_argument( "--output", default="cache_benchmark.csv", help="Output CSV path (default: cache_benchmark.csv)", ) parser.add_argument( "--seeds", type=int, default=100, help="Number of random seeds / repetitions (default: 100)", ) parser.add_argument( "--seed-start", type=int, default=0, help="First seed value; seeds run from seed-start to seed-start+seeds-1 (default: 0)", ) parser.add_argument( "--label", default="", help="Tag added to every row, e.g. machine name (default: empty)", ) parser.add_argument( "--problems", nargs="+", metavar="NAME", help="Problem names to include (default: all with JSON+CSV in data/)", ) parser.add_argument( "--algorithms", nargs="+", default=["gp", "nsgp"], metavar="ALG", help="Algorithms to benchmark (default: gp nsgp)", ) parser.add_argument( "--ls-iterations", nargs="+", type=int, default=[1, 10, 50, 100], metavar="N", help="Local-search iteration counts to sweep (default: 1 10 50 100)", ) parser.add_argument( "--jobs", type=int, default=1, help="Number of parallel benchmark processes (default: 1)", ) parser.add_argument( "--estimate", action="store_true", help="Run one seed per combo, print timing table and exit (no CSV written)", ) parser.add_argument( "--budget", type=parse_budget, metavar="DURATION", default=None, help="Time budget for --estimate feasibility report, e.g. 8h, 90m (default: none)", ) parser.add_argument( "extra_args", nargs=argparse.REMAINDER, help="Options forwarded to every CLI invocation (after --)", ) args = parser.parse_args() extra_args: list[str] = args.extra_args if extra_args and extra_args[0] == "--": extra_args = extra_args[1:] problem_names = args.problems or discover_problems() try: problems = [load_problem(n) for n in problem_names] except FileNotFoundError as e: parser.error(str(e)) if args.estimate: run_estimate(problems, args.algorithms, args.ls_iterations, extra_args, args.jobs, args.budget) return seeds = range(args.seed_start, args.seed_start + args.seeds) combos = list(product(problems, args.algorithms, args.ls_iterations, [False, True], seeds)) out_path = Path(args.output) # Resume: skip combos already present in the output file if repair_csv(out_path): print(f"Repaired incomplete final row in {out_path}.") completed: set[tuple] = set() if out_path.exists(): with out_path.open(newline="") as f: for row in csv.DictReader(f): completed.add(( row["label"], row["problem"], row["algorithm"], row["ls_iterations"], row["cache"], row["seed"], )) if completed: print(f"Resuming: {len(completed)} experiments already done, skipping.") def is_done(prob: dict, alg: str, iters: int, cache: bool, seed: int) -> bool: return (args.label, prob["name"], alg, str(iters), "yes" if cache else "no", str(seed)) in completed combos = [c for c in combos if not is_done(*c)] total = len(combos) done = 0 start = time.monotonic() print(f"Label : {args.label or '(none)'}") print(f"Problems : {', '.join(p['name'] for p in problems)}") print(f"Algorithms: {', '.join(args.algorithms)}") print(f"Iterations: {args.ls_iterations}") print(f"Seeds : {args.seed_start} … {args.seed_start + args.seeds - 1} ({args.seeds} total)") print(f"Jobs : {args.jobs}") if extra_args: print(f"Extra args: {' '.join(extra_args)}") if not combos: print("\nAll experiments already complete.") return print(f"\nRunning {total} experiments → {out_path}\n") w = len(str(total)) interrupted = False with out_path.open("a", newline="") as f: # Write header only for new files if not completed: writer = csv.DictWriter(f, fieldnames=ALL_COLUMNS) writer.writeheader() else: writer = csv.DictWriter(f, fieldnames=ALL_COLUMNS) futures_map = {} executor = ProcessPoolExecutor(max_workers=args.jobs) try: for prob, alg, iters, cache, seed in combos: fut = executor.submit(run_one, prob, alg, iters, cache, seed, extra_args, args.label) futures_map[fut] = (prob["name"], alg, iters, cache, seed) for future in as_completed(futures_map): prob_name, alg, iters, cache, seed = futures_map[future] done += 1 row, _ = future.result() if row: writer.writerow(row) f.flush() elapsed = time.monotonic() - start rate = done / elapsed eta = (total - done) / rate if rate > 0 else 0 pct = 100 * done / total lbl = f"{prob_name} {alg} iter={iters} cache={'on' if cache else 'off'} seed={seed}" print( f" [{done:>{w}}/{total}] {pct:5.1f}% " f"ETA {fmt_duration(eta):>8} elapsed {fmt_duration(elapsed):>8} {lbl}", end="\r", flush=True, ) executor.shutdown(wait=True) except KeyboardInterrupt: interrupted = True print("\n\nInterrupted — cancelling pending experiments …", flush=True) executor.shutdown(wait=False, cancel_futures=True) repair_csv(out_path) total_done = len(completed) + done status = "Interrupted" if interrupted else "Done" print(f"\n{status} in {fmt_duration(time.monotonic() - start)}. " f"{total_done} experiments saved to {out_path}") if __name__ == "__main__": main() ================================================ FILE: tools/compare_operon.py ================================================ #!/usr/bin/env python3 """ Compare operon_gp / operon_nsgp outputs between two build trees. Usage: python compare_operon.py [options] Two test modes: Determinism – identical seed → byte-identical model string on the last line Statistics – across many seeds, distributions of fitness metrics must be statistically equivalent (Mann-Whitney U when scipy is available) Examples: # Compare main branch vs refactor branch (determinism + stats) python compare_operon.py ../operon . # Quick determinism-only run, fewer datasets python compare_operon.py ../operon . --only-determinism --datasets Poly-10 # Statistical tests only, more seeds, custom significance level python compare_operon.py ../operon . --only-stats --stat-seeds 30 --alpha 0.05 """ import argparse import itertools import subprocess import sys from concurrent.futures import ThreadPoolExecutor, as_completed from dataclasses import dataclass from pathlib import Path from typing import Optional try: from scipy import stats as scipy_stats HAS_SCIPY = True except ImportError: HAS_SCIPY = False try: from tabulate import tabulate HAS_TABULATE = True except ImportError: HAS_TABULATE = False # ── Colours ──────────────────────────────────────────────────────────────────── GREEN = "\033[32m" RED = "\033[31m" YELLOW = "\033[33m" RESET = "\033[0m" BOLD = "\033[1m" def _strip_color(s: str) -> str: import re return re.sub(r"\033\[[0-9;]*m", "", s) # ── Dataset registry ─────────────────────────────────────────────────────────── @dataclass(frozen=True) class Dataset: filename: str target: str train: str test: str @property def name(self) -> str: return self.filename.removesuffix(".csv") ALL_DATASETS: list[Dataset] = [ Dataset("Poly-10.csv", "Y", "0:400", "400:500"), Dataset("Pagie-1.csv", "F", "0:1340", "1340:1676"), Dataset("Concrete.csv", "Y", "0:824", "824:1030"), Dataset("Vladislavleva-4.csv", "Y", "0:4819", "4819:6024"), ] SYMBOL_SETS: list[Optional[str]] = [ None, # default (arithmetic primitives) "add,sub,mul,div,square,sqrt", "add,sub,mul,div,exp,log,sin,cos", ] CREATORS = ["btc"] # "grow" excluded by default — it can hang with restricted symbol sets OBJECTIVES: list[str] = ["r2", "mse", "mae"] # Column order emitted by both binaries _STAT_COLS = [ "iteration", "r2_tr", "r2_te", "mae_tr", "mae_te", "nmse_tr", "nmse_te", "best_fit", "avg_fit", "best_len", "avg_len", "eval_cnt", "res_eval", "jac_eval", "opt_time", "seed", "elapsed", ] REPORT_METRICS = ["r2_tr", "r2_te", "mae_tr", "mae_te", "elapsed"] # For these metrics a negative Δmed (new < ref) means improvement. LOWER_IS_BETTER = {"mae_tr", "mae_te", "nmse_tr", "nmse_te", "elapsed"} # ── Run configuration & result ───────────────────────────────────────────────── @dataclass(frozen=True) class RunConfig: binary: str # "operon_gp" or "operon_nsgp" dataset: Dataset seed: int creator: str = "btc" symbols: Optional[str] = None objective: str = "r2" generations: int = 5 iterations: int = 1 population_size: int = 200 def cli_args(self, datadir: Path) -> list[str]: a = [ "--dataset", str(datadir / self.dataset.filename), "--target", self.dataset.target, "--train", self.dataset.train, "--test", self.dataset.test, "--seed", str(self.seed), "--creator", self.creator, "--objective", self.objective, "--generations", str(self.generations), "--iterations", str(self.iterations), "--population-size", str(self.population_size), "--pool-size", str(self.population_size), "--threads", "2", ] if self.symbols: a += ["--enable-symbols", self.symbols] return a def label(self) -> str: sym = self.symbols or "default" return (f"{self.binary} ds={self.dataset.name:<20}" f" creator={self.creator} obj={self.objective} sym={sym!s:<30} seed={self.seed}") def group_key(self) -> tuple: """Key that identifies a group for statistical tests (everything except seed).""" return (self.binary, self.dataset, self.creator, self.objective, self.symbols, self.generations, self.iterations, self.population_size) @dataclass class RunResult: returncode: int model_line: str # last stdout line (the symbolic expression) final_stats: dict # parsed last numeric row error: str = "" # ── Binary runner ────────────────────────────────────────────────────────────── def _parse_stats_line(line: str) -> dict: parts = line.split() if not parts or not parts[0].isdigit(): return {} row = {} for col, val in zip(_STAT_COLS, parts): try: row[col] = float(val) if ("." in val or "e" in val.lower()) else int(val) except ValueError: pass return row def run_binary(binary: Path, config: RunConfig, datadir: Path, timeout: int) -> RunResult: cmd = [str(binary)] + config.cli_args(datadir) try: proc = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout) except subprocess.TimeoutExpired: return RunResult(-1, "", {}, "timeout") except Exception as exc: return RunResult(-1, "", {}, str(exc)) lines = proc.stdout.strip().splitlines() if not lines: return RunResult(proc.returncode, "", {}, proc.stderr[:400]) model_line = lines[-1] final_stats = {} for line in reversed(lines[:-1]): s = _parse_stats_line(line) if s: final_stats = s break err = proc.stderr[:200] if proc.returncode != 0 else "" return RunResult(proc.returncode, model_line, final_stats, err) # ── Printing helpers ─────────────────────────────────────────────────────────── def _ok(msg, no_color=False): tag = "PASS" if no_color else f"{GREEN}PASS{RESET}" print(f" [{tag}] {msg}") def _fail(msg, no_color=False): tag = "FAIL" if no_color else f"{RED}FAIL{RESET}" print(f" [{tag}] {msg}") def _warn(msg, no_color=False): tag = "WARN" if no_color else f"{YELLOW}WARN{RESET}" print(f" [{tag}] {msg}") # ── Determinism tests ────────────────────────────────────────────────────────── def test_determinism(ref_bin: Path, new_bin: Path, datadir: Path, args) -> int: """Run every config × seed and assert model strings are byte-identical.""" bold = "" if args.no_color else BOLD rst = "" if args.no_color else RESET print(f"\n{bold}{'='*60}{rst}") print(f"{bold} DETERMINISM TESTS{rst}") print(f"{bold}{'='*60}{rst}") seeds = list(range(1, args.det_seeds + 1)) configs = [ RunConfig( binary = binary, dataset = ds, seed = seed, creator = creator, objective = objective, symbols = symbols, generations = args.generations, iterations = args.iterations, population_size = args.population_size, ) for binary, ds, seed, creator, objective, symbols in itertools.product( ["operon_gp", "operon_nsgp"], args.datasets, seeds, args.creators, args.objectives, SYMBOL_SETS, ) ] passed = failed = skipped = 0 def run_pair(cfg: RunConfig): r = run_binary(ref_bin / cfg.binary, cfg, datadir, args.timeout) n = run_binary(new_bin / cfg.binary, cfg, datadir, args.timeout) return cfg, r, n with ThreadPoolExecutor(max_workers=args.jobs) as pool: futures = {pool.submit(run_pair, cfg): cfg for cfg in configs} results = [] for fut in as_completed(futures): results.append(fut.result()) # Print in a stable order results.sort(key=lambda t: t[0].label()) for cfg, ref, new in results: label = cfg.label() nc = args.no_color if ref.error and new.error: # Both failed — not a regression, but note it _warn(f"{label} ref_err={ref.error!r} new_err={new.error!r}", nc) skipped += 1 elif ref.error or new.error: # Asymmetric failure — one build broke, the other didn't _fail(f"{label} ref_err={ref.error!r} new_err={new.error!r}", nc) failed += 1 elif not args.exact: # --no-exact: only verify both runs completed successfully if ref.returncode == 0 and new.returncode == 0: _ok(f"{label} (rc=0, no-exact)", nc) passed += 1 else: _fail(f"{label} ref_rc={ref.returncode} new_rc={new.returncode}", nc) failed += 1 elif ref.model_line == new.model_line: _ok(label, nc) passed += 1 else: _fail(label, nc) if args.verbose: print(f" REF: {ref.model_line[:160]}") print(f" NEW: {new.model_line[:160]}") failed += 1 total = passed + failed red = "" if args.no_color else RED grn = "" if args.no_color else GREEN rst = "" if args.no_color else RESET summary = (f" {red}{failed} FAILED{rst}" if failed else f" {grn}all match{rst}") print(f"\n Determinism: {passed}/{total} passed{summary}" + (f" ({skipped} skipped)" if skipped else "")) return failed # ── Statistical equivalence tests ───────────────────────────────────────────── def test_statistics(ref_bin: Path, new_bin: Path, datadir: Path, args) -> int: """ For each (binary, dataset, creator, symbols) group, run --stat-seeds seeds and compare fitness metric distributions between the two builds. """ bold = "" if args.no_color else BOLD rst = "" if args.no_color else RESET print(f"\n{bold}{'='*60}{rst}") print(f"{bold} STATISTICAL EQUIVALENCE TESTS{rst}") scipy_note = "scipy Mann-Whitney U" if HAS_SCIPY else "no scipy – descriptive only" tabulate_note = "tabulate" if HAS_TABULATE else "plain text" print(f"{bold} seeds={args.stat_seeds} α={args.alpha} ({scipy_note}, {tabulate_note}){rst}") print(f"{bold}{'='*60}{rst}") seeds = list(range(1, args.stat_seeds + 1)) all_configs = [ RunConfig( binary = binary, dataset = ds, seed = seed, creator = creator, objective = objective, symbols = symbols, generations = args.generations, iterations = args.iterations, population_size = args.population_size, ) for binary, ds, seed, creator, objective, symbols in itertools.product( ["operon_gp", "operon_nsgp"], args.datasets, seeds, args.creators, args.objectives, SYMBOL_SETS, ) ] # Collect results in parallel ref_results: dict[tuple, list[RunResult]] = {} new_results: dict[tuple, list[RunResult]] = {} for cfg in all_configs: ref_results.setdefault(cfg.group_key(), []) new_results.setdefault(cfg.group_key(), []) def run_pair(cfg: RunConfig): r = run_binary(ref_bin / cfg.binary, cfg, datadir, args.timeout) n = run_binary(new_bin / cfg.binary, cfg, datadir, args.timeout) return cfg, r, n with ThreadPoolExecutor(max_workers=args.jobs) as pool: futures = {pool.submit(run_pair, cfg): cfg for cfg in all_configs} for fut in as_completed(futures): cfg, r, n = fut.result() ref_results[cfg.group_key()].append(r) new_results[cfg.group_key()].append(n) # Report per group failed_groups = 0 red = "" if args.no_color else RED grn = "" if args.no_color else GREEN ylw = "" if args.no_color else YELLOW rst2 = "" if args.no_color else RESET bld = "" if args.no_color else BOLD for key in sorted(ref_results.keys(), key=str): binary, ds, creator, objective, symbols, generations, iterations, _pop = key sym_label = symbols or "default" group_label = (f"{binary} ds={ds.name:<20}" f" creator={creator} obj={objective} sym={sym_label!s:<30}") print(f"\n {bld}{group_label}{rst2}") ref_rows = [r.final_stats for r in ref_results[key] if r.final_stats] new_rows = [r.final_stats for r in new_results[key] if r.final_stats] if not ref_rows or not new_rows: print(f" {ylw}No data collected — check for errors{rst2}") failed_groups += 1 continue def _median(xs): s = sorted(xs); n = len(s) return (s[n//2] + s[~(n//2)]) / 2 def _mean(xs): return sum(xs) / len(xs) def _stdev(xs): m = _mean(xs) return (sum((x - m)**2 for x in xs) / len(xs)) ** 0.5 group_significant = False table_rows = [] table_headers = ["metric", "ref med", "ref mean±sd", "new med", "new mean±sd", "Δmed", "p-value"] for metric in REPORT_METRICS: rv = [row[metric] for row in ref_rows if metric in row] nv = [row[metric] for row in new_rows if metric in row] if not rv or not nv: table_rows.append([metric, "n/a", "n/a", "n/a", "n/a", "n/a", "n/a"]) continue ref_med = _median(rv) new_med = _median(nv) ref_mn = _mean(rv) new_mn = _mean(nv) delta = new_med - ref_med if HAS_SCIPY: _, pval = scipy_stats.mannwhitneyu(rv, nv, alternative="two-sided") sig = pval < args.alpha if sig: group_significant = True pval_str = f"{red}{pval:.4f} ***{rst2}" if sig else f"{grn}{pval:.4f}{rst2}" else: pval_str = "n/a" if abs(delta) <= 1e-6: delta_str = f"{grn}{delta:+.4f}{rst2}" elif (delta < 0) == (metric in LOWER_IS_BETTER): delta_str = f"{grn}{delta:+.4f}{rst2}" # improvement else: delta_str = f"{red}{delta:+.4f}{rst2}" # regression table_rows.append([ metric, f"{ref_med:+.4f}", f"{ref_mn:+.4f}±{_stdev(rv):.4f}", f"{new_med:+.4f}", f"{new_mn:+.4f}±{_stdev(nv):.4f}", delta_str, pval_str, ]) if HAS_TABULATE: tbl = tabulate(table_rows, headers=table_headers, tablefmt="simple") for line in tbl.splitlines(): print(f" {line}") else: # Plain fallback — drop sd column to stay readable for row in table_rows: metric, ref_med, _, new_med, _, delta, pval = row print(f" {metric:10s} ref={ref_med} new={new_med} Δmed={delta} p={pval}") if group_significant: failed_groups += 1 total_groups = len(ref_results) ok_groups = total_groups - failed_groups summary = (f" {red}{failed_groups} groups differ significantly{rst2}" if failed_groups else f" {grn}all equivalent{rst2}") print(f"\n Statistics: {ok_groups}/{total_groups} groups passed{summary}") return failed_groups # ── Entry point ──────────────────────────────────────────────────────────────── def main() -> None: p = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, ) p.add_argument("ref", type=Path, help="Reference operon root (e.g. ../operon)") p.add_argument("new", type=Path, help="New operon root to validate (e.g. .)") # Dataset selection ds_names = [d.name for d in ALL_DATASETS] p.add_argument("--datasets", default=",".join(ds_names), help=f"Comma-separated dataset names to include " f"(default: all — {', '.join(ds_names)})") # Test selection p.add_argument("--only-determinism", action="store_true", help="Run only determinism tests") p.add_argument("--only-stats", action="store_true", help="Run only statistical tests") # Run parameters p.add_argument("--creators", default="btc", help="Comma-separated list of tree creators to test " "(default: btc; 'grow' can hang with restricted symbol sets)") p.add_argument("--objectives", default="r2", help="Comma-separated list of fitness objectives to test " f"(default: r2; available: {', '.join(OBJECTIVES)})") p.add_argument("--no-exact", dest="exact", action="store_false", default=True, help="Disable exact string comparison in determinism tests — " "only verify both runs complete with rc=0 (useful when " "results are statistically equivalent but not numerically identical)") p.add_argument("--generations", type=int, default=5, help="GP generations per run (default: 5)") p.add_argument("--iterations", type=int, default=1, help="Local optimisation iterations per run (default: 1)") p.add_argument("--population-size", type=int, default=200, help="Population (and pool) size per run (default: 200; " "keep small to limit memory usage)") p.add_argument("--det-seeds", type=int, default=3, help="Number of seeds for determinism tests (default: 3, seeds 1..N)") p.add_argument("--stat-seeds", type=int, default=20, help="Number of seeds for statistical tests (default: 20, seeds 1..N)") p.add_argument("--alpha", type=float, default=0.01, help="Significance level for Mann-Whitney U (default: 0.01)") # Infrastructure p.add_argument("--datadir", type=Path, default=None, help="Dataset directory (default: /data)") p.add_argument("--jobs", type=int, default=2, help="Parallel run pairs (default: 2; each pair = 2 processes, " "so --jobs 2 means 4 operon processes at once)") p.add_argument("--timeout", type=int, default=300, help="Per-run timeout in seconds (default: 300)") p.add_argument("--verbose", "-v", action="store_true", help="Show mismatched model strings in determinism failures") p.add_argument("--no-color", action="store_true", help="Disable ANSI colour output") args = p.parse_args() # Resolve dataset list selected_names = {s.strip() for s in args.datasets.split(",")} args.datasets = [d for d in ALL_DATASETS if d.name in selected_names] if not args.datasets: sys.exit(f"No matching datasets for: {selected_names!r}") # Resolve creator list args.creators = [c.strip() for c in args.creators.split(",") if c.strip()] if not args.creators: sys.exit("--creators must not be empty") # Resolve objectives list args.objectives = [o.strip() for o in args.objectives.split(",") if o.strip()] if not args.objectives: sys.exit("--objectives must not be empty") ref_bin = args.ref / "build" / "cli" new_bin = args.new / "build" / "cli" datadir = args.datadir or (args.ref / "data") # Validate paths for d in (ref_bin, new_bin): for b in ("operon_gp", "operon_nsgp"): exe = d / b if not exe.exists(): sys.exit(f"Binary not found: {exe}") if not datadir.is_dir(): sys.exit(f"Data directory not found: {datadir}") bld = "" if args.no_color else BOLD rst = "" if args.no_color else RESET print(f"{bld}Operon branch comparator{rst}") print(f" ref : {args.ref}") print(f" new : {args.new}") print(f" data: {datadir}") print(f" datasets : {[d.name for d in args.datasets]}") print(f" creators : {args.creators}") print(f" objectives : {args.objectives}") print(f" exact cmp : {args.exact}") print(f" sym sets : {len(SYMBOL_SETS)}") print(f" pop size : {args.population_size}") print(f" jobs/timeout: {args.jobs} / {args.timeout}s") ylw = "" if args.no_color else YELLOW rst2 = "" if args.no_color else RESET if not HAS_SCIPY: print(f" {ylw}scipy not found — statistical tests will be descriptive only{rst2}") if not HAS_TABULATE: print(f" {ylw}tabulate not found — stats output will use plain text{rst2}") total_failures = 0 if not args.only_stats: total_failures += test_determinism(ref_bin, new_bin, datadir, args) if not args.only_determinism: total_failures += test_statistics(ref_bin, new_bin, datadir, args) bld = "" if args.no_color else BOLD rst = "" if args.no_color else RESET red = "" if args.no_color else RED grn = "" if args.no_color else GREEN colour = red if total_failures else grn print(f"\n{bld}{colour}Total failures: {total_failures}{rst}") sys.exit(0 if total_failures == 0 else 1) if __name__ == "__main__": main() ================================================ FILE: vcpkg.json ================================================ { "$schema": "https://raw.githubusercontent.com/microsoft/vcpkg/master/scripts/vcpkg.schema.json", "name": "operon", "version-string": "0.3.1", "dependencies": [ "aria-csv", "cpp-sort", "cxxopts", "eigen3", "eve", "fast-float", "fluky", "fmt", "lbfgs", "libassert", "mdspan", "ms-gsl", "infix-parser", "scnlib", "taskflow", "tl-expected", "unordered-dense", "vstat", "xxhash" ] }