Tapkee
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
arpack_wrapper< Scalar, RealScalar >
arpack_wrapper< double, double >
arpack_wrapper< float, float >
ArpackGeneralizedSelfAdjointEigenSolver< LMatrixType, RMatrixType, MatrixOperation, BisSPD >
BatchCallbackTraits< Callback >
CallbacksInitializedState< KernelCallback, DistanceCallback, FeaturesCallback >
cancelled_exceptionAn exception type that is thrown when computations were cancelled
Cell
CheckedParameter
CheckerPolicyBase
compare_impl< DistanceType, RandomAccessIterator, DistanceCallback >
compare_impl< KernelType, RandomAccessIterator, DistanceCallback >
conditional_select< bool, T >
conditional_select< false, T >
conditional_select< true, T >
Context
CoverTreePoint< RandomAccessIterator >Class Point to use with John Langford's CoverTree. This class must have some associated functions defined (distance, and print, see below) so it can be used with the CoverTree implementation
d_node< P >
DataForErrorFuncData needed to compute error function
DataPoint
DefaultLoggerImplementationDefault std::cout implementation of LoggerImplementation
DefaultValue
DenseImplicitSquareMatrixOperationMatrix-matrix operation used to compute largest eigenvalues and associated eigenvectors of X*X^T like matrix implicitly. Essentially computes matrix product with provided right-hand side part *twice*
DenseImplicitSquareSymmetricMatrixOperationMatrix-matrix operation used to compute largest eigenvalues and associated eigenvectors of X*X^T like matrix implicitly. Essentially computes matrix product with provided right-hand side part *twice*
DenseInverseMatrixOperationMatrix-matrix operation used to compute smallest eigenvalues and associated eigenvectors of a dense matrix Essentially solves linear system with provided right-hand side part
DenseMatrixOperationMatrix-matrix operation used to compute largest eigenvalues and associated eigenvectors. Essentially computes matrix product with provided right-hand side part
distance_impl< DistanceType, RandomAccessIterator, Callback >
distance_impl< KernelType, RandomAccessIterator, Callback >
DistanceAndFeaturesInitializedState< DistanceCallback, FeaturesCallback >
VpTree< T, distance >::DistanceComparator
DistanceComparator< RandomAccessIterator, DistanceCallback >
DistanceFirstInitializedState< DistanceCallback >
distances_comparator< DistanceRecord >
DistanceType
ds_node< P >
dummy_distance_callback< Data >
dummy_features_callback< Data >
dummy_kernel_callback< Data >
eigen_distance_callback
eigen_features_callback
eigen_kernel_callback
eigendecomposition_errorAn exception type that is thrown when eigendecomposition is failed
EmptyType
FeaturesFirstInitializedState< FeaturesCallback >
fibonacci_heapClass fibonacci_heap, a fibonacci heap. Generally used by Isomap for Dijkstra heap algorithm
fibonacci_heap_node
VpTree< T, distance >::HeapItem
VantagePointTree< RandomAccessIterator, DistanceCallback >::HeapItem
ImplementationBase< RandomAccessIterator, KernelCallback, DistanceCallback, FeaturesCallback >
initialize
is_dummy< T >
KernelAndDistanceInitializedState< KernelCallback, DistanceCallback >
KernelAndFeaturesInitializedState< KernelCallback, FeaturesCallback >
KernelDistance< RandomAccessIterator, Callback >
KernelFirstInitializedState< KernelCallback >
KernelType
LoggerImplementationA base class for logger required by the library
LoggingSingletonMain logging singleton used by the library. Can use provided LoggerImplementation if necessary. By default uses DefaultLoggerImplementation
MatrixProjectionImplementationBasic ProjectionImplementation that subtracts mean from the vector and multiplies projecting matrix with it
Message
MethodTraits< method >Traits used to obtain information about dimension reduction methods compile-time
missed_parameter_errorAn exception type that is thrown in case of missed parameter, i.e. when some required parameter is not set
multiple_parameter_errorAn exception type that is thrown when some parameter is passed more than once
neighbors_finder< RandomAccessIterator >
node< P >
VantagePointTree< RandomAccessIterator, DistanceCallback >::Node
VpTree< T, distance >::Node
not_enough_memory_errorAn exception type that is thrown when the library can't get enough memory
Parameter
ParameterKeyword< T >
ParametersInitializedState
ParametersSet
pimpl_distance_callback< Implementation >
pimpl_kernel_callback< Implementation >
PlainDistance< RandomAccessIterator, Callback >
PointerCheckerPolicyImpl< T >
PointerCheckerPolicyImpl< double >
PointerCheckerPolicyImpl< float >
PointerCheckerPolicyImpl< int >
PointerTypePolicyImpl< T >
precomputed_distance_callback
precomputed_kernel_callback
ProjectingFunctionA pimpl wrapper for projecting function
ProjectionImplementationA base class for implementation of projecting
QuadTree
reservable_priority_queue< T, Comparator >
SparseInverseMatrixOperationMatrix-matrix operation used to compute smallest eigenvalues and associated eigenvectors of a sparse matrix Essentially solves linear system with provided right-hand side part
TapkeeOutputReturn result of the library - a pair of DenseMatrix (embedding) and ProjectingFunction
timed_context
TSNE
TypePolicyBase
unsupported_method_errorAn exception type that is thrown when unsupported method is called
v_array< T >Class v_array taken directly from JL's implementation
ValueKeeper
VantagePointTree< RandomAccessIterator, DistanceCallback >
VpTree< T, distance >
wrong_parameter_errorAn exception type that is thrown in case if wrong parameter value is passed
wrong_parameter_type_errorAn exception type that is thrown in case if wrong parameter value is passed
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