Implements train/test data splitting: use all data both for training and then once more for testing. More...

#include <data_splitter_resub.hpp>

Inheritance diagram for FST::Data_Splitter_Resub< INTERVALCONTAINER, IDXTYPE >:

Public Types
typedef Clonable_Impl< Data_Splitter_Resub, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >	parent_impl
	implements stateless_clone(); clone/sharing_clone throw in Data_Splitter
Public Types inherited from FST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >
typedef std::shared_ptr< INTERVALCONTAINER >	PIntervaller
	shared pointer to one class's list of train/test data intervals

Public Member Functions
	Data_Splitter_Resub (const Data_Splitter_Resub &dsp)
	copies configuration (this splitter is parameterless, so there is nothing else to copy)
virtual IDXTYPE	getNoOfSplits () const override
virtual std::ostream &	print (std::ostream &os) const override
	writes a human-readable identification of the splitter and its configuration; base default writes nothing
Public Member Functions inherited from FST::Clonable_Impl< Data_Splitter_Resub< INTERVALCONTAINER, IDXTYPE >, Data_Splitter< INTERVALCONTAINER, IDXTYPE >, CLONE_INHERIT, CLONE_INHERIT, CLONE_DEEP >
Data_Splitter< INTERVALCONTAINER, IDXTYPE > *	clone () const override
	dispatches to the DEEP_KIND mechanism (default CLONE_DEEP: Derived's copy-constructor)
Data_Splitter< INTERVALCONTAINER, IDXTYPE > *	sharing_clone () const override
	dispatches to the SHARING_KIND mechanism (default CLONE_INHERIT: defers to Base, which throws by family convention)
Data_Splitter< INTERVALCONTAINER, IDXTYPE > *	stateless_clone () const override
	dispatches to the STATELESS_KIND mechanism (default CLONE_INHERIT: defers to Base, which throws by family convention)
Public Member Functions inherited from FST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >
	Data_Splitter (const Data_Splitter &dsp)
	Deep-copies every class's TClassSplitter (own train/test interval lists, not shared with `dsp`).
void	assign (const unsigned int _clsidx, const PIntervaller _train, const PIntervaller _test)
	Registers class `_clsidx's` train/test interval lists (growing csplit as needed); the caller retains ownership via the shared pointers.
void	set_n (const unsigned int _clsidx, const IDXTYPE _n)
	Sets class `_clsidx's` pattern count, which getNoOfSplits() and the concrete prepare‍/splitClass hooks size their splits against (growing csplit as needed).
IDXTYPE	get_n (const unsigned int _clsidx) const
	the pattern count previously set for class `_clsidx` via set_n()
bool	makeFirstSplit ()
	Produces the first split: prepareFirstSplit(), then splitClassFirst() per class, then finishSplit().
bool	makeNextSplit ()
	Produces the next split: prepareNextSplit(), then splitClassNext() per class, then finishSplit().
Data_Splitter *	clone () const override
Data_Splitter *	sharing_clone () const override
Public Member Functions inherited from FST::Clonable
bool	is_clone () const
bool	is_sharing () const
	true if this instance is a sharing_clone() result (referencing, not owning, the source's structures)
bool	is_sharing_clone () const
	true if this instance is BOTH a clone and sharing – i.e. specifically a sharing_clone() result, not a clone()/stateless_clone() one

Protected Types
typedef Data_Splitter< INTERVALCONTAINER, IDXTYPE >	TCC
	alias for the Data_Splitter base, used by the per-class hook
Protected Types inherited from FST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >
typedef std::shared_ptr< TClassSplitter >	PClassSplitter
	shared pointer to one class's TClassSplitter
typedef std::vector< PClassSplitter >::const_iterator	ClassIterator
	iterator over csplit, as passed to the per-class splitClassFirst()/splitClassNext() hooks

Protected Member Functions
virtual const char *	splitter_name () const override
	class name, used in error messages
virtual bool	prepareNextSplit () override
	advance split-sequence state; false = no more splits
virtual void	splitClassFirst (typename TCC::ClassIterator c, const unsigned int) override
	write first-split train/test intervals of class `cidx` (`c` is that class's entry in csplit)
Protected Member Functions inherited from FST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >
void	assert_csplit () const
	DEBUG-only: asserts csplit is non-empty and every class has n>0 and non-null train/test lists.
virtual bool	prepareFirstSplit ()
	set up split-sequence state; false = no split can be made
virtual void	splitClassNext (ClassIterator c, const unsigned int cidx)
	write next-split train/test intervals of class `cidx` (a single-split splitter never gets here, since its prepareNextSplit() returns false)
virtual void	finishSplit ()
	post-loop state update shared by first/next split
Protected Member Functions inherited from FST::Clonable
void	set_cloned (const bool cloned=true)
	marks/unmarks this instance as a clone; call from clone()/stateless_clone() overrides (Clonable_Impl does this automatically)
void	set_sharing (const bool sharing=true)
	marks/unmarks this instance as sharing (see is_sharing())
void	set_sharing_cloned (const bool cloned=true, const bool sharing=true)
	sets both flags at once; call from sharing_clone() overrides (Clonable_Impl does this automatically)

Additional Inherited Members
Protected Attributes inherited from FST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >
std::vector< PClassSplitter >	csplit
	one TClassSplitter per data class, indexed by class index; grown on demand by assign()/set_n()

Detailed Description

template<class INTERVALCONTAINER, typename IDXTYPE>
class FST::Data_Splitter_Resub< INTERVALCONTAINER, IDXTYPE >

Implements train/test data splitting: use all data both for training and then once more for testing.

Yields exactly one split, in which the train and test parts are the same samples. This is the one case where the contract's "train and test must not overlap" is deliberately broken, and the resulting accuracy is the RESUBSTITUTION estimate: how well a model reproduces the data it was fitted on.

Warning: A resubstitution estimate is optimistically biased, severely so for flexible models, and says nothing about generalization. It is legitimate as a training-fit diagnostic, as the upper reference point against which a held-out estimate is read, and for criteria that need no held-out part at all (the statistical filter criteria, which are computed on the training part). It is not a substitute for an honest estimate of classification accuracy.