ebl::class_datasource< Tnet, Tdata, Tlabel > Class Template Reference

#include <datasource.h>

Inheritance diagram for ebl::class_datasource< Tnet, Tdata, Tlabel >:

List of all members.

Public Member Functions
	class_datasource ()
	CAUTION: This empty constructor requires a subsequent call to init().
	class_datasource (midx< Tdata > &data, idx< Tlabel > &labels, vector< string * > *lblstr=NULL, const char *name=NULL)
	class_datasource (idx< Tdata > &data, idx< Tlabel > &labels, vector< string * > *lblstr=NULL, const char *name=NULL)
	class_datasource (midx< Tdata > &data, idx< Tlabel > &labels, idx< ubyte > &classes, const char *name=NULL)
	class_datasource (idx< Tdata > &data, idx< Tlabel > &labels, idx< ubyte > &classes, const char *name=NULL)
	class_datasource (const char *data_name, const char *labels_name, const char *jitters_name=NULL, const char *scales_name=NULL, const char *classes_name=NULL, const char *name=NULL)
	class_datasource (const class_datasource< Tnet, Tdata, Tlabel > &ds)
	(Shallow) copy constructor (only the class strings are deeply copied).
virtual	~class_datasource ()
	Destructor.
void	defaults ()
	Initialize variables to their default value.
virtual void	init_strings (idx< ubyte > &classes)
	Allocate class strings vector from ubyte matrix 'classes'.
void	init_local (vector< string * > *lblstr)
void	init (midx< Tdata > &data, idx< Tlabel > &labels, vector< string * > *lblstr, const char *name)
	Initialize from matrices where the data is a multi-matrix matrix.
void	init (idx< Tdata > &data, idx< Tlabel > &labels, vector< string * > *lblstr, const char *name)
	Initialize from matrices.
void	init (const char *data_fname, const char *labels_fname, const char *jitters_fname=NULL, const char *scales_fname=NULL, const char *classes_fname=NULL, const char *name=NULL, uint max_size=0)
void	init_root (const char *root, const char *data_fname, const char *labels_fname, const char *jitters_fname=NULL, const char *scales_fname=NULL, const char *classes_fname=NULL, const char *name=NULL)
	Intialize from root and partial matrices filenames.
void	init_root (const char *root_dsname, const char *name=NULL)
virtual void	init_class_labels ()
virtual Tlabel	get_label ()
virtual bool	included_sample (intg index)
	This returns true if sample with 'index' is considered for training.
virtual intg	count_included_samples ()
	This returns the number of samples actually considered for training.
virtual void	seek_begin ()
virtual void	seek_begin_train ()
virtual bool	next ()
virtual bool	next_train ()
virtual void	next_balanced_class ()
virtual void	reset_class_order ()
virtual void	set_random_class_order (bool ran)
virtual void	limit_classes (intg n, intg offset=0, bool random=false)
virtual void	set_balanced (bool bal=true)
virtual bool	epoch_done ()
virtual void	init_epoch ()
virtual void	normalize_all_probas ()
virtual void	normalize_probas (int classid=-1)
virtual intg	get_nclasses ()
	Return the number of classes.
virtual int	get_class_id (const char *name)
	Return the label id corresponding to name, or -1 if not found.
virtual string &	get_class_name (int id)
	Return the label string for index id.
virtual vector< string * > &	get_label_strings ()
	Returns a reference to a vector of each label string.
virtual intg	get_lowest_common_size ()
virtual void	save_pickings (const char *name=NULL)
template<typename T >
void	write_classed_pickings (idx< T > &m, idx< ubyte > &correct, string &name_, const char *name2_=NULL, bool plot_correct=true, const char *ylabel="")
	Write plot of m organized by class and correctness.
virtual void	save_state ()
virtual void	restore_state ()
	Restore previously saved internal iterators.
virtual void	pretty ()
	Print info about the datasource on the standard output.
virtual void	pretty_scales ()
	Print info about the scales data on the standard output.
virtual void	pretty_progress (bool newline=true)
Protected Member Functions
virtual bool	pick_current ()
Protected Attributes
intg	nclasses
	Number of classes.
vector< string * > *	lblstr
	Name of each class.
vector< string * >	clblstr
	Name of each class, may differ.
bool	bexclusion
	Exclusion is used.
vector< bool >	excluded
	Vector of excluded classes.
intg	included
	Number of included classes.
idx< Tlabel >	olabels
	Original class labels (may differ from labels).
bool	balance
	Balance iterating or not.
vector< vector< intg > >	bal_indices
	Balanced iterating indices.
vector< uint >	bal_it
	Sample iterators for each class.
vector< uint >	class_order
	Order in which to balance classes.
bool	random_class_order
uint	class_it
	Iterator on classes.
uint	class_it_it
	Iterator on classes iterator.
bool	perclass_norm
	Normalize probas per class.
vector< vector< intg > >	bal_indices_saved
vector< uint >	bal_it_saved
uint	class_it_saved
uint	class_it_it_saved
Friends
class	class_datasource_gui
class	supervised_trainer

---

Detailed Description

template<typename Tnet, typename Tdata, typename Tlabel>
class ebl::class_datasource< Tnet, Tdata, Tlabel >

class_datasource A datasource associating samples with a discrete class. The order of samples distributed to training can be balanced to present the same amount of samples for each class if these are unbalanced.

---

Constructor & Destructor Documentation

template<typename Tnet , typename Tdata , typename Tlabel >

ebl::class_datasource< Tnet, Tdata, Tlabel >::class_datasource	(	midx< Tdata > &	data,
		idx< Tlabel > &	labels,
		vector< string * > *	lblstr = NULL,
		const char *	name = NULL
	)

Construct dataset with 'data' and its corresponding 'labels', where data is a multi-matrix matrix (midx type). This allows for dynamic loading of data and avoids the need to fit all data in memory.

Parameters:

lblstr	An optional vector of strings describing each class.
name	An optional name for this dataset.

template<typename Tnet , typename Tdata , typename Tlabel >

ebl::class_datasource< Tnet, Tdata, Tlabel >::class_datasource	(	idx< Tdata > &	data,
		idx< Tlabel > &	labels,
		vector< string * > *	lblstr = NULL,
		const char *	name = NULL
	)

Construct dataset with 'data' and its corresponding 'labels'.

Parameters:

lblstr	An optional vector of strings describing each class.
name	An optional name for this dataset.

template<typename Tnet , typename Tdata , typename Tlabel >

ebl::class_datasource< Tnet, Tdata, Tlabel >::class_datasource	(	midx< Tdata > &	data,
		idx< Tlabel > &	labels,
		idx< ubyte > &	classes,
		const char *	name = NULL
	)

Construct dataset with 'data' and its corresponding 'labels', where data is a multi-matrix matrix (midx type). This allows for dynamic loading of data and avoids the need to fit all data in memory.

Parameters:

classes	A vector of strings describing each class.
name	An optional name for this dataset.

template<typename Tnet , typename Tdata , typename Tlabel >

ebl::class_datasource< Tnet, Tdata, Tlabel >::class_datasource	(	idx< Tdata > &	data,
		idx< Tlabel > &	labels,
		idx< ubyte > &	classes,
		const char *	name = NULL
	)

Construct dataset with 'data' and its corresponding 'labels'.

Parameters:

classes	A vector of strings describing each class.
name	An optional name for this dataset.

template<typename Tnet , typename Tdata , typename Tlabel >

ebl::class_datasource< Tnet, Tdata, Tlabel >::class_datasource	(	const char *	data_name,
		const char *	labels_name,
		const char *	jitters_name = NULL,
		const char *	scales_name = NULL,
		const char *	classes_name = NULL,
		const char *	name = NULL
	)

Constructor from full names for each dataset file. Note: jitters and classes files are optional.

Parameters:

name	An optional name for this dataset.

---

Member Function Documentation

template<typename Tnet , typename Tdata , typename Tlabel >

bool ebl::class_datasource< Tnet, Tdata, Tlabel >::epoch_done

(

)

[virtual]

Return true if current epoch is finished. Call init_epoch() to restart a new epoch.

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

Tlabel ebl::class_datasource< Tnet, Tdata, Tlabel >::get_label

(

)

[virtual]

Return the value contained in current sample's label. This supposes a label is only constituted of 1 element and will produce and error otherwise.

Reimplemented in ebl::hierarchy_datasource< Tnet, Tdata, Tlabel >.

template<typename Tnet , typename Tdata , typename Tlabel >

intg ebl::class_datasource< Tnet, Tdata, Tlabel >::get_lowest_common_size

(

)

[virtual]

Return the lowest (non-zero) size per class, multiplied by the number of classes. e.g. if a dataset has 10 classes with 100 examples and 5 classes with 50 examples, it will return 50 * (10 + 5) = 750, whereas size() will return 1250. This is useful to keep iterations to a meaningful size when a class has many more examples than another.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::init	(	const char *	data_fname,
		const char *	labels_fname,
		const char *	jitters_fname = NULL,
		const char *	scales_fname = NULL,
		const char *	classes_fname = NULL,
		const char *	name = NULL,
		uint	max_size = 0
	)

Intialize from matrices filenames.

Parameters:

max_size

If > 0, limit the number of samples to this value.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::init_class_labels

(

)

[virtual]

Fills 'clabels' and 'clblstr' which are the true class labels if labels are not consecutive values starting from 0. If they are, then 'clabels' is equivalent to 'labels', same for 'clblstr' and 'lblstr'.

Reimplemented in ebl::hierarchy_datasource< Tnet, Tdata, Tlabel >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::init_epoch

(

)

[virtual]

Restarts a new epoch, i.e. resets counters but do not reset iterators positions.

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::init_local

(

vector< string * > *

lblstr

)

Initialize things specific to this class. The rest can be initialized with parent init methods.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::init_root	(	const char *	root_dsname,
		const char *	name = NULL
	)

Constructor from directory 'root' and individual names for data, labels, jitters and classes files (appending names and extension of each subfile of a dataset created with dscompile tool).

Reimplemented from ebl::labeled_datasource< Tnet, Tdata, Tlabel >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::limit_classes	(	intg	n,
		intg	offset = 0,
		bool	random = false
	)		[virtual]

Excludes use of classes other than ones in [offset, offset + n] range.

Parameters:

random

If true, keep only n classes at random.

template<typename Tnet , typename Tdata , typename Tlabel >

bool ebl::class_datasource< Tnet, Tdata, Tlabel >::next

(

)

[virtual]

Move to the next datum (in the original order of the dataset). Returns false if we reached the end. This should be used during testing. It will always return the data in the same order with the same probability of 1. See next_train() for data returned with variable probability, balance, etc. (used for training only).

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::next_balanced_class

(

)

[virtual]

This move class iterator 'class_it' to the next class. If reaching the end of the classes, then the class order is shuffled and iterator starts at first class again.

template<typename Tnet , typename Tdata , typename Tlabel >

bool ebl::class_datasource< Tnet, Tdata, Tlabel >::next_train

(

)

[virtual]

Move to the next datum in a way suited for training (_not_ for testing, for testing see next()). If balance is activated (see set_balanced()) this will return samples in a class-balanced way, i.e. showing each class sequentially, with different probabilities based on sample's difficulty, or/and in a random order after each pass. When all samples of a class have been shown, it loops back to the first sample of that class. This should be used during training only. If a sample was not selected because of a low probability, this will return false, if it was selected it returns true. In any case, internal iterators will always be set to the next sample, regardless if it was selected or not. It is up to the caller, to train on the sample if selected, or only test and update its energy if not selected.

Reimplemented from ebl::datasource< Tnet, Tdata >.

Reimplemented in ebl::hierarchy_datasource< Tnet, Tdata, Tlabel >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::normalize_all_probas

(

)

[virtual]

Normalize picking probabilities by maximum probability for all classes if perclass_norm is true, or globally otherwise.

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::normalize_probas

(

int

classid = -1

)

[virtual]

Normalize picking probabilities by maximum probability of classid if perclass_norm is true, or globally otherwise.

template<typename Tnet , typename Tdata , typename Tlabel >

bool ebl::class_datasource< Tnet, Tdata, Tlabel >::pick_current

(

)

[protected, virtual]

Draw a random number between 0 and 1 and return true if higher than current sample's probability.

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::pretty_progress

(

bool

newline = true

)

[virtual]

Pretty the progress of current training epoch.

Parameters:

newline

If true, end pretty with a new line.

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::reset_class_order

(

)

[virtual]

If 'random_class_order' is true, this will randomly change to order in which classes are presented for balanced training, otherwise it will do nothing and leave them in the original order.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::save_pickings

(

const char *

name = NULL

)

[virtual]

Output statistics of samples picking, i.e. the number of times each sample has been picked for training.

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::save_state

(

)

[virtual]

Save internal iterators. Calling restore_state() will return to the current sample.

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::seek_begin

(

)

[virtual]

Move to the beginning of the data, for the test iterators only, i.e. only next() is affected, next_train() is unaffected.

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::seek_begin_train

(

)

[virtual]

Move to the beginning of the data, for the train iterators only, i.e. only next_train() is affected, next() is unaffected.

Reimplemented from ebl::datasource< Tnet, Tdata >.

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::set_balanced

(

bool

bal = true

)

[virtual]

If 'bal' is true, make the next_train() method call sequentially one sample of each class instead of following the dataset's distribution. This is important to use when the dataset is unbalanced. This is set to true by default. Balance is used only by next_train(), not by next().

template<typename Tnet , typename Tdata , typename Tlabel >

void ebl::class_datasource< Tnet, Tdata, Tlabel >::set_random_class_order

(

bool

ran

)

[virtual]

If 'ran' is true, then the order in which classes are presented during balanced training is randomized.

---

The documentation for this class was generated from the following files:

• /home/rex/ebltrunk/core/libeblearn/include/datasource.h
• /home/rex/ebltrunk/core/libeblearn/include/datasource.hpp