//! constructor. Arguments are a pointer to a parameter	//! in which the trainable weights will be appended,	//! the number of inputs, and the number of outputs.	nn_layer_full(parameter *p, intg ninputs, intg noutputs);	virtual ~nn_layer_full();	//! fprop from in to out	void fprop(state_idx *in, state_idx *out);	//! bprop	void bprop(state_idx *in, state_idx *out);	//! bbprop	void bbprop(state_idx *in, state_idx *out);	//! initialize the weights to random values	void forget(forget_param_linear &fp);};//////////////////////////////////////////////////////////////////! full connection between replicable 3D layers//! the full connection is only across the first dimension//! of the input and output layer.//! the other two dimensions are spatial dimensions accross which the//! weight matrix is shared. This is much more efficient than using//! a c-layer with a 1x1 convolution and a full-table.class f_layer: public module_1_1<state_idx, state_idx> {public:	//! weight matrix	state_idx *weight;	//! bias vector	state_idx *bias;	//! weighted sums	state_idx *sum;	//! squashing function module	module_1_1<state_idx, state_idx> *squash;	virtual ~f_layer();	//! constructor. Arguments are:	//! p: parameter object in which the trainable parameters will be allocated,	//! tin: number of slices of the input	//! tout: number of slices of the output	//! si: initial size in first spatial dimension	//! sj: initial size in decond spatial dimension	//! sq: pointer to squashing module	f_layer(parameter *p, intg tin, intg tout, intg si, intg sj, module_1_1<			state_idx, state_idx> *sq);	//! fprop from in to out	void fprop(state_idx *in, state_idx *out);	//! bprop	void bprop(state_idx *in, state_idx *out);	//! bbprop	void bbprop(state_idx *in, state_idx *out);	//! initialize the weights to random values	void forget(forget_param_linear &fp);};//////////////////////////////////////////////////////////////////! convolutional layer module. Performs multiple convolutions//! between an idx3-state input and an idx3-state output.//! includes bias and sigmoid.class c_layer: public module_1_1<state_idx, state_idx> {public:	//! thickness of output layer (number of feature maps)	int thickness;	//! vertical size for preallocation of internal state	int stridei;	//! horizontal size for preallocation of internal state	int stridej;	//! N by 2 matrix containing source and destination	//! feature maps for coresponding kernel	Idx<intg> *table;	//! convolution kernel	state_idx *kernel;	//! bias vector	state_idx *bias;	//! weighted sum	state_idx *sum;	//! squashing function module	module_1_1<state_idx, state_idx> *squash;	virtual ~c_layer();	//! constructor. Arguments are:	//! ki: vertical kernel size.	//! kj: horizontal kernel size.	//! ri: vertical stride (number of pixels by which the kernels are stepped)	//! rj: horizontal stride	//! tbl: N by 2 matrix containing source and destination	//!   feature maps for coresponding kernel	//! thick: thickness of output layer (number of feature maps)	//! si: vertical size for preallocation of internal state	//! sj: horizontal size for preallocation of internal state	//! sqsh: a squashing function module that operates on idx3-state.	//! prm: an idx1-ddparam from which the parameters will be allocated	c_layer(parameter *prm, intg ki, intg kj, intg ri, intg rj, Idx<intg> *tbl,			intg thick, intg si, intg sj,			module_1_1<state_idx, state_idx> *sqsh);	//! set the convolution stride	void set_stride(intg ri, intg rj);	//! initialize the weights to random values	void forget(forget_param_linear &fp);	//! fprop from in to out	virtual void fprop(state_idx *in, state_idx *out);	//! bprop	virtual void bprop(state_idx *in, state_idx *out);	//! bbprop	virtual void bbprop(state_idx *in, state_idx *out);};////////////////////////////////////////////////////////////////#ifdef USE_IPP#include "Ipp.h"//! same as c_layer class but using Intel IPP functions for optimization.class c_layer_ipp : public c_layer{public:	//! constructor. Arguments are:	//! ki: vertical kernel size.	//! kj: horizontal kernel size.	//! ri: vertical stride (number of pixels by which the kernels are stepped)	//! rj: horizontal stride	//! tbl: N by 2 matrix containing source and destination	//!   feature maps for coresponding kernel	//! thick: thickness of output layer (number of feature maps)	//! si: vertical size for preallocation of internal state	//! sj: horizontal size for preallocation of internal state	//! sqsh: a squashing function module that operates on idx3-state.	//! prm: an idx1-ddparam from which the parameters will be allocated	c_layer_ipp(parameter *prm, intg ki, intg kj, intg ri, intg rj, Idx<intg> *tbl,			intg thick, intg si, intg sj, module_1_1<state_idx,state_idx> *sqsh)	: c_layer(prm, ki, kj, ri, rj, tbl, thick, si, sj, sqsh)	{}	//! fprop	virtual void fprop(state_idx *in, state_idx *out);	//! bprop	virtual void bprop(state_idx *in, state_idx *out);};#endif//////////////////////////////////////////////////////////////////! Creates a table of full connections between layers.//! An Idx<intg> is allocated and returned. The caller is responsible//! for deleting this Idx.Idx<intg> full_table(intg a, intg b);//////////////////////////////////////////////////////////////////! subsampling layer classclass s_layer: public module_1_1<state_idx, state_idx> {public:	intg stridei, stridej;	state_idx *coeff;	state_idx *bias;	state_idx *sub;	state_idx *sum;	module_1_1<state_idx, state_idx> *squash;	//! constructor arguments are:	//! <prm> pointer to a "parameter" object, from which the parameters will be allocated	//! <ki>  vertical subsampling ratio.	//! <kj>  horizontal subsampling ratio.	//! <thick> thickness of output layer (number of feature maps)	//! <si>  vertical size for preallocation of internal state	//! <sj>  horizontal size for preallocation of internal state	//! <sqsh> pointer to a squashing function module that operates	s_layer(parameter *p, intg ki, intg kj, intg thick, intg si, intg sj,			module_1_1<state_idx, state_idx> *sqsh);	virtual ~s_layer();	void fprop(state_idx *in, state_idx *out);	void bprop(state_idx *in, state_idx *out);	void bbprop(state_idx *in, state_idx *out);	void forget(forget_param_linear &fp);};//////////////////////////////////////////////////////////////////! performs a log-add over spatial dimensions of an idx3-state//! output is an idx1-stateclass logadd_layer { //: public module_1_1<state_idx, state_idx> { // TODOpublic:	Idx<double> expdist;	Idx<double> sumexp;	logadd_layer(intg thick, intg si, intg sj);	virtual ~logadd_layer() {	}	void fprop(state_idx *in, state_idx *out);	void bprop(state_idx *in, state_idx *out);	//! this is not algebraically correct, but it's	//! numerically more stable (at least, I think so).	void bbprop(state_idx *in, state_idx *out);};////////////////////////////////////////////////////////////////// TODO: for all classes below, templatize type of classes (currently ubyte).//! a replicable Euclidean distance cost function.//! computes the log-sum over the 2D spatial output of//! the half squared error between the output and the//! prototype with the desired label.//! this does not generate gradients on the prototypesclass edist_cost { //: public module_1_1<state_idx, state_idx> { // TODOpublic:	logadd_layer *logadder;	state_idx *dist;	state_idx *logadded_dist;	Idx<double> *proto;	Idx<ubyte> label2classindex;	//! make a new edist-cost. <classes> is an integer vector	//! which contains the labels associated with each output.	//! From that vector, the reverse table is constructed	//! to map labels to class indices.	//! Elements in <classes> must be positive or 0, and	//! not be too large, as a table as large as the	//! maximum value is allocated.	//! <si> and <sj> are the expected maximum sizes in	//! the spatial dimensions (used for preallocation to	//! prevent memory fragmentation).	//! <p> is an idx2 containing the prototype for each	//! class label. The first dimension of <p> should be	//! equal to the dimension of <classes>.	//! the second dimension of <p> should be equal to the	//! number of outputs of the previous module.	//! The costs are "log-summed" over spatial dimensions	edist_cost(Idx<ubyte> *classes, intg ini, intg inj, Idx<double> *p);	virtual ~edist_cost() {	}	virtual void fprop(state_idx *in, Idx<ubyte> *desired, state_idx *energy);	virtual void bprop(state_idx *in, Idx<ubyte> *desired, state_idx *energy);	virtual void bbprop(state_idx *in, Idx<ubyte> *desired, state_idx *energy);};//////////////////////////////////////////////////////////////////! a special kind of state used to store the output of a classifier.//! class-state are generated by modules such as class-max, and used//! by meters such as classifier-meter. No backprop is possible through//! a class-state.class class_state {public:	ubyte output_class;	float confidence;	Idx<ubyte> *sorted_classes;	Idx<float> *sorted_scores;	class_state(ubyte n);	~class_state();	void resize(ubyte n);};//////////////////////////////////////////////////////////////////! Meters are classes used to measure the performance//! of learning machines. There are several types//! of meters for each specific situation.//! meters are generally assumed to have at least//! the following methods://! {<ul>//!  {<li> update: updates the meter with the objects//!        and values passed as argument.}//!  {<li> clear: resets the meter, so it can be used//!        for a new series of measurements.}//!  {<li> test: simply prints performance information//!        for the data passed as argument. This//!        does not update any internal state.}//! }//! Methods are provided to compute and display the//! information measured by a meter.//! {<ul>//!  {<li> display: display performance information on the terminal}//!  {<li> info: returns a list of the informations printed by display}//! }//! a class that can be used to measure the performance of//! classifiers. This is a simple version that does not//! record anything but simply computes performance measures.// TODO: allow definition of different comparison functions.class classifier_meter {public:	double energy;	float confidence;	intg size;	intg age;	intg total_correct;	intg total_error;	intg total_punt;	intg total_energy;	//! Create a new <classifier-meter> using <comparison-function>	//! to compare actual and desired answers. By default	//! the <same-class?> function is used for that purpose.	//! It takes two integer arguments, and returns 1 if they	//! are equal, -1 if they are different, and 0 if	//! the first argument is -1 (which means reject).	classifier_meter(); // TODO: allow passing of comparison function	~classifier_meter() {	}	;	//! return 0 if <actual> equals -1, otherwise, return 1 if <actual>	//! and <desired> are equal, -1 otherwise.	int correctp(ubyte co, ubyte cd);	//! reset the meter. This must be called	//! before a measurement campaign is started.	void clear();	void resize(intg sz);	//! update the meter with results from a new sample.	//! <age> is the number of training iterations so far,	//! <actual> (a <class-state>) the actual output of the machine,	//! <desired> (an idx0 of int) the desired category,	//! and <energy> (an idx0-state) the energy.	char update(intg a, class_state *co, ubyte cd, state_idx *en);	void test(class_state *co, ubyte cd, state_idx *en);	//! return a list with the age, the number of samples	//! (number of calls to update since the last clear),	//! the average energy, the percentage of correctly	//! recognize samples, the percentage of erroneously	//! recognized samples, and the percentage of rejected samples.	void info();	void info_sprint();	void info_print();	//! Display the meter's information on the terminal.	//! namely, the age, the number of samples	//! (number of calls to update since the last clear),	//! the average energy, the percentage of correctly	//! recognize samples, the percentage of erroneously	//! recognized samples, and the percentage of rejected samples.	void display();	bool save();	bool load();};//////////////////////////////////////////////////////////////////! a module that takes an state_idx, finds the lowest value//! and output the label associated with the index (in the first dimension//! of the state) of this lowest value.//! It actually sorts the labels according to their score (or costs)//! and outputs the sorted list.class max_classer { // TODO: idx3-classerpublic:	Idx<ubyte> *classindex2label; //! a vector that maps output unit index to a label	//! makes a new max-classer. <classes> is an integer vector	//! which contains the labels associated with each output.	max_classer(Idx<ubyte> *classes);	~max_classer() {	}	;	void fprop(state_idx *in, class_state *out);};//////////////////////////////////////////////////////////////////! softmax module//! if in is idx0 -> out is idx0 and equal to 1//! if in is idx1 -> it is just one pool//! if in is idx2 -> it is just one pool//! if in is idx3 -> the last two dimensions are pools//! if in is idx4 -> the last two dimensions are pools//! if in is idx5 -> the last four dimensions are pools//! if in is idx6 -> the last four dimensions are poolsclass softmax: public module_1_1<state_idx, state_idx> {public:	double beta;	// <b> is the parameter beta in the softmax	// large <b> turns the softmax into a max	// <b> equal to 0 turns the softmax into 1/Nprivate:	void resize_nsame(state_idx *in, state_idx *out, int n);public:	softmax(double b);	~softmax() {	}	;	void fprop(state_idx *in, state_idx *out);	void bprop(state_idx *in, state_idx *out);	void bbprop(state_idx *in, state_idx *out);};////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////! Generic Jacobian tester//!class Jacobian_tester {public:	Jacobian_tester() {	}	;	~Jacobian_tester() {	}	;	// this function take any module_1_1 with a fprop et bprop implemented, and tests	// if the jacobian is correct (by pertubation) (on a state_idx with 3 dimensions)	void test(module_1_1<state_idx, state_idx> *module);};////////////////////////////////////////////////////////////////////! Generic BBprop tester tester//!class Bbprop_tester {public:	Bbprop_tester() {	}	;	~Bbprop_tester() {	}	;	// this function take any module_1_1 with a fprop et bbprop implemented, and tests	// if the Bbprop is correct (by pertubation) (on a state_idx with 3 dimensions)	void test(module_1_1<state_idx, state_idx> *module);};////////////////////////////////////////////////////////////////////! Generic Bprop tester tester//!class Bprop_tester {public:	Bprop_tester() {	}	;	~Bprop_tester() {	}	;	// this function take any module_1_1 with a fprop et bprop implemented, and tests	// if the bprop is correct (by pertubation) (on a state_idx with 3 dimensions)	void test(module_1_1<state_idx, state_idx> *module);};} // namespace ebl {#include "Ebm.hpp"#endif /* EBM_H_ */