gpmp::ml::PrimaryMLP Class Reference

Primary Multi-Layer Perceptron Class. More...

#include <mlp_net.hpp>

Public Member Functions
	PrimaryMLP (int64_t nl, int64_t npl[])
	~PrimaryMLP ()
int64_t	train (const char *fnames)
int64_t	test (const char *fname)
int64_t	evaluate ()
void	run (const char *fname, const int64_t &max_iters)

Public Attributes
long double	_Eta
long double	_Alpha
long double	_Gain
long double	_AvgTestError

Private Member Functions
void	rand_init ()
int64_t	rand_int (int64_t low, int64_t hi)
long double	rand_real (long double low, long double hi)
void	set_signal_in (long double *input)
void	get_signal_out (long double *output)
void	weights_save ()
void	weights_rand ()
void	weights_restore ()
void	weights_adjust ()
void	prop_signal ()
void	output_err (long double *target)
void	back_prop_err ()
void	simulate (long double *input, long double *output, long double *target, bool training)

Private Attributes
int64_t	num_layers
layer *	layer_ptr
long double	_MSE
	Mean Squared Error. More...
long double	_MAE
	Mean Absolute Error. More...

Detailed Description

Primary Multi-Layer Perceptron Class.

Definition at line 87 of file mlp_net.hpp.

Constructor & Destructor Documentation

PrimaryMLP()

gpmp::ml::PrimaryMLP::PrimaryMLP	(	int64_t	nl,
		int64_t	npl[]
	)

Definition at line 64 of file mlp_network.cpp.

    : num_layers(0), layer_ptr(0), _MSE(0.0), _MAE(0.0), _Eta(0.25),
      _Alpha(0.9), _Gain(1.0), _AvgTestError(0.0) {
    int64_t _LAYER, _NEURON;
 
    // create network layers
    num_layers = nl;
    layer_ptr = new layer[nl];
 
    // intialize the data of the created network layers
    for (_LAYER = 0; _LAYER < nl; _LAYER++) {
        // intialize values to neuron struct information
        layer_ptr[_LAYER].num_neurons = npl[_LAYER];
        layer_ptr[_LAYER].neuron_ptr = new neuron[npl[_LAYER]];
 
        // intialize date of the neurons of the created network layers
        for (_NEURON = 0; _NEURON < npl[_LAYER]; _NEURON++) {
            // initialize exit value
            layer_ptr[_LAYER].neuron_ptr[_NEURON].sortir = 1.0;
            // save the error
            layer_ptr[_LAYER].neuron_ptr[_NEURON].err = 0.0;
 
            // check if there is at least 1 layer
            if (_LAYER > 0) {
                /* initialize weight, last weight, and saved weight
                 * values to _LAYER - 1
                 */
                layer_ptr[_LAYER].neuron_ptr[_NEURON].wt =
                    new long double[npl[_LAYER - 1]];
 
                layer_ptr[_LAYER].neuron_ptr[_NEURON].wt_last =
                    new long double[npl[_LAYER - 1]];
 
                layer_ptr[_LAYER].neuron_ptr[_NEURON].wt_saved =
                    new long double[npl[_LAYER - 1]];
            }
            // otherwise
            else {
                /*
                 * initialize weight, last weight, and saved weight
                 * to NULL
                 */
                layer_ptr[_LAYER].neuron_ptr[_NEURON].wt = NULL;
                layer_ptr[_LAYER].neuron_ptr[_NEURON].wt_last = NULL;
                layer_ptr[_LAYER].neuron_ptr[_NEURON].wt_saved = NULL;
            }
        }
    }
}

References gpmp::ml::neuron::err, layer_ptr, gpmp::ml::layer::neuron_ptr, num_layers, gpmp::ml::layer::num_neurons, gpmp::ml::neuron::sortir, gpmp::ml::neuron::wt, gpmp::ml::neuron::wt_last, and gpmp::ml::neuron::wt_saved.

~PrimaryMLP()

gpmp::ml::PrimaryMLP::~PrimaryMLP

(

)

Definition at line 115 of file mlp_network.cpp.

                              {
    int64_t _LAYER, _NEURON;
 
    // TODO : thread the loops dealing with rudimentary computations
 
    for (_LAYER = 0; _LAYER < num_layers; _LAYER++) {
        if (layer_ptr[_LAYER].neuron_ptr) {
            for (_NEURON = 0; _NEURON < layer_ptr[_LAYER].num_neurons;
                 _NEURON++) {
                if (layer_ptr[_LAYER].neuron_ptr[_NEURON].wt) {
                    delete[] layer_ptr[_LAYER].neuron_ptr[_NEURON].wt;
                }
 
                if (layer_ptr[_LAYER].neuron_ptr[_NEURON].wt_last) {
                    delete[] layer_ptr[_LAYER].neuron_ptr[_NEURON].wt_last;
                }
 
                if (layer_ptr[_LAYER].neuron_ptr[_NEURON].wt_saved) {
                    delete[] layer_ptr[_LAYER].neuron_ptr[_NEURON].wt_saved;
                }
            }
        }
        delete[] layer_ptr[_LAYER].neuron_ptr;
    }
    delete[] layer_ptr;
}

Member Function Documentation

back_prop_err()

void gpmp::ml::PrimaryMLP::back_prop_err ( )

private

evaluate()

int64_t gpmp::ml::PrimaryMLP::evaluate

(

)

get_signal_out()

void gpmp::ml::PrimaryMLP::get_signal_out ( long double *  output )

private

output_err()

void gpmp::ml::PrimaryMLP::output_err ( long double *  target )

private

prop_signal()

void gpmp::ml::PrimaryMLP::prop_signal ( )

private

rand_init()

void gpmp::ml::PrimaryMLP::rand_init ( )

private

Definition at line 48 of file mlp_network.cpp.

                                 {
    srand(4711);
    // srand((uint64_t)time(NULL));
}

rand_int()

int64_t gpmp::ml::PrimaryMLP::rand_int ( int64_t  low, int64_t  hi  )

private

Definition at line 54 of file mlp_network.cpp.

                                                          {
    return rand() % (hi - low + 1) + low;
}

rand_real()

long double gpmp::ml::PrimaryMLP::rand_real ( long double  low, long double  hi  )

private

Definition at line 59 of file mlp_network.cpp.

                                                                       {
    return ((long double)rand() / RAND_MAX) * (hi - low) + low;
}

run()

void gpmp::ml::PrimaryMLP::run	(	const char *	fname,
		const int64_t &	max_iters
	)

set_signal_in()

void gpmp::ml::PrimaryMLP::set_signal_in ( long double *  input )

private

simulate()

void gpmp::ml::PrimaryMLP::simulate ( long double *  input, long double *  output, long double *  target, bool  training  )

private

test()

int64_t gpmp::ml::PrimaryMLP::test

(

const char *

fname

)

train()

int64_t gpmp::ml::PrimaryMLP::train

(

const char *

fnames

)

weights_adjust()

void gpmp::ml::PrimaryMLP::weights_adjust ( )

private

weights_rand()

void gpmp::ml::PrimaryMLP::weights_rand ( )

private

weights_restore()

void gpmp::ml::PrimaryMLP::weights_restore ( )

private

weights_save()

void gpmp::ml::PrimaryMLP::weights_save ( )

private

Member Data Documentation

_Alpha

long double gpmp::ml::PrimaryMLP::_Alpha

Definition at line 129 of file mlp_net.hpp.

_AvgTestError

long double gpmp::ml::PrimaryMLP::_AvgTestError

Definition at line 131 of file mlp_net.hpp.

_Eta

long double gpmp::ml::PrimaryMLP::_Eta

Definition at line 128 of file mlp_net.hpp.

_Gain

long double gpmp::ml::PrimaryMLP::_Gain

Definition at line 130 of file mlp_net.hpp.

_MAE

long double gpmp::ml::PrimaryMLP::_MAE

private

Mean Absolute Error.

Definition at line 104 of file mlp_net.hpp.

_MSE

long double gpmp::ml::PrimaryMLP::_MSE

private

Mean Squared Error.

Definition at line 100 of file mlp_net.hpp.

layer_ptr

layer* gpmp::ml::PrimaryMLP::layer_ptr

private

Definition at line 96 of file mlp_net.hpp.

Referenced by PrimaryMLP().

num_layers

int64_t gpmp::ml::PrimaryMLP::num_layers

private

Definition at line 95 of file mlp_net.hpp.

Referenced by PrimaryMLP().

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

• include/openGPMP/ml/mlp_net.hpp
• modules/ml/mlp_network.cpp