fann.h

一个功能强大的神经网络分析程序

/*
Fast Artificial Neural Network Library (fann)
Copyright (C) 2003 Steffen Nissen ([e-mail])

This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
*/

/* This file defines the user interface to the fann library.
   It is included from fixedfann.h, floatfann.h and doublefann.h and should
   NOT be included directly. If included directly it will react as if
   floatfann.h was included.
*/

#ifndef FANN_INCLUDE
/* just to allow for inclusion of fann.h in normal stuations where only floats are needed */
#ifdef FIXEDFANN
#include "fixedfann.h"
#else
#include "floatfann.h"
#endif /* FIXEDFANN  */

#else

#include "compat_time.h"
#include "fann_data.h"
#include "fann_internal.h"
#include "fann_activation.h"

#ifndef __fann_h__
#define __fann_h__

#ifdef __cplusplus
extern "C" {
#endif /* __cplusplus */
	
#ifndef NULL
#define NULL 0
#endif /* NULL */

/* ----- Macros used to define DLL external entrypoints ----- */
/*
 DLL Export, import and calling convention for Windows.
 Only defined for Microsoft VC++ FANN_EXTERNAL indicates
 that a function will be exported/imported from a dll
 FANN_API ensures that the DLL calling convention
 will be used for  a function regardless of the calling convention
 used when compiling.

 For a function to be exported from a DLL its prototype and
 declaration must be like this:
    FANN_EXTERNAL void FANN_API function(char *argument)

 The following ifdef block is a way of creating macros which
 make exporting from a DLL simple. All files within a DLL are
 compiled with the FANN_DLL_EXPORTS symbol defined on the
 command line. This symbol should not be defined on any project
 that uses this DLL. This way any other project whose source
 files include this file see FANN_EXTERNAL functions as being imported
 from a DLL, whereas a DLL sees symbols defined with this
 macro as being exported which makes calls more efficient.
 The __stdcall calling convention is used for functions in a
 windows DLL.

 The callback functions for fann_train_on_data_callback and
 fann_train_on_file_callback must be declared as FANN_API
 so the DLL and the application program both use the same
 calling convention. The callback functions must of this form:
     int FANN_API user_callback(unsigned int epochs, float error)
*/

/*
 The following sets the default for MSVC++ 2003 or later to use
 the fann dll's. To use a lib or fixedfann.c, floatfann.c or doublefann.c
 with those compilers FANN_NO_DLL has to be defined before
 including the fann headers.
 The default for previous MSVC compilers such as VC++ 6 is not
 to use dll's. To use dll's FANN_USE_DLL has to be defined before
 including the fann headers.
*/
#if (_MSC_VER > 1300)
#ifndef FANN_NO_DLL
#define FANN_USE_DLL
#endif /* FANN_USE_LIB */
#endif /* _MSC_VER */
#if defined(_MSC_VER) && (defined(FANN_USE_DLL) || defined(FANN_DLL_EXPORTS))
#ifdef FANN_DLL_EXPORTS
#define FANN_EXTERNAL __declspec(dllexport)
#else
#define FANN_EXTERNAL __declspec(dllimport)
#endif /* FANN_DLL_EXPORTS*/
#define FANN_API __stdcall
#else
#define FANN_EXTERNAL
#define FANN_API
#endif /* _MSC_VER */
/* ----- End of macros used to define DLL external entrypoints ----- */


/* ----- Implemented in fann.c Creation, running and destruction of ANNs ----- */
	
/* Constructs a backpropagation neural network, from an connection rate,
   a learning rate, the number of layers and the number of neurons in each
   of the layers.

   The connection rate controls how many connections there will be in the
   network. If the connection rate is set to 1, the network will be fully
   connected, but if it is set to 0.5 only half of the connections will be set.

   There will be a bias neuron in each layer (except the output layer),
   and this bias neuron will be connected to all neurons in the next layer.
   When running the network, the bias nodes always emits 1
*/
FANN_EXTERNAL struct fann * FANN_API fann_create(float connection_rate, float learning_rate,
	/* the number of layers, including the input and output layer */
	unsigned int num_layers,
	/* the number of neurons in each of the layers, starting with
	   the input layer and ending with the output layer */
	...);

/* Just like fann_create, but with an array of layer sizes
   instead of individual parameters.
*/
FANN_EXTERNAL struct fann * FANN_API fann_create_array(float connection_rate, float learning_rate,
	unsigned int num_layers, unsigned int * layers);

/* create a fully connected neural network with shortcut connections.
 */
FANN_EXTERNAL struct fann * FANN_API fann_create_shortcut(float learning_rate,
	unsigned int num_layers, /* the number of layers, including the input and output layer */
	...); /* the number of neurons in each of the layers, starting with the input layer and ending with the output layer */

/* create a neural network with shortcut connections.
 */
FANN_EXTERNAL struct fann * FANN_API fann_create_shortcut_array(float learning_rate, unsigned int num_layers, unsigned int * layers);	
	
/* Runs a input through the network, and returns the output.
 */
FANN_EXTERNAL fann_type* FANN_API fann_run(struct fann *ann, fann_type *input);

/* Destructs the entire network.
   Be sure to call this function after finished using the network.
 */
FANN_EXTERNAL void FANN_API fann_destroy(struct fann *ann);
	
/* Randomize weights (from the beginning the weights are random between -0.1 and 0.1)
 */
FANN_EXTERNAL void FANN_API fann_randomize_weights(struct fann *ann, fann_type min_weight, fann_type max_weight);

/* Initialize the weights using Widrow + Nguyen's algorithm.
*/
FANN_EXTERNAL void FANN_API fann_init_weights(struct fann *ann, struct fann_train_data * train_data);

/* print out which connections there are in the ann */
FANN_EXTERNAL void FANN_API fann_print_connections(struct fann *ann);	
	
/* ----- Implemented in fann_io.c Saving and loading of ANNs ----- */

/* Constructs a backpropagation neural network from a configuration file.
 */
FANN_EXTERNAL struct fann * FANN_API fann_create_from_file(const char *configuration_file);

/* Save the entire network to a configuration file.
 */
FANN_EXTERNAL void FANN_API fann_save(struct fann *ann, const char *configuration_file);

/* Saves the entire network to a configuration file.
   But it is saved in fixed point format no matter which
   format it is currently in.

   This is usefull for training a network in floating points,
   and then later executing it in fixed point.

   The function returns the bit position of the fix point, which
   can be used to find out how accurate the fixed point network will be.
   A high value indicates high precision, and a low value indicates low
   precision.

   A negative value indicates very low precision, and a very
   strong possibility for overflow.
   (the actual fix point will be set to 0, since a negative
   fix point does not make sence).

   Generally, a fix point lower than 6 is bad, and should be avoided.
   The best way to avoid this, is to have less connections to each neuron,
   or just less neurons in each layer.

   The fixed point use of this network is only intended for use on machines that
   have no floating point processor, like an iPAQ. On normal computers the floating
   point version is actually faster.
*/
FANN_EXTERNAL int FANN_API fann_save_to_fixed(struct fann *ann, const char *configuration_file);


/* ----- Implemented in fann_train.c Training and testing of ANNs ----- */

#ifndef FIXEDFANN
/* Train one iteration with a set of inputs, and a set of desired outputs.
 */
FANN_EXTERNAL void FANN_API fann_train(struct fann *ann, fann_type *input, fann_type *desired_output);

#endif /* NOT FIXEDFANN */

/* Test with a set of inputs, and a set of desired outputs.
   This operation updates the mean square error, but does not
   change the network in any way.
*/
FANN_EXTERNAL fann_type * FANN_API fann_test(struct fann *ann, fann_type *input, fann_type *desired_output);

/* Reads the mean square error from the network.
   (obsolete will be removed at some point, use fann_get_MSE)
 */
FANN_EXTERNAL float FANN_API fann_get_error(struct fann *ann);

/* Reads the mean square error from the network.
 */
FANN_EXTERNAL float FANN_API fann_get_MSE(struct fann *ann);

/* Resets the mean square error from the network.
   (obsolete will be removed at some point, use fann_reset_MSE)
 */
FANN_EXTERNAL void FANN_API fann_reset_error(struct fann *ann);

/* Resets the mean square error from the network.
 */
FANN_EXTERNAL void FANN_API fann_reset_MSE(struct fann *ann);

/* ----- Implemented in fann_train_data.c Data for training of ANNs ----- */

/* Reads a file that stores training data, in the format:
   num_train_data num_input num_output\n
   inputdata seperated by space\n
   outputdata seperated by space\n

   .
   .
   .
   
   inputdata seperated by space\n