/************************************************************************************ This file is part of SnnsCLib, a fork of the kernel and parts of the gui of the Stuttgart Neural Network Simulator (SNNS), version 4.3. The file's original version is part of SNNS 4.3. It's source code can be found at http://www.ra.cs.uni-tuebingen.de/SNNS/ SNNS 4.3 is under the license LGPL v2. We note that source code files of SNNS 4.3 state as version "4.2". Base of this fork is SNNS 4.3 with a reverse-applied python patch (see http://developer.berlios.de/projects/snns-dev/). SnnsCLib was developed in 2010 by Christoph Bergmeir under supervision of José M. Benítez, both affiliated to DiCITS Lab, Sci2s group, DECSAI, University of Granada Changes done to the original code were performed with the objective to port it from C to C++ and to encapsulate all code in one class named SnnsCLib. Changes in header files mainly include: * removed all static keywords * moved initializations of variables to the constructor of SnnsCLib Changes in cpp code files mainly include: * changed file ending from .c to .cpp * removed all SNNS internal includes and only include SnnsCLib * static variables within functions were turned into member variables of SnnsCLib * function declarations were changed to method declarations, i.e. "SnnsCLib::.." was added * calls to the function table are now "C++-style", using the "this"-pointer License of SnnsCLib: This library is free software; you can redistribute it and/or modify it under the terms of the GNU Library General Public License as published by the Free Software Foundation; either version 2 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 Library General Public License for more details. You should have received a copy of the GNU Library General Public License along with this library; see the file COPYING.LIB. If not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ************************************************************************************/ /***************************************************************************** FILE : $Source: /projects/higgs1/SNNS/CVS/SNNS/kernel/sources/ext_typ.h,v $ SHORTNAME : SNNS VERSION : 4.2 PURPOSE : SNNS MasPar Kernel: Datatypes and Constants for Internal Usage NOTES : AUTHOR : Niels Mache DATE : 01.01.92 CHANGED BY : Sven Doering RCS VERSION : $Revision: 2.7 $ LAST CHANGE : $Date: 1998/02/25 15:26:11 $ Copyright (c) 1990-1995 SNNS Group, IPVR, Univ. Stuttgart, FRG Copyright (c) 1996-1998 SNNS Group, WSI, Univ. Tuebingen, FRG ******************************************************************************/ #ifndef KR_EXTERNAL_TYPES #define KR_EXTERNAL_TYPES class SnnsCLib ; typedef void (SnnsCLib::*FunctionPtr) (void *,...); typedef krui_err (SnnsCLib::*NetFunctionPtr) (void *,...); struct UnitValues { unsigned short selector; int lun, lln; FlintType out, act, i_act, bias; }; struct UnitDescriptor { int srcUnitLLN, destUnitLLN, srcUnitLUN, destUnitLUN; }; struct NetFuncParameters { int start_pattern_no, end_pattern_no, cycles, no_of_input_parameters, no_of_output_parameters; float input_parameters[NO_OF_LEARN_PARAMS], output_parameters[NO_OF_LEARN_PARAMS]; FunctionPtr function; /* pointer to the function */ }; /*################################################# GROUP: Type Definition of Function Table #################################################*/ /* Function table structure */ struct FuncTable { const char *func_name; /* name of the function */ unsigned short func_type, /* type of the function */ no_of_input_parameters, /* no. of input parameters used by this function */ /* (only for Update, Init and Learning functions) */ no_of_output_parameters; /* no. of ouput parameters returned by this function */ /* (only for Update and Learning functions) */ FunctionPtr function; /* pointer to the function */ #ifdef PARAGON_KERNEL bool parallelized; /* learning function parallelized for */ /* the Intel Paragon service process */ #endif }; #define FUNCTION_NAME_MAX_LEN 100 struct FuncInfoDescriptor { int number; char func_name[FUNCTION_NAME_MAX_LEN]; /* name of the function */ unsigned short func_type, /* type of the function */ no_of_input_parameters, /* no. of input parameters used by this function */ /* (only for Update, Init and Learning functions) */ no_of_output_parameters; /* no. of ouput parameters returned by this function */ /* (only for Update and Learning functions) */ FunctionPtr function; /* pointer to the function */ #ifdef PARAGON_KERNEL bool parallelized; /* learning function parallelized for */ /* the Intel Paragon */ #endif }; /*################################################# GROUP: Feedforward network descriptor #################################################*/ #ifdef MASPAR_KERNEL /* max. number of layers of a feedforward network in superscalar kernel mode */ #define FF_MAX_LAYERS 8 struct FFnetDescriptor { int no_of_layers; /* no. of layers of the network */ int no_of_weights; /* no. of weights in network */ struct { /* describes the layers of the network (layers[i] describes the units in layer i, layer 0 is the input layer) */ int no_of_units; /* no. of units in this layer */ struct { /* inputs[k] describes the input connections of the units in the layer i to units in layer k */ int no_of_inputs; /* stores the total no. of connections from units in layer k with units in layer i. */ FlintType *weight_array; /* pointer to the weight array containing the connection weights of connections from layer i to layer k. */ /* if the network has partial connected units: */ /* first input connection of unit i to unit in layer k */ /* gap count of the input connections of units in layer i with units in layer k */ } inputs[FF_MAX_LAYERS - 1]; } layers[FF_MAX_LAYERS]; }; struct MasParInfo { int no_of_PEs, memory_perPE, no_of_network_copies, no_of_used_PEs; float efficiency; }; #endif #endif