/*************************************************************************************** * * WRITEPAD(r): Handwriting Recognition Engine (HWRE) and components. * Copyright (c) 2001-2016 PhatWare (r) Corp. All rights reserved. * * Licensing and other inquires: * Developer: Stan Miasnikov, et al. (c) PhatWare Corp. * * WRITEPAD HWRE is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * THE MATERIAL EMBODIED ON THIS SOFTWARE IS PROVIDED TO YOU "AS-IS" * AND WITHOUT WARRANTY OF ANY KIND, EXPRESS, IMPLIED OR OTHERWISE, * INCLUDING WITHOUT LIMITATION, ANY WARRANTY OF MERCHANTABILITY OR * FITNESS FOR A PARTICULAR PURPOSE. IN NO EVENT SHALL PHATWARE CORP. * BE LIABLE TO YOU OR ANYONE ELSE FOR ANY DIRECT, SPECIAL, INCIDENTAL, * INDIRECT OR CONSEQUENTIAL DAMAGES OF ANY KIND, OR ANY DAMAGES WHATSOEVER, * INCLUDING WITHOUT LIMITATION, LOSS OF PROFIT, LOSS OF USE, SAVINGS * OR REVENUE, OR THE CLAIMS OF THIRD PARTIES, WHETHER OR NOT PHATWARE CORP. * HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH LOSS, HOWEVER CAUSED AND ON * ANY THEORY OF LIABILITY, ARISING OUT OF OR IN CONNECTION WITH THE * POSSESSION, USE OR PERFORMANCE OF THIS SOFTWARE. * See the GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with WritePad. If not, see . * **************************************************************************************/ #ifndef MAIN_HEADER_INCLUDED #define MAIN_HEADER_INCLUDED // -------------------- Defines ------------------------------------------------ #define ver_h "MLP analysis lab v 1.03" #define SNN_DATA_VER "SNN data file Ver. 1.7." #define MAIN_FILENAME_LEN 128 #define MAIN_SIZE_SAMPLE 16 #define MAIN_NUMSYMINSAMP 8 #define MAIN_MAX_STEP 0.2 #define MAIN_SIZE_BLOCK (64*1024) #define MAIN_MAX_BLOCKS 1000 #define MAIN_MLP_INI_NAME "mlp.ini" #define MAIN_DMP_NET_NAME "net.dmp" #define DEF_PARAM_NAMES { \ "Step_control_mode ", \ "Step_coeff ", \ "Step_decay_coeff ", \ "Step_min ", \ "Step_layer_coeff_1 ", \ "Step_layer_coeff_2 ", \ "Step_layer_coeff_3 ", \ "Step_layer_coeff_4 ", \ "Step_coeff_1 ", \ "Step_coeff_2 ", \ "Step_coeff_3 ", \ "Step_coeff_4 ", \ "Logistic_coeff ", \ "Logistic_decay_coeff ", \ "Logistic_max ", \ "Weigth_init_coeff ", \ "Inert_coeff ", \ "Zero_ballance_coeff ", \ "Epoch_size ", \ "Epoch_size_decay ", \ "Epoch_size_max ", \ "Testing_rate ", \ "Learn_data_file ", \ "Test_data_file ", \ "OSym_bush_size ", \ "" \ } // -------------------- Structures and classes --------------------------------- typedef unsigned char uchar; typedef struct { uchar flags; uchar nsym; uchar nvs[MAIN_NUMSYMINSAMP/2]; uchar syms[MAIN_NUMSYMINSAMP]; uchar data[MAIN_SIZE_SAMPLE]; } sample_type, * p_sample_type; typedef struct { int step_mode; float step_size; float step_decay_coeff; float step_min; float step_layer_coeffs[MLP_NET_NUMLAYERS]; float step_coeffs[MLP_NET_NUMLAYERS]; float logistic_coeff; float logistic_decay_coeff; float logistic_max; float weigth_init_coeff; float inert_coeff; float zero_ballance_coeff; float epoch_size; float epoch_size_decay; float epoch_size_max; int testing_rate; char learn_data_file[128]; char test_data_file[128]; uchar symco[MLP_NET_NUMOUTPUTS]; uchar bushes[MLP_NET_NUMOUTPUTS]; } params_type, * p_params_type; class samples_storage_class { int num_blocks; int num_in_block; int max_in_block; p_sample_type blocks[MAIN_MAX_BLOCKS]; public: int num_samples; int num_antisamp; int counter; samples_storage_class(); ~samples_storage_class(); AddSample(p_sample_type sample); GetSample(int num, p_sample_type * pp_sample); ResetSampleFlags(uchar flags); SetSampleFlags(int num, uchar flags); GetSampleFlags(int num); friend ostream& operator << (ostream& os, samples_storage_class *storage); }; typedef struct { uchar rsym; float w; int ns; int nv; int points[2][32][2]; uchar syms[MAIN_NUMSYMINSAMP]; } view_dialog_type, * p_view_dialog_type; // -------------------- Function definitions ----------------------------------- int MainLearnNet(int mode); int MainTestNet(int mode); int MainAnalyzeData(int mode); int MainDumpNetImage(int mode); int AskForParams(p_params_type pm, float * shake); int InitParams(int mode, char * fname, p_params_type pm); int PS_Yield(void); int LoadSamples(char * data_name, samples_storage_class * samples); float LearnNet(mlp_net_class * net, samples_storage_class * samples, p_params_type mp); int CreateDesiredOutputs(uchar corr_sym, uchar * syms, uchar * symco, float * desired_outputs); float TestNet(mlp_net_class * net, samples_storage_class * samples, char * log_name, int mode, float lfc); int AdjustStepCoeffs(mlp_net_class * net, p_params_type pm); int LogSamplesStat(samples_storage_class * samples, char * log_name); int WriteBackSomeSamples(samples_storage_class * samples, char * log_name); int DumpNetImage(char * dat_name, mlp_net_class * net); int GetInputs(int num, uchar *syms, uchar * inputs, samples_storage_class * samples); int GetInputsAtRandom(int flags, uchar *what, uchar * inps, uchar * syms, int * ns, samples_storage_class * samples); int GetInputsSequencial(int flags, uchar *what, uchar * inps, uchar * syms, int * ns, samples_storage_class * samples); int RockCoeffs(int mode, uchar * inps); int DrawCoeff(uchar rsym, float w, int num, samples_storage_class * samples, uchar * coeff); #endif // MAIN_HEADER_INCLUDED /* **************************************************************************** */ /* * End OF all * */ /* **************************************************************************** */ //