1. Tài liệu:

   1. Hướng dẫn sử dụng:

      - English

      - Vietnamese

      
      

   2. Mạng Nơ-ron Tự Tổ chức (Self-Organizing Map) và MLP – Ứng dụng thực tế:

      - English

   - Vietnamese

   
   

2. Sample Code:

   1. Example of using Spice-MLP Neural Network:

// MLP_Data_Modeling.cpp
// How to compiler
// g++ MLP_Data_Modeling.cpp mlpsom_source/spice_mlp_lib.cpp -lm -O3  -ffast-math -o s.x
// source /opt/intel/system_studio_2018/bin/compilervars.sh intel64
// icpc MLP_Data_Modeling.cpp mlpsom_source/spice_mlp_lib.cpp -lm -O3  -ffast-math -o s.x
// $ ./s.x 
#include <omp.h>
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
#include <math.h> 
#include <iostream>
#include <fstream>
#include <string>
#include <time.h>
#include <fstream>
#include <cstdio>

#include "mlpsom_include/spice_mlp_lib.h"
using namespace std;
using namespace SpiceNeuro;
 struct MlpTrainingSet
 {
     double **fInputs;                           //input data
     double **fOutputs;                          //output data
     char  (*strLabel)[255];                    //label of the dataset
     int nDataSet;                              //number of datasets
     int nInputs;                               //number of Inputs
     int nOutputs;                              //number of Outputs
 };

void InitMlpDataSet(MlpTrainingSet *DataSets, int nTrainingDataSet, int nInputs, int nOutputs);
void FreeDataSet(MlpTrainingSet *DataSets);
int findMaxIndex(double *data, const int nData);

// find max in output of a neuron
int findMaxIndex(double *data, const int nData)
{
   float max = data[0];
   int ind = 0;
   for (int i = 1; i < nData; i++)
   {
      if (max < data[i]){
         max = data[i];
         ind = i;
      }
   }
   return ind;
}

//initialize new dataset with number of datasets = nTrainingDataSet
void InitMlpDataSet(MlpTrainingSet *DataSets, int nTrainingDataSet, int nInputs, int nOutputs)
{

   DataSets->fInputs = new double*[nTrainingDataSet];
   DataSets->fOutputs = new double*[nTrainingDataSet];
   DataSets->strLabel = new char[nTrainingDataSet][255]; 
   for (int i = 0; i < nTrainingDataSet; i++)
   {
      DataSets->fInputs[i] = new double[nInputs];
      DataSets->fOutputs[i] = new double[nOutputs];
   }
   DataSets->nDataSet = nTrainingDataSet;
   DataSets->nInputs = nInputs;
   DataSets->nOutputs = nOutputs;
   return;
}
void FreeDataSet(MlpTrainingSet *DataSets)
{
   for (int i = 0; i < DataSets->nDataSet; i++)
   {
      delete [] DataSets->fInputs[i];
      delete [] DataSets->fOutputs[i];
   }
   delete [] DataSets->fInputs; 
   delete [] DataSets->fOutputs;
   delete [] DataSets->strLabel;
   return;
}
int CreateTrainingDataFromCsv(char *txtFilename, int nData, int nInputs, int nOutputs)
{
   printf("creating %s\n", txtFilename);
   MlpTrainingSet newData;
   InitMlpDataSet(&newData, nData, nInputs, nOutputs); // initialize 
   FILE *f = fopen(txtFilename, "r");
   char tmp[255];
   /*
   // data without header? comment it
   for (int i = 0; i < nInputs + nOutputs + 2; i++){
      fscanf(f, "%s", tmp);
   }
   */
   for (int j = 0; j < nData; j++)
   {
	  // data without ID, comment ID 
      // fscanf(f, "%s", tmp); // ID
      for (int i = 0; i < nInputs; i++){
         fscanf(f, "%s", tmp);
         newData.fInputs[j][i] = atof(tmp);
      }
      for (int i = 0; i < nOutputs; i++){
         fscanf(f, "%s", tmp);
         newData.fOutputs[j][i] = atof(tmp);
      }
      fscanf(f, "%s", tmp); // label
      sprintf(newData.strLabel[j], tmp); // assign label for the i-th dataset, please remember length of the label is 255
   }
   fclose(f);
   char fname[255];
   sprintf(fname, "%s_read.txt", txtFilename);
   f = fopen(fname, "w");
   for (int j = 0; j < nData; j++){
      for (int i = 0; i < nInputs; i++){
         fprintf(f, "%f ", newData.fInputs[j][i]);
      }
      for (int i = 0; i < nOutputs; i++){
         fprintf(f, "%f ", newData.fOutputs[j][i]);
      }
      fprintf(f, "%s \n", newData.strLabel[j]);
   }
   fclose(f);
   sprintf(fname, "%s.dat", txtFilename);
   // write data to binary file

   FILE *fout = fopen(fname, "wb");
   fwrite(&newData.nDataSet, sizeof(int), 1, fout);
   fwrite(&newData.nInputs, sizeof(int),  1, fout);
   fwrite(&newData.nOutputs, sizeof(int), 1, fout);
   for (int j = 0; j < newData.nDataSet; j++) 
   {
      fwrite(newData.fInputs[j], sizeof(double), newData.nInputs, fout);
      fwrite(newData.fOutputs[j], sizeof(double), newData.nOutputs, fout);
      fwrite(newData.strLabel[j], sizeof(char), 255, fout);
   };
   fclose(fout);

   FreeDataSet(&newData);

   return 0;
}

// create data from csv file
int main_CreateTrainingDataFromCsv()
{
   CreateTrainingDataFromCsv("TrainingData_V3-sized/Boxes.txt", 474, 36*54, 33);
   return 0;
}
char label[33][24] =
{
	"0-clear",
	"0-unclear",
	"1-clear",
	"1-unclear",
	"2-clear",
	"2-unclear",
	"7-clear",
	"7-unclear",
	"A-clear",
	"A-unclear",
	"B-clear",
	"BI-clear",
	"BI-unclear",
	"C-clear",
	"C-unclear",
	"D-clear",
	"D-unclear",
	"E-clear",
	"F-clear",
	"F-unclear",
	"G-clear",
	"G-unclear",
	"H-clear",
	"J-clear",
	"K-clear",
	"L-clear",
	"naka-clear",
	"naka-unclear",
	"RU-clear",
	"RU-unclear",
	"T-clear",
	"T-unclear",
	"Y-clear" };
    
int main()//_train_MLP()
{
	int nHiddens = 33;
	char dataFile[255], modelingFile[255], fClassInClass[255], fClassInClassBrief[255];

	char *folder = "data/";
    sprintf(fClassInClass, "%s/ClassInClass.txt", folder);
	sprintf(fClassInClassBrief, "%s/ClassInClassBrief.txt", folder);
	sprintf(dataFile, "%s/Boxes.txt.dat", folder);
	printf("datafile = %s\n", dataFile);
    SpiceMLP *MLP = new SpiceMLP("Example of MLP", 36 * 54, nHiddens,33); // initialize a MLP with 36*54 input, 33 hiddens and 33 outputs

	printf("MLP->DataSets.nInputs = %i\n", MLP->DataSets.nInputs);
	printf("MLP->DataSets.nOutputs = %i\n", MLP->DataSets.nOutputs);
	printf("MLP->GetLayers.GetInputNeurons  = %i\n", MLP->GetLayers.GetInputNeurons());
	printf("MLP->GetLayers.GetHiddenNeurons = %i\n", MLP->GetLayers.GetHiddenNeurons());
	printf("MLP->GetLayers.GetOutputNeurons = %i\n", MLP->GetLayers.GetOutputNeurons());


    MLP->InitDataSet(474, 36 * 54, 33);           // initialize data structure
    MLP->DataSplitOptions.iMiddle = 20;

      // set training parameters
      MLP->TrainingParam.bAdaptiveLearning = false;
      MLP->TrainingParam.bRandomInput      = true;
      MLP->TrainingParam.bEnableTesting    = true;
      MLP->TrainingParam.fLearningRate     = 0.0005f;
      MLP->TrainingParam.fMseRequired      = 0.0;
      // AF_Gaussian->AF_Sigmoid is ??
      MLP->TrainingParam.HiddenActivatedFunction = SpiceNeuro::AF_HyperTanh;//AF_HyperTanh, AF_Sigmoid
      MLP->TrainingParam.OutputActivatedFunction = SpiceNeuro::AF_Sigmoid;

	  printf("MLP->LoadTrainingData(dataFile) = %s\n", dataFile);
	  
      MLP->LoadTrainingData(dataFile);
	  printf("MLP->LoadTrainingData(dataFile) = %s OK\n", dataFile);
      //MLP->NormalizeInputData(Normalization_Linear, 0, 1);

      //asign split option, then do split data
      
      //MLP->DataSplitOptions.iRandomPercent = 70;
      //MLP->DataSplitOptions.SplitOptions   = SpiceNeuro::Split_Random;
      //MLP->DataSplitOptions.SplitOptions   = SpiceNeuro::Split_Middle;
      //MLP->SplitData();
      MLP->AssignTrainingData(SpiceNeuro::Use_AllDataSets); // Use_FirstSplittedDataSets, ot Use_AllDataSets

      //Train the MLP in some iterations
      double fTimeStart, fTimeEnd, fTimeLast=0;
      fTimeStart = MLP->fGetTimeNow();
      // train mlp in 1000 iterations
      printf("start to train\n");

      int nIteration =  1000;

      MLP->ResetWeights();
      double fFinal = MLP->DoTraining(nIteration);

      fTimeEnd = MLP->fGetTimeNow();
      printf("1000 iteration take %f (%f)sec, fFinal = '%f'\n", fTimeEnd - fTimeLast, fTimeEnd - fTimeStart, fFinal);
      fTimeLast = fTimeEnd;


      //Now, test the modeling data
      double *dOneSampleInputs   = new double[MLP->GetLayers.GetInputNeurons()];
      double *dOneSampleReponse = new double[MLP->GetLayers.GetOutputNeurons()];
      double **newResponse       = new double*[MLP->DataSets.nDataSet];
      for (int i = 0; i < MLP->DataSets.nDataSet; i++){
            newResponse[i] = new double[MLP->GetLayers.GetOutputNeurons()];
       }

      for (int j = 0; j < MLP->DataSets.nDataSet; j++)
      {
           //Get inputs of one dataset
           for (int i = 0; i < MLP->GetLayers.GetInputNeurons(); i++){
               dOneSampleInputs[i] = MLP->DataSets.fInputs[j][i];
           }

           MLP->ModelingOneDataSet(dOneSampleInputs, dOneSampleReponse);

           //Get modeling value of the one dataset
           for (int i = 0; i < MLP->GetLayers.GetOutputNeurons(); i++){
               newResponse[j][i] = dOneSampleReponse[i];
           }
      }
     //print modeling data to screen and file
	 sprintf(modelingFile, "%s/Boxes.modeling_%i-iter-%i-hiddens.csv", folder, nIteration, nHiddens);

     FILE *fc = fopen(modelingFile, "w");
	 fprintf(fc, "strLabel[j], label[indDesired], indDesired, indModeling, label[indModeling], newResponse[][indModeling]\n");
	 for (int j = 0; j < MLP->DataSets.nDataSet; j++)
	 {
		 int indDesired = findMaxIndex(MLP->DataSets.fOutputs[j], MLP->DataSets.nOutputs);
		 int indModeling = findMaxIndex(newResponse[j], MLP->DataSets.nOutputs);
		 fprintf(fc, "%s,%s,%i,%i,%s,%f,", MLP->DataSets.strLabel[j], label[indDesired], indDesired, indModeling, label[indModeling], newResponse[j][indModeling]);
		 for (int i = 0; i < MLP->GetLayers.GetOutputNeurons(); i++) {
			 fprintf(fc, "%f, ", newResponse[j][i]);
		 }
		 fprintf(fc, "\n");
	 }
     fclose(fc);
	 // save training & testing error
	 sprintf(modelingFile, "%s/Boxes-%i-iter-%i-hiddens-error.csv", folder, nIteration, nHiddens);
	 fc = fopen(modelingFile, "w");
	 for (int i = 0; i < nIteration; i++)
	 {
		 fprintf(fc, "%i,%f,%f\n", i, MLP->GetErrors.GetTrainingMSE(i), MLP->GetErrors.GetTestingMSE(i));
	 }
	 fclose(fc);
	 //*********************************
	 /*
	 // find the matched output
	 // search for the output
	 const int nOutputs = 10;
	 // match of output[op] on all classes
	 int nMatched_I_in_J[nOutputs][nOutputs];
	 int nSampleOfClass[nOutputs];
	 for (int j = 0; j < nOutputs; j++) {
		 nSampleOfClass[j] = 0;
		 for (int i = 0; i < nOutputs; i++) {
			 nMatched_I_in_J[j][i] = 0;
		 }
	 }
	 // calculate nMatched class in class
	 for (int op = 0; op < MLP->DataSets.nOutputs; op++)
	 {
		 for (int j = 0; j < MLP->DataSets.nDataSet; j++)
		 {
			 int indDesired = findMaxIndex(MLP->DataSets.fOutputs[j], MLP->DataSets.nOutputs);
			 if (op != indDesired)
				 continue;
			 int indModeling = findMaxIndex(newResponse[j], MLP->DataSets.nOutputs);
			 nSampleOfClass[op] += 1;
			 nMatched_I_in_J[indDesired][indModeling] += 1;
		 }
	 }

	 // save class in class to file
	 fc = fopen(fClassInClass, "w");
	 fprintf(fc, "Class In Class\n");
	 for (int class1 = 0; class1 < MLP->DataSets.nOutputs; class1++)
	 {
		 for (int class2 = 0; class2 < MLP->DataSets.nOutputs; class2++)
		 {
			 if (class1 == class2)
				 fprintf(fc, "Class %i on Class %i (correct)\n", class1, class2);
			 else
				 fprintf(fc, "Class %i on Class %i (mistake)\n", class1, class2);
			 for (int j = 0; j < MLP->DataSets.nDataSet; j++)
			 {
				 int indDesired = findMaxIndex(MLP->DataSets.fOutputs[j], MLP->DataSets.nOutputs);
				 if (indDesired != class1)
					 continue;
				 // class1 on class2
				 int indModeling = findMaxIndex(newResponse[j], MLP->DataSets.nOutputs);
				 if (indModeling != class2)
					 continue;
				 fprintf(fc, "%s\n", MLP->DataSets.strLabel[j]);
			 }
		 }
	 }
	 fclose(fc);

	 FILE *f = fopen(fClassInClassBrief, "w");
	 printf("modeling for %s\n", dataFile);
	 fprintf(f, "Class,Total,Correct,");
	 for (int j = 0; j < nOutputs; j++)
		fprintf(f, "on_Class_%i,", j);
	 fprintf(f, "\n");
	 for (int j = 0; j < nOutputs; j++) {
		 fprintf(f, "Class_%i,%i,", j, nSampleOfClass[j]);
		 fprintf(f, "%i,", nMatched_I_in_J[j][j]);
		 for (int i = 0; i < nOutputs; i++) {
			 fprintf(f, "%i,", nMatched_I_in_J[j][i]);
			 printf("%i,", nMatched_I_in_J[j][i]);
		 }
		 fprintf(f, "\n");
		 printf("\n");
	 }

	 fclose(f);
	 */

	 //*********************************
   
     delete [] dOneSampleInputs;
     delete [] dOneSampleReponse;
     for (int i = 0; i < MLP->DataSets.nDataSet; i++)
        delete [] newResponse[i];
     delete [] newResponse;
     delete MLP;
	 printf("Press Enter to exit\n");
	 getchar();
     return 0;
}

b. Activated Functions in Spice-MLP Neural Network:

AF_Linear,    //  1. y = a*x
AF_Identity,  //  2. y = x
AF_Sigmoid,   //  3. y = (1.0 / (1.0 + exp(-x)))
AF_HyperTanh, //  4. y = (3.432 / (1 + exp(-0.667 * x)) – 1.716)
AF_Tanh,      //  5. y = (exp(x) – exp(-x)) / (exp(x) + exp(-x))
AF_ArcTan,    //  6. y = atan(x)
AF_ArcCotan,  //  7. y = pi/2 – atan(x)
AF_ArcSinh,   //  8. y = log(x + sqr(x * x + 1))
AF_InvertAbs, //  9. y = x/(1+abs(x))
AF_Scah,      // 10. y = scah Hyperbolic Secant, ex. scah(x)
AF_Sin,       // 11. y = sin(x)
AF_Cos,       // 12. y = cos(x)
AF_ExpMinusX, // 13. y = exp (-x)
AF_ExpX,      // 14. y = exp (x)

AF_Cubic,             // 15. y = x*x*x
AF_Quadratic,         // 16. y = x*x
AF_SinXoverX,         // 17. y = sin(x)/x
AF_AtanXoverX,        // 18. y = atan(x)/x
AF_XoverEpx,          // 19. y = x/exp(x) == x *exp(-1.0 * x)
AF_Gaussian,          // 20. y = exp(-x*x)
AF_SinGaussian,       // 21. y = 3*sin(x)*exp(-x*x)
AF_CosGaussian,       // 22. y = cos(x)*exp(-x*x)
AF_LinearGaussian,    // 23. y = 2.5*x*exp(-x*x)
AF_QuadraticGaussian, // 24. y = x*x*exp(-x*x)
AF_CubicGaussian,     // 25. y = 3*x*x*x*exp(-x*x)
AF_XsinX,             // 26. y = x*sin(x)
AF_XcosX,             // 27. y = x*cos(x)