Version: 9.15.0
Functions
Methods dedicated to file export

Functions

void PMMLlib::PMMLlib::fillVectorsForExport (int nInput, int nOutput, int nHidden, int normType, std::vector< double > &minInput, std::vector< double > &maxInput, std::vector< double > &minOutput, std::vector< double > &maxOutput, std::vector< double > &valW)
 Specific to NeuralNetwork. More...
 
PMMLLIB_EXPORT void PMMLlib::PMMLlib::ExportCpp (std::string file, std::string functionName, std::string header)
 
PMMLLIB_EXPORT void PMMLlib::PMMLlib::ExportFortran (std::string file, std::string functionName, std::string header)
 
PMMLLIB_EXPORT void PMMLlib::PMMLlib::ExportPython (std::string file, std::string functionName, std::string header)
 
PMMLLIB_EXPORT std::string PMMLlib::PMMLlib::ExportPyStr (std::string functionName, std::string header)
 
void PMMLlib::PMMLlib::ExportNeuralNetworkCpp (std::string file, std::string functionName, std::string header)
 Specific to NeuralNetwork. More...
 
void PMMLlib::PMMLlib::ExportNeuralNetworkFortran (std::string file, std::string functionName, std::string header)
 Specific to NeuralNetwork. More...
 
void PMMLlib::PMMLlib::ExportNeuralNetworkPython (std::string file, std::string functionName, std::string header)
 Specific to NeuralNetwork. More...
 
std::string PMMLlib::PMMLlib::ExportNeuralNetworkPyStr (std::string functionName, std::string header)
 Specific to NeuralNetwork. More...
 
void PMMLlib::PMMLlib::ExportLinearRegressionCpp (std::string, std::string, std::string)
 Specific to RegressionModel
More...
 
void PMMLlib::PMMLlib::ExportLinearRegressionFortran (std::string, std::string, std::string)
 Specific to Regression. More...
 
void PMMLlib::PMMLlib::ExportLinearRegressionPython (std::string, std::string, std::string)
 Specific to Regression. More...
 
std::string PMMLlib::PMMLlib::ExportLinearRegressionPyStr (std::string functionName, std::string header)
 Specific to Regression. More...
 

Detailed Description

Methods dedicated to file export

Function Documentation

◆ ExportCpp()

void PMMLlib::PMMLlib::ExportCpp ( std::string  file,
std::string  functionName,
std::string  header 
)

Export the current model as a function in a Cpp file.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function

Definition at line 596 of file PMMLlib.cxx.

599 {
600  if ( _currentModelType == kANN )
601  ExportNeuralNetworkCpp(file,functionName, header);
602  else if ( _currentModelType == kLR )
603  {
604  ExportLinearRegressionCpp(file, functionName, header);
605  }
606  else
607  throw string("ExportCpp : PMML type not handled.");
608 }
PMMLlib::PMMLlib::_currentModelType
PMMLType _currentModelType
Type of the current model.
Definition: PMMLlib.hxx:83
PMMLlib::PMMLlib::ExportLinearRegressionCpp
void ExportLinearRegressionCpp(std::string, std::string, std::string)
Specific to RegressionModel
Definition: PMMLlib.cxx:2640
PMMLlib::PMMLlib::ExportNeuralNetworkCpp
void ExportNeuralNetworkCpp(std::string file, std::string functionName, std::string header)
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:1695
PMMLlib::kANN
@ kANN
Definition: PMMLlib.hxx:54

References PMMLlib::kANN, and PMMLlib::kLR.

◆ ExportFortran()

void PMMLlib::PMMLlib::ExportFortran ( std::string  file,
std::string  functionName,
std::string  header 
)

Export the current model as a function in a Fortran file.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function

Definition at line 616 of file PMMLlib.cxx.

619 {
620  if ( _currentModelType == kANN )
621  ExportNeuralNetworkFortran(file,functionName, header);
622  else if ( _currentModelType == kLR )
623  ExportLinearRegressionFortran(file,functionName, header);
624  else
625  throw string("ExportFortran : PMML type not handled.");
626 }
PMMLlib::PMMLlib::ExportLinearRegressionFortran
void ExportLinearRegressionFortran(std::string, std::string, std::string)
Specific to Regression.
Definition: PMMLlib.cxx:2705
PMMLlib::PMMLlib::ExportNeuralNetworkFortran
void ExportNeuralNetworkFortran(std::string file, std::string functionName, std::string header)
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:1865

References PMMLlib::kANN, and PMMLlib::kLR.

◆ ExportLinearRegressionCpp()

void PMMLlib::PMMLlib::ExportLinearRegressionCpp ( std::string  file,
std::string  functionName,
std::string  header 
)
private

Specific to RegressionModel

Export the current model as a NeuralNetwork function in a Cpp file.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function

Definition at line 2640 of file PMMLlib.cxx.

2643 {
2644  CheckRegression();
2645 
2646  // Write the file
2647  ofstream exportfile(file.c_str());
2648 
2649  exportfile << "void " << functionName <<"(double *param, double *res)" << endl;
2650  exportfile << "{" << endl;
2651  // header
2652  exportfile << " ////////////////////////////// " << endl;
2653  exportfile << " //" << endl;
2654  // insert comments in header
2655  header = " // " + header;
2656  size_t pos = 0;
2657  while ((pos = header.find("\n", pos)) != std::string::npos)
2658  {
2659  header.replace(pos, 1, "\n //");
2660  pos += 5;
2661  }
2662  exportfile << header << endl;
2663  exportfile << " //" << endl;
2664  exportfile << " ////////////////////////////// " << endl << endl;
2665 
2666  double intercept = 0.0;
2667  if ( HasIntercept() )
2668  {
2669  exportfile << " // Intercept"<< endl;
2670  intercept = GetRegressionTableIntercept();
2671  }
2672  else
2673  exportfile << " // No Intercept"<< endl;
2674  exportfile << " double y = " << intercept << ";";
2675  exportfile << endl << endl;
2676 
2677  int nPred = GetNumericPredictorNb();
2678  for (int i=0; i<nPred; i++)
2679  {
2680  exportfile << " // Attribute : " << GetNumericPredictorName(i) << endl;
2681  exportfile << " y += param["<<i<<"]*" << GetNumericPredictorCoefficient(i) << ";";
2682  exportfile << endl << endl;
2683  }
2684  nPred = GetPredictorTermNb();
2685  for (int i=0; i<nPred; i++)
2686  {
2687  exportfile << " // Attribute : " << GetPredictorTermName(i) << endl;
2688  exportfile << " y += param["<<(i+nPred)<<"]*" << GetPredictorTermCoefficient(i) << ";";
2689  exportfile << endl << endl;
2690  }
2691 
2692  exportfile << " // Return the value"<< endl;
2693  exportfile << " res[0] = y;" << endl;
2694  exportfile << "}" << endl;
2695  exportfile.close();
2696 }
PMMLlib::PMMLlib::GetPredictorTermCoefficient
PMMLLIB_EXPORT double GetPredictorTermCoefficient(int pred_term_index)
(The coefficient is the value of property "coefficient")
Definition: PMMLlib.cxx:2534
PMMLlib::PMMLlib::GetRegressionTableIntercept
PMMLLIB_EXPORT double GetRegressionTableIntercept()
Specific to RegressionModel
Definition: PMMLlib.cxx:2376
PMMLlib::PMMLlib::GetNumericPredictorName
PMMLLIB_EXPORT std::string GetNumericPredictorName(int num_pred_index)
Specific to RegressionModel
Definition: PMMLlib.cxx:2438
PMMLlib::PMMLlib::GetNumericPredictorNb
PMMLLIB_EXPORT int GetNumericPredictorNb()
Specific to RegressionModel
Definition: PMMLlib.cxx:2392
PMMLlib::PMMLlib::GetPredictorTermNb
PMMLLIB_EXPORT int GetPredictorTermNb()
Specific to RegressionModel
Definition: PMMLlib.cxx:2415
PMMLlib::PMMLlib::HasIntercept
PMMLLIB_EXPORT bool HasIntercept()
Specific to RegressionModel
Definition: PMMLlib.cxx:2351
PMMLlib::PMMLlib::GetPredictorTermName
PMMLLIB_EXPORT std::string GetPredictorTermName(int num_pred_index)
Specific to RegressionModel
Definition: PMMLlib.cxx:2467
PMMLlib::PMMLlib::CheckRegression
void CheckRegression()
Called in all methods specific to the RegressionModel model.
Definition: PMMLlib.cxx:2225
PMMLlib::PMMLlib::GetNumericPredictorCoefficient
PMMLLIB_EXPORT double GetNumericPredictorCoefficient(int num_pred_index)
(The coefficient is the value of property "coefficient")
Definition: PMMLlib.cxx:2503

References yacsorb.CORBAEngineTest::i.

◆ ExportLinearRegressionFortran()

void PMMLlib::PMMLlib::ExportLinearRegressionFortran ( std::string  file,
std::string  functionName,
std::string  header 
)
private

Specific to Regression.

Export the current model as a NeuralNetwork function in a Fortran file.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function

Definition at line 2705 of file PMMLlib.cxx.

2708 {
2709  CheckRegression();
2710 
2711  int nNumPred = GetNumericPredictorNb();
2712  int nPredTerm = GetPredictorTermNb();
2713  vector<string>strParam(nNumPred+nPredTerm);
2714  for(int i=0; i<(nNumPred+nPredTerm); i++)
2715  {
2716  strParam[i] = "P" + NumberToString(i) ;
2717  }
2718 
2719  // Write the file
2720  ofstream exportfile(file.c_str());
2721 
2722  exportfile << " SUBROUTINE " << functionName <<"(";
2723  for(int i=0; i<(nNumPred+nPredTerm); i++)
2724  {
2725  exportfile << strParam[i] << ", ";
2726  }
2727  exportfile << "RES)" << endl;
2728 
2729  // header
2730  exportfile << "C --- *********************************************" << endl;
2731  exportfile << "C --- " << endl;
2732  // insert comments in header
2733  header = "C --- " + header;
2734  size_t pos = 0;
2735  while ((pos = header.find("\n", pos)) != std::string::npos)
2736  {
2737  header.replace(pos, 1, "\nC --- ");
2738  pos += 5;
2739  }
2740  exportfile << header << endl;
2741  exportfile << "C --- " << endl;
2742  exportfile << "C --- *********************************************" << endl << endl;
2743 
2744  exportfile << " IMPLICIT DOUBLE PRECISION (P)" << endl;
2745  exportfile << " DOUBLE PRECISION RES" << endl;
2746  exportfile << " DOUBLE PRECISION Y" << endl;
2747  exportfile << endl;
2748 
2749  double intercept = 0.0;
2750  if ( HasIntercept() )
2751  {
2752  exportfile << "C --- Intercept"<< endl;
2753  intercept = GetRegressionTableIntercept();
2754  }
2755  else
2756  exportfile << "C --- No Intercept"<< endl;
2757  exportfile << " Y = " << intercept << ";";
2758  exportfile << endl << endl;
2759 
2760  for (int i=0; i<nNumPred; i++)
2761  {
2762  exportfile << "C --- Attribute : " << GetNumericPredictorName(i) << endl;
2763  exportfile << " Y += P["<<i<<"]*" << GetNumericPredictorCoefficient(i) << ";";
2764  exportfile << endl << endl;
2765  }
2766 
2767  for (int i=0; i<nPredTerm; i++)
2768  {
2769  exportfile << "C --- Attribute : " << GetPredictorTermName(i) << endl;
2770  exportfile << " Y += P["<<(i+nNumPred)<<"]*" << GetPredictorTermCoefficient(i) << ";";
2771  exportfile << endl << endl;
2772  }
2773 
2774  exportfile << "C --- Return the value"<< endl;
2775  exportfile << " RES = Y " << endl;
2776  exportfile << " RETURN" << endl;
2777  exportfile << " END" << endl;
2778  exportfile.close();
2779 }
PMMLlib::NumberToString
std::string NumberToString(T Number)
Definition: PMMLlib.hxx:39

References yacsorb.CORBAEngineTest::i, and PMMLlib::NumberToString().

◆ ExportLinearRegressionPyStr()

std::string PMMLlib::PMMLlib::ExportLinearRegressionPyStr ( std::string  functionName,
std::string  header 
)
private

Specific to Regression.

Export the current model as a NeuralNetwork function in a Python string.

Parameters
functionNameName of the function
headerHeader of the function

Definition at line 2805 of file PMMLlib.cxx.

2807 {
2808  CheckRegression();
2809 
2810  ostringstream out;
2811 
2812  // Shebang et imports
2813  out << "#!/usr/bin/env python3" << endl;
2814  out << "# -*- coding: utf-8 -*-" << endl;
2815  out << endl;
2816 
2817  // Function
2818  out << "def " << functionName <<"(param):" << endl;
2819  out << endl;
2820 
2821  // header
2822  out << " ############################## " << endl;
2823  out << " # " << endl;
2824  // insert comments in header
2825  header = " # " + header;
2826  size_t pos = 0;
2827  while ((pos = header.find("\n", pos)) != std::string::npos)
2828  {
2829  header.replace(pos, 1, "\n #");
2830  pos += 5;
2831  }
2832  out << header << endl;
2833  out << " # " << endl;
2834  out << " ############################## " << endl << endl;
2835 
2836  double intercept = 0.0;
2837  if ( HasIntercept() )
2838  {
2839  out << " # Intercept"<< endl;
2840  intercept = GetRegressionTableIntercept();
2841  }
2842  else
2843  out << " # No Intercept"<< endl;
2844  out << " y = " << intercept << ";";
2845  out << endl << endl;
2846 
2847  int nPred = GetNumericPredictorNb();
2848  for (int i=0; i<nPred; i++)
2849  {
2850  out << " # Attribute : " << GetNumericPredictorName(i) << endl;
2851  out << " y += param["<<i<<"]*" << GetNumericPredictorCoefficient(i) << ";";
2852  out << endl << endl;
2853  }
2854  nPred = GetPredictorTermNb();
2855  for (int i=0; i<nPred; i++)
2856  {
2857  out << " # Attribute : " << GetPredictorTermName(i) << endl;
2858  out << " y += param["<<(i+nPred)<<"]*" << GetPredictorTermCoefficient(i) << ";";
2859  out << endl << endl;
2860  }
2861 
2862  out << " # Return the value"<< endl;
2863  out << " return [y];" << endl;
2864 
2865  return out.str() ;
2866 }

References yacsorb.CORBAEngineTest::i.

◆ ExportLinearRegressionPython()

void PMMLlib::PMMLlib::ExportLinearRegressionPython ( std::string  file,
std::string  functionName,
std::string  header 
)
private

Specific to Regression.

Export the current model as a NeuralNetwork function in a Python file.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function

Definition at line 2788 of file PMMLlib.cxx.

2791 {
2792  string str(ExportLinearRegressionPyStr(functionName, header));
2793  // Write the file
2794  ofstream exportfile(file.c_str());
2795  exportfile << str;
2796  exportfile.close();
2797 }
PMMLlib::PMMLlib::ExportLinearRegressionPyStr
std::string ExportLinearRegressionPyStr(std::string functionName, std::string header)
Specific to Regression.
Definition: PMMLlib.cxx:2805

◆ ExportNeuralNetworkCpp()

void PMMLlib::PMMLlib::ExportNeuralNetworkCpp ( std::string  file,
std::string  functionName,
std::string  header 
)
private

Specific to NeuralNetwork.

Export the current model as a NeuralNetwork function in a Cpp file.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function

Definition at line 1695 of file PMMLlib.cxx.

1698 {
1699  CheckNeuralNetwork();
1700 
1701  // Get the different values required
1702  int nInput = GetNbInputs();
1703  int nOutput = GetNbOutputs();
1704  int nHidden = GetNbNeuronsAtLayer(0);
1705  int nNeurons = nInput+nOutput+nHidden;
1706  int nWeights = nHidden*(nInput+nOutput+1)+nOutput;
1707  int normType = GetNormalizationType();
1708  // Build min/max input/output vectors
1709  vector<double> minInput(nInput);
1710  vector<double> maxInput(nInput);
1711  vector<double> minOutput(nOutput);
1712  vector<double> maxOutput(nOutput);
1713  vector<double> valW(nWeights);
1714  fillVectorsForExport(nInput,nOutput,nHidden,normType,minInput,maxInput,minOutput,maxOutput,valW);
1715  // Write the file
1716  ofstream sourcefile(file.c_str());
1717  // ActivationFunction
1718  if( normType==0 )
1719  { // kMinusOneOne
1720  sourcefile << "#define ActivationFunction(sum) ( tanh(sum) )" << endl;
1721  }
1722  else
1723  { // kCR, kZeroOne
1724  sourcefile << "#define ActivationFunction(sum) ( 1.0 / ( 1.0 + exp( -1.0 * sum )) )" << endl;
1725  }
1726  //
1727  sourcefile << "void " << functionName <<"(double *param, double *res)" << endl;
1728  sourcefile << "{" << endl;
1729  // header
1730  sourcefile << " ////////////////////////////// " << endl;
1731  sourcefile << " //" << endl;
1732  // insert comments in header
1733  header = " // " + header;
1734  size_t pos = 0;
1735  while ((pos = header.find("\n", pos)) != std::string::npos)
1736  {
1737  header.replace(pos, 1, "\n //");
1738  pos += 5;
1739  }
1740  sourcefile << header << endl;
1741  sourcefile << " //" << endl;
1742  sourcefile << " ////////////////////////////// " << endl;
1743  sourcefile << endl;
1744  sourcefile << " int nInput = " << nInput << ";" << endl;
1745  sourcefile << " int nOutput = " << nOutput << ";" << endl;
1746  // sourcefile << " int nWeights = " << _nWeight << ";" << endl;
1747  sourcefile << " int nHidden = " << nHidden << ";" << endl;
1748  sourcefile << " const int nNeurones = " << nNeurons << ";" << endl;
1749  sourcefile << " double " << functionName << "_act[nNeurones];" << endl;
1750  sourcefile << endl;
1751  sourcefile << " // --- Preprocessing of the inputs and outputs" << endl;
1752  sourcefile << " double " << functionName << "_minInput[] = {" << endl << " ";
1753  for(int i=0 ; i<nInput ; i++)
1754  {
1755  sourcefile << minInput[i] << ", ";
1756  if( (i+1)%5==0 )
1757  sourcefile << "\n ";
1758  }
1759  if( nInput%5 != 0 )
1760  sourcefile << endl;
1761  sourcefile << " };" << endl;
1762  //
1763  sourcefile << " double " << functionName << "_minOutput[] = {" << endl << " ";
1764  sourcefile << minOutput[0] << ", ";
1765  sourcefile << " };" << endl;
1766  //
1767  sourcefile << " double " << functionName << "_maxInput[] = {" << endl << " ";
1768  for(int i=0 ; i<nInput ; i++)
1769  {
1770  sourcefile << maxInput[i] << ", ";
1771  if( (i+1)%5==0 )
1772  sourcefile << "\n ";
1773  }
1774  if( nInput%5 != 0 )
1775  sourcefile << endl;
1776  sourcefile << " };" << endl;
1777  //
1778  sourcefile << " double " << functionName << "_maxOutput[] = {" << endl << " ";
1779  sourcefile << maxOutput[0] << ", ";
1780  sourcefile << " };" << endl;
1781  // Weights vector
1782  sourcefile << endl;
1783  sourcefile << " // --- Values of the weights" << endl;
1784  sourcefile << " double " << functionName << "_valW[] = {" << endl << " ";
1785  for(int i=0 ; i<nWeights ; i++)
1786  {
1787  sourcefile << valW[i] << ", ";
1788  if ( (i+1)%5 == 0 )
1789  sourcefile << endl << " ";
1790  }
1791  sourcefile << endl << " };"<<endl;
1792  //
1793  sourcefile << " // --- Constants";
1794  sourcefile << endl;
1795  sourcefile << " int indNeurone = 0;"<<endl;
1796  sourcefile << " int CrtW;"<<endl;
1797  sourcefile << " double sum;"<<endl;
1798 
1799  // couche entree
1800  sourcefile << endl;
1801  sourcefile << " // --- Input Layers"<<endl;
1802  sourcefile << " for(int i = 0; i < nInput; i++) {"<<endl;
1803  if( normType==0 )
1804  { // kMinusOneOne
1805  sourcefile << " " << functionName << "_act[indNeurone++] = 2.0 * ( param[i] - "
1806  << functionName << "_minInput[i] ) / ( " << functionName << "_maxInput[i] - "
1807  << functionName << "_minInput[i] ) - 1.0;"<<endl;
1808  }
1809  else
1810  { // kCR, kZeroOne
1811  sourcefile << " " << functionName << "_act[indNeurone++] = ( param[i] - "
1812  << functionName << "_minInput[i] ) / " << functionName << "_maxInput[i];"
1813  << endl;
1814  }
1815  sourcefile << " }"<<endl;
1816 
1817 
1818  // couche cachee
1819  sourcefile << endl;
1820  sourcefile << " // --- Hidden Layers"<<endl;
1821  sourcefile << " for (int member = 0; member < nHidden; member++) {"<<endl;
1822  sourcefile << " int CrtW = member * ( nInput + 2) + 2;" << endl;
1823  sourcefile << " sum = " << functionName << "_valW[CrtW++];" << endl;
1824  sourcefile << " for (int source = 0; source < nInput; source++) {" << endl;
1825  sourcefile << " sum += " << functionName << "_act[source] * " << functionName << "_valW[CrtW++];" << endl;
1826  sourcefile << " }" << endl;
1827  sourcefile << " " << functionName << "_act[indNeurone++] = ActivationFunction(sum);" << endl;
1828  sourcefile << " }"<<endl;
1829  // couche sortie
1830  sourcefile << endl;
1831  sourcefile << " // --- Output"<<endl;
1832  sourcefile << " for (int member = 0; member < nOutput; member++) {"<<endl;
1833  sourcefile << " sum = " << functionName << "_valW[0];"<<endl;
1834  sourcefile << " for (int source = 0; source < nHidden; source++) {"<<endl;
1835  sourcefile << " CrtW = source * ( nInput + 2) + 1;"<<endl;
1836  sourcefile << " sum += " << functionName << "_act[nInput+source] * " << functionName << "_valW[CrtW];"<<endl;
1837  sourcefile << " }"<<endl;
1838  sourcefile << " " << functionName << "_act[indNeurone++] = sum;"<<endl;
1839  if( normType==0 )
1840  { // kMinusOneOne
1841  sourcefile << " res[member] = " << functionName
1842  << "_minOutput[member] + 0.5 * ( " << functionName
1843  << "_maxOutput[member] - " << functionName
1844  << "_minOutput[member] ) * ( sum + 1.0);" << endl;
1845  }
1846  else
1847  { // kCR, kZeroOne
1848  sourcefile << " res[member] = " << functionName
1849  << "_minOutput[member] + " << functionName
1850  << "_maxOutput[member] * sum;" << endl;
1851  }
1852  sourcefile << " }"<<endl;
1853  //
1854  sourcefile << "}" << endl;
1855  sourcefile.close();
1856 }
PMMLlib::PMMLlib::GetNbOutputs
PMMLLIB_EXPORT int GetNbOutputs()
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:871
PMMLlib::PMMLlib::GetNbNeuronsAtLayer
PMMLLIB_EXPORT int GetNbNeuronsAtLayer(int layer_index)
Definition: PMMLlib.cxx:1170
PMMLlib::PMMLlib::GetNormalizationType
PMMLLIB_EXPORT int GetNormalizationType()
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:971
PMMLlib::PMMLlib::CheckNeuralNetwork
void CheckNeuralNetwork()
Called in all methods specific to the NeuralNetwork model.
Definition: PMMLlib.cxx:728
PMMLlib::PMMLlib::GetNbInputs
PMMLLIB_EXPORT int GetNbInputs()
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:847
PMMLlib::PMMLlib::fillVectorsForExport
void fillVectorsForExport(int nInput, int nOutput, int nHidden, int normType, std::vector< double > &minInput, std::vector< double > &maxInput, std::vector< double > &minOutput, std::vector< double > &maxOutput, std::vector< double > &valW)
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:1602

References yacsorb.CORBAEngineTest::i.

◆ ExportNeuralNetworkFortran()

void PMMLlib::PMMLlib::ExportNeuralNetworkFortran ( std::string  file,
std::string  functionName,
std::string  header 
)
private

Specific to NeuralNetwork.

Export the current model as a NeuralNetwork function in a Fortran file.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function

Definition at line 1865 of file PMMLlib.cxx.

1868 {
1869  CheckNeuralNetwork();
1870 
1871  // Get the different values required
1872  int nInput = GetNbInputs();
1873  int nOutput = GetNbOutputs();
1874  int nHidden = GetNbNeuronsAtLayer(0);
1875  int nWeights = nHidden*(nInput+nOutput+1)+nOutput;
1876  int normType = GetNormalizationType();
1877  // Build min/max input/output vectors
1878  vector<double> minInput(nInput);
1879  vector<double> maxInput(nInput);
1880  vector<double> minOutput(nOutput);
1881  vector<double> maxOutput(nOutput);
1882  vector<double> valW(nWeights);
1883  fillVectorsForExport(nInput,nOutput,nHidden,normType,minInput,maxInput,minOutput,maxOutput,valW);
1884  // Write the file
1885  ofstream sourcefile(file.c_str());
1886 
1887  sourcefile << " SUBROUTINE " << functionName << "(";
1888  for(int i=0 ; i<GetNbInputs() ; i++)
1889  {
1890  sourcefile << GetNameInput(i) << ",";
1891  }
1892  sourcefile << GetNameOutput(0) << ")" << endl;
1893  // header
1894  sourcefile << "C --- *********************************************" << endl;
1895  sourcefile << "C --- " << endl;
1896  // insert comments in header
1897  header = "C --- " + header;
1898  size_t pos = 0;
1899  while ((pos = header.find("\n", pos)) != std::string::npos)
1900  {
1901  header.replace(pos, 1, "\nC --- ");
1902  pos += 5;
1903  }
1904  sourcefile << header << endl;
1905  sourcefile << "C --- " << endl;
1906  sourcefile << "C --- *********************************************" << endl;
1907 
1908  sourcefile << " IMPLICIT DOUBLE PRECISION (V)" << endl;
1909  for(int i=0 ; i<GetNbInputs() ; i++)
1910  {
1911  sourcefile << " DOUBLE PRECISION " << GetNameInput(i) << endl;
1912  }
1913  sourcefile << " DOUBLE PRECISION " << GetNameOutput(0) << endl;
1914  sourcefile << endl;
1915 
1916  sourcefile << "C --- Preprocessing of the inputs" << endl;
1917  for(int i=0 ; i<GetNbInputs() ; i++)
1918  {
1919  sourcefile << " VXN" << GetNameInput(i) << " = ";
1920 
1921  if( normType==0 )
1922  { // kMinusOneOne
1923  sourcefile << "2.D0 * ( " << GetNameInput(i) << " - " << minInput[i] << "D0 ) / " << maxInput[i] - minInput[i] << "D0 - 1.0" << endl;
1924  }
1925  else
1926  { // kCR, kZeroOne
1927  sourcefile << "( " << GetNameInput(i) << " - " << minInput[i] << "D0 ) / " << maxInput[i] << "D0" << endl;
1928  }
1929  }
1930 
1931  // Weights vector
1932  sourcefile << endl;
1933  sourcefile << "C --- Values of the weights" << endl;
1934  for(int i=0 ; i<nWeights ; i++)
1935  {
1936  sourcefile << " VW" << i+1 << " = " << valW[i] << endl;
1937  }
1938  // Loop on hidden neurons
1939  sourcefile << endl;
1940  for(int member = 0; member < nHidden; member++)
1941  {
1942  sourcefile << "C --- hidden neural number " << member+1 << endl;
1943  int CrtW = member * ( nInput + 2) + 3;
1944  sourcefile << " VAct" << member+1 << " = VW" << CrtW++ << endl;
1945  for (int source = 0; source < nInput; source++)
1946  {
1947  sourcefile << " 1 + VW"<< CrtW++ << " * VXN" << GetNameInput(source) << endl;
1948  }
1949  sourcefile << endl;
1950 
1951 
1952  if( normType==0 )
1953  { // kMinusOneOne
1954  sourcefile << " VPot" << member+1 << " = 2.D0 / (1.D0 + DEXP(-2.D0 * VAct" << member+1 <<")) - 1.D0" << endl;
1955  }
1956  else
1957  { // kCR, kZeroOne
1958  sourcefile << " VPot" << member+1 << " = 1.D0 / (1.D0 + DEXP(-1.D0 * VAct" << member+1 <<"))" << endl;
1959  }
1960  sourcefile << endl;
1961  }
1962 
1963  // Ouput of the model
1964  sourcefile << "C --- Output" << endl;
1965  sourcefile << " VOut = VW1" << endl;
1966  for(int source=0 ; source < nHidden ; source++)
1967  {
1968  int CrtW = source * ( nInput + 2) + 2;
1969  sourcefile << " 1 + VW"<< CrtW << " * VPot" << source+1 << endl;
1970  }
1971 
1972  // Denormalize Output
1973  sourcefile << endl;
1974  sourcefile << "C --- Pretraitment of the output" << endl;
1975  if( normType==0 )
1976  { // kMinusOneOne
1977  sourcefile << " VDelta = " << 0.5*(maxOutput[0]-minOutput[0]) << "D0 * ( VOut + 1.0D0)" << endl;
1978  sourcefile << " " << GetNameOutput(0) << " = " << minOutput[0] << "D0 + VDelta" << endl;
1979 
1980  }
1981  else
1982  { // kCR, kZeroOne
1983  sourcefile << " " << GetNameOutput(0) << " = "<< minOutput[0] << "D0 + " << maxOutput[0] << "D0 * VOut;" << endl;
1984  }
1985 
1986  sourcefile << endl;
1987  sourcefile << "C --- " << endl;
1988  sourcefile << " RETURN" << endl;
1989  sourcefile << " END" << endl;
1990 
1991  sourcefile.close();
1992 }
PMMLlib::PMMLlib::GetNameOutput
PMMLLIB_EXPORT std::string GetNameOutput(int output_index)
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:936
PMMLlib::PMMLlib::GetNameInput
PMMLLIB_EXPORT std::string GetNameInput(int input_index)
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:897

References yacsorb.CORBAEngineTest::i.

◆ ExportNeuralNetworkPyStr()

std::string PMMLlib::PMMLlib::ExportNeuralNetworkPyStr ( std::string  functionName,
std::string  header 
)
private

Specific to NeuralNetwork.

Export the current model as a function in a Python string.

Parameters
functionNameName of the function
headerHeader of the function
Returns
Function as a string

Definition at line 2020 of file PMMLlib.cxx.

2022 {
2023  CheckNeuralNetwork();
2024 
2025  ostringstream out;
2026 
2027  // Get the different values required
2028  int nInput = GetNbInputs();
2029  int nOutput = GetNbOutputs();
2030  int nHidden = GetNbNeuronsAtLayer(0);
2031  int nNeurons = nInput+nOutput+nHidden;
2032  int nWeights = nHidden*(nInput+nOutput+1)+nOutput;
2033  int normType = GetNormalizationType();
2034  // Build min/max input/output vectors
2035  vector<double> minInput(nInput);
2036  vector<double> maxInput(nInput);
2037  vector<double> minOutput(nOutput);
2038  vector<double> maxOutput(nOutput);
2039  vector<double> valW(nWeights);
2040  fillVectorsForExport(nInput,nOutput,nHidden,normType,minInput,maxInput,minOutput,maxOutput,valW);
2041 
2042  // Shebang et imports
2043  out << "#!/usr/bin/env python3" << endl;
2044  out << "# -*- coding: utf-8 -*-" << endl;
2045  out << endl;
2046  out << "from math import tanh, exp" << endl;
2047  out << endl;
2048 
2049  // ActivationFunction
2050  if( normType==0 )
2051  { // kMinusOneOne
2052  out << "def ActivationFunction(sum): " << endl;
2053  out << " return tanh(sum); " << endl;
2054  }
2055  else
2056  { // kCR, kZeroOne
2057  out << "def ActivationFunction(sum): " << endl;
2058  out << " return ( 1.0 / ( 1.0 + exp( -1.0 * sum ) ) ); " << endl;
2059  }
2060 
2061  out << endl;
2062  out << "def " << functionName <<"(param):" << endl;
2063  out << endl;
2064 
2065  // header
2066  out << " ############################## " << endl;
2067  out << " #" << endl;
2068  // insert comments in header
2069  header = " # " + header;
2070  size_t pos = 0;
2071  while ((pos = header.find("\n", pos)) != std::string::npos)
2072  {
2073  header.replace(pos, 1, "\n #");
2074  pos += 5;
2075  }
2076  out << header << endl;
2077  out << " #" << endl;
2078  out << " ############################## " << endl;
2079  out << endl;
2080 
2081  // Initialisations
2082  out << " nInput = " << nInput << ";" << endl;
2083  out << " nOutput = " << nOutput << ";" << endl;
2084  out << " nHidden = " << nHidden << ";" << endl;
2085  out << " nNeurones = " << nNeurons << ";" << endl;
2086  out << " " << functionName << "_act = [];" << endl;
2087  out << " res = [];" << endl;
2088  out << endl;
2089 
2090  out << " # --- Preprocessing of the inputs and outputs" << endl;
2091  out << " " << functionName << "_minInput = [" << endl << " ";
2092  out << " " ;
2093  for(int i=0 ; i<nInput ; i++)
2094  {
2095  out << minInput[i] << ", ";
2096  if( (i+1)%5==0 )
2097  {
2098  out << endl ;
2099  out << " " ;
2100  }
2101  }
2102  out << endl << " ];" << endl;
2103 
2104  out << " " << functionName << "_minOutput = [" << endl << " ";
2105  out << " " << minOutput[0] ;
2106  out << endl << " ];" << endl;
2107 
2108  out << " " << functionName << "_maxInput = [" << endl << " ";
2109  for(int i=0 ; i<nInput ; i++)
2110  {
2111  out << maxInput[i] << ", ";
2112  if( (i+1)%5==0 )
2113  {
2114  out << endl;
2115  out << " " ;
2116  }
2117  }
2118  out << endl << " ];" << endl;
2119 
2120  out << " " << functionName << "_maxOutput = [" << endl << " ";
2121  out << " " << maxOutput[0] ;
2122  out << endl << " ];" << endl;
2123 
2124  // Weights vector
2125  out << " # --- Values of the weights" << endl;
2126  out << " " << functionName << "_valW = [" << endl << " ";
2127  for(int i=0 ; i<nWeights ; i++)
2128  {
2129  out << valW[i] << ", ";
2130  if ( (i+1)%5 == 0 )
2131  {
2132  out << endl;
2133  out << " " ;
2134  }
2135  }
2136  out << endl << " ];"<<endl;
2137 
2138  out << " # --- Constants" << endl;
2139  out << " indNeurone = 0;" << endl;
2140  out << endl;
2141 
2142  // couche entree
2143  out << " # --- Input Layers" << endl;
2144  out << " for i in range(nInput) :" << endl;
2145  if( normType==0 )
2146  { // kMinusOneOne
2147  out << " " << functionName << "_act.append( 2.0 * ( param[i] - "
2148  << functionName << "_minInput[i] ) / ( " << functionName << "_maxInput[i] - "
2149  << functionName << "_minInput[i] ) - 1.0 ) ;"
2150  << endl;
2151  }
2152  else
2153  { // kCR, kZeroOne
2154  out << " " << functionName << "_act.append( ( param[i] - "
2155  << functionName << "_minInput[i] ) / " << functionName << "_maxInput[i] ) ;"
2156  << endl;
2157  }
2158  out << " indNeurone += 1 ;" << endl;
2159  out << " pass" << endl;
2160 
2161  // couche cachee
2162  out << endl;
2163  out << " # --- Hidden Layers" << endl;
2164  out << " for member in range(nHidden):" << endl;
2165  out << " CrtW = member * ( nInput + 2) + 2;" << endl;
2166  out << " sum = " << functionName << "_valW[CrtW];" << endl;
2167  out << " CrtW += 1 ;" << endl;
2168  out << " for source in range(nInput) :" << endl;
2169  out << " sum += " << functionName << "_act[source] * " << functionName << "_valW[CrtW];" << endl;
2170  out << " CrtW += 1 ;" << endl;
2171  out << " pass" << endl;
2172  out << " " << functionName << "_act.append( ActivationFunction(sum) ) ;" << endl;
2173  out << " indNeurone += 1 ;" << endl;
2174  out << " pass" << endl;
2175  out << endl;
2176 
2177  // couche sortie
2178  out << " # --- Output"<<endl;
2179  out << " for member in range(nOutput):" << endl;
2180  out << " sum = " << functionName << "_valW[0];" << endl;
2181  out << " for source in range(nHidden):" << endl;
2182  out << " CrtW = source * ( nInput + 2) + 1;"<<endl;
2183  out << " sum += " << functionName << "_act[nInput+source] * " << functionName << "_valW[CrtW];" << endl;
2184  out << " pass" << endl;
2185  out << " " << functionName << "_act.append( sum );" << endl;
2186  out << " indNeurone += 1 ;" << endl;
2187  if( normType==0 )
2188  { // kMinusOneOne
2189  out << " res[member] = " << functionName
2190  << "_minOutput[member] + 0.5 * ( " << functionName
2191  << "_maxOutput[member] - " << functionName
2192  << "_minOutput[member] ) * ( sum + 1.0);" << endl;
2193  }
2194  else
2195  { // kCR, kZeroOne
2196  out << " res.append( " << functionName
2197  << "_minOutput[member] + " << functionName
2198  << "_maxOutput[member] * sum );" << endl;
2199  }
2200  out << " pass" << endl;
2201  out << endl;
2202 
2203  // return result
2204  out << " return res;" << endl << endl;
2205  out << endl;
2206 
2207  return out.str();
2208 }

References yacsorb.CORBAEngineTest::i.

◆ ExportNeuralNetworkPython()

void PMMLlib::PMMLlib::ExportNeuralNetworkPython ( std::string  file,
std::string  functionName,
std::string  header 
)
private

Specific to NeuralNetwork.

Export the current model as a NeuralNetwork function in a Python file.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function

Definition at line 2001 of file PMMLlib.cxx.

2004 {
2005  string str(ExportNeuralNetworkPyStr(functionName, header));
2006  // Write the file
2007  ofstream exportfile(file.c_str());
2008  exportfile << str;
2009  exportfile.close();
2010 }
PMMLlib::PMMLlib::ExportNeuralNetworkPyStr
std::string ExportNeuralNetworkPyStr(std::string functionName, std::string header)
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:2020

◆ ExportPyStr()

std::string PMMLlib::PMMLlib::ExportPyStr ( std::string  functionName,
std::string  header 
)

Export the current model as a function in a Python string.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function
Returns
Function as a string

Definition at line 653 of file PMMLlib.cxx.

655 {
656  if ( _currentModelType == kANN )
657  return ExportNeuralNetworkPyStr(functionName, header);
658  else if ( _currentModelType == kLR )
659  return ExportLinearRegressionPyStr(functionName, header);
660  else
661  throw string("ExportPyStr : PMML type not handled.");
662 }

References PMMLlib::kANN, and PMMLlib::kLR.

◆ ExportPython()

void PMMLlib::PMMLlib::ExportPython ( std::string  file,
std::string  functionName,
std::string  header 
)

Export the current model as a function in a Python file.

Parameters
fileName of the file
functionNameName of the function
headerHeader of the function

Definition at line 634 of file PMMLlib.cxx.

637 {
638  if ( _currentModelType == kANN )
639  ExportNeuralNetworkPython(file,functionName, header);
640  else if ( _currentModelType == kLR )
641  ExportLinearRegressionPython(file,functionName, header);
642  else
643  throw string("ExportPython : PMML type not handled.");
644 }
PMMLlib::PMMLlib::ExportNeuralNetworkPython
void ExportNeuralNetworkPython(std::string file, std::string functionName, std::string header)
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:2001
PMMLlib::PMMLlib::ExportLinearRegressionPython
void ExportLinearRegressionPython(std::string, std::string, std::string)
Specific to Regression.
Definition: PMMLlib.cxx:2788

References PMMLlib::kANN, and PMMLlib::kLR.

◆ fillVectorsForExport()

void PMMLlib::PMMLlib::fillVectorsForExport ( int  nInput,
int  nOutput,
int  nHidden,
int  normType,
std::vector< double > &  minInput,
std::vector< double > &  maxInput,
std::vector< double > &  minOutput,
std::vector< double > &  maxOutput,
std::vector< double > &  valW 
)
private

Specific to NeuralNetwork.

Fill the vectors used by the ExportXXX methods.

Parameters
nInput
nOutput
nHidden
normType
minInput
maxInput
minOutput
maxOutput
valW

Definition at line 1602 of file PMMLlib.cxx.

1611 {
1612  CheckNeuralNetwork();
1613 
1614  xmlNodePtr netNode = _currentModelNode ;
1615  // Get the different values required
1616  // Build min/max input/output vectors
1617  for(int i=0 ; i<nInput ; i++)
1618  {
1619  xmlNodePtr node_inputs = GetChildByName(netNode,"NeuralInputs");
1620  node_inputs = node_inputs->children;
1621  for(int j = 0;j<i;j++)
1622  {
1623  node_inputs = node_inputs->next;
1624  }
1625  node_inputs = node_inputs->children; // DerivedField
1626  node_inputs = node_inputs->children; // NormContinuous
1627  node_inputs = node_inputs->children; // LinearNorm
1628  string strOrig1 = _getProp(node_inputs, string("orig") );
1629  double orig1 = atof( strOrig1.c_str() );
1630  string strNorm1 = _getProp(node_inputs, string("norm") );
1631  double norm1 = atof( strNorm1.c_str() );
1632  node_inputs = node_inputs->next;
1633  string strOrig2 = _getProp(node_inputs, string("orig") );
1634  double orig2 = atof( strOrig2.c_str() );
1635  string strNorm2 = _getProp(node_inputs, string("norm") );
1636  if( normType==0 )
1637  { // kMinusOneOne
1638  minInput[i] = orig1;
1639  maxInput[i] = orig2;
1640  }
1641  else
1642  { // kCR, kZeroOne
1643  minInput[i] = orig2;
1644  maxInput[i] = -1.0*norm1*orig2;
1645  }
1646  }
1647  xmlNodePtr node_outputs = GetChildByName(netNode,"NeuralOutputs");
1648  node_outputs = node_outputs->children;
1649  node_outputs = node_outputs->children; // DerivedField
1650  node_outputs = node_outputs->children; // NormContinuous
1651  node_outputs = node_outputs->children; // LinearNorm
1652  string strOrig1 = _getProp(node_outputs, string("orig") );
1653  double orig1 = atof( strOrig1.c_str() );
1654  string strNorm1 = _getProp(node_outputs, string("norm") );
1655  double norm1 = atof( strNorm1.c_str() );
1656  node_outputs = node_outputs->next;
1657  string strOrig2 = _getProp(node_outputs, string("orig") );
1658  double orig2 = atof( strOrig2.c_str() );
1659  if( normType==0 )
1660  { // kMinusOneOne
1661  minOutput[0] = orig1;
1662  maxOutput[0] = orig2;
1663  }
1664  else
1665  { // kCR, kZeroOne
1666  minOutput[0] = orig2;
1667  maxOutput[0] = -1.0*norm1*orig2;
1668  }
1669  // Build weight vector
1670  for(int j=0 ; j<nHidden ; j++) // hidden layers
1671  {
1672  valW[j*(nInput+nOutput+1)+2] = GetNeuronBias( 0, j);
1673  for(int i=0 ; i<nInput ; i++)
1674  {
1675  valW[j*(nInput+nOutput+1)+3+i] = GetPrecNeuronSynapse( 0, j, i);
1676  }
1677  }
1678  for(int j=0 ; j<nOutput ; j++) // output layers
1679  {
1680  valW[0] = GetNeuronBias( 1, j);
1681  for(int i=0 ; i<nHidden ; i++)
1682  {
1683  valW[i*(nInput+nOutput+1)+1] = GetPrecNeuronSynapse( 1, j, i);
1684  }
1685  }
1686 }
PMMLlib::PMMLlib::_currentModelNode
xmlNodePtr _currentModelNode
Pointer to the current model node
Definition: PMMLlib.hxx:84
PMMLlib::PMMLlib::_getProp
std::string _getProp(const xmlNodePtr node, std::string const &prop) const
Definition: PMMLlib.cxx:694
PMMLlib::PMMLlib::GetPrecNeuronSynapse
PMMLLIB_EXPORT double GetPrecNeuronSynapse(int layer_index, int neu_index, int prec_index)
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:1241
PMMLlib::PMMLlib::GetNeuronBias
PMMLLIB_EXPORT double GetNeuronBias(int layer_index, int neu_index)
Specific to NeuralNetwork.
Definition: PMMLlib.cxx:1204
PMMLlib::PMMLlib::GetChildByName
xmlNodePtr GetChildByName(xmlNodePtr node, std::string nodename)
Definition: PMMLlib.cxx:310

References yacsorb.CORBAEngineTest::i.