onelayerffnn.cpp

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#include "onelayerffnn.h"

using namespace matrix;

/// initialisation of the network with the given number of input and output units
void OneLayerFFNN::init(unsigned int inputDim, unsigned  int outputDim) {
  weights.set(outputDim, inputDim);
  bias.set(outputDim, 1);
  initialised = true;
}

/// passive processing of the input
const Matrix OneLayerFFNN::process (const Matrix& input) const {
  assert(initialised);
  return (weights * input + bias).map(actfun);
}
/// performs learning and returns the network output before learning
const Matrix OneLayerFFNN::learn (const Matrix& input, 
                                  const Matrix& nom_output, 
                                  double learnRateFactor) {    
  assert(initialised);
  double epsilon = eps*learnRateFactor;
  const Matrix& z       = weights * input + bias;
  const Matrix& g_prime = z.map(dactfun);      
  const Matrix& output  = z.map(actfun);
  const Matrix& xsi     = nom_output - output;
  
  weights += xsi.multrowwise(g_prime) * (input^T) * epsilon; 
  bias    += xsi.multrowwise(g_prime) * epsilon * factor_bias;
  return output;
}

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