// if (FormattedOutput(yt[data_idx]) !=FormattedOutput(youtputpre) )
// cout <<" (mismatch)" ;
// else
// cout <<" (match)" ;
// cout << endl;
if (FormattedOutput(yt[data_idx])==FormattedOutput(youtputpre)) num+=1;
}
cout <<" Done! ( Rate of Correction : " << 100.0 * (float)num / (float)NUM_DATA << "% )\n" << endl;
}
}; // end of class
//////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////
int main( int argc, char * argv[] ){
for( int number_of_patterns_4_training=0;number_of_patterns_4_training<=200 ; number_of_patterns_4_training+=20)
{
NeuralNet *ass2net = new NeuralNet();
ass2net->CreateTrainingPatterns();
ass2net->CreateTestData();
ass2net->Train(number_of_patterns_4_training);
// ass2net->PrintWeights();
ass2net->Test();
}
}
/*-------------------------------------------------------------------------------------------
Development Notes:
This is deliberately designed to be close to the form (identifier setc) that Russell& Norvig use. See their perceptron pseudo-code,
Neural Networks section.
