コード例 #1
0
/**
 * Validate a network for training.
 *
 * @param ContainsFlat $network
 *        	The network to validate.
 * @param MLDataSet $training
 *        	The training set to validate.
 */
function validateNetworkForTraining(ContainsFlat $network, MLDataSet $training)
{
    $inputCount = $network->getFlat()->getInputCount();
    $outputCount = $network->getFlat()->getOutputCount();
    if ($inputCount != $training->getInputSize()) {
        throw new NeuralNetworkError("The input layer size of " + $inputCount + " must match the training input size of " + $training->getInputSize() + ".");
    }
    if ($training->getIdealSize() > 0 && $outputCount != $training->getIdealSize()) {
        throw new NeuralNetworkError("The output layer size of " + $outputCount + " must match the training input size of " + $training->getIdealSize() + ".");
    }
}
コード例 #2
0
 /**
  * Copy whatever dataset type is specified into a memory dataset.
  *
  * @param
  *        	MLDataSet set
  *        	The dataset to copy.
  */
 public function copy(MLDataSet $set)
 {
     $inputCount = $set->getInputSize();
     $idealCount = $set->getIdealSize();
     foreach ($set as $pair) {
         $input = null;
         $ideal = null;
         if ($inputCount > 0) {
             $input = new BasicMLData($inputCount);
             EngineArray\arrayCopy($pair->getInputArray(), $input->getData());
         }
         if ($idealCount > 0) {
             $ideal = new BasicMLData($idealCount);
             EngineArray\arrayCopy($pair->getIdealArray(), $ideal->getData());
         }
         $this->add(new BasicMLDataPair($input, $ideal));
     }
 }
コード例 #3
0
 /**
  * Calculate the error for this neural network.
  * The error is calculated
  * using root-mean-square(RMS).
  *
  * @param
  *        	MLDataSet data
  *        	The training set.
  * @return double The error percentage.
  */
 public function calculateError(MLDataSet $data)
 {
     $errorCalculation = new ErrorCalculation();
     $actual = array();
     $pair = BasicMLDataPair::createPair($data->getInputSize(), $data->getIdealSize());
     for ($i = 0; $i < $data->getRecordCount(); ++$i) {
         $data->getRecord($i, $pair);
         $this->compute($pair->getInputArray(), $actual);
         $errorCalculation->updateError($actual, $pair->getIdealArray(), $pair->getSignificance());
     }
     return $errorCalculation->calculate();
 }