/**
* Apply a thresholding function to the data elements.
* This does not modify the object.
*
* @param
* double thesdholdValue the value to which elements are compared
* @param
* double lowValue the value to use if <= threshold
* @param
* double highValue the value to use if > threshold
* @return MLData The result.
*/
public function threshold($thresholdValue, $lowValue, $highValue)
{
$result = new BasicMLData($size());
for ($i = 0; $i < $this->size(); ++$i) {
if ($this->getData($i) > $thresholdValue) {
$result->setData($i, $highValue);
} else {
$result->setData($i, $lowValue);
}
}
return $result;
}
/**
* Compute the output for a given input to the neural network.
*
* @param MLData input
* The input to the neural network.
* @return MLData The output from the neural network.
*/
public function compute(MLData $input)
{
try {
$result = new BasicMLData($this->structure->getFlat()->getOutputCount());
$this->structure->getFlat()->computeArray($input->getData(), $result->getData());
return $result;
} catch (ArrayIndexOutOfBoundsException $ex) {
throw new NeuralNetworkError("Index exception: there was likely a mismatch between layer sizes, or the size of the input presented to the network.", $ex);
}
}
/**
* 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));
}
}
