/**
* Note: for Hopfield networks, you will usually want to call the "run"
* method to compute the output.
*
* This method can be used to copy the input data to the current state. A
* single iteration is then run, and the new current state is returned.
*
* @param
* MLData input
* The input pattern.
* @return MLData The new current state.
*/
public function compute(MLData $input)
{
$result = new BiPolarNeuralData($input->size());
EngineArray\arrayCopy($input->getData(), $this->getCurrentState()->getData());
$this->run();
for ($i = 0; $i < $this->getCurrentState()->size(); ++$i) {
$result->setData($i, BiPolarUtil\double2bipolar($this->getCurrentState()->getData($i)));
}
EngineArray\arrayCopy($this->getCurrentState()->getData(), $result->getData());
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);
}
}
/**
* Subtract one data element from another.
* This does not modify the object.
*
* @param
* MLData o The other data element
* @return MLData The result.
*/
public function minus(MLData $o)
{
if ($this->size() != $o->size()) {
throw new IllegalArgumentException();
}
$result = new BasicMLData($this->size());
for ($i = 0; $i < $this->size(); ++$i) {
$result->setData($i, $this->getData($i) - $o->getData($i));
}
return $result;
}
