public function testClear()
{
$d = [1, 1];
$data = new BiPolarNeuralData(2);
$data->setDataFromArray($d);
$data->clear();
$this->assertEquals(-1, $data->getData(0), 0.5);
$data->setData(0, true);
$this->assertEquals(true, $data->getBoolean(0));
}
/**
*
* @return MLData A cloned copy of this object.
*/
public function __clone()
{
$result = new BiPolarNeuralData($this->size());
$result->setDataFromArray($this->data);
return $result;
}
/**
* 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;
}