public function testDouble2bipolar()
{
// test a 1x4
$doubleData1 = [1, -1, 1, -1];
$checkData1 = [true, false, true, false];
$result1 = BiPolarUtil\double2bipolar($doubleData1);
for ($i = 0; $i < count($checkData1); ++$i) {
$this->assertEquals($checkData1[$i], $result1[$i]);
}
// test a 2x2
$doubleData2 = [[1, -1], [-1, 1]];
$checkData2 = [[true, false], [false, true]];
$result2 = BiPolarUtil\double2bipolar($doubleData2);
for ($r = 0; $r < count($doubleData2); ++$r) {
for ($c = 0; $c < count($doubleData2[0]); ++$c) {
$this->assertEquals($result2[$r][$c], $checkData2[$r][$c]);
}
}
}
/**
* Set the specified index of this object as a boolean.
* This value will be
* converted into bipolar.
*
* @param int $index
* The index to set.
* @param boolean $value
* The value to set.
*/
public function setData($index, $value)
{
if (is_bool($value)) {
$this->data[$index] = $value;
} else {
if (is_number($value)) {
$this->data[$index] = BiPolarUtil\double2bipolar($value);
} else {
throw new MLDataError("Unsupported value type, must be a double or a boolean.");
}
}
}
/**
* 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;
}