/**
* Takes some input and runs the network
* @param $inputarray Input data
* @return The output data
*/
function run($inputarray)
{
$output = array();
$l_count = count($this->layer);
for ($l = 0; $l < $l_count; ++$l) {
// Now we walk through each layer of the net
foreach ($this->layer[$l]->neuron as $neuron) {
$inputs = $l === 0 ? $inputarray : $output[$l];
$x = 0;
$sum = 0;
foreach ($neuron->synapse as $synapse) {
$synapse->input = $inputs[$x];
$sum += $synapse->weight * $synapse->input;
++$x;
}
$value = -1 + 2 / (1 + exp(-1 * ($sum + $neuron->bias)));
//bipolar sigmoid
$neuron->value = $value;
$output[$l + 1][] = $value;
}
}
$data = $output[$l];
self::$cache = $data;
return $data;
}
/**
* Get a nmesh instance from authkey
* @param $authkey Unique key to network
* @return NeuralMesh instance (singleton)
*/
public static function getNetwork($authkey)
{
self::init();
$data = self::$app->model->get("network")->getAuth($authkey);
$nn = self::$app->model->get("network")->nn;
nmesh::$momentumrate = $data['momentumrate'];
return $data['networkType'] == "managed" ? new ManagedNetwork($data['networkID'], $data['learningrate'], $nn) : new UnmanagedNetwork($data['networkID'], $data['learningrate'], $nn);
}
<?php include '../nm-admin/lib/proxy/nmesh.class.php'; $inputs = 1; $outputs = 1; $hidden_neurons_per_layer = 1; $hidden_layers = 1; $inputarray = array(); $nmesh = new nmesh($inputs, $outputs, $hidden_neurons_per_layer, $hidden_layers); $nmesh->run($inputarray);
