/**
* Gather and parse assessments made about user
* Input: set of assessments already filtered
* Output: karma term
*/
function userKarma_decisionMaking($valorationlist)
{
if (sizeof($valorationlist) <= 0) {
return elgg_echo('hflts:karma:none');
}
$count = 0;
$data = array('_', '_');
$enablePesos = elgg_get_plugin_setting('weight_assessments', 'hflts');
$C_weight = null;
$enableExpertos = elgg_get_plugin_setting('weight_experts', 'hflts');
$E_weight = null;
if (is_array($valorationlist)) {
foreach ($valorationlist as $evaluation) {
$data[$count] = array('ref' => $evaluation->user_guid, 'co_codigo' => $evaluation->owner_guid);
//more to come
if (!is_array($evaluation->criterion1)) {
$data[$count]['U1'] = $evaluation->criterion1;
$data[$count]['L1'] = $evaluation->criterion1;
} else {
$n = count($evaluation->criterion1) - 1;
$data[$count]['U1'] = $evaluation->criterion1[$n];
$data[$count]['L1'] = $evaluation->criterion1[0];
}
if (!is_array($evaluation->criterion2)) {
$data[$count]['U2'] = $evaluation->criterion2;
$data[$count]['L2'] = $evaluation->criterion2;
} else {
$n = count($evaluation->criterion2) - 1;
$data[$count]['U2'] = $evaluation->criterion2[$n];
$data[$count]['L2'] = $evaluation->criterion2[0];
}
if (!is_array($evaluation->criterion3)) {
$data[$count]['U3'] = $evaluation->criterion3;
$data[$count]['L3'] = $evaluation->criterion3;
} else {
$n = count($evaluation->criterion3) - 1;
$data[$count]['U3'] = $evaluation->criterion3[$n];
$data[$count]['L3'] = $evaluation->criterion3[0];
}
if (!is_array($evaluation->criterion4)) {
$data[$count]['U4'] = $evaluation->criterion4;
$data[$count]['L4'] = $evaluation->criterion4;
} else {
$n = count($evaluation->criterion4) - 1;
$data[$count]['U4'] = $evaluation->criterion4[$n];
$data[$count]['L4'] = $evaluation->criterion4[0];
}
if ($enablePesos) {
$C_weight[$count] = array($evaluation->weight1, $evaluation->weight2, $evaluation->weight3, $evaluation->weight4);
}
if ($enableExpertos) {
$expert = get_user($evaluation->owner_guid);
$E_weight[$count] = $expert->userpoints_points;
}
//$evaluation->delete();//to clean
$count++;
}
}
$computed_weight = relativeUserExpertise($E_weight);
if ($count >= 2) {
$method = new AggregationHFLTS($evaluation->user_guid);
$method->setData($data, $C_weight, $E_weight, $count, $evaluation->granularity);
$model->collectiveValoration = $method->run();
unset($method);
//destroys the object
//set valoration on user's profile
$user = get_user($evaluation->user_guid);
//system_message($count . "# " . $user->username . " @ " . $model->collectiveValoration);
return $model->collectiveValoration;
} else {
return elgg_echo("hflts:karma:none");
}
}
function update_allusers_expertise()
{
$base = elgg_get_plugin_setting('base_expertise', 'hflts');
//system_message("Computing with B=" . $base . " %");
$options = array('type' => 'user', 'limit' => $limit, 'offset' => $offset);
//,
$entities = elgg_get_entities_from_metadata($options);
$i = 0;
//counter
$points = array();
$values = array();
if (is_array($entities)) {
foreach ($entities as $entity) {
$entity->karma = userKarma($entity->guid);
//recalculo todo de cara a overview
$points[$i] = $entity->userpoints_points;
$i++;
}
}
$values = relativeUserExpertise($points);
$i = 0;
foreach ($entities as $entity) {
$entity->expertise = $values[$i];
//system_message($entity->name ." with ". $entity->expertise);
$i++;
}
}
/**
* get from the system the values needed in the model. Called from driver, from icon,... but not from collective
* input: array of $size assessments
* input: array of $size x M criteria weights
* input: array of $size x 1 expert weights
*/
public function setData($values, $C_weight, $E_weight, $size, $granularity)
{
if ($size == 0) {
return;
}
$this->data = $values;
if ($this->information) {
echo "#" . $size . ' Dt: <pre>';
print_r($this->data);
echo '</pre><br>';
}
if (sizeof($values) != $size) {
//return;
system_message($size . " DMCM setData " . sizeof($values));
}
//
$this->P = $this->num = $size;
//no necesariamente dos valoraciones vienen de 2 expertos
$this->G = $granularity;
// compute the averaged expert preference over criteria. Not normalized
$this->superE = $C_weight;
if ($C_weight != null) {
$this->W = averagedUserPreference($C_weight, $this->M);
}
if ($E_weight != null) {
$this->E = relativeUserExpertise($E_weight);
}
//Idea: no normalizar aqui sino fuera en driver + mcdm lib
if ($this->information) {
echo '! W: <pre>';
print_r($this->W);
echo '</pre><br>';
echo '! E: <pre>';
print_r($this->E);
echo '</pre><br>';
echo '! superE: <pre>';
print_r($this->superE);
echo '</pre><br>';
}
}