/**
* Compute \Phi_j(Hi_,H_k) when F(H_ij) < F(H_kj)
* as -1/theta sqrt ( sum w_jr d_(H_kj,H_ij) / w_jr)
*/
private function dominanceDegreeCaseUnder($i, $j, $k)
{
$d = euclideanDistance($this->hesitants[$i][$j], $this->hesitants[$k][$j], $this->lambda, $this->G, $this->chi);
$factor = -1.0 / $this->theta;
//echo " (" .$d.") W_r " . $this->W_r[$j] . " / " . $this->T_Wr ;
$dd = $factor * sqrt($d * $this->T_Wr / $this->W_r[$j]);
return $dd;
}
/**
* Compare best distances and worst distances to the evaluation matrix
*/
private function idealSolution()
{
$l_metric = array();
for ($j = 0; $j < $this->M; $j++) {
$max = array_keys($this->score[$j], max($this->score[$j]));
$this->positive[$j] = $this->hesitants[$j][$max[0]];
$min = array_keys($this->score[$j], min($this->score[$j]));
$this->negative[$j] = $this->hesitants[$j][$min[0]];
}
if ($this->debug) {
echo 'positive: <pre>';
print_r($this->positive);
echo '</pre><br>';
echo 'negative: <pre>';
print_r($this->negative);
echo '</pre><br>';
}
for ($j = 0; $j < $this->M; $j++) {
//echo "C".($j+1)."<br>" ;
$d_IN = euclideanDistance($this->negative[$j], $this->positive[$j], $this->lambda, $this->G, $this->xhi);
for ($i = 0; $i < $this->N; $i++) {
$d_IP = euclideanDistance($this->hesitants[$j][$i], $this->positive[$j], $this->lambda, $this->G, $this->xhi);
$l_metric[$i][$j] = $d_IP / $d_IN * $this->W[$j];
//echo "d=".$d_IP." .... " . $l_metric[$i][$j] . "<br>";
}
}
for ($i = 0; $i < $this->N; $i++) {
$sumGU = 0;
for ($j = 0; $j < $this->M; $j++) {
$sumGU += $l_metric[$i][$j];
}
$this->HFLGU[$i] = $sumGU;
// HFLGU group utility measure = Lp metric with p =1
$this->HFLIR[$i] = max($l_metric[$i]);
// HFLIR individual regret measure = Lp metric con p \inf
}
}
