CI_ASS2_Single Neuron Regression_PHP

<?php

$inputs = [
    [-0.74347,-0.15948],
    [-0.49418,-0.072128],
    [0.76792,0.37009],
    [-0.60745,-0.11181],
    [-0.75729,-0.16432],
    [0.08739,0.13164],
    [-0.37076,-0.028884],
    [-0.23592,0.018362],
    [0.58308,0.30532],
    [0.67836,0.33871]
];

$sets = [
    [0.36047,0.22733],
    [-0.16616,0.042805],
    [0.28578,0.20116],
    [-0.57184,-0.099338],
    [0.23454,0.1832],
];

$learning_rate = .001;

$weights = [
    mt_rand(-.001 * 10000000, .001 * 10000000) / 10000000,
    mt_rand(-.001 * 10000000, .001 * 10000000) / 10000000
];
$bias = 1;

$learning_count = count($inputs);

echo '<pre>';
$previous_error = 999;
for ($i = 1; ; ++$i) {
    $total_error = 0;
    foreach ($inputs as $input) {
        $out          = $input[0] * $weights[0] + $weights[1];
        $total_error += pow($error = $input[1] - $out, 2);
        $weights[0]  += $learning_rate * $input[0] * $error;
        $weights[1]  += $learning_rate * $error;
    }
    $total_error = $total_error / $learning_count;
    if ($total_error === $previous_error || $i > 500000) break;
    $previous_error = $total_error;
}

echo '<table>';
echo '<tr><td colspan="3"><b>Solving</b></td></tr>';
foreach ($sets as $set) {
    $out = $set[0] * $weights[0] + $bias * $weights[1];
    echo '<tr><td>' . $out . '</td><td>' . $set[1] . '</td><td>' . ($out === $set[1] ? '' : '×') . '</td></tr>';
}
echo '</table>';