A simple example of logistic regression via gradient descent in PHP.

classifier.php

<?php

error_reporting(E_ALL);

define('NUM_FEATURES', 3);

// My dataset describes cities around the world where I might consider living.
// Each sample (city) consists of 3 features:
//  * Feature 1: average low winter temperature in the city
//  * Feature 2: city population, in millions
//  * Feature 3: does the city have an airport I can fly to from USA directly?
//
// The labels (categories) are 1 (yes) and 0 (no).
// All the data is floating-point.

$training = array(
    array(-11., 2.6,  1.),
    array(  8., 0.78, 1.),
    array( 15., 4.2,  0.),
    array(-16., 0.18, 0.),
    array(  3., 1.1,  0.),
    array(  7., 1.4,  1.),
    array( -3., 1.44, 1.),
    array( -7., 0.52, 0.),
    array( 30., 0.82, 1.),
    array( 20., 1.32, 0.),
);

$labels = array(
    0.,
    1.,
    0.,
    0.,
    1.,
    1.,
    1.,
    0.,
    0.,
    1
);

$NUM_SAMPLES = sizeof($training);

// Initialize the weights array to random starting values.
// There are always 1+NUM_FEATURES weights, because the first weight
// does not correspond to a feature value, since:
//   weights * features = weight0 + weight1 * feature1 + weight2 * feature2 + ...
$weights = array();
for ($j=0; $j < NUM_FEATURES+1; $j++)
    $weights[$j] = mt_rand()/mt_getrandmax()*5.0;

// Calculate the data we need for feature scaling (mean/variance)
$scaling = calc_feature_scaling($training);

$learning_rate = 0.05;
$steps = 20000; // number of steps to take for gradient descent

$temp = array(); // temp array to hold updates for weights during the loop
for ($n = 0; $n < $steps; $n++) {

    // For each weight, perform the gradient descent step and save the result to temp
    for ($j = 0; $j < NUM_FEATURES+1; $j++) {
        $sum_m = 0.0;
        for ($i = 0; $i < $NUM_SAMPLES; $i++) {
            $scaled_data = scale($training[$i], $scaling);
            $h = hypothesis($scaled_data, $weights);
            // The first weight has a dummy 1 "feature" value
            $part = ($h - $labels[$i]) * ($j==0 ? 1.0 : $scaled_data[$j-1]);
            $sum_m = $sum_m + $part;
        }
        $temp[$j] = $weights[$j] - $learning_rate * $sum_m/$NUM_SAMPLES;
    }

    $weights = $temp;
}

echo "Executed $n steps\n";
echo "Weights: ", vector_to_str($weights), "\n";

// Validate the results
print "\nValidating training\n";
$correct = 0;
for ($i = 0; $i < $NUM_SAMPLES; $i++) {
    $predict = predict(scale($training[$i], $scaling), $weights);
    printf("Input: %-16s actual: %d, predict: %d", vector_to_str($training[$i]), $labels[$i], $predict);
    if ($labels[$i] != $predict)
        print " - miss";
    print "\n";
    if ($predict == $labels[$i])
        $correct++;
}
printf("Correctness = %.0f%%\n", $correct/$NUM_SAMPLES*100.0);

// Try some predictions
print "\nTesting the model\n";
$test = array(
    array(-1., 1.1, 1.),
    array(23., 0.9, 0.),
    array( 4., 1.9, 0.),
    array(-14., 1.1, 1.),
);
for ($i = 0; $i < sizeof($test); $i++) {
    $predict = predict(scale($test[$i], $scaling), $weights);
    printf("Input: %-16s predict: %d\n", vector_to_str($test[$i]), $predict);
}

function hypothesis($x, $weights)
{
    $score = $weights[0]; // free weight
    $k = sizeof($x);
    // Calculate dot product
    for ($i = 0; $i < $k; $i++)
        $score += $weights[$i+1] * $x[$i];
    // Run through the sigmoid (logistic) function
    return 1.0/(1.0 + exp(-$score));
}

function predict($input, $weights)
{
    $output = hypothesis($input, $weights);
    // Threshold on 0.5
    if ($output >= 0.50)
        $predict = 1;
    else
        $predict = 0;
    return $predict;
}

function scale($input, $scaling)
{
    foreach ($input as $f => &$value) {
        $value = ($value - $scaling['mean'][$f]) /
                    $scaling['variance'][$f];
    }
    return $input;
}

function calc_feature_scaling($data)
{
    $mins = array_fill(0, NUM_FEATURES, INF);
    $maxs = array_fill(0, NUM_FEATURES, -INF);
    $sums = array_fill(0, NUM_FEATURES, 0);
    $scaling = array('mean' => array(),
                     'variance' => array());
    $N = sizeof($data);
    foreach ($data as $i => $row) {
        foreach ($row as $f => $value) {
            if ($value > $maxs[$f])
                $maxs[$f] = $value;
            if ($value < $mins[$f])
                $mins[$f] = $value;
            $sums[$f] += $value;
        }
    }

    for ($f = 0; $f < NUM_FEATURES; $f++) {
        $scaling['mean'][$f] = $sums[$f] / $N;
        $scaling['variance'][$f] = $maxs[$f] - $mins[$f];
        if ($scaling['variance'][$f] == 0)
            throw new Exception("Feature #$f has the same value in all the samples, invalid data");
    }

    return $scaling;
}

function vector_to_str($x)
{
    return '['.implode(", ", $x).']';
}

?>