$docs = [
    'The queen does something',
    'Queen is very good queen',
    'Mission mission mission',
    'What is mission your mission'
];

$tok = new WhitespaceTokenizer();
$tset = new TrainingSet();
foreach ($docs as $line) {
    $tset->addDocument(
        '', // the class is not used by the lda model
        new TokensDocument(
            $tok->tokenize(
                mb_strtolower($line)
            )
        )
    );
}

$lda = new Lda(
    new DataAsFeatures(), // a feature factory to transform the document data
    2, // the number of topics we want
    1, // the dirichlet prior assumed for the per document topic distribution
    1  // the dirichlet prior assumed for the per word topic distribution
);

$lda->train($tset,50);

$lda->getDocumentsPerTopicsProbabilities(2);

if (!isset($count_topics_docs[$doc])) {
    $count_topics_docs[$doc] = [];
}
if (!isset($count_topics_docs[$doc][$t])) {
    $count_topics_docs[$doc][$t] = 0;
}

$docs = [
    'The queen does something',
    'Queen is very good queen',

    'Mission mission mission',
    'What is mission your mission'
];
...
$lda->getDocumentsPerTopicsProbabilities(2);