// n: expected insertions
  // b: m/n, bits per insertion
  // p: expected false positive probability
  //
  // 1) Optimal k = b * ln2
  // 2) p = (1 - e ^ (-kn/m))^k
  // 3) For optimal k: p = 2 ^ (-k) ~= 0.6185^b
  // 4) For optimal k: m = -nlnp / ((ln2) ^ 2)

  /**
   * Computes the optimal number of hash functions (k) for a given false positive probability (p).
   *

---

Für dieses Beispiel verwenden wir jedoch ein sehr einfaches HTML-Dokument mit etwas JavaScript, alles in einem langen String.

Das ist natürlich nicht optimal und man würde das nicht in der Produktion machen.

In der Produktion hätten Sie eine der oben genannten Optionen.

---

Ancak bu örnek için, tamamı uzun bir string içinde olacak şekilde biraz JavaScript içeren çok basit bir HTML dokümanı kullanacağız.

Elbette bu optimal değil ve production için kullanmazsınız.

Production'da yukarıdaki seçeneklerden birini kullanırsınız.

Ama WebSockets'in server tarafına odaklanmak ve çalışan bir örnek görmek için en basit yol bu:

Or you might have any other way to communicate with the WebSocket endpoint.

---

But for this example, we'll use a very simple HTML document with some JavaScript, all inside a long string.

This, of course, is not optimal and you wouldn't use it for production.

In production you would have one of the options above.

But it's the simplest way to focus on the server-side of WebSockets and have a working example:

---

Mais pour cet exemple, nous utiliserons un document HTML très simple avec un peu de JavaScript, le tout dans une longue chaîne.

Cela, bien entendu, n'est pas optimal et vous ne l'utiliseriez pas en production.

En production, vous auriez l'une des options ci-dessus.

Mais c'est la façon la plus simple de se concentrer sur la partie serveur des WebSockets et d'avoir un exemple fonctionnel :

    tester.testAllPublicInstanceMethods(BloomFilter.create(unencodedCharsFunnel(), 100));
    tester.testAllPublicStaticMethods(BloomFilter.class);
  }

  /** Tests that we never get an optimal hashes number of zero. */
  public void testOptimalHashes() {
    for (int n = 1; n < 1000; n++) {
      for (double p = 0.1; p > 1e-10; p /= 10) {

Voir par exemple la section `### List of English terms and their preferred German translations` dans `docs/de/llm-prompt.md`.

 * Second, it is always backed by an array large enough to hold a value for every possible
 * combination of row and column keys. (This is rarely optimal unless the table is extremely dense.)
 * Finally, every possible combination of row and column keys is always considered to have a value
 * associated with it: It is not possible to "remove" a value, only to replace it with {@code null},

//     operation still costs ~10% performance compared to direct indexing
//
// The 256-element array allows direct indexing with no bounds checks, no branches,
// and no masking operations, providing optimal performance. The additional 128 bytes
// of memory is a worthwhile tradeoff for the simpler, faster code.
type asciiSet [256]bool

// makeASCIISet creates a set of ASCII characters and reports whether all

    *   Added APIs for switching between interactive logging and absl logging.
        By default, Keras always writes the logs to stdout. However, this is not
        optimal in a non-interactive environment, where you don't have access to
        stdout, but can only view the logs. You can use
        `tf.keras.utils.disable_interactive_logging()` to write the logs to ABSL