* Return the maximum number of buckets supported by the policy. */

Take a branch from the left node with enough space

Take a branch from the left node with enough space

Take a branch from the right node with enough space

Take a branch from the right node with enough space

* Convert a const_iterator to an iterator. */

* Convert a const_iterator to an iterator. */

* Other */

* Return the number of buckets that should be used on next growth. */

* @brief Returns the sliced dataframe * * @param indices * @return DataFrame */

* @brief Repeatedly partitions the specified dataframe until it satisfies * k-anonymous * * @param df * @param quasiid_columns * @param

* Called on the hash table creation and on rehash. The number of buckets for * the table is passed in parameter. This number is a minimum, the poli

predict whether the given prediction came from training data or not Args: y_pred_prob (torch.Tensor): predicted probabilities on

Predict whether the given prediction came from training data or not Args: pred (torch.Tensor): predicted probabilities on the dat

* @brief Printout the structure of this tree * * @param show_purity Show leaf purity of each leaf node, if true * @param binary_color Color t

* @brief Print out thie oject * * @param max_row */

Privatizes the optimizer. Args: noise_multiplier (float): Noise multiplier for privacy. Returns: tu

Parse requirements from requirements.txt.

* Grow the table if m_grow_on_next_insert is true or we reached the * max_load_factor. Shrink the table if m_try_shrink_on_next_insert is true *