Function update_tree

machine_learning/frequent_pattern_growth.py:128–170

Update the FP-Tree with a transaction. Args: items: List of items in the transaction. in_tree: The current node in the FP-Tree. header_table: The header table dictionary with item information. count: The count of the transaction. Example: >>> data_s

(items: list, in_tree: TreeNode, header_table: dict, count: int)

def update_tree(items: list, in_tree: TreeNode, header_table: dict, count: int) -> None:
    """
    Update the FP-Tree with a transaction.

    Args:
        items: List of items in the transaction.
        in_tree: The current node in the FP-Tree.
        header_table: The header table dictionary with item information.
        count: The count of the transaction.

    Example:
    >>> data_set = [
    ...    ['A', 'B', 'C'],
    ...    ['A', 'C'],
    ...    ['A', 'B', 'E'],
    ...    ['A', 'B', 'C', 'E'],
    ...    ['B', 'E']
    ... ]
    >>> min_sup = 2
    >>> fp_tree, header_table = create_tree(data_set, min_sup)
    >>> fp_tree
    TreeNode('Null Set', 1, None)
    >>> transaction = ['A', 'B', 'E']
    >>> update_tree(transaction, fp_tree, header_table, 1)
    >>> fp_tree
    TreeNode('Null Set', 1, None)
    >>> fp_tree.children['A'].children['B'].children['E'].children
    {}
    >>> fp_tree.children['A'].children['B'].children['E'].count
    2
    >>> header_table['E'][1].name
    'E'
    """
    if items[0] in in_tree.children:
        in_tree.children[items[0]].inc(count)
    else:
        in_tree.children[items[0]] = TreeNode(items[0], count, in_tree)
        if header_table[items[0]][1] is None:
            header_table[items[0]][1] = in_tree.children[items[0]]
        else:
            update_header(header_table[items[0]][1], in_tree.children[items[0]])
    if len(items) > 1:
        update_tree(items[1:], in_tree.children[items[0]], header_table, count)


def update_header(node_to_test: TreeNode, target_node: TreeNode) -> TreeNode: