def rptree_leaf_array(rp_forest):
    """Generate an array of sets of candidate nearest neighbors by
    constructing a random projection forest and taking the leaves of all the
    trees. Any given tree has leaves that are a set of potential nearest
    neighbors. Given enough trees the set of all such leaves gives a good
    likelihood of getting a good set of nearest neighbors in composite. Since
    such a random projection forest is inexpensive to compute, this can be a
    useful means of seeding other nearest neighbor algorithms.
    Parameters
    ----------
    data: array of shape (n_samples, n_features)
        The data for which to generate nearest neighbor approximations.
    n_neighbors: int
        The number of nearest neighbors to attempt to approximate.
    rng_state: array of int64, shape (3,)
        The internal state of the rng
    n_trees: int (optional, default 10)
        The number of trees to build in the forest construction.
    angular: bool (optional, default False)
        Whether to use angular/cosine distance for random projection tree
        construction.
    Returns
    -------
    leaf_array: array of shape (n_leaves, max(10, n_neighbors))
        Each row of leaf array is a list of indices found in a given leaf.
        Since not all leaves are the same size the arrays are padded out with -1
        to ensure we can return a single ndarray.
    """
    if len(rp_forest) > 0:
        leaf_array = np.vstack([tree.indices for tree in rp_forest])
    else:
        leaf_array = np.array([[-1]])

    return leaf_array
def rptree_leaf_array ( rp_forest ) : if len ( rp_forest ) > 0 : leaf_array = np . vstack ( [ tree . indices for tree in rp_forest ] ) else : leaf_array = np . array ( [ [ - 1 ] ] ) return leaf_array
Generate an array of sets of candidate nearest neighbors by
    constructing a random projection forest and taking the leaves of all the
    trees. Any given tree has leaves that are a set of potential nearest
    neighbors. Given enough trees the set of all such leaves gives a good
    likelihood of getting a good set of nearest neighbors in composite. Since
    such a random projection forest is inexpensive to compute, this can be a
    useful means of seeding other nearest neighbor algorithms.
    Parameters
    ----------
    data: array of shape (n_samples, n_features)
        The data for which to generate nearest neighbor approximations.
    n_neighbors: int
        The number of nearest neighbors to attempt to approximate.
    rng_state: array of int64, shape (3,)
        The internal state of the rng
    n_trees: int (optional, default 10)
        The number of trees to build in the forest construction.
    angular: bool (optional, default False)
        Whether to use angular/cosine distance for random projection tree
        construction.
    Returns
    -------
    leaf_array: array of shape (n_leaves, max(10, n_neighbors))
        Each row of leaf array is a list of indices found in a given leaf.
        Since not all leaves are the same size the arrays are padded out with -1
        to ensure we can return a single ndarray.