def approximate_predict(clusterer, points_to_predict):
    """Predict the cluster label of new points. The returned labels
    will be those of the original clustering found by ``clusterer``,
    and therefore are not (necessarily) the cluster labels that would
    be found by clustering the original data combined with
    ``points_to_predict``, hence the 'approximate' label.

    If you simply wish to assign new points to an existing clustering
    in the 'best' way possible, this is the function to use. If you
    want to predict how ``points_to_predict`` would cluster with
    the original data under HDBSCAN the most efficient existing approach
    is to simply recluster with the new point(s) added to the original dataset.

    Parameters
    ----------
    clusterer : HDBSCAN
        A clustering object that has been fit to the data and
        either had ``prediction_data=True`` set, or called the
        ``generate_prediction_data`` method after the fact.

    points_to_predict : array, or array-like (n_samples, n_features)
        The new data points to predict cluster labels for. They should
        have the same dimensionality as the original dataset over which
        clusterer was fit.

    Returns
    -------
    labels : array (n_samples,)
        The predicted labels of the ``points_to_predict``

    probabilities : array (n_samples,)
        The soft cluster scores for each of the ``points_to_predict``

    See Also
    --------
    :py:func:`hdbscan.predict.membership_vector`
    :py:func:`hdbscan.predict.all_points_membership_vectors`

    """
    if clusterer.prediction_data_ is None:
        raise ValueError('Clusterer does not have prediction data!'
                         ' Try fitting with prediction_data=True set,'
                         ' or run generate_prediction_data on the clusterer')

    points_to_predict = np.asarray(points_to_predict)

    if points_to_predict.shape[1] != \
            clusterer.prediction_data_.raw_data.shape[1]:
        raise ValueError('New points dimension does not match fit data!')

    if clusterer.prediction_data_.cluster_tree.shape[0] == 0:
        warn('Clusterer does not have any defined clusters, new data'
             ' will be automatically predicted as noise.')
        labels = -1 * np.ones(points_to_predict.shape[0], dtype=np.int32)
        probabilities = np.zeros(points_to_predict.shape[0], dtype=np.float32)
        return labels, probabilities

    labels = np.empty(points_to_predict.shape[0], dtype=np.int)
    probabilities = np.empty(points_to_predict.shape[0], dtype=np.float64)

    min_samples = clusterer.min_samples or clusterer.min_cluster_size
    neighbor_distances, neighbor_indices = \
        clusterer.prediction_data_.tree.query(points_to_predict,
                                              k=2 * min_samples)

    for i in range(points_to_predict.shape[0]):
        label, prob = _find_cluster_and_probability(
            clusterer.condensed_tree_,
            clusterer.prediction_data_.cluster_tree,
            neighbor_indices[i],
            neighbor_distances[i],
            clusterer.prediction_data_.core_distances,
            clusterer.prediction_data_.cluster_map,
            clusterer.prediction_data_.max_lambdas,
            min_samples
        )
        labels[i] = label
        probabilities[i] = prob

    return labels, probabilities
def approximate_predict ( clusterer , points_to_predict ) : if clusterer . prediction_data_ is None : raise ValueError ( 'Clusterer does not have prediction data!' ' Try fitting with prediction_data=True set,' ' or run generate_prediction_data on the clusterer' ) points_to_predict = np . asarray ( points_to_predict ) if points_to_predict . shape [ 1 ] != clusterer . prediction_data_ . raw_data . shape [ 1 ] : raise ValueError ( 'New points dimension does not match fit data!' ) if clusterer . prediction_data_ . cluster_tree . shape [ 0 ] == 0 : warn ( 'Clusterer does not have any defined clusters, new data' ' will be automatically predicted as noise.' ) labels = - 1 * np . ones ( points_to_predict . shape [ 0 ] , dtype = np . int32 ) probabilities = np . zeros ( points_to_predict . shape [ 0 ] , dtype = np . float32 ) return labels , probabilities labels = np . empty ( points_to_predict . shape [ 0 ] , dtype = np . int ) probabilities = np . empty ( points_to_predict . shape [ 0 ] , dtype = np . float64 ) min_samples = clusterer . min_samples or clusterer . min_cluster_size neighbor_distances , neighbor_indices = clusterer . prediction_data_ . tree . query ( points_to_predict , k = 2 * min_samples ) for i in range ( points_to_predict . shape [ 0 ] ) : label , prob = _find_cluster_and_probability ( clusterer . condensed_tree_ , clusterer . prediction_data_ . cluster_tree , neighbor_indices [ i ] , neighbor_distances [ i ] , clusterer . prediction_data_ . core_distances , clusterer . prediction_data_ . cluster_map , clusterer . prediction_data_ . max_lambdas , min_samples ) labels [ i ] = label probabilities [ i ] = prob return labels , probabilities
Predict the cluster label of new points. The returned labels
    will be those of the original clustering found by ``clusterer``,
    and therefore are not (necessarily) the cluster labels that would
    be found by clustering the original data combined with
    ``points_to_predict``, hence the 'approximate' label.

    If you simply wish to assign new points to an existing clustering
    in the 'best' way possible, this is the function to use. If you
    want to predict how ``points_to_predict`` would cluster with
    the original data under HDBSCAN the most efficient existing approach
    is to simply recluster with the new point(s) added to the original dataset.

    Parameters
    ----------
    clusterer : HDBSCAN
        A clustering object that has been fit to the data and
        either had ``prediction_data=True`` set, or called the
        ``generate_prediction_data`` method after the fact.

    points_to_predict : array, or array-like (n_samples, n_features)
        The new data points to predict cluster labels for. They should
        have the same dimensionality as the original dataset over which
        clusterer was fit.

    Returns
    -------
    labels : array (n_samples,)
        The predicted labels of the ``points_to_predict``

    probabilities : array (n_samples,)
        The soft cluster scores for each of the ``points_to_predict``

    See Also
    --------
    :py:func:`hdbscan.predict.membership_vector`
    :py:func:`hdbscan.predict.all_points_membership_vectors`