utils.py 文件源码

def affinity_propagation(location, location_callback):
    """Returns one or more clusters of a set of points, using an affinity
    propagation algorithm.
    The result is sorted with the first value being the largest cluster.

    Returns:
        A list of NamedTuples (see get_cluster_named_tuple for a definition
        of the tuple).
    """
    pts = location._tuple_points()
    if not pts:
        return None
    X = np.array(pts).reshape((len(pts), len(pts[0])))
    if np.any(np.isnan(X)) or not np.all(np.isfinite(X)):
        return None
    X = Imputer().fit_transform(X)
    X = X.astype(np.float32)
    afkwargs = {
        'damping': 0.5,
        'convergence_iter': 15,
        'max_iter': 200,
        'copy': True,
        'preference': None,
        'affinity': 'euclidean',
        'verbose': False
    }
    af = AffinityPropagation(**afkwargs).fit(X)
    cluster_centers_indices = af.cluster_centers_indices_
    clusters = []
    for cluster_id, cluster_centre in enumerate(af.cluster_centers_):
        locations = []
        for j, label in enumerate(af.labels_):
            if not label == cluster_id:
                continue
            locations.append(location.locations[j])
        if not locations:
            continue
        clusters.append(cluster_named_tuple()(label=cluster_id,
                                              centroid=Point(cluster_centre),
                                              location=location_callback(
                                                  locations)))
    return clusters