methods_kharita.py 文件源码

def point2cluster(datapointwts,seeds,theta):
    cluster = {};p2cluster = []; gedges = {}; gedges1 = {}; nedges = {}; std = {}; seeds1 = []; seedweight = [];
    X = [(lonconst * xx[0], latconst * xx[1], theta / 180 * xx[2]) for xx in datapointwts];    S = [(lonconst * xx[0], latconst * xx[1], theta / 180 * xx[2]) for xx in seeds];
    Xrot = [(lonconst * xx[0], latconst * xx[1], theta / 180 * (xx[2]%360)) for xx in datapointwts];    Srot = [(lonconst * xx[0], latconst * xx[1], theta / 180 * (xx[2]%360)) for xx in seeds];
    for cd in range(len(seeds)):
        cluster[cd] = []
    nbrs = NearestNeighbors(n_neighbors=1, algorithm='ball_tree').fit(S)
    distances, indices = nbrs.kneighbors(X)
    nbrsrot = NearestNeighbors(n_neighbors=1, algorithm='ball_tree').fit(Srot)
    distancesrot, indicesrot = nbrsrot.kneighbors(Xrot)
    for ii, ll in enumerate(indices):
        #        print(distances[ii],distancesrot[ii],ll,indices[ii],indicesrot[ii])
        cd = indicesrot[ii][0]
        if distances[ii][0] < distancesrot[ii][0]:
            cd = indices[ii][0];
            #        print(cd,distances[ii],distancesrot[ii],ll,indices[ii],indicesrot[ii])
        cluster[cd].append(datapointwts[ii])
        p2cluster.append(cd)
    return(cluster,p2cluster)