algorithms.py 文件源码

def rho_estimation(data, update=None, compute_rho=True, mratio=0.01):

    N     = len(data)
    rho   = numpy.zeros(N, dtype=numpy.float32)

    if update is None:
        dist = distancematrix(data)
        didx = lambda i,j: i*N + j - i*(i+1)//2 - i - 1
        nb_selec = max(5, int(mratio*N))
        sdist    = {}

        if compute_rho:
            for i in xrange(N):
                indices  = numpy.concatenate((didx(i, numpy.arange(i+1, N)), didx(numpy.arange(0, i-1), i)))
                tmp      = numpy.argsort(numpy.take(dist, indices))[:nb_selec]
                sdist[i] = numpy.take(dist, numpy.take(indices, tmp))
                rho[i]   = numpy.mean(sdist[i])

    else:
        M        = len(update[0])
        nb_selec = max(5, int(mratio*M))
        sdist    = {}

        for i in xrange(N):
            dist     = distancematrix(data[i].reshape(1, len(data[i])), update[0]).ravel()
            all_dist = numpy.concatenate((dist, update[1][i]))
            idx      = numpy.argsort(all_dist)[:nb_selec]
            sdist[i] = numpy.take(all_dist, idx)
            rho[i]   = numpy.mean(sdist[i])
    return rho, dist, sdist, nb_selec