MCEvidence.py 文件源码

def weighted_thin(weights,thin_unit):
    '''
    Given a weight array, perform thinning.
    If the all weights are equal, this should 
    be equivalent to selecting every N/((thinfrac*N)
    where N=len(weights).
    '''

    N=len(weights)
    if thin_unit==0: return range(N),weights

    if thin_unit<1:
        N2=np.int(N*thin_unit)
    else:
        N2=N//thin_unit

    #bin the weight index to have the desired length
    #this defines the bin edges
    bins = np.linspace(-1, N, N2+1) 
    #this collects the indices of the weight array in each bin
    ind = np.digitize(np.arange(N), bins)  
    #this gets the maximum weight in each bin
    thin_ix=pd.Series(weights).groupby(ind).idxmax().tolist()
    thin_ix=np.array(thin_ix,dtype=np.intp)
    logger.info('Thinning with weighted binning: thinfrac={}. new_nsamples={},old_nsamples={}'.format(thin_unit,len(thin_ix),len(w)))

    return {'ix':thin_ix, 'w':weights[thin_ix]}