pca.py 文件源码

def get_normalized_dispersion(mat_mean, mat_var, nbins=20):
    mat_disp = (mat_var - mat_mean) / np.square(mat_mean)

    quantiles = np.percentile(mat_mean, np.arange(0, 100, 100 / nbins))
    quantiles = np.append(quantiles, mat_mean.max())

    # merge bins with no difference in value
    quantiles = np.unique(quantiles)

    if len(quantiles) <= 1:
        # pathological case: the means are all identical. just return raw dispersion.
        return mat_disp

    # calc median dispersion per bin
    (disp_meds, _, disp_bins) = scipy.stats.binned_statistic(mat_mean, mat_disp, statistic='median', bins=quantiles)

    # calc median absolute deviation of dispersion per bin
    disp_meds_arr = disp_meds[disp_bins-1] # 0th bin is empty since our quantiles start from 0
    disp_abs_dev = abs(mat_disp - disp_meds_arr)
    (disp_mads, _, disp_bins) = scipy.stats.binned_statistic(mat_mean, disp_abs_dev, statistic='median', bins=quantiles)

    # calculate normalized dispersion
    disp_mads_arr = disp_mads[disp_bins-1]
    disp_norm = (mat_disp - disp_meds_arr) / disp_mads_arr
    return disp_norm