dsb_utils.py 文件源码

def ll_of_count(counts, means, stds):
    cm = np.copy(counts)
    cm = (cm*255./cm.max()).astype(np.uint8)
    cm = cm[np.where(cm.sum(axis=1))]
    if cm.shape[0] == 0:
        cm = np.zeros((10, 30), dtype = np.uint8)
    im = Image.fromarray(cm).resize((30,10), Image.ANTIALIAS)
    counts_resized_arr = np.array(im.getdata(), dtype=np.float32).reshape(10,30)/255.
    max_ll = -10000000
    for roll_by in xrange(30):
        resized_counts = np.roll(counts_resized_arr, roll_by, axis=1).flatten()
        ll = 0.
        for i in xrange(resized_counts.shape[0]):
            ll += np.log(scipy.stats.norm.pdf(resized_counts[i], loc=means[i], scale=stds[i]))
        if ll > max_ll:
            max_ll = ll
    return max_ll