( Round to nearest with ties going to nearest even integer. ) 银行家舍入法的具体计算规则: •(1)被修约的数字小于5时,该数字舍去; •(2)被修约的数字大于5时,则进位; •(3)被修约的数字等于5时,要看5前面的数字,若是奇数则进位,若是偶数则将5舍掉,即修约后末尾数字都成为偶数;若5的后面还
round_to_nearest(n, 5))) -21 => -20 -18 => -20 -15 => -15 -12 => -10 -9 => -10 -6 => -5 -3 => -5 0 => 0 3 => 5 6 => 5 9 => 10 12 => 10 15 => 15 18 => 20 21 => 20 24 => 25 27 => 25 ...
print(round(2.5)) # 2 (rounds to even) print(round(3.5)) # 4 (rounds to even) print(round(4.5)) # 4 (rounds to even) print(round(5.5)) # 6 (rounds to even) print(round(-2.5)) # -2 print(round(-3.5)) # -4 Bankers rounding rounds 0.5 cases to the nearest even number. ...
ROUND_HALF_DOWN (to nearest with ties going towards zero), ROUND_HALF_EVEN (to nearest with ties going to nearest even integer), ROUND_HALF_UP (to nearest with ties going awayfromzero),orROUND_UP (awayfromzero). ROUND_05UP (awayfromzeroiflast digit after rounding towards zero would have bee...
我最开始其实是由于疑惑ROUND_FLOOR和 ROUND_DOWN的表现区别才看了一波文档,但是感觉拉出一票以前没有留意过的东西。 贴一个decimal文档里面的解释: ROUND_CEILING (towards Infinity), ROUND_DOWN (towards zero), ROUND_FLOOR (towards-Infinity), ROUND_HALF_DOWN (to nearest with ties going towards zero), ...
ROUND_HALF_UP (to nearest with ties going away from zero), or ROUND_UP (away from zero). ROUND_05UP (away from zero if last digit after rounding towards zero would have been 0 or 5; otherwise towards zero) x = Decimal('-3.1415926535') + Decimal('-2.7182818285') ...
im = mpimg.imread("../images/lena_small.jpg") # read the image from disk as a numpy ndarray methods = ['none', 'nearest', 'bilinear', 'bicubic', 'spline16', 'lanczos'] fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15, 30), subplot_kw={'xticks': [], 'yticks': ...
0.3 False补充：round 函数对应IEEE 754定义的第一种 Rounding 方法 "Round to nearest, ties to ...
(towards Infinity), ROUND_DOWN (towards zero), ROUND_FLOOR (towards -Infinity), ROUND_HALF_DOWN (to nearest with ties going towards zero), ROUND_HALF_EVEN (to nearest with ties going to nearest even integer), ROUND_HALF_UP (to nearest with ties going away from zero), or ROUND_UP (...
(im, sharpen_kernel, mode='nearest') im_sharp = np.clip(im_sharp, 0, 255).astype(np.uint8) # clip (0 to 255) and convert to unsigned int im_emboss = ndimage.convolve(im, emboss_kernel, mode='nearest') im_emboss = np.clip(im_emboss, 0, 255).astype(np.uint8) pylab.figure(...