如何为每行用numpy random.choice创建2d数组?
我正在尝试创建一个numpy随机选择的2d数组(由六列和许多行组成),每行的唯一值介于1到50之间,不是数组的全部
np.sort(np.random.choice(np.arange(1,50),size=(100,6),replace=False))
但这会引起错误。
ValueError: Cannot take a larger sample than population when 'replace=False'
有没有可能用一个衬套做到这一点
编辑
好的,我得到了答案。
这些是jupyter%time cellmagic的结果
#@James' solution
np.stack([np.random.choice(np.arange(1,50),size=6,replace=False) for i in range(1_000_000)])
Wall time: 25.1 s
#@Divakar's solution
np.random.rand(1_000_000, 50).argpartition(6,axis=1)[:,:6]+1
Wall time: 1.36 s
#@CoryKramer's solution
np.array([np.random.choice(np.arange(1, 50), size=6, replace=False) for _ in range(1_000_000)])
Wall time: 25.5 s
我在@Paul Panzer的解决方案上更改了 np.empty和np.random.randint的dtypes ,因为它在我的电脑上不起作用。
3.6.0 |Anaconda custom (64-bit)| (default, Dec 23 2016, 11:57:41) [MSC v.1900 64 bit (AMD64)]
最快的是
def pp(n):
draw = np.empty((n, 6), dtype=np.int64)
# generating random numbers is expensive, so draw a large one and
# make six out of one
draw[:, 0] = np.random.randint(0, 50*49*48*47*46*45, (n,),dtype=np.uint64)
draw[:, 1:] = np.arange(50, 45, -1)
draw = np.floor_divide.accumulate(draw, axis=-1)
draw[:, :-1] -= draw[:, 1:] * np.arange(50, 45, -1)
# map the shorter ranges (:49, :48, :47) to the non-occupied
# positions; this amounts to incrementing for each number on the
# left that is not larger. the nasty bit: if due to incrementing
# new numbers on the left are "overtaken" then for them we also
# need to increment.
for i in range(1, 6):
coll = np.sum(draw[:, :i] <= draw[:, i, None], axis=-1)
collidx = np.flatnonzero(coll)
if collidx.size == 0:
continue
coll = coll[collidx]
tot = coll
while True:
draw[collidx, i] += coll
coll = np.sum(draw[collidx, :i] <= draw[collidx, i, None], axis=-1)
relidx = np.flatnonzero(coll > tot)
if relidx.size == 0:
break
coll, tot = coll[relidx]-tot[relidx], coll[relidx]
collidx = collidx[relidx]
return draw + 1
#@Paul Panzer' solution
pp(1_000_000)
Wall time: 557 ms
谢谢你们。
-
这是一种有建设性的方法,首先绘制(50个选择),然后绘制(49个选择),等等。对于大集合,这是很有竞争力的(表中的pp):
# n = 10 # pp 0.18564210 ms # Divakar 0.01960790 ms # James 0.20074140 ms # CK 0.17823420 ms # n = 1000 # pp 0.80046050 ms # Divakar 1.31817130 ms # James 18.93511460 ms # CK 20.83670820 ms # n = 1000000 # pp 655.32905590 ms # Divakar 1352.44713990 ms # James 18471.08987370 ms # CK 18369.79808050 ms # pp checking plausibility... # var (exp obs) 208.333333333 208.363840259 # mean (exp obs) 25.5 25.5064865 # Divakar checking plausibility... # var (exp obs) 208.333333333 208.21113972 # mean (exp obs) 25.5 25.499471 # James checking plausibility... # var (exp obs) 208.333333333 208.313436938 # mean (exp obs) 25.5 25.4979035 # CK checking plausibility... # var (exp obs) 208.333333333 208.169585249 # mean (exp obs) 25.5 25.49
代码包括基准测试。算法有点复杂,因为映射到自由点很麻烦:
import numpy as np import types from timeit import timeit def f_pp(n): draw = np.empty((n, 6), dtype=int) # generating random numbers is expensive, so draw a large one and # make six out of one draw[:, 0] = np.random.randint(0, 50*49*48*47*46*45, (n,)) draw[:, 1:] = np.arange(50, 45, -1) draw = np.floor_divide.accumulate(draw, axis=-1) draw[:, :-1] -= draw[:, 1:] * np.arange(50, 45, -1) # map the shorter ranges (:49, :48, :47) to the non-occupied # positions; this amounts to incrementing for each number on the # left that is not larger. the nasty bit: if due to incrementing # new numbers on the left are "overtaken" then for them we also # need to increment. for i in range(1, 6): coll = np.sum(draw[:, :i] <= draw[:, i, None], axis=-1) collidx = np.flatnonzero(coll) if collidx.size == 0: continue coll = coll[collidx] tot = coll while True: draw[collidx, i] += coll coll = np.sum(draw[collidx, :i] <= draw[collidx, i, None], axis=-1) relidx = np.flatnonzero(coll > tot) if relidx.size == 0: break coll, tot = coll[relidx]-tot[relidx], coll[relidx] collidx = collidx[relidx] return draw + 1 def check_result(draw, name): print(name[2:], ' checking plausibility...') import scipy.stats assert all(len(set(row)) == 6 for row in draw) assert len(set(draw.ravel())) == 50 print(' var (exp obs)', scipy.stats.uniform(0.5, 50).var(), draw.var()) print(' mean (exp obs)', scipy.stats.uniform(0.5, 50).mean(), draw.mean()) def f_Divakar(n): return np.random.rand(n, 50).argpartition(6,axis=1)[:,:6]+1 def f_James(n): return np.stack([np.random.choice(np.arange(1,51),size=6,replace=False) for i in range(n)]) def f_CK(n): return np.array([np.random.choice(np.arange(1, 51), size=6, replace=False) for _ in range(n)]) for n in (10, 1_000, 1_000_000): print(f'n = {n}') for name, func in list(globals().items()): if not name.startswith('f_') or not isinstance(func, types.FunctionType): continue try: print("{:16s}{:16.8f} ms".format(name[2:], timeit( 'f(n)', globals={'f':func, 'n':n}, number=10)*100)) except: print("{:16s} apparently failed".format(name[2:])) if(n >= 10000): for name, func in list(globals().items()): if name.startswith('f_') and isinstance(func, types.FunctionType): check_result(func(n), name)