背景
我想生成具有分布的亂數（np.random.normal,np.random.poisson等），將幾個關鍵字引數（loc, scale,等）傳遞size給它。
loc = [1, 2, 3, 6, 10]
scale = [4, 6, 7, 8, 5]
size = [10, 9, 7, 8, 5]
# When I know which kwargs is in use, this lambda function works
list(map(lambda x, y: np.random.normal(loc=x, size=y), loc, size))
# however, the number of kwargs may change and the kwargs themselves may change. It won't work with codes below. How to generlize the function above?
list(map(lambda **params: np.random.normal(**params),**{'loc': loc, 'scale': scale, 'size': size}))
list(map(lambda x, y: np.random.poisson(loc=x, size=y), loc, size))
輸出：
TypeError Traceback (most recent call last)
<ipython-input-48-1f3449886ea1> in <module>
----> 1 list(map(lambda x, y: np.random.poisson(loc=x, size=y), loc, size))
<ipython-input-48-1f3449886ea1> in <lambda>(x, y)
----> 1 list(map(lambda x, y: np.random.poisson(loc=x, size=y), loc, size))
mtrand.pyx in numpy.random.mtrand.RandomState.poisson()
TypeError: poisson() got an unexpected keyword argument 'loc'
問題
有沒有辦法使用built-in/numpy來迭代kwargs's 元素？
uj5u.com熱心網友回復：
你可以試試這個：
import numpy as np
params = dict(loc=[1,2,3,6,10],
scale=[4,6,7,8,5],
size=[10,9,7,8,5])
ds = (dict(zip(params.keys(), vals)) for vals in zip(*params.values()))
list(np.random.normal(**d) for d in ds)
uj5u.com熱心網友回復：
如果你想使用map，你可以這樣做：
params = (loc, scale, size)
names = ('loc', 'scale', 'size')
list(map(lambda p: np.random.normal(**dict(zip(names, p))),
zip(*params)))
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