Python acak dari distribusi normal
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)
Difficult Dunlin
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)
import numpy as np
np.random.normal(loc=0.0, scale=1.0, size=None) #Example below
mean, standard_deviation, samples = 23 ,0.8 ,1000
normal_samples = np.random.normal(loc=mean,scale=standard_deviation, size=samples)
'''Parameters
loc :float or array_like of floats
Mean (“centre”) of the distribution.
scale: float or array_like of floats
Standard deviation (spread or “width”) of the distribution.
Must be non-negative.
size :int or tuple of ints, optional Output shape.
If the given shape is, e.g., (m, n, k),
then m * n * k samples are drawn.
If size is None (default),
a single value is returned if loc and scale are both scalars.
Otherwise, np.broadcast(loc, scale).size samples are drawn.'''
'''Returns
outndarray or scalar
Drawn samples from the parameterized normal distribution.'''