随心笔谈11个月前发布 admin
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def uniform(low=0.0, high=1.0, size=None): # real signature unknown; restored from __doc__
uniform(low=0.0, high=1.0, size=None)
Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval
“[low, high)“ (includes low, but excludes high). In other words,
any value within the given interval is equally likely to be drawn
by `uniform`.
low : float or array_like of floats, optional
Lower boundary of the output interval. All values generated will be
greater than or equal to low. The default value is 0.
high : float or array_like of floats
Upper boundary of the output interval. All values generated will be
less than high. The default value is 1.0.
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 “low“ and “high“ are both scalars.
Otherwise, “np.broadcast(low, high).size“ samples are drawn.
out : ndarray or scalar
Drawn samples from the parameterized uniform distribution.
See Also
randint : Discrete uniform distribution, yielding integers.
random_integers : Discrete uniform distribution over the closed
interval “[low, high]“.
random_sample : Floats uniformly distributed over “[0, 1)“.
random : Alias for `random_sample`.
rand : Convenience function that accepts dimensions as input, e.g.,
“rand(2,2)“ would generate a 2-by-2 array of floats,
uniformly distributed over “[0, 1)“.
The probability density function of the uniform distribution is
.. math:: p(x)=\frac{1}{b – a}
anywhere within the interval “[a, b)“, and zero elsewhere.
When “high“==“low“, values of “low“ will be returned.
If “high“ < “low“, the results are officially undefined
and may eventually raise an error, i.e. do not rely on this
function to behave when passed arguments satisfying that
inequality condition.
Draw samples from the distribution:
>>> s=np.random.uniform(-1,0,1000)
All values are within the given interval:
>>> np.all(s >=-1)
>>> np.all(s < 0)
Display the histogram of the samples, along with the
probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored=plt.hist(s, 15, density=True)
>>> plt.plot(bins, np.ones_like(bins), linewidth=2, color=’r’)

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