numpy.random.uniform¶

numpy.random.
uniform
(low=0.0, high=1.0, size=None)¶ Draw samples from a uniform distribution.
Samples are uniformly distributed over the halfopen interval
[low, high)
(includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn byuniform
.Parameters: 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)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned iflow
andhigh
are both scalars. Otherwise,np.broadcast(low, high).size
samples are drawn.Returns: 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 2by2 array of floats, uniformly distributed over[0, 1)
.
Notes
The probability density function of the uniform distribution is
anywhere within the interval
[a, b)
, and zero elsewhere.When
high
==low
, values oflow
will be returned. Ifhigh
<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.Examples
Draw samples from the distribution:
>>> s = np.random.uniform(1,0,1000)
All values are within the given interval:
>>> np.all(s >= 1) True >>> np.all(s < 0) True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s, 15, normed=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()
(Source code, png, pdf)