numpy.random.Generator.uniform#
method
- random.Generator.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 byuniform.- Parameters
- lowfloat 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.
- highfloat or array_like of floats
Upper boundary of the output interval. All values generated will be less than high. The high limit may be included in the returned array of floats due to floating-point rounding in the equation
low + (high-low) * random_sample(). high - low must be non-negative. The default value is 1.0.- sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k), thenm * n * ksamples are drawn. If size isNone(default), a single value is returned iflowandhighare both scalars. Otherwise,np.broadcast(low, high).sizesamples are drawn.
- Returns
- outndarray or scalar
Drawn samples from the parameterized uniform distribution.
See also
Notes
The probability density function of the uniform distribution is
\[p(x) = \frac{1}{b - a}\]anywhere within the interval
[a, b), and zero elsewhere.When
high==low, values oflowwill be returned.Examples
Draw samples from the distribution:
>>> s = np.random.default_rng().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, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()
