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
New code should use the
uniformmethod of a
default_rng()instance instead; see random-quick-start.
- 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 or equal to high. The default value is 1.0.
- sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k), then
m * n * ksamples are drawn. If size is
None(default), a single value is returned if
highare both scalars. Otherwise,
np.broadcast(low, high).sizesamples are drawn.
- outndarray or scalar
Drawn samples from the parameterized uniform distribution.
Discrete uniform distribution, yielding integers.
Discrete uniform distribution over the closed interval
Floats uniformly distributed over
Convenience function that accepts dimensions as input, e.g.,
rand(2,2)would generate a 2-by-2 array of floats, uniformly distributed over
which should be used for new code.
The probability density function of the uniform distribution is
anywhere within the interval
[a, b), and zero elsewhere.
low, values of
lowwill be returned. If
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. The
highlimit may be included in the returned array of floats due to floating-point rounding in the equation
low + (high-low) * random_sample(). For example:
>>> x = np.float32(5*0.99999999) >>> x 5.0
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, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()