method
RandomState.
uniform
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, high)
Note
New code should use the uniform method of a default_rng() instance instead; see random-quick-start.
default_rng()
Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0.
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.
(m, n, k)
m * n * k
None
low
high
np.broadcast(low, high).size
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].
[low, high]
random_sample
Floats uniformly distributed over [0, 1).
[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).
rand(2,2)
Generator.uniform
which should be used for new code.
Notes
The probability density function of the uniform distribution is
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anywhere within the interval [a, b), and zero elsewhere.
[a, b)
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.
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, density=True) >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') >>> plt.show()
