`numpy.random._generator:Generator.uniform`

Signature

def uniform ( low = 0.0 , high = 1.0 , size = None )

Summary

Draw samples from a uniform distribution.

Extended Summary

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.

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 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.
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.

Returns

out : ndarray or scalar: Drawn samples from the parameterized uniform distribution.

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 of low will be returned.

Examples

Draw samples from the distribution:

rng = np.random.default_rng()
s = rng.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, _ = plt.hist(s, 15, density=True)

✓

plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')

✗

plt.show()

✓

Aliases

numpy.random.Generator.uniform