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bundles / numpy 2.4.4 / numpy / random / RandomState / uniform

cython_function_or_method

numpy.random:RandomState.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 or equal to 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(). 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

anywhere within the interval [a, b), and zero elsewhere.

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. 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(). For example:

>>> x = np.float32(5*0.99999999)
>>> x
np.float32(5.0)

Examples

Draw samples from the distribution: 
s = np.random.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, ignored = plt.hist(s, 15, density=True)
✓
plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
✗
plt.show()
✓
fig-eb88bdd9d6943b75.png

See also

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).

randint

Discrete uniform distribution, yielding integers.

random

Alias for random_sample.

random.Generator.uniform

which should be used for new code.

random_integers

Discrete uniform distribution over the closed interval [low, high].

random_sample

Floats uniformly distributed over [0, 1).

Aliases

  • numpy.random.uniform
  • numpy.random.RandomState.uniform