flopscope.

flopscope.numpy.random.Generator.uniform

fnp.random.Generator.uniform(self, low=0.0, high=1.0, size=None)

Draw samples from a uniform distribution.

Adapted from NumPy docs np.random.Generator.uniform

Arearandom
Typecounted
NumPy Refnp.random.Generator.uniform
Cost
numel(output)\text{numel}(\text{output})
Flopscope Context

Uniform distribution; cost = numel(output).

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, flops.broadcast(low, high).size samples are drawn.

Returns

out:ndarray or scalar

Drawn samples from the parameterized uniform distribution.

See also

  • integers Discrete uniform distribution, yielding integers.
  • random Floats uniformly distributed over [0, 1).

Notes

The probability density function of the uniform distribution is

p(x)=1b−ap(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 = flops.random.default_rng()
>>> s = rng.uniform(-1,0,1000)

All values are within the given interval:

>>> flops.all(s >= -1)
True
>>> flops.all(s < 0)
True

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, flops.ones_like(bins), linewidth=2, color='r')
>>> plt.show()