nlcpy.random.RandomState.uniform

RandomState.uniform(self, low=0.0, high=1.0, size=None)

Draws 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.

Parameters

lowfloat, optional

Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.

highfloat

Upper boundary of the output interval. All values generated will be less than 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 * k samples are drawn.

Returns

outndarray

Drawn samples from the parameterized uniform distribution.

See also

RandomState.randint

Returns random integers from low (inclusive) to high (exclusive).

RandomState.random_integers

Random integers of type vp.int between low and high, inclusive.

RandomState.random_sample

Returns random floats in the half-open interval [0.0, 1.0).

RandomState.random

Returns random floats in the half-open interval [0.0, 1.0).

RandomState.rand

Random values in a given shape.

Note

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.

Restriction

• If low is neither a scalar nor None : NotImplementedError occurs.

• If high is neither a scalar nor None : NotImplementedError occurs.

Examples

Draw samples from the distribution:

>>> import nlcpy as vp
>>> s = vp.random.uniform(-1,0,1000)

All values are within the given interval:

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

Display the histogram of the samples, along with the probability density function:

>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s.get(), 15, density=True)
>>> plt.plot(bins, vp.ones_like(bins),
... linewidth=2, color='r') 
>>> plt.show()