larray.random.uniform

larray.random.uniform(low=0.0, high=1.0, axes=None, meta=None)

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.

Parameters

lowfloat or array_like of floats, optional

Lower boundary of the output interval. All values generated will be greater than or equal to low. Defaults to 0.0.

highfloat or array_like of floats, optional

Upper boundary of the output interval. All values generated will be less than high. Defaults to 1.0.

axesint, tuple of int, str, Axis or tuple/list/AxisCollection of Axis, optional

Minimum axes the resulting array must have. Defaults to None. The resulting array axes will be the union of those mentioned in axes and those of low and high. If low and high are scalars and axes is None, a single value is returned. Otherwise, if the resulting axes have a shape of, e.g., (m, n, k), then m * n * k samples are drawn.

metalist of pairs or dict or OrderedDict or Metadata, optional

Metadata (title, description, author, creation_date, …) associated with the array. Keys must be strings. Values must be of type string, int, float, date, time or datetime.

Returns

LArray or scalar

Drawn samples from the parameterized uniform distribution.

See also

randint

Discrete uniform distribution, yielding integers.

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. 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

Generate a single sample from the distribution:

>>> la.random.uniform()                                                          # doctest: +SKIP
0.4616049008844396

Generate a 2 x 3 array with numbers drawn from the distribution:

>>> la.random.uniform(0, 5, axes=(2, 3))                                         # doctest: +SKIP
{0}*\{1}*                   0                  1                  2
        0  3.4951791043804192  3.888533056628081  4.347461073315136
        1   2.146211610940853  0.509146487437932  2.790852715735223

With named and labelled axes

>>> la.random.uniform(1, 2, axes='a=a0,a1;b=b0..b2')                             # doctest: +SKIP
a\b                  b0                  b1                  b2
 a0  1.4167729850467825  1.6953091052066793  1.2321770607672526
 a1  1.4386221912579358  1.8480607144284926  1.1726213637670433

With varying low and high (each depending on a different axis)

>>> a = la.Axis('a=a0,a1')
>>> b = la.Axis('b=b0..b2')
>>> low = la.sequence(a)
>>> low
a  a0  a1
    0   1
>>> high = la.sequence(b, initial=1, inc=0.5)
>>> high
b   b0   b1   b2
   1.0  1.5  2.0
>>> la.random.uniform(low, high)                                                 # doctest: +SKIP
a\b                   b0                  b1                  b2
 a0  0.44608671494167573   0.948315996350121    1.74189664009661
 a1                  1.0  1.1099944474264194  1.1362792569316835

Draw 1000 samples from the distribution:

>>> s = la.random.uniform(-1, 0, 1000)

All values are within the given interval:

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

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

>>> import matplotlib.pyplot as plt                                     # doctest: +SKIP
>>> count, bins, ignored = plt.hist(s, 15, normed=True)                 # doctest: +SKIP
>>> _ = plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')      # doctest: +SKIP
>>> plt.show()                                                          # doctest: +SKIP