uniform(self, low=0.0, high=1.0, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.uniform.
Some inconsistencies with the Dask version may exist.
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
.
New code should use the uniform
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`.
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. 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) # doctest: +SKIP >>> x # doctest: +SKIP 5.0
Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
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.
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.
Drawn samples from the parameterized uniform distribution.
Draw samples from a uniform distribution.
Generator.uniform
which should be used for new code.
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 :None:None:`random_sample`.
random_integers
Discrete uniform distribution over the closed interval [low, high]
.
random_sample
Floats uniformly distributed over [0, 1)
.
Draw samples from the distribution:
This example is valid syntax, but we were not able to check execution>>> s = np.random.uniform(-1,0,1000) # doctest: +SKIP
All values are within the given interval:
This example is valid syntax, but we were not able to check execution>>> np.all(s >= -1) # doctest: +SKIP TrueThis example is valid syntax, but we were not able to check execution
>>> np.all(s < 0) # doctest: +SKIP True
Display the histogram of the samples, along with the probability density function:
This example is valid syntax, but we were not able to check execution>>> import matplotlib.pyplot as plt # doctest: +SKIPSee :
... count, bins, ignored = plt.hist(s, 15, density=True) # doctest: +SKIP
... plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') # doctest: +SKIP
... plt.show() # doctest: +SKIP
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.routines.histogramdd
dask.array.random.RandomState.uniform
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them