random_integers(self, low, high=None, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.random_integers.
Some inconsistencies with the Dask version may exist.
Return random integers of type :None:None:`np.int_` from the "discrete uniform" distribution in the closed interval [`low`, :None:None:`high`]. If :None:None:`high` is None (the default), then results are from [1, :None:None:`low`]. The :None:None:`np.int_` type translates to the C long integer type and its precision is platform dependent.
This function has been deprecated. Use randint instead.
To sample from N evenly spaced floating-point numbers between a and b, use:
a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)
Lowest (signed) integer to be drawn from the distribution (unless high=None
, in which case this parameter is the highest such integer).
If provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None
).
Output shape. If the given shape is, e.g., (m, n, k)
, then m * n * k
samples are drawn. Default is None, in which case a single value is returned.
:None:None:`size`-shaped array of random integers from the appropriate distribution, or a single such random int if :None:None:`size` not provided.
Random integers of type :None:None:`np.int_` between :None:None:`low` and :None:None:`high`, inclusive.
randint
Similar to :None:None:`random_integers`, only for the half-open interval [`low`, :None:None:`high`), and 0 is the lowest value if :None:None:`high` is omitted.
>>> np.random.random_integers(5) # doctest: +SKIP 4 # randomThis example is valid syntax, but we were not able to check execution
>>> type(np.random.random_integers(5)) # doctest: +SKIP <class 'numpy.int64'>This example is valid syntax, but we were not able to check execution
>>> np.random.random_integers(5, size=(3,2)) # doctest: +SKIP array([[5, 4], # random [3, 3], [4, 5]])
Choose five random numbers from the set of five evenly-spaced numbers between 0 and 2.5, inclusive (i.e., from the set ${0, 5/8, 10/8, 15/8, 20/8}$ ):
This example is valid syntax, but we were not able to check execution>>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4. # doctest: +SKIP array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ]) # random
Roll two six sided dice 1000 times and sum the results:
This example is valid syntax, but we were not able to check execution>>> d1 = np.random.random_integers(1, 6, 1000) # doctest: +SKIP
... d2 = np.random.random_integers(1, 6, 1000) # doctest: +SKIP
... dsums = d1 + d2 # doctest: +SKIP
Display results as a histogram:
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(dsums, 11, density=True) # doctest: +SKIP
... plt.show() # doctest: +SKIP
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.random.RandomState.tomaxint
dask.array.random.RandomState.randint
dask.array.random.RandomState.uniform
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