geometric(self, p, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.geometric.
Some inconsistencies with the Dask version may exist.
Bernoulli trials are experiments with one of two outcomes: success or failure (an example of such an experiment is flipping a coin). The geometric distribution models the number of trials that must be run in order to achieve success. It is therefore supported on the positive integers, k = 1, 2, ...
.
The probability mass function of the geometric distribution is
$$f(k) = (1 - p)^{k - 1} p$$where p is the probability of success of an individual trial.
New code should use the geometric
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`.
The probability of success of an individual trial.
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 p
is a scalar. Otherwise, np.array(p).size
samples are drawn.
Drawn samples from the parameterized geometric distribution.
Draw samples from the geometric distribution.
Generator.geometric
which should be used for new code.
Draw ten thousand values from the geometric distribution, with the probability of an individual success equal to 0.35:
This example is valid syntax, but we were not able to check execution>>> z = np.random.geometric(p=0.35, size=10000) # doctest: +SKIP
How many trials succeeded after a single run?
This example is valid syntax, but we were not able to check execution>>> (z == 1).sum() / 10000. # doctest: +SKIP 0.34889999999999999 #randomSee :
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