brainpy.math.random.geometric#
- brainpy.math.random.geometric(p, size=None, key=None)[source]#
Draw samples from the geometric distribution.
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.
- Parameters:
p (float or array_like of floats) – The probability of success of an individual trial.
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * n * k
samples are drawn. If size isNone
(default), a single value is returned ifp
is a scalar. Otherwise,np.array(p).size
samples are drawn.
- Returns:
out – Drawn samples from the parameterized geometric distribution.
- Return type:
ndarray or scalar
Examples
Draw ten thousand values from the geometric distribution, with the probability of an individual success equal to 0.35:
>>> z = bm.random.geometric(p=0.35, size=10000)
How many trials succeeded after a single run?
>>> (z == 1).sum() / 10000. 0.34889999999999999 #random