# mars.tensor.random.geometric#

mars.tensor.random.geometric(p, size=None, chunk_size=None, gpu=None, dtype=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), then m * n * k samples are drawn. If size is None (default), a single value is returned if p is a scalar. Otherwise, mt.array(p).size samples are drawn.

• chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension

• gpu (bool, optional) – Allocate the tensor on GPU if True, False as default

• dtype (data-type, optional) – Data-type of the returned tensor.

Returns

out – Drawn samples from the parameterized geometric distribution.

Return type

Tensor or scalar

Examples

Draw ten thousand values from the geometric distribution, with the probability of an individual success equal to 0.35:

>>> import mars.tensor as mt

>>> z = mt.random.geometric(p=0.35, size=10000)


How many trials succeeded after a single run?

>>> ((z == 1).sum() / 10000.).execute()
0.34889999999999999 #random