mars.tensor.random.geometric¶

mars.tensor.random.geometric(p, size=None, chunk_size=None, gpu=None, dtype=None)[源代码]¶

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.

参数

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.

返回

out – Drawn samples from the parameterized geometric distribution.

返回类型

Tensor or scalar

实际案例

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

mars.tensor.random.gamma

mars.tensor.random.gumbel