mars.tensor.random.poisson¶
- mars.tensor.random.poisson(lam=1.0, size=None, chunk_size=None, gpu=None, dtype=None)[源代码]¶
Draw samples from a Poisson distribution.
The Poisson distribution is the limit of the binomial distribution for large N.
- 参数
lam (float or array_like of floats) – Expectation of interval, should be >= 0. A sequence of expectation intervals must be broadcastable over the requested size.
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 iflam
is a scalar. Otherwise,mt.array(lam).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 Poisson distribution.
- 返回类型
Tensor or scalar
提示
The Poisson distribution
\[f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}\]For events with an expected separation \(\lambda\) the Poisson distribution \(f(k; \lambda)\) describes the probability of \(k\) events occurring within the observed interval \(\lambda\).
Because the output is limited to the range of the C long type, a ValueError is raised when lam is within 10 sigma of the maximum representable value.
引用
- 1
Weisstein, Eric W. “Poisson Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html
- 2
Wikipedia, “Poisson distribution”, http://en.wikipedia.org/wiki/Poisson_distribution
实际案例
Draw samples from the distribution:
>>> import mars.tensor as mt >>> s = mt.random.poisson(5, 10000)
Display histogram of the sample:
>>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(s.execute(), 14, normed=True) >>> plt.show()
Draw each 100 values for lambda 100 and 500:
>>> s = mt.random.poisson(lam=(100., 500.), size=(100, 2))