mars.tensor.random.
poisson
Draw samples from a Poisson distribution.
The Poisson distribution is the limit of the binomial distribution for large N.
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), then m * n * k samples are drawn. If size is None (default), a single value is returned if lam is a scalar. Otherwise, mt.array(lam).size samples are drawn.
(m, n, k)
m * n * k
None
lam
mt.array(lam).size
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.
out – Drawn samples from the parameterized Poisson distribution.
Tensor or scalar
Notes
The Poisson distribution
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.
References
Weisstein, Eric W. “Poisson Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html
Wikipedia, “Poisson distribution”, http://en.wikipedia.org/wiki/Poisson_distribution
Examples
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))