mars.tensor.random.rayleigh(scale=1.0, size=None, chunk_size=None, gpu=None, dtype=None)[source]#

Draw samples from a Rayleigh distribution.

The \(\chi\) and Weibull distributions are generalizations of the Rayleigh.

  • scale (float or array_like of floats, optional) – Scale, also equals the mode. Should be >= 0. Default is 1.

  • 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 scale is a scalar. Otherwise, mt.array(scale).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.


out – Drawn samples from the parameterized Rayleigh distribution.

Return type

Tensor or scalar


The probability density function for the Rayleigh distribution is

\[P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}}\]

The Rayleigh distribution would arise, for example, if the East and North components of the wind velocity had identical zero-mean Gaussian distributions. Then the wind speed would have a Rayleigh distribution.



Brighton Webs Ltd., “Rayleigh Distribution,”


Wikipedia, “Rayleigh distribution”


Draw values from the distribution and plot the histogram

>>> import matplotlib.pyplot as plt
>>> import mars.tensor as mt
>>> values = plt.hist(mt.random.rayleigh(3, 100000).execute(), bins=200, normed=True)

Wave heights tend to follow a Rayleigh distribution. If the mean wave height is 1 meter, what fraction of waves are likely to be larger than 3 meters?

>>> meanvalue = 1
>>> modevalue = mt.sqrt(2 / mt.pi) * meanvalue
>>> s = mt.random.rayleigh(modevalue, 1000000)

The percentage of waves larger than 3 meters is:

>>> (100.*mt.sum(s>3)/1000000.).execute()