# mars.tensor.random.standard_cauchy#

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

Draw samples from a standard Cauchy distribution with mode = 0.

Also known as the Lorentz distribution.

Parameters
• 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. Default is None, in which case a single value is returned.

• 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

samples – The drawn samples.

Return type

Tensor or scalar

Notes

The probability density function for the full Cauchy distribution is

$P(x; x_0, \gamma) = \frac{1}{\pi \gamma \bigl[ 1+ (\frac{x-x_0}{\gamma})^2 \bigr] }$

and the Standard Cauchy distribution just sets $$x_0=0$$ and $$\gamma=1$$

The Cauchy distribution arises in the solution to the driven harmonic oscillator problem, and also describes spectral line broadening. It also describes the distribution of values at which a line tilted at a random angle will cut the x axis.

When studying hypothesis tests that assume normality, seeing how the tests perform on data from a Cauchy distribution is a good indicator of their sensitivity to a heavy-tailed distribution, since the Cauchy looks very much like a Gaussian distribution, but with heavier tails.

References

1

NIST/SEMATECH e-Handbook of Statistical Methods, “Cauchy Distribution”, http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm

2

Weisstein, Eric W. “Cauchy Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/CauchyDistribution.html

3

Wikipedia, “Cauchy distribution” http://en.wikipedia.org/wiki/Cauchy_distribution

Examples

Draw samples and plot the distribution:

>>> import mars.tensor as mt
>>> import matplotlib.pyplot as plt

>>> s = mt.random.standard_cauchy(1000000)
>>> s = s[(s>-25) & (s<25)]  # truncate distribution so it plots well
>>> plt.hist(s.execute(), bins=100)
>>> plt.show()