# dask.array.random.chisquare

dask.array.random.chisquare(*args, **kwargs)

  Draw samples from a chi-square distribution.

  This docstring was copied from numpy.random.mtrand.RandomState.chisquare.

  Some inconsistencies with the Dask version may exist.

  When df independent random variables, each with standard normal distributions (mean 0, variance 1), are squared and summed, the resulting distribution is chi-square (see Notes). This distribution is often used in hypothesis testing.

  Note

  New code should use the ~numpy.random.Generator.chisquare method of a ~numpy.random.Generator instance instead; please see the random-quick-start.

  Parameters:
    df float or array_like of floats

      Number of degrees of freedom, must be > 0.

    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 `df` is a scalar. Otherwise, `np.array(df).size` samples are drawn.

  Returns:
    out ndarray or scalar

      Drawn samples from the parameterized chi-square distribution.

  Raises:
    ValueError

      When df <= 0 or when an inappropriate size (e.g. `size=-1`) is given.

  See also

  `random.Generator.chisquare`

    which should be used for new code.

  Notes

  The variable obtained by summing the squares of df independent, standard normally distributed random variables:

  \[Q = \sum_{i=1}^{\mathtt{df}} X^2_i\]

  is chi-square distributed, denoted

  \[Q \sim \chi^2_k.\]

  The probability density function of the chi-squared distribution is

  \[p(x) = \frac{(1/2)^{k/2}}{\Gamma(k/2)} x^{k/2 - 1} e^{-x/2},\]

  where \(\Gamma\) is the gamma function,

  \[\Gamma(x) = \int_0^{-\infty} t^{x - 1} e^{-t} dt.\]

  References

  [1]

  NIST “Engineering Statistics Handbook” https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm

  Examples

      >>> np.random.chisquare(2,4)
      array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272]) # random