dask.array.random.chisquare
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 Quick Start.
- Parameters
- dffloat or array_like of floats
Number of degrees of freedom, must be > 0.
- sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k), thenm * n * ksamples are drawn. If size isNone(default), a single value is returned ifdfis a scalar. Otherwise,np.array(df).sizesamples are drawn.
- Returns
- outndarray 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.chisquarewhich should be used for new code.
Notes
The variable obtained by summing the squares of df independent, standard normally distributed random variables:
\[Q = \sum_{i=0}^{\mathtt{df}} X^2_i\]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