chisquare(self, df, size=None, chunks='auto', **kwargs)

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

Some inconsistencies with the Dask version may exist.

When :None:None:`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.

Notes

The variable obtained by summing the squares of :None:None:`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,

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

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.

Raises

ValueError

When :None:None:`df` <= 0 or when an inappropriate :None:None:`size` (e.g. size=-1 ) is given.

Returns

out : ndarray or scalar

Drawn samples from the parameterized chi-square distribution.

Draw samples from a chi-square distribution.

See Also

Generator.chisquare

which should be used for new code.

Examples

This example is valid syntax, but we were not able to check execution

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

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: /dask/array/random.py#200
type: <class 'function'>
Commit: