fips.Gaussian#

class fips.Gaussian(levels, scale, *, within=None, metric='euclidean')[source]#

Bases: _Decay

A Gaussian correlation, \(\exp(-d^2 / (2 \ell^2))\) at a distance \(d\).

The limit of Matern as \(\nu \to \infty\): very smooth. On the Earth it takes metric="chordal" (with the great-circle distance it is not positive-definite).

Parameters:

levels (str | Sequence[str]) – The levels the distance is measured along (see Exponential).
scale (float | str | Timedelta) – The length scale \(\ell\), positive.
within (str | Sequence[str] | None) – Correlate only labels the same on these levels (see Exponential).
metric (str) – How the distance is measured (see Exponential).

Raises:

ValueError – As Exponential, and for metric="haversine".

Examples

>>> import pandas as pd
>>> from fips import Gaussian
>>> Gaussian("x", 1.0).correlation(pd.DataFrame({"x": [0.0, 1.0]})).round(4)
array([[1.    , 0.6065],
       [0.6065, 1.    ]])

Attributes

`metric`	How the distance is measured, `"euclidean"`, `"haversine"` or `"chordal"`.
`scale`	The length scale, a time for a datetime or timedelta level, else in the levels' units (km on the Earth).
`within`	The levels on which two labels must be the same to be correlated (`"time.hour"` is an attribute of a datetime level).

Methods

correlation(coords)

Return the correlation between the rows of coords, zero between groups (see CorrelationModel.correlation()).

Inherited

From CorrelationModel: levels, with_std