API Reference¶

The namespace of scikit-guess is called skg. It exports all of the parameter estimation functions provided by the scikit.

The functions themselves live in individual sub-modules. Each module containing a fitting function also defines a model attribute, which refers to the model function being fitted. Each fitting routine has a model attribute pointing to the module-level model.

Sub-modules

`exp`	Exponential fit with and additive bias of the form $A + Be^{Cx}$ .
`gauss_cdf`	Gaussian cumulative distribution fit.
`gauss_pdf`	Gaussian probability density fit.
`gauss`	Unnormalized Gaussian bell curve fit.
`pow`	Power fit with additive bias of the form $A + Bx^C$ .
`weibull_cdf`	Weibull cumulative distribution fit with three parameters, of the form $F(t) = 1 - e^{-\left( \frac{t - \mu}{\beta} \right)^{\alpha}}$ .
`ngauss`	N-dimensional, unnormalized Gaussian fit.
`nsphere`	N-sphere fit with center and radius.
`util`	Shared utility functions used by the fitting routines.

Exported Functions

These functions are available directly in the skg namespace in addition to the modules that define them.

`exp_fit`(x, y[, sorted])	Exponential fit of the form $A + Be^{Cx}$ .
`gauss_cdf_fit`(x, y[, sorted])	Gaussian CDF fit of the form $\frac{1}{2}+\frac{1}{2}erf\left(\frac{x-\mu}{\sqrt{2}\sigma}\right)$ .
`gauss_pdf_fit`(x, y[, sorted])	Gaussian PDF fit of the form $\frac{1}{\sigma \sqrt{2 \pi}} e^{-\frac{1}{2}\left(\frac{x - \mu}{\sigma}\right)^2}$ .
`gauss_fit`(x, y[, sorted])	Gaussian bell curve fit of the form $a e^{-\frac{1}{2}\left(\frac{x - \mu}{\sigma}\right)^2}$ .
`pow_fit`(x, y[, sorted])	Power fit of the form $A + Bx^C$ .
`weibull_cdf_fit`(x, y[, sorted])	Weibull CDF fit of the form $F(t) = 1 - e^{-\left( \frac{t - \mu}{\beta} \right)^{\alpha}}$ .
`ngauss_fit`(x, y[, axis, weights, scaling])	An N-dimensional Gaussian fit.
`ngauss_from_image`(img[, weights, scaling])	Compute a Gaussian fit to an entire image.
`nsphere_fit`(x[, axis, scaling])	Fit an n-sphere to ND data.