skg.pow

Power fit with additive bias of the form A + Bx^C.

As a general rule, pow_fit(x, y, ...) is equivalent to exp_fit(log(x), y, ...) since A + Be^{Cx} = A + B \left( e^x \right)^C.

Todo

Add proper handling of colinear inputs (and other singular matrix cases).

Todo

Add tests.

Todo

Add nan_policy argument.

Functions

model(x, a, b, c) Compute y = A + Bx^C.
pow_fit(x, y[, sorted]) Power fit of the form A + Bx^C.
skg.pow.model(x, a, b, c)[source]

Compute y = A + Bx^C.

Parameters:
  • x (array-like) – The value of the model will be the same shape as the input.
  • a (float) – The additive bias.
  • b (float) – The multiplicative bias.
  • c (float) – The power.
Returns:

y – An array of the same shape as x, containing the model computed for the given parameters.
Return type:

array-like
skg.pow.pow_fit(x, y, sorted=True)[source]

Power fit of the form A + Bx^C.

This implementation is based on the approximate solution to integral equation (22), presented in Régressions et équations intégrales. A power fit is regarded as an exponential fit with a logarithmically scaled x-axis in this algorightm.

Parameters:
  • x (array-like) – The x-values of the data points. The fit will be performed on a raveled version of this array. All elements must be positive.
  • y (array-like) – The y-values of the data points corresponding to x. Must be the same size as x. The fit will be performed on a raveled version of this array.
  • sorted (bool) – Set to True if x is already monotonically increasing or decreasing. If False, x will be sorted into increasing order, and y will be sorted along with it.
Returns:

a, b, c – A three-element array containing the estimated additive and multiplicative biases and power, in that order.
Return type:

ndarray

References