Model

class pyoptex.analysis.estimators.sams.models.model.Model(X, y, forced=None, mode='weak', dep=None)

Base class of a model specifying sampling and mutation functionalities in the SAMS algorithm.

This class can be extended by overwriting the fit function.

Attributes

Xnp.array(2d)

The encoded, normalized model matrix of the data

ynp.array(1d)

The output variable.

forcednp.array(1d)

Any terms that must be included in the model.

modeNone or ‘weak’ or ‘strong’

The heredity model during sampling.

depnp.array(2d)

The dependency matrix of size (N, N) with N the number of terms in the encoded model (output from Y2X). Term i depends on term j if dep(i, j) = true.

__init__(X, y, forced=None, mode='weak', dep=None)

Initialize of the base model in the SAMS algorithm.

Parameters

Xnp.array(2d)

The encoded, normalized model matrix of the data

ynp.array(1d)

The output variable.

forcednp.array(1d)

Any terms that must be included in the model.

modeNone or ‘weak’ or ‘strong’

The heredity model during sampling.

depnp.array(2d)

The dependency matrix of size (N, N) with N the number of terms in the encoded model (output from Y2X). Term i depends on term j if dep(i, j) = true.

Methods

Model.all(model_size)	Generates an array of all possible models.
Model.fit(model)	Fit a regression model on the current terms specified by model.
Model.init(model)	Create a random model by sequential sampling.
Model.mutate(model)	Mutate the model by removing atleast one term and replacing it.