# revrand.basis_functions.PolynomialBasis¶

class revrand.basis_functions.PolynomialBasis(order, include_bias=True, regularizer=None)

Polynomial basis class.

This essentially creates the concatenation,

$\phi(\mathbf{X}) = [\mathbf{1}, \mathbf{X}^1, \ldots, \mathbf{X}^p]$

where $$p$$ is the order of the polynomial.

Parameters: order (int) – the order of the polynomial to create. include_bias (bool, optional) – If True (default), include the bias column (column of ones which acts as the intercept term in a linear model) regularizer (None, Parameter, optional) – The (initial) value of the regularizer/prior variance to apply to the regression weights of this basis function. The Parameter object must have a scalar value. If it is not set, it will take on a default value of Parameter(gamma(1.), Positive()).
__init__(order, include_bias=True, regularizer=None)

See this class’s docstring.

Methods

 __init__(order[, include_bias, regularizer]) See this class’s docstring. get_dim(X) Get the output dimensionality of this basis. grad(X) Return the gradient of the basis function for each parameter. params_values() Get a list of the Parameter values if they have a value. regularizer_diagonal(X[, regularizer]) Get the diagonal of the prior variance on the weights (regularizer). transform(X) Return this basis applied to X.

Attributes

 params Get this basis’ Parameter types. regularizer Get the Parameter value of this basis’ regularizer.