revrand.basis_functions.PolynomialBasis¶
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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
orderof 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()).
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__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 Parametervalues 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
paramsGet this basis’ Parameter types. regularizerGet the Parametervalue of this basis’ regularizer.
