Class RidgeRegression

:param array-like X: the inputs X of the regression problem :param array-like Y: the outputs Y :param :py:class:`paramz.examples.ridge_regression.Regularizer` regularizer: the regularizer to use :param str name: the name of this regression object

Overrides: object.__init__

Compute the design matrix for this model using the degrees given by the index array in degrees

:param array-like Xpred: inputs to compute the design matrix for :param array-like degrees: array of degrees to use [default=range(self.degree+1)] :returns array-like phi: The design matrix [degree x #samples x #dimensions]

This method gets called when parameters have changed. Another way of listening to param changes is to add self as a listener to the param, such that updates get passed through. See :py:function:``paramz.param.Observable.add_observer``

Overrides: core.parameter_core.Parameterizable.parameters_changed

(inherited documentation)

The objective function for the given algorithm.

This function is the true objective, which wants to be minimized. Note that all parameters are already set and in place, so you just need to return the objective function here.

For probabilistic models this is the negative log_likelihood (including the MAP prior), so we return it here. If your model is not probabilistic, just return your objective to minimize here!

Overrides: model.Model.objective_function

(inherited documentation)

weights

Get Method:

unreachable.weights(self)

degree

Get Method:

unreachable.degree(self)