FutureUtilityRegressor

class negmas.models.future.FutureUtilityRegressor(regressor_factory=<class 'sklearn.gaussian_process._gpr.GaussianProcessRegressor'>, **kwargs)[source]

Bases: object

Represents a regressor for own-utility for the future of the negotiation.

Remarks:

We assume that the negotiation goes from time 0 to 1 (relative_time).

Methods Summary

`fit`(times, utils)	rtype: `FutureUtilityRegressor`
`predict_time`(utils)	rtype: `ndarray`
`predict_time_prob`(utils[, return_cov])	rtype: `ndarray`
`predict_utility`(times)	rtype: `ndarray`
`predict_utility_prob`(times[, return_cov])	rtype: `ndarray`

Methods Documentation

fit(times, utils)[source]

Return type:: FutureUtilityRegressor

predict_time(utils)[source]

Return type:: ndarray

predict_time_prob(utils, return_cov=False)[source]

Return type:: ndarray

predict_utility(times)[source]

Return type:: ndarray

predict_utility_prob(times, return_cov=False)[source]

Return type:: ndarray