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)
`predict_time`(utils)
`predict_time_prob`(utils[, return_cov])
`predict_utility`(times)
`predict_utility_prob`(times[, return_cov])

Methods Documentation

fit(times, utils) → FutureUtilityRegressor[source]

predict_time(utils) → ndarray[source]

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

predict_utility(times) → ndarray[source]

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