deepaugment/controller.py
def ask(self):
        """Ask controller for the next hyperparameter search.


        If Bayesian Optimizer, samples next hyperparameters by its internal statistic calculations (Random Forest Estimators, Gaussian Processes, etc.). If Random Search, samples randomly
        Based on ask-tell design pattern

        Returns:
            list: list of hyperparameters
        """
        if self.method == "bayesian_optimization":
            return self.opt.ask()
        elif self.method == "random_search":
            return [func() for func in random_search_space]
Ask controller for the next hyperparameter search.


        If Bayesian Optimizer, samples next hyperparameters by its internal statistic calculations (Random Forest Estimators, Gaussian Processes, etc.). If Random Search, samples randomly
        Based on ask-tell design pattern

        Returns:
            list: list of hyperparameters
        
Ask controller for the next hyperparameter search.
If Bayesian Optimizer, samples next hyperparameters by its internal statistic calculations (Random Forest Estimators, Gaussian Processes, etc.). If Random Search, samples randomly
Based on ask-tell design pattern
Ask controller for the next hyperparameter search . If Bayesian Optimizer samples next hyperparameters by its internal statistic calculations ( Random Forest Estimators Gaussian Processes etc . ) . If Random Search samples randomly Based on ask - tell design pattern
def ask(self):
        if self.method == "bayesian_optimization":
            return self.opt.ask()
        elif self.method == "random_search":
            return [func() for func in random_search_space]
def ask ( self ) : if self . method == "bayesian_optimization" : return self . opt . ask ( ) elif self . method == "random_search" : return [ func ( ) for func in random_search_space ]
Ask controller for the next hyperparameter search.
Ask controller for the next hyperparameter search .
"""Ask controller for the next hyperparameter search.


        If Bayesian Optimizer, samples next hyperparameters by its internal statistic calculations (Random Forest Estimators, Gaussian Processes, etc.). If Random Search, samples randomly
        Based on ask-tell design pattern

        Returns:
            list: list of hyperparameters
        """
list of hyperparameters
list of hyperparameters
list
self