(self, input_sample, num_samples, num_params, k_choices,
               num_groups)
return self._strategy.sample(input_sample, num_samples, num_params,
                                     k_choices, num_groups)
Computes the optimum k_choices of trajectories
        from the input_sample.

        Parameters
        ----------
        input_sample : numpy.ndarray
        num_samples : int
            The number of samples to generate
        num_params : int
            The number of parameters
        k_choices : int
            The number of optimal trajectories
        num_groups : int
            The number of groups

        Returns
        -------
        numpy.ndarray
            An array of optimal trajectories
Computes the optimum k_choices of trajectories
        from the input_sample.
Computes the optimum k_choices of trajectories from the input_sample .
def sample(self, input_sample, num_samples, num_params, k_choices,
               num_groups):
        """Computes the optimum k_choices of trajectories
        from the input_sample.

        Parameters
        ----------
        input_sample : numpy.ndarray
        num_samples : int
            The number of samples to generate
        num_params : int
            The number of parameters
        k_choices : int
            The number of optimal trajectories
        num_groups : int
            The number of groups

        Returns
        -------
        numpy.ndarray
            An array of optimal trajectories
        """
        return self._strategy.sample(input_sample, num_samples, num_params,
                                     k_choices, num_groups)
def sample ( self , input_sample , num_samples , num_params , k_choices , num_groups ) : return self . _strategy . sample ( input_sample , num_samples , num_params , k_choices , num_groups )