Port a trained estimator to the syntax of a chosen programming
        language.

        Parameters
        ----------
        :param class_name : string, default: 'Brain'
            The name of the class in the returned result.
        :param method_name : string, default: 'predict'
            The name of the method in the returned result.
        :param export_data : bool, default: False
            Whether the model data should be saved or not.
        :param export_dir : string, default: '.' (current directory)
            The directory where the model data should be saved.
        :param export_filename : string, default: 'data.json'
            The filename of the exported model data.
        :param export_append_checksum : bool, default: False
            Whether to append the checksum to the filename or not.

        Returns
        -------
        :return : string
            The transpiled algorithm with the defined placeholders.

def export(self, class_name, method_name, export_data=False,
               export_dir='.', export_filename='data.json',
               export_append_checksum=False, **kwargs):
        """
        Port a trained estimator to the syntax of a chosen programming
        language.

        Parameters
        ----------
        :param class_name : string, default: 'Brain'
            The name of the class in the returned result.
        :param method_name : string, default: 'predict'
            The name of the method in the returned result.
        :param export_data : bool, default: False
            Whether the model data should be saved or not.
        :param export_dir : string, default: '.' (current directory)
            The directory where the model data should be saved.
        :param export_filename : string, default: 'data.json'
            The filename of the exported model data.
        :param export_append_checksum : bool, default: False
            Whether to append the checksum to the filename or not.

        Returns
        -------
        :return : string
            The transpiled algorithm with the defined placeholders.
        """
        # Arguments:
        self.class_name = class_name
        self.method_name = method_name

        # Estimator:
        est = self.estimator

        self.n_features = len(est.coef_[0])
        self.n_classes = len(est.classes_)
        self.is_binary = self.n_classes == 2
        self.prefix = 'binary' if self.is_binary else 'multi'

        temp_type = self.temp('type')
        temp_arr = self.temp('arr')
        temp_arr_ = self.temp('arr[]')
        temp_arr__ = self.temp('arr[][]')

        # Coefficients:
        if self.is_binary:
            coefs = est.coef_[0]
            coefs = [temp_type.format(self.repr(c)) for c in coefs]
            coefs = ', '.join(coefs)
            coefs = temp_arr_.format(type='double', name='coefficients',
                                     values=coefs, n=self.n_features)
        else:
            coefs = []
            for coef in est.coef_:
                tmp = [temp_type.format(self.repr(c)) for c in coef]
                tmp = temp_arr.format(', '.join(tmp))
                coefs.append(tmp)
            coefs = ', '.join(coefs)
            coefs = temp_arr__.format(type='double', name='coefficients',
                                      values=coefs, n=self.n_classes,
                                      m=self.n_features)
        self.coefficients = coefs

        # Intercepts:
        if self.is_binary:
            inters = est.intercept_[0]
            temp_init = self.temp('init')
            inters = temp_init.format(type='double', name='intercepts',
                                      value=self.repr(inters))
        else:
            inters = est.intercept_
            inters = [temp_type.format(self.repr(i)) for i in inters]
            inters = ', '.join(inters)
            inters = temp_arr_.format(type='double', name='intercepts',
                                      values=inters, n=self.n_classes)
        self.intercepts = inters

        if self.target_method == 'predict':
            # Exported:
            if export_data and os.path.isdir(export_dir):
                self.export_data(export_dir, export_filename,
                                 export_append_checksum)
                return self.predict('exported')
            # Separated:
            return self.predict('separated')