(
        self, X, y_dtype, y_type, nn_data, nn_num, n_samples, step_size=1.0
    )
return X_new, y_new
A support function that returns artificial samples constructed along
        the line connecting nearest neighbours.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Points from which the points will be created.

        y_dtype : dtype
            The data type of the targets.

        y_type : str or int
            The minority target value, just so the function can return the
            target values for the synthetic variables with correct length in
            a clear format.

        nn_data : ndarray of shape (n_samples_all, n_features)
            Data set carrying all the neighbours to be used

        nn_num : ndarray of shape (n_samples_all, k_nearest_neighbours)
            The nearest neighbours of each sample in `nn_data`.

        n_samples : int
            The number of samples to generate.

        step_size : float, default=1.0
            The step size to create samples.

        Returns
        -------
        X_new : {ndarray, sparse matrix} of shape (n_samples_new, n_features)
            Synthetically generated samples.

        y_new : ndarray of shape (n_samples_new,)
            Target values for synthetic samples.
A support function that returns artificial samples constructed along
        the line connecting nearest neighbours.
A support function that returns artificial samples constructed along the line connecting nearest neighbours .
def _make_samples(
        self, X, y_dtype, y_type, nn_data, nn_num, n_samples, step_size=1.0
    ):
        """A support function that returns artificial samples constructed along
        the line connecting nearest neighbours.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Points from which the points will be created.

        y_dtype : dtype
            The data type of the targets.

        y_type : str or int
            The minority target value, just so the function can return the
            target values for the synthetic variables with correct length in
            a clear format.

        nn_data : ndarray of shape (n_samples_all, n_features)
            Data set carrying all the neighbours to be used

        nn_num : ndarray of shape (n_samples_all, k_nearest_neighbours)
            The nearest neighbours of each sample in `nn_data`.

        n_samples : int
            The number of samples to generate.

        step_size : float, default=1.0
            The step size to create samples.

        Returns
        -------
        X_new : {ndarray, sparse matrix} of shape (n_samples_new, n_features)
            Synthetically generated samples.

        y_new : ndarray of shape (n_samples_new,)
            Target values for synthetic samples.
        """
        random_state = check_random_state(self.random_state)
        samples_indices = random_state.randint(low=0, high=nn_num.size, size=n_samples)

        # np.newaxis for backwards compatability with random_state
        steps = step_size * random_state.uniform(size=n_samples)[:, np.newaxis]
        rows = np.floor_divide(samples_indices, nn_num.shape[1])
        cols = np.mod(samples_indices, nn_num.shape[1])

        X_new = self._generate_samples(X, nn_data, nn_num, rows, cols, steps)
        y_new = np.full(n_samples, fill_value=y_type, dtype=y_dtype)
        return X_new, y_new
def _make_samples ( self , X , y_dtype , y_type , nn_data , nn_num , n_samples , step_size = 1.0 ) : random_state = check_random_state ( self . random_state ) samples_indices = random_state . randint ( low = 0 , high = nn_num . size , size = n_samples ) # np.newaxis for backwards compatability with random_state steps = step_size * random_state . uniform ( size = n_samples ) [ : , np . newaxis ] rows = np . floor_divide ( samples_indices , nn_num . shape [ 1 ] ) cols = np . mod ( samples_indices , nn_num . shape [ 1 ] ) X_new = self . _generate_samples ( X , nn_data , nn_num , rows , cols , steps ) y_new = np . full ( n_samples , fill_value = y_type , dtype = y_dtype ) return X_new , y_new