def _SMOTE(T, N, k, h = 1.0):
    """
    Returns (N/100) * n_minority_samples synthetic minority samples.

    Parameters
    ----------
    T : array-like, shape = [n_minority_samples, n_features]
        Holds the minority samples
    N : percetange of new synthetic samples:
        n_synthetic_samples = N/100 * n_minority_samples. Can be < 100.
    k : int. Number of nearest neighbours.

    Returns
    -------
    S : Synthetic samples. array,
        shape = [(N/100) * n_minority_samples, n_features].
    """
    n_minority_samples, n_features = T.shape

    if N < 100:
        #create synthetic samples only for a subset of T.
        #TODO: select random minortiy samples
        N = 100
        pass

    if (N % 100) != 0:
        raise ValueError("N must be < 100 or multiple of 100")

    N = N/100
    n_synthetic_samples = N * n_minority_samples
    S = np.zeros(shape=(n_synthetic_samples, n_features))

    #Learn nearest neighbours
    neigh = NearestNeighbors(n_neighbors = k)
    neigh.fit(T)

    #Calculate synthetic samples
    for i in range(n_minority_samples):
        nn = neigh.kneighbors(T[i], return_distance=False)
        for n in range(int(N)):
            nn_index = choice(nn[0])
            #NOTE: nn includes T[i], we don't want to select it
            while nn_index == i:
                nn_index = choice(nn[0])

            dif = T[nn_index] - T[i]
            gap = np.random.uniform(low = 0.0, high = h)
            S[n + i * N, :] = T[i,:] + gap * dif[:]

    return S
Returns (N/100) * n_minority_samples synthetic minority samples.

    Parameters
    ----------
    T : array-like, shape = [n_minority_samples, n_features]
        Holds the minority samples
    N : percetange of new synthetic samples:
        n_synthetic_samples = N/100 * n_minority_samples. Can be < 100.
    k : int. Number of nearest neighbours.

    Returns
    -------
    S : Synthetic samples. array,
        shape = [(N/100) * n_minority_samples, n_features].