def _compute_mi_cc(x, y, n_neighbors):
    n_samples = x.size

    x = x.reshape((-1, 1))
    y = y.reshape((-1, 1))
    xy = np.hstack((x, y))

    # Here we rely on NearestNeighbors to select the fastest algorithm.
    nn = NearestNeighbors(metric='chebyshev', n_neighbors=n_neighbors)

    nn.fit(xy)
    radius = nn.kneighbors()[0]
    radius = np.nextafter(radius[:, -1], 0)

    # Algorithm is selected explicitly to allow passing an array as radius
    # later (not all algorithms support this).
    nn.set_params(algorithm='kd_tree')

    nn.fit(x)
    ind = nn.radius_neighbors(radius=radius, return_distance=False)
    nx = np.array([i.size for i in ind])

    nn.fit(y)
    ind = nn.radius_neighbors(radius=radius, return_distance=False)
    ny = np.array([i.size for i in ind])

    mi = (digamma(n_samples) + digamma(n_neighbors) -
          np.mean(digamma(nx + 1)) - np.mean(digamma(ny + 1)))

    return max(0, mi)
Generate RBF centers
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/feature_selection/mutual_info_.py/_compute_mi_cc--crossover with--dataset/ETHPy150Open dclambert/Python-ELM/random_layer.py/RandomLayer._compute_centers