0b0afd2d644c65554c907f8e8320e507f8caaefc,sklearn/feature_selection/_mutual_info.py,,_compute_mi_cc,#Any#Any#Any#,18

Before Change

    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)

After Change

    nx = kd.query_radius(x, radius, count_only=True, return_distance=False)
    nx = np.array(nx) - 1.0

    kd = KDTree(y, metric="chebyshev")
    ny = kd.query_radius(y, radius, count_only=True, return_distance=False)
    ny = np.array(ny) - 1.0

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

Instances