skl_groups/divergences/knn.py
def tsallis(alphas, Ks, dim, required, clamp=True, to_self=False):
    r'''
    Estimate the Tsallis-alpha divergence between distributions, based on kNN
    distances:  (\int p^alpha q^(1-\alpha) - 1) / (\alpha - 1)

    If clamp (the default), enforces the estimate is nonnegative.

    Returns an array of shape (num_alphas, num_Ks).
    '''
    alphas = np.reshape(alphas, (-1, 1))
    alpha_est = required

    est = alpha_est - 1
    est /= alphas - 1
    if clamp:
        np.maximum(est, 0, out=est)
    return est
def tsallis(alphas, Ks, dim, required, clamp=True, to_self=False):
    r'''
    Estimate the Tsallis-alpha divergence between distributions, based on kNN
    distances:  (\int p^alpha q^(1-\alpha) - 1) / (\alpha - 1)

    If clamp (the default), enforces the estimate is nonnegative.

    Returns an array of shape (num_alphas, num_Ks).
    '''
    alphas = np.reshape(alphas, (-1, 1))
    alpha_est = required

    est = alpha_est - 1
    est /= alphas - 1
    if clamp:
        np.maximum(est, 0, out=est)
    return est
def tsallis ( alphas , Ks , dim , required , clamp = True , to_self = False ) : alphas = np . reshape ( alphas , ( - 1 , 1 ) ) alpha_est = required est = alpha_est - 1 est /= alphas - 1 if clamp : np . maximum ( est , 0 , out = est ) return est
r'''
    Estimate the Tsallis-alpha divergence between distributions, based on kNN
    distances:  (\int p^alpha q^(1-\alpha) - 1) / (\alpha - 1)

    If clamp (the default), enforces the estimate is nonnegative.

    Returns an array of shape (num_alphas, num_Ks).
r Estimate the Tsallis - alpha divergence between distributions based on kNN distances : ( \ int p^alpha q^ ( 1 - \ alpha ) - 1 ) / ( \ alpha - 1 )