I'm trying to understand how does the KernelDensity class in scikit-learn work. Consider the following two cases which build a kernel from two different arrays (a). I'm wondering why the result of scoring on the array b in both cases is the same? Shouldn't a kernel from 10 same points be different from that from 2 points? The one from the 10 points should indicate more density. So, why the final scoring result is the same in both cases?

 case A:
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    a = np.array([[1],[1],[1],[1],[1],[1],[1],[1],[1],[1]])
    
    kde = KernelDensity(bandwidth=0.1)
    
    kde.fit(a)
    
    b = np.array([[1]])
    
    log_dens = kde.score_samples(b)
    
    print('Probability is: {}'.format(np.exp(log_dens)))

Probability is: [3.9894228]


 case B:
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    a = np.array([[1],[1]])
    
    kde = KernelDensity(bandwidth=0.1)
    
    kde.fit(a)
    
    b = np.array([[1]])
    
    log_dens = kde.score_samples(b)
    
    print('Probability is: {}'.format(np.exp(log_dens)))

Probability is: [3.9894228]