I have a set of (100, 3) Jaccarad-Index values, where the '3' denotes the number of model-input parameters. I want to compute the KL-Divergence for each combination of features using the sampling distribution of Jaccard indices. I'm truly at a loss on how to compute (numerically) probability distributions (or specifically kernel densities) for each column of Jaccard Index values with the hope of computing the KL-Divergence. At this point, I feel as if I don't know what I'm doing or, at least, there's a gap in understanding (wherever it may be). If someone can 'shepherd' me in the right direction, I'd greatly appreciate it. Below, I provided the array of Jaccard Index values. I apologize if this isn't the correct format for displaying a dataset this large (idk any other way), but I really need help.
np.array([[0.12 , 1. , 0.272727],
[0.074074, 0.882353, 0.208333],
[0.16 , 0.933333, 0.25 ],
[0.192308, 0.888889, 0.148148],
[0.238095, 0.473684, 0.45 ],
[0.26087 , 0.933333, 0.26087 ],
[0.333333, 0.75 , 0.3 ],
[0.2 , 0.722222, 0.166667],
[0.318182, 0.823529, 0.25 ],
[0.076923, 0.631579, 0.217391],
[0.142857, 0.722222, 0.26087 ],
[0.115385, 0.555556, 0.125 ],
[0.136364, 0.875 , 0.3 ],
[0.166667, 0.722222, 0.318182],
[0.115385, 0.764706, 0.166667],
[0.166667, 1. , 0.130435],
[0.125 , 1. , 0.08 ],
[0.272727, 0.666667, 0.181818],
[0.222222, 0.571429, 0.142857],
[0.26087 , 0.5 , 0.304348],
[0.076923, 1. , 0.333333],
[0.470588, 0.8 , 0.25 ],
[0.125 , 0.611111, 0.285714],
[0.083333, 0.625 , 0.2 ],
[0.26087 , 0.866667, 0.16 ],
[0.181818, 0.555556, 0.25 ],
[0.304348, 0.9375 , 0.318182],
[0.166667, 0.928571, 0.388889],
[0.192308, 0.684211, 0.304348],
[0.26087 , 0.866667, 0.190476],
[0.2 , 0.555556, 0.3 ],
[0.076923, 0.705882, 0.16 ],
[0.115385, 1. , 0.444444],
[0.208333, 0.611111, 0.2 ],
[0.2 , 0.833333, 0.333333],
[0.125 , 0.555556, 0.333333],
[0.166667, 0.681818, 0.103448],
[0.166667, 1. , 0.166667],
[0.173913, 0.764706, 0.153846],
[0.208333, 0.6 , 0.16 ],
[0.2 , 0.444444, 0.2 ],
[0.153846, 0.736842, 0.166667],
[0.074074, 0.777778, 0.4 ],
[0.071429, 0.9375 , 0.16 ],
[0.153846, 0.705882, 0.173913],
[0.111111, 1. , 0.130435],
[0.142857, 0.789474, 0.148148],
[0.166667, 0.722222, 0.148148],
[0.142857, 0.888889, 0.16 ],
[0.285714, 0.526316, 0.125 ],
[0.103448, 0.6 , 0.107143],
[0.148148, 0.777778, 0.192308],
[0.137931, 0.714286, 0.2 ],
[0.181818, 0.8125 , 0.285714],
[0.136364, 0.388889, 0.125 ],
[0.227273, 0.588235, 0.25 ],
[0.136364, 0.625 , 0.238095],
[0.157895, 0.5625 , 0.095238],
[0.3 , 0.473684, 0.181818],
[0.130435, 1. , 0.285714],
[0.318182, 0.75 , 0.388889],
[0.24 , 0.6 , 0.26087 ],
[0.173913, 0.55 , 0.26087 ],
[0.173913, 0.684211, 0.217391],
[0.111111, 1. , 0.208333],
[0.115385, 0.684211, 0.2 ],
[0.227273, 0.8125 , 0.368421],
[0.227273, 1. , 0.130435],
[0.24 , 0.7 , 0.192308],
[0.173913, 0.764706, 0.272727],
[0.304348, 0.8125 , 0.333333],
[0.291667, 0.666667, 0.347826],
[0.107143, 0.6 , 0.208333],
[0.192308, 0.842105, 0.2 ],
[0.217391, 0.714286, 0.12 ],
[0.217391, 1. , 0.076923],
[0.190476, 0.764706, 0.208333],
[0.033333, 0.571429, 0.153846],
[0.208333, 0.941176, 0.272727],
[0.333333, 0.722222, 0.153846],
[0.153846, 0.823529, 0.217391],
[0.136364, 0.8 , 0.529412],
[0.1 , 0.714286, 0.185185],
[0.142857, 0.785714, 0.333333],
[0.173913, 0.526316, 0.25 ],
[0.208333, 0.388889, 0.25 ],
[0.16 , 0.6 , 0.16 ],
[0.272727, 0.764706, 0.238095],
[0.086957, 0.625 , 0.190476],
[0.103448, 0.684211, 0.24 ],
[0.238095, 0.444444, 0.173913],
[0.35 , 0.875 , 0.227273],
[0.12 , 0.823529, 0.238095],
[0.28 , 0.722222, 0.178571],
[0.208333, 0.944444, 0.185185],
[0.2 , 0.611111, 0.208333],
[0.095238, 0.473684, 0.12 ],
[0.190476, 0.733333, 0.210526],
[0.192308, 0.75 , 0.217391],
[0.12 , 0.764706, 0.217391]])
I have used Seaborn's 'displot' (with "kind='kde'") to return plots for each column of Jaccard Index values: KDE for features 0, 1, and 2. I have also used scipy.gaussian_kde, with the hope that it'll return probabilities but to no avail (of course), and I've tried plt.hist from matplotlib.pyplot.
