dist, ind = tree.query(data[:100], k=5) print(dist[0]) print(ind[0]) dist, ind = tree.query(np.zeros([1, 784], dtype=np.float32), k=5) print(dist[0]) print(ind[0]) nbrs = { "d0": dist[:, 0], "d1": dist[:, 1], "d2": dist[:, 2], "d3": dist[:, 3], "d4": dist[:, 4], "i0": ind[:, 0], "i1": ind[:, 1], "i2": ind[:, 2], "i3": ind[:, 3], "i4": ind[:, 4], } csv = pd.DataFrame(nbrs) csv.to_csv("../../data/mnist_nbrs.csv") tree = pygrandma.PyGrandma() tree.set_cutoff(10) tree.set_scale_base(1.3) tree.fit(data) print(tree.knn(data[0], 5))