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))
