Knn via kdtree (elixir-nx#211)

* KNN via KDTree

* Remove redundant chunk of code

* Update lib/scholar/neighbors/kd_tree.ex

* Remove redundant variable, change spelling, change handling type

* Format

* Add amendments

* Add benchmark, tests, documentation, and metric option

* Format

* Change API

* Update knn benchmark

benchmarks/knn.exs

@@ -0,0 +1,27 @@
+# mix run benchmarks/knn.exs
+Nx.global_default_backend(EXLA.Backend)
+Nx.Defn.global_default_options(compiler: EXLA)
+
+key = Nx.Random.key(System.os_time())
+
+inputs_knn = %{
+  "100x10" => elem(Nx.Random.uniform(key, 0, 100, shape: {100, 10}), 0),
+  "1000x10" => elem(Nx.Random.uniform(key, 0, 1000, shape: {1000, 10}), 0),
+  "10000x10" => elem(Nx.Random.uniform(key, 0, 10000, shape: {10000, 10}), 0)
+}
+
+Benchee.run(
+  %{
+    "kdtree" => fn x ->
+      kdtree = Scholar.Neighbors.KDTree.fit_bounded(x, Nx.axis_size(x, 0))
+      Scholar.Neighbors.KDTree.predict(kdtree, x, k: 4)
+    end,
+    "brute force knn" => fn x ->
+      model = Scholar.Neighbors.KNearestNeighbors.fit(x, Nx.broadcast(1, {Nx.axis_size(x, 0)}), num_classes: 2, num_neighbors: 4)
+      Scholar.Neighbors.KNearestNeighbors.k_neighbors(model, x)
+    end
+  },
+  time: 10,
+  memory_time: 2,
+  inputs: inputs_knn
+)