Remove unused second argument

elixir-nx · Dec 18, 2023 · f3b382b · f3b382b
1 parent e8fee47
commit f3b382b
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Showing 3 changed files with 6 additions and 6 deletions.
diff --git a/benchmarks/knn.exs b/benchmarks/knn.exs
@@ -13,7 +13,7 @@ inputs_knn = %{
 Benchee.run(
   %{
     "kdtree" => fn x ->
-      kdtree = Scholar.Neighbors.KDTree.fit(x, Nx.axis_size(x, 0))
+      kdtree = Scholar.Neighbors.KDTree.fit(x)
       Scholar.Neighbors.KDTree.predict(kdtree, x, k: 4)
     end,
     "brute force knn" => fn x ->

diff --git a/lib/scholar/neighbors/kd_tree.ex b/lib/scholar/neighbors/kd_tree.ex
@@ -49,7 +49,7 @@ defmodule Scholar.Neighbors.KDTree do
 
   ## Examples
 
-      iex> Scholar.Neighbors.KDTree.fit(Nx.iota({5, 2}), 10)
+      iex> Scholar.Neighbors.KDTree.fit(Nx.iota({5, 2}))
       %Scholar.Neighbors.KDTree{
         data: Nx.iota({5, 2}),
         levels: 3,
@@ -233,7 +233,7 @@ defmodule Scholar.Neighbors.KDTree do
 
       iex> x = Nx.iota({10, 2})
       iex> x_predict = Nx.tensor([[2, 5], [1, 9], [6, 4]])
-      iex> kdtree = Scholar.Neighbors.KDTree.fit(x, 20)
+      iex> kdtree = Scholar.Neighbors.KDTree.fit(x)
       iex> Scholar.Neighbors.KDTree.predict(kdtree, x_predict, k: 3)
       #Nx.Tensor<
         s64[3][3]

diff --git a/test/scholar/neighbors/kd_tree_test.exs b/test/scholar/neighbors/kd_tree_test.exs
@@ -56,21 +56,21 @@ defmodule Scholar.Neighbors.KDTreeTest do
 
   describe "predict knn" do
     test "all defaults" do
-      kdtree = KDTree.fit(x(), 10)
+      kdtree = KDTree.fit(x())
 
       assert KDTree.predict(kdtree, x_pred()) ==
                Nx.tensor([[0, 6, 4], [5, 2, 9], [0, 9, 2], [5, 2, 7]])
     end
 
     test "metric set to {:minkowski, 1.5}" do
-      kdtree = KDTree.fit(x(), 10)
+      kdtree = KDTree.fit(x())
 
       assert KDTree.predict(kdtree, x_pred(), metric: {:minkowski, 1.5}) ==
                Nx.tensor([[0, 6, 2], [5, 2, 9], [0, 9, 2], [5, 2, 7]])
     end
 
     test "k set to 4" do
-      kdtree = KDTree.fit(x(), 10)
+      kdtree = KDTree.fit(x())
 
       assert KDTree.predict(kdtree, x_pred(), k: 4) ==
                Nx.tensor([[0, 6, 4, 2], [5, 2, 9, 0], [0, 9, 2, 5], [5, 2, 7, 4]])