Update CI (#317)

Author: josevalim

.github/workflows/ci.yml

@@ -11,10 +11,10 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - elixir: "1.15.6"
-            otp: "26.1"
+          - elixir: "1.18.1"
+            otp: "27.1"
             lint: true
-          - elixir: "1.14.5"
+          - elixir: "1.15.8"
             otp: "26.1"
     steps:
       - uses: actions/checkout@v2

test/scholar/neighbors/knn_regressor_test.exs

@@ -93,7 +93,18 @@ defmodule Scholar.Neighbors.KNNRegressorTest do
 
     test "predict with 2D labels" do
       y =
-        Nx.tensor([[1, 4], [0, 3], [2, 5], [0, 3], [0, 3], [1, 4], [2, 5], [0, 3], [1, 4], [2, 5]])
+        Nx.tensor([
+          [1, 4],
+          [0, 3],
+          [2, 5],
+          [0, 3],
+          [0, 3],
+          [1, 4],
+          [2, 5],
+          [0, 3],
+          [1, 4],
+          [2, 5]
+        ])
 
       model = KNNRegressor.fit(x_train(), y, num_neighbors: 3)
       y_pred = KNNRegressor.predict(model, x())
@@ -111,7 +122,18 @@ defmodule Scholar.Neighbors.KNNRegressorTest do
 
     test "predict with 2D labels, cosine metric and weights set to :distance" do
       y =
-        Nx.tensor([[1, 4], [0, 3], [2, 5], [0, 3], [0, 3], [1, 4], [2, 5], [0, 3], [1, 4], [2, 5]])
+        Nx.tensor([
+          [1, 4],
+          [0, 3],
+          [2, 5],
+          [0, 3],
+          [0, 3],
+          [1, 4],
+          [2, 5],
+          [0, 3],
+          [1, 4],
+          [2, 5]
+        ])
 
       model =
         KNNRegressor.fit(x_train(), y, num_neighbors: 3, metric: :cosine, weights: :distance)

test/scholar/neighbors/rnn_regressor_test.exs

@@ -54,7 +54,18 @@ defmodule Scholar.Neighbors.RadiusNNRegressorTest do
 
     test "predict with weights set to :distance and with specific metric and 2d labels" do
       y =
-        Nx.tensor([[1, 4], [0, 3], [2, 5], [0, 3], [0, 3], [1, 4], [2, 5], [0, 3], [1, 4], [2, 5]])
+        Nx.tensor([
+          [1, 4],
+          [0, 3],
+          [2, 5],
+          [0, 3],
+          [0, 3],
+          [1, 4],
+          [2, 5],
+          [0, 3],
+          [1, 4],
+          [2, 5]
+        ])
 
       model =
         RadiusNNRegressor.fit(x(), y,