Add core jdk 17 support to the scala library

jelmerk · Jan 1, 2024 · ac9c808 · ac9c808
1 parent 13349cf
commit ac9c808
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diff --git a/build.sbt b/build.sbt
@@ -145,6 +145,7 @@ lazy val hnswlibMetricsDropwizard = (project in file("hnswlib-metrics-dropwizard
 lazy val hnswlibScala = (project in file("hnswlib-scala"))
   .dependsOn(hnswlibCore)
   .dependsOn(hnswlibMetricsDropwizard % Optional)
+  .dependsOn(hnswlibCoreJdk17 % Optional)
   .settings(
     name               := "hnswlib-scala",
     crossScalaVersions := List("2.11.12", "2.12.18", "2.13.10"),

diff --git a/hnswlib-scala/src/main/scala/com/github/jelmerk/knn/scalalike/package.scala b/hnswlib-scala/src/main/scala/com/github/jelmerk/knn/scalalike/package.scala
@@ -126,4 +126,68 @@ package object scalalike {
     */
   val doubleSparseVectorInnerProduct: DistanceFunction[SparseVector[Array[Double]], Double] =
     JDistanceFunctions.DOUBLE_SPARSE_VECTOR_INNER_PRODUCT.distance
+
+  object jdk17DistanceFunctions {
+
+    /**
+     * Calculates the cosine distance.
+     */
+    val vectorFloat128CosineDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_128_COSINE_DISTANCE.distance
+
+    /**
+     * Calculates the cosine distance.
+     */
+    val vectorFloat256CosineDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_256_COSINE_DISTANCE.distance
+
+    /**
+     * Calculates the inner product.
+     */
+    val vectorFloat128InnerProduct: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_128_INNER_PRODUCT.distance
+
+    /**
+     * Calculates the inner product.
+     */
+    val vectorFloat256InnerProduct: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_256_INNER_PRODUCT.distance
+
+    /**
+     * Calculates the euclidean distance.
+     */
+    val vectorFloat128EuclideanDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_128_EUCLIDEAN_DISTANCE.distance
+
+    /**
+     * Calculates the euclidean distance.
+     */
+    val vectorFloat256EuclideanDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_256_EUCLIDEAN_DISTANCE.distance
+
+    /**
+     * Calculates the manhattan distance.
+     */
+    val vectorFloat128ManhattanDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_128_MANHATTAN_DISTANCE.distance
+
+    /**
+     * Calculates the manhattan distance.
+     */
+    val vectorFloat256ManhattanDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_256_MANHATTAN_DISTANCE.distance
+
+    /**
+     * Calculates the canberra distance.
+     */
+    val vectorFloat128CanberraDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_128_CANBERRA_DISTANCE.distance
+
+    /**
+     * Calculates the canberra distance.
+     */
+    val vectorFloat256CanberraDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_256_CANBERRA_DISTANCE.distance
+
+    /**
+     * Calculates the bray curtis distance.
+     */
+    val vectorFloat128BrayCurtisDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_128_BRAY_CURTIS_DISTANCE.distance
+
+    /**
+     * Calculates the bray curtis distance.
+     */
+    val vectorFloat256BrayCurtisDistance: DistanceFunction[Array[Float], Float] = Jdk17DistanceFunctions.VECTOR_FLOAT_256_BRAY_CURTIS_DISTANCE.distance
+
+  }
 }