add euclidean_distance

2 files changed, 100 insertions, 0 deletions

diff --git a/vespajlib/src/main/java/com/yahoo/tensor/functions/EuclideanDistance.java b/vespajlib/src/main/java/com/yahoo/tensor/functions/EuclideanDistance.java
new file mode 100644
index 00000000000..4feddf9f808
--- /dev/null
+++ b/vespajlib/src/main/java/com/yahoo/tensor/functions/EuclideanDistance.java
@@ -0,0 +1,57 @@
+// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.tensor.functions;
+
+import com.yahoo.tensor.evaluation.Name;
+
+import java.util.Collections;
+import java.util.List;
+import java.util.Objects;
+
+/**
+ * @author arnej
+ */
+public class EuclideanDistance<NAMETYPE extends Name> extends CompositeTensorFunction<NAMETYPE> {
+
+    private final TensorFunction<NAMETYPE> arg1;
+    private final TensorFunction<NAMETYPE> arg2;
+    private final String dimension;
+
+    public EuclideanDistance(TensorFunction<NAMETYPE> argument1,
+                             TensorFunction<NAMETYPE> argument2,
+                             String dimension)
+    {
+        this.arg1 = argument1;
+        this.arg2 = argument2;
+        this.dimension = dimension;
+    }
+
+    @Override
+    public List<TensorFunction<NAMETYPE>> arguments() { return List.of(arg1, arg2); }
+
+    @Override
+    public TensorFunction<NAMETYPE> withArguments(List<TensorFunction<NAMETYPE>> arguments) {
+        if ( arguments.size() != 2)
+            throw new IllegalArgumentException("EuclideanDistance must have 2 arguments, got " + arguments.size());
+        return new EuclideanDistance<>(arguments.get(0), arguments.get(1), dimension);
+    }
+
+    @Override
+    public PrimitiveTensorFunction<NAMETYPE> toPrimitive() {
+        TensorFunction<NAMETYPE> primitive1 = arg1.toPrimitive();
+        TensorFunction<NAMETYPE> primitive2 = arg2.toPrimitive();
+        // this should match the C++ optimized "l2_distance"
+        var diffs = new Join<>(primitive1, primitive2, ScalarFunctions.subtract());
+        var squaredDiffs = new Map<>(diffs, ScalarFunctions.square());
+        var sumOfSquares = new Reduce<>(squaredDiffs, Reduce.Aggregator.sum, dimension);
+        return new Map<>(sumOfSquares, ScalarFunctions.sqrt());
+    }
+
+    @Override
+    public String toString(ToStringContext<NAMETYPE> context) {
+        return "euclidean_distance(" + arg1.toString(context) + ", " + arg2.toString(context) + ", " + dimension + ")";
+    }
+
+    @Override
+    public int hashCode() { return Objects.hash("euclidean_distance", arg1, arg2, dimension); }
+
+}
diff --git a/vespajlib/src/test/java/com/yahoo/tensor/functions/EuclideanDistanceTestCase.java b/vespajlib/src/test/java/com/yahoo/tensor/functions/EuclideanDistanceTestCase.java
new file mode 100644
index 00000000000..9d06c313ecc
--- /dev/null
+++ b/vespajlib/src/test/java/com/yahoo/tensor/functions/EuclideanDistanceTestCase.java
@@ -0,0 +1,43 @@
+// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.tensor.functions;
+
+import com.yahoo.tensor.Tensor;
+import com.yahoo.tensor.TensorType;
+import org.junit.Test;
+
+import java.util.List;
+
+import static org.junit.Assert.assertEquals;
+
+/**
+ * @author arnej
+ */
+public class EuclideanDistanceTestCase {
+
+    @Test
+    public void testVectorDistances() {
+        var a = Tensor.from("tensor(x[3]):[1.0, 2.0, 3.0]");
+        var b = Tensor.from("tensor(x[3]):[4.0, 2.0, 7.0]");
+        var c = Tensor.from("tensor(x[3]):[1.0, 6.0, 6.0]");
+        var op = new EuclideanDistance<>(new ConstantTensor<>(a), new ConstantTensor<>(b), "x");
+        Tensor result = op.evaluate();
+        assertEquals(5.0, result.asDouble(), 0.000001);
+        op = new EuclideanDistance<>(new ConstantTensor<>(b), new ConstantTensor<>(a), "x");
+        result = op.evaluate();
+        assertEquals(5.0, result.asDouble(), 0.000001);
+        op = new EuclideanDistance<>(new ConstantTensor<>(c), new ConstantTensor<>(a), "x");
+        result = op.evaluate();
+        assertEquals(5.0, result.asDouble(), 0.000001);
+    }
+
+    @Test
+    public void testDistancesInMixed() {
+        var a = Tensor.from("tensor(c{},x[3]):{foo:[1.0, 2.0, 3.0],bar:[0.0, 0.0, 0.0]}");
+        var b = Tensor.from("tensor(c{},x[3]):{foo:[4.0, 2.0, 7.0],bar:[12.0, 0.0, 5.0]}");
+        var op = new EuclideanDistance<>(new ConstantTensor<>(a), new ConstantTensor<>(b), "x");
+        Tensor result = op.evaluate();
+        var expect = Tensor.from("tensor(c{}):{foo:5.0,bar:13.0}");
+        assertEquals(expect, result);
+    }
+
+}