diff options
Diffstat (limited to 'model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Range.java')
-rw-r--r-- | model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Range.java | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Range.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Range.java index 5c4e8cd6cd0..9a38ab9dfde 100644 --- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Range.java +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Range.java @@ -5,8 +5,8 @@ import ai.vespa.rankingexpression.importer.DimensionRenamer; import ai.vespa.rankingexpression.importer.OrderedTensorType; import com.yahoo.searchlib.rankingexpression.Reference; import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue; -import com.yahoo.searchlib.rankingexpression.rule.ArithmeticNode; -import com.yahoo.searchlib.rankingexpression.rule.ArithmeticOperator; +import com.yahoo.searchlib.rankingexpression.rule.OperationNode; +import com.yahoo.searchlib.rankingexpression.rule.Operator; import com.yahoo.searchlib.rankingexpression.rule.ConstantNode; import com.yahoo.searchlib.rankingexpression.rule.EmbracedNode; import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode; @@ -65,8 +65,8 @@ public class Range extends IntermediateOperation { ExpressionNode startExpr = new ConstantNode(new DoubleValue(start)); ExpressionNode deltaExpr = new ConstantNode(new DoubleValue(delta)); ExpressionNode dimExpr = new EmbracedNode(new ReferenceNode(dimensionName)); - ExpressionNode stepExpr = new ArithmeticNode(deltaExpr, ArithmeticOperator.MULTIPLY, dimExpr); - ExpressionNode addExpr = new ArithmeticNode(startExpr, ArithmeticOperator.PLUS, stepExpr); + ExpressionNode stepExpr = new OperationNode(deltaExpr, Operator.multiply, dimExpr); + ExpressionNode addExpr = new OperationNode(startExpr, Operator.plus, stepExpr); TensorFunction<Reference> function = Generate.bound(type.type(), wrapScalar(addExpr)); return function; } |