diff options
Diffstat (limited to 'vespajlib/src/test/java/com/yahoo/tensor/TensorFunctionBenchmark.java')
| -rw-r--r-- | vespajlib/src/test/java/com/yahoo/tensor/TensorFunctionBenchmark.java | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/vespajlib/src/test/java/com/yahoo/tensor/TensorFunctionBenchmark.java b/vespajlib/src/test/java/com/yahoo/tensor/TensorFunctionBenchmark.java index d199dd3a876..abdb3071bf7 100644 --- a/vespajlib/src/test/java/com/yahoo/tensor/TensorFunctionBenchmark.java +++ b/vespajlib/src/test/java/com/yahoo/tensor/TensorFunctionBenchmark.java @@ -13,14 +13,14 @@ import java.util.stream.Collectors; /** * Microbenchmark of tensor operations. - * + * * @author bratseth */ public class TensorFunctionBenchmark { private final static Random random = new Random(); - - public double benchmark(int iterations, List<Tensor> modelVectors, TensorType.Dimension.Type dimensionType, + + public double benchmark(int iterations, List<Tensor> modelVectors, TensorType.Dimension.Type dimensionType, boolean extraSpace) { Tensor queryVector = vectors(1, 300, dimensionType).get(0); if (extraSpace) { @@ -34,7 +34,7 @@ public class TensorFunctionBenchmark { long totalTime = System.currentTimeMillis() - startTime; return (double)totalTime / (double)iterations; } - + private Tensor unitVector(String dimension) { return Tensor.Builder.of(new TensorType.Builder().indexed(dimension, 1).build()) .cell().label(dimension, 0).value(1).build(); @@ -49,11 +49,11 @@ public class TensorFunctionBenchmark { private double dotProduct(Tensor tensor, List<Tensor> tensors) { double largest = Double.MIN_VALUE; - TensorFunction dotProductFunction = new Reduce(new Join(new ConstantTensor(tensor), - new VariableTensor("argument"), (a, b) -> a * b), + TensorFunction dotProductFunction = new Reduce(new Join(new ConstantTensor(tensor), + new VariableTensor("argument"), (a, b) -> a * b), Reduce.Aggregator.sum).toPrimitive(); MapEvaluationContext context = new MapEvaluationContext(); - + for (Tensor tensorElement : tensors) { // tensors.size() = 1 for larger tensor context.put("argument", tensorElement); double dotProduct = dotProductFunction.evaluate(context).asDouble(); |