Examples with Tensor com.yahoo.tensor.Tensor used on opensource projects

Example 56 with Tensor

use of com.yahoo.tensor.Tensor in project vespa by vespa-engine.

the class Rename method evaluate.

@Override
public <NAMETYPE extends TypeContext.Name> Tensor evaluate(EvaluationContext<NAMETYPE> context) {
    Tensor tensor = argument.evaluate(context);
    TensorType renamedType = type(tensor.type());
    // an array which lists the index of each label in the renamed type
    int[] toIndexes = new int[tensor.type().dimensions().size()];
    for (int i = 0; i < tensor.type().dimensions().size(); i++) {
        String dimensionName = tensor.type().dimensions().get(i).name();
        String newDimensionName = fromToMap.getOrDefault(dimensionName, dimensionName);
        toIndexes[i] = renamedType.indexOfDimension(newDimensionName).get();
    }
    Tensor.Builder builder = Tensor.Builder.of(renamedType);
    for (Iterator<Tensor.Cell> i = tensor.cellIterator(); i.hasNext(); ) {
        Map.Entry<TensorAddress, Double> cell = i.next();
        TensorAddress renamedAddress = rename(cell.getKey(), toIndexes);
        builder.cell(renamedAddress, cell.getValue());
    }
    return builder.build();
}

Example 57 with Tensor

use of com.yahoo.tensor.Tensor in project vespa by vespa-engine.

the class JsonReaderTestCase method testParsingOfIndexedTensorWithCells.

@Test
public void testParsingOfIndexedTensorWithCells() {
    Tensor tensor = assertTensorField("{{x:0,y:0}:2.0,{x:1,y:0}:3.0}}", createPutWithTensor("{ " + "  \"cells\": [ " + "    { \"address\": { \"x\": \"0\", \"y\": \"0\" }, " + "      \"value\": 2.0 }, " + "    { \"address\": { \"x\": \"1\", \"y\": \"0\" }, " + "      \"value\": 3.0 } " + "  ]" + "}", "indexedtensorfield"), "indexedtensorfield");
    // this matters for performance
    assertTrue(tensor instanceof IndexedTensor);
}

Example 58 with Tensor

use of com.yahoo.tensor.Tensor in project vespa by vespa-engine.

the class ExpandDims method lazyGetType.

@Override
protected OrderedTensorType lazyGetType() {
    if (!allInputTypesPresent(2)) {
        return null;
    }
    TensorFlowOperation axisOperation = inputs().get(1);
    if (!axisOperation.getConstantValue().isPresent()) {
        throw new IllegalArgumentException("ExpandDims in " + node.getName() + ": " + "axis must be a constant.");
    }
    Tensor axis = axisOperation.getConstantValue().get().asTensor();
    if (axis.type().rank() != 0) {
        throw new IllegalArgumentException("ExpandDims in " + node.getName() + ": " + "axis argument must be a scalar.");
    }
    OrderedTensorType inputType = inputs.get(0).type().get();
    int dimensionToInsert = (int) axis.asDouble();
    if (dimensionToInsert < 0) {
        dimensionToInsert = inputType.dimensions().size() - dimensionToInsert;
    }
    OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(node);
    expandDimensions = new ArrayList<>();
    int dimensionIndex = 0;
    for (TensorType.Dimension dimension : inputType.dimensions()) {
        if (dimensionIndex == dimensionToInsert) {
            String name = String.format("%s_%d", vespaName(), dimensionIndex);
            expandDimensions.add(name);
            typeBuilder.add(TensorType.Dimension.indexed(name, 1L));
        }
        typeBuilder.add(dimension);
        dimensionIndex++;
    }
    return typeBuilder.build();
}