Examples of cc.redberry.core.indexmapping.Mapping

cc.redberry.core.indexmapping.Mapping
This is the main class which represents the mapping from one tensor to another.
{@code Mapping} implements {@code Transformation} and perform transformation by simple invocation of{@link ApplyIndexMapping#applyIndexMappingAutomatically(cc.redberry.core.tensor.Tensor,Mapping)} ))} with{@code this} as a second argument.

The underlying data structure is a simple pair of sorted arrays {@code fromNames} and {@code toData}, such that i-th mapping entry is {@code fromNames[i] -> toData[i]}. The array {@code fromNames} contains from indices names. The array {@code toData}contains names of to indices where the first bit indicates whether the state of from index should be inverted.
@author Dmitry Bolotin @author Stanislav Poslavsky @since 1.1.5

                for (int j = 0; j < metricInds.length; ++j)
                    if (temp[j] == 0xFFFFFFFF)
                        temp[j] = createIndex(counter++, getType(metricInds[j]), false);//lower index
                IntArrayList _result = nonMetricIndices.clone();
                _result.addAll(temp);
                Tensor renamed = ApplyIndexMapping.applyIndexMapping(st, new Mapping(indicesArray, _result.toArray()));
                //todo bottleneck
                for (Tensor existing : combinationArray)
                    if (TensorUtils.compare1(existing, renamed) != null)
                        continue combinations;
                combinationArray.add(renamed);

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                    }
                    for (k = 0; k < termLow.length(); ++k) {
                        oldIndices[k + termUp.length()] = termLow.get(k);
                        newIndices[k + termUp.length()] = lowerArray[l++];
                    }
                    temp = ApplyIndexMapping.applyIndexMapping(temp, new Mapping(oldIndices, newIndices), indices.getAllIndices().copy());
                    tCombination.add(temp);
                }


            //creating term & processing combinatorics
            Tensor[] prodArray = tCombination.toArray(new Tensor[tCombination.size()]);

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                if (list == null) {
                    list = new ArrayList<>();
                    coefficients.put(oldCoefficient.hashCode(), list);
                }


                Mapping match = null;
                for (Tensor[] transformed : list) {
                    match = IndexMappings.getFirst(transformed[0], oldCoefficient);
                    if (match != null) {
                        newCoefficient = match.getSign() ? Tensors.negate(transformed[1]) : transformed[1];
                        break;
                    }
                }
                if (match == null) {
                    if (oldCoefficient instanceof SimpleTensor) {

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            Tensor riman2 = Tensors.parse(e1);


            long start = System.currentTimeMillis();
            for (int i = 0; i < 1000; ++i) {
                MappingsPort mp = IndexMappings.createPort(riman1, riman2);
                Mapping buffera;
                while ((buffera = mp.take()) != null) ;
            }
            time += System.currentTimeMillis() - start;
        }
        return time;

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            System.arraycopy(toAdd, 0, to, oldFromLength, toAdd.length);
        }


        assert from.length == freeIndices.length;


        return applyIndexMapping(tensor, new Mapping(from, to, mapping.getSign()), forbidden);
    }

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    public static Symmetries getSymmetriesFromMappings(final int[] indices, MappingsPort mappingsPort) {
        Symmetries symmetries = SymmetriesFactory.createSymmetries(indices.length);
        int[] sortedIndicesNames = IndicesUtils.getIndicesNames(indices);
        int[] sortPermutation = ArraysUtils.quickSortP(sortedIndicesNames);
        Mapping buffer;
        while ((buffer = mappingsPort.take()) != null)
            symmetries.add(getSymmetryFromMapping1(sortedIndicesNames, sortPermutation, buffer));
        return symmetries;
    }

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                    symmetric,
                    allFreeVarIndices,
                    varIndices.getSymmetries().getInnerSymmetries());
            derivative = applyIndexMapping(
                    derivative,
                    new Mapping(allIndices,
                            addAll(varIndices.getInverted().getAllIndices().copy(), allFreeArgIndices)),
                    new int[0]);
            ProductBuilder builder = new ProductBuilder(0, length);
            for (i = 0; i < length; ++i)
                builder.put(createMetricOrKronecker(allFreeArgIndices[i], allFreeVarIndices[i]));

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        @Override
        Tensor differentiateSimpleTensorWithoutCheck(SimpleTensor simpleTensor) {
            int[] to = simpleTensor.getIndices().getAllIndices().copy();
            to = addAll(to, freeVarIndices);
            return applyIndexMapping(derivative, new Mapping(allFreeFrom, to), new int[0]);
        }

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            int[] match;
            for (Split base : nodes) {
                if ((match = matchFactors(base.factors, toAdd.factors)) != null) {
                    Tensor[] toAddFactors = Combinatorics.reorder(toAdd.factors, match);
                    Mapping mapping =
                            IndexMappings.createBijectiveProductPort(toAddFactors, base.factors).take();


//                    mapping =  mapping.inverseStates();
//                    for (Map.Entry<Integer, IndexMappingBufferRecord> entry : mapping.getMap().entrySet())
//                        entry.getValue().invertStates();

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        return Tensors.multiply(product.getIndexlessSubProduct(), Tensors.multiply(data));
    }


    private static Tensor setIndices(Tensor t, Indices from, Indices to) {
        return ApplyIndexMapping.applyIndexMapping(t,
                new Mapping(from.getAllIndices().copy(), to.getAllIndices().copy()), new int[0]);
    }

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Related Classes of cc.redberry.core.indexmapping.Mapping

cc.redberry.core.performance.IndexMappingPerformance

cc.redberry.core.tensor.ApplyIndexMapping

cc.redberry.core.tensor.random.RandomTensor

cc.redberry.core.tensorgenerator.TensorGenerator

cc.redberry.core.tensorgenerator.TensorGeneratorUtils

cc.redberry.core.transformations.collect.CollectTransformation

cc.redberry.core.transformations.DifferentiateTransformation$SymmetricDifferentiationRule

cc.redberry.core.transformations.reverse.ReverseTransformation

cc.redberry.core.transformations.substitutions.PrimitiveProductSubstitution

cc.redberry.core.transformations.substitutions.PrimitiveSimpleTensorSubstitution

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