Examples of cc.redberry.core.indices.Indices

cc.redberry.core.indices.Indices
This interface states common tensor indices functionality. For specification and more information see methods summary and implementations. Indices objects are considered to be immutable, so there is no way to change the indices object.
For individual index structure (bit masks in the int representation) see IndicesUtils.
@author Dmitry Bolotin @author Stanislav Poslavsky @see IndicesBuilder @see IndexMapping @see IndicesUtils

        return set;
    }


    private static void getAllIndicesT1(ParseToken node, TIntSet set) {
        if (node instanceof ParseTokenSimpleTensor) {
            Indices indices = node.getIndices();
            for (int i = indices.size() - 1; i >= 0; --i)
                set.add(IndicesUtils.getNameWithType(indices.get(i)));
        } else
            for (ParseToken pn : node.content)
                if (!(pn instanceof ParseTokenScalarFunction))
                    getAllIndicesT1(pn, set);
    }

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        }


        int tensorIndex;
        for (tensorIndex = 0; tensorIndex < data.length; ++tensorIndex) {
            //Main algorithm
            Indices tInds = data[tensorIndex].getIndices();
            short[] diffIds = tInds.getDiffIds();


            //FUTURE move to other place
            if (tInds.size() >= 0x10000)
                throw new RuntimeException("Too many indices!!! max count = 2^16");


            for (i = 0; i < tInds.size(); ++i) {
                index = tInds.get(i);
                state = IndicesUtils.getStateInt(index);
                info[state][pointer[state]] = packToLong(tensorIndex, diffIds[i], i);
                indices[state][pointer[state]++] = IndicesUtils.getNameWithType(index);
            }


            //Result allocation
            contractions[tensorIndex] = new long[tInds.size()];
        }


        //Here we can use unstable sorting algorithm (all indices are different)
        ArraysUtils.quickSort(indices[0], info[0]);
        ArraysUtils.quickSort(indices[1], info[1]);

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        appendAllIndicesNamesT(tensor, set, true);
    }


    private static void appendAllIndicesNamesT(Tensor tensor, TIntHashSet set, boolean includeScalarFunctions) {
        if (tensor instanceof SimpleTensor) {
            Indices ind = tensor.getIndices();
            set.ensureCapacity(ind.size());
            final int size = ind.size();
            for (int i = 0; i < size; ++i)
                set.add(IndicesUtils.getNameWithType(ind.get(i)));
        } else if (tensor instanceof Power) {
            appendAllIndicesNamesT(tensor.get(0), set);
        } else if (tensor instanceof ScalarFunction && !includeScalarFunctions)
            return;
        else {

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        assertIndicesConsistency(t, new TIntHashSet());
    }


    private static void assertIndicesConsistency(Tensor t, TIntHashSet indices) {
        if (t instanceof SimpleTensor) {
            Indices ind = t.getIndices();
            for (int i = ind.size() - 1; i >= 0; --i)
                if (indices.contains(ind.get(i)))
                    throw new AssertionError(new InconsistentIndicesException(ind.get(i)));
                else
                    indices.add(ind.get(i));
        }
        if (t instanceof Product)
            for (int i = t.size() - 1; i >= 0; --i)
                assertIndicesConsistency(t.get(i), indices);
        if (t instanceof Sum) {

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                assertIndicesConsistency(c, new TIntHashSet(indices));
    }


    private static void appendAllIndicesT(Tensor tensor, TIntHashSet set) {
        if (tensor instanceof SimpleTensor) {
            Indices ind = tensor.getIndices();
            final int size = ind.size();
            for (int i = 0; i < size; ++i)
                set.add(ind.get(i));
        } else if (tensor instanceof Power) {
            appendAllIndicesT(tensor.get(0), set);
        } else {
            Tensor t;
            for (int i = tensor.size() - 1; i >= 0; --i) {

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        //Processing data with indices
        int i;
        ProductContent content;
        Indices indices;
        Tensor[] data = dataContainer.list.toArray(new Tensor[dataContainer.list.size()]);
        if (dataContainer.count == 1) {
            content = dataContainer.content;
            indices = dataContainer.indices;
            if (indices == null) {

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     * @return {@code true} if specified tensors are mathematically (not programming) equal
     */
    public static boolean equals(Tensor u, Tensor v) {
        if (u == v)
            return true;
        Indices freeIndices = u.getIndices().getFree();
        if (!freeIndices.equalsRegardlessOrder(v.getIndices().getFree()))
            return false;
        int[] free = freeIndices.getAllIndices().copy();
        IndexMappingBuffer tester = new IndexMappingBufferTester(free, false);
        OutputPortUnsafe<IndexMappingBuffer> mp = IndexMappings.createPortOfBuffers(tester, u, v);
        IndexMappingBuffer buffer;


        while ((buffer = mp.take()) != null)

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     * @param v tensor
     * @return {@code true} {@code true} if tensor u mathematically (not programming) equals to tensor v,
     *         {@code false} if they they differ only in the sign and {@code null} otherwise
     */
    public static Boolean compare1(Tensor u, Tensor v) {
        Indices freeIndices = u.getIndices().getFree();
        if (!freeIndices.equalsRegardlessOrder(v.getIndices().getFree()))
            return null;
        int[] free = freeIndices.getAllIndices().copy();
        IndexMappingBuffer tester = new IndexMappingBufferTester(free, false);
        IndexMappingBuffer buffer = IndexMappings.createPortOfBuffers(tester, u, v).take();
        if (buffer == null)
            return null;
        return buffer.getSign();

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        }


        int tensorIndex;
        for (tensorIndex = 0; tensorIndex < data.length; ++tensorIndex) {
            //Main algorithm
            Indices tInds = data[tensorIndex].getIndices();
            short[] diffIds = tInds.getDiffIds();
            for (i = 0; i < tInds.size(); ++i) {
                index = tInds.get(i);
                state = IndicesUtils.getStateInt(index);
                info[state][pointer[state]] = packToLong(tensorIndex, stretchIndices[tensorIndex], diffIds[i]);
                indices[state][pointer[state]++] = IndicesUtils.getNameWithType(index);
            }


            //Result allocation
            contractions[tensorIndex] = new TensorContraction(stretchIndices[tensorIndex], new long[tInds.size()]);
        }


        //Here we can use unstable sorting algorithm (all indices are different)
        ArraysUtils.quickSort(indices[0], info[0]);
        ArraysUtils.quickSort(indices[1], info[1]);

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            if (sp.getName() != var.getName())
                return null;
            if (sp.getIndices().size() == 0)
                return TensorNumber.createONE();
            Product kroneckers = new Product();
            Indices targetIndices = sp.getIndices();
            Indices varIndices = var.getIndices();
            for (int i = 0; i < sp.getIndices().size(); ++i)
                kroneckers.add(CC.createMetricOrKronecker(targetIndices.get(i), varIndices.get(i)));
            return kroneckers.equivalent();
        } else if (target.getClass() == TensorField.class) {
            TensorField field = (TensorField) target;
            Tensor[] args = field.getArgs();
            for (int i = 0; i < args.length; ++i)

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Related Classes of cc.redberry.core.indices.Indices

cc.redberry.core.graph.PrimitiveSubgraphPartition

cc.redberry.core.indexmapping.IndexMappings

cc.redberry.core.parser.ParseUtils

cc.redberry.core.tensor.Derivative

cc.redberry.core.tensor.FullContractionsStructure

cc.redberry.core.tensor.Product

cc.redberry.core.tensor.ProductBuilder

cc.redberry.core.tensor.ProductContentImpl

cc.redberry.core.tensor.ProductFactory

cc.redberry.core.tensor.ScalarsBackedProductBuilder

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