Examples of SimpleTensor

cc.redberry.core.tensor.SimpleTensor
Implementation of simple tensor. @author Dmitry Bolotin @author Stanislav Poslavsky @since 1.0
edu.stanford.nlp.neural.SimpleTensor
This class defines a block tensor, somewhat like a three dimensional matrix. This can be created in various ways, such as by providing an array of SimpleMatrix slices, by providing the initial size to create a 0-initialized tensor, or by creating a random matrix. @author John Bauer @author Richard Socher

Examples of cc.redberry.core.tensor.SimpleTensor

        generator = new SymbolsGenerator(name);
    }


    @Override
    public SimpleTensor take() {
        SimpleTensor st = generator.take();
        generated.add(st);
        return st;
    }

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Examples of cc.redberry.core.tensor.SimpleTensor

            for (Tensor t : tf)
                content[i++] = tensor2AST(t);
            return new ParseTokenTensorField(tf.getIndices(), tf.getStringName(), content, tf.getArgIndices());
        }
        if (tensor instanceof SimpleTensor) {
            SimpleTensor st = (SimpleTensor) tensor;
            return new ParseTokenSimpleTensor(st.getIndices(), st.getStringName());
        }
        if (tensor instanceof Complex)
            return new ParseTokenNumber((Complex) tensor);


        ParseToken[] content = new ParseToken[tensor.size()];

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Examples of cc.redberry.core.tensor.SimpleTensor

            throw new IllegalArgumentException();
        int i;
        for (i = 0; i < actualInput; ++i) {
            if (!(inputValues[i].get(0) instanceof SimpleTensor))
                throw new IllegalArgumentException();
            SimpleTensor st = (SimpleTensor) inputValues[i].get(0);
            NameDescriptor nd = CC.getNameDescriptor(st.getName());
            if (!nd.getName(null).equals(getStringInputName(i)))
                throw new IllegalArgumentException();
        }
        for (; i < INPUT_VALUES_GENERAL_COUNT; ++i)
            if (inputValues[i] != null)

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Examples of cc.redberry.core.tensor.SimpleTensor


    public static Tensor applyIndexMapping(Tensor tensor, IndexMapper mapper) {
        TreeTraverseIterator iterator = new TreeTraverseIterator(tensor, TraverseGuide.EXCEPT_FUNCTIONS_AND_FIELDS);
        TraverseState state;
        SimpleIndices oldIndices, newIndices;
        SimpleTensor simpleTensor;
        while ((state = iterator.next()) != null) {
            if (state == TraverseState.Leaving)
                continue;
            if (!(iterator.current() instanceof SimpleTensor))
                continue;
            simpleTensor = (SimpleTensor) iterator.current();
            oldIndices = simpleTensor.getIndices();
            newIndices = oldIndices.applyIndexMapping(mapper);
            if (oldIndices != newIndices)
                if (simpleTensor instanceof TensorField)
                    iterator.set(Tensors.setIndicesToField((TensorField) simpleTensor, newIndices));
                else

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Examples of cc.redberry.core.tensor.SimpleTensor

        return setMandelstam(tt);
    }


    public static Expression[] setMandelstam(Tensor[][] momentums) {
        checkMandelstamInput(momentums);
        SimpleTensor s = parseSimple("s"), t = parseSimple("t"), u = parseSimple("u");


        Expression[] result = new Expression[10];
        int i;
        // (k1,k1) = m1^2, (k2,k2) = m2^2, (k3,k3) = m3^2, (k4,k4) = m4^2
        for (i = 0; i < 4; ++i)

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Examples of cc.redberry.core.tensor.SimpleTensor

    private void checkConsistency() {
        int i;
        for (i = 0; i < actualInput; ++i) {
            if (!(inputValues[i].get(0) instanceof SimpleTensor))
                throw new IllegalArgumentException();
            SimpleTensor st = (SimpleTensor) inputValues[i].get(0);
            NameDescriptor nd = CC.getNameDescriptor(st.getName());
            if (!nd.getName(null).equals(getStringInputName(i)))
                throw new IllegalArgumentException();
        }
        for (; i < INPUT_VALUES_GENERAL_COUNT; ++i)
            if (inputValues[i] != null)

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Examples of edu.stanford.nlp.neural.SimpleTensor

    // System.err.println(unaryClassification);
  }


  SimpleTensor randomBinaryTensor() {
    double range = 1.0 / (4.0 * numHid);
    SimpleTensor tensor = SimpleTensor.random(numHid * 2, numHid * 2, numHid, -range, range, rand);
    return tensor.scale(op.trainOptions.scalingForInit);
  }

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Examples of edu.stanford.nlp.neural.SimpleTensor


    SimpleMatrix[] slices = new SimpleMatrix[numSlices];
    for (int i = 0; i < numSlices; ++i) {
      slices[i] = loadMatrix(basePath + "bin/Wt_" + (i + 1) + ".bin", basePath + "Wt_" + (i + 1) + ".txt");
    }
    SimpleTensor tensor = new SimpleTensor(slices);
    System.err.println("W tensor size: " + tensor.numRows() + "x" + tensor.numCols() + "x" + tensor.numSlices());


    SimpleMatrix W = loadMatrix(basePath + "bin/W.bin", basePath + "W.txt");
    System.err.println("W matrix size: " + W.numRows() + "x" + W.numCols());


    SimpleMatrix Wcat = loadMatrix(basePath + "bin/Wcat.bin", basePath + "Wcat.txt");

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Examples of edu.stanford.nlp.neural.SimpleTensor

      for (TwoDimensionalMap.Entry<String, String, SimpleTensor> entry : model.binaryTensors) {
        int numRows = entry.getValue().numRows();
        int numCols = entry.getValue().numCols();
        int numSlices = entry.getValue().numSlices();


        binaryTensorTD.put(entry.getFirstKey(), entry.getSecondKey(), new SimpleTensor(numRows, numCols, numSlices));
      }
    }


    for (Map.Entry<String, SimpleMatrix> entry : model.unaryClassification.entrySet()) {
      int numRows = entry.getValue().numRows();

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Examples of edu.stanford.nlp.neural.SimpleTensor

                                  TwoDimensionalMap<String, String, SimpleTensor> currentMatrices,
                                  double scale,
                                  double regCost) {
    double cost = 0.0; // the regularization cost
    for (TwoDimensionalMap.Entry<String, String, SimpleTensor> entry : currentMatrices) {
      SimpleTensor D = derivatives.get(entry.getFirstKey(), entry.getSecondKey());
      D = D.scale(scale).plus(entry.getValue().scale(regCost));
      derivatives.put(entry.getFirstKey(), entry.getSecondKey(), D);
      cost += entry.getValue().elementMult(entry.getValue()).elementSum() * regCost / 2.0;
    }
    return cost;
  }

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