Examples with SparseVector org.tribuo.math.la.SparseVector used on opensource projects

Example 26 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class MultiLabelConverter method convertToTensor.

@Override
public Tensor convertToTensor(List<Example<MultiLabel>> examples, ImmutableOutputInfo<MultiLabel> outputIDInfo) {
    TFloat32 returnVal = TFloat32.tensorOf(Shape.of(examples.size(), outputIDInfo.size()));
    int i = 0;
    for (Example<MultiLabel> e : examples) {
        SparseVector vec = e.getOutput().convertToSparseVector(outputIDInfo);
        for (int j = 0; j < outputIDInfo.size(); j++) {
            returnVal.setFloat(0.0f, i, j);
        }
        for (VectorTuple v : vec) {
            returnVal.setFloat((float) v.value, i, v.index);
        }
        i++;
    }
    return returnVal;
}

Example 27 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class MultiLabelConverter method convertToTensor.

@Override
public Tensor convertToTensor(MultiLabel example, ImmutableOutputInfo<MultiLabel> outputIDInfo) {
    SparseVector vec = example.convertToSparseVector(outputIDInfo);
    TFloat32 returnVal = TFloat32.tensorOf(Shape.of(1, outputIDInfo.size()));
    for (int j = 0; j < outputIDInfo.size(); j++) {
        returnVal.setFloat(0.0f, 0, j);
    }
    for (VectorTuple v : vec) {
        returnVal.setFloat((float) v.value, 0, v.index);
    }
    return returnVal;
}

Example 28 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class TreeModel method predict.

@Override
public Prediction<T> predict(Example<T> example) {
    // 
    // Ensures we handle collisions correctly
    SparseVector vec = SparseVector.createSparseVector(example, featureIDMap, false);
    if (vec.numActiveElements() == 0) {
        throw new IllegalArgumentException("No features found in Example " + example.toString());
    }
    Node<T> oldNode = root;
    Node<T> curNode = root;
    while (curNode != null) {
        oldNode = curNode;
        curNode = oldNode.getNextNode(vec);
    }
    // oldNode must be a LeafNode.
    return ((LeafNode<T>) oldNode).getPrediction(vec.numActiveElements(), example);
}

Example 29 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class HeapMerger method merge.

@Override
public DenseSparseMatrix merge(DenseSparseMatrix[] inputs) {
    int denseLength = inputs[0].getDimension1Size();
    int sparseLength = inputs[0].getDimension2Size();
    int[] totalLengths = new int[inputs[0].getDimension1Size()];
    for (int i = 0; i < inputs.length; i++) {
        for (int j = 0; j < totalLengths.length; j++) {
            totalLengths[j] += inputs[i].numActiveElements(j);
        }
    }
    int maxLength = 0;
    for (int i = 0; i < totalLengths.length; i++) {
        if (totalLengths[i] > maxLength) {
            maxLength = totalLengths[i];
        }
    }
    SparseVector[] output = new SparseVector[denseLength];
    int[] indicesBuffer = new int[maxLength];
    double[] valuesBuffer = new double[maxLength];
    List<SparseVector> vectors = new ArrayList<>();
    for (int i = 0; i < denseLength; i++) {
        vectors.clear();
        for (DenseSparseMatrix m : inputs) {
            SparseVector vec = m.getRow(i);
            if (vec.numActiveElements() > 0) {
                vectors.add(vec);
            }
        }
        output[i] = merge(vectors, sparseLength, indicesBuffer, valuesBuffer);
    }
    return DenseSparseMatrix.createFromSparseVectors(output);
}

Example 30 with SparseVector

use of org.tribuo.math.la.SparseVector in project tribuo by oracle.

the class Hinge method lossAndGradient.

/**
 * Returns a {@link Pair} of {@link Double} and {@link SGDVector} representing the loss
 * and per label gradients respectively.
 * @param truth The true label id.
 * @param prediction The prediction for each label id.
 * @return The loss and per label gradient.
 */
@Override
public Pair<Double, SGDVector> lossAndGradient(Integer truth, SGDVector prediction) {
    prediction.add(truth, -margin);
    int predIndex = prediction.indexOfMax();
    if (truth == predIndex) {
        return new Pair<>(0.0, SparseVector.createSparseVector(prediction.size(), new int[0], new double[0]));
    } else {
        int[] indices = new int[2];
        double[] values = new double[2];
        if (truth < predIndex) {
            indices[0] = truth;
            values[0] = margin;
            indices[1] = predIndex;
            values[1] = -margin;
        } else {
            indices[0] = predIndex;
            values[0] = -margin;
            indices[1] = truth;
            values[1] = margin;
        }
        SparseVector output = SparseVector.createSparseVector(prediction.size(), indices, values);
        double loss = prediction.get(truth) - prediction.get(predIndex);
        return new Pair<>(loss, output);
    }
}