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

Example 1 with DenseVector

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

the class CRFParameters method getEmptyCopy.

/**
 * Returns a 3 element {@link Tensor} array.
 *
 * The first element is a {@link DenseVector} of label biases.
 * The second element is a {@link DenseMatrix} of feature-label weights.
 * The third element is a {@link DenseMatrix} of label-label transition weights.
 * @return A {@link Tensor} array.
 */
@Override
public Tensor[] getEmptyCopy() {
    Tensor[] output = new Tensor[3];
    output[0] = new DenseVector(biases.size());
    output[1] = new DenseMatrix(featureLabelWeights.getDimension1Size(), featureLabelWeights.getDimension2Size());
    output[2] = new DenseMatrix(labelLabelWeights.getDimension1Size(), labelLabelWeights.getDimension2Size());
    return output;
}

Example 2 with DenseVector

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

the class CRFParameters method merge.

@Override
public Tensor[] merge(Tensor[][] gradients, int size) {
    DenseVector biasUpdate = new DenseVector(biases.size());
    List<DenseSparseMatrix> updates = new ArrayList<>(size);
    DenseMatrix denseUpdates = null;
    DenseMatrix labelLabelUpdate = new DenseMatrix(labelLabelWeights.getDimension1Size(), labelLabelWeights.getDimension2Size());
    for (int j = 0; j < gradients.length; j++) {
        biasUpdate.intersectAndAddInPlace(gradients[j][0]);
        Matrix tmpUpdate = (Matrix) gradients[j][1];
        if (tmpUpdate instanceof DenseSparseMatrix) {
            updates.add((DenseSparseMatrix) tmpUpdate);
        } else {
            // is dense
            if (denseUpdates == null) {
                denseUpdates = (DenseMatrix) tmpUpdate;
            } else {
                denseUpdates.intersectAndAddInPlace(tmpUpdate);
            }
        }
        labelLabelUpdate.intersectAndAddInPlace(gradients[j][2]);
    }
    // Merge the combination of any dense and sparse updates
    Matrix featureLabelUpdate;
    if (updates.size() > 0) {
        featureLabelUpdate = merger.merge(updates.toArray(new DenseSparseMatrix[0]));
        if (denseUpdates != null) {
            denseUpdates.intersectAndAddInPlace(featureLabelUpdate);
            featureLabelUpdate = denseUpdates;
        }
    } else {
        featureLabelUpdate = denseUpdates;
    }
    return new Tensor[] { biasUpdate, featureLabelUpdate, labelLabelUpdate };
}

Example 3 with DenseVector

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

the class ChainHelper method viterbi.

/**
 * Runs Viterbi on a linear chain CRF. Uses the
 * linear predictions for each token and the label transition probabilities.
 * @param scores Tuple containing the label-label transition matrix, and the per token label scores.
 * @return Tuple containing the score of the maximum path and the maximum predicted label per token.
 */
public static ChainViterbiResults viterbi(ChainCliqueValues scores) {
    DenseMatrix markovScores = scores.transitionValues;
    DenseVector[] localScores = scores.localValues;
    int numLabels = markovScores.getDimension1Size();
    DenseVector[] costs = new DenseVector[scores.localValues.length];
    int[][] backPointers = new int[scores.localValues.length][];
    for (int i = 0; i < scores.localValues.length; i++) {
        costs[i] = new DenseVector(numLabels, Double.NEGATIVE_INFINITY);
        backPointers[i] = new int[numLabels];
        Arrays.fill(backPointers[i], -1);
    }
    costs[0].setElements(localScores[0]);
    for (int i = 1; i < scores.localValues.length; i++) {
        DenseVector curLocalScores = localScores[i];
        DenseVector curCost = costs[i];
        int[] curBackPointers = backPointers[i];
        DenseVector prevCost = costs[i - 1];
        for (int vi = 0; vi < numLabels; vi++) {
            double maxScore = Double.NEGATIVE_INFINITY;
            int maxIndex = -1;
            double curLocalScore = curLocalScores.get(vi);
            for (int vj = 0; vj < numLabels; vj++) {
                double curScore = markovScores.get(vj, vi) + prevCost.get(vj) + curLocalScore;
                if (curScore > maxScore) {
                    maxScore = curScore;
                    maxIndex = vj;
                }
            }
            curCost.set(vi, maxScore);
            if (maxIndex < 0) {
                maxIndex = 0;
            }
            curBackPointers[vi] = maxIndex;
        }
    }
    int[] mapValues = new int[scores.localValues.length];
    mapValues[mapValues.length - 1] = costs[costs.length - 1].indexOfMax();
    for (int j = mapValues.length - 2; j >= 0; j--) {
        mapValues[j] = backPointers[j + 1][mapValues[j + 1]];
    }
    return new ChainViterbiResults(costs[costs.length - 1].maxValue(), mapValues, scores);
}

Example 4 with DenseVector

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

the class HdbscanModel method copy.

@Override
protected HdbscanModel copy(String newName, ModelProvenance newProvenance) {
    DenseVector copyOutlierScoresVector = outlierScoresVector.copy();
    List<Integer> copyClusterLabels = Collections.unmodifiableList(clusterLabels);
    List<HdbscanTrainer.ClusterExemplar> copyExemplars = new ArrayList<>(clusterExemplars);
    return new HdbscanModel(newName, newProvenance, featureIDMap, outputIDInfo, copyClusterLabels, copyOutlierScoresVector, copyExemplars, distType, noisePointsOutlierScore);
}

Example 5 with DenseVector

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

the class HdbscanTrainer method train.

@Override
public HdbscanModel train(Dataset<ClusterID> examples, Map<String, Provenance> runProvenance) {
    // increment the invocation count.
    TrainerProvenance trainerProvenance;
    synchronized (this) {
        trainerProvenance = getProvenance();
        trainInvocationCounter++;
    }
    ImmutableFeatureMap featureMap = examples.getFeatureIDMap();
    SGDVector[] data = new SGDVector[examples.size()];
    int n = 0;
    for (Example<ClusterID> example : examples) {
        if (example.size() == featureMap.size()) {
            data[n] = DenseVector.createDenseVector(example, featureMap, false);
        } else {
            data[n] = SparseVector.createSparseVector(example, featureMap, false);
        }
        n++;
    }
    DenseVector coreDistances = calculateCoreDistances(data, k, neighboursQueryFactory);
    ExtendedMinimumSpanningTree emst = constructEMST(data, coreDistances, distType);
    // The levels at which each point becomes noise
    double[] pointNoiseLevels = new double[data.length];
    // The last label of each point before becoming noise
    int[] pointLastClusters = new int[data.length];
    // The HDBSCAN* hierarchy
    Map<Integer, int[]> hierarchy = new HashMap<>();
    List<HdbscanCluster> clusters = computeHierarchyAndClusterTree(emst, minClusterSize, pointNoiseLevels, pointLastClusters, hierarchy);
    propagateTree(clusters);
    List<Integer> clusterLabels = findProminentClusters(hierarchy, clusters, data.length);
    DenseVector outlierScoresVector = calculateOutlierScores(pointNoiseLevels, pointLastClusters, clusters);
    Map<Integer, List<Pair<Double, Integer>>> clusterAssignments = generateClusterAssignments(clusterLabels, outlierScoresVector);
    // Use the cluster assignments to establish the clustering info
    Map<Integer, MutableLong> counts = new HashMap<>();
    for (Entry<Integer, List<Pair<Double, Integer>>> e : clusterAssignments.entrySet()) {
        counts.put(e.getKey(), new MutableLong(e.getValue().size()));
    }
    ImmutableOutputInfo<ClusterID> outputMap = new ImmutableClusteringInfo(counts);
    // Compute the cluster exemplars.
    List<ClusterExemplar> clusterExemplars = computeExemplars(data, clusterAssignments, distType);
    // Get the outlier score value for points that are predicted as noise points.
    double noisePointsOutlierScore = getNoisePointsOutlierScore(clusterAssignments);
    logger.log(Level.INFO, "Hdbscan is done.");
    ModelProvenance provenance = new ModelProvenance(HdbscanModel.class.getName(), OffsetDateTime.now(), examples.getProvenance(), trainerProvenance, runProvenance);
    return new HdbscanModel("hdbscan-model", provenance, featureMap, outputMap, clusterLabels, outlierScoresVector, clusterExemplars, distType, noisePointsOutlierScore);
}