Examples with TIntDoubleHashMap gnu.trove.map.hash.TIntDoubleHashMap used on opensource projects

Example 11 with TIntDoubleHashMap

use of gnu.trove.map.hash.TIntDoubleHashMap in project cogcomp-nlp by CogComp.

the class FeatureUtilities method convert.

/**
     * Convert a feature set into a pair of arrays of integers and doubles by looking up the feature
     * name in the provided lexicon.
     *
     * @param features The feature set
     * @param lexicon The lexicon
     * @param trainingMode Should an unseen feature string be added to the lexicon? If this is
     *        false, unseen features will be given an ID whose value is one more than the number of
     *        features.
     * @return a pair of int[] and double[], representing the feature ids and values.
     */
public static Pair<int[], double[]> convert(Set<Feature> features, Lexicon lexicon, boolean trainingMode) {
    TIntDoubleHashMap fMap = new TIntDoubleHashMap(features.size());
    for (Feature feature : features) {
        final int featureId = FeatureUtilities.getFeatureId(lexicon, trainingMode, feature);
        if (featureId < 0)
            continue;
        double value = feature.getValue() + fMap.get(featureId);
        fMap.put(featureId, value);
    }
    int[] idsOriginal = fMap.keys();
    int[] ids = new int[idsOriginal.length];
    System.arraycopy(idsOriginal, 0, ids, 0, ids.length);
    Arrays.sort(ids);
    double[] vals = new double[fMap.size()];
    int count = 0;
    for (int key : ids) {
        vals[count++] = fMap.get(key);
    }
    return new Pair<>(ids, vals);
}

Example 12 with TIntDoubleHashMap

use of gnu.trove.map.hash.TIntDoubleHashMap in project ProPPR by TeamCohen.

the class SRWTest method moreSetup.

@Override
public void moreSetup(LearningGraphBuilder lgb) {
    uniformParams = srw.getRegularizer().setupParams(new SimpleParamVector<String>(new ConcurrentHashMap<String, Double>()));
    for (String n : new String[] { "fromb", "tob", "fromr", "tor" }) uniformParams.put(n, srw.getSquashingFunction().defaultValue());
    startVec = new TIntDoubleHashMap();
    startVec.put(nodes.getId("r0"), 1.0);
}

Example 13 with TIntDoubleHashMap

use of gnu.trove.map.hash.TIntDoubleHashMap in project ProPPR by TeamCohen.

the class TrainerTest method setup.

@Before
public void setup() {
    super.setup();
    this.srw = new SRW();
    this.srw.setRegularizer(new RegularizationSchedule(this.srw, new RegularizeL2()));
    this.srw.setSquashingFunction(new ReLU<String>());
    this.initTrainer();
    query = new TIntDoubleHashMap();
    query.put(nodes.getId("r0"), 1.0);
    examples = new ArrayList<String>();
    for (int k = 0; k < this.magicNumber; k++) {
        for (int p = 0; p < this.magicNumber; p++) {
            examples.add(new PosNegRWExample(brGraph, query, new int[] { nodes.getId("b" + k) }, new int[] { nodes.getId("r" + p) }).serialize());
        }
    }
}

Example 14 with TIntDoubleHashMap

use of gnu.trove.map.hash.TIntDoubleHashMap in project ProPPR by TeamCohen.

the class FiniteDifferenceTest method moreSetup.

@Override
public void moreSetup(LearningGraphBuilder lgb) {
    startVec = new TIntDoubleHashMap();
    startVec.put(nodes.getId("r0"), 1.0);
}

Example 15 with TIntDoubleHashMap

use of gnu.trove.map.hash.TIntDoubleHashMap in project ProPPR by TeamCohen.

the class SRW method inferenceUpdate.

protected void inferenceUpdate(PosNegRWExample example, StatusLogger status) {
    PprExample ex = (PprExample) example;
    double[] pNext = new double[ex.getGraph().node_hi];
    TIntDoubleMap[] dNext = new TIntDoubleMap[ex.getGraph().node_hi];
    // p: 2. for each node u
    for (int uid = 0; uid < ex.getGraph().node_hi; uid++) {
        if (log.isInfoEnabled() && status.due(4))
            log.info("Inference: node " + (uid + 1) + " of " + (ex.getGraph().node_hi));
        // p: 2(a) p_u^{t+1} += alpha * s_u
        pNext[uid] += c.apr.alpha * Dictionary.safeGet(ex.getQueryVec(), uid, 0.0);
        // p: 2(b) for each neighbor v of u:
        for (int eid = ex.getGraph().node_near_lo[uid], xvi = 0; eid < ex.getGraph().node_near_hi[uid]; eid++, xvi++) {
            int vid = ex.getGraph().edge_dest[eid];
            // p: 2(b)i. p_v^{t+1} += (1-alpha) * p_u^t * M_uv
            if (vid >= pNext.length) {
                throw new IllegalStateException("vid=" + vid + " > pNext.length=" + pNext.length);
            }
            pNext[vid] += (1 - c.apr.alpha) * ex.p[uid] * ex.M[uid][xvi];
            // d: i. for each feature i in dM_uv:
            if (dNext[vid] == null)
                dNext[vid] = new TIntDoubleHashMap(ex.dM_hi[uid][xvi] - ex.dM_lo[uid][xvi]);
            for (int dmi = ex.dM_lo[uid][xvi]; dmi < ex.dM_hi[uid][xvi]; dmi++) {
                // d_vi^{t+1} += (1-alpha) * p_u^{t} * dM_uvi
                if (ex.dM_value[dmi] == 0)
                    continue;
                double inc = (1 - c.apr.alpha) * ex.p[uid] * ex.dM_value[dmi];
                dNext[vid].adjustOrPutValue(ex.dM_feature_id[dmi], inc, inc);
            }
            // skip when d is empty
            if (ex.dp[uid] == null)
                continue;
            for (TIntDoubleIterator it = ex.dp[uid].iterator(); it.hasNext(); ) {
                it.advance();
                if (it.value() == 0)
                    continue;
                // d_vi^{t+1} += (1-alpha) * d_ui^t * M_uv
                double inc = (1 - c.apr.alpha) * it.value() * ex.M[uid][xvi];
                dNext[vid].adjustOrPutValue(it.key(), inc, inc);
            }
        }
    }
    // sanity check on p
    if (log.isDebugEnabled()) {
        double sum = 0;
        for (double d : pNext) sum += d;
        if (Math.abs(sum - 1.0) > c.apr.epsilon)
            log.error("invalid p computed: " + sum);
    }
    ex.p = pNext;
    ex.dp = dNext;
}