Examples with TIntDoubleMap gnu.trove.map.TIntDoubleMap used on opensource projects

Example 1 with TIntDoubleMap

use of gnu.trove.map.TIntDoubleMap in project ProPPR by TeamCohen.

the class RWExampleParser method parse.

public PosNegRWExample parse(String line, LearningGraphBuilder builder, SRW learner) throws GraphFormatException {
    //String[] parts = line.trim().split(MAJOR_DELIM,5);
    // first parse the query metadata
    //LearningGraphBuilder.split(line,'\t',4);
    String[] parts = new String[4];
    int last = 0, i = 0;
    for (int next = last; i < parts.length; last = next + 1, i++) {
        if (next == -1)
            throw new GraphFormatException("Need 8 distinct tsv fields in the grounded example:" + line);
        next = line.indexOf(MAJOR_DELIM, last);
        parts[i] = next < 0 ? line.substring(last) : line.substring(last, next);
    }
    TIntDoubleMap queryVec = new TIntDoubleHashMap();
    //for(String u : parts[1].split(MINOR_DELIM)) queryVec.put(Integer.parseInt(u), 1.0);
    for (int u : parseNodes(parts[1])) queryVec.put(u, 1.0);
    int[] posList, negList;
    if (//stringToInt(parts[2].split(MINOR_DELIM));
    parts[2].length() > 0)
        //stringToInt(parts[2].split(MINOR_DELIM));
        posList = parseNodes(parts[2]);
    else
        posList = new int[0];
    if (//stringToInt(parts[3].split(MINOR_DELIM));
    parts[3].length() > 0)
        //stringToInt(parts[3].split(MINOR_DELIM));
        negList = parseNodes(parts[3]);
    else
        negList = new int[0];
    LearningGraph g = builder.deserialize(line.substring(last));
    return learner.makeExample(parts[0], g, queryVec, posList, negList);
}

Example 2 with TIntDoubleMap

use of gnu.trove.map.TIntDoubleMap in project ProPPR by TeamCohen.

the class AdaGradSRW method agd.

/**
	 * AdaGrad Descent Algo
	 * 
	 * edits params using totSqGrad as well
	 * 
	 * @author rosecatherinek
	 */
protected void agd(ParamVector<String, ?> params, PosNegRWExample ex) {
    TIntDoubleMap gradient = gradient(params, ex);
    // apply gradient to param vector
    for (TIntDoubleIterator grad = gradient.iterator(); grad.hasNext(); ) {
        grad.advance();
        // avoid underflow since we're summing the square
        if (Math.abs(grad.value()) < MIN_GRADIENT)
            continue;
        String feature = ex.getGraph().featureLibrary.getSymbol(grad.key());
        if (trainable(feature)) {
            Double g = grad.value();
            //first update the running total of the square of the gradient
            totSqGrad.adjustValue(feature, g * g);
            //now get the running total
            //				Double rt = totSqGrad.get(feature);
            //w_{t+1, i} = w_{t, i} - \eta * g_{t,i} / \sqrt{ G,i }
            //				Double descentVal = - c.eta * g / Math.sqrt(rt);
            params.adjustValue(feature, -learningRate(feature) * g);
            if (params.get(feature).isInfinite()) {
                log.warn("Infinity at " + feature + "; gradient " + grad.value() + "; rt " + totSqGrad.get(feature));
            }
        }
    }
}

Example 3 with TIntDoubleMap

use of gnu.trove.map.TIntDoubleMap in project ProPPR by TeamCohen.

the class SRW method load.

/** fills M, dM in ex **/
protected void load(ParamVector<String, ?> params, PosNegRWExample example) {
    PprExample ex = (PprExample) example;
    int dM_cursor = 0;
    for (int uid = 0; uid < ex.getGraph().node_hi; uid++) {
        // (a); (b): initialization
        double tu = 0;
        TIntDoubleMap dtu = new TIntDoubleHashMap();
        int udeg = ex.getGraph().node_near_hi[uid] - ex.getGraph().node_near_lo[uid];
        double[] suv = new double[udeg];
        double[][] dfu = new double[udeg][];
        // begin (c): 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];
            // i. s_{uv} = w * phi_{uv}, a scalar:
            suv[xvi] = 0;
            for (int lid = ex.getGraph().edge_labels_lo[eid]; lid < ex.getGraph().edge_labels_hi[eid]; lid++) {
                suv[xvi] += params.get(ex.getGraph().featureLibrary.getSymbol(ex.getGraph().label_feature_id[lid])) * ex.getGraph().label_feature_weight[lid];
            }
            // ii. t_u += f(s_{uv}), a scalar:
            tu += c.squashingFunction.edgeWeight(suv[xvi]);
            // iii. df_{uv} = f'(s_{uv})* phi_{uv}, a vector, as sparse as phi_{uv}
            // by looping over features i in phi_{uv}
            double[] dfuv = new double[ex.getGraph().edge_labels_hi[eid] - ex.getGraph().edge_labels_lo[eid]];
            double cee = c.squashingFunction.computeDerivative(suv[xvi]);
            for (int lid = ex.getGraph().edge_labels_lo[eid], dfuvi = 0; lid < ex.getGraph().edge_labels_hi[eid]; lid++, dfuvi++) {
                // iii. again
                dfuv[dfuvi] = cee * ex.getGraph().label_feature_weight[lid];
                // iv. dt_u += df_{uv}, a vector, as sparse as sum_{v'} phi_{uv'}
                // by looping over features i in df_{uv} 
                // (identical to features i in phi_{uv}, so we use the same loop)
                dtu.adjustOrPutValue(ex.getGraph().label_feature_id[lid], dfuv[dfuvi], dfuv[dfuvi]);
            }
            dfu[xvi] = dfuv;
        }
        // end (c)
        // begin (d): for each neighbor v of u,
        double scale = (1 / (tu * tu));
        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];
            //dM_features.size();
            ex.dM_lo[uid][xvi] = dM_cursor;
            // create the vector dM_{uv} = (1/t^2_u) * (t_u * df_{uv} - f(s_{uv}) * dt_u)
            // by looping over features i in dt_u
            // getting the df offset for features in dt_u is awkward, so we'll first iterate over features in df_uv,
            // then fill in the rest
            int[] seenFeatures = new int[ex.getGraph().edge_labels_hi[eid] - ex.getGraph().edge_labels_lo[eid]];
            for (int lid = ex.getGraph().edge_labels_lo[eid], dfuvi = 0; lid < ex.getGraph().edge_labels_hi[eid]; lid++, dfuvi++) {
                int fid = ex.getGraph().label_feature_id[lid];
                //dM_features.add(fid);
                ex.dM_feature_id[dM_cursor] = fid;
                double dMuvi = (tu * dfu[xvi][dfuvi] - c.squashingFunction.edgeWeight(suv[xvi]) * dtu.get(fid));
                if (tu == 0) {
                    if (dMuvi != 0)
                        throw new IllegalStateException("tu=0 at u=" + uid + "; example " + ex.toString());
                } else
                    dMuvi *= scale;
                //dM_values.add(dMuvi);
                ex.dM_value[dM_cursor] = dMuvi;
                dM_cursor++;
                //save this feature so we can skip it later
                seenFeatures[dfuvi] = fid;
            }
            Arrays.sort(seenFeatures);
            // we've hit all the features in df_uv, now we do the remaining features in dt_u:
            for (TIntDoubleIterator it = dtu.iterator(); it.hasNext(); ) {
                it.advance();
                // skip features we already added in the df_uv loop
                if (Arrays.binarySearch(seenFeatures, it.key()) >= 0)
                    continue;
                //dM_features.add(it.key());
                ex.dM_feature_id[dM_cursor] = it.key();
                // zero the first term, since df_uv doesn't cover this feature
                double dMuvi = scale * (-c.squashingFunction.edgeWeight(suv[xvi]) * it.value());
                //dM_values.add(dMuvi);
                ex.dM_value[dM_cursor] = dMuvi;
                dM_cursor++;
            }
            //dM_features.size();
            ex.dM_hi[uid][xvi] = dM_cursor;
            // also create the scalar M_{uv} = f(s_{uv}) / t_u
            ex.M[uid][xvi] = c.squashingFunction.edgeWeight(suv[xvi]);
            if (tu == 0) {
                if (ex.M[uid][xvi] != 0)
                    throw new IllegalStateException("tu=0 at u=" + uid + "; example " + ex.toString());
            } else
                ex.M[uid][xvi] /= tu;
        }
    }
}

Example 4 with TIntDoubleMap

use of gnu.trove.map.TIntDoubleMap in project ProPPR by TeamCohen.

the class SRW method accumulateGradient.

public void accumulateGradient(ParamVector<String, ?> params, PosNegRWExample example, ParamVector<String, ?> accumulator, StatusLogger status) {
    log.debug("Gradient calculating on " + example);
    initializeFeatures(params, example.getGraph());
    ParamVector<String, Double> prepare = new SimpleParamVector<String>();
    regularizer.prepareForExample(params, example.getGraph(), prepare);
    load(params, example);
    inference(params, example, status);
    TIntDoubleMap gradient = gradient(params, example);
    for (Map.Entry<String, Double> e : prepare.entrySet()) {
        if (trainable(e.getKey()))
            accumulator.adjustValue(e.getKey(), -e.getValue() / example.length());
    }
    for (TIntDoubleIterator it = gradient.iterator(); it.hasNext(); ) {
        it.advance();
        String feature = example.getGraph().featureLibrary.getSymbol(it.key());
        if (trainable(feature))
            accumulator.adjustValue(example.getGraph().featureLibrary.getSymbol(it.key()), it.value() / example.length());
    }
}

Example 5 with TIntDoubleMap

use of gnu.trove.map.TIntDoubleMap in project ProPPR by TeamCohen.

the class RedBlueGraph method colorPart.

public TIntDoubleMap colorPart(final Set<String> color, TIntDoubleMap vec) {
    final TIntDoubleMap result = new TIntDoubleHashMap();
    vec.forEachEntry(new TIntDoubleProcedure() {

        @Override
        public boolean execute(int k, double v) {
            if (color.contains(nodes.getSymbol(k)))
                result.put(k, v);
            return true;
        }
    });
    return result;
}