Example usage for org.apache.commons.math3.linear ArrayRealVector ArrayRealVector

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear ArrayRealVector ArrayRealVector.

Prototype

public ArrayRealVector(ArrayRealVector v) throws NullArgumentException

Source Link

Document

Construct a vector from another vector, using a deep copy.

Usage

From source file:org.lambda3.indra.service.impl.MockedVectorResourceImpl.java

@Override
public VectorResponse getVector(VectorRequest request) {
    Map<String, Map<Integer, Double>> terms = new HashMap<>();
    request.getTerms()/* w  w  w  .  j a  v  a  2  s  .c o m*/
            .forEach(t -> terms.put(t, RealVectorUtil.vectorToMap(new ArrayRealVector(rvg.nextVector()))));

    return new VectorResponse(request, terms);
}

From source file:org.lambda3.indra.service.mock.MockedVectorResourceImpl.java

@Override
public VectorResponse getVector(VectorRequest request) {
    Map<String, Map<Integer, Double>> terms = new HashMap<>();
    request.getTerms()//from www.  j  a  v  a2 s.co m
            .forEach(t -> terms.put(t, RealVectorUtil.vectorToMap(new ArrayRealVector(rvg.nextVector()))));
    VectorResponse response = new SparseVectorResponse(request, terms);

    return response;
}

From source file:org.lenskit.predict.ordrec.OrdRecModel.java

/**
 * The constructor of OrdRecParameter./*from w  w  w .ja va2s  .  co m*/
 * It use the quantized values of rating to initialize t1 and beta.
 * Each threshold is initialized as the mean of two contiguous rating values.
 * Since the index of quantizer is always an successive non-negative integer
 * begin from 0, so t1 will initialize as 0.5, and the interval between two
 * thresholds will be 1.
 * @param qtz The quantizer for ratings
 */
OrdRecModel(Quantizer qtz) {
    qtzValues = qtz.getValues();
    levelCount = qtzValues.getDimension();
    t1 = (qtzValues.getEntry(0) + qtzValues.getEntry(1)) / 2;
    beta = new ArrayRealVector(levelCount - 2);

    double tr = t1;
    for (int i = 1; i <= beta.getDimension(); i++) {
        double trnext = (qtzValues.getEntry(i) + qtzValues.getEntry(i + 1)) * 0.5;
        beta.setEntry(i - 1, Math.log(trnext - tr));
        tr = trnext;
    }
}

From source file:org.lenskit.predict.ordrec.OrdRecRatingPredictor.java

/**
 * The train function of OrdRec. Get all parameters after learning process.
 *//*from   www  . j a va  2s  .co m*/
@SuppressWarnings("ConstantConditions")
private void trainModel(OrdRecModel model, SparseVector ratings, MutableSparseVector scores) {
    RealVector beta = model.getBeta();
    RealVector deltaBeta = new ArrayRealVector(beta.getDimension());
    double dt1;
    // n is the number of iteration;
    for (int j = 0; j < iterationCount; j++) {
        for (VectorEntry rating : ratings) {
            long iid = rating.getKey();
            double score = scores.get(iid);
            int r = quantizer.index(rating.getValue());

            double probEqualR = model.getProbEQ(score, r);
            double probLessR = model.getProbLE(score, r);
            double probLessR_1 = model.getProbLE(score, r - 1);

            double t1 = model.getT1();
            dt1 = learningRate / probEqualR * (probLessR * (1 - probLessR) * dBeta(r, 0, t1)
                    - probLessR_1 * (1 - probLessR_1) * dBeta(r - 1, 0, t1) - regTerm * t1);

            double dbetaK;
            for (int k = 0; k < beta.getDimension(); k++) {
                dbetaK = learningRate / probEqualR
                        * (probLessR * (1 - probLessR) * dBeta(r, k + 1, beta.getEntry(k))
                                - probLessR_1 * (1 - probLessR_1) * dBeta(r - 1, k + 1, beta.getEntry(k))
                                - regTerm * beta.getEntry(k));
                deltaBeta.setEntry(k, dbetaK);
            }
            model.update(dt1, deltaBeta);
        }
    }
}

From source file:org.lenskit.predict.ordrec.OrdRecRatingPredictor.java

@Nonnull
private ResultMap computePredictions(long user, @Nonnull Collection<Long> items, boolean includeDetails) {
    logger.debug("predicting {} items for {}", items.size(), user);
    SparseVector ratings = makeUserVector(user, userEventDao);
    LongSet allItems = new LongOpenHashSet(ratings.keySet());
    allItems.addAll(items);/*from   www .  j av a  2s.c  o m*/

    ResultMap baseResults = null;
    Map<Long, Double> scores;
    if (includeDetails) {
        baseResults = itemScorer.scoreWithDetails(user, allItems);
        scores = baseResults.scoreMap();
    } else {
        scores = itemScorer.score(user, allItems);
    }
    MutableSparseVector scoreVector = MutableSparseVector.create(scores);

    OrdRecModel params = new OrdRecModel(quantizer);
    trainModel(params, ratings, scoreVector);
    logger.debug("trained parameters for {}: {}", user, params);

    RealVector probabilities = new ArrayRealVector(params.getLevelCount());

    List<Result> results = new ArrayList<>();

    LongIterator iter = LongIterators.asLongIterator(items.iterator());
    while (iter.hasNext()) {
        final long item = iter.nextLong();
        double score = scoreVector.get(item, Double.NaN);
        if (Double.isNaN(score)) {
            continue;
        }
        params.getProbDistribution(score, probabilities);
        int mlIdx = probabilities.getMaxIndex();
        double pred = quantizer.getIndexValue(mlIdx);
        if (includeDetails) {
            results.add(new FullResult(baseResults.get(item), pred, new ArrayRealVector(probabilities)));
        } else {
            results.add(Results.create(item, pred));
        }
    }

    return Results.newResultMap(results);
}

From source file:org.lenskit.transform.quantize.PreferenceDomainQuantizer.java

static RealVector makeValues(PreferenceDomain domain) {
    if (!domain.hasPrecision()) {
        throw new IllegalArgumentException("domain is not discrete");
    }/*from ww  w .  j a  va 2  s  . c  o m*/
    final double min = domain.getMinimum();
    final double max = domain.getMaximum();
    final double prec = domain.getPrecision();
    final double nv = (max - min) / prec;
    int n = (int) nv;
    if (Math.abs(nv - n) > 1.0e-6) {
        n += 1; // one more to cover everything...
    }
    if (n == 0) {
        throw new IllegalArgumentException("range has no elements");
    }
    double[] values = new double[n + 1];
    for (int i = 0; i <= n; i++) {
        values[i] = min + (prec * i);
    }
    return new ArrayRealVector(values);
}

From source file:org.lenskit.transform.quantize.PreferenceDomainQuantizerTest.java

@Test
public void testMakeValues() {
    RealVector vals = PreferenceDomainQuantizer.makeValues(domain);
    RealVector evals = new ArrayRealVector(new double[] { 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0 });
    assertThat(vals.getDimension(), equalTo(evals.getDimension()));
    for (int i = 0; i < vals.getDimension(); i++) {
        assertThat("element " + i, vals.getEntry(i), closeTo(evals.getEntry(i), 1.0e-6));
    }/*w  w  w .  j  ava  2 s.co m*/
}

From source file:org.lenskit.transform.quantize.ValueArrayQuantizer.java

/**
 * Construct a new quantizer using the specified array of values.
 *
 * @param vs The discrete values to quantize to.
 *///from w  w w. ja v  a 2  s.  c o  m
public ValueArrayQuantizer(double[] vs) {
    Preconditions.checkArgument(vs.length > 0, "must have at least one value");
    values = new ArrayRealVector(vs);
}

From source file:org.lenskit.transform.quantize.ValueArrayQuantizer.java

public ValueArrayQuantizer(RealVector vs) {
    Preconditions.checkArgument(vs.getDimension() > 0, "must have at least one value");
    values = new ArrayRealVector(vs);
}

From source file:org.lenskit.util.math.RowView.java

@Override
public RealVector append(RealVector v) {
    return new ArrayRealVector(this).append(v);
}