Examples with Vector org.apache.ignite.ml.math.primitives.vector.Vector used on opensource projects

Example 41 with Vector

use of org.apache.ignite.ml.math.primitives.vector.Vector in project ignite by apache.

the class BinarizationExample method createCache.

/**
 */
private static IgniteCache<Integer, Vector> createCache(Ignite ignite) {
    CacheConfiguration<Integer, Vector> cacheConfiguration = new CacheConfiguration<>();
    cacheConfiguration.setName("PERSONS");
    cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 2));
    IgniteCache<Integer, Vector> persons = ignite.createCache(cacheConfiguration);
    persons.put(1, new DenseVector(new Serializable[] { "Mike", 42, 10000 }));
    persons.put(2, new DenseVector(new Serializable[] { "John", 32, 64000 }));
    persons.put(3, new DenseVector(new Serializable[] { "George", 53, 120000 }));
    persons.put(4, new DenseVector(new Serializable[] { "Karl", 24, 70000 }));
    return persons;
}

Example 42 with Vector

use of org.apache.ignite.ml.math.primitives.vector.Vector in project ignite by apache.

the class MaxAbsScalerExample method createCache.

/**
 */
private static IgniteCache<Integer, Vector> createCache(Ignite ignite) {
    CacheConfiguration<Integer, Vector> cacheConfiguration = new CacheConfiguration<>();
    cacheConfiguration.setName("PERSONS");
    cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 2));
    IgniteCache<Integer, Vector> persons = ignite.createCache(cacheConfiguration);
    persons.put(1, new DenseVector(new Serializable[] { "Mike", 42, 10000 }));
    persons.put(2, new DenseVector(new Serializable[] { "John", 32, 64000 }));
    persons.put(3, new DenseVector(new Serializable[] { "George", 53, 120000 }));
    persons.put(4, new DenseVector(new Serializable[] { "Karl", 24, 70000 }));
    return persons;
}

Example 43 with Vector

use of org.apache.ignite.ml.math.primitives.vector.Vector in project ignite by apache.

the class GaussianNaiveBayesTrainer method updateModel.

/**
 * {@inheritDoc}
 */
@Override
protected <K, V> GaussianNaiveBayesModel updateModel(GaussianNaiveBayesModel mdl, DatasetBuilder<K, V> datasetBuilder, Preprocessor<K, V> extractor) {
    assert datasetBuilder != null;
    try (Dataset<EmptyContext, GaussianNaiveBayesSumsHolder> dataset = datasetBuilder.build(envBuilder, (env, upstream, upstreamSize) -> new EmptyContext(), (env, upstream, upstreamSize, ctx) -> {
        GaussianNaiveBayesSumsHolder res = new GaussianNaiveBayesSumsHolder();
        while (upstream.hasNext()) {
            UpstreamEntry<K, V> entity = upstream.next();
            LabeledVector lv = extractor.apply(entity.getKey(), entity.getValue());
            Vector features = lv.features();
            Double label = (Double) lv.label();
            double[] toMeans;
            double[] sqSum;
            if (!res.featureSumsPerLbl.containsKey(label)) {
                toMeans = new double[features.size()];
                Arrays.fill(toMeans, 0.);
                res.featureSumsPerLbl.put(label, toMeans);
            }
            if (!res.featureSquaredSumsPerLbl.containsKey(label)) {
                sqSum = new double[features.size()];
                res.featureSquaredSumsPerLbl.put(label, sqSum);
            }
            if (!res.featureCountersPerLbl.containsKey(label))
                res.featureCountersPerLbl.put(label, 0);
            res.featureCountersPerLbl.put(label, res.featureCountersPerLbl.get(label) + 1);
            toMeans = res.featureSumsPerLbl.get(label);
            sqSum = res.featureSquaredSumsPerLbl.get(label);
            for (int j = 0; j < features.size(); j++) {
                double x = features.get(j);
                toMeans[j] += x;
                sqSum[j] += x * x;
            }
        }
        return res;
    }, learningEnvironment())) {
        GaussianNaiveBayesSumsHolder sumsHolder = dataset.compute(t -> t, (a, b) -> {
            if (a == null)
                return b;
            if (b == null)
                return a;
            return a.merge(b);
        });
        if (mdl != null && mdl.getSumsHolder() != null)
            sumsHolder = sumsHolder.merge(mdl.getSumsHolder());
        List<Double> sortedLabels = new ArrayList<>(sumsHolder.featureCountersPerLbl.keySet());
        sortedLabels.sort(Double::compareTo);
        assert !sortedLabels.isEmpty() : "The dataset should contain at least one feature";
        int labelCount = sortedLabels.size();
        int featureCount = sumsHolder.featureSumsPerLbl.get(sortedLabels.get(0)).length;
        double[][] means = new double[labelCount][featureCount];
        double[][] variances = new double[labelCount][featureCount];
        double[] classProbabilities = new double[labelCount];
        double[] labels = new double[labelCount];
        long datasetSize = sumsHolder.featureCountersPerLbl.values().stream().mapToInt(i -> i).sum();
        int lbl = 0;
        for (Double label : sortedLabels) {
            int count = sumsHolder.featureCountersPerLbl.get(label);
            double[] sum = sumsHolder.featureSumsPerLbl.get(label);
            double[] sqSum = sumsHolder.featureSquaredSumsPerLbl.get(label);
            for (int i = 0; i < featureCount; i++) {
                means[lbl][i] = sum[i] / count;
                variances[lbl][i] = (sqSum[i] - sum[i] * sum[i] / count) / count;
            }
            if (equiprobableClasses)
                classProbabilities[lbl] = 1. / labelCount;
            else if (priorProbabilities != null) {
                assert classProbabilities.length == priorProbabilities.length;
                classProbabilities[lbl] = priorProbabilities[lbl];
            } else
                classProbabilities[lbl] = (double) count / datasetSize;
            labels[lbl] = label;
            ++lbl;
        }
        return new GaussianNaiveBayesModel(means, variances, classProbabilities, labels, sumsHolder);
    } catch (Exception e) {
        throw new RuntimeException(e);
    }
}

Example 44 with Vector

use of org.apache.ignite.ml.math.primitives.vector.Vector in project ignite by apache.

the class ReplicatedVectorMatrix method assignColumn.

/**
 * {@inheritDoc}
 */
@Override
public Matrix assignColumn(int col, Vector vec) {
    int rows = asCol ? vector.size() : replicationCnt;
    int cols = asCol ? replicationCnt : vector.size();
    int times = asCol ? cols : rows;
    Matrix res = new DenseMatrix(rows, cols);
    IgniteBiConsumer<Integer, Vector> replicantAssigner = asCol ? res::assignColumn : res::assignRow;
    IgniteBiConsumer<Integer, Vector> assigner = res::assignColumn;
    assign(replicantAssigner, assigner, vector, vec, times, col);
    return res;
}

Example 45 with Vector

use of org.apache.ignite.ml.math.primitives.vector.Vector in project ignite by apache.

the class ReplicatedVectorMatrix method minus.

/**
 * Specialized optimized version of minus for ReplicatedVectorMatrix.
 *
 * @param mtx Matrix to be subtracted.
 * @return New ReplicatedVectorMatrix resulting from subtraction.
 */
public Matrix minus(ReplicatedVectorMatrix mtx) {
    if (isColumnReplicated() == mtx.isColumnReplicated()) {
        checkCardinality(mtx.rowSize(), mtx.columnSize());
        Vector minus = vector.minus(mtx.replicant());
        return new ReplicatedVectorMatrix(minus, replicationCnt, asCol);
    }
    throw new UnsupportedOperationException();
}