Examples with DenseLocalOnHeapVector org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector used on opensource projects

Example 56 with DenseLocalOnHeapVector

use of org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector in project ignite by apache.

the class SVMMultiClassClassificationExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws InterruptedException {
    System.out.println();
    System.out.println(">>> SVM Multi-class classification model over cached dataset usage example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), SVMMultiClassClassificationExample.class.getSimpleName(), () -> {
            IgniteCache<Integer, double[]> dataCache = getTestCache(ignite);
            SVMLinearMultiClassClassificationTrainer<Integer, double[]> trainer = new SVMLinearMultiClassClassificationTrainer<>();
            SVMLinearMultiClassClassificationModel mdl = trainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), (k, v) -> Arrays.copyOfRange(v, 1, v.length), (k, v) -> v[0], 5);
            System.out.println(">>> SVM Multi-class model");
            System.out.println(mdl.toString());
            NormalizationTrainer<Integer, double[]> normalizationTrainer = new NormalizationTrainer<>();
            NormalizationPreprocessor<Integer, double[]> preprocessor = normalizationTrainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), (k, v) -> Arrays.copyOfRange(v, 1, v.length), 5);
            SVMLinearMultiClassClassificationModel mdlWithNormalization = trainer.fit(new CacheBasedDatasetBuilder<>(ignite, dataCache), preprocessor, (k, v) -> v[0], 5);
            System.out.println(">>> SVM Multi-class model with normalization");
            System.out.println(mdlWithNormalization.toString());
            System.out.println(">>> ----------------------------------------------------------------");
            System.out.println(">>> | Prediction\t| Prediction with Normalization\t| Ground Truth\t|");
            System.out.println(">>> ----------------------------------------------------------------");
            int amountOfErrors = 0;
            int amountOfErrorsWithNormalization = 0;
            int totalAmount = 0;
            // Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix
            int[][] confusionMtx = { { 0, 0, 0 }, { 0, 0, 0 }, { 0, 0, 0 } };
            int[][] confusionMtxWithNormalization = { { 0, 0, 0 }, { 0, 0, 0 }, { 0, 0, 0 } };
            try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
                for (Cache.Entry<Integer, double[]> observation : observations) {
                    double[] val = observation.getValue();
                    double[] inputs = Arrays.copyOfRange(val, 1, val.length);
                    double groundTruth = val[0];
                    double prediction = mdl.apply(new DenseLocalOnHeapVector(inputs));
                    double predictionWithNormalization = mdlWithNormalization.apply(new DenseLocalOnHeapVector(inputs));
                    totalAmount++;
                    // Collect data for model
                    if (groundTruth != prediction)
                        amountOfErrors++;
                    int idx1 = (int) prediction == 1 ? 0 : ((int) prediction == 3 ? 1 : 2);
                    int idx2 = (int) groundTruth == 1 ? 0 : ((int) groundTruth == 3 ? 1 : 2);
                    confusionMtx[idx1][idx2]++;
                    // Collect data for model with normalization
                    if (groundTruth != predictionWithNormalization)
                        amountOfErrorsWithNormalization++;
                    idx1 = (int) predictionWithNormalization == 1 ? 0 : ((int) predictionWithNormalization == 3 ? 1 : 2);
                    idx2 = (int) groundTruth == 1 ? 0 : ((int) groundTruth == 3 ? 1 : 2);
                    confusionMtxWithNormalization[idx1][idx2]++;
                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n", prediction, predictionWithNormalization, groundTruth);
                }
                System.out.println(">>> ----------------------------------------------------------------");
                System.out.println("\n>>> -----------------SVM model-------------");
                System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
                System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double) totalAmount));
                System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtx));
                System.out.println("\n>>> -----------------SVM model with Normalization-------------");
                System.out.println("\n>>> Absolute amount of errors " + amountOfErrorsWithNormalization);
                System.out.println("\n>>> Accuracy " + (1 - amountOfErrorsWithNormalization / (double) totalAmount));
                System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtxWithNormalization));
            }
        });
        igniteThread.start();
        igniteThread.join();
    }
}

Example 57 with DenseLocalOnHeapVector

use of org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector in project ignite by apache.

the class VectorExample method main.

/**
 * Executes example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String[] args) {
    System.out.println();
    System.out.println(">>> Basic Vector API usage example started.");
    System.out.println("\n>>> Creating perpendicular vectors.");
    double[] data1 = new double[] { 1, 0, 3, 0, 5, 0 };
    double[] data2 = new double[] { 0, 2, 0, 4, 0, 6 };
    Vector v1 = new DenseLocalOnHeapVector(data1);
    Vector v2 = new DenseLocalOnHeapVector(data2);
    System.out.println(">>> First vector: " + Arrays.toString(data1));
    System.out.println(">>> Second vector: " + Arrays.toString(data2));
    double dotProduct = v1.dot(v2);
    boolean dotProductIsAsExp = dotProduct == 0;
    System.out.println("\n>>> Dot product of vectors: [" + dotProduct + "], it is 0 as expected: [" + dotProductIsAsExp + "].");
    Vector hypotenuse = v1.plus(v2);
    System.out.println("\n>>> Hypotenuse (sum of vectors): " + Arrays.toString(hypotenuse.getStorage().data()));
    double lenSquared1 = v1.getLengthSquared();
    double lenSquared2 = v2.getLengthSquared();
    double lenSquaredHypotenuse = hypotenuse.getLengthSquared();
    boolean lenSquaredHypotenuseIsAsExp = lenSquaredHypotenuse == lenSquared1 + lenSquared2;
    System.out.println(">>> Squared length of first vector: [" + lenSquared1 + "].");
    System.out.println(">>> Squared length of second vector: [" + lenSquared2 + "].");
    System.out.println(">>> Squared length of hypotenuse: [" + lenSquaredHypotenuse + "], equals sum of squared lengths of two original vectors as expected: [" + lenSquaredHypotenuseIsAsExp + "].");
    System.out.println("\n>>> Basic Vector API usage example completed.");
}

Example 58 with DenseLocalOnHeapVector

use of org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector in project ignite by apache.

the class FuzzyCMeansDistributedClusterer method calculateMembership.

/**
 * Calculate matrix of membership coefficients for each point and each center.
 *
 * @param points Matrix with source points.
 * @param centers Array of current centers.
 * @return Membership matrix and sums of membership coefficients for each center.
 */
private MembershipsAndSums calculateMembership(SparseDistributedMatrix points, Vector[] centers) {
    String cacheName = ((SparseDistributedMatrixStorage) points.getStorage()).cacheName();
    UUID uuid = points.getUUID();
    double fuzzyMembershipCoefficient = 2 / (exponentialWeight - 1);
    MembershipsAndSumsSupplier supplier = new MembershipsAndSumsSupplier(centers.length);
    return CacheUtils.distributedFold(cacheName, (IgniteBiFunction<Cache.Entry<SparseMatrixKey, ConcurrentHashMap<Integer, Double>>, MembershipsAndSums, MembershipsAndSums>) (vectorWithIndex, membershipsAndSums) -> {
        Integer idx = vectorWithIndex.getKey().index();
        Vector pnt = VectorUtils.fromMap(vectorWithIndex.getValue(), false);
        Vector distances = new DenseLocalOnHeapVector(centers.length);
        Vector pntMemberships = new DenseLocalOnHeapVector(centers.length);
        for (int i = 0; i < centers.length; i++) distances.setX(i, distance(centers[i], pnt));
        for (int i = 0; i < centers.length; i++) {
            double invertedFuzzyWeight = 0.0;
            for (int j = 0; j < centers.length; j++) {
                double val = Math.pow(distances.getX(i) / distances.getX(j), fuzzyMembershipCoefficient);
                if (Double.isNaN(val))
                    val = 1.0;
                invertedFuzzyWeight += val;
            }
            double membership = Math.pow(1.0 / invertedFuzzyWeight, exponentialWeight);
            pntMemberships.setX(i, membership);
        }
        membershipsAndSums.memberships.put(idx, pntMemberships);
        membershipsAndSums.membershipSums = membershipsAndSums.membershipSums.plus(pntMemberships);
        return membershipsAndSums;
    }, key -> key.dataStructureId().equals(uuid), (mem1, mem2) -> {
        mem1.merge(mem2);
        return mem1;
    }, supplier);
}

Example 59 with DenseLocalOnHeapVector

use of org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector in project ignite by apache.

the class LocalModelsTest method importExportLinearRegressionModelTest.

/**
 */
@Test
public void importExportLinearRegressionModelTest() throws IOException {
    executeModelTest(mdlFilePath -> {
        LinearRegressionModel model = new LinearRegressionModel(new DenseLocalOnHeapVector(new double[] { 1, 2 }), 3);
        Exporter<LinearRegressionModel, String> exporter = new FileExporter<>();
        model.saveModel(exporter, mdlFilePath);
        LinearRegressionModel load = exporter.load(mdlFilePath);
        Assert.assertNotNull(load);
        Assert.assertEquals("", model, load);
        return null;
    });
}

Example 60 with DenseLocalOnHeapVector

use of org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector in project ignite by apache.

the class LocalModelsTest method importExportSVMMulticlassClassificationModelTest.

/**
 */
@Test
public void importExportSVMMulticlassClassificationModelTest() throws IOException {
    executeModelTest(mdlFilePath -> {
        SVMLinearBinaryClassificationModel binaryMdl1 = new SVMLinearBinaryClassificationModel(new DenseLocalOnHeapVector(new double[] { 1, 2 }), 3);
        SVMLinearBinaryClassificationModel binaryMdl2 = new SVMLinearBinaryClassificationModel(new DenseLocalOnHeapVector(new double[] { 2, 3 }), 4);
        SVMLinearBinaryClassificationModel binaryMdl3 = new SVMLinearBinaryClassificationModel(new DenseLocalOnHeapVector(new double[] { 3, 4 }), 5);
        SVMLinearMultiClassClassificationModel mdl = new SVMLinearMultiClassClassificationModel();
        mdl.add(1, binaryMdl1);
        mdl.add(2, binaryMdl2);
        mdl.add(3, binaryMdl3);
        Exporter<SVMLinearMultiClassClassificationModel, String> exporter = new FileExporter<>();
        mdl.saveModel(exporter, mdlFilePath);
        SVMLinearMultiClassClassificationModel load = exporter.load(mdlFilePath);
        Assert.assertNotNull(load);
        Assert.assertEquals("", mdl, load);
        return null;
    });
}