Examples with OLSMultipleLinearRegression org.apache.ignite.ml.regressions.OLSMultipleLinearRegression used on opensource projects

Example 1 with OLSMultipleLinearRegression

use of org.apache.ignite.ml.regressions.OLSMultipleLinearRegression in project ignite by apache.

the class DistributedRegressionExample method main.

/** Run example. */
public static void main(String[] args) throws InterruptedException {
    System.out.println();
    System.out.println(">>> Linear regression over sparse distributed matrix API usage example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
        // because we create ignite cache internally.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), SparseDistributedMatrixExample.class.getSimpleName(), () -> {
            double[] data = { 8, 78, 284, 9.100000381, 109, 9.300000191, 68, 433, 8.699999809, 144, 7.5, 70, 739, 7.199999809, 113, 8.899999619, 96, 1792, 8.899999619, 97, 10.19999981, 74, 477, 8.300000191, 206, 8.300000191, 111, 362, 10.89999962, 124, 8.800000191, 77, 671, 10, 152, 8.800000191, 168, 636, 9.100000381, 162, 10.69999981, 82, 329, 8.699999809, 150, 11.69999981, 89, 634, 7.599999905, 134, 8.5, 149, 631, 10.80000019, 292, 8.300000191, 60, 257, 9.5, 108, 8.199999809, 96, 284, 8.800000191, 111, 7.900000095, 83, 603, 9.5, 182, 10.30000019, 130, 686, 8.699999809, 129, 7.400000095, 145, 345, 11.19999981, 158, 9.600000381, 112, 1357, 9.699999809, 186, 9.300000191, 131, 544, 9.600000381, 177, 10.60000038, 80, 205, 9.100000381, 127, 9.699999809, 130, 1264, 9.199999809, 179, 11.60000038, 140, 688, 8.300000191, 80, 8.100000381, 154, 354, 8.399999619, 103, 9.800000191, 118, 1632, 9.399999619, 101, 7.400000095, 94, 348, 9.800000191, 117, 9.399999619, 119, 370, 10.39999962, 88, 11.19999981, 153, 648, 9.899999619, 78, 9.100000381, 116, 366, 9.199999809, 102, 10.5, 97, 540, 10.30000019, 95, 11.89999962, 176, 680, 8.899999619, 80, 8.399999619, 75, 345, 9.600000381, 92, 5, 134, 525, 10.30000019, 126, 9.800000191, 161, 870, 10.39999962, 108, 9.800000191, 111, 669, 9.699999809, 77, 10.80000019, 114, 452, 9.600000381, 60, 10.10000038, 142, 430, 10.69999981, 71, 10.89999962, 238, 822, 10.30000019, 86, 9.199999809, 78, 190, 10.69999981, 93, 8.300000191, 196, 867, 9.600000381, 106, 7.300000191, 125, 969, 10.5, 162, 9.399999619, 82, 499, 7.699999809, 95, 9.399999619, 125, 925, 10.19999981, 91, 9.800000191, 129, 353, 9.899999619, 52, 3.599999905, 84, 288, 8.399999619, 110, 8.399999619, 183, 718, 10.39999962, 69, 10.80000019, 119, 540, 9.199999809, 57, 10.10000038, 180, 668, 13, 106, 9, 82, 347, 8.800000191, 40, 10, 71, 345, 9.199999809, 50, 11.30000019, 118, 463, 7.800000191, 35, 11.30000019, 121, 728, 8.199999809, 86, 12.80000019, 68, 383, 7.400000095, 57, 10, 112, 316, 10.39999962, 57, 6.699999809, 109, 388, 8.899999619, 94 };
            final int nobs = 53;
            final int nvars = 4;
            System.out.println(">>> Create new SparseDistributedMatrix inside IgniteThread.");
            // Create SparseDistributedMatrix, new cache will be created automagically.
            SparseDistributedMatrix distributedMatrix = new SparseDistributedMatrix(0, 0, StorageConstants.ROW_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
            System.out.println(">>> Create new linear regression object");
            OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
            regression.newSampleData(data, nobs, nvars, distributedMatrix);
            System.out.println();
            System.out.println(">>> Estimates the regression parameters b:");
            System.out.println(Arrays.toString(regression.estimateRegressionParameters()));
            System.out.println(">>> Estimates the residuals, ie u = y - X*b:");
            System.out.println(Arrays.toString(regression.estimateResiduals()));
            System.out.println(">>> Standard errors of the regression parameters:");
            System.out.println(Arrays.toString(regression.estimateRegressionParametersStandardErrors()));
            System.out.println(">>> Estimates the variance of the regression parameters, ie Var(b):");
            Tracer.showAscii(regression.estimateRegressionParametersVariance());
            System.out.println(">>> Estimates the standard error of the regression:");
            System.out.println(regression.estimateRegressionStandardError());
            System.out.println(">>> R-Squared statistic:");
            System.out.println(regression.calculateRSquared());
            System.out.println(">>> Adjusted R-squared statistic:");
            System.out.println(regression.calculateAdjustedRSquared());
            System.out.println(">>> Returns the variance of the regressand, ie Var(y):");
            System.out.println(regression.estimateErrorVariance());
        });
        igniteThread.start();
        igniteThread.join();
    }
}