LinRegNormEqDenseBatch.java

/* file: LinRegNormEqDenseBatch.java */
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/*
 //  Content:
 //     Java example of multiple linear regression in the batch processing mode.
 //
 //     The program trains the multiple linear regression model on a training
 //     data set with the normal equations method and computes regression for
 //     the test data.
 */

package com.intel.daal.examples.linear_regression;

import com.intel.daal.algorithms.linear_regression.Model;
import com.intel.daal.algorithms.linear_regression.prediction.*;
import com.intel.daal.algorithms.linear_regression.training.*;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.data_management.data.MergedNumericTable;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.data_management.data_source.FileDataSource;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;

class LinRegNormEqDenseBatch {
    /* Input data set parameters */
    private static final String trainDatasetFileName = "../data/batch/linear_regression_train.csv";

    private static final String testDatasetFileName  = "../data/batch/linear_regression_test.csv";

    private static final int nFeatures           = 10;  /* Number of features in training and testing data sets */
    private static final int nDependentVariables = 2;   /* Number of dependent variables that correspond to each observation */

    static Model        model;
    static NumericTable results;
    static NumericTable testDependentVariables;

    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {

        trainModel();

        testModel();

        printResults();

        context.dispose();
    }

    private static void trainModel() {

        /* Initialize FileDataSource to retrieve the input data from a .csv file */
        FileDataSource trainDataSource = new FileDataSource(context, trainDatasetFileName,
                DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
                DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);

        /* Create Numeric Tables for training data and labels */
        NumericTable trainData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
        NumericTable trainDependentVariables = new HomogenNumericTable(context, Float.class, nDependentVariables, 0,
                                                                       NumericTable.AllocationFlag.DoNotAllocate);
        MergedNumericTable mergedData = new MergedNumericTable(context);
        mergedData.addNumericTable(trainData);
        mergedData.addNumericTable(trainDependentVariables);

        /* Retrieve the data from an input file */
        trainDataSource.loadDataBlock(mergedData);

        /* Create an algorithm object to train the multiple linear regression model with the normal equations method */
        TrainingBatch linearRegressionTrain = new TrainingBatch(context, Float.class, TrainingMethod.normEqDense);

        linearRegressionTrain.input.set(TrainingInputId.data, trainData);
        linearRegressionTrain.input.set(TrainingInputId.dependentVariable, trainDependentVariables);

        /* Build the multiple linear regression model */
        TrainingResult trainingResult = linearRegressionTrain.compute();

        model = trainingResult.get(TrainingResultId.model);
    }

    private static void testModel() {
        /* Initialize FileDataSource to retrieve the input data from a .csv file */
        FileDataSource testDataSource = new FileDataSource(context, testDatasetFileName,
                DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
                DataSource.NumericTableAllocationFlag.NotAllocateNumericTable);

        /* Create Numeric Tables for testing data and labels */
        NumericTable testData = new HomogenNumericTable(context, Float.class, nFeatures, 0, NumericTable.AllocationFlag.DoNotAllocate);
        testDependentVariables = new HomogenNumericTable(context, Float.class, nDependentVariables, 0, NumericTable.AllocationFlag.DoNotAllocate);
        MergedNumericTable mergedData = new MergedNumericTable(context);
        mergedData.addNumericTable(testData);
        mergedData.addNumericTable(testDependentVariables);

        /* Retrieve the data from an input file */
        testDataSource.loadDataBlock(mergedData);

        /* Create algorithm objects to predict values of multiple linear regression with the default method */
        PredictionBatch linearRegressionPredict = new PredictionBatch(context, Float.class,
                PredictionMethod.defaultDense);

        linearRegressionPredict.input.set(PredictionInputId.data, testData);
        linearRegressionPredict.input.set(PredictionInputId.model, model);

        /* Compute prediction results */
        PredictionResult predictionResult = linearRegressionPredict.compute();

        results = predictionResult.get(PredictionResultId.prediction);
    }

    private static void printResults() {
        NumericTable beta = model.getBeta();
        NumericTable expected = testDependentVariables;
        Service.printNumericTable("Linear Regression coefficients:", beta);
        Service.printNumericTable("Linear Regression prediction results: (first 10 rows):", results, 10);
        Service.printNumericTable("Ground truth (first 10 rows):", expected, 10);
    }
}
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