Package org.apache.mahout.cf.taste.impl.recommender.svd

Examples of org.apache.mahout.cf.taste.impl.recommender.svd.Factorizer



    KDDCupFactorizablePreferences factorizablePreferences =
        new KDDCupFactorizablePreferences(KDDCupDataModel.getTrainingFile(dataFileDirectory));

    Factorizer sgdFactorizer = new ParallelArraysSGDFactorizer(factorizablePreferences, numFeatures, numIterations,
        learningRate, preventOverfitting, randomNoise);

    Factorization factorization = sgdFactorizer.factorize();

    log.info("Estimating validation preferences...");
    int prefsProcessed = 0;
    RunningAverage average = new FullRunningAverage();
    for (Pair<PreferenceArray,long[]> validationPair
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    KDDCupFactorizablePreferences factorizablePreferences =
        new KDDCupFactorizablePreferences(KDDCupDataModel.getTrainingFile(dataFileDirectory));

    Factorizer sgdFactorizer = new ParallelArraysSGDFactorizer(factorizablePreferences, numFeatures, numIterations,
        learningRate, preventOverfitting, randomNoise);

    Factorization factorization = sgdFactorizer.factorize();

    log.info("Estimating validation preferences...");
    int prefsProcessed = 0;
    RunningAverage average = new FullRunningAverage();
    DataFileIterable validations = new DataFileIterable(KDDCupDataModel.getValidationFile(dataFileDirectory));
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    KDDCupFactorizablePreferences factorizablePreferences =
        new KDDCupFactorizablePreferences(KDDCupDataModel.getTrainingFile(dataFileDirectory));

    Factorizer sgdFactorizer = new ParallelArraysSGDFactorizer(factorizablePreferences, numFeatures, numIterations,
        learningRate, preventOverfitting, randomNoise);

    Factorization factorization = sgdFactorizer.factorize();

    log.info("Estimating validation preferences...");
    int prefsProcessed = 0;
    RunningAverage average = new FullRunningAverage();
    for (Pair<PreferenceArray,long[]> validationPair :
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