Package org.apache.mahout.math

Examples of org.apache.mahout.math.SparseRowMatrix.iterateAll()

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    info.append('\n');

    log.info(info.toString());

    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      Iterator<Vector.Element> elementIterator = slice.vector().iterateNonZero();
      while (elementIterator.hasNext()) {
        Vector.Element e = elementIterator.next();
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    info.append('\n');

    log.info(info.toString());

    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      for (Vector.Element e : slice.vector()) {
        if (!Double.isNaN(e.get())) {
          double pref = e.get();
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    ALSWRFactorizer factorizer = new ALSWRFactorizer(dataModel, 3, 0.065, 5, true, alpha);

    SVDRecommender svdRecommender = new SVDRecommender(dataModel, factorizer);

    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      for (Vector.Element e : slice.vector().all()) {

        long userID = slice.index() + 1;
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        new DenseVector(new double[] { na, 5.0,  na, 3.0 }),
        new DenseVector(new double[] {3.0,  na,  na, 5.0 }) });

    StringBuilder prefsAsText = new StringBuilder();
    String separator = "";
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      Iterator<Vector.Element> elementIterator = slice.vector().iterateNonZero();
      while (elementIterator.hasNext()) {
        Vector.Element e = elementIterator.next();
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        preferences.numRows(), numFeatures);
    Matrix m = MathHelper.readEntries(conf, new Path(outputDir.getAbsolutePath(), "M/part-r-00000"),
      preferences.numCols(), numFeatures);

    RunningAverage avg = new FullRunningAverage();
    sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      Iterator<Vector.Element> elementIterator = slice.vector().iterateNonZero();
      while (elementIterator.hasNext()) {
        Vector.Element e = elementIterator.next();
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    ALSWRFactorizer factorizer = new ALSWRFactorizer(dataModel, 3, 0.065, 5, true, alpha);

    SVDRecommender svdRecommender = new SVDRecommender(dataModel, factorizer);

    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      for (Vector.Element e : slice.vector().all()) {

        long userID = slice.index() + 1;
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    info.append('\n');

    log.info(info.toString());

    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      Iterator<Vector.Element> elementIterator = slice.vector().iterateNonZero();
      while (elementIterator.hasNext()) {
        Vector.Element e = elementIterator.next();
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    info.append('\n');

    log.info(info.toString());

    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      for (Vector.Element e : slice.vector()) {
        if (!Double.isNaN(e.get())) {
          double pref = e.get();
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