Package org.apache.mahout.math

Examples of org.apache.mahout.math.SparseRowMatrix

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  public void trainSync(Vector document, Vector docTopicCounts, boolean update,
      int numDocTopicIters) {
    new TrainerRunnable(readModel, update
        ? writeModel
        : null, document, docTopicCounts, new SparseRowMatrix(numTopics, numTerms, true), numDocTopicIters).run();
  }
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        : null, document, docTopicCounts, new SparseRowMatrix(numTopics, numTerms, true), numDocTopicIters).run();
  }

  public double calculatePerplexity(Vector document, Vector docTopicCounts, int numDocTopicIters) {
    TrainerRunnable runner =  new TrainerRunnable(readModel, null, document, docTopicCounts,
        new SparseRowMatrix(numTopics, numTerms, true), numDocTopicIters);
    return runner.call();
  }
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    if (inMemory) {
      List<Vector> eigenVectors = Lists.newArrayList();
      for (MatrixSlice slice : eigens) {
        eigenVectors.add(slice.vector());
      }
      eigensToVerify = new SparseRowMatrix(eigenVectors.size(), eigenVectors.get(0).size(),
          eigenVectors.toArray(new Vector[eigenVectors.size()]), true, true);

    } else {
      eigensToVerify = eigens;
    }
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      alphaI = in.readFloat();
      weightsPerFeature = VectorWritable.readVector(in);
      weightsPerLabel = new DenseVector(VectorWritable.readVector(in));
      perLabelThetaNormalizer = new DenseVector(VectorWritable.readVector(in));

      weightsPerLabelAndFeature = new SparseRowMatrix(weightsPerLabel.size(), weightsPerFeature.size());
      for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) {
        weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in));
      }
    } finally {
      Closeables.close(in, true);
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  }

  @Test
  public void toyExampleImplicit() throws Exception {

    Matrix observations = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 5.0, 5.0, 2.0, 0 }),
        new DenseVector(new double[] { 2.0, 0,   3.0, 5.0 }),
        new DenseVector(new double[] { 0,   5.0, 0,   3.0 }),
        new DenseVector(new double[] { 3.0, 0,   0,   5.0 }) });

    Matrix preferences = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 1.0, 1.0, 1.0, 0 }),
        new DenseVector(new double[] { 1.0, 0,   1.0, 1.0 }),
        new DenseVector(new double[] { 0,   1.0, 0,   1.0 }),
        new DenseVector(new double[] { 1.0, 0,   0,   1.0 }) });

    double alpha = 20;

    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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    numRows++;
    Vector[] rowVectors = new Vector[numRows];
    for (Pair<Integer, Vector> pair : rowList) {
      rowVectors[pair.getFirst()] = pair.getSecond();
    }
    return new SparseRowMatrix(numRows, numCols, rowVectors, true, !sequentialAccess);

  }
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  public static Matrix randomSequentialAccessSparseMatrix(int numRows,
                                                          int nonNullRows,
                                                          int numCols,
                                                          int entriesPerRow,
                                                          double entryMean) {
    Matrix m = new SparseRowMatrix(numRows, numCols);
    //double n = 0;
    Random r = RandomUtils.getRandom();
    for (int i = 0; i < nonNullRows; i++) {
      Vector v = new SequentialAccessSparseVector(numCols);
      for (int j = 0; j < entriesPerRow; j++) {
        int col = r.nextInt(numCols);
        double val = r.nextGaussian();
        v.set(col, val * entryMean);
      }
      int c = r.nextInt(numRows);
      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);
      } else {
        Vector other = m.viewRow(r.nextInt(numRows));
        if (other != null && other.getLengthSquared() > 0) {
          m.assignRow(c, other.clone());
        }
      }
      //n += m.getRow(c).getLengthSquared();
    }
    return m;
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   * </pre>
   */
  private void explicitExample(int numThreads) throws Exception {

    Double na = Double.NaN;
    Matrix preferences = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 5.0, 5.0, 2.0, na }),
        new DenseVector(new double[] { 2.0, na,  3.0, 5.0 }),
        new DenseVector(new double[] { na,  5.0, na,  3.0 }),
        new DenseVector(new double[] { 3.0, na,  na,  5.0 }) });

    writeLines(inputFile, preferencesAsText(preferences));

    ParallelALSFactorizationJob alsFactorization = new ParallelALSFactorizationJob();
    alsFactorization.setConf(conf);

    int numFeatures = 3;
    int numIterations = 5;
    double lambda = 0.065;

    alsFactorization.run(new String[] { "--input", inputFile.getAbsolutePath(), "--output", outputDir.getAbsolutePath(),
        "--tempDir", tmpDir.getAbsolutePath(), "--lambda", String.valueOf(lambda),
        "--numFeatures", String.valueOf(numFeatures), "--numIterations", String.valueOf(numIterations),
        "--numThreadsPerSolver", String.valueOf(numThreads) });

    Matrix u = MathHelper.readMatrix(conf, new Path(outputDir.getAbsolutePath(), "U/part-m-00000"),
        preferences.numRows(), numFeatures);
    Matrix m = MathHelper.readMatrix(conf, new Path(outputDir.getAbsolutePath(), "M/part-m-00000"),
        preferences.numCols(), numFeatures);

    StringBuilder info = new StringBuilder();
    info.append("\nA - users x items\n\n");
    info.append(MathHelper.nice(preferences));
    info.append("\nU - users x features\n\n");
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  public void completeJobImplicitToyExampleMultithreaded() throws Exception {
    implicitExample(2);
  }

  public void implicitExample(int numThreads) throws Exception {
    Matrix observations = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 5.0, 5.0, 2.0, 0 }),
        new DenseVector(new double[] { 2.0, 0,   3.0, 5.0 }),
        new DenseVector(new double[] { 0,   5.0, 0,   3.0 }),
        new DenseVector(new double[] { 3.0, 0,   0,   5.0 }) });

    Matrix preferences = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 1.0, 1.0, 1.0, 0 }),
        new DenseVector(new double[] { 1.0, 0,   1.0, 1.0 }),
        new DenseVector(new double[] { 0,   1.0, 0,   1.0 }),
        new DenseVector(new double[] { 1.0, 0,   0,   1.0 }) });
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    }
  }

  public static Matrix sampledCorpus(Matrix matrix, Random random,
      int numDocs, int numSamples, int numTopicsPerDoc) {
    Matrix corpus = new SparseRowMatrix(numDocs, matrix.numCols());
    LDASampler modelSampler = new LDASampler(matrix, random);
    Vector topicVector = new DenseVector(matrix.numRows());
    for (int i = 0; i < numTopicsPerDoc; i++) {
      int topic = random.nextInt(topicVector.size());
      topicVector.set(topic, topicVector.get(topic) + 1);
    }
    for (int docId = 0; docId < numDocs; docId++) {
      for (int sample : modelSampler.sample(topicVector, numSamples)) {
        corpus.set(docId, sample, corpus.get(docId, sample) + 1);
      }
    }
    return corpus;
  }
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