Examples of SparseRowMatrix

mikera.matrixx.impl.SparseRowMatrix
Matrix stored as a sparse collection of sparse row vectors. This format is especially efficient for: - innerProduct() with another matrix, especially one with efficient column access like SparseColumnMatrix - access via getRow() operation - transpose into SparseColumnMatrix @author Mike
org.apache.mahout.math.SparseRowMatrix
sparse matrix with general element values whose rows are accessible quickly. Implemented as a row array of either SequentialAccessSparseVectors or RandomAccessSparseVectors.

Examples of org.apache.mahout.math.SparseRowMatrix

   * </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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Examples of org.apache.mahout.math.SparseRowMatrix

  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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Examples of org.apache.mahout.math.SparseRowMatrix

    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 {

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Examples of org.apache.mahout.math.SparseRowMatrix

    }
  }


  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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Examples of org.apache.mahout.math.SparseRowMatrix

    while (true) {
      try {
        List<TrainerRunnable> runnables = Lists.newArrayList();
        for (Map.Entry<Vector, Vector> entry : batch.entrySet()) {
          runnables.add(new TrainerRunnable(readModel, null, entry.getKey(),
              entry.getValue(), new SparseRowMatrix(numTopics, numTerms, true),
              numDocTopicsIters));
        }
        threadPool.invokeAll(runnables);
        if (update) {
          for (TrainerRunnable runnable : runnables) {

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Examples of org.apache.mahout.math.SparseRowMatrix


  public void train(Vector document, Vector docTopicCounts, boolean update, int numDocTopicIters) {
    while (true) {
      try {
        workQueue.put(new TrainerRunnable(readModel,
            update ? writeModel : null, document, docTopicCounts, new SparseRowMatrix(
            numTopics, numTerms, true), numDocTopicIters));
        return;
      } catch (InterruptedException e) {
        log.warn("Interrupted waiting to submit document to work queue: " + document, e);
      }

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Examples of org.apache.mahout.math.SparseRowMatrix

  }


  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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Examples of org.apache.mahout.math.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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Examples of org.apache.mahout.math.SparseRowMatrix

  @Override
  public void map(IntWritable docId, VectorWritable doc, Context context)
      throws IOException, InterruptedException {
    int numTopics = getNumTopics();
    Vector docTopics = new DenseVector(new double[numTopics]).assign(1.0 /numTopics);
    Matrix docModel = new SparseRowMatrix(numTopics, doc.get().size());
    int maxIters = getMaxIters();
    ModelTrainer modelTrainer = getModelTrainer();
    for (int i = 0; i < maxIters; i++) {
      modelTrainer.getReadModel().trainDocTopicModel(doc.get(), docTopics, docModel);
    }

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Examples of org.apache.mahout.math.SparseRowMatrix

        vectorList.add(record.getSecond().get());
      }
    }
    int numRows = vectorList.size();
    int numCols = vectorList.get(0).size();
    return new SparseRowMatrix(numRows, numCols,
        vectorList.toArray(new Vector[vectorList.size()]), true,
        vectorList.get(0).isSequentialAccess());
  }

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