Package org.apache.mahout.math

Examples of org.apache.mahout.math.Matrix

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      }
    });
  }

  public static Matrix randomStructuredModel(int numTopics, int numTerms, DoubleFunction decay) {
    Matrix model = new DenseMatrix(numTopics, numTerms);
    int width = numTerms / numTopics;
    for(int topic = 0; topic < numTopics; topic++) {
      int topicCentroid = width * (1+topic);
      for(int i = 0; i < numTerms; i++) {
        int distance = Math.abs(topicCentroid - i);
        if(distance > numTerms / 2) {
          distance = numTerms - distance;
        }
        double v = decay.apply(distance);
        model.set(topic, i, v);
      }
    }
    return model;
  }
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    rowSimilarityJob.run(new String[] { "--input", inputFile.getAbsolutePath(), "--output", outputDir.getAbsolutePath(),
        "--numberOfColumns", String.valueOf(6), "--similarityClassname", TanimotoCoefficientSimilarity.class.getName(),
        "--excludeSelfSimilarity", String.valueOf(true), "--threshold", String.valueOf(0.5),
        "--tempDir", tmpDir.getAbsolutePath() });

    Matrix similarityMatrix = MathHelper.readMatrix(conf, new Path(outputDir.getAbsolutePath(), "part-r-00000"), 3, 3);

    assertNotNull(similarityMatrix);
    assertEquals(3, similarityMatrix.numCols());
    assertEquals(3, similarityMatrix.numRows());

    assertEquals(0.0, similarityMatrix.get(0, 0), EPSILON);
    assertEquals(0.5, similarityMatrix.get(0, 1), EPSILON);
    assertEquals(0.0, similarityMatrix.get(0, 2), EPSILON);

    assertEquals(0.5, similarityMatrix.get(1, 0), EPSILON);
    assertEquals(0.0, similarityMatrix.get(1, 1), EPSILON);
    assertEquals(0.0, similarityMatrix.get(1, 2), EPSILON);

    assertEquals(0.0, similarityMatrix.get(2, 0), EPSILON);
    assertEquals(0.0, similarityMatrix.get(2, 1), EPSILON);
    assertEquals(0.0, similarityMatrix.get(2, 2), EPSILON);
  }
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  @Test
  public void testMatrixTimesVector() throws Exception {
    Vector v = new RandomAccessSparseVector(50);
    v.assign(1.0);
    Matrix m = SolverTest.randomSequentialAccessSparseMatrix(100, 90, 50, 20, 1.0);
    DistributedRowMatrix dm = randomDistributedMatrix(100, 90, 50, 20, 1.0, false);

    Vector expected = m.times(v);
    Vector actual = dm.times(v);
    assertEquals(0.0, expected.getDistanceSquared(actual), EPSILON);
  }
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  @Test
  public void testMatrixTimesSquaredVector() throws Exception {
    Vector v = new RandomAccessSparseVector(50);
    v.assign(1.0);
    Matrix m = SolverTest.randomSequentialAccessSparseMatrix(100, 90, 50, 20, 1.0);
    DistributedRowMatrix dm = randomDistributedMatrix(100, 90, 50, 20, 1.0, false);

    Vector expected = m.timesSquared(v);
    Vector actual = dm.timesSquared(v);
    assertEquals(0.0, expected.getDistanceSquared(actual), 1.0e-9);
  }
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    assertEquals(0.0, expected.getDistanceSquared(actual), 1.0e-9);
  }

  @Test
  public void testMatrixTimesMatrix() throws Exception {
    Matrix inputA = SolverTest.randomSequentialAccessSparseMatrix(20, 19, 15, 5, 10.0);
    Matrix inputB = SolverTest.randomSequentialAccessSparseMatrix(20, 13, 25, 10, 5.0);
    Matrix expected = inputA.transpose().times(inputB);

    DistributedRowMatrix distA = randomDistributedMatrix(20, 19, 15, 5, 10.0, false, "distA");
    DistributedRowMatrix distB = randomDistributedMatrix(20, 13, 25, 10, 5.0, false, "distB");
    DistributedRowMatrix product = distA.times(distB);
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                                                                       int numCols,
                                                                       boolean isSymmetric,
                                                                       String baseTmpDirSuffix)
    throws IOException {
    Path baseTmpDirPath = getTestTempDirPath(baseTmpDirSuffix);
    Matrix c = SolverTest.randomHierarchicalMatrix(numRows, numCols, isSymmetric);
    return saveToFs(c, baseTmpDirPath);
  }
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                                                      int entriesPerRow,
                                                      double entryMean,
                                                      boolean isSymmetric,
                                                      String baseTmpDirSuffix) throws IOException {
    Path baseTmpDirPath = getTestTempDirPath(baseTmpDirSuffix);
    Matrix c = SolverTest.randomSequentialAccessSparseMatrix(numRows, nonNullRows, numCols, entriesPerRow, entryMean);
    if(isSymmetric) {
      c = c.times(c.transpose());
    }
    return saveToFs(c, baseTmpDirPath);
  }
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    return v;
  }

  private LDAState generateRandomState(int numWords, int numTopics) {
    double topicSmoothing = 50.0 / numTopics; // whatever
    Matrix m = new DenseMatrix(numTopics,numWords);
    double[] logTotals = new double[numTopics];
    for(int k = 0; k < numTopics; ++k) {
      double total = 0.0; // total number of pseudo counts we made
      for(int w = 0; w < numWords; ++w) {
        // A small amount of random noise, minimized by having a floor.
        double pseudocount = random.nextDouble() + 1.0E-10;
        total += pseudocount;
        m.setQuick(k,w,Math.log(pseudocount));
      }

      logTotals[k] = Math.log(total);
    }
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  @Test
  @Ignore("MAHOUT-399")
  public void testEndToEnd() throws Exception {
    int numGeneratingTopics = 5;
    int numTerms = 26;
    Matrix matrix = ClusteringTestUtils.randomStructuredModel(numGeneratingTopics, numTerms,
                                                              new DoubleFunction() {
                                                                @Override
                                                                public double apply(double d) {
                                                                  return 1.0 / Math.pow(d + 1, 3);
                                                                }
                                                              });

    int numDocs = 500;
    int numSamples = 10;
    int numTopicsPerDoc = 1;

    Matrix sampledCorpus = ClusteringTestUtils.sampledCorpus(matrix, RandomUtils.getRandom(),
        numDocs, numSamples, numTopicsPerDoc);

    Path sampleCorpusPath = getTestTempDirPath("corpus");
    MatrixUtils.write(sampleCorpusPath, new Configuration(), sampledCorpus);
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        "--workingDir", workingDir.toString()
    };
    new DistributedLanczosSolver().new DistributedLanczosSolverJob().run(args);

    Path rawEigenvectors = new Path(output, DistributedLanczosSolver.RAW_EIGENVECTORS);
    Matrix eigenVectors = new DenseMatrix(7, corpus.numCols());
    Configuration conf = new Configuration();

    int i = 0;
    for (VectorWritable value : new SequenceFileValueIterable<VectorWritable>(rawEigenvectors, conf)) {
      Vector v = value.get();
      eigenVectors.assignRow(i, v);
      i++;
    }
    assertEquals("number of eigenvectors", 7, i);
  }
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