Package org.apache.mahout.matrix

Examples of org.apache.mahout.matrix.Vector$Element

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  @Override
  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;

    Vector dv = new DenseVector(2);
    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        AsymmetricSampledNormalModel mm = (AsymmetricSampledNormalModel) m;
        dv.set(0, mm.getStdDev().get(0) * 3);
        dv.set(1, mm.getStdDev().get(1) * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.getMean(), dv);
      }
    }
  }
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    @Override
    public Vector next() {
      if (!hasNext()) {
        throw new NoSuchElementException();
      }
      Vector result = type == VectorType.SPARSE ? new SparseVector(numItems) : new DenseVector(numItems);
      result.assign(new UnaryFunction(){
        @Override
        public double apply(double arg1) {
          return random.nextDouble();
        }
      });
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  @Override
  public void reduce(Text key, Iterator<Vector> values,
                     OutputCollector<Text, Canopy> output, Reporter reporter) throws IOException {
    while (values.hasNext()) {
      Vector point = values.next();
      Canopy.addPointToCanopies(point, canopies);
    }
    for (Canopy canopy : canopies) {
      output.collect(new Text(canopy.getIdentifier()), canopy);
    }
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  }

  @Override
  public void close() throws IOException {
    for (Canopy canopy : canopies) {
      Vector centroid = canopy.computeCentroid();
      outputCollector.collect(new Text("centroid"), centroid);
    }
    super.close();
  }
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    Map<String, Integer> bindings = iterable.getModel().getLabelBindings();
    assertNotNull(bindings);
    assertEquals(5, bindings.size());
    Iterator<Vector> iter = iterable.iterator();
    assertTrue(iter.hasNext());
    Vector next = iter.next();
    assertNotNull(next);
    assertTrue("Wrong instanceof", next instanceof DenseVector);
    assertEquals(1.0, next.get(0));
    assertEquals(2.0, next.get(1));
    assertTrue(iter.hasNext());
    next = iter.next();
    assertNotNull(next);
    assertTrue("Wrong instanceof", next instanceof DenseVector);
    assertEquals(2.0, next.get(0));
    assertEquals(3.0, next.get(1));

    assertTrue(iter.hasNext());
    next = iter.next();
    assertNotNull(next);
    assertTrue("Wrong instanceof", next instanceof SparseVector);
    assertEquals(5.0, next.get(0));
    assertEquals(23.0, next.get(1));

    assertFalse(iter.hasNext());
  }
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  @Override
  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;

    Vector dv = new DenseVector(2);
    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        AsymmetricSampledNormalModel mm = (AsymmetricSampledNormalModel) m;
        dv.assign(mm.getStdDev().times(3));
        if (isSignificant(mm))
          plotEllipse(g2, mm.getMean(), dv);
      }
    }
  }
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  @Override
  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;

    Vector dv = new DenseVector(2);
    int i = result.size() - 1;
    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        NormalModel mm = (NormalModel) m;
        dv.assign(mm.getStdDev() * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.getMean(), dv);
      }
    }
  }
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      }
    }

    @Override
    public Vector next() {
      Vector result;
      int doc = termDocs.doc();
      //
      try {
        indexReader.getTermFreqVector(doc, field, mapper);
        result = mapper.getVector();
        if (idField != null) {
          String id = indexReader.document(doc, idFieldSelector).get(idField);
          result.setName(id);
        } else {
          result.setName(String.valueOf(doc));
        }
        if (normPower != NO_NORMALIZING) {
          result = result.normalize(normPower);
        }
      } catch (IOException e) {
        //Log?
        throw new IllegalStateException(e);
      }
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   * Generate random document vector
   * @param numWords int number of words in the vocabulary
   * @param numWords E[count] for each word
   */
  private Vector generateRandomDoc(int numWords, double sparsity) throws MathException {
    Vector v = new DenseVector(numWords);
    PoissonDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.setQuick(i, dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }
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  private void runTest(int numWords, double sparsity, int numTests) throws MathException {
    LDAState state = generateRandomState(numWords, NUM_TOPICS);
    LDAInference lda = new LDAInference(state);
    for (int t = 0; t < numTests; ++t) {
      Vector v = generateRandomDoc(numWords, sparsity);
      LDAInference.InferredDocument doc = lda.infer(v);

      assertEquals("wordCounts", doc.getWordCounts(), v);
      assertNotNull("gamma", doc.getGamma());
      for (Iterator<Vector.Element> iter = v.iterateNonZero();
          iter.hasNext(); ) {
        int w = iter.next().index();
        for (int k = 0; k < NUM_TOPICS; ++k) {
          double logProb = doc.phi(k, w);
          assertTrue(k + " " + w + " logProb " + logProb, logProb <= 0.0);
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