Package org.apache.mahout.math

Examples of org.apache.mahout.math.Matrix.viewRow()

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    }
    numTopics++;
    Matrix model = new DenseMatrix(numTopics, numTerms);
    Vector topicSums = new DenseVector(numTopics);
    for (Pair<Integer, Vector> pair : rows) {
      model.viewRow(pair.getFirst()).assign(pair.getSecond());
      topicSums.set(pair.getFirst(), pair.getSecond().norm(1));
    }
    return Pair.of(model, topicSums);
  }
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          double f = ortPiece.dot(hessenBerg.viewColumn(j).viewPart(m, high - m + 1)) / h;
          hessenBerg.viewColumn(j).viewPart(m, high - m + 1).assign(ortPiece, Functions.plusMult(-f));
        }

        for (int i = 0; i <= high; i++) {
          double f = ortPiece.dot(hessenBerg.viewRow(i).viewPart(m, high - m + 1)) / h;
          hessenBerg.viewRow(i).viewPart(m, high - m + 1).assign(ortPiece, Functions.plusMult(-f));
        }
        ort.setQuick(m, scale * ort.getQuick(m));
        hessenBerg.setQuick(m, m - 1, scale * g);
      }
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          hessenBerg.viewColumn(j).viewPart(m, high - m + 1).assign(ortPiece, Functions.plusMult(-f));
        }

        for (int i = 0; i <= high; i++) {
          double f = ortPiece.dot(hessenBerg.viewRow(i).viewPart(m, high - m + 1)) / h;
          hessenBerg.viewRow(i).viewPart(m, high - m + 1).assign(ortPiece, Functions.plusMult(-f));
        }
        ort.setQuick(m, scale * ort.getQuick(m));
        hessenBerg.setQuick(m, m - 1, scale * g);
      }
    }
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    /*
     * Step 1: orthogonalize currentPseudoEigen by subtracting off eigen(i) * helper.get(i)
     * Step 2: zero-out the helper vector because it has already helped.
     */
    for (int i = 0; i < state.getNumEigensProcessed(); i++) {
      Vector previousEigen = previousEigens.viewRow(i);
      currentPseudoEigen.assign(previousEigen, new PlusMult(-state.getHelperVector().get(i)));
      state.getHelperVector().set(i, 0);
    }
    if (DEBUG && currentPseudoEigen.norm(2) > 0) {
      for (int i = 0; i < state.getNumEigensProcessed(); i++) {
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      currentPseudoEigen.assign(previousEigen, new PlusMult(-state.getHelperVector().get(i)));
      state.getHelperVector().set(i, 0);
    }
    if (DEBUG && currentPseudoEigen.norm(2) > 0) {
      for (int i = 0; i < state.getNumEigensProcessed(); i++) {
        Vector previousEigen = previousEigens.viewRow(i);
        log.info("dot with previous: {}", previousEigen.dot(currentPseudoEigen) / currentPseudoEigen.norm(2));
      }
    }
    /*
     * Step 3: verify how eigen-like the prospective eigen is.  This is potentially asynchronous.
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    ratings = userRatings.iterateNonZero();
    while (ratings.hasNext()) {
      Vector.Element e = ratings.next();
      for (Vector.Element feature : Y.get(e.index())) {
        Vector partial = CuMinusIY.get(e.index()).times(feature.get());
        YtransponseCuMinusIY.viewRow(feature.index()).assign(partial, Functions.PLUS);
      }
    }

    /* Y' (Cu - I) Y + λ I  add lambda on the diagonal */
    for (int feature = 0; feature < numFeatures; feature++) {
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   *         last category.
   */
  public Matrix classifyFull(Matrix data) {
    Matrix r = new DenseMatrix(data.numRows(), numCategories());
    for (int row = 0; row < data.numRows(); row++) {
      classifyFull(r.viewRow(row), data.viewRow(row));
    }
    return r;
  }

  /**
 
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            .learningRate(5);
    int k = 0;
    int[] ordering = permute(gen, data.numRows());
    for (int epoch = 0; epoch < 100; epoch++) {
      for (int row : ordering) {
        lr.train(row, (int) data.get(row, 9), data.viewRow(row));
        System.out.printf("%d,%d,%.3f\n", epoch, k++, lr.auc());
      }
      assertEquals(1, lr.auc(), 0.2);
    }
    assertEquals(1, lr.auc(), 0.1);
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      }
      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();
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    DistributedRowMatrix dm =
        randomDistributedMatrix(100, 90, 50, 20, 1.0, false);

    Vector expected = new DenseVector(50);
    for (int i = 0; i < m.numRows(); i++) {
      expected.assign(m.viewRow(i), Functions.PLUS);
    }
    expected.assign(Functions.DIV, m.numRows());
    Vector actual = dm.columnMeans("DenseVector");
    assertEquals(0.0, expected.getDistanceSquared(actual), EPSILON);
  }
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