Examples of org.apache.mahout.math.Vector.dot()

org.apache.mahout.math.Vector.dot()
Return the dot product of the recipient and the argument @param x a Vector @return a new Vector @throws CardinalityException if the cardinalities differ


    itemIDs = dataModel.getItemIDs();
    while (itemIDs.hasNext()) {
      long itemID = itemIDs.nextLong();
      Vector itemVector = new DenseVector(factorization.getUserFeatures(itemID));
      double regularization = itemVector.dot(itemVector);
      sum += regularization;
    }


    double rmse = Math.sqrt(avg.getAverage());
    double loss = avg.getAverage() / 2 + lambda / 2 * sum;

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    M.forEachPair(new IntObjectProcedure<Vector>() {
      @Override
      public boolean apply(int itemID, Vector itemFeatures) {
        if (!alreadyRatedItems.contains(itemID)) {
          double predictedRating = userFeatures.dot(itemFeatures);


          MutableRecommendedItem top = topItemsQueue.top();
          if (predictedRating > top.getValue()) {
            top.set(itemID, (float) predictedRating);
            topItemsQueue.updateTop();

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        // Apply Householder similarity transformation
        // H = (I-u*u'/h)*H*(I-u*u')/h)


        Vector ortPiece = ort.viewPart(m, high - m + 1);
        for (int j = m; j < n; j++) {
          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;

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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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    Vector x = new DenseVector(b.size());


    iterations = 0;
    Vector residual = b.minus(a.times(x));
    residualNormSquared = residual.dot(residual);


    log.info("Conjugate gradient initial residual norm = {}", Math.sqrt(residualNormSquared));
    double previousConditionedNormSqr = 0.0;
    Vector updateDirection = null;
    while (Math.sqrt(residualNormSquared) > maxError && iterations < maxIterations) {

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      boolean isSymmetric) {
    if (e.getLengthSquared() == 0) {
      return;
    }
    Vector afterMultiply = isSymmetric ? corpus.times(e) : corpus.timesSquared(e);
    double dot = afterMultiply.dot(e);
    double afterNorm = afterMultiply.getLengthSquared();
    double error = 1 - Math.abs(dot / Math.sqrt(afterNorm * e.getLengthSquared()));
    log.info("the eigen-error: {} for eigen {}", error, i);
    assertTrue("Error: {" + error + " too high! (for eigen " + i + ')', Math.abs(error) < errorMargin);
  }

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      throws IOException, InterruptedException {
      Vector qRow = value.get();
      if (sValues != null) {
        for (int i = 0; i < k; i++) {
          uRow.setQuick(i,
                        qRow.dot(uHat.viewColumn(i)) * sValues.getQuick(i));
        }
      } else {
        for (int i = 0; i < k; i++) {
          uRow.setQuick(i, qRow.dot(uHat.viewColumn(i)));
        }

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          uRow.setQuick(i,
                        qRow.dot(uHat.viewColumn(i)) * sValues.getQuick(i));
        }
      } else {
        for (int i = 0; i < k; i++) {
          uRow.setQuick(i, qRow.dot(uHat.viewColumn(i)));
        }
      }


      /*
       * MAHOUT-1067: inherit A names too.

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    double oldNorm = vector.norm(2);
    double eigenValue;
    double cosAngle;
    if (newNorm > 0 && oldNorm > 0) {
      eigenValue = newNorm / oldNorm;
      cosAngle = resultantVector.dot(vector) / newNorm * oldNorm;
    } else {
      eigenValue = 1.0;
      cosAngle = 0.0;
    }
    return new EigenStatus(eigenValue, cosAngle, false);

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      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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