Examples of times()


Examples of flanagan.complex.Complex.times()

                    bi = null;
                    productbi = null;
                    break;
            case 14: Complex[] cc = this.getArray_as_Complex();
                    Complex productcc = Complex.plusOne();
                    for(int i=0; i<this.length; i++)productcc.times(cc[i]);
                    this.productt.add(productcc);
                    break;
            case 15: Phasor[] pp = this.getArray_as_Phasor();
                    Phasor productpp = Phasor.plusOne();
                    for(int i=0; i<this.length; i++)productpp.times(pp[i]);
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Examples of flanagan.complex.ComplexPoly.times()

            super.setSnumer(fpNumer.copy());
            super.setSdenom((cpNumer.plus(fpDenom)).copy());
        }
        else{
            super.setSnumer(fpNumer.times(cpDenom));
            super.setSdenom((cpNumer.times(fpDenom)).plus(cpDenom.times(fpDenom)));
        }
        this.checkConsolidate = true;
        this.deadTimeSum  = this.closedPath.getDeadTime();
        super.deadTime = 0.0;
        this.checkConsolidate = true;
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Examples of gov.sandia.cognition.math.matrix.Matrix.times()

      /*
       * Construct the measurement prior predictive likelihood
       * t_n (H*m^psi, d*C^psi/n)
       */
      final Vector mPriorPredMean = H.times(priorPsi.getMean());
      final Matrix mPriorPredCov = H.times(priorPsi.getCovariance()).times(H.transpose())
          .plus(Iy.scale(2d));

      // TODO FIXME
      final Matrix stPriorPredPrec = mPriorPredCov.inverse().scale(
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Examples of net.br410bury.graphics.GraphicsMatrix.times()

  {
    GraphicsMatrix local;
    Bone localbone = new Bone();
   
    local = global.times(node.getTransformation(frame, motion));
    localbone.setStart(local.times(matrixToPoint));
    bone.addEnd(localbone);
    for(Joint child : node.getChildren())
    {
      if(!child.isEndSite()) recursiveSegment(frame, localbone, child, local);
    }
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Examples of org.apache.mahout.math.DenseMatrix.times()

      }
      v.assign(Functions.MULT, 1/((row + 1) * v.norm(2)));
      matrix.assignRow(row, v);
    }
    if(symmetric) {
      return matrix.times(matrix.transpose());
    }
    return matrix;
  }

  public static Matrix randomHierarchicalSymmetricMatrix(int size) {
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Examples of org.apache.mahout.math.DenseVector.times()

      blckCnt++;
      cnt = 0;
    }

    for (int i = 0; i < n; i++) {
      btEmitter.emitRow(i, qRowV.times(aRow.getQuick(i)));
    }

  }

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

    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), 1.0e-9);
  }

  public void testMatrixTimesSquaredVector() throws Exception {
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Examples of org.apache.mahout.math.Matrix.times()

                                                             boolean isSymmetric,
                                                             String baseTmpDir) throws IOException {
    baseTmpDir = TESTDATA + baseTmpDir;
    Matrix c = SolverTest.randomSequentialAccessSparseMatrix(numRows, nonNullRows, numCols, entriesPerRow, entryMean);
    if(isSymmetric) {
      c = c.times(c.transpose());
    }
    final Matrix m = c;
    Configuration conf = new Configuration();
    FileSystem fs = FileSystem.get(conf);
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Examples of org.apache.mahout.math.Matrix.times()

        sInv.set(i, i, 1 / diagElem);
      } else {
        throw new IllegalStateException("Eigen Value equals to 0 found.");
      }
    }
    inverseCovarianceMatrix = svd.getU().times(sInv.times(svd.getU().transpose()));
  }
 
  public Matrix getInverseCovarianceMatrix() {
    return inverseCovarianceMatrix;
  }
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Examples of org.apache.mahout.math.Matrix.times()

        sInv.set(i, i, 1 / diagElem);
      } else {
        throw new IllegalStateException("Eigen Value equals to 0 found.");
      }
    }
    inverseCovarianceMatrix = svd.getU().times(sInv.times(svd.getU().transpose()));
    Preconditions.checkArgument(inverseCovarianceMatrix != null, "inverseCovarianceMatrix not initialized");
  }

  public Matrix getInverseCovarianceMatrix() {
    return inverseCovarianceMatrix;
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