Examples of org.apache.commons.math3.complex.Complex.multiply()

Package org.apache.commons.math3.complex

Class org.apache.commons.math3.complex.Complex

Examples of org.apache.commons.math3.complex.Complex.multiply()

org.apache.commons.math3.complex.Complex.multiply()
Returns a {@code Complex} whose value is {@code this * factor}. Implements preliminary checks for {@code NaN} and infinity followed bythe definitional formula:
```
  (a + bi)(c + di) = (ac - bd) + (ad + bc)i  
```
Returns {@link #NaN} if either {@code this} or {@code factor} has one ormore {@code NaN} parts.
Returns {@link #INF} if neither {@code this} nor {@code factor} has oneor more {@code NaN} parts and if either {@code this} or {@code factor}has one or more infinite parts (same result is returned regardless of the sign of the components).
Returns finite values in components of the result per the definitional formula in all remaining cases. @param factor value to be multiplied by this {@code Complex}. @return {@code this * factor}. @throws NullArgumentException if {@code factor} is {@code null}.

            }


            // degree k coefficient
            final BigFraction ckPrev = ck;
            ck = coefficients.get(startK + k);
            coefficients.add(ck.multiply(ai[0]).add(ckPrev.multiply(ai[1])));


            // degree k+1 coefficient
            coefficients.add(ck.multiply(ai[1]));


        }

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        final FieldMatrix<BigFraction> Hpower = H.power(n);


        BigFraction pFrac = Hpower.getEntry(k - 1, k - 1);


        for (int i = 1; i <= n; ++i) {
            pFrac = pFrac.multiply(i).divide(n);
        }


        /*
         * BigFraction.doubleValue converts numerator to double and the
         * denominator to double and divides afterwards. That gives NaN quite

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         * hPowers[0] = h^1 ... hPowers[m-1] = h^m
         */
        final BigFraction[] hPowers = new BigFraction[m];
        hPowers[0] = h;
        for (int i = 1; i < m; ++i) {
            hPowers[i] = h.multiply(hPowers[i - 1]);
        }


        /*
         * First column and last row has special values (each other reversed).
         */

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         * [1] states: "For 1/2 < h < 1 the bottom left element of the matrix
         * should be (1 - 2*h^m + (2h - 1)^m )/m!" Since 0 <= h < 1, then if h >
         * 1/2 is sufficient to check:
         */
        if (h.compareTo(BigFraction.ONE_HALF) == 1) {
            Hdata[m - 1][0] = Hdata[m - 1][0].add(h.multiply(2).subtract(1).pow(m));
        }


        /*
         * Aside from the first column and last row, the (i, j)-th element is
         * 1/(i - j + 1)! if i - j + 1 >= 0, else 0. 1's and 0's are already

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            for (int i = 0; i < n; i++) {
                for (int j = 0; j < k; j++) {
                    final RealMatrix vec
                        = new Array2DRowRealMatrix(MathArrays.ebeSubtract(data[i], newMeans[j]));
                    final RealMatrix dataCov
                        = vec.multiply(vec.transpose()).scalarMultiply(gamma[i][j]);
                    newCovMats[j] = newCovMats[j].add(dataCov);
                }
            }


            // Converting to arrays for use by fitted model

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            for (int col = 0; col < dim; col++) {
                tmpMatrix.multiplyEntry(row, col, factor);
            }
        }


        samplingMatrix = covMatEigenvectors.multiply(tmpMatrix);
    }


    /**
     * Gets the mean vector.
     *

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            for (int i = 0; i < n; i++) {
                for (int j = 0; j < k; j++) {
                    final RealMatrix vec
                        = new Array2DRowRealMatrix(MathArrays.ebeSubtract(data[i], newMeans[j]));
                    final RealMatrix dataCov
                        = vec.multiply(vec.transpose()).scalarMultiply(gamma[i][j]);
                    newCovMats[j] = newCovMats[j].add(dataCov);
                }
            }


            // Converting to arrays for use by fitted model

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            for (int col = 0; col < dim; col++) {
                tmpMatrix.multiplyEntry(row, col, factor);
            }
        }


        samplingMatrix = covMatEigenvectors.multiply(tmpMatrix);
    }


    /**
     * Gets the mean vector.
     *

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            for (int i = 0; i < n; i++) {
                for (int j = 0; j < k; j++) {
                    final RealMatrix vec
                        = new Array2DRowRealMatrix(MathArrays.ebeSubtract(data[i], newMeans[j]));
                    final RealMatrix dataCov
                        = vec.multiply(vec.transpose()).scalarMultiply(gamma[i][j]);
                    newCovMats[j] = newCovMats[j].add(dataCov);
                }
            }


            // Converting to arrays for use by fitted model

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            for (int col = 0; col < dim; col++) {
                tmpMatrix.multiplyEntry(row, col, factor);
            }
        }


        samplingMatrix = covMatEigenvectors.multiply(tmpMatrix);
    }


    /**
     * Gets the mean vector.
     *

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