Package com.opengamma.analytics.math.matrix

Examples of com.opengamma.analytics.math.matrix.DoubleMatrix1D

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  // @export "initMatrixDemo"
  static double[] ARRAY_1D = {4.0, 5.0, 10.0 };
  public static double[][] ARRAY_2D = { {1.0, 2.0, 3.0 }, {4.0, 5.0, 6.0 } };

  public static void initMatrixDemo(PrintStream out) {
    DoubleMatrix1D matrix_1d = new DoubleMatrix1D(ARRAY_1D);
    out.println(matrix_1d);

    DoubleMatrix2D matrix_2d = new DoubleMatrix2D(ARRAY_2D);
    out.println(matrix_2d);
  }
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      final DoublesPair[] keys = keySet.toArray(new DoublesPair[keySet.size()]);
      dPvCaldAlpha[loopcal][loopcal] = dPvCaldSABR[loopcal].getAlpha().getMap().get(keys[0]);
      dPvCaldRho[loopcal][loopcal] = dPvCaldSABR[loopcal].getRho().getMap().get(keys[0]);
      dPvCaldNu[loopcal][loopcal] = dPvCaldSABR[loopcal].getNu().getMap().get(keys[0]);
    }
    final DoubleMatrix1D dPvAmdLambdaMatrix = new DoubleMatrix1D(dPvAmdLambda);
    final DoubleMatrix2D dPvCaldAlphaMatrix = new DoubleMatrix2D(dPvCaldAlpha);
    final DoubleMatrix2D dLambdadAlphaMatrix = (DoubleMatrix2D) matrix.multiply(dPvCaldLambdaMatrixInverse, dPvCaldAlphaMatrix);
    final DoubleMatrix2D dPvAmdAlphaMatrix = (DoubleMatrix2D) matrix.multiply(matrix.getTranspose(dLambdadAlphaMatrix), dPvAmdLambdaMatrix);
    final DoubleMatrix2D dPvCaldRhoMatrix = new DoubleMatrix2D(dPvCaldRho);
    final DoubleMatrix2D dLambdadRhoMatrix = (DoubleMatrix2D) matrix.multiply(dPvCaldLambdaMatrixInverse, dPvCaldRhoMatrix);
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  }

  // @export "matrixAlgebraDemo"
  public static void matrixAlgebraDemo(PrintStream out) {
    ColtMatrixAlgebra colt = new ColtMatrixAlgebra();
    DoubleMatrix1D v = new DoubleMatrix1D(ARRAY_1D);
    DoubleMatrix2D m = new DoubleMatrix2D(ARRAY_2D);

    out.println(colt.getTranspose(m));
    out.println(colt.multiply(m, v));
  }
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      }
      for (int loops = 0; loops < nbNewParamSensiCurve[loopcurve]; loops++) {
        sensiClean[startCleanParameter[loopcurve] + loops] += sensiDirty[startDirtyParameter[loopcurve][indexOtherSensiCurve[loopcurve].length] + loops];
      }
    }
    return new DoubleMatrix1D(sensiClean);
  }
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        sensiClean[loopcurve][loops] += sensiDirty[startDirtyParameter[loopcurve][indexOtherSensiCurve[loopcurve].length] + loops];
      }
    }
    final LinkedHashMap<Pair<String, Currency>, DoubleMatrix1D> result = new LinkedHashMap<>();
    for (int loopcurve = 0; loopcurve < nbSensitivityCurve; loopcurve++) {
      result.put(new ObjectsPair<>(curveNamesArray[loopcurve], ccy), new DoubleMatrix1D(sensiClean[loopcurve]));
    }
    return new MultipleCurrencyParameterSensitivity(result);
  }
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    Validate.notNull(function, "function");
    Validate.notNull(startPosition, "start position");
    final MultivariateRealOptimizer optimizer = new NelderMead();
    final MultivariateRealFunction commonsFunction = CommonsMathWrapper.wrapMultivariate(function);
    try {
      return new DoubleMatrix1D(CommonsMathWrapper.unwrap(optimizer.optimize(commonsFunction, MINIMIZER, startPosition.getData())));
    } catch (final ConvergenceException e) {
      throw new MathException(e);
    } catch (final FunctionEvaluationException e) {
      throw new MathException(e);
    }
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      final YieldCurveBundle knownData,
      final InstrumentDerivativeVisitor<YieldCurveBundle, Double> calculator, final InstrumentDerivativeVisitor<YieldCurveBundle, InterestRateCurveSensitivity> sensitivityCalculator) {
    final MultipleYieldCurveFinderGeneratorDataBundle data = new MultipleYieldCurveFinderGeneratorDataBundle(instruments, knownData, curveGenerators);
    final Function1D<DoubleMatrix1D, DoubleMatrix1D> curveCalculator = new MultipleYieldCurveFinderGeneratorFunction(calculator, data);
    final Function1D<DoubleMatrix1D, DoubleMatrix2D> jacobianCalculator = new MultipleYieldCurveFinderGeneratorJacobian(new ParameterUnderlyingSensitivityCalculator(sensitivityCalculator), data);
    final double[] parameters = _rootFinder.getRoot(curveCalculator, jacobianCalculator, new DoubleMatrix1D(initGuess)).getData();
    final YieldCurveBundle newCurves = data.getBuildingFunction().evaluate(new DoubleMatrix1D(parameters));
    return new ObjectsPair<>(newCurves, ArrayUtils.toObject(parameters));
  }
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  public DoubleMatrix2D[] makeCurveMatrix(final InstrumentDerivative[] instruments, final LinkedHashMap<String, GeneratorYDCurve> curveGenerators, final int startBlock, final int[] nbParameters,
      final Double[] parameters,
      final YieldCurveBundle knownData, final InstrumentDerivativeVisitor<YieldCurveBundle, InterestRateCurveSensitivity> sensitivityCalculator) {
    final MultipleYieldCurveFinderGeneratorDataBundle data = new MultipleYieldCurveFinderGeneratorDataBundle(instruments, knownData, curveGenerators);
    final Function1D<DoubleMatrix1D, DoubleMatrix2D> jacobianCalculator = new MultipleYieldCurveFinderGeneratorJacobian(new ParameterUnderlyingSensitivityCalculator(sensitivityCalculator), data);
    final DoubleMatrix2D jacobian = jacobianCalculator.evaluate(new DoubleMatrix1D(parameters));
    final DoubleMatrix2D inverseJacobian = MATRIX_ALGEBRA.getInverse(jacobian);
    final double[][] matrixTotal = inverseJacobian.getData();
    final DoubleMatrix2D[] result = new DoubleMatrix2D[nbParameters.length];
    int startCurve = 0;
    for (int loopmat = 0; loopmat < nbParameters.length; loopmat++) {
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    bundle.addAll(newCurves);
    final double[] res = new double[_data.getNumberOfInstruments()];
    for (int i = 0; i < _data.getNumberOfInstruments(); i++) {
      res[i] = _data.getInstrument(i).accept(_calculator, bundle);
    }
    return new DoubleMatrix1D(res);
  }
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  public static void matrixMultiplyDemo(final PrintStream out) {
    final double[][] matrix_4 = identityMatrix(4);
    final DoubleMatrix2D m = new DoubleMatrix2D(matrix_4);

    final double[] data_1d = {1.0, 2.0, 3.0, 4.0 };
    final DoubleMatrix1D v = new DoubleMatrix1D(data_1d);

    final ColtMatrixAlgebra colt = new ColtMatrixAlgebra();
    out.println(colt.multiply(m, v));
  }
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