public void testBSplineFit() { final GeneralizedLeastSquare gls = new GeneralizedLeastSquare(); final LeastSquareResults results = gls.solve(X, Y, SIGMA, BASIS_FUNCTIONS); final Function<Double, Double> spline = new BasisFunctionAggregation<>(BASIS_FUNCTIONS, results.getFitParameters().toArray()); assertEquals(0.0, results.getChiSq(), 1e-12); assertEquals(-0.023605293, spline.apply(0.5), 1e-8); if (PRINT) { System.out.println("Chi^2:\t" + results.getChiSq()); System.out.println("weights:\t" + results.getFitParameters()); for (int i = 0; i < 101; i++) { final double x = 0 + i * 2.0 / 100.0; System.out.println(x + "\t" + spline.apply(x)); } for (int i = 0; i < X.length; i++) { System.out.println(X[i] + "\t" + Y[i]); } } }
final Function<double[], Double> spline = new BasisFunctionAggregation<>(BASIS_FUNCTIONS_2D, results.getFitParameters().toArray()); assertEquals(0.0, results.getChiSq(), 1e-16); assertEquals(0.05161579, spline.apply(new double[] {4, 3 }), 1e-8); System.out.println("Chi^2:\t" + results.getChiSq()); System.out.println("weights:\t" + results.getFitParameters());
public void testFit() { final GeneralizedLeastSquare gls = new GeneralizedLeastSquare(); final double[] y = new double[Y.length]; for (int i = 0; i < Y.length; i++) { y[i] = Y[i] + SIGMA[i] * NORMAL.nextRandom(); } final LeastSquareResults results = gls.solve(X, y, SIGMA, SIN_FUNCTIONS); assertTrue(results.getChiSq() < 3 * Y.length); }
vols = updateParameters(sabrDefinition, vols, resTransform.getModelParameters()); return IborCapletFloorletVolatilityCalibrationResult.ofLeastSquare(vols, res.getChiSq());
InterpolatedNodalSurface resSurface = InterpolatedNodalSurface.of( metadata, capletNodes.getFirst(), capletNodes.getSecond(), res.getFitParameters(), directDefinition.getInterpolator()); return IborCapletFloorletVolatilityCalibrationResult.ofLeastSquare(volatilitiesFunction.apply(resSurface), res.getChiSq());
public void testPerfectFit() { final GeneralizedLeastSquare gls = new GeneralizedLeastSquare(); final LeastSquareResults results = gls.solve(X, Y, SIGMA, SIN_FUNCTIONS); assertEquals(0.0, results.getChiSq(), 1e-8); final DoubleArray w = results.getFitParameters(); for (int i = 0; i < WEIGHTS.length; i++) { assertEquals(WEIGHTS[i], w.get(i), 1e-8); } }
LeastSquareResultsWithTransform resTransform = new LeastSquareResultsWithTransform(res, transform); vols = updateParameters(vols, nExpiries, i, betaFix, resTransform.getModelParameters()); totalChiSq += res.getChiSq(); prevExpiry = capList.get(startIndex[i + 1] - 1).getFinalFixingDateTime();
public void testPerfectFitVector() { final GeneralizedLeastSquare gls = new GeneralizedLeastSquare(); final LeastSquareResults results = gls.solve(X_TRIG, Y_TRIG, SIGMA_TRIG, VECTOR_TRIG_FUNCTIONS); assertEquals(0.0, results.getChiSq(), 1e-8); final DoubleArray w = results.getFitParameters(); for (int i = 0; i < WEIGHTS.length; i++) { assertEquals(WEIGHTS[i], w.get(i), 1e-8); } }
@Test public void solverTest2() { double[] w = new double[] {3.0, 4.0 }; final int n = w.length; Function<DoubleArray, DoubleArray> func = new Function<DoubleArray, DoubleArray>() { @Override public DoubleArray apply(DoubleArray x) { double a = x.get(0); double theta = x.get(1); double c1 = Math.cos(theta); return DoubleArray.of( a * c1 * c1, a * (1 - c1 * c1)); } }; DoubleArray sigma = DoubleArray.filled(n, 1e-4); DoubleArray start = DoubleArray.of(0.0, 0.8); LeastSquareResults res = SOLVER.solve(DoubleArray.copyOf(w), sigma, func, start/*, maxJump*/); assertEquals("chi sqr", 0.0, res.getChiSq(), 1e-9); double[] fit = res.getFitParameters().toArray(); assertEquals(7.0, fit[0], 1e-9); assertEquals(Math.atan(Math.sqrt(4 / 3.)), fit[1], 1e-9); }
assertEquals("chi sqr", 0.0, res.getChiSq(), 1e-9); double[] fit = res.getFitParameters().toArray(); double[] expected = trans.inverseTransform(w);
@Test public void testRecall() { final double chiSq = 12.46; LeastSquareResults res = new LeastSquareResults(chiSq, PARAMS, COVAR); assertEquals(chiSq, res.getChiSq(), 0.0); for (int i = 0; i < 2; i++) { assertEquals(PARAMS.get(i), res.getFitParameters().get(i), 0); for (int j = 0; j < 2; j++) { assertEquals(COVAR.get(i, j), res.getCovariance().get(i, j), 0); } } res = new LeastSquareResults(chiSq, PARAMS, COVAR, INV_JAC); assertEquals(chiSq, res.getChiSq(), 0.0); for (int i = 0; i < 2; i++) { assertEquals(PARAMS.get(i), res.getFitParameters().get(i), 0); for (int j = 0; j < 2; j++) { assertEquals(COVAR.get(i, j), res.getCovariance().get(i, j), 0); assertEquals(INV_JAC.get(i, j), res.getFittingParameterSensitivityToData().get(i, j), 0); } } }
public void solveExactTest2() { final DoubleArray start = DoubleArray.of(0.2, 1.8, 0.2, 0.3); final LeastSquareResults result = LS.solve(Y, SIGMA, FUNCTION, start); assertEquals(0.0, result.getChiSq(), 1e-8); assertEquals(1.0, result.getFitParameters().get(0), 1e-8); assertEquals(1.0, result.getFitParameters().get(1), 1e-8); assertEquals(0.0, result.getFitParameters().get(2), 1e-8); assertEquals(0.0, result.getFitParameters().get(3), 1e-8); }
public void solveExactWithoutGradientTest() { final DoubleArray start = DoubleArray.of(1.2, 0.8, -0.2, -0.3); final NonLinearLeastSquare ls = new NonLinearLeastSquare(); final LeastSquareResults result = ls.solve(X, Y, SIGMA, PARAM_FUNCTION, start); assertEquals(0.0, result.getChiSq(), 1e-8); assertEquals(1.0, result.getFitParameters().get(0), 1e-8); assertEquals(1.0, result.getFitParameters().get(1), 1e-8); assertEquals(0.0, result.getFitParameters().get(2), 1e-8); assertEquals(0.0, result.getFitParameters().get(3), 1e-8); }
public void solveRandomNoiseTest() { final MatrixAlgebra ma = new OGMatrixAlgebra(); final double[] y = new double[20]; for (int i = 0; i < 20; i++) { y[i] = Y.get(i) + SIGMA.get(i) * NORMAL.nextRandom(); } final DoubleArray start = DoubleArray.of(0.7, 1.4, 0.2, -0.3); final NonLinearLeastSquare ls = new NonLinearLeastSquare(); final LeastSquareResults res = ls.solve(X, DoubleArray.copyOf(y), SIGMA, PARAM_FUNCTION, PARAM_GRAD, start); final double chiSqDoF = res.getChiSq() / 16; assertTrue(chiSqDoF > 0.25); assertTrue(chiSqDoF < 3.0); final DoubleArray trueValues = DoubleArray.of(1, 1, 0, 0); final DoubleArray delta = (DoubleArray) ma.subtract(res.getFitParameters(), trueValues); final LUDecompositionCommons decmp = new LUDecompositionCommons(); final LUDecompositionResult decmpRes = decmp.apply(res.getCovariance()); final DoubleMatrix invCovariance = decmpRes.solve(DoubleMatrix.identity(4)); double z = ma.getInnerProduct(delta, ma.multiply(invCovariance, delta)); z = Math.sqrt(z); assertTrue(z < 3.0); }
public void solveExactTest() { final DoubleArray start = DoubleArray.of(1.2, 0.8, -0.2, -0.3); LeastSquareResults result = LS.solve(X, Y, SIGMA, PARAM_FUNCTION, PARAM_GRAD, start); assertEquals(0.0, result.getChiSq(), 1e-8); assertEquals(1.0, result.getFitParameters().get(0), 1e-8); assertEquals(1.0, result.getFitParameters().get(1), 1e-8); assertEquals(0.0, result.getFitParameters().get(2), 1e-8); assertEquals(0.0, result.getFitParameters().get(3), 1e-8); result = LS.solve(X, Y, SIGMA.get(0), PARAM_FUNCTION, PARAM_GRAD, start); assertEquals(0.0, result.getChiSq(), 1e-8); assertEquals(1.0, result.getFitParameters().get(0), 1e-8); assertEquals(1.0, result.getFitParameters().get(1), 1e-8); assertEquals(0.0, result.getFitParameters().get(2), 1e-8); assertEquals(0.0, result.getFitParameters().get(3), 1e-8); result = LS.solve(X, Y, PARAM_FUNCTION, PARAM_GRAD, start); assertEquals(0.0, result.getChiSq(), 1e-8); assertEquals(1.0, result.getFitParameters().get(0), 1e-8); assertEquals(1.0, result.getFitParameters().get(1), 1e-8); assertEquals(0.0, result.getFitParameters().get(2), 1e-8); assertEquals(0.0, result.getFitParameters().get(3), 1e-8); }