Examples with NumberContext org.ojalgo.type.context.NumberContext used on opensource projects

Example 86 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo by optimatika.

the class PolynomialImplTest method testEstimation.

@Test
public void testEstimation() {
    final int tmpMaxSamples = 9;
    for (int tmpSamples = 1; tmpSamples <= tmpMaxSamples; tmpSamples++) {
        final Uniform tmpRndm = new Uniform(-100, 200);
        final double[] x = new double[tmpSamples];
        final double[] y = new double[tmpSamples];
        for (int i = 0; i < tmpSamples; i++) {
            x[i] = tmpRndm.doubleValue();
            y[i] = tmpRndm.doubleValue();
        }
        final int tmpDegree = tmpSamples - 1;
        final PrimitivePolynomial tmpPoly = new PrimitivePolynomial(tmpDegree);
        tmpPoly.estimate(Access1D.wrap(x), Access1D.wrap(y));
        final NumberContext tmpEquals = new NumberContext(7, 14);
        for (int i = 0; i < tmpSamples; i++) {
            TestUtils.assertEquals(y[i], tmpPoly.invoke(x[i]), tmpEquals);
        }
    }
}

Example 87 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo by optimatika.

the class GaussianProcessTest method testTutorial.

@Test
public void testTutorial() {
    final GaussianField.Covariance<Double> tmpCovar = new GaussianField.Covariance<Double>() {

        public void calibrate(final Collection<ComparableToDouble<Double>> observations, final Mean<Double> mean) {
        }

        public double invoke(final Double anArg1, final Double anArg2) {
            return this.invoke(anArg1.doubleValue(), anArg2.doubleValue());
        }

        double invoke(final double anArg1, final double anArg2) {
            final double tmpSF = 1.27;
            final double tmpSN = 0.3;
            final double tmpL = 1.0;
            double retVal = tmpSF * tmpSF * PrimitiveFunction.EXP.invoke(-PrimitiveFunction.POW.invoke(anArg1 - anArg2, TWO) / (TWO * tmpL * tmpL));
            if (anArg1 == anArg2) {
                retVal += tmpSN * tmpSN;
            }
            return retVal;
        }
    };
    final GaussianProcess tmpProc = new GaussianProcess(tmpCovar);
    tmpProc.addObservation(-1.5, -1.6);
    tmpProc.addObservation(-1.0, -1.1);
    tmpProc.addObservation(-0.75, -0.4);
    tmpProc.addObservation(-0.4, 0.1);
    tmpProc.addObservation(-0.25, 0.5);
    tmpProc.addObservation(0.0, 0.8);
    final PrimitiveDenseStore tmpExpected = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 1.7029, 1.423379254178694, 1.2174807940480699, 0.8807634427271873, 0.7384394292014367, 0.5236319646022823 }, { 1.423379254178694, 1.7029, 1.5632762838868954, 1.3472073239852407, 1.2174807940480699, 0.9782733010505065 }, { 1.2174807940480699, 1.5632762838868954, 1.7029, 1.5170744874003474, 1.423379254178694, 1.2174807940480699 }, { 0.8807634427271873, 1.3472073239852407, 1.5170744874003474, 1.7029, 1.5948565596534579, 1.4888943550870049 }, { 0.7384394292014367, 1.2174807940480699, 1.423379254178694, 1.5948565596534579, 1.7029, 1.5632762838868954 }, { 0.5236319646022823, 0.9782733010505065, 1.2174807940480699, 1.4888943550870049, 1.5632762838868954, 1.7029 } });
    TestUtils.assertEquals(tmpExpected, tmpProc.getCovariances(), new NumberContext(8, 2));
    final Normal tmpDistr = tmpProc.getDistribution(0.2);
    TestUtils.assertEquals("Mean", 0.911277527445648, tmpDistr.getExpected(), 0.005);
    TestUtils.assertEquals("Variance", 0.20604504349662636, tmpDistr.getVariance(), 0.005);
}

Example 88 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo by optimatika.

the class GeometricBrownianMotionTest method testDistributionConsistency.

@Test
public void testDistributionConsistency() {
    final double tmpError = new NumberContext(7, 9).epsilon();
    GeometricBrownianMotion tmpProcess;
    LogNormal tmpDistribution;
    for (int tmpCreateHorizon = 1; tmpCreateHorizon < 10; tmpCreateHorizon++) {
        for (double tmpExpected = 1.0; tmpExpected <= 2.0; tmpExpected += 0.1) {
            for (double tmpVariance = 0.0; tmpVariance <= 1.0; tmpVariance += 0.1) {
                tmpProcess = GeometricBrownianMotion.make(tmpExpected, tmpVariance, tmpCreateHorizon);
                TestUtils.assertEquals(tmpExpected, tmpProcess.getExpected(tmpCreateHorizon), tmpError);
                TestUtils.assertEquals(tmpVariance, tmpProcess.getVariance(tmpCreateHorizon), tmpError);
                tmpDistribution = tmpProcess.getDistribution(tmpCreateHorizon);
                TestUtils.assertEquals(tmpExpected, tmpDistribution.getExpected(), tmpError);
                TestUtils.assertEquals(tmpVariance, tmpDistribution.getVariance(), tmpError);
                for (int tmpTestHorison = 0; tmpTestHorison < 10; tmpTestHorison++) {
                    tmpDistribution = tmpProcess.getDistribution(tmpTestHorison);
                    TestUtils.assertEquals(tmpDistribution.getExpected(), tmpProcess.getExpected(tmpTestHorison), tmpError);
                    TestUtils.assertEquals(tmpDistribution.getVariance(), tmpProcess.getVariance(tmpTestHorison), tmpError);
                    TestUtils.assertEquals(tmpDistribution.getStandardDeviation(), tmpProcess.getStandardDeviation(tmpTestHorison), tmpError);
                    TestUtils.assertEquals(tmpDistribution.getUpperConfidenceQuantile(0.95), tmpProcess.getUpperConfidenceQuantile(tmpTestHorison, 0.95), tmpError);
                    TestUtils.assertEquals(tmpDistribution.getLowerConfidenceQuantile(0.95), tmpProcess.getLowerConfidenceQuantile(tmpTestHorison, 0.95), tmpError);
                    TestUtils.assertEquals(tmpDistribution.getUpperConfidenceQuantile(0.05), tmpProcess.getUpperConfidenceQuantile(tmpTestHorison, 0.05), tmpError);
                    TestUtils.assertEquals(tmpDistribution.getLowerConfidenceQuantile(0.05), tmpProcess.getLowerConfidenceQuantile(tmpTestHorison, 0.05), tmpError);
                }
            }
        }
    }
}

Example 89 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo-finance by optimatika.

the class PortfolioProblems method testP20110614.

/**
 * A user claimed he got constraint breaking weights using these figures.
 */
@Test
public void testP20110614() {
    final Builder<PrimitiveMatrix> tmpCovarsBuilder = PrimitiveMatrix.FACTORY.getBuilder(3, 3);
    tmpCovarsBuilder.set(0, 0, 0.04);
    tmpCovarsBuilder.set(0, 1, 0.01);
    tmpCovarsBuilder.set(0, 2, 0.02);
    tmpCovarsBuilder.set(1, 0, 0.01);
    tmpCovarsBuilder.set(1, 1, 0.09);
    tmpCovarsBuilder.set(1, 2, 0.01);
    tmpCovarsBuilder.set(2, 0, 0.02);
    tmpCovarsBuilder.set(2, 1, 0.01);
    tmpCovarsBuilder.set(2, 2, 0.16);
    final BasicMatrix tmpCovars = tmpCovarsBuilder.build();
    final Builder<PrimitiveMatrix> tmpReturnsBuilder = PrimitiveMatrix.FACTORY.getBuilder(3, 1);
    tmpReturnsBuilder.set(0, 0, 0.10);
    tmpReturnsBuilder.set(1, 0, 0.15);
    tmpReturnsBuilder.set(2, 0, 0.18);
    final BasicMatrix tmpReturs = tmpReturnsBuilder.build();
    final MarketEquilibrium tmpME = new MarketEquilibrium(tmpCovars);
    final MarkowitzModel tmpMarkowitz = new MarkowitzModel(tmpME, tmpReturs);
    for (int i = 1; i < 10; i++) {
        tmpMarkowitz.setRiskAversion(new BigDecimal(i));
        final List<BigDecimal> tmpWeights = tmpMarkowitz.getWeights();
        for (final BigDecimal tmpBigDecimal : tmpWeights) {
            if ((tmpBigDecimal.compareTo(BigMath.ZERO) == -1) || (tmpBigDecimal.compareTo(BigMath.ONE) == 1)) {
                TestUtils.fail("!(0.0 <= " + tmpBigDecimal + " <= 1.0)");
            }
        }
    }
    // As the Markowitz model built it the problem
    final MatrixStore<Double> tmpQ = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 4.0, 1.0, 2.0 }, { 1.0, 9.0, 1.0 }, { 2.0, 1.0, 16.0 } });
    final MatrixStore<Double> tmpC = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 10.0 }, { 15.0 }, { 18.0 } });
    final MatrixStore<Double> tmpAE = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 1.0, 1.0, 1.0 } });
    final MatrixStore<Double> tmpBE = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 1.0 } });
    MatrixStore<Double> tmpAI = PrimitiveDenseStore.FACTORY.rows(new double[][] { { -1.0, 0.0, 0.0 }, { 0.0, -1.0, 0.0 }, { 0.0, 0.0, -1.0 } });
    MatrixStore<Double> tmpBI = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 0.0 }, { 0.0 }, { 0.0 } });
    final MatrixStore<Double> tmpX = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 0.0 }, { 0.5217391304347826 }, { 0.4782608695652173 } });
    ConvexSolver.Builder tmpBuilder = new ConvexSolver.Builder(tmpQ, tmpC).equalities(tmpAE, tmpBE).inequalities(tmpAI, tmpBI);
    ConvexSolver tmpSolver = tmpBuilder.build();
    // tmpSolver.options.debug(ConvexSolver.class);
    Optimisation.Result tmpResult = tmpSolver.solve();
    // BasicMatrix tmpSolution = tmpResult.getSolution();
    TestUtils.assertEquals(tmpX, tmpResult, new NumberContext(7, 6));
    // As (I believe) the user built it
    // 
    // The only *problem* I found was that he did not set lower limits
    // on the portfolio weights, which you have to do. No problem with
    // ojAlgo.
    tmpAI = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 1.0, 0.0, 0.0 }, { 0.0, 1.0, 0.0 }, { 0.0, 0.0, 1.0 } });
    tmpBI = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 1.0 }, { 1.0 }, { 1.0 } });
    tmpBuilder = new ConvexSolver.Builder(tmpQ, tmpC).equalities(tmpAE, tmpBE).inequalities(tmpAI, tmpBI);
    tmpSolver = tmpBuilder.build();
    tmpResult = tmpSolver.solve();
    // Should NOT be equal in this case!
    TestUtils.assertFalse(Access1D.equals(tmpX, tmpResult, new NumberContext(7, 6)));
    // No problem with both the lower and upper limits set.
    tmpAI = PrimitiveDenseStore.FACTORY.rows(new double[][] { { -1.0, 0.0, 0.0 }, { 0.0, -1.0, 0.0 }, { 0.0, 0.0, -1.0 }, { 1.0, 0.0, 0.0 }, { 0.0, 1.0, 0.0 }, { 0.0, 0.0, 1.0 } });
    tmpBI = PrimitiveDenseStore.FACTORY.rows(new double[][] { { 0.0 }, { 0.0 }, { 0.0 }, { 1.0 }, { 1.0 }, { 1.0 } });
    tmpBuilder = new ConvexSolver.Builder(tmpQ, tmpC).equalities(tmpAE, tmpBE).inequalities(tmpAI, tmpBI);
    tmpSolver = tmpBuilder.build();
    tmpResult = tmpSolver.solve();
    TestUtils.assertEquals(tmpX, tmpResult, new NumberContext(7, 6));
}

Example 90 with NumberContext

use of org.ojalgo.type.context.NumberContext in project ojAlgo-finance by optimatika.

the class PortfolioProblems method testP20141202.

/**
 * <p>
 * First of all, let me say that I really like ojAlgo so thank you for making it! I do, however, think
 * that you should make tmpIterCount and _0_000005 variables fields (with getters and setters) in the
 * MarkowitzModel.java class. We are finding that we get suboptimal solutions with the hard-coded limit of
 * 20 iterations in a mean variance optimisation (solving for the highest return given a target variance).
 * We are now testing it (against our own Python model) with a limit of 100. Please let me know what you
 * think when you get a chance.
 * </p>
 * <p>
 * Borrowed test data from {@link #testP20090115()}.
 * </p>
 * <p>
 * 2015-04-14: Changed test evaluation context from <7,14> to <4,4>.
 * </p>
 */
@Test
public void testP20141202() {
    final double[][] assets_return = { { -1.5905837442343828E-4, -0.03062360801781757, -0.029857534032853142, -0.011811692726036832, -0.017972310602803136, 0.017338003502626997, 0.0 }, { -0.02757158006362653, -0.02562704471101405, -0.011751538891997735, -0.024915062287655786, -0.01684088269454123, 0.013585351447135364, 0.0 }, { -0.00699300699300693, -0.033802816901408676, -0.04675196850393671, -0.021166752710376546, -0.007911392405063583, 0.03827751196172254, 0.0 }, { -0.007626310772164015, 0.0038424591738713027, 0.02488038277511978, 0.025210084033613675, -0.02003642987249557, -0.09758364312267642, 0.0 }, { -0.03965053763440893, 0.021693491952414375, 0.01643835616438392, -0.007412398921833087, 0.01765105227427014, -0.010006671114076025, 0.0 }, { -0.017821782178217872, 0.005040322580645311, 0.006018054162487363, 9.008107296569024E-4, 0.002999999999999824, -0.01196410767696908, 0.0 }, { 2.630552127527583E-4, 2.5867028174649627E-4, 2.3866431891514327E-4, 1.9564035993080523E-4, 2.351016690966669E-4, 1.9070675120065465E-4, 0.0 } };
    final P20090115 tm = new P20090115();
    final BasicMatrix tmpCovariances = tm.getCovariances(assets_return);
    final BasicMatrix tmpExpectedExcessReturns = tm.getExpectedExcessReturns(assets_return).negate();
    final MarketEquilibrium tmpME = new MarketEquilibrium(tmpCovariances).clean();
    final MarkowitzModel tmpMarkowitz = new MarkowitzModel(tmpME, tmpExpectedExcessReturns);
    final BigDecimal[] tmpRiskAversions = new BigDecimal[] { BigMath.HUNDREDTH, BigMath.TWELFTH, BigMath.EIGHTH, BigMath.HALF, BigMath.ONE, BigMath.TWO, BigMath.EIGHT, BigMath.TWELVE, BigMath.HUNDRED, BigMath.THOUSAND };
    final double[] tmpPortfolioReturn = new double[tmpRiskAversions.length];
    final double[] tmpPortfolioVariance = new double[tmpRiskAversions.length];
    final BigDecimal tmpInitialRiskAversion = tmpMarkowitz.getRiskAversion().toBigDecimal();
    for (int ra = 0; ra < tmpRiskAversions.length; ra++) {
        tmpMarkowitz.setRiskAversion(tmpRiskAversions[ra]);
        tmpMarkowitz.getWeights();
        tmpPortfolioReturn[ra] = tmpMarkowitz.getMeanReturn();
        tmpPortfolioVariance[ra] = tmpMarkowitz.getReturnVariance();
        if (DEBUG) {
            BasicLogger.debug("RA: {}\tret: {}\tvar: {}\tweights: {}", tmpRiskAversions[ra], tmpMarkowitz.getMeanReturn(), tmpMarkowitz.getReturnVariance(), tmpMarkowitz.getWeights());
        }
    }
    tmpMarkowitz.setRiskAversion(tmpInitialRiskAversion);
    // test evaluation context
    final NumberContext tmpPrecision = StandardType.PERCENT.newPrecision(4);
    for (int r = 0; r < tmpPortfolioReturn.length; r++) {
        tmpMarkowitz.setRiskAversion(tmpInitialRiskAversion);
        tmpMarkowitz.setTargetReturn(BigDecimal.valueOf(tmpPortfolioReturn[r]));
        tmpMarkowitz.getWeights();
        // BasicLogger.debug("Exp={}, Act={}, Quoat={}", tmpPortfolioReturn[r], tmpMarkowitz.getMeanReturn(), tmpMarkowitz.getMeanReturn()  / tmpPortfolioReturn[r]);
        TestUtils.assertEquals("Return: " + tmpRiskAversions[r], tmpPortfolioReturn[r], tmpMarkowitz.getMeanReturn(), tmpPrecision);
    }
    for (int v = 0; v < tmpPortfolioVariance.length; v++) {
        tmpMarkowitz.setRiskAversion(tmpInitialRiskAversion);
        tmpMarkowitz.setTargetVariance(BigDecimal.valueOf(tmpPortfolioVariance[v]));
        tmpMarkowitz.getWeights();
        TestUtils.assertEquals("Variance: " + tmpRiskAversions[v], tmpPortfolioVariance[v], tmpMarkowitz.getReturnVariance(), tmpPrecision);
    }
}