Java TestUtils.assertTrue方法代码示例

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
/**
 * Defines a problem of 57 rows and 97 columns. Seems to be the same model as adlittle at netlib. Netlib
 * also provides the solution.
 * <a href="http://www-new.mcs.anl.gov/otc/Guide/TestProblems/LPtest/netlib/adlittle.html" >
 * [email protected]</a> Found this info somewhere on the net: "With 56 constraints and 97 variables
 * adlittle is one of its smaller members. While being in fact feasible, adlittle suffers from
 * ill--posedness. Perturbing the right hand side of the equality constraints by subtracting a tiny
 * multiple of the 96th column of the equation matrix renders the linear program infeasible. Running this
 * problem through CPLEX and lp_solve does again return a solution without any warnings." FAIL: Hittar
 * bara en lösning 0.0, oavsett om jag minimerrar eller maximerar. 2010-04-19 lp_solve => 225494.96316238
 */
public void testMPSadlittle() {

    final File tmpFile = new File(PATH + "adlittle.mps");
    final MathProgSysModel tmpMPS = MathProgSysModel.make(tmpFile);
    final ExpressionsBasedModel tmpModel = tmpMPS.getExpressionsBasedModel();

    //tmpModel.options.debug(LinearSolver.class);

    TestUtils.assertTrue(tmpModel.validate());

    final BigDecimal tmpExpVal = new BigDecimal("225494.96316238446"); // Stated to be .22549496316e+6
    final double tmpActVal = tmpModel.minimise().getValue();

    TestUtils.assertEquals(tmpExpVal.doubleValue(), tmpActVal, PRECISION);

    if (!tmpModel.validate(PRECISION)) {
        TestUtils.fail(SOLUTION_NOT_VALID);
    }
}
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
/**
 * Defines a problem of 28 rows and 32 columns. Seems to be the same model as afiro at netlib. Netlib also
 * provides the solution.
 * <a href="http://www-new.mcs.anl.gov/otc/Guide/TestProblems/LPtest/netlib/afiro.html">[email protected]</a>
 * OK! 2010-04-19 lp_solve => -464.75314286
 */
public void testMPSafiro() {

    final File tmpFile = new File(PATH + "afiro.mps");
    final MathProgSysModel tmpMPS = MathProgSysModel.make(tmpFile);
    final ExpressionsBasedModel tmpModel = tmpMPS.getExpressionsBasedModel();

    TestUtils.assertTrue(tmpModel.validate());

    final BigDecimal tmpExpVal = new BigDecimal("-.46475314286e+3");
    final double tmpActVal = tmpModel.minimise().getValue();
    TestUtils.assertEquals(tmpExpVal.doubleValue(), tmpActVal, PRECISION);

    if (!tmpModel.validate(PRECISION)) {
        TestUtils.fail(SOLUTION_NOT_VALID);
    }
}
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
/**
 * <a href="https://github.com/optimatika/ojAlgo/issues/23">GitHub Issue 23</a> The problem was that since
 * the model allows shorting the pure profit maximisation is unbounded (initial LP). The algorithm did not
 * handle the case where "target" could be >= the max possible when shorting not allowed (bounded LP).
 */
public void testP20160608() {

    final BasicMatrix.Factory<PrimitiveMatrix> matrixFactory = PrimitiveMatrix.FACTORY;
    final PrimitiveMatrix cov = matrixFactory.rows(new double[][] { { 0.01, 0.0018, 0.0011 }, { 0.0018, 0.0109, 0.0026 }, { 0.0011, 0.0026, 0.0199 } });
    final PrimitiveMatrix ret = matrixFactory.columns(new double[] { 0.0427, 0.0015, 0.0285 });

    final MarketEquilibrium marketEquilibrium = new MarketEquilibrium(cov);
    final MarkowitzModel markowitz = new MarkowitzModel(marketEquilibrium, ret);
    markowitz.setShortingAllowed(true);
    markowitz.setTargetReturn(BigDecimal.valueOf(0.0427));

    List<BigDecimal> tmpWeights = markowitz.getWeights();
    TestUtils.assertTrue(markowitz.optimiser().getState().isFeasible());

    final NumberContext tmpTestPrecision = StandardType.PERCENT.newPrecision(4);

    // Solution reachable without shorting, but since it is allowed the optimal solution is different
    TestUtils.assertEquals(0.82745, tmpWeights.get(0).doubleValue(), tmpTestPrecision); // 0.82745
    TestUtils.assertEquals(-0.09075, tmpWeights.get(1).doubleValue(), tmpTestPrecision); // -0.09075
    TestUtils.assertEquals(0.26329, tmpWeights.get(2).doubleValue(), tmpTestPrecision); // 0.26329

    TestUtils.assertEquals(0.0427, markowitz.getMeanReturn(), tmpTestPrecision);
    TestUtils.assertEquals(0.0084, markowitz.getReturnVariance(), tmpTestPrecision);

    // Also verify that it's posible to reach 10% return by shorting
    markowitz.setTargetReturn(BigDecimal.valueOf(0.1));
    TestUtils.assertEquals(0.1, markowitz.getMeanReturn(), tmpTestPrecision);
    TestUtils.assertTrue(markowitz.optimiser().getState().isFeasible());

    // Min risk portfolio, very high risk aversion means minimum risk.
    markowitz.setTargetReturn(null);
    markowitz.setRiskAversion(new BigDecimal(1000000));
    tmpWeights = markowitz.getWeights();
    TestUtils.assertTrue(markowitz.optimiser().getState().isFeasible());
    TestUtils.assertEquals(0.4411, tmpWeights.get(0).doubleValue(), tmpTestPrecision); // 0.4411
    TestUtils.assertEquals(0.3656, tmpWeights.get(1).doubleValue(), tmpTestPrecision); // 0.3656
    TestUtils.assertEquals(0.1933, tmpWeights.get(2).doubleValue(), tmpTestPrecision); // 0.1933
}
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
public void testP20170508() {

        Builder<PrimitiveMatrix> tmpBuilder = PrimitiveMatrix.FACTORY.getBuilder(2, 2);
        tmpBuilder.add(0, 0, 0.040000);
        tmpBuilder.add(0, 1, 0.1000);
        tmpBuilder.add(1, 0, 0.1000);
        tmpBuilder.add(1, 1, 0.250000);
        final BasicMatrix covariances = tmpBuilder.build();

        tmpBuilder = PrimitiveMatrix.FACTORY.getBuilder(2);
        tmpBuilder.add(0, 0.20000);
        tmpBuilder.add(1, 0.40000);
        final BasicMatrix returns = tmpBuilder.build();

        final MarketEquilibrium marketEq = new MarketEquilibrium(covariances);
        final MarkowitzModel markowitzModel = new MarkowitzModel(marketEq, returns);

        for (int r = 0; r <= 10; r++) {
            final BigDecimal targetReturn = StandardType.PERCENT.enforce(new BigDecimal(0.2 + (0.02 * r)));
            markowitzModel.setTargetReturn(targetReturn);

            markowitzModel.optimiser().validate(false);
            markowitzModel.optimiser().debug(false);

            final List<BigDecimal> tmpWeights = markowitzModel.getWeights();

            if (DEBUG) {
                BasicLogger.debug("{} => {} {}", targetReturn, markowitzModel.optimiser().getState(), markowitzModel.toSimplePortfolio());
            }

            TestUtils.assertTrue("Optimiser completed normally", markowitzModel.optimiser().getState().isOptimal());
            TestUtils.assertTrue("Weights sum to 100%",
                    tmpWeights.get(0).add(tmpWeights.get(1)).setScale(2, RoundingMode.HALF_EVEN).compareTo(BigMath.ONE) == 0);
            TestUtils.assertEquals("Return is close to target", targetReturn, markowitzModel.getMeanReturn(), StandardType.PERCENT.newPrecision(2).newScale(2));
        }

    }
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
public void _testHanging() throws Exception {

        final MarkowitzModel markowitzModel = GitHubIssue24.buildMarkowitzModel(2.5E-5, false, false, false);

        final double tmpMeanReturn = markowitzModel.getMeanReturn();
        if (DEBUG) {
            BasicLogger.debug(tmpMeanReturn);
        }

        TestUtils.assertTrue(markowitzModel.optimiser().getState().isOptimal()); // Won't reach here...
    }
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
public void testOriginallyHangingButNowCleaned() throws Exception {

        final MarkowitzModel markowitzModel = GitHubIssue24.buildMarkowitzModel(2.5E-5, true, false, false);

        final double tmpMeanReturn = markowitzModel.getMeanReturn();
        if (DEBUG) {
            BasicLogger.debug(tmpMeanReturn);
        }

        TestUtils.assertTrue(markowitzModel.optimiser().getState().isOptimal()); // Won't reach here...
    }
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
public void testSuccess() throws Exception {

        final MarkowitzModel markowitzModel = GitHubIssue24.buildMarkowitzModel(0.015, false, false, false);

        final double tmpMeanReturn = markowitzModel.getMeanReturn();
        if (DEBUG) {
            BasicLogger.debug(tmpMeanReturn);
        }

        final State tmpOptimisationState = markowitzModel.optimiser().getState();
        TestUtils.assertTrue(tmpOptimisationState.isOptimal());
    }
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
private static void doTestCleaning(final double[][] original) {

        final NumberContext tmpEvalCntx = NumberContext.getGeneral(6, 12);

        final PrimitiveDenseStore tmpOriginal = PrimitiveDenseStore.FACTORY.rows(original);

        final SingularValue<Double> tmpSVD = SingularValue.make(tmpOriginal);

        tmpSVD.decompose(tmpOriginal);
        final double tmpRefCond = tmpSVD.getCondition();
        final int tmpRefRank = tmpSVD.getRank();
        final double tmpRefNorm = tmpSVD.getFrobeniusNorm();

        final PrimitiveMatrix tmpCorrelations = FinanceUtils.toCorrelations(tmpOriginal, true);
        final PrimitiveMatrix tmpVolatilities = FinanceUtils.toVolatilities(tmpOriginal, true);
        final PrimitiveMatrix tmpCovariances = FinanceUtils.toCovariances(tmpVolatilities, tmpCorrelations);

        tmpSVD.decompose(PrimitiveDenseStore.FACTORY.copy(tmpCovariances));
        final double tmpNewCond = tmpSVD.getCondition();
        final int tmpNewRank = tmpSVD.getRank();
        final double tmpNewNorm = tmpSVD.getFrobeniusNorm();

        TestUtils.assertTrue("Improved the condition", tmpNewCond <= tmpRefCond);
        TestUtils.assertTrue("Improved the rank", tmpNewRank >= tmpRefRank);
        TestUtils.assertEquals("Full rank", original.length, tmpNewRank);
        TestUtils.assertEquals("Roughly the same frob norm", tmpRefNorm, tmpNewNorm, tmpEvalCntx);

        if (DEBUG) {
            BasicLogger.debug("Original", tmpOriginal);
            BasicLogger.debug("Cleaned", tmpCovariances);
            BasicLogger.debug("Difference", tmpOriginal.subtract(PrimitiveDenseStore.FACTORY.copy(tmpCovariances)), tmpEvalCntx);
        }

    }
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
@Override
void doTest(final BasicArray<Double> array) {

    for (int i = 0; i < INDICES.length; i++) {
        array.set(INDICES[i], 1.0);
    }

    final AggregatorFunction<Double> tmpVisitor = Aggregator.SUM.getFunction(PrimitiveAggregator.getSet());

    array.visitAll(tmpVisitor);

    TestUtils.assertTrue(1 <= tmpVisitor.intValue());
    TestUtils.assertTrue(INDICES.length >= tmpVisitor.intValue());
}
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
@Override
void doTest(final BasicArray<Double> array) {

    for (int i = 0; i < INDICES.length; i++) {
        array.set(INDICES[i], 1.0);
    }

    final AggregatorFunction<Double> tmpVisitor = Aggregator.CARDINALITY.getFunction(PrimitiveAggregator.getSet());

    array.visitAll(tmpVisitor);

    TestUtils.assertTrue(1 <= tmpVisitor.intValue());
    TestUtils.assertTrue(INDICES.length >= tmpVisitor.intValue());
}
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
public void test2() {
    this.setupModel();
    vars[0].lower(BigMath.ZERO).upper(BigMath.HUNDRED);
    vars[1].level(new BigDecimal(5.0));
    final MatrixStore<Double> solution = this.solveLinear();
    TestUtils.assertTrue(solution != null);
}
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
@Override
public void testData() {

    // 3x5
    final ComplexMatrix tmpProblematic = P20050827Case.getProblematic();
    TestUtils.assertEquals(3, tmpProblematic.countRows());
    TestUtils.assertEquals(5, tmpProblematic.countColumns());

    // 5x5
    final ComplexMatrix tmpBig = tmpProblematic.conjugate().multiply(tmpProblematic);
    TestUtils.assertEquals(5, tmpBig.countRows());
    TestUtils.assertEquals(5, tmpBig.countColumns());

    // 3x3
    final ComplexMatrix tmpSmall = tmpProblematic.multiply(tmpProblematic.conjugate());
    TestUtils.assertEquals(3, tmpSmall.countRows());
    TestUtils.assertEquals(3, tmpSmall.countColumns());

    final Scalar<ComplexNumber> tmpBigTrace = tmpBig.getTrace();
    final Scalar<ComplexNumber> tmpSmallTrace = tmpSmall.getTrace();

    for (int ij = 0; ij < 3; ij++) {
        TestUtils.assertTrue(tmpSmall.toScalar(ij, ij).toString(), tmpSmall.get(ij, ij).isReal());
    }

    for (int ij = 0; ij < 5; ij++) {
        TestUtils.assertTrue(tmpBig.toScalar(ij, ij).toString(), tmpBig.get(ij, ij).isReal());
    }

    TestUtils.assertEquals("Scalar<?> != Scalar<?>", tmpBigTrace.get(), tmpSmallTrace.get(), EVALUATION);
}
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
public void testVpm2FirstBranch() {

        final File tmpFile = new File(MipLibCase.PATH + "vpm2.mps");
        final MathProgSysModel tmpMPS = MathProgSysModel.make(tmpFile);
        final ExpressionsBasedModel tmpModel = tmpMPS.getExpressionsBasedModel();

        TestUtils.assertTrue(tmpModel.validate());

        final ExpressionsBasedModel tmpLowerBranchModel = tmpModel.relax(false);
        final ExpressionsBasedModel tmpUpperBranchModel = tmpModel.relax(false);

        tmpLowerBranchModel.getVariable(106).upper(BigMath.ZERO);
        tmpUpperBranchModel.getVariable(106).lower(BigMath.ONE);

        final Optimisation.Result tmpLowerResult = tmpLowerBranchModel.minimise();
        final Optimisation.Result tmpUpperResult = tmpUpperBranchModel.minimise();

        final State tmpLowerState = tmpLowerResult.getState();
        final State tmpUpperState = tmpUpperResult.getState();

        if (!tmpLowerState.isFeasible() && !tmpUpperState.isFeasible()) {
            TestUtils.fail("Both these branches cannot be infeasible!");
        }

        tmpLowerBranchModel.minimise();
        if (tmpLowerState.isFeasible() && !tmpLowerBranchModel.validate(MipLibCase.PRECISION)) {
            TestUtils.fail(MipLibCase.SOLUTION_NOT_VALID);
        }

        tmpUpperBranchModel.minimise();
        if (tmpUpperState.isFeasible() && !tmpUpperBranchModel.validate(MipLibCase.PRECISION)) {
            TestUtils.fail(MipLibCase.SOLUTION_NOT_VALID);
        }
    }
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
public void _testQR() {

        final MatrixStore<Double> tmpProblematic = ExtremeElementsCase.getVerySmall();

        final QR<BigDecimal> tmpBig = QR.BIG.make();
        final QR<ComplexNumber> tmpComplex = QR.COMPLEX.make();
        final QR<Double> tmpPrimitive = QR.PRIMITIVE.make();
        final QR<Double> tmpJama = new RawQR();

        TestUtils.assertTrue("Big.compute()", tmpBig.decompose(MatrixStore.BIG.makeWrapper(tmpProblematic)));
        TestUtils.assertTrue("Complex.compute()", tmpComplex.decompose(MatrixStore.COMPLEX.makeWrapper(tmpProblematic)));
        TestUtils.assertTrue("Primitive.compute()", tmpPrimitive.decompose(tmpProblematic));
        TestUtils.assertTrue("Jama.compute()", tmpJama.decompose(tmpProblematic));

        if (MatrixDecompositionTests.DEBUG) {
            BasicLogger.debug("Big Q", tmpBig.getQ());
            BasicLogger.debug("Complex Q", tmpComplex.getQ());
            BasicLogger.debug("Primitive Q", tmpPrimitive.getQ());
            BasicLogger.debug("Jama Q", tmpJama.getQ());
        }

        TestUtils.assertEquals("QR.reconstruct() Big", tmpProblematic, tmpBig.reconstruct(), PRECISION);
        TestUtils.assertEquals("QR.reconstruct() Complex", tmpProblematic, tmpComplex.reconstruct(), PRECISION);
        TestUtils.assertEquals("QR.reconstruct() Primitive", tmpProblematic, tmpPrimitive.reconstruct(), PRECISION);
        TestUtils.assertEquals("QR.reconstruct() Jama", tmpProblematic, tmpJama.reconstruct(), PRECISION);

        final SingularValue<Double> tmpSVD = new RawSingularValue();
        tmpSVD.decompose(tmpProblematic);

        TestUtils.assertEquals("rank() SVD vs Big", tmpSVD.getRank(), tmpBig.getRank());
        TestUtils.assertEquals("rank() SVD vs Complex", tmpSVD.getRank(), tmpComplex.getRank());
        TestUtils.assertEquals("rank() SVD vs Primitive", tmpSVD.getRank(), tmpPrimitive.getRank());
        TestUtils.assertEquals("rank() SVD vs Jama", tmpSVD.getRank(), tmpJama.getRank());

    }
 

import org.ojalgo.TestUtils; //导入方法依赖的package包/类
public void testEmptySet() {

        final Primitive64Array tmpSamples = Primitive64Array.wrap(new double[] { });
        final SampleSet tmpSampleSet = SampleSet.wrap(tmpSamples);

        try {

            // The key thing is that all methods return something
            // 0.0, NaN, Inf... doesn't really matter...
            TestUtils.assertEquals(0.0, tmpSampleSet.getFirst());
            TestUtils.assertEquals(0.0, tmpSampleSet.getInterquartileRange());
            TestUtils.assertEquals(0.0, tmpSampleSet.getLargest());
            TestUtils.assertEquals(0.0, tmpSampleSet.getLast());
            TestUtils.assertEquals(0.0, tmpSampleSet.getMaximum());
            TestUtils.assertEquals(Double.NaN, tmpSampleSet.getMean());
            TestUtils.assertEquals(0.0, tmpSampleSet.getMedian());
            TestUtils.assertEquals(0.0, tmpSampleSet.getMinimum());
            TestUtils.assertEquals(0.0, tmpSampleSet.getQuartile1());
            TestUtils.assertEquals(0.0, tmpSampleSet.getQuartile2());
            TestUtils.assertEquals(0.0, tmpSampleSet.getQuartile3());
            TestUtils.assertTrue(Double.isInfinite(tmpSampleSet.getSmallest()));
            TestUtils.assertEquals(0.0, tmpSampleSet.getStandardDeviation());
            TestUtils.assertEquals(0.0, tmpSampleSet.getSumOfSquares());
            TestUtils.assertEquals(0.0, tmpSampleSet.getVariance());

        } catch (final Exception exception) {
            // Important NOT to throw an exception!
            TestUtils.fail(exception.getMessage());
        }
    }