Package org.apache.commons.math3.stat.descriptive.summary

Examples of org.apache.commons.math3.stat.descriptive.summary.SumOfLogs


    /**
     * Create a GeometricMean instance
     */
    public GeometricMean() {
        sumOfLogs = new SumOfLogs();
    }
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        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i= new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }

        covarianceImpl =
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    /**
     * Create a GeometricMean instance
     */
    public GeometricMean() {
        sumOfLogs = new SumOfLogs();
    }
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        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i= new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }

        covarianceImpl =
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    /**
     * Create a GeometricMean instance
     */
    public GeometricMean() {
        sumOfLogs = new SumOfLogs();
    }
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        final SecondMoment secondMoment = new SecondMoment();
        final Sum sum = new Sum();
        final SumOfSquares sumsq = new SumOfSquares();
        final Min min = new Min();
        final Max max = new Max();
        final SumOfLogs sumLog = new SumOfLogs();
       
        final Random r = new Random(0);
        for (int i = 0; i < 10; i++) {
            final int nextInt = r.nextInt(100000000);
            secondMoment.increment(nextInt);
            sum.increment(nextInt);
            sumsq.increment(nextInt);
            min.increment(nextInt);
            max.increment(nextInt);
            sumLog.increment(nextInt);
        }

        testStorelessUnivariateStatistic(secondMoment, 7.513432791665536E15);
        testStorelessUnivariateStatistic(sum, 6.01312177E8);
        testStorelessUnivariateStatistic(sumsq, 4.3671066212513456E16);
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        }
        return this.max;
    }
    private SumOfLogs _getSumLog() {
        if (this.sumLog == null) {
            this.sumLog = new SumOfLogs();
        }
        return this.sumLog;
    }
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        geoMeanImpl = new StorelessUnivariateStatistic[k];
        meanImpl    = new StorelessUnivariateStatistic[k];

        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i= new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
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     * @param checker Convergence checker.
     */
    protected BaseOptimizer(ConvergenceChecker<PAIR> checker) {
        this.checker = checker;

        evaluations = new Incrementor(0, new MaxEvalCallback());
        iterations = new Incrementor(0, new MaxIterCallback());
    }
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        DimensionMismatchException, NonSelfAdjointOperatorException,
        NonPositiveDefiniteOperatorException, IllConditionedOperatorException,
        MaxCountExceededException {
        checkParameters(a, m, b, x);

        final IterationManager manager = getIterationManager();
        /* Initialization counts as an iteration. */
        manager.resetIterationCount();
        manager.incrementIterationCount();

        final State state;
        state = new State(a, m, b, goodb, shift, delta, check);
        state.init();
        state.refineSolution(x);
        IterativeLinearSolverEvent event;
        event = new DefaultIterativeLinearSolverEvent(this,
                                                      manager.getIterations(),
                                                      x,
                                                      b,
                                                      state.getNormOfResidual());
        if (state.bEqualsNullVector()) {
            /* If b = 0 exactly, stop with x = 0. */
            manager.fireTerminationEvent(event);
            return x;
        }
        /* Cause termination if beta is essentially zero. */
        final boolean earlyStop;
        earlyStop = state.betaEqualsZero() || state.hasConverged();
        manager.fireInitializationEvent(event);
        if (!earlyStop) {
            do {
                manager.incrementIterationCount();
                event = new DefaultIterativeLinearSolverEvent(this,
                                                              manager.getIterations(),
                                                              x,
                                                              b,
                                                              state.getNormOfResidual());
                manager.fireIterationStartedEvent(event);
                state.update();
                state.refineSolution(x);
                event = new DefaultIterativeLinearSolverEvent(this,
                                                              manager.getIterations(),
                                                              x,
                                                              b,
                                                              state.getNormOfResidual());
                manager.fireIterationPerformedEvent(event);
            } while (!state.hasConverged());
        }
        event = new DefaultIterativeLinearSolverEvent(this,
                                                      manager.getIterations(),
                                                      x,
                                                      b,
                                                      state.getNormOfResidual());
        manager.fireTerminationEvent(event);
        return x;
    }
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