Package org.apache.commons.math3.analysis.integration

Examples of org.apache.commons.math3.analysis.integration.UnivariateIntegrator.integrate()

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        Collections.sort(integrationTestPoints);
        for (int i = 1; i < integrationTestPoints.size(); i++) {
            Assert.assertEquals(
                    distribution.cumulativeProbability(  // FIXME @4.0 when rename happens
                            integrationTestPoints.get(0), integrationTestPoints.get(i)),
                            integrator.integrate(
                                    1000000, // Triangle integrals are very slow to converge
                                    d, integrationTestPoints.get(0),
                                    integrationTestPoints.get(i)), tol);
        }
    }
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        final double[] upper = {5, 12, 1030, 5010, 10000};
        for (int i = 1; i < 5; i++) {
            Assert.assertEquals(
                    distribution.cumulativeProbability(
                            lower[i], upper[i]),
                            integrator.integrate(
                                    1000000, // Triangle integrals are very slow to converge
                                    d, lower[i], upper[i]), tol);
        }
    }
   
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    k[k.length - 1] = n - lastJ;

    // now verify probabilities by comparing to integral of pdf
    UnivariateIntegrator integrator = new RombergIntegrator();
    for (int i = 0; i < xs.length - 1; i++) {
      double delta = integrator.integrate(1000000, new UnivariateFunction() {
        @Override
        public double value(double v) {
          return dist.pdf(v);
        }
      }, xs[i], xs[i + 1]);
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    k[k.length - 1] = n - lastJ;

    // now verify probabilities by comparing to integral of pdf
    UnivariateIntegrator integrator = new RombergIntegrator();
    for (int i = 0; i < xs.length - 1; i++) {
      double delta = integrator.integrate(1000000, new UnivariateFunction() {
        @Override
        public double value(double v) {
          return dist.pdf(v);
        }
      }, xs[i], xs[i + 1]);
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                    public double value(final double x) {
                        return x * density(x);
                    }
                };
                final UnivariateIntegrator integrator = new RombergIntegrator();
                return integrator.integrate(Integer.MAX_VALUE, f, x0, x4);
            }

            public double getNumericalVariance() {
                final double meanX = getNumericalMean();
                final UnivariateFunction f = new UnivariateFunction() {
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                    public double value(final double x) {
                        return x * x * density(x);
                    }
                };
                final UnivariateIntegrator integrator = new RombergIntegrator();
                final double meanX2 = integrator.integrate(Integer.MAX_VALUE,
                        f, x0, x4);
                return meanX2 - meanX * meanX;
            }

            public double getSupportLowerBound() {
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      // new
      // SimpsonIntegrator();//
      // new
      // RombergIntegrator();

      final double val = ri.integrate(10000000, new IntensityFunctionWrapper(
          intensity), 0, lengthOfScenario);
      System.out
          .printf("%1d relative height: %1.3f%n", i, relHeight);

      final double newAmpl = orders / val;
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      final double newAmpl = orders / val;

      final SineIntensity finalIntensity = new SineIntensity(
          newAmpl, freq, relHeight, newAmpl * min);
      final double compensatedArea = ri.integrate(10000000,
          new IntensityFunctionWrapper(
              finalIntensity), 0, lengthOfScenario);
      // System.out.printf("compensated area: %1.3f%n", compensatedArea);

      final List<Double> sineTimes = FluentIterable
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                    public double value(final double x) {
                        return x * density(x);
                    }
                };
                final UnivariateIntegrator integrator = new RombergIntegrator();
                return integrator.integrate(Integer.MAX_VALUE, f, x0, x4);
            }

            public double getNumericalVariance() {
                final double meanX = getNumericalMean();
                final UnivariateFunction f = new UnivariateFunction() {
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                    public double value(final double x) {
                        return x * x * density(x);
                    }
                };
                final UnivariateIntegrator integrator = new RombergIntegrator();
                final double meanX2 = integrator.integrate(Integer.MAX_VALUE,
                        f, x0, x4);
                return meanX2 - meanX * meanX;
            }

            public double getSupportLowerBound() {
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