Examples of org.apache.commons.math3.distribution.NormalDistribution.cumulativeProbability()

Package org.apache.commons.math3.distribution

Class org.apache.commons.math3.distribution.NormalDistribution

Examples of org.apache.commons.math3.distribution.NormalDistribution.cumulativeProbability()

org.apache.commons.math3.distribution.NormalDistribution.cumulativeProbability()
{@inheritDoc}If {@code x} is more than 40 standard deviations from the mean, 0 or 1is returned, as in these cases the actual value is within {@code Double.MIN_VALUE} of 0 or 1.

        ExponentialFamily normal = new UnivariateGaussian();
        PVector p = new PVector(1);
        p.array[0] = 32;


        System.out.println(bn.cumulativeProbability(32));
        System.out.println(n.cumulativeProbability(32));
        System.out.println(normal.density(p, param_norm));


        p.array[0] = 27;
        System.out.println(bn.cumulativeProbability(27));
        System.out.println(n.cumulativeProbability(27));

View Full Code Here

        System.out.println(n.cumulativeProbability(32));
        System.out.println(normal.density(p, param_norm));


        p.array[0] = 27;
        System.out.println(bn.cumulativeProbability(27));
        System.out.println(n.cumulativeProbability(27));
        System.out.println(n.density(27));
        System.out.println(normal.density(p, param_norm));


        p.array[0] = 60;
        System.out.println(bn.cumulativeProbability(60));

View Full Code Here

        System.out.println(n.density(27));
        System.out.println(normal.density(p, param_norm));


        p.array[0] = 60;
        System.out.println(bn.cumulativeProbability(60));
        System.out.println(n.cumulativeProbability(60));
        System.out.println(n.density(60));
        System.out.println(normal.density(p, param_norm));

View Full Code Here

      final double mean = mean(samples);
      final double variance = variance(samples);
      // N.B.: NormalDistribution commons math v2.0 will cause init Node hanged.
      final NormalDistribution cal = new NormalDistribution(mean, variance);
      final double rt = (double) timestamp - (double) getLatestHeartbeat();
      cdf = cal.cumulativeProbability(rt);
      if (LOG.isDebugEnabled())
        LOG.debug("Calcuated cdf:" + cdf + " END");
      return cdf;
    }

View Full Code Here

            presentLife = getPresent();
        }
        //System.out.println(expectedValue);
        //System.out.println(standardDeviation);
        NormalDistribution normDist = new NormalDistribution(expectedValue, standardDeviation);
        normalHazardRate = (normDist.density(presentLife)/(1-normDist.cumulativeProbability(presentLife)))/timeDiv;
        //System.out.println(1-normDist.cumulativeProbability(presentLife));
        //System.out.println(presentLife);
        if(breakPoint>0f){
            //Use the reduction Factor or Not
            if("Y".equals(repMod)){

View Full Code Here

      final double mean = mean(samples);
      final double variance = variance(samples);
      // N.B.: NormalDistribution commons math v2.0 will cause init Node hanged.
      final NormalDistribution cal = new NormalDistribution(mean, variance);
      final double rt = (double) timestamp - (double) getLatestHeartbeat();
      cdf = cal.cumulativeProbability(rt);
      if (LOG.isDebugEnabled())
        LOG.debug("Calcuated cdf:" + cdf + " END");
      return cdf;
    }

View Full Code Here

        final double z = (Umin - EU) / FastMath.sqrt(VarU);


        // No try-catch or advertised exception because args are valid
        final NormalDistribution standardNormal = new NormalDistribution(0, 1);


        return 2 * standardNormal.cumulativeProbability(z);
    }


    /**
     * Returns the asymptotic <i>observed significance level</i>, or <a href=
     * "http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">

View Full Code Here

        final double z = (Wmin - ES - 0.5) / FastMath.sqrt(VarS);


        // No try-catch or advertised exception because args are valid
        final NormalDistribution standardNormal = new NormalDistribution(0, 1);


        return 2*standardNormal.cumulativeProbability(z);
    }


    /**
     * Returns the <i>observed significance level</i>, or <a href=
     * "http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">

View Full Code Here


        final NormalDistribution normal = new NormalDistribution();
        List<Double> scores = Ordering.natural().sortedCopy(Iterables.transform(k1.elementSet(),
                new Function<String, Double>() {
                    public Double apply(String s) {
                        return normal.cumulativeProbability(LogLikelihood.rootLogLikelihoodRatio(k1.count(s), 50000 - k1.count(s), k2.count(s), 50000 - k2.count(s)));
                    }
                }));
        int n = scores.size();
//        System.out.printf("%.5f, %.5f, %.5f, %.5f, %.5f, %.5f, %.5f", scores.get(0), scores.get((int) (0.05*n)), scores.get(n / 4), scores.get(n / 2), scores.get(3 * n / 4), scores.get((int) (0.95 * n)), scores.get(n - 1));
        int i = 0;

View Full Code Here

            assertEquals(q, uG1, (1 - q) * q * 10e-2);


            double uG2 = gd2.cumulativeProbability(tdG2.quantile(q));
            assertEquals(q, uG2, (1 - q) * q * 10e-2);


            double u1 = normalDistribution.cumulativeProbability(td1.quantile(q));
            assertEquals(q, u1, (1 - q) * q * 10e-2);


            double u2 = normalDistribution.cumulativeProbability(td2.quantile(q) / 2);
            assertEquals(q, u2, (1 - q) * q * 10e-2);

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.