Running (normal) statistics.

This class computes the running variance using Welford’s method. Running statistics need only one pass over the data, and do not require all data to be available prior to computing.

References: John D. Cook. Accurately computing running variance. Available on: http://www.johndcook.com/standard_deviation.html Chan, Tony F.; Golub, Gene H.; LeVeque, Randall J. (1983). Algorithms for Computing the Sample Variance: Analysis and Recommendations. The American Statistician 37, 242-247. Ling, Robert F. (1974). Comparison of Several Algorithms for Computing Sample Means and Variances. Journal of the American Statistical Association, Vol. 69, No. 348, 859-866.

Inheritance: IRunningStatistics
RunningNormalStatistics Class Documentation
Esempio n. 1
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        public void StandardDeviationTest()
        {
            double[] values = { 0.5, -1.2, 0.7, 0.2, 1.1 };

            RunningNormalStatistics target = new RunningNormalStatistics();

            for (int i = 0; i < values.Length; i++)
                target.Push(values[i]);

            double expected = values.StandardDeviation();
            double actual = target.StandardDeviation;

            Assert.AreEqual(expected, actual);
        }
Esempio n. 2
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        public void ClearTest()
        {
            double[] values = { 0.5, -1.2, 0.7, 0.2, 1.1 };

            RunningNormalStatistics target = new RunningNormalStatistics();

            for (int i = 0; i < values.Length; i++)
                target.Push(values[i]);

            target.Clear();

            double[] values2 = { 1.5, -5.2, 0.7, 1.2, 9.1 };

            for (int i = 0; i < values.Length; i++)
                target.Push(values2[i]);

            Assert.AreEqual(values2.Mean(), target.Mean, 1e-10);
            Assert.AreEqual(values2.StandardDeviation(), target.StandardDeviation, 1e-10);
            Assert.AreEqual(values2.Variance(), target.Variance, 1e-10);
        }
Esempio n. 3
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        public void VarianceTest()
        {
            double[] values = { 0.5, -1.2, 0.7, 0.2, 1.1 };

            RunningNormalStatistics target = new RunningNormalStatistics();

            for (int i = 0; i < values.Length; i++)
                target.Push(values[i]);

            double expected = Tools.Variance(values);
            double actual = target.Variance;

            Assert.AreEqual(expected, actual);
        }
Esempio n. 4
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 public void RunningNormalStatisticsConstructorTest()
 {
     RunningNormalStatistics target = new RunningNormalStatistics();
     Assert.AreEqual(0, target.Mean);
     Assert.AreEqual(0, target.StandardDeviation);
     Assert.AreEqual(0, target.Variance);
 }
Esempio n. 5
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 public double TestUtility()
 {
     RunningNormalStatistics utilityAve = new RunningNormalStatistics();
     int counter = 0;
     foreach (var a in stats) {
         if (a.Value.ElementsSeen > 20) {
             utilityAve.Push(a.Value.ProbabilisticUtility());
             counter++;
         }
     }
     if (counter > 8) {
         return utilityAve.Mean;
     } else {
         //Debug.Print(counter.ToString());
         return -1;
     }
 }