Example #1
0
        // Helper functions
        private static double ComputeDensityEstimation(double pickupDelay, double duration, double interval, IEnumerable <Leg> legs)
        {
            MultivariateEmpiricalDistribution dist = new MultivariateEmpiricalDistribution(legs
                                                                                           .Where(l => l.NumOfPassengersPickedUp > 0)
                                                                                           .Select(l => (new double[] {
                GetPickupDelay(l),
                l.ArrivalTime.Subtract(l.StartTime).TotalMinutes
            })).ToArray());

            double probDist = 1 - dist.DistributionFunction(new double[] { pickupDelay, duration });

            // compute appropriate univariate distribution
            if (Math.Abs(dist.Variance[0]) < Double.Epsilon)
            {
                dist = new MultivariateEmpiricalDistribution(legs
                                                             .Where(l => l.NumOfPassengersPickedUp > 0)
                                                             .Select(l => (new double[] {
                    l.ArrivalTime.Subtract(l.StartTime).TotalMinutes
                })).ToArray());
                probDist = 1 - dist.DistributionFunction(new double[] { duration });
            }

            double frequency = legs.Count(l => Math.Abs(l.StartTime.Subtract(DateTime.Now).TotalDays) < 2 &&
                                          l.NumOfPassengersPickedUp > 0) / 5760.0;

            return(probDist * frequency * interval);
        }
        public void WeightedEmpiricalDistributionConstructorTest2()
        {
            double[] original     = { 5, 5, 1, 4, 1, 2, 2, 3, 3, 3, 4, 3, 3, 3, 4, 3, 2, 3 };
            var      distribution = new MultivariateEmpiricalDistribution(original.ToArray());

            double[]   weights = { 2, 1, 1, 1, 2, 3, 1, 3, 1, 1, 1, 1 };
            double[]   source  = { 5, 1, 4, 1, 2, 3, 4, 3, 4, 3, 2, 3 };
            double[][] samples = source.ToArray();

            weights = weights.Divide(weights.Sum());

            var target = new MultivariateEmpiricalDistribution(samples,
                                                               weights, distribution.Smoothing);

            Assert.AreEqual(distribution.Mean[0], target.Mean[0]);
            Assert.AreEqual(distribution.Median[0], target.Median[0]);
            Assert.AreEqual(distribution.Mode[0], target.Mode[0]);
            Assert.AreEqual(distribution.Smoothing[0, 0], target.Smoothing[0, 0]);
            Assert.AreEqual(1.3655172413793104, target.Variance[0]);
            Assert.AreEqual(target.Weights, weights);
            Assert.AreEqual(target.Samples, samples);

            for (double x = 0; x < 6; x += 0.1)
            {
                double actual, expected;
                expected = distribution.ComplementaryDistributionFunction(x);
                actual   = target.ComplementaryDistributionFunction(x);
                Assert.AreEqual(expected, actual, 1e-15);

                expected = distribution.DistributionFunction(x);
                actual   = target.DistributionFunction(x);
                Assert.AreEqual(expected, actual, 1e-15);

                expected = distribution.LogProbabilityDensityFunction(x);
                actual   = target.LogProbabilityDensityFunction(x);
                Assert.AreEqual(expected, actual, 1e-15);

                expected = distribution.ProbabilityDensityFunction(x);
                actual   = target.ProbabilityDensityFunction(x);
                Assert.AreEqual(expected, actual, 1e-15);
            }
        }
        public void FitTest()
        {
            double[] original     = { 5, 5, 1, 4, 1, 2, 2, 3, 3, 3, 4, 3, 3, 3, 4, 3, 2, 3 };
            var      distribution = new MultivariateEmpiricalDistribution(original.ToJagged());

            int[]      weights = { 2, 1, 1, 1, 2, 3, 1, 3, 1, 1, 1, 1 };
            double[]   sources = { 5, 1, 4, 1, 2, 3, 4, 3, 4, 3, 2, 3 };
            double[][] samples = sources.ToJagged();
            var        target  = new MultivariateEmpiricalDistribution(Jagged.Zeros(1, 1));

            target.Fit(samples, weights);

            Assert.AreEqual(distribution.Mean[0], target.Mean[0]);
            Assert.AreEqual(distribution.Median[0], target.Median[0]);
            Assert.AreEqual(distribution.Mode[0], target.Mode[0]);
            Assert.AreEqual(distribution.Smoothing[0, 0], target.Smoothing[0, 0]);
            Assert.AreEqual(distribution.Variance[0], target.Variance[0]);
            Assert.IsTrue(target.Weights.IsEqual(weights.Divide(weights.Sum())));
            Assert.AreEqual(target.Samples, samples);

            for (double x = 0; x < 6; x += 0.1)
            {
                double actual, expected;
                expected = distribution.ComplementaryDistributionFunction(x);
                actual   = target.ComplementaryDistributionFunction(x);
                Assert.AreEqual(expected, actual);

                expected = distribution.DistributionFunction(x);
                actual   = target.DistributionFunction(x);
                Assert.AreEqual(expected, actual);

                expected = distribution.LogProbabilityDensityFunction(x);
                actual   = target.LogProbabilityDensityFunction(x);
                Assert.AreEqual(expected, actual, 1e-15);

                expected = distribution.ProbabilityDensityFunction(x);
                actual   = target.ProbabilityDensityFunction(x);
                Assert.AreEqual(expected, actual, 1e-15);
            }
        }
        public void WeightedEmpiricalDistribution_DistributionFunction()
        {
            double[][] samples =
            {
                new double[] { 5, 2 },
                new double[] { 1, 5 },
                new double[] { 4, 7 },
                new double[] { 1, 6 },
                new double[] { 2, 2 },
                new double[] { 3, 4 },
                new double[] { 4, 8 },
                new double[] { 3, 2 },
                new double[] { 4, 4 },
                new double[] { 3, 7 },
                new double[] { 2, 4 },
                new double[] { 3, 1 },
            };


            var target = new MultivariateEmpiricalDistribution(samples);

            double[] expected =
            {
                0.33333333333333331, 0.083333333333333329, 0.83333333333333337,
                0.16666666666666666, 0.083333333333333329, 0.41666666666666669,
                0.91666666666666663,                 0.25,                 0.5,
                0.66666666666666663,  0.16666666666666666, 0.083333333333333329
            };

            for (int i = 0; i < samples.Length; i++)
            {
                double e = expected[i];
                double a = target.DistributionFunction(samples[i]);
                Assert.AreEqual(e, a);
            }
        }