/// <summary> /// Creates a new object that is a copy of the current instance. /// </summary> /// <returns> /// A new object that is a copy of this instance. /// </returns> /// public override object Clone() { var e = new MultivariateEmpiricalDistribution(Dimension); e.chol = (CholeskyDecomposition)chol.Clone(); e.determinant = determinant; e.kernel = kernel; e.numberOfSamples = numberOfSamples; e.repeats = (int[])repeats.Clone(); e.samples = (double[][])samples.MemberwiseClone(); e.smoothing = smoothing.MemberwiseClone(); e.sumOfWeights = sumOfWeights; e.type = type; if (e.weights != null) { e.weights = (double[])weights.Clone(); } if (e.repeats != null) { e.repeats = (int[])repeats.Clone(); } return(e); }
/// <summary> /// Creates a new object that is a copy of the current instance. /// </summary> /// <returns> /// A new object that is a copy of this instance. /// </returns> /// public override object Clone() { var e = new MultivariateEmpiricalDistribution(Dimension); e.initialize(kernel, samples.MemberwiseClone(), (double[, ])smoothing.Clone()); return(e); }
public void ConstructorTest4() { // Suppose we have the following data, and we would // like to estimate a distribution from this data double[][] samples = { new double[] { 0, 1 }, new double[] { 1, 2 }, new double[] { 5, 1 }, new double[] { 7, 1 }, new double[] { 6, 1 }, new double[] { 5, 7 }, new double[] { 2, 1 }, }; // Start by specifying a density kernel IDensityKernel kernel = new EpanechnikovKernel(dimension: 2); // Create a multivariate Empirical distribution from the samples var dist = new MultivariateEmpiricalDistribution(kernel, samples); // Common measures double[] mean = dist.Mean; // { 3.71, 2.00 } double[] median = dist.Median; // { 3.71, 2.00 } double[] var = dist.Variance; // { 7.23, 5.00 } (diagonal from cov) double[,] cov = dist.Covariance; // { { 7.23, 0.83 }, { 0.83, 5.00 } } // Probability mass functions double pdf1 = dist.ProbabilityDensityFunction(new double[] { 2, 1 }); // 0.039131176997318849 double pdf2 = dist.ProbabilityDensityFunction(new double[] { 4, 2 }); // 0.010212109770266639 double pdf3 = dist.ProbabilityDensityFunction(new double[] { 5, 7 }); // 0.02891906722705221 double lpdf = dist.LogProbabilityDensityFunction(new double[] { 5, 7 }); // -3.5432541357714742 Assert.AreEqual(3.7142857142857144, mean[0]); Assert.AreEqual(2.0, mean[1]); Assert.AreEqual(3.7142857142857144, median[0]); Assert.AreEqual(2.0, median[1]); Assert.AreEqual(7.2380952380952381, var[0]); Assert.AreEqual(5.0, var[1]); Assert.AreEqual(7.2380952380952381, cov[0, 0]); Assert.AreEqual(0.83333333333333337, cov[0, 1]); Assert.AreEqual(0.83333333333333337, cov[1, 0]); Assert.AreEqual(5.0, cov[1, 1]); Assert.AreEqual(0.039131176997318849, pdf1); Assert.AreEqual(0.010212109770266639, pdf2); Assert.AreEqual(0.02891906722705221, pdf3); Assert.AreEqual(-3.5432541357714742, lpdf); }
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); } }
public void WeightedEmpiricalDistributionConstructorTest3() { double[] weights = { 2, 1, 1, 1, 2, 3, 1, 3, 1, 1, 1, 1 }; double[] samples = { 5, 1, 4, 1, 2, 3, 4, 3, 4, 3, 2, 3 }; weights = weights.Divide(weights.Sum()); var target = new MultivariateEmpiricalDistribution(samples.ToArray(), weights); Assert.AreEqual(1.2377597081667415, target.Smoothing[0, 0]); }
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 GenerateTest1() { Accord.Math.Tools.SetupGenerator(0); double[] mean = { 2, 6 }; double[,] cov = { { 2, 1 }, { 1, 5 } }; var normal = new MultivariateNormalDistribution(mean, cov); double[][] source = normal.Generate(10000000); var target = new MultivariateEmpiricalDistribution(source); Assert.IsTrue(mean.IsEqual(target.Mean, 0.001)); Assert.IsTrue(cov.IsEqual(target.Covariance, 0.003)); double[][] samples = target.Generate(10000000); double[] sampleMean = samples.Mean(); double[,] sampleCov = samples.Covariance(); Assert.AreEqual(2, sampleMean[0], 1e-2); Assert.AreEqual(6, sampleMean[1], 1e-2); Assert.AreEqual(2, sampleCov[0, 0], 1e-2); Assert.AreEqual(1, sampleCov[0, 1], 1e-2); Assert.AreEqual(1, sampleCov[1, 0], 1e-2); Assert.AreEqual(5, sampleCov[1, 1], 2e-2); }
public void FitTest2() { double[][] observations = { new double[] { 0.1000, -0.2000 }, new double[] { 0.4000, 0.6000 }, new double[] { 2.0000, 0.2000 }, new double[] { 2.0000, 0.3000 } }; double[] mean = Accord.Statistics.Tools.Mean(observations); double[,] cov = Accord.Statistics.Tools.Covariance(observations); var target = new MultivariateEmpiricalDistribution(observations); target.Fit(observations); Assert.IsTrue(Matrix.IsEqual(mean, target.Mean)); Assert.IsTrue(Matrix.IsEqual(cov, target.Covariance, 1e-10)); }
public void FitTest() { double[][] observations = { new double[] { 0.1000, -0.2000 }, new double[] { 0.4000, 0.6000 }, new double[] { 2.0000, 0.2000 }, new double[] { 2.0000, 0.3000 } }; var target = new MultivariateEmpiricalDistribution(observations); double[] weigths = { 0.25, 0.25, 0.25, 0.25 }; bool thrown = false; try { target.Fit(observations, weigths); } catch (ArgumentException) { thrown = true; } Assert.IsTrue(thrown); }
/// <summary> /// Creates a new object that is a copy of the current instance. /// </summary> /// <returns> /// A new object that is a copy of this instance. /// </returns> /// public override object Clone() { var e = new MultivariateEmpiricalDistribution(Dimension); e.initialize(kernel, samples.MemberwiseClone(), (double[,])smoothing.Clone()); return e; }