Ejemplo n.º 1
0
            public void Initialize(bool skipStringDistributions = false)
            {
                // DO NOT make this a constructor, because it makes the test not notice complete lack of serialization as an empty object is set up exactly as the thing
                // you are trying to deserialize.
                this.pareto  = new Pareto(1.2, 3.5);
                this.poisson = new Poisson(2.3);
                this.wishart = new Wishart(20, new PositiveDefiniteMatrix(new double[, ] {
                    { 22, 21 }, { 21, 23 }
                }));
                this.vectorGaussian = new VectorGaussian(Vector.FromArray(13, 14), new PositiveDefiniteMatrix(new double[, ] {
                    { 16, 15 }, { 15, 17 }
                }));
                this.unnormalizedDiscrete = UnnormalizedDiscrete.FromLogProbs(DenseVector.FromArray(5.1, 5.2, 5.3));
                this.pointMass            = PointMass <double> .Create(1.1);

                this.gaussian             = new Gaussian(11.0, 12.0);
                this.nonconjugateGaussian = new NonconjugateGaussian(1.2, 2.3, 3.4, 4.5);
                this.gamma              = new Gamma(9.0, 10.0);
                this.gammaPower         = new GammaPower(5.6, 2.8, 3.4);
                this.discrete           = new Discrete(6.0, 7.0, 8.0);
                this.conjugateDirichlet = new ConjugateDirichlet(1.2, 2.3, 3.4, 4.5);
                this.dirichlet          = new Dirichlet(3.0, 4.0, 5.0);
                this.beta      = new Beta(2.0, 1.0);
                this.binomial  = new Binomial(5, 0.8);
                this.bernoulli = new Bernoulli(0.6);

                this.sparseBernoulliList    = SparseBernoulliList.Constant(4, new Bernoulli(0.1));
                this.sparseBernoulliList[1] = new Bernoulli(0.9);
                this.sparseBernoulliList[3] = new Bernoulli(0.7);

                this.sparseBetaList    = SparseBetaList.Constant(5, new Beta(2.0, 2.0));
                this.sparseBetaList[0] = new Beta(3.0, 4.0);
                this.sparseBetaList[1] = new Beta(5.0, 6.0);

                this.sparseGaussianList    = SparseGaussianList.Constant(6, Gaussian.FromMeanAndPrecision(0.1, 0.2));
                this.sparseGaussianList[4] = Gaussian.FromMeanAndPrecision(0.3, 0.4);
                this.sparseGaussianList[5] = Gaussian.FromMeanAndPrecision(0.5, 0.6);

                this.sparseGammaList = SparseGammaList.Constant(1, Gamma.FromShapeAndRate(1.0, 2.0));

                this.truncatedGamma    = new TruncatedGamma(1.2, 2.3, 3.4, 4.5);
                this.truncatedGaussian = new TruncatedGaussian(1.2, 3.4, 5.6, 7.8);
                this.wrappedGaussian   = new WrappedGaussian(1.2, 2.3, 3.4);

                ga = Distribution <double> .Array(new[] { this.gaussian, this.gaussian });

                vga = Distribution <Vector> .Array(new[] { this.vectorGaussian, this.vectorGaussian });

                ga2D = Distribution <double> .Array(new[, ] {
                    { this.gaussian, this.gaussian }, { this.gaussian, this.gaussian }
                });

                vga2D = Distribution <Vector> .Array(new[, ] {
                    { this.vectorGaussian, this.vectorGaussian }, { this.vectorGaussian, this.vectorGaussian }
                });

                gaJ = Distribution <double> .Array(new[] { new[] { this.gaussian, this.gaussian }, new[] { this.gaussian, this.gaussian } });

                vgaJ = Distribution <Vector> .Array(new[] { new[] { this.vectorGaussian, this.vectorGaussian }, new[] { this.vectorGaussian, this.vectorGaussian } });

                var gp    = new GaussianProcess(new ConstantFunction(0), new SquaredExponential(0));
                var basis = Util.ArrayInit(2, i => Vector.FromArray(1.0 * i));

                this.sparseGp = new SparseGP(new SparseGPFixed(gp, basis));

                this.quantileEstimator = new QuantileEstimator(0.01);
                this.quantileEstimator.Add(5);
                this.outerQuantiles = OuterQuantiles.FromDistribution(3, this.quantileEstimator);
                this.innerQuantiles = InnerQuantiles.FromDistribution(3, this.outerQuantiles);

                if (!skipStringDistributions)
                {
                    // String distributions can not be serialized by some formatters (namely BinaryFormatter)
                    // That is fine because this combination is never used in practice
                    this.stringDistribution1 = StringDistribution.String("aa")
                                               .Append(StringDistribution.OneOf("b", "ccc")).Append("dddd");
                    this.stringDistribution2 = new StringDistribution();
                    this.stringDistribution2.SetToProduct(StringDistribution.OneOf("a", "b"),
                                                          StringDistribution.OneOf("b", "c"));
                }
            }
Ejemplo n.º 2
0
            public void Initialize()
            {
                // DO NOT make this a constructor, because it makes the test not notice complete lack of serialization as an empty object is set up exactly as the thing
                // you are trying to deserialize.
                this.pareto  = new Pareto(1.2, 3.5);
                this.poisson = new Poisson(2.3);
                this.wishart = new Wishart(20, new PositiveDefiniteMatrix(new double[, ] {
                    { 22, 21 }, { 21, 23 }
                }));
                this.vectorGaussian = new VectorGaussian(Vector.FromArray(13, 14), new PositiveDefiniteMatrix(new double[, ] {
                    { 16, 15 }, { 15, 17 }
                }));
                this.unnormalizedDiscrete = UnnormalizedDiscrete.FromLogProbs(DenseVector.FromArray(5.1, 5.2, 5.3));
                this.pointMass            = PointMass <double> .Create(1.1);

                this.gaussian             = new Gaussian(11.0, 12.0);
                this.nonconjugateGaussian = new NonconjugateGaussian(1.2, 2.3, 3.4, 4.5);
                this.gamma              = new Gamma(9.0, 10.0);
                this.gammaPower         = new GammaPower(5.6, 2.8, 3.4);
                this.discrete           = new Discrete(6.0, 7.0, 8.0);
                this.conjugateDirichlet = new ConjugateDirichlet(1.2, 2.3, 3.4, 4.5);
                this.dirichlet          = new Dirichlet(3.0, 4.0, 5.0);
                this.beta      = new Beta(2.0, 1.0);
                this.binomial  = new Binomial(5, 0.8);
                this.bernoulli = new Bernoulli(0.6);

                this.sparseBernoulliList    = SparseBernoulliList.Constant(4, new Bernoulli(0.1));
                this.sparseBernoulliList[1] = new Bernoulli(0.9);
                this.sparseBernoulliList[3] = new Bernoulli(0.7);

                this.sparseBetaList    = SparseBetaList.Constant(5, new Beta(2.0, 2.0));
                this.sparseBetaList[0] = new Beta(3.0, 4.0);
                this.sparseBetaList[1] = new Beta(5.0, 6.0);

                this.sparseGaussianList    = SparseGaussianList.Constant(6, Gaussian.FromMeanAndPrecision(0.1, 0.2));
                this.sparseGaussianList[4] = Gaussian.FromMeanAndPrecision(0.3, 0.4);
                this.sparseGaussianList[5] = Gaussian.FromMeanAndPrecision(0.5, 0.6);

                this.sparseGammaList = SparseGammaList.Constant(1, Gamma.FromShapeAndRate(1.0, 2.0));

                this.truncatedGamma    = new TruncatedGamma(1.2, 2.3, 3.4, 4.5);
                this.truncatedGaussian = new TruncatedGaussian(1.2, 3.4, 5.6, 7.8);
                this.wrappedGaussian   = new WrappedGaussian(1.2, 2.3, 3.4);

                ga = Distribution <double> .Array(new[] { this.gaussian, this.gaussian });

                vga = Distribution <Vector> .Array(new[] { this.vectorGaussian, this.vectorGaussian });

                ga2D = Distribution <double> .Array(new[, ] {
                    { this.gaussian, this.gaussian }, { this.gaussian, this.gaussian }
                });

                vga2D = Distribution <Vector> .Array(new[, ] {
                    { this.vectorGaussian, this.vectorGaussian }, { this.vectorGaussian, this.vectorGaussian }
                });

                gaJ = Distribution <double> .Array(new[] { new[] { this.gaussian, this.gaussian }, new[] { this.gaussian, this.gaussian } });

                vgaJ = Distribution <Vector> .Array(new[] { new[] { this.vectorGaussian, this.vectorGaussian }, new[] { this.vectorGaussian, this.vectorGaussian } });

                var gp    = new GaussianProcess(new ConstantFunction(0), new SquaredExponential(0));
                var basis = Util.ArrayInit(2, i => Vector.FromArray(1.0 * i));

                this.sparseGp = new SparseGP(new SparseGPFixed(gp, basis));

                this.quantileEstimator = new QuantileEstimator(0.01);
                this.quantileEstimator.Add(5);
            }