C# MarkovDiscreteFunction类代码示例

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本文整理汇总了C#中MarkovDiscreteFunction类的典型用法代码示例。如果您正苦于以下问题：C# MarkovDiscreteFunction类的具体用法？C# MarkovDiscreteFunction怎么用？C# MarkovDiscreteFunction使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。

MarkovDiscreteFunction类属于命名空间，在下文中一共展示了MarkovDiscreteFunction类的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的C#代码示例。

示例1: ComputeTest

        public void ComputeTest()
        {

            HiddenMarkovModel hmm = DiscreteHiddenMarkovModelFunctionTest.CreateModel2();

            int states = hmm.States;


            var function = new MarkovDiscreteFunction(hmm);
            var target = new ConditionalRandomField<int>(states, function);
            double p1, p2;

            int[] observations, expected, actual;

            observations = new int[] { 0, 0, 1, 1, 1, 2 };
            expected = hmm.Decode(observations, out p1);
            actual = target.Compute(observations, out p2);

            Assert.IsTrue(expected.IsEqual(actual));
            Assert.AreEqual(p1, p2, 1e-6);


            observations = new int[] { 0, 1, 2, 2, 2 };
            expected = hmm.Decode(observations, out p1);
            actual = target.Compute(observations, out p2);

            Assert.IsTrue(expected.IsEqual(actual));
            Assert.AreEqual(p1, p2, 1e-6);
        }

开发者ID:RLaumeyer，项目名称:framework，代码行数:29，代码来源:ConditionalRandomFieldTest.cs

示例2: BackwardTest

        public void BackwardTest()
        {
            HiddenMarkovModel hmm = Accord.Tests.Statistics.Models.Markov.
                ForwardBackwardAlgorithmTest.CreateModel2();

            var function = new MarkovDiscreteFunction(hmm);


            //                     A  B  B  A
            int[] observations = { 0, 1, 1, 0 };

            double logLikelihood;
            double[,] actual = Accord.Statistics.Models.Fields.
                ForwardBackwardAlgorithm.Backward(function.Factors[0], observations, 0, out logLikelihood);

            var A = Matrix.Exp(hmm.Transitions);
            var B = Matrix.Exp(hmm.Emissions);
            var P = Matrix.Exp(hmm.Probabilities);

            double a30 = 1;
            double a31 = 1;

            double a20 = A[0, 0] * B[0, 0] * a30 + A[0, 1] * B[1, 0] * a31;
            double a21 = A[1, 0] * B[0, 0] * a30 + A[1, 1] * B[1, 0] * a31;

            double a10 = A[0, 0] * B[0, 1] * a20 + A[0, 1] * B[1, 1] * a21;
            double a11 = A[1, 0] * B[0, 1] * a20 + A[1, 1] * B[1, 1] * a21;

            double a00 = A[0, 0] * B[0, 1] * a10 + A[0, 1] * B[1, 1] * a11;
            double a01 = A[1, 0] * B[0, 1] * a10 + A[1, 1] * B[1, 1] * a11;


            Assert.AreEqual(actual[0, 0], a00, 1e-10);
            Assert.AreEqual(actual[0, 1], a01, 1e-10);

            Assert.AreEqual(actual[1, 0], a10, 1e-10);
            Assert.AreEqual(actual[1, 1], a11, 1e-10);

            Assert.AreEqual(actual[2, 0], a20, 1e-10);
            Assert.AreEqual(actual[2, 1], a21, 1e-10);

            Assert.AreEqual(actual[3, 0], a30, 1e-10);
            Assert.AreEqual(actual[3, 1], a31, 1e-10);

            foreach (double e in actual)
                Assert.IsFalse(double.IsNaN(e));

            double p = 0;
            for (int i = 0; i < hmm.States; i++)
                p += actual[0, i] * P[i] * B[i, observations[0]];

            Assert.AreEqual(0.054814695, p, 1e-8);
            Assert.IsFalse(double.IsNaN(p));

            p = System.Math.Exp(logLikelihood);
            Assert.AreEqual(0.054814695, p, 1e-8);
            Assert.IsFalse(double.IsNaN(p));
        }

开发者ID:accord-net，项目名称:framework，代码行数:58，代码来源:ForwardBackwardAlgorithmTest.cs

示例3: HiddenConditionalRandomFieldConstructorTest

        public void HiddenConditionalRandomFieldConstructorTest()
        {
            HiddenMarkovClassifier hmm = HiddenMarkovClassifierPotentialFunctionTest.CreateModel1();

            var function = new MarkovDiscreteFunction(hmm);
            var target = new HiddenConditionalRandomField<int>(function);

            Assert.AreEqual(function, target.Function);
            Assert.AreEqual(2, target.Function.Factors[0].States);
        }

开发者ID:huanzl0503，项目名称:framework，代码行数:10，代码来源:HiddenConditionalRandomFieldTest.cs

示例4: ConditionalRandomFieldConstructorTest

        public void ConditionalRandomFieldConstructorTest()
        {
            HiddenMarkovModel hmm = DiscreteHiddenMarkovModelFunctionTest.CreateModel1();

            int states = 2;
            var function = new MarkovDiscreteFunction(hmm);
            var target = new ConditionalRandomField<int>(states, function);


            Assert.AreEqual(function, target.Function);
            Assert.AreEqual(2, target.States);
        }

开发者ID:RLaumeyer，项目名称:framework，代码行数:12，代码来源:ConditionalRandomFieldTest.cs

示例5: ComputeTest

        public void ComputeTest()
        {
            HiddenMarkovClassifier hmm = DiscreteHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();

            // Declare some testing data
            int[][] inputs = new int[][]
            {
                new int[] { 0,1,1,0 },   // Class 0
                new int[] { 0,0,1,0 },   // Class 0
                new int[] { 0,1,1,1,0 }, // Class 0
                new int[] { 0,1,0 },     // Class 0

                new int[] { 1,0,0,1 },   // Class 1
                new int[] { 1,1,0,1 },   // Class 1
                new int[] { 1,0,0,0,1 }, // Class 1
                new int[] { 1,0,1 },     // Class 1
            };

            int[] outputs = new int[]
            {
                0,0,0,0, // First four sequences are of class 0
                1,1,1,1, // Last four sequences are of class 1
            };


            var function = new MarkovDiscreteFunction(hmm);
            var target = new HiddenConditionalRandomField<int>(function);


            for (int i = 0; i < inputs.Length; i++)
            {
                int expected = hmm.Compute(inputs[i]);

                int actual = target.Compute(inputs[i]);

                double h0 = hmm.LogLikelihood(inputs[i], 0);
                double h1 = hmm.LogLikelihood(inputs[i], 1);

                double c0 = target.LogLikelihood(inputs[i], 0);
                double c1 = target.LogLikelihood(inputs[i], 1);

                Assert.AreEqual(expected, actual);
                Assert.AreEqual(h0, c0, 1e-10);
                Assert.AreEqual(h1, c1, 1e-10);

                Assert.IsFalse(double.IsNaN(c0));
                Assert.IsFalse(double.IsNaN(c1));
            }
        }

开发者ID:accord-net，项目名称:framework，代码行数:49，代码来源:HiddenConditionalRandomFieldTest.cs

示例6: RunTest

        public void RunTest()
        {
            var inputs = QuasiNewtonHiddenLearningTest.inputs;
            var outputs = QuasiNewtonHiddenLearningTest.outputs;

            HiddenMarkovClassifier hmm = HiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
            var function = new MarkovDiscreteFunction(hmm);

            var model = new HiddenConditionalRandomField<int>(function);
            var target = new HiddenGradientDescentLearning<int>(model);
            target.LearningRate = 1000;

            double[] actual = new double[inputs.Length];
            double[] expected = new double[inputs.Length];

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }

            for (int i = 0; i < inputs.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            double ll0 = model.LogLikelihood(inputs, outputs);

            double error = Double.NegativeInfinity;
            for (int i = 0; i < 50; i++)
                error = target.RunEpoch(inputs, outputs);

            double ll1 = model.LogLikelihood(inputs, outputs);

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }

            Assert.AreEqual(-0.00046872579976353634, ll0, 1e-10);
            Assert.AreEqual(0.00027018722449589916, error, 1e-10);
            Assert.IsFalse(Double.IsNaN(ll0));
            Assert.IsFalse(Double.IsNaN(error));

            for (int i = 0; i < inputs.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            Assert.IsTrue(ll1 > ll0);
        }

开发者ID:qusma，项目名称:framework，代码行数:48，代码来源:GradientDescentHiddenLearningTest.cs

示例7: RunTest

        public void RunTest()
        {
            var inputs = QuasiNewtonHiddenLearningTest.inputs;
            var outputs = QuasiNewtonHiddenLearningTest.outputs;

            HiddenMarkovClassifier hmm = DiscreteHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
            var function = new MarkovDiscreteFunction(hmm);

            var model = new HiddenConditionalRandomField<int>(function);
            var target = new HiddenConjugateGradientLearning<int>(model);

            double[] actual = new double[inputs.Length];
            double[] expected = new double[inputs.Length];

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }

            for (int i = 0; i < inputs.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            double ll0 = model.LogLikelihood(inputs, outputs);

            double error = target.Run(inputs, outputs);

            double ll1 = model.LogLikelihood(inputs, outputs);

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }

            Assert.AreEqual(-0.0019419916698781847, ll0, 1e-10);
            Assert.AreEqual(0.00050271005636426391, error, 1e-10);
            Assert.AreEqual(error, -ll1);
            Assert.IsFalse(Double.IsNaN(ll0));
            Assert.IsFalse(Double.IsNaN(error));

            for (int i = 0; i < inputs.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            Assert.IsTrue(ll1 > ll0);
        }

开发者ID:RLaumeyer，项目名称:framework，代码行数:46，代码来源:ConjugateGradientHiddenLearningTest.cs

示例8: ComputeTest

        public void ComputeTest()
        {
            var hmm = DiscreteHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();

            IPotentialFunction<int> owner = new MarkovDiscreteFunction(hmm);

            int[] x = new int[] { 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0 };

            foreach (var factor in owner.Factors)
            {
                for (int y = 0; y < owner.Outputs; y++)
                {
                    double[,] fwd = Accord.Statistics.Models.Fields
                        .ForwardBackwardAlgorithm.Forward(factor, x, y);

                    double[,] bwd = Accord.Statistics.Models.Fields
                        .ForwardBackwardAlgorithm.Backward(factor, x, y);

                    double[,] lnfwd = Accord.Statistics.Models.Fields
                        .ForwardBackwardAlgorithm.LogForward(factor, x, y);

                    double[,] lnbwd = Accord.Statistics.Models.Fields
                        .ForwardBackwardAlgorithm.LogBackward(factor, x, y);

                    for (int i = 0; i < fwd.GetLength(0); i++)
                        for (int j = 0; j < fwd.GetLength(1); j++)
                            Assert.AreEqual(System.Math.Log(fwd[i, j]), lnfwd[i, j], 1e-10);

                    for (int i = 0; i < bwd.GetLength(0); i++)
                        for (int j = 0; j < bwd.GetLength(1); j++)
                            Assert.AreEqual(System.Math.Log(bwd[i, j]), lnbwd[i, j], 1e-10);


                    foreach (var feature in factor)
                    {
                        double expected = System.Math.Log(feature.Marginal(fwd, bwd, x, y));
                        double actual = feature.LogMarginal(lnfwd, lnbwd, x, y);

                        Assert.AreEqual(expected, actual, 1e-10);
                        Assert.IsFalse(Double.IsNaN(actual));
                    }

                }
            }
        }

开发者ID:accord-net，项目名称:framework，代码行数:45，代码来源:FeatureTest.cs

示例9: RunTest

        public void RunTest()
        {
            HiddenMarkovClassifier hmm = HiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
            var function = new MarkovDiscreteFunction(hmm);

            var model = new HiddenConditionalRandomField<int>(function);
            var target = new HiddenQuasiNewtonLearning<int>(model);

            double[] actual = new double[inputs.Length];
            double[] expected = new double[inputs.Length];

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }

            for (int i = 0; i < inputs.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            double ll0 = model.LogLikelihood(inputs, outputs);

            double error = target.Run(inputs, outputs);

            double ll1 = model.LogLikelihood(inputs, outputs);

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }

            Assert.AreEqual(-0.00046872579976353634, ll0, 1e-10);
            Assert.AreEqual(0.0, error, 1e-10);
            Assert.AreEqual(error, -ll1);
            Assert.IsFalse(Double.IsNaN(ll0));
            Assert.IsFalse(Double.IsNaN(error));

            for (int i = 0; i < inputs.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            Assert.IsTrue(ll1 > ll0);
        }

开发者ID:KommuSoft，项目名称:accord_framework，代码行数:43，代码来源:QuasiNewtonHiddenLearningTest.cs

示例10: ComputeTest

        public void ComputeTest()
        {
            HiddenMarkovModel model = CreateModel1();

            MarkovDiscreteFunction target = new MarkovDiscreteFunction(model);

            double actual;
            double expected;

            int[] x = { 0, 1 };

            for (int i = 0; i < model.States; i++)
            {
                // Check initial state transitions
                expected = Math.Exp(model.Probabilities[i]) * Math.Exp(model.Emissions[i, x[0]]);
                actual = Math.Exp(target.Factors[0].Compute(-1, i, x, 0));
                Assert.AreEqual(expected, actual, 1e-6);
            }

            for (int t = 0; t < x.Length; t++)
            {
                for (int i = 0; i < model.States; i++)
                {
                    // Check initial state transitions
                    expected = Math.Exp(model.Probabilities[i]) * Math.Exp(model.Emissions[i, x[0]]);
                    actual = Math.Exp(target.Factors[0].Compute(-1, i, x, 0));
                    Assert.AreEqual(expected, actual, 1e-6);

                    // Check normal state transitions
                    for (int j = 0; j < model.States; j++)
                    {
                        double xb = Math.Exp(model.Transitions[i, j]);
                        double xc = Math.Exp(model.Emissions[j, x[t]]);
                        expected = xb * xc;
                        actual = Math.Exp(target.Factors[0].Compute(i, j, x, t));
                        Assert.AreEqual(expected, actual, 1e-6);
                    }
                }
            }

        }

开发者ID:RLaumeyer，项目名称:framework，代码行数:41，代码来源:DiscreteHiddenMarkovModelFunctionTest.cs

示例11: HiddenMarkovModelFunctionConstructorTest

        public void HiddenMarkovModelFunctionConstructorTest()
        {
            HiddenMarkovModel model = CreateModel1();

            MarkovDiscreteFunction target = new MarkovDiscreteFunction(model);

            var features = target.Features;
            double[] weights = target.Weights;

            Assert.AreEqual(features.Length, 12);
            Assert.AreEqual(weights.Length, 12);

            int k = 0;

            for (int i = 0; i < model.States; i++)
                Assert.AreEqual(model.Probabilities[i], weights[k++]);

            for (int i = 0; i < model.States; i++)
                for (int j = 0; j < model.States; j++)
                    Assert.AreEqual(model.Transitions[i, j], weights[k++]);

            for (int i = 0; i < model.States; i++)
                for (int j = 0; j < model.Symbols; j++)
                    Assert.AreEqual(model.Emissions[i, j], weights[k++]);
        }

开发者ID:RLaumeyer，项目名称:framework，代码行数:25，代码来源:DiscreteHiddenMarkovModelFunctionTest.cs

示例12: LikelihoodTest

        public void LikelihoodTest()
        {
            HiddenMarkovModel hmm = HiddenMarkovModelFunctionTest.CreateModel2();

            int states = hmm.States;
            int symbols = hmm.Symbols;


            var function1 = new MarkovDiscreteFunction(hmm);
            var target1 = new ConditionalRandomField<int>(states, function1);

            var function2 = new MarkovDiscreteFunction(states, symbols);
            var target2 = new ConditionalRandomField<int>(states, function2);


            int[] observations;

            double a, b, la, lb;

            observations = new int[] { 0, 0, 1, 1, 1, 2 };
            a = target1.LogLikelihood(observations, observations);
            b = target2.LogLikelihood(observations, observations);
            Assert.IsTrue(a > b);

            observations = new int[] { 0, 0, 1, 1, 1, 2 };
            la = target1.LogLikelihood(observations, observations);
            lb = target2.LogLikelihood(observations, observations);
            Assert.IsTrue(la > lb);

            double lla = System.Math.Log(a);
            double llb = System.Math.Log(b);

            Assert.AreEqual(lla, la, 1e-6);
            Assert.AreEqual(llb, lb, 1e-6);
        }

开发者ID:BiYiTuan，项目名称:framework，代码行数:35，代码来源:ConditionalRandomFieldTest.cs

示例13: RunTest

        public void RunTest()
        {
            int nstates = 3;
            int symbols = 3;

            int[][] sequences = new int[][] 
            {
                new int[] { 0, 1, 1, 1, 2 },
                new int[] { 0, 1, 1, 1, 2, 2, 2 },
                new int[] { 0, 0, 1, 1, 2, 2 },
                new int[] { 0, 1, 1, 1, 2, 2, 2 },
                new int[] { 0, 1, 1, 1, 2, 2 },
                new int[] { 0, 1, 1, 2, 2 },
                new int[] { 0, 0, 1, 1, 1, 2, 2 },
                new int[] { 0, 0, 0, 1, 1, 1, 2, 2 },
                new int[] { 0, 1, 1, 2, 2, 2 },
            };


            var function = new MarkovDiscreteFunction(nstates, symbols);
            var model = new ConditionalRandomField<int>(nstates, function);


            for (int i = 0; i < sequences.Length; i++)
            {
                double p;
                int[] s = sequences[i];
                int[] r = model.Compute(s, out p);
                Assert.IsFalse(s.IsEqual(r));
            }

            var target = new QuasiNewtonLearning<int>(model); 

            int[][] labels = sequences;
            int[][] observations = sequences;

            double ll0 = model.LogLikelihood(observations, labels);

            double actual = target.Run(observations, labels);

            double ll1 = model.LogLikelihood(observations, labels);

            Assert.IsTrue(ll1 > ll0);


            Assert.AreEqual(0, actual, 1e-8);

            for (int i = 0; i < sequences.Length; i++)
            {
                double p;
                int[] s = sequences[i];
                int[] r = model.Compute(s, out p);
                Assert.IsTrue(s.IsEqual(r));
            }
            
        }

开发者ID:KommuSoft，项目名称:accord_framework，代码行数:56，代码来源:QuasiNewtonLearningTest.cs

示例14: RunTest2

        public void RunTest2()
        {
            var inputs = QuasiNewtonHiddenLearningTest.inputs;
            var outputs = QuasiNewtonHiddenLearningTest.outputs;


            Accord.Math.Tools.SetupGenerator(0);

            var function = new MarkovDiscreteFunction(2, 2, 2);

            var model = new HiddenConditionalRandomField<int>(function);
            var target = new HiddenConjugateGradientLearning<int>(model);

            double[] actual = new double[inputs.Length];
            double[] expected = new double[inputs.Length];

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }


            double ll0 = model.LogLikelihood(inputs, outputs);
            double error = target.Run(inputs, outputs);
            double ll1 = model.LogLikelihood(inputs, outputs);

            for (int i = 0; i < inputs.Length; i++)
            {
                actual[i] = model.Compute(inputs[i]);
                expected[i] = outputs[i];
            }


            Assert.AreEqual(-5.5451774444795623, ll0, 1e-10);
            Assert.AreEqual(0, error, 1e-10);
            Assert.IsFalse(double.IsNaN(error));

            for (int i = 0; i < inputs.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            Assert.IsTrue(ll1 > ll0);
        }

开发者ID:RLaumeyer，项目名称:framework，代码行数:43，代码来源:ConjugateGradientHiddenLearningTest.cs

示例15: GradientTest

        public void GradientTest()
        {
            var function = new MarkovDiscreteFunction(2, 2, 2);
            var model = new HiddenConditionalRandomField<int>(function);
            var target = new ForwardBackwardGradient<int>(model);

            FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);

            diff.Function = parameters => func(model, parameters);

            double[] expected = diff.Compute(function.Weights);
            double[] actual = target.Gradient(function.Weights, inputs, outputs);


            for (int i = 0; i < actual.Length; i++)
            {
                Assert.AreEqual(expected[i], actual[i], 1e-4);
                Assert.IsFalse(double.IsNaN(actual[i]));
                Assert.IsFalse(double.IsNaN(expected[i]));
            }
        }

开发者ID:KommuSoft，项目名称:accord_framework，代码行数:21，代码来源:QuasiNewtonHiddenLearningTest.cs

示例16: LogBackwardTest2

        public void LogBackwardTest2()
        {
            HiddenMarkovModel hmm = Accord.Tests.Statistics.Models.Markov.
                ForwardBackwardAlgorithmTest.CreateModel3();

            MarkovDiscreteFunction function = new MarkovDiscreteFunction(hmm);

            int[] observations = { 0, 0, 1, 1 };

            double[,] expected = Matrix.Log(Accord.Statistics.Models.Fields.
              ForwardBackwardAlgorithm.Backward(function.Factors[0], observations, 0));

            double[,] actual = Accord.Statistics.Models.Fields.
                ForwardBackwardAlgorithm.LogBackward(function.Factors[0], observations, 0);

            Assert.IsTrue(expected.IsEqual(actual, 1e-10));

            foreach (double p in actual)
                Assert.IsFalse(double.IsNaN(p));
        }

开发者ID:accord-net，项目名称:framework，代码行数:20，代码来源:ForwardBackwardAlgorithmTest.cs

示例17: ForwardScalingTest

        public void ForwardScalingTest()
        {
            HiddenMarkovModel hmm = Accord.Tests.Statistics.Models.Markov.
                ForwardBackwardAlgorithmTest.CreateModel1();

            MarkovDiscreteFunction function = new MarkovDiscreteFunction(hmm);


            //                     G  G  C  A
            int[] observations = { 2, 2, 1, 0 };

            double[] scaling;
            double logLikelihood;

            double[,] actual = Accord.Statistics.Models.Fields.
                ForwardBackwardAlgorithm.Forward(function.Factors[0], observations, 0, out scaling, out logLikelihood);

            double[] P = Matrix.Exp(hmm.Probabilities);
            double[,] B = Matrix.Exp(hmm.Emissions);
            double[,] A = Matrix.Exp(hmm.Transitions);

            double a00 = P[0] * B[0, 2];
            double a01 = P[1] * B[1, 2];
            double t0 = a00 + a01;

            a00 /= t0;
            a01 /= t0;

            double a10 = (a00 * A[0, 0] + a01 * A[1, 0]) * B[0, 2];
            double a11 = (a01 * A[1, 1] + a00 * A[0, 1]) * B[1, 2];
            double t1 = a10 + a11;

            a10 /= t1;
            a11 /= t1;

            double a20 = (a10 * A[0, 0] + a11 * A[1, 0]) * B[0, 1];
            double a21 = (a11 * A[1, 1] + a10 * A[0, 1]) * B[1, 1];
            double t2 = a20 + a21;

            a20 /= t2;
            a21 /= t2;

            double a30 = (a20 * A[0, 0] + a21 * A[1, 0]) * B[0, 0];
            double a31 = (a21 * A[1, 1] + a20 * A[0, 1]) * B[1, 0];
            double t3 = a30 + a31;

            a30 /= t3;
            a31 /= t3;

            Assert.AreEqual(a00, actual[0, 0], 1e-10);
            Assert.AreEqual(a01, actual[0, 1], 1e-10);

            Assert.AreEqual(a10, actual[1, 0], 1e-10);
            Assert.AreEqual(a11, actual[1, 1], 1e-10);

            Assert.AreEqual(a20, actual[2, 0], 1e-10);
            Assert.AreEqual(a21, actual[2, 1], 1e-10);

            Assert.AreEqual(a30, actual[3, 0], 1e-10);
            Assert.AreEqual(a31, actual[3, 1], 1e-10);

            foreach (double e in actual)
                Assert.IsFalse(double.IsNaN(e));


            double p = System.Math.Exp(logLikelihood);
            Assert.AreEqual(0.00384315, p, 1e-8);
            Assert.IsFalse(double.IsNaN(p));
        }

开发者ID:accord-net，项目名称:framework，代码行数:69，代码来源:ForwardBackwardAlgorithmTest.cs

示例18: ForwardScalingTest2

        public void ForwardScalingTest2()
        {
            HiddenMarkovModel hmm = Accord.Tests.Statistics.Models.Markov.
                ForwardBackwardAlgorithmTest.CreateModel2();

            MarkovDiscreteFunction function = new MarkovDiscreteFunction(hmm);

            //                     A  B  B  A
            int[] observations = { 0, 1, 1, 0 };

            double[] scaling;
            double logLikelihood;

            double[,] actual = Accord.Statistics.Models.Fields.
                ForwardBackwardAlgorithm.Forward(function.Factors[0], observations, 0, out scaling, out logLikelihood);

            double p = System.Math.Exp(logLikelihood);
            Assert.AreEqual(0.054814695, p, 1e-7);
            Assert.IsFalse(double.IsNaN(p));
        }

开发者ID:accord-net，项目名称:framework，代码行数:20，代码来源:ForwardBackwardAlgorithmTest.cs

示例19: ForwardTest3

        public void ForwardTest3()
        {
            HiddenMarkovModel hmm = Accord.Tests.Statistics.Models.Markov.
                ForwardBackwardAlgorithmTest.CreateModel3();

            MarkovDiscreteFunction function = new MarkovDiscreteFunction(hmm);

            //                     L  L  R  R
            int[] observations = { 0, 0, 1, 1 };

            double[,] actual = Accord.Statistics.Models.Fields.
                ForwardBackwardAlgorithm.Forward(function.Factors[0], observations);

            // Forward matrices from R's HMM package are
            // transposed in relation to the framework's:

            Assert.AreEqual(4, actual.GetLength(0));
            Assert.AreEqual(2, actual.GetLength(1));

            Assert.AreEqual(0.675, actual[0, 0], 1e-10);
            Assert.AreEqual(0.012, actual[0, 1], 1e-10);

            Assert.AreEqual(0.4077, actual[1, 0], 1e-10);
            Assert.AreEqual(0.017208, actual[1, 1], 1e-10);

            Assert.AreEqual(0.0828306, actual[2, 0], 1e-10);
            Assert.AreEqual(0.082355328, actual[2, 1], 1e-10);

            Assert.AreEqual(0.0227427696, actual[3, 0], 1e-10);
            Assert.AreEqual(0.065309067648, actual[3, 1], 1e-10);

            foreach (double p in actual)
                Assert.IsFalse(double.IsNaN(p));
        }

开发者ID:accord-net，项目名称:framework，代码行数:34，代码来源:ForwardBackwardAlgorithmTest.cs

示例20: ForwardTest2

        public void ForwardTest2()
        {
            HiddenMarkovModel hmm = Accord.Tests.Statistics.Models.Markov.
                ForwardBackwardAlgorithmTest.CreateModel2();

            MarkovDiscreteFunction function = new MarkovDiscreteFunction(hmm);


            //                     A  B  B  A
            int[] observations = { 0, 1, 1, 0 };

            double[,] actual = Accord.Statistics.Models.Fields.
                ForwardBackwardAlgorithm.Forward(function.Factors[0], observations);

            var A = Matrix.Exp(hmm.Transitions);
            var B = Matrix.Exp(hmm.Emissions);
            var P = Matrix.Exp(hmm.Probabilities);

            double a00 = P[0] * B[0, 0];
            double a01 = P[1] * B[1, 0];

            double a10 = (a00 * A[0, 0] + a01 * A[1, 0]) * B[0, 1];
            double a11 = (a01 * A[1, 1] + a00 * A[0, 1]) * B[1, 1];

            double a20 = (a10 * A[0, 0] + a11 * A[1, 0]) * B[0, 1];
            double a21 = (a11 * A[1, 1] + a10 * A[0, 1]) * B[1, 1];

            double a30 = (a20 * A[0, 0] + a21 * A[1, 0]) * B[0, 0];
            double a31 = (a21 * A[1, 1] + a20 * A[0, 1]) * B[1, 0];


            Assert.AreEqual(a00, actual[0, 0], 1e-10);
            Assert.AreEqual(a01, actual[0, 1], 1e-10);

            Assert.AreEqual(a10, actual[1, 0], 1e-10);
            Assert.AreEqual(a11, actual[1, 1], 1e-10);

            Assert.AreEqual(a20, actual[2, 0], 1e-10);
            Assert.AreEqual(a21, actual[2, 1], 1e-10);

            Assert.AreEqual(a30, actual[3, 0], 1e-10);
            Assert.AreEqual(a31, actual[3, 1], 1e-10);

            foreach (double e in actual)
                Assert.IsFalse(double.IsNaN(e));

            double p = 0;
            for (int i = 0; i < hmm.States; i++)
                p += actual[observations.Length - 1, i];

            Assert.AreEqual(0.054814695, p, 1e-8);
            Assert.IsFalse(double.IsNaN(p));
        }

开发者ID:accord-net，项目名称:framework，代码行数:53，代码来源:ForwardBackwardAlgorithmTest.cs

注：本文中的MarkovDiscreteFunction类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。

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C# MarkovDiscreteFunction类代码示例

示例1: ComputeTest

示例2: BackwardTest

示例3: HiddenConditionalRandomFieldConstructorTest

示例4: ConditionalRandomFieldConstructorTest

示例5: ComputeTest

示例6: RunTest

示例7: RunTest

示例8: ComputeTest

示例9: RunTest

示例10: ComputeTest

示例11: HiddenMarkovModelFunctionConstructorTest

示例12: LikelihoodTest

示例13: RunTest

示例14: RunTest2

示例15: GradientTest

示例16: LogBackwardTest2

示例17: ForwardScalingTest

示例18: ForwardScalingTest2

示例19: ForwardTest3

示例20: ForwardTest2

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