public void GradientTest3() { var hmm = NormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1(); var function = new NormalMarkovClassifierFunction(hmm); var model = new HiddenConditionalRandomField <double>(function); var target = new QuasiNewtonHiddenLearning <double>(model); target.Regularization = 2; FiniteDifferences diff = new FiniteDifferences(function.Weights.Length); diff.Function = parameters => func(model, parameters, target.Regularization); 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-2); Assert.IsFalse(double.IsNaN(actual[i])); Assert.IsFalse(double.IsNaN(expected[i])); } }
public void ComputeTest2() { var hmm = NormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1(); IPotentialFunction <double> owner = new NormalMarkovClassifierFunction(hmm); double[] x = new double[] { 0, 1, 2, 1, 7, 2, 1, -2, 5, 3, 4 }; 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)); } } } }
public void RunTest() { var hmm = NormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1(); var function = new NormalMarkovClassifierFunction(hmm); var model = new HiddenConditionalRandomField <double>(function); var target = new QuasiNewtonHiddenLearning <double>(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 llm = hmm.LogLikelihood(inputs, outputs); double ll0 = model.LogLikelihood(inputs, outputs); Assert.AreEqual(llm, ll0, 1e-10); Assert.IsFalse(Double.IsNaN(llm)); Assert.IsFalse(Double.IsNaN(ll0)); double error = target.RunEpoch(inputs, outputs); double ll1 = model.LogLikelihood(inputs, outputs); Assert.AreEqual(-ll1, error, 1e-10); Assert.IsFalse(Double.IsNaN(ll1)); Assert.IsFalse(Double.IsNaN(error)); for (int i = 0; i < inputs.Length; i++) { actual[i] = model.Compute(inputs[i]); expected[i] = outputs[i]; } Assert.AreEqual(-0.0000041736023099758768, ll0, 1e-10); for (int i = 0; i < inputs.Length; i++) { Assert.AreEqual(expected[i], actual[i]); } Assert.IsTrue(ll1 > ll0); }
public void HiddenMarkovHiddenPotentialFunctionConstructorTest() { HiddenMarkovClassifier <NormalDistribution> model = CreateModel1(); var target = new NormalMarkovClassifierFunction(model); var features = target.Features; double[] weights = target.Weights; Assert.AreEqual(26, features.Length); Assert.AreEqual(26, weights.Length); int k = 0; for (int c = 0; c < model.Classes; c++) { Assert.AreEqual(Math.Log(model.Priors[c]), weights[k++]); for (int i = 0; i < model[c].States; i++) { Assert.AreEqual(model[c].Probabilities[i], weights[k++]); } for (int i = 0; i < model[c].States; i++) { for (int j = 0; j < model[c].States; j++) { Assert.AreEqual(model[c].Transitions[i, j], weights[k++]); } } for (int i = 0; i < model[c].States; i++) { for (int j = 0; j < model[c].Dimension; j++) { double mean = model[c].Emissions[i].Mean; double var = model[c].Emissions[i].Variance; double l2ps = System.Math.Log(2 * System.Math.PI * var); Assert.AreEqual(-0.5 * (l2ps + (mean * mean) / var), weights[k++]); Assert.AreEqual(mean / var, weights[k++]); Assert.AreEqual(-1.0 / (2 * var), weights[k++]); } } } }
public void ComputeTest() { var model = CreateModel1(); var target = new NormalMarkovClassifierFunction(model); double actual; double expected; double[] x = { 0, 1 }; for (int c = 0; c < model.Classes; c++) { for (int i = 0; i < model[c].States; i++) { // Check initial state transitions expected = model.Priors[c] * Math.Exp(model[c].Probabilities[i]) * model[c].Emissions[i].ProbabilityDensityFunction(x[0]); actual = Math.Exp(target.Factors[c].Compute(-1, i, x, 0, c)); Assert.AreEqual(expected, actual, 1e-6); Assert.IsFalse(double.IsNaN(actual)); } for (int t = 1; t < x.Length; t++) { // Check normal state transitions for (int i = 0; i < model[c].States; i++) { for (int j = 0; j < model[c].States; j++) { expected = model.Priors[c] * Math.Exp(model[c].Transitions[i, j]) * model[c].Emissions[j].ProbabilityDensityFunction(x[t]); actual = Math.Exp(target.Factors[c].Compute(i, j, x, t, c)); Assert.AreEqual(expected, actual, 1e-6); Assert.IsFalse(double.IsNaN(actual)); } } } } }
public void RunTest() { var hmm = NormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1(); var function = new NormalMarkovClassifierFunction(hmm); var model = new HiddenConditionalRandomField<double>(function); var target = new QuasiNewtonHiddenLearning<double>(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 llm = hmm.LogLikelihood(inputs, outputs); double ll0 = model.LogLikelihood(inputs, outputs); Assert.AreEqual(llm, ll0, 1e-10); Assert.IsFalse(Double.IsNaN(llm)); Assert.IsFalse(Double.IsNaN(ll0)); double error = target.RunEpoch(inputs, outputs); double ll1 = model.LogLikelihood(inputs, outputs); Assert.AreEqual(-ll1, error, 1e-10); Assert.IsFalse(Double.IsNaN(ll1)); Assert.IsFalse(Double.IsNaN(error)); for (int i = 0; i < inputs.Length; i++) { actual[i] = model.Compute(inputs[i]); expected[i] = outputs[i]; } Assert.AreEqual(-0.0000041736023099758768, ll0, 1e-10); for (int i = 0; i < inputs.Length; i++) Assert.AreEqual(expected[i], actual[i]); Assert.IsTrue(ll1 > ll0); }
public void GradientTest3() { var hmm = NormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1(); var function = new NormalMarkovClassifierFunction(hmm); var model = new HiddenConditionalRandomField<double>(function); var target = new QuasiNewtonHiddenLearning<double>(model); target.Regularization = 2; FiniteDifferences diff = new FiniteDifferences(function.Weights.Length); diff.Function = parameters => func(model, parameters, target.Regularization); 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-2); Assert.IsFalse(double.IsNaN(actual[i])); Assert.IsFalse(double.IsNaN(expected[i])); } }
public void ComputeTest2() { var hmm = NormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1(); IPotentialFunction<double> owner = new NormalMarkovClassifierFunction(hmm); double[] x = new double[] { 0, 1, 2, 1, 7, 2, 1, -2, 5, 3, 4 }; 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)); } } } }
public void HiddenMarkovHiddenPotentialFunctionConstructorTest() { HiddenMarkovClassifier<NormalDistribution> model = CreateModel1(); var target = new NormalMarkovClassifierFunction(model); var features = target.Features; double[] weights = target.Weights; Assert.AreEqual(26, features.Length); Assert.AreEqual(26, weights.Length); int k = 0; for (int c = 0; c < model.Classes; c++) { Assert.AreEqual(Math.Log(model.Priors[c]), weights[k++]); for (int i = 0; i < model[c].States; i++) Assert.AreEqual(model[c].Probabilities[i], weights[k++]); for (int i = 0; i < model[c].States; i++) for (int j = 0; j < model[c].States; j++) Assert.AreEqual(model[c].Transitions[i, j], weights[k++]); for (int i = 0; i < model[c].States; i++) { for (int j = 0; j < model[c].Dimension; j++) { double mean = model[c].Emissions[i].Mean; double var = model[c].Emissions[i].Variance; double l2ps = System.Math.Log(2 * System.Math.PI * var); Assert.AreEqual(-0.5 * (l2ps + (mean * mean) / var), weights[k++]); Assert.AreEqual(mean / var, weights[k++]); Assert.AreEqual(-1.0 / (2 * var), weights[k++]); } } } }