本文整理汇总了C#中MarkovMultivariateFunction类的典型用法代码示例。如果您正苦于以下问题：C# MarkovMultivariateFunction类的具体用法？C# MarkovMultivariateFunction怎么用？C# MarkovMultivariateFunction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。
MarkovMultivariateFunction类属于命名空间，在下文中一共展示了MarkovMultivariateFunction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: RunTest
public void RunTest()
{
var hmm = MultivariateNormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new HiddenQuasiNewtonLearning<double[]>(model);
var inputs = inputs1;
var outputs = outputs1;
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.Run(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(-0.0000041736023117522336, ll0, 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:53，代码来源:MultivariateNormalQuasiNewtonHiddenLearningTest.cs
示例2: ForwardTest
public void ForwardTest()
{
double[][][] observations;
int[] labels;
var hmm = IndependentMarkovFunctionTest.CreateModel2(out observations, out labels);
var function = new MarkovMultivariateFunction(hmm, includePriors: false);
foreach (double[][] x in observations)
{
foreach (int y in labels)
{
double[] scaling1;
double logLikelihood1;
double[,] actual = Accord.Statistics.Models.Fields.
ForwardBackwardAlgorithm.Forward(function.Factors[y], x, y, out scaling1, out logLikelihood1);
double[] scaling2;
double logLikelihood2;
double[,] expected = Accord.Statistics.Models.Markov.
ForwardBackwardAlgorithm.Forward(hmm.Models[y], x, out scaling2, out logLikelihood2);
for (int i = 0; i < actual.GetLength(0); i++)
for (int j = 0; j < actual.GetLength(1); j++)
{
Assert.AreEqual(expected[i, j], actual[i, j], 1e-10);
Assert.IsFalse(Double.IsNaN(actual[i, j]));
}
Assert.AreEqual(logLikelihood1, logLikelihood2, 1e-10);
for (int i = 0; i < scaling1.Length; i++)
Assert.AreEqual(scaling1[i], scaling2[i], 1e-10);
}
}
}
开发者ID:accord-net，项目名称:framework，代码行数:39，代码来源:IndependentForwardBackwardAlgorithmTest.cs
示例3: LogForwardGesturesPriorsDeoptimizedTest
public void LogForwardGesturesPriorsDeoptimizedTest()
{
int[] labels;
double[][][] words;
var classifier = IndependentMarkovFunctionTest.CreateModel4(out words, out labels, true);
var deopFun = new MarkovMultivariateFunction(classifier);
deopFun.Deoptimize();
var target1 = new HiddenConditionalRandomField<double[]>(deopFun);
var function = new MarkovMultivariateFunction(classifier);
var target2 = new HiddenConditionalRandomField<double[]>(function);
foreach (var word in words)
{
for (int c = 0; c < 3; c++)
{
for (int y = 0; y < 3; y++)
{
var actual = Accord.Statistics.Models.Fields.ForwardBackwardAlgorithm
.LogForward(target1.Function.Factors[c], word, y);
var expected = Accord.Statistics.Models.Fields.ForwardBackwardAlgorithm
.LogForward(target2.Function.Factors[c], word, y);
for (int i = 0; i < actual.GetLength(0); i++)
{
for (int j = 0; j < actual.GetLength(1); j++)
{
double a = actual[i, j];
double e = expected[i, j];
Assert.IsTrue(e.IsRelativelyEqual(a, 0.1));
}
}
}
}
}
}
开发者ID:accord-net，项目名称:framework，代码行数:39，代码来源:IndependentForwardBackwardAlgorithmTest.cs
示例4: SaveLoadTest
public void SaveLoadTest()
{
double[][] hello =
{
new double[] { 1.0, 0.1, 0.0, 0.0 }, // let's say the word
new double[] { 0.0, 1.0, 0.1, 0.1 }, // hello took 6 frames
new double[] { 0.0, 1.0, 0.1, 0.1 }, // to be recorded.
new double[] { 0.0, 0.0, 1.0, 0.0 },
new double[] { 0.0, 0.0, 1.0, 0.0 },
new double[] { 0.0, 0.0, 0.1, 1.1 },
};
double[][] car =
{
new double[] { 0.0, 0.0, 0.0, 1.0 }, // the car word
new double[] { 0.1, 0.0, 1.0, 0.1 }, // took only 4.
new double[] { 0.0, 0.0, 0.1, 0.0 },
new double[] { 1.0, 0.0, 0.0, 0.0 },
};
double[][] wardrobe =
{
new double[] { 0.0, 0.0, 1.0, 0.0 }, // same for the
new double[] { 0.1, 0.0, 1.0, 0.1 }, // wardrobe word.
new double[] { 0.0, 0.1, 1.0, 0.0 },
new double[] { 0.1, 0.0, 1.0, 0.1 },
};
double[][][] words = { hello, car, wardrobe };
int[] labels = { 0, 1, 2 };
var initial = new Independent
(
new NormalDistribution(0, 1),
new NormalDistribution(0, 1),
new NormalDistribution(0, 1),
new NormalDistribution(0, 1)
);
int numberOfWords = 3;
int numberOfStates = 5;
var classifier = new HiddenMarkovClassifier<Independent>
(
classes: numberOfWords,
topology: new Forward(numberOfStates),
initial: initial
);
var teacher = new HiddenMarkovClassifierLearning<Independent>(classifier,
modelIndex => new BaumWelchLearning<Independent>(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 100,
FittingOptions = new IndependentOptions()
{
InnerOption = new NormalOptions() { Regularization = 1e-5 }
}
}
);
double logLikelihood = teacher.Run(words, labels);
var function = new MarkovMultivariateFunction(classifier);
var hcrf = new HiddenConditionalRandomField<double[]>(function);
MemoryStream stream = new MemoryStream();
hcrf.Save(stream);
stream.Seek(0, SeekOrigin.Begin);
var target = HiddenConditionalRandomField<double[]>.Load(stream);
Assert.AreEqual(hcrf.Function.Factors.Length, target.Function.Factors.Length);
for (int i = 0; i < hcrf.Function.Factors.Length; i++)
{
var e = hcrf.Function.Factors[i];
var a = target.Function.Factors[i];
Assert.AreEqual(e.Index, target.Function.Factors[i].Index);
Assert.AreEqual(e.States, target.Function.Factors[i].States);
Assert.AreEqual(e.EdgeParameters.Count, a.EdgeParameters.Count);
Assert.AreEqual(e.EdgeParameters.Offset, a.EdgeParameters.Offset);
Assert.AreEqual(e.FactorParameters.Count, a.FactorParameters.Count);
Assert.AreEqual(e.FactorParameters.Offset, a.FactorParameters.Offset);
Assert.AreEqual(e.OutputParameters.Count, a.OutputParameters.Count);
Assert.AreEqual(e.OutputParameters.Offset, a.OutputParameters.Offset);
Assert.AreEqual(e.StateParameters.Count, a.StateParameters.Count);
Assert.AreEqual(e.StateParameters.Offset, a.StateParameters.Offset);
Assert.AreEqual(target.Function, a.Owner);
Assert.AreEqual(hcrf.Function, e.Owner);
}
Assert.AreEqual(hcrf.Function.Features.Length, target.Function.Features.Length);
for (int i = 0; i < hcrf.Function.Factors.Length; i++)
//.........这里部分代码省略.........
开发者ID:accord-net，项目名称:framework，代码行数:101，代码来源:HiddenConditionalRandomFieldTest.cs
示例5: ComputeTest
public void ComputeTest()
{
var model = CreateModel1();
var target = new MarkovMultivariateFunction(model);
double actual;
double expected;
double[][] x = { new double[] { 0 }, new double[] { 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 = 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));
}
}
}
}
}
开发者ID:qusma，项目名称:framework，代码行数:38，代码来源:MultivariateNormalHiddenMarkovClassifierFunctionTest.cs
示例6: ComputeTest3
public void ComputeTest3()
{
var hmm = MultivariateMarkovFunctionTest.CreateModel1();
var owner = new MarkovMultivariateFunction(hmm);
double[][] x =
{
new double[] { 0 },
new double[] { 1 },
new double[] { 3 },
new double[] { 1 },
new double[] { 2 },
new double[] { 8 },
new double[] { 0 },
new double[] { 10 },
new double[] { System.Math.PI },
};
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:58，代码来源:FeatureTest.cs
示例7: GradientTest3
public void GradientTest3()
{
var hmm = MultivariateNormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
target.Regularization = 2;
var inputs = inputs1;
var outputs = outputs1;
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters, inputs, outputs, 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-3);
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:30，代码来源:MultivariateNormalQuasiNewtonHiddenLearningTest.cs
示例8: LogForwardTest4
public void LogForwardTest4()
{
var hmmc = Accord.Tests.Statistics.Models.Fields.
MultivariateMarkovFunctionTest.CreateModel3();
for (int c = 0; c < hmmc.Classes; c++)
{
var hmm = hmmc[c];
var function = new MarkovMultivariateFunction(hmm);
var sequences = Accord.Tests.Statistics.Models.Fields.
MultivariateMarkovFunctionTest.inputTest;
for (int i = 0; i < sequences.Length; i++)
{
var observations = sequences[i];
double expectedLogLikelihood;
double[,] expected = Accord.Statistics.Models.Markov
.ForwardBackwardAlgorithm.LogForward(hmm, observations, out expectedLogLikelihood);
double actualLogLikelihood;
double[,] actual = Accord.Statistics.Models.Fields
.ForwardBackwardAlgorithm.LogForward(function.Factors[0], observations, 0, out actualLogLikelihood);
Assert.IsTrue(expected.IsEqual(actual, 1e-10));
Assert.AreEqual(expectedLogLikelihood, actualLogLikelihood, 1e-6);
Assert.IsFalse(Double.IsNaN(actualLogLikelihood));
}
}
}
开发者ID:accord-net，项目名称:framework，代码行数:34，代码来源:ForwardBackwardAlgorithmTest.cs
示例9: LogForwardTest3
public void LogForwardTest3()
{
MultivariateNormalDistribution density = new MultivariateNormalDistribution(3);
var hmm = new HiddenMarkovClassifier<MultivariateNormalDistribution>(2, new Ergodic(2), density);
double[][][] inputs =
{
new [] { new double[] { 0, 1, 0 }, new double[] { 0, 1, 0 }, new double[] { 0, 1, 0 } },
new [] { new double[] { 1, 6, 2 }, new double[] { 2, 1, 6 }, new double[] { 1, 1, 0 } },
new [] { new double[] { 9, 1, 0 }, new double[] { 0, 1, 5 }, new double[] { 0, 0, 0 } },
};
int[] outputs =
{
0, 0, 1
};
var function = new MarkovMultivariateFunction(hmm);
var observations = inputs[0];
double[,] expected = Matrix.Log(Accord.Statistics.Models.Fields.
ForwardBackwardAlgorithm.Forward(function.Factors[0], observations, 0));
double logLikelihood;
double[,] actual = Accord.Statistics.Models.Fields.
ForwardBackwardAlgorithm.LogForward(function.Factors[0], observations, 0, out logLikelihood);
Assert.IsTrue(expected.IsEqual(actual, 1e-10));
double p = 0;
for (int i = 0; i < hmm[0].States; i++)
p += Math.Exp(actual[observations.Length - 1, i]);
Assert.AreEqual(Math.Exp(logLikelihood), p, 1e-8);
Assert.IsFalse(double.IsNaN(p));
}
开发者ID:accord-net，项目名称:framework，代码行数:37，代码来源:ForwardBackwardAlgorithmTest.cs
示例10: ComputeDeoptimizeTest4
public void ComputeDeoptimizeTest4()
{
int[] labels;
double[][][] words;
var model = CreateModel4(out words, out labels, false);
var target = new MarkovMultivariateFunction(model);
#pragma warning disable 0618
target.Deoptimize();
#pragma warning restore 0618
var hcrf = new HiddenConditionalRandomField<double[]>(target);
Assert.AreEqual(3, model.Priors.Length);
Assert.AreEqual(1 / 3.0, model.Priors[0]);
Assert.AreEqual(1 / 3.0, model.Priors[1]);
Assert.AreEqual(1 / 3.0, model.Priors[2]);
check4(words, model, target, hcrf);
}
开发者ID:RLaumeyer，项目名称:framework，代码行数:22，代码来源:IndependentMarkovFunctionTest.cs
示例11: ComputeDeoptimizeTest3
public void ComputeDeoptimizeTest3()
{
double[][][] sequences;
int[] labels;
var model = CreateModel3(out sequences, out labels);
var target = new MarkovMultivariateFunction(model);
#pragma warning disable 0618
target.Deoptimize();
#pragma warning restore 0618
var hcrf = new HiddenConditionalRandomField<double[]>(target);
Assert.AreEqual(2, model.Priors.Length);
Assert.AreEqual(1 / 2.0, model.Priors[0]);
Assert.AreEqual(1 / 2.0, model.Priors[1]);
check4(sequences, model, target, hcrf);
}
开发者ID:RLaumeyer，项目名称:framework，代码行数:21，代码来源:IndependentMarkovFunctionTest.cs
示例12: ComputeTest4
public void ComputeTest4()
{
int[] labels;
double[][][] words;
HiddenMarkovClassifier<Independent<NormalDistribution>> model =
CreateModel4(out words, out labels, false);
var target = new MarkovMultivariateFunction(model);
var hcrf = new HiddenConditionalRandomField<double[]>(target);
Assert.AreEqual(3, model.Priors.Length);
Assert.AreEqual(1 / 3.0, model.Priors[0]);
Assert.AreEqual(1 / 3.0, model.Priors[1]);
Assert.AreEqual(1 / 3.0, model.Priors[2]);
check4(words, model, target, hcrf);
}
开发者ID:RLaumeyer，项目名称:framework，代码行数:19，代码来源:IndependentMarkovFunctionTest.cs
示例13: ComputeTest2
public void ComputeTest2()
{
double[][][] sequences;
int[] labels;
var model = CreateModel2(out sequences, out labels);
var target = new MarkovMultivariateFunction(model);
var hcrf = new HiddenConditionalRandomField<double[]>(target);
double actual;
double expected;
double[][] x = { new double[] { 0, 1.7 }, new double[] { 2, 2.1 } };
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++)
{
double xb = Math.Exp(model[c].Transitions[i, j]);
double xc = model[c].Emissions[j].ProbabilityDensityFunction(x[t]);
expected = xb * xc;
actual = Math.Exp(target.Factors[c].Compute(i, j, x, t, c));
Assert.AreEqual(expected, actual, 1e-6);
Assert.IsFalse(double.IsNaN(actual));
}
}
}
actual = model.LogLikelihood(x, c);
expected = hcrf.LogLikelihood(x, c);
Assert.AreEqual(expected, actual);
Assert.IsFalse(double.IsNaN(actual));
}
}
开发者ID:RLaumeyer，项目名称:framework，代码行数:49，代码来源:IndependentMarkovFunctionTest.cs
示例14: LogBackwardTest2
public void LogBackwardTest2()
{
HiddenMarkovClassifier<Independent> hmm = IndependentMarkovClassifierPotentialFunctionTest
.CreateModel3();
MarkovMultivariateFunction function = new MarkovMultivariateFunction(hmm);
double[][][] observations = IndependentMarkovClassifierPotentialFunctionTest.sequences2;
int[] labels = IndependentMarkovClassifierPotentialFunctionTest.labels2;
foreach (double[][] x in observations)
{
foreach (int y in labels)
{
double[,] actual = new double[x.Length, 5];
Accord.Statistics.Models.Fields.
ForwardBackwardAlgorithm.LogBackward(function.Factors[y], x, y, actual);
double[,] expected = new double[x.Length, 5];
Accord.Statistics.Models.Markov.
ForwardBackwardAlgorithm.LogBackward(hmm.Models[y], x, expected);
for (int i = 0; i < actual.GetLength(0); i++)
for (int j = 0; j < actual.GetLength(1); j++)
{
Assert.AreEqual(expected[i, j], actual[i, j], 1e-10);
Assert.IsFalse(Double.IsNaN(actual[i, j]));
}
}
}
}
开发者ID:KommuSoft，项目名称:accord_framework，代码行数:31，代码来源:IndependentForwardBackwardAlgorithmTest.cs
示例15: NumberOfFeaturesTest
public void NumberOfFeaturesTest()
{
Independent initial = new Independent(
new GeneralDiscreteDistribution(46), // output,
new NormalDistribution(0, 1), // headAngle,
new NormalDistribution(0, 1), // handAngle,
new NormalDistribution(0, 1), // relHandX,
new NormalDistribution(0, 1) // relHandY,
);
var model = new HiddenMarkovClassifier<Independent>(
classes: 13, topology: new Forward(5), initial: initial);
var function = new MarkovMultivariateFunction(model);
var field = new HiddenConditionalRandomField<double[]>(function);
int discreteDistributions = 1;
int continuousDistributions = 4;
int symbols = 46;
int states = 5;
int classes = 13;
int expected = classes *
(1 + states + states * states +
states * discreteDistributions * symbols +
states * continuousDistributions * 3);
Assert.AreEqual(expected, field.Function.Features.Length);
Assert.AreEqual(expected, field.Function.Weights.Length);
Assert.AreEqual(4173, expected);
}
开发者ID:KommuSoft，项目名称:accord_framework，代码行数:34，代码来源:HiddenConditionalRandomFieldTest.cs
示例16: LogForwardTest
public void LogForwardTest()
{
double[][][] observations;
int[] labels;
var hmm = IndependentMarkovFunctionTest.CreateModel2(out observations, out labels);
MarkovMultivariateFunction function = new MarkovMultivariateFunction(hmm, includePriors: false);
foreach (double[][] x in observations)
{
foreach (int y in labels)
{
double[,] actual = new double[5, 2];
Accord.Statistics.Models.Fields.
ForwardBackwardAlgorithm.LogForward(function.Factors[y], x, y, actual);
double[,] expected = new double[5, 2];
Accord.Statistics.Models.Markov.
ForwardBackwardAlgorithm.LogForward(hmm.Models[y], x, expected);
for (int i = 0; i < actual.GetLength(0); i++)
for (int j = 0; j < actual.GetLength(1); j++)
Assert.AreEqual(expected[i, j], actual[i, j], 1e-10);
}
}
}
开发者ID:accord-net，项目名称:framework，代码行数:26，代码来源:IndependentForwardBackwardAlgorithmTest.cs
示例17: check4
private static void check4(double[][][] words, HiddenMarkovClassifier<Independent> model, MarkovMultivariateFunction target, HiddenConditionalRandomField<double[]> hcrf)
{
double actual;
double expected;
foreach (var x in words)
{
for (int c = 0; c < model.Classes; c++)
{
for (int i = 0; i < model[c].States; i++)
{
// Check initial state transitions
double xa = model.Priors[c];
double xb = Math.Exp(model[c].Probabilities[i]);
double xc = model[c].Emissions[i].ProbabilityDensityFunction(x[0]);
expected = xa * xb * xc;
actual = Math.Exp(target.Factors[c].Compute(-1, i, x, 0, c));
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-10));
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++)
{
double xb = Math.Exp(model[c].Transitions[i, j]);
double xc = model[c].Emissions[j].ProbabilityDensityFunction(x[t]);
expected = xb * xc;
actual = Math.Exp(target.Factors[c].Compute(i, j, x, t, c));
Assert.IsTrue(expected.IsRelativelyEqual(actual, 1e-10));
Assert.IsFalse(double.IsNaN(actual));
}
}
}
actual = Math.Exp(model.LogLikelihood(x, c));
expected = Math.Exp(hcrf.LogLikelihood(x, c));
Assert.AreEqual(expected, actual, 1e-10);
Assert.IsFalse(double.IsNaN(actual));
actual = model.Compute(x);
expected = hcrf.Compute(x);
Assert.AreEqual(expected, actual);
Assert.IsFalse(double.IsNaN(actual));
}
}
}
开发者ID:RLaumeyer，项目名称:framework，代码行数:50，代码来源:IndependentMarkovFunctionTest.cs
示例18: ForwardTest4
public void ForwardTest4()
{
var hmmc = Accord.Tests.Statistics.Models.Fields.
MultivariateMarkovFunctionTest.CreateModel3();
var hmm = hmmc[0];
var function = new MarkovMultivariateFunction(hmm);
var observations = Accord.Tests.Statistics.Models.Fields.
MultivariateMarkovFunctionTest.inputTest[3];
double expectedLogLikelihood;
double[,] expected = Accord.Statistics.Models.Markov
.ForwardBackwardAlgorithm.Forward(hmm, observations, out expectedLogLikelihood);
double actualLogLikelihood;
double[,] actual = Accord.Statistics.Models.Fields.
ForwardBackwardAlgorithm.Forward(function.Factors[0], observations, 0, out actualLogLikelihood);
Assert.IsTrue(expected.IsEqual(actual, 1e-10));
Assert.AreEqual(expectedLogLikelihood, actualLogLikelihood, 1e-6);
Assert.IsFalse(Double.IsNaN(actualLogLikelihood));
}
开发者ID:accord-net，项目名称:framework，代码行数:28，代码来源:ForwardBackwardAlgorithmTest.cs
示例19: GradientDeoptimizeTest3
public void GradientDeoptimizeTest3()
{
double[][][] sequences2;
int[] labels2;
var hmm = CreateModel3(out sequences2, out labels2);
var function = new MarkovMultivariateFunction(hmm);
#pragma warning disable 0618
function.Deoptimize();
#pragma warning restore 0618
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
target.Regularization = 2;
var inputs = sequences2;
var outputs = labels2;
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters, inputs, outputs, target.Regularization);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights, inputs, outputs);
for (int i = 0; i < actual.Length; i++)
{
double e = expected[i];
double a = actual[i];
Assert.AreEqual(e, a, 1e-3);
Assert.IsFalse(double.IsNaN(actual[i]));
Assert.IsFalse(double.IsNaN(expected[i]));
}
}
开发者ID:RLaumeyer，项目名称:framework，代码行数:37，代码来源:IndependentMarkovFunctionTest.cs
示例20: GradientTest
public void GradientTest()
{
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
MultivariateNormalDistribution density = new MultivariateNormalDistribution(3);
var hmm = new HiddenMarkovClassifier<MultivariateNormalDistribution>(2, new Ergodic(2), density);
double[][][] inputs =
{
new [] { new double[] { 0, 1, 0 }, new double[] { 0, 1, 0 }, new double[] { 0, 1, 0 } },
new [] { new double[] { 1, 6, 2 }, new double[] { 2, 1, 6 }, new double[] { 1, 1, 0 } },
new [] { new double[] { 9, 1, 0 }, new double[] { 0, 1, 5 }, new double[] { 0, 0, 0 } },
};
int[] outputs =
{
0, 0, 1
};
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters, inputs, outputs);
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], 0.05);
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:39，代码来源:MultivariateNormalQuasiNewtonHiddenLearningTest.cs
注：本文中的MarkovMultivariateFunction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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