public void GradientTest3()
{
var hmm = MultivariateNormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new MultivariateNormalMarkovClassifierFunction(hmm);
var model = new HiddenConditionalRandomField <double[]>(function);
var target = new QuasiNewtonHiddenLearning <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]));
}
}
public void RunTest()
{
var hmm = MultivariateNormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new MultivariateNormalMarkovClassifierFunction(hmm);
var model = new HiddenConditionalRandomField <double[]>(function);
var target = new QuasiNewtonHiddenLearning <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.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(-0.0000041736023117522336, 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);
}
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 MultivariateNormalMarkovClassifierFunction(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));
}
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 MultivariateNormalMarkovClassifierFunction(hmm);
var model = new HiddenConditionalRandomField <double[]>(function);
var target = new QuasiNewtonHiddenLearning <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]));
}
}
public void GradientTest3()
{
var hmm = MultivariateNormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new MultivariateNormalMarkovClassifierFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new QuasiNewtonHiddenLearning<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]));
}
}
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 MultivariateNormalMarkovClassifierFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new QuasiNewtonHiddenLearning<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]));
}
}
public void RunTest()
{
var hmm = MultivariateNormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new MultivariateNormalMarkovClassifierFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new QuasiNewtonHiddenLearning<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.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(-0.0000041736023117522336, 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);
}
public void ComputeTest5()
{
var model = CreateModel3(states: 7);
var target = new MultivariateNormalMarkovClassifierFunction(model);
double actual;
double expected;
double[][] x = { new double[] { 0, 1 }, new double[] { 3, 2 } };
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));
}
}
}
}
var hcrf = new HiddenConditionalRandomField<double[]>(target);
for (int i = 0; i < inputTest.Length; i++)
{
int h = model.Compute(inputTest[i]);
int c = hcrf.Compute(inputTest[i]);
Assert.AreEqual(h, c);
}
}
public void HiddenMarkovHiddenPotentialFunctionConstructorTest2()
{
var model = CreateModel2();
var target = new MultivariateNormalMarkovClassifierFunction(model);
var features = target.Features;
double[] weights = target.Weights;
Assert.AreEqual(38, features.Length);
Assert.AreEqual(38, 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[j];
double var = model[c].Emissions[i].Variance[j];
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 ComputeTest3()
{
var hmm = MultivariateNormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var owner = new MultivariateNormalMarkovClassifierFunction(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));
}
}
}
}
public void ComputeTest3()
{
var hmm = MultivariateNormalHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var owner = new MultivariateNormalMarkovClassifierFunction(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));
}
}
}
}
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 MultivariateNormalMarkovClassifierFunction(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));
}