public void RunTest()
{
var inputs = QuasiNewtonHiddenLearningTest.inputs;
var outputs = QuasiNewtonHiddenLearningTest.outputs;
HiddenMarkovClassifier hmm = HiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new DiscreteMarkovClassifierFunction(hmm);
var model = new HiddenConditionalRandomField <int>(function);
var target = new GradientDescentHiddenLearning <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);
}
public void RunTest2()
{
var inputs = QuasiNewtonHiddenLearningTest.inputs;
var outputs = QuasiNewtonHiddenLearningTest.outputs;
Accord.Math.Tools.SetupGenerator(0);
var function = new DiscreteMarkovClassifierFunction(2, 2, 2);
var model = new HiddenConditionalRandomField <int>(function);
var target = new GradientDescentHiddenLearning <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 = Double.PositiveInfinity;
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(-5.5451774444795623, ll0, 1e-10);
Assert.AreEqual(0, error, 1e-5);
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 RunTest()
{
var inputs = QuasiNewtonHiddenLearningTest.inputs;
var outputs = QuasiNewtonHiddenLearningTest.outputs;
HiddenMarkovClassifier hmm = HiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new DiscreteMarkovClassifierFunction(hmm);
var model = new HiddenConditionalRandomField<int>(function);
var target = new GradientDescentHiddenLearning<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);
}
public void RunTest2()
{
var inputs = QuasiNewtonHiddenLearningTest.inputs;
var outputs = QuasiNewtonHiddenLearningTest.outputs;
Accord.Math.Tools.SetupGenerator(0);
var function = new DiscreteMarkovClassifierFunction(2, 2, 2);
var model = new HiddenConditionalRandomField<int>(function);
var target = new GradientDescentHiddenLearning<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 = Double.PositiveInfinity;
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(-5.5451774444795623, ll0, 1e-10);
Assert.AreEqual(0, error, 1e-5);
Assert.IsFalse(double.IsNaN(error));
for (int i = 0; i < inputs.Length; i++)
Assert.AreEqual(expected[i], actual[i]);
Assert.IsTrue(ll1 > ll0);
}