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);
}
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));
}
}
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);
}
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);
}
public void RunTest()
{
var hmm = MarkovContinuousFunctionTest.CreateModel1();
var function = new MarkovContinuousFunction(hmm);
var model = new HiddenConditionalRandomField<double>(function);
var target = new HiddenQuasiNewtonLearning<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.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(-0.0000041736023099758768, ll0, 1e-10);
for (int i = 0; i < inputs.Length; i++)
Assert.AreEqual(expected[i], actual[i]);
Assert.IsTrue(ll1 > ll0);
}
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);
}
public void GradientTest_DiscreteMarkov()
{
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);
var inputs = QuasiNewtonHiddenLearningTest.inputs;
var outputs = QuasiNewtonHiddenLearningTest.outputs;
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], 1e-4);
Assert.IsFalse(double.IsNaN(actual[i]));
Assert.IsFalse(double.IsNaN(expected[i]));
}
}
private double func(HiddenConditionalRandomField <double[]> model, double[] parameters, double[][][] inputs, int[] outputs, double beta)
{
model.Function.Weights = parameters;
// Regularization
double sumSquaredWeights = 0;
if (beta != 0)
{
for (int i = 0; i < parameters.Length; i++)
{
if (!(Double.IsInfinity(parameters[i]) || Double.IsNaN(parameters[i])))
{
sumSquaredWeights += parameters[i] * parameters[i];
}
}
sumSquaredWeights = sumSquaredWeights * 0.5 / beta;
}
double logLikelihood = model.LogLikelihood(inputs, outputs);
// Maximize the log-likelihood and minimize the sum of squared weights
return(-logLikelihood + sumSquaredWeights);
}
public void SimpleGestureRecognitionTest()
{
// Let's say we would like to do a very simple mechanism for
// gesture recognition. In this example, we will be trying to
// create a classifier that can distinguish between the words
// "hello", "car", and "wardrobe".
// Let's say we decided to acquire some data, and we asked some
// people to perform those words in front of a Kinect camera, and,
// using Microsoft's SDK, we were able to captured the x and y
// coordinates of each hand while the word was being performed.
// Let's say we decided to represent our frames as:
//
// double[] frame = { leftHandX, leftHandY, rightHandX, rightHandY };
//
// Since we captured words, this means we captured sequences of
// frames as we described above. Let's write some of those as
// rough examples to explain how gesture recognition can be done:
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 },
};
// Here, please note that a real-world example would involve *lots*
// of samples for each word. Here, we are considering just one from
// each class which is clearly sub-optimal and should _never_ be done
// on practice. For example purposes, however, please disregard this.
// Those are the words we have in our vocabulary:
//
double[][][] words = { hello, car, wardrobe };
// Now, let's associate integer labels with them. This is needed
// for the case where there are multiple samples for each word.
//
int[] labels = { 0, 1, 2 };
// We will create our classifiers assuming an independent
// Gaussian distribution for each component in our feature
// vectors (like assuming a Naive Bayes assumption).
var initial = new Independent <NormalDistribution>
(
new NormalDistribution(0, 1),
new NormalDistribution(0, 1),
new NormalDistribution(0, 1),
new NormalDistribution(0, 1)
);
// Now, we can proceed and create our classifier.
//
int numberOfWords = 3; // we are trying to distinguish between 3 words
int numberOfStates = 5; // this value can be found by trial-and-error
var hmm = new HiddenMarkovClassifier <Independent <NormalDistribution> >
(
classes: numberOfWords,
topology: new Forward(numberOfStates), // word classifiers should use a forward topology
initial: initial
);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning <Independent <NormalDistribution> >(hmm,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning <Independent <NormalDistribution> >(hmm.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 100,
// This is necessary so the code doesn't blow up when it realize
// there is only one sample per word class. But this could also be
// needed in normal situations as well.
//
FittingOptions = new IndependentOptions()
{
InnerOption = new NormalOptions()
{
Regularization = 1e-5
}
}
}
);
// Finally, we can run the learning algorithm!
double logLikelihood = teacher.Run(words, labels);
// At this point, the classifier should be successfully
// able to distinguish between our three word classes:
//
int tc1 = hmm.Compute(hello);
int tc2 = hmm.Compute(car);
int tc3 = hmm.Compute(wardrobe);
Assert.AreEqual(0, tc1);
Assert.AreEqual(1, tc2);
Assert.AreEqual(2, tc3);
// Now, we can use the Markov classifier to initialize a HCRF
var function = new MarkovMultivariateFunction(hmm);
var hcrf = new HiddenConditionalRandomField <double[]>(function);
// We can check that both are equivalent, although they have
// formulations that can be learned with different methods
//
for (int i = 0; i < words.Length; i++)
{
// Should be the same
int expected = hmm.Compute(words[i]);
int actual = hcrf.Compute(words[i]);
// Should be the same
double h0 = hmm.LogLikelihood(words[i], 0);
double c0 = hcrf.LogLikelihood(words[i], 0);
double h1 = hmm.LogLikelihood(words[i], 1);
double c1 = hcrf.LogLikelihood(words[i], 1);
double h2 = hmm.LogLikelihood(words[i], 2);
double c2 = hcrf.LogLikelihood(words[i], 2);
Assert.AreEqual(expected, actual);
Assert.AreEqual(h0, c0, 1e-10);
Assert.IsTrue(h1.IsRelativelyEqual(c1, 1e-10));
Assert.IsTrue(h2.IsRelativelyEqual(c2, 1e-10));
Assert.IsFalse(double.IsNaN(c0));
Assert.IsFalse(double.IsNaN(c1));
Assert.IsFalse(double.IsNaN(c2));
}
// Now we can learn the HCRF using one of the best learning
// algorithms available, Resilient Backpropagation learning:
// Create a learning algorithm
var rprop = new HiddenResilientGradientLearning <double[]>(hcrf)
{
Iterations = 50,
Tolerance = 1e-5
};
// Run the algorithm and learn the models
double error = rprop.Run(words, labels);
// At this point, the HCRF should be successfully
// able to distinguish between our three word classes:
//
int hc1 = hcrf.Compute(hello);
int hc2 = hcrf.Compute(car);
int hc3 = hcrf.Compute(wardrobe);
Assert.AreEqual(0, hc1);
Assert.AreEqual(1, hc2);
Assert.AreEqual(2, hc3);
}
private static void resilientgradienthiddenlearning()
{
// Suppose we would like to learn how to classify the
// following set of sequences among three class labels:
int[][] inputSequences =
{
// First class of sequences: starts and
// ends with zeros, ones in the middle:
new[] { 0, 1, 1, 1, 0 },
new[] { 0, 0, 1, 1, 0, 0 },
new[] { 0, 1, 1, 1, 1, 0 },
// Second class of sequences: starts with
// twos and switches to ones until the end.
new[] { 2, 2, 2, 2, 1, 1, 1, 1, 1 },
new[] { 2, 2, 1, 2, 1, 1, 1, 1, 1 },
new[] { 2, 2, 2, 2, 2, 1, 1, 1, 1 },
// Third class of sequences: can start
// with any symbols, but ends with three.
new[] { 0, 0, 1, 1, 3, 3, 3, 3 },
new[] { 0, 0, 0, 3, 3, 3, 3 },
new[] { 1, 0, 1, 2, 2, 2, 3, 3 },
new[] { 1, 1, 2, 3, 3, 3, 3 },
new[] { 0, 0, 1, 1, 3, 3, 3, 3 },
new[] { 2, 2, 0, 3, 3, 3, 3 },
new[] { 1, 0, 1, 2, 3, 3, 3, 3 },
new[] { 1, 1, 2, 3, 3, 3, 3 },
};
// Now consider their respective class labels
int[] outputLabels =
{
/* Sequences 1-3 are from class 0: */ 0, 0, 0,
/* Sequences 4-6 are from class 1: */ 1, 1, 1,
/* Sequences 7-14 are from class 2: */ 2, 2, 2, 2, 2, 2, 2, 2
};
// Create the Hidden Conditional Random Field using a set of discrete features
var function = new MarkovDiscreteFunction(states: 3, symbols: 4, outputClasses: 3);
var classifier = new HiddenConditionalRandomField<int>(function);
// Create a learning algorithm
var teacher = new HiddenResilientGradientLearning<int>(classifier)
{
Iterations = 50
};
// Run the algorithm and learn the models
teacher.Run(inputSequences, outputLabels);
int[] answers = inputSequences.Apply(classifier.Compute);
}
private void btnLearnHCRF_Click(object sender, EventArgs e)
{
if (gridSamples.Rows.Count == 0)
{
MessageBox.Show("Please load or insert some data first.");
return;
}
var samples = database.Samples;
var classes = database.Classes;
double[][][] inputs = new double[samples.Count][][];
int[] outputs = new int[samples.Count];
for (int i = 0; i < inputs.Length; i++)
{
inputs[i] = samples[i].Input;
outputs[i] = samples[i].Output;
}
int iterations = 100;
double tolerance = 0.01;
hcrf = new HiddenConditionalRandomField<double[]>(
new MarkovMultivariateFunction(hmm));
// Create the learning algorithm for the ensemble classifier
var teacher = new HiddenResilientGradientLearning<double[]>(hcrf)
{
Iterations = iterations,
Tolerance = tolerance
};
// Run the learning algorithm
double error = teacher.Run(inputs, outputs);
foreach (var sample in database.Samples)
{
sample.RecognizedAs = hcrf.Compute(sample.Input);
}
foreach (DataGridViewRow row in gridSamples.Rows)
{
var sample = row.DataBoundItem as Sequence;
row.DefaultCellStyle.BackColor = (sample.RecognizedAs == sample.Output) ?
Color.LightGreen : Color.White;
}
}
private double func(HiddenConditionalRandomField<double[]> model, double[] parameters, double[][][] inputs, int[] outputs)
{
model.Function.Weights = parameters;
return -model.LogLikelihood(inputs, outputs);
}
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]));
}
}
static void runHiddenConditionalRandomFieldLearningExample()
{
// Observation sequences should only contain symbols that are greater than or equal to 0, and lesser than the number of symbols.
int[][] observationSequences =
{
// First class of sequences: starts and ends with zeros, ones in the middle.
new[] { 0, 1, 1, 1, 0 },
new[] { 0, 0, 1, 1,0, 0 },
new[] { 0, 1, 1, 1,1, 0 },
// Second class of sequences: starts with twos and switches to ones until the end.
new[] { 2, 2, 2, 2,1, 1, 1, 1, 1 },
new[] { 2, 2, 1, 2,1, 1, 1, 1, 1 },
new[] { 2, 2, 2, 2,2, 1, 1, 1, 1 },
// Third class of sequences: can start with any symbols, but ends with three.
new[] { 0, 0, 1, 1,3, 3, 3, 3 },
new[] { 0, 0, 0, 3,3, 3, 3 },
new[] { 1, 0, 1, 2,2, 2, 3, 3 },
new[] { 1, 1, 2, 3,3, 3, 3 },
new[] { 0, 0, 1, 1,3, 3, 3, 3 },
new[] { 2, 2, 0, 3,3, 3, 3 },
new[] { 1, 0, 1, 2,3, 3, 3, 3 },
new[] { 1, 1, 2, 3,3, 3, 3 },
};
// Consider their respective class labels.
// Class labels have to be zero-based and successive integers.
int[] classLabels =
{
0, 0, 0, // Sequences 1-3 are from class 0.
1, 1, 1, // Sequences 4-6 are from class 1.
2, 2, 2, 2, 2, 2, 2, 2 // Sequences 7-14 are from class 2.
};
// Create the Hidden Conditional Random Field using a set of discrete features.
var function = new MarkovDiscreteFunction(states: 3, symbols: 4, outputClasses: 3);
var hcrf = new HiddenConditionalRandomField <int>(function);
// Create a learning algorithm.
var trainer = new HiddenResilientGradientLearning <int>(hcrf)
{
Iterations = 50
};
// Run the algorithm and learn the models.
double error = trainer.Run(observationSequences, classLabels);
Console.WriteLine("the error in the last iteration = {0}", error);
// Check the output classificaton label for some sequences.
int y1 = hcrf.Compute(new[] { 0, 1, 1, 1, 0 }); // output is y1 = 0.
Console.WriteLine("output class = {0}", y1);
int y2 = hcrf.Compute(new[] { 0, 0, 1, 1, 0, 0 }); // output is y2 = 0.
Console.WriteLine("output class = {0}", y2);
int y3 = hcrf.Compute(new[] { 2, 2, 2, 2, 1, 1 }); // output is y3 = 1.
Console.WriteLine("output class = {0}", y3);
int y4 = hcrf.Compute(new[] { 2, 2, 1, 1 }); // output is y4 = 1.
Console.WriteLine("output class = {0}", y4);
int y5 = hcrf.Compute(new[] { 0, 0, 1, 3, 3, 3 }); // output is y5 = 2.
Console.WriteLine("output class = {0}", y5);
int y6 = hcrf.Compute(new[] { 2, 0, 2, 2, 3, 3 }); // output is y6 = 2.
Console.WriteLine("output class = {0}", y6);
}
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);
}
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);
}
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);
}
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));
}
}
public void GradientTest()
{
var function = new DiscreteMarkovClassifierFunction(2, 2, 2);
var model = new HiddenConditionalRandomField<int>(function);
var target = new QuasiNewtonHiddenLearning<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]));
}
}
/// <summary>
/// Initializes a new instance of the <see cref="HiddenGradientDescentLearning<T>"/> class.
/// </summary>
///
/// <param name="model">The model to be trained.</param>
///
public HiddenGradientDescentLearning(HiddenConditionalRandomField <T> model)
: this()
{
Model = model;
init();
}
private void btnLearnHMM_Click(object sender, EventArgs e)
{
if (gridSamples.Rows.Count == 0)
{
MessageBox.Show("Please load or insert some data first.");
return;
}
BindingList<Sequence> samples = database.Samples;
BindingList<String> classes = database.Classes;
double[][][] inputs = new double[samples.Count][][];
int[] outputs = new int[samples.Count];
for (int i = 0; i < inputs.Length; i++)
{
inputs[i] = samples[i].Input;
outputs[i] = samples[i].Output;
}
int states = 5;
int iterations = 0;
double tolerance = 0.01;
bool rejection = false;
hmm = new HiddenMarkovClassifier<MultivariateNormalDistribution>(classes.Count,
new Forward(states), new MultivariateNormalDistribution(2), classes.ToArray());
// Create the learning algorithm for the ensemble classifier
var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution>(hmm,
// Train each model using the selected convergence criteria
i => new BaumWelchLearning<MultivariateNormalDistribution>(hmm.Models[i])
{
Tolerance = tolerance,
Iterations = iterations,
FittingOptions = new NormalOptions()
{
Regularization = 1e-5
}
}
);
teacher.Empirical = true;
teacher.Rejection = rejection;
// Run the learning algorithm
double error = teacher.Run(inputs, outputs);
// Classify all training instances
foreach (var sample in database.Samples)
{
sample.RecognizedAs = hmm.Compute(sample.Input);
}
foreach (DataGridViewRow row in gridSamples.Rows)
{
var sample = row.DataBoundItem as Sequence;
row.DefaultCellStyle.BackColor = (sample.RecognizedAs == sample.Output) ?
Color.LightGreen : Color.White;
}
btnLearnHCRF.Enabled = true;
hcrf = null;
}
public void LogForwardGesturesPriorsTest()
{
int[] labels;
double[][][] words;
var classifier = IndependentMarkovFunctionTest.CreateModel4(out words, out labels, true);
var function = new MarkovMultivariateFunction(classifier);
var target = new HiddenConditionalRandomField<double[]>(function);
foreach (var word in words)
{
for (int c = 0; c < 3; c++)
{
var actual = Accord.Statistics.Models.Fields.ForwardBackwardAlgorithm.LogForward(
target.Function.Factors[c], word, c);
var expected = Accord.Statistics.Models.Markov.ForwardBackwardAlgorithm.LogForward(
classifier[c], word);
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];
// TODO: Verify if is possible to reduce this tolerance
Assert.IsTrue(e.IsRelativelyEqual(a, 0.1));
}
}
}
}
}
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);
}
/// <summary>
/// Constructs a new L-BFGS learning algorithm.
/// </summary>
///
public HiddenQuasiNewtonLearning(HiddenConditionalRandomField <T> model)
{
Model = model;
}
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));
}
}
}
/// <summary>
/// Initializes a new instance of the <see cref="BaseHiddenConditionalRandomFieldLearning<T>"/> class.
/// </summary>
///
/// <param name="model">The model to be trained.</param>
///
protected BaseHiddenConditionalRandomFieldLearning(HiddenConditionalRandomField<T> model)
{
this.model = model;
this.function = model.Function;
}
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));
}
}
}
}
}
}
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]));
}
}
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);
}
/// <summary>
/// Constructs a new Conjugate Gradient learning algorithm.
/// </summary>
///
public HiddenConjugateGradientLearning(HiddenConditionalRandomField <T> model)
{
Model = model;
}
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]));
}
}
public void ComputeTest3()
{
var model = CreateModel3();
var target = new MarkovMultivariateFunction(model);
var hcrf = new HiddenConditionalRandomField<double[]>(target);
double actual;
double expected;
for (int k = 0; k < 5; k++)
{
foreach (var x in sequences2)
{
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.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));
}
}
}
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));
}
}
}
}
private double func(HiddenConditionalRandomField<double[]> model, double[] parameters, double[][][] inputs, int[] outputs, double beta)
{
model.Function.Weights = parameters;
// Regularization
double sumSquaredWeights = 0;
if (beta != 0)
{
for (int i = 0; i < parameters.Length; i++)
if (!(Double.IsInfinity(parameters[i]) || Double.IsNaN(parameters[i])))
sumSquaredWeights += parameters[i] * parameters[i];
sumSquaredWeights = sumSquaredWeights * 0.5 / beta;
}
double logLikelihood = model.LogLikelihood(inputs, outputs);
// Maximize the log-likelihood and minimize the sum of squared weights
return -logLikelihood + sumSquaredWeights;
}
public void GradientTest2()
{
var hmm = CreateModel3();
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
var inputs = sequences2;
var outputs = labels2;
double[] actual = target.Gradient(function.Weights, inputs, outputs);
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters, inputs, outputs);
double[] expected = diff.Compute(function.Weights);
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]));
}
}
private void openDataDialog_FileOk(object sender, System.ComponentModel.CancelEventArgs e)
{
hmm = null;
hcrf = null;
using (var stream = openDataDialog.OpenFile())
database.Load(stream);
btnLearnHMM.Enabled = true;
btnLearnHCRF.Enabled = false;
panelClassification.Visible = false;
panelUserLabeling.Visible = false;
}
public void ComputeTest3()
{
double[][][] sequences2;
int[] labels2;
var model = CreateModel3(out sequences2, out labels2);
var target = new MarkovMultivariateFunction(model);
var hcrf = new HiddenConditionalRandomField <double[]>(target);
double actual;
double expected;
for (int k = 0; k < 5; k++)
{
foreach (var x in sequences2)
{
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.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 = 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));
}
}
}
}
/// <summary>
/// Initializes a new instance of the <see cref="GradientDescentHiddenLearning<T>"/> class.
/// </summary>
///
/// <param name="model">The model to be trained.</param>
///
public GradientDescentHiddenLearning(HiddenConditionalRandomField <T> model)
: base(model)
{
aObservations = new T[1][];
aOutput = new int[1];
}
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));
}
}
}
/// <summary>
/// Initializes a new instance of the <see cref="GradientDescentHiddenLearning<T>"/> class.
/// </summary>
///
/// <param name="model">The model to be trained.</param>
///
public GradientDescentHiddenLearning(HiddenConditionalRandomField <T> model)
: base(model)
{
gradient = new double[Model.Function.Weights.Length];
}
/// <summary>
/// Initializes a new instance of the <see cref="ForwardBackwardGradient{T}"/> class.
/// </summary>
///
/// <param name="model">The model to be trained.</param>
///
public ForwardBackwardGradient(HiddenConditionalRandomField<T> model)
: this()
{
this.model = model;
this.function = model.Function;
}
public void ComputeTest5()
{
var model = CreateModel3(states: 7);
var target = new MarkovMultivariateFunction(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 GradientTest2()
{
HiddenMarkovClassifier hmm = HiddenMarkovClassifierPotentialFunctionTest.CreateModel1();
var function = new MarkovDiscreteFunction(hmm);
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-5);
Assert.IsFalse(double.IsNaN(actual[i]));
Assert.IsFalse(double.IsNaN(expected[i]));
}
}
private void btnLearnHMM_Click(object sender, EventArgs e)
{
if (gridSamples.Rows.Count == 0)
{
MessageBox.Show("Please load or insert some data first.");
return;
}
BindingList <Sequence> samples = database.Samples;
BindingList <String> classes = database.Classes;
double[][][] inputs = new double[samples.Count][][];
int[] outputs = new int[samples.Count];
for (int i = 0; i < inputs.Length; i++)
{
inputs[i] = samples[i].Input;
outputs[i] = samples[i].Output;
}
int states = 5;
int iterations = 0;
double tolerance = 0.01;
bool rejection = false;
hmm = new HiddenMarkovClassifier <MultivariateNormalDistribution, double[]>(classes.Count,
new Forward(states), new MultivariateNormalDistribution(2), classes.ToArray());
// Create the learning algorithm for the ensemble classifier
var teacher = new HiddenMarkovClassifierLearning <MultivariateNormalDistribution, double[]>(hmm)
{
// Train each model using the selected convergence criteria
Learner = i => new BaumWelchLearning <MultivariateNormalDistribution, double[]>(hmm.Models[i])
{
Tolerance = tolerance,
Iterations = iterations,
FittingOptions = new NormalOptions()
{
Regularization = 1e-5
}
}
};
teacher.Empirical = true;
teacher.Rejection = rejection;
// Run the learning algorithm
teacher.Learn(inputs, outputs);
// Classify all training instances
foreach (var sample in database.Samples)
{
sample.RecognizedAs = hmm.Decide(sample.Input);
}
foreach (DataGridViewRow row in gridSamples.Rows)
{
var sample = row.DataBoundItem as Sequence;
row.DefaultCellStyle.BackColor = (sample.RecognizedAs == sample.Output) ?
Color.LightGreen : Color.White;
}
btnLearnHCRF.Enabled = true;
hcrf = null;
}
public void GradientTest4()
{
var hmm = IndependentMarkovClassifierPotentialFunctionTest.CreateModel2();
var function = new MarkovMultivariateFunction(hmm);
var model = new HiddenConditionalRandomField<double[]>(function);
var target = new ForwardBackwardGradient<double[]>(model);
target.Regularization = 0;
FiniteDifferences diff = new FiniteDifferences(function.Weights.Length);
diff.Function = parameters => func(model, parameters,
IndependentMarkovClassifierPotentialFunctionTest.sequences,
IndependentMarkovClassifierPotentialFunctionTest.labels);
double[] expected = diff.Compute(function.Weights);
double[] actual = target.Gradient(function.Weights,
IndependentMarkovClassifierPotentialFunctionTest.sequences,
IndependentMarkovClassifierPotentialFunctionTest.labels);
for (int i = 0; i < actual.Length; i++)
{
if (double.IsNaN(expected[i]))
continue;
Assert.AreEqual(expected[i], actual[i], 1e-5);
Assert.IsFalse(double.IsNaN(actual[i]));
}
}