public static HiddenMarkovClassifier <NormalDistribution> CreateModel1()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a univariate sequence and the same sequence backwards.
double[][] sequences = new double[][]
{
new double[] { 0, 1, 2, 3, 4 }, // This is the first sequence with label = 0
new double[] { 4, 3, 2, 1, 0 }, // This is the second sequence with label = 1
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
NormalDistribution density = new NormalDistribution();
var classifier = new HiddenMarkovClassifier <NormalDistribution>(2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning <NormalDistribution>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning <NormalDistribution>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
return(classifier);
}
private static HiddenMarkovClassifier <NormalDistribution> createClassifier(
out double[][] sequences, bool rejection = false)
{
sequences = new double[][]
{
new double[] { 0, 1, 2, 3, 4 },
new double[] { 4, 3, 2, 1, 0 },
};
int[] labels = { 0, 1 };
NormalDistribution density = new NormalDistribution();
HiddenMarkovClassifier <NormalDistribution> classifier =
new HiddenMarkovClassifier <NormalDistribution>(2, new Ergodic(2), density);
var teacher = new HiddenMarkovClassifierLearning <NormalDistribution>(classifier,
modelIndex => new BaumWelchLearning <NormalDistribution>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
teacher.Rejection = rejection;
teacher.Run(sequences, labels);
return(classifier);
}
private void LoadAndTrain_Click(object sender, EventArgs e)
{
List <int> outputLabels = new List <int>();
List <int[]> inputSequences = new List <int[]>();
OpenFileDialog dlg = new OpenFileDialog();
dlg.Filter = "Gestures (*.xml)|*.xml";
dlg.Title = "Load Gestures";
dlg.RestoreDirectory = false;
dlg.Multiselect = true;
if (dlg.ShowDialog(this) == DialogResult.OK)
{
lblResult.Text = "Training...";
for (int i = 0; i < dlg.FileNames.Length; i++)
{
string name = dlg.FileNames[i];
List <int[]> inputSequencesTemp = _rec.LoadDirectionalCodewordsFile(name);
for (int j = 0; j < inputSequencesTemp.Count; j++)
{
inputSequences.Add(inputSequencesTemp[j]);
outputLabels.Add(i);
}
}
ReloadViewForm();
}
//ITopology forward = new Forward(4,3);
ITopology[] forwards = new Forward[4];
forwards[0] = new Forward(5, 3);
forwards[1] = new Forward(5, 3);
forwards[2] = new Forward(5, 3);
forwards[3] = new Forward(5, 3);
_hmmc = new HiddenMarkovClassifier(4, forwards, 16);
//hmmc.Models[0] = new HiddenMarkovModel();
//hmmc.Models[0].Transitions = null;kovModel();
// And create a algorithms to teach each of the inner models
var teacher = new HiddenMarkovClassifierLearning(_hmmc,
// We can specify individual training options for each inner model:
modelIndex => new BaumWelchLearning(_hmmc.Models[modelIndex])
{
Tolerance = 0.001, // iterate until log-likelihood changes less than 0.001
Iterations = 0 // don't place an upper limit on the number of iterations
});
teacher.Run((int[][])inputSequences.ToArray(), (int[])outputLabels.ToArray());
_hmmc.Threshold = teacher.Threshold();
_hmmc.Sensitivity = 1;
_hmms = _hmmc.Models;
for (int i = 0; i < dlg.FileNames.Length; i++)
{
_hmms[i].Tag = Gesture.ParseName(dlg.FileNames[i]);
}
lblResult.Text = "Success!!";
}
public static HiddenMarkovClassifier<NormalDistribution> CreateModel1()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a univariate sequence and the same sequence backwards.
double[][] sequences = new double[][]
{
new double[] { 0,1,2,3,4 }, // This is the first sequence with label = 0
new double[] { 4,3,2,1,0 }, // This is the second sequence with label = 1
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
NormalDistribution density = new NormalDistribution();
var classifier = new HiddenMarkovClassifier<NormalDistribution>(2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<NormalDistribution>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning<NormalDistribution>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
return classifier;
}
private static HiddenMarkovClassifier createClassifier(
out int[][] sequences, bool rejection = false)
{
sequences = new int[][]
{
new int[] { 0, 1, 2, 3, 4 },
new int[] { 4, 3, 2, 1, 0 },
};
int[] labels = { 0, 1 };
HiddenMarkovClassifier classifier =
new HiddenMarkovClassifier(2, new Ergodic(2), symbols: 5);
var teacher = new HiddenMarkovClassifierLearning(classifier,
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
teacher.Rejection = rejection;
teacher.Run(sequences, labels);
return(classifier);
}
public int Classify(double[][][] trainDataSet, int[] trainLabels, double[][] testData, String[] classes)
{
int states = 5;
int dimensionsOfFeatures = 12;
int numberOfClasses = classes.Length;
int iterations = 0;
double tolerance = 0.01;
HiddenMarkovClassifier <MultivariateNormalDistribution> hmm = new HiddenMarkovClassifier <MultivariateNormalDistribution>
(numberOfClasses, new Forward(states), new MultivariateNormalDistribution(dimensionsOfFeatures), classes);
// 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 = false;
// Run the learning algorithm
double error = teacher.Run(trainDataSet, trainLabels);
int predictedResult = hmm.Compute(testData);
return(predictedResult);
}
public static HiddenMarkovClassifier <Independent> CreateModel3()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
var comp1 = new GeneralDiscreteDistribution(3);
var comp2 = new NormalDistribution(1);
var comp3 = new NormalDistribution(2);
var comp4 = new NormalDistribution(3);
var comp5 = new NormalDistribution(4);
var density = new Independent(comp1, comp2, comp3, comp4, comp5);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier <Independent>(
2, new Forward(5), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning <Independent>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning <Independent>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences2, labels2);
return(classifier);
}
public void LearnTest()
{
// 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to two symbols (0 or 1)
int symbols = 2;
// Nested models will have two states each
int[] states = new int[] { 2, 2 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
// Will assert the models have learned the sequences correctly.
for (int i = 0; i < inputs.Length; i++)
{
int expected = outputs[i];
int actual = classifier.Compute(inputs[i], out likelihood);
Assert.AreEqual(expected, actual);
}
}
public void LearnTest()
{
// 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to two symbols (0 or 1)
int symbols = 2;
// Nested models will have two states each
int[] states = new int[] { 2, 2 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
// Will assert the models have learned the sequences correctly.
for (int i = 0; i < inputs.Length; i++)
{
int expected = outputs[i];
int actual = classifier.Compute(inputs[i], out likelihood);
Assert.AreEqual(expected, actual);
}
}
public static HiddenMarkovClassifier <Independent> CreateModel2(out double[][][] sequences, out int[] labels)
{
sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0, 1.1 },
new double[] { 1, 2.5 },
new double[] { 1, 3.4 },
new double[] { 1, 4.7 },
new double[] { 2, 5.8 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 2, 3.2 },
new double[] { 2, 2.6 },
new double[] { 1, 1.2 },
new double[] { 1, 0.8 },
new double[] { 0, 1.1 },
}
};
labels = new[] { 0, 1 };
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
var comp1 = new GeneralDiscreteDistribution(3);
var comp2 = new NormalDistribution(1);
var density = new Independent(comp1, comp2);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier <Independent>(
2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning <Independent>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning <Independent>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
Assert.AreEqual(double.NegativeInfinity, logLikelihood); // only one training sample per class
return(classifier);
}
public void LearnTest2()
{
// Declare some testing data
int[][] inputs = new int[][]
{
new int[] { 0, 0, 1, 2 }, // Class 0
new int[] { 0, 1, 1, 2 }, // Class 0
new int[] { 0, 0, 0, 1, 2 }, // Class 0
new int[] { 0, 1, 2, 2, 2 }, // Class 0
new int[] { 2, 2, 1, 0 }, // Class 1
new int[] { 2, 2, 2, 1, 0 }, // Class 1
new int[] { 2, 2, 2, 1, 0 }, // Class 1
new int[] { 2, 2, 2, 2, 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to 3 symbols (0,1,2)
int symbols = 3;
// Nested models will have 3 states each
int[] states = new int[] { 3, 3 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Enable support for sequence rejection
teacher.Rejection = true;
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
//Assert.AreEqual(-0.84036002169161428, likelihood, 1e-15);
likelihood = testThresholdModel(inputs, outputs, classifier, likelihood);
}
public void Run()
{
/*Initialize the model
* Read more tut on Code project for better understanding
* http://www.codeproject.com/Articles/541428/Sequence-Classifiers-in-Csharp-Part-I-Hidden-Marko?msg=5219822#xx5219822xx
* states is parameters for running forward algo
* intteration is parameters for iterations
* tolerance is parameters for threshold
* */
int states = 3;
int iterations = 100;
double tolerance = 0.01;
bool rejection = false;
string[] classes = ActivityIndex.Keys.ToArray();
ITopology foward = new Forward(states: 3);
hmm = new HiddenMarkovClassifier(classes: 12, topology: foward, symbols: 5);
// Create the learning algorithm for the ensemble classifier
var teacher = new HiddenMarkovClassifierLearning(hmm,
// Train each model using the selected convergence criteria
i => new BaumWelchLearning(hmm.Models[i])
{
Tolerance = tolerance,
Iterations = iterations,
}
);
teacher.Empirical = true;
teacher.Rejection = rejection;
// Run the learning algorithm
double error = teacher.Run(input, output);
Console.WriteLine("Error: {0}", error);
//Run the test and compare the real value
using (StreamWriter writer = new StreamWriter("compare.txt"))
{
for (int i = 0; i < outpuTest.Length; ++i)
{
int val = hmm.Compute(inpuTest[i]);
if (val != outpuTest[i])
{
string labelTestRetrieve = ActivityIndex.FirstOrDefault(x => x.Value == val).Key;
string labelTestActity = ActivityIndex.FirstOrDefault(x => x.Value == outpuTest[i]).Key;
writer.WriteLine(outputTestLabelFolder[i] + " - " + "false, label test retrieve: " + labelTestRetrieve + " label activity: " + labelTestActity);
}
else
{
string labelTestActity = ActivityIndex.FirstOrDefault(x => x.Value == outpuTest[i]).Key;
writer.WriteLine(outputTestLabelFolder[i] + " - " + "true, label activity: " + labelTestActity);
}
}
}
}
public void LearnTest1()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a univariate sequence and the same sequence backwards.
double[][] sequences = new double[][]
{
new double[] { 0,1,2,3,4 }, // This is the first sequence with label = 0
new double[] { 4,3,2,1,0 }, // This is the second sequence with label = 1
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
NormalDistribution density = new NormalDistribution();
var classifier = new HiddenMarkovClassifier<NormalDistribution>(2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<NormalDistribution>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning<NormalDistribution>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
// Calculate the probability that the given
// sequences originated from the model
double likelihood1, likelihood2;
// Try to classify the first sequence (output should be 0)
int c1 = classifier.Compute(sequences[0], out likelihood1);
// Try to classify the second sequence (output should be 1)
int c2 = classifier.Compute(sequences[1], out likelihood2);
Assert.AreEqual(0, c1);
Assert.AreEqual(1, c2);
Assert.AreEqual(-13.271981026832929, logLikelihood, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood1, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood2, 1e-10);
Assert.IsFalse(double.IsNaN(logLikelihood));
Assert.IsFalse(double.IsNaN(likelihood1));
Assert.IsFalse(double.IsNaN(likelihood2));
}
public static HiddenMarkovClassifier <Independent> CreateModel1()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
double[][][] sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0 },
new double[] { 1 },
new double[] { 2 },
new double[] { 3 },
new double[] { 4 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 4 },
new double[] { 3 },
new double[] { 2 },
new double[] { 1 },
new double[] { 0 },
}
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
NormalDistribution component = new NormalDistribution();
Independent density = new Independent(component);
var classifier = new HiddenMarkovClassifier <Independent>(2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning <Independent>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning <Independent>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
Assert.AreEqual(-13.271981026832929d, logLikelihood, 1e-10);
return(classifier);
}
/// <summary>
/// learn Hmm model for samples in given database using Baum Welch unsupervised learning algorithm
/// then used the learned model to classify the training samples
/// </summary>
/// <param name="database"></param>
/// <returns></returns>
static HiddenMarkovClassifier <MultivariateNormalDistribution> learnHMM(Database database)
{
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 = 0.1;
bool rejection = true;
HiddenMarkovClassifier <MultivariateNormalDistribution> 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);
}
return(hmm);
}
public void LearnTest1()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a univariate sequence and the same sequence backwards.
double[][] sequences = new double[][]
{
new double[] { 0, 1, 2, 3, 4 }, // This is the first sequence with label = 0
new double[] { 4, 3, 2, 1, 0 }, // This is the second sequence with label = 1
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
NormalDistribution density = new NormalDistribution();
var classifier = new HiddenMarkovClassifier <NormalDistribution>(2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning <NormalDistribution>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning <NormalDistribution>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
// Calculate the probability that the given
// sequences originated from the model
double likelihood1, likelihood2;
// Try to classify the first sequence (output should be 0)
int c1 = classifier.Compute(sequences[0], out likelihood1);
// Try to classify the second sequence (output should be 1)
int c2 = classifier.Compute(sequences[1], out likelihood2);
Assert.AreEqual(0, c1);
Assert.AreEqual(1, c2);
Assert.AreEqual(-13.271981026832929, logLikelihood, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood1, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood2, 1e-10);
Assert.IsFalse(double.IsNaN(logLikelihood));
Assert.IsFalse(double.IsNaN(likelihood1));
Assert.IsFalse(double.IsNaN(likelihood2));
}
public static HiddenMarkovClassifier 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to two symbols (0 or 1)
int symbols = 2;
// Nested models will have two states each
int[] states = new int[] { 2, 2 };
// Creates a new Hidden Markov Model Classifier with the given parameters
var classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
return classifier;
}
public static HiddenMarkovClassifier 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to two symbols (0 or 1)
int symbols = 2;
// Nested models will have two states each
int[] states = new int[] { 2, 2 };
// Creates a new Hidden Markov Model Classifier with the given parameters
var classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
return(classifier);
}
public static void LearnAndPredictContinuous()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a univariate sequence and the same sequence backwards.
double[][] sequences = new double[][]
{
new double[] { 0, 1, 2, 3, 4 }, // This is the first sequence with label = 0
new double[] { 4, 3, 2, 1, 0 }, // This is the second sequence with label = 1
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a new Continuous-density Hidden Markov Model Sequence Classifier
// containing 2 hidden Markov Models with 2 states and an underlying Normal
// distribution as the continuous probability density.
Gaussian density = new Gaussian();
var classifier = new HiddenMarkovClassifier(2, new Ergodic(2), density);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
teacher.Run(sequences, labels);
// Calculate the probability that the given
// sequences originated from the model
double likelihood;
// Try to classify the first sequence (output should be 0)
int c1 = classifier.Compute(sequences[0], out likelihood);
Console.WriteLine("c1: {0}", c1);
// Try to classify the second sequence (output should be 1)
int c2 = classifier.Compute(sequences[1], out likelihood);
Console.WriteLine("c2: {0}", c2);
}
public void Aprender(IDadosSinaisDinamicos dados)
{
var quantidadeCaracteristicas = dados.CaracteristicasSinais[0][0].Length;
hmm = new HiddenMarkovClassifier<MultivariateNormalDistribution>(
classes: dados.QuantidadeClasses,
topology: new Forward(QuantidadeEstados),
initial: new MultivariateNormalDistribution(quantidadeCaracteristicas)
);
var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution>(hmm,
modelIndex => new BaumWelchLearning<MultivariateNormalDistribution>(hmm.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 100,
FittingOptions = new NormalOptions { Regularization = 1e-5}
});
teacher.Run(dados.CaracteristicasSinais, dados.IdentificadoresSinais);
}
private void button1_Click(object sender, EventArgs e)
{
var classes = 4;
var states = new[] { 1, 2, 2, 3 };
var cat = new[] { "ខ្ញុំ", "ទៅ", "ខ្លួន", "ក" };
//var cat = new[] { "A", "B" };
_hmmc = new HiddenMarkovClassifier(classes, states, 4, cat);
// Train the ensemble
var sequences = new[]
{
new[] { 1, 1, 1 },
new[] { 0, 2 },
new[] { 0, 1, 2 },
new[] { 1, 2 }
};
var labels = new[] { 0, 1, 2, 3 };
var teacher = new HiddenMarkovClassifierLearning(_hmmc, i =>
new BaumWelchLearning(_hmmc.Models[i])
{
Iterations = 0,
Tolerance = 0.0001
}
);
teacher.Run(sequences, labels);
var m = _hmmc.Models;
var test = new[] { 1, 2 };
double likelihood;
var label = _hmmc.Compute(test, out likelihood);
MessageBox.Show(_hmmc.Models[label].Tag.ToString() + " P =" + likelihood);
}
private void button1_Click(object sender, EventArgs e)
{
var classes = 4;
var states = new[]{1,2,2,3};
var cat = new[] {"ខ្ញុំ", "ទៅ", "ខ្លួន", "ក"};
//var cat = new[] { "A", "B" };
_hmmc = new HiddenMarkovClassifier(classes, states, 4, cat);
// Train the ensemble
var sequences = new[]
{
new[] {1, 1, 1},
new[] {0, 2},
new[] {0, 1, 2},
new[] {1, 2}
};
var labels = new[] {0, 1, 2, 3};
var teacher = new HiddenMarkovClassifierLearning(_hmmc, i =>
new BaumWelchLearning(_hmmc.Models[i])
{
Iterations = 0,
Tolerance = 0.0001
}
);
teacher.Run(sequences, labels);
var m = _hmmc.Models;
var test = new[]{1,2};
double likelihood;
var label = _hmmc.Compute(test, out likelihood);
MessageBox.Show(_hmmc.Models[label].Tag.ToString()+ " P =" + likelihood);
}
public static HiddenMarkovClassifier<MultivariateNormalDistribution> CreateModel3(
int states = 4, bool priors = true)
{
MultivariateNormalDistribution density = new MultivariateNormalDistribution(2);
var classifier = new HiddenMarkovClassifier<MultivariateNormalDistribution>(6,
new Forward(states), density);
string[] labels = { "1", "2", "3", "4", "5", "6" };
for (int i = 0; i < classifier.Models.Length; i++)
classifier.Models[i].Tag = labels[i];
// Create the learning algorithm for the ensemble classifier
var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution>(classifier,
// Train each model using the selected convergence criteria
i => new BaumWelchLearning<MultivariateNormalDistribution>(classifier.Models[i])
{
Tolerance = 0.1,
Iterations = 0,
FittingOptions = new NormalOptions() { Diagonal = true, Regularization = 1e-10 }
}
);
teacher.Empirical = priors;
// Run the learning algorithm
teacher.Run(inputTest, outputTest);
return classifier;
}
public void LearnTest2()
{
// Declare some testing data
int[][] inputs = new int[][]
{
new int[] { 0,0,1,2 }, // Class 0
new int[] { 0,1,1,2 }, // Class 0
new int[] { 0,0,0,1,2 }, // Class 0
new int[] { 0,1,2,2,2 }, // Class 0
new int[] { 2,2,1,0 }, // Class 1
new int[] { 2,2,2,1,0 }, // Class 1
new int[] { 2,2,2,1,0 }, // Class 1
new int[] { 2,2,2,2,1 }, // 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to 3 symbols (0,1,2)
int symbols = 3;
// Nested models will have 3 states each
int[] states = new int[] { 3, 3 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Enable support for sequence rejection
teacher.Rejection = true;
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
HiddenMarkovModel threshold = classifier.Threshold;
Assert.AreEqual(6, threshold.States);
Assert.AreEqual(classifier.Models[0].Transitions[0, 0], threshold.Transitions[0, 0], 1e-10);
Assert.AreEqual(classifier.Models[0].Transitions[1, 1], threshold.Transitions[1, 1], 1e-10);
Assert.AreEqual(classifier.Models[0].Transitions[2, 2], threshold.Transitions[2, 2], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[0, 0], threshold.Transitions[3, 3], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[1, 1], threshold.Transitions[4, 4], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[2, 2], threshold.Transitions[5, 5], 1e-10);
for (int i = 0; i < 3; i++)
for (int j = 3; j < 6; j++)
Assert.AreEqual(Double.NegativeInfinity, threshold.Transitions[i, j]);
for (int i = 3; i < 6; i++)
for (int j = 0; j < 3; j++)
Assert.AreEqual(Double.NegativeInfinity, threshold.Transitions[i, j]);
Assert.IsFalse(Matrix.HasNaN(threshold.Transitions));
classifier.Sensitivity = 0.5;
// Will assert the models have learned the sequences correctly.
for (int i = 0; i < inputs.Length; i++)
{
int expected = outputs[i];
int actual = classifier.Compute(inputs[i], out likelihood);
Assert.AreEqual(expected, actual);
}
int[] r0 = new int[] { 1, 1, 0, 0, 2 };
double logRejection;
int c = classifier.Compute(r0, out logRejection);
Assert.AreEqual(-1, c);
Assert.AreEqual(0.99906957195279988, logRejection);
Assert.IsFalse(double.IsNaN(logRejection));
logRejection = threshold.Evaluate(r0);
Assert.AreEqual(-4.5653702970734793, logRejection, 1e-10);
Assert.IsFalse(double.IsNaN(logRejection));
threshold.Decode(r0, out logRejection);
Assert.AreEqual(-8.21169955167614, logRejection, 1e-10);
Assert.IsFalse(double.IsNaN(logRejection));
foreach (var model in classifier.Models)
{
double[,] A = model.Transitions;
for (int i = 0; i < A.GetLength(0); i++)
{
double[] row = A.Exp().GetRow(i);
double sum = row.Sum();
Assert.AreEqual(1, sum, 1e-10);
}
}
{
double[,] A = classifier.Threshold.Transitions;
for (int i = 0; i < A.GetLength(0); i++)
{
double[] row = A.GetRow(i);
double sum = row.Exp().Sum();
Assert.AreEqual(1, sum, 1e-6);
}
}
}
public void LearnTest4()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
double[][][] sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0 },
new double[] { 1 },
new double[] { 2 },
new double[] { 3 },
new double[] { 4 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 4 },
new double[] { 3 },
new double[] { 2 },
new double[] { 1 },
new double[] { 0 },
}
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Create a mixture of two 1-dimensional normal distributions (by default,
// initialized with zero mean and unit covariance matrices).
var density = new MultivariateMixture<MultivariateNormalDistribution>(
new MultivariateNormalDistribution(1),
new MultivariateNormalDistribution(1));
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier<MultivariateMixture<MultivariateNormalDistribution>>(
2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<MultivariateMixture<MultivariateNormalDistribution>>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning<MultivariateMixture<MultivariateNormalDistribution>>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
// Calculate the probability that the given
// sequences originated from the model
double likelihood1, likelihood2;
// Try to classify the 1st sequence (output should be 0)
int c1 = classifier.Compute(sequences[0], out likelihood1);
// Try to classify the 2nd sequence (output should be 1)
int c2 = classifier.Compute(sequences[1], out likelihood2);
Assert.AreEqual(0, c1);
Assert.AreEqual(1, c2);
Assert.AreEqual(-13.271981026832933, logLikelihood, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood1, 1e-10);
Assert.AreEqual(0.99999791320102149, likelihood2, 1e-10);
Assert.IsFalse(double.IsNaN(logLikelihood));
Assert.IsFalse(double.IsNaN(likelihood1));
Assert.IsFalse(double.IsNaN(likelihood2));
}
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++)
{
Assert.AreEqual(hcrf.Function.Features[i].GetType(), target.Function.Features[i].GetType());
}
Assert.AreEqual(hcrf.Function.Outputs, target.Function.Outputs);
for (int i = 0; i < hcrf.Function.Weights.Length; i++)
{
Assert.AreEqual(hcrf.Function.Weights[i], target.Function.Weights[i]);
}
}
public void LearnTest6()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
double[][][] sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0, 1 },
new double[] { 1, 2 },
new double[] { 2, 3 },
new double[] { 3, 4 },
new double[] { 4, 5 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 4, 3 },
new double[] { 3, 2 },
new double[] { 2, 1 },
new double[] { 1, 0 },
new double[] { 0, -1 },
}
};
// Labels for the sequences
int[] labels = { 0, 1 };
var density = new MultivariateNormalDistribution(2);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier<MultivariateNormalDistribution>(
2, new Custom(new double[2, 2], new double[2]), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning<MultivariateNormalDistribution>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
FittingOptions = new NormalOptions() { Diagonal = true }
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
// Calculate the probability that the given
// sequences originated from the model
double response1, response2;
// Try to classify the 1st sequence (output should be 0)
int c1 = classifier.Compute(sequences[0], out response1);
// Try to classify the 2nd sequence (output should be 1)
int c2 = classifier.Compute(sequences[1], out response2);
Assert.AreEqual(double.NegativeInfinity, logLikelihood);
Assert.AreEqual(0, response1);
Assert.AreEqual(0, response2);
Assert.IsFalse(double.IsNaN(logLikelihood));
Assert.IsFalse(double.IsNaN(response1));
Assert.IsFalse(double.IsNaN(response2));
}
public void LearnTest2()
{
// Declare some testing data
int[][] inputs = new int[][]
{
new int[] { 0, 0, 1, 2 }, // Class 0
new int[] { 0, 1, 1, 2 }, // Class 0
new int[] { 0, 0, 0, 1, 2 }, // Class 0
new int[] { 0, 1, 2, 2, 2 }, // Class 0
new int[] { 2, 2, 1, 0 }, // Class 1
new int[] { 2, 2, 2, 1, 0 }, // Class 1
new int[] { 2, 2, 2, 1, 0 }, // Class 1
new int[] { 2, 2, 2, 2, 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to 3 symbols (0,1,2)
int symbols = 3;
// Nested models will have 3 states each
int[] states = new int[] { 3, 3 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Enable support for sequence rejection
teacher.Rejection = true;
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
// Will assert the models have learned the sequences correctly.
for (int i = 0; i < inputs.Length; i++)
{
int expected = outputs[i];
int actual = classifier.Compute(inputs[i], out likelihood);
Assert.AreEqual(expected, actual);
}
HiddenMarkovModel threshold = classifier.Threshold;
Assert.AreEqual(6, threshold.States);
Assert.AreEqual(classifier.Models[0].Transitions[0, 0], threshold.Transitions[0, 0], 1e-10);
Assert.AreEqual(classifier.Models[0].Transitions[1, 1], threshold.Transitions[1, 1], 1e-10);
Assert.AreEqual(classifier.Models[0].Transitions[2, 2], threshold.Transitions[2, 2], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[0, 0], threshold.Transitions[3, 3], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[1, 1], threshold.Transitions[4, 4], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[2, 2], threshold.Transitions[5, 5], 1e-10);
Assert.IsFalse(Matrix.HasNaN(threshold.Transitions));
int[] r0 = new int[] { 1, 1, 0, 0, 2 };
double logRejection;
int c = classifier.Compute(r0, out logRejection);
Assert.AreEqual(-1, c);
Assert.AreEqual(0.99569011079012049, logRejection);
Assert.IsFalse(double.IsNaN(logRejection));
logRejection = threshold.Evaluate(r0);
Assert.AreEqual(-6.7949285513628528, logRejection, 1e-10);
Assert.IsFalse(double.IsNaN(logRejection));
threshold.Decode(r0, out logRejection);
Assert.AreEqual(-8.902077561009957, logRejection, 1e-10);
Assert.IsFalse(double.IsNaN(logRejection));
}
public void LearnTest2()
{
// Declare some testing data
int[][] inputs = new int[][]
{
new int[] { 0,0,1,2 }, // Class 0
new int[] { 0,1,1,2 }, // Class 0
new int[] { 0,0,0,1,2 }, // Class 0
new int[] { 0,1,2,2,2 }, // Class 0
new int[] { 2,2,1,0 }, // Class 1
new int[] { 2,2,2,1,0 }, // Class 1
new int[] { 2,2,2,1,0 }, // Class 1
new int[] { 2,2,2,2,1 }, // 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to 3 symbols (0,1,2)
int symbols = 3;
// Nested models will have 3 states each
int[] states = new int[] { 3, 3 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Enable support for sequence rejection
teacher.Rejection = true;
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
HiddenMarkovModel threshold = classifier.Threshold;
Assert.AreEqual(6, threshold.States);
Assert.AreEqual(classifier.Models[0].Transitions[0, 0], threshold.Transitions[0, 0], 1e-10);
Assert.AreEqual(classifier.Models[0].Transitions[1, 1], threshold.Transitions[1, 1], 1e-10);
Assert.AreEqual(classifier.Models[0].Transitions[2, 2], threshold.Transitions[2, 2], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[0, 0], threshold.Transitions[3, 3], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[1, 1], threshold.Transitions[4, 4], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[2, 2], threshold.Transitions[5, 5], 1e-10);
for (int i = 0; i < 3; i++)
for (int j = 3; j < 6; j++)
Assert.AreEqual(Double.NegativeInfinity, threshold.Transitions[i, j]);
for (int i = 3; i < 6; i++)
for (int j = 0; j < 3; j++)
Assert.AreEqual(Double.NegativeInfinity, threshold.Transitions[i, j]);
Assert.IsFalse(Matrix.HasNaN(threshold.Transitions));
classifier.Sensitivity = 0.5;
// Will assert the models have learned the sequences correctly.
for (int i = 0; i < inputs.Length; i++)
{
int expected = outputs[i];
int actual = classifier.Compute(inputs[i], out likelihood);
Assert.AreEqual(expected, actual);
}
int[] r0 = new int[] { 1, 1, 0, 0, 2 };
double logRejection;
int c = classifier.Compute(r0, out logRejection);
Assert.AreEqual(-1, c);
Assert.AreEqual(0.99906957195279988, logRejection);
Assert.IsFalse(double.IsNaN(logRejection));
logRejection = threshold.Evaluate(r0);
Assert.AreEqual(-4.5653702970734793, logRejection, 1e-10);
Assert.IsFalse(double.IsNaN(logRejection));
threshold.Decode(r0, out logRejection);
Assert.AreEqual(-8.21169955167614, logRejection, 1e-10);
Assert.IsFalse(double.IsNaN(logRejection));
foreach (var model in classifier.Models)
{
double[,] A = model.Transitions;
for (int i = 0; i < A.GetLength(0); i++)
{
double[] row = A.Exp().GetRow(i);
double sum = row.Sum();
Assert.AreEqual(1, sum, 1e-10);
}
}
{
double[,] A = classifier.Threshold.Transitions;
for (int i = 0; i < A.GetLength(0); i++)
{
double[] row = A.GetRow(i);
double sum = row.Exp().Sum();
Assert.AreEqual(1, sum, 1e-6);
}
}
}
static void runDiscreteDensityHiddenMarkovClassifierLearningExample()
{
// 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.
};
// Use a single topology for all inner models.
ITopology forward = new Forward(states: 3);
// Create a hidden Markov classifier with the given topology.
HiddenMarkovClassifier hmc = new HiddenMarkovClassifier(classes: 3, topology: forward, symbols: 4);
// Create a algorithms to teach each of the inner models.
var trainer = new HiddenMarkovClassifierLearning(
hmc,
// Specify individual training options for each inner model.
modelIndex => new BaumWelchLearning(hmc.Models[modelIndex])
{
Tolerance = 0.001, // iterate until log-likelihood changes less than 0.001.
Iterations = 0 // don't place an upper limit on the number of iterations.
}
);
// Call its Run method to start learning.
double averageLogLikelihood = trainer.Run(observationSequences, classLabels);
Console.WriteLine("average log-likelihood for the observations = {0}", averageLogLikelihood);
// Check the output classificaton label for some sequences.
int y1 = hmc.Compute(new[] { 0, 1, 1, 1, 0 }); // output is y1 = 0.
Console.WriteLine("output class = {0}", y1);
int y2 = hmc.Compute(new[] { 0, 0, 1, 1, 0, 0 }); // output is y2 = 0.
Console.WriteLine("output class = {0}", y2);
int y3 = hmc.Compute(new[] { 2, 2, 2, 2, 1, 1 }); // output is y3 = 1.
Console.WriteLine("output class = {0}", y3);
int y4 = hmc.Compute(new[] { 2, 2, 1, 1 }); // output is y4 = 1.
Console.WriteLine("output class = {0}", y4);
int y5 = hmc.Compute(new[] { 0, 0, 1, 3, 3, 3 }); // output is y5 = 2.
Console.WriteLine("output class = {0}", y4);
int y6 = hmc.Compute(new[] { 2, 0, 2, 2, 3, 3 }); // output is y6 = 2.
Console.WriteLine("output class = {0}", y6);
}
public void LearnTest2()
{
// Declare some testing data
int[][] inputs = new int[][]
{
new int[] { 0,0,1,2 }, // Class 0
new int[] { 0,1,1,2 }, // Class 0
new int[] { 0,0,0,1,2 }, // Class 0
new int[] { 0,1,2,2,2 }, // Class 0
new int[] { 2,2,1,0 }, // Class 1
new int[] { 2,2,2,1,0 }, // Class 1
new int[] { 2,2,2,1,0 }, // Class 1
new int[] { 2,2,2,2,1 }, // 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to 3 symbols (0,1,2)
int symbols = 3;
// Nested models will have 3 states each
int[] states = new int[] { 3, 3 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Enable support for sequence rejection
teacher.Rejection = true;
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
// Will assert the models have learned the sequences correctly.
for (int i = 0; i < inputs.Length; i++)
{
int expected = outputs[i];
int actual = classifier.Compute(inputs[i], out likelihood);
Assert.AreEqual(expected, actual);
}
HiddenMarkovModel threshold = classifier.Threshold;
Assert.AreEqual(6, threshold.States);
Assert.AreEqual(classifier.Models[0].Transitions[0, 0], threshold.Transitions[0, 0], 1e-10);
Assert.AreEqual(classifier.Models[0].Transitions[1, 1], threshold.Transitions[1, 1], 1e-10);
Assert.AreEqual(classifier.Models[0].Transitions[2, 2], threshold.Transitions[2, 2], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[0, 0], threshold.Transitions[3, 3], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[1, 1], threshold.Transitions[4, 4], 1e-10);
Assert.AreEqual(classifier.Models[1].Transitions[2, 2], threshold.Transitions[5, 5], 1e-10);
Assert.IsFalse(Matrix.HasNaN(threshold.Transitions));
int[] r0 = new int[] { 1, 1, 0, 0, 2 };
double logRejection;
int c = classifier.Compute(r0, out logRejection);
Assert.AreEqual(-1, c);
Assert.AreEqual(0.99569011079012049, logRejection);
Assert.IsFalse(double.IsNaN(logRejection));
logRejection = threshold.Evaluate(r0);
Assert.AreEqual(-6.7949285513628528, logRejection, 1e-10);
Assert.IsFalse(double.IsNaN(logRejection));
threshold.Decode(r0, out logRejection);
Assert.AreEqual(-8.902077561009957, logRejection, 1e-10);
Assert.IsFalse(double.IsNaN(logRejection));
}
public void CrossvalidationConstructorTest2()
{
Accord.Math.Tools.SetupGenerator(0);
// This is a sample code on how to use Cross-Validation
// to assess the performance of Hidden Markov Models.
// 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, 1, 1, 0 }, // Class 0
new int[] { 0, 1, 1, 0 }, // Class 0
new int[] { 0, 1, 1, 1, 0 }, // Class 0
new int[] { 0, 1, 1, 1, 0 }, // Class 0
new int[] { 0, 1, 0, 1, 0 }, // Class 0
new int[] { 0, 1, 0 }, // Class 0
new int[] { 0, 1, 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
new int[] { 1, 1, 0, 1 }, // Class 1
new int[] { 1, 0, 1 }, // Class 1
new int[] { 1, 0, 0, 1 }, // Class 1
new int[] { 1, 0, 0, 0, 1 }, // Class 1
new int[] { 1, 0, 1 }, // Class 1
new int[] { 1, 0, 0, 0, 1 }, // Class 1
};
int[] outputs = new int[]
{
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, // First 10 sequences are of class 0
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, // Last 10 sequences are of class 1
};
// Create a new Cross-validation algorithm passing the data set size and the number of folds
var crossvalidation = new CrossValidation <HiddenMarkovClassifier>(size: inputs.Length, folds: 3);
// Define a fitting function using Support Vector Machines. The objective of this
// function is to learn a SVM in the subset of the data indicated by cross-validation.
crossvalidation.Fitting = delegate(int k, int[] indicesTrain, int[] indicesValidation)
{
// The fitting function is passing the indices of the original set which
// should be considered training data and the indices of the original set
// which should be considered validation data.
// Lets now grab the training data:
var trainingInputs = inputs.Submatrix(indicesTrain);
var trainingOutputs = outputs.Submatrix(indicesTrain);
// And now the validation data:
var validationInputs = inputs.Submatrix(indicesValidation);
var validationOutputs = outputs.Submatrix(indicesValidation);
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to two symbols (0 or 1)
int symbols = 2;
// Nested models will have two states each
int[] states = new int[] { 2, 2 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(trainingInputs, trainingOutputs);
double trainingError = teacher.ComputeError(trainingInputs, trainingOutputs);
// Now we can compute the validation error on the validation data:
double validationError = teacher.ComputeError(validationInputs, validationOutputs);
// Return a new information structure containing the model and the errors achieved.
return(new CrossValidationValues <HiddenMarkovClassifier>(classifier, trainingError, validationError));
};
// Compute the cross-validation
var result = crossvalidation.Compute();
// Finally, access the measured performance.
double trainingErrors = result.Training.Mean;
double validationErrors = result.Validation.Mean;
Assert.AreEqual(3, crossvalidation.K);
Assert.AreEqual(0, result.Training.Mean);
Assert.AreEqual(0, result.Validation.Mean);
Assert.AreEqual(3, crossvalidation.Folds.Length);
Assert.AreEqual(3, result.Models.Length);
}
public void LearnTest8()
{
// Declare some testing data
double[][] inputs = new double[][]
{
new double[] { 0,0,1,2 }, // Class 0
new double[] { 0,1,1,2 }, // Class 0
new double[] { 0,0,0,1,2 }, // Class 0
new double[] { 0,1,2,2,2 }, // Class 0
new double[] { 2,2,1,0 }, // Class 1
new double[] { 2,2,2,1,0 }, // Class 1
new double[] { 2,2,2,1,0 }, // Class 1
new double[] { 2,2,2,2,1 }, // 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
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to 3 symbols (0,1,2)
int symbols = 3;
// Nested models will have 3 states each
int[] states = new int[] { 3, 3 };
// Creates a new Hidden Markov Model Classifier with the given parameters
var classifier = HiddenMarkovClassifier.CreateGeneric(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<GeneralDiscreteDistribution>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning<GeneralDiscreteDistribution>(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Enable support for sequence rejection
teacher.Rejection = true;
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(inputs, outputs);
Assert.AreEqual(-0.84036002169162149, likelihood);
likelihood = testThresholdModel(inputs, outputs, classifier, likelihood);
}
public static HiddenMarkovClassifier<Independent> CreateModel1()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
double[][][] sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0 },
new double[] { 1 },
new double[] { 2 },
new double[] { 3 },
new double[] { 4 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 4 },
new double[] { 3 },
new double[] { 2 },
new double[] { 1 },
new double[] { 0 },
}
};
// Labels for the sequences
int[] labels = { 0, 1 };
// Creates a sequence classifier containing 2 hidden Markov Models
// with 2 states and an underlying Normal distribution as density.
NormalDistribution component = new NormalDistribution();
Independent density = new Independent(component);
var classifier = new HiddenMarkovClassifier<Independent>(2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<Independent>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning<Independent>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
Assert.AreEqual(-13.271981026832929d, logLikelihood);
return classifier;
}
private static HiddenMarkovClassifier<NormalDistribution> createClassifier(
out double[][] sequences, bool rejection = false)
{
sequences = new double[][]
{
new double[] { 0,1,2,3,4 },
new double[] { 4,3,2,1,0 },
};
int[] labels = { 0, 1 };
NormalDistribution density = new NormalDistribution();
HiddenMarkovClassifier<NormalDistribution> classifier =
new HiddenMarkovClassifier<NormalDistribution>(2, new Ergodic(2), density);
var teacher = new HiddenMarkovClassifierLearning<NormalDistribution>(classifier,
modelIndex => new BaumWelchLearning<NormalDistribution>(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
teacher.Rejection = rejection;
teacher.Run(sequences, labels);
return classifier;
}
private void btnTrain_Click(object sender, EventArgs e)
{
if (dataGridView1.Rows.Count == 0)
{
MessageBox.Show("Please load or insert some data first.");
return;
}
int states = (int)numStates.Value;
int iterations = (int)numIterations.Value;
double tolerance = (double)numConvergence.Value;
if (rbStopIterations.Checked) tolerance = 0.0;
if (rbStopConvergence.Checked) iterations = 0;
// Retrieve the training data from the data grid view
int rows = dataGridView1.Rows.Count;
int[] outputs = new int[rows];
var sequences = new int[rows][];
for (int i = 0; i < rows; i++)
{
outputs[i] = (int)dataGridView1.Rows[i].Cells["colLabel"].Value - 1;
sequences[i] = GetFeatures((double[][])dataGridView1.Rows[i].Tag);
}
int classes = outputs.Distinct().Count();
string[] labels = new string[classes];
for (int i = 0; i < labels.Length; i++)
labels[i] = (i+1).ToString();
// Create a sequence classifier for 3 classes
classifier = new HiddenMarkovClassifier(labels.Length,
new Forward(states), symbols: 20, names: labels);
// Create the learning algorithm for the ensemble classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model using the selected convergence criteria
i => new BaumWelchLearning(classifier.Models[i])
{
Tolerance = tolerance,
Iterations = iterations,
}
);
// Create and use a rejection threshold model
teacher.Rejection = cbRejection.Checked;
teacher.Empirical = true;
teacher.Smoothing = (double)numSmoothing.Value;
// Run the learning algorithm
teacher.Run(sequences, outputs);
double error = classifier.LogLikelihood(sequences, outputs);
int hits = 0;
toolStripProgressBar1.Visible = true;
toolStripProgressBar1.Value = 0;
toolStripProgressBar1.Step = 1;
toolStripProgressBar1.Maximum = dataGridView1.Rows.Count;
for (int i = 0; i < rows; i++)
{
double likelihood;
int index = classifier.Compute(sequences[i], out likelihood);
DataGridViewRow row = dataGridView1.Rows[i];
if (index == -1)
{
row.Cells["colClassification"].Value = String.Empty;
}
else
{
row.Cells["colClassification"].Value = classifier.Models[index].Tag;
}
int expected = (int)row.Cells["colLabel"].Value;
if (expected == index + 1)
{
row.Cells[0].Style.BackColor = Color.LightGreen;
row.Cells[1].Style.BackColor = Color.LightGreen;
row.Cells[2].Style.BackColor = Color.LightGreen;
hits++;
}
else
{
row.Cells[0].Style.BackColor = Color.White;
row.Cells[1].Style.BackColor = Color.White;
row.Cells[2].Style.BackColor = Color.White;
}
toolStripProgressBar1.PerformStep();
}
dgvModels.DataSource = classifier.Models;
toolStripProgressBar1.Visible = false;
toolStripStatusLabel1.Text = String.Format("Training complete. Hits: {0}/{1} ({2:0%})",
hits, dataGridView1.Rows.Count, (double)hits / dataGridView1.Rows.Count);
}
/// <summary>
/// Trains the ensemble
/// </summary>
private void btnTrain_Click(object sender, EventArgs e)
{
DataTable source = dgvSequenceSource.DataSource as DataTable;
if (source == null || hmmc == null)
{
MessageBox.Show("Please create a sequence classifier first.");
return;
}
int rows = source.Rows.Count;
// Gets the input sequences
int[][] sequences = new int[rows][];
int[] labels = new int[rows];
for (int i = 0; i < rows; i++)
{
DataRow row = source.Rows[i];
string label = row["Label"] as string;
for (int j = 0; j < hmmc.Models.Length; j++)
{
if (hmmc.Models[j].Tag.Equals(label))
{
labels[i] = j;
break;
}
}
sequences[i] = decode(row["Sequences"] as string);
}
// Grab training parameters
int iterations = (int)numIterations.Value;
double limit = (double)numConvergence.Value;
if (rbStopIterations.Checked)
{
limit = 0;
}
else
{
iterations = 0;
}
// Train the ensemble
var teacher = new HiddenMarkovClassifierLearning(hmmc, i =>
new BaumWelchLearning(hmmc.Models[i])
{
Iterations = iterations,
Tolerance = limit
}
);
teacher.Run(sequences, labels);
// Update the GUI
dgvModels_CurrentCellChanged(this, EventArgs.Empty);
}
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;
}
public static HiddenMarkovClassifier <Independent <NormalDistribution> > CreateModel4(out double[][][] words, out int[] labels, bool usePriors)
{
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[][] wardrobe2 =
{
new double[] { 0.0, 0.0, 1.0, 0.0 }, // same for the
new double[] { 0.2, 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, 0.2 },
};
words = new double[][][] { hello, car, wardrobe, wardrobe2 };
labels = new [] { 0, 1, 2, 2 };
var initial = new Independent <NormalDistribution>
(
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 <NormalDistribution> >
(
classes: numberOfWords,
topology: new Forward(numberOfStates),
initial: initial
);
var teacher = new HiddenMarkovClassifierLearning <Independent <NormalDistribution> >(classifier,
modelIndex => new BaumWelchLearning <Independent <NormalDistribution> >(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 100,
FittingOptions = new IndependentOptions()
{
InnerOption = new NormalOptions()
{
Regularization = 1e-5
}
}
}
);
if (usePriors)
{
teacher.Empirical = true;
}
double logLikelihood = teacher.Run(words, labels);
Assert.AreEqual(208.38345600145777d, logLikelihood);
return(classifier);
}
public void LearnTest7()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
double[][][] sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0, 1 },
new double[] { 1, 2 },
new double[] { 2, 3 },
new double[] { 3, 4 },
new double[] { 4, 5 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 4, 3 },
new double[] { 3, 2 },
new double[] { 2, 1 },
new double[] { 1, 0 },
new double[] { 0, -1 },
}
};
// Labels for the sequences
int[] labels = { 0, 1 };
var initialDensity = new MultivariateNormalDistribution(2);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier<MultivariateNormalDistribution>(
classes: 2, topology: new Forward(2), initial: initialDensity);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning<MultivariateNormalDistribution>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
FittingOptions = new NormalOptions()
{
Diagonal = true, // only diagonal covariance matrices
Regularization = 1e-5 // avoid non-positive definite errors
}
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
// Calculate the probability that the given
// sequences originated from the model
double likelihood, likelihood2;
// Try to classify the 1st sequence (output should be 0)
int c1 = classifier.Compute(sequences[0], out likelihood);
// Try to classify the 2nd sequence (output should be 1)
int c2 = classifier.Compute(sequences[1], out likelihood2);
Assert.AreEqual(0, c1);
Assert.AreEqual(1, c2);
Assert.AreEqual(-24.560663315259973, logLikelihood, 1e-10);
Assert.AreEqual(0.99999999998805045, likelihood, 1e-10);
Assert.AreEqual(0.99999999998805045, likelihood2, 1e-10);
Assert.IsFalse(double.IsNaN(logLikelihood));
Assert.IsFalse(double.IsNaN(likelihood));
Assert.IsFalse(double.IsNaN(likelihood2));
}
public TrainResult TrainAll(Dictionary<string, IList<ISoundSignalReader>> signalsDictionary,
SignalVisitor voiceVisitor = null)
{
var numberOfItems = 0;
foreach (var item in signalsDictionary)
{
numberOfItems += item.Value.Count;
}
double[][][][] featuresInput = new Double[signalsDictionary.Count][][][];
int[] models = new int[numberOfItems];
var allSignalIndex = 0;
var modelIndex = 0;
var featureUtility = new FeatureUtility(_engineParameters);
foreach (var item in signalsDictionary)
{
var signals = item.Value; // signals
var signalsCount = signals.Count();
featuresInput[modelIndex] = new double[signalsCount][][];
for (var signalIndex = 0; signalIndex < signalsCount; signalIndex++)
{
var signal = signals[signalIndex];
List<Double[]> features = featureUtility.ExtractFeatures(signal, voiceVisitor).First();
featuresInput[modelIndex][signalIndex] = features.ToArray();
models[allSignalIndex] = modelIndex;
allSignalIndex++;
}
modelIndex++;
}
List<int[]> observables = new List<int[]>();
for (int wordIndex = 0; wordIndex < featuresInput.Length; wordIndex++) // foreach word
{
for (var signalIndex = 0; signalIndex < featuresInput[wordIndex].Length; signalIndex++)
// foreach word signal
{
var points = featuresInput[wordIndex][signalIndex].Select(item => new Point(item));
// convert feature to points
var codeItems = _codeBook.Quantize(points.ToArray());
observables.Add(codeItems);
}
}
//HiddenMarkovModel hmm = new HiddenMarkovModel(5, _codeBook.Size, true);
//var Bauc
var hmm = new HiddenMarkovClassifier(signalsDictionary.Count, new Forward(_numberOfHiddenStates),
_codeBook.Size, signalsDictionary.Keys.ToArray());
const int iterations = 200;
const double tolerance = 0;
var teacher = new HiddenMarkovClassifierLearning(hmm,
i => new ViterbiLearning(hmm.Models[i]) {Iterations = iterations, Tolerance = tolerance}
);
teacher.Run(observables.ToArray(), models);
return new TrainResult {Catalog = _codeBook, Models = hmm.Models.ToArray()};
}
public void LearnTest9()
{
double[][][] inputs = large_gestures;
int[] outputs = large_outputs;
int states = 5;
int iterations = 100;
double tolerance = 0.01;
bool rejection = true;
double sensitivity = 1E-85;
int dimension = inputs[0][0].Length;
var hmm = new HiddenMarkovClassifier<MultivariateNormalDistribution>(2,
new Forward(states), new MultivariateNormalDistribution(dimension));
// 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 logLikelihood = teacher.Run(inputs, outputs);
hmm.Sensitivity = sensitivity;
for (int i = 0; i < large_gestures.Length; i++)
{
int actual = hmm.Compute(large_gestures[i]);
int expected = large_outputs[i];
Assert.AreEqual(expected,actual);
}
}
static void runDiscreteDensityHiddenMarkovClassifierLearningExample()
{
// 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.
};
// Use a single topology for all inner models.
ITopology forward = new Forward(states: 3);
// Create a hidden Markov classifier with the given topology.
HiddenMarkovClassifier hmc = new HiddenMarkovClassifier(classes: 3, topology: forward, symbols: 4);
// Create a algorithms to teach each of the inner models.
var trainer = new HiddenMarkovClassifierLearning(
hmc,
// Specify individual training options for each inner model.
modelIndex => new BaumWelchLearning(hmc.Models[modelIndex])
{
Tolerance = 0.001, // iterate until log-likelihood changes less than 0.001.
Iterations = 0 // don't place an upper limit on the number of iterations.
}
);
// Call its Run method to start learning.
double averageLogLikelihood = trainer.Run(observationSequences, classLabels);
Console.WriteLine("average log-likelihood for the observations = {0}", averageLogLikelihood);
// Check the output classificaton label for some sequences.
int y1 = hmc.Compute(new[] { 0, 1, 1, 1, 0 }); // output is y1 = 0.
Console.WriteLine("output class = {0}", y1);
int y2 = hmc.Compute(new[] { 0, 0, 1, 1, 0, 0 }); // output is y2 = 0.
Console.WriteLine("output class = {0}", y2);
int y3 = hmc.Compute(new[] { 2, 2, 2, 2, 1, 1 }); // output is y3 = 1.
Console.WriteLine("output class = {0}", y3);
int y4 = hmc.Compute(new[] { 2, 2, 1, 1 }); // output is y4 = 1.
Console.WriteLine("output class = {0}", y4);
int y5 = hmc.Compute(new[] { 0, 0, 1, 3, 3, 3 }); // output is y5 = 2.
Console.WriteLine("output class = {0}", y4);
int y6 = hmc.Compute(new[] { 2, 0, 2, 2, 3, 3 }); // output is y6 = 2.
Console.WriteLine("output class = {0}", y6);
}
/// <summary>
/// learn Hmm model for samples in given database using Baum Welch unsupervised learning algorithm
/// then used the learned model to classify the training samples
/// </summary>
/// <param name="database"></param>
/// <returns></returns>
static HiddenMarkovClassifier<MultivariateNormalDistribution> learnHMM(Database database)
{
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 = 0.1;
bool rejection = true;
HiddenMarkovClassifier<MultivariateNormalDistribution> 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);
}
return hmm;
}
/// <summary>
/// Trains the hidden Markov classifier
/// </summary>
///
private void btnTrain_Click(object sender, EventArgs e)
{
DataTable source = dgvSequenceSource.DataSource as DataTable;
if (source == null || hmmc == null)
{
MessageBox.Show("Please create a sequence classifier first.");
return;
}
int rows = source.Rows.Count;
// Gets the input sequences
int[][] sequences = new int[rows][];
int[] labels = new int[rows];
// Foreach row in the datagridview
for (int i = 0; i < rows; i++)
{
// Get the row at the index
DataRow row = source.Rows[i];
// Get the label associated with this sequence
string label = row["Label"] as string;
// Extract the sequence and the expected label for it
sequences[i] = decode(row["Sequences"] as string);
labels[i] = hmmc.Models.Find(x => x.Tag as string == label)[0];
}
// Grab training parameters
int iterations = (int)numIterations.Value;
double limit = (double)numConvergence.Value;
if (rbStopIterations.Checked)
{
limit = 0;
}
else
{
iterations = 0;
}
// Create a new hidden Markov model learning algorithm
var teacher = new HiddenMarkovClassifierLearning(hmmc, i =>
{
return new BaumWelchLearning(hmmc.Models[i])
{
Iterations = iterations,
Tolerance = limit
};
});
// Learn the classifier
teacher.Run(sequences, labels);
// Update the GUI
dgvModels_CurrentCellChanged(this, EventArgs.Empty);
}
private void btnTrain_Click(object sender, EventArgs e)
{
if (dataGridView1.Rows.Count == 0)
{
MessageBox.Show("Please load or insert some data first.");
return;
}
int states = (int)numStates.Value;
int iterations = (int)numIterations.Value;
double tolerance = (double)numConvergence.Value;
if (rbStopIterations.Checked)
{
tolerance = 0.0;
}
if (rbStopConvergence.Checked)
{
iterations = 0;
}
// Retrieve the training data from the data grid view
int rows = dataGridView1.Rows.Count;
int[] outputs = new int[rows];
var sequences = new int[rows][];
for (int i = 0; i < rows; i++)
{
outputs[i] = (int)dataGridView1.Rows[i].Cells["colLabel"].Value - 1;
sequences[i] = GetFeatures((double[][])dataGridView1.Rows[i].Tag);
}
int classes = outputs.Distinct().Count();
string[] labels = new string[classes];
for (int i = 0; i < labels.Length; i++)
{
labels[i] = (i + 1).ToString();
}
// Create a sequence classifier for 3 classes
classifier = new HiddenMarkovClassifier(labels.Length,
new Forward(states), symbols: 20, names: labels);
// Create the learning algorithm for the ensemble classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model using the selected convergence criteria
i => new BaumWelchLearning(classifier.Models[i])
{
Tolerance = tolerance,
Iterations = iterations,
}
);
// Create and use a rejection threshold model
teacher.Rejection = cbRejection.Checked;
teacher.Empirical = true;
teacher.Smoothing = (double)numSmoothing.Value;
// Run the learning algorithm
teacher.Run(sequences, outputs);
double error = classifier.LogLikelihood(sequences, outputs);
int hits = 0;
toolStripProgressBar1.Visible = true;
toolStripProgressBar1.Value = 0;
toolStripProgressBar1.Step = 1;
toolStripProgressBar1.Maximum = dataGridView1.Rows.Count;
for (int i = 0; i < rows; i++)
{
double likelihood;
int index = classifier.Compute(sequences[i], out likelihood);
DataGridViewRow row = dataGridView1.Rows[i];
if (index == -1)
{
row.Cells["colClassification"].Value = String.Empty;
}
else
{
row.Cells["colClassification"].Value = classifier.Models[index].Tag;
}
int expected = (int)row.Cells["colLabel"].Value;
if (expected == index + 1)
{
row.Cells[0].Style.BackColor = Color.LightGreen;
row.Cells[1].Style.BackColor = Color.LightGreen;
row.Cells[2].Style.BackColor = Color.LightGreen;
hits++;
}
else
{
row.Cells[0].Style.BackColor = Color.White;
row.Cells[1].Style.BackColor = Color.White;
row.Cells[2].Style.BackColor = Color.White;
}
toolStripProgressBar1.PerformStep();
}
dgvModels.DataSource = classifier.Models;
toolStripProgressBar1.Visible = false;
toolStripStatusLabel1.Text = String.Format("Training complete. Hits: {0}/{1} ({2:0%})",
hits, dataGridView1.Rows.Count, (double)hits / dataGridView1.Rows.Count);
}
/// <summary>
/// Trains the hidden Markov classifier
/// </summary>
///
private void btnTrain_Click(object sender, EventArgs e)
{
DataTable source = dgvSequenceSource.DataSource as DataTable;
if (source == null || hmmc == null)
{
MessageBox.Show("Please create a sequence classifier first.");
return;
}
int rows = source.Rows.Count;
// Gets the input sequences
int[][] sequences = new int[rows][];
int[] labels = new int[rows];
// Foreach row in the datagridview
for (int i = 0; i < rows; i++)
{
// Get the row at the index
DataRow row = source.Rows[i];
// Get the label associated with this sequence
string label = row["Label"] as string;
// Extract the sequence and the expected label for it
sequences[i] = decode(row["Sequences"] as string);
labels[i] = hmmc.Models.Find(x => x.Tag as string == label)[0];
}
// Grab training parameters
int iterations = (int)numIterations.Value;
double limit = (double)numConvergence.Value;
if (rbStopIterations.Checked)
{
limit = 0;
}
else
{
iterations = 0;
}
// Create a new hidden Markov model learning algorithm
var teacher = new HiddenMarkovClassifierLearning(hmmc, i =>
{
return(new BaumWelchLearning(hmmc.Models[i])
{
Iterations = iterations,
Tolerance = limit
});
});
// Learn the classifier
teacher.Run(sequences, labels);
// Update the GUI
dgvModels_CurrentCellChanged(this, EventArgs.Empty);
}
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 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);
}
public static HiddenMarkovClassifier<Independent> CreateModel2(out double[][][] sequences, out int[] labels)
{
sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0, 1.1 },
new double[] { 1, 2.5 },
new double[] { 1, 3.4 },
new double[] { 1, 4.7 },
new double[] { 2, 5.8 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 2, 3.2 },
new double[] { 2, 2.6 },
new double[] { 1, 1.2 },
new double[] { 1, 0.8 },
new double[] { 0, 1.1 },
}
};
labels = new[] { 0, 1 };
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
var comp1 = new GeneralDiscreteDistribution(3);
var comp2 = new NormalDistribution(1);
var density = new Independent(comp1, comp2);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier<Independent>(
2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<Independent>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning<Independent>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
return classifier;
}
/// <summary>
/// Trains the ensemble
/// </summary>
private void btnTrain_Click(object sender, EventArgs e)
{
DataTable source = dgvSequenceSource.DataSource as DataTable;
if (source == null || hmmc == null)
{
MessageBox.Show("Please create a sequence classifier first.");
return;
}
int rows = source.Rows.Count;
// Gets the input sequences
int[][] sequences = new int[rows][];
int[] labels = new int[rows];
for (int i = 0; i < rows; i++)
{
DataRow row = source.Rows[i];
string label = row["Label"] as string;
for (int j = 0; j < hmmc.Models.Length; j++)
{
if (hmmc.Models[j].Tag.Equals(label))
{
labels[i] = j;
break;
}
}
sequences[i] = decode(row["Sequences"] as string);
}
// Grab training parameters
int iterations = (int)numIterations.Value;
double limit = (double)numConvergence.Value;
if (rbStopIterations.Checked)
{
limit = 0;
}
else
{
iterations = 0;
}
// Train the ensemble
var teacher = new HiddenMarkovClassifierLearning(hmmc, i =>
new BaumWelchLearning(hmmc.Models[i])
{
Iterations = iterations,
Tolerance = limit
}
);
teacher.Run(sequences, labels);
// Update the GUI
dgvModels_CurrentCellChanged(this, EventArgs.Empty);
}
public static HiddenMarkovClassifier<Independent> CreateModel3(out double[][][] sequences2, out int[] labels2)
{
sequences2 = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 1, 1.12, 2.41, 1.17, 9.3 },
new double[] { 1, 2.54, 1.45, 0.16, 4.5 },
new double[] { 1, 3.46, 2.63, 1.15, 9.2 },
new double[] { 1, 4.73, 0.41, 1.54, 5.5 },
new double[] { 2, 5.81, 2.42, 1.13, 9.1 },
},
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0, 1.49, 2.48, 1.18, 9.37 },
new double[] { 1, 2.18, 1.44, 2.19, 1.56 },
new double[] { 1, 3.77, 2.62, 1.10, 9.25 },
new double[] { 2, 4.76, 5.44, 3.58, 5.54 },
new double[] { 2, 5.85, 2.46, 1.16, 5.13 },
new double[] { 2, 4.84, 5.44, 3.54, 5.52 },
new double[] { 2, 5.83, 3.41, 1.22, 5.11 },
},
new double[][]
{
// This is the first sequence with label = 0
new double[] { 2, 1.11, 2.41, 1.12, 2.31 },
new double[] { 1, 2.52, 3.73, 0.12, 4.50 },
new double[] { 1, 3.43, 2.61, 1.24, 9.29 },
new double[] { 1, 4.74, 2.42, 2.55, 6.57 },
new double[] { 2, 5.85, 2.43, 1.16, 9.16 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 0, 1.26, 5.44, 1.56, 9.55 },
new double[] { 2, 2.67, 5.45, 4.27, 1.54 },
new double[] { 1, 1.28, 3.46, 2.18, 4.13 },
new double[] { 1, 5.89, 2.57, 1.79, 5.02 },
new double[] { 0, 1.40, 2.48, 2.10, 6.41 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 2, 3.21, 2.49, 1.54, 9.17 },
new double[] { 2, 2.62, 5.40, 4.25, 1.54 },
new double[] { 1, 1.53, 6.49, 2.17, 4.52 },
new double[] { 1, 2.84, 2.58, 1.73, 6.04 },
new double[] { 1, 1.45, 2.47, 2.28, 5.42 },
new double[] { 1, 1.46, 2.46, 2.35, 5.41 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 1, 5.27, 5.45, 1.4, 9.5 },
new double[] { 2, 2.68, 2.54, 3.2, 2.2 },
new double[] { 1, 2.89, 3.83, 2.6, 4.1 },
new double[] { 1, 1.80, 1.32, 1.2, 4.2 },
new double[] { 0, 1.41, 2.41, 2.1, 6.4 },
}
};
labels2 = new[] { 0, 0, 0, 1, 1, 1 };
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
var comp1 = new GeneralDiscreteDistribution(3);
var comp2 = new NormalDistribution(1);
var comp3 = new NormalDistribution(2);
var comp4 = new NormalDistribution(3);
var comp5 = new NormalDistribution(4);
var density = new Independent(comp1, comp2, comp3, comp4, comp5);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier<Independent>(
2, new Forward(5), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<Independent>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning<Independent>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences2, labels2);
return classifier;
}
public static HiddenMarkovClassifier<Independent<NormalDistribution>> CreateModel4(out double[][][] words, out int[] labels, bool usePriors)
{
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[][] wardrobe2 =
{
new double[] { 0.0, 0.0, 1.0, 0.0 }, // same for the
new double[] { 0.2, 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, 0.2 },
};
words = new double[][][] { hello, car, wardrobe, wardrobe2 };
labels = new [] { 0, 1, 2, 2 };
var initial = new Independent<NormalDistribution>
(
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<NormalDistribution>>
(
classes: numberOfWords,
topology: new Forward(numberOfStates),
initial: initial
);
var teacher = new HiddenMarkovClassifierLearning<Independent<NormalDistribution>>(classifier,
modelIndex => new BaumWelchLearning<Independent<NormalDistribution>>(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 100,
FittingOptions = new IndependentOptions()
{
InnerOption = new NormalOptions() { Regularization = 1e-5 }
}
}
);
if (usePriors)
teacher.Empirical = true;
double logLikelihood = teacher.Run(words, labels);
return classifier;
}
public static HiddenMarkovClassifier<Independent> CreateModel3()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
var comp1 = new GeneralDiscreteDistribution(3);
var comp2 = new NormalDistribution(1);
var comp3 = new NormalDistribution(2);
var comp4 = new NormalDistribution(3);
var comp5 = new NormalDistribution(4);
var density = new Independent(comp1, comp2, comp3, comp4, comp5);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier<Independent>(
2, new Forward(5), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<Independent>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning<Independent>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences2, labels2);
return classifier;
}
public static void LearnAndPredict()
{
// 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
};
// We will use a single topology for all inner models, but we
// could also have explicit led different topologies for each:
ITopology forward = new Forward(state_count: 3);
// Now we create a hidden Markov classifier with the given topology
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(class_count: 3, topology: forward, symbol_count: 4);
// And create a algorithms to teach each of the inner models
var teacher = new HiddenMarkovClassifierLearning(classifier,
// We can specify individual training options for each inner model:
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001, // iterate until log-likelihood changes less than 0.001
Iterations = 0 // don't place an upper limit on the number of iterations
});
// Then let's call its Run method to start learning
double error = teacher.Run(inputSequences, outputLabels);
// After training has finished, we can check the
// output classificaton label for some sequences.
int y1 = classifier.Compute(new[] { 0, 1, 1, 1, 0 }); // output is y1 = 0
int y2 = classifier.Compute(new[] { 0, 0, 1, 1, 0, 0 }); // output is y1 = 0
int y3 = classifier.Compute(new[] { 2, 2, 2, 2, 1, 1 }); // output is y2 = 1
int y4 = classifier.Compute(new[] { 2, 2, 1, 1 }); // output is y2 = 1
int y5 = classifier.Compute(new[] { 0, 0, 1, 3, 3, 3 }); // output is y3 = 2
int y6 = classifier.Compute(new[] { 2, 0, 2, 2, 3, 3 }); // output is y3 = 2
Console.WriteLine("y1 = {0}", y1);
Console.WriteLine("y2 = {0}", y2);
Console.WriteLine("y3 = {0}", y3);
Console.WriteLine("y4 = {0}", y4);
Console.WriteLine("y5 = {0}", y5);
Console.WriteLine("y6 = {0}", y6);
}
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).
Independent initial = new Independent
(
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 classifier = new HiddenMarkovClassifier <Independent>
(
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>(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning <Independent>(classifier.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 c1 = classifier.Compute(hello);
int c2 = classifier.Compute(car);
int c3 = classifier.Compute(wardrobe);
Assert.AreEqual(0, c1);
Assert.AreEqual(1, c2);
Assert.AreEqual(2, c3);
}
public static HiddenMarkovClassifier<MultivariateNormalDistribution> CreateModel2()
{
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
double[][][] sequences = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0, 1 },
new double[] { 1, 2 },
new double[] { 2, 3 },
new double[] { 3, 4 },
new double[] { 4, 5 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 4, 3 },
new double[] { 3, 2 },
new double[] { 2, 1 },
new double[] { 1, 0 },
new double[] { 0, -1 },
}
};
// Labels for the sequences
int[] labels = { 0, 1 };
var density = new MultivariateNormalDistribution(2);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier<MultivariateNormalDistribution>(
2, new Ergodic(2), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning<MultivariateNormalDistribution>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
FittingOptions = new NormalOptions() { Diagonal = true }
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences, labels);
return classifier;
}
public static HiddenMarkovClassifier <Independent> CreateModel3(out double[][][] sequences2, out int[] labels2)
{
sequences2 = new double[][][]
{
new double[][]
{
// This is the first sequence with label = 0
new double[] { 1, 1.12, 2.41, 1.17, 9.3 },
new double[] { 1, 2.54, 1.45, 0.16, 4.5 },
new double[] { 1, 3.46, 2.63, 1.15, 9.2 },
new double[] { 1, 4.73, 0.41, 1.54, 5.5 },
new double[] { 2, 5.81, 2.42, 1.13, 9.1 },
},
new double[][]
{
// This is the first sequence with label = 0
new double[] { 0, 1.49, 2.48, 1.18, 9.37 },
new double[] { 1, 2.18, 1.44, 2.19, 1.56 },
new double[] { 1, 3.77, 2.62, 1.10, 9.25 },
new double[] { 2, 4.76, 5.44, 3.58, 5.54 },
new double[] { 2, 5.85, 2.46, 1.16, 5.13 },
new double[] { 2, 4.84, 5.44, 3.54, 5.52 },
new double[] { 2, 5.83, 3.41, 1.22, 5.11 },
},
new double[][]
{
// This is the first sequence with label = 0
new double[] { 2, 1.11, 2.41, 1.12, 2.31 },
new double[] { 1, 2.52, 3.73, 0.12, 4.50 },
new double[] { 1, 3.43, 2.61, 1.24, 9.29 },
new double[] { 1, 4.74, 2.42, 2.55, 6.57 },
new double[] { 2, 5.85, 2.43, 1.16, 9.16 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 0, 1.26, 5.44, 1.56, 9.55 },
new double[] { 2, 2.67, 5.45, 4.27, 1.54 },
new double[] { 1, 1.28, 3.46, 2.18, 4.13 },
new double[] { 1, 5.89, 2.57, 1.79, 5.02 },
new double[] { 0, 1.40, 2.48, 2.10, 6.41 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 2, 3.21, 2.49, 1.54, 9.17 },
new double[] { 2, 2.62, 5.40, 4.25, 1.54 },
new double[] { 1, 1.53, 6.49, 2.17, 4.52 },
new double[] { 1, 2.84, 2.58, 1.73, 6.04 },
new double[] { 1, 1.45, 2.47, 2.28, 5.42 },
new double[] { 1, 1.46, 2.46, 2.35, 5.41 },
},
new double[][]
{
// This is the second sequence with label = 1
new double[] { 1, 5.27, 5.45, 1.4, 9.5 },
new double[] { 2, 2.68, 2.54, 3.2, 2.2 },
new double[] { 1, 2.89, 3.83, 2.6, 4.1 },
new double[] { 1, 1.80, 1.32, 1.2, 4.2 },
new double[] { 0, 1.41, 2.41, 2.1, 6.4 },
}
};
labels2 = new[] { 0, 0, 0, 1, 1, 1 };
// Create a Continuous density Hidden Markov Model Sequence Classifier
// to detect a multivariate sequence and the same sequence backwards.
var comp1 = new GeneralDiscreteDistribution(3);
var comp2 = new NormalDistribution(1);
var comp3 = new NormalDistribution(2);
var comp4 = new NormalDistribution(3);
var comp5 = new NormalDistribution(4);
var density = new Independent(comp1, comp2, comp3, comp4, comp5);
// Creates a sequence classifier containing 2 hidden Markov Models with 2 states
// and an underlying multivariate mixture of Normal distributions as density.
var classifier = new HiddenMarkovClassifier <Independent>(
2, new Forward(5), density);
// Configure the learning algorithms to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning <Independent>(
classifier,
// Train each model until the log-likelihood changes less than 0.0001
modelIndex => new BaumWelchLearning <Independent>(
classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0,
}
);
// Train the sequence classifier using the algorithm
double logLikelihood = teacher.Run(sequences2, labels2);
Assert.AreEqual(-3.0493028798326081d, logLikelihood, 1e-10);
return(classifier);
}
private static HiddenMarkovClassifier createClassifier(
out int[][] sequences, bool rejection = false)
{
sequences = new int[][]
{
new int[] { 0,1,2,3,4 },
new int[] { 4,3,2,1,0 },
};
int[] labels = { 0, 1 };
HiddenMarkovClassifier classifier =
new HiddenMarkovClassifier(2, new Ergodic(2), symbols: 5);
var teacher = new HiddenMarkovClassifierLearning(classifier,
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.0001,
Iterations = 0
}
);
teacher.Rejection = rejection;
teacher.Run(sequences, labels);
return classifier;
}
public void CrossvalidationConstructorTest2()
{
Accord.Math.Tools.SetupGenerator(0);
// This is a sample code on how to use Cross-Validation
// to assess the performance of Hidden Markov Models.
// 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[] { 0,1,1,1,0 }, // Class 0
new int[] { 0,1,1,0 }, // Class 0
new int[] { 0,1,1,1,0 }, // Class 0
new int[] { 0,1,1,1,0 }, // Class 0
new int[] { 0,1,0,1,0 }, // Class 0
new int[] { 0,1,0 }, // Class 0
new int[] { 0,1,1,0 }, // 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
new int[] { 1,1,0,1 }, // Class 1
new int[] { 1,0,1 }, // Class 1
new int[] { 1,0,0,1 }, // Class 1
new int[] { 1,0,0,0,1 }, // Class 1
new int[] { 1,0,1 }, // Class 1
new int[] { 1,0,0,0,1 }, // Class 1
};
int[] outputs = new int[]
{
0,0,0,0,0,0,0,0,0,0, // First 10 sequences are of class 0
1,1,1,1,1,1,1,1,1,1, // Last 10 sequences are of class 1
};
// Create a new Cross-validation algorithm passing the data set size and the number of folds
var crossvalidation = new CrossValidation<HiddenMarkovClassifier>(size: inputs.Length, folds: 3);
// Define a fitting function using Support Vector Machines. The objective of this
// function is to learn a SVM in the subset of the data indicated by cross-validation.
crossvalidation.Fitting = delegate(int k, int[] indicesTrain, int[] indicesValidation)
{
// The fitting function is passing the indices of the original set which
// should be considered training data and the indices of the original set
// which should be considered validation data.
// Lets now grab the training data:
var trainingInputs = inputs.Submatrix(indicesTrain);
var trainingOutputs = outputs.Submatrix(indicesTrain);
// And now the validation data:
var validationInputs = inputs.Submatrix(indicesValidation);
var validationOutputs = outputs.Submatrix(indicesValidation);
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to two symbols (0 or 1)
int symbols = 2;
// Nested models will have two states each
int[] states = new int[] { 2, 2 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
Iterations = 0
}
);
// Train the sequence classifier using the algorithm
double likelihood = teacher.Run(trainingInputs, trainingOutputs);
double trainingError = teacher.ComputeError(trainingInputs, trainingOutputs);
// Now we can compute the validation error on the validation data:
double validationError = teacher.ComputeError(validationInputs, validationOutputs);
// Return a new information structure containing the model and the errors achieved.
return new CrossValidationValues<HiddenMarkovClassifier>(classifier, trainingError, validationError);
};
// Compute the cross-validation
var result = crossvalidation.Compute();
// Finally, access the measured performance.
double trainingErrors = result.Training.Mean;
double validationErrors = result.Validation.Mean;
Assert.AreEqual(3, crossvalidation.K);
Assert.AreEqual(0, result.Training.Mean);
Assert.AreEqual(0.055555555555555552, result.Validation.Mean);
Assert.AreEqual(3, crossvalidation.Folds.Length);
Assert.AreEqual(3, result.Models.Length);
}