public void Run_3ObservationsAnd2StatesAnd123Model_PathCount3()
{
var algo = new Viterbi(false);
var observations = new List <IObservation>
{
new Observation(new double[] { 0 }, "Red"),
new Observation(new double[] { 1 }, "Blue"),
new Observation(new double[] { 0 }, "Red")
};
var startDistribution = new [] { 1 / 3d, 1 / 3d, 1 / 3d };
var states = new List <IState> {
new State(0, "One"), new State(1, "Two"), new State(2, "Three")
};
var tpm = new double[][] { new[] { 0.3, 0.6, 0.1 }, new[] { 0.5, 0.2, 0.3 }, new[] { 0.4, 0.1, 0.5 } };
var distributions = new List <IDistribution>
{
new FirstDistribution(), new SecondDistribution(), new ThirdDistribution()
};
var path = algo.Run(observations, states, startDistribution, tpm, distributions);
Assert.AreEqual(path.Count, 3);
Assert.AreEqual("One", path[0].Description);
Assert.AreEqual("Two", path[1].Description);
Assert.AreEqual("One", path[2].Description);
}
public void Run_3ObservationsAnd2StatesAndRainySunnyModel_PathCount3()
{
var algo = new Viterbi(false);
var observations = new List <IObservation>
{
new Observation(new double[] { 0 }, "walk"),
new Observation(new double[] { 1 }, "shop"),
new Observation(new double[] { 2 }, "clean")
};
var startDistribution = new [] { 0.6, 0.4 };
var states = new List <IState> {
new State(0, "Rainy"), new State(1, "Sunny")
};
var tpm = new double[][] { new[] { 0.7, 0.3 }, new[] { 0.4, 0.6 } };
var distributions = new List <IDistribution> {
new RainyDistribution(), new SunnyDistribution()
};
var path = algo.Run(observations, states, startDistribution, tpm, distributions);
Assert.AreEqual(path.Count, 3);
Assert.AreEqual("Sunny", path[0].Description);
Assert.AreEqual("Rainy", path[1].Description);
Assert.AreEqual("Rainy", path[2].Description);
}
public void Run_4ObservationsAnd2StatesAndSTModel_PathCount4()
{
var observations = new List <IObservation>
{
new Observation(new double[] { 0 }, "A"),
new Observation(new double[] { 1 }, "B"),
new Observation(new double[] { 1 }, "B"),
new Observation(new double[] { 0 }, "A")
};
var startDistribution = new[] { 0.85, 0.15 };
var states = new List <IState> {
new State(0, "s"), new State(1, "t")
};
var tpm = new double[2][];
tpm[0] = new[] { 0.3, 0.7 };
tpm[1] = new[] { 0.1, 0.9 };
var distributions = new List <IDistribution> {
new HealthyDistribution(), new OkDistribution(), new SickDistribution()
};
var algo = new Viterbi(false);
var path = algo.Run(observations, states, startDistribution, tpm, distributions);
Assert.AreEqual(path.Count, 4);
Assert.AreEqual("s", path[0].Description);
Assert.AreEqual("t", path[1].Description);
Assert.AreEqual("t", path[2].Description);
Assert.AreEqual("t", path[3].Description);
}
public void Run_Normalized5ObservationsAnd3StatesAndHealthySickModel_PathCount5()
{
var algo = new Viterbi(true);
var observations = new List <IObservation>
{
new Observation(new double[] { 0 }, "high"),
new Observation(new double[] { 1 }, "average"),
new Observation(new double[] { 1 }, "average"),
new Observation(new double[] { 2 }, "low"),
new Observation(new double[] { 2 }, "low")
};
var startDistribution = new[] { 1 / 3d, 1 / 3d, 1 / 3d };
var states = new List <IState> {
new State(0, "Healthy"), new State(1, "OK"), new State(2, "Sick")
};
var tpm = new double[][] { new[] { 0.4, 0.3, 0.3 }, new[] { 0.2, 0.6, 0.2 }, new[] { 0, 0.4, 0.6 } };
var distributions = new List <IDistribution> {
new HealthyDistribution(), new OkDistribution(), new SickDistribution()
};
var path = algo.Run(observations, states, startDistribution, tpm, distributions);
Assert.AreEqual(path.Count, 5);
Assert.AreEqual("Healthy", path[0].Description);
Assert.AreEqual("OK", path[1].Description);
Assert.AreEqual("OK", path[2].Description);
Assert.AreEqual("Sick", path[3].Description);
Assert.AreEqual("Sick", path[4].Description);
}
public void Run_100LengthObservations2MixtureDistribution_PathCount100()
{
var algo = new Viterbi(false);
var path = algo.Run(_observations, _states, _startDistribution, _tpm, _mixtures);
Assert.AreEqual(path.Count, 100);
}
private double[] PredictNextValue <TDistribution>(IHiddenMarkovModel <TDistribution> model, double[][] trainingSet) where TDistribution : IDistribution
{
var alg = new Viterbi(model.Normalized);
var mpp = alg.Run(Helper.Convert(trainingSet), model.GetStates(), model.Pi, model.TransitionProbabilityMatrix, model.GetEmissions());
var emission = model.Emission[mpp[trainingSet.Length - 1].Index];
var prediction = CalculatePredictionValue(emission, trainingSet);
return(prediction);
}
private ViterbiResult RunViterbi(FilterResult filterResult, string fileSha1)
{
// Load any user-defined state machines.
List <UserState> userStates;
try
{
userStates = Loader.LoadUserStates(MainForm.Program.UserStatesEnabled,
MainForm.Program.UserStatesPath);
}
catch (Exception ex)
{
DisplayExceptionMessages(ex, "User-Defined States");
return(null);
}
ViterbiResult viterbiResultFields = null;
try
{
write("Running Viterbi on fields");
DateTime dt = DateTime.Now;
#if _GENERALPARSE
Viterbi viterbi = new Viterbi(RunType.GeneralParse, false);
viterbiResultFields = viterbi.Run(filterResult.UnfilteredBlocks, this.filePath);
#else
#if SKIPREALWORK
viterbiResultFields = new ViterbiResult();
viterbiResultFields.Fields = new List <ViterbiField>();
#else
if (filterResult.UnfilteredBlocks.Count > 0)
{
ThreadedViterbi tv = new ThreadedViterbi(filterResult.UnfilteredBlocks, RunType.GeneralParse, userStates, this.filePath, this.fileSha1);
viterbiResultFields = tv.RunThreadedViterbi();
TimeSpan ts = DateTime.Now.Subtract(dt);
write("Time elapsed for Viterbi fields: {0}", ts.ToString("c"));
write("Field count: {0}", viterbiResultFields.Fields.Count);
}
#endif
#endif
filterResult.UnfilteredBlocks.Clear(); // Allow gc to clean things up
}
catch (ThreadAbortException)
{
return(null);
}
catch (Exception ex)
{
DisplayExceptionMessages(ex, "Viterbi Fields");
return(null);
}
return(viterbiResultFields);
}
public void Run_Normalized9ObservationsAnd2StatesAndHLModel_PathCount9()
{
var algo = new Viterbi(true);
var observations = new List <IObservation>
{
new Observation(new double[] { 2 }, "G"),
new Observation(new double[] { 2 }, "G"),
new Observation(new double[] { 1 }, "C"),
new Observation(new double[] { 0 }, "A"),
new Observation(new double[] { 1 }, "C"),
new Observation(new double[] { 3 }, "T"),
new Observation(new double[] { 2 }, "G"),
new Observation(new double[] { 0 }, "A"),
new Observation(new double[] { 0 }, "A")
};
var startDistribution = new[] { 0.5, 0.5 };
var states = new List <IState> {
new State(0, "H"), new State(1, "L")
};
var tpm = new double[][] { new[] { 0.5, 0.5 }, new[] { 0.4, 0.6 } };
var distributions = new List <IDistribution> {
new HDistribution(), new LDistribution()
};
var path = algo.Run(observations, states, startDistribution, tpm, distributions);
Assert.AreEqual(path.Count, 9);
Assert.AreEqual("H", path[0].Description);
Assert.AreEqual("H", path[1].Description);
Assert.AreEqual("H", path[2].Description);
Assert.AreEqual("L", path[3].Description);
Assert.AreEqual("L", path[4].Description);
Assert.AreEqual("L", path[5].Description);
Assert.AreEqual("L", path[6].Description);
Assert.AreEqual("L", path[7].Description);
Assert.AreEqual("L", path[8].Description);
}
