public void Iteration()
{
HiddenMarkovModel nhmm;
nhmm = _method.Clone();
var allGamma = new double[_training.SequenceCount][][];
double[][] aijNum = EngineArray.AllocateDouble2D(_method.StateCount, _method.StateCount);
var aijDen = new double[_method.StateCount];
EngineArray.Fill(aijDen, 0.0);
for (int i = 0; i < _method.StateCount; i++)
{
EngineArray.Fill(aijNum[i], 0.0);
}
int g = 0;
foreach (IMLDataSet obsSeq in _training.Sequences)
{
ForwardBackwardCalculator fbc = GenerateForwardBackwardCalculator(
obsSeq, _method);
double[][][] xi = EstimateXi(obsSeq, fbc, _method);
double[][] gamma = allGamma[g++] = EstimateGamma(xi, fbc);
for (int i = 0; i < _method.StateCount; i++)
{
for (int t = 0; t < (obsSeq.Count - 1); t++)
{
aijDen[i] += gamma[t][i];
for (int j = 0; j < _method.StateCount; j++)
{
aijNum[i][j] += xi[t][i][j];
}
}
}
}
for (int i = 0; i < _method.StateCount; i++)
{
if (aijDen[i] == 0.0)
{
for (int j = 0; j < _method.StateCount; j++)
{
nhmm.TransitionProbability[i][j] =
_method.TransitionProbability[i][j];
}
}
else
{
for (int j = 0; j < _method.StateCount; j++)
{
nhmm.TransitionProbability[i][j] = aijNum[i][j]
/ aijDen[i];
}
}
}
/* compute pi */
for (int i = 0; i < _method.StateCount; i++)
{
nhmm.Pi[i] = 0.0;
}
for (int o = 0; o < _training.SequenceCount; o++)
{
for (int i = 0; i < _method.StateCount; i++)
{
nhmm.Pi[i] += (allGamma[o][0][i] / _training
.SequenceCount);
}
}
/* compute pdfs */
for (int i = 0; i < _method.StateCount; i++)
{
var weights = new double[_training.Count];
double sum = 0.0;
int j = 0;
int o = 0;
foreach (IMLDataSet obsSeq in _training.Sequences)
{
for (int t = 0; t < obsSeq.Count; t++, j++)
{
sum += weights[j] = allGamma[o][t][i];
}
o++;
}
for (j--; j >= 0; j--)
{
weights[j] /= sum;
}
IStateDistribution opdf = nhmm.StateDistributions[i];
opdf.Fit(_training, weights);
}
_method = nhmm;
Iterations++;
}
/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
///
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
///
public override object Clone()
{
return(model.Clone());
}
public void issue_154()
{
int[][] sequences = new int[][]
{
new int[] { 0, 1, 1, 1, 1, 0, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 0, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
new int[] { 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
};
var hmm0 = new HiddenMarkovModel(states: 3, symbols: 2);
var hmm1 = hmm0.Clone() as HiddenMarkovModel;
var hmm2 = hmm0.Clone() as HiddenMarkovModel;
var hmm3 = hmm0.Clone() as HiddenMarkovModel;
var hmm4 = hmm0.Clone() as HiddenMarkovModel;
{
Accord.Math.Random.Generator.Seed = 0;
var teacher = new BaumWelchLearning(hmm1)
{
Tolerance = 0.0001, Iterations = 1
};
Assert.AreEqual(0, teacher.CurrentIteration);
teacher.Learn(sequences);
Assert.AreEqual(1, teacher.CurrentIteration);
teacher = new BaumWelchLearning(hmm1)
{
Tolerance = 0.0001, Iterations = 1
};
Assert.AreEqual(0, teacher.CurrentIteration);
teacher.Learn(sequences);
Assert.AreEqual(1, teacher.CurrentIteration);
teacher = new BaumWelchLearning(hmm1)
{
Tolerance = 0.0001, Iterations = 1
};
Assert.AreEqual(0, teacher.CurrentIteration);
teacher.Learn(sequences);
Assert.AreEqual(1, teacher.CurrentIteration);
teacher = new BaumWelchLearning(hmm1)
{
Tolerance = 0.0001, Iterations = 1
};
Assert.AreEqual(0, teacher.CurrentIteration);
teacher.Learn(sequences);
Assert.AreEqual(1, teacher.CurrentIteration);
}
{
Accord.Math.Random.Generator.Seed = 0;
var teacher = new BaumWelchLearning(hmm2)
{
Tolerance = 0.0001, Iterations = 2
};
Assert.AreEqual(0, teacher.CurrentIteration);
teacher.Learn(sequences);
Assert.AreEqual(2, teacher.CurrentIteration);
teacher.MaxIterations = 4;
teacher.Learn(sequences);
Assert.AreEqual(4, teacher.CurrentIteration);
}
{
Accord.Math.Random.Generator.Seed = 0;
var teacher = new BaumWelchLearning(hmm3)
{
Tolerance = 0.0001, Iterations = 3
};
teacher.Learn(sequences);
Assert.AreEqual(3, teacher.CurrentIteration);
teacher = new BaumWelchLearning(hmm3)
{
Tolerance = 0.0001, Iterations = 1
};
Assert.AreEqual(0, teacher.CurrentIteration);
teacher.Learn(sequences);
Assert.AreEqual(1, teacher.CurrentIteration);
}
{
Accord.Math.Random.Generator.Seed = 0;
var teacher = new BaumWelchLearning(hmm4)
{
Tolerance = 0.0001, Iterations = 4
};
Assert.AreEqual(0, teacher.CurrentIteration);
teacher.Learn(sequences);
Assert.AreEqual(4, teacher.CurrentIteration);
}
{
var teacher = new BaumWelchLearning(hmm0)
{
Tolerance = 0.0001, Iterations = 0
};
teacher.Learn(sequences);
Assert.AreEqual(13, teacher.CurrentIteration);
}
compare(hmm1, hmm2);
compare(hmm1, hmm3);
compare(hmm1, hmm4);
}
