/// <summary>
/// Creates an instance of the model to be learned. Inheritors of this abstract
/// class must define this method so new models can be created from the training data.
/// </summary>
///
protected override HiddenMarkovModel <TDistribution, TObservation> Create(TObservation[][] x, int numberOfClasses)
{
var hmm = new HiddenMarkovModel <TDistribution, TObservation>(states: numberOfClasses, emissions: Emissions);
MarkovHelperMethods.CheckObservationDimensions(x, hmm);
return(hmm);
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
/// <returns>A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.</returns>
public TModel Learn(TObservation[][] x, int[][] y, double[] weights = null)
{
if (weights != null)
{
throw new ArgumentException(Accord.Properties.Resources.NotSupportedWeights, "weights");
}
if (Model == null)
{
Model = Create(x, numberOfClasses: y.Max() + 1);
}
MarkovHelperMethods.CheckObservationDimensions(x, Model);
// Grab model information
var model = Model;
var fittingOptions = FittingOptions;
int N = x.Length;
int states = model.NumberOfStates;
int[] initial = new int[states];
int[,] transitions = new int[states, states];
// 1. Count first state occurrences
for (int i = 0; i < y.Length; i++)
{
initial[y[i][0]]++;
}
// 2. Count all state transitions
foreach (int[] path in y)
{
for (int j = 1; j < path.Length; j++)
{
transitions[path[j - 1], path[j]]++;
}
}
if (UseWeights)
{
int totalObservations = 0;
for (int i = 0; i < x.Length; i++)
{
totalObservations += x[i].Length;
}
double[][] totalWeights = new double[states][];
for (int i = 0; i < totalWeights.Length; i++)
{
totalWeights[i] = new double[totalObservations];
}
var all = new TObservation[totalObservations];
for (int i = 0, c = 0; i < y.Length; i++)
{
for (int t = 0; t < y[i].Length; t++, c++)
{
int state = y[i][t];
all[c] = x[i][t];
totalWeights[state][c] = 1;
}
}
for (int i = 0; i < model.NumberOfStates; i++)
{
model.Emissions[i].Fit(all, totalWeights[i], fittingOptions);
}
}
else
{
// 3. Count emissions for each state
var clusters = new List <TObservation> [model.NumberOfStates];
for (int i = 0; i < clusters.Length; i++)
{
clusters[i] = new List <TObservation>();
}
// Count symbol frequencies per state
for (int i = 0; i < y.Length; i++)
{
for (int t = 0; t < y[i].Length; t++)
{
int state = y[i][t];
var symbol = x[i][t];
clusters[state].Add(symbol);
}
}
// Estimate probability distributions
for (int i = 0; i < model.NumberOfStates; i++)
{
if (clusters[i].Count > 0)
{
model.Emissions[i].Fit(clusters[i].ToArray(), fittingOptions);
}
}
}
// 4. Form log-probabilities, using the Laplace
// correction to avoid zero probabilities
if (UseLaplaceRule)
{
// Use Laplace's rule of succession correction
// http://en.wikipedia.org/wiki/Rule_of_succession
for (int i = 0; i < initial.Length; i++)
{
initial[i]++;
for (int j = 0; j < states; j++)
{
transitions[i, j]++;
}
}
}
// Form probabilities
int initialCount = initial.Sum();
int[] transitionCount = transitions.Sum(1);
if (initialCount == 0)
{
initialCount = 1;
}
for (int i = 0; i < transitionCount.Length; i++)
{
if (transitionCount[i] == 0)
{
transitionCount[i] = 1;
}
}
for (int i = 0; i < initial.Length; i++)
{
model.LogInitial[i] = Math.Log(initial[i] / (double)initialCount);
}
for (int i = 0; i < transitionCount.Length; i++)
{
for (int j = 0; j < states; j++)
{
model.LogTransitions[i][j] = Math.Log(transitions[i, j] / (double)transitionCount[i]);
}
}
Accord.Diagnostics.Debug.Assert(!model.LogInitial.HasNaN());
Accord.Diagnostics.Debug.Assert(!model.LogTransitions.HasNaN());
return(Model);
}
/// <summary>
/// Learns a model that can map the given inputs to the desired outputs.
/// </summary>
///
/// <param name="x">The model inputs.</param>
/// <param name="weights">The weight of importance for each input sample.</param>
///
/// <returns>A model that has learned how to produce suitable outputs
/// given the input data <paramref name="x"/>.</returns>
///
public TModel Learn(TObservation[][] x, double[] weights = null)
{
// Initial argument checks
CheckArgs(x, weights);
// Baum-Welch algorithm.
// The Baum–Welch algorithm is a particular case of a generalized expectation-maximization
// (GEM) algorithm. It can compute maximum likelihood estimates and posterior mode estimates
// for the parameters (transition and emission probabilities) of an HMM, when given only
// emissions as training data.
// The algorithm has two steps:
// - Calculating the forward probability and the backward probability for each HMM state;
// - On the basis of this, determining the frequency of the transition-emission pair values
// and dividing it by the probability of the entire string. This amounts to calculating
// the expected count of the particular transition-emission pair. Each time a particular
// transition is found, the value of the quotient of the transition divided by the probability
// of the entire string goes up, and this value can then be made the new value of the transition.
this.samples = x.Concatenate();
this.vectorObservations = x;
if (Model == null)
Model = Create(x);
MarkovHelperMethods.CheckObservationDimensions(x, Model);
if (MaxIterations > 0 && CurrentIteration >= MaxIterations)
return Model;
// Grab model information
int states = Model.NumberOfStates;
var logA = Model.LogTransitions;
var logP = Model.LogInitial;
// Initialize the algorithm
int N = x.Length;
double logN = Math.Log(N);
LogKsi = new double[N][][,];
LogGamma = new double[N][,];
LogWeights = new double[N];
if (weights != null)
weights.Log(result: LogWeights);
for (int i = 0; i < x.Length; i++)
{
int T = x[i].Length;
LogKsi[i] = new double[T][,];
LogGamma[i] = new double[T, states];
for (int t = 0; t < LogKsi[i].Length; t++)
LogKsi[i][t] = new double[states, states];
}
int TMax = x.Max(x_i => x_i.Length);
#if SERIAL
lnFwd = new double[TMax, states];
lnBwd = new double[TMax, states];
sampleWeights = new double[samples.Length];
#endif
convergence.CurrentIteration--;
bool hasUpdated = false;
do
{
if (Token.IsCancellationRequested)
break;
// Initialize the model log-likelihood
LogLikelihood = Expectation(x, TMax);
convergence.NewValue = LogLikelihood;
// Check for convergence
if (hasUpdated && convergence.HasConverged)
break;
if (Token.IsCancellationRequested)
break;
// 3. Continue with parameter re-estimation
// 3.1 Re-estimation of initial state probabilities
#if SERIAL
for (int i = 0; i < logP.Length; i++)
#else
Parallel.For(0, logP.Length, ParallelOptions, i =>
#endif
{
double lnsum = Double.NegativeInfinity;
for (int k = 0; k < LogGamma.Length; k++)
lnsum = Special.LogSum(lnsum, LogGamma[k][0, i]);
logP[i] = lnsum - logN;
}
#if !SERIAL
);
#endif
// 3.2 Re-estimation of transition probabilities
#if SERIAL
for (int i = 0; i < states; i++)
#else
Parallel.For(0, states, ParallelOptions, i =>
#endif
{
for (int j = 0; j < states; j++)
{
double lnnum = Double.NegativeInfinity;
double lnden = Double.NegativeInfinity;
for (int k = 0; k < LogGamma.Length; k++)
{
int T = x[k].Length;
for (int t = 0; t < T - 1; t++)
{
lnnum = Special.LogSum(lnnum, LogKsi[k][t][i, j]);
lnden = Special.LogSum(lnden, LogGamma[k][t, i]);
}
}
logA[i][j] = (lnnum == lnden) ? 0 : lnnum - lnden;
Accord.Diagnostics.Debug.Assert(!Double.IsNaN(logA[i][j]));
}
}
#if !SERIAL
);
#endif
// 3.3 Re-estimation of emission probabilities
UpdateEmissions(); // discrete and continuous
hasUpdated = true;
} while (true);
return Model;
}