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- Hmm = new HiddenMarkovClassifier<MultivariateMixture<MultivariateNormalDistribution>>(classes.Count,
- new Forward(states), new MultivariateMixture<MultivariateNormalDistribution>(new MultivariateNormalDistribution), classes.ToArray());
- // I tried to change the topology from Forward to Ergodic and the performence is nearly the same
- // Ergodic models are commonly used to represent models in which a single (large) sequence of observations is used for training (such as when a training sequence does not have well defined starting and ending points and can potentially be infinitely long).
- // Create the learning algorithm for the ensemble classifier
- var teacher = new HiddenMarkovClassifierLearning<MultivariateMixture<MultivariateNormalDistribution>>(Hmm,
- // Train each model using the selected convergence criteria
- i => new BaumWelchLearning<MultivariateMixture<MultivariateNormalDistribution>>(Hmm.Models[i])
- Tolerance = tolerance,
- Iterations = iterations,
- FittingOptions = new NormalOptions()
- Regularization = 1e-5
- // , Diagonal = true
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