Method | Description | |
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ContinuousHiddenMarkovModel ( ITopology topology, IDistribution emissions ) : System |
Constructs a new Hidden Markov Model with discrete state probabilities.
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ContinuousHiddenMarkovModel ( ITopology topology, int symbols ) : System |
Constructs a new Hidden Markov Model with discrete state probabilities.
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ContinuousHiddenMarkovModel ( double transitions, IDistribution emissions, double probabilities ) : System |
Constructs a new Hidden Markov Model.
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ContinuousHiddenMarkovModel ( double transitions, double emissions, double probabilities ) : System |
Constructs a new Hidden Markov Model.
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ContinuousHiddenMarkovModel ( int states, IDistribution emissions ) : System |
Constructs a new Hidden Markov Model.
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ContinuousHiddenMarkovModel ( int states, int symbols ) : System |
Constructs a new Hidden Markov Model with discrete state probabilities.
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Decode ( |
Calculates the most likely sequence of hidden states that produced the given observation sequence. Decoding problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1,o2, ..., oK}, calculate the most likely sequence of hidden states Si that produced this observation sequence O. This can be computed efficiently using the Viterbi algorithm. |
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Decode ( |
Calculates the most likely sequence of hidden states that produced the given observation sequence. Decoding problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1,o2, ..., oK}, calculate the most likely sequence of hidden states Si that produced this observation sequence O. This can be computed efficiently using the Viterbi algorithm. |
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Evaluate ( Array observations ) : double |
Calculates the probability that this model has generated the given sequence. Evaluation problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1, o2, ..., oK}, calculate the probability that model M has generated sequence O. This can be computed efficiently using the either the Viterbi or the Forward algorithms. |
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Evaluate ( Array observations, bool logarithm ) : double |
Calculates the probability that this model has generated the given sequence. Evaluation problem. Given the HMM M = (A, B, pi) and the observation sequence O = {o1, o2, ..., oK}, calculate the probability that model M has generated sequence O. This can be computed efficiently using the either the Viterbi or the Forward algorithms. |
Method | Description | |
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convert ( Array array ) : double[][] |
Converts a univariate or multivariate array of observations into a two-dimensional jagged array.
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public ContinuousHiddenMarkovModel ( ITopology topology, IDistribution emissions ) : System | ||
topology | ITopology |
/// A |
emissions | IDistribution | /// The initial emission probability distribution to be used by each of the states. /// |
return | System |
public ContinuousHiddenMarkovModel ( ITopology topology, int symbols ) : System | ||
topology | ITopology |
/// A |
symbols | int | The number of output symbols used for this model. |
return | System |
public ContinuousHiddenMarkovModel ( double transitions, IDistribution emissions, double probabilities ) : System | ||
transitions | double | The transitions matrix A for this model. |
emissions | IDistribution | The emissions matrix B for this model. |
probabilities | double | The initial state probabilities for this model. |
return | System |
public ContinuousHiddenMarkovModel ( double transitions, double emissions, double probabilities ) : System | ||
transitions | double | The transitions matrix A for this model. |
emissions | double | The emissions matrix B for this model. |
probabilities | double | The initial state probabilities for this model. |
return | System |
public ContinuousHiddenMarkovModel ( int states, IDistribution emissions ) : System | ||
states | int | The number of states for the model. |
emissions | IDistribution | A initial distribution to be copied to all states in the model. |
return | System |
public ContinuousHiddenMarkovModel ( int states, int symbols ) : System | ||
states | int | The number of states for this model. |
symbols | int | The number of output symbols used for this model. |
return | System |
public Decode ( |
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observations | A sequence of observations. | |
logarithm | bool | True to return the log-likelihood, false to return /// the likelihood. Default is false (default is to return the likelihood). |
probability | double | The state optimized probability. |
return | int[] |
public Decode ( |
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observations | A sequence of observations. | |
probability | double | The state optimized probability. |
return | int[] |
public Evaluate ( Array observations ) : double | ||
observations | Array | /// A sequence of observations. /// |
return | double |
public Evaluate ( Array observations, bool logarithm ) : double | ||
observations | Array | /// A sequence of observations. /// |
logarithm | bool | /// True to return the log-likelihood, false to return /// the likelihood. Default is false. /// |
return | double |