A C D E F G H M O S T V

A

annotationToFile(String) - Method in class HMM

Outputs the optimal annotation and its score to a file.

arrayToString(double[][]) - Method in class HMM

Returns a String representation of a 2D array.

arrayToString(int[][]) - Method in class HMM

Returns a String representation of a 2D array.

C

comparePredictedAnnotationToDocumentedGenes(String, String) - Static method in class EvaluateGenePredictions

Compares gene predictions (as output by a HMM) with documented genes in a genome.

D

determineOptimalAnnotation() - Method in class HMM

Determines the optimal annotation of the observation sequence.

E

EvaluateGenePredictions - Class in <Unnamed>

The EvaluateGenePredictions class takes a file of gene predictions output by the HMM application and compares it to a file of documented genes to determine how well the predictions match up with the documented genes - the program reports the sensitivity and specificity of the gene predictions.

EvaluateGenePredictions() - Constructor for class EvaluateGenePredictions

F

fillInTableEntry(int, int) - Method in class HMM

Fills in the entry at row row and column col in both the dynamic programming table and the backtracking table.

G

getBacktrackTable() - Method in class HMM

Returns the backtracking table used by the Viterbi algorithm.

getEmissionLength() - Method in class State

Returns the length of the characters that this State can output (all characters output by a State are the same length).

getEmissionProbability(String) - Method in class State

Returns the probability that this State emits the specified character s, or 0.0 if this State cannot emit character s.

getName() - Method in class State

Returns the name of this State.

getScoreOfOptimalAnnotation() - Method in class HMM

Returns the score (the natural logarithm of the probability) of the optimal annotation of the observation sequence.

getTable() - Method in class HMM

Returns the dynamic programming table used by the Viterbi algorithm.

H

HMM - Class in <Unnamed>

An instance of the HMM class represents a hidden Markov model, including the model's states, transitions, and the optimal annotation (i.e., parse) of an observation sequence.

HMM(String, String, String) - Constructor for class HMM

Creates a hidden Markov model based on the specified states, transitions, and observation sequence.

M

main(String[]) - Static method in class EvaluateGenePredictions

The main method takes two files, a file containing information about documented genes and a file containing gene predictions output by a HMM, and it compares the two, reporting the sensitivity and specificity of the gene predictions.

main(String[]) - Static method in class HMM

The main method generates a HMM based on the state and transition information in two files specified by command line arguments.

main(String[]) - Static method in class State

The main method creates a new State, for testing purposes, and prints out this newly created State to the screen.

O

optimalAnnotationToString() - Method in class HMM

Returns a String representation of the optimal annotation.

S

State - Class in <Unnamed>

An instance of the State class represents a single state in a hidden Markov model, including the state's name, a collection of all characters that the state can output, and the probability of outputting each character.

State(String) - Constructor for class State

Construct a new State based on information in a tab-delimited line read-in from a file.

T

toString() - Method in class HMM

Returns a String representation of this HMM.

toString() - Method in class State

Returns a String representation of this State.

V

viterbi() - Method in class HMM

Implements the Viterbi algorithm by filling in a dynamic programming table and a backtracking table.

A C D E F G H M O S T V