public class EMHyperErlangFit extends MLContPhaseFitter
| Modifier and Type | Field and Description |
|---|---|
static int |
maxIter
Maximum number of iterations for the algorithm execution
|
static double |
precision
Precision for the convergence criterion
in the algorithm
|
static double |
precisionCV
Precision for the convergence criterion in
the coefficient of variance
|
| Constructor and Description |
|---|
EMHyperErlangFit(double[] data) |
| Modifier and Type | Method and Description |
|---|---|
HyperErlangVar |
doFitHyperErlang()
Returns a HyperErlang variable with the best fit
experiments to be fitted
|
double |
doFitNM(HyperErlangVar var)
This method returns a completely specified HyperErlang
variable, such that it has the best likelihood between
all the possible combinations of N phases in M branches
|
double |
doFitNMR(HyperErlangVar var)
This method returns a completely specified HyperErlang
variable, such that it has the best likelihood after the
execution of the EM algorithm for the case where the
variable has N phases in M branches, distributed as determined
by the vector r
experiments to be fitted
|
ContPhaseVar |
fit()
Returns a HyperErlang variable with the best fit,
in the form of a Dense Continuous Phase variable
|
ContPhaseVar |
fit(int N)
Returns a HyperErlang variable with the best fit,
in the form of a Dense Continuous Phase variable
|
getLogLikelihoodpublic static double precision
public static double precisionCV
public static int maxIter
public ContPhaseVar fit()
fit in interface PhaseFitterfit in class ContPhaseFitterPhaseFitter.fit()public ContPhaseVar fit(int N)
N - number of phases in the distributionpublic HyperErlangVar doFitHyperErlang()
public double doFitNM(HyperErlangVar var)
var - HyperErlang variable with the parameters
N and M determinedpublic double doFitNMR(HyperErlangVar var)
var - HyperErlang variable with the parameters
N, M and r determined