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Packages that use EMResult | |
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org.metaqtl | |
org.metaqtl.adapter | |
org.metaqtl.algo | |
org.metaqtl.factory |
Uses of EMResult in org.metaqtl |
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Fields in org.metaqtl declared as EMResult | |
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EMResult[] |
MetaQtlResult.clusterings
The meta qtl clusterings. |
Methods in org.metaqtl that return EMResult | |
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EMResult |
MetaQtlResult.getBestClustering(int criterion)
This methods returns the best models for each criterion defined to select the optimal number of clusters per trait. |
static EMResult |
EMCriteria.getBestResult(EMResult[] clusterings,
int criterion)
For the given criterion criterion this methods
looks into the array of EMResult and find the model
which is optimal. |
static EMResult |
EMCriteria.getBestResult(EMResult[] clusterings,
int n,
int criterion)
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static EMResult |
EMCriteria.getBestResult(EMResult[] clusterings,
int n,
java.lang.String criterion)
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static EMResult |
EMCriteria.getBestResult(EMResult[] clusterings,
java.lang.String criterion)
|
EMResult |
MetaQtlResult.getClustering(int k)
Returns the Qtl clustering result with k
clusters. |
Methods in org.metaqtl with parameters of type EMResult | |
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static void |
EMResult.copy(EMResult dest,
EMResult src)
|
static EMResult |
EMCriteria.getBestResult(EMResult[] clusterings,
int criterion)
For the given criterion criterion this methods
looks into the array of EMResult and find the model
which is optimal. |
static EMResult |
EMCriteria.getBestResult(EMResult[] clusterings,
int n,
int criterion)
|
static EMResult |
EMCriteria.getBestResult(EMResult[] clusterings,
int n,
java.lang.String criterion)
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static EMResult |
EMCriteria.getBestResult(EMResult[] clusterings,
java.lang.String criterion)
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double[] |
MetaQtlResult.getUSD(EMResult best,
int kmin,
int kmax,
java.lang.String criterion)
|
void |
MetaQtlResult.setClusterings(EMResult[] results)
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Uses of EMResult in org.metaqtl.adapter |
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Methods in org.metaqtl.adapter with parameters of type EMResult | |
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static Qtl[] |
QtlAdapter.adapt(EMResult result)
From a result of a EM clustering returns the mixture components as an array of Qtl. |
Uses of EMResult in org.metaqtl.algo |
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Methods in org.metaqtl.algo that return EMResult | |
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static EMResult |
EMAlgorithm.doEM(double[] x,
double[] sd,
int k,
EMResult spoint)
Apply the EM-Algorithm on the given data set where x
is an array of observed value and sd an array of same
size than x which stores the standard deviations of
the observed values. |
Methods in org.metaqtl.algo with parameters of type EMResult | |
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static void |
EMAlgorithm.computeCOV(double[] sd,
EMResult theta)
Computes the variance-covariance matrix of the mixture component estimates. |
static void |
EMAlgorithm.computeDM(double[] x,
double[] sd,
double[] mu,
EMResult theta)
Computes the matrix of derivatives of the update function. |
static EMResult |
EMAlgorithm.doEM(double[] x,
double[] sd,
int k,
EMResult spoint)
Apply the EM-Algorithm on the given data set where x
is an array of observed value and sd an array of same
size than x which stores the standard deviations of
the observed values. |
static void |
EMAlgorithm.emRate(EMResult theta,
double[] mu,
double[] pi)
Compute the euclidean distance between the new parameters and the old ones to obtain a simple approximation of the EM convergence rate. |
static void |
EMAlgorithm.eStep(double[] x,
double[] sd,
EMResult theta)
The Expectation step : it just consists in updating the Z matrix, i.e the cluster membership probabilities. |
static void |
EMAlgorithm.initEM(double[] x,
double[] sd,
EMResult theta,
java.util.Random rng)
Initializes the EM algorithm by randomly assigning observations to the clusters and do one M-Step to compute the first values of the parameters. |
static int |
EMAlgorithm.iterate(double[] x,
double[] sd,
EMResult theta)
This method performs one iteration of the EM-algorithm. |
static int |
EMAlgorithm.mStep(double[] x,
double[] sd,
EMResult theta,
double err)
The Maximization step : here this step is straighforward and simple analytical formula are applied to obtain the new parameter estimates. |
static void |
EMAlgorithm.updateLoglikelihood(double[] x,
double[] sd,
EMResult theta)
Updates the loglikelihood value. |
static double[] |
EMAlgorithm.updateMuVector(double[] x,
double[] sd,
EMResult theta)
Updates the vector of mixture components and returns the new values. |
static double[] |
EMAlgorithm.updatePiVector(EMResult theta)
Updates the vector of mixture mixings and returns the new values. |
static void |
EMAlgorithm.updateZMatrix(double[] x,
double[] sd,
EMResult theta)
Updates the Z matrix. |
Uses of EMResult in org.metaqtl.factory |
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Methods in org.metaqtl.factory that return EMResult | |
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static EMResult |
EMResultFactory.read(java.io.BufferedReader buffer)
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Methods in org.metaqtl.factory with parameters of type EMResult | |
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static void |
EMResultFactory.write(EMResult result,
java.io.OutputStream stream)
|
static void |
EMResultFactory.write(EMResult result,
java.io.PrintWriter writer)
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