Uses of Class
org.metaqtl.EMResult

Packages that use EMResult
org.metaqtl   
org.metaqtl.adapter   
org.metaqtl.algo   
org.metaqtl.factory   
 

Uses of EMResult in org.metaqtl
 

Fields in org.metaqtl declared as EMResult
 EMResult[] MetaQtlResult.clusterings
          The meta qtl clusterings.
 

Methods in org.metaqtl that return EMResult
 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)
           
static EMResult EMCriteria.getBestResult(EMResult[] clusterings, int n, java.lang.String criterion)
           
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
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)
           
static EMResult EMCriteria.getBestResult(EMResult[] clusterings, java.lang.String criterion)
           
 double[] MetaQtlResult.getUSD(EMResult best, int kmin, int kmax, java.lang.String criterion)
           
 void MetaQtlResult.setClusterings(EMResult[] results)
           
 

Uses of EMResult in org.metaqtl.adapter
 

Methods in org.metaqtl.adapter with parameters of type EMResult
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
 

Methods in org.metaqtl.algo that return EMResult
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
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
 

Methods in org.metaqtl.factory that return EMResult
static EMResult EMResultFactory.read(java.io.BufferedReader buffer)
           
 

Methods in org.metaqtl.factory with parameters of type EMResult
static void EMResultFactory.write(EMResult result, java.io.OutputStream stream)
           
static void EMResultFactory.write(EMResult result, java.io.PrintWriter writer)