List of fit() Examples

Examples of fit()

ca.nengo.math.CurveFitter.fit()
@param x Example x points @param y Example y points (must be same length as x) @return A Function that approximates the mapping Y=f(X) exemplified by x and y.
ca.nengo.math.impl.LinearCurveFitter.fit()
Note that inputs x must be sorted from lowest to highest. @see ca.nengo.math.CurveFitter#fit(float[],float[])
com.nr.model.Fitlin.fit()
com.nr.model.Fitmrq.fit()
com.nr.model.Fitsvd.fit()
jaolho.data.lma.LMA.fit()
The default fit. If used after calling fit(lambda, minDeltaChi2, maxIterations), uses those values. The stop condition is fetched from this.stop(). Override this.stop() if you want to use another stop condition.
org.apache.commons.math.optimization.fitting.CurveFitter.fit()
Fit a curve.
This method compute the coefficients of the curve that best fit the sample of observed points previously given through calls to the {@link #addObservedPoint(WeightedObservedPoint) addObservedPoint} method.
@param f parametric function to fit @param initialGuess first guess of the function parameters @return fitted parameters @exception FunctionEvaluationException if the objective function throws one during the search @exception OptimizationException if the algorithm failed to converge @exception IllegalArgumentException if the start point dimension is wrong
org.apache.commons.math.optimization.fitting.PolynomialFitter.fit()
Get the polynomial fitting the weighted (x, y) points. @return polynomial function best fitting the observed points @exception OptimizationException if the algorithm failed to converge
org.encog.ml.hmm.distributions.StateDistribution.fit()
Fit this distribution to the specified data set. @param set The data set to fit to.
weka.classifiers.functions.pace.ChisqMixture.fit()
weka.classifiers.functions.pace.NormalMixture.fit()

Examples of weka.classifiers.functions.pace.ChisqMixture.fit()

    case pace2Estimator: 
    case pace4Estimator:
    case pace6Estimator:
      DoubleVector AHat = aHat.square();
      ChisqMixture dc = new ChisqMixture();
      dc.fit( AHat, MixtureDistribution.NNMMethod ); 
      DoubleVector ATilde; 
      if( paceEstimator == pace6Estimator ) 
  ATilde = dc.pace6( AHat );
      else if( paceEstimator == pace2Estimator ) 
  ATilde = dc.pace2( AHat );

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Examples of weka.classifiers.functions.pace.NormalMixture.fit()

    switch( paceEstimator) {
    case ebEstimator: 
    case nestedEstimator:
    case subsetEstimator:
      NormalMixture d = new NormalMixture();
      d.fit( aHat, MixtureDistribution.NNMMethod ); 
      if( paceEstimator == ebEstimator ) 
  aTilde = d.empiricalBayesEstimate( aHat );
      else if( paceEstimator == ebEstimator ) 
  aTilde = d.subsetEstimate( aHat );
      else aTilde = d.nestedEstimate( aHat );

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Examples of weka.classifiers.functions.pace.NormalMixture.fit()

    switch( paceEstimator) {
    case ebEstimator: 
    case nestedEstimator:
    case subsetEstimator:
      NormalMixture d = new NormalMixture();
      d.fit( aHat, MixtureDistribution.NNMMethod ); 
      if( paceEstimator == ebEstimator ) 
  aTilde = d.empiricalBayesEstimate( aHat );
      else if( paceEstimator == ebEstimator ) 
  aTilde = d.subsetEstimate( aHat );
      else aTilde = d.nestedEstimate( aHat );

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