PEATDB/Ekin/Base.py
def fitDatasets(self, datasets='ALL', update=True, findbest=False, models=None,
                        noiter=300, conv=1e-6, grad=1e-6, silent=False, callback=None):
        """Fit multiple datasets in this project, if no datasets are given, we
           fit all of them.
           If findbest=True, finds the best fit model using f-testing for the given
           dataset.
           models is optional and provides a list of models to try for best fitting
        """
        datasets = self.checkDatasetParam(datasets)
        print 'fitting %s datasets..' %len(datasets)
        if findbest == True:
            for name in datasets:
                self.findBestModel(name, update=update, models=models)
        else:
            for name in datasets:
                self.fitDataset(name, update=update, model=models[0],
                                noiter=noiter, conv=conv, grad=grad,
                                silent=silent)
                if callback != None:
                    callback()
        return
Fit multiple datasets in this project, if no datasets are given, we
           fit all of them.
           If findbest=True, finds the best fit model using f-testing for the given
           dataset.
           models is optional and provides a list of models to try for best fitting
        
Fit multiple datasets in this project, if no datasets are given, we
fit all of them.
If findbest=True, finds the best fit model using f-testing for the given
dataset.
models is optional and provides a list of models to try for best fitting
Fit multiple datasets in this project if no datasets are given we fit all of them . If findbest = True finds the best fit model using f - testing for the given dataset . models is optional and provides a list of models to try for best fitting
def fitDatasets(self, datasets='ALL', update=True, findbest=False, models=None,
                        noiter=300, conv=1e-6, grad=1e-6, silent=False, callback=None):
        datasets = self.checkDatasetParam(datasets)
        print 'fitting %s datasets..' %len(datasets)
        if findbest == True:
            for name in datasets:
                self.findBestModel(name, update=update, models=models)
        else:
            for name in datasets:
                self.fitDataset(name, update=update, model=models[0],
                                noiter=noiter, conv=conv, grad=grad,
                                silent=silent)
                if callback != None:
                    callback()
        return
def fitDatasets ( self , datasets = 'ALL' , update = True , findbest = False , models = None , noiter = 300 , conv = 1e-6 , grad = 1e-6 , silent = False , callback = None ) : datasets = self . checkDatasetParam ( datasets ) print 'fitting %s datasets..' % len ( datasets ) if findbest == True : for name in datasets : self . findBestModel ( name , update = update , models = models ) else : for name in datasets : self . fitDataset ( name , update = update , model = models [ 0 ] , noiter = noiter , conv = conv , grad = grad , silent = silent ) if callback != None : callback ( ) return
Fit multiple datasets in this project, if no datasets are given, we
fit all of them.
Fit multiple datasets in this project if no datasets are given we fit all of them .
"""Fit multiple datasets in this project, if no datasets are given, we
           fit all of them.
           If findbest=True, finds the best fit model using f-testing for the given
           dataset.
           models is optional and provides a list of models to try for best fitting
        """
self
datasets
update
findbest
models
noiter
conv
grad
silent
callback