["BayesicFitting"]

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class ClassicProblem( Problem )	Source

A ClassicProblem is an optimization of parameters which involves the fitting of data to a Model at a fixed set of x values.

Problems can be solved by NestedSampler, with appropriate Engines and ErrorDistributions.

The result of the function for certain x and p is given by problem.result( x, p ) The parameters, p, are to be optimized while the x provide additional information.

Attributes from Problem

model, xdata, ydata, weights, accuracy, varyy

Author : Do Kester

ClassicProblem( model=None, xdata=None, ydata=None, weights=None, accuracy=None, copy=None )

Constructor for classic problems.

Parameters

model : Model
the model to be solved
xdata : array_like or None
independent variable
ydata : array_like or None
dependent variable
weights : array_like or None
weights associated with ydata
accuracy : float or array_like
accuracy scale for the datapoints
all the same or one for each data point
copy : Problem
to be copied

copy( )

Copy.

The copy points to the same instance of model.

result( param )

Returns the result calculated at the xdatas.

Parameters

param : array_like
values for the parameters.

partial( param )

Return the partials of the internal model.

Parameters

param : array_like
list of model parameters

derivative( param )

Return the derivative of the internal model.

Parameters

param : array_like
list of model parameters

myEngines( )

Return a default list of preferred engines

myStartEngine( )

Return a default preferred start engines: "start"

myDistribution( )

Return a default preferred ErrorDistribution: "gauss"

baseName( )

Returns a string representation of the model.

Methods inherited from Problem