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ClassicProblem = class ClassicProblem(Problem) |
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ClassicProblem(model=None, xdata=None, ydata=None, weights=None, accuracy=None, copy=None)
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
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model, xdata, ydata, weights, accuracy, varyy
Author : Do Kester
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- Method resolution order:
- ClassicProblem
- Problem
- builtins.object
Constructor:
- 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
Methods defined here:
- baseName()
- Returns a string representation of the model.
- copy()
- Copy.
The copy points to the same instance of model.
- derivative( param )
- Return the derivative of the internal model.
Parameters
----------
param : array_like
list of model parameters
- myDistribution()
- Return a default preferred ErrorDistribution: "gauss"
- myEngines()
- Return a default list of preferred engines
- myStartEngine()
- Return a default preferred start engines: "start"
- partial( param )
- Return the partials of the internal model.
Parameters
----------
param : array_like
list of model parameters
- result( param )
- Returns the result calculated at the xdatas.
Parameters
----------
param : array_like
values for the parameters.
Methods inherited from Problem:
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