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MultipleOutputProblem = class MultipleOutputProblem(Problem) |
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MultipleOutputProblem(model=None, xdata=None, ydata=None, weights=None, copy=None)
A MultipleOutputProblem is an optimization of parameters where the model
has multiple outputs. E.g. the orbit of a double star or the outcome of
a game.
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( 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, partype
Author : Do Kester
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- Method resolution order:
- MultipleOutputProblem
- Problem
- builtins.object
Constructor:
- MultipleOutputProblem( model=None, xdata=None, ydata=None, weights=None, copy=None )
- Problem Constructor.
Parameters
----------
model : Model
the model to be solved. One with multiple outputs: model.ndout > 1
xdata : array_like
independent variable
ydata : array_like
dependent variable. shape = (len(xdata), model.ndout)
weights : array_like or None
weights associated with ydata: shape = as xdata or as ydata
copy : Problem
to be copied
Methods defined here:
- copy()
- Copy.
- derivative( param )
- Return the derivative of the internal model.
Parameters
----------
param : array_like
list of model parameters
- myDistribution()
- Return the name of the preferred error distribution
- myEngines()
- Return a default list of preferred engines
- myStartEngine()
- Return the default preferred startengines
- partial( param )
- Returns the partials (df/dp) calculated at the xdata.
Parameters
----------
param : array_like
values for the parameters + nuisance params.
- residuals( param, mockdata=None )
- Returns residuals in a flattened array.
- result( param )
- Returns the result calculated at the xdata.
Parameters
----------
param : array_like
values for the parameters + nuisance params.
Methods inherited from Problem:
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