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
model, xdata, ydata, weights, partype
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MultipleOutputProblem( model=None, xdata=None, ydata=None, weights=None,
accuracy=None, copy=None )
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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
- accuracy : float or ndarray of shape (ndata,)
accuracy scale for the datapoints
all the same or one for each data point
- copy : Problem
to be copied
Copy.
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expandFlat( weights, ndout )
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Expand and flatten the arrays.
Returns the result calculated at the xdata.
Parameters
- param : array_like
values for the parameters + nuisance params.
Returns the partials (df/dp) calculated at the xdata.
Parameters
- param : array_like
values for the parameters + nuisance params.
Return the derivative of the internal model.
Parameters
- param : array_like
list of model parameters
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residuals( param, mockdata=None )
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Returns residuals in a flattened array.
Return a default list of preferred engines
Return the default preferred startengines
Return the name of the preferred error distribution
Returns a string representation of the model.