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class Problem( object )	Source

Problem implements the common parts of specialized Problems.

A Problem is an optimization of parameters which does not involve the fitting of data to a Model.

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.

This class is a base class. Further specializations will define the result method.

Attributes

• model : Model
       to be optimized

• xdata : array_like
       independent variable (static)

• ydata : array_like
       dependent variable (static)

• weights : array_like
       weights associated with ydata

• varyy : float or ndarry of shape (ndata,)
       Variance in the errors of the ydata

• npars : int
       number of parameters in the model of the problem

• partype : float | int
       type of the parameters

• Author : Do Kester

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

Problem Constructor.

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 ndarray of shape (ndata,)
       accuracy scale for the datapoints
       all the same or one for each data point
• copy : Problem
       to be copied

copy( )

Copy.

setAccuracy( accuracy=None )

set the value for accuracy.

Paramaters

• accuracy : float or array of NDATA floats or None
       Either one value for all or one for each data point
       When None the value is set to 0 (for easy computational reasons)

hasWeights( )

Return whether it has weights.

result( param )

Returns the result using the parameters.

In this (base)class it is a placeholder.

Parameters

• param : array_like
       values for the parameters.

residuals( param, mockdata=None )

Returns the residuals, calculated at the xdata.

Parameters

• param : array_like
       values for the parameters.
• mockdata : array_like
       model fit at xdata

cyclicCorrection( res )

No correction.

Returns

     the residuals, unadultered

Parameters

• res : array_like
       residuals

cycor1( res )

Returns the residuals, all corrected for periodicity in residuals

Parameters

• res : array_like
       residuals

cycor2( res )

Returns the residuals corrected for periodicity in residuals, only the result dimensions listed in the model.cyclic dictionary.

Parameters

• res : array_like
       residuals

cyclize( res, period )

Apply correction on residuals which are cyclic in some phase space.

If the model results in a phase value of +ε while the data give that phase value as (p - ε) to keep all data in the range [0,p], the naive residual would be (p - 2 ε) while the actual distance should be measured the other way around as (2 ε). Here p = period and ε = small deviation.

Parameters

• res : array_like
       original residuals
• period : float
       of the phase space

Returns

corrected residuals.

weightedResSq( allpars, mockdata=None, extra=False )

Returns the (weighted) squared residuals, calculated at the xdata.

Optionally (extra=True) the weighted residuals themselves are returned too.

Parameters

• allpars : array_like
       values for the parameters.
• mockdata : array_like
       model fit at xdata
• extra : bool (False)
       true : return ( wgt * res^2, wgt * res )
       false : return wgt * res²

isDynamic( )

domain2Unit( dval, kpar )

Return value in [0,1] for the selected parameter.

Parameters

• dval : float
       domain value for the selected parameter
• kpar : array_like
       selected parameter index, where kp is index in [parameters, hyperparams]

unit2Domain( uval, kpar )

Return domain value for the selected parameter.

Parameters

• uval : array_like
       unit value for the selected parameter
• kpar : array_like
       selected parameter indices, where kp is index in [parameters, hyperparams]

shortName( )

baseName( )