["BayesicFitting"]

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class Fitter( BaseFitter )	[source]

Fitter for linear models.

The Fitter class is to be used in conjunction with Model classes.

The Fitter class and its descendants fit data to a model. Fitter itself is the variant for linear models, ie. models linear in its parameters.

Examples

# assume x and y are numpy.asarray data arrays
x = numpy.arange( 100 )
y = numpy.arange( 100 ) // 4        # digitization noise
poly = PolynomialModel( 1 )         # line
fitter = Fitter( x, poly )
param = fitter.fit( y )
stdev = fitter.stdevs               # stdevs on the parameters
chisq = fitter.chisq
scale = fitter.scale                # noise scale
yfit  = fitter.getResult( )         # fitted values
yfit  = poly( x )                   # same as previous
yband = fitter.monteCarloError( )        # 1 sigma confidence region

Limitations

The Fitter does not work with limits.
The calculation of the evidence is an Gaussian approximation which is
only exact for linear models with a fixed scale.

Author Do Kester

Fitter( xdata, model, map=False, keep=None, fixedScale=None )	[source]

Create a new Fitter, providing xdatas and model.

A Fitter class is defined by its model and the input vector (the independent variable). When a fit to another model and/or another input vector is needed a new object should be created.

Parameters

xdata : array_like
array of independent input values
model : Model
the model function to be fitted
map : bool (False)
     When true, the xdata should be interpreted as a map.
     The fitting is done on the pixel indices of the map,
     using ImageAssistant
keep : dict of {int:float}
     dictionary of indices (int) to be kept at a fixed value (float)
     The values of keep will be used by the Fitter as long as the Fitter exists.
     See also fit( ..., keep=dict )
fixedScale : float
the fixed noise scale

fit( ydata, weights=None, accuracy=None, keep=None, plot=False )	[source]

Return model parameters fitted to the data, including weights.

For Linear models the matrix equation

H * p = β

is solved for p. H is the Hessian matrix ( D * w * D^T ) and β is the inproduct of the data with the D, design matrix.

β = y * w * D^T

Parameters

ydata : array_like
the data vector to be fitted
weights : array_like
weights pertaining to the data ( = 1.0 / sigma^2 )
accuracy : float or array_like
accuracy of (individual) data
keep : dict of {int:float}
     dictionary of indices (int) to be kept at a fixed value (float)
     The values will override those at initialization.
     They are only used in this call of fit.
plot : bool
Plot the results

Raises
ValueError when ydata or weights contain a NaN

Methods inherited from BaseFitter