| class MonteCarlo( object ) | Source |
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Helper class to calculate the confidence region of a fitted model.
MonteCarlo for models.
The MonteCarlo class is to be used in conjunction with Model classes.
Author: Do Kester
Attributes
- xdata : array_like
array of independent input values - model : Model
the model function to be fitted - mcycles : int
Sets number of cycles in the MonteCarlo procedure to estimate
error bars. Default = 25
Hidden Attributes
- _eigenvectors : array_like (read only)
from eigenvalue decomposition of covariance matrix - _eigenvalues : array_like (read only)
from eigenvalue decomposition of covariance matrix - _random : random
random number generator
| MonteCarlo( xdata, model, covariance, index=None, seed=12345, mcycles=25 ) |
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Create a new MonteCarlo, providing inputs and model.
A MonteCarlo object 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 - covariance : matrix
the covariance matrix of the problem. Default from the Model. - index : list of int
indices of parameters to fit - seed : int
seed for random number generator - mcycles : int
number of cycles in the MonteCarlo procedure to estimate error bars.
Raises
ValueError when model and input have different dimensions
| decompose( covariance ) |
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| getError( xdata=None ) |
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From the full covariance matrix ( = inverse of the Hessian ) random samples are drawn, which are added to the parameters. With this new set of parameters the model is calculated. This procedure is done by default, 25 times. The standard deviation of the models is returned as the error bar.
Parameters
- xdata : array_like
input data over which to calculate the error bars. default provided xdata
| randomVariant( xdata ) |
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Parameters
- xdata : array_like
input data at these indpendent points