AmoebaFitter

AmoebaFitter = class AmoebaFitter(MaxLikelihoodFitter)

AmoebaFitter(xdata, model, **kwargs) Fitter using the simulated annealing simplex minimum finding algorithm, See also: AnnealingAmoeba Author Do Kester Examples -------- # assume x and y are Double1d data arrays. >>> x = numpy.arange( 100, dtype=float ) / 10 >>> y = 3.5 * SIN( x + 0.4 ) # make sine >>> numpy.random.seed( 12345L ) # Gaussian random number generator >>> y += numpy.random.randn( 100 ) * 0.2 # add noise >>> sine = SineModel( ) # sinusiodal model >>> lolim = numpy.asarray( [1,-10,-10], dtype=float ) >>> hilim = numpy.asarray( [100,10,10], dtype=float ) >>> sine.setLimits( lolim, hilim ) # set limits on the model parameters >>> amfit = AmoebaFitter( x, sine ) >>> param = amfit.fit( y, temp=10 ) >>> stdev = amfit.getStandardDeviation( ) # stdevs on the parameters >>> chisq = amfit.getChiSquared( ) >>> scale = amfit.getScale( ) # noise scale >>> yfit = amfit.getResult( ) # fitted values >>> yfit = sine( x ) # fitted values ( same as previous ) >>> yband = amfit.monteCarloError( ) # 1 sigma confidence region # for diagnostics ( or just for fun ) >>> amfit = AmoebaFitter( x, sine ) >>> amfit.setTemperature( 10 ) # set a temperature to escape local minima >>> amfit.setVerbose( 10 ) # report every 10th iteration >>> plotter = IterationPlotter( ) # from BayesicFitting >>> amfit.setPlotter( plotter, 20 ) # make a plot every 20th iteration >>> param = amfit.fit( y ) Notes ----- 1. AmoebaFitter is not guaranteed to find the global minimum. 2. The calculation of the evidence is an Gaussian approximation which is only exact for linear models with a fixed scale. Author : Do Kester.

Method resolution order:: AmoebaFitter; MaxLikelihoodFitter; IterativeFitter; BaseFitter; builtins.object

Constructor:

AmoebaFitter( xdata, model, **kwargs ): Create a new Amoeba class, providing inputs and model. Parameters ---------- xdata : array_like independent input values model : Model the model function to be fitted kwargs : dict Possibly includes keywords from MaxLikelihoodFitter : errdis, scale, power IterativeFitter : maxIter, tolerance, verbose BaseFitter : map, keep, fixedScale

Methods defined here:

fit( data, weights=None, par0=None, keep=None, size=None,
         seed=4567, temp=0, limits=None, maxiter=1000, tolerance=0.0001,
         cooling=0.95, steps=10, verbose=0, plot=False, accuracy=None,
         callback=None): Return Model fitted to the data array. When done, it also calculates the hessian matrix and chisq. Parameters ---------- data : array_like the data vector to be fitted weights : array_like weights pertaining to the data The weights are relative weights unless `scale` is set. accuracy : float or array_like accuracy of (individual) data par0 : array_like initial values of teh parameters of the model default: from model keep : dict of {int:float} dictionary of indices (int) to be kept at a fixed value (float) The values of keep are only valid for *this* fit See also `AmoebaFitter( ..., keep=dict )` size : float or array_like step size of the simplex seed : int for random number generator temp : float temperature of annealing (0 is no annealing) limits : None or list of 2 floats or list of 2 array_like None : no limits applied [lo,hi] : low and high limits for all values [la,ha] : low array and high array limits for the values maxiter : int max number of iterations tolerance : float stops when ( |hi-lo| / (|hi|+|lo|) ) < tolerance cooling : float cooling factor when annealing steps : int number of cycles in each cooling step. verbose : int 0 : silent 1 : print results to output 2 : print some info every 100 iterations 3 : print some info all iterations plot : bool plot the results. callback : callable is called each iteration as `val = callback( val )` where `val` is the minimizable array

Methods inherited from MaxLikelihoodFitter:

Methods inherited from IterativeFitter:

Methods inherited from BaseFitter: