24 from __future__
import absolute_import, division, print_function
26 __all__ = (
"fitMixture",
"SemiEmpiricalPriorConfig",
27 "SoftenedLinearPriorControl")
29 from builtins
import range
36 from ..mixture
import Mixture
37 from .priors
import (SemiEmpiricalPriorControl, SemiEmpiricalPrior,
38 SoftenedLinearPriorControl, SoftenedLinearPrior,
42 SemiEmpiricalPriorConfig = makeConfigClass(SemiEmpiricalPriorControl)
44 SoftenedLinearPriorConfig = makeConfigClass(SoftenedLinearPriorControl)
50 ConfigClass = SemiEmpiricalPriorConfig
56 ConfigClass = SoftenedLinearPriorConfig
59 def fitMixture(data, nComponents, minFactor=0.25, maxFactor=4.0,
60 nIterations=20, df=float(
"inf")):
61 """Fit a ``Mixture`` distribution to a set of (e1, e2, r) data points, 62 returing a ``MixturePrior`` object. 67 array of data points to fit; shape=(N,3) 69 number of components in the mixture distribution 71 ellipticity variance of the smallest component in the initial mixture, 72 relative to the measured variance 74 ellipticity variance of the largest component in the initial mixture, 75 relative to the measured variance 77 number of expectation-maximization update iterations 79 number of degrees of freedom for component Student's T distributions 83 rMu = data[:, 2].mean()
84 rSigma = data[:, 2].var()
85 eSigma = 0.5*(data[:, 0].var() + data[:, 1].var())
86 mu = np.array([0.0, 0.0, rMu], dtype=float)
87 baseSigma = np.array([[eSigma, 0.0, 0.0],
90 for factor
in np.linspace(minFactor, maxFactor, nComponents):
91 sigma = baseSigma.copy()
92 sigma[:2, :2] *= factor
94 mixture = Mixture(3, components, df)
95 restriction = MixturePrior.getUpdateRestriction()
96 for i
in range(nIterations):
97 mixture.updateEM(data, restriction)
def fitMixture(data, nComponents, minFactor=0.25, maxFactor=4.0, nIterations=20, df=float("inf"))
A weighted Student's T or Gaussian distribution used as a component in a Mixture. ...