Inheritance diagram for nipy.algorithms.clustering.von_mises_fisher_mixture:
Implementation of Von-Mises-Fisher Mixture models, i.e. the equaivalent of mixture of Gaussian on the sphere.
Author: Bertrand Thirion, 2010-2011
Bases: object
Model for Von Mises mixture distribution with fixed variance on a two-dimensional sphere
Methods
density_per_component(x) | Compute the per-component density of the data |
estimate(x[, maxiter, miniter, bias]) | Return average log density across samples |
estimate_means(x, z) | Calculate and set means from x and z |
estimate_weights(z) | Calculate and set weights from z |
log_density_per_component(x) | Compute the per-component density of the data |
log_weighted_density(x) | Return log weighted density |
mixture_density(x) | Return mixture density |
responsibilities(x) | Return responsibilities |
show(x) | Visualization utility |
weighted_density(x) | Return weighted density |
Initialize Von Mises mixture
Parameters : | k: int, :
precision: float, :
means: array of shape(self.k, 3), optional :
weights: array of shape(self.k), optional :
null_class: bool, optional :
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Compute the per-component density of the data
Parameters : | x: array fo shape(n,3) :
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Returns : | like: array of shape(n, self.k), with non-neagtive values :
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Return average log density across samples
Parameters : | x: array fo shape(n,3) :
maxiter: int, optional, :
miniter=1: int, optional, :
bias: array of shape(n), optional :
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Calculate and set means from x and z
Parameters : | x: array fo shape(n,3) :
z: array of shape(self.k) : |
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Calculate and set weights from z
Parameters : | z: array of shape(self.k) : |
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Compute the per-component density of the data
Parameters : | x: array fo shape(n,3) :
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Returns : | like: array of shape(n, self.k), with non-neagtive values :
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Return log weighted density
Parameters : | x: array fo shape(n,3) :
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Returns : | log_like: array of shape(n, self.k) : |
Return mixture density
Parameters : | x: array fo shape(n,3) :
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Returns : | like: array of shape(n) : |
Return responsibilities
Parameters : | x: array fo shape(n,3) :
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Returns : | resp: array of shape(n, self.k) : |
Visualization utility
Parameters : | x: array fo shape(n,3) :
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Return weighted density
Parameters : | x: array shape(n,3) :
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Returns : | like: array :
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Return the best von_mises mixture after severla initialization
Parameters : | k: int, number of classes : precision: float, priori precision parameter : null class: bool, optional, :
x: array fo shape(n,3) :
ninit: int, optional, :
bias: array of shape(n), optional :
maxiter: int, optional, :
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Return the best von_mises mixture after severla initialization
Parameters : | krange: list of ints, :
precision: : null class: : x: array fo shape(n,3) :
ninit: int, optional, :
maxiter: int, optional, : bias: array of shape(n), :
verbose: Bool, optional : |
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Return the best von_mises mixture after severla initialization
Parameters : | krange: list of ints, :
precision: float, :
x: array fo shape(n, 3) :
null class: bool, whether a null class should be included or not : cv_index: set of indices for cross validation : ninit: int, optional, :
maxiter: int, optional, : bias: array of shape (n), prior : |
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Return the points and area of a npoints**2 points sampled on a sphere
Returns : | s : array of shape(npoints ** 2, 3) area: array of shape(npoints) : |
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