algorithms.clustering.von_mises_fisher_mixture¶
Module: algorithms.clustering.von_mises_fisher_mixture
¶
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
Class¶
VonMisesMixture
¶
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class
nipy.algorithms.clustering.von_mises_fisher_mixture.
VonMisesMixture
(k, precision, means=None, weights=None, null_class=False)¶ 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 -
__init__
(k, precision, means=None, weights=None, null_class=False)¶ Initialize Von Mises mixture
Parameters: k: int, :
number of components
precision: float, :
the fixed precision parameter
means: array of shape(self.k, 3), optional :
input component centers
weights: array of shape(self.k), optional :
input components weights
null_class: bool, optional :
Inclusion of a null class within the model (related to k=0)
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density_per_component
(x)¶ Compute the per-component density of the data
Parameters: x: array fo shape(n,3) :
should be on the unit sphere
Returns: like: array of shape(n, self.k), with non-neagtive values :
the density
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estimate
(x, maxiter=100, miniter=1, bias=None)¶ Return average log density across samples
Parameters: x: array of shape (n,3) :
should be on the unit sphere
maxiter : int, optional
maximum number of iterations of the algorithms
miniter : int, optional
minimum number of iterations
bias : array of shape(n), optional
prior probability of being in a non-null class
Returns: ll : float
average (across samples) log-density
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estimate_means
(x, z)¶ Calculate and set means from x and z
Parameters: x: array fo shape(n,3) :
should be on the unit sphere
z: array of shape(self.k) :
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estimate_weights
(z)¶ Calculate and set weights from z
Parameters: z: array of shape(self.k) :
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log_density_per_component
(x)¶ Compute the per-component density of the data
Parameters: x: array fo shape(n,3) :
should be on the unit sphere
Returns: like: array of shape(n, self.k), with non-neagtive values :
the density
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log_weighted_density
(x)¶ Return log weighted density
Parameters: x: array fo shape(n,3) :
should be on the unit sphere
Returns: log_like: array of shape(n, self.k) :
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mixture_density
(x)¶ Return mixture density
Parameters: x: array fo shape(n,3) :
should be on the unit sphere
Returns: like: array of shape(n) :
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responsibilities
(x)¶ Return responsibilities
Parameters: x: array fo shape(n,3) :
should be on the unit sphere
Returns: resp: array of shape(n, self.k) :
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show
(x)¶ Visualization utility
Parameters: x: array fo shape(n,3) :
should be on the unit sphere
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weighted_density
(x)¶ Return weighted density
Parameters: x: array shape(n,3) :
should be on the unit sphere
Returns: like: array :
of shape(n, self.k)
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Functions¶
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nipy.algorithms.clustering.von_mises_fisher_mixture.
estimate_robust_vmm
(k, precision, null_class, x, ninit=10, bias=None, maxiter=100)¶ Return the best von_mises mixture after severla initialization
Parameters: k: int, number of classes :
precision: float, priori precision parameter :
null class: bool, optional, :
should a null class be included or not
x: array fo shape(n,3) :
input data, should be on the unit sphere
ninit: int, optional, :
number of iterations
bias: array of shape(n), optional :
prior probability of being in a non-null class
maxiter: int, optional, :
maximum number of iterations after each initialization
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nipy.algorithms.clustering.von_mises_fisher_mixture.
example_cv_nonoise
()¶
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nipy.algorithms.clustering.von_mises_fisher_mixture.
example_noisy
()¶
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nipy.algorithms.clustering.von_mises_fisher_mixture.
select_vmm
(krange, precision, null_class, x, ninit=10, bias=None, maxiter=100, verbose=0)¶ Return the best von_mises mixture after severla initialization
Parameters: krange: list of ints, :
number of classes to consider
precision: :
null class: :
x: array fo shape(n,3) :
should be on the unit sphere
ninit: int, optional, :
number of iterations
maxiter: int, optional, :
bias: array of shape(n), :
a prior probability of not being in the null class
verbose: Bool, optional :
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nipy.algorithms.clustering.von_mises_fisher_mixture.
select_vmm_cv
(krange, precision, x, null_class, cv_index, ninit=5, maxiter=100, bias=None, verbose=0)¶ Return the best von_mises mixture after severla initialization
Parameters: krange: list of ints, :
number of classes to consider
precision: float, :
precision parameter of the von-mises densities
x: array fo shape(n, 3) :
should be on the unit sphere
null class: bool, whether a null class should be included or not :
cv_index: set of indices for cross validation :
ninit: int, optional, :
number of iterations
maxiter: int, optional, :
bias: array of shape (n), prior :
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nipy.algorithms.clustering.von_mises_fisher_mixture.
sphere_density
(npoints)¶ 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) :