Referencia de la Clase vsm.extensions.clustering.manifold.Manifold

Diagrama de herencias de vsm.extensions.clustering.manifold.Manifold

Diagrama de colaboración para vsm.extensions.clustering.manifold.Manifold:

Métodos públicos
def	__init__
def	__str__
def	cls
def	KMeans
def	AffinityPropagation
def	SpectralClustering
def	mds
def	isomap
def	plot

Atributos públicos
	dismat
	labels
	pos

Documentación de las funciones miembro

def vsm.extensions.clustering.manifold.Manifold.AffinityPropagation	(		self,
			show = True
	)

Clusters objects in `dismat` using affinity propagation algorithm.

:param show: Shows the resulting clusters if true.
:type n_clusters: boolean, optional

def vsm.extensions.clustering.manifold.Manifold.cls

(

self

)

views clusters as lists

def vsm.extensions.clustering.manifold.Manifold.isomap	(		self,
			n_components = 2,
			n_neighbors = 3,
			show = False
	)

Calculates lower dimention coordinates using the isomap algorithm.

:param n_components: dimentionality of the reduced space
:type n_components: int, optional

:param n_neighbors: Used by isomap to determine the number of neighbors
    for each point. Large neighbor size tends to produce a denser map.
:type n_neighbors: int, optional

:param show: Shows the calculated coordinates if true.
:type show: boolean, optional

def vsm.extensions.clustering.manifold.Manifold.KMeans	(		self,
			n_clusters = 10,
			init = 'k-means++',
			max_iter = 100,
			n_init = 1,
			verbose = 1,
			show = True
	)

Clusters the objects in `dismat` using k-means algorithm. This requires 
`pos` be  precomputed by `mds` or `isomap`. For parameters of the 
algorithms see: 
http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.
html#sklearn.cluster.KMeans

:param n_clusters: Number of clusters used as the parameter for K-means.
:type n_clusters: int, optional

:param show: Shows the resulting clusters if true.
:type n_clusters: boolean, optional

def vsm.extensions.clustering.manifold.Manifold.mds	(		self,
			n_components = 2,
			dissimilarity = 'precomputed',
			show = False
	)

Calculates lower dimention coordinates using the mds algorithm.
This requires sklearn ver 0.14 due to the dissimilarity argument.

:param n_components: dimentionality of the reduced space.
:type n_components: int, optional

:param show: Shows the calculated coordinates if true.
:type show: boolean, optional

def vsm.extensions.clustering.manifold.Manifold.plot	(		self,
			xy = (0,1
	)

Outputs 2d embeded plot based on `pos`

:param xy: specifies the dimsntions of pos to be plotted.
:type xy: tuple, optional

def vsm.extensions.clustering.manifold.Manifold.SpectralClustering	(		self,
			n_clusters = 10,
			show = True
	)

Clusters objects in `dismat` using spectral clustering. 

:param n_clusters: Number of clusters used as the parameter for K-means.
:type n_clusters: int, optional

:param show: Shows the resulting clusters if true.
:type n_clusters: boolean, optional

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La documentación para esta clase fue generada a partir del siguiente fichero:

• vsm/vsm/extensions/clustering/manifold.py