Inheritance diagram for nipy.neurospin.eda.dimension_reduction:
This modules contains several classes to perform non-linear dimension reduction. Each class has 2 methods, ‘train’ and ‘test’ - ‘train’ performs the computation of low-simensional data embedding and the information to generalize to new data - ‘test’ computes the embedding for new dsamples of data This is done for - Multi-dimensional scaling - Isompap (knn or eps-neighb implementation) - Locality Preseving projections (LPP) - laplacian embedding (train only)
Future developpements will include some supervised cases, e.g. LDA,LDE and the estimation of the latent dimension, at least in simple cases.
Bases: nipy.neurospin.eda.dimension_reduction.NLDR
This is a particular class that perfoms linear dimension reduction using multi-dimensional scaling besides the fields of NDLR, it contains the following ones: - trained: trained==1 means that the system has been trained and can generalize - embedding: array of shape (nbitems,rdim) this is representation of the training data - offset: array of shape(nbitems) affine part of the embedding - projector: array of shape(fdim,rdim) linear part of the embedding
This is a generic class for dimension reduction techniques the main fields are - train_data : the input dataset from which the DR is perfomed - fdim=1 - rdim=1
Bases: nipy.neurospin.eda.dimension_reduction.NLDR
This is a particular class that perfoms linear dimension reduction using eps-ball neighbor modelling and isomapping. besides the fields of NDLR, it contains the following ones: - eps : eps-ball model used in the knn graph building - G : resulting graph based on the training data - trained: trained==1 means that the system has been trained and can generalize - embedding: array of shape (nbitems,rdim) this is representation of the training data - offset: array of shape(nbitems) affine part of the embedding - projector: array of shape(fdim,rdim) linear part of the embedding
Bases: nipy.neurospin.eda.dimension_reduction.NLDR
This is a particular class that perfoms linear dimension reduction using k nearest neighbor modelling and isomapping. besides the fields of NDLR, it contains the following ones: - k : number of neighbors in the knn graph building - G : resulting graph based on the training data - trained: trained==1 means that the system has been trained and can generalize - embedding: array of shape (nbitems,rdim) this is representation of the training data - offset: array of shape(nbitems) affine part of the embedding - projector: array of shape(fdim,rdim) linear part of the embedding
Bases: nipy.neurospin.eda.dimension_reduction.NLDR
This is a particular class that perfoms linear dimension reduction using k nearest neighbor modelling and laplacian embedding. besides the fields of NDLR, it contains the following ones: - k : number of neighbors in the knn graph building - G : resulting graph based on the training data - trained: trained==1 means that the system has been trained and can generalize - embedding: array of shape (nbitems,rdim) this is representation of the training data NB: to date, only the training part (embedding computation) is considered
Bases: nipy.neurospin.eda.dimension_reduction.NLDR
This is a particular class that perfoms linear dimension reduction using k nearest neighbor modelling and locality preserving projection (LPP). besides the fields of NDLR, it contains the following ones: - k : number of neighbors in the knn graph building - G : resulting graph based on the training data - trained: trained==1 means that the system has been trained and can generalize - embedding: array of shape (nbitems,rdim) this is representation of the training data - projector: array of shape(fdim,rdim) linear part of the embedding