Uses of Package
com.aliasi.matrix

Packages that use com.aliasi.matrix

Package	Description
com.aliasi.classify	Classes for classifying data and evaluation.
com.aliasi.crf	Classes and interfaces for conditional random fields.
com.aliasi.dca	Classes for fitting and running discrete choice analysis (DCA) models.
com.aliasi.features	Classes for extracting feature vectors from objects and parsing objects for feature handlers.
com.aliasi.matrix	Classes for matrices and vectors.
com.aliasi.stats	Classes for handling basic statical distributions and estimators.
com.aliasi.test.unit.matrix

Classes in com.aliasi.matrix used by com.aliasi.classify

Class and Description
KernelFunction A KernelFunction computes real-valued proximities between vectors.
Vector A Vector represents an n-dimensional value.

Classes in com.aliasi.matrix used by com.aliasi.crf

Class and Description
Vector A Vector represents an n-dimensional value.

Classes in com.aliasi.matrix used by com.aliasi.dca

Class and Description
Vector A Vector represents an n-dimensional value.

Classes in com.aliasi.matrix used by com.aliasi.features

Class and Description
Vector A Vector represents an n-dimensional value.

Classes in com.aliasi.matrix used by com.aliasi.matrix

Class and Description
AbstractMatrix An AbstractMatrix implements most of a matrix's functionality in terms of methods for accessing numbers of rows and columns and values.
AbstractVector An AbstractVector implements most of a vector's functionality in terms of methods for dimensionality and values.
CosineDistance The CosineDistance class implements proximity as vector cosine.
DenseVector A DenseVector is a vector implementation suitable for vectors with primarily non-zero values.
EuclideanDistance The EuclideanDistance class implements standard Euclidean distance between vectors.
KernelFunction A KernelFunction computes real-valued proximities between vectors.
Matrix A Matrix represents a 2-dimensional matrix.
SvdMatrix An SvdMatrix provides a means of storing a matrix that has been factored via a singular-value decomposition (SVD).
TaxicabDistance The TaxicabDistance class implements standard taxicab, or Manhattan distance between vectors.
Vector A Vector represents an n-dimensional value.

Classes in com.aliasi.matrix used by com.aliasi.stats

Class and Description
Vector A Vector represents an n-dimensional value.

Classes in com.aliasi.matrix used by com.aliasi.test.unit.matrix

Class and Description
AbstractVector An AbstractVector implements most of a vector's functionality in terms of methods for dimensionality and values.
Vector A Vector represents an n-dimensional value.