Home >> All

•

jmat.data.* (13)

•

jmat.function.* (10)

•

jmat.io.* (33)

•

jmat.test.* (1)

• jmat: Javadoc index of package jmat.


Package Samples:

• jmat.data.arrayTools
• jmat.data.matrixDecompositions
• jmat.data
• jmat.function.expressionParser
• jmat.function
• jmat.io.data.fileTools
• jmat.io.data
• jmat.io.gui.plotTools
• jmat.io.gui
• jmat.test

Classes:

• EigenvalueDecomposition: Eigenvalues and eigenvectors of a real matrix. If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is diagonal and the eigenvector matrix V is orthogonal. I.e. A = V.times(D.times(V.transpose())) and V.times(V.transpose()) equals the identity matrix. If A is not symmetric, then the eigenvalue matrix D is block diagonal with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues, lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda]. The columns of V represent the eigenvectors in the sense that A*V = V*D, i.e. A.times(V) equals V.times(D). The matrix V may be badly conditioned, ...
• LUDecomposition: LU Decomposition. For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n unit lower triangular matrix L, an n-by-n upper triangular matrix U, and a permutation vector piv of length m so that A(piv,:) = L*U. In other words, assuming P the permutation Matrix, P*A = L*U. If m The LU decompostion with pivoting always exists, even if the matrix is singular, so the constructor will never fail. The primary use of the LU decomposition is in the solution of square systems of simultaneous linear equations. This will fail if isNonsingular() returns false.
• SingularValueDecomposition: Singular Value Decomposition. For an m-by-n matrix A with m >= n, the singular value decomposition is an m-by-n orthogonal matrix U, an n-by-n diagonal matrix S, and an n-by-n orthogonal matrix V so that A = U*S*V'. The singular values, sigma[k] = S[k][k], are ordered so that sigma[0] >= sigma[1] >= ... >= sigma[n-1]. The singular value decompostion always exists, so the constructor will never fail. The matrix condition number and the effective numerical rank can be computed from this decomposition.
• QRDecomposition: QR Decomposition. For an m-by-n matrix A with m >= n, the QR decomposition is an m-by-n orthogonal matrix Q and an n-by-n upper triangular matrix R so that A = Q*R. The QR decompostion always exists, even if the matrix does not have full rank, so the constructor will never fail. The primary use of the QR decomposition is in the least squares solution of nonsquare systems of simultaneous linear equations. This will fail if isFullRank() returns false.
• Evaluator: Mathematic expression evaluator. Supports the following functions: +, -, *, /, ^, %, cos, sin, tan, acos, asin, atan, sqrt, sqr, log, min, max, ceil, floor, abs, neg, rndr. When the getValue() is called, a Double object is returned. If it returns null, an error occured. Sample: MathEvaluator m = new MathEvaluator("-5-6/(-2) + sqr(15+x)"); m.addVariable("x", 15.1d); System.out.println( m.getValue() );
• CholeskyDecomposition: Cholesky Decomposition. For a symmetric, positive definite matrix A, the Cholesky decomposition is an lower triangular matrix L so that A = L*L'. If the matrix is not symmetric or positive definite, the constructor returns a partial decomposition and sets an internal flag that may be queried by the isSPD() method.
• Matrix: The Matrix Class provides the fundamental operations of numerical linear algebra (from the package JAMA), basic manipulations, and visualization tools. All the operations in this version of the Matrix Class involve only real matrices.
• Text: The Text Class is just designed to provide easy-to-use string operations like building log files, displaying text in a window, converting matrix to String format...
• Sort: Quick Sort algoritm. Allows to sort a column quickly, Using a generic version of C.A.R Hoare's Quick Sort algorithm.
• RandomMatrix: The RandomMatrix Class provides tools for statistical simulations,it extends the Matrix Class and adds many methods.
• MatlabSyntax: Description : Enables an easy to use Matlab-like syntax for invokation of main features. Copyright : GPL
• CharFile: Titre : JAva MAtrix TOols Description : Copyright : Copyright (c) 2002 Société : IRSN
• XMLFile: Titre : JAva MAtrix TOols Description : Copyright : Copyright (c) 2002 Société : IRSN
• PanelParameters: Titre : JAva MAtrix TOols Description : builds a JPanel containing fields for setting parameters.
• RandomVariable: The RandomVaraibale Class provides static methods for generating random numbers.
• Find: Find a value or a verified condition in a 2D-Array of double.
• DoubleFunction: Titre : JAva MAtrix TOols Description :
• MatrixFunction: Titre : JAva MAtrix TOols Description :
• TestDoubleFunction: Titre : JAva MAtrix TOols Description :
• Shuffle: Shuffle algoritm.
• Mathfun: DOCUMENT ME!
• DoubleFunctionExpression: DOCUMENT ME!
• DoubleFunctionInterpolation: DOCUMENT ME!
• InvokeDoubleFunction: DOCUMENT ME!
• InvokeMatrixFunction: DOCUMENT ME!

Home | Contact Us | Privacy Policy | Terms of Service