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Efficient Java Matrix Library (EJML) is a [http://en.wikipedia.org/wiki/Linear_algebra linear algebra] library for manipulating dense matrices. Its design goals are; 1) to be as computationally and memory efficient as possible for both small and large matrices, and 2) to be accessible to both novices and experts. These goals are accomplished by dynamically selecting the best algorithms to use at runtime, clean API, and multiple interfaces. EJML is free, written in 100% Java and has been released under an Apache v2.0 license. | Efficient Java Matrix Library (EJML) is a [http://en.wikipedia.org/wiki/Linear_algebra linear algebra] library for manipulating dense matrices. Its design goals are; 1) to be as computationally and memory efficient as possible for both small and large matrices, and 2) to be accessible to both novices and experts. These goals are accomplished by dynamically selecting the best algorithms to use at runtime, clean API, and multiple interfaces. EJML is free, written in 100% Java and has been released under an Apache v2.0 license. | ||
| − | EJML has three distinct ways to interact with it: 1) procedural, 2) object oriented, and 3) equations. Procedure provides all capabilities of EJML and almost complete control over memory creation, speed, and specific algorithms. Object oriented provides a simplified subset of the core capabilities in an easy to use API, inspired by Jama. Equations is a symbolic interface, similar in spirit to Matlab and other CAS, that provides a compact way of writing equations. | + | EJML has three distinct ways to interact with it: 1) ''procedural'', 2) ''object oriented'', and 3) ''equations''. ''Procedure'' provides all capabilities of EJML and almost complete control over memory creation, speed, and specific algorithms. ''Object oriented'' provides a simplified subset of the core capabilities in an easy to use API, inspired by [http://math.nist.gov/javanumerics/jama/ Jama]. ''Equations'' is a symbolic interface, similar in spirit to Matlab and other [http://en.wikipedia.org/wiki/Computer_algebra_system CAS], that provides a compact way of writing equations. |
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{| | {| | ||
| − | | colspan=" | + | | colspan="3" width="500pt" align="center" | |
{|width="280pt" style="font-size:120%; text-align:left;" | {|width="280pt" style="font-size:120%; text-align:left;" | ||
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| [[Frequently Asked Questions|FAQ]] | | [[Frequently Asked Questions|FAQ]] | ||
|} | |} | ||
| + | | width="220pt" | | ||
| + | {| width="200pt" border="1" align="center" style="font-size:120%; text-align:center;" | ||
| + | |- | ||
| + | | [[Acknowledgments|Acknowledgments]] | ||
| + | |- | ||
| + | | [[Performance|Performance]] | ||
| + | |- | ||
| + | | [[Users|Users]] | ||
| + | |} | ||
| + | |} | ||
| + | |||
| + | |||
| + | == Code Examples == | ||
| + | |||
| + | |||
| + | {| width="500pt" | | ||
| + | |- | ||
| + | | | ||
| + | '''Equations''' | ||
| + | <syntaxhighlight lang="java"> | ||
| + | eq.process("K = P*H'*inv( H*P*H' + R )"); | ||
| + | </syntaxhighlight> | ||
| + | |||
| + | '''Object Oriented''' | ||
| + | <syntaxhighlight lang="java"> | ||
| + | SimpleMatrix S = H.mult(P).mult(H.transpose()).plus(R); | ||
| + | SimpleMatrix K = P.mult(H.transpose().mult(S.invert())); | ||
| + | </syntaxhighlight> | ||
| + | |||
| + | '''Procedural''' | ||
| + | <syntaxhighlight lang="java"> | ||
| + | mult(H,P,c); | ||
| + | multTransB(c,H,S); | ||
| + | addEquals(S,R); | ||
| + | if( !invert(S,S_inv) ) throw new RuntimeException("Invert failed"); | ||
| + | multTransA(H,S_inv,d); | ||
| + | mult(P,d,K); | ||
| + | </syntaxhighlight> | ||
| + | |} | ||
| + | |||
| + | == Functionality == | ||
| + | |||
| + | {| width="850pt" border="1" | | ||
| + | ! Data Structures || Operations | ||
| + | |- | ||
| + | | style="vertical-align:top;" | | ||
| + | * Dense Real | ||
| + | * Dense Complex (Next Stable Release) | ||
| + | | style="vertical-align:top;" | | ||
| + | * Basic Operators (addition, multiplication, ... ) | ||
| + | * Matrix Manipulation (extract, insert, combine, ... ) | ||
| + | * Linear Solvers (linear, least squares, incremental, ... ) | ||
| + | * Decompositions (LU, QR, Cholesky, SVD, Eigenvalue, ...) | ||
| + | * Matrix Features (rank, symmetric, definitiveness, ... ) | ||
| + | * Random Matrices (covariance, orthogonal, symmetric, ... ) | ||
| + | * Different Internal Formats (row-major, block) | ||
| + | * Unit Testing | ||
|} | |} | ||
</center> | </center> | ||
Revision as of 18:14, 14 March 2015
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Efficient Java Matrix Library (EJML) is a linear algebra library for manipulating dense matrices. Its design goals are; 1) to be as computationally and memory efficient as possible for both small and large matrices, and 2) to be accessible to both novices and experts. These goals are accomplished by dynamically selecting the best algorithms to use at runtime, clean API, and multiple interfaces. EJML is free, written in 100% Java and has been released under an Apache v2.0 license. EJML has three distinct ways to interact with it: 1) procedural, 2) object oriented, and 3) equations. Procedure provides all capabilities of EJML and almost complete control over memory creation, speed, and specific algorithms. Object oriented provides a simplified subset of the core capabilities in an easy to use API, inspired by Jama. Equations is a symbolic interface, similar in spirit to Matlab and other CAS, that provides a compact way of writing equations. |
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Code Examples
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Equations eq.process("K = P*H'*inv( H*P*H' + R )");
Object Oriented SimpleMatrix S = H.mult(P).mult(H.transpose()).plus(R);
SimpleMatrix K = P.mult(H.transpose().mult(S.invert()));
Procedural mult(H,P,c);
multTransB(c,H,S);
addEquals(S,R);
if( !invert(S,S_inv) ) throw new RuntimeException("Invert failed");
multTransA(H,S_inv,d);
mult(P,d,K);
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Functionality
| Data Structures | Operations |
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