Efficient Java Matrix Library - Revision history

Deltaspace42: added Category:Software using the Apache license using HotCat

2023-12-22T15:30:26Z

added Category:Software using the Apache license using HotCat

← Previous revision		Revision as of 15:30, 22 December 2023
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	[[Category:Numerical libraries]]		[[Category:Numerical libraries]]
			[[Category:Software using the Apache license]]

BattyBot: /* top */Expanded Template:Notability and General fixes, replaced: {{Notability|date=January 2023}} → {{Notability|Product|date=January 2023}}

2023-02-02T20:59:31Z

top: Expanded Template:Notability and General fixes, replaced: {{Notability|date=January 2023}} → {{Notability|Product|date=January 2023}}

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	{{COI\|date=January 2023}}		{{COI\|date=January 2023}}
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	{{Infobox software		{{Infobox software
	\| name =		\| name =
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	\| latest_release_date = {{Start date and age\|2022\|12\|04}}		\| latest_release_date = {{Start date and age\|2022\|12\|04}}
	\| genre = Library		\| genre = Library
	\| license = [[~~Apache_License~~]]		\| license = [[Apache License]]
	\| website = {{url\|http://ejml.org/}}		\| website = {{url\|http://ejml.org/}}
	}}		}}
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	'''Efficient Java Matrix Library''' (EJML) is a linear algebra library for manipulating real/complex/dense/sparse 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 linear algebra library for manipulating real/complex/dense/sparse 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) SimpleMatrix, and 3) Equations. The procedural style provides all capabilities of EJML and almost complete control over matrix creation, speed, and specific algorithms. The SimpleMatrix style provides a simplified subset of the core capabilities in an easy to use flow-styled object-oriented API, inspired by [[JAMA (numerical linear algebra library)\|JAMA]]. The Equations style provides a symbolic interface, similar in spirit to Matlab and other CAS, that provides a compact way of writing equations. <ref name="ProjectPage"/>		EJML has three distinct ways to interact with it: 1) Procedural, 2) SimpleMatrix, and 3) Equations. The procedural style provides all capabilities of EJML and almost complete control over matrix creation, speed, and specific algorithms. The SimpleMatrix style provides a simplified subset of the core capabilities in an easy to use flow-styled object-oriented API, inspired by [[JAMA (numerical linear algebra library)\|JAMA]]. The Equations style provides a symbolic interface, similar in spirit to Matlab and other CAS, that provides a compact way of writing equations.<ref name="ProjectPage"/>

	== Capabilities ==		== Capabilities ==

Finngall: Connected contributor template goes on the talk page

2023-01-19T17:25:50Z

Connected contributor template goes on the talk page

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2023-01-19T17:23:23Z

rv AfD tag placed without followup -- see WP:AFDHOWTO

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2023-01-19T13:24:36Z

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2023-01-19T09:49:36Z

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Frap: /* Usage Examples */ MOS:HEAD

2023-01-19T09:44:57Z

Usage Examples: MOS:HEAD

← Previous revision		Revision as of 09:44, 19 January 2023
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	* Unit Testing		* Unit Testing

	== Usage ~~Examples~~ ==		== Usage examples ==
	=== Equation style ===		=== Equation style ===

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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	mult(H,P,c);		mult(H, P, c);
	multTransB(c,H,S);		multTransB(c, H, S);
	addEquals(S,R);		addEquals(S, R);
	if( !invert(S,S_inv) )		if (!invert(S, S_inv))
	throw new RuntimeException("Invert failed");		throw new RuntimeException("Invert failed");
	multTransA(H,S_inv,d);		multTransA(H, S_inv, d);
	mult(P,d,K);		mult(P, d, K);
	</syntaxhighlight>		</syntaxhighlight>

	=== SimpleMatrix style ===		=== SimpleMatrix style ===

	Example of [[~~Singular~~ ~~Value~~ ~~Decomposition~~]] (SVD):		Example of [[singular value decomposition]] (SVD):
	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	SimpleSVD s = matA.svd();		SimpleSVD s = matA.svd();

220.133.210.243: Update to latest release information at the moment.

2023-01-19T09:37:26Z

Update to latest release information at the moment.

← Previous revision		Revision as of 09:37, 19 January 2023
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	\| author = Peter Abeles		\| author = Peter Abeles
	\| operating system = [[Cross-platform]]		\| operating system = [[Cross-platform]]
	\| latest_release_version = 0.40		\| latest_release_version = 0.41.1
	\| latest_release_date = {{Start date and age\|~~2020~~\|11\|04}}		\| latest_release_date = {{Start date and age\|2022\|12\|04}}
	\| genre = Library		\| genre = Library
	\| license = [[Apache_License]]		\| license = [[Apache_License]]

Salix alba: update release number, reorganise examples, reword and add links

2021-04-24T05:02:52Z

update release number, reorganise examples, reword and add links

← Previous revision		Revision as of 05:02, 24 April 2021
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	\| author = Peter Abeles		\| author = Peter Abeles
	\| operating system = [[Cross-platform]]		\| operating system = [[Cross-platform]]
	\| latest_release_version = 0.39		\| latest_release_version = 0.40
	\| latest_release_date = {{Start date and age\|2020\|04\|06}}		\| latest_release_date = {{Start date and age\|2020\|11\|04}}
	\| genre = Library		\| genre = Library
	\| license = [[Apache_License]]		\| license = [[Apache_License]]
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	'''Efficient Java Matrix Library''' (EJML) is a linear algebra library for manipulating real/complex/dense/sparse 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 linear algebra library for manipulating real/complex/dense/sparse 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) SimpleMatrix, and 3) Equations. ~~Procedure~~ provides all capabilities of EJML and almost complete control over ~~memory~~ creation, speed, and specific algorithms. SimpleMatrix provides a simplified subset of the core capabilities in an easy to use flow styled object-oriented 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. <ref name="ProjectPage"/>		EJML has three distinct ways to interact with it: 1) Procedural, 2) SimpleMatrix, and 3) Equations. The procedural style provides all capabilities of EJML and almost complete control over matrix creation, speed, and specific algorithms. The SimpleMatrix style provides a simplified subset of the core capabilities in an easy to use flow-styled object-oriented API, inspired by [[JAMA (numerical linear algebra library)\|JAMA]]. The Equations style provides a symbolic interface, similar in spirit to Matlab and other CAS, that provides a compact way of writing equations. <ref name="ProjectPage"/>

	== Capabilities ==		== Capabilities ==
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	* Unit Testing		* Unit Testing

	== Usage ~~Example (Equations)~~ ==		== Usage Examples ==
			=== Equation style ===

	Computing the Kalman gain:		Computing the [[Kalman gain]]:

	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
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	</syntaxhighlight>		</syntaxhighlight>

			=== Procedural style ===
	== Usage Example (SimpleMatrix) ==

			Computing Kalman gain:
⚫	Example of Singular Value Decomposition (SVD):

			<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>

			=== SimpleMatrix style ===

		⚫	Example of [[Singular Value Decomposition]] (SVD):
	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	SimpleSVD s = matA.svd();		SimpleSVD s = matA.svd();
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	</syntaxhighlight>		</syntaxhighlight>

			=== DecompositionFactory ===
	== Usage Example (DenseMatrix64F) ==

			Use of a DecompositionFactory to compute a Singular Value Decomposition with a Dense Double Row Major matrix (DDRM):<ref>{{cite web\|access-date=2021-04-24\|title=Matrix Decompositions - Efficient Java Matrix Library\|url=http://ejml.org/wiki/index.php?title=Matrix_Decompositions\|website=ejml.org}}</ref>

	Example of Singular Value Decomposition (SVD):
	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	SingularValueDecomposition_F64<DenseMatrix64F> svd =		SingularValueDecomposition_F64<DenseMatrix64F> svd =
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	== References ==		== References ==
	{{reflist\|refs=		{{reflist\|refs=
	<ref name=ProjectPage>{{cite web \|url=http://ejml.org/ \|title=EJML Project Page \|work=EJML\|publisher=Peter Abeles\|~~accessdate~~=Jan 21, 2019}}</ref>		<ref name=ProjectPage>{{cite web \|url=http://ejml.org/ \|title=EJML Project Page \|work=EJML\|publisher=Peter Abeles\|access-date=Jan 21, 2019}}</ref>
	}}		}}

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	* [http://ejml.org/ Efficient Java Matrix Library (EJML) homepage]		* [http://ejml.org/ Efficient Java Matrix Library (EJML) homepage]

	[[Category:Numerical ~~software~~]]		[[Category:Numerical libraries]]

Frap at 09:04, 25 June 2020

2020-06-25T09:04:57Z

← Previous revision		Revision as of 09:04, 25 June 2020
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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	SimpleSVD s = matA.svd();		SimpleSVD s = matA.svd();
	SimpleMatrix U=s.getU();		SimpleMatrix U = s.getU();
	SimpleMatrix W=s.getW();		SimpleMatrix W = s.getW();
	SimpleMatrix V=s.getV();		SimpleMatrix V = s.getV();
	</syntaxhighlight>		</syntaxhighlight>

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	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	SingularValueDecomposition_F64<DenseMatrix64F> svd =		SingularValueDecomposition_F64<DenseMatrix64F> svd =
	DecompositionFactory_DDRM.svd(true,true,true);		DecompositionFactory_DDRM.svd(true, true, true);

	if( !DecompositionFactory.decomposeSafe(svd,matA) )		if (!DecompositionFactory.decomposeSafe(svd, matA))
	throw new DetectedException("Decomposition failed");		throw new DetectedException("Decomposition failed.");

	DenseMatrix64F U = svd.getU(null,false);		DenseMatrix64F U = svd.getU(null, false);
	DenseMatrix64F S = svd.getW(null);		DenseMatrix64F S = svd.getW(null);
	DenseMatrix64F V = svd.getV(null,false);		DenseMatrix64F V = svd.getV(null, false);
	</syntaxhighlight>		</syntaxhighlight>

	Example of matrix multiplication:		Example of matrix multiplication:
	<syntaxhighlight lang="java">		<syntaxhighlight lang="java">
	CommonOps_DDRM.mult(matA,matB,result);		CommonOps_DDRM.mult(matA, matB, result);
	</syntaxhighlight>		</syntaxhighlight>