Parents (Total: 12)

The searched file hash is included in 12 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	D6BC55AF6175A19DB531F98501489B54
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	3.fc21
PackageVersion	1.0
SHA-1	23F518AF8636F0A389D487BCBC63D13977CC8FF2
SHA-256	D9E068E75E45305FEB3388C96D95D4F1B9C880AE59B9A305A036176C9D05CCA8

Key	Value
MD5	985F905EB488F64647F841E671DCF8F6
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	6.fc24
PackageVersion	1.0
SHA-1	096ADC455DCDD18882895C0053AC21CCBB46B6E4
SHA-256	6614BCF50CF7A1D9DF0FFCCE48F4DF68FF161C41602C09B630847F2BE2613FDC

Key	Value
MD5	96111852CB997E2075BB85F9EDC78A6C
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	1.fc20
PackageVersion	1.0
SHA-1	91ED93F0B6C597BD83D492FEA4FD7D6F749BA62A
SHA-256	6DA9D2610A62518200E7296E81EADAC6CD9C0FB17329EEC681B4F02305779A9F

Key	Value
MD5	DC295A46D4C9716ADC18B487E3576DAD
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	3.fc22
PackageVersion	1.0
SHA-1	72B3C97AC2721F66CE43A3C7069B0CFCDF15F350
SHA-256	1E142B74F9521CAB5080714A85147C1B448340141D914B45A24A256B3BFB27B0

Key	Value
MD5	E23CA819EC76B84A32FB805065AE3FB9
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	3.fc21
PackageVersion	1.0
SHA-1	4E60E958E794F1482D88CB20D760C02956C37E3C
SHA-256	0106DBF4F8EAEAEA75B69C722C9934D0DB2B363F0D3BAEAE400A6A0E3B01FA65

Key	Value
MD5	6EDD9AC4BEDAA26E3CB35270B87A5461
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	1.fc20
PackageVersion	1.0
SHA-1	CB1748B51A403F3F180F3468821D255D10E12799
SHA-256	4352257583D98B86A8BA5D7043BF472774B0A090DE1BBC02D92E453B12644C99

Key	Value
MD5	56AB4FB085399BD298F5B20CEE745E1A
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	5.fc23
PackageVersion	1.0
SHA-1	2DF3CF8071268FBDF482DD310592237A7835D7BE
SHA-256	FD42B6EC5EE63C089561DBFCA8F9E7D525BCB73D3F989E1821527FB717779A9A

Key	Value
MD5	382882382DA1800821F5E0BD55C58609
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	5.fc23
PackageVersion	1.0
SHA-1	7F6632A5F8BFE3372AD7043A77D939A15098C1C4
SHA-256	2C6AB410016CA839F3B46582EE2F7676FC415E1E6FEFDD8DFA4125D791D1739E

Key	Value
MD5	D035E1D34EBC29CDFCBB09869678554F
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	3.fc22
PackageVersion	1.0
SHA-1	A1BE3E0116611A8E70FDFEB12AF3BF545DEDF8D1
SHA-256	828E180109FFACA71EF705A8BFD1AC3471A0809B83061FB0832ED479473BC507

Key	Value
MD5	89A2BB1637C591F276519840B0FDEB05
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	3.fc21
PackageVersion	1.0
SHA-1	1FC7803F82A70E0F6EF722F6DBC1903403C3D6E9
SHA-256	2F11CC7925AADB52FC5EC712A9C0D2D60A895D711844FC91B4A15CD7575F29E0

Key	Value
MD5	F5B820FF80EB66DD13C42B242A776995
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	3.fc22
PackageVersion	1.0
SHA-1	8B99E5AB67A3BCE2156C9A259B191D0292DB6C40
SHA-256	52F0421D96395EB92B594CD27A565895CD21925C474D8523ADF91B532EC2B8AB

Key	Value
MD5	C45535012611D34B7FBE2703DACD4D55
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	3.fc22
PackageVersion	1.0
SHA-1	DCCAB89354A505F670AE2DE29AD06B98D210C955
SHA-256	B07FF131864B1B8FCD8C59B7F0F8E04070131957B67365ED5B64CCE0F4988842