Translation components API.

See the Weblate's Web API documentation for detailed description of the API.

GET /api/translations/packages-i18n/s-master/id/units/?format=api&page=3
HTTP 200 OK
Allow: GET, POST, HEAD, OPTIONS
Content-Type: application/json
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            "source": [
                "This packages provides the C and C++ CMPI header files needed by provider developers and can be used standalone. If used for C++ provider development it is also necessary to have tog-pegasus-devel installed."
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                "Paket ini menyediakan file header C dan C++ CMPI yang diperlukan oleh pengembang penyedia dan dapat digunakan secara mandiri. Jika digunakan untuk pengembangan penyedia C++ juga perlu menginstal tog-pegasus-devel."
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            "source": [
                "Column Approximate Minimum Degree"
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                "Kolom Perkiraan Tingkat Minimum"
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                "The COLAMD column approximate minimum degree ordering algorithm computes a permutation vector P such that the LU factorization of A (:,P) tends to be sparser than that of A. The Cholesky factorization of (A (:,P))'*(A (:,P)) will also tend to be sparser than that of A'*A. SYMAMD is a symmetric minimum degree ordering method based on COLAMD, available as a MATLAB-callable function. It constructs a matrix M such that M'*M has the same pattern as A, and then uses COLAMD to compute a column ordering of M. Colamd and symamd tend to be faster and generate better orderings than their MATLAB counterparts, colmmd and symmmd.\n\nCOLAMD is part of the SuiteSparse sparse matrix suite."
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                "Instructional Sparse Matrix Package"
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                "CSparse is a small yet feature-rich sparse matrix package written specifically for a book. The purpose of the package is to demonstrate a wide range of sparse matrix algorithms in as concise a code as possible. CSparse is about 2,200 lines long (excluding its MATLAB interface, demo codes, and test codes), yet it contains algorithms (either asympotical optimal or fast in practice) for all of the following functions described below. A MATLAB interface is included.\n\nNote that the LU and Cholesky factorization algorithms are not as fast as UMFPACK or CHOLMOD. Other functions have comparable performance as their MATLAB equivalents (some are faster).\n\nDocumentation is very terse in the code; it is fully documented in the book. Some indication of how to call the C functions in CSparse is given by the CSparse/MATLAB/*.m help files.\n\nCSparse is part of the SuiteSparse sparse matrix suite."
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                "Development files for the FreeIPA HBAC Evaluator library"
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