Package space.kscience.kmath.linear
Types
BufferedLinearSpace
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class BufferedLinearSpace<T : Any, A : Ring<T>>(elementAlgebra: A, bufferFactory: BufferFactory<T>) : LinearSpace<T, A>
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CholeskyDecompositionFeature
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DeterminantFeature
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DiagonalFeature
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InverseMatrixFeature
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LFeature
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LinearSolver
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LinearSpace
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LupDecomposition
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class LupDecomposition<T : Any>(context: LinearSpace<T, *>, elementContext: Field<T>, lu: Matrix<T>, pivot: IntArray, even: Boolean) : LupDecompositionFeature<T> , DeterminantFeature<T>
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Common implementation of LupDecompositionFeature.
LupDecompositionFeature
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Matrix
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MatrixBuilder
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MatrixFeature
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MatrixWrapper
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MutableMatrix
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OrthogonalFeature
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Matrices with this feature are orthogonal ones: a · aT = u where a is the owning matrix, u is the unit matrix (UnitFeature).
QRDecompositionFeature
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SingularValueDecompositionFeature
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TransposedFeature
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UFeature
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UnitFeature
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VirtualMatrix
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class VirtualMatrix<T : Any>(rowNum: Int, colNum: Int, generator: (i: Int, j: Int) -> T) : Structure2D<T>
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ZeroFeature
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Functions
asMatrix
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Creates an n × 1 VirtualMatrix, where n is the size of the given buffer.
DeterminantFeature
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getFeature
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inline fun <T : Any, F : StructureFeature> LinearSpace<T, *>.getFeature(structure: Matrix<T>): F?
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inverseWithLup
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fun LinearSpace<Double, DoubleField>.inverseWithLup(matrix: Matrix<Double>): Matrix<Double>
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inline fun <T : Comparable<T>> LinearSpace<T, Field<T>>.inverseWithLup(matrix: Matrix<T>, noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto, noinline checkSingular: (T) -> Boolean): Matrix<T>
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lup
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fun LinearSpace<Double, DoubleField>.lup(matrix: Matrix<Double>): LupDecomposition<Double>
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inline fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(matrix: Matrix<T>, noinline checkSingular: (T) -> Boolean): LupDecomposition<T>
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fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(factory: MutableBufferFactory<T>, matrix: Matrix<T>, checkSingular: (T) -> Boolean): LupDecomposition<T>
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plus
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operator fun <T : Any> Matrix<T>.plus(newFeatures: Collection<MatrixFeature>): MatrixWrapper<T>
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Add a collection of features to a Matrix
operator fun <T : Any> Matrix<T>.plus(newFeature: MatrixFeature): MatrixWrapper<T>
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Add a single feature to a Matrix
solveWithLup
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inline fun <T : Any> LupDecomposition<T>.solveWithLup(matrix: Matrix<T>): Matrix<T>
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fun LinearSpace<Double, DoubleField>.solveWithLup(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double>
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fun <T : Any> LupDecomposition<T>.solveWithLup(factory: MutableBufferFactory<T>, matrix: Matrix<T>): Matrix<T>
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inline fun <T : Comparable<T>> LinearSpace<T, Field<T>>.solveWithLup(a: Matrix<T>, b: Matrix<T>, noinline bufferFactory: MutableBufferFactory<T> = MutableBuffer.Companion::auto, noinline checkSingular: (T) -> Boolean): Matrix<T>
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