Package org.jblas
Class Singular
java.lang.Object
org.jblas.Singular
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic ComplexDoubleMatrix[]
Compute a singular-value decomposition of A.static ComplexFloatMatrix[]
Compute a singular-value decomposition of A.static DoubleMatrix[]
Compute a singular-value decomposition of A.static FloatMatrix[]
Compute a singular-value decomposition of A.static ComplexDoubleMatrix[]
Compute a singular-value decomposition of A (sparse variant).static ComplexFloatMatrix[]
Compute a singular-value decomposition of A (sparse variant).static DoubleMatrix[]
Compute a singular-value decomposition of A (sparse variant).static FloatMatrix[]
Compute a singular-value decomposition of A (sparse variant).static DoubleMatrix
Compute the singular values of a complex matrix.static FloatMatrix
Compute the singular values of a complex matrix.static DoubleMatrix
Compute the singular values of a matrix.static FloatMatrix
Compute the singular values of a matrix.
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Constructor Details
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Singular
public Singular()
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Method Details
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fullSVD
Compute a singular-value decomposition of A.- Returns:
- A DoubleMatrix[3] array of U, S, V such that A = U * diag(S) * V'
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sparseSVD
Compute a singular-value decomposition of A (sparse variant). Sparse means that the matrices U and V are not square but only have as many columns (or rows) as necessary.- Parameters:
A
-- Returns:
- A DoubleMatrix[3] array of U, S, V such that A = U * diag(S) * V'
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sparseSVD
Compute a singular-value decomposition of A (sparse variant). Sparse means that the matrices U and V are not square but only have as many columns (or rows) as necessary.- Parameters:
A
-- Returns:
- A ComplexDoubleMatrix[3] array of U, S, V such that A = U * diag(S) * V*
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fullSVD
Compute a singular-value decomposition of A.- Returns:
- A ComplexDoubleMatrix[3] array of U, S, V such that A = U * diag(S) * V'
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SVDValues
Compute the singular values of a matrix.- Parameters:
A
- DoubleMatrix of dimension m * n- Returns:
- A min(m, n) vector of singular values.
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SVDValues
Compute the singular values of a complex matrix.- Parameters:
A
- ComplexDoubleMatrix of dimension m * n- Returns:
- A real-valued (!) min(m, n) vector of singular values.
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fullSVD
Compute a singular-value decomposition of A.- Returns:
- A FloatMatrix[3] array of U, S, V such that A = U * diag(S) * V'
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sparseSVD
Compute a singular-value decomposition of A (sparse variant). Sparse means that the matrices U and V are not square but only have as many columns (or rows) as necessary.- Parameters:
A
-- Returns:
- A FloatMatrix[3] array of U, S, V such that A = U * diag(S) * V'
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sparseSVD
Compute a singular-value decomposition of A (sparse variant). Sparse means that the matrices U and V are not square but only have as many columns (or rows) as necessary.- Parameters:
A
-- Returns:
- A ComplexFloatMatrix[3] array of U, S, V such that A = U * diag(S) * V*
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fullSVD
Compute a singular-value decomposition of A.- Returns:
- A ComplexFloatMatrix[3] array of U, S, V such that A = U * diag(S) * V'
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SVDValues
Compute the singular values of a matrix.- Parameters:
A
- FloatMatrix of dimension m * n- Returns:
- A min(m, n) vector of singular values.
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SVDValues
Compute the singular values of a complex matrix.- Parameters:
A
- ComplexFloatMatrix of dimension m * n- Returns:
- A real-valued (!) min(m, n) vector of singular values.
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