Packages

c

scalation.linalgebra.gen

SparseMatrixN

class SparseMatrixN[T] extends Matrix[T] with Error with Serializable

The SparseMatrixN class stores and operates on Numeric Matrices of various sizes and types. The element type may be any subtype of Numeric. Rather than storing the matrix as a 2 dimensional array, it is stored as an array of list-maps, which record all the non-zero values for each particular row, along with their j-index.

Linear Supertypes
Serializable, Serializable, Matrix[T], Error, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. SparseMatrixN
  2. Serializable
  3. Serializable
  4. Matrix
  5. Error
  6. AnyRef
  7. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new SparseMatrixN(u: SymTriMatrixN[T], _0: T)(implicit arg0: ClassTag[T], arg1: Numeric[T])

    Construct a sparse matrix and assign values from SymTriMatrixN matrix 'u'.

    Construct a sparse matrix and assign values from SymTriMatrixN matrix 'u'.

    u

    the matrix of values to assign

    _0

    the value zero for type T

  2. new SparseMatrixN(u: MatrixN[T], _0: T)(implicit arg0: ClassTag[T], arg1: Numeric[T])

    Construct a sparse matrix and assign values from MatrixN matrix 'u'.

    Construct a sparse matrix and assign values from MatrixN matrix 'u'.

    u

    the matrix of values to assign

    _0

    the value zero for type T

  3. new SparseMatrixN(u: SparseMatrixN[T], _0: T)(implicit arg0: ClassTag[T], arg1: Numeric[T])

    Construct a sparse matrix and assign values from matrix 'u'.

    Construct a sparse matrix and assign values from matrix 'u'.

    u

    the matrix of values to assign

    _0

    the value zero for type T

  4. new SparseMatrixN(dim1: Int, x: T, _0: T)(implicit arg0: ClassTag[T], arg1: Numeric[T])

    Construct a 'dim1-by-dim1' square sparse matrix with 'x' assigned on the diagonal and 0 assigned off the diagonal.

    Construct a 'dim1-by-dim1' square sparse matrix with 'x' assigned on the diagonal and 0 assigned off the diagonal. To obtain an identity matrix, let 'x = 1'.

    dim1

    the row and column dimension

    x

    the scalar value to assign on the diagonal

    _0

    the value zero for type T

  5. new SparseMatrixN(dim1: Int, dim2: Int, x: T, _0: T)(implicit arg0: ClassTag[T], arg1: Numeric[T])

    Construct a 'dim1-by-dim2' sparse matrix and assign each element the value 'x'.

    Construct a 'dim1-by-dim2' sparse matrix and assign each element the value 'x'.

    dim1

    the row dimension

    dim2

    the column dimension

    x

    the scalar value to assign

    _0

    the value zero for type T

  6. new SparseMatrixN(dim1: Int, _0: T)(implicit arg0: ClassTag[T], arg1: Numeric[T])

    Construct a 'dim1-by-dim1' square sparse matrix.

    Construct a 'dim1-by-dim1' square sparse matrix.

    dim1

    the row and column dimension

    _0

    the value zero for type T

  7. new SparseMatrixN(dim1: Int, dim2: Int, u: Array[SortedLinkedHashMap[Int, T]], _0: T)(implicit arg0: ClassTag[T], arg1: Numeric[T])

    Construct a 'dim1-by-dim2' sparse matrix from an array of sorted-linked-maps.

    Construct a 'dim1-by-dim2' sparse matrix from an array of sorted-linked-maps.

    dim1

    the row dimension

    dim2

    the column dimension

    u

    the array of sorted-linked-maps

    _0

    the value zero for type T

  8. new SparseMatrixN(d1: Int, d2: Int, _0: T)(implicit arg0: ClassTag[T], arg1: Numeric[T])

    d1

    the first/row dimension

    d2

    the second/column dimension

    _0

    the value zero for type T

Type Members

  1. type RowMap = SortedLinkedHashMap[Int, T]

    Type definition for matrix rows

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. def *(x: T): SparseMatrixN[T]

    Multiply this matrix by scalar 'x'.

    Multiply this matrix by scalar 'x'.

    x

    the scalar to multiply by

    Definition Classes
    SparseMatrixN → Matrix
  4. def *(u: VectorN[T]): VectorN[T]

    Multiply this matrix by vector 'u'.

    Multiply this matrix by vector 'u'.

    u

    the vector to multiply by

    Definition Classes
    SparseMatrixN → Matrix
  5. def *(b: Matrix[T]): SparseMatrixN[T]

    Multiply this matrix by matrix 'b'.

    Multiply this matrix by matrix 'b'.

    b

    the matrix to multiply by (requires 'sameCrossDimensions')

  6. def *(b: SparseMatrixN[T]): SparseMatrixN[T]

    Multiply this sparse matrix by sparse matrix 'b', by performing a merge operation on the rows on this sparse matrix and the transpose of the 'b' matrix.

    Multiply this sparse matrix by sparse matrix 'b', by performing a merge operation on the rows on this sparse matrix and the transpose of the 'b' matrix.

    b

    the matrix to multiply by (requires 'sameCrossDimensions')

  7. def **(u: VectorN[T]): SparseMatrixN[T]

    Multiply this matrix by vector 'u' to produce another matrix 'a_ij * b_j'.

    Multiply this matrix by vector 'u' to produce another matrix 'a_ij * b_j'.

    u

    the vector to multiply by

    Definition Classes
    SparseMatrixN → Matrix
  8. def **=(u: VectorN[T]): SparseMatrixN[T]

    Multiply in-place this matrix by vector 'u' to produce another matrix 'a_ij * b_j'.

    Multiply in-place this matrix by vector 'u' to produce another matrix 'a_ij * b_j'.

    u

    the vector to multiply by

    Definition Classes
    SparseMatrixN → Matrix
  9. def *=(x: T): SparseMatrixN[T]

    Multiply in-place this matrix by scalar 'x'.

    Multiply in-place this matrix by scalar 'x'.

    x

    the scalar to multiply by

    Definition Classes
    SparseMatrixN → Matrix
  10. def *=(b: Matrix[T]): SparseMatrixN[T]

    Multiply in-place this matrix by matrix 'b'.

    Multiply in-place this matrix by matrix 'b'. If 'b' and this reference the same matrix 'b == this', a copy of the this matrix is made.

    b

    the matrix to multiply by (requires 'sameCrossDimensions')

  11. def *=(b: SparseMatrixN[T]): SparseMatrixN[T]

    Multiply in-place this sparse matrix by sparse matrix 'b', by performing a merge operation on the rows on this sparse matrix and the transpose of the 'b' matrix.

    Multiply in-place this sparse matrix by sparse matrix 'b', by performing a merge operation on the rows on this sparse matrix and the transpose of the 'b' matrix.

    b

    the matrix to multiply by (requires square and 'sameCrossDimensions')

  12. def +(x: T): SparseMatrixN[T]

    Add this matrix and scalar x.

    Add this matrix and scalar x.

    x

    the scalar to add

    Definition Classes
    SparseMatrixN → Matrix
  13. def +(b: Matrix[T]): SparseMatrixN[T]

    Add this matrix and matrix 'b'.

    Add this matrix and matrix 'b'.

    b

    the matrix to add (requires 'leDimensions')

  14. def +(b: SparseMatrixN[T]): SparseMatrixN[T]

    Add this sparse matrix and sparse matrix 'b'.

    Add this sparse matrix and sparse matrix 'b'.

    b

    the matrix to add (requires 'sameCrossDimensions')

  15. def ++(u: VectorN[T]): SparseMatrixN[T]

    Concatenate this sparse matrix and vector 'u'.

    Concatenate this sparse matrix and vector 'u'.

    u

    the vector to be concatenated as the new last row in matrix

    Definition Classes
    SparseMatrixN → Matrix
  16. def +=(x: T): SparseMatrixN[T]

    Add in-place this matrix and scalar 'x'.

    Add in-place this matrix and scalar 'x'.

    x

    the scalar to add

    Definition Classes
    SparseMatrixN → Matrix
  17. def +=(b: Matrix[T]): SparseMatrixN[T]

    Add in-place this matrix and matrix 'b'.

    Add in-place this matrix and matrix 'b'.

    b

    the matrix to add (requires 'leDimensions')

  18. def +=(b: SparseMatrixN[T]): SparseMatrixN[T]

    Add in-place this sparse matrix and sparse matrix 'b'.

    Add in-place this sparse matrix and sparse matrix 'b'.

    b

    the matrix to add (requires 'sameCrossDimensions')

  19. def -(x: T): SparseMatrixN[T]

    From this matrix subtract scalar 'x'.

    From this matrix subtract scalar 'x'.

    x

    the scalar to subtract

    Definition Classes
    SparseMatrixN → Matrix
  20. def -(b: Matrix[T]): SparseMatrixN[T]

    From this matrix subtract matrix 'b'.

    From this matrix subtract matrix 'b'.

    b

    the matrix to subtract (requires 'leDimensions')

  21. def -(b: SparseMatrixN[T]): SparseMatrixN[T]

    From this sparse matrix subtract matrix 'b'.

    From this sparse matrix subtract matrix 'b'.

    b

    the sparse matrix to subtract (requires 'sameCrossDimensions')

  22. def -=(x: T): SparseMatrixN[T]

    From this matrix subtract in-place scalar 'x'.

    From this matrix subtract in-place scalar 'x'.

    x

    the scalar to subtract

    Definition Classes
    SparseMatrixN → Matrix
  23. def -=(b: Matrix[T]): SparseMatrixN[T]

    From this matrix subtract in-place matrix 'b'.

    From this matrix subtract in-place matrix 'b'.

    b

    the matrix to subtract (requires 'leDimensions')

  24. def -=(b: SparseMatrixN[T]): SparseMatrixN[T]

    From this sparse matrix subtract in-place sparse matrix 'b'.

    From this sparse matrix subtract in-place sparse matrix 'b'.

    b

    the sparse matrix to subtract (requires 'sameCrossDimensions')

  25. def /(x: T)(implicit fr: Fractional[T]): SparseMatrixN[T]

    Divide this sparse matrix by scalar 'x'.

    Divide this sparse matrix by scalar 'x'.

    x

    the scalar to divide by

    Definition Classes
    SparseMatrixN → Matrix
  26. def /=(x: T)(implicit fr: Fractional[T]): SparseMatrixN[T]

    Divide in-place this sparse matrix by scalar 'x'.

    Divide in-place this sparse matrix by scalar 'x'.

    x

    the scalar to divide by

    Definition Classes
    SparseMatrixN → Matrix
  27. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. val _1: T

    Numeric one (1)

  29. val _1n: T

    Numeric minus one (-1)

  30. def apply(i: Int, jr: Range): VectorN[T]

    Get a slice this matrix row-wise at index 'i' and column-wise on range 'jr'.

    Get a slice this matrix row-wise at index 'i' and column-wise on range 'jr'. Ex: u = a(2, 3..5)

    i

    the row index

    jr

    the column range

    Definition Classes
    SparseMatrixN → Matrix
  31. def apply(ir: Range, j: Int): VectorN[T]

    Get a slice this matrix row-wise on range 'ir' and column-wise at index 'j'.

    Get a slice this matrix row-wise on range 'ir' and column-wise at index 'j'. Ex: u = a(2..4, 3)

    ir

    the row range

    j

    the column index

    Definition Classes
    SparseMatrixN → Matrix
  32. def apply(ir: Range, jr: Range): SparseMatrixN[T]

    Get a slice this matrix row-wise on range 'ir' and column-wise on range 'jr'.

    Get a slice this matrix row-wise on range 'ir' and column-wise on range 'jr'. Ex: b = a(2..4, 3..5)

    ir

    the row range

    jr

    the column range

    Definition Classes
    SparseMatrixN → Matrix
  33. def apply(i: Int): VectorN[T]

    Get this matrix's vector at the 'i'th index position ('i'th row).

    Get this matrix's vector at the 'i'th index position ('i'th row).

    i

    the row index

    Definition Classes
    SparseMatrixN → Matrix
  34. def apply(i: Int, j: Int): T

    Get this matrix's element at the 'i,j'-th index position.

    Get this matrix's element at the 'i,j'-th index position.

    i

    the row index

    j

    the column index

    Definition Classes
    SparseMatrixN → Matrix
  35. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  36. def clean(thres: T, relative: Boolean = true)(implicit fr: Fractional[T]): SparseMatrixN[T]

    Clean values in matrix at or below the threshold by setting them to zero.

    Clean values in matrix at or below the threshold by setting them to zero. Iterative algorithms give approximate values and if very close to zero, may throw off other calculations, e.g., in computing eigenvectors.

    thres

    the cutoff threshold (a small value)

    relative

    whether to use relative or absolute cutoff

    Definition Classes
    SparseMatrixN → Matrix
  37. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  38. def col(col: Int, from: Int = 0): VectorN[T]

    Get column 'col' from the matrix, returning it as a vector.

    Get column 'col' from the matrix, returning it as a vector.

    col

    the column to extract from the matrix

    from

    the position to start extracting from

    Definition Classes
    SparseMatrixN → Matrix
  39. def det: T

    Compute the determinant of this matrix.

    Compute the determinant of this matrix.

    Definition Classes
    SparseMatrixN → Matrix
  40. def diag(p: Int, q: Int): SparseMatrixN[T]

    Form a matrix '[Ip, this, Iq]' where 'Ir' is a 'r-by-r' identity matrix, by positioning the three matrices 'Ip', this and 'Iq' along the diagonal.

    Form a matrix '[Ip, this, Iq]' where 'Ir' is a 'r-by-r' identity matrix, by positioning the three matrices 'Ip', this and 'Iq' along the diagonal.

    p

    the size of identity matrix Ip

    q

    the size of identity matrix Iq

    Definition Classes
    SparseMatrixN → Matrix
  41. def diag(b: Matrix[T]): SparseMatrixN[T]

    Combine this matrix with matrix 'b', placing them along the diagonal and filling in the bottom left and top right regions with zeros; '[this, b]'.

    Combine this matrix with matrix 'b', placing them along the diagonal and filling in the bottom left and top right regions with zeros; '[this, b]'.

    b

    the matrix to combine with this matrix

  42. lazy val dim1: Int

    Dimension 1

    Dimension 1

    Definition Classes
    SparseMatrixN → Matrix
  43. lazy val dim2: Int

    Dimension 2

    Dimension 2

    Definition Classes
    SparseMatrixN → Matrix
  44. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  45. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  46. final def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  47. def foreach[U](f: (Array[T]) ⇒ U): Unit

    Iterate over the matrix row by row.

    Iterate over the matrix row by row.

    f

    the function to apply

    Definition Classes
    Matrix
  48. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  49. def getDiag(k: Int = 0): VectorN[T]

    Get the 'k'th diagonal of this matrix.

    Get the 'k'th diagonal of this matrix. Assumes 'dim2 >= dim1'.

    k

    how far above the main diagonal, e.g., (-1, 0, 1) for (sub, main, super)

    Definition Classes
    SparseMatrixN → Matrix
  50. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  51. def inverse(implicit fr: Fractional[T]): SparseMatrixN[T]

    Invert this matrix (requires a 'squareMatrix') using partial pivoting.

    Invert this matrix (requires a 'squareMatrix') using partial pivoting.

    Definition Classes
    SparseMatrixN → Matrix
  52. def inverse_ip(implicit fr: Fractional[T]): SparseMatrixN[T]

    Invert in-place this matrix (requires a 'squareMatrix').

    Invert in-place this matrix (requires a 'squareMatrix'). This version uses partial pivoting.

    Definition Classes
    SparseMatrixN → Matrix
  53. def inverse_npp(implicit fr: Fractional[T]): SparseMatrixN[T]

    Invert this matrix (requires a 'squareMatrix') and not using partial pivoting.

    Invert this matrix (requires a 'squareMatrix') and not using partial pivoting.

    Definition Classes
    SparseMatrixN → Matrix
  54. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  55. def isNonnegative: Boolean

    Check whether this matrix is nonnegative (has no negative elements).

    Check whether this matrix is nonnegative (has no negative elements).

    Definition Classes
    SparseMatrixN → Matrix
  56. def isRectangular: Boolean

    Check whether this matrix is rectangular (all rows have the same number of columns).

    Check whether this matrix is rectangular (all rows have the same number of columns).

    Definition Classes
    SparseMatrixN → Matrix
  57. def isSquare: Boolean

    Check whether this matrix is square (same row and column dimensions).

    Check whether this matrix is square (same row and column dimensions).

    Definition Classes
    Matrix
  58. def isSymmetric: Boolean

    Check whether this matrix is symmetric.

    Check whether this matrix is symmetric.

    Definition Classes
    Matrix
  59. def leDimensions(b: Matrix[T]): Boolean

    Check whether this matrix dimensions are less than or equal to 'le' those of the other Matrix.

    Check whether this matrix dimensions are less than or equal to 'le' those of the other Matrix.

    b

    the other matrix

    Definition Classes
    Matrix
  60. def lud(implicit fr: Fractional[T]): (SparseMatrixN[T], SparseMatrixN[T])

    Decompose this matrix into the product of lower and upper triangular matrices '(l, u)' using the 'LU' Decomposition algorithm.

    Decompose this matrix into the product of lower and upper triangular matrices '(l, u)' using the 'LU' Decomposition algorithm. This version uses partial pivoting.

    Definition Classes
    SparseMatrixN → Matrix
  61. def lud_ip(implicit fr: Fractional[T]): (SparseMatrixN[T], SparseMatrixN[T])

    Decompose in-place this matrix into the product of lower and upper triangular matrices '(l, u)' using the 'LU' Decomposition algorithm.

    Decompose in-place this matrix into the product of lower and upper triangular matrices '(l, u)' using the 'LU' Decomposition algorithm. This version uses partial pivoting.

    Definition Classes
    SparseMatrixN → Matrix
  62. def mag: T

    Find the magnitude of this matrix, the element value farthest from zero.

    Find the magnitude of this matrix, the element value farthest from zero.

    Definition Classes
    SparseMatrixN → Matrix
  63. def max(e: Int = dim1): T

    Find the maximum element in this sparse matrix.

    Find the maximum element in this sparse matrix.

    e

    the ending row index (exclusive) for the search

    Definition Classes
    SparseMatrixN → Matrix
  64. def min(e: Int = dim1): T

    Find the minimum element in this sparse matrix.

    Find the minimum element in this sparse matrix.

    e

    the ending row index (exclusive) for the search

    Definition Classes
    SparseMatrixN → Matrix
  65. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  66. def norm1: T

    Compute the 1-norm of this matrix, i.e., the maximum 1-norm of the column vectors.

    Compute the 1-norm of this matrix, i.e., the maximum 1-norm of the column vectors. This is useful for comparing matrices '(a - b).norm1'.

    Definition Classes
    SparseMatrixN → Matrix
  67. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  68. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  69. val nu: Numeric[T]

    Import Numeric evidence (nu value from superclass)

  70. def nullspace(implicit fr: Fractional[T]): VectorN[T]

    Compute the (right) nullspace of this 'm-by-n' matrix (requires 'n = m + 1') by performing Gauss-Jordan reduction and extracting the negation of the last column augmented by 1.

    Compute the (right) nullspace of this 'm-by-n' matrix (requires 'n = m + 1') by performing Gauss-Jordan reduction and extracting the negation of the last column augmented by 1. The nullspace of matrix 'a' is "this vector 'v' times any scalar 's'", i.e., 'a*(v*s) = 0'. The left nullspace of matrix 'a' is the same as the right nullspace of 'a.t' ('a' transpose).

    Definition Classes
    SparseMatrixN → Matrix
  71. def nullspace_ip(implicit fr: Fractional[T]): VectorN[T]

    Compute the (right) nullspace in-place of this 'm-by-n' matrix (requires 'n = m + 1') by performing Gauss-Jordan reduction and extracting the negation of the last column augmented by 1.

    Compute the (right) nullspace in-place of this 'm-by-n' matrix (requires 'n = m + 1') by performing Gauss-Jordan reduction and extracting the negation of the last column augmented by 1. The nullspace of matrix 'a' is "this vector 'v' times any scalar 's'", i.e., 'a*(v*s) = 0'. The left nullspace of matrix 'a' is the same as the right nullspace of 'a.t' ('a' transpose).

    Definition Classes
    SparseMatrixN → Matrix
  72. val range1: Range

    Range for the storage array on dimension 1 (rows)

    Range for the storage array on dimension 1 (rows)

    Attributes
    protected
    Definition Classes
    Matrix
  73. val range2: Range

    Range for the storage array on dimension 2 (columns)

    Range for the storage array on dimension 2 (columns)

    Attributes
    protected
    Definition Classes
    Matrix
  74. def rank(implicit fr: Fractional[T]): Int

    Determine the rank of this 'm-by-n' matrix by taking the upper triangular matrix from the 'LU' Decomposition and counting the number of non-zero diagonal elements.

    Determine the rank of this 'm-by-n' matrix by taking the upper triangular matrix from the 'LU' Decomposition and counting the number of non-zero diagonal elements. FIX: should implement in implementing classes.

    Definition Classes
    Matrix
  75. def reduce(implicit fr: Fractional[T]): SparseMatrixN[T]

    Use Gauss-Jordan reduction on this matrix to make the left part embed an identity matrix.

    Use Gauss-Jordan reduction on this matrix to make the left part embed an identity matrix. A constraint on this 'm-by-n' matrix is that 'n >= m'.

    Definition Classes
    SparseMatrixN → Matrix
  76. def reduce_ip(implicit fr: Fractional[T]): Unit

    Use Gauss-Jordan reduction in-place on this matrix to make the left part embed an identity matrix.

    Use Gauss-Jordan reduction in-place on this matrix to make the left part embed an identity matrix. A constraint on this 'm-by-n' matrix is that 'n >= m'.

    Definition Classes
    SparseMatrixN → Matrix
  77. def sameCrossDimensions(b: Matrix[T]): Boolean

    Check whether this matrix and the other matrix have the same cross dimensions.

    Check whether this matrix and the other matrix have the same cross dimensions.

    b

    the other matrix

    Definition Classes
    Matrix
  78. def sameDimensions(b: Matrix[T]): Boolean

    Check whether this matrix and the other Matrix have the same dimensions.

    Check whether this matrix and the other Matrix have the same dimensions.

    b

    the other matrix

    Definition Classes
    Matrix
  79. def selectCols(colIndex: Array[Int]): SparseMatrixN[T]

    Select columns from this matrix according to the given index/basis.

    Select columns from this matrix according to the given index/basis. Ex: Can be used to divide a matrix into a basis and a non-basis.

    colIndex

    the column index positions (e.g., (0, 2, 5))

    Definition Classes
    SparseMatrixN → Matrix
  80. def selectRows(rowIndex: Array[Int]): SparseMatrixN[T]

    Select rows from this matrix according to the given index/basis.

    Select rows from this matrix according to the given index/basis.

    rowIndex

    the row index positions (e.g., (0, 2, 5))

    Definition Classes
    SparseMatrixN → Matrix
  81. def set(i: Int, u: VectorN[T], j: Int = 0): Unit

    Set this matrix's 'i'th row starting at column 'j' to the vector 'u'.

    Set this matrix's 'i'th row starting at column 'j' to the vector 'u'.

    i

    the row index

    u

    the vector value to assign

    j

    the starting column index

    Definition Classes
    SparseMatrixN → Matrix
  82. def set(u: Array[Array[T]]): Unit

    Set all the values in this matrix as copies of the values in 2D array 'u'.

    Set all the values in this matrix as copies of the values in 2D array 'u'.

    u

    the 2D array of values to assign

    Definition Classes
    SparseMatrixN → Matrix
  83. def set(x: T): Unit

    Set all the elements in this matrix to the scalar 'x'.

    Set all the elements in this matrix to the scalar 'x'.

    x

    the scalar value to assign

    Definition Classes
    SparseMatrixN → Matrix
  84. def setCol(col: Int, u: VectorN[T]): Unit

    Set column 'col' of the matrix to a vector.

    Set column 'col' of the matrix to a vector.

    col

    the column to set

    u

    the vector to assign to the column

    Definition Classes
    SparseMatrixN → Matrix
  85. def setDiag(x: T): Unit

    Set the main diagonal of this matrix to the scalar 'x'.

    Set the main diagonal of this matrix to the scalar 'x'. Assumes 'dim2 >= dim1'.

    x

    the scalar to set the diagonal to

    Definition Classes
    SparseMatrixN → Matrix
  86. def setDiag(u: VectorN[T], k: Int = 0): Unit

    Set the 'k'th diagonal of this matrix to the vector 'u'.

    Set the 'k'th diagonal of this matrix to the vector 'u'. Assumes 'dim2 >= dim1'.

    u

    the vector to set the diagonal to

    k

    how far above the main diagonal, e.g., (-1, 0, 1) for (sub, main, super)

    Definition Classes
    SparseMatrixN → Matrix
  87. def showAll(): Unit

    Show all elements in this sparse matrix.

  88. def slice(r_from: Int, r_end: Int, c_from: Int, c_end: Int): SparseMatrixN[T]

    Slice this matrix row-wise 'r_from' to 'r_end' and column-wise 'c_from' to 'c_end'.

    Slice this matrix row-wise 'r_from' to 'r_end' and column-wise 'c_from' to 'c_end'.

    r_from

    the start of the row slice

    r_end

    the end of the row slice

    c_from

    the start of the column slice

    c_end

    the end of the column slice

    Definition Classes
    SparseMatrixN → Matrix
  89. def slice(from: Int, end: Int): SparseMatrixN[T]

    Slice this matrix row-wise 'from' to 'end'.

    Slice this matrix row-wise 'from' to 'end'.

    from

    the start row of the slice

    end

    the end row of the slice

    Definition Classes
    SparseMatrixN → Matrix
  90. def sliceExclude(row: Int, col: Int): SparseMatrixN[T]

    Slice this matrix excluding the given row and column.

    Slice this matrix excluding the given row and column.

    row

    the row to exclude

    col

    the column to exclude

    Definition Classes
    SparseMatrixN → Matrix
  91. def solve(b: VectorN[T])(implicit fr: Fractional[T]): VectorN[T]

    Solve for 'x' in the equation 'a*x = b' where 'a' is this matrix (see 'lud' above).

    Solve for 'x' in the equation 'a*x = b' where 'a' is this matrix (see 'lud' above).

    b

    the constant vector.

    Definition Classes
    SparseMatrixN → Matrix
  92. def solve(lu: (Matrix[T], Matrix[T]), b: VectorN[T])(implicit fr: Fractional[T]): VectorN[T]

    Solve for 'x' in the equation 'l*u*x = b' (see 'lud' above).

    Solve for 'x' in the equation 'l*u*x = b' (see 'lud' above).

    lu

    the lower and upper triangular matrices

    b

    the constant vector

    Definition Classes
    SparseMatrixN → Matrix
  93. def solve(l: Matrix[T], u: Matrix[T], b: VectorN[T])(implicit fr: Fractional[T]): VectorN[T]

    Solve for 'x' in the equation 'l*u*x = b' (see 'lud' above).

    Solve for 'x' in the equation 'l*u*x = b' (see 'lud' above).

    l

    the lower triangular matrix

    u

    the upper triangular matrix

    b

    the constant vector

    Definition Classes
    SparseMatrixN → Matrix
  94. def sum: T

    Compute the sum of this matrix, i.e., the sum of its elements.

    Compute the sum of this matrix, i.e., the sum of its elements.

    Definition Classes
    SparseMatrixN → Matrix
  95. def sumAbs: T

    Compute the 'abs' sum of this matrix, i.e., the sum of the absolute value of its elements.

    Compute the 'abs' sum of this matrix, i.e., the sum of the absolute value of its elements. This is useful for comparing matrices '(a - b).sumAbs'.

    Definition Classes
    SparseMatrixN → Matrix
  96. def sumLower: T

    Compute the sum of the lower triangular region of this matrix.

    Compute the sum of the lower triangular region of this matrix.

    Definition Classes
    SparseMatrixN → Matrix
  97. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  98. def t: SparseMatrixN[T]

    Transpose this matrix (rows => columns).

    Transpose this matrix (rows => columns).

    Definition Classes
    SparseMatrixN → Matrix
  99. def toString(): String

    Show the non-zero elements in this sparse matrix.

    Show the non-zero elements in this sparse matrix.

    Definition Classes
    SparseMatrixN → AnyRef → Any
  100. def trace: T

    Compute the trace of this matrix, i.e., the sum of the elements on the main diagonal.

    Compute the trace of this matrix, i.e., the sum of the elements on the main diagonal. Should also equal the sum of the eigenvalues.

    Definition Classes
    SparseMatrixN → Matrix
    See also

    Eigen.scala

  101. def update(i: Int, jr: Range, u: VectorN[T]): Unit

    Set a slice this matrix row-wise at index 'i' and column-wise on range 'jr'.

    Set a slice this matrix row-wise at index 'i' and column-wise on range 'jr'. Ex: a(2, 3..5) = u

    i

    the row index

    jr

    the column range

    u

    the vector to assign

    Definition Classes
    SparseMatrixN → Matrix
  102. def update(ir: Range, j: Int, u: VectorN[T]): Unit

    Set a slice this matrix row-wise on range 'ir' and column-wise at index 'j'.

    Set a slice this matrix row-wise on range 'ir' and column-wise at index 'j'. Ex: a(2..4, 3) = u

    ir

    the row range

    j

    the column index

    u

    the vector to assign

    Definition Classes
    SparseMatrixN → Matrix
  103. def update(ir: Range, jr: Range, b: SparseMatrixN[T]): Unit

    Set a slice this matrix row-wise on range 'ir' and column-wise on range 'jr'.

    Set a slice this matrix row-wise on range 'ir' and column-wise on range 'jr'. Ex: a(2..4, 3..5) = b

    ir

    the row range

    jr

    the column range

    b

    the matrix to assign

  104. def update(i: Int, u: RowMap): Unit

    Set this matrix's row at the 'i'th index position to the list-map 'u'.

    Set this matrix's row at the 'i'th index position to the list-map 'u'.

    i

    the row index

    u

    the list-map of non-zero values to assign

  105. def update(i: Int, u: VectorN[T]): Unit

    Set this matrix's row at the 'i'th index position to the vector 'u'.

    Set this matrix's row at the 'i'th index position to the vector 'u'.

    i

    the row index

    u

    the vector value to assign

    Definition Classes
    SparseMatrixN → Matrix
  106. def update(i: Int, j: Int, x: T): Unit

    Set this matrix's element at the 'i,j'-th index position to the scalar 'x'.

    Set this matrix's element at the 'i,j'-th index position to the scalar 'x'. Only store x if it is non-zero.

    i

    the row index

    j

    the column index

    x

    the scalar value to assign

    Definition Classes
    SparseMatrixN → Matrix
  107. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  108. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  109. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  110. def ~^(p: Int): SparseMatrixN[T]

    Raise this sparse matrix to the 'p'th power (for some integer 'p >= 2').

    Raise this sparse matrix to the 'p'th power (for some integer 'p >= 2'). Caveat: should be replace by a divide and conquer algorithm.

    p

    the power to raise this matrix to

    Definition Classes
    SparseMatrixN → Matrix

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from Serializable

Inherited from Serializable

Inherited from Matrix[T]

Inherited from Error

Inherited from AnyRef

Inherited from Any

Ungrouped