trait MatriI extends Error
The MatriI
trait specifies the operations to be defined by the concrete
classes implementing Int
matrices, i.e.,
MatrixI
- dense matrix
BidMatrixI
- bidiagonal matrix - useful for computing Singular Values
RleMatrixI
- compressed matrix - Run Length Encoding (RLE)
SparseMatrixI
- sparse matrix - majority of elements should be zero
SymTriMatrixI
- symmetric triangular matrix - useful for computing Eigenvalues
par.MatrixI
- parallel dense matrix
par.SparseMatrixI
- parallel sparse matrix
Some of the classes provide a few custom methods, e.g., methods beginning with "times"
or ending with 'npp'.
------------------------------------------------------------------------------
row-wise column-wise
Prepend: vector +: matrix vector +: matrix (right associative)
Append: matrix :+ vector matrix :+ vector
Concatenate: matrix ++ matrix matrix ++^ matrix
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-
abstract
def
*(x: Int): MatriI
Multiply 'this' matrix by scalar 'x'.
Multiply 'this' matrix by scalar 'x'.
- x
the scalar to multiply by
-
abstract
def
*(u: VectoI): VectoI
Multiply 'this' matrix by vector 'u'.
Multiply 'this' matrix by vector 'u'.
- u
the vector to multiply by
-
abstract
def
*(b: MatriI): MatriI
Multiply 'this' matrix and matrix 'b' for any type extending
MatriI
.Multiply 'this' matrix and matrix 'b' for any type extending
MatriI
. Note, subtypes ofMatriI
should also implement a more efficient version, e.g.,def * (b: MatrixI): MatrixI
.- b
the matrix to add (requires 'leDimensions')
-
abstract
def
**(u: VectoI): MatriI
Multiply 'this' matrix by vector 'u' to produce another matrix 'a_ij * u_j'.
Multiply 'this' matrix by vector 'u' to produce another matrix 'a_ij * u_j'.
- u
the vector to multiply by
-
abstract
def
**:(u: VectoI): MatriI
Multiply vector 'u' by 'this' matrix to produce another matrix 'u_i * a_ij'.
Multiply vector 'u' by 'this' matrix to produce another matrix 'u_i * a_ij'. E.g., multiply a diagonal matrix represented as a vector by a matrix. This operator is right associative.
- u
the vector to multiply by
-
abstract
def
**=(u: VectoI): MatriI
Multiply in-place 'this' matrix by vector 'u' to produce another matrix 'a_ij * u_j'.
Multiply in-place 'this' matrix by vector 'u' to produce another matrix 'a_ij * u_j'.
- u
the vector to multiply by
-
abstract
def
*=(x: Int): MatriI
Multiply in-place 'this' matrix by scalar 'x'.
Multiply in-place 'this' matrix by scalar 'x'.
- x
the scalar to multiply by
-
abstract
def
*=(b: MatriI): MatriI
Multiply in-place 'this' matrix and matrix 'b' for any type extending
MatriI
.Multiply in-place 'this' matrix and matrix 'b' for any type extending
MatriI
. Note, subtypes ofMatriI
should also implement a more efficient version, e.g.,def *= (b: MatrixI): MatrixI
.- b
the matrix to multiply by (requires 'leDimensions')
-
abstract
def
+(x: Int): MatriI
Add 'this' matrix and scalar 'x'.
Add 'this' matrix and scalar 'x'.
- x
the scalar to add
-
abstract
def
+(u: VectoI): MatriI
Add 'this' matrix and (row) vector 'u'.
Add 'this' matrix and (row) vector 'u'.
- u
the vector to add
-
abstract
def
+(b: MatriI): MatriI
Add 'this' matrix and matrix 'b' for any type extending
MatriI
.Add 'this' matrix and matrix 'b' for any type extending
MatriI
. Note, subtypes ofMatriI
should also implement a more efficient version, e.g.,def + (b: MatrixI): MatrixI
.- b
the matrix to add (requires 'leDimensions')
-
abstract
def
++(b: MatriI): MatriI
Concatenate (row-wise) 'this' matrix and matrix 'b'.
Concatenate (row-wise) 'this' matrix and matrix 'b'.
- b
the matrix to be concatenated as the new last rows in new matrix
-
abstract
def
++^(b: MatriI): MatriI
Concatenate (column-wise) 'this' matrix and matrix 'b'.
Concatenate (column-wise) 'this' matrix and matrix 'b'.
- b
the matrix to be concatenated as the new last columns in new matrix
-
abstract
def
+:(u: VectoI): MatriI
Concatenate (row) vector 'u' and 'this' matrix, i.e., prepend 'u' to 'this'.
Concatenate (row) vector 'u' and 'this' matrix, i.e., prepend 'u' to 'this'.
- u
the vector to be prepended as the new first row in new matrix
-
abstract
def
+=(x: Int): MatriI
Add in-place 'this' matrix and scalar 'x'.
Add in-place 'this' matrix and scalar 'x'.
- x
the scalar to add
-
abstract
def
+=(u: VectoI): MatriI
Add in-place 'this' matrix and (row) vector 'u'.
Add in-place 'this' matrix and (row) vector 'u'.
- u
the vector to add
-
abstract
def
+=(b: MatriI): MatriI
Add in-place 'this' matrix and matrix 'b' for any type extending
MatriI
.Add in-place 'this' matrix and matrix 'b' for any type extending
MatriI
. Note, subtypes ofMatriI
should also implement a more efficient version, e.g.,def += (b: MatrixI): MatrixI
.- b
the matrix to add (requires 'leDimensions')
-
abstract
def
+^:(u: VectoI): MatriI
Concatenate (column) vector 'u' and 'this' matrix, i.e., prepend 'u' to 'this'.
Concatenate (column) vector 'u' and 'this' matrix, i.e., prepend 'u' to 'this'.
- u
the vector to be prepended as the new first column in new matrix
-
abstract
def
-(x: Int): MatriI
From 'this' matrix subtract scalar 'x'.
From 'this' matrix subtract scalar 'x'.
- x
the scalar to subtract
-
abstract
def
-(u: VectoI): MatriI
From 'this' matrix subtract (row) vector 'u'.
From 'this' matrix subtract (row) vector 'u'.
- u
the vector to subtract
-
abstract
def
-(b: MatriI): MatriI
From 'this' matrix subtract matrix 'b' for any type extending
MatriI
.From 'this' matrix subtract matrix 'b' for any type extending
MatriI
. Note, subtypes ofMatriI
should also implement a more efficient version, e.g.,def - (b: MatrixI): MatrixI
.- b
the matrix to subtract (requires 'leDimensions')
-
abstract
def
-=(x: Int): MatriI
From 'this' matrix subtract in-place scalar 'x'.
From 'this' matrix subtract in-place scalar 'x'.
- x
the scalar to subtract
-
abstract
def
-=(u: VectoI): MatriI
From 'this' matrix subtract in-place (row) vector 'u'.
From 'this' matrix subtract in-place (row) vector 'u'.
- u
the vector to subtract
-
abstract
def
-=(b: MatriI): MatriI
From 'this' matrix subtract in-place matrix 'b' for any type extending
MatriI
.From 'this' matrix subtract in-place matrix 'b' for any type extending
MatriI
. Note, subtypes ofMatriI
should also implement a more efficient version, e.g.,def -= (b: MatrixI): MatrixI
.- b
the matrix to subtract (requires 'leDimensions')
-
abstract
def
/(x: Int): MatriI
Divide 'this' matrix by scalar 'x'.
Divide 'this' matrix by scalar 'x'.
- x
the scalar to divide by
-
abstract
def
/=(x: Int): MatriI
Divide in-place 'this' matrix by scalar 'x'.
Divide in-place 'this' matrix by scalar 'x'.
- x
the scalar to divide by
-
abstract
def
:+(u: VectoI): MatriI
Concatenate 'this' matrix and (row) vector 'u', i.e., append 'u' to 'this'.
Concatenate 'this' matrix and (row) vector 'u', i.e., append 'u' to 'this'.
- u
the vector to be appended as the new last row in new matrix
-
abstract
def
:^+(u: VectoI): MatriI
Concatenate 'this' matrix and (column) vector 'u', i.e., append 'u' to 'this'.
Concatenate 'this' matrix and (column) vector 'u', i.e., append 'u' to 'this'.
- u
the vector to be appended as the new last column in new matrix
-
abstract
def
apply(ir: Range, jr: Range): MatriI
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
-
abstract
def
apply(i: Int): VectoI
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
-
abstract
def
apply(i: Int, j: Int): Int
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
-
abstract
def
bsolve(y: VectoI): VectoI
Solve for 'x' using back substitution in the equation 'u*x = y' where 'this' matrix ('u') is upper triangular (see 'lud' above).
Solve for 'x' using back substitution in the equation 'u*x = y' where 'this' matrix ('u') is upper triangular (see 'lud' above).
- y
the constant vector
-
abstract
def
clean(thres: Double, relative: Boolean = true): MatriI
Clean values in 'this' matrix at or below the threshold 'thres' by setting them to zero.
Clean values in 'this' matrix at or below the threshold 'thres' 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
-
abstract
def
col(col: Int, from: Int = 0): VectoI
Get column 'col' starting 'from' in 'this' matrix, returning it as a vector.
Get column 'col' starting 'from' in 'this' matrix, returning it as a vector.
- col
the column to extract from the matrix
- from
the position to start extracting from
-
abstract
def
copy(): MatriI
Create an exact copy of 'this' m-by-n matrix.
-
abstract
def
det: Int
Compute the determinant of 'this' matrix.
-
abstract
def
diag(p: Int, q: Int = 0): MatriI
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
-
abstract
def
diag(b: MatriI): MatriI
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
-
abstract
val
dim1: Int
Matrix dimension 1 (# rows)
-
abstract
val
dim2: Int
Matrix dimension 2 (# columns)
-
abstract
def
dot(b: MatriI): VectoI
Compute the dot product of 'this' matrix and matrix 'b' that results in a vector, by taking the dot product for each column 'j' of both matrices.
Compute the dot product of 'this' matrix and matrix 'b' that results in a vector, by taking the dot product for each column 'j' of both matrices.
- b
the matrix to multiply by (requires same first dimensions)
- See also
www.mathworks.com/help/matlab/ref/dot.html
-
abstract
def
dot(u: VectoI): VectoI
Compute the dot product of 'this' matrix and vector 'u', by first transposing 'this' matrix and then multiplying by 'u' (i.e., 'a dot u = a.t * u').
Compute the dot product of 'this' matrix and vector 'u', by first transposing 'this' matrix and then multiplying by 'u' (i.e., 'a dot u = a.t * u').
- u
the vector to multiply by (requires same first dimensions)
-
abstract
def
getDiag(k: Int = 0): VectoI
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)
-
abstract
def
inverse: MatriI
Invert 'this' matrix (requires a 'squareMatrix') and use partial pivoting.
-
abstract
def
inverse_ip(): MatriI
Invert in-place 'this' matrix (requires a 'squareMatrix') and use partial pivoting.
-
abstract
def
isRectangular: Boolean
Check whether 'this' matrix is rectangular (all rows have the same number of columns).
-
abstract
def
lowerT: MatriI
Return the lower triangular of 'this' matrix (rest are zero).
-
abstract
def
lud_ip(): (MatriI, MatriI)
Factor in-place 'this' matrix into the product of lower and upper triangular matrices '(l, u)' using the 'LU' Decomposition algorithm.
-
abstract
def
lud_npp: (MatriI, MatriI)
Factor 'this' matrix into the product of lower and upper triangular matrices '(l, u)' using the 'LU' Decomposition algorithm.
-
abstract
def
max(e: Int = dim1): Int
Find the maximum element in 'this' matrix.
Find the maximum element in 'this' matrix.
- e
the ending row index (exclusive) for the search
-
abstract
def
mdot(b: MatriI): MatriI
Compute the matrix dot product of 'this' matrix and matrix 'b', by first transposing 'this' matrix and then multiplying by 'b' (i.e., 'a dot b = a.t * b').
Compute the matrix dot product of 'this' matrix and matrix 'b', by first transposing 'this' matrix and then multiplying by 'b' (i.e., 'a dot b = a.t * b').
- b
the matrix to multiply by (requires same first dimensions)
-
abstract
def
min(e: Int = dim1): Int
Find the minimum element in 'this' matrix.
Find the minimum element in 'this' matrix.
- e
the ending row index (exclusive) for the search
-
abstract
def
nullspace: VectoI
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.
nullspace (a) = set of orthogonal vectors v s.t. a * v = 0
The left nullspace of matrix 'a' is the same as the right nullspace of 'a.t'. FIX: need a more robust algorithm for computing nullspace (@see Fac_QR.scala). FIX: remove the 'n = m+1' restriction.
- See also
/solving-ax-0-pivot-variables-special-solutions/MIT18_06SCF11_Ses1.7sum.pdf
http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/ax-b-and-the-four-subspaces
-
abstract
def
nullspace_ip(): VectoI
Compute in-place 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 in-place 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.
nullspace (a) = set of orthogonal vectors v s.t. a * v = 0
The left nullspace of matrix 'a' is the same as the right nullspace of 'a.t'. FIX: need a more robust algorithm for computing nullspace (@see Fac_QR.scala). FIX: remove the 'n = m+1' restriction.
- See also
/solving-ax-0-pivot-variables-special-solutions/MIT18_06SCF11_Ses1.7sum.pdf
http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/ax-b-and-the-four-subspaces
-
abstract
def
reduce: MatriI
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.
-
abstract
def
reduce_ip(): 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.
-
abstract
def
selectCols(colIndex: Array[Int]): MatriI
Select columns from 'this' matrix according to the given index/basis 'colIndex'.
Select columns from 'this' matrix according to the given index/basis 'colIndex'. 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))
-
abstract
def
selectRows(rowIndex: Array[Int]): MatriI
Select rows from 'this' matrix according to the given index/basis 'rowIndex'.
Select rows from 'this' matrix according to the given index/basis 'rowIndex'.
- rowIndex
the row index positions (e.g., (0, 2, 5))
-
abstract
def
set(i: Int, u: VectoI, j: Int = 0): Unit
Set 'this' matrix's 'i'th row starting a column 'j' to the vector 'u'.
Set 'this' matrix's 'i'th row starting a column 'j' to the vector 'u'.
- i
the row index
- u
the vector value to assign
- j
the starting column index
-
abstract
def
set(u: Array[Array[Int]]): Unit
Set the values in 'this' matrix as copies of the values in 2D array 'u'.
Set the values in 'this' matrix as copies of the values in 2D array 'u'.
- u
the 2D array of values to assign
-
abstract
def
set(x: Int): 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
-
abstract
def
setCol(col: Int, u: VectoI): Unit
Set column 'col' of 'this' matrix to vector 'u'.
Set column 'col' of 'this' matrix to vector 'u'.
- col
the column to set
- u
the vector to assign to the column
-
abstract
def
setDiag(x: Int): 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
-
abstract
def
setDiag(u: VectoI, 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)
-
abstract
def
slice(from: Int, end: Int): MatriI
Slice 'this' matrix row-wise 'from' to 'end'.
Slice 'this' matrix row-wise 'from' to 'end'.
- from
the start row of the slice (inclusive)
- end
the end row of the slice (exclusive)
-
abstract
def
slice(r_from: Int, r_end: Int, c_from: Int, c_end: Int): MatriI
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 (inclusive)
- r_end
the end of the row slice (exclusive)
- c_from
the start of the column slice (inclusive)
- c_end
the end of the column slice (exclusive)
-
abstract
def
sliceCol(from: Int, end: Int): MatriI
Slice 'this' matrix column-wise 'from' to 'end'.
Slice 'this' matrix column-wise 'from' to 'end'.
- from
the start column of the slice (inclusive)
- end
the end column of the slice (exclusive)
-
abstract
def
sliceEx(row: Int, col: Int): MatriI
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
-
abstract
def
solve(b: VectoI): VectoI
Solve for 'x' in the equation 'a*x = b' where 'a' is 'this' matrix.
Solve for 'x' in the equation 'a*x = b' where 'a' is 'this' matrix.
- b
the constant vector.
-
abstract
def
solve(l: MatriI, u: MatriI, b: VectoI): VectoI
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
-
abstract
def
sum: Int
Compute the sum of 'this' matrix, i.e., the sum of its elements.
-
abstract
def
sumAbs: Int
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'.
-
abstract
def
sumLower: Int
Compute the sum of the lower triangular region of 'this' matrix.
-
abstract
def
t: MatriI
Transpose 'this' matrix (rows => columns).
-
abstract
def
toDense: MatriI
Convert 'this' matrix to a dense matrix.
-
abstract
def
toInt: MatriI
Convert 'this'
MatriI
into aMatriI
. -
abstract
def
trace: Int
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.
- See also
Eigen.scala
-
abstract
def
update(ir: Range, jr: Range, b: MatriI): Unit
Set a slice of 'this' matrix row-wise on range 'ir' and column-wise on range 'jr' to matrix 'b'.
Set a slice of 'this' matrix row-wise on range 'ir' and column-wise on range 'jr' to matrix 'b'. Ex: a(2..4, 3..5) = b
- ir
the row range
- jr
the column range
- b
the matrix to assign
-
abstract
def
update(i: Int, u: VectoI): 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
-
abstract
def
update(i: Int, j: Int, x: Int): 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'.
- i
the row index
- j
the column index
- x
the scalar value to assign
-
abstract
def
upperT: MatriI
Return the upper triangular of 'this' matrix (rest are zero).
-
abstract
def
write(fileName: String): Unit
Write 'this' matrix to a CSV-formatted text file with name 'fileName'.
Write 'this' matrix to a CSV-formatted text file with name 'fileName'.
- fileName
the name of file to hold the data
-
abstract
def
zero(m: Int = dim1, n: Int = dim2): MatriI
Create an m-by-n matrix with all elements initialized to zero.
Create an m-by-n matrix with all elements initialized to zero.
- m
the number of rows
- n
the number of columns
-
abstract
def
~^(p: Int): MatriI
Raise 'this' matrix to the 'p'th power (for some integer p >= 2).
Raise 'this' matrix to the 'p'th power (for some integer p >= 2).
- p
the power to raise 'this' matrix to
Concrete Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
def
**(b: MatriI): MatriI
Multiply 'this' matrix by matrix 'b' elementwise (Hadamard product).
Multiply 'this' matrix by matrix 'b' elementwise (Hadamard product).
- b
the matrix to multiply by
- See also
en.wikipedia.org/wiki/Hadamard_product_(matrices) FIX - remove ??? and implement in all implementing classes
-
def
*:(u: VectoI): VectoI
Multiply (row) vector 'u' by 'this' matrix.
Multiply (row) vector 'u' by 'this' matrix. Note '*:' is right associative. vector = vector *: matrix
- u
the vector to multiply by
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
apply(iv: VectoI): MatriI
Get the rows indicated by the index vector 'iv' FIX - implement in all implementing classes
Get the rows indicated by the index vector 'iv' FIX - implement in all implementing classes
- iv
the vector of row indices
-
def
apply(i: Int, jr: Range): VectoI
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
-
def
apply(ir: Range, j: Int): VectoI
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
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
val
fString: String
Format string used for printing vector values (change using 'setFormat')
Format string used for printing vector values (change using 'setFormat')
- Attributes
- protected
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
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
-
def
foreach[U](f: (Array[Int]) ⇒ U): Unit
Iterate over 'this' matrix row by row applying method 'f'.
Iterate over 'this' matrix row by row applying method 'f'.
- f
the function to apply
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
isBidiagonal: Boolean
Check whether 'this' matrix is bidiagonal (has non-zero elements only in main diagonal and super-diagonal).
Check whether 'this' matrix is bidiagonal (has non-zero elements only in main diagonal and super-diagonal). The method may be overriding for efficiency.
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
def
isNonnegative: Boolean
Check whether 'this' matrix is nonnegative (has no negative elements).
-
def
isSquare: Boolean
Check whether 'this' matrix is square (same row and column dimensions).
-
def
isSymmetric: Boolean
Check whether 'this' matrix is symmetric.
-
def
isTridiagonal: Boolean
Check whether 'this' matrix is bidiagonal (has non-zero elements only in main diagonal and super-diagonal).
Check whether 'this' matrix is bidiagonal (has non-zero elements only in main diagonal and super-diagonal). The method may be overriding for efficiency.
-
def
leDimensions(b: MatriI): Boolean
Check whether 'this' matrix dimensions are less than or equal to 'le' those of the other matrix 'b'.
Check whether 'this' matrix dimensions are less than or equal to 'le' those of the other matrix 'b'.
- b
the other matrix
-
def
mag: Int
Find the magnitude of 'this' matrix, the element value farthest from zero.
-
def
map(f: (VectoI) ⇒ VectoI): MatriI
Map the elements of 'this' matrix by applying the mapping function 'f'.
Map the elements of 'this' matrix by applying the mapping function 'f'. FIX - remove ??? and implement in all implementing classes
- f
the function to apply
-
def
mean: VectoI
Compute the column means of 'this' matrix.
-
def
meanNZ: VectoI
Compute the column means of 'this' matrix ignoring zero elements (e.g., a zero may indicate a missing value as in recommender systems).
-
def
meanR: VectoI
Compute the row means of 'this' matrix.
-
def
meanRNZ: VectoI
Compute the row means of 'this' matrix ignoring zero elements (e.g., a zero may indicate a missing value as in recommender systems).
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
norm1: Int
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'.
- See also
en.wikipedia.org/wiki/Matrix_norm
-
def
normF: Int
Compute the Frobenius-norm of 'this' matrix, i.e., the sum of the absolute values squared of all the elements.
Compute the Frobenius-norm of 'this' matrix, i.e., the sum of the absolute values squared of all the elements.
- See also
en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm
-
def
normINF: Int
Compute the (infinity) INF-norm of 'this' matrix, i.e., the maximum 1-norm of the row vectors.
Compute the (infinity) INF-norm of 'this' matrix, i.e., the maximum 1-norm of the row vectors.
- See also
en.wikipedia.org/wiki/Matrix_norm
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
val
range1: Range
Range for the storage array on dimension 1 (rows)
-
val
range2: Range
Range for the storage array on dimension 2 (columns)
-
def
sameCrossDimensions(b: MatriI): Boolean
Check whether 'this' matrix and the other matrix 'b' have the same cross dimensions.
Check whether 'this' matrix and the other matrix 'b' have the same cross dimensions.
- b
the other matrix
-
def
sameDimensions(b: MatriI): Boolean
Check whether 'this' matrix and the other matrix 'b' have the same dimensions.
Check whether 'this' matrix and the other matrix 'b' have the same dimensions.
- b
the other matrix
-
def
selectRowsEx(rowIndex: Array[Int]): MatriI
Select all rows from 'this' matrix excluding the rows from the given 'rowIndex'.
Select all rows from 'this' matrix excluding the rows from the given 'rowIndex'.
- rowIndex
the row indices to exclude
-
def
setFormat(newFormat: String): Unit
Set the format to the 'newFormat'.
Set the format to the 'newFormat'.
- newFormat
the new format string
-
def
slice(rg: Range): MatriI
Slice 'this' matrix row-wise over the given range 'rg'.
Slice 'this' matrix row-wise over the given range 'rg'.
- rg
the range specifying the slice
-
def
sliceEx(rg: Range): MatriI
Slice 'this' matrix row-wise excluding the given range 'rg'.
Slice 'this' matrix row-wise excluding the given range 'rg'.
- rg
the excluded range of the slice
-
def
solve(lu: (MatriI, MatriI), b: VectoI): VectoI
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
-
def
swap(i: Int, k: Int, col: Int = 0): Unit
Swap the elements in rows 'i' and 'k' starting from column 'col'.
Swap the elements in rows 'i' and 'k' starting from column 'col'.
- i
the first row in the swap
- k
the second row in the swap
- col
the starting column for the swap (default 0 => whole row)
-
def
swapCol(j: Int, l: Int, row: Int = 0): Unit
Swap the elements in columns 'j' and 'l' starting from row 'row'.
Swap the elements in columns 'j' and 'l' starting from row 'row'.
- j
the first column in the swap
- l
the second column in the swap
- row
the starting row for the swap (default 0 => whole column)
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
def
update(i: Int, jr: Range, u: VectoI): Unit
Set a slice of 'this' matrix row-wise at index 'i' and column-wise on range 'jr' to vector 'u'.
Set a slice of 'this' matrix row-wise at index 'i' and column-wise on range 'jr' to vector 'u'. Ex: a(2, 3..5) = u
- i
the row index
- jr
the column range
- u
the vector to assign
-
def
update(ir: Range, j: Int, u: VectoI): Unit
Set a slice of 'this' matrix row-wise on range 'ir' and column-wise at index 'j' to vector 'u'.
Set a slice of 'this' matrix row-wise on range 'ir' and column-wise at index 'j' to vector 'u'. Ex: a(2..4, 3) = u
- ir
the row range
- j
the column index
- u
the vector to assign
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )