class MatrixR extends MatriR with Error with Serializable
The MatrixR class stores and operates on Numeric Matrices of type Real.
This class follows the gen.MatrixN framework and is provided for efficiency.
Caveat: Only works for rectangular matrices. For matrix-like structures
based on jagged arrays, where the second dimension varies,
- See also
scalation.linalgebra.gen.HMatrix2
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- MatrixR
- Serializable
- Serializable
- MatriR
- Error
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Instance Constructors
-
new
MatrixR(b: MatriR)
Construct a matrix and assign values from matrix 'b'.
Construct a matrix and assign values from matrix 'b'.
- b
the matrix of values to assign
-
new
MatrixR(dim: (Int, Int), u: Real*)
Construct a matrix from repeated values.
Construct a matrix from repeated values.
- dim
the (row, column) dimensions
- u
the repeated values
-
new
MatrixR(u: Array[Array[Real]])
Construct a matrix and assign values from array of arrays 'u'.
Construct a matrix and assign values from array of arrays 'u'.
- u
the 2D array of values to assign
-
new
MatrixR(dim1: Int, dim2: Int, x: Real)
Construct a 'dim1' by 'dim2' matrix and assign each element the value 'x'.
Construct a 'dim1' by 'dim2' matrix and assign each element the value 'x'.
- dim1
the row dimension
- dim2
the column dimension
- x
the scalar value to assign
-
new
MatrixR(dim1: Int)
Construct a 'dim1' by 'dim1' square matrix.
Construct a 'dim1' by 'dim1' square matrix.
- dim1
the row and column dimension
-
new
MatrixR(d1: Int, d2: Int, v: Array[Array[Real]] = null)
- d1
the first/row dimension
- d2
the second/column dimension
- v
the 2D array used to store matrix elements
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
def
*(x: Real): MatrixR
Multiply 'this' matrix by scalar 'x'.
-
def
*(u: VectoR): VectorR
Multiply 'this' matrix by (column) vector 'u' (vector elements beyond 'dim2' ignored).
-
def
*(b: MatriR): MatrixR
Multiply 'this' matrix by matrix 'b', transposing 'b' to improve efficiency.
-
def
*(b: MatrixR): MatrixR
Multiply 'this' matrix by matrix 'b', transposing 'b' to improve efficiency.
Multiply 'this' matrix by matrix 'b', transposing 'b' to improve efficiency. Use 'times' method to skip the transpose step.
- b
the matrix to multiply by (requires 'sameCrossDimensions')
-
def
**(b: MatriR): MatrixR
Multiply 'this' matrix by matrix 'b' elementwise (Hadamard product).
-
def
**(u: VectoR): MatrixR
Multiply 'this' matrix by vector 'u' to produce another matrix 'a_ij * u_j'.
-
def
**:(u: VectoR): MatrixR
Multiply vector 'u' by 'this' matrix to produce another matrix 'u_i * a_ij'.
-
def
**=(u: VectoR): MatrixR
Multiply in-place 'this' matrix by vector 'u' to produce another matrix 'a_ij * u_j'.
-
def
*:(u: VectoR): VectoR
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
- Definition Classes
- MatriR
-
def
*=(x: Real): MatrixR
Multiply in-place 'this' matrix by scalar 'x'.
-
def
*=(b: MatriR): MatrixR
Multiply in-place 'this' matrix by matrix 'b', transposing 'b' to improve efficiency.
-
def
*=(b: MatrixR): MatrixR
Multiply in-place 'this' matrix by matrix 'b', transposing 'b' to improve efficiency.
Multiply in-place 'this' matrix by matrix 'b', transposing 'b' to improve efficiency. Use 'times_ip' method to skip the transpose step.
- b
the matrix to multiply by (requires square and 'sameCrossDimensions')
-
def
+(x: Real): MatrixR
Add 'this' matrix and scalar 'x'.
-
def
+(u: VectoR): MatrixR
Add 'this' matrix and (row) vector 'u'.
-
def
+(b: MatriR): MatrixR
Add 'this' matrix and matrix 'b' for any type extending MatriR.
-
def
+(b: MatrixR): MatrixR
Add 'this' matrix and matrix 'b'.
Add 'this' matrix and matrix 'b'.
- b
the matrix to add (requires 'leDimensions')
-
def
++(b: MatriR): MatrixR
Concatenate (row-wise) 'this' matrix and matrix 'b'.
-
def
++^(b: MatriR): MatrixR
Concatenate (column-wise) 'this' matrix and matrix 'b'.
-
def
+:(u: VectoR): MatrixR
Concatenate (row) vector 'u' and 'this' matrix, i.e., prepend 'u' to 'this'.
-
def
+=(x: Real): MatrixR
Add in-place 'this' matrix and scalar 'x'.
-
def
+=(u: VectoR): MatrixR
Add in-place 'this' matrix and (row) vector 'u'.
-
def
+=(b: MatriR): MatrixR
Add in-place 'this' matrix and matrix 'b' for any type extending MatriR.
-
def
+=(b: MatrixR): MatrixR
Add in-place 'this' matrix and matrix 'b'.
Add in-place 'this' matrix and matrix 'b'.
- b
the matrix to add (requires 'leDimensions')
-
def
+^:(u: VectoR): MatrixR
Concatenate (column) vector 'u' and 'this' matrix, i.e., prepend 'u' to 'this'.
-
def
-(x: Real): MatrixR
From 'this' matrix subtract scalar 'x'.
-
def
-(u: VectoR): MatrixR
From 'this' matrix subtract (row) vector 'u'.
-
def
-(b: MatriR): MatrixR
From 'this' matrix subtract matrix 'b' for any type extending MatriR.
-
def
-(b: MatrixR): MatrixR
From 'this' matrix subtract matrix 'b'.
From 'this' matrix subtract matrix 'b'.
- b
the matrix to subtract (requires 'leDimensions')
-
def
-=(x: Real): MatrixR
From 'this' matrix subtract in-place scalar 'x'.
-
def
-=(u: VectoR): MatrixR
From 'this' matrix subtract in-place (row) vector 'u'.
-
def
-=(b: MatriR): MatrixR
From 'this' matrix subtract in-place matrix 'b'.
-
def
-=(b: MatrixR): MatrixR
From 'this' matrix subtract in-place matrix 'b'.
From 'this' matrix subtract in-place matrix 'b'.
- b
the matrix to subtract (requires 'leDimensions')
-
def
/(x: Real): MatrixR
Divide 'this' matrix by scalar 'x'.
-
def
/=(x: Real): MatrixR
Divide in-place 'this' matrix by scalar 'x'.
-
def
:+(u: VectoR): MatrixR
Concatenate 'this' matrix and (row) vector 'u', i.e., append 'u' to 'this'.
-
def
:^+(u: VectoR): MatrixR
Concatenate 'this' matrix and (column) vector 'u', i.e., append 'u' to 'this'.
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
apply(iv: VectoI): MatrixR
Get the rows from 'this' matrix according to the given index/basis.
-
def
apply(): Array[Array[Real]]
Get the underlying 2D array for 'this' matrix.
-
def
apply(ir: Range, jr: Range): MatrixR
Get a slice 'this' matrix row-wise on range 'ir' and column-wise on range 'jr'.
-
def
apply(i: Int): VectorR
Get 'this' matrix's vector at the 'i'-th index position ('i'-th row).
-
def
apply(i: Int, j: Int): Real
Get 'this' matrix's element at the 'i,j'-th index position.
-
def
apply(i: Int, jr: Range): VectoR
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
- MatriR
-
def
apply(ir: Range, j: Int): VectoR
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
- MatriR
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
bsolve(y: VectoR): VectorR
Solve for 'x' using back substitution in the equation 'u*x = y' where 'this' matrix ('u') is upper triangular (see 'lud_npp' above).
-
def
clean(thres: Double = TOL, relative: Boolean = true): MatrixR
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
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
-
def
col(col: Int, from: Int = 0): VectorR
Get column 'col' from the matrix, returning it as a vector.
-
def
copy(): MatrixR
Create an exact copy of 'this' m-by-n matrix.
-
def
det: Real
Compute the determinant of 'this' matrix.
-
def
diag(p: Int, q: Int = 0): MatrixR
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.
-
def
diag(b: MatriR): MatrixR
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]'.
-
lazy val
dim1: Int
Dimension 1
-
lazy val
dim2: Int
Dimension 2
-
def
dot(b: MatrixR): VectorR
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
-
def
dot(b: MatriR): VectorR
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.
-
def
dot(u: VectoR): VectorR
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').
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(b: Any): Boolean
Override equals to determine whether 'this' matrix equals matrix 'b'.
Override equals to determine whether 'this' matrix equals matrix 'b'.
- b
the matrix to compare with this
- Definition Classes
- MatrixR → 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
- Definition Classes
- MatriR
-
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[Real]) ⇒ 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
- Definition Classes
- MatriR
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
getDiag(k: Int = 0): VectorR
Get the 'k'th diagonal of 'this' matrix.
-
def
hashCode(): Int
Must also override hashCode for 'this' matrix to be compatible with equals.
Must also override hashCode for 'this' matrix to be compatible with equals.
- Definition Classes
- MatrixR → AnyRef → Any
-
def
inverse: MatrixR
Invert 'this' matrix (requires a square matrix) and use partial pivoting.
-
def
inverse_ip(): MatrixR
Invert in-place 'this' matrix (requires a square matrix) and uses partial pivoting.
-
def
inverse_npp: MatrixR
Invert 'this' matrix (requires a square matrix) and does not use partial pivoting.
-
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.
- Definition Classes
- MatriR
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
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
- MatriR
-
def
isRectangular: Boolean
Check whether 'this' matrix is rectangular (all rows have the same number of columns).
-
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
- MatriR
-
def
isSymmetric: Boolean
Check whether 'this' matrix is symmetric.
Check whether 'this' matrix is symmetric.
- Definition Classes
- MatriR
-
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.
- Definition Classes
- MatriR
-
def
leDimensions(b: MatriR): 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
- Definition Classes
- MatriR
-
def
lowerT: MatrixR
Return the lower triangular of 'this' matrix (rest are zero).
-
def
lud_ip(): (MatrixR, MatrixR)
Factor in-place 'this' matrix into the product of lower and upper triangular matrices '(l, u)' using the 'LU' Factorization algorithm.
-
def
lud_npp: (MatrixR, MatrixR)
Factor 'this' matrix into the product of upper and lower triangular matrices '(l, u)' using the 'LU' Factorization algorithm.
-
def
mag: Real
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
- MatriR
-
def
map(f: (VectoR) ⇒ VectoR): MatrixR
Map the elements of 'this' matrix by applying the mapping function 'f'.
-
def
max(e: Int = dim1): Real
Find the maximum element in 'this' matrix.
-
def
mdot(b: MatrixR): MatrixR
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)
-
def
mdot(b: MatriR): MatrixR
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').
-
def
mean: VectoR
Compute the column means of 'this' matrix.
Compute the column means of 'this' matrix.
- Definition Classes
- MatriR
-
def
meanNZ: VectoR
Compute the column means of 'this' matrix ignoring zero elements (e.g., a zero may indicate a missing value as in recommender systems).
Compute the column means of 'this' matrix ignoring zero elements (e.g., a zero may indicate a missing value as in recommender systems).
- Definition Classes
- MatriR
-
def
meanR: VectoR
Compute the row means of 'this' matrix.
Compute the row means of 'this' matrix.
- Definition Classes
- MatriR
-
def
meanRNZ: VectoR
Compute the row means of 'this' matrix ignoring zero elements (e.g., a zero may indicate a missing value as in recommender systems).
Compute the row means of 'this' matrix ignoring zero elements (e.g., a zero may indicate a missing value as in recommender systems).
- Definition Classes
- MatriR
-
def
min(e: Int = dim1): Real
Find the minimum element in 'this' matrix.
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
norm1: Real
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
- MatriR
- See also
en.wikipedia.org/wiki/Matrix_norm
-
def
normF: Real
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.
- Definition Classes
- MatriR
- See also
en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm
-
def
normINF: Real
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.
- Definition Classes
- MatriR
- See also
en.wikipedia.org/wiki/Matrix_norm
-
def
normalizeU: MatrixR
Create a normalized version of 'this' matrix.
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
nullspace: VectorR
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.
-
def
nullspace_ip(): VectorR
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.
-
val
range1: Range
Range for the storage array on dimension 1 (rows)
Range for the storage array on dimension 1 (rows)
- Definition Classes
- MatriR
-
val
range2: Range
Range for the storage array on dimension 2 (columns)
Range for the storage array on dimension 2 (columns)
- Definition Classes
- MatriR
-
def
reduce: MatrixR
Use Gauss-Jordan reduction on 'this' matrix to make the left part embed an identity matrix.
-
def
reduce_ip(): Unit
Use Gauss-Jordan reduction in-place on 'this' matrix to make the left part embed an identity matrix.
-
def
sameCrossDimensions(b: MatriR): 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
- Definition Classes
- MatriR
-
def
sameDimensions(b: MatriR): 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
- Definition Classes
- MatriR
-
def
selectCols(colIndex: Array[Int]): MatrixR
Select columns from 'this' matrix according to the given index/basis.
-
def
selectRows(rowIndex: Array[Int]): MatrixR
Select rows from 'this' matrix according to the given index/basis.
-
def
selectRowsEx(rowIndex: Array[Int]): MatriR
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
- Definition Classes
- MatriR
-
def
set(i: Int, u: VectoR, j: Int = 0): Unit
Set 'this' matrix's 'i'-th row starting at column 'j' to the vector 'u'.
-
def
set(u: Array[Array[Real]]): Unit
Set all the values in 'this' matrix as copies of the values in 2D array 'u'.
-
def
set(x: Real): Unit
Set all the elements in 'this' matrix to the scalar 'x'.
-
def
setCol(col: Int, u: VectoR): Unit
Set column 'col' of the matrix to a vector.
-
def
setDiag(x: Real): Unit
Set the main diagonal of 'this' matrix to the scalar 'x'.
-
def
setDiag(u: VectoR, k: Int = 0): Unit
Set the 'k'th diagonal of 'this' matrix to the vector 'u'.
-
def
setFormat(newFormat: String): Unit
Set the format to the 'newFormat'.
-
def
slice(r_from: Int, r_end: Int, c_from: Int, c_end: Int): MatrixR
Slice 'this' matrix row-wise 'r_from' to 'r_end' and column-wise 'c_from' to 'c_end'.
-
def
slice(from: Int, end: Int): MatrixR
Slice 'this' matrix row-wise 'from' to 'end'.
-
def
slice(rg: Range): MatriR
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
- Definition Classes
- MatriR
-
def
sliceCol(from: Int, end: Int): MatrixR
Slice 'this' matrix column-wise 'from' to 'end'.
-
def
sliceEx(row: Int, col: Int): MatrixR
Slice 'this' matrix excluding the given row and/or column.
-
def
sliceEx(rg: Range): MatriR
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
- Definition Classes
- MatriR
-
def
solve(b: VectoR): VectoR
Solve for 'x' in the equation 'a*x = b' where 'a' is 'this' matrix.
-
def
solve(l: MatriR, u: MatriR, b: VectoR): VectoR
Solve for 'x' in the equation 'l*u*x = b' (see 'lud_npp' above).
-
def
solve(lu: (MatriR, MatriR), b: VectoR): VectoR
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
- MatriR
-
def
sum: Real
Compute the sum of 'this' matrix, i.e., the sum of its elements.
-
def
sumAbs: Real
Compute the 'abs' sum of 'this' matrix, i.e., the sum of the absolute value of its elements.
-
def
sumLower: Real
Compute the sum of the lower triangular region of 'this' matrix.
-
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)
- Definition Classes
- MatriR
-
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)
- Definition Classes
- MatriR
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
t: MatrixR
Transpose 'this' matrix (columns => rows).
-
def
times(b: MatrixR): MatrixR
Multiply 'this' matrix by matrix 'b' without first transposing 'b'.
Multiply 'this' matrix by matrix 'b' without first transposing 'b'.
- b
the matrix to multiply by (requires 'sameCrossDimensions')
-
def
times_d(b: MatriR): MatrixR
Multiply 'this' matrix by matrix 'b' using 'dot' product (concise solution).
Multiply 'this' matrix by matrix 'b' using 'dot' product (concise solution).
- b
the matrix to multiply by (requires 'sameCrossDimensions')
-
def
times_ip(b: MatrixR): Unit
Multiply in-place 'this' matrix by matrix 'b' without first transposing 'b'.
Multiply in-place 'this' matrix by matrix 'b' without first transposing '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 square and 'sameCrossDimensions')
-
def
times_ip_pre(b: MatrixR, d: Int = 0): Unit
Pre-multiply in-place 'this' ('a') matrix by matrix 'b', starting with column 'd'.
Pre-multiply in-place 'this' ('a') matrix by matrix 'b', starting with column 'd'.
a(d:m, d:n) = b a(d:m, d:n)
- b
the matrix to pre-multiply by 'this' (requires square and 'sameCrossDimensions')
- d
the column to start with
-
def
times_s(b: MatrixR): MatrixR
Multiply 'this' matrix by matrix 'b' using the Strassen matrix multiplication algorithm.
Multiply 'this' matrix by matrix 'b' using the Strassen matrix multiplication algorithm. Both matrices ('this' and 'b') must be square. Although the algorithm is faster than the traditional cubic algorithm, its requires more memory and is often less stable (due to round-off errors). FIX: could be make more efficient using a virtual slice 'vslice' method.
- b
the matrix to multiply by (it has to be a square matrix)
- See also
http://en.wikipedia.org/wiki/Strassen_algorithm
-
def
tip(): MatrixR
Transpose, in-place, 'this' matrix (columns => rows).
Transpose, in-place, 'this' matrix (columns => rows). FIX: may wish to use algorithm with better data locality.
-
def
toDense: MatrixR
Convert 'this' matrix to a dense matrix.
-
def
toDouble: MatrixD
Convert 'this'
MatrixRinto a double matrixMatrixD. -
def
toInt: MatrixI
Convert 'this'
MatrixRinto an integer matrixMatrixI. -
def
toString(): String
Convert 'this' real (double precision) matrix to a string.
Convert 'this' real (double precision) matrix to a string.
- Definition Classes
- MatrixR → AnyRef → Any
-
def
trace: Real
Compute the trace of 'this' matrix, i.e., the sum of the elements on the main diagonal.
-
def
update(ir: Range, jr: Range, b: MatriR): Unit
Set a slice 'this' matrix row-wise on range 'ir' and column-wise on range 'jr'.
-
def
update(i: Int, u: VectoR): Unit
Set 'this' matrix's row at the 'i'-th index position to the vector 'u'.
-
def
update(i: Int, j: Int, x: Real): Unit
Set 'this' matrix's element at the 'i,j'-th index position to the scalar 'x'.
-
def
update(i: Int, jr: Range, u: VectoR): 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
- Definition Classes
- MatriR
-
def
update(ir: Range, j: Int, u: VectoR): 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
- Definition Classes
- MatriR
-
def
upperT: MatrixR
Return the upper triangular of 'this' matrix (rest are zero).
-
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( ... )
-
def
write(fileName: String): Unit
Write 'this' matrix to a CSV-formatted text file with name 'fileName'.
-
def
zero(m: Int = dim1, n: Int = dim2): MatrixR
Create an m-by-n matrix with all elements initialized to zero.
-
def
~^(p: Int): MatrixR
Raise 'this' matrix to the 'p'th power (for some integer 'p' >= 1) using a divide and conquer algorithm.