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Uses of Class
org.ejml.data.D1Matrix64F

Packages that use D1Matrix64F
org.ejml.alg.dense.mult   
org.ejml.data   
org.ejml.ops   
 

Uses of D1Matrix64F in org.ejml.alg.dense.mult
 

Methods in org.ejml.alg.dense.mult with parameters of type D1Matrix64F
static void VectorVectorMult.addOuterProd(double gamma, D1Matrix64F x, D1Matrix64F y, RowD1Matrix64F A)
           Adds to A ∈ ℜ m × n the results of an outer product multiplication of the two vectors.
static void VectorVectorMult.householder(double gamma, D1Matrix64F u, D1Matrix64F x, D1Matrix64F y)
           Multiplies a householder reflection against a vector:

y = (I + γ u uT)x
static double VectorVectorMult.innerProd(D1Matrix64F x, D1Matrix64F y)
           Computes the inner product of the two vectors.
static double VectorVectorMult.innerProdA(D1Matrix64F x, D1Matrix64F A, D1Matrix64F y)
           xTAy
static double VectorVectorMult.innerProdTranA(D1Matrix64F x, D1Matrix64F A, D1Matrix64F y)
           xTATy
static void MatrixVectorMult.mult(RowD1Matrix64F a, D1Matrix64F b, D1Matrix64F c)
           Performs a matrix vector multiply.

c = A * b
and
c = A * bT

ci = Sum{ j=1:n, aij * bj}

where A is a matrix, b is a column or transposed row vector, and c is a column vector.
static void MatrixVectorMult.multAdd(RowD1Matrix64F A, D1Matrix64F B, D1Matrix64F C)
           Performs a matrix vector multiply.

C = C + A * B
or
C = C + A * BT

ci = Sum{ j=1:n, ci + aij * bj}

where A is a matrix, B is a column or transposed row vector, and C is a column vector.
static void MatrixVectorMult.multAddTransA_reorder(RowD1Matrix64F A, D1Matrix64F B, D1Matrix64F C)
          An alternative implementation of MatrixVectorMult.multAddTransA_small(org.ejml.data.RowD1Matrix64F, org.ejml.data.D1Matrix64F, org.ejml.data.D1Matrix64F) that performs well on large matrices.
static void MatrixVectorMult.multAddTransA_small(RowD1Matrix64F A, D1Matrix64F B, D1Matrix64F C)
           Performs a matrix vector multiply.

C = C + AT * B
or
C = CT + AT * BT

ci = Sum{ j=1:n, ci + aji * bj}

where A is a matrix, B is a column or transposed row vector, and C is a column vector.
static void MatrixVectorMult.multTransA_reorder(RowD1Matrix64F A, D1Matrix64F B, D1Matrix64F C)
          An alternative implementation of MatrixVectorMult.multTransA_small(org.ejml.data.RowD1Matrix64F, org.ejml.data.D1Matrix64F, org.ejml.data.D1Matrix64F) that performs well on large matrices.
static void MatrixVectorMult.multTransA_small(RowD1Matrix64F A, D1Matrix64F B, D1Matrix64F C)
           Performs a matrix vector multiply.

C = AT * B
where B is a column vector.
or
C = AT * BT
where B is a row vector.
static void VectorVectorMult.outerProd(D1Matrix64F x, D1Matrix64F y, RowD1Matrix64F A)
           Sets A ∈ ℜ m × n equal to an outer product multiplication of the two vectors.
 

Uses of D1Matrix64F in org.ejml.data
 

Subclasses of D1Matrix64F in org.ejml.data
 class BlockMatrix64F
          A row-major block matrix declared on to one continuous array.
 class DenseMatrix64F
           DenseMatrix64F is a dense matrix with elements that are 64-bit floats (doubles).
 class RowD1Matrix64F
          Interface for a row-major matrix that uses a single array internally.
 

Fields in org.ejml.data declared as D1Matrix64F
 D1Matrix64F D1Submatrix64F.original
           
 

Methods in org.ejml.data with parameters of type D1Matrix64F
 void D1Matrix64F.set(D1Matrix64F b)
          Sets the value of this matrix to be the same as the value of the provided matrix.
 

Constructors in org.ejml.data with parameters of type D1Matrix64F
D1Submatrix64F(D1Matrix64F original)
           
D1Submatrix64F(D1Matrix64F original, int row0, int row1, int col0, int col1)
           
 

Uses of D1Matrix64F in org.ejml.ops
 

Methods in org.ejml.ops with parameters of type D1Matrix64F
static void CommonOps.add(D1Matrix64F a, D1Matrix64F b, D1Matrix64F c)
          Performs the following operation:

c = a + b
cij = aij + bij
static void CommonOps.add(D1Matrix64F a, double val)
          Performs an in-place scalar addition:

a = a + val
aij = aij + val
static void CommonOps.add(D1Matrix64F a, double val, D1Matrix64F c)
          Performs scalar addition:

c = a + val
cij = aij + val
static void CommonOps.add(D1Matrix64F a, double beta, D1Matrix64F b, D1Matrix64F c)
          Performs the following operation:

c = a + β * b
cij = aij + β * bij
static void CommonOps.add(double alpha, D1Matrix64F a, D1Matrix64F b, D1Matrix64F c)
          Performs the following operation:

c = α * a + b
cij = α * aij + bij
static void CommonOps.add(double alpha, D1Matrix64F a, double beta, D1Matrix64F b, D1Matrix64F c)
          Performs the following operation:

c = α * a + β * b
cij = α * aij + β * bij
static void CommonOps.addEquals(D1Matrix64F a, D1Matrix64F b)
          Performs the following operation:

a = a + b
aij = aij + bij
static void CommonOps.addEquals(D1Matrix64F a, double beta, D1Matrix64F b)
          Performs the following operation:

a = a + β * b
aij = aij + β * bij
static void CommonOps.changeSign(D1Matrix64F a)
           Changes the sign of every element in the matrix.

aij = -aij
static double SpecializedOps.diffNormF_fast(D1Matrix64F a, D1Matrix64F b)
           
static double SpecializedOps.diffNormF(D1Matrix64F a, D1Matrix64F b)
           Computes the F norm of the difference between the two Matrices:

Sqrt{∑i=1:m ∑j=1:n ( aij - bij)2}
static double SpecializedOps.diffNormP1(D1Matrix64F a, D1Matrix64F b)
           Computes the p=1 p-norm of the difference between the two Matrices:

∑i=1:m ∑j=1:n | aij - bij|

where |x| is the absolute value of x.
static void CommonOps.divide(double alpha, D1Matrix64F a)
           Performs an in-place element by element scalar division.

aij = aij/α
static void CommonOps.divide(double alpha, D1Matrix64F a, D1Matrix64F b)
           Performs an element by element scalar division.

bij = *aij /α
static void CommonOps.elementDiv(D1Matrix64F a, D1Matrix64F b)
          Performs the an element by element division operation:

aij = aij / bij
static void CommonOps.elementDiv(D1Matrix64F a, D1Matrix64F b, D1Matrix64F c)
          Performs the an element by element division operation:

cij = aij / bij
static double CommonOps.elementMax(D1Matrix64F a)
           Returns the value of the element in the matrix that has the largest value.

Max{ aij } for all i and j
static double CommonOps.elementMaxAbs(D1Matrix64F a)
           Returns the absolute value of the element in the matrix that has the largest absolute value.

Max{ |aij| } for all i and j
static double CommonOps.elementMin(D1Matrix64F a)
           Returns the value of the element in the matrix that has the minimum value.

Min{ aij } for all i and j
static double CommonOps.elementMinAbs(D1Matrix64F a)
           Returns the absolute value of the element in the matrix that has the smallest absolute value.

Min{ |aij| } for all i and j
static void CommonOps.elementMult(D1Matrix64F a, D1Matrix64F b)
          Performs the an element by element multiplication operation:

aij = aij * bij
static void CommonOps.elementMult(D1Matrix64F a, D1Matrix64F b, D1Matrix64F c)
          Performs the an element by element multiplication operation:

cij = aij * bij
static double CommonOps.elementSum(D1Matrix64F mat)
           Computes the sum of all the elements in the matrix:

sum(i=1:m , j=1:n ; aij)
static double CommonOps.elementSumAbs(D1Matrix64F mat)
           Computes the sum of the absolute value all the elements in the matrix:

sum(i=1:m , j=1:n ; |aij|)
static double SpecializedOps.elementSumSq(D1Matrix64F m)
          Sums up the square of each element in the matrix.
static double NormOps.fastElementP(D1Matrix64F A, double p)
          Same as NormOps.elementP(org.ejml.data.RowD1Matrix64F, double) but runs faster by not mitigating overflow/underflow related problems.
static double NormOps.fastNormF(D1Matrix64F a)
           This implementation of the Frobenius norm is a straight forward implementation and can be susceptible for overflow/underflow issues.
static boolean MatrixFeatures.hasNaN(D1Matrix64F m)
          Checks to see if any element in the matrix is NaN.
static boolean MatrixFeatures.hasUncountable(D1Matrix64F m)
          Checks to see if any element in the matrix is NaN of Infinite.
static boolean MatrixFeatures.isEquals(D1Matrix64F a, D1Matrix64F b)
           Checks to see if each element in the two matrices are equal: aij == bij
static boolean MatrixFeatures.isEquals(D1Matrix64F a, D1Matrix64F b, double tol)
           Checks to see if each element in the two matrices are within tolerance of each other: tol ≥ |aij - bij|.
static boolean MatrixFeatures.isIdentical(D1Matrix64F a, D1Matrix64F b, double tol)
           Checks to see if each corresponding element in the two matrices are within tolerance of each other or have the some symbolic meaning.
static boolean MatrixFeatures.isNegative(D1Matrix64F a, D1Matrix64F b, double tol)
           Checks to see if the two matrices are the negative of each other:

aij = -bij
static boolean MatrixFeatures.isSquare(D1Matrix64F mat)
          Checks to see if it is a square matrix.
static boolean MatrixFeatures.isVector(D1Matrix64F mat)
          Checks to see if the matrix is a vector or not.
static double NormOps.normF(D1Matrix64F a)
           Computes the Frobenius matrix norm:

normF = Sqrt{ ∑i=1:m ∑j=1:n { aij2} }
static double SpecializedOps.qualityTriangular(boolean upper, D1Matrix64F T)
          Computes the quality of a triangular matrix, where the quality of a matrix is defined in LinearSolver.quality().
static void MatrixComponent.renderMatrix(D1Matrix64F M, BufferedImage image, double maxValue)
           
static void CommonOps.scale(double alpha, D1Matrix64F a)
           Performs an in-place element by element scalar multiplication.

aij = α*aij
static void CommonOps.scale(double alpha, D1Matrix64F a, D1Matrix64F b)
           Performs an element by element scalar multiplication.

bij = α*aij
static void CommonOps.set(D1Matrix64F a, double value)
           Sets every element in the matrix to the specified value.

aij = value
 void MatrixComponent.setMatrix(D1Matrix64F A)
           
static void RandomMatrices.setRandom(D1Matrix64F mat, double min, double max, Random rand)
           Sets each element in the matrix to a value drawn from an uniform distribution from 'min' to 'max' inclusive.
static void MatrixVisualization.show(D1Matrix64F A, String title)
          Creates a window visually showing the matrix's state.
static void CommonOps.sub(D1Matrix64F a, D1Matrix64F b, D1Matrix64F c)
          Performs the following subtraction operation:

c = a - b
cij = aij - bij
static void CommonOps.subEquals(D1Matrix64F a, D1Matrix64F b)
          Performs the following subtraction operation:

a = a - b
aij = aij - bij
 

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