#include <cpu_operations.h>

Static Public Member Functions
static Matrix< T >	Transpose (const Matrix< T > &a)

static Vector< T >	Transpose (const Vector< T > &a)

static Matrix< T >	Multiply (const Matrix< T > &a, const T &scalar)

static Matrix< T >	Multiply (const Matrix< T > &a, const Matrix< T > &b)

static Matrix< T >	Add (const Matrix< T > &a, const T &scalar)

static Matrix< T >	Add (const Matrix< T > &a, const Matrix< T > &b)

static Matrix< T >	Subtract (const Matrix< T > &a, const T &scalar)

static Matrix< T >	Subtract (const Matrix< T > &a, const Matrix< T > &b)

static Matrix< bool >	LogicalOr (const Matrix< bool > &a, const Matrix< bool > &b)

static Matrix< bool >	LogicalNot (const Matrix< bool > &a)

static Matrix< bool >	LogicalAnd (const Matrix< bool > &a, const Matrix< bool > &b)

static Matrix< T >	Inverse (const Matrix< T > &a)

static Vector< T >	Norm (const Matrix< T > &a, const int &p=2, const int &axis=0)

static T	Determinant (const Matrix< T > &a)

static int	Rank (const Matrix< T > &a)

static T	FrobeniusNorm (const Matrix< T > &a)

static T	Trace (const Matrix< T > &a)

static T	DotProduct (const Vector< T > &a, const Vector< T > &b)

static Matrix< T >	OuterProduct (const Vector< T > &a, const Vector< T > &b)

static Vector< bool >	LogicalAnd (const Vector< T > &a, const Vector< T > &b)

static Vector< bool >	LogicalOr (const Vector< bool > &a, const Vector< bool > &b)

static Vector< bool >	LogicalNot (const Vector< bool > &a)

static Matrix< T >	Normalize (const Matrix< T > &a, const int &p=2, const int &axis=0)

static Matrix< T >	GenKernelMatrix (const Matrix< T > &data_matrix, const KernelType kernel_type=kGaussianKernel, const float constant=1.0)

static void	GenDegreeMatrix (const Matrix< T > &kernel_matrix, Matrix< T > &degree_matrix, Matrix< T > &degree_matrix_to_the_minus_half)

Member Function Documentation

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::Add	(	const Matrix< T > &	a,
		const T &	scalar
	)

inlinestatic

This is a function that adds each element in the matrix to a scalar and returns the resulting matrix.

Parameters

a	Input Matrix
scalar	Input Scalar

Returns: This function returns a matrix that is the resulatant of adding the input matrix and scalar.

See also: Add(const Matrix<T> &a, const Matrix<T> &b)

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::Add	(	const Matrix< T > &	a,
		const Matrix< T > &	b
	)

inlinestatic

This is a function that adds two matricies and returns the resulting matrix.

Parameters

a	Input Matrix 1
b	Input Matrix 2

Returns: This function returns a matrix that is the resulatant of adding two input matricies.

See also: Add(const Matrix<T> &a, const T &scalar)

template<typename T >

static T Nice::CpuOperations< T >::Determinant ( const Matrix< T > & a )

static

template<typename T >

static T Nice::CpuOperations< T >::DotProduct	(	const Vector< T > &	a,
		const Vector< T > &	b
	)

inlinestatic

This is a function that calculates the dot product of two vectors.

Parameters

a	Input Vector 1
b	Input Vector 2

Returns: This function returns a value of type T which is the dot product of the two vectors.

template<typename T >

static T Nice::CpuOperations< T >::FrobeniusNorm ( const Matrix< T > & a )

inlinestatic

This is a function that returns the frobenius norm of Matrix a

Parameters

a

Returns: This function returns a value of type T

template<typename T >

static void Nice::CpuOperations< T >::GenDegreeMatrix	(	const Matrix< T > &	kernel_matrix,
		Matrix< T > &	degree_matrix,
		Matrix< T > &	degree_matrix_to_the_minus_half
	)

inlinestatic

Generates a degree matrix D from an input kernel matrix It also generates D^(-1/2)

Parameters

kernel_matrix	Input matrix: a squared kernel matrix
degree_matrix	Output degree matrix D
degree_matrix_to_the_minus_half	Output matrix D^(-1/2)

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::GenKernelMatrix	(	const Matrix< T > &	data_matrix,
		const KernelType	kernel_type = `kGaussianKernel`,
		const float	constant = `1.0`
	)

inlinestatic

Generates a kernel matrix from an input data_matrix

Parameters

data_matrix	Input matrix whose rows represent samples and columns represent features
kernel_type	Kernel type could be chosen from Gaussian, Linear and Polynomial
constant	In Gaussian kernel, this is sigma; In Polynomial kernel, this is constant c In Linear kernel, this is c as well

Returns: An nxn kernel matrix where n is the number of samples in data_matrix

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::Inverse ( const Matrix< T > & a )

inlinestatic

This is a function that returns the inverse of a matrix.

Parameters

a	Input Matrix

Returns: This function returns a matrix that is the inverse of the input matrix.

template<typename T >

static Matrix<bool> Nice::CpuOperations< T >::LogicalAnd	(	const Matrix< bool > &	a,
		const Matrix< bool > &	b
	)

inlinestatic

This is a function that calculates the "logical and" of the two input Matrices

Parameters

a	Input Matrix 1
b	Input Matrix 2

Returns: This function returns a Matrix of type bool

template<typename T >

static Vector<bool> Nice::CpuOperations< T >::LogicalAnd	(	const Vector< T > &	a,
		const Vector< T > &	b
	)

inlinestatic

This is a function that calculates the "logical and" of the two input Vectors

Parameters

a	Input Vector 1
b	Input Vector 2

Returns: This function returns a Vector of type bool

template<typename T >

static Matrix<bool> Nice::CpuOperations< T >::LogicalNot ( const Matrix< bool > & a )

inlinestatic

This is a funtion that returns the "logical not" of the input Matrix

Parameters

a	Input Matrix 1

Returns: This funtion returns a Matrix of type bool

template<typename T >

static Vector<bool> Nice::CpuOperations< T >::LogicalNot ( const Vector< bool > & a )

inlinestatic

This is a funtion that returns the "logical not" of the input Vector

Parameters

a	Input Vector 1

Returns: This funtion returns a Vector of type bool

template<typename T >

static Matrix<bool> Nice::CpuOperations< T >::LogicalOr	(	const Matrix< bool > &	a,
		const Matrix< bool > &	b
	)

inlinestatic

This is a function that calculates the "logical or" of the two input Matrices

Parameters

a	Input Matrix 1
b	Input Matrix 2

Returns: This function returns a Matrix of type bool

template<typename T >

static Vector<bool> Nice::CpuOperations< T >::LogicalOr	(	const Vector< bool > &	a,
		const Vector< bool > &	b
	)

inlinestatic

This is a function that calculates the "logical or" of the two input Vectors

Parameters

a	Input Vector 1
b	Input Vector 2

Returns: This function returns a Vector of type bool

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::Multiply	(	const Matrix< T > &	a,
		const T &	scalar
	)

inlinestatic

This is a function that calculates the product Matrix of the input Matrix and a scalar

Parameters

a	Input Matrix
scalar	Input scalar

Returns: This function returns a Matrix of type T

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::Multiply	(	const Matrix< T > &	a,
		const Matrix< T > &	b
	)

inlinestatic

This is a funtion that calculates the product Matrix of the two input Matrices

Parameters

a	Input Matrix 1
b	Input Matrix 2

Returns: This function returns a Matrix of type T

template<typename T >

static Vector<T> Nice::CpuOperations< T >::Norm	(	const Matrix< T > &	a,
		const int &	p = `2`,
		const int &	axis = `0`
	)

inlinestatic

static Vector <T> Norm( const Matrix <T> &a, const int &p = 2, const int &axis = 0) calculates the norm of the values in an m x n dependent of the input p and axis. The norm is returned in the form of a vector. If the axis is 0, the norm will be calulated column wise and the size of the output vector will be dependent on n. If the axis is 1, the norm will be calculated row-wise and the size of the vector will be dependent on m.

Parameters

a	const Matrix <T> &a
b	int &p
c	int &axis

Returns: Vector <T>

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::Normalize	(	const Matrix< T > &	a,
		const int &	p = `2`,
		const int &	axis = `0`
	)

inlinestatic

statix Matrix <T> Normalize(const Matrix <T> &a, const int &p =2, const int &axis = 0) normalizes a m x n matrix by element.

Parameters

a	const Matrix<T> &a
b	const int &p = 2
c	const int &axis = 0

Returns: Matrix <T>

See also: Norm

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::OuterProduct	(	const Vector< T > &	a,
		const Vector< T > &	b
	)

inlinestatic

This is a function that calculates the "Outer Product of the input Vectors

Parameters

a	Input Vector 1
b	Input Vector 2

Returns: This function returns a Matrix of type T

template<typename T >

static int Nice::CpuOperations< T >::Rank ( const Matrix< T > & a )

inlinestatic

static int Rank(const Matrix <T> &a) is a function that returns the rank of a m x n matrix

Parameters

a	Matrix<T> &a

Returns: This function returns an int value of the matrix's rank.

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::Subtract	(	const Matrix< T > &	a,
		const T &	scalar
	)

inlinestatic

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::Subtract	(	const Matrix< T > &	a,
		const Matrix< T > &	b
	)

inlinestatic

template<typename T >

static T Nice::CpuOperations< T >::Trace ( const Matrix< T > & a )

inlinestatic

This is a function that returns the sum of the diagonal coefficiants of a Matrix

Parameters

a	Input Matrix

Returns: This function returns a value of type T

template<typename T >

static Matrix<T> Nice::CpuOperations< T >::Transpose ( const Matrix< T > & a )

inlinestatic

This is a function that calculates the transpose Matrix of the input Matrix

Parameters

a	Input Matrix

Returns: This function returns a Matrix of type T

template<typename T >

static Vector<T> Nice::CpuOperations< T >::Transpose ( const Vector< T > & a )

inlinestatic

This is a function that calculates the transpose Vector of the input Vector

Parameters

a	Input Vector

Returns: This function returns a Vector of type T

The documentation for this class was generated from the following file:

include/cpu_operations.h

Static Public Member Functions

Member Function Documentation