qlibs::smootherALNF Class Reference

Adaptive Filter LMS. More...

#include <smoother.hpp>

Inheritance diagram for qlibs::smootherALNF:

Public Member Functions
virtual	~smootherALNF ()
bool	setup (const real_t a, const real_t m, const size_t wsize, real_t *window, real_t *weights, real_t *w1=nullptr)
	Setup an initialize the Adaptive Filter LMS.
template<size_t windowSize>
bool	setup (const real_t a, const real_t m, real_t(&window)[windowSize], real_t(&weights)[windowSize], real_t(&w1)[windowSize])
	Setup an initialize the Adaptive Filter LMS.
template<size_t windowSize>
bool	setup (const real_t a, real_t(&window)[windowSize], real_t(&weights)[windowSize])
	Setup an initialize the Adaptive Filter LMS.
real_t	smooth (const real_t x) override
	Perform the smooth operation recursively for the input signal x.
Public Member Functions inherited from qlibs::smoother
virtual	~smoother ()
bool	isInitialized (void) const
	Check if the smoother filter has been initialized using setup().
	operator bool () const noexcept
	Check if the smoother filter has been initialized using setup().
bool	reset (void)
	Reset the smoother filter.

Detailed Description

Adaptive Filter LMS.

Constructor & Destructor Documentation

~smootherALNF()

virtual qlibs::smootherALNF::~smootherALNF ( )

inlinevirtual

Member Function Documentation

setup() [1/3]

bool smootherALNF::setup	(	const real_t	a,
		const real_t	m,
		const size_t	wsize,
		real_t *	window,
		real_t *	weights,
		real_t *	w1 = nullptr )

Setup an initialize the Adaptive Filter LMS.

Parameters

[in]	a	Learning rate [ 0 < a < 1 ]
[in]	m	Momentum [ 0 < m < 1 ]
[in]	wsize	The size of the window array
[in]	window	An array to store the window samples.
[in]	weights	An array to store the filter weights
[in]	w1	An array to keep previous estimated weights. To ignore pass nullptr as argument.

Returns

true on success, otherwise return false.

setup() [2/3]

template<size_t windowSize>

bool qlibs::smootherALNF::setup ( const real_t a, const real_t m, real_t(&) window[windowSize], real_t(&) weights[windowSize], real_t(&) w1[windowSize] )

inline

Setup an initialize the Adaptive Filter LMS.

Parameters

[in]	a	Learning rate [ 0 < a < 1 ]
[in]	m	Momentum [ 0 < m < 1 ]
[in]	window	An array to store the window samples.
[in]	weights	An array to store the filter weights
[in]	w1	An array to keep previous estimated weights.

Returns

true on success, otherwise return false.

setup() [3/3]

template<size_t windowSize>

bool qlibs::smootherALNF::setup ( const real_t a, real_t(&) window[windowSize], real_t(&) weights[windowSize] )

inline

Setup an initialize the Adaptive Filter LMS.

Parameters

[in]	a	Learning rate [ 0 < a < 1 ]
[in]	window	An array to store the window samples.
[in]	weights	An array to store the filter weights.

Returns

true on success, otherwise return false.

smooth()

real_t smootherALNF::smooth ( const real_t x )

overridevirtual

Perform the smooth operation recursively for the input signal x.

Precondition

Instance must be previously initialized

Parameters

[in]

x

A sample of the input signal.

Returns

The smoothed output.

Implements qlibs::smoother.