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Definitions of Filter classes and functions. More...

#include <itpp/base/vec.h>
#include <itpp/itexports.h>

Classes
class	itpp::Filter< T1, T2, T3 >
	Virtual Filter Base Class.The class is templated as follows: More...

class	itpp::MA_Filter< T1, T2, T3 >
	Moving Average Filter Base Class.This class implements a moving average (MA) filter according to $y(n) = b(0)x(n) + b(1)x(n-1) + ... + b(N)*x(n-N)$ where b is the filter coefficients, x is the input and y is the output. More...

class	itpp::AR_Filter< T1, T2, T3 >
	Autoregressive (AR) Filter Base Class.This class implements a autoregressive (AR) filter according to $a(0)y(n) = x(n) - a(1)y(n-1) - ... - a(N)*y(n-N)$ where a is the filter coefficients, x is the input and y is the output. More...

class	itpp::ARMA_Filter< T1, T2, T3 >
	Autoregressive Moving Average (ARMA) Filter Base Class.This class implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ . More...

Namespaces
namespace	itpp
	itpp namespace

Functions
vec	itpp::fir1 (int N, double cutoff)
	Design a Nth order FIR filter with cut-off frequency `cutoff` using the window method.


vec	itpp::filter (const vec &b, const vec &a, const vec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const vec &b, const vec &a, const cvec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const cvec &b, const cvec &a, const cvec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const cvec &b, const cvec &a, const vec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

vec	itpp::filter (const vec &b, const int one, const vec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const vec &b, const int one, const cvec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const cvec &b, const int one, const cvec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const cvec &b, const int one, const vec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

vec	itpp::filter (const int one, const vec &a, const vec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const int one, const vec &a, const cvec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const int one, const cvec &a, const cvec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const int one, const cvec &a, const vec &input)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

vec	itpp::filter (const vec &b, const vec &a, const vec &input, const vec &state_in, vec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const vec &b, const vec &a, const cvec &input, const cvec &state_in, cvec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const cvec &b, const cvec &a, const cvec &input, const cvec &state_in, cvec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const cvec &b, const cvec &a, const vec &input, const cvec &state_in, cvec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

vec	itpp::filter (const vec &b, const int one, const vec &input, const vec &state_in, vec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const vec &b, const int one, const cvec &input, const cvec &state_in, cvec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const cvec &b, const int one, const cvec &input, const cvec &state_in, cvec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const cvec &b, const int one, const vec &input, const cvec &state_in, cvec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

vec	itpp::filter (const int one, const vec &a, const vec &input, const vec &state_in, vec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const int one, const vec &a, const cvec &input, const cvec &state_in, cvec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const int one, const cvec &a, const cvec &input, const cvec &state_in, cvec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

cvec	itpp::filter (const int one, const cvec &a, const vec &input, const cvec &state_in, cvec &state_out)
	ARMA filter functionThese functions implements a autoregressive moving average (ARMA) filter according to $a(0)y(n) = b(0)x(n) + b(1)x(n-1) + \ldots + b(N_b)x(n-N_b) - a(1)y(n-1) - \ldots - a(N_a)y(n-N_a)$ .

Detailed Description

Definitions of Filter classes and functions.

Author: Hakan Eriksson, Thomas Eriksson, Tony Ottosson and Adam Piatyszek

This file is part of IT++ - a C++ library of mathematical, signal processing, speech processing, and communications classes and functions.

IT++ is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

IT++ is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with IT++. If not, see http://www.gnu.org/licenses/.

Definition in file filter.h.

Classes

Namespaces

Functions

Detailed Description