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Adaptive filtering module for Python

Project description

Adaptfilt is an adaptive filtering module for Python. It includes simple, procedural implementations of the following filtering algorithms:

  • Least-mean-squares (LMS)
  • Normalized least-mean-squares (NLMS)
  • Affine projection (AP)

The algorithms are implemented using Numpy for computational efficiency. Further optimization have also been done, but this is very limited and only on the most intesive parts of the source code. Future implementation of the following algorithms is currently planned:

  • Recursive least squares (RLS)
  • Steepest descent (SD) (technically not an adaptive filter but included since it is closely related)
Authors: Jesper Wramberg & Mathias Tausen
Version: 0.1
License: MIT

Installation

To install from PyPI using pip simply run:

pip install adaptfilt

Alternatively, the module can also be downloaded at https://pypi.python.org/pypi/adaptfilt or https://github.com/Wramberg/adaptfilt. The latter is also used for issue tracking. Note that adaptfilt requires Numpy to be installed (tested using version 1.9.0).

Usage

Once installed, the module should be available for import by calling:

import adaptfilt

Following the reference sections, examples are provided to show the modules functionality.

Main Function Reference

In this section, the functions provided by adaptfilt are described. The descriptions corresponds with the function docstrings and are only included here for your convenience.

y, e, w = lms(u, d, M, step, leak=0., initCoeffs=None, N=None, returnCoeffs=False)

Perform least-mean-squares (LMS) adaptive filtering on u to minimize error given by e=d-y, where y is the output of the adaptive filter.

Parameters
u : array-like
One-dimensional filter input.
d : array-like
One-dimensional desired signal, i.e., the output of the unknown FIR system which the adaptive filter should identify. Must have length >= len(u), or N+M-1 if number of iterations are limited (via the N parameter).
M : int
Desired number of filter taps (desired filter order + 1), must be non-negative.
step : float
Step size of the algorithm, must be non-negative.
Optional Parameters
leak : float
Leakage factor, must be equal to or greater than zero and smaller than one. When greater than zero a leaky LMS filter is used. Defaults to 0, i.e., no leakage.
initCoeffs : array-like
Initial filter coefficients to use. Should match desired number of filter taps, defaults to zeros.
N : int
Number of iterations, must be less than or equal to len(u)-M+1 (default).
returnCoeffs : boolean
If true, will return all filter coefficients for every iteration in an N x M matrix. Does not include the initial coefficients. If false, only the latest coefficients in a vector of length M is returned. Defaults to false.
Returns
y : numpy.array
Output values of LMS filter, array of length N.
e : numpy.array
Error signal, i.e, d-y. Array of length N.
w : numpy.array
Final filter coefficients in array of length M if returnCoeffs is False. NxM array containing all filter coefficients for all iterations otherwise.
Raises
TypeError
If number of filter taps M is not type integer, number of iterations N is not type integer, or leakage leak is not type float/int.
ValueError
If number of iterations N is greater than len(u)-M, number of filter taps M is negative, or if step-size or leakage is outside specified range.

y, e, w = nlms(u, d, M, step, eps=0.001, leak=0, initCoeffs=None, N=None, returnCoeffs=False)

Perform normalized least-mean-squares (NLMS) adaptive filtering on u to minimize error given by e=d-y, where y is the output of the adaptive filter.

Parameters
u : array-like
One-dimensional filter input.
d : array-like
One-dimensional desired signal, i.e., the output of the unknown FIR system which the adaptive filter should identify. Must have length >= len(u), or N+M-1 if number of iterations are limited (via the N parameter).
M : int
Desired number of filter taps (desired filter order + 1), must be non-negative.
step : float
Step size of the algorithm, must be non-negative.
Optional Parameters
eps : float
Regularization factor to avoid nummerical issues when power of input is close to zero. Defaults to 0.001. Must be non-negative.
leak : float
Leakage factor, must be equal to or greater than zero and smaller than one. When greater than zero a leaky LMS filter is used. Defaults to 0, i.e., no leakage.
initCoeffs : array-like
Initial filter coefficients to use. Should match desired number of filter taps, defaults to zeros.
N : int
Number of iterations to run. Must be less than or equal to len(u)-M+1. Defaults to len(u)-M+1.
returnCoeffs : boolean
If true, will return all filter coefficients for every iteration in an N x M matrix. Does not include the initial coefficients. If false, only the latest coefficients in a vector of length M is returned. Defaults to false.
Returns
y : numpy.array
Output values of LMS filter, array of length N.
e : numpy.array
Error signal, i.e, d-y. Array of length N.
w : numpy.array
Final filter coefficients in array of length M if returnCoeffs is False. NxM array containing all filter coefficients for all iterations otherwise.
Raises
TypeError
If number of filter taps M is not type integer, number of iterations N is not type integer, or leakage leak is not type float/int.
ValueError
If number of iterations N is greater than len(u)-M, number of filter taps M is negative, or if step-size or leakage is outside specified range.

y, e, w = ap(u, d, M, step, K, eps=0.001, leak=0, initCoeffs=None, N=None, returnCoeffs=False)

Perform affine projection (AP) adaptive filtering on u to minimize error given by e=d-y, where y is the output of the adaptive filter.

Parameters
u : array-like
One-dimensional filter input.
d : array-like
One-dimensional desired signal, i.e., the output of the unknown FIR system which the adaptive filter should identify. Must have length >= len(u), or N+M-1 if number of iterations are limited (via the N parameter).
M : int
Desired number of filter taps (desired filter order + 1), must be non-negative.
step : float
Step size of the algorithm, must be non-negative.
K : int
Projection order, must be integer larger than zero.
Optional Parameters
eps : float
Regularization factor to avoid nummerical issues when power of input is close to zero. Defaults to 0.001. Must be non-negative.
leak : float
Leakage factor, must be equal to or greater than zero and smaller than one. When greater than zero a leaky LMS filter is used. Defaults to 0, i.e., no leakage.
initCoeffs : array-like
Initial filter coefficients to use. Should match desired number of filter taps, defaults to zeros.
N : int
Number of iterations to run. Must be less than or equal to len(u)-M+1. Defaults to len(u)-M+1.
returnCoeffs : boolean
If true, will return all filter coefficients for every iteration in an N x M matrix. Does not include the initial coefficients. If false, only the latest coefficients in a vector of length M is returned. Defaults to false.
Returns
y : numpy.array
Output values of LMS filter, array of length N.
e : numpy.array
Error signal, i.e, d-y. Array of length N.
w : numpy.array
Final filter coefficients in array of length M if returnCoeffs is False. NxM array containing all filter coefficients for all iterations otherwise.
Raises
TypeError
If number of filter taps M is not type integer, number of iterations N is not type integer, or leakage leak is not type float/int.
ValueError
If number of iterations N is greater than len(u)-M, number of filter taps M is negative, or if step-size or leakage is outside specified range.

Helper Function Reference

mswe = mswe(w, v)

Calculate mean squared weigth error between estimated and true filter coefficients, in respect to iterations.

Parameters
v : array-like
True coefficients used to generate desired signal, must be a one-dimensional array.
w : array-like
Estimated coefficients from adaptive filtering algorithm. Must be an N x M matrix where N is the number of iterations, and M is the number of filter coefficients.
Returns
mswe : numpy.array
One-dimensional array containing the mean-squared weigth error for every iteration.
Raises
TypeError
If inputs have wrong dimensions
Note
To use this function with the adaptive filter functions set the optional parameter returnCoeffs to True. This will return a coefficient matrix w corresponding with the input-parameter w.

Examples

The following examples illustrate the use of the adaptfilt module. Note that the matplotlib.pyplot module is required by some of the examples.

"""
Convergence comparison of different adaptive filtering algorithms (with
different step sizes) in white Gaussian noise.
"""

import numpy as np
import matplotlib.pyplot as plt
import adaptfilt as adf

# Generating inpud and desired signal
N = 3000
coeffs = np.concatenate(([-4, 3.2], np.zeros(20), [0.7], np.zeros(33), [-0.1]))
u = np.random.randn(N)
d = np.convolve(u, coeffs)

# Perform filtering
M = 60  # No. of taps to estimate
mu1 = 0.0008  # Step size 1 in LMS
mu2 = 0.0004  # Step size 1 in LMS
beta1 = 0.08  # Step size 2 in NLMS and AP
beta2 = 0.04  # Step size 2 in NLMS and AP
K = 3  # Projection order 1 in AP

# LMS
y_lms1, e_lms1, w_lms1 = adf.lms(u, d, M, mu1, returnCoeffs=True)
y_lms2, e_lms2, w_lms2 = adf.lms(u, d, M, mu2, returnCoeffs=True)
mswe_lms1 = adf.mswe(w_lms1, coeffs)
mswe_lms2 = adf.mswe(w_lms2, coeffs)

# NLMS
y_nlms1, e_nlms1, w_nlms1 = adf.nlms(u, d, M, beta1, returnCoeffs=True)
y_nlms2, e_nlms2, w_nlms2 = adf.nlms(u, d, M, beta2, returnCoeffs=True)
mswe_nlms1 = adf.mswe(w_nlms1, coeffs)
mswe_nlms2 = adf.mswe(w_nlms2, coeffs)

# AP
y_ap1, e_ap1, w_ap1 = adf.ap(u, d, M, beta1, K, returnCoeffs=True)
y_ap2, e_ap2, w_ap2 = adf.ap(u, d, M, beta2, K, returnCoeffs=True)
mswe_ap1 = adf.mswe(w_ap1, coeffs)
mswe_ap2 = adf.mswe(w_ap2, coeffs)

# Plot results
plt.figure()
plt.title('Convergence comparison of different adaptive filtering algorithms')
plt.plot(mswe_lms1, 'b', label='MSWE for LMS with stepsize=%.4f' % mu1)
plt.plot(mswe_lms2, 'b--', label='MSWE for LMS with stepsize=%.4f' % mu2)
plt.plot(mswe_nlms1, 'g', label='MSWE for NLMS with stepsize=%.2f' % beta1)
plt.plot(mswe_nlms2, 'g--', label='MSWE for NLMS with stepsize=%.2f' % beta2)
plt.plot(mswe_ap1, 'r', label='MSWE for AP with stepsize=%.2f' % beta1)
plt.plot(mswe_ap2, 'r--', label='MSWE for AP with stepsize=%.2f' % beta2)
plt.legend()
plt.grid()
plt.xlabel('Iterations')
plt.ylabel('Mean-squared weight error')
plt.show()

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