Toshiaki Yokoda
and 3 contributors

NAME

DSP::LinPred_XS - Linear Prediction

SYNOPSIS

    use DSP::LinPred_XS;

    # OPTIONS
    # mu       : Step size of filter. (default = 0.001)
    #
    # h_length : Filter size. (default = 100)
    #
    # dc_init  : Initial DC bias.
    #            It *SHOULD* be set value *ACCURATELY* when dc_mode => 0.
    #            (default = 0)
    #
    # stddev_init : Initial value of stddev.
    #               (default = 1)
    #

    my $lp = DSP::LinPred_XS->new;

    # set filter
    $lp->set_filter({
                     mu => 0.001,
                     filter_length => 500,
                     est_mode => 1
                    });

    # defining signal x
    my $x = [0,0.1,0.5, ... ]; # input signal

    # Updating Filter
    $lp->update($x);
    my $current_error = $lp->current_error; # get error

    # Prediction
    my $pred_length = 10;
    my $pred = $lp->predict($pred_length);
    for( 0 .. $#$pred ){ print $pred->[$_], "\n"; }

DESCRIPTION

DSP::LinPred_XS is Linear Prediction by Least Mean Squared Algorithm. Implemented by XS.

This Linear Predicting method can estimate the standard deviation, direct current component, and predict future value of input.

METHODS

set_filter

set_filter method sets filter specifications to DSP::LinPred object.

    $lp->set_filter(
        {
            mu => $step_size, # <Num>
            filter_length => $filter_length, # <Int>
            dc_init => $initial_dc_bias, # <Num>
            stddev_init => $initial_stddev, # <Num>
            est_mode => $estimation_mode # <Int>, enable when 1
        });

update

update method updates filter state by source inputs are typed ArrayRef[Num].

    my $x = [0.13,0.3,-0.2,0.5,-0.07];
    $lp->update($x);

If you would like to extract the filter state, you can access member variable directly like below.

    my $filter = $lp->h;
    for( 0 .. $#$filter ){ print $filter->[$_], "\n"; }

predict

predict method generates predicted future values of inputs by filter.

    my $predicted = $lp->predict(7);
    for( 0 .. $#$predicted ){ print $predicted->[$_], "\n";}

filter_dc

This method can calculate mean value of current filter.

    my $filter_dc = $lp->filter_dc;

filter_stddev

This method can calculate standard deviation of current filter.

    my $filter_stddev = $lp->filter_stddev;

READING STATES

current_error

    # It returns value of current prediction error
    # error = Actual - Predicted
    my $current_error = $lp->current_error;
    print 'Current Error : '.$current_error, "\n";

h

    # It returns filter state(ArrayRef)
    my $filter = $lp->h;
    print "Filter state\n";
    for( 0 .. $#$filter ){ print $_.' : '.$filter->[$_],"\n"; }

x_count

    # It returns value of input counter used in filter updating.
    my $x_count = $lp->x_count;
    print 'Input count : '.$x_count, "\n";

dc

    # Get value of current Direct Current Components of inputs.
    my $dc = $lp->dc;
    print 'Current DC-Component : '.$dc, "\n";

stddev

    # Get value of current standard deviation of inputs.
    my $stddev = $lp->dc;
    print 'Current STDDEV : '.$stddev, "\n";

LICENSE

Copyright (C) Toshiaki Yokoda.

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.

AUTHOR

Toshiaki Yokoda <>