You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: LmsFilter.m
    path of file: ./repos/StackExchangeCodes/SignalProcessing/Q59325
    
    the code of the file until where you have to start completion: function [ vW ] = LmsFilter( vW, vY, vD, paramL, numSamples, stepSize, normalizeMode )
% ----------------------------------------------------------------------------------------------- %
% [ vW ] = LmsFilter( vY, vD, stepSize, vW, paramN, paramM, paramL, normalizeMode )
% Applies the Least Mean Squares Adaptive Filter for optimal 'vW' weights
% given the reference signal vD.
% Input:
%   - vW                -   Filter Taps.
%                           The adaptive filter taps (To be updated).
%                           Structure: Vector (paramL x 1).
%                           Type: 'Single' / 'Double'.
%                           Range: (-inf, inf).
%   - vY                -   Input Signal.
%                           The signal to be filtered to by the adaptive
%                           filter to match the reference signal.
%                           Structure: Vector (numSamples x 1.
%                           Type: 'Single' / 'Double'.
%                           Range: (-inf, inf).
%   - vD                -   Reference Signal.
%                           The reference signal
%                           Structure: Vector (numSamples x 1).
%                           Type: 'Single' / 'Double'.
%                           Range: (-inf, inf).
%   - paramL            -   Number of Taps.
%                           Number of taps of the adaptive filter.
%                           Structure: Scalar.
%                           Type: 'Single' / 'Double'.

    
function [ vW ] = LmsFilter( vW, vY, vD, paramL, numSamples, stepSize, normalizeMode )
% ----------------------------------------------------------------------------------------------- %
% [ vW ] = LmsFilter( vY, vD, stepSize, vW, paramN, paramM, paramL, normalizeMode )
% Applies the Least Mean Squares Adaptive Filter for optimal 'vW' weights
% given the reference signal vD.
% Input:
%   - vW                -   Filter Taps.
%                           The adaptive filter taps (To be updated).
%                           Structure: Vector (paramL x 1).
%                           Type: 'Single' / 'Double'.
%                           Range: (-inf, inf).
%   - vY                -   Input Signal.
%                           The signal to be filtered to by the adaptive
%                           filter to match the reference signal.
%                           Structure: Vector (numSamples x 1.
%                           Type: 'Single' / 'Double'.
%                           Range: (-inf, inf).
%   - vD                -   Reference Signal.
%                           The reference signal
%                           Structure: Vector (numSamples x 1).
%                           Type: 'Single' / 'Double'.
%                           Range: (-inf, inf).
%   - paramL            -   Number of Taps.
%                           Number of taps of the adaptive filter.
%                           Structure: Scalar.
%                           Type: 'Single' / 'Double'.
%                           Range: {1, 2, 3, ...}.
%   - numSamples        -   Number of Samples.
%                           Number of samples of the input vector 'vY' and
%                           'vD'.
%                           Structure: Scalar.
%                           Type: 'Single' / 'Double'.
%                           Range: {1, 2, 3, ..., min(length(vY), length(vD)}.
%   - stepSize          -   Step Size
%                           The step size (paramMu) of the LMS filter.
%                           Basically the SGD step size.
%                           Structure: Scalar.
%                           Type: 'Single' / 'Double'.
%                           Range: (0, inf).
%   - normalizeMode     -   Normalize Mode.
%                           Normalizes the step size by the norm of the
%                           samples.
%                           Structure: Scalar.
%                           Type: 'Single' / 'Double'.
%                           Range: {0, 1} / {OFF, ON}.
% Output:
%   - vW                -   Filter Taps.
%                           The updated adaptive filter taps.
%                           Structure: Vector (paramL x 1).
%                           Type: 'Single' / 'Double'.
%                           Range: (-inf, inf).
% References:
%   1.  A
% Remarks:
%   1.  The values of the step size must be in the range (0, 2 / lambdaMax)
%       where lambdaMax is the maximum eigen value of the samples
%       covariance.
%   2.  In case of normalization it is usually will converge for stepSize
%       within the range [0, 2).
% TODO:
%   1.  C
% Release Notes:
%   -   1.0.000     07/08/2020  Royi Avital
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

% Predictor variant

FALSE   = 0;
TRUE    = 1;

OFF     = 0;
ON      = 1;

DELTA_PARAM = 1e-5;

% paramL %<! Order of the filter

for ii = paramL:numSamples
    vYn     = vY(ii:-1:(ii - paramL + 1));
    dSample = vD(ii);
    
    zSample = vW.' * vYn;
    
    eSample = dSample - zSample;
    
    if(normalizeMode == ON)
        vW(:) = vW + (stepSize * eSample * vYn);
    else
        vW(:) = vW + ((stepSize / ((vYn.' * vYn) + DELTA_PARAM)) * eSample * vYn);
    end