Determination of Standard Deviation and Means square Error

Can any one look in to the error I am getting from extracting the standard deviation and Mean Square Error of Matlab DTW of extracted features for fault signal and Steady state signals

[Florian Rössing]

Hey, not wanting to be rude, but you provided a lot of files, could you elaborate how we can reproduce your error? Or better give a minimal example to reproduce the error?

[john amoo otoo]

Florian, thanks for the response the files you actually need is the getmswtfeat.m and Processdata.m. The data file you need is John_FaultData0km0ohm.mat. Once you run it that shoud reproduce the error Standard Deviation and Variance attached

[Steven Lord]

Can you post the full and exact text of those warning and/or error messages (all the text displayed in orange and/or red in the Command Window) that you receive? Knowing that may make determining what's going on and how to avoid the warning and/or error much easier and quicker.

[john amoo otoo]

Steve,

Please attached is the error message

[Jeffrey Clark]

@john amoo otoo, @Florian Rössing, @Steven Lord the error is the same exact one you posted in Matrix Dimensional Error Index error. Please only post once for any given issue.

[john amoo otoo]

% GETMSWTFEAT Gets the Multiscale Wavelet Transform features, these

% include: Energy, Variance, Standard Deviation, and Waveform Length

% feat = getmswtfeat(x,winsize,wininc,SF)

% ------------------------------------------------------------------

% The signals in x are divided into multiple windows of size

% "winsize" and the windows are spaced "wininc" apart.

% Inputs

% ------

% signals: columns of signals

% winsize: window size (length of x)

% wininc: spacing of the windows (winsize)

% SF: sampling frequency

%

% Outputs

% -------

% =========================================================================

function feature_out = getmswtfeat(signals,winsize,wininc,SF)

if nargin < 4

if nargin < 3

if nargin < 2

error('A sliding window approach requires the window size (winsize) as input')

end

error('A sliding window approach requires the window increment (wininc) as input')

end

error('Please provide the sampling frequency of this signal')

end

%% The number of decomposition levels

decomOption = 1;

if decomOption==1

J=8; % Number of decomposition levels set manually here

elseif decomOption==2

J=wmaxlev(winsize,'sym2'); % Number of decomposition levels set based on window size and wavelet family

else

J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)

end

%% make sure you have some parameters pre-defined

% specify the number of samples

datasize = size(signals,1);

% based on the number of samples, winsize, and wininc, how many windows we

% will have? this is "numwin"

numwin = floor((datasize - winsize)/wininc)+1;

% how many signals (electrodes) are we processing

Nsignals = size(signals,2);

% how many features we plan to extract

%%

NF = 8;

% predefine zeros matrix to allocate memory for output features

%feature_out = zeros(numwin,(J+1)*NF*Nsignals);

feature_out = zeros(numwin,(J+1)*Nsignals);

for dims =1:Nsignals

x=signals(:,dims);

%% Chop the signal according to a sliding window approach

% allocate memory

feat = zeros(winsize,numwin);

st = 1;

en = winsize;

for i = 1:numwin

feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));

st = st + wininc;

en = en + wininc;

end

%% Multisignal one-dimensional wavelet transform decomposition

dec = mdwtdec('col',feat,J,'sym2');

% Proceed with Multisignal 1-D decomposition energy distribution

if isequal(dec.dirDec,'c')

dim = 1;

end

[cfs,longs] = wdec2cl(dec,'all');

level = length(longs)-2;

if dim==1

cfs = cfs';

longs = longs';

end

numOfSIGs = size(cfs,1);

num_CFS_TOT = size(cfs,2);

absCFS = abs(cfs);

absCFS0 = (cfs);

cfs_POW2 = absCFS.^2;

Energy = sum(cfs_POW2,2);

percentENER = zeros(size(cfs_POW2));

notZER = (Energy>0);

percentENER(notZER,:) = cfs_POW2(notZER,:);%./Energy(notZER,ones(1,num_CFS_TOT));

%% or try this version below and tell us which one is the best on your data

% percentENER(notZER,:) = cfs_POW2(notZER,:);

%% Pre-define and allocate memory

tab_ENER = zeros(numOfSIGs,level+1);

tab_VAR = zeros(numOfSIGs,level+1);

tab_STD = zeros(numOfSIGs,level+1);

tab_WL = zeros(numOfSIGs,level+1);

tab_entropy = zeros(numOfSIGs,level+1);

%% Feature extraction section

st = 1;

for k=1:level+1

nbCFS = longs(k);

en = st+nbCFS-1;

%tab_ENER(:,k) = sum(percentENER(:,st:en),2);% energy per waveform

%tab_VAR(:,k) = var(percentENER(:,st:en),0,2); % variance of coefficients

%tab_STD(:,k) = std(percentENER(:,st:en),[],2); % standard deviation of coefficients

%tab_WL(:,k) = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length

%percentENER(:,st:en) = percentENER(:,st:en)./repmat(sum(percentENER(:,st:en),2),1,size(percentENER(:,st:en),2));

prob = percentENER(:,st:en);%./repmat(sum(percentENER(:,st:en),2),1,longs(k)) + eps;

tab_entropy(:,k) = -sum(prob.*log(prob),2);%./size(percentENER(:,st:en),2);

st = en + 1;

end

%feature_out(:,(1:(NF*(J+1)))+(dims-1)*((J+1)*NF)) =log([tab_ENER tab_VAR tab_STD tab_WL tab_entropy]);

feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_entropy;

feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_STD;

feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_VAR;

feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_WL;

end

[john amoo otoo]

load('John_FaultData0km0ohm.mat')

%% does this signal has any NaNs, if so remove

SteadyStateNoneFaultState = rmmissing(SteadyStateNoneFaultState);

DataPG0km0hmsFaultData = rmmissing(DataPG0km0hmsFaultData);

SSTime = SteadyStateNoneFaultState.Time;

SSNFS = SteadyStateNoneFaultState.SteadyStateNoneFaultState;

DPTime = DataPG0km0hmsFaultData.Time;

DPFD = DataPG0km0hmsFaultData.DataPG0km0hmsFaultData;

%% Define the filtering and inspect

n = 8; %% defines window length

w = [-ones(n,1); ones(n,1)];

SSNFS = filter(w, n, SSNFS);

DPFD = filter(w, n, DPFD);

%% Normalize signals

med_training = prctile(SSNFS,50);

iqr_training = iqr(SSNFS);

SSNFS = (SSNFS-med_training)./iqr_training;

med_fault = prctile(DPFD,50);

iqr_fault = iqr(DPFD);

DPFD = (DPFD-med_fault)./iqr_fault;

% Plot & Observe the data

subplot(2,1,1)

plot(DPTime, DPFD)

title('filtered DataPG0km5hmsFaultData')

subplot(2,1,2)

plot(SSTime, SSNFS)

title('filtered SteadyStateNoneFaultState')

%% Let's observe the FFT power spectrum for differences

feat_fault = getmswtfeat(DPFD,32,16,100000);

feat_Good = getmswtfeat(SSNFS,32,16,100000);

[john amoo otoo]

Please posted is the script, how do I add teh script Tab standard deviation, tab mean square error and tab variance

[john amoo otoo]

%% Feature extraction section
st = 1;
for k=1:level+1
    nbCFS                = longs(k);
    en                   = st+nbCFS-1;
    %tab_ENER(:,k)        = sum(percentENER(:,st:en),2);% energy per waveform
    %tab_VAR(:,k)         = var(percentENER(:,st:en),0,2); % variance of coefficients
    %tab_STD(:,k)         = std(percentENER(:,st:en),[],2); % standard deviation of coefficients
    %tab_WL(:,k)          = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
    %percentENER(:,st:en) = percentENER(:,st:en)./repmat(sum(percentENER(:,st:en),2),1,size(percentENER(:,st:en),2));
    prob                 = percentENER(:,st:en);%./repmat(sum(percentENER(:,st:en),2),1,longs(k)) + eps;
    tab_entropy(:,k)     = -sum(prob.*log(prob),2);%./size(percentENER(:,st:en),2);
    tab_VAR(:,k)         = var(percentENER(:,st:en),0,2);% variance of coefficients
    tab_STD(:,k)         = std(percentENER(:,st:en),[],2);% standard deviation of coefficients
    tab_WL(:,k)          = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
    st = en + 1;
end
%feature_out(:,(1:(NF*(J+1)))+(dims-1)*((J+1)*NF)) =log([tab_ENER tab_VAR tab_STD tab_WL tab_entropy]);
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_entropy;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_STD;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_VAR;
feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_WL;
end

[john amoo otoo]

Please could you look into why Tab STD, tab_VAR values does not show up in the work space. See attached

[Walter Roberson]

Either you are looking at the wrong workspace, or else there is something in the code before that point that is ending the code early. Notice that the list of variables does not even include st that you set as the very first posted statement.

Remember that the variables that are set in a function typically become inaccessible after the function returns.

[john amoo otoo]

% GETMSWTFEAT Gets the Multiscale Wavelet Transform features, these
% include: Energy, Variance, Standard Deviation, and Waveform Length
% feat = getmswtfeat(x,winsize,wininc,SF)
% ------------------------------------------------------------------
% The signals in x are divided into multiple windows of size
% "winsize" and the windows are spaced "wininc" apart.
% Inputs
% ------
%    signals:	columns of signals
%    winsize:	window size (length of x)
%    wininc:	spacing of the windows (winsize)
%    SF:        sampling frequency
%
% Outputs
% -------
% =========================================================================
% REFERENCE: MATLAB CODE: Multi Scale Wavelet Decomposition: Dr. Rami Khushaba
% Email: [e-mail]
% URL: www.rami-khushaba.com (Matlab Code Section)
function feature_out = getmswtfeat(signals,winsize,wininc,SF)
if nargin < 4
    if nargin < 3
        if nargin < 2
            error('A sliding window approach requires the window size (winsize) as input')
        end
        error('A sliding window approach requires the window increment (wininc) as input')
    end
    error('Please provide the sampling frequency of this signal')
end
%% The number of decomposition levels 
decomOption = 1;
if decomOption==1
    J=8; % Number of decomposition levels set manually here
elseif decomOption==2
    J=wmaxlev(winsize,'db2'); % Number of decomposition levels set based on window size and wavelet family
else
    J=(log(SF/2)/log(2))-1; % Number of decomposition levels set based on sampling frequency (SF)
end
%% make sure you have some parameters pre-defined
% specify the number of samples
datasize = size(signals,1);
% based on the number of samples, winsize, and wininc, how many windows we
% will have? this is "numwin"
numwin = floor((datasize - winsize)/wininc)+1;
% how many signals (electrodes) are we processing
Nsignals = size(signals,2);
% how many features we plan to extract
%% 
NF = 8;
% predefine zeros matrix to allocate memory for output features
%feature_out = zeros(numwin,(J+1)*NF*Nsignals);
feature_out = zeros(numwin,(J+1)*Nsignals);
for dims =1:Nsignals
    x=signals(:,dims);
    %% Chop the signal according to a sliding window approach
    % allocate memory
    feat = zeros(winsize,numwin);
    st = 1;
    en = winsize;
    for i = 1:numwin
        feat(1:winsize,i) = x(st:en,:)-mean(x(st:en,:));
        st = st + wininc;
        en = en + wininc;
    end
    
    %% Multisignal one-dimensional wavelet transform decomposition
    dec = mdwtdec('col',feat,J,'sym8');
    % Proceed with Multisignal 1-D decomposition energy distribution
    
    if isequal(dec.dirDec,'c')
        dim = 1;
    end
    [cfs,longs] = wdec2cl(dec,'all');
    level = length(longs)-2;
    
    if dim==1
        cfs = cfs';
        longs = longs';
    end
    numOfSIGs             = size(cfs,1);
    num_CFS_TOT           = size(cfs,2);
    absCFS                = abs(cfs);
    absCFS0               = (cfs);
    cfs_POW2              = absCFS.^2;
    Energy                = sum(cfs_POW2,2);
    percentENER           = zeros(size(cfs_POW2));
    notZER                = (Energy>0);
    percentENER(notZER,:) = cfs_POW2(notZER,:);%./Energy(notZER,ones(1,num_CFS_TOT));
    
    %% or try this version below and tell us which  one is the best on your data
    % percentENER(notZER,:) = cfs_POW2(notZER,:);
    
    %% Pre-define and allocate memory
    tab_ENER    = zeros(numOfSIGs,level+1);
    tab_VAR     = zeros(numOfSIGs,level+1);
    tab_STD     = zeros(numOfSIGs,level+1);
    tab_WL      = zeros(numOfSIGs,level+1);
    tab_entropy = zeros(numOfSIGs,level+1);
    
    %% Feature extraction section
    st = 1;
    for k=1:level+1
        nbCFS                = longs(k);
        en                   = st+nbCFS-1;
        %tab_ENER(:,k)        = sum(percentENER(:,st:en),2);% energy per waveform
        %tab_VAR(:,k)         = var(percentENER(:,st:en),0,2); % variance of coefficients
        %tab_STD(:,k)         = std(percentENER(:,st:en),[],2); % standard deviation of coefficients
        %tab_WL(:,k)          = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
        %percentENER(:,st:en) = percentENER(:,st:en)./repmat(sum(percentENER(:,st:en),2),1,size(percentENER(:,st:en),2));
        prob                 = percentENER(:,st:en);%./repmat(sum(percentENER(:,st:en),2),1,longs(k)) + eps;
        tab_entropy(:,k)     = -sum(prob.*log(prob),2);%./size(percentENER(:,st:en),2);
        tab_VAR(:,k)         = var(percentENER(:,st:en),0,2);% variance of coefficients
        tab_STD(:,k)         = std(percentENER(:,st:en),[],2);% standard deviation of coefficients
        tab_WL(:,k)          = sum(abs(diff(percentENER(:,st:en)')))'; % waveform length
        st = en + 1;
    end
    %feature_out(:,(1:(NF*(J+1)))+(dims-1)*((J+1)*NF)) =log([tab_ENER tab_VAR tab_STD tab_WL tab_entropy]);
    feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_entropy;
    feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_STD;
    feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_VAR;
    feature_out(:,(1:((J+1)))+(dims-1)*(J+1)) =tab_WL;
end

[john amoo otoo]

walter, this is the full script

[Walter Roberson]

The first statement in your function after testing nargin is

decomOption = 1;

but the workspace you showed does not even have a variable decomOption in it. It does, however, have some variables that are not set in the function.

The implication is that the workspace you show is not the workspace for the function you are discussing.