how to fix the error

function [X_norm, mu, sigma] = featureNormalize(x)
%FEATURENORMALIZE Normalizes the features in X 
%   FEATURENORMALIZE(X) returns a normalized version of X where
%   the mean value of each feature is 0 and the standard deviation
%   is 1. This is often a good preprocessing step to do when
%   working with learning algorithms.

% You need to set these values correctly
X_norm= x;
mu = zeros(1, size(x, 2));
sigma = zeros(1, size(x, 2));

% ====================== YOUR CODE HERE ======================
% Instructions: First, for each feature dimension, compute the mean
%               of the feature and subtract it from the dataset,
%               storing the mean value in mu. Next, compute the 
%               standard deviation of each feature and divide
%               each feature by it's standard deviation, storing
%               the standard deviation in sigma. 
%
%               Note that X is a matrix where each column is a 
%               feature and each row is an example. You need 
%               to perform the normalization separately for 
%               each feature. 
%
% Hint: You might find the 'mean' and 'std' functions useful.
%  


for  l=1:size(x,2) 
    mu=mean(x(:,l));
    sigma=std(x(:,l));
    X_norm=(x(:, l) - mu)./sigma;
    
    
end

[KSSV]

What is the error? You have not speciffied the error.

[Zoha Yaghmayi Benis]

Not enough input arguments.

Error in featureNormalize (line 9)

X_norm= x;

[Stephen23]

@Zoha Yaghmayi Benis: did you define the input x when you called the function?

Answer 1

[David Hill]

for  l=1:size(x,2) 
    mu(l)=mean(x(:,l));
    sigma(l)=std(x(:,l));
end
X_norm=(x-mu)./sigma;

Alternatively, no for-loop is needed.

mu=mean(x);
sigma=std(x);
X_norm=(x-mu)./sigma;