how to fix the error

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Zoha Yaghmayi Benis
回答： David Hill 2022-2-28
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
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Zoha Yaghmayi Benis
Zoha Yaghmayi Benis 2022-2-28
Not enough input arguments.
Error in featureNormalize (line 9)
X_norm= x;
Stephen23
Stephen23 2022-2-28
@Zoha Yaghmayi Benis: did you define the input x when you called the function?

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回答（1 个）

David Hill
David Hill 2022-2-28
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;

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