CODEWITHSUNDEEP
matlaboctave
Rocketq
Rocketq

Reputation: 5791

Octave error: no default value for argument 1 while calling function

I need to find normalized value for each value by column. So I have such function:

function [X_norm, mu, sigma] = featureNormalize(X)

X_norm = X;
mu = zeros(1, size(X, 2));
sigma = zeros(1, size(X, 2));

for x = 1:size(X, 1)
    mu(1,x) = mean(:, x)
    sigma(1,x) = std(:, x)
    for y = 1:size(X, 2)  
        X_norm(x,y) = (X(x,y) - mu)/sigma
    end  
end    

end

And I`m trying to call this function this way:

>> X = [23 32 2 ; 23 23 1];
>> featureNormalize(X)

As a result I get such error: no default value for argument 1 while calling function. I can't get what it's wrong here, any suggestions?

Upvotes: 0

Views: 1550

Answers (2)

mehmet
mehmet

Reputation: 1631

I think there is a problem in way how you use the mean() and std() functions and also maybe subscripted assignment is an issue too.

I am not sure but please try these codes below as your function definition:

1. Row based normalization:

function [X_norm, mu, sigma] = featureNormalize(X)

X_norm = zeros(size(X));
mu     = zeros(1, size(X, 1));
sigma  = zeros(1, size(X, 1));

for x = 1:size(X, 1)
    mu(x)    = mean( X(x, :) );
    sigma(x) = std( X(x, :) );

    for y = 1 : size(X, 2)  
        X_norm(x, y) = ( X(x, y) - mu(x) ) / sigma(x);
    end  
end     
end

2. Column based normalization:

function [X_norm, mu, sigma] = featureNormalize(X)

X_norm = zeros(size(X));
mu     = zeros(1, size(X, 2));
sigma  = zeros(1, size(X, 2));

for y = 1:size(X, 2)
    mu(y)    = mean( X(:, y) );
    sigma(y) = std( X(:, y) );

    for x = 1 : size(X, 1)  
        X_norm(x, y) = ( X(x, y) - mu(y) ) / sigma(y);
    end  
end     
end

Upvotes: 2

Divakar
Divakar

Reputation: 221754

If you are calculating normalized values for each element with respect to the mean and sigma values for each column, I think you need to make some changes as your problem code doesn't really reflect that because you are using an array of mean and sigma values. So, I think you need to do something like this instead -

%// Initialize array for final normalized values
X_norm = zeros(size(X));
for x = 1:size(X, 2)

    %// Store mean and standard deviation values for each column
    mu = mean(X(:, x))
    sigma = std(X(:, x))

    %// Use an innermost loop to calculate the normalized values 
    %// for all the elements in each column. 
    %// Thus, for all these elements, you need to use the same mean
    %// and standard deviation calculated in the previous step
    for y = 1:size(X, 1)  
        X_norm(y,x) = (X(y,x) - mu)/sigma
    end  
end

You can very easily vectorize the above code with bsxfun -

X_norm = bsxfun(@rdivide, bsxfun(@minus, X, mean(X,1)),std(X,[],1))

Upvotes: 2

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