I want to feature scale a matrix (X) with 2 columns. I am using mean normalization, and I wrote the following lines in Octave:
X_norm = X
mu = mean(X);
sigma = std(X);
X_norm(:,1) = (X_norm(:,1) .- mu(:,1)) ./ sigma(:,1);
X_norm(:,2) = (X_norm(:,2) .- mu(:,2)) ./ sigma(:,2);
Can you please let me know a cleaner way to vectorize these calculation?
I checked my code by comparing with the result from zscore(X) and they matched - i.e. a sum(X_norm - zscore(X)) returned me 0 0.
I am constrained to not use zscore(), and hence the question.
Sample data as follows:
2104 3
1600 3
2400 3
1416 2
3000 4
1985 4
1534 3
1427 3
1380 3
1494 3
1940 4
2000 3
1890 3
4478 5
1268 3
2300 4
1320 2
1236 3
2609 4
3031 4
1767 3
1888 2
1604 3
1962 4
3890 3
1100 3
1458 3
2526 3
2200 3
2637 3
You could simply do:
X_norm = (X .- mean(X,1)) ./ std(X,0,1);