I am trying to normalize all rows of my matrix data at once within range 0 and 1. But I don't know how to do it.. For example, I want to normalize each "obs1", "obs2", "obs3". Thus, minimum, maximum, and sum of each "obs1", "obs2", "obs3" will be used. My data format is,
`mydata
a b c d e
obs1 8.15609 11.5379 11.1401 8.95186 7.95722
obs2 339.89800 856.3470 691.3490 590.28600 543.67200
obs3 2.12776 46.4561 136.8860 118.09100 119.86400
`
Also, When I searched to perform this, people used "function()". When/for what does this used?
Thank you very much for your help in advance! :)
Normalize data in a vector and matrix by computing the z-score. Create a vector v and compute the z-score, normalizing the data to have mean 0 and standard deviation 1. Create a matrix B and compute the z-score for each column. Then, normalize each row.
normalized_M = normr( M ) takes a single matrix or cell array of matrices, M , and returns the matrices with rows normalized to a length of one.
To normalize for each row, you can use apply
and then subtract the minimum from each column and divide by the difference between maximum and minimum:
t(apply(mydata, 1, function(x)(x-min(x))/(max(x)-min(x))))
gives you
a b c d e
obs1 0.05553973 1.0000000 0.8889038 0.2777796 0.0000000
obs2 0.00000000 1.0000000 0.6805144 0.4848262 0.3945675
obs3 0.00000000 0.3289472 1.0000000 0.8605280 0.8736849
What happens is that you apply the function
function(x){
(x-min(x))/(max(x)-min(x))
}
to each row of your data frame.
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