How can I normalize data between 0 and 1 ? I want to use logsig...
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Platon on 13 May 2013
Answered: Abhijit Bhattacharjee on 25 May 2022
All is in the question: I want to use logsig as a transfer function for the hidden neurones so I have to normalize data between 0 and 1. The mapminmax function in NN tool box normalize data between -1 and 1 so it does not correspond to what I'm looking for.
José-Luis on 15 May 2013
bla = 100.*randn(1,10)
norm_data = (bla - min(bla)) / ( max(bla) - min(bla) )
Aviral Petwal on 22 Jun 2018
No need to denormalize the data. For your Test set also you can normalize the data with the same parameters and feed it to NN. If you trained on Normalised data just normalize your test set using same parameters and feed the data to NN.
More Answers (4)
Jurgen on 15 May 2013
NDATA = mat2gray(DATA);
Greg Heath on 8 Oct 2016
Edited: Greg Heath on 8 Oct 2016
Why not just try it and find out?
close all, clear all, clc
[ x1 , t1 ] = simplefit_dataset;
DATA1 = [ x1, t1 ];
DATA2 = [ x1; t1 ];
whos DATA1 DATA2
minmax1 = minmax(DATA1)
minmax2 = minmax(DATA2)
minmaxMTG1 = minmax( mat2gray(DATA1) )
minmaxMTG2 = minmax( mat2gray(DATA2) )
Hope this helps.
Abhijit Bhattacharjee on 25 May 2022
As of MATLAB R2018a, there is an easy one-liner command that can do this for you. It's called NORMALIZE.
Here is an example, where a denotes the vector of data:
a_normalized = normalize(a, 'range');
Greg Heath on 11 May 2017
Edited: Greg Heath on 11 May 2017
I like to calculate min, mean, std and max to detect outliers with standardized data (zero mean/unit variance). For normalization and denormalization I just let the training function use defaults
tansig and linear
however, if the ouput is naturally bounded use
tansig and tansig
tansig and logsig
In short, unless you are plotting you don't have to worry about anything except outliers.
Hope this helps.
Angus Steele on 20 Sep 2017
function [ newValue ] = math_scale_values( originalValue, minOriginalRange, maxOriginalRange, minNewRange, maxNewRange )
% Converts a value from one range into another
% (maxNewRange - minNewRange)(originalValue - minOriginalRange)
% y = ----------------------------------------------------------- + minNewRange
% (maxOriginalRange - minOriginalRange)
newValue = minNewRange + (((maxNewRange - minNewRange) * (originalValue - minOriginalRange))/(maxOriginalRange - minOriginalRange));
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