# How can I normalize data between 0 and 1 ? I want to use logsig...

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Platon il 13 Mag 2013
Commentato: shazia il 10 Ago 2023
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.
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José-Luis il 15 Mag 2013
bla = 100.*randn(1,10)
norm_data = (bla - min(bla)) / ( max(bla) - min(bla) )
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José-Luis il 15 Mag 2013
Yes, provided you use the same normalization bounds (the min and max of both datasets). To rescale, please look at the below code.
bla = 100.*randn(1,10)
minVal = min(bla);
maxVal = max(bla);
norm_data = (bla - minVal) / ( maxVal - minVal )
your_original_data = minVal + norm_data.*(maxVal - minVal)
Aviral Petwal il 22 Giu 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.

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### Più risposte (4)

Jurgen il 15 Mag 2013
NDATA = mat2gray(DATA);
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JohnDylon il 8 Ott 2016
Is this exactly a min-max normalizaton?
Greg Heath il 8 Ott 2016
Modificato: Greg Heath il 8 Ott 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.
Greg

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Abhijit Bhattacharjee il 25 Mag 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');
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shazia il 10 Ago 2023
How about denormalization what comand should we use to denormalize after training to calculate the error. please guide

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Greg Heath il 11 Mag 2017
Modificato: Greg Heath il 11 Mag 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
or
tansig and logsig
In short, unless you are plotting you don't have to worry about anything except outliers.
Hope this helps.
Greg
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Angus Steele il 20 Set 2017
function [ newValue ] = math_scale_values( originalValue, minOriginalRange, maxOriginalRange, minNewRange, maxNewRange )
% MATH_SCALE_VALUES
% Converts a value from one range into another
% (maxNewRange - minNewRange)(originalValue - minOriginalRange)
% y = ----------------------------------------------------------- + minNewRange
% (maxOriginalRange - minOriginalRange)
newValue = minNewRange + (((maxNewRange - minNewRange) * (originalValue - minOriginalRange))/(maxOriginalRange - minOriginalRange));
end
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