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

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Platon
Platon 2013-5-13
评论： shazia 2023-8-10
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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采纳的回答

José-Luis
José-Luis 2013-5-15
bla = 100.*randn(1,10)
norm_data = (bla - min(bla)) / ( max(bla) - min(bla) )
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显示 1更早的评论
José-Luis
José-Luis 2013-5-15
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
Aviral Petwal 2018-6-22
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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更多回答（4 个）

Jurgen
Jurgen 2013-5-15
NDATA = mat2gray(DATA);
  2 个评论
Greg Heath
Greg Heath 2016-10-8
编辑：Greg Heath 2016-10-8
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
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
shazia 2023-8-10
How about denormalization what comand should we use to denormalize after training to calculate the error. please guide

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Greg Heath
Greg Heath 2017-5-11
编辑：Greg Heath 2017-5-11
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
Angus Steele
Angus Steele 2017-9-20
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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