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I have a dataset(145 rows, 32 columns without id and attributes https://archive.ics.uci.edu/ml/datasets/Higher+Education+Students+Performance+Evaluation+Dataset ). I can read it in matlab with code. But I can't find the centered data matrix. I can find centered data matrix in another example.
D = randi([-5, 5] , 4,2] generate 8 numbers from -5 to 5 and I use size() ones() etc.
onesd = ones(size(D,1)1)
meand= mean(D)
D=onesd*meand then i find the centered data matrix
how can i write this dataset i have with centered data matrix

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Mustafa Furkan SAHIN
Mustafa Furkan SAHIN 2022년 12월 11일
clc
clear
dataset = xlsread('DATA.csv','DATA');
[m,n] = size(dataset)
Mean = mean(dataset)
Size = size(dataset, 1)
dataset2 = repmat(Mean,Size,1)
CenterMatrix = dataset - dataset2
Is this my code correct? Is such use acceptable?

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Torsten
Torsten 2022년 12월 11일
Ran in:

1 개 추천

Ran in:
D = randi([-5, 5],4,2)
D = 4×2
4 -1 -2 -5 -4 -1 -2 -3
Cn = eye(size(D,1))-1/size(D,1)*ones(size(D,1))
Cn = 4×4
0.7500 -0.2500 -0.2500 -0.2500 -0.2500 0.7500 -0.2500 -0.2500 -0.2500 -0.2500 0.7500 -0.2500 -0.2500 -0.2500 -0.2500 0.7500
D_centered = Cn*D
D_centered = 4×2
5.0000 1.5000 -1.0000 -2.5000 -3.0000 1.5000 -1.0000 -0.5000
mean(D_centered,1)
ans = 1×2
0 0

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Mustafa Furkan SAHIN
Mustafa Furkan SAHIN 2022년 12월 12일
편집: Mustafa Furkan SAHIN 2022년 12월 12일
thank you for answering but I couldn't figure out how to run this command for a 145 row 32 column dataset
edit:I understood the logic of the formula. I tried and found the answer for Higher Education Students Performance Evaluation Dataset
Image Analyst
Image Analyst 2022년 12월 12일
Ran in:
Ran in:
D = randi([-5, 5], 145, 32); % a 145 row 32 column dataset.
Cn = eye(size(D,1))-1/size(D,1)*ones(size(D,1));
D_centered = Cn*D;
results = mean(D_centered,1)
results = 1×32
1.0e-14 * 0.0515 -0.0809 -0.0784 -0.0557 0.0294 -0.0527 0.0760 0.0263 0.0760 0.0600 -0.0343 0.0012 0.0058 -0.0178 0.0662 -0.0025 -0.0196 0.0735 -0.1176 -0.0208 -0.0098 0.0184 -0.0061 0.0098 -0.1017 -0.0472 -0.0049 0.0319 0.0319 -0.0576
If you have any more questions, then attach your data and code to read it in with the paperclip icon after you read this:
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Mustafa Furkan SAHIN
Mustafa Furkan SAHIN 2022년 12월 12일
편집: Mustafa Furkan SAHIN 2022년 12월 12일
Thank you for answering.
Actually i mean understood how to apply a centered data matrix to the dataset(Higher Education Students Performance Evaluation Dataset) But I don't understand why we write between -5 and 5. Is it default?
Torsten
Torsten 2022년 12월 12일
편집: Torsten 2022년 12월 12일
But I don't understand why we write between -5 and 5. Is it default?
It's an example.
You wrote yourself that you tested it for D = randi([-5, 5] , 4,2].
Instead of the line
D = randi([-5, 5] , 4,2]
use your own data matrix from above as D.
Mustafa Furkan SAHIN
Mustafa Furkan SAHIN 2022년 12월 12일
I understood the logic of the formula. I tried and found the answer for Higher Education Students Performance Evaluation Dataset (145 rows and 32columns). Thanks Torsten
Mustafa Furkan SAHIN
Mustafa Furkan SAHIN 2022년 12월 13일
This is my file, I encountered an error
Torsten
Torsten 2022년 12월 13일
Ran in:
Ran in:
Works without problems (see below).
D=[2 2 3 3 1 2 2 1 1 1 1 2 3 1 2 5 3 2 2 1 1 1 1 1 3 2 1 2 1 1 1 1
2 2 3 3 1 2 2 1 1 1 2 3 2 1 2 1 2 2 2 1 1 1 1 1 3 2 3 2 2 3 1 1
2 2 2 3 2 2 2 2 4 2 2 2 2 1 2 1 2 1 2 1 1 1 1 1 2 2 1 1 2 2 1 1
1 1 1 3 1 2 1 2 1 2 1 2 5 1 2 1 3 1 2 1 1 1 1 2 3 2 2 1 3 2 1 1
2 2 1 3 2 2 1 3 1 4 3 3 2 1 2 4 2 1 1 1 1 1 2 1 2 2 2 1 2 2 1 1
2 2 2 3 2 2 2 2 1 1 3 3 2 1 2 3 1 1 2 1 1 1 1 1 1 2 1 2 4 4 1 2
1 2 2 4 2 2 2 1 1 3 1 3 1 1 2 4 2 2 2 2 1 2 1 1 3 3 3 3 4 4 1 5
1 1 2 3 1 1 1 2 2 3 4 3 1 1 4 3 1 2 2 1 1 1 3 1 3 2 2 1 1 1 1 2
2 1 3 3 2 1 1 1 1 3 2 4 2 1 2 4 1 2 2 1 1 1 1 1 3 2 2 2 4 3 1 5
2 1 2 3 2 2 1 3 4 2 1 2 3 1 2 3 2 2 2 1 1 2 1 1 2 2 2 2 1 2 1 0
1 1 1 3 2 2 2 3 2 3 3 4 2 1 3 2 1 1 1 1 1 2 1 1 2 2 2 2 1 1 1 2
1 1 1 4 1 1 2 4 2 3 5 5 1 1 3 2 3 3 3 1 3 1 3 2 3 1 3 3 4 3 1 0
1 1 1 4 2 2 2 1 1 1 3 5 4 2 2 2 3 2 2 1 1 1 1 1 2 2 2 3 4 2 1 0
2 1 2 5 2 2 2 1 1 1 2 2 2 1 2 3 1 2 1 1 1 2 1 1 3 2 3 3 4 2 1 1
3 2 2 4 1 1 2 3 4 2 3 1 2 1 4 1 2 2 2 1 1 1 1 1 2 3 2 1 4 4 1 2
2 2 2 3 2 2 2 1 1 2 4 4 2 1 3 1 2 2 2 2 3 2 1 1 3 2 2 3 2 2 1 2
1 1 2 5 2 1 2 1 1 1 2 2 4 1 2 3 2 2 2 1 1 2 1 1 3 2 3 3 4 3 1 1
2 2 2 3 2 2 2 1 1 1 2 2 2 1 2 1 2 2 2 2 1 2 1 1 2 2 2 2 2 2 1 2
1 1 2 4 2 2 2 3 1 1 2 2 5 1 2 5 5 3 2 2 1 2 1 1 3 1 3 3 3 3 1 2
1 2 1 3 2 2 1 2 2 2 3 3 3 1 2 4 4 2 2 1 2 1 1 1 3 2 2 3 2 3 1 3
1 2 2 5 1 2 1 1 4 2 3 3 3 2 2 3 4 2 2 2 1 1 1 2 3 1 2 3 4 4 1 1
1 2 2 5 2 2 1 1 4 2 2 2 4 1 2 3 3 2 2 1 1 1 1 1 3 1 3 3 3 3 1 1
2 2 2 3 1 2 1 1 1 1 1 1 4 2 2 4 3 3 3 1 1 1 1 2 3 1 2 3 3 3 1 3
3 2 2 2 1 1 2 5 1 1 1 4 3 1 2 1 3 2 3 1 1 2 1 1 3 2 3 3 3 3 1 1
2 2 2 3 2 2 2 2 1 1 3 1 3 1 2 4 1 2 2 1 1 1 1 1 2 1 3 2 4 4 1 2
2 2 2 3 2 2 1 1 1 2 1 4 3 1 2 4 2 2 2 1 1 1 1 1 2 1 3 2 1 2 1 3
2 2 2 3 2 1 1 1 1 1 4 4 4 1 2 4 2 3 2 1 1 1 1 1 3 3 3 2 2 1 1 1
1 2 1 3 1 2 2 1 1 1 1 1 4 1 2 3 3 2 2 1 1 2 1 1 3 1 2 1 2 1 1 1
3 2 2 3 2 2 1 1 4 2 2 2 4 1 2 1 2 2 2 1 1 1 1 1 3 2 3 3 5 4 1 3
2 2 3 4 2 2 2 1 4 2 3 3 2 1 4 1 2 2 2 1 1 1 1 1 3 2 2 2 4 3 1 5
2 2 2 5 1 1 1 1 1 2 1 1 5 1 2 4 2 2 2 1 1 1 2 1 2 3 3 3 5 4 1 5
3 2 2 3 1 2 2 1 1 2 1 2 4 1 2 3 2 2 3 1 1 1 1 1 3 3 2 3 4 3 1 3
2 1 2 3 2 2 2 2 1 1 2 2 2 1 2 1 2 2 2 1 1 2 1 1 2 2 2 1 2 3 1 1
2 1 2 3 1 2 1 1 1 1 1 2 5 1 2 3 2 1 2 1 1 1 1 1 1 3 2 2 2 3 1 2
1 2 1 3 2 2 1 2 1 1 3 3 3 1 2 4 2 1 1 1 1 2 1 1 2 2 3 1 4 1 1 2
1 2 1 4 2 2 2 3 1 1 1 2 5 1 2 3 2 2 2 1 1 2 1 1 2 1 3 1 1 1 1 1
2 2 3 4 1 2 1 3 1 1 1 1 2 2 4 3 1 1 3 1 1 2 2 1 2 1 2 1 4 3 1 2
2 2 2 3 1 1 1 2 2 3 3 3 1 1 2 3 2 2 2 1 1 1 1 1 3 2 3 3 5 4 1 1
2 2 2 5 2 2 2 1 1 1 3 3 1 1 2 4 3 2 2 1 1 1 2 1 2 2 2 2 4 3 1 2
2 1 2 3 2 2 1 1 1 2 1 3 1 1 2 4 1 2 1 1 1 1 1 1 2 2 2 3 2 1 1 1
1 2 1 3 2 2 2 2 1 1 2 3 1 1 2 5 1 2 2 1 1 1 1 1 3 1 2 1 2 3 1 1
3 2 2 3 1 2 2 2 1 2 1 4 2 1 2 2 3 2 2 1 1 2 1 1 3 2 2 3 4 3 1 1
2 2 2 3 2 1 2 1 4 2 4 4 2 1 3 1 2 2 1 1 1 1 1 1 2 1 3 1 2 3 1 1
1 2 2 3 2 2 1 1 1 1 2 3 3 1 2 1 2 2 2 1 1 2 1 1 3 1 3 3 3 2 1 4
2 2 3 3 2 2 1 1 1 1 1 3 2 1 2 5 2 2 2 1 1 1 2 1 3 2 2 1 4 3 1 1
1 2 2 3 2 2 1 4 1 1 2 3 5 1 2 4 3 2 2 1 1 1 1 1 2 2 2 1 4 3 1 3
2 2 2 3 2 2 1 1 1 1 1 2 3 1 2 5 2 2 2 1 1 1 1 1 2 2 3 1 4 2 1 5
2 2 2 3 2 2 1 1 1 2 4 3 1 1 5 4 2 2 2 1 1 2 1 1 2 1 3 2 5 3 1 3
1 2 2 3 2 1 1 1 1 2 3 3 3 1 2 2 1 1 2 1 1 1 1 1 3 2 3 2 2 2 1 1
1 2 1 4 2 2 2 2 4 1 2 3 2 1 2 4 2 2 2 1 1 1 2 1 2 2 2 1 3 2 1 2
2 2 2 3 2 2 1 2 2 1 1 1 2 1 2 3 2 2 2 1 1 2 1 1 2 2 2 3 3 3 1 1
2 1 3 3 1 1 2 1 1 1 1 2 5 1 2 1 1 2 2 1 1 2 1 1 2 3 3 1 3 3 1 4
2 1 2 3 1 2 2 2 1 1 1 3 4 1 2 1 3 1 1 1 1 1 1 2 2 2 2 1 4 2 1 1
2 1 2 3 2 1 2 2 1 1 1 1 5 1 2 1 1 2 2 1 1 2 1 1 2 2 2 2 3 2 1 5
2 2 2 3 2 2 2 3 4 2 1 4 2 1 4 2 2 1 1 1 1 1 1 2 3 1 3 1 5 3 1 3
3 2 2 3 1 2 1 4 1 2 1 1 5 3 2 1 1 2 2 1 1 1 3 3 1 3 3 3 5 4 1 3
2 2 2 3 2 1 2 1 1 1 1 3 5 1 2 4 4 2 3 1 1 1 1 2 3 2 3 3 5 4 1 5
2 2 2 3 1 1 2 1 1 1 4 2 3 1 3 3 2 3 2 1 1 1 1 1 3 2 3 1 5 4 1 4
3 2 2 3 2 2 1 3 1 1 1 3 5 2 2 1 3 2 2 1 1 1 1 1 3 2 2 2 5 4 1 3
2 2 2 3 2 1 1 1 1 1 4 4 3 1 2 3 3 2 2 1 1 1 1 1 3 3 2 2 4 3 1 5
2 1 2 3 2 2 2 5 2 1 6 1 3 1 2 3 1 2 2 1 1 1 1 1 1 3 3 1 2 1 1 2
1 2 3 3 2 1 2 2 1 2 1 3 4 1 2 3 3 1 2 1 3 2 1 1 3 2 2 2 4 4 1 5
2 2 2 3 2 2 2 1 4 2 2 3 2 1 2 4 3 2 2 1 1 1 1 1 2 3 3 2 5 4 1 3
2 2 2 4 2 2 1 2 1 1 1 3 3 1 2 1 1 1 2 1 1 1 3 1 2 2 2 1 3 3 1 5
2 2 3 5 2 2 2 1 1 1 2 2 2 1 2 1 1 2 2 1 3 1 2 1 3 2 3 1 4 3 1 3
1 2 2 3 2 2 1 3 1 1 2 4 3 1 2 4 1 1 2 1 3 2 2 1 2 1 2 1 2 2 1 2
2 2 2 3 2 2 1 1 1 1 3 2 5 1 2 5 2 2 2 1 1 1 1 1 3 2 2 3 5 4 2 5
2 2 3 3 1 1 2 3 1 2 3 4 4 2 2 2 2 2 2 1 1 2 1 1 2 2 1 2 2 3 2 1
2 1 2 4 1 2 2 1 1 1 2 3 5 3 2 5 1 2 2 1 1 2 1 1 2 2 3 2 4 3 3 5
2 1 2 4 2 2 1 1 1 1 2 2 2 1 2 3 1 1 2 1 1 1 2 1 3 2 3 1 3 2 3 5
1 2 2 4 2 1 1 1 1 1 4 3 1 2 4 2 2 2 2 1 1 1 1 1 2 2 3 1 5 4 3 7
1 1 3 4 2 2 2 1 1 3 1 3 5 1 2 4 2 2 1 1 1 1 1 1 2 3 3 3 2 2 3 6
1 2 2 3 2 1 1 1 1 2 1 1 2 1 2 3 2 3 3 1 1 1 1 1 2 2 3 2 3 3 3 6
2 2 2 4 2 2 2 1 1 2 1 3 4 1 2 4 3 1 1 2 1 1 1 2 3 2 3 1 5 3 3 6
1 2 2 4 2 2 2 1 1 1 2 2 3 1 2 1 2 2 2 2 1 1 2 1 3 2 3 2 3 3 3 7
1 2 2 4 2 1 2 1 1 3 1 5 4 1 2 2 3 3 1 1 1 1 1 1 2 2 2 2 4 1 3 7
2 2 1 2 2 1 2 2 1 1 5 4 3 1 4 3 2 3 2 1 1 1 3 2 2 1 2 3 2 2 4 4
1 2 1 2 2 2 1 2 2 2 6 5 2 1 4 2 2 2 2 2 1 2 3 1 2 2 2 2 1 1 4 7
2 1 2 4 1 1 2 1 1 1 1 3 4 1 2 4 2 3 3 1 1 2 1 1 3 3 2 2 2 2 4 4
2 2 2 4 2 2 2 1 1 1 1 3 4 1 2 4 1 2 2 1 3 1 1 1 3 3 3 2 4 4 4 3
2 1 2 3 1 2 1 1 2 1 1 1 2 1 2 3 1 1 1 1 3 1 1 1 2 3 1 2 4 2 5 4
3 2 2 3 1 2 2 1 1 2 1 2 4 1 2 3 2 2 2 1 1 1 1 1 3 3 2 3 5 4 5 3
2 2 2 4 1 2 1 2 1 1 3 1 3 1 2 3 2 2 3 1 1 1 1 1 3 3 2 3 4 3 5 7
2 2 3 3 2 2 2 3 1 1 4 4 2 1 1 2 1 2 2 1 1 1 3 1 3 2 3 3 1 2 5 7
3 2 3 3 1 2 1 3 1 2 4 2 5 3 2 1 1 2 2 1 1 1 3 3 3 3 3 3 5 4 5 7
1 2 2 5 2 2 2 2 1 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 3 2 3 3 4 3 5 4
2 2 2 4 2 2 2 1 4 2 3 3 2 1 4 1 2 2 2 1 1 1 1 1 3 2 3 2 5 4 5 5
2 2 2 3 2 1 2 2 1 1 3 4 2 1 1 1 2 2 2 1 1 1 2 1 2 2 2 2 1 1 6 6
1 2 2 4 2 1 1 1 1 1 4 4 1 1 3 2 2 2 2 1 1 1 1 2 3 1 2 3 5 3 6 6
2 2 2 3 2 2 2 1 1 1 3 5 3 1 2 2 2 2 2 1 1 2 1 1 3 1 3 3 3 2 6 6
2 1 2 3 2 1 1 1 2 3 3 3 1 1 4 2 2 2 2 1 1 1 1 1 2 3 2 3 4 2 6 6
2 2 2 5 1 1 1 1 1 2 4 1 5 1 2 3 2 2 2 1 1 1 1 1 2 3 3 2 5 4 6 6
1 2 2 3 2 2 2 1 1 1 3 4 4 1 3 2 3 2 2 1 1 1 1 1 3 2 3 3 2 2 6 7
1 2 2 1 1 2 1 1 1 2 1 2 4 1 2 3 5 2 1 1 1 1 1 1 3 3 3 2 2 2 6 4
2 2 2 3 2 2 1 1 1 1 3 2 5 1 2 5 2 2 3 1 1 1 1 1 3 2 2 3 5 4 6 6
1 2 3 5 1 1 1 1 2 3 3 4 1 1 2 1 2 2 3 1 1 1 1 1 3 2 2 1 4 3 7 5
1 2 2 4 2 1 1 1 2 3 2 2 3 1 4 2 2 3 2 1 1 1 1 1 3 2 3 2 2 3 7 7
1 2 2 4 1 2 2 1 1 3 3 3 4 1 2 3 2 2 2 1 1 1 1 1 3 2 2 1 3 3 7 6
1 2 2 4 2 1 2 1 4 2 2 4 2 1 2 5 2 3 2 1 1 1 1 1 3 1 2 1 4 4 7 7
2 2 2 3 2 2 2 1 4 2 3 4 2 1 2 1 3 2 2 1 1 1 1 1 3 2 3 2 2 2 7 7
1 2 2 4 2 2 2 1 2 3 4 1 2 1 3 3 2 2 2 1 1 1 1 1 2 2 2 1 3 3 7 6
1 2 2 4 2 2 1 2 1 3 2 3 1 1 2 4 2 2 2 1 1 1 1 1 3 3 2 1 2 3 7 7
1 2 2 3 2 2 1 1 1 2 3 1 1 2 4 4 2 2 2 2 1 1 1 1 3 1 2 1 3 4 7 7
1 2 1 4 2 2 1 5 1 2 1 3 4 1 2 1 2 3 2 1 1 1 1 1 2 2 3 2 4 4 7 7
1 1 2 3 2 2 2 1 2 2 4 4 1 1 3 2 2 2 2 1 1 1 1 1 2 3 2 2 1 1 7 3
1 2 2 4 1 1 2 1 1 1 2 2 4 1 2 4 2 3 2 1 1 2 1 2 2 1 3 1 3 3 7 7
1 2 2 4 2 1 2 1 1 2 2 2 1 1 2 1 3 2 2 1 1 1 3 2 2 2 2 1 4 4 7 7
1 1 2 4 1 1 2 1 1 3 1 3 5 1 2 4 2 2 2 1 1 1 2 1 2 3 2 1 4 2 7 6
2 1 1 5 2 1 2 2 2 1 1 2 1 3 1 5 3 3 2 1 1 2 1 1 2 3 1 2 3 3 7 6
1 2 1 3 2 1 1 1 2 2 4 4 2 3 2 5 3 2 2 1 1 1 2 2 3 2 3 1 4 4 7 7
2 2 2 3 2 2 2 2 4 2 4 4 2 2 1 5 2 2 2 1 1 2 1 1 2 2 2 1 2 3 8 2
1 1 1 5 2 1 2 1 1 2 3 3 1 1 3 2 1 2 2 2 2 1 1 1 3 1 3 3 4 3 8 2
2 1 3 3 1 2 2 1 3 3 1 1 2 1 2 1 1 1 1 2 3 1 2 1 3 3 3 1 4 2 8 2
2 1 3 3 2 2 2 1 4 2 4 4 1 1 3 2 2 2 3 2 3 2 2 1 2 2 1 1 1 2 8 1
2 1 2 5 1 1 2 4 1 1 1 1 1 1 2 1 2 2 2 2 2 1 1 1 2 2 1 2 1 1 8 2
2 1 2 5 1 2 1 1 1 1 1 1 2 1 2 2 3 3 2 2 3 1 2 2 3 2 3 2 2 2 8 1
2 1 2 5 2 2 2 1 1 1 1 1 5 1 2 4 3 3 3 2 1 1 1 1 3 3 3 1 2 3 8 1
3 1 1 3 2 1 2 1 1 2 4 4 1 1 1 3 2 2 3 2 1 2 1 1 2 3 2 1 2 3 8 1
1 2 2 5 2 1 1 1 4 2 1 2 3 1 4 4 1 2 2 1 1 1 3 1 3 3 2 2 3 3 8 1
2 1 2 4 2 1 2 1 1 2 3 3 2 3 2 5 5 2 2 1 1 1 1 2 2 2 3 1 3 3 8 2
2 1 1 3 1 1 1 2 2 3 3 3 2 1 3 4 1 1 1 1 1 2 2 1 3 3 3 2 2 2 8 1
2 1 2 3 1 1 1 1 2 1 1 1 5 3 2 4 5 3 3 1 3 1 2 1 1 1 1 1 1 1 8 0
1 2 2 5 2 1 2 1 1 1 3 3 2 1 2 1 2 1 2 1 2 1 2 1 3 2 2 1 4 3 8 2
2 1 3 3 2 2 2 3 1 1 3 2 4 1 1 1 2 2 3 2 1 1 1 1 3 3 2 1 1 1 8 1
1 1 2 4 1 1 1 1 1 3 2 3 2 1 4 3 4 2 2 2 1 1 2 2 3 3 2 1 3 3 9 3
1 1 2 5 1 1 2 1 1 3 1 1 5 1 2 3 3 2 3 2 1 1 1 2 3 2 3 1 1 3 9 2
1 1 1 4 1 1 1 3 2 3 4 6 2 1 4 3 4 2 3 1 1 1 2 1 3 3 3 1 2 2 9 3
1 1 2 4 2 2 2 1 4 3 3 2 2 1 3 4 2 1 1 2 1 1 1 1 3 2 2 1 2 2 9 1
1 1 2 4 2 1 1 1 4 3 3 4 2 1 4 2 2 1 1 2 1 1 1 1 3 2 2 1 2 2 9 0
1 1 2 3 1 1 2 1 1 1 1 1 3 1 2 4 4 2 3 2 1 1 2 2 3 2 3 2 2 3 9 3
1 1 2 3 1 1 2 1 1 1 1 2 3 1 2 4 4 2 2 2 1 1 1 2 3 1 3 2 2 3 9 1
1 1 1 5 2 1 2 1 2 2 4 3 1 1 3 2 4 1 3 2 1 1 1 2 3 2 3 1 5 3 9 4
1 1 1 5 2 1 2 1 1 2 1 2 3 1 2 1 2 2 3 2 1 1 2 1 3 2 3 1 5 4 9 3
1 1 2 5 2 2 1 1 1 1 1 1 1 1 2 3 3 1 2 2 1 1 1 1 3 3 3 2 5 3 9 3
1 1 2 4 2 1 2 1 2 3 1 1 2 1 4 3 2 2 2 2 1 1 1 1 2 1 2 1 2 3 9 1
2 1 2 3 1 1 2 1 4 2 3 3 1 1 2 1 2 3 2 1 3 1 1 1 3 3 2 1 3 2 9 2
1 1 2 3 1 1 1 1 2 3 3 3 3 1 3 4 2 2 2 2 1 1 3 1 3 2 2 1 2 2 9 0
1 1 1 5 2 1 2 1 1 1 2 2 2 1 2 1 2 2 2 1 1 1 1 1 3 1 3 1 2 4 9 2
1 1 2 4 1 1 1 5 2 3 3 1 1 2 4 5 2 2 3 2 3 1 2 1 3 2 3 1 1 3 9 0
1 1 2 4 1 2 1 2 2 3 3 3 1 1 5 4 1 1 2 2 1 2 2 1 2 3 2 1 1 2 9 0
2 1 2 3 1 1 2 1 1 2 1 2 2 2 2 4 3 3 2 1 1 1 1 1 2 1 2 1 3 3 9 5
1 1 2 4 2 2 2 1 4 2 1 1 5 1 2 1 3 2 2 2 1 2 1 1 3 2 2 1 5 3 9 5
1 1 1 4 2 2 2 1 1 1 3 4 4 1 2 4 2 2 2 1 1 1 1 1 3 3 2 1 4 3 9 1
2 1 2 4 1 1 1 5 2 3 4 4 1 1 3 3 2 2 1 1 1 1 2 1 2 1 2 1 5 3 9 4
1 1 1 5 2 2 2 3 1 1 3 1 5 1 2 4 3 1 1 1 1 1 2 1 3 2 3 1 5 4 9 3
];
size(D)
ans = 1×2
145 32
Cn = eye(size(D,1))-1/size(D,1)*ones(size(D,1));
D_centered = Cn*D
D_centered = 145×32
0.3793 0.4000 1.0552 -0.5724 -0.6621 0.4000 0.4207 -0.6276 -0.6207 -0.7310 -1.2828 -0.6345 0.1931 -0.1724 -0.3586 2.1931 0.8000 0.0552 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 0.4552 -0.0552 -1.3931 0.1931 -2.1241 -1.7241 0.3793 0.4000 1.0552 -0.5724 -0.6621 0.4000 0.4207 -0.6276 -0.6207 -0.7310 -0.2828 0.3655 -0.8069 -0.1724 -0.3586 -1.8069 -0.2000 0.0552 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 0.4552 -0.0552 0.6069 0.1931 -1.1241 0.2759 0.3793 0.4000 0.0552 -0.5724 0.3379 0.4000 0.4207 0.3724 2.3793 0.2690 -0.2828 -0.6345 -0.8069 -0.1724 -0.3586 -1.8069 -0.2000 -0.9448 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 -0.5448 -0.0552 -1.3931 -0.8069 -1.1241 -0.7241 -0.6207 -0.6000 -0.9448 -0.5724 -0.6621 0.4000 -0.5793 0.3724 -0.6207 0.2690 -1.2828 -0.6345 2.1931 -0.1724 -0.3586 -1.8069 0.8000 -0.9448 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 0.8345 0.4552 -0.0552 -0.3931 -0.8069 -0.1241 -0.7241 0.3793 0.4000 -0.9448 -0.5724 0.3379 0.4000 -0.5793 1.3724 -0.6207 2.2690 0.7172 0.3655 -0.8069 -0.1724 -0.3586 1.1931 -0.2000 -0.9448 -1.0138 -0.2138 -0.2069 -0.2414 0.6621 -0.1655 -0.5448 -0.0552 -0.3931 -0.8069 -1.1241 -0.7241 0.3793 0.4000 0.0552 -0.5724 0.3379 0.4000 0.4207 0.3724 -0.6207 -0.7310 0.7172 0.3655 -0.8069 -0.1724 -0.3586 0.1931 -1.2000 -0.9448 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 -1.5448 -0.0552 -1.3931 0.1931 0.8759 1.2759 -0.6207 0.4000 0.0552 0.4276 0.3379 0.4000 0.4207 -0.6276 -0.6207 1.2690 -1.2828 0.3655 -1.8069 -0.1724 -0.3586 1.1931 -0.2000 0.0552 -0.0138 0.7862 -0.2069 0.7586 -0.3379 -0.1655 0.4552 0.9448 0.6069 1.1931 0.8759 1.2759 -0.6207 -0.6000 0.0552 -0.5724 -0.6621 -0.6000 -0.5793 0.3724 0.3793 1.2690 1.7172 0.3655 -1.8069 -0.1724 1.6414 0.1931 -1.2000 0.0552 -0.0138 -0.2138 -0.2069 -0.2414 1.6621 -0.1655 0.4552 -0.0552 -0.3931 -0.8069 -2.1241 -1.7241 0.3793 -0.6000 1.0552 -0.5724 0.3379 -0.6000 -0.5793 -0.6276 -0.6207 1.2690 -0.2828 1.3655 -0.8069 -0.1724 -0.3586 1.1931 -1.2000 0.0552 -0.0138 -0.2138 -0.2069 -0.2414 -0.3379 -0.1655 0.4552 -0.0552 -0.3931 0.1931 0.8759 0.2759 0.3793 -0.6000 0.0552 -0.5724 0.3379 0.4000 -0.5793 1.3724 2.3793 0.2690 -1.2828 -0.6345 0.1931 -0.1724 -0.3586 0.1931 -0.2000 0.0552 -0.0138 -0.2138 -0.2069 0.7586 -0.3379 -0.1655 -0.5448 -0.0552 -0.3931 0.1931 -2.1241 -0.7241
results = mean(D_centered,1)
results = 1×32
1.0e-14 * -0.2090 -0.2439 -0.2494 -0.0298 -0.2616 -0.2421 -0.2315 -0.0441 -0.0300 -0.1562 -0.0937 -0.1495 0.0028 -0.0992 -0.2485 -0.0466 -0.1729 -0.2456 -0.2427 -0.1441 -0.0778 -0.1648 -0.1047 -0.1227 -0.1926 -0.2309 -0.2210 -0.1158 -0.0435 -0.1629
Mustafa Furkan SAHIN
Mustafa Furkan SAHIN 2022년 12월 13일
편집: Torsten 2022년 12월 13일
Ran in:
Ran in:
DATA=[2 2 3 3 1 2 2 1 1 1 1 2 3 1 2 5 3 2 2 1 1 1 1 1 3 2 1 2 1 1 1 1
2 2 3 3 1 2 2 1 1 1 2 3 2 1 2 1 2 2 2 1 1 1 1 1 3 2 3 2 2 3 1 1
2 2 2 3 2 2 2 2 4 2 2 2 2 1 2 1 2 1 2 1 1 1 1 1 2 2 1 1 2 2 1 1
1 1 1 3 1 2 1 2 1 2 1 2 5 1 2 1 3 1 2 1 1 1 1 2 3 2 2 1 3 2 1 1
2 2 1 3 2 2 1 3 1 4 3 3 2 1 2 4 2 1 1 1 1 1 2 1 2 2 2 1 2 2 1 1
2 2 2 3 2 2 2 2 1 1 3 3 2 1 2 3 1 1 2 1 1 1 1 1 1 2 1 2 4 4 1 2
1 2 2 4 2 2 2 1 1 3 1 3 1 1 2 4 2 2 2 2 1 2 1 1 3 3 3 3 4 4 1 5
1 1 2 3 1 1 1 2 2 3 4 3 1 1 4 3 1 2 2 1 1 1 3 1 3 2 2 1 1 1 1 2
2 1 3 3 2 1 1 1 1 3 2 4 2 1 2 4 1 2 2 1 1 1 1 1 3 2 2 2 4 3 1 5
2 1 2 3 2 2 1 3 4 2 1 2 3 1 2 3 2 2 2 1 1 2 1 1 2 2 2 2 1 2 1 0
1 1 1 3 2 2 2 3 2 3 3 4 2 1 3 2 1 1 1 1 1 2 1 1 2 2 2 2 1 1 1 2
1 1 1 4 1 1 2 4 2 3 5 5 1 1 3 2 3 3 3 1 3 1 3 2 3 1 3 3 4 3 1 0
1 1 1 4 2 2 2 1 1 1 3 5 4 2 2 2 3 2 2 1 1 1 1 1 2 2 2 3 4 2 1 0
2 1 2 5 2 2 2 1 1 1 2 2 2 1 2 3 1 2 1 1 1 2 1 1 3 2 3 3 4 2 1 1
3 2 2 4 1 1 2 3 4 2 3 1 2 1 4 1 2 2 2 1 1 1 1 1 2 3 2 1 4 4 1 2
2 2 2 3 2 2 2 1 1 2 4 4 2 1 3 1 2 2 2 2 3 2 1 1 3 2 2 3 2 2 1 2
1 1 2 5 2 1 2 1 1 1 2 2 4 1 2 3 2 2 2 1 1 2 1 1 3 2 3 3 4 3 1 1
2 2 2 3 2 2 2 1 1 1 2 2 2 1 2 1 2 2 2 2 1 2 1 1 2 2 2 2 2 2 1 2
1 1 2 4 2 2 2 3 1 1 2 2 5 1 2 5 5 3 2 2 1 2 1 1 3 1 3 3 3 3 1 2
1 2 1 3 2 2 1 2 2 2 3 3 3 1 2 4 4 2 2 1 2 1 1 1 3 2 2 3 2 3 1 3
1 2 2 5 1 2 1 1 4 2 3 3 3 2 2 3 4 2 2 2 1 1 1 2 3 1 2 3 4 4 1 1
1 2 2 5 2 2 1 1 4 2 2 2 4 1 2 3 3 2 2 1 1 1 1 1 3 1 3 3 3 3 1 1
2 2 2 3 1 2 1 1 1 1 1 1 4 2 2 4 3 3 3 1 1 1 1 2 3 1 2 3 3 3 1 3
3 2 2 2 1 1 2 5 1 1 1 4 3 1 2 1 3 2 3 1 1 2 1 1 3 2 3 3 3 3 1 1
2 2 2 3 2 2 2 2 1 1 3 1 3 1 2 4 1 2 2 1 1 1 1 1 2 1 3 2 4 4 1 2
2 2 2 3 2 2 1 1 1 2 1 4 3 1 2 4 2 2 2 1 1 1 1 1 2 1 3 2 1 2 1 3
2 2 2 3 2 1 1 1 1 1 4 4 4 1 2 4 2 3 2 1 1 1 1 1 3 3 3 2 2 1 1 1
1 2 1 3 1 2 2 1 1 1 1 1 4 1 2 3 3 2 2 1 1 2 1 1 3 1 2 1 2 1 1 1
3 2 2 3 2 2 1 1 4 2 2 2 4 1 2 1 2 2 2 1 1 1 1 1 3 2 3 3 5 4 1 3
2 2 3 4 2 2 2 1 4 2 3 3 2 1 4 1 2 2 2 1 1 1 1 1 3 2 2 2 4 3 1 5
2 2 2 5 1 1 1 1 1 2 1 1 5 1 2 4 2 2 2 1 1 1 2 1 2 3 3 3 5 4 1 5
3 2 2 3 1 2 2 1 1 2 1 2 4 1 2 3 2 2 3 1 1 1 1 1 3 3 2 3 4 3 1 3
2 1 2 3 2 2 2 2 1 1 2 2 2 1 2 1 2 2 2 1 1 2 1 1 2 2 2 1 2 3 1 1
2 1 2 3 1 2 1 1 1 1 1 2 5 1 2 3 2 1 2 1 1 1 1 1 1 3 2 2 2 3 1 2
1 2 1 3 2 2 1 2 1 1 3 3 3 1 2 4 2 1 1 1 1 2 1 1 2 2 3 1 4 1 1 2
1 2 1 4 2 2 2 3 1 1 1 2 5 1 2 3 2 2 2 1 1 2 1 1 2 1 3 1 1 1 1 1
2 2 3 4 1 2 1 3 1 1 1 1 2 2 4 3 1 1 3 1 1 2 2 1 2 1 2 1 4 3 1 2
2 2 2 3 1 1 1 2 2 3 3 3 1 1 2 3 2 2 2 1 1 1 1 1 3 2 3 3 5 4 1 1
2 2 2 5 2 2 2 1 1 1 3 3 1 1 2 4 3 2 2 1 1 1 2 1 2 2 2 2 4 3 1 2
2 1 2 3 2 2 1 1 1 2 1 3 1 1 2 4 1 2 1 1 1 1 1 1 2 2 2 3 2 1 1 1
1 2 1 3 2 2 2 2 1 1 2 3 1 1 2 5 1 2 2 1 1 1 1 1 3 1 2 1 2 3 1 1
3 2 2 3 1 2 2 2 1 2 1 4 2 1 2 2 3 2 2 1 1 2 1 1 3 2 2 3 4 3 1 1
2 2 2 3 2 1 2 1 4 2 4 4 2 1 3 1 2 2 1 1 1 1 1 1 2 1 3 1 2 3 1 1
1 2 2 3 2 2 1 1 1 1 2 3 3 1 2 1 2 2 2 1 1 2 1 1 3 1 3 3 3 2 1 4
2 2 3 3 2 2 1 1 1 1 1 3 2 1 2 5 2 2 2 1 1 1 2 1 3 2 2 1 4 3 1 1
1 2 2 3 2 2 1 4 1 1 2 3 5 1 2 4 3 2 2 1 1 1 1 1 2 2 2 1 4 3 1 3
2 2 2 3 2 2 1 1 1 1 1 2 3 1 2 5 2 2 2 1 1 1 1 1 2 2 3 1 4 2 1 5
2 2 2 3 2 2 1 1 1 2 4 3 1 1 5 4 2 2 2 1 1 2 1 1 2 1 3 2 5 3 1 3
1 2 2 3 2 1 1 1 1 2 3 3 3 1 2 2 1 1 2 1 1 1 1 1 3 2 3 2 2 2 1 1
1 2 1 4 2 2 2 2 4 1 2 3 2 1 2 4 2 2 2 1 1 1 2 1 2 2 2 1 3 2 1 2
2 2 2 3 2 2 1 2 2 1 1 1 2 1 2 3 2 2 2 1 1 2 1 1 2 2 2 3 3 3 1 1
2 1 3 3 1 1 2 1 1 1 1 2 5 1 2 1 1 2 2 1 1 2 1 1 2 3 3 1 3 3 1 4
2 1 2 3 1 2 2 2 1 1 1 3 4 1 2 1 3 1 1 1 1 1 1 2 2 2 2 1 4 2 1 1
2 1 2 3 2 1 2 2 1 1 1 1 5 1 2 1 1 2 2 1 1 2 1 1 2 2 2 2 3 2 1 5
2 2 2 3 2 2 2 3 4 2 1 4 2 1 4 2 2 1 1 1 1 1 1 2 3 1 3 1 5 3 1 3
3 2 2 3 1 2 1 4 1 2 1 1 5 3 2 1 1 2 2 1 1 1 3 3 1 3 3 3 5 4 1 3
2 2 2 3 2 1 2 1 1 1 1 3 5 1 2 4 4 2 3 1 1 1 1 2 3 2 3 3 5 4 1 5
2 2 2 3 1 1 2 1 1 1 4 2 3 1 3 3 2 3 2 1 1 1 1 1 3 2 3 1 5 4 1 4
3 2 2 3 2 2 1 3 1 1 1 3 5 2 2 1 3 2 2 1 1 1 1 1 3 2 2 2 5 4 1 3
2 2 2 3 2 1 1 1 1 1 4 4 3 1 2 3 3 2 2 1 1 1 1 1 3 3 2 2 4 3 1 5
2 1 2 3 2 2 2 5 2 1 6 1 3 1 2 3 1 2 2 1 1 1 1 1 1 3 3 1 2 1 1 2
1 2 3 3 2 1 2 2 1 2 1 3 4 1 2 3 3 1 2 1 3 2 1 1 3 2 2 2 4 4 1 5
2 2 2 3 2 2 2 1 4 2 2 3 2 1 2 4 3 2 2 1 1 1 1 1 2 3 3 2 5 4 1 3
2 2 2 4 2 2 1 2 1 1 1 3 3 1 2 1 1 1 2 1 1 1 3 1 2 2 2 1 3 3 1 5
2 2 3 5 2 2 2 1 1 1 2 2 2 1 2 1 1 2 2 1 3 1 2 1 3 2 3 1 4 3 1 3
1 2 2 3 2 2 1 3 1 1 2 4 3 1 2 4 1 1 2 1 3 2 2 1 2 1 2 1 2 2 1 2
2 2 2 3 2 2 1 1 1 1 3 2 5 1 2 5 2 2 2 1 1 1 1 1 3 2 2 3 5 4 2 5
2 2 3 3 1 1 2 3 1 2 3 4 4 2 2 2 2 2 2 1 1 2 1 1 2 2 1 2 2 3 2 1
2 1 2 4 1 2 2 1 1 1 2 3 5 3 2 5 1 2 2 1 1 2 1 1 2 2 3 2 4 3 3 5
2 1 2 4 2 2 1 1 1 1 2 2 2 1 2 3 1 1 2 1 1 1 2 1 3 2 3 1 3 2 3 5
1 2 2 4 2 1 1 1 1 1 4 3 1 2 4 2 2 2 2 1 1 1 1 1 2 2 3 1 5 4 3 7
1 1 3 4 2 2 2 1 1 3 1 3 5 1 2 4 2 2 1 1 1 1 1 1 2 3 3 3 2 2 3 6
1 2 2 3 2 1 1 1 1 2 1 1 2 1 2 3 2 3 3 1 1 1 1 1 2 2 3 2 3 3 3 6
2 2 2 4 2 2 2 1 1 2 1 3 4 1 2 4 3 1 1 2 1 1 1 2 3 2 3 1 5 3 3 6
1 2 2 4 2 2 2 1 1 1 2 2 3 1 2 1 2 2 2 2 1 1 2 1 3 2 3 2 3 3 3 7
1 2 2 4 2 1 2 1 1 3 1 5 4 1 2 2 3 3 1 1 1 1 1 1 2 2 2 2 4 1 3 7
2 2 1 2 2 1 2 2 1 1 5 4 3 1 4 3 2 3 2 1 1 1 3 2 2 1 2 3 2 2 4 4
1 2 1 2 2 2 1 2 2 2 6 5 2 1 4 2 2 2 2 2 1 2 3 1 2 2 2 2 1 1 4 7
2 1 2 4 1 1 2 1 1 1 1 3 4 1 2 4 2 3 3 1 1 2 1 1 3 3 2 2 2 2 4 4
2 2 2 4 2 2 2 1 1 1 1 3 4 1 2 4 1 2 2 1 3 1 1 1 3 3 3 2 4 4 4 3
2 1 2 3 1 2 1 1 2 1 1 1 2 1 2 3 1 1 1 1 3 1 1 1 2 3 1 2 4 2 5 4
3 2 2 3 1 2 2 1 1 2 1 2 4 1 2 3 2 2 2 1 1 1 1 1 3 3 2 3 5 4 5 3
2 2 2 4 1 2 1 2 1 1 3 1 3 1 2 3 2 2 3 1 1 1 1 1 3 3 2 3 4 3 5 7
2 2 3 3 2 2 2 3 1 1 4 4 2 1 1 2 1 2 2 1 1 1 3 1 3 2 3 3 1 2 5 7
3 2 3 3 1 2 1 3 1 2 4 2 5 3 2 1 1 2 2 1 1 1 3 3 3 3 3 3 5 4 5 7
1 2 2 5 2 2 2 2 1 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 3 2 3 3 4 3 5 4
2 2 2 4 2 2 2 1 4 2 3 3 2 1 4 1 2 2 2 1 1 1 1 1 3 2 3 2 5 4 5 5
2 2 2 3 2 1 2 2 1 1 3 4 2 1 1 1 2 2 2 1 1 1 2 1 2 2 2 2 1 1 6 6
1 2 2 4 2 1 1 1 1 1 4 4 1 1 3 2 2 2 2 1 1 1 1 2 3 1 2 3 5 3 6 6
2 2 2 3 2 2 2 1 1 1 3 5 3 1 2 2 2 2 2 1 1 2 1 1 3 1 3 3 3 2 6 6
2 1 2 3 2 1 1 1 2 3 3 3 1 1 4 2 2 2 2 1 1 1 1 1 2 3 2 3 4 2 6 6
2 2 2 5 1 1 1 1 1 2 4 1 5 1 2 3 2 2 2 1 1 1 1 1 2 3 3 2 5 4 6 6
1 2 2 3 2 2 2 1 1 1 3 4 4 1 3 2 3 2 2 1 1 1 1 1 3 2 3 3 2 2 6 7
1 2 2 1 1 2 1 1 1 2 1 2 4 1 2 3 5 2 1 1 1 1 1 1 3 3 3 2 2 2 6 4
2 2 2 3 2 2 1 1 1 1 3 2 5 1 2 5 2 2 3 1 1 1 1 1 3 2 2 3 5 4 6 6
1 2 3 5 1 1 1 1 2 3 3 4 1 1 2 1 2 2 3 1 1 1 1 1 3 2 2 1 4 3 7 5
1 2 2 4 2 1 1 1 2 3 2 2 3 1 4 2 2 3 2 1 1 1 1 1 3 2 3 2 2 3 7 7
1 2 2 4 1 2 2 1 1 3 3 3 4 1 2 3 2 2 2 1 1 1 1 1 3 2 2 1 3 3 7 6
1 2 2 4 2 1 2 1 4 2 2 4 2 1 2 5 2 3 2 1 1 1 1 1 3 1 2 1 4 4 7 7
2 2 2 3 2 2 2 1 4 2 3 4 2 1 2 1 3 2 2 1 1 1 1 1 3 2 3 2 2 2 7 7
1 2 2 4 2 2 2 1 2 3 4 1 2 1 3 3 2 2 2 1 1 1 1 1 2 2 2 1 3 3 7 6
1 2 2 4 2 2 1 2 1 3 2 3 1 1 2 4 2 2 2 1 1 1 1 1 3 3 2 1 2 3 7 7
1 2 2 3 2 2 1 1 1 2 3 1 1 2 4 4 2 2 2 2 1 1 1 1 3 1 2 1 3 4 7 7
1 2 1 4 2 2 1 5 1 2 1 3 4 1 2 1 2 3 2 1 1 1 1 1 2 2 3 2 4 4 7 7
1 1 2 3 2 2 2 1 2 2 4 4 1 1 3 2 2 2 2 1 1 1 1 1 2 3 2 2 1 1 7 3
1 2 2 4 1 1 2 1 1 1 2 2 4 1 2 4 2 3 2 1 1 2 1 2 2 1 3 1 3 3 7 7
1 2 2 4 2 1 2 1 1 2 2 2 1 1 2 1 3 2 2 1 1 1 3 2 2 2 2 1 4 4 7 7
1 1 2 4 1 1 2 1 1 3 1 3 5 1 2 4 2 2 2 1 1 1 2 1 2 3 2 1 4 2 7 6
2 1 1 5 2 1 2 2 2 1 1 2 1 3 1 5 3 3 2 1 1 2 1 1 2 3 1 2 3 3 7 6
1 2 1 3 2 1 1 1 2 2 4 4 2 3 2 5 3 2 2 1 1 1 2 2 3 2 3 1 4 4 7 7
2 2 2 3 2 2 2 2 4 2 4 4 2 2 1 5 2 2 2 1 1 2 1 1 2 2 2 1 2 3 8 2
1 1 1 5 2 1 2 1 1 2 3 3 1 1 3 2 1 2 2 2 2 1 1 1 3 1 3 3 4 3 8 2
2 1 3 3 1 2 2 1 3 3 1 1 2 1 2 1 1 1 1 2 3 1 2 1 3 3 3 1 4 2 8 2
2 1 3 3 2 2 2 1 4 2 4 4 1 1 3 2 2 2 3 2 3 2 2 1 2 2 1 1 1 2 8 1
2 1 2 5 1 1 2 4 1 1 1 1 1 1 2 1 2 2 2 2 2 1 1 1 2 2 1 2 1 1 8 2
2 1 2 5 1 2 1 1 1 1 1 1 2 1 2 2 3 3 2 2 3 1 2 2 3 2 3 2 2 2 8 1
2 1 2 5 2 2 2 1 1 1 1 1 5 1 2 4 3 3 3 2 1 1 1 1 3 3 3 1 2 3 8 1
3 1 1 3 2 1 2 1 1 2 4 4 1 1 1 3 2 2 3 2 1 2 1 1 2 3 2 1 2 3 8 1
1 2 2 5 2 1 1 1 4 2 1 2 3 1 4 4 1 2 2 1 1 1 3 1 3 3 2 2 3 3 8 1
2 1 2 4 2 1 2 1 1 2 3 3 2 3 2 5 5 2 2 1 1 1 1 2 2 2 3 1 3 3 8 2
2 1 1 3 1 1 1 2 2 3 3 3 2 1 3 4 1 1 1 1 1 2 2 1 3 3 3 2 2 2 8 1
2 1 2 3 1 1 1 1 2 1 1 1 5 3 2 4 5 3 3 1 3 1 2 1 1 1 1 1 1 1 8 0
1 2 2 5 2 1 2 1 1 1 3 3 2 1 2 1 2 1 2 1 2 1 2 1 3 2 2 1 4 3 8 2
2 1 3 3 2 2 2 3 1 1 3 2 4 1 1 1 2 2 3 2 1 1 1 1 3 3 2 1 1 1 8 1
1 1 2 4 1 1 1 1 1 3 2 3 2 1 4 3 4 2 2 2 1 1 2 2 3 3 2 1 3 3 9 3
1 1 2 5 1 1 2 1 1 3 1 1 5 1 2 3 3 2 3 2 1 1 1 2 3 2 3 1 1 3 9 2
1 1 1 4 1 1 1 3 2 3 4 6 2 1 4 3 4 2 3 1 1 1 2 1 3 3 3 1 2 2 9 3
1 1 2 4 2 2 2 1 4 3 3 2 2 1 3 4 2 1 1 2 1 1 1 1 3 2 2 1 2 2 9 1
1 1 2 4 2 1 1 1 4 3 3 4 2 1 4 2 2 1 1 2 1 1 1 1 3 2 2 1 2 2 9 0
1 1 2 3 1 1 2 1 1 1 1 1 3 1 2 4 4 2 3 2 1 1 2 2 3 2 3 2 2 3 9 3
1 1 2 3 1 1 2 1 1 1 1 2 3 1 2 4 4 2 2 2 1 1 1 2 3 1 3 2 2 3 9 1
1 1 1 5 2 1 2 1 2 2 4 3 1 1 3 2 4 1 3 2 1 1 1 2 3 2 3 1 5 3 9 4
1 1 1 5 2 1 2 1 1 2 1 2 3 1 2 1 2 2 3 2 1 1 2 1 3 2 3 1 5 4 9 3
1 1 2 5 2 2 1 1 1 1 1 1 1 1 2 3 3 1 2 2 1 1 1 1 3 3 3 2 5 3 9 3
1 1 2 4 2 1 2 1 2 3 1 1 2 1 4 3 2 2 2 2 1 1 1 1 2 1 2 1 2 3 9 1
2 1 2 3 1 1 2 1 4 2 3 3 1 1 2 1 2 3 2 1 3 1 1 1 3 3 2 1 3 2 9 2
1 1 2 3 1 1 1 1 2 3 3 3 3 1 3 4 2 2 2 2 1 1 3 1 3 2 2 1 2 2 9 0
1 1 1 5 2 1 2 1 1 1 2 2 2 1 2 1 2 2 2 1 1 1 1 1 3 1 3 1 2 4 9 2
1 1 2 4 1 1 1 5 2 3 3 1 1 2 4 5 2 2 3 2 3 1 2 1 3 2 3 1 1 3 9 0
1 1 2 4 1 2 1 2 2 3 3 3 1 1 5 4 1 1 2 2 1 2 2 1 2 3 2 1 1 2 9 0
2 1 2 3 1 1 2 1 1 2 1 2 2 2 2 4 3 3 2 1 1 1 1 1 2 1 2 1 3 3 9 5
1 1 2 4 2 2 2 1 4 2 1 1 5 1 2 1 3 2 2 2 1 2 1 1 3 2 2 1 5 3 9 5
1 1 1 4 2 2 2 1 1 1 3 4 4 1 2 4 2 2 2 1 1 1 1 1 3 3 2 1 4 3 9 1
2 1 2 4 1 1 1 5 2 3 4 4 1 1 3 3 2 2 1 1 1 1 2 1 2 1 2 1 5 3 9 4
1 1 1 5 2 2 2 3 1 1 3 1 5 1 2 4 3 1 1 1 1 1 2 1 3 2 3 1 5 4 9 3
];
DATA(:,32)=[]; %I took out the output note because I'm trying to find centered data matrix
meanDATA=mean(DATA); %I got mean from the data set
DATAnorm=DATA-meanDATA; %then I subtracted it from the data
newmean=mean(DATAnorm); %again I need to take mean of the new dataset called DATAnorm
scatter(DATA,meanDATA,'red');
hold on
title('Blue is scatter of centered data matrix','Purple is old data matrix');
scatter(DATAnorm,newmean,'blue');
hold off
so is this code correct?
Torsten
Torsten 2022년 12월 13일
Yes, it's correct. See above.
Mustafa Furkan SAHIN
Mustafa Furkan SAHIN 2022년 12월 13일
That's great. Thanks for taking the time,Torsten

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추가 답변 (1개)

Steven Lord
Steven Lord 2022년 12월 12일

0 개 추천

I would use the normalize function or the Normalize Data task in Live Editor.

댓글 수: 2
없음 표시 없음 숨기기

Torsten
Torsten 2022년 12월 12일
It's only asked for mean 0, not standard deviation 1. Is this also possible with "normalize" ?
Steven Lord
Steven Lord 2022년 12월 12일
Ran in:
Ran in:
The normalize function can center without scaling:
format longg
x = rand(1, 10);
n = normalize(x, 'center');
[mean(n), std(n)]
ans = 1×2
-1.11022302462516e-17 0.266647564148699
scale without centering:
s = normalize(x, 'scale');
[mean(s), std(s)]
ans = 1×2
2.19316158923735 1
or both center and scale.
z = normalize(x); % 'zscore' is the default
[mean(z), std(z)]
ans = 1×2
-5.55111512312578e-18 1
There are other normalization options as well.

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