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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Data Analysis - Matlab Code
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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
1 件のコメント
採用された回答
Torsten
2022 年 12 月 11 日
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
10 件のコメント
Mustafa Furkan SAHIN
2022 年 12 月 12 日
編集済み: Mustafa Furkan SAHIN
2022 年 12 月 12 日
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
2022 年 12 月 12 日
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:
Mustafa Furkan SAHIN
2022 年 12 月 12 日
編集済み: Mustafa Furkan SAHIN
2022 年 12 月 12 日
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?
Mustafa Furkan SAHIN
2022 年 12 月 12 日
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
Torsten
2022 年 12 月 13 日
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
2022 年 12 月 13 日
編集済み: Torsten
2022 年 12 月 13 日
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?
その他の回答 (1 件)
Steven Lord
2022 年 12 月 12 日
2 件のコメント
Torsten
2022 年 12 月 12 日
It's only asked for mean 0, not standard deviation 1. Is this also possible with "normalize" ?
Steven Lord
2022 年 12 月 12 日
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.
参考
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