Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

transposition based on input method #67

Open
karoraw1 opened this issue Feb 9, 2018 · 3 comments
Open

transposition based on input method #67

karoraw1 opened this issue Feb 9, 2018 · 3 comments

Comments

@karoraw1
Copy link

karoraw1 commented Feb 9, 2018

[karoraw1@jhu.edu@login-node03 bhtsne]$ echo -e '1.0\t0.0\n0.0\t1.0' 
1.0	0.0
0.0	1.0
[karoraw1@jhu.edu@login-node03 bhtsne]$ echo -e '1.0\t0.0\n0.0\t1.0' | ./bhtsne.py -d 2 -p 0.1
-2227.32653069	6608.48958328
2227.32653069	-6608.48958328
[karoraw1@jhu.edu@login-node03 bhtsne]$ echo -e '1.0\t0.0\n0.0\t1.0' > a_file.txt
[karoraw1@jhu.edu@login-node03 bhtsne]$ cat a_file.txt 
1.0	0.0
0.0	1.0
[karoraw1@jhu.edu@login-node03 bhtsne]$ ./bhtsne.py -d 2 -p 0.1 -i a_file.txt 
-6863.21277159	-1236.73732294
6863.21277159	1236.73732294
@karoraw1
Copy link
Author

karoraw1 commented Feb 9, 2018

[karoraw1@jhu.edu@login-node03 bhtsne]$ ./bhtsne.py -d 2 -p 0.1 -i a_file.txt | python -c 'import numpy;  from sys import stdin, stdout; d = numpy.loadtxt(stdin); d -= d.min(axis=0); d /= d.max(axis=0); numpy.savetxt(stdout, d, fmt="%.8f", delimiter="\t")'
0.00000000	0.00000000
1.00000000	1.00000000
[karoraw1@jhu.edu@login-node03 bhtsne]$ echo -e '1.0\t0.0\n0.0\t1.0' | ./bhtsne.py -d 2 -p 0.1 | python -c 'import numpy;  from sys import stdin, stdout; d = numpy.loadtxt(stdin); d -= d.min(axis=0); d /= d.max(axis=0); numpy.savetxt(stdout, d, fmt="%.8f", delimiter="\t")'
1.00000000	0.00000000
0.00000000	1.00000000

@karoraw1
Copy link
Author

karoraw1 commented Feb 9, 2018

i don't think it makes a difference. i just wondered why it happens.

@lvdmaaten
Copy link
Owner

By definition, the loss of any multidimensional scaling method (that is, a loss that measures to what extent pairwise distances are preserved) is invariant under rotations and inflections of the embedding. I suppose that is what you're seeing here?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants