Comments (3)
[[email protected]@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
[[email protected]@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
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i don't think it makes a difference. i just wondered why it happens.
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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?
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Related Issues (20)
- Usage of random generator(s) in the source HOT 2
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