Cc analysis: Difference between revisions

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1,605 bytes added ,  5 July 2022
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The output is: <vector #> <x> <y> <length> <angle> for each vector, in this 2-dimensional case; equivalently for higher dimensions.
The output is: <vector #> <x> <y> <length> <angle> for each vector, in this 2-dimensional case; equivalently for higher dimensions.
The Python code produces the following output:
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===
Correlation matrix parsed from infile:
[[  nan 0.017  0.0222 0.0233 0.0226]
[0.017    nan 0.7026 0.7287 0.6241]
[0.0222 0.7026    nan 0.9131 0.8049]
[0.0233 0.7287 0.9131    nan 0.8432]
[0.0226 0.6241 0.8049 0.8432    nan]]
===
Correction factor for 2nd and higher eigenvalue(s):
0.8000
===
Interpretation of correlation matrix as dot product matrix:
---
all h_i by iterative approach:
initial values:
[0.1459 0.7198 0.7815 0.7919 0.7574]
refinement by iteration:
#13: [0.0242 0.7414 0.9389 0.9798 0.8551]
===
Uncorrected eigenvalue(s):
2 used:
[3.1228 0.0126]
3 unused:
[ 0.0045 -0.0004 -0.0167]
---
Corrected eigenvalue(s):
2 used:
[3.1228 0.0158]
iter      RMS  max_chg  rms_chg
  0  0.00345        -        -
  1  0.00241 -0.04680  0.00403
  2  0.00127  0.01783  0.00275
  3  0.00057  0.01297  0.00162
  4  0.00029  0.00570  0.00073
  5  0.00023  0.00182  0.00026
  6  0.00023 -0.00047  0.00008
  7  0.00022 -0.00042  0.00004
  8  0.00022 -0.00042  0.00004
  9  0.00022 -0.00045  0.00005
  10  0.00022 -0.00045  0.00005
  11  0.00022 -0.00045  0.00005
  12  0.00021 -0.00044  0.00005
  13  0.00021 -0.00043  0.00005
  14  0.00021 -0.00043  0.00005
  15  0.00021 -0.00042  0.00004
  16  0.00021 -0.00042  0.00004
  17  0.00020 -0.00042  0.00004
  18  0.00020 -0.00042  0.00004
  19  0.00020 -0.00042  0.00004
  20  0.00020 -0.00042  0.00004
    1  0.0241 -0.0098
    2  0.7484  0.1425
    3  0.9359  0.0150
    4  0.9758 -0.0112
    5  0.8624 -0.1501
===
Finished outputting 2-dimensional representative vectors! =)
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