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keichi/_kedm.py

Last active March 23, 2023 13:48
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Multivariate Simplex projection using pyEDM and kEDM
Raw
_kedm.py
# pip3 install kedm==0.4.0
import numpy as np
import kedm
data = np.loadtxt("block_3sp.csv", delimiter=",", skiprows=1)
# Columns #1, #4 and #7 correspond to x_t, y_t and z_t
library = data[:99, [1, 4, 7]]
target = data[97:198, [1, 4, 7]]
prediction = kedm.simplex(library, target, E=3)
np.savetxt("kedm_output.csv", prediction[:, 0], delimiter=",")
Raw
_pyedm.py
# pip3 install pyEDM==1.14.0.0
import pandas as pd
import pyEDM as EDM
df = pd.read_csv("block_3sp.csv")
S = EDM.Simplex(dataFrame=df,
lib="1 99", pred="100 198", E=3, Tp=1,
tau=-1, columns="x_t y_t z_t", target="x_t")
S.to_csv("pyedm_output.csv")
Raw
block_3sp.csv
time x_t x_t-1 x_t-2 y_t y_t-1 y_t-2 z_t z_t-1 z_t-2
3 -1.917685158 1.244881764 -0.741862518 -0.113188052 1.488887456 -1.268103609 1.535238778 -0.481582539 -1.863980166
4 -0.962317629 -1.917685158 1.244881764 -1.106778594 -0.113188052 1.488887456 -1.492955761 1.535238778 -0.481582539
5 1.331875102 -0.962317629 -1.917685158 2.385040811 -1.106778594 -0.113188052 -1.119476212 -1.492955761 1.535238778
6 -0.8170829 1.331875102 -0.962317629 -0.675346281 2.385040811 -1.106778594 0.746657902 -1.119476212 -1.492955761
7 0.743585963 -0.8170829 1.331875102 -0.010639353 -0.675346281 2.385040811 0.063088675 0.746657902 -1.119476212
8 -1.280173167 0.743585963 -0.8170829 1.019141615 -0.010639353 -0.675346281 0.812235161 0.063088675 0.746657902
9 0.555004181 -1.280173167 0.743585963 -0.194615118 1.019141615 -0.010639353 -1.731141339 0.812235161 0.063088675
10 -0.076886143 0.555004181 -1.280173167 1.633441402 -0.194615118 1.019141615 0.357270473 -1.731141339 0.812235161
11 0.842846283 -0.076886143 0.555004181 -1.167448221 1.633441402 -0.194615118 -0.080599755 0.357270473 -1.731141339
12 -1.050086389 0.842846283 -0.076886143 0.217246161 -1.167448221 1.633441402 1.020736743 -0.080599755 0.357270473
13 0.992532049 -1.050086389 0.842846283 -0.672663789 0.217246161 -1.167448221 -0.326734623 1.020736743 -0.080599755
14 -0.314656087 0.992532049 -1.050086389 -0.296778866 -0.672663789 0.217246161 0.307151369 -0.326734623 1.020736743
15 1.466061576 -0.314656087 0.992532049 -0.510561933 -0.296778866 -0.672663789 -0.226960645 0.307151369 -0.326734623
16 -1.61357238 1.466061576 -0.314656087 1.465883289 -0.510561933 -0.296778866 1.523424867 -0.226960645 0.307151369
17 -0.480080269 -1.61357238 1.466061576 -0.433485545 1.465883289 -0.510561933 -1.540322935 1.523424867 -0.226960645
18 0.997189335 -0.480080269 -1.61357238 0.118151313 -0.433485545 1.465883289 0.29119956 -1.540322935 1.523424867
19 -2.034038346 0.997189335 -0.480080269 0.50729183 0.118151313 -0.433485545 1.487987281 0.29119956 -1.540322935
20 -1.560020076 -2.034038346 0.997189335 -1.226802982 0.50729183 0.118151313 -2.443141567 1.487987281 0.29119956
21 0.201774357 -1.560020076 -2.034038346 0.697495314 -1.226802982 0.50729183 -1.453055089 -2.443141567 1.487987281
22 1.113258907 0.201774357 -1.560020076 0.862965977 0.697495314 -1.226802982 -0.401897498 -1.453055089 -2.443141567
23 -1.866068578 1.113258907 0.201774357 -0.639584919 0.862965977 0.697495314 1.284663607 -0.401897498 -1.453055089
24 -0.809943472 -1.866068578 1.113258907 -0.222526696 -0.639584919 0.862965977 -1.983265314 1.284663607 -0.401897498
25 1.325859915 -0.809943472 -1.866068578 0.836702462 -0.222526696 -0.639584919 -0.088878572 -1.983265314 1.284663607
26 -1.421913953 1.325859915 -0.809943472 1.319094345 0.836702462 -0.222526696 0.375582297 -0.088878572 -1.983265314
27 0.137876449 -1.421913953 1.325859915 -1.36366474 1.319094345 0.836702462 -1.568808696 0.375582297 -0.088878572
28 1.095383021 0.137876449 -1.421913953 -0.161693602 -1.36366474 1.319094345 0.18878508 -1.568808696 0.375582297
29 -1.058729002 1.095383021 0.137876449 -0.493608195 -0.161693602 -1.36366474 1.00710109 0.18878508 -1.568808696
30 0.711817732 -1.058729002 1.095383021 0.096733727 -0.493608195 -0.161693602 -1.734990086 1.00710109 0.18878508
31 -0.031449534 0.711817732 -1.058729002 1.072348624 0.096733727 -0.493608195 0.272353833 -1.734990086 1.00710109
32 1.180607043 -0.031449534 0.711817732 -0.569276899 1.072348624 0.096733727 0.132415967 0.272353833 -1.734990086
33 -1.387745561 1.180607043 -0.031449534 1.736596897 -0.569276899 1.072348624 1.051856981 0.132415967 0.272353833
34 0.181451349 -1.387745561 1.180607043 -0.873817764 1.736596897 -0.569276899 -1.260551265 1.051856981 0.132415967
35 0.468995118 0.181451349 -1.387745561 2.095509948 -0.873817764 1.736596897 0.162404132 -1.260551265 1.051856981
36 0.222637162 0.468995118 0.181451349 -0.605192292 2.095509948 -0.873817764 -0.313141874 0.162404132 -1.260551265
37 0.677274174 0.222637162 0.468995118 0.009175989 -0.605192292 2.095509948 0.487723889 -0.313141874 0.162404132
38 0.045755424 0.677274174 0.222637162 -0.717945927 0.009175989 -0.605192292 1.24830621 0.487723889 -0.313141874
39 1.084118867 0.045755424 0.677274174 -0.070537156 -0.717945927 0.009175989 -0.2085223 1.24830621 0.487723889
40 -1.167567319 1.084118867 0.045755424 0.440672258 -0.070537156 -0.717945927 0.992379018 -0.2085223 1.24830621
41 0.879658686 -1.167567319 1.084118867 -1.644488587 0.440672258 -0.070537156 -0.668460951 0.992379018 -0.2085223
42 0.055981494 0.879658686 -1.167567319 1.564518782 -1.644488587 0.440672258 1.134456953 -0.668460951 0.992379018
43 0.900566621 0.055981494 0.879658686 -1.490914226 1.564518782 -1.644488587 -0.250573861 1.134456953 -0.668460951
44 -0.669431425 0.900566621 0.055981494 -0.079992629 -1.490914226 1.564518782 1.567439149 -0.250573861 1.134456953
45 1.383144271 -0.669431425 0.900566621 0.197416957 -0.079992629 -1.490914226 -0.822636271 1.567439149 -0.250573861
46 -1.345021232 1.383144271 -0.669431425 0.685564146 0.197416957 -0.079992629 0.844851844 -0.822636271 1.567439149
47 0.248406163 -1.345021232 1.383144271 -0.32166416 0.685564146 0.197416957 -1.180108739 0.844851844 -0.822636271
48 0.529035215 0.248406163 -1.345021232 0.784805545 -0.32166416 0.685564146 0.092095802 -1.180108739 0.844851844
49 -0.349430762 0.529035215 0.248406163 -0.864682834 0.784805545 -0.32166416 0.571996061 0.092095802 -1.180108739
50 1.257521747 -0.349430762 0.529035215 0.777882787 -0.864682834 0.784805545 -0.196853164 0.571996061 0.092095802
51 -0.978601366 1.257521747 -0.349430762 -0.538724191 0.777882787 -0.864682834 0.334697755 -0.196853164 0.571996061
52 0.97713909 -0.978601366 1.257521747 -0.309352105 -0.538724191 0.777882787 -0.661837398 0.334697755 -0.196853164
53 0.133081125 0.97713909 -0.978601366 0.000553478 -0.309352105 -0.538724191 0.15671314 -0.661837398 0.334697755
54 0.790843191 0.133081125 0.97713909 -0.012580698 0.000553478 -0.309352105 0.021106833 0.15671314 -0.661837398
55 -1.184901302 0.790843191 0.133081125 0.563513365 -0.012580698 0.000553478 1.02158984 0.021106833 0.15671314
56 0.674038408 -1.184901302 0.790843191 0.546839213 0.563513365 -0.012580698 -0.726263679 1.02158984 0.021106833
57 0.155118223 0.674038408 -1.184901302 1.708684621 0.546839213 0.563513365 -0.130247701 -0.726263679 1.02158984
58 0.710373896 0.155118223 0.674038408 -0.924342793 1.708684621 0.546839213 -0.547197196 -0.130247701 -0.726263679
59 0.373405131 0.710373896 0.155118223 0.229086666 -0.924342793 1.708684621 0.534906868 -0.547197196 -0.130247701
60 0.604152687 0.373405131 0.710373896 -1.149584409 0.229086666 -0.924342793 0.476250759 0.534906868 -0.547197196
61 -0.599674799 0.604152687 0.373405131 1.567761516 -1.149584409 0.229086666 0.715422 0.476250759 0.534906868
62 1.337703056 -0.599674799 0.604152687 -1.52822511 1.567761516 -1.149584409 -0.009870978 0.715422 0.476250759
63 -1.346487515 1.337703056 -0.599674799 -0.089572253 -1.52822511 1.567761516 0.469972378 -0.009870978 0.715422
64 0.603144928 -1.346487515 1.337703056 -0.620200975 -0.089572253 -1.52822511 -1.502500994 0.469972378 -0.009870978
65 0.42916742 0.603144928 -1.346487515 -0.285002774 -0.620200975 -0.089572253 0.843343598 -1.502500994 0.469972378
66 0.326290509 0.42916742 0.603144928 -0.930041843 -0.285002774 -0.620200975 1.002675239 0.843343598 -1.502500994
67 0.645812851 0.326290509 0.42916742 -0.705679112 -0.930041843 -0.285002774 0.800796457 1.002675239 0.843343598
68 0.162594361 0.645812851 0.326290509 -1.115939859 -0.705679112 -0.930041843 0.900459719 0.800796457 1.002675239
69 0.551451867 0.162594361 0.645812851 -0.888741683 -1.115939859 -0.705679112 1.093252848 0.900459719 0.800796457
70 0.091405526 0.551451867 0.162594361 0.418740121 -0.888741683 -1.115939859 0.468702066 1.093252848 0.900459719
71 0.759119771 0.091405526 0.551451867 -0.190223442 0.418740121 -0.888741683 0.460332592 0.468702066 1.093252848
72 -0.652498947 0.759119771 0.091405526 -0.935922238 -0.190223442 0.418740121 0.88342704 0.460332592 0.468702066
73 1.255777886 -0.652498947 0.759119771 -0.126204993 -0.935922238 -0.190223442 -0.820047555 0.88342704 0.460332592
74 -1.06419718 1.255777886 -0.652498947 0.191106276 -0.126204993 -0.935922238 0.78348598 -0.820047555 0.88342704
75 0.662830422 -1.06419718 1.255777886 -0.939949958 0.191106276 -0.126204993 -1.111759834 0.78348598 -0.820047555
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78 -1.792882981 0.907829306 -0.16963299 1.406556608 -0.705880544 1.525613482 0.50480681 0.150137651 1.50161138
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Raw
kedm_output.csv
We can make this file beautiful and searchable if this error is corrected: No commas found in this CSV file in line 0.
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Raw
pyedm_output.csv
time Observations Predictions Pred_Variance
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98 200 0.040303513 -0.4940743983053224 0.4235013634019137
99 201 1.2541465083346461 0.04086412392865021
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