last update, perhaps
This commit is contained in:
Morten Hjorth-Jensen
@@ -751,10 +751,10 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.1001408041761458
|
||||
4.2807716628772665
|
||||
[[ 1.15654145 3.54867722]
|
||||
[ 3.54867722 11.70485195]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.05665875086534638
|
||||
4.128393685824704
|
||||
[[ 0.93987367 2.98650457]
|
||||
[ 2.98650457 10.47544463]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -794,10 +794,10 @@ a more brute force way. Here we scale the mean values for each column of the des
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.09543871010617433
|
||||
1.6888043337746685
|
||||
[[1. 0.7167077]
|
||||
[0.7167077 1. ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07617734331359052
|
||||
1.6957182489166325
|
||||
[[1. 0.68029423]
|
||||
[0.68029423 1. ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -826,30 +826,30 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.20575734 0.01384583]
|
||||
[-0.89876098 -3.04065686]
|
||||
[-0.76289128 -3.17080691]
|
||||
[-0.0334136 0.16124569]
|
||||
[ 2.73970542 9.28885103]
|
||||
[ 0.75413023 2.98474769]
|
||||
[-1.87894459 -5.48121459]
|
||||
[-1.26814205 -2.4848097 ]
|
||||
[ 0.18114057 -0.9889962 ]
|
||||
[ 1.37293361 2.71779401]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.29087396 0.13244119]
|
||||
[-1.21617146 -2.69678073]
|
||||
[-1.37024276 -3.76728511]
|
||||
[ 0.49342785 1.50638863]
|
||||
[ 0.4155974 -0.07435812]
|
||||
[ 0.64813145 1.94455739]
|
||||
[-0.48364163 -2.62178739]
|
||||
[-0.3807176 -1.25007671]
|
||||
[ 1.73036439 5.00709577]
|
||||
[ 0.45412633 1.81980509]]
|
||||
0 1
|
||||
0 -0.205757 0.013846
|
||||
1 -0.898761 -3.040657
|
||||
2 -0.762891 -3.170807
|
||||
3 -0.033414 0.161246
|
||||
4 2.739705 9.288851
|
||||
5 0.754130 2.984748
|
||||
6 -1.878945 -5.481215
|
||||
7 -1.268142 -2.484810
|
||||
8 0.181141 -0.988996
|
||||
9 1.372934 2.717794
|
||||
0 -0.290874 0.132441
|
||||
1 -1.216171 -2.696781
|
||||
2 -1.370243 -3.767285
|
||||
3 0.493428 1.506389
|
||||
4 0.415597 -0.074358
|
||||
5 0.648131 1.944557
|
||||
6 -0.483642 -2.621787
|
||||
7 -0.380718 -1.250077
|
||||
8 1.730364 5.007096
|
||||
9 0.454126 1.819805
|
||||
0 1
|
||||
0 1.000000 0.970965
|
||||
1 0.970965 1.000000
|
||||
0 1.000000 0.961042
|
||||
1 0.961042 1.000000
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -906,37 +906,37 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1 2 3 4 5 6 7 \
|
||||
0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.0 0.090565 0.089065 0.093226 0.091674 0.090154 0.086287 0.084847
|
||||
2 0.0 0.089065 0.087946 0.092421 0.091068 0.089735 0.085988 0.084674
|
||||
3 0.0 0.093226 0.092421 0.102147 0.100816 0.099494 0.098144 0.096731
|
||||
4 0.0 0.091674 0.091068 0.100816 0.099622 0.098431 0.097115 0.095803
|
||||
5 0.0 0.090154 0.089735 0.099494 0.098431 0.097365 0.096077 0.094862
|
||||
6 0.0 0.086287 0.085988 0.098144 0.097115 0.096077 0.096630 0.095395
|
||||
7 0.0 0.084847 0.084674 0.096731 0.095803 0.094862 0.095395 0.094243
|
||||
8 0.0 0.083459 0.083405 0.095358 0.094527 0.093680 0.094189 0.093115
|
||||
9 0.0 0.082121 0.082180 0.094027 0.093288 0.092530 0.093011 0.092013
|
||||
10 0.0 0.078708 0.078711 0.091732 0.090935 0.090118 0.091871 0.090804
|
||||
11 0.0 0.077431 0.077523 0.090387 0.089668 0.088926 0.090626 0.089626
|
||||
12 0.0 0.076203 0.076378 0.089086 0.088441 0.087772 0.089417 0.088481
|
||||
13 0.0 0.075021 0.075274 0.087828 0.087255 0.086655 0.088242 0.087369
|
||||
14 0.0 0.073883 0.074212 0.086611 0.086107 0.085573 0.087102 0.086289
|
||||
1 0.0 0.068131 0.073423 0.069509 0.072262 0.074935 0.062568 0.064540
|
||||
2 0.0 0.073423 0.079638 0.075544 0.078741 0.081836 0.068222 0.070480
|
||||
3 0.0 0.069509 0.075544 0.075689 0.079013 0.082264 0.070991 0.073416
|
||||
4 0.0 0.072262 0.078741 0.079013 0.082596 0.086101 0.074265 0.076876
|
||||
5 0.0 0.074935 0.081836 0.082264 0.086101 0.089859 0.077488 0.080287
|
||||
6 0.0 0.062568 0.068222 0.070991 0.074265 0.077488 0.068479 0.070924
|
||||
7 0.0 0.064540 0.070480 0.073416 0.076876 0.080287 0.070924 0.073514
|
||||
8 0.0 0.066545 0.072773 0.075883 0.079535 0.083138 0.073418 0.076157
|
||||
9 0.0 0.068597 0.075114 0.078409 0.082256 0.086058 0.075974 0.078867
|
||||
10 0.0 0.055105 0.060160 0.064322 0.067367 0.070380 0.063327 0.065650
|
||||
11 0.0 0.056730 0.062004 0.066330 0.069526 0.072694 0.065375 0.067819
|
||||
12 0.0 0.058409 0.063910 0.068406 0.071759 0.075086 0.067492 0.070063
|
||||
13 0.0 0.060149 0.065884 0.070556 0.074073 0.077566 0.069686 0.072388
|
||||
14 0.0 0.061956 0.067932 0.072787 0.076473 0.080139 0.071962 0.074802
|
||||
|
||||
8 9 10 11 12 13 14
|
||||
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.083459 0.082121 0.078708 0.077431 0.076203 0.075021 0.073883
|
||||
2 0.083405 0.082180 0.078711 0.077523 0.076378 0.075274 0.074212
|
||||
3 0.095358 0.094027 0.091732 0.090387 0.089086 0.087828 0.086611
|
||||
4 0.094527 0.093288 0.090935 0.089668 0.088441 0.087255 0.086107
|
||||
5 0.093680 0.092530 0.090118 0.088926 0.087772 0.086655 0.085573
|
||||
6 0.094189 0.093011 0.091871 0.090626 0.089417 0.088242 0.087102
|
||||
7 0.093115 0.092013 0.090804 0.089626 0.088481 0.087369 0.086289
|
||||
8 0.092064 0.091034 0.089755 0.088642 0.087560 0.086508 0.085486
|
||||
9 0.091034 0.090075 0.088726 0.087675 0.086653 0.085659 0.084694
|
||||
10 0.089755 0.088726 0.088455 0.087327 0.086227 0.085155 0.084112
|
||||
11 0.088642 0.087675 0.087327 0.086256 0.085212 0.084195 0.083203
|
||||
12 0.087560 0.086653 0.086227 0.085212 0.084222 0.083257 0.082316
|
||||
13 0.086508 0.085659 0.085155 0.084195 0.083257 0.082342 0.081450
|
||||
14 0.085486 0.084694 0.084112 0.083203 0.082316 0.081450 0.080605
|
||||
1 0.066545 0.068597 0.055105 0.056730 0.058409 0.060149 0.061956
|
||||
2 0.072773 0.075114 0.060160 0.062004 0.063910 0.065884 0.067932
|
||||
3 0.075883 0.078409 0.064322 0.066330 0.068406 0.070556 0.072787
|
||||
4 0.079535 0.082256 0.067367 0.069526 0.071759 0.074073 0.076473
|
||||
5 0.083138 0.086058 0.070380 0.072694 0.075086 0.077566 0.080139
|
||||
6 0.073418 0.075974 0.063327 0.065375 0.067492 0.069686 0.071962
|
||||
7 0.076157 0.078867 0.065650 0.067819 0.070063 0.072388 0.074802
|
||||
8 0.078953 0.081823 0.068024 0.070318 0.072693 0.075155 0.077711
|
||||
9 0.081823 0.084858 0.070460 0.072885 0.075396 0.078000 0.080704
|
||||
10 0.068024 0.070460 0.059480 0.061447 0.063482 0.065591 0.067780
|
||||
11 0.070318 0.072885 0.061447 0.063517 0.065661 0.067883 0.070190
|
||||
12 0.072693 0.075396 0.063482 0.065661 0.067917 0.070258 0.072688
|
||||
13 0.075155 0.078000 0.065591 0.067883 0.070258 0.072721 0.075281
|
||||
14 0.077711 0.080704 0.067780 0.070190 0.072688 0.075281 0.077975
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1125,12 +1125,10 @@ We can write our own code or simply use either the functionaly of <strong>numpy<
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1
|
||||
0 4.114499 2.071143
|
||||
1 2.071143 2.061388
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[4.11449851 2.07114326]
|
||||
[2.07114326 2.0613875 ]]
|
||||
0 4.060824 2.038890
|
||||
1 2.038890 2.029664
|
||||
[[4.06082419 2.03888998]
|
||||
[2.03888998 2.02966421]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1157,8 +1155,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Centered covariance using own code
|
||||
[[4.11449851 2.07114326]
|
||||
[2.07114326 2.0613875 ]]
|
||||
[[4.06082419 2.03888998]
|
||||
[2.03888998 2.02966421]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapter8_65_1.png" src="_images/chapter8_65_1.png" />
|
||||
@@ -1218,16 +1216,16 @@ questions.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvalues of Covariance matrix
|
||||
5.399533503407795
|
||||
0.776352510835556
|
||||
5.323066643161066
|
||||
0.7674217625964539
|
||||
First eigenvector
|
||||
[0.84973247 0.52721412]
|
||||
[0.85025162 0.52637646]
|
||||
Second eigenvector
|
||||
[-0.52721412 0.84973247]
|
||||
[-0.52637646 0.85025162]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvector of largest eigenvalue
|
||||
[-0.84973247 -0.52721412]
|
||||
[-0.85025162 -0.52637646]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Reference in New Issue
Block a user