update on jupyter-book
This commit is contained in:
Morten Hjorth-Jensen
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
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Exercises week 38
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week38.html">
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Week 38: Logistic Regression and Optimization
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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@@ -1270,10 +1275,10 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.0369544130635358
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3.662836197064178
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[[1.058997 3.11439407]
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[3.11439407 9.99498272]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.07978850553011713
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3.8232102961414203
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[[ 1.25705685 3.70704566]
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[ 3.70704566 12.1664372 ]]
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</pre></div>
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</div>
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</div>
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@@ -1310,10 +1315,10 @@ a more brute force way. Here we scale the mean values for each column of the des
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08826182458028335
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1.7026722043092946
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[[1. 0.61113781]
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[0.61113781 1. ]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08931169286872433
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2.047447724189861
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[[1. 0.66729685]
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[0.66729685 1. ]]
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</pre></div>
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</div>
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</div>
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@@ -1343,30 +1348,30 @@ this matrix we easily see that it is a positive definite matrix.</p>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.7252563 -2.26264849]
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[ 1.19052935 3.11261935]
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[-0.62158409 -2.99662602]
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[-0.06216141 -0.18120973]
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[ 1.32065614 3.50269821]
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[ 0.83995705 2.80855691]
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[ 0.1571284 0.96919021]
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[-0.03404758 1.01551815]
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[-0.23596934 0.77449804]
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[-1.82925222 -6.74259663]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[ 2.37582891 5.4242743 ]
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[-0.81925302 -3.60052901]
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[ 0.37467515 3.16158074]
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[-0.40478305 -2.22601408]
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[-0.0776532 -1.70594608]
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[-0.99434472 -2.2026985 ]
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[ 0.71376303 2.77725472]
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[ 0.94637206 2.69303453]
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[-0.19791352 0.93366198]
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[-1.91669165 -5.2546186 ]]
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0 1
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0 -0.725256 -2.262648
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1 1.190529 3.112619
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2 -0.621584 -2.996626
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3 -0.062161 -0.181210
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4 1.320656 3.502698
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5 0.839957 2.808557
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6 0.157128 0.969190
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7 -0.034048 1.015518
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8 -0.235969 0.774498
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9 -1.829252 -6.742597
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0 2.375829 5.424274
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1 -0.819253 -3.600529
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2 0.374675 3.161581
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3 -0.404783 -2.226014
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4 -0.077653 -1.705946
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5 -0.994345 -2.202698
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6 0.713763 2.777255
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7 0.946372 2.693035
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8 -0.197914 0.933662
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9 -1.916692 -5.254619
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0 1
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0 1.000000 0.963187
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1 0.963187 1.000000
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0 1.000000 0.932382
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1 0.932382 1.000000
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</pre></div>
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</div>
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</div>
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@@ -1423,37 +1428,37 @@ this matrix we easily see that it is a positive definite matrix.</p>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1 2 3 4 5 6 7 \
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0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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1 0.0 0.077382 0.076459 0.074114 0.076542 0.079074 0.064978 0.067074
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2 0.0 0.076459 0.078118 0.070150 0.073866 0.078123 0.059624 0.062356
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3 0.0 0.074114 0.070150 0.076618 0.077248 0.077529 0.070432 0.071572
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4 0.0 0.076542 0.073866 0.077248 0.078731 0.080082 0.069802 0.071449
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5 0.0 0.079074 0.078123 0.077529 0.080082 0.082803 0.068607 0.070859
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6 0.0 0.064978 0.059624 0.070432 0.069802 0.068607 0.066865 0.067168
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7 0.0 0.067074 0.062356 0.071572 0.071449 0.070859 0.067168 0.067809
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8 0.0 0.069439 0.065556 0.072752 0.073253 0.073422 0.067367 0.068408
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9 0.0 0.072092 0.069317 0.073923 0.075198 0.076333 0.067380 0.068897
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10 0.0 0.056856 0.051021 0.063637 0.062282 0.060288 0.061848 0.061588
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11 0.0 0.058361 0.052856 0.064604 0.063560 0.061921 0.062263 0.062231
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12 0.0 0.060085 0.055000 0.065679 0.065007 0.063802 0.062700 0.062932
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13 0.0 0.062053 0.057514 0.066855 0.066633 0.065963 0.063133 0.063673
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14 0.0 0.064294 0.060470 0.068116 0.068446 0.068446 0.063525 0.064427
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1 0.0 0.080947 0.086913 0.082764 0.085436 0.088029 0.075956 0.077683
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2 0.0 0.086913 0.094044 0.088199 0.091365 0.094439 0.080171 0.082174
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3 0.0 0.082764 0.088199 0.090624 0.093094 0.095465 0.086730 0.088375
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4 0.0 0.085436 0.091365 0.093094 0.095805 0.098414 0.088651 0.090440
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5 0.0 0.088029 0.094439 0.095465 0.098414 0.101263 0.090468 0.092401
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6 0.0 0.075956 0.080171 0.086730 0.088651 0.090468 0.085390 0.086712
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7 0.0 0.077683 0.082174 0.088375 0.090440 0.092401 0.086712 0.088127
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8 0.0 0.079444 0.084218 0.090038 0.092252 0.094362 0.088038 0.089548
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9 0.0 0.081252 0.086318 0.091731 0.094099 0.096365 0.089376 0.090985
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10 0.0 0.068684 0.071868 0.080702 0.082126 0.083446 0.081103 0.082115
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11 0.0 0.069978 0.073339 0.081978 0.083499 0.084915 0.082170 0.083248
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12 0.0 0.071314 0.074859 0.083288 0.084910 0.086428 0.083260 0.084406
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13 0.0 0.072697 0.076435 0.084637 0.086364 0.087988 0.084376 0.085592
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14 0.0 0.074130 0.078071 0.086026 0.087864 0.089600 0.085518 0.086809
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8 9 10 11 12 13 14
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0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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1 0.069439 0.072092 0.056856 0.058361 0.060085 0.062053 0.064294
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2 0.065556 0.069317 0.051021 0.052856 0.055000 0.057514 0.060470
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3 0.072752 0.073923 0.063637 0.064604 0.065679 0.066855 0.068116
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4 0.073253 0.075198 0.062282 0.063560 0.065007 0.066633 0.068446
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5 0.073422 0.076333 0.060288 0.061921 0.063802 0.065963 0.068446
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6 0.067367 0.067380 0.061848 0.062263 0.062700 0.063133 0.063525
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7 0.068408 0.068897 0.061588 0.062231 0.062932 0.063673 0.064427
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8 0.069486 0.070556 0.061154 0.062058 0.063064 0.064166 0.065352
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9 0.070556 0.072344 0.060453 0.061656 0.063017 0.064547 0.066256
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10 0.061154 0.060453 0.058245 0.058254 0.058234 0.058152 0.057961
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11 0.062058 0.061656 0.058254 0.058427 0.058593 0.058721 0.058770
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12 0.063064 0.063017 0.058234 0.058593 0.058970 0.059340 0.059669
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13 0.064166 0.064547 0.058152 0.058721 0.059340 0.059992 0.060652
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14 0.065352 0.066256 0.057961 0.058770 0.059669 0.060652 0.061706
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1 0.079444 0.081252 0.068684 0.069978 0.071314 0.072697 0.074130
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2 0.084218 0.086318 0.071868 0.073339 0.074859 0.076435 0.078071
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3 0.090038 0.091731 0.080702 0.081978 0.083288 0.084637 0.086026
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4 0.092252 0.094099 0.082126 0.083499 0.084910 0.086364 0.087864
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5 0.094362 0.096365 0.083446 0.084915 0.086428 0.087988 0.089600
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6 0.088038 0.089376 0.081103 0.082170 0.083260 0.084376 0.085518
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7 0.089548 0.090985 0.082115 0.083248 0.084406 0.085592 0.086809
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8 0.091068 0.092607 0.083119 0.084319 0.085548 0.086808 0.088102
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9 0.092607 0.094254 0.084122 0.085392 0.086694 0.088030 0.089404
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10 0.083119 0.084122 0.078238 0.079089 0.079953 0.080832 0.081728
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11 0.084319 0.085392 0.079089 0.079989 0.080903 0.081835 0.082785
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12 0.085548 0.086694 0.079953 0.080903 0.081871 0.082857 0.083864
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13 0.086808 0.088030 0.080832 0.081835 0.082857 0.083900 0.084967
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14 0.088102 0.089404 0.081728 0.082785 0.083864 0.084967 0.086096
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</pre></div>
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</div>
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</div>
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