update
This commit is contained in:
Morten Hjorth-Jensen
@@ -343,6 +343,11 @@ const thebe_selector_output = ".output, .cell_output"
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Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations
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</a>
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<li class="toctree-l1">
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<a class="reference internal" href="exercisesweek43.html">
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Exercises week 43
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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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@@ -1325,10 +1330,10 @@ 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>-0.1982973034395375
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3.328462040651484
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[[ 1.08036056 3.23565337]
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[ 3.23565337 10.80845707]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.06291984474217532
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4.095077148135215
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[[0.8942779 2.56719397]
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[2.56719397 8.13485566]]
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</pre></div>
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</div>
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</div>
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@@ -1365,10 +1370,10 @@ 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>0.08154221680664263
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2.12282958127703
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[[1. 0.6177137]
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[0.6177137 1. ]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07603598322691016
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1.4323043635926456
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[[1. 0.58435095]
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[0.58435095 1. ]]
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</pre></div>
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</div>
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</div>
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@@ -1398,30 +1403,30 @@ 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>[[ 0.14690374 -1.08896995]
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[-0.14465374 -0.8889085 ]
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[-0.01877081 0.85085928]
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[-0.20536579 -1.03613164]
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[ 0.28162714 0.25445 ]
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[-0.25588843 -2.27916593]
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[-0.11479519 -0.41788797]
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[ 0.19483625 1.37363888]
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[-0.03282533 0.94863824]
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[ 0.14893216 2.28347758]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-1.2105986 -2.92044261]
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[-0.59585933 -2.0072114 ]
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[-0.7336627 -1.27166993]
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[ 0.50493371 0.54649725]
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[-0.76911228 -2.66017222]
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[ 0.70843895 3.92226956]
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[ 0.61516316 2.7399476 ]
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[ 0.3114655 -0.76958689]
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[ 1.49817031 3.74791021]
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[-0.32893873 -1.32754157]]
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0 1
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0 0.146904 -1.088970
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1 -0.144654 -0.888908
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2 -0.018771 0.850859
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3 -0.205366 -1.036132
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4 0.281627 0.254450
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5 -0.255888 -2.279166
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6 -0.114795 -0.417888
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7 0.194836 1.373639
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8 -0.032825 0.948638
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9 0.148932 2.283478
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0 -1.210599 -2.920443
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1 -0.595859 -2.007211
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2 -0.733663 -1.271670
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3 0.504934 0.546497
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4 -0.769112 -2.660172
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5 0.708439 3.922270
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6 0.615163 2.739948
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7 0.311466 -0.769587
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8 1.498170 3.747910
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9 -0.328939 -1.327542
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0 1
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0 1.000000 0.631471
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1 0.631471 1.000000
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0 1.000000 0.915842
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1 0.915842 1.000000
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</pre></div>
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</div>
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</div>
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@@ -1478,37 +1483,37 @@ 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 0.072263 0.073863 0.075238 0.072577 0.069597 0.068931 0.065674
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2 0.0 0.073863 0.076665 0.078474 0.076248 0.073525 0.072653 0.069507
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3 0.0 0.075238 0.078474 0.083331 0.081232 0.078589 0.079443 0.076163
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4 0.0 0.072577 0.076248 0.081232 0.079487 0.077139 0.077899 0.074866
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5 0.0 0.069597 0.073525 0.078589 0.077139 0.075058 0.075761 0.072967
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6 0.0 0.068931 0.072653 0.079443 0.077899 0.075761 0.077837 0.074911
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7 0.0 0.065674 0.069507 0.076163 0.074866 0.072967 0.074911 0.072226
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8 0.0 0.062481 0.066354 0.072859 0.071774 0.070087 0.071919 0.069457
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9 0.0 0.059398 0.063264 0.069606 0.068701 0.067204 0.068939 0.066680
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10 0.0 0.061779 0.065469 0.073182 0.072011 0.070273 0.073144 0.070581
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11 0.0 0.058670 0.062345 0.069785 0.068797 0.067249 0.069948 0.067597
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12 0.0 0.055715 0.059348 0.066520 0.065690 0.064311 0.066854 0.064699
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13 0.0 0.052918 0.056491 0.063401 0.062709 0.061482 0.063883 0.061907
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14 0.0 0.050281 0.053781 0.060435 0.059864 0.058774 0.061045 0.059233
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1 0.0 0.083046 0.078912 0.083582 0.082947 0.081772 0.074931 0.074984
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2 0.0 0.078912 0.078913 0.075718 0.076758 0.077825 0.065957 0.066785
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3 0.0 0.083582 0.075718 0.089055 0.086655 0.083266 0.082876 0.082018
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4 0.0 0.082947 0.076758 0.086655 0.085096 0.082753 0.079657 0.079265
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5 0.0 0.081772 0.077825 0.083266 0.082753 0.081750 0.075343 0.075494
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6 0.0 0.074931 0.065957 0.082876 0.079657 0.075343 0.079164 0.077785
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7 0.0 0.074984 0.066785 0.082018 0.079265 0.075494 0.077785 0.076698
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8 0.0 0.074979 0.067759 0.080910 0.078709 0.075600 0.076076 0.075321
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9 0.0 0.074820 0.068872 0.079393 0.077861 0.075576 0.073869 0.073497
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10 0.0 0.066329 0.057411 0.075255 0.071789 0.067261 0.073246 0.071643
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11 0.0 0.066412 0.057907 0.074831 0.071648 0.067437 0.072501 0.071093
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12 0.0 0.066546 0.058532 0.074371 0.071511 0.067667 0.071672 0.070480
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13 0.0 0.066707 0.059294 0.073827 0.071341 0.067930 0.070701 0.069750
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14 0.0 0.066858 0.060197 0.073125 0.071081 0.068193 0.069506 0.068828
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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 0.062481 0.059398 0.061779 0.058670 0.055715 0.052918 0.050281
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2 0.066354 0.063264 0.065469 0.062345 0.059348 0.056491 0.053781
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3 0.072859 0.069606 0.073182 0.069785 0.066520 0.063401 0.060435
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4 0.071774 0.068701 0.072011 0.068797 0.065690 0.062709 0.059864
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5 0.070087 0.067204 0.070273 0.067249 0.064311 0.061482 0.058774
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6 0.071919 0.068939 0.073144 0.069948 0.066854 0.063883 0.061045
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7 0.069457 0.066680 0.070581 0.067597 0.064699 0.061907 0.059233
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8 0.066896 0.064314 0.067935 0.065156 0.062446 0.059827 0.057312
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9 0.064314 0.061915 0.065282 0.062695 0.060163 0.057711 0.055349
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10 0.067935 0.065282 0.069784 0.066877 0.064054 0.061333 0.058726
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11 0.065156 0.062695 0.066877 0.064176 0.061545 0.059003 0.056561
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12 0.062446 0.060163 0.064054 0.061545 0.059093 0.056718 0.054432
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13 0.059827 0.057711 0.061333 0.059003 0.056718 0.054500 0.052360
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14 0.057312 0.055349 0.058726 0.056561 0.054432 0.052360 0.050357
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1 0.074979 0.074820 0.066329 0.066412 0.066546 0.066707 0.066858
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2 0.067759 0.068872 0.057411 0.057907 0.058532 0.059294 0.060197
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3 0.080910 0.079393 0.075255 0.074831 0.074371 0.073827 0.073125
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4 0.078709 0.077861 0.071789 0.071648 0.071511 0.071341 0.071081
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5 0.075600 0.075576 0.067261 0.067437 0.067667 0.067930 0.068193
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6 0.076076 0.073869 0.073246 0.072501 0.071672 0.070701 0.069506
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7 0.075321 0.073497 0.071643 0.071093 0.070480 0.069750 0.068828
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8 0.074328 0.072954 0.069693 0.069359 0.068986 0.068528 0.067920
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9 0.072954 0.072121 0.067239 0.067143 0.067040 0.066893 0.066650
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10 0.069693 0.067239 0.068729 0.067831 0.066827 0.065661 0.064254
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11 0.069359 0.067143 0.067831 0.067072 0.066218 0.065217 0.063990
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12 0.068986 0.067040 0.066827 0.066218 0.065529 0.064708 0.063682
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13 0.068528 0.066893 0.065661 0.065217 0.064708 0.064088 0.063287
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14 0.067920 0.066650 0.064254 0.063990 0.063682 0.063287 0.062745
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</pre></div>
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</div>
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</div>
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