adding dft slides
This commit is contained in:
Morten Hjorth-Jensen
@@ -333,6 +333,16 @@ const thebe_selector_output = ".output, .cell_output"
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Week 42 Constructing a Neural Network code with introduction to Tensor flow
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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="exercisesweek43.html">
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Exercises weeks 43 and 44
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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="week43.html">
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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>
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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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@@ -1315,10 +1325,10 @@ 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>0.12765651865754318
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4.348676117830458
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[[0.98073929 2.88442538]
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[2.88442538 9.24128917]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.023888460698069384
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4.161573669199933
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[[0.708589 2.01323615]
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[2.01323615 6.75406265]]
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</pre></div>
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</div>
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</div>
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@@ -1355,10 +1365,10 @@ 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>0.08652153831327969
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1.7893215781870513
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[[1. 0.70344416]
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[0.70344416 1. ]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08737007811453563
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1.792898603630095
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[[1. 0.65673455]
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[0.65673455 1. ]]
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</pre></div>
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</div>
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</div>
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@@ -1388,30 +1398,30 @@ 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>[[ 1.71727268 5.22388434]
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[ 0.31027702 0.17469167]
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[-0.26149831 -0.93082933]
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[ 0.04107874 1.47244548]
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[-2.10812381 -5.28818554]
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[-1.62910047 -4.07706814]
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[ 0.92136836 2.27309401]
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[-0.3175938 -1.42457498]
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[ 0.68037392 0.16481217]
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[ 0.64594566 2.41173033]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[ 0.1036544 0.17432695]
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[ 0.51758232 1.25700419]
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[-1.37236385 -4.22805937]
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[-0.46766277 -1.52501047]
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[ 0.99175336 3.59187177]
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[-2.34718587 -5.3512747 ]
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[ 1.94980712 6.99450177]
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[ 0.24849282 -0.56424167]
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[-0.35408251 -2.3930301 ]
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[ 0.73000497 2.04391163]]
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0 1
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0 1.717273 5.223884
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1 0.310277 0.174692
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2 -0.261498 -0.930829
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3 0.041079 1.472445
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4 -2.108124 -5.288186
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5 -1.629100 -4.077068
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6 0.921368 2.273094
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7 -0.317594 -1.424575
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8 0.680374 0.164812
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9 0.645946 2.411730
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0 0.103654 0.174327
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1 0.517582 1.257004
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2 -1.372364 -4.228059
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3 -0.467663 -1.525010
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4 0.991753 3.591872
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5 -2.347186 -5.351275
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6 1.949807 6.994502
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7 0.248493 -0.564242
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8 -0.354083 -2.393030
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9 0.730005 2.043912
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0 1
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0 1.000000 0.962653
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1 0.962653 1.000000
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0 1.000000 0.966337
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1 0.966337 1.000000
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</pre></div>
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</div>
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</div>
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@@ -1468,37 +1478,37 @@ 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 0.082246 0.081621 0.082225 0.081617 0.081120 0.073421 0.072973
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2 0.0 0.081621 0.081679 0.081960 0.081804 0.081742 0.073498 0.073387
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3 0.0 0.082225 0.081960 0.087271 0.086900 0.086636 0.081057 0.080755
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4 0.0 0.081617 0.081804 0.086900 0.086868 0.086932 0.080935 0.080903
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5 0.0 0.081120 0.081742 0.086636 0.086932 0.087311 0.080906 0.081136
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6 0.0 0.073421 0.073498 0.081057 0.080935 0.080906 0.077455 0.077330
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7 0.0 0.072973 0.073387 0.080755 0.080903 0.081136 0.077330 0.077429
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8 0.0 0.072637 0.073376 0.080571 0.080980 0.081466 0.077313 0.077630
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9 0.0 0.072410 0.073465 0.080502 0.081164 0.081896 0.077403 0.077931
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10 0.0 0.064640 0.064948 0.073406 0.073471 0.073618 0.071662 0.071685
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11 0.0 0.064320 0.064896 0.073187 0.073476 0.073840 0.071579 0.071792
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12 0.0 0.064101 0.064938 0.073080 0.073586 0.074161 0.071601 0.072000
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13 0.0 0.063980 0.065069 0.073079 0.073797 0.074577 0.071726 0.072305
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14 0.0 0.063953 0.065289 0.073184 0.074108 0.075089 0.071951 0.072707
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1 0.0 0.084489 0.079202 0.083269 0.079700 0.076331 0.074355 0.071456
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2 0.0 0.079202 0.075352 0.079849 0.077042 0.074328 0.072527 0.070086
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3 0.0 0.083269 0.079849 0.087761 0.084946 0.082203 0.081779 0.079170
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4 0.0 0.079700 0.077042 0.084946 0.082590 0.080248 0.079820 0.077517
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5 0.0 0.076331 0.074328 0.082203 0.080248 0.078258 0.077833 0.075804
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6 0.0 0.074355 0.072527 0.081779 0.079820 0.077833 0.078423 0.076337
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7 0.0 0.071456 0.070086 0.079170 0.077517 0.075804 0.076337 0.074477
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8 0.0 0.068743 0.067765 0.076678 0.075294 0.073824 0.074307 0.072650
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9 0.0 0.066200 0.065559 0.074301 0.073154 0.071901 0.072338 0.070865
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10 0.0 0.065631 0.064814 0.074306 0.072976 0.071564 0.072718 0.071080
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11 0.0 0.063225 0.062696 0.071960 0.070845 0.069629 0.070705 0.069239
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12 0.0 0.060971 0.060691 0.069733 0.068809 0.067769 0.068771 0.067462
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13 0.0 0.058856 0.058793 0.067619 0.066865 0.065984 0.066919 0.065753
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14 0.0 0.056870 0.056996 0.065613 0.065012 0.064275 0.065147 0.064113
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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 0.072637 0.072410 0.064640 0.064320 0.064101 0.063980 0.063953
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2 0.073376 0.073465 0.064948 0.064896 0.064938 0.065069 0.065289
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3 0.080571 0.080502 0.073406 0.073187 0.073080 0.073079 0.073184
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4 0.080980 0.081164 0.073471 0.073476 0.073586 0.073797 0.074108
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5 0.081466 0.081896 0.073618 0.073840 0.074161 0.074577 0.075089
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6 0.077313 0.077403 0.071662 0.071579 0.071601 0.071726 0.071951
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7 0.077630 0.077931 0.071685 0.071792 0.072000 0.072305 0.072707
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8 0.078041 0.078548 0.071805 0.072098 0.072486 0.072967 0.073541
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9 0.078548 0.079255 0.072022 0.072495 0.073059 0.073712 0.074455
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10 0.071805 0.072022 0.067420 0.067457 0.067591 0.067820 0.068141
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11 0.072098 0.072495 0.067457 0.067660 0.067955 0.068340 0.068815
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12 0.072486 0.073059 0.067591 0.067955 0.068407 0.068945 0.069570
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13 0.072967 0.073712 0.067820 0.068340 0.068945 0.069634 0.070406
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14 0.073541 0.074455 0.068141 0.068815 0.069570 0.070406 0.071323
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1 0.068743 0.066200 0.065631 0.063225 0.060971 0.058856 0.056870
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2 0.067765 0.065559 0.064814 0.062696 0.060691 0.058793 0.056996
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3 0.076678 0.074301 0.074306 0.071960 0.069733 0.067619 0.065613
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4 0.075294 0.073154 0.072976 0.070845 0.068809 0.066865 0.065012
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5 0.073824 0.071901 0.071564 0.069629 0.067769 0.065984 0.064275
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6 0.074307 0.072338 0.072718 0.070705 0.068771 0.066919 0.065147
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7 0.072650 0.070865 0.071080 0.069239 0.067462 0.065753 0.064113
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8 0.071008 0.069391 0.069456 0.067774 0.066143 0.064568 0.063051
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9 0.069391 0.067929 0.067859 0.066323 0.064827 0.063378 0.061977
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10 0.069456 0.067859 0.068437 0.066752 0.065119 0.063542 0.062023
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11 0.067774 0.066323 0.066752 0.065207 0.063705 0.062250 0.060845
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12 0.066143 0.064827 0.065119 0.063705 0.062325 0.060983 0.059685
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13 0.064568 0.063378 0.063542 0.062250 0.060983 0.059749 0.058550
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14 0.063051 0.061977 0.062023 0.060845 0.059685 0.058550 0.057446
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</pre></div>
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</div>
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</div>
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