update
This commit is contained in:
Morten Hjorth-Jensen
@@ -313,6 +313,26 @@ const thebe_selector_output = ".output, .cell_output"
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Week 40: Gradient descent methods (continued) and start Neural networks
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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="exercisesweek41.html">
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Exercises week 41
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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="week41.html">
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Week 41 Neural networks and constructing a neural network code
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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="exercisesweek42.html">
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Exercises week 42
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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="week42.html">
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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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</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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@@ -325,6 +345,11 @@ const thebe_selector_output = ".output, .cell_output"
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Project 1 on Machine Learning, deadline October 9 (midnight), 2023
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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="project2.html">
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Project 2 on Machine Learning, deadline November 13 (Midnight)
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</a>
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</li>
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</ul>
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</div>
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@@ -1290,10 +1315,10 @@ 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>-0.033790755027115954
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3.888577549915147
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[[ 1.2697447 3.97644118]
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[ 3.97644118 13.38465596]]
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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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</pre></div>
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</div>
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</div>
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@@ -1330,10 +1355,10 @@ 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>0.08238863600759742
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1.795225339396409
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[[1. 0.64391062]
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[0.64391062 1. ]]
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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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</pre></div>
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</div>
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</div>
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@@ -1363,30 +1388,30 @@ 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>[[ 1.29135778 4.3399612 ]
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[ 0.08815506 -1.48140137]
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[ 0.34149655 1.18571316]
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[-1.00375475 -2.69226802]
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[ 0.42198678 2.56858701]
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[ 0.53278871 2.8969113 ]
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[-1.38020451 -4.26263837]
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[-0.64969451 -2.00778523]
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[-0.32632463 -1.7413913 ]
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[ 0.68419351 1.19431161]]
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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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0 1
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0 1.291358 4.339961
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1 0.088155 -1.481401
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2 0.341497 1.185713
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3 -1.003755 -2.692268
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4 0.421987 2.568587
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5 0.532789 2.896911
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6 -1.380205 -4.262638
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7 -0.649695 -2.007785
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8 -0.326325 -1.741391
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9 0.684194 1.194312
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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 1
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0 1.000000 0.943439
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1 0.943439 1.000000
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0 1.000000 0.962653
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1 0.962653 1.000000
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</pre></div>
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</div>
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</div>
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@@ -1443,40 +1468,37 @@ 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 0.089710 0.084075 0.088697 0.086518 0.084247 0.079455 0.077756
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2 0.0 0.084075 0.079226 0.082189 0.080406 0.078545 0.073008 0.071611
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3 0.0 0.088697 0.082189 0.093408 0.090365 0.087247 0.087250 0.084843
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4 0.0 0.086518 0.080406 0.090365 0.087603 0.084764 0.083853 0.081680
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5 0.0 0.084247 0.078545 0.087247 0.084764 0.082205 0.080411 0.078467
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6 0.0 0.079455 0.073008 0.087250 0.083853 0.080411 0.083988 0.081246
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7 0.0 0.077756 0.071611 0.084843 0.081680 0.078467 0.081246 0.078707
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8 0.0 0.076125 0.070275 0.082517 0.079581 0.076592 0.078593 0.076249
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9 0.0 0.074545 0.068987 0.080256 0.077542 0.074772 0.076012 0.073858
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10 0.0 0.070597 0.064412 0.079882 0.076354 0.072805 0.078656 0.075758
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11 0.0 0.068974 0.063055 0.077650 0.074330 0.070986 0.076136 0.073422
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12 0.0 0.067437 0.061775 0.075523 0.072404 0.069257 0.073728 0.071191
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13 0.0 0.065982 0.060567 0.073494 0.070569 0.067611 0.071423 0.069055
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14 0.0 0.064602 0.059427 0.071554 0.068816 0.066042 0.069213 0.067009
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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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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 0.076125 0.074545 0.070597 0.068974 0.067437 0.065982 0.064602
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2 0.070275 0.068987 0.064412 0.063055 0.061775 0.060567 0.059427
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3 0.082517 0.080256 0.079882 0.077650 0.075523 0.073494 0.071554
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4 0.079581 0.077542 0.076354 0.074330 0.072404 0.070569 0.068816
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5 0.076592 0.074772 0.072805 0.070986 0.069257 0.067611 0.066042
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6 0.078593 0.076012 0.078656 0.076136 0.073728 0.071423 0.069213
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7 0.076249 0.073858 0.075758 0.073422 0.071191 0.069055 0.067009
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8 0.073980 0.071773 0.072953 0.070795 0.068734 0.066762 0.064874
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9 0.071773 0.069746 0.070228 0.068241 0.066344 0.064532 0.062797
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10 0.072953 0.070228 0.074969 0.072310 0.069766 0.067328 0.064987
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11 0.070795 0.068241 0.072310 0.069822 0.067440 0.065158 0.062967
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12 0.068734 0.066344 0.069766 0.067440 0.065214 0.063081 0.061034
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13 0.066762 0.064532 0.067328 0.065158 0.063081 0.061092 0.059182
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14 0.064874 0.062797 0.064987 0.062967 0.061034 0.059182 0.057406
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
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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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</pre></div>
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</div>
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</div>
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