tensorflow problems with chapter 12
This commit is contained in:
Morten Hjorth-Jensen
@@ -1111,10 +1111,10 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.05805002932374468
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4.309519578637819
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[[ 1.23033954 3.6804336 ]
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[ 3.6804336 11.92527504]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0119839668275544
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3.9026821944532157
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[[ 1.21805483 3.6417002 ]
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[ 3.6417002 11.67950066]]
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</pre></div>
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@@ -1151,10 +1151,10 @@ a more brute force way. Here we scale the mean values for each column of the des
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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>0.09198004868226574
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1.9493620821393187
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[[1. 0.68904673]
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[0.68904673 1. ]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08283349271892
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1.6319972672199556
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[[1. 0.65062105]
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[0.65062105 1. ]]
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</pre></div>
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@@ -1184,30 +1184,30 @@ 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>[[-1.13352411e+00 -4.86145508e+00]
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[ 5.06793578e-01 2.24339370e+00]
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[ 4.08359441e-03 -1.44109702e+00]
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[-2.02972824e-01 -1.17393096e+00]
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[ 9.99703985e-01 5.00745587e+00]
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[-1.05430325e-01 3.31446832e-02]
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[ 4.31082669e-01 6.38783434e-01]
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[ 3.40259257e-02 5.84099240e-01]
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[-8.44862840e-01 -2.71546272e+00]
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[ 3.11100342e-01 1.68506886e+00]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[ 0.30199745 0.72164987]
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[ 1.04858244 2.95539603]
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[ 0.46840512 1.52940309]
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[ 0.24117177 1.78848481]
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[-1.38690487 -4.17705119]
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[-0.65735283 -2.46141305]
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[ 0.94916469 2.81774574]
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[-0.52449732 -2.22140879]
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[-0.66999836 -2.66511337]
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[ 0.22943192 1.71230687]]
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0 1
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0 -1.133524 -4.861455
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1 0.506794 2.243394
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2 0.004084 -1.441097
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3 -0.202973 -1.173931
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4 0.999704 5.007456
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5 -0.105430 0.033145
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6 0.431083 0.638783
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7 0.034026 0.584099
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8 -0.844863 -2.715463
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9 0.311100 1.685069
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0 0.301997 0.721650
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1 1.048582 2.955396
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2 0.468405 1.529403
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3 0.241172 1.788485
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4 -1.386905 -4.177051
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5 -0.657353 -2.461413
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6 0.949165 2.817746
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7 -0.524497 -2.221409
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8 -0.669998 -2.665113
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9 0.229432 1.712307
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0 1
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0 1.000000 0.958527
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1 0.958527 1.000000
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0 1.000000 0.975127
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1 0.975127 1.000000
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</pre></div>
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@@ -1264,37 +1264,37 @@ 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 0.082212 0.080493 0.081616 0.077560 0.073611 0.073557 0.069528
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2 0.0 0.080493 0.080583 0.082413 0.079267 0.075998 0.075624 0.072071
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3 0.0 0.081616 0.082413 0.087107 0.084071 0.080840 0.082136 0.078414
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4 0.0 0.077560 0.079267 0.084071 0.081732 0.079085 0.080069 0.076853
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5 0.0 0.073611 0.075998 0.080840 0.079085 0.076946 0.077662 0.074900
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6 0.0 0.073557 0.075624 0.082136 0.080069 0.077662 0.079815 0.076731
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7 0.0 0.069528 0.072071 0.078414 0.076853 0.074900 0.076731 0.074079
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8 0.0 0.065794 0.068700 0.074858 0.073728 0.072170 0.073713 0.071447
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9 0.0 0.062352 0.065534 0.071499 0.070739 0.069526 0.070812 0.068888
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10 0.0 0.065582 0.068203 0.075503 0.074125 0.072352 0.074961 0.072447
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11 0.0 0.062022 0.064909 0.071917 0.070915 0.069494 0.071782 0.069625
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12 0.0 0.058765 0.061857 0.068581 0.067904 0.066793 0.068790 0.066951
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13 0.0 0.055788 0.059038 0.065492 0.065096 0.064256 0.065991 0.064434
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14 0.0 0.053069 0.056441 0.062636 0.062486 0.061885 0.063380 0.062076
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1 0.0 0.096033 0.089499 0.100065 0.095636 0.091356 0.093135 0.089366
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2 0.0 0.089499 0.084679 0.095430 0.091860 0.088348 0.090343 0.087109
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3 0.0 0.100065 0.095430 0.109996 0.106272 0.102568 0.105983 0.102429
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4 0.0 0.095636 0.091860 0.106272 0.103072 0.099840 0.103254 0.100074
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5 0.0 0.091356 0.088348 0.102568 0.099840 0.097041 0.100443 0.097606
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6 0.0 0.093135 0.090343 0.105983 0.103254 0.100443 0.104606 0.101680
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7 0.0 0.089366 0.087109 0.102429 0.100074 0.097606 0.101680 0.099049
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8 0.0 0.085816 0.084032 0.099028 0.097007 0.094850 0.098834 0.096471
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9 0.0 0.082468 0.081101 0.095771 0.094052 0.092175 0.096070 0.093951
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10 0.0 0.085269 0.083748 0.099374 0.097448 0.095375 0.099827 0.097488
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11 0.0 0.082003 0.080845 0.096087 0.094440 0.092628 0.096955 0.094852
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12 0.0 0.078938 0.078099 0.092968 0.091572 0.089996 0.094201 0.092313
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13 0.0 0.076058 0.075503 0.090008 0.088838 0.087475 0.091561 0.089869
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14 0.0 0.073350 0.073045 0.087198 0.086231 0.085061 0.089033 0.087519
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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 0.065794 0.062352 0.065582 0.062022 0.058765 0.055788 0.053069
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2 0.068700 0.065534 0.068203 0.064909 0.061857 0.059038 0.056441
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3 0.074858 0.071499 0.075503 0.071917 0.068581 0.065492 0.062636
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4 0.073728 0.070739 0.074125 0.070915 0.067904 0.065096 0.062486
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5 0.072170 0.069526 0.072352 0.069494 0.066793 0.064256 0.061885
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6 0.073713 0.070812 0.074961 0.071782 0.068790 0.065991 0.063380
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7 0.071447 0.068888 0.072447 0.069625 0.066951 0.064434 0.062076
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8 0.069161 0.066912 0.069940 0.067442 0.065059 0.062803 0.060678
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9 0.066912 0.064945 0.067492 0.065289 0.063171 0.061154 0.059245
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10 0.069940 0.067492 0.071541 0.068800 0.066196 0.063738 0.061430
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11 0.067442 0.065289 0.068800 0.066372 0.064050 0.061846 0.059767
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12 0.065059 0.063171 0.066196 0.064050 0.061983 0.060011 0.058141
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13 0.062803 0.061154 0.063738 0.061846 0.060011 0.058249 0.056570
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14 0.060678 0.059245 0.061430 0.059767 0.058141 0.056570 0.055065
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1 0.085816 0.082468 0.085269 0.082003 0.078938 0.076058 0.073350
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2 0.084032 0.081101 0.083748 0.080845 0.078099 0.075503 0.073045
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3 0.099028 0.095771 0.099374 0.096087 0.092968 0.090008 0.087198
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4 0.097007 0.094052 0.097448 0.094440 0.091572 0.088838 0.086231
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5 0.094850 0.092175 0.095375 0.092628 0.089996 0.087475 0.085061
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6 0.098834 0.096070 0.099827 0.096955 0.094201 0.091561 0.089033
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7 0.096471 0.093951 0.097488 0.094852 0.092313 0.089869 0.087519
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8 0.094138 0.091843 0.095173 0.092754 0.090414 0.088152 0.085968
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9 0.091843 0.089754 0.092891 0.090673 0.088516 0.086423 0.084394
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10 0.095173 0.092891 0.096568 0.094135 0.091779 0.089500 0.087297
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11 0.092754 0.090673 0.094135 0.091901 0.089727 0.087616 0.085567
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12 0.090414 0.088516 0.091779 0.089727 0.087721 0.085765 0.083859
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13 0.088152 0.086423 0.089500 0.087616 0.085765 0.083951 0.082179
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14 0.085968 0.084394 0.087297 0.085567 0.083859 0.082179 0.080529
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</pre></div>
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@@ -2150,15 +2150,13 @@ set of <span class="math notranslate nohighlight">\(\lambda\)</span> values.</p>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.03099776 -0.17917768 5.18029127]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Training MSE for OLS
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Training MSE for OLS
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0.009163470508352228
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Test MSE OLS
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0.008675369724976777
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</pre></div>
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<img alt="_images/chapter2_249_2.png" src="_images/chapter2_249_2.png" />
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<img alt="_images/chapter2_249_1.png" src="_images/chapter2_249_1.png" />
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<p>Both these example send a clear message. The addition of a
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