tensorflow problems with chapter 12
This commit is contained in:
Morten Hjorth-Jensen
@@ -532,10 +532,10 @@ 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>-0.1014841451296029
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3.744674093727182
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[[ 1.04621283 3.2509618 ]
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[ 3.2509618 11.28244928]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.13740265686642364
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3.647867896223553
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[[0.88732076 2.58645106]
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[2.58645106 8.40379027]]
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</pre></div>
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@@ -575,10 +575,10 @@ 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>0.08899392007617646
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1.960919045835183
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[[1. 0.64606848]
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[0.64606848 1. ]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.09282061240353208
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2.0679953259809554
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[[1. 0.65276752]
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[0.65276752 1. ]]
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</pre></div>
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@@ -607,30 +607,30 @@ 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>[[-0.26338419 -0.43128913]
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[-0.40052348 -0.40977892]
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[-1.21495309 -3.03292103]
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[-0.68288031 -2.86961626]
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[ 1.847187 7.221629 ]
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[-0.01324363 0.06365882]
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[-0.08321393 -1.11171059]
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[ 0.6497097 1.58382502]
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[-0.3196681 -1.46807752]
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[ 0.48097003 0.45428062]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[ 0.27416797 2.69235507]
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[-0.30895532 -1.36796587]
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[ 1.03285324 2.17106672]
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[-0.35304998 -2.85655181]
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[-0.79084976 -1.67951873]
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[ 0.19247017 0.868215 ]
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[-0.45745139 -2.25170803]
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[-0.63165748 -1.85258411]
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[-0.19636844 0.14649905]
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[ 1.238841 4.13019269]]
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0 1
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0 -0.263384 -0.431289
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1 -0.400523 -0.409779
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2 -1.214953 -3.032921
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3 -0.682880 -2.869616
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4 1.847187 7.221629
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5 -0.013244 0.063659
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6 -0.083214 -1.111711
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7 0.649710 1.583825
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8 -0.319668 -1.468078
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9 0.480970 0.454281
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0 0.274168 2.692355
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1 -0.308955 -1.367966
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2 1.032853 2.171067
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3 -0.353050 -2.856552
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4 -0.790850 -1.679519
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5 0.192470 0.868215
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6 -0.457451 -2.251708
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7 -0.631657 -1.852584
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8 -0.196368 0.146499
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9 1.238841 4.130193
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0 1
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0 1.000000 0.959043
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1 0.959043 1.000000
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0 1.000000 0.899606
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1 0.899606 1.000000
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</pre></div>
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@@ -687,37 +687,37 @@ 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 0.077527 0.078731 0.077076 0.078129 0.079210 0.068328 0.069362
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2 0.0 0.078731 0.081031 0.079460 0.081106 0.082747 0.071284 0.072687
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3 0.0 0.077076 0.079460 0.081529 0.083368 0.085189 0.075205 0.076784
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4 0.0 0.078129 0.081106 0.083368 0.085569 0.087738 0.077404 0.079228
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5 0.0 0.079210 0.082747 0.085189 0.087738 0.090239 0.079562 0.081623
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6 0.0 0.068328 0.071284 0.075205 0.077404 0.079562 0.071309 0.073110
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7 0.0 0.069362 0.072687 0.076784 0.079228 0.081623 0.073110 0.075086
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8 0.0 0.070455 0.074134 0.078402 0.081085 0.083711 0.074934 0.077084
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9 0.0 0.071609 0.075631 0.080066 0.082982 0.085835 0.076789 0.079111
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10 0.0 0.059604 0.062733 0.067433 0.069731 0.071979 0.065235 0.067080
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11 0.0 0.060603 0.063986 0.068834 0.071311 0.073734 0.066778 0.068758
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12 0.0 0.061660 0.065292 0.070286 0.072940 0.075537 0.068363 0.070479
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13 0.0 0.062777 0.066653 0.071794 0.074624 0.077395 0.069997 0.072248
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14 0.0 0.063954 0.068071 0.073361 0.076367 0.079312 0.071682 0.074070
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1 0.0 0.090368 0.089828 0.084745 0.089694 0.094385 0.072722 0.077607
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2 0.0 0.089828 0.091016 0.082203 0.087699 0.093293 0.069684 0.074647
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3 0.0 0.084745 0.082203 0.084433 0.088351 0.091653 0.075222 0.079774
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4 0.0 0.089694 0.087699 0.088351 0.092763 0.096661 0.078226 0.083104
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5 0.0 0.094385 0.093293 0.091653 0.096661 0.101332 0.080548 0.085760
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6 0.0 0.072722 0.069684 0.075222 0.078226 0.080548 0.068780 0.072668
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7 0.0 0.077607 0.074647 0.079774 0.083104 0.085760 0.072668 0.076854
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8 0.0 0.082726 0.079963 0.084408 0.088121 0.091191 0.076564 0.081069
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9 0.0 0.088020 0.085632 0.089011 0.093180 0.096774 0.080344 0.085192
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10 0.0 0.062054 0.059107 0.065855 0.068244 0.069993 0.061366 0.064683
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11 0.0 0.066283 0.063260 0.070074 0.072690 0.074644 0.065135 0.068701
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12 0.0 0.070793 0.067729 0.074516 0.077390 0.079587 0.069075 0.072910
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13 0.0 0.075587 0.072539 0.079162 0.082333 0.084822 0.073160 0.077286
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14 0.0 0.080656 0.077711 0.083974 0.087490 0.090338 0.077343 0.081784
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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 0.070455 0.071609 0.059604 0.060603 0.061660 0.062777 0.063954
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2 0.074134 0.075631 0.062733 0.063986 0.065292 0.066653 0.068071
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3 0.078402 0.080066 0.067433 0.068834 0.070286 0.071794 0.073361
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4 0.081085 0.082982 0.069731 0.071311 0.072940 0.074624 0.076367
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5 0.083711 0.085835 0.071979 0.073734 0.075537 0.077395 0.079312
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6 0.074934 0.076789 0.065235 0.066778 0.068363 0.069997 0.071682
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7 0.077084 0.079111 0.067080 0.068758 0.070479 0.072248 0.074070
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8 0.079253 0.081450 0.068941 0.070753 0.072608 0.074513 0.076473
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9 0.081450 0.083817 0.070824 0.072770 0.074761 0.076803 0.078901
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10 0.068941 0.070824 0.060591 0.062149 0.063743 0.065378 0.067060
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11 0.070753 0.072770 0.062149 0.063814 0.065517 0.067262 0.069055
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12 0.072608 0.074761 0.063743 0.065517 0.067329 0.069185 0.071092
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13 0.074513 0.076803 0.065378 0.067262 0.069185 0.071155 0.073178
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14 0.076473 0.078901 0.067060 0.069055 0.071092 0.073178 0.075318
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1 0.082726 0.088020 0.062054 0.066283 0.070793 0.075587 0.080656
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2 0.079963 0.085632 0.059107 0.063260 0.067729 0.072539 0.077711
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3 0.084408 0.089011 0.065855 0.070074 0.074516 0.079162 0.083974
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4 0.088121 0.093180 0.068244 0.072690 0.077390 0.082333 0.087490
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5 0.091191 0.096774 0.069993 0.074644 0.079587 0.084822 0.090338
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6 0.076564 0.080344 0.061366 0.065135 0.069075 0.073160 0.077343
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7 0.081069 0.085192 0.064683 0.068701 0.072910 0.077286 0.081784
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8 0.085636 0.090149 0.067977 0.072252 0.076743 0.081428 0.086266
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9 0.090149 0.095107 0.071137 0.075674 0.080455 0.085465 0.090671
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10 0.067977 0.071137 0.055542 0.058856 0.062305 0.065864 0.069488
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11 0.072252 0.075674 0.058856 0.062396 0.066086 0.069899 0.073789
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12 0.076743 0.080455 0.062305 0.066086 0.070032 0.074118 0.078298
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13 0.081428 0.085465 0.065864 0.069899 0.074118 0.078497 0.082989
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14 0.086266 0.090671 0.069488 0.073789 0.078298 0.082989 0.087822
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</pre></div>
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@@ -906,10 +906,10 @@ We can write our own code or simply use either the functionaly of <strong>numpy<
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1
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0 3.982025 2.012742
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1 2.012742 2.042269
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[[3.98202489 2.01274179]
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[2.01274179 2.04226949]]
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0 3.900266 1.942180
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1 1.942180 1.965724
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[[3.90026646 1.9421795 ]
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[1.9421795 1.96572363]]
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</pre></div>
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@@ -936,8 +936,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Centered covariance using own code
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[[3.98202489 2.01274179]
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[2.01274179 2.04226949]]
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[[3.90026646 1.9421795 ]
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[1.9421795 1.96572363]]
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</pre></div>
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<img alt="_images/chapter8_65_1.png" src="_images/chapter8_65_1.png" />
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@@ -997,16 +997,16 @@ questions.</p>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvalues of Covariance matrix
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5.246379112732898
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0.7779152676639822
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5.102712819944882
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0.763277270613554
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First eigenvector
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[0.84678774 0.53193095]
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[0.85023678 0.52640043]
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Second eigenvector
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[-0.53193095 0.84678774]
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[-0.52640043 0.85023678]
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</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvector of largest eigenvalue
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[0.84678774 0.53193095]
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[-0.85023678 -0.52640043]
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</pre></div>
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</div>
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</div>
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