book update
This commit is contained in:
Morten Hjorth-Jensen
@@ -128,6 +128,11 @@
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1. Elements of Probability Theory and Statistical Data Analysis
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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="linalg.html">
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2. Linear Algebra, Handling of Arrays and more Python Features
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</a>
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</li>
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</ul>
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<p class="caption collapsible-parent">
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<span class="caption-text">
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@@ -206,6 +211,11 @@
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2. Building a Feed Forward Neural Network
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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="chapter11.html">
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3. Solving Differential Equations with Deep Learning
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</a>
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</li>
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</ul>
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</nav> <!-- To handle the deprecated key -->
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@@ -876,10 +886,10 @@ 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>0.05952725829739267
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4.386262724250306
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[[ 1.1558068 3.35522344]
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[ 3.35522344 10.90654923]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.062127739929490035
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4.226441441217558
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[[0.95977893 2.78652875]
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[2.78652875 9.09409124]]
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</pre></div>
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</div>
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</div>
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@@ -916,10 +926,10 @@ 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>0.09168618934140788
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1.945123703671126
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[[1. 0.72291211]
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[0.72291211 1. ]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.09053391104887817
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1.9755272664385481
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[[1. 0.64723729]
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[0.64723729 1. ]]
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</pre></div>
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</div>
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</div>
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@@ -948,30 +958,30 @@ 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>[[ 0.2701033 0.76102689]
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[ 0.51167594 2.72484235]
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[ 0.32455001 -1.43364263]
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[-0.53774919 -1.34290169]
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[-0.05260429 -0.42595379]
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[ 0.07893835 0.61280302]
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[-1.6055745 -4.25652444]
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[ 0.85403864 3.64196375]
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[ 0.57068381 2.34793363]
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[-0.41406207 -2.62954709]]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[ 0.52374318 0.7528421 ]
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[-1.07892554 -3.5697027 ]
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[ 0.65536057 2.84854 ]
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[-0.9936011 -1.75368597]
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[-0.22233456 -1.63866932]
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[ 1.10799046 4.51410028]
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[ 1.30401938 5.04686521]
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[ 0.70055962 1.28566384]
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[-1.69423925 -6.23684061]
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[-0.30257276 -1.24911284]]
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0 1
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0 0.270103 0.761027
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1 0.511676 2.724842
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2 0.324550 -1.433643
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3 -0.537749 -1.342902
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4 -0.052604 -0.425954
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5 0.078938 0.612803
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6 -1.605575 -4.256524
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7 0.854039 3.641964
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8 0.570684 2.347934
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9 -0.414062 -2.629547
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0 0.523743 0.752842
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1 -1.078926 -3.569703
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2 0.655361 2.848540
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3 -0.993601 -1.753686
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4 -0.222335 -1.638669
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5 1.107990 4.514100
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6 1.304019 5.046865
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7 0.700560 1.285664
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8 -1.694239 -6.236841
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9 -0.302573 -1.249113
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0 1
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0 1.000000 0.894794
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1 0.894794 1.000000
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0 1.000000 0.967871
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1 0.967871 1.000000
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</pre></div>
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</div>
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</div>
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@@ -1028,37 +1038,37 @@ 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 0.090640 0.079152 0.086791 0.086033 0.083807 0.076613 0.076853
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2 0.0 0.079152 0.070665 0.073700 0.073719 0.072669 0.064120 0.064658
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3 0.0 0.086791 0.073700 0.089130 0.086860 0.082986 0.082366 0.081697
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4 0.0 0.086033 0.073719 0.086860 0.085104 0.081822 0.079399 0.079040
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5 0.0 0.083807 0.072669 0.082986 0.081822 0.079272 0.074974 0.074942
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6 0.0 0.076613 0.064120 0.082366 0.079399 0.074974 0.078615 0.077369
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7 0.0 0.076853 0.064658 0.081697 0.079040 0.074942 0.077369 0.076344
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8 0.0 0.076988 0.065180 0.080804 0.078501 0.074783 0.075867 0.075083
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9 0.0 0.076812 0.065537 0.079464 0.077568 0.074305 0.073910 0.073388
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10 0.0 0.067721 0.056185 0.075172 0.071927 0.067396 0.073456 0.071888
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11 0.0 0.067838 0.056481 0.074711 0.071675 0.067356 0.072592 0.071185
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12 0.0 0.068022 0.056864 0.074259 0.071451 0.067365 0.071702 0.070467
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13 0.0 0.068244 0.057318 0.073774 0.071219 0.067394 0.070744 0.069695
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14 0.0 0.068449 0.057811 0.073183 0.070913 0.067391 0.069650 0.068802
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1 0.0 0.076075 0.081429 0.075275 0.076780 0.077999 0.067453 0.067971
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2 0.0 0.081429 0.088214 0.081300 0.083371 0.085063 0.072811 0.073594
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3 0.0 0.075275 0.081300 0.080335 0.082127 0.083567 0.075400 0.075990
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4 0.0 0.076780 0.083371 0.082127 0.084184 0.085857 0.076996 0.077729
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5 0.0 0.077999 0.085063 0.083567 0.085857 0.087738 0.078264 0.079128
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6 0.0 0.067453 0.072811 0.075400 0.076996 0.078264 0.072961 0.073444
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7 0.0 0.067971 0.073594 0.075990 0.077729 0.079128 0.073444 0.074016
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8 0.0 0.068431 0.074291 0.076495 0.078367 0.079889 0.073843 0.074498
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9 0.0 0.068860 0.074936 0.076947 0.078943 0.080582 0.074186 0.074922
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10 0.0 0.059693 0.064192 0.068842 0.070144 0.071159 0.068084 0.068427
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11 0.0 0.059875 0.064519 0.069009 0.070400 0.071499 0.068172 0.068575
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12 0.0 0.060056 0.064837 0.069164 0.070641 0.071822 0.068246 0.068709
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13 0.0 0.060243 0.065156 0.069319 0.070878 0.072139 0.068315 0.068837
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14 0.0 0.060442 0.065483 0.069478 0.071119 0.072459 0.068387 0.068966
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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 0.076988 0.076812 0.067721 0.067838 0.068022 0.068244 0.068449
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2 0.065180 0.065537 0.056185 0.056481 0.056864 0.057318 0.057811
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3 0.080804 0.079464 0.075172 0.074711 0.074259 0.073774 0.073183
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4 0.078501 0.077568 0.071927 0.071675 0.071451 0.071219 0.070913
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5 0.074783 0.074305 0.067396 0.067356 0.067365 0.067394 0.067391
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6 0.075867 0.073910 0.073456 0.072592 0.071702 0.070744 0.069650
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7 0.075083 0.073388 0.071888 0.071185 0.070467 0.069695 0.068802
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8 0.074088 0.072685 0.070067 0.069534 0.069000 0.068429 0.067755
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9 0.072685 0.071614 0.067818 0.067465 0.067127 0.066772 0.066339
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10 0.070067 0.067818 0.069854 0.068745 0.067594 0.066361 0.064984
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11 0.069534 0.067465 0.068745 0.067758 0.066737 0.065641 0.064409
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12 0.069000 0.067127 0.067594 0.066737 0.065852 0.064903 0.063827
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13 0.068429 0.066772 0.066361 0.065641 0.064903 0.064110 0.063205
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14 0.067755 0.066339 0.064984 0.064409 0.063827 0.063205 0.062484
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1 0.068431 0.068860 0.059693 0.059875 0.060056 0.060243 0.060442
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2 0.074291 0.074936 0.064192 0.064519 0.064837 0.065156 0.065483
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3 0.076495 0.076947 0.068842 0.069009 0.069164 0.069319 0.069478
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4 0.078367 0.078943 0.070144 0.070400 0.070641 0.070878 0.071119
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5 0.079889 0.080582 0.071159 0.071499 0.071822 0.072139 0.072459
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6 0.073843 0.074186 0.068084 0.068172 0.068246 0.068315 0.068387
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7 0.074498 0.074922 0.068427 0.068575 0.068709 0.068837 0.068966
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8 0.075062 0.075564 0.068693 0.068901 0.069093 0.069278 0.069465
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9 0.075564 0.076143 0.068909 0.069174 0.069423 0.069665 0.069908
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10 0.068693 0.068909 0.064578 0.064582 0.064574 0.064559 0.064545
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11 0.068901 0.069174 0.064582 0.064632 0.064668 0.064698 0.064728
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12 0.069093 0.069423 0.064574 0.064668 0.064748 0.064822 0.064896
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13 0.069278 0.069665 0.064559 0.064698 0.064822 0.064940 0.065058
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14 0.069465 0.069908 0.064545 0.064728 0.064896 0.065058 0.065220
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</pre></div>
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</div>
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</div>
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