update
This commit is contained in:
Morten Hjorth-Jensen
@@ -10,7 +10,7 @@ edge [fontname="helvetica"] ;
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4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ;
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3 -> 4 ;
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5 [label="mean perimeter <= 78.51\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
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5 [label="worst compactness <= 0.085\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
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3 -> 5 ;
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6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ;
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@@ -30,11 +30,11 @@ edge [fontname="helvetica"] ;
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11 -> 13 ;
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14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ;
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0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
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15 [label="worst perimeter <= 116.8\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
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15 [label="worst concavity <= 0.318\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
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14 -> 15 ;
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16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ;
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15 -> 16 ;
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17 [label="smoothness error <= 0.005\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
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17 [label="area error <= 23.16\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
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15 -> 17 ;
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18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
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@@ -42,13 +42,13 @@ edge [fontname="helvetica"] ;
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17 -> 19 ;
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20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#ea985d"] ;
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14 -> 20 ;
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21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ;
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21 [label="compactness error <= 0.016\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ;
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20 -> 21 ;
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22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139"] ;
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21 -> 22 ;
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23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ;
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21 -> 23 ;
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24 [label="mean smoothness <= 0.079\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
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24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
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20 -> 24 ;
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25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
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@@ -357,6 +357,11 @@ const thebe_selector_output = ".output, .cell_output"
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Week 46: Decision Trees, Ensemble methods and Random Forests
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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="week47.html">
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Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
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</a>
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</li>
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</ul>
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Load Diff
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
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Week 45, Recurrent Neural Networks
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</a>
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</li>
|
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<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
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</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
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||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
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@@ -1086,13 +1101,13 @@ example of the functionality of <strong>Scikit-Learn</strong>.</p>
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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>The intercept alpha:
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[1.79503422]
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[2.07575375]
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Coefficient beta :
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[[5.33918941]]
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Mean squared error: 0.21
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Variance score: 0.92
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[[4.80180325]]
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Mean squared error: 0.28
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Variance score: 0.88
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Mean squared log error: 0.01
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Mean absolute error: 0.37
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Mean absolute error: 0.40
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</pre></div>
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</div>
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<img alt="_images/chapter1_19_1.png" src="_images/chapter1_19_1.png" />
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@@ -1192,7 +1207,7 @@ a linear <span class="math notranslate nohighlight">\(x\)</span>-dependence we s
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</div>
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<div class="cell_output docutils container">
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<img alt="_images/chapter1_33_0.png" src="_images/chapter1_33_0.png" />
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.005
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.005000000000000002
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</pre></div>
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</div>
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</div>
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@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -1403,7 +1418,7 @@ the <em>Hadamard product</em>, meaning element-wise multiplication.</p>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old accuracy on training data: 0.1440501043841336
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</pre></div>
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||||
</div>
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15302/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
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return 1/(1 + np.exp(-x))
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</pre></div>
|
||||
</div>
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@@ -1677,305 +1692,30 @@ Lambda = 0.0001
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Accuracy score on test set: 0.85
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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>Learning rate = 0.001
|
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Lambda = 0.001
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Accuracy score on test set: 0.875
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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>Learning rate = 0.001
|
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Lambda = 0.01
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Accuracy score on test set: 0.8666666666666667
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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>Learning rate = 0.001
|
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Lambda = 0.1
|
||||
Accuracy score on test set: 0.8638888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.9555555555555556
|
||||
</pre></div>
|
||||
</div>
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||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.925
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.9472222222222222
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.9277777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.9472222222222222
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.9305555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.9555555555555556
|
||||
</pre></div>
|
||||
</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
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Lambda = 1.0
|
||||
Accuracy score on test set: 0.7694444444444445
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.19166666666666668
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.08611111111111111
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.08888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.08611111111111111
|
||||
</pre></div>
|
||||
</div>
|
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
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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>Learning rate = 0.1
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.08888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.09166666666666666
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.07777777777777778
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
|
||||
<span class="nn">Input In [8],</span> in <span class="ni"><cell line: 7></span><span class="nt">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">lmbd</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lmbd_vals</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="n">dnn</span> <span class="o">=</span> <span class="n">NeuralNetwork</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train_onehot</span><span class="p">,</span> <span class="n">eta</span><span class="o">=</span><span class="n">eta</span><span class="p">,</span> <span class="n">lmbd</span><span class="o">=</span><span class="n">lmbd</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">10</span> <span class="n">n_hidden_neurons</span><span class="o">=</span><span class="n">n_hidden_neurons</span><span class="p">,</span> <span class="n">n_categories</span><span class="o">=</span><span class="n">n_categories</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">11</span> <span class="n">dnn</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">13</span> <span class="n">DNN_numpy</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">dnn</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">15</span> <span class="n">test_predict</span> <span class="o">=</span> <span class="n">dnn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">Input In [6],</span> in <span class="ni">NeuralNetwork.train</span><span class="nt">(self)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">95</span> <span class="bp">self</span><span class="o">.</span><span class="n">X_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">X_data_full</span><span class="p">[</span><span class="n">chosen_datapoints</span><span class="p">]</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">96</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Y_data_full</span><span class="p">[</span><span class="n">chosen_datapoints</span><span class="p">]</span>
|
||||
<span class="ne">---> </span><span class="mi">98</span> <span class="bp">self</span><span class="o">.</span><span class="n">feed_forward</span><span class="p">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">99</span> <span class="bp">self</span><span class="o">.</span><span class="n">backpropagation</span><span class="p">()</span>
|
||||
|
||||
<span class="nn">Input In [6],</span> in <span class="ni">NeuralNetwork.feed_forward</span><span class="nt">(self)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">36</span> <span class="k">def</span> <span class="nf">feed_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">37</span> <span class="c1"># feed-forward for training</span>
|
||||
<span class="ne">---> </span><span class="mi">38</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_h</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">X_data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_bias</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">39</span> <span class="bp">self</span><span class="o">.</span><span class="n">a_h</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">z_h</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">41</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_o</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">a_h</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_weights</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_bias</span>
|
||||
|
||||
<span class="ne">KeyboardInterrupt</span>:
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -2021,22 +1761,6 @@ Accuracy score on test set: 0.07777777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8624/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapter10_59_1.png" src="_images/chapter10_59_1.png" />
|
||||
<img alt="_images/chapter10_59_2.png" src="_images/chapter10_59_2.png" />
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="scikit-learn-implementation">
|
||||
@@ -2072,332 +1796,6 @@ performance overall.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.18333333333333332
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.18611111111111112
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.13055555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.24444444444444444
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.23333333333333334
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.12777777777777777
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.1527777777777778
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.9111111111111111
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.8888888888888888
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.8722222222222222
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.8305555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.8888888888888888
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.8805555555555555
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.8944444444444445
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.975
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.9777777777777777
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.9805555555555555
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.9861111111111112
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.9805555555555555
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.9777777777777777
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.001
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.9444444444444444
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.9861111111111112
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.9888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.9888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.9861111111111112
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.9888888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.9722222222222222
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.01
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.9527777777777777
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.9027777777777778
|
||||
|
||||
Learning rate = 0.1
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.8583333333333333
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.8722222222222222
|
||||
|
||||
Learning rate = 0.1
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.9055555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.8805555555555555
|
||||
|
||||
Learning rate = 0.1
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.8722222222222222
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.1
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.8666666666666667
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.08611111111111111
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.17777777777777778
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.08333333333333333
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.08888888888888889
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.09444444444444444
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.17222222222222222
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.0001
|
||||
Accuracy score on test set: 0.11666666666666667
|
||||
|
||||
Learning rate = 10.0
|
||||
Lambda = 0.001
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 0.01
|
||||
Accuracy score on test set: 0.1388888888888889
|
||||
|
||||
Learning rate = 10.0
|
||||
Lambda = 0.1
|
||||
Accuracy score on test set: 0.11388888888888889
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 1.0
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
|
||||
Lambda = 10.0
|
||||
Accuracy score on test set: 0.09444444444444444
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="id1">
|
||||
@@ -2441,10 +1839,6 @@ Accuracy score on test set: 0.09444444444444444
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/chapter10_63_0.png" src="_images/chapter10_63_0.png" />
|
||||
<img alt="_images/chapter10_63_1.png" src="_images/chapter10_63_1.png" />
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="building-neural-networks-in-tensorflow-and-keras">
|
||||
@@ -2483,14 +1877,6 @@ and/or if you use <strong>anaconda</strong>, just write (or install from the gra
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span> <span class="n">Input</span> <span class="n">In</span> <span class="p">[</span><span class="mi">12</span><span class="p">]</span>
|
||||
<span class="n">conda</span> <span class="n">create</span> <span class="o">-</span><span class="n">n</span> <span class="n">tf</span> <span class="n">tensorflow</span>
|
||||
<span class="o">^</span>
|
||||
<span class="ne">SyntaxError</span>: invalid syntax
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<p>To install the current release of GPU TensorFlow</p>
|
||||
<div class="cell docutils container">
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -2657,140 +2672,20 @@ Using TensorFlow results in a much better execution time. Try it!</p>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">19</span> <span class="n">x</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">argnum</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">20</span> <span class="k">return</span> <span class="n">unary_operator</span><span class="p">(</span><span class="n">unary_f</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">nary_op_args</span><span class="p">,</span> <span class="o">**</span><span class="n">nary_op_kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25,</span> in <span class="ni">grad</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">18</span> <span class="nd">@unary_to_nary</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">19</span> <span class="k">def</span> <span class="nf">grad</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">20</span><span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">21</span><span class="sd"> Returns a function which computes the gradient of `fun` with respect to</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">22</span><span class="sd"> positional argument number `argnum`. The returned function takes the same</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">23</span><span class="sd"> arguments as `fun`, but returns the gradient instead. The function `fun`</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">24</span><span class="sd"> should be scalar-valued. The gradient has the same type as the argument."""</span>
|
||||
<span class="ne">---> </span><span class="mi">25</span> <span class="n">vjp</span><span class="p">,</span> <span class="n">ans</span> <span class="o">=</span> <span class="n">_make_vjp</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">26</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">vspace</span><span class="p">(</span><span class="n">ans</span><span class="p">)</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">27</span> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Grad only applies to real scalar-output functions. "</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">28</span> <span class="s2">"Try jacobian, elementwise_grad or holomorphic_grad."</span><span class="p">)</span>
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:29,</span> in <span class="ni">grad</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">26</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">vspace</span><span class="p">(</span><span class="n">ans</span><span class="p">)</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">27</span> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Grad only applies to real scalar-output functions. "</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">28</span> <span class="s2">"Try jacobian, elementwise_grad or holomorphic_grad."</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">29</span> <span class="k">return</span> <span class="n">vjp</span><span class="p">(</span><span class="n">vspace</span><span class="p">(</span><span class="n">ans</span><span class="p">)</span><span class="o">.</span><span class="n">ones</span><span class="p">())</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10,</span> in <span class="ni">make_vjp</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="k">def</span> <span class="nf">make_vjp</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="n">start_node</span> <span class="o">=</span> <span class="n">VJPNode</span><span class="o">.</span><span class="n">new_root</span><span class="p">()</span>
|
||||
<span class="ne">---> </span><span class="mi">10</span> <span class="n">end_value</span><span class="p">,</span> <span class="n">end_node</span> <span class="o">=</span> <span class="n">trace</span><span class="p">(</span><span class="n">start_node</span><span class="p">,</span> <span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">11</span> <span class="k">if</span> <span class="n">end_node</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">12</span> <span class="k">def</span> <span class="nf">vjp</span><span class="p">(</span><span class="n">g</span><span class="p">):</span> <span class="k">return</span> <span class="n">vspace</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">zeros</span><span class="p">()</span>
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14,</span> in <span class="ni">make_vjp.<locals>.vjp</span><span class="nt">(g)</span>
|
||||
<span class="ne">---> </span><span class="mi">14</span> <span class="k">def</span> <span class="nf">vjp</span><span class="p">(</span><span class="n">g</span><span class="p">):</span> <span class="k">return</span> <span class="n">backward_pass</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="n">end_node</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10,</span> in <span class="ni">trace</span><span class="nt">(start_node, fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="k">with</span> <span class="n">trace_stack</span><span class="o">.</span><span class="n">new_trace</span><span class="p">()</span> <span class="k">as</span> <span class="n">t</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="n">start_box</span> <span class="o">=</span> <span class="n">new_box</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">start_node</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">10</span> <span class="n">end_box</span> <span class="o">=</span> <span class="n">fun</span><span class="p">(</span><span class="n">start_box</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">11</span> <span class="k">if</span> <span class="n">isbox</span><span class="p">(</span><span class="n">end_box</span><span class="p">)</span> <span class="ow">and</span> <span class="n">end_box</span><span class="o">.</span><span class="n">_trace</span> <span class="o">==</span> <span class="n">start_box</span><span class="o">.</span><span class="n">_trace</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">12</span> <span class="k">return</span> <span class="n">end_box</span><span class="o">.</span><span class="n">_value</span><span class="p">,</span> <span class="n">end_box</span><span class="o">.</span><span class="n">_node</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15,</span> in <span class="ni">unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f</span><span class="nt">(x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">13</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">14</span> <span class="n">subargs</span> <span class="o">=</span> <span class="n">subvals</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="nb">zip</span><span class="p">(</span><span class="n">argnum</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
|
||||
<span class="ne">---> </span><span class="mi">15</span> <span class="k">return</span> <span class="n">fun</span><span class="p">(</span><span class="o">*</span><span class="n">subargs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">Input In [9],</span> in <span class="ni">cost_function</span><span class="nt">(P, x, t)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">78</span> <span class="n">g_t</span> <span class="o">=</span> <span class="n">g_trial</span><span class="p">(</span><span class="n">point</span><span class="p">,</span><span class="n">P</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">79</span> <span class="n">g_t_jacobian</span> <span class="o">=</span> <span class="n">g_t_jacobian_func</span><span class="p">(</span><span class="n">point</span><span class="p">,</span><span class="n">P</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">80</span> <span class="n">g_t_hessian</span> <span class="o">=</span> <span class="n">g_t_hessian_func</span><span class="p">(</span><span class="n">point</span><span class="p">,</span><span class="n">P</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">82</span> <span class="n">g_t_dt</span> <span class="o">=</span> <span class="n">g_t_jacobian</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">83</span> <span class="n">g_t_d2x</span> <span class="o">=</span> <span class="n">g_t_hessian</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20,</span> in <span class="ni">unary_to_nary.<locals>.nary_operator.<locals>.nary_f</span><span class="nt">(*args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">18</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">19</span> <span class="n">x</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">argnum</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">20</span> <span class="k">return</span> <span class="n">unary_operator</span><span class="p">(</span><span class="n">unary_f</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">nary_op_args</span><span class="p">,</span> <span class="o">**</span><span class="n">nary_op_kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:78,</span> in <span class="ni">hessian</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">75</span> <span class="nd">@unary_to_nary</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">76</span> <span class="k">def</span> <span class="nf">hessian</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">77</span> <span class="s2">"Returns a function that computes the exact Hessian."</span>
|
||||
<span class="ne">---> </span><span class="mi">78</span> <span class="k">return</span> <span class="n">jacobian</span><span class="p">(</span><span class="n">jacobian</span><span class="p">(</span><span class="n">fun</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20,</span> in <span class="ni">unary_to_nary.<locals>.nary_operator.<locals>.nary_f</span><span class="nt">(*args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">18</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">19</span> <span class="n">x</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">argnum</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">20</span> <span class="k">return</span> <span class="n">unary_operator</span><span class="p">(</span><span class="n">unary_f</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">nary_op_args</span><span class="p">,</span> <span class="o">**</span><span class="n">nary_op_kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:57,</span> in <span class="ni">jacobian</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">47</span> <span class="nd">@unary_to_nary</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">48</span> <span class="k">def</span> <span class="nf">jacobian</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">49</span><span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">50</span><span class="sd"> Returns a function which computes the Jacobian of `fun` with respect to</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">51</span><span class="sd"> positional argument number `argnum`, which must be a scalar or array. Unlike</span>
|
||||
<span class="sd"> (...)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">55</span><span class="sd"> (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">56</span><span class="sd"> """</span>
|
||||
<span class="ne">---> </span><span class="mi">57</span> <span class="n">vjp</span><span class="p">,</span> <span class="n">ans</span> <span class="o">=</span> <span class="n">_make_vjp</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">58</span> <span class="n">ans_vspace</span> <span class="o">=</span> <span class="n">vspace</span><span class="p">(</span><span class="n">ans</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">59</span> <span class="n">jacobian_shape</span> <span class="o">=</span> <span class="n">ans_vspace</span><span class="o">.</span><span class="n">shape</span> <span class="o">+</span> <span class="n">vspace</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10,</span> in <span class="ni">make_vjp</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="k">def</span> <span class="nf">make_vjp</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="n">start_node</span> <span class="o">=</span> <span class="n">VJPNode</span><span class="o">.</span><span class="n">new_root</span><span class="p">()</span>
|
||||
<span class="ne">---> </span><span class="mi">10</span> <span class="n">end_value</span><span class="p">,</span> <span class="n">end_node</span> <span class="o">=</span> <span class="n">trace</span><span class="p">(</span><span class="n">start_node</span><span class="p">,</span> <span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">11</span> <span class="k">if</span> <span class="n">end_node</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">12</span> <span class="k">def</span> <span class="nf">vjp</span><span class="p">(</span><span class="n">g</span><span class="p">):</span> <span class="k">return</span> <span class="n">vspace</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">zeros</span><span class="p">()</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10,</span> in <span class="ni">trace</span><span class="nt">(start_node, fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="k">with</span> <span class="n">trace_stack</span><span class="o">.</span><span class="n">new_trace</span><span class="p">()</span> <span class="k">as</span> <span class="n">t</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">9</span> <span class="n">start_box</span> <span class="o">=</span> <span class="n">new_box</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">start_node</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">10</span> <span class="n">end_box</span> <span class="o">=</span> <span class="n">fun</span><span class="p">(</span><span class="n">start_box</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">11</span> <span class="k">if</span> <span class="n">isbox</span><span class="p">(</span><span class="n">end_box</span><span class="p">)</span> <span class="ow">and</span> <span class="n">end_box</span><span class="o">.</span><span class="n">_trace</span> <span class="o">==</span> <span class="n">start_box</span><span class="o">.</span><span class="n">_trace</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">12</span> <span class="k">return</span> <span class="n">end_box</span><span class="o">.</span><span class="n">_value</span><span class="p">,</span> <span class="n">end_box</span><span class="o">.</span><span class="n">_node</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15,</span> in <span class="ni">unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f</span><span class="nt">(x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">13</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">14</span> <span class="n">subargs</span> <span class="o">=</span> <span class="n">subvals</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="nb">zip</span><span class="p">(</span><span class="n">argnum</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
|
||||
<span class="ne">---> </span><span class="mi">15</span> <span class="k">return</span> <span class="n">fun</span><span class="p">(</span><span class="o">*</span><span class="n">subargs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20,</span> in <span class="ni">unary_to_nary.<locals>.nary_operator.<locals>.nary_f</span><span class="nt">(*args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">18</span> <span class="k">else</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">19</span> <span class="n">x</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">argnum</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">20</span> <span class="k">return</span> <span class="n">unary_operator</span><span class="p">(</span><span class="n">unary_f</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">nary_op_args</span><span class="p">,</span> <span class="o">**</span><span class="n">nary_op_kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:61,</span> in <span class="ni">jacobian</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">59</span> <span class="n">jacobian_shape</span> <span class="o">=</span> <span class="n">ans_vspace</span><span class="o">.</span><span class="n">shape</span> <span class="o">+</span> <span class="n">vspace</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">60</span> <span class="n">grads</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="n">vjp</span><span class="p">,</span> <span class="n">ans_vspace</span><span class="o">.</span><span class="n">standard_basis</span><span class="p">())</span>
|
||||
<span class="ne">---> </span><span class="mi">61</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">grads</span><span class="p">),</span> <span class="n">jacobian_shape</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:103,</span> in <span class="ni">stack</span><span class="nt">(arrays, axis)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">100</span> <span class="n">axis</span> <span class="o">+=</span> <span class="n">result_ndim</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">102</span> <span class="n">sl</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">),)</span> <span class="o">*</span> <span class="n">axis</span> <span class="o">+</span> <span class="p">(</span><span class="kc">None</span><span class="p">,)</span>
|
||||
<span class="ne">--> </span><span class="mi">103</span> <span class="k">return</span> <span class="n">concatenate</span><span class="p">([</span><span class="n">arr</span><span class="p">[</span><span class="n">sl</span><span class="p">]</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:103,</span> in <span class="ni"><listcomp></span><span class="nt">(.0)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">100</span> <span class="n">axis</span> <span class="o">+=</span> <span class="n">result_ndim</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">102</span> <span class="n">sl</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">),)</span> <span class="o">*</span> <span class="n">axis</span> <span class="o">+</span> <span class="p">(</span><span class="kc">None</span><span class="p">,)</span>
|
||||
<span class="ne">--> </span><span class="mi">103</span> <span class="k">return</span> <span class="n">concatenate</span><span class="p">([</span><span class="n">arr</span><span class="p">[</span><span class="n">sl</span><span class="p">]</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:44,</span> in <span class="ni">primitive.<locals>.f_wrapped</span><span class="nt">(*args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">42</span> <span class="n">parents</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">box</span><span class="o">.</span><span class="n">_node</span> <span class="k">for</span> <span class="n">_</span> <span class="p">,</span> <span class="n">box</span> <span class="ow">in</span> <span class="n">boxed_args</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">43</span> <span class="n">argnums</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">argnum</span> <span class="k">for</span> <span class="n">argnum</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">boxed_args</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">44</span> <span class="n">ans</span> <span class="o">=</span> <span class="n">f_wrapped</span><span class="p">(</span><span class="o">*</span><span class="n">argvals</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">45</span> <span class="n">node</span> <span class="o">=</span> <span class="n">node_constructor</span><span class="p">(</span><span class="n">ans</span><span class="p">,</span> <span class="n">f_wrapped</span><span class="p">,</span> <span class="n">argvals</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">argnums</span><span class="p">,</span> <span class="n">parents</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">46</span> <span class="k">return</span> <span class="n">new_box</span><span class="p">(</span><span class="n">ans</span><span class="p">,</span> <span class="n">trace</span><span class="p">,</span> <span class="n">node</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45,</span> in <span class="ni">primitive.<locals>.f_wrapped</span><span class="nt">(*args, **kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">43</span> <span class="n">argnums</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">argnum</span> <span class="k">for</span> <span class="n">argnum</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">boxed_args</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">44</span> <span class="n">ans</span> <span class="o">=</span> <span class="n">f_wrapped</span><span class="p">(</span><span class="o">*</span><span class="n">argvals</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">45</span> <span class="n">node</span> <span class="o">=</span> <span class="n">node_constructor</span><span class="p">(</span><span class="n">ans</span><span class="p">,</span> <span class="n">f_wrapped</span><span class="p">,</span> <span class="n">argvals</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">argnums</span><span class="p">,</span> <span class="n">parents</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">46</span> <span class="k">return</span> <span class="n">new_box</span><span class="p">(</span><span class="n">ans</span><span class="p">,</span> <span class="n">trace</span><span class="p">,</span> <span class="n">node</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">47</span> <span class="k">else</span><span class="p">:</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36,</span> in <span class="ni">VJPNode.__init__</span><span class="nt">(self, value, fun, args, kwargs, parent_argnums, parents)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">33</span> <span class="n">fun_name</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="s1">'__name__'</span><span class="p">,</span> <span class="n">fun</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">34</span> <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"VJP of </span><span class="si">{}</span><span class="s2"> wrt argnums </span><span class="si">{}</span><span class="s2"> not defined"</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">35</span> <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fun_name</span><span class="p">,</span> <span class="n">parent_argnums</span><span class="p">))</span>
|
||||
<span class="ne">---> </span><span class="mi">36</span> <span class="bp">self</span><span class="o">.</span><span class="n">vjp</span> <span class="o">=</span> <span class="n">vjpmaker</span><span class="p">(</span><span class="n">parent_argnums</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:56,</span> in <span class="ni">defvjp.<locals>.vjp_argnums</span><span class="nt">(argnums, ans, args, kwargs)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">53</span> <span class="n">argnums</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'argnums'</span><span class="p">,</span> <span class="n">count</span><span class="p">())</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">54</span> <span class="n">vjps_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">argnum</span> <span class="p">:</span> <span class="n">translate_vjp</span><span class="p">(</span><span class="n">vjpmaker</span><span class="p">,</span> <span class="n">fun</span><span class="p">,</span> <span class="n">argnum</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">55</span> <span class="k">for</span> <span class="n">argnum</span><span class="p">,</span> <span class="n">vjpmaker</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">argnums</span><span class="p">,</span> <span class="n">vjpmakers</span><span class="p">)}</span>
|
||||
<span class="ne">---> </span><span class="mi">56</span> <span class="k">def</span> <span class="nf">vjp_argnums</span><span class="p">(</span><span class="n">argnums</span><span class="p">,</span> <span class="n">ans</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">57</span> <span class="n">L</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">argnums</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">58</span> <span class="c1"># These first two cases are just optimizations</span>
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:23,</span> in <span class="ni">backward_pass</span><span class="nt">(g, end_node)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">21</span> <span class="n">ingrads</span> <span class="o">=</span> <span class="n">node</span><span class="o">.</span><span class="n">vjp</span><span class="p">(</span><span class="n">outgrad</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">22</span> <span class="k">for</span> <span class="n">parent</span><span class="p">,</span> <span class="n">ingrad</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">parents</span><span class="p">,</span> <span class="n">ingrads</span><span class="p">):</span>
|
||||
<span class="ne">---> </span><span class="mi">23</span> <span class="n">outgrads</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="n">add_outgrads</span><span class="p">(</span><span class="n">outgrads</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">parent</span><span class="p">),</span> <span class="n">ingrad</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">24</span> <span class="k">return</span> <span class="n">outgrad</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="ne">KeyboardInterrupt</span>:
|
||||
</pre></div>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -1317,7 +1332,27 @@ labels = (n_inputs) = (1797,)
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
|
||||
super(SGD, self).__init__(name, **kwargs)
|
||||
2023-11-08 15:24:42.293245: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
|
||||
2023-11-21 06:09:14.627153: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 1/12 [=>............................] - ETA: 1s - loss: 2.5762 - accuracy: 0.2188
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
7/12 [================>.............] - ETA: 0s - loss: 2.7312 - accuracy: 0.2277
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
12/12 [==============================] - ETA: 0s - loss: 2.7089 - accuracy: 0.2389
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
12/12 [==============================] - 1s 35ms/step - loss: 2.7089 - accuracy: 0.2389
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
|
||||
Lambda = 1e-05
|
||||
Test accuracy: 0.239
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -720,321 +735,345 @@ systems such as automatic translation and speech-to-text.</p>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 1/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2023-11-08 15:25:18.982829: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2023-11-21 06:09:51.383315: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 3s - loss: 1.7073 - 3s/epoch - 65ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 2s - loss: 0.6262 - 2s/epoch - 43ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 2/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.5091 - 450ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4148 - 440ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 3/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4349 - 447ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4134 - 485ms/epoch - 10ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 4/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4192 - 447ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.4111 - 507ms/epoch - 10ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 5/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4095 - 448ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4099 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 6/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4056 - 450ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4051 - 449ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 7/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4055 - 451ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4082 - 448ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 8/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4025 - 449ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4052 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 9/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3975 - 452ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4032 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 10/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3994 - 451ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4053 - 439ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 11/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3976 - 449ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4007 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 12/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3973 - 455ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4036 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 13/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3951 - 452ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4008 - 449ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 14/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3957 - 454ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3978 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 15/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3953 - 472ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.4018 - 449ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 16/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3940 - 479ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3984 - 448ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 17/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3939 - 455ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3975 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 18/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3897 - 483ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3954 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 19/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3936 - 467ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3995 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 20/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3929 - 455ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3987 - 449ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 21/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3913 - 455ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3961 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 22/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3900 - 463ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3948 - 444ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 23/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3921 - 516ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3968 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 24/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3903 - 485ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3970 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 25/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3902 - 471ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3929 - 444ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 26/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3887 - 472ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3971 - 445ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 27/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3895 - 519ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3960 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 28/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3873 - 477ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3915 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 29/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3879 - 500ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3942 - 449ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 30/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3876 - 485ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3937 - 445ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 31/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3865 - 472ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3944 - 445ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 32/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3856 - 459ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3912 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 33/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3856 - 502ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3939 - 445ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 34/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3839 - 479ms/epoch - 10ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3932 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 35/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3853 - 459ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3863 - 445ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 36/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3827 - 457ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3880 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 37/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3824 - 453ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3888 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 38/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3824 - 455ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3893 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 39/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3812 - 455ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3895 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 40/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3823 - 469ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3904 - 448ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 41/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 1s - loss: 0.3814 - 527ms/epoch - 11ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3877 - 448ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 42/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3814 - 464ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3852 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 43/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3812 - 453ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3902 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 44/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3779 - 456ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3856 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 45/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3797 - 457ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3876 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 46/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3808 - 460ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3880 - 444ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 47/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3788 - 460ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3873 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 48/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3792 - 455ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3848 - 448ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 49/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3765 - 457ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3852 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 50/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3775 - 454ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3877 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 51/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3777 - 458ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3852 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 52/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3770 - 458ms/epoch - 9ms/step
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3840 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 53/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3834 - 444ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 54/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3851 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 55/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3864 - 446ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 56/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>50/50 - 0s - loss: 0.3812 - 447ms/epoch - 9ms/step
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 57/100
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
|
||||
<span class="nn">Input In [1],</span> in <span class="ni"><cell line: 58></span><span class="nt">()</span>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -1335,10 +1350,10 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07752620206774397
|
||||
4.3068687590657415
|
||||
[[1.01031184 2.85759522]
|
||||
[2.85759522 9.0542566 ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.031405320178937886
|
||||
3.7508636683863235
|
||||
[[ 1.07728714 3.41235715]
|
||||
[ 3.41235715 11.76274779]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1375,10 +1390,10 @@ a more brute force way. Here we scale the mean values for each column of the des
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07472152457534222
|
||||
1.4560786541572335
|
||||
[[1. 0.61219726]
|
||||
[0.61219726 1. ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08460107110703337
|
||||
1.9541100061668464
|
||||
[[1. 0.73391309]
|
||||
[0.73391309 1. ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1408,30 +1423,30 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.59265811 -0.57943748]
|
||||
[ 0.08641073 0.06656566]
|
||||
[-0.68596176 -2.59060904]
|
||||
[ 0.47558206 1.64482901]
|
||||
[ 0.21597684 -0.67723533]
|
||||
[ 1.22935165 5.02293408]
|
||||
[-0.51249881 -3.38478181]
|
||||
[ 0.29220202 1.35149796]
|
||||
[ 0.10955639 0.86177342]
|
||||
[-0.61796102 -1.71553646]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.80226723 -3.04272582]
|
||||
[ 0.43201921 2.14816084]
|
||||
[ 1.57309147 3.36212275]
|
||||
[-1.88172029 -7.76015446]
|
||||
[ 1.07203435 4.92153252]
|
||||
[ 0.60376519 3.40447029]
|
||||
[-0.63613568 -1.37836163]
|
||||
[-1.93626155 -5.91514067]
|
||||
[ 1.59593425 4.28910504]
|
||||
[-0.02045972 -0.02900886]]
|
||||
0 1
|
||||
0 -0.592658 -0.579437
|
||||
1 0.086411 0.066566
|
||||
2 -0.685962 -2.590609
|
||||
3 0.475582 1.644829
|
||||
4 0.215977 -0.677235
|
||||
5 1.229352 5.022934
|
||||
6 -0.512499 -3.384782
|
||||
7 0.292202 1.351498
|
||||
8 0.109556 0.861773
|
||||
9 -0.617961 -1.715536
|
||||
0 -0.802267 -3.042726
|
||||
1 0.432019 2.148161
|
||||
2 1.573091 3.362123
|
||||
3 -1.881720 -7.760154
|
||||
4 1.072034 4.921533
|
||||
5 0.603765 3.404470
|
||||
6 -0.636136 -1.378362
|
||||
7 -1.936262 -5.915141
|
||||
8 1.595934 4.289105
|
||||
9 -0.020460 -0.029009
|
||||
0 1
|
||||
0 1.000000 0.921567
|
||||
1 0.921567 1.000000
|
||||
0 1.000000 0.965295
|
||||
1 0.965295 1.000000
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1488,37 +1503,40 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1 2 3 4 5 6 7 \
|
||||
0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.0 0.079330 0.084471 0.080541 0.081753 0.082306 0.073011 0.072990
|
||||
2 0.0 0.084471 0.092487 0.087280 0.089513 0.090777 0.079391 0.079777
|
||||
3 0.0 0.080541 0.087280 0.087211 0.088900 0.089752 0.082231 0.082329
|
||||
4 0.0 0.081753 0.089513 0.088900 0.091023 0.092206 0.083848 0.084164
|
||||
5 0.0 0.082306 0.090777 0.089752 0.092206 0.093657 0.084672 0.085172
|
||||
6 0.0 0.073011 0.079391 0.082231 0.083848 0.084672 0.079611 0.079715
|
||||
7 0.0 0.072990 0.079777 0.082329 0.084164 0.085172 0.079715 0.079958
|
||||
8 0.0 0.072802 0.079892 0.082196 0.084212 0.085382 0.079597 0.079964
|
||||
9 0.0 0.072527 0.079854 0.081937 0.084110 0.085425 0.079353 0.079836
|
||||
10 0.0 0.064985 0.070571 0.075171 0.076586 0.077304 0.074181 0.074265
|
||||
11 0.0 0.064627 0.070400 0.074809 0.076355 0.077194 0.073842 0.074026
|
||||
12 0.0 0.064245 0.070170 0.074403 0.076066 0.077017 0.073458 0.073736
|
||||
13 0.0 0.063864 0.069919 0.073987 0.075758 0.076814 0.073063 0.073431
|
||||
14 0.0 0.063500 0.069672 0.073582 0.075454 0.076612 0.072676 0.073131
|
||||
1 0.0 0.090537 0.085374 0.090091 0.089893 0.089356 0.080696 0.081199
|
||||
2 0.0 0.085374 0.081563 0.083636 0.083982 0.084114 0.074319 0.075079
|
||||
3 0.0 0.090091 0.083636 0.094993 0.094026 0.092598 0.088156 0.088323
|
||||
4 0.0 0.089893 0.083982 0.094026 0.093386 0.092327 0.086892 0.087259
|
||||
5 0.0 0.089356 0.084114 0.092598 0.092327 0.091697 0.085163 0.085746
|
||||
6 0.0 0.080696 0.074319 0.088156 0.086892 0.085163 0.083797 0.083762
|
||||
7 0.0 0.081199 0.075079 0.088323 0.087259 0.085746 0.083762 0.083873
|
||||
8 0.0 0.081680 0.075858 0.088410 0.087566 0.086291 0.083628 0.083894
|
||||
9 0.0 0.082105 0.076636 0.088369 0.087768 0.086762 0.083348 0.083777
|
||||
10 0.0 0.071274 0.065396 0.079741 0.078433 0.076697 0.077121 0.077004
|
||||
11 0.0 0.071854 0.066119 0.080197 0.079026 0.077431 0.077466 0.077460
|
||||
12 0.0 0.072470 0.066891 0.080665 0.079640 0.078196 0.077810 0.077920
|
||||
13 0.0 0.073113 0.067709 0.081133 0.080262 0.078981 0.078139 0.078370
|
||||
14 0.0 0.073773 0.068568 0.081581 0.080878 0.079775 0.078433 0.078791
|
||||
|
||||
8 9 10 11 12 13 14
|
||||
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.072802 0.072527 0.064985 0.064627 0.064245 0.063864 0.063500
|
||||
2 0.079892 0.079854 0.070571 0.070400 0.070170 0.069919 0.069672
|
||||
3 0.082196 0.081937 0.075171 0.074809 0.074403 0.073987 0.073582
|
||||
4 0.084212 0.084110 0.076586 0.076355 0.076066 0.075758 0.075454
|
||||
5 0.085382 0.085425 0.077304 0.077194 0.077017 0.076814 0.076612
|
||||
6 0.079597 0.079353 0.074181 0.073842 0.073458 0.073063 0.072676
|
||||
7 0.079964 0.079836 0.074265 0.074026 0.073736 0.073431 0.073131
|
||||
8 0.080086 0.080069 0.074152 0.074008 0.073810 0.073592 0.073378
|
||||
9 0.080069 0.080157 0.073929 0.073876 0.073766 0.073635 0.073504
|
||||
10 0.074152 0.073929 0.070129 0.069821 0.069475 0.069119 0.068771
|
||||
11 0.074008 0.073876 0.069821 0.069595 0.069327 0.069048 0.068774
|
||||
12 0.073810 0.073766 0.069475 0.069327 0.069136 0.068931 0.068731
|
||||
13 0.073592 0.073635 0.069119 0.069048 0.068931 0.068800 0.068671
|
||||
14 0.073378 0.073504 0.068771 0.068774 0.068731 0.068671 0.068612
|
||||
1 0.081680 0.082105 0.071274 0.071854 0.072470 0.073113 0.073773
|
||||
2 0.075858 0.076636 0.065396 0.066119 0.066891 0.067709 0.068568
|
||||
3 0.088410 0.088369 0.079741 0.080197 0.080665 0.081133 0.081581
|
||||
4 0.087566 0.087768 0.078433 0.079026 0.079640 0.080262 0.080878
|
||||
5 0.086291 0.086762 0.076697 0.077431 0.078196 0.078981 0.079775
|
||||
6 0.083628 0.083348 0.077121 0.077466 0.077810 0.078139 0.078433
|
||||
7 0.083894 0.083777 0.077004 0.077460 0.077920 0.078370 0.078791
|
||||
8 0.084078 0.084137 0.076786 0.077358 0.077938 0.078514 0.079067
|
||||
9 0.084137 0.084387 0.076427 0.077117 0.077822 0.078529 0.079223
|
||||
10 0.076786 0.076427 0.071915 0.072200 0.072478 0.072735 0.072954
|
||||
11 0.077358 0.077117 0.072200 0.072576 0.072948 0.073302 0.073621
|
||||
12 0.077938 0.077822 0.072478 0.072948 0.073417 0.073871 0.074292
|
||||
13 0.078514 0.078529 0.072735 0.073302 0.073871 0.074428 0.074956
|
||||
14 0.079067 0.079223 0.072954 0.073621 0.074292 0.074956 0.075597
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -889,10 +904,10 @@ number <span class="math notranslate nohighlight">\(i\)</span> is left out. Usin
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Runtime: 0.155664 sec
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Runtime: 0.155073 sec
|
||||
Jackknife Statistics :
|
||||
original bias std. error
|
||||
99.9688 99.9588 0.15043
|
||||
100.27 100.26 0.150592
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1111,7 +1126,7 @@ theorem.</p>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Bootstrap Statistics :
|
||||
original bias std. error
|
||||
100.132 14.8115 100.132 0.147896
|
||||
100.186 14.9807 100.189 0.148711
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1660,15 +1675,15 @@ Mean squared error on test data: 1371.99051150
|
||||
Degree of polynomial: 20
|
||||
Mean squared error on training data: 0.00137818
|
||||
Mean squared error on test data: 1887.86252988
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 21
|
||||
Degree of polynomial: 21
|
||||
Mean squared error on training data: 0.00118508
|
||||
Mean squared error on test data: 14859.69908626
|
||||
Degree of polynomial: 22
|
||||
Mean squared error on training data: 0.00092647
|
||||
Mean squared error on test data: 876.51191552
|
||||
Degree of polynomial: 23
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 23
|
||||
Mean squared error on training data: 0.00085889
|
||||
Mean squared error on test data: 5594.60815105
|
||||
Degree of polynomial: 24
|
||||
@@ -1683,19 +1698,19 @@ Mean squared error on test data: 19003.94822514
|
||||
Degree of polynomial: 27
|
||||
Mean squared error on training data: 0.00068946
|
||||
Mean squared error on test data: 2379.66219404
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 28
|
||||
Degree of polynomial: 28
|
||||
Mean squared error on training data: 0.00062595
|
||||
Mean squared error on test data: 4082.19983530
|
||||
Degree of polynomial: 29
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 29
|
||||
Mean squared error on training data: 0.00060705
|
||||
Mean squared error on test data: 3250.17647619
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(testerror), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1929,7 +1944,7 @@ cross-validation (LOOCV).</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2818,9 +2833,9 @@ linear system as an equation would reduce this down to
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
cb = fig.colorbar(im)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2964,9 +2979,9 @@ with the form utilized in linear regression, viz.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
cb = fig.colorbar(im)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -3006,9 +3021,9 @@ cost function is given by</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
cb = fig.colorbar(im)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -3043,9 +3058,9 @@ cost function is given by</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
cb = fig.colorbar(im)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -3098,43 +3113,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0%| | 0/10 [00:00<?, ?it/s]
|
||||
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|
||||
</pre></div>
|
||||
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|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:647: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00
|
||||
model = cd_fast.enet_coordinate_descent(
|
||||
|
||||
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|
||||
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|
||||
</pre></div>
|
||||
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|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 20%|███████████████████████████████ | 2/10 [00:02<00:08, 1.03s/it]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 20%|████████████████████████████▌ | 2/10 [00:01<00:07, 1.01it/s]
|
||||
</pre></div>
|
||||
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|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 30%|██████████████████████████████████████████████▌ | 3/10 [00:02<00:06, 1.16it/s]
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 30%|██████████████████████████████████████████▉ | 3/10 [00:02<00:05, 1.24it/s]
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||||
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 40%|██████████████████████████████████████████████████████████████ | 4/10 [00:03<00:04, 1.28it/s]
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 40%|█████████████████████████████████████████████████████████▏ | 4/10 [00:03<00:04, 1.39it/s]
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||||
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 50%|█████████████████████████████████████████████████████████████████████████████▌ | 5/10 [00:03<00:03, 1.42it/s]
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 50%|███████████████████████████████████████████████████████████████████████▌ | 5/10 [00:03<00:03, 1.53it/s]
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||||
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||||
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 60%|█████████████████████████████████████████████████████████████████████████████████████████████ | 6/10 [00:04<00:02, 1.47it/s]
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 60%|█████████████████████████████████████████████████████████████████████████████████████▊ | 6/10 [00:04<00:02, 1.61it/s]
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||||
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 7/10 [00:05<00:01, 1.55it/s]
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 70%|████████████████████████████████████████████████████████████████████████████████████████████████████ | 7/10 [00:04<00:01, 1.65it/s]
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||||
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 8/10 [00:05<00:01, 1.58it/s]
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 80%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 8/10 [00:05<00:01, 1.66it/s]
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 9/10 [00:06<00:00, 1.69it/s]
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 90%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 9/10 [00:05<00:00, 1.67it/s]
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.70it/s]
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.59it/s]
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||||
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.46it/s]
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.49it/s]
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||||
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||||
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||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
@@ -3281,9 +3296,9 @@ which polynomial fits the data best.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
|
||||
ax = fig.gca(projection='3d')
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8674/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15405/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
|
||||
fig.colorbar(surf, shrink=0.5, aspect=5)
|
||||
</pre></div>
|
||||
</div>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
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|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -817,9 +832,9 @@ predicting the target features of query instances is as follows:</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2nd degree coefficients:
|
||||
zero power: 0.9887034589972739
|
||||
first power: -0.10518426027535331
|
||||
second power: 0.0005840075008020406
|
||||
zero power: 2.7023746599300384
|
||||
first power: 0.03407676546787885
|
||||
second power: 9.208257931205295e-06
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapter6_1_1.png" src="_images/chapter6_1_1.png" />
|
||||
@@ -1417,16 +1432,61 @@ humidity and weak and strong for wind.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
|
||||
<span class="ne">FileNotFoundError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
|
||||
<span class="nn">Input In [6],</span> in <span class="ni"><cell line: 37></span><span class="nt">()</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">34</span> <span class="k">def</span> <span class="nf">save_fig</span><span class="p">(</span><span class="n">fig_id</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">35</span> <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">image_path</span><span class="p">(</span><span class="n">fig_id</span><span class="p">)</span> <span class="o">+</span> <span class="s2">".png"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">'png'</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">37</span> <span class="n">infile</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">data_path</span><span class="p">(</span><span class="s2">"rideclass.csv"</span><span class="p">),</span><span class="s1">'r'</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">39</span> <span class="c1"># Read the experimental data with Pandas</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">40</span> <span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
|
||||
|
||||
<span class="ne">FileNotFoundError</span>: [Errno 2] No such file or directory: 'DataFiles/rideclass.csv'
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> (0, 0) 1.0
|
||||
(0, 7) 1.0
|
||||
(0, 9) 1.0
|
||||
(0, 13) 1.0
|
||||
(1, 3) 1.0
|
||||
(1, 5) 1.0
|
||||
(1, 8) 1.0
|
||||
(1, 12) 1.0
|
||||
(2, 3) 1.0
|
||||
(2, 5) 1.0
|
||||
(2, 8) 1.0
|
||||
(2, 11) 1.0
|
||||
(3, 1) 1.0
|
||||
(3, 5) 1.0
|
||||
(3, 8) 1.0
|
||||
(3, 12) 1.0
|
||||
(4, 2) 1.0
|
||||
(4, 6) 1.0
|
||||
(4, 8) 1.0
|
||||
(4, 12) 1.0
|
||||
(5, 2) 1.0
|
||||
(5, 4) 1.0
|
||||
(5, 10) 1.0
|
||||
(5, 12) 1.0
|
||||
(6, 2) 1.0
|
||||
: :
|
||||
(8, 12) 1.0
|
||||
(9, 3) 1.0
|
||||
(9, 4) 1.0
|
||||
(9, 10) 1.0
|
||||
(9, 12) 1.0
|
||||
(10, 2) 1.0
|
||||
(10, 6) 1.0
|
||||
(10, 10) 1.0
|
||||
(10, 12) 1.0
|
||||
(11, 3) 1.0
|
||||
(11, 6) 1.0
|
||||
(11, 10) 1.0
|
||||
(11, 11) 1.0
|
||||
(12, 1) 1.0
|
||||
(12, 6) 1.0
|
||||
(12, 8) 1.0
|
||||
(12, 11) 1.0
|
||||
(13, 1) 1.0
|
||||
(13, 5) 1.0
|
||||
(13, 10) 1.0
|
||||
(13, 12) 1.0
|
||||
(14, 2) 1.0
|
||||
(14, 6) 1.0
|
||||
(14, 8) 1.0
|
||||
(14, 11) 1.0
|
||||
Train set accuracy with Decision Tree: 0.73
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1501,6 +1561,67 @@ algorithm ID3.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>X1 < 0.000 Gini=0.408
|
||||
X1 < 0.000 Gini=0.408
|
||||
X1 < 1.000 Gini=0.394
|
||||
X1 < 2.000 Gini=0.394
|
||||
X1 < 2.000 Gini=0.394
|
||||
X1 < 2.000 Gini=0.394
|
||||
X1 < 1.000 Gini=0.394
|
||||
X1 < 0.000 Gini=0.408
|
||||
X1 < 0.000 Gini=0.408
|
||||
X1 < 2.000 Gini=0.394
|
||||
X1 < 0.000 Gini=0.408
|
||||
X1 < 1.000 Gini=0.394
|
||||
X1 < 1.000 Gini=0.394
|
||||
X1 < 2.000 Gini=0.394
|
||||
X2 < 0.000 Gini=0.408
|
||||
X2 < 0.000 Gini=0.408
|
||||
X2 < 0.000 Gini=0.408
|
||||
X2 < 1.000 Gini=0.407
|
||||
X2 < 2.000 Gini=0.407
|
||||
X2 < 2.000 Gini=0.407
|
||||
X2 < 2.000 Gini=0.407
|
||||
X2 < 1.000 Gini=0.407
|
||||
X2 < 2.000 Gini=0.407
|
||||
X2 < 1.000 Gini=0.407
|
||||
X2 < 1.000 Gini=0.407
|
||||
X2 < 1.000 Gini=0.407
|
||||
X2 < 0.000 Gini=0.408
|
||||
X2 < 1.000 Gini=0.407
|
||||
X3 < 0.000 Gini=0.408
|
||||
X3 < 0.000 Gini=0.408
|
||||
X3 < 0.000 Gini=0.408
|
||||
X3 < 0.000 Gini=0.408
|
||||
X3 < 1.000 Gini=0.367
|
||||
X3 < 1.000 Gini=0.367
|
||||
X3 < 1.000 Gini=0.367
|
||||
X3 < 0.000 Gini=0.408
|
||||
X3 < 1.000 Gini=0.367
|
||||
X3 < 1.000 Gini=0.367
|
||||
X3 < 1.000 Gini=0.367
|
||||
X3 < 0.000 Gini=0.408
|
||||
X3 < 1.000 Gini=0.367
|
||||
X3 < 0.000 Gini=0.408
|
||||
X4 < 0.000 Gini=0.408
|
||||
X4 < 1.000 Gini=0.405
|
||||
X4 < 0.000 Gini=0.408
|
||||
X4 < 0.000 Gini=0.408
|
||||
X4 < 0.000 Gini=0.408
|
||||
X4 < 1.000 Gini=0.405
|
||||
X4 < 1.000 Gini=0.405
|
||||
X4 < 0.000 Gini=0.408
|
||||
X4 < 0.000 Gini=0.408
|
||||
X4 < 0.000 Gini=0.408
|
||||
X4 < 1.000 Gini=0.405
|
||||
X4 < 1.000 Gini=0.405
|
||||
X4 < 0.000 Gini=0.408
|
||||
X4 < 1.000 Gini=0.405
|
||||
Split: [X3 < 1.000]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1573,6 +1694,32 @@ attributes at each step while growing the tree.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(426, 30)
|
||||
(143, 30)
|
||||
Test set accuracy with Logistic Regression: 0.94
|
||||
Test set accuracy with SVM: 0.63
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test set accuracy with Decision Trees: 0.90
|
||||
Test set accuracy Logistic Regression with scaled data: 0.96
|
||||
Test set accuracy SVM with scaled data: 0.96
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test set accuracy with Decision Trees and scaled data: 0.89
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
|
||||
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
||||
|
||||
Increase the number of iterations (max_iter) or scale the data as shown in:
|
||||
https://scikit-learn.org/stable/modules/preprocessing.html
|
||||
Please also refer to the documentation for alternative solver options:
|
||||
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
||||
n_iter_i = _check_optimize_result(
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="another-example-the-moons-again">
|
||||
@@ -1646,6 +1793,9 @@ attributes at each step while growing the tree.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/chapter6_42_0.png" src="_images/chapter6_42_0.png" />
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
@@ -1672,6 +1822,9 @@ attributes at each step while growing the tree.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/chapter6_43_0.png" src="_images/chapter6_43_0.png" />
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
@@ -1694,6 +1847,11 @@ attributes at each step while growing the tree.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>DecisionTreeRegressor(max_depth=2, random_state=42)
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
@@ -1738,6 +1896,9 @@ attributes at each step while growing the tree.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/chapter6_46_0.png" src="_images/chapter6_46_0.png" />
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell docutils container">
|
||||
<div class="cell_input docutils container">
|
||||
@@ -1772,6 +1933,9 @@ attributes at each step while growing the tree.</p>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/chapter6_47_0.png" src="_images/chapter6_47_0.png" />
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -771,10 +786,10 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.11743722141098414
|
||||
3.5452708224046345
|
||||
[[ 1.27880068 3.85600299]
|
||||
[ 3.85600299 12.61955303]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.06776308367941637
|
||||
4.128711548693024
|
||||
[[0.73875685 2.21831419]
|
||||
[2.21831419 7.61137175]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -814,10 +829,10 @@ a more brute force way. Here we scale the mean values for each column of the des
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07178264457746288
|
||||
1.6714298027296224
|
||||
[[1. 0.59987612]
|
||||
[0.59987612 1. ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07327275854723572
|
||||
1.4831993814276723
|
||||
[[1. 0.63304358]
|
||||
[0.63304358 1. ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -846,30 +861,30 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-1.05589275 -2.32845846]
|
||||
[-1.43650129 -5.0020496 ]
|
||||
[ 0.30685269 -0.25002882]
|
||||
[ 1.1511986 3.95940231]
|
||||
[-0.84931504 -2.84538739]
|
||||
[-0.63401971 -1.90876452]
|
||||
[ 0.39256409 1.76775004]
|
||||
[ 1.07828283 3.52988562]
|
||||
[-0.18753987 0.14133772]
|
||||
[ 1.23437046 2.9363131 ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[-0.85835723 -3.41774143]
|
||||
[ 1.61213079 4.0877162 ]
|
||||
[ 0.45424136 1.21829201]
|
||||
[-1.21728232 -4.60356071]
|
||||
[-0.22144968 1.11721416]
|
||||
[-2.47068328 -7.69488259]
|
||||
[ 0.99346318 3.77312796]
|
||||
[-0.18802913 -0.87085636]
|
||||
[ 2.10355817 6.71082182]
|
||||
[-0.20759186 -0.32013106]]
|
||||
0 1
|
||||
0 -1.055893 -2.328458
|
||||
1 -1.436501 -5.002050
|
||||
2 0.306853 -0.250029
|
||||
3 1.151199 3.959402
|
||||
4 -0.849315 -2.845387
|
||||
5 -0.634020 -1.908765
|
||||
6 0.392564 1.767750
|
||||
7 1.078283 3.529886
|
||||
8 -0.187540 0.141338
|
||||
9 1.234370 2.936313
|
||||
0 -0.858357 -3.417741
|
||||
1 1.612131 4.087716
|
||||
2 0.454241 1.218292
|
||||
3 -1.217282 -4.603561
|
||||
4 -0.221450 1.117214
|
||||
5 -2.470683 -7.694883
|
||||
6 0.993463 3.773128
|
||||
7 -0.188029 -0.870856
|
||||
8 2.103558 6.710822
|
||||
9 -0.207592 -0.320131
|
||||
0 1
|
||||
0 1.000000 0.972745
|
||||
1 0.972745 1.000000
|
||||
0 1.000000 0.982252
|
||||
1 0.982252 1.000000
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -926,37 +941,37 @@ this matrix we easily see that it is a positive definite matrix.</p>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1 2 3 4 5 6 7 \
|
||||
0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.0 0.093993 0.092566 0.095420 0.093559 0.091714 0.087802 0.086054
|
||||
2 0.0 0.092566 0.091571 0.094206 0.092560 0.090919 0.086830 0.085223
|
||||
3 0.0 0.095420 0.094206 0.103273 0.101409 0.099552 0.098879 0.097049
|
||||
4 0.0 0.093559 0.092560 0.101409 0.099701 0.097995 0.097238 0.095528
|
||||
5 0.0 0.091714 0.090919 0.099552 0.097995 0.096434 0.095596 0.094001
|
||||
6 0.0 0.087802 0.086830 0.098879 0.097238 0.095596 0.097294 0.095624
|
||||
7 0.0 0.086054 0.085223 0.097049 0.095528 0.094001 0.095624 0.094050
|
||||
8 0.0 0.084365 0.083669 0.095273 0.093866 0.092450 0.093996 0.092516
|
||||
9 0.0 0.082734 0.082168 0.093551 0.092254 0.090945 0.092412 0.091021
|
||||
10 0.0 0.079914 0.079165 0.092493 0.091090 0.089678 0.092852 0.091372
|
||||
11 0.0 0.078377 0.077731 0.090832 0.089523 0.088202 0.091293 0.089893
|
||||
12 0.0 0.076897 0.076349 0.089227 0.088007 0.086773 0.089781 0.088456
|
||||
13 0.0 0.075471 0.075017 0.087674 0.086540 0.085390 0.088314 0.087062
|
||||
14 0.0 0.074096 0.073734 0.086172 0.085121 0.084051 0.086891 0.085709
|
||||
1 0.0 0.093096 0.078035 0.092051 0.085918 0.079156 0.082615 0.078445
|
||||
2 0.0 0.078035 0.067042 0.077110 0.072585 0.067659 0.069711 0.066564
|
||||
3 0.0 0.092051 0.077110 0.096788 0.090301 0.083242 0.090313 0.085815
|
||||
4 0.0 0.085918 0.072585 0.090301 0.084631 0.078425 0.084457 0.080543
|
||||
5 0.0 0.079156 0.067659 0.083242 0.078425 0.073141 0.078154 0.074826
|
||||
6 0.0 0.082615 0.069711 0.090313 0.084457 0.078154 0.086511 0.082368
|
||||
7 0.0 0.078445 0.066564 0.085815 0.080543 0.074826 0.082368 0.078667
|
||||
8 0.0 0.074411 0.063533 0.081472 0.076756 0.071602 0.078379 0.075092
|
||||
9 0.0 0.070370 0.060524 0.077146 0.072971 0.068378 0.074425 0.071535
|
||||
10 0.0 0.073425 0.062485 0.082390 0.077304 0.071857 0.080383 0.076734
|
||||
11 0.0 0.070072 0.059925 0.078744 0.074128 0.069148 0.076993 0.073706
|
||||
12 0.0 0.066944 0.057537 0.075342 0.071160 0.066614 0.073828 0.070876
|
||||
13 0.0 0.064000 0.055294 0.072140 0.068364 0.064224 0.070852 0.068211
|
||||
14 0.0 0.061186 0.053157 0.069084 0.065690 0.061938 0.068017 0.065666
|
||||
|
||||
8 9 10 11 12 13 14
|
||||
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.084365 0.082734 0.079914 0.078377 0.076897 0.075471 0.074096
|
||||
2 0.083669 0.082168 0.079165 0.077731 0.076349 0.075017 0.073734
|
||||
3 0.095273 0.093551 0.092493 0.090832 0.089227 0.087674 0.086172
|
||||
4 0.093866 0.092254 0.091090 0.089523 0.088007 0.086540 0.085121
|
||||
5 0.092450 0.090945 0.089678 0.088202 0.086773 0.085390 0.084051
|
||||
6 0.093996 0.092412 0.092852 0.091293 0.089781 0.088314 0.086891
|
||||
7 0.092516 0.091021 0.091372 0.089893 0.088456 0.087062 0.085709
|
||||
8 0.091072 0.089664 0.089925 0.088521 0.087159 0.085835 0.084550
|
||||
9 0.089664 0.088339 0.088510 0.087180 0.085888 0.084633 0.083414
|
||||
10 0.089925 0.088510 0.089979 0.088563 0.087184 0.085842 0.084536
|
||||
11 0.088521 0.087180 0.088563 0.087212 0.085898 0.084617 0.083371
|
||||
12 0.087159 0.085888 0.087184 0.085898 0.084644 0.083423 0.082234
|
||||
13 0.085835 0.084633 0.085842 0.084617 0.083423 0.082260 0.081126
|
||||
14 0.084550 0.083414 0.084536 0.083371 0.082234 0.081126 0.080045
|
||||
1 0.074411 0.070370 0.073425 0.070072 0.066944 0.064000 0.061186
|
||||
2 0.063533 0.060524 0.062485 0.059925 0.057537 0.055294 0.053157
|
||||
3 0.081472 0.077146 0.082390 0.078744 0.075342 0.072140 0.069084
|
||||
4 0.076756 0.072971 0.077304 0.074128 0.071160 0.068364 0.065690
|
||||
5 0.071602 0.068378 0.071857 0.069148 0.066614 0.064224 0.061938
|
||||
6 0.078379 0.074425 0.080383 0.076993 0.073828 0.070852 0.068017
|
||||
7 0.075092 0.071535 0.076734 0.073706 0.070876 0.068211 0.065666
|
||||
8 0.071908 0.068726 0.073223 0.070535 0.068019 0.065645 0.063374
|
||||
9 0.068726 0.065909 0.069753 0.067388 0.065171 0.063076 0.061068
|
||||
10 0.073223 0.069753 0.075692 0.072679 0.069865 0.067220 0.064702
|
||||
11 0.070535 0.067388 0.072679 0.069968 0.067433 0.065045 0.062768
|
||||
12 0.068019 0.065171 0.069865 0.067433 0.065155 0.063006 0.060952
|
||||
13 0.065645 0.063076 0.067220 0.065045 0.063006 0.061078 0.059232
|
||||
14 0.063374 0.061068 0.064702 0.062768 0.060952 0.059232 0.057582
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1145,10 +1160,10 @@ We can write our own code or simply use either the functionaly of <strong>numpy<
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0 1
|
||||
0 4.068439 2.030371
|
||||
1 2.030371 2.006046
|
||||
[[4.06843936 2.03037095]
|
||||
[2.03037095 2.00604596]]
|
||||
0 3.959839 1.973209
|
||||
1 1.973209 1.963889
|
||||
[[3.95983949 1.97320866]
|
||||
[1.97320866 1.96388867]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1175,8 +1190,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Centered covariance using own code
|
||||
[[4.06843936 2.03037095]
|
||||
[2.03037095 2.00604596]]
|
||||
[[3.95983949 1.97320866]
|
||||
[1.97320866 1.96388867]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapter8_65_1.png" src="_images/chapter8_65_1.png" />
|
||||
@@ -1236,16 +1251,16 @@ questions.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvalues of Covariance matrix
|
||||
5.314471861842257
|
||||
0.7600134536106469
|
||||
5.173087120899479
|
||||
0.7506410412799234
|
||||
First eigenvector
|
||||
[0.8522997 0.52305374]
|
||||
[0.85185762 0.52377342]
|
||||
Second eigenvector
|
||||
[-0.52305374 0.8522997 ]
|
||||
[-0.52377342 0.85185762]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvector of largest eigenvalue
|
||||
[0.8522997 0.52305374]
|
||||
[-0.85185762 -0.52377342]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -1067,11 +1082,11 @@ which equals</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8738/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15458/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
|
||||
ax = fig.gca(projection="3d")
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x12b1c2700>
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x12463a760>
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapteroptimization_61_2.png" src="_images/chapteroptimization_61_2.png" />
|
||||
@@ -1129,7 +1144,7 @@ which equals</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D at 0x12e9e6280>]
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D at 0x127461970>]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapteroptimization_69_1.png" src="_images/chapteroptimization_69_1.png" />
|
||||
@@ -1386,11 +1401,11 @@ when <span class="math notranslate nohighlight">\(||\nabla_\beta C(\beta_k) || \
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.33015882 4.55906894]
|
||||
[[4.11836068]
|
||||
[2.85949635]]
|
||||
[[4.11836068]
|
||||
[2.85949635]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.27151245 4.21908927]
|
||||
[[4.2144122 ]
|
||||
[2.86528733]]
|
||||
[[4.2144122 ]
|
||||
[2.86528733]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapteroptimization_123_1.png" src="_images/chapteroptimization_123_1.png" />
|
||||
@@ -1419,9 +1434,9 @@ when <span class="math notranslate nohighlight">\(||\nabla_\beta C(\beta_k) || \
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[4.13451895]
|
||||
[2.8383548 ]]
|
||||
[4.19783086] [2.93535577]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[4.08838465]
|
||||
[3.00383795]]
|
||||
[4.10031273] [3.04626011]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1492,10 +1507,10 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[4.12575322]
|
||||
[2.87918262]]
|
||||
[[4.08876865]
|
||||
[2.90510842]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[3.81243166]
|
||||
[3.07844345]]
|
||||
[[3.80101942]
|
||||
[3.08513055]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapteroptimization_132_1.png" src="_images/chapteroptimization_132_1.png" />
|
||||
@@ -1745,15 +1760,15 @@ function.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[3.78515112]
|
||||
[3.19029687]]
|
||||
Eigenvalues of Hessian Matrix:[0.30739146 4.15768662]
|
||||
[[3.56486674]
|
||||
[3.29918458]]
|
||||
Eigenvalues of Hessian Matrix:[0.2756303 4.36170438]
|
||||
theta from own gd
|
||||
[[3.78515112]
|
||||
[3.19029687]]
|
||||
[[3.56486674]
|
||||
[3.29918458]]
|
||||
theta from own sdg
|
||||
[[3.81425332]
|
||||
[3.25285802]]
|
||||
[[3.52208436]
|
||||
[3.31774469]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapteroptimization_148_1.png" src="_images/chapteroptimization_148_1.png" />
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
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|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
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|
||||
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|
||||
<li class="toctree-l1">
|
||||
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|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
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|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
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|
||||
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|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
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|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
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|
||||
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|
||||
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|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
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|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
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|
||||
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|
||||
<li class="toctree-l1">
|
||||
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|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
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|
||||
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|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
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|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
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|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -351,6 +351,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
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|
||||
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|
||||
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|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -365,7 +375,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
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|
||||
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|
||||
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|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
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|
||||
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|
||||
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|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -824,15 +839,15 @@ regression.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[3.69887085]
|
||||
[3.34977681]]
|
||||
Eigenvalues of Hessian Matrix:[0.25923926 4.67193435]
|
||||
[[4.16422434]
|
||||
[2.90414392]]
|
||||
Eigenvalues of Hessian Matrix:[0.29405153 4.56291744]
|
||||
theta from own gd
|
||||
[[3.69887085]
|
||||
[3.34977681]]
|
||||
[[4.16422434]
|
||||
[2.90414392]]
|
||||
theta from own sdg
|
||||
[[3.63685221]
|
||||
[3.37369014]]
|
||||
[[4.13747168]
|
||||
[2.89670222]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/exercisesweek41_5_1.png" src="_images/exercisesweek41_5_1.png" />
|
||||
@@ -954,14 +969,14 @@ first example shows results with ordinary leats squares.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[3.64342603]
|
||||
[3.30485583]]
|
||||
Eigenvalues of Hessian Matrix:[0.29030069 4.6608358 ]
|
||||
[[3.6398684 ]
|
||||
[3.27790702]]
|
||||
Eigenvalues of Hessian Matrix:[0.35845332 4.35403494]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own gd
|
||||
[[3.64342603]
|
||||
[3.30485583]]
|
||||
[[3.6398684 ]
|
||||
[3.27790702]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/exercisesweek41_16_2.png" src="_images/exercisesweek41_16_2.png" />
|
||||
@@ -1032,73 +1047,73 @@ Eigenvalues of Hessian Matrix:[0.29030069 4.6608358 ]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[4.]
|
||||
[3.]]
|
||||
Eigenvalues of Hessian Matrix:[0.27335131 4.46000649]
|
||||
0 [-13.27099835] [-15.86570776]
|
||||
1 [0.01163425] [-0.00974702]
|
||||
2 [0.01092119] [-0.00914964]
|
||||
3 [0.01025184] [-0.00858886]
|
||||
4 [0.00962351] [-0.00806245]
|
||||
5 [0.00903369] [-0.00756831]
|
||||
6 [0.00848002] [-0.00710445]
|
||||
7 [0.00796028] [-0.00666902]
|
||||
8 [0.0074724] [-0.00626028]
|
||||
9 [0.00701442] [-0.00587659]
|
||||
10 [0.00658451] [-0.00551642]
|
||||
11 [0.00618095] [-0.00517832]
|
||||
12 [0.00580212] [-0.00486095]
|
||||
13 [0.00544651] [-0.00456302]
|
||||
14 [0.0051127] [-0.00428336]
|
||||
15 [0.00479935] [-0.00402083]
|
||||
16 [0.0045052] [-0.0037744]
|
||||
17 [0.00422908] [-0.00354307]
|
||||
18 [0.00396988] [-0.00332591]
|
||||
19 [0.00372657] [-0.00312207]
|
||||
20 [0.00349817] [-0.00293072]
|
||||
21 [0.00328377] [-0.0027511]
|
||||
22 [0.00308251] [-0.00258249]
|
||||
23 [0.00289358] [-0.00242421]
|
||||
24 [0.00271624] [-0.00227563]
|
||||
25 [0.00254976] [-0.00213616]
|
||||
26 [0.00239349] [-0.00200523]
|
||||
27 [0.00224679] [-0.00188233]
|
||||
28 [0.00210909] [-0.00176697]
|
||||
29 [0.00197982] [-0.00165867]
|
||||
Eigenvalues of Hessian Matrix:[0.3431178 4.16725475]
|
||||
0 [-14.28194794] [-15.81268342]
|
||||
1 [-0.23712617] [0.2073339]
|
||||
2 [-0.21760199] [0.19026272]
|
||||
3 [-0.19968537] [0.17459712]
|
||||
4 [-0.18324395] [0.16022138]
|
||||
5 [-0.16815626] [0.14702929]
|
||||
6 [-0.15431083] [0.13492339]
|
||||
7 [-0.1416054] [0.12381425]
|
||||
8 [-0.12994608] [0.1136198]
|
||||
9 [-0.11924676] [0.10426473]
|
||||
10 [-0.10942838] [0.09567992]
|
||||
11 [-0.10041841] [0.08780195]
|
||||
12 [-0.0921503] [0.08057264]
|
||||
13 [-0.08456295] [0.07393855]
|
||||
14 [-0.07760032] [0.0678507]
|
||||
15 [-0.07121097] [0.0622641]
|
||||
16 [-0.0653477] [0.05713748]
|
||||
17 [-0.05996719] [0.05243298]
|
||||
18 [-0.05502969] [0.04811582]
|
||||
19 [-0.05049873] [0.04415412]
|
||||
20 [-0.04634083] [0.04051862]
|
||||
21 [-0.04252528] [0.03718245]
|
||||
22 [-0.03902389] [0.03412098]
|
||||
23 [-0.0358108] [0.03131157]
|
||||
24 [-0.03286226] [0.02873348]
|
||||
25 [-0.03015649] [0.02636766]
|
||||
26 [-0.0276735] [0.02419664]
|
||||
27 [-0.02539496] [0.02220437]
|
||||
28 [-0.02330402] [0.02037613]
|
||||
29 [-0.02138525] [0.01869843]
|
||||
theta from own gd
|
||||
[[4.00679887]
|
||||
[2.994304 ]]
|
||||
0 [0.00185848] [-0.00155701]
|
||||
1 [0.00174457] [-0.00146158]
|
||||
2 [0.00160348] [-0.00134337]
|
||||
3 [0.00146287] [-0.00122558]
|
||||
4 [0.00133103] [-0.00111512]
|
||||
5 [0.0012099] [-0.00101364]
|
||||
6 [0.00109941] [-0.00092107]
|
||||
7 [0.00099888] [-0.00083685]
|
||||
8 [0.0009075] [-0.00076029]
|
||||
9 [0.00082447] [-0.00069073]
|
||||
10 [0.00074902] [-0.00062752]
|
||||
11 [0.00068048] [-0.0005701]
|
||||
12 [0.00061822] [-0.00051793]
|
||||
13 [0.00056165] [-0.00047054]
|
||||
14 [0.00051025] [-0.00042748]
|
||||
15 [0.00046356] [-0.00038836]
|
||||
16 [0.00042114] [-0.00035283]
|
||||
17 [0.0003826] [-0.00032054]
|
||||
18 [0.00034759] [-0.00029121]
|
||||
19 [0.00031579] [-0.00026456]
|
||||
20 [0.00028689] [-0.00024035]
|
||||
21 [0.00026064] [-0.00021836]
|
||||
22 [0.00023679] [-0.00019838]
|
||||
23 [0.00021512] [-0.00018022]
|
||||
24 [0.00019544] [-0.00016373]
|
||||
25 [0.00017755] [-0.00014875]
|
||||
26 [0.0001613] [-0.00013514]
|
||||
27 [0.00014654] [-0.00012277]
|
||||
28 [0.00013313] [-0.00011154]
|
||||
29 [0.00012095] [-0.00010133]
|
||||
[[3.94280548]
|
||||
[3.05000867]]
|
||||
0 [-0.01962446] [0.01715886]
|
||||
1 [-0.01800865] [0.01574606]
|
||||
2 [-0.01604113] [0.01402574]
|
||||
3 [-0.0141301] [0.01235481]
|
||||
4 [-0.01239337] [0.01083628]
|
||||
5 [-0.01085192] [0.0094885]
|
||||
6 [-0.00949598] [0.00830291]
|
||||
7 [-0.00830733] [0.0072636]
|
||||
8 [-0.00726673] [0.00635375]
|
||||
9 [-0.00635624] [0.00555765]
|
||||
10 [-0.00555974] [0.00486122]
|
||||
11 [-0.00486302] [0.00425203]
|
||||
12 [-0.0042536] [0.00371918]
|
||||
13 [-0.00372054] [0.0032531]
|
||||
14 [-0.00325429] [0.00284542]
|
||||
15 [-0.00284647] [0.00248884]
|
||||
16 [-0.00248975] [0.00217694]
|
||||
17 [-0.00217774] [0.00190413]
|
||||
18 [-0.00190483] [0.00166551]
|
||||
19 [-0.00166612] [0.00145679]
|
||||
20 [-0.00145732] [0.00127423]
|
||||
21 [-0.00127469] [0.00111454]
|
||||
22 [-0.00111495] [0.00097487]
|
||||
23 [-0.00097522] [0.0008527]
|
||||
24 [-0.00085301] [0.00074584]
|
||||
25 [-0.00074611] [0.00065237]
|
||||
26 [-0.00065261] [0.00057062]
|
||||
27 [-0.00057083] [0.00049911]
|
||||
28 [-0.00049929] [0.00043656]
|
||||
29 [-0.00043672] [0.00038185]
|
||||
theta from own gd wth momentum
|
||||
[[4.00040199]
|
||||
[2.99966322]]
|
||||
[[3.9988867 ]
|
||||
[3.00097342]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1151,17 +1166,17 @@ theta from own gd wth momentum
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[3.71369789]
|
||||
[3.2314999 ]]
|
||||
Eigenvalues of Hessian Matrix:[0.30237154 4.4642383 ]
|
||||
0 [-17.75091492] [-21.33108943]
|
||||
1 [-4.60742555e-15] [5.64228618e-16]
|
||||
2 [-5.34294831e-16] [-5.79981535e-16]
|
||||
3 [-5.34294831e-16] [-5.79981535e-16]
|
||||
4 [-5.34294831e-16] [-5.79981535e-16]
|
||||
[[3.87043345]
|
||||
[3.22475203]]
|
||||
Eigenvalues of Hessian Matrix:[0.38540656 3.68548206]
|
||||
0 [-12.40634991] [-11.15578306]
|
||||
1 [-5.99520433e-15] [-8.10731958e-15]
|
||||
2 [-2.87964097e-16] [-5.20946226e-16]
|
||||
3 [4.12864187e-16] [4.15804451e-16]
|
||||
4 [4.12864187e-16] [4.15804451e-16]
|
||||
beta from own Newton code
|
||||
[[3.71369789]
|
||||
[3.2314999 ]]
|
||||
[[3.87043345]
|
||||
[3.22475203]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1250,20 +1265,20 @@ beta from own Newton code
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[4.39917327]
|
||||
[2.69542733]]
|
||||
Eigenvalues of Hessian Matrix:[0.29765192 4.0375827 ]
|
||||
[[3.98225665]
|
||||
[3.11495144]]
|
||||
Eigenvalues of Hessian Matrix:[0.29378469 4.27347181]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own gd
|
||||
[[4.39917327]
|
||||
[2.69542733]]
|
||||
[[3.98225665]
|
||||
[3.11495144]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/exercisesweek41_22_2.png" src="_images/exercisesweek41_22_2.png" />
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own sdg
|
||||
[[4.32234998]
|
||||
[2.64530585]]
|
||||
[[3.89184354]
|
||||
[3.13010854]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1345,17 +1360,17 @@ Eigenvalues of Hessian Matrix:[0.29765192 4.0375827 ]
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Own inversion
|
||||
[[3.7635689 ]
|
||||
[3.10080981]]
|
||||
Eigenvalues of Hessian Matrix:[0.31633433 3.9824638 ]
|
||||
[[4.11385399]
|
||||
[2.90239437]]
|
||||
Eigenvalues of Hessian Matrix:[0.2870363 4.27221341]
|
||||
theta from own gd
|
||||
[[3.76366462]
|
||||
[3.1007216 ]]
|
||||
[[4.11265847]
|
||||
[2.90343239]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own sdg with momentum
|
||||
[[3.75707243]
|
||||
[3.12422141]]
|
||||
[[4.07560398]
|
||||
[2.82894312]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1430,9 +1445,9 @@ theta from own gd
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own AdaGrad
|
||||
[[1.99999956]
|
||||
[3.00000215]
|
||||
[3.99999797]]
|
||||
[[2.00036163]
|
||||
[2.99784051]
|
||||
[4.00208264]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1514,9 +1529,9 @@ theta from own gd
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own RMSprop
|
||||
[[1.99907985]
|
||||
[2.99897733]
|
||||
[3.99754609]]
|
||||
[[1.99976935]
|
||||
[2.99966842]
|
||||
[4.00437973]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1602,9 +1617,9 @@ theta from own gd
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>theta from own ADAM
|
||||
[[2.00003617]
|
||||
[2.99986253]
|
||||
[4.00012569]]
|
||||
[[1.99998899]
|
||||
[3.00009903]
|
||||
[3.99988697]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1677,7 +1692,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
|
||||
return asarray(x, dtype=self.dtype)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D at 0x11cb23a60>]
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[<matplotlib.lines.Line2D at 0x126f4fa00>]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/exercisesweek41_39_2.png" src="_images/exercisesweek41_39_2.png" />
|
||||
@@ -1716,7 +1731,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
|
||||
return asarray(x, dtype=self.dtype)
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><matplotlib.collections.PathCollection at 0x11cb23fd0>
|
||||
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><matplotlib.collections.PathCollection at 0x126f922b0>
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/exercisesweek41_41_2.png" src="_images/exercisesweek41_41_2.png" />
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -358,6 +358,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -957,8 +962,9 @@ Accuracy score on data set: 0.5
|
||||
Learning rate = 0.0001
|
||||
Lambda = 0.001
|
||||
Accuracy score on data set: 0.5
|
||||
|
||||
Learning rate = 0.0001
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 0.0001
|
||||
Lambda = 0.01
|
||||
Accuracy score on data set: 0.5
|
||||
|
||||
@@ -1137,7 +1143,7 @@ Accuracy score on data set: 0.5
|
||||
warnings.warn(
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/exercisesweek43_26_2.png" src="_images/exercisesweek43_26_2.png" />
|
||||
<img alt="_images/exercisesweek43_26_3.png" src="_images/exercisesweek43_26_3.png" />
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -5444,9 +5450,6 @@ case.</p>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.001, Lambda=0
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -8483,9 +8486,6 @@ case.</p>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.0001, Lambda=0
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.000% | train_error: 11.3 | train_acc: 0.453 | val_error: 10.4 | val_acc: 0.497
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -14567,8 +14567,9 @@ digits between the range of 0 to 9.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.1, Lambda=0
|
||||
|
||||
[----------------------------------------] 0.000% | train_error: 10.4 | train_acc: 0.500
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.000% | train_error: 10.4 | train_acc: 0.500
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.1000% | train_error: 10.4 | train_acc: 0.500
|
||||
|
||||
@@ -354,6 +354,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
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|
||||
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|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
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|
||||
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|
||||
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|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -355,6 +355,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
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|
||||
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|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<span class="caption-text">
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -673,8 +688,8 @@ matrices and vectors.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 0.44079937 -0.14839786 -1.00862798 -0.22996417 0.53459992 0.28570701
|
||||
-0.40043644 0.43989497 -0.27463692 -0.17644873]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[-0.35325013 1.40714201 -1.34943652 0.57794243 -0.3501046 -0.08502551
|
||||
0.22939767 0.37351692 0.87604059 -0.16082975]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -895,26 +910,26 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.51727541 0.09312344 0.68284332 0.10399743 0.11110246 0.17604689
|
||||
0.83716603 0.72598009 0.89142728 0.54640368]
|
||||
[0.33656494 0.55847112 0.87450434 0.13621148 0.38488879 0.68655761
|
||||
0.74147751 0.2544422 0.33708747 0.6848187 ]
|
||||
[0.54213329 0.22197349 0.8702764 0.36674564 0.59816099 0.3225819
|
||||
0.18856622 0.88595314 0.46132345 0.55685628]
|
||||
[0.88866133 0.65822169 0.05062537 0.20513942 0.41671085 0.48333258
|
||||
0.12786653 0.31532451 0.56548318 0.65766777]
|
||||
[0.55206229 0.11704038 0.2856881 0.26961519 0.13371503 0.26805987
|
||||
0.34460089 0.09215672 0.53367133 0.34362409]
|
||||
[0.34642944 0.54617756 0.22752605 0.86192438 0.49901588 0.98157799
|
||||
0.69802962 0.6513444 0.88050263 0.66725024]
|
||||
[0.88657125 0.3591093 0.14600426 0.66667985 0.95921115 0.09607524
|
||||
0.0470705 0.42777999 0.65318467 0.59602968]
|
||||
[0.37187359 0.6945612 0.7840642 0.78347558 0.60307141 0.95682858
|
||||
0.99534399 0.1859082 0.4281152 0.11232098]
|
||||
[0.8459616 0.64874236 0.39148625 0.284499 0.35322418 0.37391132
|
||||
0.39837199 0.47950427 0.44298022 0.31921368]
|
||||
[0.89881513 0.0062825 0.19942021 0.86850693 0.90715001 0.3426926
|
||||
0.98597638 0.71365128 0.79367372 0.86629645]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.79473273 0.85144525 0.58083888 0.95534385 0.74978227 0.09375511
|
||||
0.3762603 0.55342366 0.69948916 0.52180757]
|
||||
[0.41125816 0.53375204 0.78459969 0.63860117 0.68632877 0.3135197
|
||||
0.64378812 0.1228485 0.23374497 0.16365729]
|
||||
[0.06106228 0.41042542 0.72277345 0.87499269 0.25312591 0.97624609
|
||||
0.30902533 0.35567049 0.01001307 0.60893494]
|
||||
[0.68303739 0.09712817 0.45884458 0.56841764 0.80038919 0.6266335
|
||||
0.15169011 0.58487995 0.78177874 0.84856859]
|
||||
[0.35193678 0.09087594 0.88887574 0.11148997 0.77155539 0.02374036
|
||||
0.0382287 0.46294168 0.72359843 0.78427913]
|
||||
[0.97784379 0.14479759 0.46138883 0.85159661 0.18534538 0.96863656
|
||||
0.84143964 0.01707943 0.61695829 0.41936371]
|
||||
[0.00693764 0.08822806 0.64624281 0.59033885 0.00622235 0.56069111
|
||||
0.11141718 0.8736902 0.22015945 0.70367282]
|
||||
[0.55011282 0.56603693 0.98849276 0.79256181 0.97555188 0.731344
|
||||
0.50153896 0.88292369 0.05203331 0.10856388]
|
||||
[0.73582138 0.28478815 0.03418359 0.54608238 0.33959063 0.34619711
|
||||
0.93037027 0.31960205 0.97702512 0.13990198]
|
||||
[0.48668089 0.23908241 0.22812848 0.56640341 0.8840647 0.56933711
|
||||
0.46660343 0.91471524 0.52960552 0.85067652]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -974,13 +989,13 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.10095106250934528
|
||||
3.73293228266057
|
||||
-0.589971818845805
|
||||
[[ 1.44967228 4.20536556 4.14179769]
|
||||
[ 4.20536556 13.10814421 12.2161908 ]
|
||||
[ 4.14179769 12.2161908 17.03056169]]
|
||||
[28.710746 0.0846527 2.79297948]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.13443663368799186
|
||||
4.256841168348422
|
||||
0.26403986587386163
|
||||
[[0.69773572 2.0163462 1.40881429]
|
||||
[2.0163462 6.97266041 4.00255459]
|
||||
[1.40881429 4.00255459 5.66675932]]
|
||||
[10.95984465 0.0898612 2.2874496 ]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Binary file not shown.
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|
||||
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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<link rel="next" title="Project 2 on Machine Learning, deadline November 13 (Midnight)" href="project2.html" />
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|
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|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
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||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
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<a class="reference internal" href="week47.html">
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Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
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Project 2 on Machine Learning, deadline November 13 (Midnight)
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Project 2 on Machine Learning, deadline November 17 (Midnight)
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Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
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|
||||
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|
||||
</ul>
|
||||
@@ -720,7 +735,7 @@ which polynomial fits the data best.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8779/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15499/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
|
||||
ax = fig.gca(projection='3d')
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1079,17 +1094,17 @@ of code developers and contributors keeps increasing.</p>
|
||||
|
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@@ -55,6 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
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@@ -357,6 +358,11 @@ const thebe_selector_output = ".output, .cell_output"
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Week 46: Decision Trees, Ensemble methods and Random Forests
|
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Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
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@@ -374,6 +380,11 @@ const thebe_selector_output = ".output, .cell_output"
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Project 2 on Machine Learning, deadline November 17 (Midnight)
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Project 3 on Machine Learning, deadline December 18 (midnight), 2023
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@@ -807,6 +818,13 @@ we encourage you to collaborate. Optimal working groups consist of
|
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<p class="prev-next-title">Project 1 on Machine Learning, deadline October 9 (midnight), 2023</p>
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</div>
|
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|
||||
@@ -1,3 +1,14 @@
|
||||
Traceback (most recent call last):
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 730, in _async_poll_for_reply
|
||||
msg = await ensure_async(self.kc.shell_channel.get_msg(timeout=new_timeout))
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/util.py", line 96, in ensure_async
|
||||
result = await obj
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jupyter_client/channels.py", line 230, in get_msg
|
||||
raise Empty
|
||||
_queue.Empty
|
||||
|
||||
During handling of the above exception, another exception occurred:
|
||||
|
||||
Traceback (most recent call last):
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jupyter_cache/executors/utils.py", line 51, in single_nb_execution
|
||||
executenb(
|
||||
@@ -11,20 +22,18 @@ Traceback (most recent call last):
|
||||
return future.result()
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 663, in async_execute
|
||||
await self.async_execute_cell(
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 965, in async_execute_cell
|
||||
await self._check_raise_for_error(cell, cell_index, exec_reply)
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 862, in _check_raise_for_error
|
||||
raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
|
||||
nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
|
||||
------------------
|
||||
conda create -n tf tensorflow
|
||||
conda activate tf
|
||||
------------------
|
||||
|
||||
[0;36m Input [0;32mIn [12][0;36m[0m
|
||||
[0;31m conda create -n tf tensorflow[0m
|
||||
[0m ^[0m
|
||||
[0;31mSyntaxError[0m[0;31m:[0m invalid syntax
|
||||
|
||||
SyntaxError: invalid syntax (2259440937.py, line 1)
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 949, in async_execute_cell
|
||||
exec_reply = await self.task_poll_for_reply
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 754, in _async_poll_for_reply
|
||||
await self._async_handle_timeout(timeout, cell)
|
||||
File "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/nbclient/client.py", line 801, in _async_handle_timeout
|
||||
raise CellTimeoutError.error_from_timeout_and_cell(
|
||||
nbclient.exceptions.CellTimeoutError: A cell timed out while it was being executed, after 30 seconds.
|
||||
The message was: Cell execution timed out.
|
||||
Here is a preview of the cell contents:
|
||||
-------------------
|
||||
['eta_vals = np.logspace(-5, 1, 7)', 'lmbd_vals = np.logspace(-5, 1, 7)', '# store the models for later use', 'DNN_numpy = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)', '']
|
||||
...
|
||||
[' ', ' print("Learning rate = ", eta)', ' print("Lambda = ", lmbd)', ' print("Accuracy score on test set: ", accuracy_score(Y_test, test_predict))', ' print()']
|
||||
-------------------
|
||||
|
||||
|
||||
@@ -351,6 +351,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -365,7 +375,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -360,6 +360,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -1045,27 +1060,27 @@ uncorrelated.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2.860303069807892
|
||||
[[24.75576092 22.38035637 6.45399416 23.02131025 23.17927079 13.79482449
|
||||
14.58098325 8.09978307 10.32858131 7.04416475]
|
||||
[22.38035637 20.23288045 5.83471014 20.81233249 20.95513615 12.47116132
|
||||
13.18188532 7.32257967 9.33751667 6.36825174]
|
||||
[ 6.45399416 5.83471014 1.68259989 6.00181115 6.04299253 3.59640396
|
||||
3.80136087 2.11166818 2.69273094 1.83646135]
|
||||
[23.02131025 20.81233249 6.00181115 21.40837954 21.55527296 12.8283245
|
||||
13.55940301 7.53229196 9.60493501 6.55063291]
|
||||
[23.17927079 20.95513615 6.04299253 21.55527296 21.7031743 12.91634595
|
||||
13.65244075 7.58397472 9.67083919 6.59558002]
|
||||
[13.79482449 12.47116132 3.59640396 12.8283245 12.91634595 7.6869858
|
||||
8.12506251 4.51349834 5.75546705 3.92526882]
|
||||
[14.58098325 13.18188532 3.80136087 13.55940301 13.65244075 8.12506251
|
||||
8.58810494 4.7707199 6.08346766 4.14896753]
|
||||
[ 8.09978307 7.32257967 2.11166818 7.53229196 7.58397472 4.51349834
|
||||
4.7707199 2.65015024 3.37938584 2.3047648 ]
|
||||
[10.32858131 9.33751667 2.69273094 9.60493501 9.67083919 5.75546705
|
||||
6.08346766 3.37938584 4.30928349 2.9389615 ]
|
||||
[ 7.04416475 6.36825174 1.83646135 6.55063291 6.59558002 3.92526882
|
||||
4.14896753 2.3047648 2.9389615 2.00439232]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>3.899444949551402
|
||||
[[10.50764587 14.915888 4.01878557 10.96438655 3.9173859 11.73359971
|
||||
9.21793896 -2.24485338 7.74529434 12.25404247]
|
||||
[14.915888 21.17350714 5.70477499 15.56424378 5.56083543 16.6561627
|
||||
13.08511411 -3.18663019 10.99465516 17.39494529]
|
||||
[ 4.01878557 5.70477499 1.53703671 4.19347197 1.49825509 4.48766753
|
||||
3.52552042 -0.85857332 2.96228836 4.68671762]
|
||||
[10.96438655 15.56424378 4.19347197 11.4409806 4.08766472 12.2436295
|
||||
9.61861937 -2.34243146 8.08196262 12.78669458]
|
||||
[ 3.9173859 5.56083543 1.49825509 4.08766472 1.46045199 4.37443731
|
||||
3.43656654 -0.83691029 2.88754562 4.56846508]
|
||||
[11.73359971 16.6561627 4.48766753 12.2436295 4.37443731 13.10258872
|
||||
10.29341942 -2.50676615 8.64895758 13.68375286]
|
||||
[ 9.21793896 13.08511411 3.52552042 9.61861937 3.43656654 10.29341942
|
||||
8.0865305 -1.96932041 6.79463805 10.7499831 ]
|
||||
[-2.24485338 -3.18663019 -0.85857332 -2.34243146 -0.83691029 -2.50676615
|
||||
-1.96932041 0.47959046 -1.65470462 -2.61795354]
|
||||
[ 7.74529434 10.99465516 2.96228836 8.08196262 2.88754562 8.64895758
|
||||
6.79463805 -1.65470462 5.70913648 9.03258131]
|
||||
[12.25404247 17.39494529 4.68671762 12.78669458 4.56846508 13.68375286
|
||||
10.7499831 -2.61795354 9.03258131 14.29069448]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1333,15 +1348,15 @@ more practically oriented methods like the blocking technique.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07023654656164897
|
||||
4.208190393562401
|
||||
-0.023810076900619058
|
||||
1.0331134070762626 10.076560707521647 14.512204707520711
|
||||
3.055706923889776 3.0768224464930487 8.922010623244745
|
||||
[[ 1.03311341 3.05570692 3.07682245]
|
||||
[ 3.05570692 10.07656071 8.92201062]
|
||||
[ 3.07682245 8.92201062 14.51220471]]
|
||||
[22.35796655 0.08015655 3.18375572]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.027931820179105067
|
||||
3.8948621306998614
|
||||
0.09739722453284225
|
||||
1.281127658556346 13.800407897050107 32.990171109310765
|
||||
4.053326337300775 5.0047469652158 15.94611730275583
|
||||
[[ 1.28112766 4.05332634 5.00474697]
|
||||
[ 4.05332634 13.8004079 15.9461173 ]
|
||||
[ 5.00474697 15.9461173 32.99017111]]
|
||||
[42.9738062 0.07783419 5.02006627]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1671,7 +1686,7 @@ assumption for approximating <span class="math notranslate nohighlight">\(\sigma
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.006719367598355617 1.0020717457079393
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.005332694146660865 0.995998835818754
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/statistics_188_1.png" src="_images/statistics_188_1.png" />
|
||||
|
||||
@@ -351,6 +351,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -365,7 +375,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -351,6 +351,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -365,7 +375,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -1835,8 +1850,8 @@ developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly he
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.303107 1.38160211 0.19790229 1.36099915 -2.08992459 -0.86156954
|
||||
2.9012691 -0.18410452 -1.06886644 0.01011906]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.99876055 1.26944417 1.08159052 -0.19114964 -0.60407268 0.65062476
|
||||
1.21910072 1.34814162 -0.23692158 0.52823177]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -2061,26 +2076,26 @@ lowercase letters for vectors and uppercase letters for matrices)</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.27204759 0.8509716 0.98844754 0.6522099 0.81868633 0.74934715
|
||||
0.53632379 0.2257879 0.88228452 0.33213799]
|
||||
[0.82550815 0.97032289 0.54852248 0.45000312 0.45227801 0.44437409
|
||||
0.66608227 0.37871763 0.10320791 0.55812916]
|
||||
[0.10455569 0.18044829 0.92543216 0.81873184 0.05924492 0.00620039
|
||||
0.03106988 0.61631038 0.56830193 0.46580623]
|
||||
[0.97991527 0.62460522 0.51635486 0.08776426 0.80418542 0.94035843
|
||||
0.63870745 0.64347512 0.03019138 0.45811552]
|
||||
[0.82050873 0.4757488 0.95696156 0.2970942 0.56217428 0.16487517
|
||||
0.2522939 0.44886896 0.66951925 0.56135704]
|
||||
[0.82178144 0.58639705 0.0944958 0.69585594 0.08191117 0.42487977
|
||||
0.29009852 0.08368077 0.6691852 0.48444949]
|
||||
[0.55790428 0.58519863 0.35795044 0.65925306 0.53506617 0.60090208
|
||||
0.22830615 0.30506642 0.79142002 0.68029581]
|
||||
[0.12867125 0.34373214 0.89558707 0.05629549 0.54342461 0.10888134
|
||||
0.27485633 0.97326759 0.10477501 0.80621648]
|
||||
[0.56756375 0.18276764 0.83519995 0.23044077 0.43559429 0.0934955
|
||||
0.5712104 0.92054587 0.10120164 0.69666941]
|
||||
[0.17456211 0.42323635 0.03955811 0.54969188 0.5793788 0.49423098
|
||||
0.78834469 0.80312429 0.94756925 0.83793923]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.07178725 0.08695635 0.89462507 0.75563845 0.30666456 0.0295741
|
||||
0.79835897 0.68599651 0.09215268 0.92098926]
|
||||
[0.66013062 0.05466359 0.48935121 0.8554451 0.40293251 0.1690242
|
||||
0.62412322 0.13571683 0.5735888 0.22524684]
|
||||
[0.77325382 0.25018275 0.77615813 0.91438463 0.79009118 0.67199316
|
||||
0.35858674 0.54205622 0.95558887 0.35799174]
|
||||
[0.42109456 0.19802685 0.8478361 0.20265997 0.78724182 0.91581662
|
||||
0.92419995 0.65805069 0.64192269 0.23499198]
|
||||
[0.18302441 0.97198474 0.70796162 0.06124676 0.14967691 0.83091912
|
||||
0.9697031 0.96649164 0.48613114 0.79595791]
|
||||
[0.15400455 0.92643252 0.75309058 0.24605947 0.50661281 0.47991139
|
||||
0.54220589 0.10645534 0.55295871 0.71213744]
|
||||
[0.85824275 0.00915265 0.88395664 0.86901486 0.00286269 0.47340594
|
||||
0.5703663 0.13201296 0.99246148 0.75848064]
|
||||
[0.94578579 0.81175543 0.74233133 0.80589357 0.64101039 0.02053512
|
||||
0.98904619 0.19457423 0.98598055 0.19429581]
|
||||
[0.27377363 0.89454132 0.31780875 0.11925465 0.02636112 0.23754723
|
||||
0.20580025 0.53632918 0.39822715 0.69355844]
|
||||
[0.15449587 0.45133278 0.03230546 0.07962234 0.17757752 0.07225884
|
||||
0.99267755 0.87211615 0.36537059 0.28596284]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -2135,13 +2150,13 @@ covariance matrix through the <strong>np.linalg.eig()</strong> function.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.14482255345953607
|
||||
3.467427755242117
|
||||
-0.7005538702846336
|
||||
[[ 0.82032378 2.41772265 2.45808919]
|
||||
[ 2.41772265 8.23849741 7.23077531]
|
||||
[ 2.45808919 7.23077531 13.06343533]]
|
||||
[18.91591367 0.08817972 3.11816312]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.02192091870116783
|
||||
3.774553354452195
|
||||
-0.536915581616642
|
||||
[[ 0.97075378 2.86906683 4.23997187]
|
||||
[ 2.86906683 9.35109296 12.76954723]
|
||||
[ 4.23997187 12.76954723 33.47704686]]
|
||||
[39.63504283 0.07780377 4.08604699]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -2364,7 +2379,7 @@ Name: Aragorn, dtype: object
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8790/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_15510/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
|
||||
data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))
|
||||
</pre></div>
|
||||
</div>
|
||||
|
||||
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Week 45, Recurrent Neural Networks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week46.html">
|
||||
Week 46: Decision Trees, Ensemble methods and Random Forests
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week47.html">
|
||||
Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project2.html">
|
||||
Project 2 on Machine Learning, deadline November 13 (Midnight)
|
||||
Project 2 on Machine Learning, deadline November 17 (Midnight)
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="project3.html">
|
||||
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -1716,7 +1731,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.996738628265756
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9958946686888259
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1733,7 +1748,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.00846262916105675
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.008142188979400687
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1748,31 +1763,23 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[2.18321314e-02 4.63790586e-02 1.86599003e-02 2.57537966e-02
|
||||
7.75301638e-03 2.41708096e-03 2.05388549e-02 2.24797183e-02
|
||||
3.80669838e-02 1.77124395e-02 5.74437617e-02 8.91992985e-03
|
||||
3.79831624e-02 1.33444711e-02 2.46753261e-02 2.04450975e-02
|
||||
8.98180203e-02 1.21522960e-02 3.12747234e-03 1.31395784e-03
|
||||
1.84447599e-03 2.96525482e-03 6.28909679e-03 1.52006777e-02
|
||||
2.87135280e-03 2.40513177e-02 3.35417405e-02 5.92991719e-03
|
||||
3.54379087e-02 7.18303628e-03 1.54601264e-02 2.15148810e-02
|
||||
2.79754897e-03 4.12602928e-03 4.22227163e-02 3.09676156e-02
|
||||
1.25713219e-02 2.32108713e-02 2.44657526e-02 1.05066388e-02
|
||||
6.68324974e-02 2.97565845e-02 2.42484290e-02 1.89707309e-02
|
||||
2.19461919e-02 1.41644629e-02 1.41226929e-02 5.23396766e-03
|
||||
3.21530495e-03 3.66036618e-03 7.91408373e-03 3.18065689e-02
|
||||
5.10582403e-02 6.76220793e-03 3.09797549e-02 1.01612033e-02
|
||||
4.64257697e-02 1.98270777e-02 2.88088818e-02 5.94948363e-03
|
||||
8.94501598e-03 4.64365518e-03 4.55438359e-02 3.34347894e-03
|
||||
4.97761429e-03 2.71875845e-02 1.57316402e-02 4.15628391e-02
|
||||
4.75979803e-02 8.77079389e-03 3.22623101e-03 2.53596681e-03
|
||||
4.02206965e-02 3.06020683e-02 3.07080407e-02 9.75525377e-03
|
||||
6.45380691e-02 2.66174067e-02 1.94727053e-03 4.82766482e-03
|
||||
3.39313789e-03 5.00126617e-02 3.25794223e-02 3.97663980e-02
|
||||
3.51267283e-02 4.43226747e-02 4.45976616e-03 2.86750237e-02
|
||||
2.33197004e-02 9.78449688e-05 4.38688646e-02 2.86830766e-02
|
||||
2.90763970e-02 9.24053124e-03 1.36970119e-02 6.97177697e-02
|
||||
2.34728094e-02 2.31728952e-02 3.08484802e-03 6.25254477e-02]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.0476021 0.02689869 0.01088331 0.01783105 0.00544013 0.05110385
|
||||
0.02900389 0.01629703 0.05594058 0.02527366 0.00657884 0.04127087
|
||||
0.01925607 0.02221978 0.01212083 0.04919181 0.00745959 0.03110176
|
||||
0.010203 0.0076995 0.00298213 0.01702968 0.04557362 0.03192124
|
||||
0.06668218 0.0178392 0.00706728 0.0095239 0.00784983 0.05197707
|
||||
0.01519861 0.0134093 0.00291822 0.00311528 0.02036289 0.01136976
|
||||
0.0189559 0.04908155 0.01384493 0.01715895 0.01262581 0.00756465
|
||||
0.00473818 0.00224783 0.01773579 0.03804636 0.03945128 0.01662346
|
||||
0.05137822 0.00206124 0.06090176 0.01632212 0.01220987 0.06361921
|
||||
0.00318122 0.00362359 0.03177421 0.06554078 0.00123144 0.01091059
|
||||
0.04958045 0.00291334 0.01541622 0.00607264 0.05274561 0.007352
|
||||
0.06263415 0.01593612 0.00853836 0.01006042 0.00223784 0.02106518
|
||||
0.02410507 0.08294341 0.0043675 0.06502562 0.03422156 0.00213264
|
||||
0.02365779 0.01883403 0.00683222 0.01848399 0.02930957 0.02161016
|
||||
0.02746315 0.02774744 0.03591454 0.04814746 0.00568413 0.00215333
|
||||
0.03631783 0.02866734 0.01684326 0.00953152 0.01001378 0.00119895
|
||||
0.02603725 0.00127672 0.04770636 0.028797 ]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1841,15 +1848,15 @@ but now splitting the data into a training set and a test set.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.97864285 0.28134042 4.70594499 -0.58368727 0.70917314]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.02283241 0.15972118 3.84256187 1.89005305 -0.93145755]
|
||||
Training R2
|
||||
0.993658072083743
|
||||
0.9959044445566834
|
||||
Training MSE
|
||||
0.012874822204495243
|
||||
0.010349061754867921
|
||||
Test R2
|
||||
0.9945729062189713
|
||||
0.9961996615568259
|
||||
Test MSE
|
||||
0.007472516848671787
|
||||
0.008771887357306985
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user