update on jupyter-book
This commit is contained in:
Morten Hjorth-Jensen
@@ -6,11 +6,11 @@ edge [fontname="helvetica"] ;
|
||||
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
|
||||
2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e78946"] ;
|
||||
1 -> 2 ;
|
||||
3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ;
|
||||
3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ;
|
||||
2 -> 3 ;
|
||||
4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ;
|
||||
3 -> 4 ;
|
||||
5 [label="mean compactness <= 0.063\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
|
||||
5 [label="area error <= 51.38\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
|
||||
3 -> 5 ;
|
||||
6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ;
|
||||
5 -> 6 ;
|
||||
@@ -30,11 +30,11 @@ edge [fontname="helvetica"] ;
|
||||
11 -> 13 ;
|
||||
14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ;
|
||||
0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
|
||||
15 [label="worst perimeter <= 116.8\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
|
||||
15 [label="worst area <= 964.4\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
|
||||
14 -> 15 ;
|
||||
16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ;
|
||||
15 -> 16 ;
|
||||
17 [label="worst smoothness <= 0.106\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
|
||||
17 [label="symmetry error <= 0.014\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
|
||||
15 -> 17 ;
|
||||
18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
|
||||
17 -> 18 ;
|
||||
@@ -42,16 +42,16 @@ edge [fontname="helvetica"] ;
|
||||
17 -> 19 ;
|
||||
20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#ea985d"] ;
|
||||
14 -> 20 ;
|
||||
21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ;
|
||||
21 [label="compactness error <= 0.016\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ;
|
||||
20 -> 21 ;
|
||||
22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139"] ;
|
||||
21 -> 22 ;
|
||||
23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ;
|
||||
21 -> 23 ;
|
||||
24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
|
||||
24 [label="fractal dimension error <= 0.013\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
|
||||
20 -> 24 ;
|
||||
25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
|
||||
25 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ;
|
||||
24 -> 25 ;
|
||||
26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ;
|
||||
26 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
|
||||
24 -> 26 ;
|
||||
}
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 245 KiB After Width: | Height: | Size: 243 KiB |
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
After Width: | Height: | Size: 23 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 13 KiB After Width: | Height: | Size: 14 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 19 KiB After Width: | Height: | Size: 18 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 24 KiB After Width: | Height: | Size: 26 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 18 KiB After Width: | Height: | Size: 18 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 21 KiB After Width: | Height: | Size: 22 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 44 KiB After Width: | Height: | Size: 52 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 36 KiB After Width: | Height: | Size: 36 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 39 KiB After Width: | Height: | Size: 36 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 10 KiB After Width: | Height: | Size: 9.9 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 18 KiB After Width: | Height: | Size: 19 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 22 KiB After Width: | Height: | Size: 21 KiB |
File diff suppressed because it is too large
Load Diff
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -1021,11 +1026,11 @@ example of the functionality of <strong>Scikit-Learn</strong>.</p>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The intercept alpha:
|
||||
[1.99194201]
|
||||
[1.95647867]
|
||||
Coefficient beta :
|
||||
[[4.85108001]]
|
||||
Mean squared error: 0.28
|
||||
Variance score: 0.87
|
||||
[[5.05401912]]
|
||||
Mean squared error: 0.25
|
||||
Variance score: 0.90
|
||||
Mean squared log error: 0.01
|
||||
Mean absolute error: 0.43
|
||||
</pre></div>
|
||||
@@ -1127,7 +1132,7 @@ a linear <span class="math notranslate nohighlight">\(x\)</span>-dependence we s
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<img alt="_images/chapter1_33_0.png" src="_images/chapter1_33_0.png" />
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.005
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.004999999999999991
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -1159,9 +1164,8 @@ probability that image 0 is in category 0,1,2,...,9 =
|
||||
1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03
|
||||
9.84443254e-01 3.11507992e-04]
|
||||
probabilities sum up to: 1.0
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>predictions = (n_inputs) = (1437,)
|
||||
|
||||
predictions = (n_inputs) = (1437,)
|
||||
prediction for image 0: 8
|
||||
correct label for image 0: 6
|
||||
</pre></div>
|
||||
@@ -1339,7 +1343,7 @@ the <em>Hadamard product</em>, meaning element-wise multiplication.</p>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old accuracy on training data: 0.1440501043841336
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1673,7 +1677,7 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1682,7 +1686,7 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1691,7 +1695,7 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1700,7 +1704,7 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1709,7 +1713,7 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1718,7 +1722,7 @@ Lambda = 0.1
|
||||
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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1727,7 +1731,7 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1736,11 +1740,11 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1749,11 +1753,11 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1762,11 +1766,11 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1775,11 +1779,11 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1788,11 +1792,11 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1801,7 +1805,7 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1810,11 +1814,11 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1823,11 +1827,11 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1836,11 +1840,11 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1849,11 +1853,11 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1862,37 +1866,56 @@ 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_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.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 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="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">line</span> <span class="mi">11</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">Cell In[6], line 99,</span> in <span class="ni">NeuralNetwork.train</span><span class="nt">(self)</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="g g-Whitespace"> </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="ne">---> </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">Cell In[6], line 64,</span> in <span class="ni">NeuralNetwork.backpropagation</span><span class="nt">(self)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">61</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_weights_gradient</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="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">error_output</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">62</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_bias_gradient</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">error_output</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">64</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_weights_gradient</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="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">error_hidden</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">65</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_bias_gradient</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">error_hidden</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">67</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lmbd</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">:</span>
|
||||
|
||||
<span class="ne">KeyboardInterrupt</span>:
|
||||
<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_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.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_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.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_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.py:43: RuntimeWarning: overflow encountered in exp
|
||||
exp_term = np.exp(self.z_o)
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/1630775253.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
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1938,6 +1961,22 @@ 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_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.py:4: RuntimeWarning: overflow encountered in exp
|
||||
return 1/(1 + np.exp(-x))
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58656/953065564.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">
|
||||
@@ -1973,6 +2012,326 @@ 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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
|
||||
|
||||
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
|
||||
|
||||
Learning rate = 0.1
|
||||
Lambda = 1e-05
|
||||
Accuracy score on test set: 0.9027777777777778
|
||||
</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: 0.8583333333333333
|
||||
|
||||
Learning rate = 0.1
|
||||
Lambda = 0.001
|
||||
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 = 0.01
|
||||
Accuracy score on test set: 0.9055555555555556
|
||||
|
||||
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
|
||||
|
||||
Learning rate = 1.0
|
||||
Lambda = 0.0001
|
||||
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 = 0.001
|
||||
Accuracy score on test set: 0.10555555555555556
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
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">
|
||||
@@ -2016,6 +2375,10 @@ performance overall.</p>
|
||||
</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">
|
||||
@@ -2054,6 +2417,14 @@ 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">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">12</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</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">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -2627,138 +2632,58 @@ 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:60,</span> in <span class="ni">jacobian</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">50</span> <span class="nd">@unary_to_nary</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">51</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">52</span><span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">53</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">54</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">58</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">59</span><span class="sd"> """</span>
|
||||
<span class="ne">---> </span><span class="mi">60</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">61</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">62</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/differential_operators.py:64,</span> in <span class="ni">jacobian</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">62</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">63</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">64</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/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/numpy/numpy_wrapper.py:88,</span> in <span class="ni">stack</span><span class="nt">(arrays, axis)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">83</span> <span class="k">def</span> <span class="nf">stack</span><span class="p">(</span><span class="n">arrays</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">84</span> <span class="c1"># this code is basically copied from numpy/core/shape_base.py's stack</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">85</span> <span class="c1"># we need it here because we want to re-implement stack in terms of the</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">86</span> <span class="c1"># primitives defined in this file</span>
|
||||
<span class="ne">---> </span><span class="mi">88</span> <span class="n">arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">array</span><span class="p">(</span><span class="n">arr</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="g g-Whitespace"> </span><span class="mi">89</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">arrays</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">90</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'need at least one array to stack'</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/numpy/numpy_wrapper.py:88,</span> in <span class="ni"><listcomp></span><span class="nt">(.0)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">83</span> <span class="k">def</span> <span class="nf">stack</span><span class="p">(</span><span class="n">arrays</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">84</span> <span class="c1"># this code is basically copied from numpy/core/shape_base.py's stack</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">85</span> <span class="c1"># we need it here because we want to re-implement stack in terms of the</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">86</span> <span class="c1"># primitives defined in this file</span>
|
||||
<span class="ne">---> </span><span class="mi">88</span> <span class="n">arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">array</span><span class="p">(</span><span class="n">arr</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="g g-Whitespace"> </span><span class="mi">89</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">arrays</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">90</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'need at least one array to stack'</span><span class="p">)</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/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/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/core.py:21,</span> in <span class="ni">backward_pass</span><span class="nt">(g, end_node)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">19</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">toposort</span><span class="p">(</span><span class="n">end_node</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">20</span> <span class="n">outgrad</span> <span class="o">=</span> <span class="n">outgrads</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
|
||||
<span class="ne">---> </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="g g-Whitespace"> </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="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60,</span> in <span class="ni">jacobian</span><span class="nt">(fun, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">50</span> <span class="nd">@unary_to_nary</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">51</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">52</span><span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">53</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">54</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">58</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">59</span><span class="sd"> """</span>
|
||||
<span class="ne">---> </span><span class="mi">60</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">61</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">62</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: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">Cell In[9], line 61,</span> in <span class="ni">g_trial</span><span class="nt">(point, P)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">59</span> <span class="k">def</span> <span class="nf">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">60</span> <span class="n">x</span><span class="p">,</span><span class="n">t</span> <span class="o">=</span> <span class="n">point</span>
|
||||
<span class="ne">---> </span><span class="mi">61</span> <span class="k">return</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">t</span><span class="p">)</span><span class="o">*</span><span class="n">u</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">x</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">x</span><span class="p">)</span><span class="o">*</span><span class="n">t</span><span class="o">*</span><span class="n">deep_neural_network</span><span class="p">(</span><span class="n">P</span><span class="p">,</span><span class="n">point</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">Cell In[9], line 37,</span> in <span class="ni">deep_neural_network</span><span class="nt">(deep_params, x)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">34</span> <span class="n">x_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="n">num_points</span><span class="p">)),</span> <span class="n">x_prev</span> <span class="p">),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">36</span> <span class="n">z_hidden</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="n">w_hidden</span><span class="p">,</span> <span class="n">x_prev</span><span class="p">)</span>
|
||||
<span class="ne">---> </span><span class="mi">37</span> <span class="n">x_hidden</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z_hidden</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">39</span> <span class="c1"># Update x_prev such that next layer can use the output from this layer</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">40</span> <span class="n">x_prev</span> <span class="o">=</span> <span class="n">x_hidden</span>
|
||||
|
||||
<span class="nn">Cell In[9], line 11,</span> in <span class="ni">sigmoid</span><span class="nt">(z)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">10</span> <span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">):</span>
|
||||
<span class="ne">---> </span><span class="mi">11</span> <span class="k">return</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">z</span><span class="p">))</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:39,</span> in <span class="ni">ArrayBox.__rtruediv__</span><span class="nt">(self, other)</span>
|
||||
<span class="ne">---> </span><span class="mi">39</span> <span class="k">def</span> <span class="fm">__rtruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="k">return</span> <span class="n">anp</span><span class="o">.</span><span class="n">true_divide</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</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:66,</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">63</span> <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67,</span> in <span class="ni">defvjp.<locals>.vjp_argnums.<locals>.<lambda></span><span class="nt">(g)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">64</span> <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">65</span> <span class="s2">"VJP of </span><span class="si">{}</span><span class="s2"> wrt argnum 0 not defined"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fun</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
|
||||
<span class="ne">---> </span><span class="mi">66</span> <span class="n">vjp</span> <span class="o">=</span> <span class="n">vjpfun</span><span class="p">(</span><span class="n">ans</span><span class="p">,</span> <span class="o">*</span><span class="n">args</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">67</span> <span class="k">return</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="p">(</span><span class="n">vjp</span><span class="p">(</span><span class="n">g</span><span class="p">),)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">66</span> <span class="n">vjp</span> <span class="o">=</span> <span class="n">vjpfun</span><span class="p">(</span><span class="n">ans</span><span class="p">,</span> <span class="o">*</span><span class="n">args</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">67</span> <span class="k">return</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="p">(</span><span class="n">vjp</span><span class="p">(</span><span class="n">g</span><span class="p">),)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">68</span> <span class="k">elif</span> <span class="n">L</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">69</span> <span class="n">argnum_0</span><span class="p">,</span> <span class="n">argnum_1</span> <span class="o">=</span> <span class="n">argnums</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:53,</span> in <span class="ni"><lambda></span><span class="nt">(ans, x, y)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">48</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">logaddexp</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">*</span> <span class="n">anp</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="n">ans</span><span class="p">)),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">49</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">*</span> <span class="n">anp</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">y</span><span class="o">-</span><span class="n">ans</span><span class="p">)))</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">50</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">logaddexp2</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">*</span> <span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="n">ans</span><span class="p">)),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">51</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">*</span> <span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">y</span><span class="o">-</span><span class="n">ans</span><span class="p">)))</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">52</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">true_divide</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span> <span class="o">/</span> <span class="n">y</span><span class="p">),</span>
|
||||
<span class="ne">---> </span><span class="mi">53</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="o">-</span> <span class="n">g</span> <span class="o">*</span> <span class="n">x</span> <span class="o">/</span> <span class="n">y</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">54</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">mod</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">55</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="o">-</span><span class="n">g</span> <span class="o">*</span> <span class="n">anp</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="n">y</span><span class="p">)))</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">56</span> <span class="n">defvjp</span><span class="p">(</span><span class="n">anp</span><span class="o">.</span><span class="n">remainder</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">g</span><span class="p">),</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">57</span> <span class="k">lambda</span> <span class="n">ans</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="n">unbroadcast_f</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="o">-</span><span class="n">g</span> <span class="o">*</span> <span class="n">anp</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">x</span><span class="o">/</span><span class="n">y</span><span class="p">)))</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:658,</span> in <span class="ni">unbroadcast_f</span><span class="nt">(target, f)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">655</span> <span class="n">x</span> <span class="o">=</span> <span class="n">anp</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">656</span> <span class="k">return</span> <span class="n">x</span>
|
||||
<span class="ne">--> </span><span class="mi">658</span> <span class="k">def</span> <span class="nf">unbroadcast_f</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span>
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:660,</span> in <span class="ni">unbroadcast_f.<locals>.<lambda></span><span class="nt">(g)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">658</span> <span class="k">def</span> <span class="nf">unbroadcast_f</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">f</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">659</span> <span class="n">target_meta</span> <span class="o">=</span> <span class="n">anp</span><span class="o">.</span><span class="n">metadata</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">660</span> <span class="k">return</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">g</span><span class="p">),</span> <span class="n">target_meta</span><span class="p">)</span>
|
||||
<span class="ne">--> </span><span class="mi">660</span> <span class="k">return</span> <span class="k">lambda</span> <span class="n">g</span><span class="p">:</span> <span class="n">unbroadcast</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">g</span><span class="p">),</span> <span class="n">target_meta</span><span class="p">)</span>
|
||||
|
||||
<span class="nn">File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:651,</span> in <span class="ni">unbroadcast</span><span class="nt">(x, target_meta, broadcast_idx)</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">649</span> <span class="k">while</span> <span class="n">anp</span><span class="o">.</span><span class="n">ndim</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">></span> <span class="n">target_ndim</span><span class="p">:</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">650</span> <span class="n">x</span> <span class="o">=</span> <span class="n">anp</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">broadcast_idx</span><span class="p">)</span>
|
||||
<span class="ne">--> </span><span class="mi">651</span> <span class="k">for</span> <span class="n">axis</span><span class="p">,</span> <span class="n">size</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">target_shape</span><span class="p">):</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">652</span> <span class="k">if</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">653</span> <span class="n">x</span> <span class="o">=</span> <span class="n">anp</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</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="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="ne">KeyboardInterrupt</span>:
|
||||
</pre></div>
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -1270,10 +1275,10 @@ 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>-0.0369544130635358
|
||||
3.662836197064178
|
||||
[[1.058997 3.11439407]
|
||||
[3.11439407 9.99498272]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.07978850553011713
|
||||
3.8232102961414203
|
||||
[[ 1.25705685 3.70704566]
|
||||
[ 3.70704566 12.1664372 ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1310,10 +1315,10 @@ 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>0.08826182458028335
|
||||
1.7026722043092946
|
||||
[[1. 0.61113781]
|
||||
[0.61113781 1. ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.08931169286872433
|
||||
2.047447724189861
|
||||
[[1. 0.66729685]
|
||||
[0.66729685 1. ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1343,30 +1348,30 @@ 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>[[-0.7252563 -2.26264849]
|
||||
[ 1.19052935 3.11261935]
|
||||
[-0.62158409 -2.99662602]
|
||||
[-0.06216141 -0.18120973]
|
||||
[ 1.32065614 3.50269821]
|
||||
[ 0.83995705 2.80855691]
|
||||
[ 0.1571284 0.96919021]
|
||||
[-0.03404758 1.01551815]
|
||||
[-0.23596934 0.77449804]
|
||||
[-1.82925222 -6.74259663]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[ 2.37582891 5.4242743 ]
|
||||
[-0.81925302 -3.60052901]
|
||||
[ 0.37467515 3.16158074]
|
||||
[-0.40478305 -2.22601408]
|
||||
[-0.0776532 -1.70594608]
|
||||
[-0.99434472 -2.2026985 ]
|
||||
[ 0.71376303 2.77725472]
|
||||
[ 0.94637206 2.69303453]
|
||||
[-0.19791352 0.93366198]
|
||||
[-1.91669165 -5.2546186 ]]
|
||||
0 1
|
||||
0 -0.725256 -2.262648
|
||||
1 1.190529 3.112619
|
||||
2 -0.621584 -2.996626
|
||||
3 -0.062161 -0.181210
|
||||
4 1.320656 3.502698
|
||||
5 0.839957 2.808557
|
||||
6 0.157128 0.969190
|
||||
7 -0.034048 1.015518
|
||||
8 -0.235969 0.774498
|
||||
9 -1.829252 -6.742597
|
||||
0 2.375829 5.424274
|
||||
1 -0.819253 -3.600529
|
||||
2 0.374675 3.161581
|
||||
3 -0.404783 -2.226014
|
||||
4 -0.077653 -1.705946
|
||||
5 -0.994345 -2.202698
|
||||
6 0.713763 2.777255
|
||||
7 0.946372 2.693035
|
||||
8 -0.197914 0.933662
|
||||
9 -1.916692 -5.254619
|
||||
0 1
|
||||
0 1.000000 0.963187
|
||||
1 0.963187 1.000000
|
||||
0 1.000000 0.932382
|
||||
1 0.932382 1.000000
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1423,37 +1428,37 @@ 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 0.077382 0.076459 0.074114 0.076542 0.079074 0.064978 0.067074
|
||||
2 0.0 0.076459 0.078118 0.070150 0.073866 0.078123 0.059624 0.062356
|
||||
3 0.0 0.074114 0.070150 0.076618 0.077248 0.077529 0.070432 0.071572
|
||||
4 0.0 0.076542 0.073866 0.077248 0.078731 0.080082 0.069802 0.071449
|
||||
5 0.0 0.079074 0.078123 0.077529 0.080082 0.082803 0.068607 0.070859
|
||||
6 0.0 0.064978 0.059624 0.070432 0.069802 0.068607 0.066865 0.067168
|
||||
7 0.0 0.067074 0.062356 0.071572 0.071449 0.070859 0.067168 0.067809
|
||||
8 0.0 0.069439 0.065556 0.072752 0.073253 0.073422 0.067367 0.068408
|
||||
9 0.0 0.072092 0.069317 0.073923 0.075198 0.076333 0.067380 0.068897
|
||||
10 0.0 0.056856 0.051021 0.063637 0.062282 0.060288 0.061848 0.061588
|
||||
11 0.0 0.058361 0.052856 0.064604 0.063560 0.061921 0.062263 0.062231
|
||||
12 0.0 0.060085 0.055000 0.065679 0.065007 0.063802 0.062700 0.062932
|
||||
13 0.0 0.062053 0.057514 0.066855 0.066633 0.065963 0.063133 0.063673
|
||||
14 0.0 0.064294 0.060470 0.068116 0.068446 0.068446 0.063525 0.064427
|
||||
1 0.0 0.080947 0.086913 0.082764 0.085436 0.088029 0.075956 0.077683
|
||||
2 0.0 0.086913 0.094044 0.088199 0.091365 0.094439 0.080171 0.082174
|
||||
3 0.0 0.082764 0.088199 0.090624 0.093094 0.095465 0.086730 0.088375
|
||||
4 0.0 0.085436 0.091365 0.093094 0.095805 0.098414 0.088651 0.090440
|
||||
5 0.0 0.088029 0.094439 0.095465 0.098414 0.101263 0.090468 0.092401
|
||||
6 0.0 0.075956 0.080171 0.086730 0.088651 0.090468 0.085390 0.086712
|
||||
7 0.0 0.077683 0.082174 0.088375 0.090440 0.092401 0.086712 0.088127
|
||||
8 0.0 0.079444 0.084218 0.090038 0.092252 0.094362 0.088038 0.089548
|
||||
9 0.0 0.081252 0.086318 0.091731 0.094099 0.096365 0.089376 0.090985
|
||||
10 0.0 0.068684 0.071868 0.080702 0.082126 0.083446 0.081103 0.082115
|
||||
11 0.0 0.069978 0.073339 0.081978 0.083499 0.084915 0.082170 0.083248
|
||||
12 0.0 0.071314 0.074859 0.083288 0.084910 0.086428 0.083260 0.084406
|
||||
13 0.0 0.072697 0.076435 0.084637 0.086364 0.087988 0.084376 0.085592
|
||||
14 0.0 0.074130 0.078071 0.086026 0.087864 0.089600 0.085518 0.086809
|
||||
|
||||
8 9 10 11 12 13 14
|
||||
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.069439 0.072092 0.056856 0.058361 0.060085 0.062053 0.064294
|
||||
2 0.065556 0.069317 0.051021 0.052856 0.055000 0.057514 0.060470
|
||||
3 0.072752 0.073923 0.063637 0.064604 0.065679 0.066855 0.068116
|
||||
4 0.073253 0.075198 0.062282 0.063560 0.065007 0.066633 0.068446
|
||||
5 0.073422 0.076333 0.060288 0.061921 0.063802 0.065963 0.068446
|
||||
6 0.067367 0.067380 0.061848 0.062263 0.062700 0.063133 0.063525
|
||||
7 0.068408 0.068897 0.061588 0.062231 0.062932 0.063673 0.064427
|
||||
8 0.069486 0.070556 0.061154 0.062058 0.063064 0.064166 0.065352
|
||||
9 0.070556 0.072344 0.060453 0.061656 0.063017 0.064547 0.066256
|
||||
10 0.061154 0.060453 0.058245 0.058254 0.058234 0.058152 0.057961
|
||||
11 0.062058 0.061656 0.058254 0.058427 0.058593 0.058721 0.058770
|
||||
12 0.063064 0.063017 0.058234 0.058593 0.058970 0.059340 0.059669
|
||||
13 0.064166 0.064547 0.058152 0.058721 0.059340 0.059992 0.060652
|
||||
14 0.065352 0.066256 0.057961 0.058770 0.059669 0.060652 0.061706
|
||||
1 0.079444 0.081252 0.068684 0.069978 0.071314 0.072697 0.074130
|
||||
2 0.084218 0.086318 0.071868 0.073339 0.074859 0.076435 0.078071
|
||||
3 0.090038 0.091731 0.080702 0.081978 0.083288 0.084637 0.086026
|
||||
4 0.092252 0.094099 0.082126 0.083499 0.084910 0.086364 0.087864
|
||||
5 0.094362 0.096365 0.083446 0.084915 0.086428 0.087988 0.089600
|
||||
6 0.088038 0.089376 0.081103 0.082170 0.083260 0.084376 0.085518
|
||||
7 0.089548 0.090985 0.082115 0.083248 0.084406 0.085592 0.086809
|
||||
8 0.091068 0.092607 0.083119 0.084319 0.085548 0.086808 0.088102
|
||||
9 0.092607 0.094254 0.084122 0.085392 0.086694 0.088030 0.089404
|
||||
10 0.083119 0.084122 0.078238 0.079089 0.079953 0.080832 0.081728
|
||||
11 0.084319 0.085392 0.079089 0.079989 0.080903 0.081835 0.082785
|
||||
12 0.085548 0.086694 0.079953 0.080903 0.081871 0.082857 0.083864
|
||||
13 0.086808 0.088030 0.080832 0.081835 0.082857 0.083900 0.084967
|
||||
14 0.088102 0.089404 0.081728 0.082785 0.083864 0.084967 0.086096
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -824,10 +829,10 @@ 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: 0.165272 sec
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Runtime: 0.139224 sec
|
||||
Jackknife Statistics :
|
||||
original bias std. error
|
||||
99.6801 99.6702 0.149483
|
||||
99.9792 99.9692 0.149921
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1046,7 +1051,7 @@ 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
|
||||
100.121 15.022 100.121 0.149904
|
||||
99.8978 15.0232 99.8962 0.149063
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1258,14 +1263,14 @@ Error: 0.10398646080125035
|
||||
Bias^2: 0.1007711427354898
|
||||
Var: 0.0032153180657605116
|
||||
0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032
|
||||
Polynomial degree: 3
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 3
|
||||
Error: 0.06547790180152355
|
||||
Bias^2: 0.06208238634231949
|
||||
Var: 0.0033955154592040936
|
||||
0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 4
|
||||
Polynomial degree: 4
|
||||
Error: 0.06844519414009445
|
||||
Bias^2: 0.06453579006728324
|
||||
Var: 0.003909404072811226
|
||||
@@ -1275,14 +1280,14 @@ Error: 0.05227921801205686
|
||||
Bias^2: 0.0481872773043029
|
||||
Var: 0.004091940707753939
|
||||
0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844
|
||||
Polynomial degree: 6
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 6
|
||||
Error: 0.037813671417389005
|
||||
Bias^2: 0.033657685071527665
|
||||
Var: 0.00415598634586135
|
||||
0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 7
|
||||
Polynomial degree: 7
|
||||
Error: 0.02760977349102253
|
||||
Bias^2: 0.022999498260366312
|
||||
Var: 0.004610275230656212
|
||||
@@ -1297,7 +1302,9 @@ Error: 0.02660572763718093
|
||||
Bias^2: 0.010018312644137363
|
||||
Var: 0.016587414993043573
|
||||
0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936
|
||||
Polynomial degree: 10
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 10
|
||||
Error: 0.021592704588025025
|
||||
Bias^2: 0.010516485576645508
|
||||
Var: 0.011076219011379514
|
||||
@@ -1307,9 +1314,7 @@ Error: 0.07160048164233104
|
||||
Bias^2: 0.014436800088904942
|
||||
Var: 0.05716368155342608
|
||||
0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 12
|
||||
Polynomial degree: 12
|
||||
Error: 0.11547777218872497
|
||||
Bias^2: 0.01628578269596628
|
||||
Var: 0.09919198949275869
|
||||
@@ -1636,9 +1641,9 @@ Mean squared error on training data: 0.00060704
|
||||
Mean squared error on test data: 3262.26814548
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/626635268.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_58739/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(testerror), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -1872,7 +1877,7 @@ 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_6403/3817475779.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_58739/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2761,7 +2766,7 @@ 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_6403/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2905,7 +2910,7 @@ 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_6403/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2945,7 +2950,7 @@ 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_6403/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -2980,7 +2985,7 @@ 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_6403/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58739/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
|
||||
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
|
||||
</pre></div>
|
||||
</div>
|
||||
@@ -3033,43 +3038,43 @@ 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]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 0%| | 0/10 [00:00<?, ?it/s]
|
||||
</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/_coordinate_descent.py:628: 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(
|
||||
|
||||
10%|██████████ | 1/10 [00:00<00:07, 1.15it/s]
|
||||
10%|█████████████████▏ | 1/10 [00:00<00:07, 1.27it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 20%|████████████████████ | 2/10 [00:01<00:07, 1.12it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 20%|██████████████████████████████████▍ | 2/10 [00:01<00:06, 1.22it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 30%|██████████████████████████████ | 3/10 [00:02<00:05, 1.32it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 30%|███████████████████████████████████████████████████▌ | 3/10 [00:02<00:05, 1.32it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 40%|████████████████████████████████████████ | 4/10 [00:02<00:03, 1.50it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 40%|████████████████████████████████████████████████████████████████████▊ | 4/10 [00:02<00:04, 1.43it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 50%|██████████████████████████████████████████████████ | 5/10 [00:03<00:02, 1.69it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 50%|██████████████████████████████████████████████████████████████████████████████████████ | 5/10 [00:03<00:03, 1.48it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 60%|████████████████████████████████████████████████████████████ | 6/10 [00:03<00:02, 1.69it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 60%|███████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 6/10 [00:04<00:02, 1.55it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 70%|██████████████████████████████████████████████████████████████████████ | 7/10 [00:04<00:01, 1.65it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 70%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 7/10 [00:04<00:01, 1.50it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 80%|████████████████████████████████████████████████████████████████████████████████ | 8/10 [00:05<00:01, 1.67it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 8/10 [00:05<00:01, 1.52it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 90%|██████████████████████████████████████████████████████████████████████████████████████████ | 9/10 [00:05<00:00, 1.56it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 9/10 [00:06<00:00, 1.50it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.54it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.52it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.52it/s]
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.47it/s]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -752,9 +757,9 @@ 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: -2.767367275553824
|
||||
first power: 0.024011020121022356
|
||||
second power: -0.00021270681344726395
|
||||
zero power: -1.4725246793626128
|
||||
first power: -0.08935132374099551
|
||||
second power: 0.00034688149480437765
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapter6_1_1.png" src="_images/chapter6_1_1.png" />
|
||||
@@ -1621,7 +1626,9 @@ Test set accuracy with Logistic Regression: 0.94
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test set accuracy with SVM: 0.63
|
||||
Test set accuracy with Decision Trees: 0.90
|
||||
</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
|
||||
Test set accuracy with Decision Trees and scaled data: 0.89
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -706,10 +711,10 @@ 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>0.10541723644166373
|
||||
4.575870409023631
|
||||
[[0.84972787 2.5321613 ]
|
||||
[2.5321613 8.59875207]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.14934258650797513
|
||||
4.548263635652985
|
||||
[[ 1.0875061 3.3260513 ]
|
||||
[ 3.3260513 11.10994958]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -749,10 +754,10 @@ 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>0.0768805855280187
|
||||
1.6568154596723088
|
||||
[[1. 0.69438869]
|
||||
[0.69438869 1. ]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.09291556244521161
|
||||
2.096511363983559
|
||||
[[1. 0.7198234]
|
||||
[0.7198234 1. ]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -781,30 +786,30 @@ 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>[[-1.6629598 -6.60625144]
|
||||
[-1.59424119 -4.17676247]
|
||||
[ 0.13699574 -1.26680052]
|
||||
[ 1.67275915 7.04206048]
|
||||
[ 1.48931464 4.73718419]
|
||||
[ 0.82341746 3.16411163]
|
||||
[ 0.56141009 1.13137881]
|
||||
[ 0.38616125 0.98338288]
|
||||
[-1.28000355 -3.69734609]
|
||||
[-0.53285379 -1.31095745]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[ 0.20480187 0.26586817]
|
||||
[ 0.72601722 1.13675593]
|
||||
[ 0.02649469 -0.9834505 ]
|
||||
[ 0.97548406 1.6266783 ]
|
||||
[-1.59078383 -4.25673276]
|
||||
[-0.40596423 -0.31486917]
|
||||
[-0.34654596 -1.94627617]
|
||||
[-1.33062878 -3.73785069]
|
||||
[ 2.22810365 9.25389111]
|
||||
[-0.48697869 -1.04401421]]
|
||||
0 1
|
||||
0 -1.662960 -6.606251
|
||||
1 -1.594241 -4.176762
|
||||
2 0.136996 -1.266801
|
||||
3 1.672759 7.042060
|
||||
4 1.489315 4.737184
|
||||
5 0.823417 3.164112
|
||||
6 0.561410 1.131379
|
||||
7 0.386161 0.983383
|
||||
8 -1.280004 -3.697346
|
||||
9 -0.532854 -1.310957
|
||||
0 0.204802 0.265868
|
||||
1 0.726017 1.136756
|
||||
2 0.026495 -0.983450
|
||||
3 0.975484 1.626678
|
||||
4 -1.590784 -4.256733
|
||||
5 -0.405964 -0.314869
|
||||
6 -0.346546 -1.946276
|
||||
7 -1.330629 -3.737851
|
||||
8 2.228104 9.253891
|
||||
9 -0.486979 -1.044014
|
||||
0 1
|
||||
0 1.000000 0.972149
|
||||
1 0.972149 1.000000
|
||||
0 1.000000 0.950423
|
||||
1 0.950423 1.000000
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -861,37 +866,37 @@ 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 0.083793 0.077516 0.081955 0.073791 0.066763 0.071803 0.064695
|
||||
2 0.0 0.077516 0.074112 0.078363 0.072304 0.066838 0.070556 0.064873
|
||||
3 0.0 0.081955 0.078363 0.084619 0.077941 0.071937 0.076833 0.070462
|
||||
4 0.0 0.073791 0.072304 0.077941 0.073102 0.068533 0.072132 0.067141
|
||||
5 0.0 0.066763 0.066838 0.071937 0.068533 0.065108 0.067671 0.063791
|
||||
6 0.0 0.071803 0.070556 0.076833 0.072132 0.067671 0.071608 0.066653
|
||||
7 0.0 0.064695 0.064873 0.070462 0.067141 0.063791 0.066653 0.062797
|
||||
8 0.0 0.058641 0.059876 0.064878 0.062637 0.060171 0.062173 0.059201
|
||||
9 0.0 0.053462 0.055477 0.059977 0.058582 0.056826 0.058133 0.055876
|
||||
10 0.0 0.061862 0.062199 0.067948 0.064820 0.061637 0.064615 0.060898
|
||||
11 0.0 0.056026 0.057316 0.062429 0.060312 0.057964 0.060087 0.057216
|
||||
12 0.0 0.051042 0.053036 0.057609 0.056287 0.054609 0.056041 0.053854
|
||||
13 0.0 0.046764 0.049276 0.053387 0.052692 0.051554 0.052427 0.050796
|
||||
14 0.0 0.043072 0.045963 0.049677 0.049481 0.048781 0.049196 0.048020
|
||||
1 0.0 0.072147 0.072728 0.071758 0.072209 0.072843 0.064428 0.064668
|
||||
2 0.0 0.072728 0.075385 0.069979 0.071530 0.073408 0.061386 0.062260
|
||||
3 0.0 0.071758 0.069979 0.076968 0.076244 0.075522 0.072286 0.071935
|
||||
4 0.0 0.072209 0.071530 0.076244 0.076161 0.076150 0.070898 0.070950
|
||||
5 0.0 0.072843 0.073408 0.075522 0.076150 0.076934 0.069399 0.069885
|
||||
6 0.0 0.064428 0.061386 0.072286 0.070898 0.069399 0.069873 0.069179
|
||||
7 0.0 0.064668 0.062260 0.071935 0.070950 0.069885 0.069179 0.068758
|
||||
8 0.0 0.065062 0.063354 0.071655 0.071103 0.070514 0.068494 0.068360
|
||||
9 0.0 0.065616 0.064690 0.071433 0.071356 0.071291 0.067793 0.067967
|
||||
10 0.0 0.057287 0.053787 0.066153 0.064505 0.062691 0.065212 0.064382
|
||||
11 0.0 0.057387 0.054286 0.065949 0.064573 0.063048 0.064834 0.064202
|
||||
12 0.0 0.057607 0.054932 0.065830 0.064739 0.063518 0.064507 0.064077
|
||||
13 0.0 0.057951 0.055737 0.065788 0.065001 0.064107 0.064218 0.064000
|
||||
14 0.0 0.058422 0.056717 0.065818 0.065358 0.064820 0.063954 0.063959
|
||||
|
||||
8 9 10 11 12 13 14
|
||||
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
|
||||
1 0.058641 0.053462 0.061862 0.056026 0.051042 0.046764 0.043072
|
||||
2 0.059876 0.055477 0.062199 0.057316 0.053036 0.049276 0.045963
|
||||
3 0.064878 0.059977 0.067948 0.062429 0.057609 0.053387 0.049677
|
||||
4 0.062637 0.058582 0.064820 0.060312 0.056287 0.052692 0.049481
|
||||
5 0.060171 0.056826 0.061637 0.057964 0.054609 0.051554 0.048781
|
||||
6 0.062173 0.058133 0.064615 0.060087 0.056041 0.052427 0.049196
|
||||
7 0.059201 0.055876 0.060898 0.057216 0.053854 0.050796 0.048020
|
||||
8 0.056328 0.053599 0.057420 0.054434 0.051645 0.049060 0.046678
|
||||
9 0.053599 0.051368 0.054197 0.051787 0.049479 0.047299 0.045258
|
||||
10 0.057420 0.054197 0.059243 0.055653 0.052370 0.049380 0.046663
|
||||
11 0.054434 0.051787 0.055653 0.052738 0.050015 0.047489 0.045161
|
||||
12 0.051645 0.049479 0.052370 0.050015 0.047760 0.045630 0.043637
|
||||
13 0.049060 0.047299 0.049380 0.047489 0.045630 0.043839 0.042136
|
||||
14 0.046678 0.045258 0.046663 0.045161 0.043637 0.042136 0.040684
|
||||
1 0.065062 0.065616 0.057287 0.057387 0.057607 0.057951 0.058422
|
||||
2 0.063354 0.064690 0.053787 0.054286 0.054932 0.055737 0.056717
|
||||
3 0.071655 0.071433 0.066153 0.065949 0.065830 0.065788 0.065818
|
||||
4 0.071103 0.071356 0.064505 0.064573 0.064739 0.065001 0.065358
|
||||
5 0.070514 0.071291 0.062691 0.063048 0.063518 0.064107 0.064820
|
||||
6 0.068494 0.067793 0.065212 0.064834 0.064507 0.064218 0.063954
|
||||
7 0.068360 0.067967 0.064382 0.064202 0.064077 0.064000 0.063959
|
||||
8 0.068268 0.068206 0.063526 0.063549 0.063637 0.063781 0.063977
|
||||
9 0.068206 0.068504 0.062616 0.062853 0.063163 0.063545 0.063995
|
||||
10 0.063526 0.062616 0.061724 0.061284 0.060870 0.060471 0.060071
|
||||
11 0.063549 0.062853 0.061284 0.060994 0.060734 0.060490 0.060251
|
||||
12 0.063637 0.063163 0.060870 0.060734 0.060629 0.060547 0.060475
|
||||
13 0.063781 0.063545 0.060471 0.060490 0.060547 0.060631 0.060735
|
||||
14 0.063977 0.063995 0.060071 0.060251 0.060475 0.060735 0.061025
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1080,10 +1085,10 @@ 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 3.986362 1.994474
|
||||
1 1.994474 2.001468
|
||||
[[3.98636199 1.99447418]
|
||||
[1.99447418 2.00146807]]
|
||||
0 3.935972 1.991047
|
||||
1 1.991047 2.000783
|
||||
[[3.93597168 1.99104747]
|
||||
[1.99104747 2.00078324]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1110,8 +1115,8 @@ 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
|
||||
[[3.98636199 1.99447418]
|
||||
[1.99447418 2.00146807]]
|
||||
[[3.93597168 1.99104747]
|
||||
[1.99104747 2.00078324]]
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/chapter8_65_1.png" src="_images/chapter8_65_1.png" />
|
||||
@@ -1171,16 +1176,16 @@ 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
|
||||
5.221666864828611
|
||||
0.766163196245293
|
||||
5.182086698929565
|
||||
0.7546682196464342
|
||||
First eigenvector
|
||||
[0.85014487 0.52654886]
|
||||
[0.84767088 0.53052247]
|
||||
Second eigenvector
|
||||
[-0.52654886 0.85014487]
|
||||
[-0.53052247 0.84767088]
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Eigenvector of largest eigenvalue
|
||||
[-0.85014487 -0.52654886]
|
||||
[0.84767088 0.53052247]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
<link rel="next" title="Project 1 on Machine Learning, deadline October 7 (midnight), 2024" href="project1.html" />
|
||||
<link rel="next" title="Week 38: Logistic Regression and Optimization" href="week38.html" />
|
||||
<link rel="prev" title="Week 37: Statistical interpretations and Resampling Methods" href="week37.html" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
<meta name="docsearch:language" content="None">
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -504,10 +509,10 @@ You can follow the code example in the jupyter-book at <a class="reference exter
|
||||
<p class="prev-next-title">Week 37: Statistical interpretations and Resampling Methods</p>
|
||||
</div>
|
||||
</a>
|
||||
<a class='right-next' id="next-link" href="project1.html" title="next page">
|
||||
<a class='right-next' id="next-link" href="week38.html" title="next page">
|
||||
<div class="prev-next-info">
|
||||
<p class="prev-next-subtitle">next</p>
|
||||
<p class="prev-next-title">Project 1 on Machine Learning, deadline October 7 (midnight), 2024</p>
|
||||
<p class="prev-next-title">Week 38: Logistic Regression and Optimization</p>
|
||||
</div>
|
||||
<i class="fas fa-angle-right"></i>
|
||||
</a>
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -608,8 +613,8 @@ 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>[ 0.93937564 0.92308867 0.34427423 -1.37685349 2.80413696 0.25555619
|
||||
1.121292 -0.42392359 -0.13913033 -0.7228852 ]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[-0.29015871 0.69417174 -1.07998756 0.34332677 0.19547923 -1.09755017
|
||||
0.86197958 -0.15546887 0.14369927 1.96251859]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -830,36 +835,26 @@ 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>[[7.39090680e-02 6.22755839e-01 6.49123502e-01 2.88135577e-01
|
||||
8.94704941e-01 3.03201179e-01 3.18840034e-01 8.41366128e-01
|
||||
8.95033155e-01 5.99812668e-01]
|
||||
[9.16312434e-01 9.43984553e-02 9.29253213e-01 5.26464303e-01
|
||||
2.21390371e-01 1.24224776e-01 6.89710385e-01 4.27861634e-01
|
||||
7.43779698e-03 8.17834506e-01]
|
||||
[1.51914183e-01 5.02447063e-01 5.99647992e-01 8.36235052e-01
|
||||
8.14046224e-01 1.06662069e-01 2.91416944e-01 5.89463761e-01
|
||||
7.07712197e-01 4.57819238e-01]
|
||||
[6.21192602e-01 1.27597777e-01 9.92527681e-01 3.95743466e-01
|
||||
2.66757105e-01 7.72309979e-01 1.18019623e-01 9.40415979e-03
|
||||
9.27352535e-01 9.26378176e-02]
|
||||
[3.52989287e-02 1.01080465e-01 7.27602210e-01 4.01826811e-01
|
||||
2.33826500e-01 8.47478922e-01 4.73646130e-01 4.59491404e-01
|
||||
1.56857739e-01 8.65889641e-01]
|
||||
[1.29100624e-01 1.30387411e-01 2.49156266e-01 1.86123808e-01
|
||||
3.06942115e-01 7.77873845e-01 6.61979664e-01 7.08699295e-01
|
||||
4.47297587e-02 5.88738394e-01]
|
||||
[5.17405911e-01 9.84789579e-01 7.30334592e-01 5.61602014e-01
|
||||
9.95053694e-01 5.73305640e-01 9.36545697e-01 5.29033075e-01
|
||||
1.00989834e-04 6.96379806e-01]
|
||||
[8.23093598e-01 9.99201684e-01 9.42197297e-01 8.48268005e-01
|
||||
1.65900052e-01 2.60605083e-01 3.83884553e-01 5.91410559e-02
|
||||
6.89766337e-01 7.91434750e-01]
|
||||
[6.39621036e-03 4.53512750e-01 2.85259666e-01 6.82623709e-01
|
||||
4.62905281e-01 9.88236598e-01 6.74272285e-02 5.17547294e-01
|
||||
5.67238764e-01 5.67819487e-01]
|
||||
[5.16172219e-01 1.42083463e-01 2.01779091e-01 8.29541992e-02
|
||||
8.23994188e-01 1.28183460e-01 4.64564368e-01 4.61106937e-01
|
||||
7.93891335e-01 6.25458506e-01]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.08233627 0.43727575 0.01759442 0.01179415 0.34483937 0.32733949
|
||||
0.71332117 0.20347912 0.57521514 0.56042437]
|
||||
[0.64909048 0.55179339 0.13847167 0.04224929 0.89540936 0.5142856
|
||||
0.85871255 0.75080943 0.34026086 0.50850373]
|
||||
[0.03239393 0.87884833 0.41310208 0.82418008 0.01014331 0.24284399
|
||||
0.93668828 0.79247398 0.48062913 0.77803692]
|
||||
[0.73068535 0.47910545 0.95715036 0.11844773 0.76686148 0.31453949
|
||||
0.6029291 0.5443909 0.29398019 0.96585824]
|
||||
[0.0179826 0.51643579 0.53723188 0.03844073 0.25989189 0.20834188
|
||||
0.98619989 0.40833375 0.52876551 0.76612827]
|
||||
[0.44830934 0.94099173 0.07123177 0.96305058 0.11596834 0.69756791
|
||||
0.60664448 0.89457785 0.86016288 0.33615742]
|
||||
[0.35243372 0.77465911 0.37532078 0.38399622 0.20728198 0.1108407
|
||||
0.83020679 0.48170743 0.99003341 0.99956033]
|
||||
[0.82832579 0.42553096 0.02355756 0.28542801 0.96894842 0.70711022
|
||||
0.8825782 0.04072235 0.24369936 0.79622905]
|
||||
[0.60762863 0.21699817 0.20218457 0.63071772 0.02358547 0.62886648
|
||||
0.44286735 0.78745927 0.30982436 0.76277254]
|
||||
[0.52173381 0.9495484 0.47433659 0.73203674 0.95226607 0.40072098
|
||||
0.35098705 0.46544987 0.66456459 0.25512598]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -919,13 +914,13 @@ 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>0.2160073466353432
|
||||
4.720889407643293
|
||||
1.0362483569261072
|
||||
[[ 1.17260271 3.60999067 4.15283463]
|
||||
[ 3.60999067 12.23423987 13.18824338]
|
||||
[ 4.15283463 13.18824338 21.31571995]]
|
||||
[31.70587399 0.09156055 2.92512799]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.0018310257999764163
|
||||
4.092608577084595
|
||||
0.31555734196707536
|
||||
[[ 0.78671736 2.38337498 2.04657426]
|
||||
[ 2.38337498 8.33235647 6.05002471]
|
||||
[ 2.04657426 6.05002471 10.13152828]]
|
||||
[15.98413606 0.08227668 3.18418938]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
<link rel="prev" title="Exercises week 38" href="exercisesweek38.html" />
|
||||
<link rel="prev" title="Week 38: Logistic Regression and Optimization" href="week38.html" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
<meta name="docsearch:language" content="None">
|
||||
|
||||
@@ -292,6 +292,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -1033,11 +1038,11 @@ of code developers and contributors keeps increasing.</p>
|
||||
|
||||
<!-- Previous / next buttons -->
|
||||
<div class='prev-next-area'>
|
||||
<a class='left-prev' id="prev-link" href="exercisesweek38.html" title="previous page">
|
||||
<a class='left-prev' id="prev-link" href="week38.html" title="previous page">
|
||||
<i class="fas fa-angle-left"></i>
|
||||
<div class="prev-next-info">
|
||||
<p class="prev-next-subtitle">previous</p>
|
||||
<p class="prev-next-title">Exercises week 38</p>
|
||||
<p class="prev-next-title">Week 38: Logistic Regression and Optimization</p>
|
||||
</div>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
@@ -291,6 +291,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</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
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -980,27 +985,27 @@ uncorrelated.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="cell_output docutils container">
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2.076776262573027
|
||||
[[ 5.24692014 11.86416941 1.87818331 3.59971727 6.93989887 8.39223923
|
||||
1.96991192 6.69391797 5.22667819 3.39929428]
|
||||
[11.86416941 26.82688363 4.24688853 8.13956654 15.69227923 18.97626519
|
||||
4.45430236 15.13607502 11.81839897 7.68637642]
|
||||
[ 1.87818331 4.24688853 0.67231298 1.2885519 2.48420061 3.0040792
|
||||
0.70514808 2.39614949 1.87093752 1.21680864]
|
||||
[ 3.59971727 8.13956654 1.2885519 2.46963249 4.76120718 5.75760403
|
||||
1.3514835 4.59244881 3.58583003 2.33212971]
|
||||
[ 6.93989887 15.69227923 2.48420061 4.76120718 9.17913653 11.1000911
|
||||
2.60552651 8.85378708 6.91312563 4.49611541]
|
||||
[ 8.39223923 18.97626519 3.0040792 5.75760403 11.1000911 13.42305151
|
||||
3.15079545 10.70665448 8.35986305 5.43703545]
|
||||
[ 1.96991192 4.45430236 0.70514808 1.3514835 2.60552651 3.15079545
|
||||
0.73958682 2.51317506 1.96231225 1.27623637]
|
||||
[ 6.69391797 15.13607502 2.39614949 4.59244881 8.85378708 10.70665448
|
||||
2.51317506 8.53996947 6.66809369 4.33675308]
|
||||
[ 5.22667819 11.81839897 1.87093752 3.58583003 6.91312563 8.35986305
|
||||
1.96231225 6.66809369 5.20651434 3.38618024]
|
||||
[ 3.39929428 7.68637642 1.21680864 2.33212971 4.49611541 5.43703545
|
||||
1.27623637 4.33675308 3.38618024 2.20228273]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9841870203276504
|
||||
[[ 3.57492326 2.52888979 5.61382924 2.32419549 4.2986082 3.83718206
|
||||
4.7658599 4.76527062 8.55196383 8.05747369]
|
||||
[ 2.52888979 1.78892891 3.97120565 1.64412879 3.0408223 2.71441086
|
||||
3.37135473 3.37093787 6.04963309 5.69983227]
|
||||
[ 5.61382924 3.97120565 8.81559586 3.64976691 6.75025744 6.02566356
|
||||
7.48399942 7.48307405 13.42945319 12.65293772]
|
||||
[ 2.32419549 1.64412879 3.64976691 1.51104913 2.79469098 2.49470005
|
||||
3.09846933 3.09808622 5.55996153 5.23847441]
|
||||
[ 4.2986082 3.0408223 6.75025744 2.79469098 5.16879134 4.61395701
|
||||
5.73063053 5.72992196 10.28316949 9.68857788]
|
||||
[ 3.83718206 2.71441086 6.02566356 2.49470005 4.61395701 4.11868033
|
||||
5.11548661 5.1148541 9.1793417 8.64857542]
|
||||
[ 4.7658599 3.37135473 7.48399942 3.09846933 5.73063053 5.11548661
|
||||
6.35354073 6.35275513 11.40093324 10.74171049]
|
||||
[ 4.76527062 3.37093787 7.48307405 3.09808622 5.72992196 5.1148541
|
||||
6.35275513 6.35196964 11.39952356 10.74038232]
|
||||
[ 8.55196383 6.04963309 13.42945319 5.55996153 10.28316949 9.1793417
|
||||
11.40093324 11.39952356 20.4580854 19.2751616 ]
|
||||
[ 8.05747369 5.69983227 12.65293772 5.23847441 9.68857788 8.64857542
|
||||
10.74171049 10.74038232 19.2751616 18.16063661]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1268,15 +1273,15 @@ 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>0.09790216282081063
|
||||
4.184866696796263
|
||||
0.2373286546935638
|
||||
0.9722865317690382 10.417644792969273 7.499394932852416
|
||||
3.055091049054481 2.2630723514841553 7.22977659964799
|
||||
[[ 0.97228653 3.05509105 2.26307235]
|
||||
[ 3.05509105 10.41764479 7.2297766 ]
|
||||
[ 2.26307235 7.2297766 7.49939493]]
|
||||
[17.22169087 0.0637712 1.6038642 ]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.07950356694388497
|
||||
4.124228866018077
|
||||
0.3908470004999506
|
||||
1.2475908482537097 11.548432150659993 27.870704644539707
|
||||
3.6568686180915178 4.6156409232714415 13.395980256477532
|
||||
[[ 1.24759085 3.65686862 4.61564092]
|
||||
[ 3.65686862 11.54843215 13.39598026]
|
||||
[ 4.61564092 13.39598026 27.87070464]]
|
||||
[36.35957915 0.07241855 4.23472994]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1606,7 +1611,7 @@ 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>0.023295599127611474 0.9904314810719695
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.05577845931438246 1.0303586629618948
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/statistics_188_1.png" src="_images/statistics_188_1.png" />
|
||||
|
||||
@@ -291,6 +291,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -291,6 +291,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -1668,8 +1673,8 @@ 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>[ 2.93289669 0.72236654 0.6671767 1.31936237 -0.39865452 -0.86247117
|
||||
0.64411666 -1.56072658 1.17367225 0.96391888]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[-1.24454968 1.12382443 0.67088558 1.12498355 1.0342028 -0.44563226
|
||||
0.14599499 0.07378525 0.03479236 -1.2976637 ]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1894,26 +1899,26 @@ 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>[[0.84159992 0.17563274 0.40632663 0.78278086 0.22919456 0.7510197
|
||||
0.57584612 0.46342934 0.4745474 0.65543115]
|
||||
[0.59730078 0.77482712 0.70301068 0.29223521 0.15202654 0.74883972
|
||||
0.48827791 0.09346106 0.58890784 0.7766443 ]
|
||||
[0.59184268 0.65554958 0.91546928 0.87340054 0.48200792 0.14824254
|
||||
0.36185989 0.98654811 0.0473916 0.24032019]
|
||||
[0.87788573 0.61774362 0.82914126 0.23139242 0.32651488 0.61621902
|
||||
0.59908884 0.49381549 0.97716508 0.21531156]
|
||||
[0.01654574 0.32393078 0.91854134 0.93909866 0.75300068 0.53942728
|
||||
0.66063786 0.48867802 0.53149078 0.6831505 ]
|
||||
[0.57847325 0.42774546 0.24433117 0.07531349 0.98190064 0.68879472
|
||||
0.18485685 0.85422602 0.58493681 0.00348246]
|
||||
[0.8517571 0.29620357 0.3096154 0.18409254 0.54880148 0.29881308
|
||||
0.7509571 0.46891823 0.42124182 0.38203725]
|
||||
[0.59963873 0.51154388 0.28399125 0.60026673 0.49074536 0.32906581
|
||||
0.4069157 0.89724282 0.48326853 0.43373107]
|
||||
[0.28415767 0.95518112 0.68257097 0.59215613 0.64373221 0.81283649
|
||||
0.04262217 0.80979265 0.73337355 0.2077068 ]
|
||||
[0.87696461 0.09529067 0.39540235 0.68352799 0.1598058 0.03648711
|
||||
0.73018893 0.60921896 0.33220123 0.50887105]]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[0.50306335 0.66487438 0.75677341 0.01349244 0.2813829 0.80056922
|
||||
0.07974741 0.63046054 0.05098714 0.26479234]
|
||||
[0.17861414 0.3388112 0.30005983 0.35520169 0.5399913 0.51709529
|
||||
0.36339156 0.71582784 0.34568909 0.23164401]
|
||||
[0.11922031 0.771128 0.20051449 0.07890044 0.96866031 0.34346829
|
||||
0.5116375 0.52732966 0.80637385 0.69435454]
|
||||
[0.94291287 0.29238145 0.84711297 0.22849742 0.56967917 0.1636508
|
||||
0.15833751 0.84254917 0.05068486 0.54057582]
|
||||
[0.17856374 0.71524686 0.66498292 0.00256622 0.72427854 0.50667812
|
||||
0.0894015 0.22688898 0.54873252 0.31727523]
|
||||
[0.73578772 0.81479491 0.45620975 0.2662452 0.47757553 0.64322974
|
||||
0.54921401 0.70630967 0.51136852 0.59683811]
|
||||
[0.37837864 0.71860732 0.35320952 0.67495943 0.16188604 0.41925189
|
||||
0.28956161 0.06685171 0.75654448 0.28923 ]
|
||||
[0.91099021 0.8387133 0.18277213 0.40418675 0.7249499 0.46415522
|
||||
0.35018806 0.87148597 0.94141801 0.50911289]
|
||||
[0.86066395 0.12235593 0.82418352 0.57881465 0.82559478 0.96826039
|
||||
0.291056 0.41675053 0.06430789 0.96432396]
|
||||
[0.91270333 0.64362404 0.18816387 0.81318307 0.47989224 0.20375464
|
||||
0.33145794 0.92192012 0.33596404 0.18085537]]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1968,13 +1973,13 @@ 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>-0.03681479262838276
|
||||
3.936877889972962
|
||||
-0.43976975777097144
|
||||
[[ 1.18742521 3.6407249 4.14122256]
|
||||
[ 3.6407249 12.06804775 12.20775115]
|
||||
[ 4.14122256 12.20775115 20.86009093]]
|
||||
[30.46593404 0.0604888 3.58914105]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.1385510301651882
|
||||
4.344176915013831
|
||||
0.13134148011170077
|
||||
[[ 0.87981676 2.52698542 2.65591748]
|
||||
[ 2.52698542 8.29861395 7.76790092]
|
||||
[ 2.65591748 7.76790092 13.4831139 ]]
|
||||
[19.77845284 0.09028702 2.79280475]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -2199,7 +2204,7 @@ Name: Aragorn, dtype: object
|
||||
<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">AttributeError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
|
||||
<span class="nn">/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6690/1326197715.py</span> in <span class="ni">?</span><span class="nt">()</span>
|
||||
<span class="nn">/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58840/1326197715.py</span> in <span class="ni">?</span><span class="nt">()</span>
|
||||
<span class="ne">----> </span><span class="mi">6</span> <span class="n">new_hobbit</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'First Name'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Peregrin"</span><span class="p">],</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">7</span> <span class="s1">'Last Name'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Took"</span><span class="p">],</span>
|
||||
<span class="g g-Whitespace"> </span><span class="mi">8</span> <span class="s1">'Place of birth'</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Shire"</span><span class="p">],</span>
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -1636,7 +1641,7 @@ 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>0.9958983289118531
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9969513794144311
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1653,7 +1658,7 @@ 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>0.010348289064969316
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.007658477904313023
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1668,23 +1673,23 @@ 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>[0.01250964 0.00679013 0.06416459 0.02126205 0.01247909 0.00080497
|
||||
0.07373479 0.00940611 0.04794409 0.02361665 0.03311432 0.00114401
|
||||
0.06392846 0.0813156 0.03578526 0.00691012 0.05978512 0.02928194
|
||||
0.00209403 0.02014577 0.13600596 0.0178462 0.02016185 0.00574735
|
||||
0.029003 0.04877931 0.03796359 0.02374496 0.06622369 0.02965159
|
||||
0.01655384 0.00290613 0.00560098 0.00641423 0.05549529 0.03916813
|
||||
0.01288787 0.02472936 0.02564828 0.03365012 0.03642173 0.02102403
|
||||
0.00633289 0.02631942 0.02164667 0.04899367 0.00593362 0.03664879
|
||||
0.00250787 0.00791263 0.01480256 0.01329403 0.02151521 0.00133638
|
||||
0.01717549 0.01681612 0.03358833 0.01003519 0.00659604 0.02026756
|
||||
0.01234261 0.058967 0.0126711 0.0039259 0.00254973 0.04315855
|
||||
0.02909574 0.02386932 0.02404706 0.02769274 0.05804539 0.00732665
|
||||
0.03868372 0.00513051 0.00172865 0.00353938 0.03076019 0.01643795
|
||||
0.00458631 0.01817048 0.08667591 0.01706166 0.00637188 0.0930438
|
||||
0.01506354 0.02787184 0.0002926 0.09398153 0.00570321 0.03511387
|
||||
0.01075018 0.00170809 0.00943959 0.02654585 0.00399972 0.03057995
|
||||
0.00211418 0.00685287 0.02330799 0.04191323]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.00053122 0.03115122 0.00789262 0.02218076 0.00573727 0.00557893
|
||||
0.00099778 0.01234 0.03960002 0.03596557 0.0134113 0.00556946
|
||||
0.00136125 0.10719554 0.02754248 0.01409478 0.01760144 0.01032533
|
||||
0.01241284 0.01039879 0.00100913 0.02944152 0.00512599 0.00747773
|
||||
0.06260611 0.0231353 0.01624447 0.02923006 0.0046544 0.07332248
|
||||
0.02338085 0.02920675 0.02286267 0.04353549 0.00569512 0.02664408
|
||||
0.01098247 0.02156565 0.03529801 0.00507531 0.00554202 0.05141614
|
||||
0.02031987 0.01244297 0.01551724 0.00174738 0.01044475 0.01161645
|
||||
0.02622039 0.03285784 0.00522055 0.00687309 0.0195302 0.04101344
|
||||
0.00816675 0.0206033 0.04046513 0.02189863 0.06777772 0.04832356
|
||||
0.00114855 0.08660891 0.00586355 0.00625051 0.00939407 0.00108471
|
||||
0.03948301 0.02527621 0.03205795 0.11042239 0.02594314 0.05176711
|
||||
0.03396658 0.00889475 0.02632742 0.02502325 0.01266999 0.00455966
|
||||
0.03853313 0.01543076 0.00617221 0.00552462 0.01573062 0.01035006
|
||||
0.00162921 0.00974758 0.00812487 0.01881237 0.06690071 0.01499192
|
||||
0.04652794 0.04061345 0.04495752 0.00566707 0.01006984 0.00519717
|
||||
0.00151416 0.03214829 0.00891702 0.01844822]
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1753,15 +1758,15 @@ 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>[ 2.04641529 -0.77354301 7.7511273 -3.70241548 1.69515531]
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.97243946 0.15593478 4.54011398 0.56342158 -0.19299283]
|
||||
Training R2
|
||||
0.9958589197366403
|
||||
0.9948998579029953
|
||||
Training MSE
|
||||
0.008560581831215528
|
||||
0.009396472959497925
|
||||
Test R2
|
||||
0.997252717901263
|
||||
0.9951059931014423
|
||||
Test MSE
|
||||
0.00683875021199284
|
||||
0.010612904886352397
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
|
||||
@@ -293,6 +293,11 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
Exercises week 38
|
||||
</a>
|
||||
</li>
|
||||
<li class="toctree-l1">
|
||||
<a class="reference internal" href="week38.html">
|
||||
Week 38: Logistic Regression and Optimization
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
@@ -1624,7 +1629,7 @@ 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
|
||||
100.213 14.98 100.211 0.149466
|
||||
99.8244 15.0449 99.8227 0.150527
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -1844,9 +1849,7 @@ Error: 0.08426840630693411
|
||||
Bias^2: 0.0796891867672603
|
||||
Var: 0.004579219539673834
|
||||
0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 2
|
||||
Polynomial degree: 2
|
||||
Error: 0.10398646080125035
|
||||
Bias^2: 0.1007711427354898
|
||||
Var: 0.0032153180657605116
|
||||
@@ -1873,9 +1876,7 @@ Error: 0.037813671417389005
|
||||
Bias^2: 0.033657685071527665
|
||||
Var: 0.00415598634586135
|
||||
0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 7
|
||||
Polynomial degree: 7
|
||||
Error: 0.02760977349102253
|
||||
Bias^2: 0.022999498260366312
|
||||
Var: 0.004610275230656212
|
||||
@@ -1890,16 +1891,17 @@ Error: 0.02660572763718093
|
||||
Bias^2: 0.010018312644137363
|
||||
Var: 0.016587414993043573
|
||||
0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 10
|
||||
Polynomial degree: 10
|
||||
Error: 0.021592704588025025
|
||||
Bias^2: 0.010516485576645508
|
||||
Var: 0.011076219011379514
|
||||
0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 11
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree:
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 11
|
||||
Error: 0.07160048164233104
|
||||
Bias^2: 0.014436800088904942
|
||||
Var: 0.05716368155342608
|
||||
@@ -1916,7 +1918,7 @@ Var: 0.20867052175034223
|
||||
0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/week37_139_6.png" src="_images/week37_139_6.png" />
|
||||
<img alt="_images/week37_139_4.png" src="_images/week37_139_4.png" />
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -2267,31 +2269,29 @@ Mean squared error on test data: 10.50427787
|
||||
Degree of polynomial: 7
|
||||
Mean squared error on training data: 0.47313680
|
||||
Mean squared error on test data: 1.53738247
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 8
|
||||
Degree of polynomial: 8
|
||||
Mean squared error on training data: 0.04926746
|
||||
Mean squared error on test data: 0.14629156
|
||||
Degree of polynomial: 9
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 9
|
||||
Mean squared error on training data: 0.02546675
|
||||
Mean squared error on test data: 0.11202337
|
||||
Degree of polynomial: 10
|
||||
Mean squared error on training data: 0.02424794
|
||||
Mean squared error on test data: 0.22467274
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 11
|
||||
Degree of polynomial: 11
|
||||
Mean squared error on training data: 0.01594452
|
||||
Mean squared error on test data: 1.07641937
|
||||
Degree of polynomial: 12
|
||||
Mean squared error on training data: 0.00805074
|
||||
Mean squared error on test data: 0.04295757
|
||||
Degree of polynomial: 13
|
||||
Mean squared error on training data: 0.00781918
|
||||
Mean squared error on test data: 0.56965674
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 14
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 13
|
||||
Mean squared error on training data: 0.00781918
|
||||
Mean squared error on test data: 0.56965674
|
||||
Degree of polynomial: 14
|
||||
Mean squared error on training data: 0.00465099
|
||||
Mean squared error on test data: 0.28443039
|
||||
Degree of polynomial: 15
|
||||
@@ -2311,31 +2311,29 @@ Mean squared error on test data: 429.25695398
|
||||
Degree of polynomial: 19
|
||||
Mean squared error on training data: 0.00154853
|
||||
Mean squared error on test data: 239.97065359
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 20
|
||||
Degree of polynomial: 20
|
||||
Mean squared error on training data: 0.00140846
|
||||
Mean squared error on test data: 1350.24493666
|
||||
Degree of polynomial: 21
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 21
|
||||
Mean squared error on training data: 0.00119688
|
||||
Mean squared error on test data: 1840.50530832
|
||||
Degree of polynomial: 22
|
||||
Mean squared error on training data: 0.00092898
|
||||
Mean squared error on test data: 1184.60929685
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 23
|
||||
Degree of polynomial: 23
|
||||
Mean squared error on training data: 0.00089193
|
||||
Mean squared error on test data: 3892.17483760
|
||||
Degree of polynomial: 24
|
||||
Mean squared error on training data: 0.00083355
|
||||
Mean squared error on test data: 1332.46736215
|
||||
Degree of polynomial: 25
|
||||
Mean squared error on training data: 0.00079904
|
||||
Mean squared error on test data: 7577.76690383
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 26
|
||||
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Degree of polynomial: 25
|
||||
Mean squared error on training data: 0.00079904
|
||||
Mean squared error on test data: 7577.76690383
|
||||
Degree of polynomial: 26
|
||||
Mean squared error on training data: 0.00075590
|
||||
Mean squared error on test data: 1079.36895644
|
||||
Degree of polynomial: 27
|
||||
@@ -2351,13 +2349,13 @@ Mean squared error on training data: 0.00063866
|
||||
Mean squared error on test data: 3099.60342978
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.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_58862/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58862/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(testerror), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
<img alt="_images/week37_148_11.png" src="_images/week37_148_11.png" />
|
||||
<img alt="_images/week37_148_9.png" src="_images/week37_148_9.png" />
|
||||
</div>
|
||||
</div>
|
||||
<p>Note that we kept the intercept column in the fitting here. This means that we need to set the <strong>intercept</strong> in the call to the <strong>Scikit-Learn</strong> function as <strong>False</strong>. Alternatively, we could have set up the design matrix <span class="math notranslate nohighlight">\(X\)</span> without the first column of ones.</p>
|
||||
@@ -2438,7 +2436,7 @@ Mean squared error on test data: 3099.60342978
|
||||
</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_6729/3817475779.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_58862/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
|
||||
plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
|
||||
</pre></div>
|
||||
</div>
|
||||
|
||||
@@ -1099,6 +1099,7 @@ doconce format html week38.do.txt --no_mako -->
|
||||
<li><p>Resampling techniques, cross-validation examples included here, see also the lectures from last week on the bootstrap method</p></li>
|
||||
<li><p>Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at <a class="reference external" href="https://youtu.be/omLmp_kkie0">https://youtu.be/omLmp_kkie0</a></p></li>
|
||||
<li><p>Work on project 1, in particular resampling methods like cross-validation and bootstrap.</p></li>
|
||||
<li><p><a class="reference external" href="https://youtu.be/T9jjWsmsd1o">Video on cross-validation from exercise session</a></p></li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section" id="material-for-lecture-monday-september-16">
|
||||
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
Binary file not shown.
|
After Width: | Height: | Size: 23 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
@@ -2989,25 +2989,14 @@
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:81\u001b[0m, in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhessian\u001b[39m(fun, x):\n\u001b[1;32m 80\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturns a function that computes the exact Hessian.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjacobian\u001b[39m(fun, x):\n\u001b[1;32m 52\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;124;03m Returns a function which computes the Jacobian of `fun` with respect to\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`, which must be a scalar or array. Unlike\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;124;03m (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).\u001b[39;00m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 61\u001b[0m ans_vspace \u001b[38;5;241m=\u001b[39m vspace(ans)\n\u001b[1;32m 62\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m 9\u001b[0m start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m end_value, end_node \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 9\u001b[0m start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:60\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjacobian\u001b[39m(fun, x):\n\u001b[1;32m 52\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;124;03m Returns a function which computes the Jacobian of `fun` with respect to\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`, which must be a scalar or array. Unlike\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;124;03m (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).\u001b[39;00m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 61\u001b[0m ans_vspace \u001b[38;5;241m=\u001b[39m vspace(ans)\n\u001b[1;32m 62\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m 9\u001b[0m start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m end_value, end_node \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 9\u001b[0m start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"Cell \u001b[0;32mIn[9], line 61\u001b[0m, in \u001b[0;36mg_trial\u001b[0;34m(point, P)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mg_trial\u001b[39m(point,P):\n\u001b[1;32m 60\u001b[0m x,t \u001b[38;5;241m=\u001b[39m point\n\u001b[0;32m---> 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m-\u001b[39mt)\u001b[38;5;241m*\u001b[39mu(x) \u001b[38;5;241m+\u001b[39m x\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m-\u001b[39mx)\u001b[38;5;241m*\u001b[39mt\u001b[38;5;241m*\u001b[39m\u001b[43mdeep_neural_network\u001b[49m\u001b[43m(\u001b[49m\u001b[43mP\u001b[49m\u001b[43m,\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"Cell \u001b[0;32mIn[9], line 37\u001b[0m, in \u001b[0;36mdeep_neural_network\u001b[0;34m(deep_params, x)\u001b[0m\n\u001b[1;32m 34\u001b[0m x_prev \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate((np\u001b[38;5;241m.\u001b[39mones((\u001b[38;5;241m1\u001b[39m,num_points)), x_prev ), axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 36\u001b[0m z_hidden \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(w_hidden, x_prev)\n\u001b[0;32m---> 37\u001b[0m x_hidden \u001b[38;5;241m=\u001b[39m \u001b[43msigmoid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz_hidden\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# Update x_prev such that next layer can use the output from this layer\u001b[39;00m\n\u001b[1;32m 40\u001b[0m x_prev \u001b[38;5;241m=\u001b[39m x_hidden\n",
|
||||
"Cell \u001b[0;32mIn[9], line 11\u001b[0m, in \u001b[0;36msigmoid\u001b[0;34m(z)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msigmoid\u001b[39m(z):\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexp\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mz\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:39\u001b[0m, in \u001b[0;36mArrayBox.__rtruediv__\u001b[0;34m(self, other)\u001b[0m\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__rtruediv__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrue_divide\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45\u001b[0m, in \u001b[0;36mprimitive.<locals>.f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 43\u001b[0m argnums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(argnum \u001b[38;5;28;01mfor\u001b[39;00m argnum, _ \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[1;32m 44\u001b[0m ans \u001b[38;5;241m=\u001b[39m f_wrapped(\u001b[38;5;241m*\u001b[39margvals, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 45\u001b[0m node \u001b[38;5;241m=\u001b[39m \u001b[43mnode_constructor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_wrapped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36\u001b[0m, in \u001b[0;36mVJPNode.__init__\u001b[0;34m(self, value, fun, args, kwargs, parent_argnums, parents)\u001b[0m\n\u001b[1;32m 33\u001b[0m fun_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fun, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__name__\u001b[39m\u001b[38;5;124m'\u001b[39m, fun)\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnums \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;241m.\u001b[39mformat(fun_name, parent_argnums))\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpmaker\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent_argnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:66\u001b[0m, in \u001b[0;36mdefvjp.<locals>.vjp_argnums\u001b[0;34m(argnums, ans, args, kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[0;32m---> 66\u001b[0m vjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (vjp(g),)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:53\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(ans, x, y)\u001b[0m\n\u001b[1;32m 48\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlogaddexp, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39mexp(x\u001b[38;5;241m-\u001b[39mans)),\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39mexp(y\u001b[38;5;241m-\u001b[39mans)))\n\u001b[1;32m 50\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mlogaddexp2, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(x\u001b[38;5;241m-\u001b[39mans)),\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(y\u001b[38;5;241m-\u001b[39mans)))\n\u001b[1;32m 52\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mtrue_divide, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m y),\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : \u001b[43munbroadcast_f\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 54\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mmod, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39mg \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39mfloor(x\u001b[38;5;241m/\u001b[39my)))\n\u001b[1;32m 56\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mremainder, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39mg \u001b[38;5;241m*\u001b[39m anp\u001b[38;5;241m.\u001b[39mfloor(x\u001b[38;5;241m/\u001b[39my)))\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:658\u001b[0m, in \u001b[0;36munbroadcast_f\u001b[0;34m(target, f)\u001b[0m\n\u001b[1;32m 655\u001b[0m x \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mreal(x)\n\u001b[1;32m 656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n\u001b[0;32m--> 658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munbroadcast_f\u001b[39m(target, f):\n\u001b[1;32m 659\u001b[0m target_meta \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mmetadata(target)\n\u001b[1;32m 660\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: unbroadcast(f(g), target_meta)\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:64\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 62\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 63\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(vjp, ans_vspace\u001b[38;5;241m.\u001b[39mstandard_basis())\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m)\u001b[49m, jacobian_shape)\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp.<locals>.vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m toposort(end_node):\n\u001b[1;32m 20\u001b[0m outgrad \u001b[38;5;241m=\u001b[39m outgrads\u001b[38;5;241m.\u001b[39mpop(node)\n\u001b[0;32m---> 21\u001b[0m ingrads \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrad\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[1;32m 23\u001b[0m outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(outgrads\u001b[38;5;241m.\u001b[39mget(parent), ingrad)\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67\u001b[0m, in \u001b[0;36mdefvjp.<locals>.vjp_argnums.<locals>.<lambda>\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[1;32m 66\u001b[0m vjp \u001b[38;5;241m=\u001b[39m vjpfun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m,)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 69\u001b[0m argnum_0, argnum_1 \u001b[38;5;241m=\u001b[39m argnums\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:660\u001b[0m, in \u001b[0;36munbroadcast_f.<locals>.<lambda>\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munbroadcast_f\u001b[39m(target, f):\n\u001b[1;32m 659\u001b[0m target_meta \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mmetadata(target)\n\u001b[0;32m--> 660\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[43munbroadcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_meta\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:651\u001b[0m, in \u001b[0;36munbroadcast\u001b[0;34m(x, target_meta, broadcast_idx)\u001b[0m\n\u001b[1;32m 649\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m anp\u001b[38;5;241m.\u001b[39mndim(x) \u001b[38;5;241m>\u001b[39m target_ndim:\n\u001b[1;32m 650\u001b[0m x \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39msum(x, axis\u001b[38;5;241m=\u001b[39mbroadcast_idx)\n\u001b[0;32m--> 651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, size \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtarget_shape\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 653\u001b[0m x \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39msum(x, axis\u001b[38;5;241m=\u001b[39maxis, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
|
||||
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||
]
|
||||
}
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 13 KiB After Width: | Height: | Size: 14 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 19 KiB After Width: | Height: | Size: 18 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 24 KiB After Width: | Height: | Size: 26 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 18 KiB After Width: | Height: | Size: 18 KiB |
@@ -1798,10 +1798,10 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"-0.0369544130635358\n",
|
||||
"3.662836197064178\n",
|
||||
"[[1.058997 3.11439407]\n",
|
||||
" [3.11439407 9.99498272]]\n"
|
||||
"-0.07978850553011713\n",
|
||||
"3.8232102961414203\n",
|
||||
"[[ 1.25705685 3.70704566]\n",
|
||||
" [ 3.70704566 12.1664372 ]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1845,10 +1845,10 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.08826182458028335\n",
|
||||
"1.7026722043092946\n",
|
||||
"[[1. 0.61113781]\n",
|
||||
" [0.61113781 1. ]]\n"
|
||||
"0.08931169286872433\n",
|
||||
"2.047447724189861\n",
|
||||
"[[1. 0.66729685]\n",
|
||||
" [0.66729685 1. ]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1905,30 +1905,30 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[-0.7252563 -2.26264849]\n",
|
||||
" [ 1.19052935 3.11261935]\n",
|
||||
" [-0.62158409 -2.99662602]\n",
|
||||
" [-0.06216141 -0.18120973]\n",
|
||||
" [ 1.32065614 3.50269821]\n",
|
||||
" [ 0.83995705 2.80855691]\n",
|
||||
" [ 0.1571284 0.96919021]\n",
|
||||
" [-0.03404758 1.01551815]\n",
|
||||
" [-0.23596934 0.77449804]\n",
|
||||
" [-1.82925222 -6.74259663]]\n",
|
||||
"[[ 2.37582891 5.4242743 ]\n",
|
||||
" [-0.81925302 -3.60052901]\n",
|
||||
" [ 0.37467515 3.16158074]\n",
|
||||
" [-0.40478305 -2.22601408]\n",
|
||||
" [-0.0776532 -1.70594608]\n",
|
||||
" [-0.99434472 -2.2026985 ]\n",
|
||||
" [ 0.71376303 2.77725472]\n",
|
||||
" [ 0.94637206 2.69303453]\n",
|
||||
" [-0.19791352 0.93366198]\n",
|
||||
" [-1.91669165 -5.2546186 ]]\n",
|
||||
" 0 1\n",
|
||||
"0 -0.725256 -2.262648\n",
|
||||
"1 1.190529 3.112619\n",
|
||||
"2 -0.621584 -2.996626\n",
|
||||
"3 -0.062161 -0.181210\n",
|
||||
"4 1.320656 3.502698\n",
|
||||
"5 0.839957 2.808557\n",
|
||||
"6 0.157128 0.969190\n",
|
||||
"7 -0.034048 1.015518\n",
|
||||
"8 -0.235969 0.774498\n",
|
||||
"9 -1.829252 -6.742597\n",
|
||||
"0 2.375829 5.424274\n",
|
||||
"1 -0.819253 -3.600529\n",
|
||||
"2 0.374675 3.161581\n",
|
||||
"3 -0.404783 -2.226014\n",
|
||||
"4 -0.077653 -1.705946\n",
|
||||
"5 -0.994345 -2.202698\n",
|
||||
"6 0.713763 2.777255\n",
|
||||
"7 0.946372 2.693035\n",
|
||||
"8 -0.197914 0.933662\n",
|
||||
"9 -1.916692 -5.254619\n",
|
||||
" 0 1\n",
|
||||
"0 1.000000 0.963187\n",
|
||||
"1 0.963187 1.000000\n"
|
||||
"0 1.000000 0.932382\n",
|
||||
"1 0.932382 1.000000\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1974,37 +1974,37 @@
|
||||
"text": [
|
||||
" 0 1 2 3 4 5 6 7 \\\n",
|
||||
"0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
|
||||
"1 0.0 0.077382 0.076459 0.074114 0.076542 0.079074 0.064978 0.067074 \n",
|
||||
"2 0.0 0.076459 0.078118 0.070150 0.073866 0.078123 0.059624 0.062356 \n",
|
||||
"3 0.0 0.074114 0.070150 0.076618 0.077248 0.077529 0.070432 0.071572 \n",
|
||||
"4 0.0 0.076542 0.073866 0.077248 0.078731 0.080082 0.069802 0.071449 \n",
|
||||
"5 0.0 0.079074 0.078123 0.077529 0.080082 0.082803 0.068607 0.070859 \n",
|
||||
"6 0.0 0.064978 0.059624 0.070432 0.069802 0.068607 0.066865 0.067168 \n",
|
||||
"7 0.0 0.067074 0.062356 0.071572 0.071449 0.070859 0.067168 0.067809 \n",
|
||||
"8 0.0 0.069439 0.065556 0.072752 0.073253 0.073422 0.067367 0.068408 \n",
|
||||
"9 0.0 0.072092 0.069317 0.073923 0.075198 0.076333 0.067380 0.068897 \n",
|
||||
"10 0.0 0.056856 0.051021 0.063637 0.062282 0.060288 0.061848 0.061588 \n",
|
||||
"11 0.0 0.058361 0.052856 0.064604 0.063560 0.061921 0.062263 0.062231 \n",
|
||||
"12 0.0 0.060085 0.055000 0.065679 0.065007 0.063802 0.062700 0.062932 \n",
|
||||
"13 0.0 0.062053 0.057514 0.066855 0.066633 0.065963 0.063133 0.063673 \n",
|
||||
"14 0.0 0.064294 0.060470 0.068116 0.068446 0.068446 0.063525 0.064427 \n",
|
||||
"1 0.0 0.080947 0.086913 0.082764 0.085436 0.088029 0.075956 0.077683 \n",
|
||||
"2 0.0 0.086913 0.094044 0.088199 0.091365 0.094439 0.080171 0.082174 \n",
|
||||
"3 0.0 0.082764 0.088199 0.090624 0.093094 0.095465 0.086730 0.088375 \n",
|
||||
"4 0.0 0.085436 0.091365 0.093094 0.095805 0.098414 0.088651 0.090440 \n",
|
||||
"5 0.0 0.088029 0.094439 0.095465 0.098414 0.101263 0.090468 0.092401 \n",
|
||||
"6 0.0 0.075956 0.080171 0.086730 0.088651 0.090468 0.085390 0.086712 \n",
|
||||
"7 0.0 0.077683 0.082174 0.088375 0.090440 0.092401 0.086712 0.088127 \n",
|
||||
"8 0.0 0.079444 0.084218 0.090038 0.092252 0.094362 0.088038 0.089548 \n",
|
||||
"9 0.0 0.081252 0.086318 0.091731 0.094099 0.096365 0.089376 0.090985 \n",
|
||||
"10 0.0 0.068684 0.071868 0.080702 0.082126 0.083446 0.081103 0.082115 \n",
|
||||
"11 0.0 0.069978 0.073339 0.081978 0.083499 0.084915 0.082170 0.083248 \n",
|
||||
"12 0.0 0.071314 0.074859 0.083288 0.084910 0.086428 0.083260 0.084406 \n",
|
||||
"13 0.0 0.072697 0.076435 0.084637 0.086364 0.087988 0.084376 0.085592 \n",
|
||||
"14 0.0 0.074130 0.078071 0.086026 0.087864 0.089600 0.085518 0.086809 \n",
|
||||
"\n",
|
||||
" 8 9 10 11 12 13 14 \n",
|
||||
"0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
|
||||
"1 0.069439 0.072092 0.056856 0.058361 0.060085 0.062053 0.064294 \n",
|
||||
"2 0.065556 0.069317 0.051021 0.052856 0.055000 0.057514 0.060470 \n",
|
||||
"3 0.072752 0.073923 0.063637 0.064604 0.065679 0.066855 0.068116 \n",
|
||||
"4 0.073253 0.075198 0.062282 0.063560 0.065007 0.066633 0.068446 \n",
|
||||
"5 0.073422 0.076333 0.060288 0.061921 0.063802 0.065963 0.068446 \n",
|
||||
"6 0.067367 0.067380 0.061848 0.062263 0.062700 0.063133 0.063525 \n",
|
||||
"7 0.068408 0.068897 0.061588 0.062231 0.062932 0.063673 0.064427 \n",
|
||||
"8 0.069486 0.070556 0.061154 0.062058 0.063064 0.064166 0.065352 \n",
|
||||
"9 0.070556 0.072344 0.060453 0.061656 0.063017 0.064547 0.066256 \n",
|
||||
"10 0.061154 0.060453 0.058245 0.058254 0.058234 0.058152 0.057961 \n",
|
||||
"11 0.062058 0.061656 0.058254 0.058427 0.058593 0.058721 0.058770 \n",
|
||||
"12 0.063064 0.063017 0.058234 0.058593 0.058970 0.059340 0.059669 \n",
|
||||
"13 0.064166 0.064547 0.058152 0.058721 0.059340 0.059992 0.060652 \n",
|
||||
"14 0.065352 0.066256 0.057961 0.058770 0.059669 0.060652 0.061706 \n"
|
||||
"1 0.079444 0.081252 0.068684 0.069978 0.071314 0.072697 0.074130 \n",
|
||||
"2 0.084218 0.086318 0.071868 0.073339 0.074859 0.076435 0.078071 \n",
|
||||
"3 0.090038 0.091731 0.080702 0.081978 0.083288 0.084637 0.086026 \n",
|
||||
"4 0.092252 0.094099 0.082126 0.083499 0.084910 0.086364 0.087864 \n",
|
||||
"5 0.094362 0.096365 0.083446 0.084915 0.086428 0.087988 0.089600 \n",
|
||||
"6 0.088038 0.089376 0.081103 0.082170 0.083260 0.084376 0.085518 \n",
|
||||
"7 0.089548 0.090985 0.082115 0.083248 0.084406 0.085592 0.086809 \n",
|
||||
"8 0.091068 0.092607 0.083119 0.084319 0.085548 0.086808 0.088102 \n",
|
||||
"9 0.092607 0.094254 0.084122 0.085392 0.086694 0.088030 0.089404 \n",
|
||||
"10 0.083119 0.084122 0.078238 0.079089 0.079953 0.080832 0.081728 \n",
|
||||
"11 0.084319 0.085392 0.079089 0.079989 0.080903 0.081835 0.082785 \n",
|
||||
"12 0.085548 0.086694 0.079953 0.080903 0.081871 0.082857 0.083864 \n",
|
||||
"13 0.086808 0.088030 0.080832 0.081835 0.082857 0.083900 0.084967 \n",
|
||||
"14 0.088102 0.089404 0.081728 0.082785 0.083864 0.084967 0.086096 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
File diff suppressed because one or more lines are too long
Binary file not shown.
|
Before Width: | Height: | Size: 21 KiB After Width: | Height: | Size: 22 KiB |
File diff suppressed because one or more lines are too long
Binary file not shown.
|
Before Width: | Height: | Size: 44 KiB After Width: | Height: | Size: 52 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 36 KiB After Width: | Height: | Size: 36 KiB |
File diff suppressed because one or more lines are too long
Binary file not shown.
|
Before Width: | Height: | Size: 39 KiB After Width: | Height: | Size: 36 KiB |
@@ -225,8 +225,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 0.93937564 0.92308867 0.34427423 -1.37685349 2.80413696 0.25555619\n",
|
||||
" 1.121292 -0.42392359 -0.13913033 -0.7228852 ]\n"
|
||||
"[-0.29015871 0.69417174 -1.07998756 0.34332677 0.19547923 -1.09755017\n",
|
||||
" 0.86197958 -0.15546887 0.14369927 1.96251859]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -662,36 +662,26 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[7.39090680e-02 6.22755839e-01 6.49123502e-01 2.88135577e-01\n",
|
||||
" 8.94704941e-01 3.03201179e-01 3.18840034e-01 8.41366128e-01\n",
|
||||
" 8.95033155e-01 5.99812668e-01]\n",
|
||||
" [9.16312434e-01 9.43984553e-02 9.29253213e-01 5.26464303e-01\n",
|
||||
" 2.21390371e-01 1.24224776e-01 6.89710385e-01 4.27861634e-01\n",
|
||||
" 7.43779698e-03 8.17834506e-01]\n",
|
||||
" [1.51914183e-01 5.02447063e-01 5.99647992e-01 8.36235052e-01\n",
|
||||
" 8.14046224e-01 1.06662069e-01 2.91416944e-01 5.89463761e-01\n",
|
||||
" 7.07712197e-01 4.57819238e-01]\n",
|
||||
" [6.21192602e-01 1.27597777e-01 9.92527681e-01 3.95743466e-01\n",
|
||||
" 2.66757105e-01 7.72309979e-01 1.18019623e-01 9.40415979e-03\n",
|
||||
" 9.27352535e-01 9.26378176e-02]\n",
|
||||
" [3.52989287e-02 1.01080465e-01 7.27602210e-01 4.01826811e-01\n",
|
||||
" 2.33826500e-01 8.47478922e-01 4.73646130e-01 4.59491404e-01\n",
|
||||
" 1.56857739e-01 8.65889641e-01]\n",
|
||||
" [1.29100624e-01 1.30387411e-01 2.49156266e-01 1.86123808e-01\n",
|
||||
" 3.06942115e-01 7.77873845e-01 6.61979664e-01 7.08699295e-01\n",
|
||||
" 4.47297587e-02 5.88738394e-01]\n",
|
||||
" [5.17405911e-01 9.84789579e-01 7.30334592e-01 5.61602014e-01\n",
|
||||
" 9.95053694e-01 5.73305640e-01 9.36545697e-01 5.29033075e-01\n",
|
||||
" 1.00989834e-04 6.96379806e-01]\n",
|
||||
" [8.23093598e-01 9.99201684e-01 9.42197297e-01 8.48268005e-01\n",
|
||||
" 1.65900052e-01 2.60605083e-01 3.83884553e-01 5.91410559e-02\n",
|
||||
" 6.89766337e-01 7.91434750e-01]\n",
|
||||
" [6.39621036e-03 4.53512750e-01 2.85259666e-01 6.82623709e-01\n",
|
||||
" 4.62905281e-01 9.88236598e-01 6.74272285e-02 5.17547294e-01\n",
|
||||
" 5.67238764e-01 5.67819487e-01]\n",
|
||||
" [5.16172219e-01 1.42083463e-01 2.01779091e-01 8.29541992e-02\n",
|
||||
" 8.23994188e-01 1.28183460e-01 4.64564368e-01 4.61106937e-01\n",
|
||||
" 7.93891335e-01 6.25458506e-01]]\n"
|
||||
"[[0.08233627 0.43727575 0.01759442 0.01179415 0.34483937 0.32733949\n",
|
||||
" 0.71332117 0.20347912 0.57521514 0.56042437]\n",
|
||||
" [0.64909048 0.55179339 0.13847167 0.04224929 0.89540936 0.5142856\n",
|
||||
" 0.85871255 0.75080943 0.34026086 0.50850373]\n",
|
||||
" [0.03239393 0.87884833 0.41310208 0.82418008 0.01014331 0.24284399\n",
|
||||
" 0.93668828 0.79247398 0.48062913 0.77803692]\n",
|
||||
" [0.73068535 0.47910545 0.95715036 0.11844773 0.76686148 0.31453949\n",
|
||||
" 0.6029291 0.5443909 0.29398019 0.96585824]\n",
|
||||
" [0.0179826 0.51643579 0.53723188 0.03844073 0.25989189 0.20834188\n",
|
||||
" 0.98619989 0.40833375 0.52876551 0.76612827]\n",
|
||||
" [0.44830934 0.94099173 0.07123177 0.96305058 0.11596834 0.69756791\n",
|
||||
" 0.60664448 0.89457785 0.86016288 0.33615742]\n",
|
||||
" [0.35243372 0.77465911 0.37532078 0.38399622 0.20728198 0.1108407\n",
|
||||
" 0.83020679 0.48170743 0.99003341 0.99956033]\n",
|
||||
" [0.82832579 0.42553096 0.02355756 0.28542801 0.96894842 0.70711022\n",
|
||||
" 0.8825782 0.04072235 0.24369936 0.79622905]\n",
|
||||
" [0.60762863 0.21699817 0.20218457 0.63071772 0.02358547 0.62886648\n",
|
||||
" 0.44286735 0.78745927 0.30982436 0.76277254]\n",
|
||||
" [0.52173381 0.9495484 0.47433659 0.73203674 0.95226607 0.40072098\n",
|
||||
" 0.35098705 0.46544987 0.66456459 0.25512598]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -810,13 +800,13 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.2160073466353432\n",
|
||||
"4.720889407643293\n",
|
||||
"1.0362483569261072\n",
|
||||
"[[ 1.17260271 3.60999067 4.15283463]\n",
|
||||
" [ 3.60999067 12.23423987 13.18824338]\n",
|
||||
" [ 4.15283463 13.18824338 21.31571995]]\n",
|
||||
"[31.70587399 0.09156055 2.92512799]\n"
|
||||
"-0.0018310257999764163\n",
|
||||
"4.092608577084595\n",
|
||||
"0.31555734196707536\n",
|
||||
"[[ 0.78671736 2.38337498 2.04657426]\n",
|
||||
" [ 2.38337498 8.33235647 6.05002471]\n",
|
||||
" [ 2.04657426 6.05002471 10.13152828]]\n",
|
||||
"[15.98413606 0.08227668 3.18418938]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
File diff suppressed because one or more lines are too long
Binary file not shown.
|
Before Width: | Height: | Size: 10 KiB After Width: | Height: | Size: 9.9 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 18 KiB After Width: | Height: | Size: 19 KiB |
@@ -874,8 +874,8 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 2.93289669 0.72236654 0.6671767 1.31936237 -0.39865452 -0.86247117\n",
|
||||
" 0.64411666 -1.56072658 1.17367225 0.96391888]\n"
|
||||
"[-1.24454968 1.12382443 0.67088558 1.12498355 1.0342028 -0.44563226\n",
|
||||
" 0.14599499 0.07378525 0.03479236 -1.2976637 ]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1313,26 +1313,26 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[0.84159992 0.17563274 0.40632663 0.78278086 0.22919456 0.7510197\n",
|
||||
" 0.57584612 0.46342934 0.4745474 0.65543115]\n",
|
||||
" [0.59730078 0.77482712 0.70301068 0.29223521 0.15202654 0.74883972\n",
|
||||
" 0.48827791 0.09346106 0.58890784 0.7766443 ]\n",
|
||||
" [0.59184268 0.65554958 0.91546928 0.87340054 0.48200792 0.14824254\n",
|
||||
" 0.36185989 0.98654811 0.0473916 0.24032019]\n",
|
||||
" [0.87788573 0.61774362 0.82914126 0.23139242 0.32651488 0.61621902\n",
|
||||
" 0.59908884 0.49381549 0.97716508 0.21531156]\n",
|
||||
" [0.01654574 0.32393078 0.91854134 0.93909866 0.75300068 0.53942728\n",
|
||||
" 0.66063786 0.48867802 0.53149078 0.6831505 ]\n",
|
||||
" [0.57847325 0.42774546 0.24433117 0.07531349 0.98190064 0.68879472\n",
|
||||
" 0.18485685 0.85422602 0.58493681 0.00348246]\n",
|
||||
" [0.8517571 0.29620357 0.3096154 0.18409254 0.54880148 0.29881308\n",
|
||||
" 0.7509571 0.46891823 0.42124182 0.38203725]\n",
|
||||
" [0.59963873 0.51154388 0.28399125 0.60026673 0.49074536 0.32906581\n",
|
||||
" 0.4069157 0.89724282 0.48326853 0.43373107]\n",
|
||||
" [0.28415767 0.95518112 0.68257097 0.59215613 0.64373221 0.81283649\n",
|
||||
" 0.04262217 0.80979265 0.73337355 0.2077068 ]\n",
|
||||
" [0.87696461 0.09529067 0.39540235 0.68352799 0.1598058 0.03648711\n",
|
||||
" 0.73018893 0.60921896 0.33220123 0.50887105]]\n"
|
||||
"[[0.50306335 0.66487438 0.75677341 0.01349244 0.2813829 0.80056922\n",
|
||||
" 0.07974741 0.63046054 0.05098714 0.26479234]\n",
|
||||
" [0.17861414 0.3388112 0.30005983 0.35520169 0.5399913 0.51709529\n",
|
||||
" 0.36339156 0.71582784 0.34568909 0.23164401]\n",
|
||||
" [0.11922031 0.771128 0.20051449 0.07890044 0.96866031 0.34346829\n",
|
||||
" 0.5116375 0.52732966 0.80637385 0.69435454]\n",
|
||||
" [0.94291287 0.29238145 0.84711297 0.22849742 0.56967917 0.1636508\n",
|
||||
" 0.15833751 0.84254917 0.05068486 0.54057582]\n",
|
||||
" [0.17856374 0.71524686 0.66498292 0.00256622 0.72427854 0.50667812\n",
|
||||
" 0.0894015 0.22688898 0.54873252 0.31727523]\n",
|
||||
" [0.73578772 0.81479491 0.45620975 0.2662452 0.47757553 0.64322974\n",
|
||||
" 0.54921401 0.70630967 0.51136852 0.59683811]\n",
|
||||
" [0.37837864 0.71860732 0.35320952 0.67495943 0.16188604 0.41925189\n",
|
||||
" 0.28956161 0.06685171 0.75654448 0.28923 ]\n",
|
||||
" [0.91099021 0.8387133 0.18277213 0.40418675 0.7249499 0.46415522\n",
|
||||
" 0.35018806 0.87148597 0.94141801 0.50911289]\n",
|
||||
" [0.86066395 0.12235593 0.82418352 0.57881465 0.82559478 0.96826039\n",
|
||||
" 0.291056 0.41675053 0.06430789 0.96432396]\n",
|
||||
" [0.91270333 0.64362404 0.18816387 0.81318307 0.47989224 0.20375464\n",
|
||||
" 0.33145794 0.92192012 0.33596404 0.18085537]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1446,13 +1446,13 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"-0.03681479262838276\n",
|
||||
"3.936877889972962\n",
|
||||
"-0.43976975777097144\n",
|
||||
"[[ 1.18742521 3.6407249 4.14122256]\n",
|
||||
" [ 3.6407249 12.06804775 12.20775115]\n",
|
||||
" [ 4.14122256 12.20775115 20.86009093]]\n",
|
||||
"[30.46593404 0.0604888 3.58914105]\n"
|
||||
"0.1385510301651882\n",
|
||||
"4.344176915013831\n",
|
||||
"0.13134148011170077\n",
|
||||
"[[ 0.87981676 2.52698542 2.65591748]\n",
|
||||
" [ 2.52698542 8.29861395 7.76790092]\n",
|
||||
" [ 2.65591748 7.76790092 13.4831139 ]]\n",
|
||||
"[19.77845284 0.09028702 2.79280475]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1808,7 +1808,7 @@
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6690/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58840/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6200\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6201\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6202\u001b[0m ):\n\u001b[1;32m 6203\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'"
|
||||
]
|
||||
|
||||
@@ -1533,7 +1533,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.9958983289118531\n"
|
||||
"0.9969513794144311\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1564,7 +1564,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.010348289064969316\n"
|
||||
"0.007658477904313023\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1599,23 +1599,23 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[0.01250964 0.00679013 0.06416459 0.02126205 0.01247909 0.00080497\n",
|
||||
" 0.07373479 0.00940611 0.04794409 0.02361665 0.03311432 0.00114401\n",
|
||||
" 0.06392846 0.0813156 0.03578526 0.00691012 0.05978512 0.02928194\n",
|
||||
" 0.00209403 0.02014577 0.13600596 0.0178462 0.02016185 0.00574735\n",
|
||||
" 0.029003 0.04877931 0.03796359 0.02374496 0.06622369 0.02965159\n",
|
||||
" 0.01655384 0.00290613 0.00560098 0.00641423 0.05549529 0.03916813\n",
|
||||
" 0.01288787 0.02472936 0.02564828 0.03365012 0.03642173 0.02102403\n",
|
||||
" 0.00633289 0.02631942 0.02164667 0.04899367 0.00593362 0.03664879\n",
|
||||
" 0.00250787 0.00791263 0.01480256 0.01329403 0.02151521 0.00133638\n",
|
||||
" 0.01717549 0.01681612 0.03358833 0.01003519 0.00659604 0.02026756\n",
|
||||
" 0.01234261 0.058967 0.0126711 0.0039259 0.00254973 0.04315855\n",
|
||||
" 0.02909574 0.02386932 0.02404706 0.02769274 0.05804539 0.00732665\n",
|
||||
" 0.03868372 0.00513051 0.00172865 0.00353938 0.03076019 0.01643795\n",
|
||||
" 0.00458631 0.01817048 0.08667591 0.01706166 0.00637188 0.0930438\n",
|
||||
" 0.01506354 0.02787184 0.0002926 0.09398153 0.00570321 0.03511387\n",
|
||||
" 0.01075018 0.00170809 0.00943959 0.02654585 0.00399972 0.03057995\n",
|
||||
" 0.00211418 0.00685287 0.02330799 0.04191323]\n"
|
||||
"[0.00053122 0.03115122 0.00789262 0.02218076 0.00573727 0.00557893\n",
|
||||
" 0.00099778 0.01234 0.03960002 0.03596557 0.0134113 0.00556946\n",
|
||||
" 0.00136125 0.10719554 0.02754248 0.01409478 0.01760144 0.01032533\n",
|
||||
" 0.01241284 0.01039879 0.00100913 0.02944152 0.00512599 0.00747773\n",
|
||||
" 0.06260611 0.0231353 0.01624447 0.02923006 0.0046544 0.07332248\n",
|
||||
" 0.02338085 0.02920675 0.02286267 0.04353549 0.00569512 0.02664408\n",
|
||||
" 0.01098247 0.02156565 0.03529801 0.00507531 0.00554202 0.05141614\n",
|
||||
" 0.02031987 0.01244297 0.01551724 0.00174738 0.01044475 0.01161645\n",
|
||||
" 0.02622039 0.03285784 0.00522055 0.00687309 0.0195302 0.04101344\n",
|
||||
" 0.00816675 0.0206033 0.04046513 0.02189863 0.06777772 0.04832356\n",
|
||||
" 0.00114855 0.08660891 0.00586355 0.00625051 0.00939407 0.00108471\n",
|
||||
" 0.03948301 0.02527621 0.03205795 0.11042239 0.02594314 0.05176711\n",
|
||||
" 0.03396658 0.00889475 0.02632742 0.02502325 0.01266999 0.00455966\n",
|
||||
" 0.03853313 0.01543076 0.00617221 0.00552462 0.01573062 0.01035006\n",
|
||||
" 0.00162921 0.00974758 0.00812487 0.01881237 0.06690071 0.01499192\n",
|
||||
" 0.04652794 0.04061345 0.04495752 0.00566707 0.01006984 0.00519717\n",
|
||||
" 0.00151416 0.03214829 0.00891702 0.01844822]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1669,15 +1669,15 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 2.04641529 -0.77354301 7.7511273 -3.70241548 1.69515531]\n",
|
||||
"[ 1.97243946 0.15593478 4.54011398 0.56342158 -0.19299283]\n",
|
||||
"Training R2\n",
|
||||
"0.9958589197366403\n",
|
||||
"0.9948998579029953\n",
|
||||
"Training MSE\n",
|
||||
"0.008560581831215528\n",
|
||||
"0.009396472959497925\n",
|
||||
"Test R2\n",
|
||||
"0.997252717901263\n",
|
||||
"0.9951059931014423\n",
|
||||
"Test MSE\n",
|
||||
"0.00683875021199284\n"
|
||||
"0.010612904886352397\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
File diff suppressed because one or more lines are too long
Binary file not shown.
|
Before Width: | Height: | Size: 22 KiB After Width: | Height: | Size: 21 KiB |
File diff suppressed because it is too large
Load Diff
@@ -48,6 +48,8 @@
|
||||
# * Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at <https://youtu.be/omLmp_kkie0>
|
||||
#
|
||||
# * Work on project 1, in particular resampling methods like cross-validation and bootstrap.
|
||||
#
|
||||
# * [Video on cross-validation from exercise session](https://youtu.be/T9jjWsmsd1o)
|
||||
|
||||
# ## Material for lecture Monday September 16
|
||||
|
||||
|
||||
Binary file not shown.
+179
-177
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user