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b/doc/LectureNotes/_build/html/_images/week37_121_0.png differ diff --git a/doc/LectureNotes/_build/html/_images/week37_139_6.png b/doc/LectureNotes/_build/html/_images/week37_139_6.png new file mode 100644 index 000000000..9ebeeb751 Binary files /dev/null and b/doc/LectureNotes/_build/html/_images/week37_139_6.png differ diff --git a/doc/LectureNotes/_build/html/_images/week37_148_11.png b/doc/LectureNotes/_build/html/_images/week37_148_11.png new file mode 100644 index 000000000..9fa6833f5 Binary files /dev/null and b/doc/LectureNotes/_build/html/_images/week37_148_11.png differ diff --git a/doc/LectureNotes/_build/html/chapter1.html b/doc/LectureNotes/_build/html/chapter1.html index 612d9296d..c2190833b 100644 --- a/doc/LectureNotes/_build/html/chapter1.html +++ b/doc/LectureNotes/_build/html/chapter1.html @@ -1021,10 +1021,10 @@ example of the functionality of Scikit-Learn.

The intercept alpha: 
- [2.00955125]
+ [1.99194201]
 Coefficient beta : 
- [[4.94419718]]
-Mean squared error: 0.31
+ [[4.85108001]]
+Mean squared error: 0.28
 Variance score: 0.87
 Mean squared log error: 0.01
 Mean absolute error: 0.43
@@ -1127,7 +1127,7 @@ a linear \(x\)-dependence we s
_images/chapter1_33_0.png -
0.005000000000000001
+
0.005
diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index a0840cda8..ba3b56012 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -1159,8 +1159,9 @@ 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 - -predictions = (n_inputs) = (1437,) +
+
+
predictions = (n_inputs) = (1437,)
 prediction for image 0: 8
 correct label for image 0: 6
@@ -1338,7 +1339,7 @@ the Hadamard product, meaning element-wise multiplication.

Old accuracy on training data: 0.1440501043841336
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1672,7 +1673,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1681,7 +1682,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1690,7 +1691,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1699,7 +1700,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1708,7 +1709,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1717,7 +1718,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1726,7 +1727,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1735,11 +1736,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1748,11 +1749,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1761,11 +1762,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1774,11 +1775,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1787,11 +1788,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1800,7 +1801,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 

 

@@ -1809,11 +1810,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1822,11 +1823,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

@@ -1835,11 +1836,37 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1323/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+

+

+
Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on test set:  0.07777777777777778
+

+

+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.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
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+

+

+
Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on test set:  0.07777777777777778
+

+

+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/953065564.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
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6316/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 

 

diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html
index 0b5ab5340..0c8b78b4e 100644
--- a/doc/LectureNotes/_build/html/chapter11.html
+++ b/doc/LectureNotes/_build/html/chapter11.html
@@ -2706,20 +2706,19 @@ Using TensorFlow results in a much better execution time. Try it!

      60     x,t = point
 ---> 61     return (1-t)*u(x) + x*(1-x)*t*deep_neural_network(P,point)
 
-Cell In[9], line 48, in deep_neural_network(deep_params, x)
-     45 w_output = deep_params[-1]
-     47 # Include bias:
----> 48 x_prev = np.concatenate((np.ones((1,num_points)), x_prev), axis = 0)
-     50 z_output = np.matmul(w_output, x_prev)
-     51 x_output = z_output
+Cell In[9], line 37, in deep_neural_network(deep_params, x)
+     34 x_prev = np.concatenate((np.ones((1,num_points)), x_prev ), axis = 0)
+     36 z_hidden = np.matmul(w_hidden, x_prev)
+---> 37 x_hidden = sigmoid(z_hidden)
+     39 # Update x_prev such that next layer can use the output from this layer
+     40 x_prev = x_hidden
 
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:38, in <lambda>(arr_list, axis)
-     35 @primitive
-     36 def concatenate_args(axis, *args):
-     37     return _np.concatenate(args, axis).view(ndarray)
----> 38 concatenate = lambda arr_list, axis=0 : concatenate_args(axis, *arr_list)
-     39 vstack = row_stack = lambda tup: concatenate([atleast_2d(_m) for _m in tup], axis=0)
-     40 def hstack(tup):
+Cell In[9], line 11, in sigmoid(z)
+     10 def sigmoid(z):
+---> 11     return 1/(1 + np.exp(-z))
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:39, in ArrayBox.__rtruediv__(self, other)
+---> 39 def __rtruediv__(self, other): return anp.true_divide(other, self)
 
 File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45, in primitive.<locals>.f_wrapped(*args, **kwargs)
      43     argnums = tuple(argnum    for argnum, _   in boxed_args)
@@ -2734,24 +2733,32 @@ Using TensorFlow results in a much better execution time. Try it!

      35                               .format(fun_name, parent_argnums))
 ---> 36 self.vjp = vjpmaker(parent_argnums, value, args, kwargs)
 
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:48, in defvjp_argnum.<locals>.vjp_argnums(argnums, *args)
-     47 def vjp_argnums(argnums, *args):
----> 48     vjps = [vjpmaker(argnum, *args) for argnum in argnums]
-     49     return lambda g: (vjp(g) for vjp in vjps)
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:66, in defvjp.<locals>.vjp_argnums(argnums, ans, args, kwargs)
+     63     except KeyError:
+     64         raise NotImplementedError(
+     65             "VJP of {} wrt argnum 0 not defined".format(fun.__name__))
+---> 66     vjp = vjpfun(ans, *args, **kwargs)
+     67     return lambda g: (vjp(g),)
+     68 elif L == 2:
 
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:48, in <listcomp>(.0)
-     47 def vjp_argnums(argnums, *args):
----> 48     vjps = [vjpmaker(argnum, *args) for argnum in argnums]
-     49     return lambda g: (vjp(g) for vjp in vjps)
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:53, in <lambda>(ans, x, y)
+     48 defvjp(anp.logaddexp,   lambda ans, x, y : unbroadcast_f(x, lambda g: g * anp.exp(x-ans)),
+     49                         lambda ans, x, y : unbroadcast_f(y, lambda g: g * anp.exp(y-ans)))
+     50 defvjp(anp.logaddexp2,  lambda ans, x, y : unbroadcast_f(x, lambda g: g * 2**(x-ans)),
+     51                         lambda ans, x, y : unbroadcast_f(y, lambda g: g * 2**(y-ans)))
+     52 defvjp(anp.true_divide, lambda ans, x, y : unbroadcast_f(x, lambda g: g / y),
+---> 53                         lambda ans, x, y : unbroadcast_f(y, lambda g: - g * x / y**2))
+     54 defvjp(anp.mod,         lambda ans, x, y : unbroadcast_f(x, lambda g: g),
+     55                         lambda ans, x, y : unbroadcast_f(y, lambda g: -g * anp.floor(x/y)))
+     56 defvjp(anp.remainder,   lambda ans, x, y : unbroadcast_f(x, lambda g: g),
+     57                         lambda ans, x, y : unbroadcast_f(y, lambda g: -g * anp.floor(x/y)))
 
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:537, in grad_concatenate_args(argnum, ans, axis_args, kwargs)
-    532 defvjp(tensordot_adjoint_1, lambda ans, A, G, axes, An, Bn: lambda B: match_complex(A, tensordot_adjoint_0(B, G, axes, An, Bn)),
-    533                             lambda ans, A, G, axes, An, Bn: lambda B: match_complex(G, anp.tensordot(A, B, axes)))
-    534 defvjp(anp.outer, lambda ans, a, b : lambda g: match_complex(a, anp.dot(g, b.T)),
-    535                   lambda ans, a, b : lambda g: match_complex(b, anp.dot(a.T, g)))
---> 537 def grad_concatenate_args(argnum, ans, axis_args, kwargs):
-    538     axis, args = axis_args[0], axis_args[1:]
-    539     sizes = [anp.shape(a)[axis] for a in args[:argnum]]
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:658, in unbroadcast_f(target, f)
+    655         x = anp.real(x)
+    656     return x
+--> 658 def unbroadcast_f(target, f):
+    659     target_meta = anp.metadata(target)
+    660     return lambda g: unbroadcast(f(g), target_meta)
 
 KeyboardInterrupt:
diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html index fa7ffa3ee..29f9ec9cf 100644 --- a/doc/LectureNotes/_build/html/chapter2.html +++ b/doc/LectureNotes/_build/html/chapter2.html @@ -1270,10 +1270,10 @@ covariance matrix through the np.linalg.eig() function.

-
0.05005634426334421
-4.366375489616074
-[[ 0.93787605  2.95563211]
- [ 2.95563211 10.33025801]]
+
-0.0369544130635358
+3.662836197064178
+[[1.058997   3.11439407]
+ [3.11439407 9.99498272]]
@@ -1310,10 +1310,10 @@ a more brute force way. Here we scale the mean values for each column of the des
-
0.07808426989543932
-1.4121966338442804
-[[1.         0.70362677]
- [0.70362677 1.        ]]
+
0.08826182458028335
+1.7026722043092946
+[[1.         0.61113781]
+ [0.61113781 1.        ]]
@@ -1343,30 +1343,30 @@ this matrix we easily see that it is a positive definite matrix.

-
[[-0.34661376 -1.6809195 ]
- [ 0.05792927  0.30915293]
- [ 0.65183066  3.00564344]
- [ 1.75018686  4.35667342]
- [-0.75682834 -1.67875366]
- [ 1.16654048  3.9065894 ]
- [-1.86267497 -5.53585173]
- [ 0.29803738  2.45731144]
- [-0.63031478 -2.76157429]
- [-0.3280928  -2.37827145]]
+
[[-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]]
           0         1
-0 -0.346614 -1.680920
-1  0.057929  0.309153
-2  0.651831  3.005643
-3  1.750187  4.356673
-4 -0.756828 -1.678754
-5  1.166540  3.906589
-6 -1.862675 -5.535852
-7  0.298037  2.457311
-8 -0.630315 -2.761574
-9 -0.328093 -2.378271
+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         1
-0  1.000000  0.959076
-1  0.959076  1.000000
+0  1.000000  0.963187
+1  0.963187  1.000000
@@ -1423,37 +1423,37 @@ this matrix we easily see that it is a positive definite matrix.

     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  0.084996  0.084434  0.084712  0.085455  0.086212  0.075771  0.076638   
-2   0.0  0.084434  0.084514  0.084042  0.085120  0.086230  0.075176  0.076259   
-3   0.0  0.084712  0.084042  0.089718  0.090439  0.091159  0.083479  0.084358   
-4   0.0  0.085455  0.085120  0.090439  0.091388  0.092347  0.084091  0.085139   
-5   0.0  0.086212  0.086230  0.091159  0.092347  0.093554  0.084695  0.085918   
-6   0.0  0.075771  0.075176  0.083479  0.084091  0.084695  0.079903  0.080657   
-7   0.0  0.076638  0.076259  0.084358  0.085139  0.085918  0.080657  0.081544   
-8   0.0  0.077567  0.077411  0.085294  0.086250  0.087210  0.081457  0.082482   
-9   0.0  0.078557  0.078636  0.086286  0.087424  0.088574  0.082303  0.083471   
-10  0.0  0.066997  0.066458  0.075909  0.076389  0.076857  0.074221  0.074824   
-11  0.0  0.067764  0.067380  0.076693  0.077304  0.077906  0.074892  0.075602   
-12  0.0  0.068592  0.068369  0.077539  0.078284  0.079027  0.075615  0.076436   
-13  0.0  0.069484  0.069427  0.078447  0.079334  0.080222  0.076393  0.077329   
-14  0.0  0.070441  0.070558  0.079420  0.080453  0.081494  0.077226  0.078282   
+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   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.077567  0.078557  0.066997  0.067764  0.068592  0.069484  0.070441  
-2   0.077411  0.078636  0.066458  0.067380  0.068369  0.069427  0.070558  
-3   0.085294  0.086286  0.075909  0.076693  0.077539  0.078447  0.079420  
-4   0.086250  0.087424  0.076389  0.077304  0.078284  0.079334  0.080453  
-5   0.087210  0.088574  0.076857  0.077906  0.079027  0.080222  0.081494  
-6   0.081457  0.082303  0.074221  0.074892  0.075615  0.076393  0.077226  
-7   0.082482  0.083471  0.074824  0.075602  0.076436  0.077329  0.078282  
-8   0.083564  0.084702  0.075463  0.076351  0.077301  0.078313  0.079391  
-9   0.084702  0.085996  0.076138  0.077141  0.078210  0.079347  0.080555  
-10  0.075463  0.076138  0.070095  0.070630  0.071208  0.071831  0.072500  
-11  0.076351  0.077141  0.070630  0.071252  0.071921  0.072638  0.073405  
-12  0.077301  0.078210  0.071208  0.071921  0.072684  0.073499  0.074368  
-13  0.078313  0.079347  0.071831  0.072638  0.073499  0.074417  0.075392  
-14  0.079391  0.080555  0.072500  0.073405  0.074368  0.075392  0.076479  
+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
diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index 68df75ce1..91c973fc4 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -824,10 +824,10 @@ number \(i\) is left out. Usin
-
Runtime: 0.14708 sec
+
Runtime: 0.165272 sec
 Jackknife Statistics :
 original           bias      std. error
- 100.034        100.024        0.147836
+ 99.6801        99.6702        0.149483
@@ -1046,7 +1046,7 @@ theorem.

Bootstrap Statistics :
 original           bias      std. error
- 100.179  15.0422        100.179        0.151522
+ 100.121   15.022        100.121        0.149904
@@ -1263,14 +1263,14 @@ Error: 0.06547790180152355 Bias^2: 0.06208238634231949 Var: 0.0033955154592040936 0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359 -Polynomial degree: 4 +
+
+
Polynomial degree: 4
 Error: 0.06844519414009445
 Bias^2: 0.06453579006728324
 Var: 0.003909404072811226
 0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446
-
-
-
Polynomial degree: 5
+Polynomial degree: 5
 Error: 0.05227921801205686
 Bias^2: 0.0481872773043029
 Var: 0.004091940707753939
@@ -1280,7 +1280,9 @@ Error: 0.037813671417389005
 Bias^2: 0.033657685071527665
 Var: 0.00415598634586135
 0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902
-Polynomial degree: 7
+
+
+
Polynomial degree: 7
 Error: 0.02760977349102253
 Bias^2: 0.022999498260366312
 Var: 0.004610275230656212
@@ -1290,9 +1292,7 @@ Error: 0.017355848195593347
 Bias^2: 0.010331721306655127
 Var: 0.007024126888938232
 0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336
-
-
-
Polynomial degree: 9
+Polynomial degree: 9
 Error: 0.02660572763718093
 Bias^2: 0.010018312644137363
 Var: 0.016587414993043573
@@ -1307,7 +1307,9 @@ Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
 0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
-Polynomial degree: 12
+
+
+
Polynomial degree: 12
 Error: 0.11547777218872497
 Bias^2: 0.01628578269596628
 Var: 0.09919198949275869
@@ -1319,7 +1321,7 @@ Var: 0.20867052175034223
 0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
-_images/chapter3_66_3.png +_images/chapter3_66_4.png

The bias-variance tradeoff summarizes the fundamental tension in @@ -1634,9 +1636,9 @@ Mean squared error on training data: 0.00060704 Mean squared error on test data: 3262.26814548 -

/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1390/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1390/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 

 

@@ -1870,7 +1872,7 @@ cross-validation (LOOCV).
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1390/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6403/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 

 

@@ -2759,7 +2761,7 @@ linear system as an equation would reduce this down to
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1390/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
/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.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

@@ -2903,7 +2905,7 @@ with the form utilized in linear regression, viz.
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1390/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
/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.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

@@ -2943,7 +2945,7 @@ cost function is given by
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1390/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
/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.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

@@ -2978,7 +2980,7 @@ cost function is given by
-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1390/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
/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.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

@@ -3031,43 +3033,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
-
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+
  0%|                                                                                                            | 0/10 [00:00<?, ?it/s]
 

 

 
/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,  1.14it/s]
+ 10%|██████████                                                                                          | 1/10 [00:00<00:07,  1.15it/s]
 

 

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+
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+
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-
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+
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+
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+
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-
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+
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-
 80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                      | 8/10 [00:06<00:01,  1.33it/s]
+
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-
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-
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diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html
index cf830fab8..08392b396 100644
--- a/doc/LectureNotes/_build/html/chapter6.html
+++ b/doc/LectureNotes/_build/html/chapter6.html
@@ -752,9 +752,9 @@ predicting the target features of query instances is as follows:

 

 

 
2nd degree coefficients:
-zero power:  2.1810415856976313
-first power:  -0.2546817701709956
-second power:  0.0008297120772365539
+zero power:  -2.767367275553824
+first power:  0.024011020121022356
+second power:  -0.00021270681344726395
 

 

 _images/chapter6_1_1.png
@@ -1621,9 +1621,7 @@ Test set accuracy with Logistic Regression: 0.94
 

 

 
Test set accuracy with SVM: 0.63
-

-

-
Test set accuracy with Decision Trees: 0.90
+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
diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html
index 9878afa42..97a59af11 100644
--- a/doc/LectureNotes/_build/html/chapter8.html
+++ b/doc/LectureNotes/_build/html/chapter8.html
@@ -706,10 +706,10 @@ covariance matrix through the np.linalg.eig() function.

 

 

 

-
-0.1477190177681485
-3.5426270409877345
-[[1.01393496 3.02432309]
- [3.02432309 9.86643649]]
+
0.10541723644166373
+4.575870409023631
+[[0.84972787 2.5321613 ]
+ [2.5321613  8.59875207]]
 

 

 

@@ -749,10 +749,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 

 

 

-
0.08793554992813543
-1.9271707090281667
-[[1.        0.6690108]
- [0.6690108 1.       ]]
+
0.0768805855280187
+1.6568154596723088
+[[1.         0.69438869]
+ [0.69438869 1.        ]]
 

 

 

@@ -781,30 +781,30 @@ this matrix we easily see that it is a positive definite matrix.

 

 

 

-
[[-0.95395895 -3.11535632]
- [ 1.05641352  3.6533977 ]
- [ 0.801356    4.56475921]
- [-0.69136414 -1.7642448 ]
- [ 0.68822559  0.63896182]
- [ 0.30916988  1.25233253]
- [ 0.10008326  0.10539984]
- [-0.52155823 -1.85777073]
- [ 0.24377554  0.94616709]
- [-1.03214247 -4.42364634]]
+
[[-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]]
           0         1
-0 -0.953959 -3.115356
-1  1.056414  3.653398
-2  0.801356  4.564759
-3 -0.691364 -1.764245
-4  0.688226  0.638962
-5  0.309170  1.252333
-6  0.100083  0.105400
-7 -0.521558 -1.857771
-8  0.243776  0.946167
-9 -1.032142 -4.423646
+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         1
-0  1.000000  0.947607
-1  0.947607  1.000000
+0  1.000000  0.972149
+1  0.972149  1.000000
 

 

 

@@ -861,37 +861,37 @@ this matrix we easily see that it is a positive definite matrix.

 

 
     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  0.079977  0.079947  0.079510  0.081431  0.083259  0.070891  0.072689   
-2   0.0  0.079947  0.081195  0.081235  0.083734  0.086125  0.073415  0.075557   
-3   0.0  0.079510  0.081235  0.084255  0.086970  0.089578  0.078324  0.080630   
-4   0.0  0.081431  0.083734  0.086970  0.090033  0.092982  0.081221  0.083765   
-5   0.0  0.083259  0.086125  0.089578  0.092982  0.096270  0.084018  0.086799   
-6   0.0  0.070891  0.073415  0.078324  0.081221  0.084018  0.074971  0.077341   
-7   0.0  0.072689  0.075557  0.080630  0.083765  0.086799  0.077341  0.079887   
-8   0.0  0.074531  0.077736  0.082971  0.086346  0.089619  0.079739  0.082464   
-9   0.0  0.076418  0.079959  0.085353  0.088970  0.092486  0.082173  0.085079   
-10  0.0  0.062300  0.065028  0.070886  0.073685  0.076396  0.069322  0.071578   
-11  0.0  0.063862  0.066824  0.072817  0.075794  0.078684  0.071279  0.073672   
-12  0.0  0.065482  0.068680  0.074807  0.077965  0.081038  0.073288  0.075822   
-13  0.0  0.067164  0.070600  0.076859  0.080205  0.083465  0.075356  0.078035   
-14  0.0  0.068912  0.072590  0.078981  0.082519  0.085973  0.077486  0.080316   
+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   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.074531  0.076418  0.062300  0.063862  0.065482  0.067164  0.068912  
-2   0.077736  0.079959  0.065028  0.066824  0.068680  0.070600  0.072590  
-3   0.082971  0.085353  0.070886  0.072817  0.074807  0.076859  0.078981  
-4   0.086346  0.088970  0.073685  0.075794  0.077965  0.080205  0.082519  
-5   0.089619  0.092486  0.076396  0.078684  0.081038  0.083465  0.085973  
-6   0.079739  0.082173  0.069322  0.071279  0.073288  0.075356  0.077486  
-7   0.082464  0.085079  0.071578  0.073672  0.075822  0.078035  0.080316  
-8   0.085222  0.088023  0.073858  0.076091  0.078386  0.080747  0.083182  
-9   0.088023  0.091015  0.076167  0.078544  0.080987  0.083501  0.086095  
-10  0.073858  0.076167  0.065149  0.067003  0.068903  0.070855  0.072863  
-11  0.076091  0.078544  0.067003  0.068967  0.070980  0.073048  0.075177  
-12  0.078386  0.080987  0.068903  0.070980  0.073109  0.075298  0.077553  
-13  0.080747  0.083501  0.070855  0.073048  0.075298  0.077613  0.079998  
-14  0.083182  0.086095  0.072863  0.075177  0.077553  0.079998  0.082518  
+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  
 

 

 

@@ -1080,10 +1080,10 @@ We can write our own code or simply use either the functionaly of numpy<
 

 

 
          0         1
-0  3.970827  1.972533
-1  1.972533  1.968650
-[[3.97082748 1.97253307]
- [1.97253307 1.96865004]]
+0  3.986362  1.994474
+1  1.994474  2.001468
+[[3.98636199 1.99447418]
+ [1.99447418 2.00146807]]
 

 

 

@@ -1110,8 +1110,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
 

 

 
Centered covariance using own code
-[[3.97082748 1.97253307]
- [1.97253307 1.96865004]]
+[[3.98636199 1.99447418]
+ [1.99447418 2.00146807]]
 

 

 _images/chapter8_65_1.png
@@ -1171,16 +1171,16 @@ questions.

 

 

 
Eigenvalues of Covariance matrix
-5.181766185664273
-0.7577113351177733
+5.221666864828611
+0.766163196245293
 First eigenvector
-[0.85222243 0.52317963]
+[0.85014487 0.52654886]
 Second eigenvector
-[-0.52317963  0.85222243]
+[-0.52654886  0.85014487]
 

 

 
Eigenvector of largest eigenvalue
-[0.85222243 0.52317963]
+[-0.85014487 -0.52654886]
 

 

 

diff --git a/doc/LectureNotes/_build/html/linalg.html b/doc/LectureNotes/_build/html/linalg.html
index 930740b76..2e55d00a7 100644
--- a/doc/LectureNotes/_build/html/linalg.html
+++ b/doc/LectureNotes/_build/html/linalg.html
@@ -608,8 +608,8 @@ matrices and vectors.

 

 

 

-
[ 1.02808229  1.3194467  -1.8476874  -0.00537955 -0.47991892 -1.54490887
- -0.04110474  0.70857635 -1.39855569 -0.11081083]
+
[ 0.93937564  0.92308867  0.34427423 -1.37685349  2.80413696  0.25555619
+  1.121292   -0.42392359 -0.13913033 -0.7228852 ]
 

 

 

@@ -830,26 +830,36 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
 

 

 

-
[[0.04413243 0.8917148  0.26113912 0.87399194 0.30400113 0.35432563
-  0.05060379 0.65641173 0.77507653 0.83369175]
- [0.71261809 0.93968601 0.91961463 0.79866484 0.81919129 0.73062648
-  0.1488598  0.24254071 0.39922082 0.24398826]
- [0.09171886 0.59348521 0.078588   0.74613334 0.18094575 0.61883807
-  0.89408972 0.86978877 0.82802004 0.75433448]
- [0.26715191 0.8905826  0.19852045 0.06432267 0.72771857 0.63030526
-  0.97272223 0.66289235 0.41744203 0.6663569 ]
- [0.91114704 0.01530321 0.55020649 0.40140374 0.67100236 0.5847256
-  0.80410179 0.37055062 0.4729218  0.26775644]
- [0.34271514 0.45193407 0.55542568 0.82242798 0.40266454 0.64713979
-  0.03873507 0.81506255 0.72848736 0.16118615]
- [0.72602818 0.13825388 0.03701105 0.76807288 0.58493493 0.1441031
-  0.72744372 0.20755569 0.0317606  0.67313212]
- [0.85005989 0.9485531  0.81622636 0.32003025 0.57914918 0.36482524
-  0.17934801 0.84726382 0.52397611 0.14829228]
- [0.18510775 0.22528536 0.56977352 0.53105728 0.43962226 0.06444224
-  0.01772779 0.20912557 0.08839544 0.06984502]
- [0.71469351 0.70474145 0.97836358 0.65475653 0.14213876 0.6816947
-  0.65082441 0.01573768 0.06410638 0.64425744]]
+
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@@ -909,13 +919,13 @@ covariance matrix through the np.linalg.eig() function.

 

 

 

-
0.030645975175292262
-4.057913350391706
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-[[ 0.87584434  2.75131415  3.30124108]
- [ 2.75131415  9.73994248 10.78758569]
- [ 3.30124108 10.78758569 24.86174943]]
-[31.05888195  0.08758013  4.33107417]
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+ [ 3.60999067 12.23423987 13.18824338]
+ [ 4.15283463 13.18824338 21.31571995]]
+[31.70587399  0.09156055  2.92512799]
 

 

 

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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling 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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/statistics.html b/doc/LectureNotes/_build/html/statistics.html
index 268af36d6..69fec610d 100644
--- a/doc/LectureNotes/_build/html/statistics.html
+++ b/doc/LectureNotes/_build/html/statistics.html
@@ -980,37 +980,27 @@ uncorrelated.

 

 

 

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@@ -1278,15 +1268,15 @@ more practically oriented methods like the blocking technique.

 

 

 

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+ [ 2.26307235  7.2297766   7.49939493]]
+[17.22169087  0.0637712   1.6038642 ]
 

 

 

@@ -1616,7 +1606,7 @@ assumption for approximating \(\sigma
 

 

 

-
0.011156605304609659 0.9767506308987675
+
0.023295599127611474 0.9904314810719695
 

 

 _images/statistics_188_1.png
diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html
index 384285098..aa6d3b36c 100644
--- a/doc/LectureNotes/_build/html/week34.html
+++ b/doc/LectureNotes/_build/html/week34.html
@@ -1668,8 +1668,8 @@ developed in the 1970s, namely EISPACK and LINPACK.  We describe them shortly he
 

 

 

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@@ -1894,26 +1894,26 @@ lowercase letters for vectors and uppercase letters for matrices)

 

 

 

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+ [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]]
 

 

 

@@ -1968,13 +1968,13 @@ covariance matrix through the np.linalg.eig() function.

 

 

 

-
0.028638258927275215
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-[[ 1.07405556  3.2781456   2.97935648]
- [ 3.2781456  11.21805426  9.35966688]
- [ 2.97935648  9.35966688 14.77152924]]
-[23.38440469  0.10036694  3.57886743]
+
-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]
 

 

 

@@ -2199,7 +2199,7 @@ Name: Aragorn, dtype: object
 

 
---------------------------------------------------------------------------
 AttributeError                            Traceback (most recent call last)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1539/1326197715.py in ?()
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6690/1326197715.py in ?()
 ----> 6 new_hobbit = {'First Name': ["Peregrin"],
       7               'Last Name': ["Took"],
       8               'Place of birth': ["Shire"],
diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
index 47e881dfc..0c9d8440f 100644
--- a/doc/LectureNotes/_build/html/week35.html
+++ b/doc/LectureNotes/_build/html/week35.html
@@ -1636,7 +1636,7 @@ Since we are not using Scikit-Learn here we can define our own
 

 

 

-
0.9952537939995855
+
0.9958983289118531
 

 

 

@@ -1653,7 +1653,7 @@ Since we are not using Scikit-Learn here we can define our own
 

 

 

-
0.011208613520466846
+
0.010348289064969316
 

 

 

@@ -1668,23 +1668,23 @@ Since we are not using Scikit-Learn here we can define our own
 

 

 

-
[0.05040878 0.02601643 0.01922269 0.05006037 0.02572685 0.10595991
- 0.04487298 0.00334047 0.00330046 0.00606382 0.02500488 0.03247316
- 0.01897462 0.0241039  0.0606958  0.00472276 0.01756114 0.06536971
- 0.02809972 0.04955942 0.00956827 0.00667611 0.02576358 0.04216532
- 0.04808723 0.01625794 0.00282226 0.00220013 0.00017733 0.0211429
- 0.02207054 0.02156196 0.0694226  0.01119738 0.0041148  0.01783096
- 0.0062202  0.03317599 0.02032056 0.00798909 0.06901081 0.01353638
- 0.01863203 0.01179128 0.01178857 0.00634299 0.01793261 0.00018053
- 0.13055762 0.02441422 0.05029018 0.0253208  0.01979808 0.02693015
- 0.05336637 0.01373484 0.09291806 0.00168745 0.04588592 0.01013849
- 0.04018985 0.03887801 0.03033791 0.01811279 0.02540212 0.02980537
- 0.02784266 0.03158013 0.01060492 0.01620955 0.00942574 0.0043587
- 0.02651857 0.00053001 0.0337609  0.01131771 0.00023813 0.02091662
- 0.01315875 0.00434043 0.04161572 0.05045    0.0121289  0.01532738
- 0.02334754 0.01206221 0.00930146 0.03244944 0.00702721 0.02576685
- 0.05224117 0.0262517  0.02946852 0.09604976 0.01406777 0.02183817
- 0.0164974  0.02322594 0.04238763 0.00647029]
+
[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]
 

 

 

@@ -1753,15 +1753,15 @@ but now splitting the data into a training set and a test set.

 

 

 

-
[ 1.82079885  2.45560415 -4.73595198 14.38102552 -7.04838148]
+
[ 2.04641529 -0.77354301  7.7511273  -3.70241548  1.69515531]
 Training R2
-0.9952183728736417
+0.9958589197366403
 Training MSE
-0.009338082195270294
+0.008560581831215528
 Test R2
-0.9969461173312454
+0.997252717901263
 Test MSE
-0.008043811612683473
+0.00683875021199284
 

 

 

diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html
index 98bd859b9..f98298114 100644
--- a/doc/LectureNotes/_build/html/week37.html
+++ b/doc/LectureNotes/_build/html/week37.html
@@ -1624,7 +1624,7 @@ theorem.

 

 
Bootstrap Statistics :
 original           bias      std. error
- 100.211  14.8834        100.212        0.149388
+ 100.213    14.98        100.211        0.149466
 

 

 

@@ -1844,7 +1844,9 @@ Error: 0.08426840630693411
 Bias^2: 0.0796891867672603
 Var: 0.004579219539673834
 0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413
-Polynomial degree: 2
+

+

+
Polynomial degree: 2
 Error: 0.10398646080125035
 Bias^2: 0.1007711427354898
 Var: 0.0032153180657605116
@@ -1866,14 +1868,14 @@ Error: 0.05227921801205686
 Bias^2: 0.0481872773043029
 Var: 0.004091940707753939
 0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844
-

-

-
Polynomial degree: 6
+Polynomial degree: 6
 Error: 0.037813671417389005
 Bias^2: 0.033657685071527665
 Var: 0.00415598634586135
 0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902
-Polynomial degree: 7
+

+

+
Polynomial degree: 7
 Error: 0.02760977349102253
 Bias^2: 0.022999498260366312
 Var: 0.004610275230656212
@@ -1895,7 +1897,9 @@ Error: 0.021592704588025025
 Bias^2: 0.010516485576645508
 Var: 0.011076219011379514
 0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022
-Polynomial degree: 11
+

+

+
Polynomial degree: 11
 Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
@@ -1912,7 +1916,7 @@ Var: 0.20867052175034223
 0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
 

 

-_images/week37_139_4.png
+_images/week37_139_6.png
 

 

 

@@ -2263,29 +2267,31 @@ 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
-Degree of polynomial:   8
-Mean squared error on training data: 0.04926746
-Mean squared error on test data: 0.14629156
 

 

-
Degree of polynomial:   9
+
Degree of polynomial:   8
+Mean squared error on training data: 0.04926746
+Mean squared error on test data: 0.14629156
+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
-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
+Degree of polynomial:  13
 Mean squared error on training data: 0.00781918
 Mean squared error on test data: 0.56965674
-Degree of polynomial:  14
+

+

+
Degree of polynomial:  14
 Mean squared error on training data: 0.00465099
 Mean squared error on test data: 0.28443039
 Degree of polynomial:  15
@@ -2305,29 +2311,31 @@ 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
-Degree of polynomial:  20
-Mean squared error on training data: 0.00140846
-Mean squared error on test data: 1350.24493666
 

 

-
Degree of polynomial:  21
+
Degree of polynomial:  20
+Mean squared error on training data: 0.00140846
+Mean squared error on test data: 1350.24493666
+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
-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
+Degree of polynomial:  25
 Mean squared error on training data: 0.00079904
 Mean squared error on test data: 7577.76690383
-Degree of polynomial:  26
+

+

+
Degree of polynomial:  26
 Mean squared error on training data: 0.00075590
 Mean squared error on test data: 1079.36895644
 Degree of polynomial:  27
@@ -2343,13 +2351,13 @@ Mean squared error on training data: 0.00063866
 Mean squared error on test data: 3099.60342978
 

 

-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1579/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1579/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 

 

-_images/week37_148_9.png
+_images/week37_148_11.png
 

 

 
Note that we kept the intercept column in the fitting here. This means that we need to set the intercept in the call to the Scikit-Learn function as False. Alternatively, we could have set up the design matrix \(X\) without the first column of ones.

@@ -2430,7 +2438,7 @@ Mean squared error on test data: 3099.60342978
 

 

 

-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1579/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 

 

diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
index 2e84b69e5..df7d9c752 100644
--- a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
@@ -1344,37 +1344,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.1732863504708044\n",
-      "[[8.55460327e+00 4.59630232e+00 7.71507714e+00 3.56832496e-01\n",
-      "  1.62620041e+01 3.84661230e+00 8.38865681e+00 1.18285689e+01\n",
-      "  1.78876155e+01 4.77352152e+00]\n",
-      " [4.59630232e+00 2.46954702e+00 4.14523337e+00 1.91722512e-01\n",
-      "  8.73741131e+00 2.06674611e+00 4.50714096e+00 6.35537114e+00\n",
-      "  9.61083596e+00 2.56476512e+00]\n",
-      " [7.71507714e+00 4.14523337e+00 6.95793988e+00 3.21813899e-01\n",
-      "  1.46660941e+01 3.46911594e+00 7.56541622e+00 1.06677444e+01\n",
-      "  1.61321722e+01 4.30506075e+00]\n",
-      " [3.56832496e-01 1.91722512e-01 3.21813899e-01 1.48843174e-02\n",
-      "  6.78326199e-01 1.60451189e-01 3.49910481e-01 4.93397254e-01\n",
-      "  7.46134248e-01 1.99114739e-01]\n",
-      " [1.62620041e+01 8.73741131e+00 1.46660941e+01 6.78326199e-01\n",
-      "  3.09135059e+01 7.31227657e+00 1.59465457e+01 2.24856992e+01\n",
-      "  3.40037367e+01 9.07429886e+00]\n",
-      " [3.84661230e+00 2.06674611e+00 3.46911594e+00 1.60451189e-01\n",
-      "  7.31227657e+00 1.72964493e+00 3.77199379e+00 5.31876431e+00\n",
-      "  8.04323936e+00 2.14643345e+00]\n",
-      " [8.38865681e+00 4.50714096e+00 7.56541622e+00 3.49910481e-01\n",
-      "  1.59465457e+01 3.77199379e+00 8.22592946e+00 1.15991124e+01\n",
-      "  1.75406226e+01 4.68092237e+00]\n",
-      " [1.18285689e+01 6.35537114e+00 1.06677444e+01 4.93397254e-01\n",
-      "  2.24856992e+01 5.31876431e+00 1.15991124e+01 1.63555267e+01\n",
-      "  2.47334547e+01 6.60041459e+00]\n",
-      " [1.78876155e+01 9.61083596e+00 1.61321722e+01 7.46134248e-01\n",
-      "  3.40037367e+01 8.04323936e+00 1.75406226e+01 2.47334547e+01\n",
-      "  3.74028787e+01 9.98140006e+00]\n",
-      " [4.77352152e+00 2.56476512e+00 4.30506075e+00 1.99114739e-01\n",
-      "  9.07429886e+00 2.14643345e+00 4.68092237e+00 6.60041459e+00\n",
-      "  9.98140006e+00 2.66365453e+00]]\n"
+      "2.076776262573027\n",
+      "[[ 5.24692014 11.86416941  1.87818331  3.59971727  6.93989887  8.39223923\n",
+      "   1.96991192  6.69391797  5.22667819  3.39929428]\n",
+      " [11.86416941 26.82688363  4.24688853  8.13956654 15.69227923 18.97626519\n",
+      "   4.45430236 15.13607502 11.81839897  7.68637642]\n",
+      " [ 1.87818331  4.24688853  0.67231298  1.2885519   2.48420061  3.0040792\n",
+      "   0.70514808  2.39614949  1.87093752  1.21680864]\n",
+      " [ 3.59971727  8.13956654  1.2885519   2.46963249  4.76120718  5.75760403\n",
+      "   1.3514835   4.59244881  3.58583003  2.33212971]\n",
+      " [ 6.93989887 15.69227923  2.48420061  4.76120718  9.17913653 11.1000911\n",
+      "   2.60552651  8.85378708  6.91312563  4.49611541]\n",
+      " [ 8.39223923 18.97626519  3.0040792   5.75760403 11.1000911  13.42305151\n",
+      "   3.15079545 10.70665448  8.35986305  5.43703545]\n",
+      " [ 1.96991192  4.45430236  0.70514808  1.3514835   2.60552651  3.15079545\n",
+      "   0.73958682  2.51317506  1.96231225  1.27623637]\n",
+      " [ 6.69391797 15.13607502  2.39614949  4.59244881  8.85378708 10.70665448\n",
+      "   2.51317506  8.53996947  6.66809369  4.33675308]\n",
+      " [ 5.22667819 11.81839897  1.87093752  3.58583003  6.91312563  8.35986305\n",
+      "   1.96231225  6.66809369  5.20651434  3.38618024]\n",
+      " [ 3.39929428  7.68637642  1.21680864  2.33212971  4.49611541  5.43703545\n",
+      "   1.27623637  4.33675308  3.38618024  2.20228273]]\n"
      ]
     }
    ],
@@ -2011,15 +2001,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.03872795610436844\n",
-      "3.8505072899794293\n",
-      "0.03015288036252618\n",
-      "0.8896094720230128 9.323184775146844 7.56423957069263\n",
-      "2.7487814387207385 2.000626681519836 6.325228546249804\n",
-      "[[0.88960947 2.74878144 2.00062668]\n",
-      " [2.74878144 9.32318478 6.32522855]\n",
-      " [2.00062668 6.32522855 7.56423957]]\n",
-      "[15.60940356  0.06849372  2.09913654]\n"
+      "0.09790216282081063\n",
+      "4.184866696796263\n",
+      "0.2373286546935638\n",
+      "0.9722865317690382 10.417644792969273 7.499394932852416\n",
+      "3.055091049054481 2.2630723514841553 7.22977659964799\n",
+      "[[ 0.97228653  3.05509105  2.26307235]\n",
+      " [ 3.05509105 10.41764479  7.2297766 ]\n",
+      " [ 2.26307235  7.2297766   7.49939493]]\n",
+      "[17.22169087  0.0637712   1.6038642 ]\n"
      ]
     }
    ],
@@ -2648,7 +2638,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -2774,12 +2764,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.011156605304609659 0.9767506308987675\n"
+      "0.023295599127611474 0.9904314810719695\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "
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diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png b/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png
index d53f7e367..0f32a44c0 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png and b/doc/LectureNotes/_build/jupyter_execute/statistics_181_0.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png
index d053c0539..038108733 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png and b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
index a6a3fc849..76f39b5e6 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb
@@ -874,8 +874,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.27600259 -1.0831367  -2.92599642 -0.36091273 -0.39398622 -1.03979291\n",
-      "  0.93237862  1.4635149   0.01552874  0.4761747 ]\n"
+      "[ 2.93289669  0.72236654  0.6671767   1.31936237 -0.39865452 -0.86247117\n",
+      "  0.64411666 -1.56072658  1.17367225  0.96391888]\n"
      ]
     }
    ],
@@ -1313,26 +1313,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.89184351 0.97435908 0.73122034 0.58084123 0.86891741 0.74957448\n",
-      "  0.65593984 0.06020264 0.48306473 0.47729832]\n",
-      " [0.86445334 0.75532041 0.31162122 0.57261202 0.2257449  0.31160886\n",
-      "  0.68591105 0.01084359 0.57192197 0.83466807]\n",
-      " [0.39525059 0.32684035 0.91648657 0.34904819 0.47052809 0.7029975\n",
-      "  0.0057065  0.1074792  0.6389681  0.16567361]\n",
-      " [0.46984559 0.3241988  0.77559615 0.16431956 0.92604454 0.08624193\n",
-      "  0.17567503 0.96919836 0.00859693 0.39692815]\n",
-      " [0.26817703 0.49224801 0.05240397 0.14260706 0.91365263 0.2876045\n",
-      "  0.38381781 0.23526911 0.28109198 0.01024875]\n",
-      " [0.47892661 0.88377332 0.51858116 0.02345858 0.52087499 0.63309898\n",
-      "  0.589841   0.19433318 0.24071032 0.76262775]\n",
-      " [0.67464946 0.69903406 0.44838546 0.72952595 0.41987877 0.0907958\n",
-      "  0.21998973 0.54856099 0.54725976 0.29390668]\n",
-      " [0.75600095 0.3882273  0.13024288 0.19677112 0.6401576  0.34599598\n",
-      "  0.62351789 0.49211524 0.6267112  0.82121282]\n",
-      " [0.77959897 0.35714377 0.08946029 0.70039784 0.60418171 0.618746\n",
-      "  0.46107913 0.41636775 0.94625725 0.85079059]\n",
-      " [0.0525733  0.16201555 0.18147009 0.41408221 0.15173932 0.88501323\n",
-      "  0.01376216 0.8030191  0.61192203 0.64399916]]\n"
+      "[[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"
      ]
     }
    ],
@@ -1446,13 +1446,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.028638258927275215\n",
-      "4.036527092051294\n",
-      "0.19009085621304586\n",
-      "[[ 1.07405556  3.2781456   2.97935648]\n",
-      " [ 3.2781456  11.21805426  9.35966688]\n",
-      " [ 2.97935648  9.35966688 14.77152924]]\n",
-      "[23.38440469  0.10036694  3.57886743]\n"
+      "-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"
      ]
     }
    ],
@@ -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_1539/1326197715.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_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~/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'"
      ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
index 76eaf2b07..4d27b5af7 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
@@ -1533,7 +1533,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9952537939995855\n"
+      "0.9958983289118531\n"
      ]
     }
    ],
@@ -1564,7 +1564,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.011208613520466846\n"
+      "0.010348289064969316\n"
      ]
     }
    ],
@@ -1599,23 +1599,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.05040878 0.02601643 0.01922269 0.05006037 0.02572685 0.10595991\n",
-      " 0.04487298 0.00334047 0.00330046 0.00606382 0.02500488 0.03247316\n",
-      " 0.01897462 0.0241039  0.0606958  0.00472276 0.01756114 0.06536971\n",
-      " 0.02809972 0.04955942 0.00956827 0.00667611 0.02576358 0.04216532\n",
-      " 0.04808723 0.01625794 0.00282226 0.00220013 0.00017733 0.0211429\n",
-      " 0.02207054 0.02156196 0.0694226  0.01119738 0.0041148  0.01783096\n",
-      " 0.0062202  0.03317599 0.02032056 0.00798909 0.06901081 0.01353638\n",
-      " 0.01863203 0.01179128 0.01178857 0.00634299 0.01793261 0.00018053\n",
-      " 0.13055762 0.02441422 0.05029018 0.0253208  0.01979808 0.02693015\n",
-      " 0.05336637 0.01373484 0.09291806 0.00168745 0.04588592 0.01013849\n",
-      " 0.04018985 0.03887801 0.03033791 0.01811279 0.02540212 0.02980537\n",
-      " 0.02784266 0.03158013 0.01060492 0.01620955 0.00942574 0.0043587\n",
-      " 0.02651857 0.00053001 0.0337609  0.01131771 0.00023813 0.02091662\n",
-      " 0.01315875 0.00434043 0.04161572 0.05045    0.0121289  0.01532738\n",
-      " 0.02334754 0.01206221 0.00930146 0.03244944 0.00702721 0.02576685\n",
-      " 0.05224117 0.0262517  0.02946852 0.09604976 0.01406777 0.02183817\n",
-      " 0.0164974  0.02322594 0.04238763 0.00647029]\n"
+      "[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"
      ]
     }
    ],
@@ -1669,15 +1669,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.82079885  2.45560415 -4.73595198 14.38102552 -7.04838148]\n",
+      "[ 2.04641529 -0.77354301  7.7511273  -3.70241548  1.69515531]\n",
       "Training R2\n",
-      "0.9952183728736417\n",
+      "0.9958589197366403\n",
       "Training MSE\n",
-      "0.009338082195270294\n",
+      "0.008560581831215528\n",
       "Test R2\n",
-      "0.9969461173312454\n",
+      "0.997252717901263\n",
       "Test MSE\n",
-      "0.008043811612683473\n"
+      "0.00683875021199284\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
index ab9222d55..fca6a2395 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb
@@ -1671,7 +1671,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 100.211  14.8834        100.212        0.149388\n"
+      " 100.213    14.98        100.211        0.149466\n"
      ]
     }
    ],
@@ -1737,7 +1737,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -2075,7 +2075,13 @@
       "Error: 0.08426840630693411\n",
       "Bias^2: 0.0796891867672603\n",
       "Var: 0.004579219539673834\n",
-      "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n",
+      "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Polynomial degree: 2\n",
       "Error: 0.10398646080125035\n",
       "Bias^2: 0.1007711427354898\n",
@@ -2101,18 +2107,18 @@
       "Error: 0.05227921801205686\n",
       "Bias^2: 0.0481872773043029\n",
       "Var: 0.004091940707753939\n",
-      "0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844\n"
+      "0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844\n",
+      "Polynomial degree: 6\n",
+      "Error: 0.037813671417389005\n",
+      "Bias^2: 0.033657685071527665\n",
+      "Var: 0.00415598634586135\n",
+      "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Polynomial degree: 6\n",
-      "Error: 0.037813671417389005\n",
-      "Bias^2: 0.033657685071527665\n",
-      "Var: 0.00415598634586135\n",
-      "0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n",
       "Polynomial degree: 7\n",
       "Error: 0.02760977349102253\n",
       "Bias^2: 0.022999498260366312\n",
@@ -2138,7 +2144,13 @@
       "Error: 0.021592704588025025\n",
       "Bias^2: 0.010516485576645508\n",
       "Var: 0.011076219011379514\n",
-      "0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022\n",
+      "0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Polynomial degree: 11\n",
       "Error: 0.07160048164233104\n",
       "Bias^2: 0.014436800088904942\n",
@@ -2165,7 +2177,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_139_4.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_139_6.png"
       }
      },
      "output_type": "display_data"
@@ -2592,37 +2604,43 @@
       "Mean squared error on test data: 10.50427787\n",
       "Degree of polynomial:   7\n",
       "Mean squared error on training data: 0.47313680\n",
-      "Mean squared error on test data: 1.53738247\n",
-      "Degree of polynomial:   8\n",
-      "Mean squared error on training data: 0.04926746\n",
-      "Mean squared error on test data: 0.14629156\n"
+      "Mean squared error on test data: 1.53738247\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:   8\n",
+      "Mean squared error on training data: 0.04926746\n",
+      "Mean squared error on test data: 0.14629156\n",
       "Degree of polynomial:   9\n",
       "Mean squared error on training data: 0.02546675\n",
       "Mean squared error on test data: 0.11202337\n",
       "Degree of polynomial:  10\n",
       "Mean squared error on training data: 0.02424794\n",
-      "Mean squared error on test data: 0.22467274\n",
-      "Degree of polynomial:  11\n",
-      "Mean squared error on training data: 0.01594452\n",
-      "Mean squared error on test data: 1.07641937\n",
-      "Degree of polynomial:  12\n",
-      "Mean squared error on training data: 0.00805074\n",
-      "Mean squared error on test data: 0.04295757\n"
+      "Mean squared error on test data: 0.22467274\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  11\n",
+      "Mean squared error on training data: 0.01594452\n",
+      "Mean squared error on test data: 1.07641937\n",
+      "Degree of polynomial:  12\n",
+      "Mean squared error on training data: 0.00805074\n",
+      "Mean squared error on test data: 0.04295757\n",
       "Degree of polynomial:  13\n",
       "Mean squared error on training data: 0.00781918\n",
-      "Mean squared error on test data: 0.56965674\n",
+      "Mean squared error on test data: 0.56965674\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Degree of polynomial:  14\n",
       "Mean squared error on training data: 0.00465099\n",
       "Mean squared error on test data: 0.28443039\n",
@@ -2646,37 +2664,43 @@
       "Mean squared error on test data: 429.25695398\n",
       "Degree of polynomial:  19\n",
       "Mean squared error on training data: 0.00154853\n",
-      "Mean squared error on test data: 239.97065359\n",
-      "Degree of polynomial:  20\n",
-      "Mean squared error on training data: 0.00140846\n",
-      "Mean squared error on test data: 1350.24493666\n"
+      "Mean squared error on test data: 239.97065359\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  20\n",
+      "Mean squared error on training data: 0.00140846\n",
+      "Mean squared error on test data: 1350.24493666\n",
       "Degree of polynomial:  21\n",
       "Mean squared error on training data: 0.00119688\n",
       "Mean squared error on test data: 1840.50530832\n",
       "Degree of polynomial:  22\n",
       "Mean squared error on training data: 0.00092898\n",
-      "Mean squared error on test data: 1184.60929685\n",
-      "Degree of polynomial:  23\n",
-      "Mean squared error on training data: 0.00089193\n",
-      "Mean squared error on test data: 3892.17483760\n",
-      "Degree of polynomial:  24\n",
-      "Mean squared error on training data: 0.00083355\n",
-      "Mean squared error on test data: 1332.46736215\n"
+      "Mean squared error on test data: 1184.60929685\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Degree of polynomial:  23\n",
+      "Mean squared error on training data: 0.00089193\n",
+      "Mean squared error on test data: 3892.17483760\n",
+      "Degree of polynomial:  24\n",
+      "Mean squared error on training data: 0.00083355\n",
+      "Mean squared error on test data: 1332.46736215\n",
       "Degree of polynomial:  25\n",
       "Mean squared error on training data: 0.00079904\n",
-      "Mean squared error on test data: 7577.76690383\n",
+      "Mean squared error on test data: 7577.76690383\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Degree of polynomial:  26\n",
       "Mean squared error on training data: 0.00075590\n",
       "Mean squared error on test data: 1079.36895644\n",
@@ -2701,9 +2725,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1579/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1579/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
      ]
     },
@@ -2716,7 +2740,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_148_9.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_148_11.png"
       }
      },
      "output_type": "display_data"
@@ -2838,7 +2862,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_1579/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_6729/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
       "  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
      ]
     },
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