updated book chapter

doc/BookChapters/chapter1.do.txt

@@ -1,7 +1,5 @@
 ======= Linear Regression =======
 
-"Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h20/forelesningsvideoer/LectureAug21.mp4?vrtx=view-as-webpage"
-
 
 ===== Introduction =====
 
@@ -9,7 +7,7 @@
 
 
 
-Our emphasis throughout this series of lectures (small change)  
+Our emphasis throughout this series of lectures  
 is on understanding the mathematical aspects of
 different algorithms used in the fields of data analysis and machine learning. 
 

doc/LectureNotes/_build/html/genindex.html

@@ -142,12 +142,12 @@
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="chapter2.html">
-   4. Resampling Methods
+   4. Ridge and Lasso Regression
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="chapter3.html">
-   5. Ridge and Lasso Regression
+   5. Resampling Methods
   </a>
  </li>
  <li class="toctree-l1">

doc/LectureNotes/_build/html/intro.html

@@ -143,12 +143,12 @@
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="chapter2.html">
-   4. Resampling Methods
+   4. Ridge and Lasso Regression
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="chapter3.html">
-   5. Ridge and Lasso Regression
+   5. Resampling Methods
   </a>
  </li>
  <li class="toctree-l1">

doc/LectureNotes/_build/html/reports/chapter2.log

@@ -17,88 +17,21 @@ Traceback (most recent call last):
     raise CellExecutionError.from_cell_and_msg(cell, exec_reply['content'])
 nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
 ------------------
-%matplotlib inline
-
-from numpy import *
-from numpy.random import randint, randn
-from time import time
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-
-# Returns mean of bootstrap samples                                                                                                                                                
-def stat(data):
-    return mean(data)
-
-# Bootstrap algorithm
-def bootstrap(data, statistic, R):
-    t = zeros(R); n = len(data); inds = arange(n); t0 = time()
-    # non-parametric bootstrap         
-    for i in range(R):
-        t[i] = statistic(data[randint(0,n,n)])
-
-    # analysis    
-    print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :")
-    print("original           bias      std. error")
-    print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t)))
-    return t
-
-
-mu, sigma = 100, 15
-datapoints = 10000
-x = mu + sigma*random.randn(datapoints)
-# bootstrap returns the data sample                                    
-t = bootstrap(x, stat, datapoints)
-# the histogram of the bootstrapped  data                                                                                                    
-n, binsboot, patches = plt.hist(t, 50, normed=1, facecolor='red', alpha=0.75)
-
-# add a 'best fit' line  
-y = mlab.normpdf( binsboot, mean(t), std(t))
-lt = plt.plot(binsboot, y, 'r--', linewidth=1)
-plt.xlabel('Smarts')
-plt.ylabel('Probability')
-plt.axis([99.5, 100.6, 0, 3.0])
-plt.grid(True)
-
-plt.show()
+Ainv = np.linlag.pinv(A)
 ------------------
 
 ---------------------------------------------------------------------------
 AttributeError                            Traceback (most recent call last)
-<ipython-input-2-772b904ae9cb> in <module>
-     31 t = bootstrap(x, stat, datapoints)
-     32 # the histogram of the bootstrapped  data
----> 33 n, binsboot, patches = plt.hist(t, 50, normed=1, facecolor='red', alpha=0.75)
-     34 
-     35 # add a 'best fit' line
+<ipython-input-6-52d2c51caad1> in <module>
+----> 1 Ainv = np.linlag.pinv(A)
+
+~/opt/anaconda3/lib/python3.8/site-packages/numpy/__init__.py in __getattr__(attr)
+    212                 return Tester
+    213             else:
+--> 214                 raise AttributeError("module {!r} has no attribute "
+    215                                      "{!r}".format(__name__, attr))
+    216 
 
-~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py in hist(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, data, **kwargs)
-   2683         orientation='vertical', rwidth=None, log=False, color=None,
-   2684         label=None, stacked=False, *, data=None, **kwargs):
--> 2685     return gca().hist(
-   2686         x, bins=bins, range=range, density=density, weights=weights,
-   2687         cumulative=cumulative, bottom=bottom, histtype=histtype,
-
-~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
-   1445     def inner(ax, *args, data=None, **kwargs):
-   1446         if data is None:
--> 1447             return func(ax, *map(sanitize_sequence, args), **kwargs)
-   1448 
-   1449         bound = new_sig.bind(ax, *args, **kwargs)
-
-~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py in hist(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
-   6813             if patch:
-   6814                 p = patch[0]
--> 6815                 p.update(kwargs)
-   6816                 if lbl is not None:
-   6817                     p.set_label(lbl)
-
-~/opt/anaconda3/lib/python3.8/site-packages/matplotlib/artist.py in update(self, props)
-    994                     func = getattr(self, f"set_{k}", None)
-    995                     if not callable(func):
---> 996                         raise AttributeError(f"{type(self).__name__!r} object "
-    997                                              f"has no property {k!r}")
-    998                     ret.append(func(v))
-
-AttributeError: 'Rectangle' object has no property 'normed'
-AttributeError: 'Rectangle' object has no property 'normed'
+AttributeError: module 'numpy' has no attribute 'linlag'
+AttributeError: module 'numpy' has no attribute 'linlag'
 

doc/LectureNotes/_build/html/search.html

@@ -147,12 +147,12 @@
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="chapter2.html">
-   4. Resampling Methods
+   4. Ridge and Lasso Regression
   </a>
  </li>
  <li class="toctree-l1">
   <a class="reference internal" href="chapter3.html">
-   5. Ridge and Lasso Regression
+   5. Resampling Methods
   </a>
  </li>
  <li class="toctree-l1">