From 7bcbcaa1f3b82db8a7c0433c0b79dff3e7a66509 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Sun, 14 Sep 2025 08:00:55 +0200 Subject: [PATCH] update week 38 --- doc/pub/week38/html/._week38-bs000.html | 41 +- doc/pub/week38/html/._week38-bs001.html | 43 +- doc/pub/week38/html/._week38-bs002.html | 42 +- doc/pub/week38/html/._week38-bs003.html | 41 +- doc/pub/week38/html/._week38-bs004.html | 41 +- doc/pub/week38/html/._week38-bs005.html | 41 +- doc/pub/week38/html/._week38-bs006.html | 41 +- doc/pub/week38/html/._week38-bs007.html | 41 +- doc/pub/week38/html/._week38-bs008.html | 41 +- doc/pub/week38/html/._week38-bs009.html | 41 +- doc/pub/week38/html/._week38-bs010.html | 41 +- doc/pub/week38/html/._week38-bs011.html | 41 +- doc/pub/week38/html/._week38-bs012.html | 41 +- doc/pub/week38/html/._week38-bs013.html | 41 +- doc/pub/week38/html/._week38-bs014.html | 41 +- doc/pub/week38/html/._week38-bs015.html | 41 +- doc/pub/week38/html/._week38-bs016.html | 41 +- doc/pub/week38/html/._week38-bs017.html | 41 +- doc/pub/week38/html/._week38-bs018.html | 41 +- doc/pub/week38/html/._week38-bs019.html | 41 +- doc/pub/week38/html/._week38-bs020.html | 41 +- doc/pub/week38/html/._week38-bs021.html | 41 +- doc/pub/week38/html/._week38-bs022.html | 41 +- doc/pub/week38/html/._week38-bs023.html | 41 +- doc/pub/week38/html/._week38-bs024.html | 41 +- doc/pub/week38/html/._week38-bs025.html | 41 +- doc/pub/week38/html/._week38-bs026.html | 41 +- doc/pub/week38/html/._week38-bs027.html | 41 +- doc/pub/week38/html/._week38-bs028.html | 41 +- doc/pub/week38/html/._week38-bs029.html | 41 +- doc/pub/week38/html/._week38-bs030.html | 41 +- doc/pub/week38/html/._week38-bs031.html | 41 +- doc/pub/week38/html/._week38-bs032.html | 41 +- doc/pub/week38/html/._week38-bs033.html | 41 +- doc/pub/week38/html/._week38-bs034.html | 42 +- doc/pub/week38/html/._week38-bs035.html | 43 +- doc/pub/week38/html/._week38-bs036.html | 44 +- doc/pub/week38/html/._week38-bs037.html | 45 +- doc/pub/week38/html/._week38-bs038.html | 46 +- doc/pub/week38/html/._week38-bs039.html | 45 +- doc/pub/week38/html/._week38-bs040.html | 45 +- doc/pub/week38/html/._week38-bs041.html | 45 +- doc/pub/week38/html/._week38-bs042.html | 53 +- doc/pub/week38/html/week38-bs.html | 41 +- doc/pub/week38/html/week38-reveal.html | 210 +----- doc/pub/week38/html/week38-solarized.html | 224 +----- doc/pub/week38/html/week38.html | 224 +----- doc/pub/week38/ipynb/ipynb-week38-src.tar.gz | Bin 1022756 -> 1022756 bytes doc/pub/week38/ipynb/week38.ipynb | 740 ++++--------------- doc/src/week38/week38.do.txt | 186 +---- 50 files changed, 400 insertions(+), 3030 deletions(-) diff --git a/doc/pub/week38/html/._week38-bs000.html b/doc/pub/week38/html/._week38-bs000.html index ae5e73f71..b468e462d 100644 --- a/doc/pub/week38/html/._week38-bs000.html +++ b/doc/pub/week38/html/._week38-bs000.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -322,7 +291,7 @@ MathJax.Hub.Config({
  • 9
  • 10
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs001.html b/doc/pub/week38/html/._week38-bs001.html index a5a443594..fdd3b8515 100644 --- a/doc/pub/week38/html/._week38-bs001.html +++ b/doc/pub/week38/html/._week38-bs001.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -290,7 +259,7 @@ MathJax.Hub.Config({
    1. Statistical interpretation of Ridge and Lasso regression
    2. Resampling techniques, Bootstrap and cross validation and bias-variance tradeoff (this may partly be discussed during the exercise sessions as well.
      3. -
    3. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at https://youtu.be/ +
    4. The material we did not cover last week, that is on more advanced methods for updating the learning rate, are covered by its own video. We will briefly discuss these topics at the beginning of the lecture and during the lab sessions. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at https://youtu.be/
    @@ -314,7 +283,7 @@ MathJax.Hub.Config({
  • 10
  • 11
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs002.html b/doc/pub/week38/html/._week38-bs002.html index 39f2a5fb3..335ebeafd 100644 --- a/doc/pub/week38/html/._week38-bs002.html +++ b/doc/pub/week38/html/._week38-bs002.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -293,6 +262,7 @@ MathJax.Hub.Config({
  • Video on Bootstrapping
  • Video on cross validation
    • +

    For the lab session, the following video on cross validation (from 2024), could be helpful, see https://www.youtube.com/watch?v=T9jjWsmsd1o

    @@ -314,7 +284,7 @@ MathJax.Hub.Config({
  • 11
  • 12
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs003.html b/doc/pub/week38/html/._week38-bs003.html index df8d90f90..729f1adce 100644 --- a/doc/pub/week38/html/._week38-bs003.html +++ b/doc/pub/week38/html/._week38-bs003.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -343,7 +312,7 @@ row number \( i \) and perform a sum over all values \( p \).
  • 12
  • 13
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs004.html b/doc/pub/week38/html/._week38-bs004.html index 92d818f08..68febe9e3 100644 --- a/doc/pub/week38/html/._week38-bs004.html +++ b/doc/pub/week38/html/._week38-bs004.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -319,7 +288,7 @@ $$
  • 13
  • 14
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs005.html b/doc/pub/week38/html/._week38-bs005.html index 94ad80d2a..dc4b0349e 100644 --- a/doc/pub/week38/html/._week38-bs005.html +++ b/doc/pub/week38/html/._week38-bs005.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -335,7 +304,7 @@ mean value \( \boldsymbol{X}\boldsymbol{\beta} \) and variance \( \sigma^2 \) (n
  • 14
  • 15
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs006.html b/doc/pub/week38/html/._week38-bs006.html index 7a7a12b3c..7da46d9c2 100644 --- a/doc/pub/week38/html/._week38-bs006.html +++ b/doc/pub/week38/html/._week38-bs006.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -380,7 +349,7 @@ This means the variance we obtain with the standard OLS will always for \( \lamb
  • 15
  • 16
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs007.html b/doc/pub/week38/html/._week38-bs007.html index d6afaff85..45a7dcfd9 100644 --- a/doc/pub/week38/html/._week38-bs007.html +++ b/doc/pub/week38/html/._week38-bs007.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -326,7 +295,7 @@ $$
  • 16
  • 17
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs008.html b/doc/pub/week38/html/._week38-bs008.html index d99d8559a..a9b8a2f52 100644 --- a/doc/pub/week38/html/._week38-bs008.html +++ b/doc/pub/week38/html/._week38-bs008.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -338,7 +307,7 @@ $$
  • 17
  • 18
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs009.html b/doc/pub/week38/html/._week38-bs009.html index 6e83177d8..05c5ab4a5 100644 --- a/doc/pub/week38/html/._week38-bs009.html +++ b/doc/pub/week38/html/._week38-bs009.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -333,7 +302,7 @@ is equivalent to the maximization/minimization of the function itself.
  • 18
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  • ...
    • -
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    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs010.html b/doc/pub/week38/html/._week38-bs010.html index 01f3eed0c..923142ef3 100644 --- a/doc/pub/week38/html/._week38-bs010.html +++ b/doc/pub/week38/html/._week38-bs010.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -333,7 +302,7 @@ $$
  • 19
  • 20
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs011.html b/doc/pub/week38/html/._week38-bs011.html index c7c7b26e5..b2e8d42b9 100644 --- a/doc/pub/week38/html/._week38-bs011.html +++ b/doc/pub/week38/html/._week38-bs011.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -319,7 +288,7 @@ our regression analysis. In what follows we will
  • 20
  • 21
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs012.html b/doc/pub/week38/html/._week38-bs012.html index af65cec5f..f5c627683 100644 --- a/doc/pub/week38/html/._week38-bs012.html +++ b/doc/pub/week38/html/._week38-bs012.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -335,7 +304,7 @@ cross-validation and the bootstrap method.
  • 21
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  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs013.html b/doc/pub/week38/html/._week38-bs013.html index bb3463244..be9a22264 100644 --- a/doc/pub/week38/html/._week38-bs013.html +++ b/doc/pub/week38/html/._week38-bs013.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -332,7 +301,7 @@ bootstrap is widely used.
  • 22
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  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs014.html b/doc/pub/week38/html/._week38-bs014.html index b9d520dd1..bf9314ac2 100644 --- a/doc/pub/week38/html/._week38-bs014.html +++ b/doc/pub/week38/html/._week38-bs014.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -321,7 +290,7 @@ MathJax.Hub.Config({
  • 23
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  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs015.html b/doc/pub/week38/html/._week38-bs015.html index 46630e78e..79cf8e0f3 100644 --- a/doc/pub/week38/html/._week38-bs015.html +++ b/doc/pub/week38/html/._week38-bs015.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -325,7 +294,7 @@ MathJax.Hub.Config({
  • 24
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  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs016.html b/doc/pub/week38/html/._week38-bs016.html index 89c683064..4e4dc6fe0 100644 --- a/doc/pub/week38/html/._week38-bs016.html +++ b/doc/pub/week38/html/._week38-bs016.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -332,7 +301,7 @@ training error reaches a saturation.
  • 25
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  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs017.html b/doc/pub/week38/html/._week38-bs017.html index 5739a55d7..9dc69805a 100644 --- a/doc/pub/week38/html/._week38-bs017.html +++ b/doc/pub/week38/html/._week38-bs017.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -330,7 +299,7 @@ advantages:
  • 26
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  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs018.html b/doc/pub/week38/html/._week38-bs018.html index 58ae431ce..73ed302ec 100644 --- a/doc/pub/week38/html/._week38-bs018.html +++ b/doc/pub/week38/html/._week38-bs018.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -325,7 +294,7 @@ $$
  • 27
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  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs019.html b/doc/pub/week38/html/._week38-bs019.html index fffcc1523..37eb3b102 100644 --- a/doc/pub/week38/html/._week38-bs019.html +++ b/doc/pub/week38/html/._week38-bs019.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -325,7 +294,7 @@ product of individual \( p(x_i) \). The independence assumption is important in
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    • -
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs020.html b/doc/pub/week38/html/._week38-bs020.html index c69d58ecd..f3c96de7b 100644 --- a/doc/pub/week38/html/._week38-bs020.html +++ b/doc/pub/week38/html/._week38-bs020.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -334,7 +303,7 @@ $$
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    • +
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs021.html b/doc/pub/week38/html/._week38-bs021.html index 0bf8d3783..2d5ec0a71 100644 --- a/doc/pub/week38/html/._week38-bs021.html +++ b/doc/pub/week38/html/._week38-bs021.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -336,7 +305,7 @@ and \( \mu \) is also the mean of the PDF \( p(x) \).
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs022.html b/doc/pub/week38/html/._week38-bs022.html index 779eaf591..46d086436 100644 --- a/doc/pub/week38/html/._week38-bs022.html +++ b/doc/pub/week38/html/._week38-bs022.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -348,7 +317,7 @@ finite \( m \), it is not always possible to find a closed form /analytic expres
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    • -
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs023.html b/doc/pub/week38/html/._week38-bs023.html index 030151a95..8c2b83067 100644 --- a/doc/pub/week38/html/._week38-bs023.html +++ b/doc/pub/week38/html/._week38-bs023.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -326,7 +295,7 @@ construct a confidence interval for the estimates.
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    • diff --git a/doc/pub/week38/html/._week38-bs024.html b/doc/pub/week38/html/._week38-bs024.html index d0e33972d..1beb11850 100644 --- a/doc/pub/week38/html/._week38-bs024.html +++ b/doc/pub/week38/html/._week38-bs024.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -333,7 +302,7 @@ Bootstrap method, why it works and various theorems related to it.
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs025.html b/doc/pub/week38/html/._week38-bs025.html index 920e5eaa3..ce808117c 100644 --- a/doc/pub/week38/html/._week38-bs025.html +++ b/doc/pub/week38/html/._week38-bs025.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -320,7 +289,7 @@ estimators.
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    • -
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    • +
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs026.html b/doc/pub/week38/html/._week38-bs026.html index eb40a50db..f65e1fd4e 100644 --- a/doc/pub/week38/html/._week38-bs026.html +++ b/doc/pub/week38/html/._week38-bs026.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -325,7 +294,7 @@ idea is to use the relative frequency of \( \widehat{\beta}^* \)
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    • -
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    • +
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs027.html b/doc/pub/week38/html/._week38-bs027.html index 9fa029e54..a82e7c109 100644 --- a/doc/pub/week38/html/._week38-bs027.html +++ b/doc/pub/week38/html/._week38-bs027.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -322,7 +291,7 @@ result in some asymptotic sense? The answer is yes.
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    • -
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs028.html b/doc/pub/week38/html/._week38-bs028.html index b2ddd4ae4..c99c39b20 100644 --- a/doc/pub/week38/html/._week38-bs028.html +++ b/doc/pub/week38/html/._week38-bs028.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -328,7 +297,7 @@ example, if you are interested in estimating the variance of \( \widehat
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  • ...
    • -
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    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs029.html b/doc/pub/week38/html/._week38-bs029.html index 151b0dfe2..d80a351d7 100644 --- a/doc/pub/week38/html/._week38-bs029.html +++ b/doc/pub/week38/html/._week38-bs029.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -373,7 +342,7 @@ t = bootstrap(x, datapoints)
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    • -
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    • +
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs030.html b/doc/pub/week38/html/._week38-bs030.html index 0f96420c4..ae9659eeb 100644 --- a/doc/pub/week38/html/._week38-bs030.html +++ b/doc/pub/week38/html/._week38-bs030.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -340,7 +309,7 @@ plt.show()
  • 39
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  • ...
    • -
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    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs031.html b/doc/pub/week38/html/._week38-bs031.html index aca968327..fe761b456 100644 --- a/doc/pub/week38/html/._week38-bs031.html +++ b/doc/pub/week38/html/._week38-bs031.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -366,7 +335,7 @@ $$
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  • ...
    • -
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    • +
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs032.html b/doc/pub/week38/html/._week38-bs032.html index c98b45e1e..acff1d63e 100644 --- a/doc/pub/week38/html/._week38-bs032.html +++ b/doc/pub/week38/html/._week38-bs032.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -315,7 +284,7 @@ MathJax.Hub.Config({
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  • ...
    • -
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    • +
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs033.html b/doc/pub/week38/html/._week38-bs033.html index 4e8953e6d..a7fab434a 100644 --- a/doc/pub/week38/html/._week38-bs033.html +++ b/doc/pub/week38/html/._week38-bs033.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -384,8 +353,6 @@ plt.show()
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    • -
  • ...
    • -
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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs034.html b/doc/pub/week38/html/._week38-bs034.html index 22ad5ed8c..8f2b4e765 100644 --- a/doc/pub/week38/html/._week38-bs034.html +++ b/doc/pub/week38/html/._week38-bs034.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -375,9 +344,6 @@ plt.show()
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    • -
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    • -
  • ...
    • -
  • 49
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs035.html b/doc/pub/week38/html/._week38-bs035.html index 359687312..063d9d50b 100644 --- a/doc/pub/week38/html/._week38-bs035.html +++ b/doc/pub/week38/html/._week38-bs035.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -337,10 +306,6 @@ flexible statistical methods have higher variance.
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    • -
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    • -
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    • -
  • ...
    • -
  • 49
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs036.html b/doc/pub/week38/html/._week38-bs036.html index 165bd6129..1000de440 100644 --- a/doc/pub/week38/html/._week38-bs036.html +++ b/doc/pub/week38/html/._week38-bs036.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -394,11 +363,6 @@ plt.show()
  • 41
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    • -
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    • -
  • 45
    • -
  • 46
    • -
  • ...
    • -
  • 49
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs037.html b/doc/pub/week38/html/._week38-bs037.html index 130d984a1..17c7acd86 100644 --- a/doc/pub/week38/html/._week38-bs037.html +++ b/doc/pub/week38/html/._week38-bs037.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -320,12 +289,6 @@ cross-validation (LOOCV).
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    • -
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    • -
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    • -
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    • -
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    • -
  • ...
    • -
  • 49
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs038.html b/doc/pub/week38/html/._week38-bs038.html index f964db141..d71c27c30 100644 --- a/doc/pub/week38/html/._week38-bs038.html +++ b/doc/pub/week38/html/._week38-bs038.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -317,13 +286,6 @@ MathJax.Hub.Config({
  • 41
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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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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs039.html b/doc/pub/week38/html/._week38-bs039.html index 741169f75..4f07ef8da 100644 --- a/doc/pub/week38/html/._week38-bs039.html +++ b/doc/pub/week38/html/._week38-bs039.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -415,12 +384,6 @@ plt.show()
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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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  • »
    • diff --git a/doc/pub/week38/html/._week38-bs040.html b/doc/pub/week38/html/._week38-bs040.html index a356423d5..8410ce312 100644 --- a/doc/pub/week38/html/._week38-bs040.html +++ b/doc/pub/week38/html/._week38-bs040.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -403,12 +372,6 @@ plt.show()
  • 41
  • 42
  • 43
    • -
  • 44
    • -
  • 45
    • -
  • 46
    • -
  • 47
    • -
  • 48
    • -
  • 49
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs041.html b/doc/pub/week38/html/._week38-bs041.html index 58480d296..20ae1dda2 100644 --- a/doc/pub/week38/html/._week38-bs041.html +++ b/doc/pub/week38/html/._week38-bs041.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -391,12 +360,6 @@ plt.show()
  • 41
  • 42
  • 43
    • -
  • 44
    • -
  • 45
    • -
  • 46
    • -
  • 47
    • -
  • 48
    • -
  • 49
  • »
    • diff --git a/doc/pub/week38/html/._week38-bs042.html b/doc/pub/week38/html/._week38-bs042.html index da965f36d..47c5a5b87 100644 --- a/doc/pub/week38/html/._week38-bs042.html +++ b/doc/pub/week38/html/._week38-bs042.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -284,6 +253,13 @@ MathJax.Hub.Config({

    Material for the lab sessions

    +

    This week we will discuss during the first hour of each lab session +some technicalities related to the project and methods for updating +the learning like ADAgrad, RMSprop and ADAM. As teaching material, see +the jupyter-notebook from week 37 (September 12-16). +

    + +

    For the lab session, the following video on cross validation (from 2024), could be helpful, see https://www.youtube.com/watch?v=T9jjWsmsd1o

    diff --git a/doc/pub/week38/html/week38-bs.html b/doc/pub/week38/html/week38-bs.html index ae5e73f71..b468e462d 100644 --- a/doc/pub/week38/html/week38-bs.html +++ b/doc/pub/week38/html/week38-bs.html @@ -163,32 +163,7 @@ doconce format html week38.do.txt --html_style=bootstrap --pygments_html_style=d ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -259,18 +234,12 @@ MathJax.Hub.Config({
  • Understanding what happens
  • Summing up
  • Another Example from Scikit-Learn's Repository
    • -
  • Various steps in cross-validation
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • +
  • Various steps in cross-validation
    • +
  • Cross-validation in brief
    • +
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
  • More examples on bootstrap and cross-validation and errors
  • The same example but now with cross-validation
  • Material for the lab sessions
    • -
  • Plans for the lab sessions
    • -
  • Lab session: Material relevant for the first project
    • -
  • Various steps in cross-validation
    • -
  • How to set up the cross-validation for Ridge and/or Lasso
    • -
  • Cross-validation in brief
    • -
  • Code Example for Cross-validation and \( k \)-fold Cross-validation
    • @@ -322,7 +291,7 @@ MathJax.Hub.Config({
  • 9
  • 10
  • ...
    • -
  • 49
    • +
  • 43
  • »
    • diff --git a/doc/pub/week38/html/week38-reveal.html b/doc/pub/week38/html/week38-reveal.html index 4da3a29cb..b6ca7426c 100644 --- a/doc/pub/week38/html/week38-reveal.html +++ b/doc/pub/week38/html/week38-reveal.html @@ -200,7 +200,7 @@ MathJax.Hub.Config({

    1. Statistical interpretation of Ridge and Lasso regression

    2. Resampling techniques, Bootstrap and cross validation and bias-variance tradeoff (this may partly be discussed during the exercise sessions as well.
      3. -

    3. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at https://youtu.be/ +

    4. The material we did not cover last week, that is on more advanced methods for updating the learning rate, are covered by its own video. We will briefly discuss these topics at the beginning of the lecture and during the lab sessions. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at https://youtu.be/
    @@ -219,6 +219,8 @@ MathJax.Hub.Config({

  • Video on Bootstrapping

  • Video on cross validation
    • +

    +

    For the lab session, the following video on cross validation (from 2024), could be helpful, see https://www.youtube.com/watch?v=T9jjWsmsd1o

    @@ -1820,210 +1822,14 @@ plt.show()

    Material for the lab sessions

    -
    -
    -

    Plans for the lab sessions

    - -
    -Material for the active learning sessions on Tuesday and Wednesday -

    -

      - -

    • bias-variance tradeoff
      • - -

    • Resampling techniques, cross-validation examples included here, see also the lectures from last week on the bootstrap method
      • - -

    • Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at https://youtu.be/omLmp_kkie0
      • -
    -
    -
    - -
    -

    Lab session: Material relevant for the first project

    -
    - -
    -

    Various steps in cross-validation

    - -

    When the repetitive splitting of the data set is done randomly, -samples may accidently end up in a fast majority of the splits in -either training or test set. Such samples may have an unbalanced -influence on either model building or prediction evaluation. To avoid -this \( k \)-fold cross-validation structures the data splitting. The -samples are divided into \( k \) more or less equally sized exhaustive and -mutually exclusive subsets. In turn (at each split) one of these -subsets plays the role of the test set while the union of the -remaining subsets constitutes the training set. Such a splitting -warrants a balanced representation of each sample in both training and -test set over the splits. Still the division into the \( k \) subsets -involves a degree of randomness. This may be fully excluded when -choosing \( k=n \). This particular case is referred to as leave-one-out -cross-validation (LOOCV). +

    This week we will discuss during the first hour of each lab session +some technicalities related to the project and methods for updating +the learning like ADAgrad, RMSprop and ADAM. As teaching material, see +the jupyter-notebook from week 37 (September 12-16).

    -
    -
    -

    How to set up the cross-validation for Ridge and/or Lasso

    - - -

    -

     
    -$$ -\begin{align*} -\boldsymbol{\beta}_{-i}(\lambda) & = ( \boldsymbol{X}_{-i, \ast}^{T} -\boldsymbol{X}_{-i, \ast} + \lambda \boldsymbol{I}_{pp})^{-1} -\boldsymbol{X}_{-i, \ast}^{T} \boldsymbol{y}_{-i} -\end{align*} -$$ -

     
    - - -

    -
    - -
    -

    Cross-validation in brief

    - -

    For the various values of \( k \)

    - -
      -

    1. shuffle the dataset randomly.
      2. -

    2. Split the dataset into \( k \) groups.
      3. -

    3. For each unique group: -
        -

      1. Decide which group to use as set for test data
        2. -

      2. Take the remaining groups as a training data set
        3. -

      3. Fit a model on the training set and evaluate it on the test set
        4. -

      4. Retain the evaluation score and discard the model
        5. -
      -

      -

    4. Summarize the model using the sample of model evaluation scores
      5. -
    -
    - -
    -

    Code Example for Cross-validation and \( k \)-fold Cross-validation

    - -

    The code here uses Ridge regression with cross-validation (CV) resampling and \( k \)-fold CV in order to fit a specific polynomial.

    - - -
    -
    -
    -
    -
    - 
    import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.model_selection import KFold
-from sklearn.linear_model import Ridge
-from sklearn.model_selection import cross_val_score
-from sklearn.preprocessing import PolynomialFeatures
-
-# A seed just to ensure that the random numbers are the same for every run.
-# Useful for eventual debugging.
-np.random.seed(3155)
-
-# Generate the data.
-nsamples = 100
-x = np.random.randn(nsamples)
-y = 3*x**2 + np.random.randn(nsamples)
-
-## Cross-validation on Ridge regression using KFold only
-
-# Decide degree on polynomial to fit
-poly = PolynomialFeatures(degree = 6)
-
-# Decide which values of lambda to use
-nlambdas = 500
-lambdas = np.logspace(-3, 5, nlambdas)
-
-# Initialize a KFold instance
-k = 5
-kfold = KFold(n_splits = k)
-
-# Perform the cross-validation to estimate MSE
-scores_KFold = np.zeros((nlambdas, k))
-
-i = 0
-for lmb in lambdas:
-    ridge = Ridge(alpha = lmb)
-    j = 0
-    for train_inds, test_inds in kfold.split(x):
-        xtrain = x[train_inds]
-        ytrain = y[train_inds]
-
-        xtest = x[test_inds]
-        ytest = y[test_inds]
-
-        Xtrain = poly.fit_transform(xtrain[:, np.newaxis])
-        ridge.fit(Xtrain, ytrain[:, np.newaxis])
-
-        Xtest = poly.fit_transform(xtest[:, np.newaxis])
-        ypred = ridge.predict(Xtest)
-
-        scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)
-
-        j += 1
-    i += 1
-
-
-estimated_mse_KFold = np.mean(scores_KFold, axis = 1)
-
-## Cross-validation using cross_val_score from sklearn along with KFold
-
-# kfold is an instance initialized above as:
-# kfold = KFold(n_splits = k)
-
-estimated_mse_sklearn = np.zeros(nlambdas)
-i = 0
-for lmb in lambdas:
-    ridge = Ridge(alpha = lmb)
-
-    X = poly.fit_transform(x[:, np.newaxis])
-    estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)
-
-    # cross_val_score return an array containing the estimated negative mse for every fold.
-    # we have to the the mean of every array in order to get an estimate of the mse of the model
-    estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)
-
-    i += 1
-
-## Plot and compare the slightly different ways to perform cross-validation
-
-plt.figure()
-
-plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')
-plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')
-
-plt.xlabel('log10(lambda)')
-plt.ylabel('mse')
-
-plt.legend()
-
-plt.show()
-
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    +

    For the lab session, the following video on cross validation (from 2024), could be helpful, see https://www.youtube.com/watch?v=T9jjWsmsd1o

    diff --git a/doc/pub/week38/html/week38-solarized.html b/doc/pub/week38/html/week38-solarized.html index 4781b53fe..4dce90021 100644 --- a/doc/pub/week38/html/week38-solarized.html +++ b/doc/pub/week38/html/week38-solarized.html @@ -190,32 +190,7 @@ div.toc p,a { ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -263,7 +238,7 @@ MathJax.Hub.Config({
    1. Statistical interpretation of Ridge and Lasso regression
    2. Resampling techniques, Bootstrap and cross validation and bias-variance tradeoff (this may partly be discussed during the exercise sessions as well.
      3. -
    3. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at https://youtu.be/ +
    4. The material we did not cover last week, that is on more advanced methods for updating the learning rate, are covered by its own video. We will briefly discuss these topics at the beginning of the lecture and during the lab sessions. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at https://youtu.be/
    @@ -282,6 +257,7 @@ MathJax.Hub.Config({
  • Video on Bootstrapping
  • Video on cross validation
    • +

    For the lab session, the following video on cross validation (from 2024), could be helpful, see https://www.youtube.com/watch?v=T9jjWsmsd1o

    @@ -1775,197 +1751,13 @@ plt.show()









    Material for the lab sessions

    -









    -

    Plans for the lab sessions

    - -
    -Material for the active learning sessions on Tuesday and Wednesday -

    -

    -
    - - -









    -

    Lab session: Material relevant for the first project

    - - -

    Various steps in cross-validation

    - -

    When the repetitive splitting of the data set is done randomly, -samples may accidently end up in a fast majority of the splits in -either training or test set. Such samples may have an unbalanced -influence on either model building or prediction evaluation. To avoid -this \( k \)-fold cross-validation structures the data splitting. The -samples are divided into \( k \) more or less equally sized exhaustive and -mutually exclusive subsets. In turn (at each split) one of these -subsets plays the role of the test set while the union of the -remaining subsets constitutes the training set. Such a splitting -warrants a balanced representation of each sample in both training and -test set over the splits. Still the division into the \( k \) subsets -involves a degree of randomness. This may be fully excluded when -choosing \( k=n \). This particular case is referred to as leave-one-out -cross-validation (LOOCV). +

    This week we will discuss during the first hour of each lab session +some technicalities related to the project and methods for updating +the learning like ADAgrad, RMSprop and ADAM. As teaching material, see +the jupyter-notebook from week 37 (September 12-16).

    - -

    How to set up the cross-validation for Ridge and/or Lasso

    - - -$$ -\begin{align*} -\boldsymbol{\beta}_{-i}(\lambda) & = ( \boldsymbol{X}_{-i, \ast}^{T} -\boldsymbol{X}_{-i, \ast} + \lambda \boldsymbol{I}_{pp})^{-1} -\boldsymbol{X}_{-i, \ast}^{T} \boldsymbol{y}_{-i} -\end{align*} -$$ - - - -









    -

    Cross-validation in brief

    - -

    For the various values of \( k \)

    - -
      -
    1. shuffle the dataset randomly.
      2. -
    2. Split the dataset into \( k \) groups.
      3. -
    3. For each unique group: -
        -
      1. Decide which group to use as set for test data
        2. -
      2. Take the remaining groups as a training data set
        3. -
      3. Fit a model on the training set and evaluate it on the test set
        4. -
      4. Retain the evaluation score and discard the model
        5. -
      -
    4. Summarize the model using the sample of model evaluation scores
      5. -
    -









    -

    Code Example for Cross-validation and \( k \)-fold Cross-validation

    - -

    The code here uses Ridge regression with cross-validation (CV) resampling and \( k \)-fold CV in order to fit a specific polynomial.

    - - -
    -
    -
    -
    -
    - 
    import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.model_selection import KFold
-from sklearn.linear_model import Ridge
-from sklearn.model_selection import cross_val_score
-from sklearn.preprocessing import PolynomialFeatures
-
-# A seed just to ensure that the random numbers are the same for every run.
-# Useful for eventual debugging.
-np.random.seed(3155)
-
-# Generate the data.
-nsamples = 100
-x = np.random.randn(nsamples)
-y = 3*x**2 + np.random.randn(nsamples)
-
-## Cross-validation on Ridge regression using KFold only
-
-# Decide degree on polynomial to fit
-poly = PolynomialFeatures(degree = 6)
-
-# Decide which values of lambda to use
-nlambdas = 500
-lambdas = np.logspace(-3, 5, nlambdas)
-
-# Initialize a KFold instance
-k = 5
-kfold = KFold(n_splits = k)
-
-# Perform the cross-validation to estimate MSE
-scores_KFold = np.zeros((nlambdas, k))
-
-i = 0
-for lmb in lambdas:
-    ridge = Ridge(alpha = lmb)
-    j = 0
-    for train_inds, test_inds in kfold.split(x):
-        xtrain = x[train_inds]
-        ytrain = y[train_inds]
-
-        xtest = x[test_inds]
-        ytest = y[test_inds]
-
-        Xtrain = poly.fit_transform(xtrain[:, np.newaxis])
-        ridge.fit(Xtrain, ytrain[:, np.newaxis])
-
-        Xtest = poly.fit_transform(xtest[:, np.newaxis])
-        ypred = ridge.predict(Xtest)
-
-        scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)
-
-        j += 1
-    i += 1
-
-
-estimated_mse_KFold = np.mean(scores_KFold, axis = 1)
-
-## Cross-validation using cross_val_score from sklearn along with KFold
-
-# kfold is an instance initialized above as:
-# kfold = KFold(n_splits = k)
-
-estimated_mse_sklearn = np.zeros(nlambdas)
-i = 0
-for lmb in lambdas:
-    ridge = Ridge(alpha = lmb)
-
-    X = poly.fit_transform(x[:, np.newaxis])
-    estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)
-
-    # cross_val_score return an array containing the estimated negative mse for every fold.
-    # we have to the the mean of every array in order to get an estimate of the mse of the model
-    estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)
-
-    i += 1
-
-## Plot and compare the slightly different ways to perform cross-validation
-
-plt.figure()
-
-plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')
-plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')
-
-plt.xlabel('log10(lambda)')
-plt.ylabel('mse')
-
-plt.legend()
-
-plt.show()
-
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    - - +

    For the lab session, the following video on cross validation (from 2024), could be helpful, see https://www.youtube.com/watch?v=T9jjWsmsd1o

    © 1999-2025, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license diff --git a/doc/pub/week38/html/week38.html b/doc/pub/week38/html/week38.html index 09541e6d7..98dabdcc7 100644 --- a/doc/pub/week38/html/week38.html +++ b/doc/pub/week38/html/week38.html @@ -267,32 +267,7 @@ div.toc p,a { ('Material for the lab sessions', 2, None, - 'material-for-the-lab-sessions'), - ('Plans for the lab sessions', - 2, - None, - 'plans-for-the-lab-sessions'), - ('Lab session: Material relevant for the first project', - 2, - None, - 'lab-session-material-relevant-for-the-first-project'), - ('Various steps in cross-validation', - 2, - None, - 'various-steps-in-cross-validation'), - ('How to set up the cross-validation for Ridge and/or Lasso', - 2, - None, - 'how-to-set-up-the-cross-validation-for-ridge-and-or-lasso'), - ('Cross-validation in brief', - 2, - None, - 'cross-validation-in-brief'), - ('Code Example for Cross-validation and $k$-fold ' - 'Cross-validation', - 2, - None, - 'code-example-for-cross-validation-and-k-fold-cross-validation')]} + 'material-for-the-lab-sessions')]} end of tocinfo --> @@ -340,7 +315,7 @@ MathJax.Hub.Config({
    1. Statistical interpretation of Ridge and Lasso regression
    2. Resampling techniques, Bootstrap and cross validation and bias-variance tradeoff (this may partly be discussed during the exercise sessions as well.
      3. -
    3. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at https://youtu.be/ +
    4. The material we did not cover last week, that is on more advanced methods for updating the learning rate, are covered by its own video. We will briefly discuss these topics at the beginning of the lecture and during the lab sessions. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at https://youtu.be/
    @@ -359,6 +334,7 @@ MathJax.Hub.Config({
  • Video on Bootstrapping
  • Video on cross validation
    • +

    For the lab session, the following video on cross validation (from 2024), could be helpful, see https://www.youtube.com/watch?v=T9jjWsmsd1o

    @@ -1852,197 +1828,13 @@ plt.show()









    Material for the lab sessions

    -









    -

    Plans for the lab sessions

    - -
    -Material for the active learning sessions on Tuesday and Wednesday -

    -

      -
    • bias-variance tradeoff
      • -
    • Resampling techniques, cross-validation examples included here, see also the lectures from last week on the bootstrap method
      • -
    • Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at https://youtu.be/omLmp_kkie0
      • -
    -
    - - -









    -

    Lab session: Material relevant for the first project

    - - -

    Various steps in cross-validation

    - -

    When the repetitive splitting of the data set is done randomly, -samples may accidently end up in a fast majority of the splits in -either training or test set. Such samples may have an unbalanced -influence on either model building or prediction evaluation. To avoid -this \( k \)-fold cross-validation structures the data splitting. The -samples are divided into \( k \) more or less equally sized exhaustive and -mutually exclusive subsets. In turn (at each split) one of these -subsets plays the role of the test set while the union of the -remaining subsets constitutes the training set. Such a splitting -warrants a balanced representation of each sample in both training and -test set over the splits. Still the division into the \( k \) subsets -involves a degree of randomness. This may be fully excluded when -choosing \( k=n \). This particular case is referred to as leave-one-out -cross-validation (LOOCV). +

    This week we will discuss during the first hour of each lab session +some technicalities related to the project and methods for updating +the learning like ADAgrad, RMSprop and ADAM. As teaching material, see +the jupyter-notebook from week 37 (September 12-16).

    - -

    How to set up the cross-validation for Ridge and/or Lasso

    - - -$$ -\begin{align*} -\boldsymbol{\beta}_{-i}(\lambda) & = ( \boldsymbol{X}_{-i, \ast}^{T} -\boldsymbol{X}_{-i, \ast} + \lambda \boldsymbol{I}_{pp})^{-1} -\boldsymbol{X}_{-i, \ast}^{T} \boldsymbol{y}_{-i} -\end{align*} -$$ - - - -









    -

    Cross-validation in brief

    - -

    For the various values of \( k \)

    - -
      -
    1. shuffle the dataset randomly.
      2. -
    2. Split the dataset into \( k \) groups.
      3. -
    3. For each unique group: -
        -
      1. Decide which group to use as set for test data
        2. -
      2. Take the remaining groups as a training data set
        3. -
      3. Fit a model on the training set and evaluate it on the test set
        4. -
      4. Retain the evaluation score and discard the model
        5. -
      -
    4. Summarize the model using the sample of model evaluation scores
      5. -
    -









    -

    Code Example for Cross-validation and \( k \)-fold Cross-validation

    - -

    The code here uses Ridge regression with cross-validation (CV) resampling and \( k \)-fold CV in order to fit a specific polynomial.

    - - -
    -
    -
    -
    -
    - 
    import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.model_selection import KFold
-from sklearn.linear_model import Ridge
-from sklearn.model_selection import cross_val_score
-from sklearn.preprocessing import PolynomialFeatures
-
-# A seed just to ensure that the random numbers are the same for every run.
-# Useful for eventual debugging.
-np.random.seed(3155)
-
-# Generate the data.
-nsamples = 100
-x = np.random.randn(nsamples)
-y = 3*x**2 + np.random.randn(nsamples)
-
-## Cross-validation on Ridge regression using KFold only
-
-# Decide degree on polynomial to fit
-poly = PolynomialFeatures(degree = 6)
-
-# Decide which values of lambda to use
-nlambdas = 500
-lambdas = np.logspace(-3, 5, nlambdas)
-
-# Initialize a KFold instance
-k = 5
-kfold = KFold(n_splits = k)
-
-# Perform the cross-validation to estimate MSE
-scores_KFold = np.zeros((nlambdas, k))
-
-i = 0
-for lmb in lambdas:
-    ridge = Ridge(alpha = lmb)
-    j = 0
-    for train_inds, test_inds in kfold.split(x):
-        xtrain = x[train_inds]
-        ytrain = y[train_inds]
-
-        xtest = x[test_inds]
-        ytest = y[test_inds]
-
-        Xtrain = poly.fit_transform(xtrain[:, np.newaxis])
-        ridge.fit(Xtrain, ytrain[:, np.newaxis])
-
-        Xtest = poly.fit_transform(xtest[:, np.newaxis])
-        ypred = ridge.predict(Xtest)
-
-        scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)
-
-        j += 1
-    i += 1
-
-
-estimated_mse_KFold = np.mean(scores_KFold, axis = 1)
-
-## Cross-validation using cross_val_score from sklearn along with KFold
-
-# kfold is an instance initialized above as:
-# kfold = KFold(n_splits = k)
-
-estimated_mse_sklearn = np.zeros(nlambdas)
-i = 0
-for lmb in lambdas:
-    ridge = Ridge(alpha = lmb)
-
-    X = poly.fit_transform(x[:, np.newaxis])
-    estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)
-
-    # cross_val_score return an array containing the estimated negative mse for every fold.
-    # we have to the the mean of every array in order to get an estimate of the mse of the model
-    estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)
-
-    i += 1
-
-## Plot and compare the slightly different ways to perform cross-validation
-
-plt.figure()
-
-plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')
-plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')
-
-plt.xlabel('log10(lambda)')
-plt.ylabel('mse')
-
-plt.legend()
-
-plt.show()
-
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    - - +

    For the lab session, the following video on cross validation (from 2024), could be helpful, see https://www.youtube.com/watch?v=T9jjWsmsd1o

    © 1999-2025, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license diff --git a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz index 385fae9901228ff029310ad725e4ccf3c6c9561d..2f6495f662db0ef9ba1b98537cc64202d07416c9 100644 GIT binary patch delta 63 zcmWN_IT3&`002S$3xr1jgOdos4aHSRpp6BPfG*f_^ON$E*v?SI{XLLUDygNBRyz4J PNH2p#8D*0BW?8Qf9as-B delta 63 zcmWN_IT3&`002S$3%>#eC-Fvc6%rV_SO5n|zz=M>>5l1+tb54L_t}tK3Mr+MS{nH? PNGqN6GRP>?#k?FJQ%?{* diff --git a/doc/pub/week38/ipynb/week38.ipynb b/doc/pub/week38/ipynb/week38.ipynb index 303d4a616..9f8f7a307 100644 --- a/doc/pub/week38/ipynb/week38.ipynb +++ b/doc/pub/week38/ipynb/week38.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d78710f8", + "id": "ebf95654", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "c0346fc9", + "id": "abe89759", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "efd985cb", + "id": "856fcd30", "metadata": { "editable": true }, @@ -40,14 +40,14 @@ "\n", "2. Resampling techniques, Bootstrap and cross validation and bias-variance tradeoff (this may partly be discussed during the exercise sessions as well.\n", "\n", - "3. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at \n", + "3. The material we did not cover last week, that is on more advanced methods for updating the learning rate, are covered by its own video. We will briefly discuss these topics at the beginning of the lecture and during the lab sessions. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at \n", "\n", "" ] }, { "cell_type": "markdown", - "id": "be400d40", + "id": "77b936c1", "metadata": { "editable": true }, @@ -61,12 +61,14 @@ "\n", "4. [Video on Bootstrapping](https://www.youtube.com/watch?v=Xz0x-8-cgaQ)\n", "\n", - "5. [Video on cross validation](https://www.youtube.com/watch?v=fSytzGwwBVw)" + "5. [Video on cross validation](https://www.youtube.com/watch?v=fSytzGwwBVw)\n", + "\n", + "For the lab session, the following video on cross validation (from 2024), could be helpful, see " ] }, { "cell_type": "markdown", - "id": "227b89cf", + "id": "fea30749", "metadata": { "editable": true }, @@ -94,7 +96,7 @@ }, { "cell_type": "markdown", - "id": "c746b141", + "id": "2ef00215", "metadata": { "editable": true }, @@ -110,7 +112,7 @@ }, { "cell_type": "markdown", - "id": "a8a616d7", + "id": "b64f9c5d", "metadata": { "editable": true }, @@ -129,7 +131,7 @@ }, { "cell_type": "markdown", - "id": "7a38fe3c", + "id": "ba50dc5c", "metadata": { "editable": true }, @@ -143,7 +145,7 @@ }, { "cell_type": "markdown", - "id": "1c903423", + "id": "4809e53f", "metadata": { "editable": true }, @@ -155,7 +157,7 @@ }, { "cell_type": "markdown", - "id": "1a56f951", + "id": "370aa933", "metadata": { "editable": true }, @@ -166,7 +168,7 @@ }, { "cell_type": "markdown", - "id": "6b20370c", + "id": "1632b45a", "metadata": { "editable": true }, @@ -178,7 +180,7 @@ }, { "cell_type": "markdown", - "id": "aeed813c", + "id": "0558b720", "metadata": { "editable": true }, @@ -190,7 +192,7 @@ }, { "cell_type": "markdown", - "id": "1f94ef1e", + "id": "36bf611d", "metadata": { "editable": true }, @@ -206,7 +208,7 @@ }, { "cell_type": "markdown", - "id": "da5a6018", + "id": "39166940", "metadata": { "editable": true }, @@ -217,7 +219,7 @@ }, { "cell_type": "markdown", - "id": "68c0362a", + "id": "711a67a0", "metadata": { "editable": true }, @@ -240,7 +242,7 @@ }, { "cell_type": "markdown", - "id": "37553c8f", + "id": "46c737e3", "metadata": { "editable": true }, @@ -251,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "b2510323", + "id": "50d44e22", "metadata": { "editable": true }, @@ -263,7 +265,7 @@ }, { "cell_type": "markdown", - "id": "6227b9f5", + "id": "064ff732", "metadata": { "editable": true }, @@ -275,7 +277,7 @@ }, { "cell_type": "markdown", - "id": "9c3899c3", + "id": "aed344c5", "metadata": { "editable": true }, @@ -289,7 +291,7 @@ }, { "cell_type": "markdown", - "id": "731e67f5", + "id": "81cde45e", "metadata": { "editable": true }, @@ -320,7 +322,7 @@ }, { "cell_type": "markdown", - "id": "17735638", + "id": "42c56971", "metadata": { "editable": true }, @@ -342,7 +344,7 @@ }, { "cell_type": "markdown", - "id": "d21b8264", + "id": "7b968dd2", "metadata": { "editable": true }, @@ -354,7 +356,7 @@ }, { "cell_type": "markdown", - "id": "c844f949", + "id": "406a7032", "metadata": { "editable": true }, @@ -367,7 +369,7 @@ }, { "cell_type": "markdown", - "id": "b48d4c26", + "id": "cb5c6efc", "metadata": { "editable": true }, @@ -379,7 +381,7 @@ }, { "cell_type": "markdown", - "id": "d161bce5", + "id": "6d6432a9", "metadata": { "editable": true }, @@ -391,7 +393,7 @@ }, { "cell_type": "markdown", - "id": "12b26ab6", + "id": "d89db325", "metadata": { "editable": true }, @@ -403,7 +405,7 @@ }, { "cell_type": "markdown", - "id": "4503a1be", + "id": "e191c8f7", "metadata": { "editable": true }, @@ -415,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "4645bbe3", + "id": "bbbbc0bd", "metadata": { "editable": true }, @@ -438,7 +440,7 @@ }, { "cell_type": "markdown", - "id": "d93b0448", + "id": "7ac9d52b", "metadata": { "editable": true }, @@ -450,7 +452,7 @@ }, { "cell_type": "markdown", - "id": "cc4eea57", + "id": "6e73a9f0", "metadata": { "editable": true }, @@ -463,7 +465,7 @@ }, { "cell_type": "markdown", - "id": "f077e491", + "id": "7a2d54a2", "metadata": { "editable": true }, @@ -475,7 +477,7 @@ }, { "cell_type": "markdown", - "id": "9bd5e3c0", + "id": "85b02f2a", "metadata": { "editable": true }, @@ -487,7 +489,7 @@ }, { "cell_type": "markdown", - "id": "927ff3d4", + "id": "5de13f57", "metadata": { "editable": true }, @@ -499,7 +501,7 @@ }, { "cell_type": "markdown", - "id": "89feaf37", + "id": "52057d9d", "metadata": { "editable": true }, @@ -510,7 +512,7 @@ }, { "cell_type": "markdown", - "id": "95dd8b04", + "id": "e58e03d3", "metadata": { "editable": true }, @@ -522,7 +524,7 @@ }, { "cell_type": "markdown", - "id": "25e42098", + "id": "484b7f0f", "metadata": { "editable": true }, @@ -533,7 +535,7 @@ }, { "cell_type": "markdown", - "id": "aab7e6ff", + "id": "f9e72522", "metadata": { "editable": true }, @@ -545,7 +547,7 @@ }, { "cell_type": "markdown", - "id": "db273591", + "id": "36c242ac", "metadata": { "editable": true }, @@ -555,7 +557,7 @@ }, { "cell_type": "markdown", - "id": "633e3ad6", + "id": "d140b182", "metadata": { "editable": true }, @@ -586,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "fd0b4f36", + "id": "26be96fd", "metadata": { "editable": true }, @@ -598,7 +600,7 @@ }, { "cell_type": "markdown", - "id": "5d460ae8", + "id": "ece8720a", "metadata": { "editable": true }, @@ -610,7 +612,7 @@ }, { "cell_type": "markdown", - "id": "57c177c2", + "id": "2131bf21", "metadata": { "editable": true }, @@ -620,7 +622,7 @@ }, { "cell_type": "markdown", - "id": "e760b8b0", + "id": "d04c15e3", "metadata": { "editable": true }, @@ -632,7 +634,7 @@ }, { "cell_type": "markdown", - "id": "62c01e7b", + "id": "865cc731", "metadata": { "editable": true }, @@ -642,7 +644,7 @@ }, { "cell_type": "markdown", - "id": "eb69463b", + "id": "f77ed57c", "metadata": { "editable": true }, @@ -654,7 +656,7 @@ }, { "cell_type": "markdown", - "id": "261eaf2e", + "id": "c1f7c00e", "metadata": { "editable": true }, @@ -664,7 +666,7 @@ }, { "cell_type": "markdown", - "id": "8fb419fe", + "id": "5addc988", "metadata": { "editable": true }, @@ -676,7 +678,7 @@ }, { "cell_type": "markdown", - "id": "93916f6d", + "id": "9b99e4cc", "metadata": { "editable": true }, @@ -686,7 +688,7 @@ }, { "cell_type": "markdown", - "id": "92d4fffd", + "id": "bc001de2", "metadata": { "editable": true }, @@ -705,7 +707,7 @@ }, { "cell_type": "markdown", - "id": "d96afaf1", + "id": "76e5d39d", "metadata": { "editable": true }, @@ -733,7 +735,7 @@ }, { "cell_type": "markdown", - "id": "17911f95", + "id": "912488e0", "metadata": { "editable": true }, @@ -759,7 +761,7 @@ }, { "cell_type": "markdown", - "id": "2c551883", + "id": "08b5d022", "metadata": { "editable": true }, @@ -776,7 +778,7 @@ }, { "cell_type": "markdown", - "id": "868c3d19", + "id": "fc7a103a", "metadata": { "editable": true }, @@ -796,7 +798,7 @@ }, { "cell_type": "markdown", - "id": "1b1b6bb2", + "id": "2daa5891", "metadata": { "editable": true }, @@ -825,7 +827,7 @@ }, { "cell_type": "markdown", - "id": "296bcf91", + "id": "3e5aed9f", "metadata": { "editable": true }, @@ -850,7 +852,7 @@ }, { "cell_type": "markdown", - "id": "b19af9f2", + "id": "b46c3356", "metadata": { "editable": true }, @@ -870,7 +872,7 @@ }, { "cell_type": "markdown", - "id": "30fffd56", + "id": "83b98cd1", "metadata": { "editable": true }, @@ -882,7 +884,7 @@ }, { "cell_type": "markdown", - "id": "4bb19cc7", + "id": "bfd72849", "metadata": { "editable": true }, @@ -892,7 +894,7 @@ }, { "cell_type": "markdown", - "id": "93a6835d", + "id": "2df8aa1c", "metadata": { "editable": true }, @@ -907,7 +909,7 @@ }, { "cell_type": "markdown", - "id": "69b6b47b", + "id": "38e7f561", "metadata": { "editable": true }, @@ -920,7 +922,7 @@ }, { "cell_type": "markdown", - "id": "15de9df3", + "id": "9f40e55c", "metadata": { "editable": true }, @@ -933,7 +935,7 @@ }, { "cell_type": "markdown", - "id": "987da2d9", + "id": "f302be9b", "metadata": { "editable": true }, @@ -945,7 +947,7 @@ }, { "cell_type": "markdown", - "id": "499f912d", + "id": "e5a58ece", "metadata": { "editable": true }, @@ -958,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "d9ab85a8", + "id": "8ceb9d77", "metadata": { "editable": true }, @@ -969,7 +971,7 @@ }, { "cell_type": "markdown", - "id": "defa5f6f", + "id": "82bf620e", "metadata": { "editable": true }, @@ -983,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "5fb3e2ee", + "id": "bfb87add", "metadata": { "editable": true }, @@ -993,7 +995,7 @@ }, { "cell_type": "markdown", - "id": "2a26a261", + "id": "e85f74a4", "metadata": { "editable": true }, @@ -1007,7 +1009,7 @@ }, { "cell_type": "markdown", - "id": "c8897e38", + "id": "2a9dbc78", "metadata": { "editable": true }, @@ -1020,7 +1022,7 @@ }, { "cell_type": "markdown", - "id": "364a2eaf", + "id": "bcd19592", "metadata": { "editable": true }, @@ -1033,7 +1035,7 @@ }, { "cell_type": "markdown", - "id": "b9ef8d41", + "id": "acae1f4d", "metadata": { "editable": true }, @@ -1043,7 +1045,7 @@ }, { "cell_type": "markdown", - "id": "10b892e5", + "id": "4fc70174", "metadata": { "editable": true }, @@ -1056,7 +1058,7 @@ }, { "cell_type": "markdown", - "id": "5c5340ed", + "id": "19456461", "metadata": { "editable": true }, @@ -1066,7 +1068,7 @@ }, { "cell_type": "markdown", - "id": "6348866a", + "id": "13a72ddb", "metadata": { "editable": true }, @@ -1079,7 +1081,7 @@ }, { "cell_type": "markdown", - "id": "4e7b83d1", + "id": "9a4fcddb", "metadata": { "editable": true }, @@ -1091,7 +1093,7 @@ }, { "cell_type": "markdown", - "id": "e840251b", + "id": "01a8f001", "metadata": { "editable": true }, @@ -1110,7 +1112,7 @@ }, { "cell_type": "markdown", - "id": "7a134bc3", + "id": "66cfe5b0", "metadata": { "editable": true }, @@ -1123,7 +1125,7 @@ }, { "cell_type": "markdown", - "id": "f4d0b07d", + "id": "f5c688c8", "metadata": { "editable": true }, @@ -1135,7 +1137,7 @@ }, { "cell_type": "markdown", - "id": "577334f2", + "id": "0b92a021", "metadata": { "editable": true }, @@ -1148,7 +1150,7 @@ }, { "cell_type": "markdown", - "id": "a2f24763", + "id": "79b3759b", "metadata": { "editable": true }, @@ -1168,7 +1170,7 @@ }, { "cell_type": "markdown", - "id": "59a7d784", + "id": "50e10779", "metadata": { "editable": true }, @@ -1191,7 +1193,7 @@ }, { "cell_type": "markdown", - "id": "79693dfc", + "id": "bf2f3b79", "metadata": { "editable": true }, @@ -1206,7 +1208,7 @@ }, { "cell_type": "markdown", - "id": "46d3ea64", + "id": "9c0ad203", "metadata": { "editable": true }, @@ -1218,7 +1220,7 @@ }, { "cell_type": "markdown", - "id": "4cdaad6b", + "id": "9afc9e91", "metadata": { "editable": true }, @@ -1238,7 +1240,7 @@ }, { "cell_type": "markdown", - "id": "88dfa9fa", + "id": "2ab72214", "metadata": { "editable": true }, @@ -1258,7 +1260,7 @@ }, { "cell_type": "markdown", - "id": "18195c5f", + "id": "af9cb8e8", "metadata": { "editable": true }, @@ -1282,7 +1284,7 @@ }, { "cell_type": "markdown", - "id": "a56cc2b9", + "id": "38b903a0", "metadata": { "editable": true }, @@ -1303,7 +1305,7 @@ }, { "cell_type": "markdown", - "id": "b6f32c85", + "id": "fbc520a3", "metadata": { "editable": true }, @@ -1333,7 +1335,7 @@ }, { "cell_type": "markdown", - "id": "5a7afda1", + "id": "a8b55d71", "metadata": { "editable": true }, @@ -1357,25 +1359,12 @@ { "cell_type": "code", "execution_count": 1, - "id": "994cc8dc", + "id": "aa554cc4", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrap Statistics :\n", - "original bias std. error\n", - " 100.171 14.9711 100.174 0.151379\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -1409,7 +1398,7 @@ }, { "cell_type": "markdown", - "id": "37bee1f6", + "id": "9cfdc8b9", "metadata": { "editable": true }, @@ -1419,7 +1408,7 @@ }, { "cell_type": "markdown", - "id": "be798fd6", + "id": "e45bf26e", "metadata": { "editable": true }, @@ -1430,26 +1419,12 @@ { "cell_type": "code", "execution_count": 2, - "id": "f369a227", + "id": "fd69eea3", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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LCwMSCcSSQ0TVsjO9B56ucxW+1rdFR9Frclkp/lsvGj//DJTyWolEQrDkEJHalKVy7L3bhbOq1DSq3kHcvAmc4PnZREKw5BCR2qLvPY2skrq8yrGautr/CWdnYO9e0UmITBNLDhGpbdfd7vC2TEW7uldFRzEIclkpBg0C9uwRnYTINHFZByJSiySplnIY7nIUMpnoNIZjSMxCbPxzAW50HQMfqyrOY+LSD0RawT05RKSWuPtNkVzoxvNxqinQ6Q+Yy4qx924X0VGITA5LDhGpZWd6DziY56CXPZfXrg5781z0sj+LPSw5RDrHkkNEatmV3h2DnU5AYVYiOorBGeJ8HL9ndkBuiZXoKEQmhSWHiJ4oMd8dZ3Mbc1ZVDQ1xPo5CyQIHMjuIjkJkUlhyiOiJdqV3h4WsCIFOf4iOYpCa2txEM+sk7LnbVXQUIpMitOQsXboUnTp1gq2tLVxdXTF8+HAkJCQ88XnR0dHw8/ODlZUVGjVqhFWrVukgLZHp2pneA/0dY2Frni86isEa4nwCe+524YKdRDoktORER0djypQpOHHiBCIjI1FcXIyAgADk5uZW+ZzExEQMGjQIPXv2RFxcHN59911Mnz4d27Zt02FyItNxV2mHw1ltOauqloY4H0dKkQvi7jcVHYXIZAi9Ts6+ffvKfb1u3Tq4uroiJiYGvXr1qvQ5q1atgre3N0JCQgAALVu2xOnTp7Fs2TKMHDlS25GJTM6eu10hQYZhziw5tdHD/hzs5ffx692u6GB7RXQcIpOgVxcDzMrKAgA4OTlV+Zjjx48jICCg3LbAwECsXbsWSqUSCoWi3H2FhYUoLCws+zo7OxsAoFQqoVQqNRW92h68t8gMhoDjpB6tjZOFBXZk9EBn+0twts2FEhaafX0dU/7z80H5yM8JXQmodxq/ZnTDu822lL9Dz76/+e9OfRwr9WhinGryXJkk6ccRYkmS8OyzzyIzMxOHDx+u8nHNmjXDiy++iHfffbds27Fjx9C9e3fcunULHh4e5R7/wQcfYOHChRVeZ9OmTbCxsdHcByAyQoWFZhg/fiBGj07AiBFcyqG2oqIaICTED99/vw9OToVPfgIRlcnLy8MLL7yArKws2NnZqfUcvdmTM3XqVJw9exZHjjx5iqrskWvKP+hpj24HgLlz5yI4OLjs6+zsbHh5eSEgIEDtQdIGpVKJyMhIDBgwoMLeJ/oXx0k92hqnPZ0XobBwKGb/9SVarEjW2OuKolQoEDlpEgasWQOFgN+8OxfZYTm2QLnsTwxqEPHvHXq2uBX/3amPY6UeTYzTgyMx1aEXJWfatGnYvXs3Dh06hAYNGjz2se7u7khNTS23LS0tDebm5nB2dq7weEtLS1haWlbYrlAo9OIbUl9y6DuOk3o0PU57UruguXUS2ir+Aoo09rLCKZRKKIp0/4E8kI6udn8iPK0TXnX99aFA+vm9zX936uNYqac241ST5wmdXSVJEqZOnYrt27fj999/R8OGDZ/4nK5duyIyMrLctv3796Njx478BiPSoJIS4Ne7XTmrSsOGOB9HZEZHFJTw5xWRtgktOVOmTEFoaCg2bdoEW1tbpKamIjU1Ffn5/16LY+7cuRg/fnzZ15MnT8aNGzcQHByMixcv4vvvv8fatWsxe/ZsER+ByGgdPw7cUTryKscaNsT5OHJLrRGd1U50FCKjJ7TkrFy5EllZWejTpw88PDzKblu3bi17TEpKCpKSksq+btiwIcLCwhAVFYV27dph0aJFWL58OaePE2nYnj1APUUmnrG7KDqKUWld5zp8rVK4YCeRDgg9J0ediV3r16+vsK13796IjY3VQiIieiA8HAhy+gNmMr2YgGk0ZDLV3pxf07theZOvUcl8CSLSEK5dRUQV3LoFnD0LBDmdEh3FKA1xPoEbhe64kOsrOgqRUWPJIaIK9u1T7XEIcOSCnNrQ2z4edczyuWAnkZax5BBRBeHhQOfOgItF9a9LQU9mJVdigNNplhwiLWPJIaJyiouByEhg4EDRSYzbEOcTOJ7dCulF4i5KSmTsWHKIqJzjx4GsLJYcbRvsdBylkGNfRmfRUYiMFksOEZUTHg64uAAdO4pOYtzcLTPRyfYiD1kRaZFeLOtARAL4+1e6Ofz0agTaXIdZvyU6DmR6hjifwBfJ/4VSqbcrOxAZNO7JIaIyKYVOiL/fFAOdOXVcF4Y4H0dWSV2osS4xEdUASw4RldmX0RkylCLQkSVHF9rXvQJPizv6tgg5kdFgySGiMuEZz6CTbQKnjuuITAYMdj7JkkOkJSw5RAQAKC41Q2SmHwY6nRQdxaQMcT6Oy5eBK1dEJyEyPiw5RAQAOJHdCveKbXk+jo71dYiFhYXqKtNEpFksOUQEQHWoytk8Cx1tE0RHMSl1zQvQsydLDpE2sOQQEQAgPKMzAp3+gFxWKjqKyQkKAg4eBAoKRCchMi4sOUSE1EJHxN1vxvNxBAkMBPLzwankRBrGkkNE/04dd+Kq4yK0aQN4evKQFZGmseQQEcIznkFH2wTUs8gSHcUkyWSqQ1YsOUSaxZJDZOKKS82wP7MjBjpxVpVIQUHAhQtAcrLoJETGgyWHyMSdzPln6jjPxxGqf3/AzAzYv190EiLjwZJDZOL2ZXSCs3kWOtlx6rhIjo7AM8/wkBWRJrHkEJm48LvPIMDpNKeO64GgICAyEiguFp2EyDiw5BCZsNtFjoi535yHqvREUBCQlQWc5H8OIo1gySEyYREZnQCAU8f1hJ8f4OzMQ1ZEmsKSQ2TCwjM6o6PtJbha3BMdhQDI5cCAAUBEhOgkRMaBJYfIRJVIZtifwanj+iYoCDh9GrhzR3QSIsPHkkNkok5lt0BGsT3Px9EzAQGAJKlOQCai2mHJITJR4RnPwMk8C53tLomOQg/x8ADateN5OUSawJJDZKLCMzpz6rieCgxUXRSwlP9piGqlRiVn/fr1yMvL03QWItKRtDTgdE4Lno+jp4KCgNu3gTNnRCchMmw1Kjlz586Fu7s7Xn75ZRw7dkzTmYhIyx7M3uHUcf3UrRtQty4PWRHVVo1Kzt9//43Q0FBkZmbC398fLVq0wCeffILU1FRN5yMiLYiIADrUvQw3i0zRUagSFhZAv34sOUS1VaOSI5fLMWzYMGzfvh3Jycl49dVX8eOPP8Lb2xvDhg3Drl27UMqDyUR6qbRUVXK4F0e/BQUBx44B2dmikxAZLvPavoCrqyu6d++OhIQEXL58GefOncOLL74IBwcHrFu3Dn369NFATCLSlLg4ID0dCGzHkqM3/P0rbArMd0dx8Wb83m0+htc7qtp48KCOgxEZthrPrrp9+zaWLVuG1q1bo0+fPsjOzsaePXuQmJiIW7duYcSIEZgwYYImsxKRBkREqM736Gp3QXQUeoyG1qloZp2EfRmdRUchMlg1KjlDhw6Fl5cX1q9fj0mTJuHmzZvYvHkz+vfvDwCwtrbGm2++ieTkZI2GJaLai4hQne9hYcalrvVdkNMf2JfRGZIkOgmRYapRyXF1dUV0dDTOnz+PmTNnwsnJqcJjPDw8kJiYWOuARKQ52dmq8zwCA0UnIXUEOZ3CjUJ3JOR5iY5CZJBqVHJ69+6NDh06VNheVFSEDRs2AABkMhl8fHxql46INOr334HiYpYcQ9Hb4QwsZUU8ZEVUQzUqORMnTkRWVlaF7Tk5OZg4cWKtQxGRdkREAE2aAI0aiU5C6rCRF6KXwxlEZHYSHYXIINWo5EiSBJlMVmH733//DXt7+1qHIiLNkyTVdVeCgkQnoeoIcvoDUffaIb/EQnQUIoNTrSnk7du3h0wmg0wmQ79+/WBu/u/TS0pKkJiYiCD+BCXSS1euANev81CVoQlyOoU3/3oDh7KeBv/TEVVPtUrO8OHDAQDx8fEIDAxE3bp1y+6zsLCAr68vRo4cqdGARKQZERGAQgHw0lWGpaXNDXhZ3sa+jE4sOUTVVK2Ss2DBAgCAr68vRo8eDSsrK62EIqJaquTichFnl6BHHUvUHfqmgEBUUzKZ6urUERk8L4eoump0Ts6ECRNYcIgMSGGpAgfvtUMQl3IwSEFOf+Bini9u3BCdhMiwqF1ynJyckJ6eDgBwdHSEk5NTlTci0i9Hstoir9Sa61UZqH4OMZCjpGz1eCJSj9qHq7788kvY2tqW/b2y2VVEpJ8iMjrB3eIunqrzl+goVAMOilx0sfsT+/a1xauvik5DZDjULjkPr0P14osvaiMLEWlJREYnBDieBn83MVxBTqfw2YG2UCpVJ5AT0ZOpfbgqOztb7RsR6Y9bhc44m9sYQU6nREehWghyOoXsbODECdFJiAyH2ntyHBwcnniI6sFFAktKSmodjIg0Y39GR8hQigGOp0VHoVroYHsFLi6qCzr27Ck6DZFhULvkHDx4UJs5iEhLIjI7wc/2MlwsuJfVkJnJJAQEqK53tHix6DREhkHtktO7d29t5iAiLSiRzBCZ0RGTPXeLjkIaEBQEbNoEpKUBrq6i0xDpP7VLztmzZ9GmTRuYmZnh7Nmzj33sU089VetgRFR7MTnNcLfYnufjGImAANWf+/cDY8eKzUJkCNQuOe3atUNqaipcXV3Rrl07yGQySJJU4XE8J4dIf0RkdIKd/D6esbsoOgppgJsb0L696pAVSw7Rk6k9uyoxMRH16tUr+/u1a9eQmJhY4Xbt2jW13/zQoUMYOnQoPD09IZPJsHPnzsc+PioqqmyB0Idvly5dUvs9iUxJREYn9HOMhcKMv3gYi6AgVckpLRWdhEj/qb0nx8fHp9K/10Zubi6efvppTJw4sVoLeyYkJMDOzq7s6wfli4j+dU9ZByeyW+HbZl+JjkIaFBgILF0KxMUBfn6i0xDpt2ot0PmwhIQEfP3117h48SJkMhlatGiBadOmoXnz5mq/xsCBAzFw4MBqv7erqyscHByq/TwiU3Lgnh9KIEegI8/HMSZduwK2tqq9OSw5RI9Xo5Lzyy+/4Pnnn0fHjh3RtWtXAMCJEyfQpk0bbNq0Cf/97381GvJR7du3R0FBAVq1aoX58+fDv5IVlx8oLCxEYWFh2dcPLlaoVCqhVCq1mvNxHry3yAyGgOOkngrjZGGB8HvPoJlNMurbZ0IJC4Hp9Ifyn0sFKw31ksFKJWQywN9fjvBw4K23tHMYkv/u1MexUo8mxqkmz5VJlZ09/ASNGjXC2LFj8eGHH5bbvmDBAmzcuLFa5+WUBZHJsGPHDgwfPrzKxyQkJODQoUPw8/NDYWEhNm7ciFWrViEqKgq9evWq9DkffPABFi5cWGH7pk2bYGNjU+2cRIZAkoBXXx2AZ55JwSuvnBcdhzRs3z5frF7dFhs3hqNOnWLRcYh0Ii8vDy+88AKysrLKnbLyODUqOTY2Njh79iyaNGlSbvuVK1fw9NNPIy8vr7ovqVbJqczQoUMhk8mwe3fl1wGpbE+Ol5cX0tPT1R4kbVAqlYiMjMSAAQOgMNTfKnWA46SeR8fpkv/reOro/7Cr/XwMrMeVxx9QKhSInDQJA9asgcIQf/PeswcAkJgING+uwE8/FWP48Gr/CH8i/rtTH8dKPZoYp+zsbLi4uFSr5NTocFWfPn1w+PDhCiXnyJEj6Knj64136dIFoaGhVd5vaWkJS0vLCtsVCoVefEPqSw59x3FSz4NxOpDaDhayIvSrGwNFUZHoWHpHoVQa5rj882+gWTOgeXPgt9/Moc2zA/jvTn0cK/XUZpxq8jy1S87De0qGDRuGt99+GzExMejSpQsA1Tk5P//8c6WHhrQpLi4OHh4eOn1PIn0XkdkJvRzOoo68QHQU0pLAQGDnTtWhSa4uT1Q5tUtOZYeRVqxYgRUrVpTbNmXKFEyePFmt17x//z6uXr1a9nViYiLi4+Ph5OQEb29vzJ07Fzdv3sSGDRsAACEhIfD19UXr1q1RVFSE0NBQbNu2Ddu2bVP3YxAZvfx8IPre0/jQd53oKKRFQUHA8uVAQgLQooXoNET6Se2SU6qFK0+dPn263Myo4OBgAMCECROwfv16pKSkICkpqez+oqIizJ49Gzdv3oS1tTVat26NvXv3YtCgQRrPRmSoDh8G8kutEOjEc3GMWe/egKWlalVylhyiytX4Ojma0KdPn0qXhnhg/fr15b6eM2cO5syZo+VURIZt3z7A0+IO2tRJFB2FtMjGBujVS/Xfe+ZM0WmI9FONS05ubi6io6ORlJSEokdO4Js+fXqtgxFRzYSHAwOdTvE8DRMQFATMm6c6RGltLToNkf6pUcmJi4vDoEGDkJeXh9zcXDg5OSE9PR02NjZwdXVlySESJDERuHQJ+Kg1r3JsCoKCgDffBA4dUp2ITETlqb1A58NmzZqFoUOHIiMjA9bW1jhx4gRu3LgBPz8/LFu2TNMZiUhNERFmMDcH+jvGiI5COtCyJdCggeqQFRFVVKOSEx8fjzfffBNyuRxyuRyFhYXw8vLCp59+infffVfTGYlITfv2ydC9O2Bvnis6CumATPbvquREVFGNSo5CoYDsnwP+bm5uZTOg7O3ty82GIiLdKSoyQ1SUDDVY85YMWFAQcPEicOOG6CRE+qdGJad9+/Y4ffo0AMDf3x/vv/8+fvzxR8ycORNt27bVaEAiUs+ffzojL48lx9T06wfI5dybQ1SZGpWcJUuWlF1leNGiRXB2dsbrr7+OtLQ0rF69WqMBiUg9MTFuqF9fAn/PMC0ODkCXLiw5RJWp0eyqjh07lv29Xr16CAsL01ggIqqZ2FhXBAZKZYeSyXQEBgLLlgFKZdnyVkSEGu7JeSAtLQ2HDx/GkSNHcOfOHU1lIqJqunYNuHnTFoGBmr8yOem/oCAgOxs4cUJ0EiL9UqOSk52djXHjxqF+/fro3bs3evXqBU9PT4wdOxZZWVmazkhETxARYQa5vBT9+lV9BXEyXn5+gIsLp5ITPapGJeeVV17ByZMnsWfPHty7dw9ZWVnYs2cPTp8+jUmTJmk6IxE9QUSEDC1b3oWdnegkJIKZGTBgAM/LIXpUjUrO3r178f333yMwMBB2dnawtbVFYGAg1qxZg71792o6IxE9RkEBcPCgDB06pImOQgIFBQExMUAavw2IytSo5Dg7O8Pe3r7Cdnt7ezg6OtY6FBGp79AhID9fhg4dbouOQgI9WNaBh6yI/lWjkjN//nwEBwcjJSWlbFtqaireeustvPfeexoLR0RPFhYGNGggwccnR3QUEsjNDejYEeDOdKJ/qT2FvH379uWmpl65cgU+Pj7w9vYGACQlJcHS0hJ37tzBa6+9pvmkRFSp8HD8M3VcdBISbfBgICSEU8mJHlC75AwfPlyLMYioJq5dAy5fBj76iFPHSVVyFi4Ejh0DevcWnYZIPLVLzoIFC7SZg4hqIDwcMDcH+vaVcOSI6DSkdf7+j73bT5LBze137N3LkkME1PCKxw/ExMTg4sWLkMlkaNWqFdq3b6+pXESkhrAwoGdPcOo4AQDMZBIGDlSdl/Ppp6LTEIlXo5KTlpaGMWPGICoqCg4ODpAkCVlZWfD398eWLVtQr149Teckokeopo6rDk8QPTB4MLB+PXD9OuDrKzgMkWA1ml01bdo0ZGdn48KFC8jIyEBmZibOnz+P7OxsTJ8+XdMZiagS0dFAfj646jiVM2CA6hAmZ1kR1bDk7Nu3DytXrkTLli3LtrVq1QrffvstwsPDNRaOiKoWHg54eQGtW4tOQvrE3l51CJMlh6iGJae0tBSKSuYnKhQKlJZylgeRLoSFqfbicOo4PWrwYNWhzLw80UmIxKpRyenbty9mzJiBW7dulW27efMmZs2ahX79+mksHBFV7q+/gCtXeKiKKjdkiOqcrd9/F52ESKwalZxvvvkGOTk58PX1RePGjdGkSRM0bNgQOTk5+PrrrzWdkYgeER6uutgbf6egyjRrBjRuzENWRDWaXeXl5YXY2FhERkbi0qVLkCQJrVq1Qv/+/TWdj4gqER6uOu/C1lZ0EtJHMpnqkNWOHYAk8ZAmma5ql5zi4mJYWVkhPj4eAwYMwIABA7SRi4iqkN8rEL8f3Y1Fvt8D/j+pNlpYAG+8oTpOUVQkNiDphcGDgeXLgfPngbZtRachEqPaJcfc3Bw+Pj4oKSnRRh4ieoLoe0+joNQSA51PiY5C+uifqyL3LlWgjtku7H12I9r6bC7/mIMHBQQj0r0ar0I+d+5cZGRkaDoPET1BeMYz8LZMRSub66KjkB6zNFOiv2MM9mZ0ER2FSJganZOzfPlyXL16FZ6envDx8UGdOnXK3R8bG6uRcERUUXhGZwx0OsXzLOiJBjufwOTLs5ChtIWTIkd0HCKdq1HJGT58OGQyGSRJ0nQeInqMq1eBK/le+Kzxd6KjkAEY5HwSpZAjIqMTnnfjfHIyPdUqOXl5eXjrrbewc+dOKJVK9OvXD19//TVcXFy0lY+IHhIWBihkSvR14N5SerL6luloV/cK9t7twpJDJqla5+QsWLAA69evx+DBg/H888/jt99+w+uvv66tbET0iN27AX+HeNia54uOQgZisNMJ7MvojBKpRqdgEhm0au3J2b59O9auXYsxY8YAAP7v//4P3bt3R0lJCeRyuVYCEpHKvXuqRTm/anhUdBQyIIOdT2Bx0jiczG6JbvYXRMch0qlqVfvk5GT07Nmz7OvOnTvD3Ny83PIORKQd4eFAcTEwzJklh9TX2e4SXBT3sPcuZ1mR6alWySkpKYGFhUW5bebm5iguLtZoKCKqaNcuoEMHoIFVuugoZEDkslIEOZ3CnrtdRUch0rlqHa6SJAkvvvgiLC0ty7YVFBRg8uTJ5aaRb9++XXMJiQhFRao9OW++CYDXcaNqGux0AqG3A5BcUA9eVndExyHSmWqVnAkTJlTYNnbsWI2FIaLKRUUB2dnAs8+CJYeqLdDpD8hRgrCMLnjN81fRcYh0plolZ926ddrKQUSPsXs34OMDPPWU6CRkiBwV99HN/jz23n2GJYdMCucUEuk5SVKVnGHDuJo01dxg5xM4kNkBBSUK0VGIdIYlh0jPxcUBycn/HKoiqqHBTieQV2qNqHvtREch0hmWHCI9t2sX4OAA9OolOgkZstZ1rsPbMpULdpJJYckh0nO7dwODBgEKHmWgWpDJgMHOJ7H3bhdw2UEyFSw5RHrsxg0gPl51Pg5RbQ12PoHEAk9cuiQ6CZFusOQQ6bHdu1V7cAYOFJ2EjEFfh1jUMcvHzp2ikxDpBksOkR7btQvw9wfs7EQnIWNgLS/CIOeT4PVayVSw5BDpqQcLcnJWFWnSCJdDOH0aSEoSnYRI+1hyiPTUgwU5hw4VnYSMySDnk7CwAHbsEJ2ESPtYcoj01IMFOb28RCchY2JnnocBA8BDVmQSWHKI9NCDBTl5qIq0YcQI4PBh4PZt0UmItIslh0gPRUc/tCAnkYY9WCJk1y7RSYi0iyWHSA/t2sUFOUl7XFyA3r15yIqMH0sOkZ7hgpykCyNGAAcOqGbxERkrlhwiPcMFOUkXnntONXtvzx7RSYi0R2jJOXToEIYOHQpPT0/IZDLsVOMynNHR0fDz84OVlRUaNWqEVatWaT8okQ7t3s0FOUn76tcHunThISsybkJLTm5uLp5++ml88803aj0+MTERgwYNQs+ePREXF4d3330X06dPx7Zt27SclEh3du3igpykGyNGAPv2Abm5opMQaYe5yDcfOHAgBlZjUZ5Vq1bB29sbISEhAICWLVvi9OnTWLZsGUaOHFnpcwoLC1FYWFj2dXZ2NgBAqVRCqVTWPHwtPXhvkRkMgamNk2pBTgVmzy6GUlnFUtEWFhU2Kf9pREo2o8fiOP3jn39PQ4YAc+YosHdvMZ57TnrobtP6d1cbHCv1aGKcavJcmSRJVfwk1S2ZTIYdO3Zg+PDhVT6mV69eaN++Pb766quybTt27MCoUaOQl5cHRSU/uD744AMsXLiwwvZNmzbBxsZGI9mJNGXPnoZYv74NNmwIh41Nseg4ZAJmzuwDH59szJoVKzoK0WPl5eXhhRdeQFZWFuzUXNBP6J6c6kpNTYWbm1u5bW5ubiguLkZ6ejo8PDwqPGfu3LkIDg4u+zo7OxteXl4ICAhQe5C0QalUIjIyEgMGDKi0nJGKqY3T1y+dg799LP6zfnm1nqdUKBA5aRIGrFkDBX+jrBLHqaIYeRa+OjoS/RWrYWGmKtZl42Qi/+5qw9R+RtWUJsbpwZGY6jCokgOo9vg87MGOqEe3P2BpaQlLS8sK2xUKhV58Q+pLDn1nCuN07x4QnfkUvmryDRRFRTV6DYVSWePnmhKO07/+6xiFRX+Nx+HbrRHk/Ee5+0zh352mcKzUU5txqsnzDGoKubu7O1JTU8ttS0tLg7m5OZydnQWlItKM8HCgWDLHUOdjoqOQCWlTJxFNrP/G9nRO5yPjY1Alp2vXroiMjCy3bf/+/ejYsSMbNBm8n38GOtlehJfVHdFRyITIZMAIl8PYmd4dJZJB/S+B6ImEfkffv38f8fHxiI+PB6CaIh4fH4+kpCQAqvNpxo8fX/b4yZMn48aNGwgODsbFixfx/fffY+3atZg9e7aI+EQak5UFhIUBY1wPio5CJmhEvcO4o3TE0aw2oqMQaZTQknP69Gm0b98e7du3BwAEBwejffv2eP/99wEAKSkpZYUHABo2bIiwsDBERUWhXbt2WLRoEZYvX17l9HEiQ7Fzp2rl8dEsOSRAJ9tLqG9xB9vv9BQdhUijhJ543KdPHzxuBvv69esrbOvduzdiYznVkYzLli1Az55AfbN00VHIBJnJJDxX7zC2p/fEl02+FR2HSGN4AJZIsPR0IDISGDNGdBIyZSNcDiO50A0xOc1ERyHSGJYcIsEerErCo64kUk/7s3A2z8I2zrIiI8KSQyTYli1Av36Aq6voJGTKzM1KMdzlCLbd6QX9uA4+Ue2x5BAJdOsWEB3NQ1WkH0bUO4wr+V74M9dHdBQijWDJIRLop59Uq40/95zoJERAP8dY2MpzsfN2d9FRiDSCJYdIoC1bgIEDAQcH0UmIAEszJYY4n8DONJYcMg4sOUSCJCYCJ0/yUBXplxEuh3AmpwlSU21ERyGqNZYcIkG2bgVsbIChQ0UnIfrXQOdTqCPPx+HDDURHIao1lhwiQbZsURWcOnVEJyH6Vx15AYa7HkVUVAPOsiKDx5JDJMDFi8CZMzxURfrp/zwP4OZNW5w+LRMdhahWWHKIBNi6FbC3V510TKRv/J3i4eSUjx9/ZMkhw8aSQ6RjkgRs3qyaNm5pKToNUUVyWSl69fobP/1khqIi0WmIao4lh0jH4uOBy5d5qIr0W58+yUhPl2HfPtFJiGqOJYdIx7ZsAVxcgL59RSchqpqvbw6eekrCxo2ikxDVHEsOkQ5Jkqrk/Oc/qisdE+mz//u/Uvz6K5CZKToJUc2w5BDp0IkTQFISD1WRYRgzphRKJfDzz6KTENUMSw6RDm3ZAnh6Aj16iE5C9GQeHsCAAeAhKzJYLDlEOlJSolqQc/RoQC4XnYZIPePGAUeOANeuiU5CVH3mogMQmYpov2Ckpn6BMdGvA/6XRMchUsvw4UDdukBoKPD++6LTEFUP9+QQ6ciWtL5oaHULnWxZcMhw1KkDjBypOmTFZR7I0HBPDpEOFBUB2+70xGueeyDjRWTJUAwZAhQVYVxmB/xw9XOc9HsDXewv/nv/wYPishGpgXtyiHTgt9+AjGJ7jHH9XXQUomrr4xCP+hZ3sPF2gOgoRNXCkkOkA+vWAa1tEtG2Ds/eJMMjl5VirFsktqT5o6iUBwDIcLDkEGnZ7dvAzp3AqzxURQZsnHskMortEXb3GdFRiNTGkkOkZevWAebmwDi3/aKjENVY6zrX0b7uZR6yIoPCkkOkRaWlwJo1wKhRgKPivug4RLUyzi0Sv97tigylregoRGphySHSogMHVBdRe/VV0UmIau951wMolczwU1of0VGI1MKSQ6RFq1cDrVsD3bqJTkJUe+6WmQhw+oOHrMhgsOQQaUnZCcevgicck9EY5xaJY9ltcDXPU3QUoidiySHSkrITjseJTkKkOc+6HIWtPBehtweIjkL0RCw5RFpQ7oRjR9FpiDTHRl6I/9Q7hNDbA7jMA+k9lhwiLeAJx2TMxrntx18F9XHkiOgkRI/HkkOkBTzhmIxZb4czaGqdjG+/FZ2E6PFYcog0LDWVJxyTcTOTSZhWfwd++QX4+2/RaYiqxpJDpGHr1/OEYzJ+E9wjYGMDrFolOglR1bjSGpEm+PsDAEolGdac3IhR9ufhOOJjwaGItMfOPA8TJwLffQfMnw9YWYlORFQR9+QQadCBzA64VlAfr3n+KjoKkdZNnQqkpwNbtohOQlQ57skh0qDVKUPQ2iYRXe0uiI5CpHVNX/XHIKelWD7NCRPWv1b5OWgHD+o8F9ED3JNDpCGphY7Ymd4Dr3ru4QnHZDKmN9iOuPvNcDSrjegoRBWw5BBpyPrUIJjLSjDObb/oKEQ6M8DxNJpbJ2H5zZGioxBVwJJDpAGlkgxrUgZjVL0oOCrui45DpDNmMgnTGuzA9js9kVxQT3QconJYcog0gCcckykb7xaBOvJ8rLz1rOgoROWw5BBpwHe3hvKEYzJZtub5eMkjHKtvDUF+iYXoOERlWHKIaik1Fdh1tztPOCaTNsVzJzKKbbE5rZ/oKERlWHKIaul//wNPOCaT18TmFgY7n8Dyv0dwdXLSGyw5RLWQmwuEhAAT3ffxhGMyedPrb8eZ3CY4nPWU6ChEAFhyiGpl9WogKwuY47VZdBQi4fo7xqClzXUs/3uE6ChEAFhyiGqsoABYtgwYOxbwtb4tOg6RcDIZMK3+DuxI74GkAlfRcYhYcohqav16ICUFeOcd0UmI9Mc4t/2wledjxU1OJyfxWHKIakCpBD75BBg1CmjeXHQaIv1R17wAL3uEYU3KYOSVWIqOQyaOJYeoBjZvBq5fB959V3QSIv0zpf5OZBbbYtNtTicnsbgKOVE1lZQAS5cCQ4cCT3ESCVEFjaxTMNT5OJbfHIGX+/g/+fpRXKmctET4npwVK1agYcOGsLKygp+fHw4fPlzlY6OioiCTySrcLl26pMPEZOq2bwcuXQLmzROdhEh/TW+wHedyG+NAZgfRUciECS05W7duxcyZMzFv3jzExcWhZ8+eGDhwIJKSkh77vISEBKSkpJTdmjZtqqPEZOokCVi8GOjfH3jmGdFpiPRXX4dYdLK9iPeuv8SLA5IwQg9XffHFF3j55ZfxyiuvAABCQkIQERGBlStXYunSpVU+z9XVFQ4ODmq9R2FhIQoLC8u+zs7OBgAolUoolcqah6+lB+8tMoMh0LdxCguT4cwZc0RGFkOpfOgnt4XY9XqUCkW5P6lyHCf1aGqcPmz2AwbGfIydWT0xxPXkY95QP/5914S+/YzSV5oYp5o8VyZJYjp2UVERbGxs8PPPP+O5554r2z5jxgzEx8cjOjq6wnOioqLg7+8PX19fFBQUoFWrVpg/fz78/f2rfJ8PPvgACxcurLB906ZNsLGx0cyHIZMgScA77/SEmZmEJUuOcJ0qoieQJOD997shO9sCX34ZBTPhJ0iQIcvLy8MLL7yArKws2NnZqfUcYXty0tPTUVJSAjc3t3Lb3dzckJqaWulzPDw8sHr1avj5+aGwsBAbN25Ev379EBUVhV69elX6nLlz5yI4OLjs6+zsbHh5eSEgIEDtQdIGpVKJyMhIDBgwAAr+VlklfRqngwdlSEgwx+7dxQgKGlT+ziFDxIT6h1KhQOSkSRiwZg0U/I2yShwn9WhynFysf0fPc18hZ8FFPO9RxQnGe/bU6j1E0qefUfpME+P04EhMdQifXSV75NdhSZIqbHugefPmaP7QRUm6du2K5ORkLFu2rMqSY2lpCUvLitdqUCgUevENqS859J0+jNMnnwAdOgBDhphX3ItTVCQk06MUSiUUepJFn3Gc1KOJcephcxbDnI/iwyvj8Lzjb1CYlVTyRob/M1AffkYZgtqMU02eJ2znoYuLC+RyeYW9NmlpaRX27jxOly5dcOXKFU3HIyrn+HHg999VM6p4mIqoehY1/B7XCjywLnWg6ChkYoSVHAsLC/j5+SEyMrLc9sjISHTr1k3t14mLi4OHh4em4xH9y98fi4ccRyub6xi+vC/g71/xRkRVeqruNTzv+jsWXh+P/BKxJ+mTaRF6uCo4OBjjxo1Dx44d0bVrV6xevRpJSUmYPHkyANX5NDdv3sSGDRsAqGZf+fr6onXr1igqKkJoaCi2bduGbdu2ifwYZOTicxpjb0ZXhLZcDDMZ58IS1cRC33Vo+ccPWHHrWbzp9bPoOGQihJac0aNH4+7du/jwww+RkpKCNm3aICwsDD4+PgCAlJSUctfMKSoqwuzZs3Hz5k1YW1ujdevW2Lt3LwYNGlTVWxDV2pKksWhkdROj6/0uOgqRwWpicwsvu4dh6Y3/wySPvbAzzxMdiUyA8BOP33jjDbzxxhuV3rd+/fpyX8+ZMwdz5szRQSoilYsXgV/u9MJ3zb6AuVmp6DhEBu09341YnxqEL//+Dxb4bhAdh0wAr1pA9Bjz5gH1LdMx3n2/6ChEBq++ZTqm1t+Bz5NHIb1I3CU8yHSw5BBVISIC2LED+KzRKlia8ZoqRJrwjvcmAMDHSS8ITkKmgCWHqBKFhcC0aaqJU6NduUIykaa4WGTjTa+f8M3N5/B3gYvoOGTkWHKIKvH550BiIvD117wuDpGmzWrwC+rK8/HRjXGio5CRY8khesSNG8BHHwEzZgCtW4tOQ2R87MzzMNfnR6xNHYSreZ6i45AREz67ikioSi7kF3x+IRxLWmLByQmAf76AUETG7w3PXfgy+b9YcH0ifhQdhowW9+QQPSQioxO2p/fCssarYGvOgkOkLdbyIrzvuwGb0/oiLk50GjJWLDlE/ygsVWDalWno4xCHMa688B+Rtk10D0frOtfxyisAF4UnbWDJIfrH58mjkFjggW+aLufJxkQ6oDArwbrmn+DMGeDTT0WnIWPEkkMEIKnAFR/dGIsZ9behdZ3rouMQmYyOdpfx1lvAhx8CFy6ITkPGhiWHCMCsq1PgaJ6DBb4/iI5CZHIWLAAaNwYmTgSKi0WnIWPCkkMmjycbE4llZQV8/z0QE6O6RhWRprDkkEnjycZE+qFLFyA4WLVX59Il0WnIWLDkkEnjycZE+uPDDwEfH9Vhq5IS0WnIGLDkkMlKSgJPNibSI9bWqsNWJ08CISGi05AxYMkhk1RSovptkScbE+mX7t1VS6rMnw9cviw6DRk6lhwySe+/D0RFAT+2XMyTjYn0zOLFQP36wEsv8bAV1Q5LDpmcX38FlixR3fo4nhEdh4geYWOjOmx19CjwzTei05AhY8khk3LtGjB+PDBsGDBnjug0RFSVXr2AqVOBuXOBv/4SnYYMFUsOmYyCAuA//wGcnIAffgBnUxHpuaVLAXd34OWXgdJS0WnIELHkkMmYNg24eBHYtg1wcBCdhoiepG5d4H//A6KjVefpEFUXSw6ZhPXrVT8sv/0WaNdOdBoiUlffvqrr57z/PrBpk+g0ZGjMRQcg0hp/fwDAmfuN8Xrst3jJ/QBe2vgZsFFwLiIq759/q1WZLwF/ub2NiWP7wuuz2ejpcK7yBx48qIVwZMi4J4eM2j1lHYw8vxAtbJLwTdOvRMchohqQyYDVzT9HN/sLGH5+Ea7k1RcdiQwESw4ZLUkCJia8jXSlPba1XgBreZHoSERUQxZmxdje+n24WtzDoHMfI73ITnQkMgAsOWS0liWPxs70ntjQcikaWaeIjkNEteSouI+9beciq7gOhp//CAUlCtGRSM+x5JBROngQmHttEt722oRhLsdExyEiDWlknYLdbeYh5n4zvJTwNkolXguCqsaSQ0YnKgoYOhTo6xiLjxquFR2HiDSsi/1FbGyxBJvT+mHB9RdFxyE9xpJDRuW334BBg4Bu3YCdbd6DuRmvIEZkjP7jegifNlqFj26Mx7qUINFxSE9xCjkZpkqmnO672wnDz3+Efo6x2FbwPqzkSgHBiEhXZnttxdX8+nj18pvwsbqNvqIDkd7hnhwyCr+md8Wz5z9CoNMf2N6GBYfIFMhkwLdNQ9DPIRYjzn+IuDjRiUjfsOSQwdt+pydGXPgQQ52P4+fWH8DSjAWHyFSYm5Xip9YL0cwmGT17AmFhohORPmHJIYO2Nc0foy4swEiXQ9jcahEszIpFRyIiHbMzz8PBdsHo31816WDFCtGJSF+w5JDBCk3tjxf+nIfn3Q4gtOViKMxKREciIkHqyAuwbRswfTowZQowezZXLieeeEwGan1KIF5KmIMX3SOwpvkyyGX8aUZk6uT9/fElgEZNnsPMz6cg8Yej2NhyCWzkhf8+iOtbmRTuySGDUloKfP45MDHhHUzy2Iv/Nf+MBYeIypnWYAd2tnkP+zI6wT/+S9wuchQdiQRhySGDkZKiugbO7NnAHK/NWNXsC5jJJNGxiEgPDXU5jkPtZyKp0BVdYr/FxVxv0ZFIAJYc0j/+/v/ehgwBAOx9ZhGe8rmH+N8zEN52Dj5pvBoyXs2diB7Dz/YyTnZ4A3XMCtAt7hsczGwnOhLpGEsO6bX8Egt8991TeC7uQ3Sx+xPnOr2EIOc/RMciIgPhbZWGox2moZPtJQSe/RSffAIoeZUJk8GSQ3rrzP3G6HLiGxw44I3lLb7G7jbzUM8iS3QsIjIw9ua52Nt2LmY02IZ58wA/P+D4cdGpSBdYckjvlEoyfJn8H3SOWQFzWQmWLYvGZO89PDxFRDWmMCvBZ42/wx/tJsHq+kV06wZM9tyNzB5Dyx8ir2TJGDJcLDmkV27eBAad/RjBf03BG/V34egz0+HtnSM6FhEZifa2V3G8w1R83eQrbErrh5anfsDm230hcQ6DUeJ1cki3qvgt6XaRIz5Jeh4rbw2DvbwJwtvOQZDzH1DKLXQckIiMnVxWiqkNduK5ekcw8+oUvHDxPaxPDcKKZiFobH1LdDzSIJYcEupOkT0+Sx6Db24Oh0JWjLe9NmNmg1/goMgVHY2IjFx9y3T83Hoh9qRHYMqVGWjzx/d4z2cjggsAKyvR6UgTWHJIiLtKO3yePArL/x4BmUzCm14/IbjBz3BU3BcdjYhMzBCXE/B3jMfC6xPwfuJEfOUDTJ4MvP464O4uOh3VBs/JIZ3KVNbFe4kT0fDEJiz/ewSmN9iO612ex6KG61hwiEiYOvICfNr4O1zsPAGjRqmurO7tDUyYAMTGik5HNcU9OaQ5VZxvI0lA7P1m2HS7H/6XshnFkhxT6u/CW15bOCWciPRKU5ub+PprYNEiYO1a4OuvgQ0bgF69gJkzgWHDALlcdEpSF0sOac2lXC9sTuuHzWl9cSXfC66KDLzquQezvX6Cm0Wm6HhERFVycADefBOYMQPYtQsICQFGjAB8fYFp04AXXuChLEPAkkMalVxQD1vS+mJzWl/E3W8GO/l9jKh3GN80XY6+DrEwN+NimkSk5x7aK20OYCSAkebA6Q7N8NXNkXhntj/efFOBzrYXMdD1FOyn+EAaPARQFpV/Ha54LhxLDtVKUZHqePWRI8DuuBAcznoaVmaFGOJ8HPN9QjHI6QSs5LyGOhEZvo52l7HRbim+bPwtwjK6YM/drvjyxn+QHVwHn1u2xmCnExjifBz9HGNhIy8UHZfAkkPVdO+e6nLoR46obqdOAQUFgI0N0NsqHz+0WIrhLkdgZ54nOioRkVa4WGRjvPt+jHffj1xzG3zR613cXZuKvWnPYHXKUFiZFaKvQxx6fgx06KC6ubiITm2aWHKoUpIE3LkDXL4MJCQAMTGqUnP+vOo+V1egRw9gyRLVn+3aAYqAuaJjExHplIVZMZ56Kh2Dmq/Gl77f4HK+F/bc7YKwu12wZAmQ888F2729VWtmPSg9fn6Am5vY7KZAeMlZsWIFPvvsM6SkpKB169YICQlBz549q3x8dHQ0goODceHCBXh6emLOnDmYPHmyDhMbl9xcIDFRVWQevd27p3qMTAY0a6YqM8HBQPfuQJMm4FpSREQPkcmA5jbJaG6TjDe9fkbpgYO4elV1SD82VvXL4uef//uz1dMTaN5cdTJzw4aqPx/83cODs7g0QWjJ2bp1K2bOnIkVK1age/fu+O677zBw4ED8+eef8Pb2rvD4xMREDBo0CJMmTUJoaCiOHj2KN954A/Xq1cPIkSMFfAL9U1gIZGX9e0tPB1JTgZSUyv+8/9ClaRzMc9DcWvUPdJhjMprXT0Zz6yQ0tr4Fa3kR8BdUtx9EfToiIsNh1s8fzQA0AzDmn23S08D1AnfE3m+G2JymuFpvLC5cAPbuBdLS/n2uQqHa++Prqyo8Li6qW716//79wddOTixEVRFacr744gu8/PLLeOWVVwAAISEhiIiIwMqVK7F06dIKj1+1ahW8vb0REhICAGjZsiVOnz6NZcuW6UXJycwE8vOBkpLH34qLgfx8Gc6dc4aFhQylpYBSWf5WUKB6rcfd7t8vX2iyslTPq4yTeRbcLTLhYXEX3hYZeMbyLtzdMuDhlQFvy9tobpMMF0UW984QEWmRTAY0tE5FQ+tUjKx3CNg6tuy+3Fzgxg3g+nXVHvbr11W3GzdUe4HS04G7d4HS0oqvWbduxZutbfmvra0BS0vVzcrq378/vE2hAMzN//3z4duDbXI5YGamuj3894e/trZW3UQTVnKKiooQExODd955p9z2gIAAHDt2rNLnHD9+HAEBAeW2BQYGYu3atVAqlVAoFBWeU1hYiMLCf89yz8pSXXwuIyMDSqVmZ/0MGybHsWPVuYj0UwCqOkFXgrW16pvuwZ+qv0tl22zOHIe7eR7szHNhZ5MHO9tc2JrnwU6RBzvzPNjJ8+CoyIab5T1YmhU/MU2GHl4AWwkgLy8PdwEozPQvn77gOKmH46QejpP6aj1W/fqV+9Ltn9szD2/c8VPZX0tLVb/Q3r0LZGTIkJ4OZGQAOd/8gNwSa+QUWiEv1xq5t6xwv8QKt0us8VeJFe4XW6PIzRuFhapfhouKVHv+lUrt/Gb7+uslWLTo3zamVCpV43T3bqX/r1ZHzj8nOEnVWTJeEuTmzZsSAOno0aPlti9evFhq1qxZpc9p2rSptHjx4nLbjh49KgGQbt26VelzFixYIAHgjTfeeOONN96M4JacnKx21xB+4rHskeMjkiRV2Pakx1e2/YG5c+ciODi47OvS0lJkZGTA2dn5se+jbdnZ2fDy8kJycjLs7OyE5dB3HCf1cJzUw3FSD8dJfRwr9WhinCRJQk5ODjw9PdV+jrCS4+LiArlcjtTU1HLb09LS4FbFvDp3d/dKH29ubg5nZ+dKn2NpaQlLS8ty2xwcHGoeXMPs7Oz4D0MNHCf1cJzUw3FSD8dJfRwr9dR2nOzt7av1eGEHWy0sLODn54fIyMhy2yMjI9GtW7dKn9O1a9cKj9+/fz86duxY42N8REREZJyEnlEWHByM//3vf/j+++9x8eJFzJo1C0lJSWXXvZk7dy7Gjx9f9vjJkyfjxo0bCA4OxsWLF/H9999j7dq1mD17tqiPQERERHpK6Dk5o0ePxt27d/Hhhx8iJSUFbdq0QVhYGHx8fAAAKSkpSEpKKnt8w4YNERYWhlmzZuHbb7+Fp6cnli9frhfTx6vL0tISCxYsqHAojcrjOKmH46QejpN6OE7q41ipR9Q4ySSpOnOxiIiIiAwDL4BARERERoklh4iIiIwSSw4REREZJZYcIiIiMkosORqQk5ODmTNnwsfHB9bW1ujWrRv++OOPsvtv376NF198EZ6enrCxsUFQUBCuXLnyxNe9d+8epkyZAg8PD1hZWaFly5YICwvT5kfRKm2NU0hICJo3bw5ra2t4eXlh1qxZKKhqpVI9dOjQIQwdOhSenp6QyWTYuXNnufslScIHH3wAT09PWFtbo0+fPrhw4UK5xxQWFmLatGlwcXFBnTp1MGzYMPz9999PfO8VK1agYcOGsLKygp+fHw4fPqzJj6ZRosZp6dKl6NSpE2xtbeHq6orhw4cjISFB0x9PY0R+Pz2wdOlSyGQyzJw5UwOfSDtEjtPNmzcxduxYODs7w8bGBu3atUNMTIwmP57GiBqn4uJizJ8/Hw0bNoS1tTUaNWqEDz/8EKWPrk76JGovAEFVGjVqlNSqVSspOjpaunLlirRgwQLJzs5O+vvvv6XS0lKpS5cuUs+ePaVTp05Jly5dkl599VXJ29tbun//fpWvWVhYKHXs2FEaNGiQdOTIEen69evS4cOHpfj4eB1+Ms3SxjiFhoZKlpaW0o8//iglJiZKERERkoeHhzRz5kwdfrLaCQsLk+bNmydt27ZNAiDt2LGj3P0ff/yxZGtrK23btk06d+6cNHr0aMnDw0PKzs4ue8zkyZOl+vXrS5GRkVJsbKzk7+8vPf3001JxcXGV77tlyxZJoVBIa9askf78809pxowZUp06daQbN25o66PWiqhxCgwMlNatWyedP39eio+PlwYPHvzE70uRRI3TA6dOnZJ8fX2lp556SpoxY4aGP53miBqnjIwMycfHR3rxxRelkydPSomJidJvv/0mXb16VVsftVZEjdNHH30kOTs7S3v27JESExOln3/+Wapbt64UEhJSrfwsObWUl5cnyeVyac+ePeW2P/3009K8efOkhIQECYB0/vz5svuKi4slJycnac2aNVW+7sqVK6VGjRpJRUVFWsuuS9oapylTpkh9+/Ytty04OFjq0aOHZj+Ajjz6Q6S0tFRyd3eXPv7447JtBQUFkr29vbRq1SpJkiTp3r17kkKhkLZs2VL2mJs3b0pmZmbSvn37qnyvzp07S5MnTy63rUWLFtI777yjoU+jPbocp0elpaVJAKTo6OjafxAt0/U45eTkSE2bNpUiIyOl3r1763XJeZgux+ntt9/mzyc1xmnw4MHSSy+9VG7biBEjpLFjx1YrMw9X1VJxcTFKSkpgZWVVbru1tTWOHDmCwsJCACh3v1wuh4WFBY4cOVLl6+7evRtdu3bFlClT4ObmhjZt2mDJkiUoKSnRzgfRMm2NU48ePRATE4NTp04BAK5du4awsDAMHjxYC59C9xITE5GamoqAgICybZaWlujduzeOHTsGAIiJiYFSqSz3GE9PT7Rp06bsMY8qKipCTExMuecAQEBAQJXP0WfaGqfKZGVlAQCcnJw0lF53tD1OU6ZMweDBg9G/f3/tfAAd0eY47d69Gx07dsR///tfuLq6on379lizZo32PowWaXOcevTogQMHDuDy5csAgDNnzuDIkSMYNGhQtTKy5NSSra0tunbtikWLFuHWrVsoKSlBaGgoTp48iZSUFLRo0QI+Pj6YO3cuMjMzUVRUhI8//hipqalISUmp8nWvXbuGX375BSUlJQgLC8P8+fPx+eefY/HixTr8dJqjrXEaM2YMFi1ahB49ekChUKBx48bw9/fHO++8o8NPpz0PFqR9dNFaNze3svtSU1NhYWEBR0fHKh/zqPT0dJSUlDz2dQ2JtsbpUZIkITg4GD169ECbNm00kFy3tDlOW7ZsQWxsLJYuXarh1LqnzXG6du0aVq5ciaZNmyIiIgKTJ0/G9OnTsWHDBg1/Cu3T5ji9/fbbeP7559GiRQsoFAq0b98eM2fOxPPPP1+tjCw5GrBx40ZIkoT69evD0tISy5cvxwsvvAC5XA6FQoFt27bh8uXLcHJygo2NDaKiojBw4EDI5fIqX7O0tBSurq5YvXo1/Pz8MGbMGMybNw8rV67U4SfTLG2MU1RUFBYvXowVK1YgNjYW27dvx549e7Bo0SIdfjLtk8lk5b6WJKnCtkep85iavK4+09Y4PTB16lScPXsWmzdvrnFGfaDpcUpOTsaMGTMQGhpaYW+tIdPG91NpaSk6dOiAJUuWoH379njttdcwadIkg/7Zro1x2rp1K0JDQ7Fp0ybExsbihx9+wLJly/DDDz9UKxtLjgY0btwY0dHRuH//PpKTk3Hq1CkolUo0bNgQAODn54f4+Hjcu3cPKSkp2LdvH+7evVt2f2U8PDzQrFmzcv+Db9myJVJTU1FUVKT1z6QN2hin9957D+PGjcMrr7yCtm3b4rnnnsOSJUuwdOnS6p+Fr4fc3d0BoMJvPGlpaWW/Pbm7u6OoqAiZmZlVPuZRLi4ukMvlj31dQ6KtcXrYtGnTsHv3bhw8eBANGjTQUHLd0tY4xcTEIC0tDX5+fjA3N4e5uTmio6OxfPlymJubG9xhdm1+P3l4eKBVq1bltrVs2bLcOo2GQpvj9NZbb+Gdd97BmDFj0LZtW4wbNw6zZs2q9p5ClhwNqlOnDjw8PJCZmYmIiAg8++yz5e63t7dHvXr1cOXKFZw+fbrC/Q/r3r07rl69Wu5/1JcvX4aHhwcsLCy09hl0QZPjlJeXBzOz8t/Gcrkckuqkeq3k16WGDRvC3d0dkZGRZduKiooQHR2Nbt26AVCVQ4VCUe4xKSkpOH/+fNljHmVhYQE/P79yzwGAyMjIKp+jz7Q1ToDqN86pU6di+/bt+P333x9buvWdtsapX79+OHfuHOLj48tuHTt2xP/93/8hPj7+sXtj9ZE2v5+6d+9e4RIEly9fLluY2pBoc5yq+tnOKeQC7Nu3TwoPD5euXbsm7d+/X3r66aelzp07l82M+umnn6SDBw9Kf/31l7Rz507Jx8dHGjFiRLnXGDduXLlZLUlJSVLdunWlqVOnSgkJCdKePXskV1dX6aOPPtLpZ9MkbYzTggULJFtbW2nz5s1lr9u4cWNp1KhROv1stZGTkyPFxcVJcXFxEgDpiy++kOLi4sqmcn/88ceSvb29tH37duncuXPS888/X+kUzQYNGki//fabFBsbK/Xt27fCFM2+fftKX3/9ddnXD6aQr127Vvrzzz+lmTNnSnXq1JGuX7+uuw9fDaLG6fXXX5fs7e2lqKgoKSUlpeyWl5enuw9fDaLG6VH6PrtK1DidOnVKMjc3lxYvXixduXJF+vHHHyUbGxspNDRUdx++GkSN04QJE6T69euXTSHfvn275OLiIs2ZM6da+VlyNGDr1q1So0aNJAsLC8nd3V2aMmWKdO/evbL7v/rqK6lBgwaSQqGQvL29pfnz50uFhYXlXqN3797ShAkTym07duyY9Mwzz0iWlpZSo0aNpMWLF6t1nQp9pY1xUiqV0gcffCA1btxYsrKykry8vKQ33nhDyszM1NGnqr2DBw9KACrcHnzO0tJSacGCBZK7u7tkaWkp9erVSzp37ly518jPz5emTp0qOTk5SdbW1tKQIUOkpKSkco/x8fGRFixYUG7bt99+K/n4+EgWFhZShw4d9HpatKhxquw9AUjr1q3T8ieuGZHfTw/T95Ijcpx+/fVXqU2bNpKlpaXUokULafXq1dr8qLUiapyys7OlGTNmSN7e3pKVlZXUqFEjad68eRX+n/AkMkkygn36RERERI/gOTlERERklFhyiIiIyCix5BAREZFRYskhIiIio8SSQ0REREaJJYeIiIiMEksOERERGSWWHCIiIjJKLDlERERklFhyiIiIyCix5BAREZFRYskhIoN3584duLu7Y8mSJWXbTp48CQsLC+zfv19gMiISiQt0EpFRCAsLw/Dhw3Hs2DG0aNEC7du3x+DBgxESEiI6GhEJwpJDREZjypQp+O2339CpUyecOXMGf/zxB6ysrETHIiJBWHKIyGjk5+ejTZs2SE5OxunTp/HUU0+JjkREAvGcHCIyGteuXcOtW7dQWlqKGzduiI5DRIJxTw4RGYWioiJ07twZ7dq1Q4sWLfDFF1/g3LlzcHNzEx2NiARhySEio/DWW2/hl19+wZkzZ1C3bl34+/vD1tYWe/bsER2NiATh4SoiMnhRUVEICQnBxo0bYWdnBzMzM2zcuBFHjhzBypUrRccjIkG4J4eIiIiMEvfkEBERkVFiySEiIiKjxJJDRERERoklh4iIiIwSSw4REREZJZYcIiIiMkosOURERGSUWHKIiIjIKLHkEBERkVFiySEiIiKjxJJDRERERun/AUilgEb8GGUKAAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# the histogram of the bootstrapped data (normalized data if density = True)\n", "n, binsboot, patches = plt.hist(t, 50, density=True, facecolor='red', alpha=0.75)\n", @@ -1464,7 +1439,7 @@ }, { "cell_type": "markdown", - "id": "381ecffe", + "id": "13cee84b", "metadata": { "editable": true }, @@ -1482,7 +1457,7 @@ }, { "cell_type": "markdown", - "id": "292b352a", + "id": "68a2b1e6", "metadata": { "editable": true }, @@ -1494,7 +1469,7 @@ }, { "cell_type": "markdown", - "id": "3578051e", + "id": "9a1b0f82", "metadata": { "editable": true }, @@ -1511,7 +1486,7 @@ }, { "cell_type": "markdown", - "id": "c1f692ee", + "id": "7c8d8230", "metadata": { "editable": true }, @@ -1523,7 +1498,7 @@ }, { "cell_type": "markdown", - "id": "84cbcb60", + "id": "ec3262b6", "metadata": { "editable": true }, @@ -1533,7 +1508,7 @@ }, { "cell_type": "markdown", - "id": "5c74387c", + "id": "3a88d244", "metadata": { "editable": true }, @@ -1545,7 +1520,7 @@ }, { "cell_type": "markdown", - "id": "9ec1eaac", + "id": "04c0c8a6", "metadata": { "editable": true }, @@ -1562,7 +1537,7 @@ }, { "cell_type": "markdown", - "id": "25e440c0", + "id": "7a471b5b", "metadata": { "editable": true }, @@ -1574,7 +1549,7 @@ }, { "cell_type": "markdown", - "id": "3cd51995", + "id": "6af7ebcb", "metadata": { "editable": true }, @@ -1584,7 +1559,7 @@ }, { "cell_type": "markdown", - "id": "e1936a99", + "id": "1b902364", "metadata": { "editable": true }, @@ -1596,7 +1571,7 @@ }, { "cell_type": "markdown", - "id": "df781675", + "id": "09231540", "metadata": { "editable": true }, @@ -1606,7 +1581,7 @@ }, { "cell_type": "markdown", - "id": "5106ccfd", + "id": "a86940ba", "metadata": { "editable": true }, @@ -1618,7 +1593,7 @@ }, { "cell_type": "markdown", - "id": "24fd2b68", + "id": "86fd6ba8", "metadata": { "editable": true }, @@ -1628,7 +1603,7 @@ }, { "cell_type": "markdown", - "id": "a38da956", + "id": "c9f568ef", "metadata": { "editable": true }, @@ -1644,7 +1619,7 @@ }, { "cell_type": "markdown", - "id": "d5d994f7", + "id": "3b31cafa", "metadata": { "editable": true }, @@ -1655,36 +1630,12 @@ { "cell_type": "code", "execution_count": 3, - "id": "c0b43b12", + "id": "9e45cee4", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error: 0.013121574062587286\n", - "Bias^2: 0.012073649469946107\n", - "Var: 0.0010479245926411787\n", - "0.013121574062587286 >= 0.012073649469946107 + 0.0010479245926411787 = 0.013121574062587286\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1744,7 +1695,7 @@ }, { "cell_type": "markdown", - "id": "7873e4da", + "id": "724a308d", "metadata": { "editable": true }, @@ -1755,102 +1706,12 @@ { "cell_type": "code", "execution_count": 4, - "id": "4e70511b", + "id": "50397594", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polynomial degree: 0\n", - "Error: 0.32149601703519115\n", - "Bias^2: 0.3123314713548606\n", - "Var: 0.009164545680330616\n", - "0.32149601703519115 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n", - "Polynomial degree: 1\n", - "Error: 0.08426840630693412\n", - "Bias^2: 0.0796891867672603\n", - "Var: 0.004579219539673834\n", - "0.08426840630693412 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n", - "Polynomial degree: 2\n", - "Error: 0.10398646080125037\n", - "Bias^2: 0.10077114273548984\n", - "Var: 0.0032153180657605116\n", - "0.10398646080125037 >= 0.10077114273548984 + 0.0032153180657605116 = 0.10398646080125036\n", - "Polynomial degree: 3\n", - "Error: 0.06547790180152352\n", - "Bias^2: 0.062082386342319454\n", - "Var: 0.0033955154592040923\n", - "0.06547790180152352 >= 0.062082386342319454 + 0.0033955154592040923 = 0.06547790180152355\n", - "Polynomial degree: 4\n", - "Error: 0.06844519414009445\n", - "Bias^2: 0.06453579006728322\n", - "Var: 0.003909404072811221\n", - "0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444\n", - "Polynomial degree: 5\n", - "Error: 0.05227921801205679\n", - "Bias^2: 0.04818727730430286\n", - "Var: 0.004091940707753925\n", - "0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679\n", - "Polynomial degree: 6\n", - "Error: 0.03781367141738902\n", - "Bias^2: 0.03365768507152769\n", - "Var: 0.0041559863458613296\n", - "0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902\n", - "Polynomial degree: 7\n", - "Error: 0.027609773491022394\n", - "Bias^2: 0.022999498260366198\n", - "Var: 0.004610275230656182\n", - "0.027609773491022394 >= 0.022999498260366198 + 0.004610275230656182 = 0.02760977349102238\n", - "Polynomial degree: 8\n", - "Error: 0.017355848195593312\n", - "Bias^2: 0.010331721306655165\n", - "Var: 0.007024126888938144\n", - "0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331\n", - "Polynomial degree: 9\n", - "Error: 0.026605727637184558\n", - "Bias^2: 0.010018312644139219\n", - "Var: 0.016587414993045335\n", - "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n", - "Polynomial degree: 10\n", - "Error: 0.021592704588021178\n", - "Bias^2: 0.010516485576646504\n", - "Var: 0.01107621901137467\n", - "0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174\n", - "Polynomial degree: 11\n", - "Error: 0.07160048164232538\n", - "Bias^2: 0.014436800088896381\n", - "Var: 0.05716368155342902\n", - "0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254\n", - "Polynomial degree: 12\n", - "Error: 0.11547777218876518\n", - "Bias^2: 0.016285782696017142\n", - "Var: 0.09919198949274803\n", - "0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518\n", - "Polynomial degree: 13\n", - "Error: 0.2284246870217162\n", - "Bias^2: 0.01975416527168255\n", - "Var: 0.20867052175003364\n", - "0.2284246870217162 >= 0.01975416527168255 + 0.20867052175003364 = 0.2284246870217162\n" - ] - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1902,7 +1763,7 @@ }, { "cell_type": "markdown", - "id": "092a6ff8", + "id": "660c0100", "metadata": { "editable": true }, @@ -1940,7 +1801,7 @@ }, { "cell_type": "markdown", - "id": "152432a6", + "id": "d04f669b", "metadata": { "editable": true }, @@ -1967,26 +1828,12 @@ { "cell_type": "code", "execution_count": 5, - "id": "d18911d3", + "id": "f3e8b5af", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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", 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ivs+ZMWMGDhw4gIiICOzZswcHDx5EVFRUiWonIguqUgVo21bpKixj7lygenXZY7V4sdLVkJVj2FG5nTt3YvHixdi2bRu6d++ea//JkyfNbpbZpk0bAMCmTZuQlpaGL7/8Ek2aNMGTTz6J8PBwbNiwAf/++2+u17l79y4+++wzvPfeewgODkZAQAC++OILGAyGUj9GIsqH0Sj/+PryS3lXczVxcQFWrJDr774L/P23svWQVeO9sYqrgNM5uc6FJyTk39YmR968dKnYJeWladOmuHnzJubOnYvWrVvD1dXVbH+DBg2wfft202OtVgsAOHPmDJo1awZnZ2fTvg4dOsBoNOLcuXPw9vY2e52///4ber0e7du3N23z8PBAgwYNLHo8RFQEa9YA338P7NwJdOsme0LUZOBAIDgYOHwY+O9/5RVaRHlg2CmubCFAsbaFUL16dXz33Xfo2rUrevXqhV27dpkFHgcHhzxv1CmEgEajyfM189ou1PZXI1F5d+mSvN8VIHs+1BZ0AHll1tq18g9MPz+lqyErxtNYFUDNmjVx6NAhJCQkoEePHkguxL1wGjVqhOjoaKRmGxz966+/wsbGBo899liu9vXr14e9vT2OHj1q2paYmIjz589b5iCIqPCEkPPppKYCTzwBFDDWrtyrXZtBhx6JYaeCqFGjBg4ePIhbt26hR48eSEpKKrD90KFD4ejoiBEjRuDUqVM4cOAAJk2ahGHDhuU6hQUALi4uGDVqFGbMmIH9+/fj1KlTGDlyJGxynqYjotK3di3w88+ATgd89lnu0+Vq9euvwO7dSldBVqiC/AsgQJ7SOnToEO7cuYPg4GDcuXMn37ZOTk7YvXs3bt++jdatW2PgwIHo1q0bwsPD833O0qVL0alTJ/Tv3x/du3dHx44dERgYWApHQkT5unwZmD5dri9aBPj7K1tPWfnuO6BjR2DMGF6KTrloBAdbIDk5Ge7u7khKSoKbm5vZvvv37+PixYuoU6cOHB0dFaqQKhr+3FGxrV0LjB0LdOgAHDpUficPLKq0NKBBAyA2Voa8OXOUrojKQEGf39mxZ4eISE1eeUWGnHXrKk7QAeQpu8xbYISFFXwVLFU4DDtERGrTqVPFOX2V3ZAhQMuWQEoKJxokMww7RETlnRDytM3//qd0JcqysQEWLpTrq1YB164pWw9ZDYadQuLQJipL/HmjIvn8czmXTtu2BU94WhH07CkHKqenA++8o3Q1ZCUYdh7B3t4egLxXFFFZyfx5y/z5I8pXbCwwdapcf+21Et9Lr9zTaGTvjp+feu4DRiXGGZQfwdbWFpUqVULCw8FuTk5O+c4sTFRSQgjcu3cPCQkJqFSpEmwr0gBTKjoh5IDk5GSgXTsgNFTpiqxDp07yXln8Y4EeYtgpBB8fHwAwBR6i0lapUiXTzx1RvtavB3btArTainf11aMw6FA2DDuFoNFoUK1aNVStWhUZGRlKl0MqZ29vzx4derSrV4FXX5Xrb78NNGyobD3W6MEDYMMG4ORJYPlypashBTHsFIGtrS0/hIjIOixdCiQlAW3aZI3ZIXPnzgEvvyzXR40CGjdWth5SDAcoExGVR0uXAvPn8/RVQRo3Bp57Tq5nXpJOFRJvF4HCTzdNRETlTHQ00KKFvErr9Gme7lMZ3i6CiEhthAA2bQL0eqUrKT+aNwf695ffu8zbSVCFo2jYefDgAd544w3UqVMHOp0OdevWxYIFC2A0Gk1thBCYP38+fH19odPp0KVLF8TExJi9Tnp6OiZNmgQvLy84Ozujf//+uHr1alkfDhFR6dq4EXjxReCJJwCDQelqyo8335RfN23iLNMVlKJhZ/Hixfj4448RHh6OM2fOYMmSJVi6dCk+/PBDU5slS5Zg+fLlCA8Px/Hjx+Hj44Pg4GCkpKSY2oSGhiIiIgJbtmzBkSNHcPfuXfTt2xcG/jIgIrWIiwMmT5brAwZwnE5RtGoF9OkDGI3yJqFU4Sg6Zqdv377w9vbGZ599Ztr23HPPwcnJCRs2bIAQAr6+vggNDcWsWbMAyF4cb29vLF68GGPHjkVSUhKqVKmCDRs2ICQkBABw/fp1+Pn5YceOHejZs2eu901PT0d6errpcXJyMvz8/Dhmh4iskxAy4Pzf/wGBgcDRo4AdL6YtkqNH5QzT8+YBnTsrXQ1ZSLkYs9OxY0fs378f58+fBwCcOHECR44cQZ8+fQAAFy9eRHx8PHr06GF6jlarRefOnREZGQkAiIqKQkZGhlkbX19fNGnSxNQmp7CwMLi7u5sWPz+/0jpEIqKS27xZBh17e3n1FYNO0bVrB/z8M4NOBaXov5hZs2YhKSkJDRs2hK2tLQwGAxYuXIgXXngBABAfHw8A8Pb2Nnuet7c3Ll++bGrj4OCAypUr52qT+fyc5syZg6nZ5qXI7NkhIrI68fHApElyfe5cICBA2XqIyiFFw87WrVuxceNGbN68GY0bN0Z0dDRCQ0Ph6+uLESNGmNrlvBeVEOKR96cqqI1Wq4VWqy35ARARlbZXXwUSE+Xl0w9P51MJ3LgBLFsmTw0uXqx0NVRGFA07M2bMwOzZs/H8888DAAICAnD58mWEhYVhxIgRpnsDxcfHo1q1aqbnJSQkmHp7fHx8oNfrkZiYaNa7k5CQgKCgoDI8GiKiUrBgAZCQAKxYwfs9WcKpUzLkODjIAd/VqytdEZUBRcfs3Lt3DzY25iXY2tqaLj2vU6cOfHx8sHfvXtN+vV6PQ4cOmYJMYGAg7O3tzdrExcXh1KlTDDtEVP75+wP79wNNmypdiTp06QJ07CjnKlq2TOlqqIwoGnb69euHhQsX4qeffsKlS5cQERGB5cuX45lnngEgT1+FhoZi0aJFiIiIwKlTpzBy5Eg4OTlhyJAhAAB3d3eMGjUK06ZNw/79+/Hnn3/ixRdfREBAALp3767k4RERFY8QcrZfsjyNBnjjDbn+8cfytBapn1BQcnKymDJliqhZs6ZwdHQUdevWFa+//rpIT083tTEajWLevHnCx8dHaLVa0alTJ3Hy5Emz10lLSxMTJ04UHh4eQqfTib59+4orV64Uuo6kpCQBQCQlJVns2IiIim3rViE0GiGmT1e6EnUyGoVo1UoIQIg5c5SuhkqgsJ/fvDcWeG8sIrIi//4rb2B565a8+uqtt5SuSJ22bQOefhpwdQUuXwZyXNFL5UO5mGeHiIiyEQIYO1YGnebNgddfV7oi9erXT17Gn5ICZJu1n9SJM1MREVmLjRtlj4O9PfDll/KKISodNjZyNuUDB4BsU52QOjHsEBFZg2vXsiYPnD+fkweWheeekwupHk9jEREpTQhg9GggKQlo0waYOVPpiiomDmFVLYYdIiJr8PzzgLc3sH49731V1v78Uw5WXrVK6UqolDDsEBEpTaOR40YuXQIef1zpaiqe//5XjpVavBhIT1e6GioFDDtEREoxGoHbt7MeOzoqV0tFNnIk4OsLXL0qB4aT6jDsEBEpZdkyoEkTYN8+pSup2BwdgRkz5HpYGPDggbL1kMUx7BARKeGPP+Q8OnFxclI7UtaYMUCVKsDFi8BXXyldDVkYww4RUVlLTQWGDAEyMoBnnwVeflnpisjZGZg6Va4vWgQYDMrWQxbFsENEVNamTQPOnQOqVwc++UQOUCbljR8PVKoEnD0LREQoXQ1ZEK9vJCIqSz/8AKxZIwPOl18CHh5KV0SZ3NyABQvkfDtPPaV0NWRBDDtERGXl+nU5eSAATJ8OPPmksvVQbpmzWJOqMOwQEZUVNzegZ0/gzBngnXeUroYexWiUPXA8zVjuccwOEVFZcXGRN/v8+Wfe5NPaffONnBZg/36lKyELYNghIiptf/+ddd8ljUYOgiXrduQIe+BUhGGHiKg0Xb4MtG4NPPMMkJysdDVUWDNmAPb2wKFDMvhQucawQ0RUWvR6YPBgIDFRDk7WapWuiAqrRg3gpZfk+sKFytZCJcawQ0RUWqZPB44dAypXBr7+mmGnvJk1C7C1BXbtAn7/XelqqAQYdoiISsMXXwAffijXv/wSqF1b0XKoGOrWBYYOlevs3SnXGHaIiCzt6FHglVfk+ty5QN++ytZDxTdnjhxU/sMPctZrKpc4zw4RkSVlZMj7Xun1clDyvHlKV0Ql0bAh8NZbQNu2wGOPKV0NFRPDDhGRJdnby7tmz50rT2XZsAO93HvzTaUroBJi2CEisrS2bYHdu5WugkqDXs8JIcsh/slBRGQJYWG8YkfNjEbZW1e9OnDpktLVUBEx7BARldSqVcBrrwFdugBxcUpXQ6XBxkYOPL95UwZbKlcYdoiISmLbNmDyZLk+Zw5QrZqy9VDpmTtXfv38c+Cff5SthYqEYYeIqLh++QV44QV5imPMGNm7Q+rVsaO8a/2DB8D8+UpXQ0XAsENEVBzHjgFPPQWkpcmvq1fL+VhI3TInF9y4EYiJUbYWKjSGHSKiojp7Vv6Ff/cu8OSTwDffAHa8uLVCCAwEnn1W3sU+87QWWT2GHSKioqpVC+jQAQgKkmN2dDqlK6KytGCB7MX7v/8Drl5VuhoqBP4pQkRUVDod8P33wP37gIuL0tVQWWvcGPj4Y6BbN3l3dLJ67NkhIiqMqCg5AFkI+djBAXBzU7YmUs4rrwD16ildBRUSe3aIiB7lyBE5CDk5Wf4lP3680hWRNTl/HvD35wB1K8aeHSKiguzcKQcjJycDnToBw4YpXRFZCyGAUaPkzUJ//FHpaqgADDtERPn56COgb1/g3j0ZeHbuBFxdla6KrIVGA3h5ydAzc6acf4esEsMOEVFORiMwfbo8XWU0AiNHAtu3A05OSldG1mbOHMDTU05H8NlnSldD+WDYISLKKToaWLlSrr/zjrw9AO90TXmpVAmYN0+uz50LpKQoWg7ljWGHiCinli2BtWuBzZuB11/nwFMq2NixcoByQgKwdKnS1VAeGHaIiABg0ybg5Mmsxy+/LO97RfQoDg7Au+/K9ffeA65dU7YeyoVhh4gqtpQUYPRo4MUX5W0AkpOVrojKo2eekbNqu7jIS9HJqnCeHSKquA4dkoOPL12Sp6peeAFwdla6KiqPNBpgwwY5WJmTTVodhh0iqnhSU4E335SDkIUAatcG1q8HOndWuDAq1+rUUboCygfDDhFVLNevA+3aAbGx8vHo0cDy5Zw/hyxHCGDrVsDODhg4UOlqCAw7RFTRVKsGNGgA2NoC4eHyNhBElrR5sxwDVrUqEBwMuLsrXVGFxwHKRKRusbFycsCEBPlYowG+/BI4fZpBh0rHoEEyUCckAG+9pXQ1BIYdIlKra9eAV18F6teXt30IC8vaV60aoNMpVxupm4MD8MEHcv3DD2WwJkXxNFZpCw+X526BrInJsn/94gugVi35eN06YOPG/NuuWiUnrgKAr7+W7TP3ZW+n0cg5Hxo3ltt+/BH49NP8X/eNN4AWLeTjn38GPv44/7avvgq0aSMf//abrCm/tmPGAB07yscnT8rXtbWVi41N1rqtLdC/f9brXr4MbNmSu13mevv2QNOmsu3t28C+ffKXS16Lry/g4yPbPngg22ffb2vLCePU5sQJYNky4Kuvsu5V1LkzMHiwsnVRxdKjB/D008APPwDjxgEHD8rfYaQIhp3S9s8/wJEj+e9PSzNv+/PP+bfNPg35338Du3bl33bmTPPX3bYt/7ZjxmStX7wIfPNN/m0HDcoKJZcuyYnY8tO9e1bYuXgRWL06/7Y1amS97oULwOzZ+bd9772ssHP+PBASkn/bt96SU7gD8t41AQHm+zUaGXqcnIDQ0Ky28fHA0KFye15L+/ZAr16ybUYGEBkpz8u7uWV9tbfPvy4qHYMGAd9+m/X4iSdkmA8OZqilsvf++8DevcDhw/K+Wdl/11KZYtgpbSNHyg98IeTjnF99fbPaDhoke2OEMG+XuZ7ZAwTIOzH7+ub/upk9QADw5JNy6vv82mb2AAHyQzw8PP+2mSEDkL1By5fn3VYIOeV+pscek5f6Ggzyxoo5v2YPIdWqye9b5v6cbbMfm7Oz/Ktdr5dLRkbWul4PVK6c1TYjA7kIAaSnyyX7HYvv3Ck4eIaGZoWdGzeALl1yt9HpZPAZOTLrFIpeL4Ool5dcPD3N1z09Aa02//elLAYD8OuvQFCQvOoFkL14trbyCphp04DWrZWtkSq2mjWBt98Gpk4FZs0Cnn+eV/0pRCNE5idUxZWcnAx3d3ckJSXBjZNBqZsQMvRkD0X378setkqVsk55JSUBO3YA9+7lvTz5JPDcc7LtpUsy+CQny+fdu2f+nlOmZN1U8vp1oHr1/OsbNkwOngVkjSEhsiYfHxkCM9d9fABv74p3c8rkZHk6YNs2eRfymzeBAweywmZsrPx/XLOmklUSZXnwABg+XJ7K6tRJ6WpUp7Cf3+zZoYol87SVg0PBM+W6uxf+vki1a8tTZJkyMuQpx6QkuVSqlLXPwUH+hXfzJnDrlvyauX7rluzhyZSQAERE5P++L74oZ2wFZGh7+WXzMJR98fAov+MFrl6Vp0B//hn4/XfZo5OpcmW5P5OfX9nXR1QQOzt5KTopimGHyNLs7WW48PDIvc/LK+uGgTkZjean2pyd5QDw+PisJS4ua71atay2CQkFj58aPlwOhgdkMJowIf9gpMTtEm7fluOvMpegIKBPH7nvzh3zK6nq1pX7nnlGjsnh2CgqTy5ckP/OeDqrTDHsEFkLGxvz8TqVKsn5YfKSMxg5OQFLl5oHo8zl1i15yivTv/9mXZ2Xl7Fjs67IS02Vp9IqVZK9KJUqycHXjo5yadRI3vwQkCFq/35ZW/YxVmlpwN278hLwbt1k25s35biazACX8+abI0ZkhZ2GDWWvVadOQNeuPEVF5deGDfLf1/DhWf/GqEww7BCVRzmDkYcHMH163m0zB25n0unkoMmcoSguTo43cnHJanv7NvDTT/nXMWZMVthJSckKKHkZPjwr7Li4yJtwZlejhhzI/thjWVfxAfI0wGef5f+6ROVFjRoy/K9ZAzz7rLw8ncoEww6R2mWOUcrk5SUvx85JCNkDYzRmbXN3l71Ad+4AiYnya3KyvHrt/n2geXPz92nZUgax7Iujo+yyz351nqOjnOKgShXZpV+jBu82TurXtSswcaK84nXUKDn/WPYxfVRqeDUWeDUWERGVkdRU+UfC//4np6XInByWiqWwn9/l9PIMIiKicsjZGVi/Xl4Zun498H//p3RFFQLDDhERUVnq0EFONAjI01n//qtsPRUAx+wQERGVtXfekRNi9u8vZ06nUsWwQ0REVNYcHeXNlCvaLOgKUfw01rVr1/Diiy/C09MTTk5OaN68OaKiokz7hRCYP38+fH19odPp0KVLF8TExJi9Rnp6OiZNmgQvLy84Ozujf//+uJp9VlUiIiJrkz3opKfLGzxTqVA07CQmJqJDhw6wt7fHzp07cfr0aSxbtgyVsl2Kt2TJEixfvhzh4eE4fvw4fHx8EBwcjJRsdwAPDQ1FREQEtmzZgiNHjuDu3bvo27cvDNmnlSciIrJGly4B7doBPXvmnmCTLELRS89nz56NX3/9FYcPH85zvxACvr6+CA0NxaxZswDIXhxvb28sXrwYY8eORVJSEqpUqYINGzYgJCQEAHD9+nX4+flhx44d6Nmz5yPr4KXnRESkmMREeTn6lStyssFvv5VXa9EjlYtLz7dv345WrVph0KBBqFq1Klq0aIFPPvnEtP/ixYuIj49Hj2yzTGq1WnTu3BmRkZEAgKioKGRkZJi18fX1RZMmTUxtckpPT0dycrLZQkREpIjKlYGvv5b3efv+e2DZMqUrUh1Fw84///yDjz76CP7+/ti9ezfGjRuHyZMn48svvwQAxMfHAwC8s9/X5+HjzH3x8fFwcHBA5cqV822TU1hYGNzd3U2LH++UTERESmrbFnj/fbk+e3bu26lQiSgadoxGI1q2bIlFixahRYsWGDt2LMaMGYOPPvrIrJ0mR3eeECLXtpwKajNnzhwkJSWZltjY2JIdCBERUUmNGwcMGyZvohsSAly/rnRFqqFo2KlWrRoaNWpktu3xxx/HlStXAAA+Pj4AkKuHJiEhwdTb4+PjA71ej8TExHzb5KTVauHm5ma2EBERKUqjkXdDDwiQEw1OnKh0RaqhaNjp0KEDzp07Z7bt/PnzqFWrFgCgTp068PHxwd69e0379Xo9Dh06hKCgIABAYGAg7O3tzdrExcXh1KlTpjZERETlgpOTHLfTtWvWaS0qMUUnFXz11VcRFBSERYsWYfDgwTh27BjWrl2LtWvXApCnr0JDQ7Fo0SL4+/vD398fixYtgpOTE4YMGQIAcHd3x6hRozBt2jR4enrCw8MD06dPR0BAALp3767k4RERERVd/frAzz8rXYWqKBp2WrdujYiICMyZMwcLFixAnTp1sHLlSgwdOtTUZubMmUhLS8P48eORmJiItm3bYs+ePXB1dTW1WbFiBezs7DB48GCkpaWhW7duWL9+PWxtbZU4LCIiIsv5/nt5pVa/fkpXUm4pOs+OteA8O0REZJV27QJ695Z3S4+MBJo2Vboiq1Iu5tkhIiKiAnTrJpfUVNmzwzukFwvDDhERkbWyt5cTDtavL2dY7tsXuHtX6arKHYYdIiIia+bhAezYAXh5Ab//DgwaBGRkKF1VucKwQ0REZO38/YEff5SXpu/aBbzyCsAht4XGsENERFQetG0rT2nZ2gJVqjDsFIGil54TERFRETz1FHDiBNC4sdKVlCvs2SEiIipPsgedtDROQFgIDDtERETl0b17QJ8+QI8ewPbtSldj1Rh2iIiIyiNHR8DPT94lffBgYP9+pSuyWgw7RERE5ZGNDfD558AzzwDp6cCAAcDRo0pXZZUYdoiIiMorOzvgq6+A4GA5y3Lv3nIAM5lh2CEiIirPtFogIgIICgLu3JFjeM6eVboqq8KwQ0REVN45OwM//QS0aAHo9bKXh0w4zw4REZEaVKoE7NkDXLsGNGumdDVWhWGHiIhILby85JLp2DHA0xOoV0+5mqwAT2MRERGp0fHjQPfuQNeuwD//KF2Nohh2iIiI1MjPD6heHYiNlYHn0iWlK1IMww4REZEa+fjIW0k89hhw5YoMPJcvK12VIhh2iIiI1KpaNeDAAcDfX/bsdO0qe3oqGIYdIiIiNfP1lYGnXj3g4kWgSxcgLk7pqsoUww4REZHaVa8uA0/dukCDBoCHh9IVlSleek5ERFQR+PkBv/wiL0XXapWupkyxZ4eIiKiiqF5d3i0dAIQAFi2qEJelM+wQERFVRMuXA6+/Lsfw/P230tWUKoYdIiKiimjIEKBhQ3l1VufOwIULSldUahh2iIiIKqLMy9IbNZL30+rcGTh3TumqSgXDDhERUUXl4yMDT5Mm8nL0zp2B06eVrsriGHaIiIgqsqpVZeBp2hT49195P627d5WuyqIYdoiIiCo6Ly95a4nAQODddwEXF6UrsijOs0NERERy/p2jRwG7bNFACECjUa4mC2HPDhEREUnZg058PNCxIxAVpVw9FsKwQ0RERLnNng1ERsoxPMePK11NiRQ77Dx48AD79u3DmjVrkJKSAgC4fv067qpsUBMREVGF9MEHQFAQcOeODDxHjypdUbEVK+xcvnwZAQEBGDBgACZMmIAbN24AAJYsWYLp06dbtEAiIiJSgJsbsGsX8MQTQHIy0KOH7Okph4oVdqZMmYJWrVohMTEROp3OtP2ZZ57B/v37LVYcERERKcjVFdi5U95SIiUF6NkTOHJE6aqKrFhh58iRI3jjjTfg4OBgtr1WrVq4du2aRQojIiIiK+DsDPz0E9Ctm5x/Z+JEwGhUuqoiKVbYMRqNMBgMubZfvXoVrq6uJS6KiIiIrIiTE/B//weMHi2/2pSv65uKVW1wcDBWrlxpeqzRaHD37l3MmzcPffr0sVRtREREZC10OuCTTwA/v6xt5eRsTrHCzooVK3Do0CE0atQI9+/fx5AhQ1C7dm1cu3YNixcvtnSNREREZG2++QaoVw/46iulK3mkYs2g7Ovri+joaGzZsgVRUVEwGo0YNWoUhg4dajZgmYiIiFRq714gPR0YOhS4dw8YNUrpivKlEUIIpYtQWnJyMtzd3ZGUlAQ3NzelyyEiIrJ+RqMcrPzRR/Lx++8DkyeXaQmF/fwu1mmsL774Aj/99JPp8cyZM1GpUiUEBQXh8uXLxXlJIiIiKk9sbIBVq4DM+fWmTAHCwpStKR/FCjuLFi0yna767bffEB4ejiVLlsDLywuvvvqqRQskIiIiK6XRAEuWAPPny8evvQa8/rq8gagVKdaYndjYWNSvXx8A8MMPP2DgwIF45ZVX0KFDB3Tp0sWS9REREZE102iAefPkfDwzZsjJB61MsXp2XFxccOvWLQDAnj170L17dwCAo6Mj0tLSLFcdERERlQ/TpwP79wMrV8oAZEWKPc/O6NGjMXr0aJw/fx5PPfUUACAmJga1atWyaIFERERUTjz5ZNaEg+npwBtvWEVPT7HCzqpVq9C+fXvcuHED3333HTw9PQEAUVFRGDJkiEULJCIionLoP/8BFi6UAejhDcOVUuxLz+/fv4+//voLCQkJMOa4R0b//v0tUlxZ4aXnREREFnbsGNCnD3DrFtCqlXxs4dNbhf38LtYA5V27dmH48OG4desWcmYljUaT532ziIiIqAJp0wb49Vegf39g6VJFx/EU6zTWxIkTMWjQIFy/fh1Go9FsYdAhIiIiAECDBkBMDKDwldrFCjsJCQmYOnUqvL29LV0PERERqYldsU4iWVSxws7AgQNx8OBBC5dCREREZHnFGqB87949DBo0CFWqVEFAQADs7e3N9k8u43tjlBQHKBMREZU/pTpAefPmzdi9ezd0Oh0OHjwITbZBRxqNptyFHSIiIlKvYoWdN954AwsWLMDs2bNhY1OsM2FEREREZaJYSUWv1yMkJIRBh4iIiKxesdLKiBEjsHXrVkvXQkRERGRxxTqNZTAYsGTJEuzevRtNmzbNNUB5+fLlFimOiIiIqKSKFXZOnjyJFi1aAABOnTpltk9jZXc6JSIiooqtWGHnwIEDlq6DiIiIqFRwhDERERGpGsMOERERqZrVhJ2wsDBoNBqEhoaatgkhMH/+fPj6+kKn06FLly6IiYkxe156ejomTZoELy8vODs7o3///rh69WoZV09ERETWyirCzvHjx7F27Vo0bdrUbPuSJUuwfPlyhIeH4/jx4/Dx8UFwcDBSUlJMbUJDQxEREYEtW7bgyJEjuHv3Lvr27cu7rxMREREAKwg7d+/exdChQ/HJJ5+gcuXKpu1CCKxcuRKvv/46nn32WTRp0gRffPEF7t27h82bNwMAkpKS8Nlnn2HZsmXo3r07WrRogY0bN+LkyZPYt2+fUodEREREVkTxsDNhwgQ89dRT6N69u9n2ixcvIj4+Hj169DBt02q16Ny5MyIjIwEAUVFRyMjIMGvj6+uLJk2amNrkJT09HcnJyWYLERERqVOxLj23lC1btuCPP/7A8ePHc+2Lj48HAHh7e5tt9/b2xuXLl01tHBwczHqEMttkPj8vYWFheOutt0paPhEREZUDivXsxMbGYsqUKdi4cSMcHR3zbZdzkkIhxCMnLnxUmzlz5iApKcm0xMbGFq14IiIiKjcUCztRUVFISEhAYGAg7OzsYGdnh0OHDuGDDz6AnZ2dqUcnZw9NQkKCaZ+Pjw/0ej0SExPzbZMXrVYLNzc3s4WIiIjUSbGw061bN5w8eRLR0dGmpVWrVhg6dCiio6NRt25d+Pj4YO/evabn6PV6HDp0CEFBQQCAwMBA2Nvbm7WJi4vDqVOnTG2IiIioYlNszI6rqyuaNGlits3Z2Rmenp6m7aGhoVi0aBH8/f3h7++PRYsWwcnJCUOGDAEAuLu7Y9SoUZg2bRo8PT3h4eGB6dOnIyAgINeAZyIiIqqYFB2g/CgzZ85EWloaxo8fj8TERLRt2xZ79uyBq6urqc2KFStgZ2eHwYMHIy0tDd26dcP69etha2urYOVERERkLTRCCKF0EUpLTk6Gu7s7kpKSOH6HiIionCjs57fi8+wQERERlSaGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1hh0iIiJSNYYdIiIiUjWGHSIiIlI1RcNOWFgYWrduDVdXV1StWhVPP/00zp07Z9ZGCIH58+fD19cXOp0OXbp0QUxMjFmb9PR0TJo0CV5eXnB2dkb//v1x9erVsjwUIiIislKKhp1Dhw5hwoQJOHr0KPbu3YsHDx6gR48eSE1NNbVZsmQJli9fjvDwcBw/fhw+Pj4IDg5GSkqKqU1oaCgiIiKwZcsWHDlyBHfv3kXfvn1hMBiUOCwiIiKyIhohhFC6iEw3btxA1apVcejQIXTq1AlCCPj6+iI0NBSzZs0CIHtxvL29sXjxYowdOxZJSUmoUqUKNmzYgJCQEADA9evX4efnhx07dqBnz56PfN/k5GS4u7sjKSkJbm5upXqMREREZBmF/fy2qjE7SUlJAAAPDw8AwMWLFxEfH48ePXqY2mi1WnTu3BmRkZEAgKioKGRkZJi18fX1RZMmTUxtckpPT0dycrLZQkREROpkNWFHCIGpU6eiY8eOaNKkCQAgPj4eAODt7W3W1tvb27QvPj4eDg4OqFy5cr5tcgoLC4O7u7tp8fPzs/ThEBERkZWwmrAzceJE/PXXX/jqq69y7dNoNGaPhRC5tuVUUJs5c+YgKSnJtMTGxha/cCIiIrJqVhF2Jk2ahO3bt+PAgQOoUaOGabuPjw8A5OqhSUhIMPX2+Pj4QK/XIzExMd82OWm1Wri5uZktREREpE6Khh0hBCZOnIjvv/8eP//8M+rUqWO2v06dOvDx8cHevXtN2/R6PQ4dOoSgoCAAQGBgIOzt7c3axMXF4dSpU6Y2REREVHHZKfnmEyZMwObNm7Ft2za4urqaenDc3d2h0+mg0WgQGhqKRYsWwd/fH/7+/li0aBGcnJwwZMgQU9tRo0Zh2rRp8PT0hIeHB6ZPn46AgAB0795dycMjIiIiK6Bo2Pnoo48AAF26dDHbvm7dOowcORIAMHPmTKSlpWH8+PFITExE27ZtsWfPHri6uprar1ixAnZ2dhg8eDDS0tLQrVs3rF+/Hra2tmV1KERERGSlrGqeHaVwnh0iIqLyp1zOs0NERERkaQw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkagw7REREpGoMO0RERKRqDDtERESkanZKF0BERNYhw2DEPb0B9/QP5Nf0h+sZWetpGQakphuQpn+AVL0B9/RZ62nZnmswChiFgFEga90oYBACBiMghNxmo9HAxkYDW40GNhrI9YePNRrA1kYDG40GdrYaONjaQGtnC629jVy3t4XWzgYOdjbQ2sl9WetZ27T22b/KdcfMbXY2pv2OD19Xo9Eo/b/CqjwwGJFhENA/MCLdYDCtmxaDAfczjEjTG5CWYcD9h4tcN5q29Wvmi5Y1KytyDKoJO6tXr8bSpUsRFxeHxo0bY+XKlXjiiSeULouIyKKEEEjLyAwZBqQ+DBdpegNS07PCyD39g4f7c4eRnMEkM+BkGITSh2cVTEHJPkcoyhmUHoYjWxsZ2OweBjNbm2xLtjBnZ5u5H9BAAwEB8fBbLgAIAQjIDabtIutx5v8doxAwGLOWB7nWjXhglOHyQbbt5o+NMBqBDGNWaMkwZIYXI9KzPTZa6MfCv6orw05JbN26FaGhoVi9ejU6dOiANWvWoHfv3jh9+jRq1qypWF3/S0hBXNJ9s20ijx+avH6ORB4N8/x5y+eHUOSxI8/3tnA9eb1eXi0L+775ty3+a+Z1LJZ+35LWI3Ks5PoFmMf75Nsm2+uLnM/J/EWa67Xyr++R71dAm0w2D/9q1wDQaORjANBoNNAAsNE8XDe1yVzXPGwv1x/+l/V62dpkvlbm62euA5qs18/+/tneyyb76yB3LTaarNfK/voPjAIZBvkh8cAgoM+x/sCQtT/zr+PMv3oz/yrO63H6w7+OM7eVNjsbDZwcbOHkYAcnrW3WuoP5us7BFs451nUOttDZ22b7YM/+FWZhQAOY9/w8/BA3Puz9yVqXS/oDI9IfGGQPw8MP6Ly3GeT2jMwPbvk9vP/wa3q2Nvcz5NfsP6PpD18L9x+U+ve6PHKws4HWVvao2T/8qrO3haODLXT2NnC0lz8Djg8X3cPQ+Hg1V8Vq1ojC/ua3Ym3btkXLli3x0UcfmbY9/vjjePrppxEWFparfXp6OtLT002Pk5OT4efnh6SkJLi5uVmsrtciTmLzf69Y7PWIiLLLDB/Zg0bm18IElMztOgdbOGtt4WQv1x3sKtZwTiEEMgwiVwCSwSjHtgdGpGdkbdMbjPL0nBEPT9EZYTDm7n0xiKyeFWP2rpLM4A6Y/QEgd2WFeNOWh23sbDSwtbExhUo7G/MeJbsc6zambTawtYF87sP9eYUXBzvZa2X21U4+x5pO8yUnJ8Pd3f2Rn9/lvmdHr9cjKioKs2fPNtveo0cPREZG5vmcsLAwvPXWW6Vem4+bIxr65E6yef2g5PWjk9fPU57bcjw77zaFe4NC15Fnu5K8XuHepLivV9zvZX7t8lKy48//9TSmx7mfkfVLMXcbTc42GvPt2Z+Y9Xzz98z7PXK3QY7X1uR+C9O2zO5448MV2X0vx3Zkrpu+iqyufaPI7CGS242mdjBvn31/Zte/WXvx8P3lkzNfI3v7nK+Vf73ZahQCdrY2sLfVwN7W5uGigZ2t/LDIud3e1gb2D/8i1tnLEOJoWrcx/WWcuc/0+GFIcbSzhY2N9XzolGcajQYOdvJDX7m+BypN5T7s3Lx5EwaDAd7e3mbbvb29ER8fn+dz5syZg6lTp5oeZ/bsWNrkbv6Y3M3f4q9LREREhVfuw06mnH9VCyHy7WrTarXQarVlURYREREprNyfmPXy8oKtrW2uXpyEhIRcvT1ERERU8ZT7sOPg4IDAwEDs3bvXbPvevXsRFBSkUFVERERkLVRxGmvq1KkYNmwYWrVqhfbt22Pt2rW4cuUKxo0bp3RpREREpDBVhJ2QkBDcunULCxYsQFxcHJo0aYIdO3agVq1aSpdGREREClPFPDslVdjr9ImIiMh6FPbzu9yP2SEiIiIqCMMOERERqRrDDhEREakaww4RERGpGsMOERERqRrDDhEREakaww4RERGpGsMOERERqZoqZlAuqcx5FZOTkxWuhIiIiAor83P7UfMjM+wASElJAQD4+fkpXAkREREVVUpKCtzd3fPdz9tFADAajbh+/TpcXV2h0Wgs9rrJycnw8/NDbGysam9DofZjVPvxAeo/Rh5f+af2Y+TxFZ8QAikpKfD19YWNTf4jc9izA8DGxgY1atQotdd3c3NT5Q9wdmo/RrUfH6D+Y+TxlX9qP0YeX/EU1KOTiQOUiYiISNUYdoiIiEjVGHZKkVarxbx586DVapUupdSo/RjVfnyA+o+Rx1f+qf0YeXyljwOUiYiISNXYs0NERESqxrBDREREqsawQ0RERKrGsENERESqxrBThvr374+aNWvC0dER1apVw7Bhw3D9+nWly7KIS5cuYdSoUahTpw50Oh3q1auHefPmQa/XK12axSxcuBBBQUFwcnJCpUqVlC7HIlavXo06derA0dERgYGBOHz4sNIlWcwvv/yCfv36wdfXFxqNBj/88IPSJVlUWFgYWrduDVdXV1StWhVPP/00zp07p3RZFvPRRx+hadOmpono2rdvj507dypdVqkJCwuDRqNBaGio0qVYzPz586HRaMwWHx8fRWph2ClDXbt2xddff41z587hu+++w99//42BAwcqXZZFnD17FkajEWvWrEFMTAxWrFiBjz/+GK+99prSpVmMXq/HoEGD8J///EfpUixi69atCA0Nxeuvv44///wTTzzxBHr37o0rV64oXZpFpKamolmzZggPD1e6lFJx6NAhTJgwAUePHsXevXvx4MED9OjRA6mpqUqXZhE1atTAu+++i99//x2///47nnzySQwYMAAxMTFKl2Zxx48fx9q1a9G0aVOlS7G4xo0bIy4uzrScPHlSmUIEKWbbtm1Co9EIvV6vdCmlYsmSJaJOnTpKl2Fx69atE+7u7kqXUWJt2rQR48aNM9vWsGFDMXv2bIUqKj0AREREhNJllKqEhAQBQBw6dEjpUkpN5cqVxaeffqp0GRaVkpIi/P39xd69e0Xnzp3FlClTlC7JYubNmyeaNWumdBlCCCHYs6OQ27dvY9OmTQgKCoK9vb3S5ZSKpKQkeHh4KF0G5UGv1yMqKgo9evQw296jRw9ERkYqVBWVRFJSEgCo8t+cwWDAli1bkJqaivbt2ytdjkVNmDABTz31FLp37650KaXiwoUL8PX1RZ06dfD888/jn3/+UaQOhp0yNmvWLDg7O8PT0xNXrlzBtm3blC6pVPz999/48MMPMW7cOKVLoTzcvHkTBoMB3t7eZtu9vb0RHx+vUFVUXEIITJ06FR07dkSTJk2ULsdiTp48CRcXF2i1WowbNw4RERFo1KiR0mVZzJYtW/DHH38gLCxM6VJKRdu2bfHll19i9+7d+OSTTxAfH4+goCDcunWrzGth2CmhvAZg5Vx+//13U/sZM2bgzz//xJ49e2Bra4vhw4dDWPEk1kU9PgC4fv06evXqhUGDBmH06NEKVV44xTk+NdFoNGaPhRC5tpH1mzhxIv766y989dVXSpdiUQ0aNEB0dDSOHj2K//znPxgxYgROnz6tdFkWERsbiylTpmDjxo1wdHRUupxS0bt3bzz33HMICAhA9+7d8dNPPwEAvvjiizKvxa7M31FlJk6ciOeff77ANrVr1zate3l5wcvLC4899hgef/xx+Pn54ejRo1bbNVvU47t+/Tq6du2K9u3bY+3ataVcXckV9fjUwsvLC7a2trl6cRISEnL19pB1mzRpErZv345ffvkFNWrUULoci3JwcED9+vUBAK1atcLx48fx/vvvY82aNQpXVnJRUVFISEhAYGCgaZvBYMAvv/yC8PBwpKenw9bWVsEKLc/Z2RkBAQG4cOFCmb83w04JZYaX4sjs0UlPT7dkSRZVlOO7du0aunbtisDAQKxbtw42NtbfcViS/3/lmYODAwIDA7F3714888wzpu179+7FgAEDFKyMCksIgUmTJiEiIgIHDx5EnTp1lC6p1AkhrPr3ZVF069Yt15VJL730Eho2bIhZs2apLugA8rPuzJkzeOKJJ8r8vRl2ysixY8dw7NgxdOzYEZUrV8Y///yDuXPnol69elbbq1MU169fR5cuXVCzZk289957uHHjhmmfUvMqWNqVK1dw+/ZtXLlyBQaDAdHR0QCA+vXrw8XFRdniimHq1KkYNmwYWrVqZeqJu3LlimrGWd29exf/+9//TI8vXryI6OhoeHh4oGbNmgpWZhkTJkzA5s2bsW3bNri6upp66dzd3aHT6RSuruRee+019O7dG35+fkhJScGWLVtw8OBB7Nq1S+nSLMLV1TXX+KrM8ZxqGXc1ffp09OvXDzVr1kRCQgLeeecdJCcnY8SIEWVfjJKXglUkf/31l+jatavw8PAQWq1W1K5dW4wbN05cvXpV6dIsYt26dQJAnotajBgxIs/jO3DggNKlFduqVatErVq1hIODg2jZsqWqLls+cOBAnv+/RowYoXRpFpHfv7d169YpXZpFvPzyy6afzSpVqohu3bqJPXv2KF1WqVLbpechISGiWrVqwt7eXvj6+opnn31WxMTEKFKLRggrHh1LREREVELWP6iCiIiIqAQYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIiIhI1Rh2iIiISNUYdoiIiEjVGHaIKF9dunRBaGio0mXk6datW6hatSouXboEADh48CA0Gg3u3LlTqu9b3PdZv349KlWqVKTntG7dGt9//32RnkNEuTHsEFGZiYuLw5AhQ9CgQQPY2NjkG6S+++47NGrUCFqtFo0aNUJERESuNmFhYejXr58q70qf6c0338Ts2bNhNBqVLoWoXGPYIaIyk56ejipVquD1119Hs2bN8mzz22+/ISQkBMOGDcOJEycwbNgwDB48GP/9739NbdLS0vDZZ59h9OjRZVW6Ip566ikkJSVh9+7dSpdCVK4x7BBRoSQmJmL48OGoXLkynJyc0Lt3b1y4cMGszSeffAI/Pz84OTnhmWeewfLly81O3dSuXRvvv/8+hg8fDnd39zzfZ+XKlQgODsacOXPQsGFDzJkzB926dcPKlStNbXbu3Ak7Ozu0b98+33pv3bqFF154ATVq1ICTkxMCAgLw1VdfmbXp0qULJk2ahNDQUFSuXBne3t5Yu3YtUlNT8dJLL8HV1RX16tXDzp07c73+r7/+imbNmsHR0RFt27bFyZMnzfavX78eNWvWNH0vbt26Zbb/77//xoABA+Dt7Q0XFxe0bt0a+/btM2tja2uLPn365KqbiIqGYYeICmXkyJH4/fffsX37dvz2228QQqBPnz7IyMgAID/8x40bhylTpiA6OhrBwcFYuHBhkd/nt99+Q48ePcy29ezZE5GRkabHv/zyC1q1alXg69y/fx+BgYH48ccfcerUKbzyyisYNmyYWQ8RAHzxxRfw8vLCsWPHMGnSJPznP//BoEGDEBQUhD/++AM9e/bEsGHDcO/ePbPnzZgxA++99x6OHz+OqlWron///qbvxX//+1+8/PLLGD9+PKKjo9G1a1e88847Zs+/e/cu+vTpg3379uHPP/9Ez5490a9fP1y5csWsXZs2bXD48OHCffOIKG+K3GudiMqFzp07iylTpojz588LAOLXX3817bt586bQ6XTi66+/FkIIERISIp566imz5w8dOlS4u7sX+No52dvbi02bNplt27Rpk3BwcDA9HjBggHj55ZfN2hw4cEAAEImJifkeT58+fcS0adPMaujYsaPp8YMHD4Szs7MYNmyYaVtcXJwAIH777Tez99myZYupza1bt4ROpxN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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2223,7 +2056,7 @@ }, { "cell_type": "markdown", - "id": "c77cb714", + "id": "6f1b4032", "metadata": { "editable": true }, @@ -2234,28 +2067,12 @@ { "cell_type": "code", "execution_count": 7, - "id": "52169944", + "id": "13b36be9", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'DataFiles/EoS.csv'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 33\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msave_fig\u001b[39m(fig_id):\n\u001b[1;32m 31\u001b[0m plt\u001b[38;5;241m.\u001b[39msavefig(image_path(fig_id) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 33\u001b[0m infile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata_path\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEoS.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;66;03m# Read the EoS data as csv file and organize the data into two arrays with density and energies\u001b[39;00m\n\u001b[1;32m 36\u001b[0m EoS \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(infile, names\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDensity\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEnergy\u001b[39m\u001b[38;5;124m'\u001b[39m))\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/IPython/core/interactiveshell.py:286\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 280\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 281\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 282\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 283\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 284\u001b[0m )\n\u001b[0;32m--> 286\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'DataFiles/EoS.csv'" - ] - } - ], + "outputs": [], "source": [ "# Common imports\n", "import os\n", @@ -2339,7 +2156,7 @@ }, { "cell_type": "markdown", - "id": "b09d9af0", + "id": "dd5ce3c0", "metadata": { "editable": true }, @@ -2349,7 +2166,7 @@ }, { "cell_type": "markdown", - "id": "c02fbf40", + "id": "018cdb87", "metadata": { "editable": true }, @@ -2361,14 +2178,11 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "98337a7f", + "execution_count": 8, + "id": "1b995666", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2443,277 +2257,23 @@ }, { "cell_type": "markdown", - "id": "c658961b", + "id": "e928bd64", "metadata": { "editable": true }, "source": [ - "## Material for the lab sessions" - ] - }, - { - "cell_type": "markdown", - "id": "36e6cf29", - "metadata": { - "editable": true - }, - "source": [ - "## Plans for the lab sessions\n", + "## Material for the lab sessions\n", "\n", - "**Material for the active learning sessions on Tuesday and Wednesday.**\n", + "This week we will discuss during the first hour of each lab session\n", + "some technicalities related to the project and methods for updating\n", + "the learning like ADAgrad, RMSprop and ADAM. As teaching material, see\n", + "the jupyter-notebook from week 37 (September 12-16).\n", "\n", - " * bias-variance tradeoff\n", - "\n", - " * Resampling techniques, cross-validation examples included here, see also the lectures from last week on the bootstrap method\n", - "\n", - " * Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at " - ] - }, - { - "cell_type": "markdown", - "id": "038fcf9e", - "metadata": { - "editable": true - }, - "source": [ - "## Lab session: Material relevant for the first project" - ] - }, - { - "cell_type": "markdown", - "id": "6ccae8fc", - "metadata": { - "editable": true - }, - "source": [ - "## Various steps in cross-validation\n", - "\n", - "When the repetitive splitting of the data set is done randomly,\n", - "samples may accidently end up in a fast majority of the splits in\n", - "either training or test set. Such samples may have an unbalanced\n", - "influence on either model building or prediction evaluation. To avoid\n", - "this $k$-fold cross-validation structures the data splitting. The\n", - "samples are divided into $k$ more or less equally sized exhaustive and\n", - "mutually exclusive subsets. In turn (at each split) one of these\n", - "subsets plays the role of the test set while the union of the\n", - "remaining subsets constitutes the training set. Such a splitting\n", - "warrants a balanced representation of each sample in both training and\n", - "test set over the splits. Still the division into the $k$ subsets\n", - "involves a degree of randomness. This may be fully excluded when\n", - "choosing $k=n$. This particular case is referred to as leave-one-out\n", - "cross-validation (LOOCV)." - ] - }, - { - "cell_type": "markdown", - "id": "1dd7bea2", - "metadata": { - "editable": true - }, - "source": [ - "## How to set up the cross-validation for Ridge and/or Lasso\n", - "\n", - "* Define a range of interest for the penalty parameter.\n", - "\n", - "* Divide the data set into training and test set comprising samples $\\{1, \\ldots, n\\} \\setminus i$ and $\\{ i \\}$, respectively.\n", - "\n", - "* Fit the linear regression model by means of for example Ridge or Lasso regression for each $\\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\\boldsymbol{\\sigma}_{-i}^2(\\lambda)$, as" - ] - }, - { - "cell_type": "markdown", - "id": "d31c042f", - "metadata": { - "editable": true - }, - "source": [ - "$$\n", - "\\begin{align*}\n", - "\\boldsymbol{\\beta}_{-i}(\\lambda) & = ( \\boldsymbol{X}_{-i, \\ast}^{T}\n", - "\\boldsymbol{X}_{-i, \\ast} + \\lambda \\boldsymbol{I}_{pp})^{-1}\n", - "\\boldsymbol{X}_{-i, \\ast}^{T} \\boldsymbol{y}_{-i}\n", - "\\end{align*}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "75924aa6", - "metadata": { - "editable": true - }, - "source": [ - "* Evaluate the prediction performance of these models on the test set by $C[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]$. Or, by the prediction error $|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)|$, the relative error, the error squared or the R2 score function.\n", - "\n", - "* Repeat the first three steps such that each sample plays the role of the test set once.\n", - "\n", - "* Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data." - ] - }, - { - "cell_type": "markdown", - "id": "9e3b8fc6", - "metadata": { - "editable": true - }, - "source": [ - "## Cross-validation in brief\n", - "\n", - "For the various values of $k$\n", - "\n", - "1. shuffle the dataset randomly.\n", - "\n", - "2. Split the dataset into $k$ groups.\n", - "\n", - "3. For each unique group:\n", - "\n", - "a. Decide which group to use as set for test data\n", - "\n", - "b. Take the remaining groups as a training data set\n", - "\n", - "c. Fit a model on the training set and evaluate it on the test set\n", - "\n", - "d. Retain the evaluation score and discard the model\n", - "\n", - "5. Summarize the model using the sample of model evaluation scores" - ] - }, - { - "cell_type": "markdown", - "id": "9480fea8", - "metadata": { - "editable": true - }, - "source": [ - "## Code Example for Cross-validation and $k$-fold Cross-validation\n", - "\n", - "The code here uses Ridge regression with cross-validation (CV) resampling and $k$-fold CV in order to fit a specific polynomial." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "fad06644", - "metadata": { - "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import KFold\n", - "from sklearn.linear_model import Ridge\n", - "from sklearn.model_selection import cross_val_score\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "\n", - "# A seed just to ensure that the random numbers are the same for every run.\n", - "# Useful for eventual debugging.\n", - "np.random.seed(3155)\n", - "\n", - "# Generate the data.\n", - "nsamples = 100\n", - "x = np.random.randn(nsamples)\n", - "y = 3*x**2 + np.random.randn(nsamples)\n", - "\n", - "## Cross-validation on Ridge regression using KFold only\n", - "\n", - "# Decide degree on polynomial to fit\n", - "poly = PolynomialFeatures(degree = 6)\n", - "\n", - "# Decide which values of lambda to use\n", - "nlambdas = 500\n", - "lambdas = np.logspace(-3, 5, nlambdas)\n", - "\n", - "# Initialize a KFold instance\n", - "k = 5\n", - "kfold = KFold(n_splits = k)\n", - "\n", - "# Perform the cross-validation to estimate MSE\n", - "scores_KFold = np.zeros((nlambdas, k))\n", - "\n", - "i = 0\n", - "for lmb in lambdas:\n", - " ridge = Ridge(alpha = lmb)\n", - " j = 0\n", - " for train_inds, test_inds in kfold.split(x):\n", - " xtrain = x[train_inds]\n", - " ytrain = y[train_inds]\n", - "\n", - " xtest = x[test_inds]\n", - " ytest = y[test_inds]\n", - "\n", - " Xtrain = poly.fit_transform(xtrain[:, np.newaxis])\n", - " ridge.fit(Xtrain, ytrain[:, np.newaxis])\n", - "\n", - " Xtest = poly.fit_transform(xtest[:, np.newaxis])\n", - " ypred = ridge.predict(Xtest)\n", - "\n", - " scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)\n", - "\n", - " j += 1\n", - " i += 1\n", - "\n", - "\n", - "estimated_mse_KFold = np.mean(scores_KFold, axis = 1)\n", - "\n", - "## Cross-validation using cross_val_score from sklearn along with KFold\n", - "\n", - "# kfold is an instance initialized above as:\n", - "# kfold = KFold(n_splits = k)\n", - "\n", - "estimated_mse_sklearn = np.zeros(nlambdas)\n", - "i = 0\n", - "for lmb in lambdas:\n", - " ridge = Ridge(alpha = lmb)\n", - "\n", - " X = poly.fit_transform(x[:, np.newaxis])\n", - " estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)\n", - "\n", - " # cross_val_score return an array containing the estimated negative mse for every fold.\n", - " # we have to the the mean of every array in order to get an estimate of the mse of the model\n", - " estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n", - "\n", - " i += 1\n", - "\n", - "## Plot and compare the slightly different ways to perform cross-validation\n", - "\n", - "plt.figure()\n", - "\n", - "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n", - "plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')\n", - "\n", - "plt.xlabel('log10(lambda)')\n", - "plt.ylabel('mse')\n", - "\n", - "plt.legend()\n", - "\n", - "plt.show()" + "For the lab session, the following video on cross validation (from 2024), could be helpful, see " ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/src/week38/week38.do.txt b/doc/src/week38/week38.do.txt index c50aa65dd..9ff1e5f39 100644 --- a/doc/src/week38/week38.do.txt +++ b/doc/src/week38/week38.do.txt @@ -10,7 +10,7 @@ DATE: September 15-19, 2025 !bblock Material for the lecture on Monday September 15 o Statistical interpretation of Ridge and Lasso regression o Resampling techniques, Bootstrap and cross validation and bias-variance tradeoff (this may partly be discussed during the exercise sessions as well. -o See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at URL:"https://youtu.be/" +o The material we did not cover last week, that is on more advanced methods for updating the learning rate, are covered by its own video. We will briefly discuss these topics at the beginning of the lecture and during the lab sessions. See video on ADAgrad, RMSprop and ADAM (material from last week not covered during lecture) at URL:"https://youtu.be/" # * "Video of Lecture":"https://youtu.be/omLmp_kkie0" # * "Whiteboard notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember9.pdf" !eblock @@ -24,6 +24,7 @@ o Hastie et al Chapter 7, here we recommend 7.1-7.5 and 7.10 (cross-validation) o "Video on bias-variance tradeoff":"https://www.youtube.com/watch?v=EuBBz3bI-aA" o "Video on Bootstrapping":"https://www.youtube.com/watch?v=Xz0x-8-cgaQ" o "Video on cross validation":"https://www.youtube.com/watch?v=fSytzGwwBVw" +For the lab session, the following video on cross validation (from 2024), could be helpful, see URL:"https://www.youtube.com/watch?v=T9jjWsmsd1o" !eblock @@ -1374,182 +1375,9 @@ plt.show() !split ===== Material for the lab sessions ===== +This week we will discuss during the first hour of each lab session +some technicalities related to the project and methods for updating +the learning like ADAgrad, RMSprop and ADAM. As teaching material, see +the jupyter-notebook from week 37 (September 12-16). - - -!split -===== Plans for the lab sessions ===== - -!bblock Material for the active learning sessions on Tuesday and Wednesday - * bias-variance tradeoff - * Resampling techniques, cross-validation examples included here, see also the lectures from last week on the bootstrap method - * Exercise for week 38 on the bias-variance tradeoff, see also the video from the lab session from week 37 at URL:"https://youtu.be/omLmp_kkie0" -!eblock - - - - - -!split -===== Lab session: Material relevant for the first project ===== - - -!split -===== Various steps in cross-validation ===== - -When the repetitive splitting of the data set is done randomly, -samples may accidently end up in a fast majority of the splits in -either training or test set. Such samples may have an unbalanced -influence on either model building or prediction evaluation. To avoid -this $k$-fold cross-validation structures the data splitting. The -samples are divided into $k$ more or less equally sized exhaustive and -mutually exclusive subsets. In turn (at each split) one of these -subsets plays the role of the test set while the union of the -remaining subsets constitutes the training set. Such a splitting -warrants a balanced representation of each sample in both training and -test set over the splits. Still the division into the $k$ subsets -involves a degree of randomness. This may be fully excluded when -choosing $k=n$. This particular case is referred to as leave-one-out -cross-validation (LOOCV). - -!split -===== How to set up the cross-validation for Ridge and/or Lasso ===== - -* Define a range of interest for the penalty parameter. - -* Divide the data set into training and test set comprising samples $\{1, \ldots, n\} \setminus i$ and $\{ i \}$, respectively. - -* Fit the linear regression model by means of for example Ridge or Lasso regression for each $\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\bm{\sigma}_{-i}^2(\lambda)$, as -!bt -\begin{align*} -\bm{\beta}_{-i}(\lambda) & = ( \bm{X}_{-i, \ast}^{T} -\bm{X}_{-i, \ast} + \lambda \bm{I}_{pp})^{-1} -\bm{X}_{-i, \ast}^{T} \bm{y}_{-i} -\end{align*} -!et - -* Evaluate the prediction performance of these models on the test set by $C[y_i, \bm{X}_{i, \ast}; \bm{\beta}_{-i}(\lambda), \bm{\sigma}_{-i}^2(\lambda)]$. Or, by the prediction error $|y_i - \bm{X}_{i, \ast} \bm{\beta}_{-i}(\lambda)|$, the relative error, the error squared or the R2 score function. - -* Repeat the first three steps such that each sample plays the role of the test set once. - -* Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data. - - -!split -===== Cross-validation in brief ===== - -For the various values of $k$ - -o shuffle the dataset randomly. -o Split the dataset into $k$ groups. -o For each unique group: - o Decide which group to use as set for test data - o Take the remaining groups as a training data set - o Fit a model on the training set and evaluate it on the test set - o Retain the evaluation score and discard the model -o Summarize the model using the sample of model evaluation scores - - - -!split -===== Code Example for Cross-validation and $k$-fold Cross-validation ===== - -The code here uses Ridge regression with cross-validation (CV) resampling and $k$-fold CV in order to fit a specific polynomial. -!bc pycod -import numpy as np -import matplotlib.pyplot as plt -from sklearn.model_selection import KFold -from sklearn.linear_model import Ridge -from sklearn.model_selection import cross_val_score -from sklearn.preprocessing import PolynomialFeatures - -# A seed just to ensure that the random numbers are the same for every run. -# Useful for eventual debugging. -np.random.seed(3155) - -# Generate the data. -nsamples = 100 -x = np.random.randn(nsamples) -y = 3*x**2 + np.random.randn(nsamples) - -## Cross-validation on Ridge regression using KFold only - -# Decide degree on polynomial to fit -poly = PolynomialFeatures(degree = 6) - -# Decide which values of lambda to use -nlambdas = 500 -lambdas = np.logspace(-3, 5, nlambdas) - -# Initialize a KFold instance -k = 5 -kfold = KFold(n_splits = k) - -# Perform the cross-validation to estimate MSE -scores_KFold = np.zeros((nlambdas, k)) - -i = 0 -for lmb in lambdas: - ridge = Ridge(alpha = lmb) - j = 0 - for train_inds, test_inds in kfold.split(x): - xtrain = x[train_inds] - ytrain = y[train_inds] - - xtest = x[test_inds] - ytest = y[test_inds] - - Xtrain = poly.fit_transform(xtrain[:, np.newaxis]) - ridge.fit(Xtrain, ytrain[:, np.newaxis]) - - Xtest = poly.fit_transform(xtest[:, np.newaxis]) - ypred = ridge.predict(Xtest) - - scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred) - - j += 1 - i += 1 - - -estimated_mse_KFold = np.mean(scores_KFold, axis = 1) - -## Cross-validation using cross_val_score from sklearn along with KFold - -# kfold is an instance initialized above as: -# kfold = KFold(n_splits = k) - -estimated_mse_sklearn = np.zeros(nlambdas) -i = 0 -for lmb in lambdas: - ridge = Ridge(alpha = lmb) - - X = poly.fit_transform(x[:, np.newaxis]) - estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold) - - # cross_val_score return an array containing the estimated negative mse for every fold. - # we have to the the mean of every array in order to get an estimate of the mse of the model - estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds) - - i += 1 - -## Plot and compare the slightly different ways to perform cross-validation - -plt.figure() - -plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score') -plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold') - -plt.xlabel('log10(lambda)') -plt.ylabel('mse') - -plt.legend() - -plt.show() - -!ec - - - - - - +For the lab session, the following video on cross validation (from 2024), could be helpful, see URL:"https://www.youtube.com/watch?v=T9jjWsmsd1o"