diff --git a/doc/Projects/2021/Project1/html/._Project1-bs000.html b/doc/Projects/2021/Project1/html/._Project1-bs000.html index 5f4bc9427..8e4ff2d79 100644 --- a/doc/Projects/2021/Project1/html/._Project1-bs000.html +++ b/doc/Projects/2021/Project1/html/._Project1-bs000.html @@ -307,7 +307,7 @@ $$

Your code has to include a scaling of the data (for example by subtracting the mean value), and -a split of the data in training and test data. For this part you can +a split of the data in training and test data. For this exercise you can either write your own code or use for example the function for splitting training data provided by the library Scikit-Learn (make sure you have installed it). This function is called @@ -426,7 +426,7 @@ one provided by Scikit-Learn. Write your own code for the Ridge method, either using matrix inversion or the singular value decomposition as done in the previous exercise. Perform the same bootstrap analysis as in the -Exercise 2 (for the same polynomials) and the cross-validation part in part c) but now for different values of \( \lambda \). Compare and +Exercise 2 (for the same polynomials) and the cross-validation in exercise 3 but now for different values of \( \lambda \). Compare and analyze your results with those obtained in exercises 1-3. Study the dependence on \( \lambda \). @@ -449,11 +449,14 @@ model fits the data best. Perform here as well an analysis of the bias-variance

With our codes functioning and having been tested properly on a simpler function we are now ready to look at real data. We will -essentially repeat in part g) what was done in parts a-e). However, we +essentially repeat in this exercise what was done in exercises 1-5. However, we need first to download the data and prepare properly the inputs to our codes. We are going to download digital terrain data from the website https://earthexplorer.usgs.gov/, +

+Or, if you prefer, we have placed selected datafiles at https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles +

In order to obtain data for a specific region, you need to register as a user (free) at this website and then decide upon which area you want diff --git a/doc/Projects/2021/Project1/html/Project1-bs.html b/doc/Projects/2021/Project1/html/Project1-bs.html index 5f4bc9427..8e4ff2d79 100644 --- a/doc/Projects/2021/Project1/html/Project1-bs.html +++ b/doc/Projects/2021/Project1/html/Project1-bs.html @@ -307,7 +307,7 @@ $$

Your code has to include a scaling of the data (for example by subtracting the mean value), and -a split of the data in training and test data. For this part you can +a split of the data in training and test data. For this exercise you can either write your own code or use for example the function for splitting training data provided by the library Scikit-Learn (make sure you have installed it). This function is called @@ -426,7 +426,7 @@ one provided by Scikit-Learn. Write your own code for the Ridge method, either using matrix inversion or the singular value decomposition as done in the previous exercise. Perform the same bootstrap analysis as in the -Exercise 2 (for the same polynomials) and the cross-validation part in part c) but now for different values of \( \lambda \). Compare and +Exercise 2 (for the same polynomials) and the cross-validation in exercise 3 but now for different values of \( \lambda \). Compare and analyze your results with those obtained in exercises 1-3. Study the dependence on \( \lambda \). @@ -449,11 +449,14 @@ model fits the data best. Perform here as well an analysis of the bias-variance

With our codes functioning and having been tested properly on a simpler function we are now ready to look at real data. We will -essentially repeat in part g) what was done in parts a-e). However, we +essentially repeat in this exercise what was done in exercises 1-5. However, we need first to download the data and prepare properly the inputs to our codes. We are going to download digital terrain data from the website https://earthexplorer.usgs.gov/, +

+Or, if you prefer, we have placed selected datafiles at https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles +

In order to obtain data for a specific region, you need to register as a user (free) at this website and then decide upon which area you want diff --git a/doc/Projects/2021/Project1/html/Project1.html b/doc/Projects/2021/Project1/html/Project1.html index 604ee768e..4c254e47b 100644 --- a/doc/Projects/2021/Project1/html/Project1.html +++ b/doc/Projects/2021/Project1/html/Project1.html @@ -263,7 +263,7 @@ $$

Your code has to include a scaling of the data (for example by subtracting the mean value), and -a split of the data in training and test data. For this part you can +a split of the data in training and test data. For this exercise you can either write your own code or use for example the function for splitting training data provided by the library Scikit-Learn (make sure you have installed it). This function is called @@ -382,7 +382,7 @@ one provided by Scikit-Learn. Write your own code for the Ridge method, either using matrix inversion or the singular value decomposition as done in the previous exercise. Perform the same bootstrap analysis as in the -Exercise 2 (for the same polynomials) and the cross-validation part in part c) but now for different values of \( \lambda \). Compare and +Exercise 2 (for the same polynomials) and the cross-validation in exercise 3 but now for different values of \( \lambda \). Compare and analyze your results with those obtained in exercises 1-3. Study the dependence on \( \lambda \). @@ -405,11 +405,14 @@ model fits the data best. Perform here as well an analysis of the bias-variance

With our codes functioning and having been tested properly on a simpler function we are now ready to look at real data. We will -essentially repeat in part g) what was done in parts a-e). However, we +essentially repeat in this exercise what was done in exercises 1-5. However, we need first to download the data and prepare properly the inputs to our codes. We are going to download digital terrain data from the website https://earthexplorer.usgs.gov/, +

+Or, if you prefer, we have placed selected datafiles at https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles +

In order to obtain data for a specific region, you need to register as a user (free) at this website and then decide upon which area you want diff --git a/doc/Projects/2021/Project1/ipynb/Project1.ipynb b/doc/Projects/2021/Project1/ipynb/Project1.ipynb index 5b7105db3..ebee420aa 100644 --- a/doc/Projects/2021/Project1/ipynb/Project1.ipynb +++ b/doc/Projects/2021/Project1/ipynb/Project1.ipynb @@ -194,7 +194,7 @@ "source": [ "Your code has to include a scaling of the data (for example by\n", "subtracting the mean value), and\n", - "a split of the data in training and test data. For this part you can\n", + "a split of the data in training and test data. For this exercise you can\n", "either write your own code or use for example the function for\n", "splitting training data provided by the library **Scikit-Learn** (make\n", "sure you have installed it). This function is called\n", @@ -329,7 +329,7 @@ "Write your own code for the Ridge method, either using matrix\n", "inversion or the singular value decomposition as done in the previous\n", "exercise. Perform the same bootstrap analysis as in the\n", - "Exercise 2 (for the same polynomials) and the cross-validation part in part c) but now for different values of $\\lambda$. Compare and\n", + "Exercise 2 (for the same polynomials) and the cross-validation in exercise 3 but now for different values of $\\lambda$. Compare and\n", "analyze your results with those obtained in exercises 1-3. Study the\n", "dependence on $\\lambda$.\n", "\n", @@ -349,11 +349,13 @@ "\n", "With our codes functioning and having been tested properly on a\n", "simpler function we are now ready to look at real data. We will\n", - "essentially repeat in part g) what was done in parts a-e). However, we\n", + "essentially repeat in this exercise what was done in exercises 1-5. However, we\n", "need first to download the data and prepare properly the inputs to our\n", "codes. We are going to download digital terrain data from the website\n", ",\n", "\n", + "Or, if you prefer, we have placed selected datafiles at \n", + "\n", "In order to obtain data for a specific region, you need to register as\n", "a user (free) at this website and then decide upon which area you want\n", "to fetch the digital terrain data from. In order to be able to read\n", diff --git a/doc/Projects/2021/Project1/ipynb/ipynb-Project1-src.tar.gz b/doc/Projects/2021/Project1/ipynb/ipynb-Project1-src.tar.gz index 341f3bbdd..5d23f750e 100644 Binary files a/doc/Projects/2021/Project1/ipynb/ipynb-Project1-src.tar.gz and b/doc/Projects/2021/Project1/ipynb/ipynb-Project1-src.tar.gz differ diff --git a/doc/Projects/2021/Project1/pdf/Project1.p.tex b/doc/Projects/2021/Project1/pdf/Project1.p.tex index 9bc99e99f..0e5ee96b6 100644 --- a/doc/Projects/2021/Project1/pdf/Project1.p.tex +++ b/doc/Projects/2021/Project1/pdf/Project1.p.tex @@ -262,7 +262,7 @@ where we have defined the mean value of $\hat{y}$ as Your code has to include a scaling of the data (for example by subtracting the mean value), and -a split of the data in training and test data. For this part you can +a split of the data in training and test data. For this exercise you can either write your own code or use for example the function for splitting training data provided by the library \textbf{Scikit-Learn} (make sure you have installed it). This function is called @@ -362,7 +362,7 @@ one provided by \textbf{Scikit-Learn}. Write your own code for the Ridge method, either using matrix inversion or the singular value decomposition as done in the previous exercise. Perform the same bootstrap analysis as in the -Exercise 2 (for the same polynomials) and the cross-validation part in part c) but now for different values of $\lambda$. Compare and +Exercise 2 (for the same polynomials) and the cross-validation in exercise 3 but now for different values of $\lambda$. Compare and analyze your results with those obtained in exercises 1-3. Study the dependence on $\lambda$. @@ -380,11 +380,13 @@ model fits the data best. Perform here as well an analysis of the bias-variance \paragraph{Exercise 6: Analysis of real data (score 30 points).} With our codes functioning and having been tested properly on a simpler function we are now ready to look at real data. We will -essentially repeat in part g) what was done in parts a-e). However, we +essentially repeat in this exercise what was done in exercises 1-5. However, we need first to download the data and prepare properly the inputs to our codes. We are going to download digital terrain data from the website \href{{https://earthexplorer.usgs.gov/}}{\nolinkurl{https://earthexplorer.usgs.gov/}}, +Or, if you prefer, we have placed selected datafiles at \href{{https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles}}{\nolinkurl{https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles}} + In order to obtain data for a specific region, you need to register as a user (free) at this website and then decide upon which area you want to fetch the digital terrain data from. In order to be able to read diff --git a/doc/Projects/2021/Project1/pdf/Project1.pdf b/doc/Projects/2021/Project1/pdf/Project1.pdf index f463b8e8a..598fb051b 100644 Binary files a/doc/Projects/2021/Project1/pdf/Project1.pdf and b/doc/Projects/2021/Project1/pdf/Project1.pdf differ diff --git a/doc/Projects/2021/Project1/pdf/Project1.tex b/doc/Projects/2021/Project1/pdf/Project1.tex index 616674b1d..c781d1aba 100644 --- a/doc/Projects/2021/Project1/pdf/Project1.tex +++ b/doc/Projects/2021/Project1/pdf/Project1.tex @@ -232,7 +232,7 @@ where we have defined the mean value of $\hat{y}$ as Your code has to include a scaling of the data (for example by subtracting the mean value), and -a split of the data in training and test data. For this part you can +a split of the data in training and test data. For this exercise you can either write your own code or use for example the function for splitting training data provided by the library \textbf{Scikit-Learn} (make sure you have installed it). This function is called @@ -332,7 +332,7 @@ one provided by \textbf{Scikit-Learn}. Write your own code for the Ridge method, either using matrix inversion or the singular value decomposition as done in the previous exercise. Perform the same bootstrap analysis as in the -Exercise 2 (for the same polynomials) and the cross-validation part in part c) but now for different values of $\lambda$. Compare and +Exercise 2 (for the same polynomials) and the cross-validation in exercise 3 but now for different values of $\lambda$. Compare and analyze your results with those obtained in exercises 1-3. Study the dependence on $\lambda$. @@ -350,11 +350,13 @@ model fits the data best. Perform here as well an analysis of the bias-variance \paragraph{Exercise 6: Analysis of real data (score 30 points).} With our codes functioning and having been tested properly on a simpler function we are now ready to look at real data. We will -essentially repeat in part g) what was done in parts a-e). However, we +essentially repeat in this exercise what was done in exercises 1-5. However, we need first to download the data and prepare properly the inputs to our codes. We are going to download digital terrain data from the website \href{{https://earthexplorer.usgs.gov/}}{\nolinkurl{https://earthexplorer.usgs.gov/}}, +Or, if you prefer, we have placed selected datafiles at \href{{https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles}}{\nolinkurl{https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles}} + In order to obtain data for a specific region, you need to register as a user (free) at this website and then decide upon which area you want to fetch the digital terrain data from. In order to be able to read diff --git a/doc/src/Projects/2021/Project1/Project1.do.txt b/doc/src/Projects/2021/Project1/Project1.do.txt index da1e0953c..3b58bad59 100644 --- a/doc/src/Projects/2021/Project1/Project1.do.txt +++ b/doc/src/Projects/2021/Project1/Project1.do.txt @@ -136,7 +136,7 @@ where we have defined the mean value of $\hat{y}$ as Your code has to include a scaling of the data (for example by subtracting the mean value), and -a split of the data in training and test data. For this part you can +a split of the data in training and test data. For this exercise you can either write your own code or use for example the function for splitting training data provided by the library _Scikit-Learn_ (make sure you have installed it). This function is called @@ -246,7 +246,7 @@ one provided by _Scikit-Learn_. Write your own code for the Ridge method, either using matrix inversion or the singular value decomposition as done in the previous exercise. Perform the same bootstrap analysis as in the -Exercise 2 (for the same polynomials) and the cross-validation part in part c) but now for different values of $\lambda$. Compare and +Exercise 2 (for the same polynomials) and the cross-validation in exercise 3 but now for different values of $\lambda$. Compare and analyze your results with those obtained in exercises 1-3. Study the dependence on $\lambda$. @@ -266,11 +266,13 @@ model fits the data best. Perform here as well an analysis of the bias-variance With our codes functioning and having been tested properly on a simpler function we are now ready to look at real data. We will -essentially repeat in part g) what was done in parts a-e). However, we +essentially repeat in this exercise what was done in exercises 1-5. However, we need first to download the data and prepare properly the inputs to our codes. We are going to download digital terrain data from the website URL:"https://earthexplorer.usgs.gov/", +Or, if you prefer, we have placed selected datafiles at URL:"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles" + In order to obtain data for a specific region, you need to register as a user (free) at this website and then decide upon which area you want to fetch the digital terrain data from. In order to be able to read