cleaning up

2021-09-10 10:00:31 +02:00
parent f26299b098
commit e6ba08bee0
9 changed files with 38 additions and 21 deletions
@@ -307,7 +307,7 @@ $$
 <p>
 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 <b>Scikit-Learn</b> (make
 sure you have installed it).  This function is called
@@ -426,7 +426,7 @@ one provided by <b>Scikit-Learn</b>.
 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
 <p>
 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
 <a href="https://earthexplorer.usgs.gov/" target="_self"><tt>https://earthexplorer.usgs.gov/</tt></a>,

+<p>
+Or, if you prefer, we have placed selected datafiles at <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles</tt></a>
+
 <p>
 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
@@ -307,7 +307,7 @@ $$
 <p>
 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 <b>Scikit-Learn</b> (make
 sure you have installed it).  This function is called
@@ -426,7 +426,7 @@ one provided by <b>Scikit-Learn</b>.
 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
 <p>
 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
 <a href="https://earthexplorer.usgs.gov/" target="_self"><tt>https://earthexplorer.usgs.gov/</tt></a>,

+<p>
+Or, if you prefer, we have placed selected datafiles at <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles</tt></a>
+
 <p>
 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
@@ -263,7 +263,7 @@ $$
 <p>
 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 <b>Scikit-Learn</b> (make
 sure you have installed it).  This function is called
@@ -382,7 +382,7 @@ one provided by <b>Scikit-Learn</b>.
 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
 <p>
 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
 <a href="https://earthexplorer.usgs.gov/" target="_blank"><tt>https://earthexplorer.usgs.gov/</tt></a>,

+<p>
+Or, if you prefer, we have placed selected datafiles at <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles</tt></a>
+
 <p>
 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
@@ -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",
    "<https://earthexplorer.usgs.gov/>,\n",
    "\n",
+    "Or, if you prefer, we have placed selected datafiles at <https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles>\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",
@@ -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
@@ -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
@@ -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