🔧 Updated nvimtree

.config/espanso/match/base.yml

@@ -147,6 +147,9 @@ matches:
   - trigger: ;frac
     force_clipboard: true
     replace: \frac{}{}
+  - trigger: ;perp
+    force_clipboard: true
+    replace: \perp\!\!\!\perp
   - trigger: ;int
     force_clipboard: true
     replace: \int{f(x)\, dx}
@@ -329,6 +332,10 @@ matches:
   - trigger: ;zwiki
     force_clipboard: true
     replace: https://pzwiki.net/wiki/Project_Zomboid_Wiki
+  # Routes to Vintage Story Wiki
+  - trigger: ;vswiki
+    force_clipboard: true
+    replace: https://wiki.vintagestory.at/
     # Prepares about pages for firefox
   - trigger: ;conf
     force_clipboard: true

.config/nvim/lua/plugins/nvimtree.lua

@@ -51,10 +51,6 @@ return {
 				update_cwd = false,
 				ignore_list = {},
 			},
-			system_open = {
-				cmd = nil,
-				args = {},
-			},
 			diagnostics = {
 				enable = false,
 				show_on_dirs = false,

aliases

@@ -37,6 +37,7 @@ alias Code="cd ~/Documents/Code && ls"
 alias graphics="cd ~/Documents/Graphic_Design && ls"
 alias Math="cd ~/Documents/Math && ls"
 alias sophia="cd ~/Documents/sophia && ls"
+alias study="cd ~/Documents/study && ls"
 alias cplus="cd ~/Documents/Code/cpp && ls"
 alias js="cd ~/Documents/Code/javascript && ls"
 alias py="cd ~/Documents/Code/python && ls"
@@ -273,8 +274,10 @@ alias sudoku="sku"
 # alias rbd="checkrebuild -v"
 # pip list and pip freeze command list pip packages
 # poetry virtual env
+alias svenv="python -m venv .venv"
 alias venv='source ".venv/bin/activate"'
 alias {uvenv,dvenv}="deactivate"
+alias pip="python -m pip"
 
 #Particularly useful commands more to remember than to use as aliases:
 # alias optimalbufsize= 'stat -c "%o"' # followed by filename will give you the optimal read/write BUFSIZE for a file
@@ -341,6 +344,7 @@ alias vgrind="valgrind --leak-check=full --show-leak-kinds=all --track-origins=y
 # alias {twd,thewalkingdead}="steam steam://rungameid/1449690 &"
 # alias {cbmods,cybermods}="cd ~/.local/share/Steam/steamapps/common/Cyberpunk\ 2077/archive/pc/mod && ls"
 alias vintagestory="flatpak run at.vintagestory.VintageStory"
+alias vsmods="cd ~/.var/app/at.vintagestory.VintageStory/config/VintagestoryData/Mods/ && ls"
 
 # docker specific Aliases
 alias docker-ls="docker container ls -a && docker images"

gitignore.txt

@@ -1,6 +1,8 @@
 .env*
-.gitignore
 .prettierrc
 .prettierignore
 .editorconfig
-node_modules
+node_modules
+*.vim
+.venv/
+.ipynb_checkpoints/

math_notes/machine_learning/linear_regression.md

@@ -0,0 +1,81 @@
+# Linear Regression
+
+In linear regression, given features and labels (X, Y), where Y is real-valued,
+we try to learn a function f(x) to predict Y given x. Figure 2 outlines this
+function:
+
+$$ \hat{y} = w_0 + w_1x_1 + w_2x_2 + \dots + w_mx_m = \mathbf{w}^T\mathbf{X} $$
+
+_Figure 2: Learning Function_
+
+where $\mathbf{X} = x_1\text{, } \dots \text{, } x_m$ are the feature values and
+$\mathbf{w} = w_0 \text{, } \dots \text{, } w_n$ can be seen as weights.
+
+The weights determine how the corresponding feature affects the predicted value.
+Thus, our task is to find the appropriate values of **w**.
+
+**Cost function:** The cost function helps us to figure out the best possible
+values for **w**. For the cost function, we use the Mean Squared Error (MSE),
+Figure 3.
+
+$$ MSE(\mathbf{w}) = \frac{1}{m}\sum_{i=1}^{m}{\left(\hat{y_i} - y_i\right)^2} $$
+
+_Figure 3: MSE_
+
+Using this MSE function we are going to update the values of w, such that the
+MSE value settles at the minimum. The method of updating w to minimize the cost
+function (MSE) is called gradient descent. We initialize the values of w and
+then update these values iteratively to minimize the cost. Sometimes the cost
+function can be a non-convex function where you can settle at a local minimum,
+but for linear regression, it is always a convex function. To update w, we take
+gradients from the cost function. To find these gradients, we take partial
+derivatives with respect to w. Figure 4 outlines this 'update rule'.
+
+- Initialize $w_i$
+- Repeat until convergence
+  $\{w_i := w_i - \alpha \times \frac{\partial MSE(\mathbf{w})}{\partial w_i}\}$
+  Parameter $\alpha$ is called learning rate.
+
+_Figure 4: Update Rule_
+
+Code: In order to perform linear regression, we are going to use a Python module
+called scikit learn. In the following example, we will use the California
+Housing Data Set. The data set contains information about the housing values in
+the suburbs of Boston.
+
+There are 14 attributes for each **X**. Examples of these attributes include:
+
+- MedInc per capita crime rate by town
+- HouseAge Average age of a house in years
+- AveRooms Average Rooms in a home
+- Population City population
+
+The target value **Y** is:
+
+- MedHouseVal - Median value of owner-occupied homes in $1000's
+
+Next, we split the data into training and testing sets. We train the model with
+80% of the samples and test with the remaining 20%. Finally, we will evaluate
+our model using MSE.
+
+```py
+import sklearn
+from sklearn.linear_model import LinearRegression
+from sklearn.datasets import load_boston
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import fetch_california_housing
+
+housing = fetch_california_housing()
+X = housing ['data']
+Y = housing ['target']
+
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)
+
+lr = LinearRegression()
+lr.fit(X_train, Y_train)
+Y_pred = lr.predict(X_test)
+
+mse = sklearn.metrics.mean_squared_error(Y_test, Y_pred)
+
+print('Mean squared error for test set:', mse)
+```

math_notes/machine_learning/logistic_regression.md

@@ -0,0 +1,103 @@
+# Classification
+
+Logistic regression is used in classification problems. For example, an email
+can be classified as belonging to one of two classes: 'spam' and 'not spam'.
+Given features and labels (**x**, **Y**), where **Y** can take only discrete
+values (we can also say that the target variable is categorical), we try to
+learn a function f(x) to predict Y given x. Figure 5 outlines this function.
+
+$$ \hat{y} = w_0 + w_1x_1 + w_2x_2 + \dots + w_mx_m = \mathbf{w}^T\mathbf{X} $$
+
+where $\mathbf{X} = x_1 \text{, } \dots \text{, } x_m$ are the feature values
+and $\mathbf{w} = w_0 \text{, } \dots \text{, } w_n$ can be seen as weights.
+
+_Figure 5: Learning Function_
+
+As in linear regression, the weights determine how the corresponding feature
+affects the predicted value, thus our task is to find the appropriate values of
+**w**.
+
+In this binary classification problem, the predicted function must return binary
+values (either 0 or 1). To achieve this, we apply to our function the sigmoid or
+logistic function (Figure 6). The sigmoid function has the domain of all real
+numbers, with a return value from 0 to 1. Unlike linear regression, using the
+sigmoid function we transform the output into a probability.
+
+$$ \text{Sigmoid function: } \sigma(x) = \frac{1}{1 + \mathbf{e}^{-x}} $$
+
+$$ \text{Sigmoid applied to learning function: } \sigma(\hat{y}) = \sigma\left(\mathbf{w}^T\mathbf{X}\right) = \frac{1}{1 + \mathbf{e}^{-\mathbf{w}^T\mathbf{X}}} $$
+
+$$ \text{Probability for } \mathbf{X} \text{ to belong in the positive class: } Pr\left(c_{+}\mid X\right) = \frac{1}{1 + \mathbf{e}^{-\mathbf{w}^T\mathbf{X}}} $$
+
+$$ \text{Probability for } \mathbf{X} \text{ to belong in the negative class: } Pr\left(c_{-}\mid X\right) = 1 - Pr\left(c_{+}\mid X\right) $$
+
+_Figure 6: Sigmoid Function_
+
+**Cost function**: Figure 7 outlines the cost function that is used in logistic
+regression (Maximum Likelihood).
+
+$$ J(\mathbf{w}) = \frac{1}{m}\sum_{i=1}^{m}{-\left[y_i\log \hat{y} + \left(1 - y_i\right)\left(1 - \hat{y}\right)\right]} $$
+
+_Figure 7: Cost Function in Logistic Regression_
+
+Using this cost function, we are going to update the values of **w**, such that
+the J(w) value settles at the minimum. To obtain the values of **w**, we perform
+the gradient descent algorithm. Figure 8 outlines the update rule of **w** in
+logistic regression.
+
+- Initialize $w_i$
+- Repeat until convergence
+  $\{w_i := w_i - \alpha \cdot \frac{\partial MSE(\mathbf{w})}{\partial w_i}\}$
+  Parameter $\alpha$ is called learning rate.
+
+_Figure 8: Update Rule_
+
+**Code:i** To perform logistic regression we again use the scikit learn module.
+In the following example, we will use the Breast Cancer Wisconsin (Diagnostic)
+Data Set. There are 10 attributes for every **X** including:
+
+- radius (mean of distances from the center to points on the perimeter)
+- texture (standard deviation of gray-scale values)
+- perimeter
+- area
+- smoothness (local variation in radius lengths)
+
+The **Y** classes are:
+
+- WDBC-Malignant
+- WDBC-Benign
+
+Next, we split the data into training and testing sets. We train the model with
+80% of the samples and test with the remaining 20%. Finally, we will evaluate
+our model using precision and recall metrics. The precision is the intuitive
+ability of the classifier not to label as positive a sample that is negative,
+and recall is the ability of the classifier to find all the positive samples.
+
+```py
+import sklearn
+from sklearn.linear_model import LogisticRegression
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import recall_score
+from sklearn.metrics import precision_score
+
+data = load_breast_cancer()
+X = data['data']
+Y = data['target']
+
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)
+clf = LogisticRegression()
+clf.fit(X_train, Y_train)
+Y_pred = clf.predict(X_test)
+
+print('Recall:', recall_score(Y_test, Y_pred))
+print('Precision:', precision_score(Y_test, Y_pred))
+```
+
+The disadvantage of this algorithm is that for each iteration m gradients have
+to be computed leading to m training examples. If the training set is very
+large, the above algorithm is going to be memory inefficient and might crash if
+the training set doesn't fit in the memory. The Stochastic Gradient Descent
+algorithm may be helpful in this case as it takes a sample of the training set
+to calculate the weights-parameters instead of the entire sample space for each
+iteration. This makes training much faster.

mysql_docker_pip_setup.md

@@ -0,0 +1,84 @@
+### Setup
+
+Considering that it's been a while since I've set up MySQL and Pip, I thought
+I'd document some of the basics here again for my own sake:
+
+**Setting Up Mysql**
+
+I opted to setup mysql via docker instead of installing it on bare metal. Simply
+use the contained `docker-compose.yml` file with
+[docker-compose](https://github.com/docker/compose) to get a very basic mysql
+instance up and running:
+
+```sh
+docker-compose up -d
+```
+
+Confirm it's running using `ps`:
+
+```sh
+docker ps
+```
+
+As long as it's up and running you can test it to be sure:
+
+```sh
+docker exec -it myjsql-basic mysql -u root -p
+```
+
+And put in the default root password from the `docker-compose.yml` file.
+
+**Virtual Environments**
+
+You'll want to install `pip` and also get a virtual environment running:
+
+```sh
+python -m venv .venv
+```
+
+```sh
+source .venv/bin/activate
+```
+
+Once inside the virtual environment, upgrade the local `pip`:
+
+```sh
+python -m pip install --upgrade pip
+```
+
+And use that `pip` to install all the packages from `requirements.txt`:
+
+```sh
+python -m pip install -r requirements.txt
+```
+
+If you need to create a new `requirements.txt`, then just:
+
+```sh
+pip freeze > requirements.txt
+```
+
+**Confirming MySQL Statements Were Executed:**
+
+From within the `docker exec`'ed mysql repl, for now just use `SHOW DATABASES`
+to ensure it went through:
+
+```
+mysql> SHOW DATABASES;
+```
+
+You should see output like this:
+
+```
++--------------------+
+| Database           |
++--------------------+
+| employee_db        |
+| information_schema |
+| myapp              |
+| mysql              |
+| performance_schema |
+| sys                |
++--------------------+
+6 rows in set (0.001 sec)
+```

scripts/html5

@@ -26,7 +26,8 @@ cat <<EOM
                 anim id est laborum.</p>
             </article>
         </main>
+        <script src="./index.js" defer></script>
     </body>
-    <script src="./index.js" default defer></script>
 </html>
-EOM
+EOM
+

wgu_questionnaire.md

@@ -75,11 +75,11 @@
 ## Study.com Courses with OR
 
 - [Network and System Security: SDCM-0200](https://study.com/academy/course/computer-science-202-network-and-system-security.html)
-  OR
+  TAKE THIS ONE OR
 - [Introduction to Cybersecurity: SDCM-0215](https://study.com/academy/course/computer-science-110-introduction-to-cybersecurity.html)
 
 - [Database Programming: SDCM-0218](https://study.com/academy/course/computer-science-204-database-programming.html)
-  OR
+  TAKE THIS ONE OR
 - [Database Management: SDCM-0164](https://study.com/academy/course/computer-science-303-database-management.html)
 
 ## Sophia.org Documents