.config/espanso/match/base.yml

@@ -147,9 +147,6 @@ 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}
@@ -332,10 +329,6 @@ 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

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

aliases

@@ -37,7 +37,6 @@ 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"
@@ -274,10 +273,8 @@ 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
@@ -344,7 +341,6 @@ 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

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

math_notes/machine_learning/linear_regression.md

@@ -1,81 +0,0 @@
-# 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

@@ -1,103 +0,0 @@
-# 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

@@ -1,84 +0,0 @@
-### 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

@@ -26,8 +26,7 @@ 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)
-  TAKE THIS ONE OR
+  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)
-  TAKE THIS ONE OR
+  OR
 - [Database Management: SDCM-0164](https://study.com/academy/course/computer-science-303-database-management.html)
 
 ## Sophia.org Documents