VS Code Extension that Generates Documentation Using AI

/** * return a set of all the subjects in the fullCoursesArray. * @returns A set of all the subjects. */function getAllSubjects(): ReadonlySet<string> {  const set = new Set<string>();  fullCoursesArray.forEach(it => set.add(it.subject));  return set;}/** * For each subject, if it has a color, do nothing. Otherwise, give it a random color from the colorset. * @param subjectColors - The subject colors that are currently in use. * @returns A new object with the same keys as the original object, but with new values. */export function allocateAllSubjectColor(  subjectColors: Record<string, string>): Record<string, string> {  const subjectsColorsCopy = { ...subjectColors };  getAllSubjects().forEach(subject => {    if (subjectsColorsCopy[subject]) return;    subjectsColorsCopy[subject] = coursesColorSet[      Math.floor(Math.random() * coursesColorSet.length)    ].hex.substring(1);  });  return subjectsColorsCopy;}/** * Update the subjectColors object with the new color for the given subject. * @param subjectColors - The current subject colors. * @param {string} color - The color to be applied to the subject. * @param {string} code - The subject code of the subject to update. * @returns A new object with the updated color. */export function updateSubjectColor(  subjectColors: Record<string, string>,  color: string,  code: string): Record<string, string> {  const subjectsColorsCopy = { ...subjectColors };  getAllSubjects().forEach(subject => {    if (subject === code) {      subjectsColorsCopy[subject] = color;    }  });  return subjectsColorsCopy;}/** * When the user clicks outside of the element, the `clickOutside` event handler is called. */export const clickOutside = {  // eslint-disable-next-line @typescript-eslint/no-explicit-any  beforeMount(el: any, binding: any): void {    el.clickOutsideEvent = (event: Event) => {      if (!(el === event.target || el.contains(event.target))) {        binding.value(event, el);      }    };    document.body.addEventListener('click', el.clickOutsideEvent);  },  // eslint-disable-next-line @typescript-eslint/no-explicit-any  unmounted(el: any): void {    document.body.removeEventListener('click', el.clickOutsideEvent);  },};

def collect_dataset():    '''    The function is used to collect the data from the github repository.    :return: A matrix of the dataset.    '''    response = requests.get(        "https://raw.githubusercontent.com/yashLadha/"        + "The_Math_of_Intelligence/master/Week1/ADRvs"        + "Rating.csv"    )    lines = response.text.splitlines()    data = []    for item in lines:        item = item.split(",")        data.append(item)    data.pop(0)  # This is for removing the labels from the list    dataset = np.matrix(data)    return datasetdef run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):    '''    This function runs the gradient descent algorithm.    :param data_x: the data matrix    :param data_y: the actual y values    :param len_data: the number of data points    :param alpha: learning rate    :param theta: The initial value of theta    :return: Theta    '''    n = len_data    prod = np.dot(theta, data_x.transpose())    prod -= data_y.transpose()    sum_grad = np.dot(prod, data_x)    theta = theta - (alpha / n) * sum_grad    return thetadef sum_of_square_error(data_x, data_y, len_data, theta):    '''    It calculates the sum of squared error for the given data and the given theta.    :param data_x: the data matrix    :param data_y: the y values of the data    :param len_data: the number of data points    :param theta: theta vector    :return: The sum of the squares of the errors.    '''    prod = np.dot(theta, data_x.transpose())    prod -= data_y.transpose()    sum_elem = np.sum(np.square(prod))    error = sum_elem / (2 * len_data)    return errordef run_linear_regression(data_x, data_y):    '''    Runs gradient descent on the data and returns the final theta vector.    :param data_x: The training data    :param data_y: The dependent variable    :return: Theta    '''    iterations = 100000    alpha = 0.0001550    no_features = data_x.shape[1]    len_data = data_x.shape[0] - 1    theta = np.zeros((1, no_features))    for i in range(0, iterations):        theta = run_steep_gradient_descent(            data_x, data_y, len_data, alpha, theta)        error = sum_of_square_error(data_x, data_y, len_data, theta)        print("At Iteration %d - Error is %.5f " % (i + 1, error))    return theta