SeroviICAI

function [s] = cubic_spline_interp(X, Y, options)
    arguments
        X (1,:) {mustBeVector}
        Y (1,:) {mustBeVector}
        options.Plotting (1, 1) string = 'False'
    end

    % Calculation of differences between consecutive x-coordinates.
    n = length(X);
    h = zeros(n-1, 1);

SeroviICAI

import numpy as np

__all__ = ["MultinomialNaiveBayes"]


class MultinomialNaiveBayes:
    """
    Multinomial Naive Bayes classifier for text data.

    Parameters:

SeroviICAI

import math


def miller_test(n: int) -> bool:
    """
    Check if a number is a prime (Miller Test). This algorithm is deterministic.

    :param n: integer to check
    :return: True if n is a prime, False otherwise
    """

SeroviICAI

function output = decimal_to_IEEE754(decimal)
    % Given a decimal number, this function returns the exact same number in
    % IEEE754 format (double precision)
    %   decimal: Decimal number to be converted
    %   output: Converted number (string)

    % Get the sign bit (0 for positive, 1 for negative)
    sign = 0;
    if decimal < 0
        sign = 1;

SeroviICAI

function p = newton_interpolation(x, y)
    % Plots the Newton Interpolation Polynomial and the divided difference table.
    %   x - the x-coordinates of the data points
    %   y - the y-coordinates of the data points
    
    % Number of data points
    n = length(x);
    
    % Divided difference table
    F = divided_differences(x, y);