jootse84

jootse84

-module(redblacktree).
-export([insert/2, empty_node/0,test/0]).
-define(BLACK, 'b').
-define(RED, 'r').
-define(BLACK_EMPTY_NODE, {?BLACK, nil, nil, nil}).
-define(RED_EMPTY_NODE, {?RED, nil, nil, nil}).


% 1 - black
% 0 - red

jootse84

def bubble(l_elems, is_sorted, step):
    if step is 1 or is_sorted:
        # base case: l_elems is already sorted or we pass through the list len(l_elems) times
        return l_elems
    else:
        is_swapped = False
        for i in range(len(l_elems) - 1):
            # compares each pair of adjacent items and swaps them if they are in the wrong order
            if l_elems[i] > l_elems[i + 1]:
                is_swapped = True

jootse84

# The program recommends to you a movie list based on the responses about movies ratings.
# How it works? It calculates the similarity between your responses and other users in the system, and recommends to you 
# movies from the user with higher similarity (cosine index similarity: https://en.wikipedia.org/wiki/Cosine_similarity).

## How to run it? > Rscript retflix_improvement.R --test

getRecommendations <- function () {

  # returns vector with the responses in order about the movies selected by the ids from indices
  movieSurvey <- function (indices, movies) {

jootse84

# The program recommends to you a movie list based on the responses about movies that you have already seen.
# How it works? It calculates the similarity between your responses and other users in the system, and recommends to you 
# movies from the user with higher similarity (jaccard index similarity: https://en.wikipedia.org/wiki/Jaccard_index).

## How to run it? > Rscript retflix.R --test
## Use --test command parameter to use for the user data frame a small sample with 8 movies and 3 users.

getRecommendations <- function () {

  # returns vector with the responses in order about the movies selected by the ids from indices

jootse84

'''
John Snow created a map in 1854 about the cholera outbreak in London.
Snow plotted cholera cases in the Soho area of London around Broad Street and noticed a cluster around a water pump.
This led to improved sanitation facilities and also the discovery that cholera infection was water-borne rather than airborne.

This program:
* initally have a set of sample points - x,y (representing locations of chollera infection cases)
* allow the user to introduce Points of Interest - x,y (representing water pipes, buildings, markets ...)
* calculate the final result, representing the Point of Interest closer to all the cholera infection cases.
'''

jootse84

import sys

SYMBOLS = ['X', 'O']

def main(argv):
    game = Game()
    game.welcome()
    game.start_game()

class Game:

jootse84

/**
 * @file Trie class
 * @author Jose E. Martinez <jootse.84@gmail.com>
 * @description A class that saves MQTT topics in a trie structure. The trie structure is represented by an Object which keys contains the topics levels. In addition, the topic levels with the key '$' as 'true' are the completed topics.
 *
 * @see [Project info]{@link https://jootse84.github.io/notes/create-your-own-MQTT-broker.-part-1}
 * @see [Trie structure]{@link https://en.wikipedia.org/wiki/Trie}
 * @see [MQTT topic]{@link http://www.hivemq.com/blog/mqtt-essentials-part-5-mqtt-topics-best-practices}
 *
 * @example