debonx

# To be reminded
# 1) Classifying a single point can result in a true positive (truth = 1, guess = 1), a true negative (truth = 0, guess = 0), a false positive (truth = 0, guess = 1), or a false negative (truth = 1, guess = 0).
# 2) Accuracy measures how many classifications your algorithm got correct out of every classification it made.
# 3) Recall measures the percentage of the relevant items your classifier was able to successfully find.
# 4) Precision measures the percentage of items your classifier found that were actually relevant.
# 5) Precision and recall are tied to each other. As one goes up, the other will go down.
# 6) F1 score is a combination of precision and recall.
# 7) F1 score will be low if either precision or recall is low.

from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score

debonx

/* Write a function solution(A); that, given an array A of N integers, returns the smallest positive integer (greater than 0) that does not occur in A.
For example, given A = [1, 3, 6, 4, 1, 2], the function should return 5.
Given A = [1, 2, 3], the function should return 4.
Given A = [−1, −3], the function should return 1.

Write an efficient algorithm for the following assumptions:
N is an integer within the range [1..100,000];
each element of array A is an integer within the range [−1,000,000..1,000,000]. */

const A = [-1, -4];

debonx

# Wordpress php.ini configuration

upload_max_filesize = 32M
post_max_size = 32M
memory_limit = 128M
max_execution_time = 60
max_input_vars = 10000
max_input_time = 30

debonx

<?php /**
 * Single Product Thumbnails
 *
 * @param array $args
 * @param int $attachment_id
 * @param string $image_size
 * @param bool $main_image
 * @return array
 */
function bones_gallery_image_html_attachment_image_params ( $args, $attachment_id, $image_size, $main_image )

debonx

#Import datasets and libraries
from sklearn.datasets import fetch_20newsgroups
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer

#Category selection to compare different datasets of emails and evaluate how hard is to distinguish those.
the_categories = ['comp.sys.ibm.pc.hardware', 'rec.sport.hockey']
train_emails = fetch_20newsgroups(categories = the_categories, subset = 'train', shuffle = True, random_state = 108)
test_emails = fetch_20newsgroups(categories = the_categories, subset = 'test', shuffle = True, random_state = 108)

debonx

#include <cs50.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

int get_card_length(long card)
{
    int length = 0;
    while (card > 0)
    {

debonx

@media (min-width:320px) { /* smartphones, portrait iPhone, portrait 480x320 phones (Android) */ }
@media (min-width:480px) { /* smartphones, Android phones, landscape iPhone */ }
@media (min-width:600px) { /* portrait tablets, portrait iPad, e-readers (Nook/Kindle), landscape 800x480 phones (Android) */ }
@media (min-width:801px) { /* tablet, landscape iPad, lo-res laptops ands desktops */ }
@media (min-width:1025px) { /* big landscape tablets, laptops, and desktops */ }
@media (min-width:1281px) { /* hi-res laptops and desktops */ }

debonx

CREATE TABLE friends (
  id INTEGER,
  name TEXT,
  birthday DATE
);

INSERT INTO friends (id, name, birthday) 
VALUES 
  (1, 'Jane Doe', '1990-05-30'),
  (2, 'Richard Stallman', '1953-03-16'),

debonx

// Define a rooster
Rooster = {};

// Return a morning rooster call
Rooster.announceDawn = () => {
  return 'moo!';
}

// Return hour as string
// Throws Error if hour is not between 0 and 23 inclusive

debonx

const Calculate = {
  factorial(number) {
    if(number === 0) {
      return 1;
    }
    for(let i = number - 1; i >= 1; i--) {
      number *= i;
    }
    return number;
  }