debonx

add_filter( 'user_contactmethods','huraji_contactmethods', 20, 1 );

function huraji_contactmethods( $contact_methods ) {
	  $contact_methods['linkedin'] = __( 'LinkedIn URL', 'text_domain' );
	  $contact_methods['github'] = __( 'Github URL', 'text_domain' );
    $contact_methods['youtube']  = __( 'YouTube URL', 'text_domain' );
    return $contact_methods;
}

debonx

import numpy as np

#Get data from CSV 
#Temperatures recorded 4 times a day, at 0:00, 6:00, 12:00, and 18:00. Last weeks data (Monday through Friday)
temperatures = np.genfromtxt('temperature_data.csv', delimiter=',')

#Adjust temperatures
temperatures_fixed = temperatures + 3.0

#Select Monday's Temperatures

debonx

import pandas as pd

#Read CSV file
orders = pd.read_csv('shoefly.csv')

#Inspect first 5 lines
print(orders.head(5))

#Get a specific serie of data / column
emails = orders['email']

debonx

/*
* @param $tag = tag to use for this transient
* @param $element = value to associate the transient
* @param $duration / $expiration = time in seconds > 3600*24 for 1 day
* @return true > if needed to update | false > if still not expired
*/

function update_transient($tag, $element, $duration, $expiration){

		$elements_in_tag = get_transient($tag);

debonx

/* 
* Put this function in your plugin or functions.php and pass the parameter $log to be debugged.
* Example: wpui_write_log( $log ) where $log is the data to be debugged.
* It writes into  /debug.log of your wp-content/
* Could be used as class method as well
*/

function wpui_write_log( $log )  {
      if ( is_array( $log ) || is_object( $log ) ) {
         error_log( print_r( $log, true ) );

debonx

#Import LinearRegression methods from Scikit-Learn
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np

#Data of Soup sales with their temperature
temperature = np.array(range(60, 100, 2))
temperature = temperature.reshape(-1, 1)
sales = [65, 58, 46, 45, 44, 42, 40, 40, 36, 38, 38, 28, 30, 22, 27, 25, 25, 20, 15, 5]

debonx

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

#True labels and predicted classifications
labels = [1, 0, 0, 1, 1, 1, 0, 1, 1, 1]
guesses = [0, 1, 1, 1, 1, 0, 1, 0, 1, 0]

#Calculate accuracy, recall, precision and f1 score
print(accuracy_score(labels, guesses))
print(recall_score(labels, guesses))
print(precision_score(labels, guesses))

debonx

/**
*
* Check Status Of License Activation
* More at https://docs.woocommerce.com/document/woocommerce-api-manager/
* @author: huraji (https://xilab.com/u/huraji)
* @param: $Request (string)
* @param: $Endpoint (string) 
* @return: JSON decoded formatted data
* 
**/

debonx

function get_url_param(name){
    var results = new RegExp('[\?&]' + name + '=([^]*)').exec(window.location.href);
    if (results==null){
       return null;
    } else {
    	if(results[1].indexOf("#")){
    		results = results[1].split("#");
    		results = results[0];
    	} else {
    		results = results[1] || 0;

debonx

# REQUIREMENTS
# - A tagged dataset is necessary to calculate the probabilities used in Bayes' Theorem.
# - In order to apply Bayes' Theorem, we assume that these features are independent.
# - Using Bayes' Theorem, we can find P(class|data point) for every possible class. The class with the highest probability will be the algorithm’s prediction.

# MORE Improvements for the Natural Language Processing.
# - Remove punctuation from the training set (great! into great)
# - Lowercase every word in the training set (Great into great)
# - Use a bigram or trigram model, makes the assumption of independence more reasonable ("this is" or "is great" instead of single words)