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/**
*
* 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
* 
**/

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# 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)

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#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)

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# 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

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# Importing breast cancer data and features.
# Importing training model, classifier and matplotlib

import codecademylib3_seaborn
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt

# Split data

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from sklearn.svm import SVC
from graph import points, labels

# Create SVC classifier (linear decision boundary)
classifier = SVC(kernel = 'linear')

# Training with points and labels
classifier.fit(points, labels)

# Print the prediction 

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import matplotlib.pyplot as plt
from sklearn import datasets

# From sklearn.cluster, import Kmeans class
from sklearn.cluster import KMeans

# Load data
iris = datasets.load_iris()
samples = iris.data

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from sklearn.linear_model import Perceptron
import matplotlib.pyplot as plt
import numpy as np
from itertools import product

data = [[0, 0], [0, 1], [1, 0], [1, 1]]

# Set labels basing on gate type (AND, OR)
# Multiple perceptrons can solve XOR gates
labels = [0, 0, 0, 1]

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function setUrlParam(key,value) {
  if (history.pushState) {
    var params = new URLSearchParams(window.location.search);
    params.set(key, value);
    var newUrl = window.location.protocol + "//" + window.location.host + window.location.pathname + '?' + params.toString();
    window.history.pushState({path:newUrl},'',newUrl);
  }
}

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const getUserChoice = userInput => {
  userInput = userInput.toLowerCase();
  	return userInput === 'rock' || userInput === 'paper' || userInput === 'scissors' ? userInput : console.log('Invalid input');
}

const getComputerChoice = () => {
  
  let whole = Math.floor(Math.random() * 3);
  switch( whole ) {
    case 0: