Guidosalimbeni

let question = "test question';
let answer = 'test answer';
let form = new FormData();
form.append("question", question);
form.append("answer", answer);

fetch("http://www.guidosalimbeni.it/-- xxx xxx xxx --- .php", {
  // URL
  // body: JSON.stringify(form), // data you send.
  body: new URLSearchParams(form),

Guidosalimbeni

<?php
  
    $servername = "xxx.xxx.xxx.xxx"; 
    $username = "username";
    $password = "******";
    $database = "name"; 
    $table    = "data";

    // your data here depending on things coded in the front end
     $question              = $_POST['question'];

Guidosalimbeni

Guidosalimbeni

def main():
  # prepare data
  trainingSet=df.values.tolist()[:18]
  testSet=df.values.tolist()[19:]
  
  print ('Train set: ' + repr(len(trainingSet)))
  print ('Test set: ' + repr(len(testSet)))

  # generate predictions
  predictions=[]

Guidosalimbeni

def getAccuracy(testSet, predictions):
	correct = 0
	for x in range(len(testSet)):
		if testSet[x][-1] == predictions[x]:
			correct += 1
	return (correct/float(len(testSet))) * 100.0

Guidosalimbeni

def getResponse(neighbors):
	classVotes = {}
	for x in range(len(neighbors)):
		response = neighbors[x][-1]
		if response in classVotes:
			classVotes[response] += 1
		else:
			classVotes[response] = 1
	sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)
	return sortedVotes[0][0]

Guidosalimbeni

def getNeighbors(trainingSet, testInstance, k):
	distances = []
	length = len(testInstance)-1
	for x in range(len(trainingSet)):
		dist = euclideanDistance(testInstance, trainingSet[x], length)
		distances.append((trainingSet[x], dist))
	distances.sort(key=operator.itemgetter(1))
	neighbors = []
	for x in range(k):
		neighbors.append(distances[x][0])

Guidosalimbeni

def euclideanDistance(instance1, instance2, length):
	distance = 0
	for x in range(length):
		distance += pow((float(instance1[x]) - float(instance2[x])), 2)
	return math.sqrt(distance)