def get_profile(student):
models = csv_test.getModels()
soma_dev = 0
dev = 0
soma_gest = 0
gest = 0
soma_infra = 0
infra = 0
for disc in student:
if disc in models["Dev"]:
soma_dev += float(student[disc].replace(",", "."))
dev += 1
elif disc in models["Gest"]:
soma_gest += float(student[disc].replace(",", "."))
gest += 1
elif disc in models["Infra"]:
soma_infra += float(student[disc].replace(",", "."))
infra += 1
media_dev = soma_dev / dev
media_gest = soma_gest / gest
media_infra = soma_infra / infra
return {"Dev" : media_dev, "Gest" : media_gest, "Infra" : media_infra}
from recommendations import sim_distance, sim_pearson, top_matchs, get_recommendations, get_profile
import csv_test, json, ast
from pymongo import MongoClient
client = MongoClient()
db = client.studentShareTest
csv_test.saveStudents()
studJson = csv_test.getStudents()
modelsJson = csv_test.getModels()
prefs = {stud["registry"] : stud["grades"] for stud in studJson}
db.workData.delete_many({})
for stud in studJson:
if stud == "_id":
continue
workData = {"name" : stud["name"]}
workData["registry"] = stud["registry"]
workData["warningSubjects"] = get_recommendations(prefs, stud["registry"], top_match = True)
workData["recommendedSubjects"] = get_recommendations(prefs, stud["registry"], reverse = True, top_match = True)
workData["topMatchs"] = top_matchs(prefs, stud["registry"])
workData["profile"] = get_profile(stud["grades"])
db.workData.insert_one(workData)
