from Prediction import Prediction
  import numpy as np
  import pandas as pd
  import random
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import median_absolute_error
  from math import sqrt
  
  #Prediction of complexity level (the number of levels is variable)
  #Each row is a complexity level, each pair [,] is the note and the time for a question (test with 3 levels)
  
  dataI=pd.read_csv('dataInitial.csv', sep=' ', header=0)
  data=pd.read_csv('data.csv', sep=' ', header=0)
  v1=[]
  v2=[]
  n=0
  #for name,dr in dataI.iterrows():
  for i in range(1):
  
      #initialization of Beta distribution for all the complexity levels (test with 5 levels)

      betap=[[2,1],[1,2],[1,3],[1,4],[1,5]]

  
      #Parameters: previous grades and times, a-priori paameters for beta distribution in each complexity level, step for change beta parameters, value of penalization for time and value of limit between win and loss
      pred=Prediction(dataI.iloc[i,:], data.iloc[i,:], betap, 0.2, 1/16, 6)
      pred.CalculateGradePenalization()
      pred.Calculate()

      pred.CalculateSW()