#!/bin/python3

import math
import os
import random
import re
import sys
import pandas as pd
import numpy as np
from scipy import optimize
#import matplotlib.pyplot as plt
from io import StringIO


if __name__ == '__main__':
    timeCharged = float(input().strip())

    # Read the data using pandas
    data = pd.read_csv('trainingdata.txt', header=None, names=["TimeCharged", "TimeLasted"])
    
    # training data: Range when not fully charge range
    index_values = data.query("TimeCharged <= 4.01").index.tolist()
    x = data.TimeCharged[index_values]
    y = data.TimeLasted[index_values]

    # Equation to be solved from book https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/chapter16.02-Least-Squares-Regression-Derivation-Linear-Algebra.html
    # y = b1 * f1(x)
   
    # A Matrix
    # Only one linear basis function used. Important: Method allows any kind and number of basis function, as long as beta params are constants
    # Basis function is  evaluated at the input measured values. In this case f1(x) = x 
    A = np.array(x).reshape(-1,1)

    # Y Matrix: Output measured values
    Y = np.array(y).reshape(-1,1) 

    # Here is where the magic happends: Least square regression to find beta parameters
    beta_params = np.linalg.inv(A.T @ A) @ A.T @ Y
    
    
   # Make prediction
    if timeCharged > 4.01:  # Battery fully charged
        print(8)  
    else:
        predicted_time = timeCharged *  beta_params[0][0]
        print(predicted_time)  # Output the predicted battery life