#!/bin/python3

import math
import os
import random
import re
import sys
import numpy as np  
from sklearn.linear_model import LinearRegression

data = []

with open('trainingdata.txt', 'r') as file:
    for line in file:
        charging_time, battery_lasted = map(float, line.strip().split(','))
        data.append((charging_time, battery_lasted))


data = np.array(data) 

X = data[:, 0]
y = data[:, 1]

# determines the minimum charging time at which the battery life reaches its maximum possible value (8.00 hours).

#  find the smallest charging time at which the battery life reaches 8.00.

threshold = min(X[y== 8])


filtered_X = X[X < threshold].reshape(-1, 1)
filtered_y = y[X < threshold]

lr = LinearRegression().fit(filtered_X, filtered_y)

def predict_battery_life(charge_time):
    if charge_time >= threshold:
        return 8 
    return lr.predict(np.array([[charge_time]]))[0] 

if __name__ == '__main__':
    timeCharged = float(input().strip())
    predicted_life = predict_battery_life(timeCharged)
    print(round(predicted_life, 2))