#!/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

#!/bin/python3

import math
import os
import random
import re
import sys
import pandas as pd
import numpy as np
from scipy import optimize
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]
   
    # create basis functions for least square regression 
    def basis_func_lin (x, b1):
        return b1 * pow(x,1)
        
    # or
    def basis_func_quad (x, b1, b2):
        return b1 * pow(x,1) + b2 * pow(x,2)

    #choose func
    func = basis_func_lin
    
    # do training (least square regression)
    beta_params, covariance = optimize.curve_fit(func, x, y)
    
   # Make prediction
    if timeCharged > 4.01:  # Battery fully charged
        print(8)  
    else:
        predicted_time = func(timeCharged, *beta_params)
        print(predicted_time)  # Output the predicted battery life
        
    
    

#!/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))