We use cookies to ensure you have the best browsing experience on our website. Please read our cookie policy for more information about how we use cookies.
  1. Prepare
  2. Artificial Intelligence
  3. Statistics and Machine Learning
  4. Laptop Battery Life
  5. Discussions

Laptop Battery Life

  • zdorfer
     + 0 comments

    Even not strictly necessary for this challenge, i got back to theory and implentation of least square regression, as this method opens solutions for a lot of problems, including this one.

    I super recommned this book: https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/chapter16.04-Least-Squares-Regression-in-Python.html

    #!/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