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  2. Artificial Intelligence
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  4. Laptop Battery Life
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Laptop Battery Life

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216 Discussions

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  • nonsnomani
     + 0 comments

    I have a laptop from Dell Company which has 8 GB RAM and 256 SSD hard disk. Its battery was working very well for the first year. I had taken out four to five times but suddenly it has stopped working. Now it has taken out 20 to 25 minutes. I don't understand what the problem is. If any of you know, please guide me. You can contact me for more information.

  • spirialexandre
     + 0 comments

    I've found it useful to create a scatter plot (plt. scatter(X,Y))to understand the shape before selecting a model

  • zoyashah41306
     + 0 comments

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  • zdorfer
     + 0 comments

    Using least square regression through definition (matrix inversions, dot products and transpose), no library: 

    #!/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

  • 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