I was working on “Machine learning in Robotics” , unit 2, i was trying to run the program under " 2.2 Linear regression - gradient descent", when i tried to run the program, i was getting the error shown in the screenshot. Can you tell me how to fix this?

Does it have any connection to an already reported error:

/home/user/catkin_ws/src/machine_learning_course/unit2/src/gradient_decent.py:26: RuntimeWarning: overflow encountered in double_scalars
totalError += (y[i]-(m*x[i] + b)) ** 2
/home/user/catkin_ws/src/machine_learning_course/unit2/src/gradient_decent.py:55: RuntimeWarning: overflow encountered in double_scalars
gradient_m = (-2/n) * sum(X * (Y - Y_pred))
/home/user/catkin_ws/src/machine_learning_course/unit2/src/gradient_decent.py:59: RuntimeWarning: invalid value encountered in double_scalars
m = m - alpha * gradient_m

The program is as below:

#! /usr/bin/env python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import future
MSE = []
iteration = []
data = pd.read_csv('/home/user/catkin_ws/src/machine_learning_course/dataset/test_brakes.csv')
data.drop(['Unnamed: 0'], axis=1, inplace=True)
data.drop(['payload'], axis=1, inplace=True)
X = (data.iloc[:,1].values).flatten()
Y = (data.iloc[:,:-1].values).flatten()
def compute_error_for_line_given_points(b,m,X,Y):
totalError = 0 #sum of square error formula
for i in range (0, len(X)):
x = X
y = Y
totalError += (y[i]-(m*x[i] + b)) ** 2
mse = (totalError)/totalError.size
return mse
#MSE.append(mse)
#iteration.append(i)
#print (MSE)
#return totalError/ float(len(X))
# Initial values
m = 0
b = 0
#Hyperparameters
alpha = 0.0001 # The learning Rate
epochs = 15000 # The number of iterations to perform gradient descent
#n = float(len(X))
n = len(X)
##########################################################################
# OUR OPTIMIZATOR - we use MSE to look for the best paramaters m and b
# We use Gradient Descent
#########################################################################
for i in range(epochs):
Y_pred = m*X + b
#IMPLEMENTATION OF DISCUSSED ABOVE EQATIONS FOR GRADIENT DESCENT
gradient_m = (-2/n) * sum(X * (Y - Y_pred))
gradient_b = (-2/n) * sum(Y - Y_pred)
#UPDATE OF m AND b
m = m - alpha * gradient_m
b = b - alpha * gradient_b
mse = compute_error_for_line_given_points(b,m,X,Y)
MSE.append(mse)
iteration.append(i)
# performing the regression baseon od gradient descent
Y_pred = m*X + b
plt.figure(figsize=(10, 8))
plt.scatter(X, Y)
plt.plot([min(X), max(X)], [min(Y_pred), max(Y_pred)], color='red') # predicted
plt.xlabel("% of max speed of axis 1", fontsize=16)
plt.ylabel("stop distance [deg]", fontsize=16)
plt.title('Gradient descent', fontsize=18)
plt.show()
plt.figure(figsize=(10, 8))
i = np.arange(0, len(MSE),1)
plt.xlabel("epoch", fontsize=16)
plt.ylabel("error", fontsize=16)
plt.title('Gradient descent ERROR (MSE)', fontsize=18)
plt.plot(i,MSE)
plt.show()

I ran the program as is with only the addition of the #! /usr/bin/env python line at the top. I also had to install the future using pip install future.

Please look into it, do let me know if you need anymore info