Unit 2 Linear and Logistic Regression and regularization. Running Cell Run [4] issue

Hello Guys,

I’m on unit 2 on Machine learning and I’m trying to Cell Run [4] and I’m having an issue with. It is saying
NameError: name ‘data’ is not defined. How do I fix this? By the way, I’m creating a python script within Machine Learning folder to run the cells.

Hello @leonar29 ,

Use this script:

import warnings
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import pandas as pd

# class definition

class LinearRegression(object):

    def __init__(self):

        self._m = 0
        self._b = 0
    def m(self):

        return self._m

    def b(self):

        return self._b

    def fit(self, X, y): # IMPLEMENTATION ABOVE EQUATIONS TO COMPUTE: m, b  

        X = np.array(X)
        y = np.array(y)
        X_ = X.mean()
        y_ = y.mean()
        num = ((X - X_)*(y - y_)).sum()
        den = ((X - X_)**2).sum()
        self._m = num/den
        self._b = y_ - self._m*X_

    def predict(self, x):

        x = np.array(x)
        return self._m*x + self._b

# Computation of MSE and regression (we use the same formulas as we defined earlier)

def MSE(ax, x, y, model):

    error = y - model.predict(x)
    MSE = (error**2).sum()/error.size
    ax.plot([x, x], [error*0, error])
    return MSE

def compute_regression(ax, x, y, model):

    error = y - model.predict(x)
    MSE = (error**2).sum()/error.size
    ax.scatter(x, y, label='distance')
    ax.plot([x, x], [y, model.predict(x)], ':')
    ax.plot(0, 0, ':', alpha=0.5, label='error')
    ax.plot([0, 100], model.predict([0, 100]), color='red', label='regression')
    ax.axis([0, 100, 0, 22])

# model is a object of class
model = LinearRegression()

# load dataset
data = pd.read_csv('/home/user/catkin_ws/src/machine_learning_course/dataset/test_brakes.csv')

# remove Unmaned column
data.drop(['Unnamed: 0'], axis=1, inplace=True)

# our data set
yA = (data.iloc[:,0].values).flatten()
x = (data.iloc[:,1].values).flatten()

plt.figure(figsize=(10, 8))
axA = plt.axes(xlim=(0, 100), ylim=(0, 22), autoscale_on=False)
model.fit(x, yA)
compute_regression(axA, x, yA, model)
plt.xlabel("% of max speed of axis 1", fontsize=16)
plt.ylabel("stop distance [deg]", fontsize=16)
plt.title("Linear regression", fontsize=18)


I’m going to update this course to adapt all the codes so that they can be run as scripts.

1 Like

It worked thank you!