my_lr_2d_with_noise.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun  5 19:29:48 2018

@author: ekele
"""

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

# load data
X = []
Y = []

for line in open('data_2d.csv'):
    x1,x2,y = line.split(',')
    X.append([float(x1), float(x2), 1])
    Y.append(float(y))
    
# turn X and Y into numpy arrays
X = np.array(X)
Y = np.array(Y)

print(X.shape)

noise = np.random.randn(100,1)
print(noise.shape)

X = np.concatenate((X, noise), axis=1)

# let's plot the data to see what the data looks like
fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
ax.scatter(X[:,0], X[:,1], Y)
plt.show()

# calculate weights
w = np.linalg.solve(np.dot(X.T, X), np.dot(X.T, Y))
Yhat = np.dot(X, w)

# compute r-squared
d1 = Y - Yhat
d2 = Y - Y.mean()

r2 = 1 - d1.dot(d1)/d2.dot(d2)

print("r-squared: ", r2)