<reponame>philtsmith570/Linear-Regression-Lazy-Programmer<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 25 20:06:38 2017

@author: philt
"""

import re
import numpy as np
import matplotlib.pyplot as plt

X = []
Y = []

non_decimal = re.compile(r'[^\d]+')

for line in open('moore.csv'):
    r = line.split('\t')
    x = int(non_decimal.sub('', r[2].split('[')[0]))
    y = int(non_decimal.sub('', r[1].split('[')[0]))
    X.append(x)
    Y.append(y)
    
X = np.array(X)
Y = np.array(Y)

plt.scatter(X, Y)
plt.show()

Y = np.log(Y)
plt.scatter(X, Y)
plt.show()

# Apply eqns for linear regression  a,b
denominator = X.dot(X) - X.mean()*X.sum()

a = (X.dot(Y) - Y.mean() * X.sum()) / denominator
b = (Y.mean() * X.dot(X) - X.mean() * X.dot(Y)) / denominator

Yhat = a*X + b
plt.scatter(X, Y)
plt.show()

d1 = Y - Yhat
d2 = Y - Y.mean()
r2 = 1 - d1.dot(d1) / d2.dot(d2)
print("a:", a, " b: ", b)
print("The r-squared is ", r2)

print("time to double: ", np.log(2)/a)

# -*- coding: utf-8 -*-
"""
Created on Mon Dec 25 20:06:38 2017

@author: philt
"""

import re
import numpy as np
import matplotlib.pyplot as plt

X = []
Y = []

non_decimal = re.compile(r'[^\d]+')

for line in open('moore.csv'):
    r = line.split('\t')
    x = int(non_decimal.sub('', r[2].split('[')[0]))
    y = int(non_decimal.sub('', r[1].split('[')[0]))
    X.append(x)
    Y.append(y)
    
X = np.array(X)
Y = np.array(Y)

plt.scatter(X, Y)
plt.show()

Y = np.log(Y)
plt.scatter(X, Y)
plt.show()

# Apply eqns for linear regression  a,b
denominator = X.dot(X) - X.mean()*X.sum()

a = (X.dot(Y) - Y.mean() * X.sum()) / denominator
b = (Y.mean() * X.dot(X) - X.mean() * X.dot(Y)) / denominator

Yhat = a*X + b
plt.scatter(X, Y)
plt.show()

d1 = Y - Yhat
d2 = Y - Y.mean()
r2 = 1 - d1.dot(d1) / d2.dot(d2)
print("a:", a, " b: ", b)
print("The r-squared is ", r2)

print("time to double: ", np.log(2)/a)
# -*- coding: utf-8 -*- Created on Mon Dec 25 20:06:38 2017
 
 @author: philt # Apply eqns for linear regression  a,b