linear regression.py

from statistics import mean
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as style
import random 
xs = np.array([1,2,3,4,5,6],dtype=np.float64)
ys = np.array([5,4,6,5,6,7],dtype=np.float64)

def create_dataset(hm,variance,step=2,correlation=False):
    val=1
    ys=[]
    for i in range(hm):
        y = val+ random.randrange(-variance,variance)
        ys.append(y)
        if correlation and correlation == 'pos':
            val+=step
        elif correlation and correlation == 'neg':
            val-=step
    xs = [i for i in range(len(ys))]

    
    return np.array(xs,dtype=np.float64),np.array(ys,dtype=np.float64)
"""plt.scatter(xs,ys)
plt.show()"""
def best_fit_slope_and_intercept(xs,ys):
    m = (mean(xs)*mean(ys)- mean(xs*ys))/((mean(xs))**2-mean(xs**2))
    b= mean(ys)-m*mean(xs)
    return m,b

def squared_error(ys_orig,ys_line):
    return sum((ys_line-ys_orig)**2)

def coeff_of_deter(ys_orig,ys_line):
    y_mean_line=[mean(ys_orig) for y in ys_orig]
    squared_error_regr= squared_error(ys_orig,ys_line)
    squared_error_y_mean=squared_error(ys_orig,y_mean_line)
    return 1-(squared_error_regr/squared_error_y_mean)

xs,ys=create_dataset(40,80,2,correlation='pos')


m,b = best_fit_slope_and_intercept(xs,ys)



regression_line = [(m*x)+b for x in xs]

predict_x=8
predict_y=(m*predict_x) + b

r_squared = coeff_of_deter(ys,regression_line)
print(r_squared)

plt.scatter(xs,ys)
plt.scatter(predict_x,predict_y,color='g')
plt.plot(xs,regression_line)
plt.show()