Linear_Regression.py

from statistics import mean
import random
import numpy as np
from matplotlib import style
style.use('ggplot')
import matplotlib.pyplot as plt


# 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)




def best_fit_slope_and_intercept(xs,ys):
    m = ((mean(xs)*mean(ys)) - mean(xs*ys))/((mean(xs)*mean(xs)) - mean(xs*xs))
    
    b = mean(ys) - m*mean(xs)
    return m, b

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

def coefficient_of_determination(ys_orig,ys_line):
    y_mean_line = [mean(ys_orig) for y in ys_orig]

    squared_error_regr = sum((ys_line - ys_orig) * (ys_line - ys_orig))
    squared_error_y_mean = sum((y_mean_line - ys_orig) * (y_mean_line - ys_orig))

#     print(squared_error_regr)
#     print(squared_error_y_mean)

    r_squared = 1 - (squared_error_regr/squared_error_y_mean)

    return r_squared


xs,ys=create_dataset(40,80,2,correlation=False)
m,b = best_fit_slope_and_intercept(xs,ys)


                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       
regression_line = [(m*x)+b for x in xs] # These are the predicted values for the given x's .......we are doing this to get the best fit line

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

r_squared=coefficient_of_determination(ys,regression_line)
print(r_squared)
# print(np.shape(xs),b, '\n',len(regression_line))



plt.scatter(xs,ys,color='#003F72',label='data')
plt.scatter(predict_x,predict_y,s=100,color='g')
plt.plot(xs,regression_line,label = 'regression line')
plt.legend(loc=4)
plt.show()