# -*- coding: utf-8 -*-
"""
Created on Fri Apr 23 11:49:57 2021

@author: ahmed
"""

from statistics import mean
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style

style.use('fivethirtyeight')

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

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


predict_x = 7
predict_y = m*predict_x + b

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

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