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

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 create_dataset(hm, varinace, step=2, correlation= False):
	val = 1 
	ys = []
	for i in range(hm):
		y = val + random.randrange(-varinace, varinace)
		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)**2) - 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 coeffiecent_of_determination(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, 5, 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 = coeffiecent_of_determination(ys, regression_line)
print(r_squared)

plt.scatter(xs,ys)
plt.plot(xs, regression_line)
plt.scatter(predict_x, predict_y, s = 100 ,color ='g')
plt.show()